A Brain-wide Circuit Model of Heat-Evoked Swimming Behavior in Larval Zebrafish.

PubMed

Haesemeyer, Martin; Robson, Drew N; Li, Jennifer M; Schier, Alexander F; Engert, Florian

2018-05-16

Thermosensation provides crucial information, but how temperature representation is transformed from sensation to behavior is poorly understood. Here, we report a preparation that allows control of heat delivery to zebrafish larvae while monitoring motor output and imaging whole-brain calcium signals, thereby uncovering algorithmic and computational rules that couple dynamics of heat modulation, neural activity and swimming behavior. This approach identifies a critical step in the transformation of temperature representation between the sensory trigeminal ganglia and the hindbrain: A simple sustained trigeminal stimulus representation is transformed into a representation of absolute temperature as well as temperature changes in the hindbrain that explains the observed motor output. An activity constrained dynamic circuit model captures the most prominent aspects of these sensori-motor transformations and predicts both behavior and neural activity in response to novel heat stimuli. These findings provide the first algorithmic description of heat processing from sensory input to behavioral output. Copyright © 2018 Elsevier Inc. All rights reserved.

Determination of regional brain temperature using proton magnetic resonance spectroscopy to assess brain-body temperature differences in healthy human subjects.

PubMed

Childs, Charmaine; Hiltunen, Yrjö; Vidyasagar, Rishma; Kauppinen, Risto A

2007-01-01

Proton magnetic resonance spectroscopy ((1)H MRS) was used to determine brain temperature in healthy volunteers. Partially water-suppressed (1)H MRS data sets were acquired at 3T from four different gray matter (GM)/white matter (WM) volumes. Brain temperatures were determined from the chemical-shift difference between the CH(3) of N-acetyl aspartate (NAA) at 2.01 ppm and water. Brain temperatures in (1)H MRS voxels of 2 x 2 x 2 cm(3) showed no substantial heterogeneity. The volume-averaged temperature from single-voxel spectroscopy was compared with body temperatures obtained from the oral cavity, tympanum, and temporal artery regions. The mean brain parenchyma temperature was 0.5 degrees C cooler than readings obtained from three extra-brain sites (P < 0.01). (1)H MRS imaging (MRSI) data were acquired from a slice encompassing the single-voxel volumes to assess the ability of spectroscopic imaging to determine regional brain temperature within the imaging slice. Brain temperature away from the center of the brain determined by MRSI differed from that obtained by single-voxel MRS in the same brain region, possibly due to a poor line width (LW) in MRSI. The data are discussed in the light of proposed brain-body temperature gradients and the use of (1)H MRSI to monitor brain temperature in pathologies, such as brain trauma.

Effects of global warming on fish reproductive endocrine axis, with special emphasis in pejerrey Odontesthes bonariensis.

PubMed

Miranda, Leandro Andrés; Chalde, Tomás; Elisio, Mariano; Strüssmann, Carlos Augusto

2013-10-01

The ongoing of global warming trend has led to an increase in temperature of several water bodies. Reproduction in fish, compared with other physiological processes, only occurs in a bounded temperature range; therefore, small changes in water temperature could significantly affect this process. This review provides evidence that fish reproduction may be directly affected by further global warming and that abnormal high water temperature impairs the expression of important genes throughout the brain-pituitary-gonad axis. In all fishes studied, gonads seem to be the organ more readily damaged by heat treatments through the inhibition of the gene expression and subsequent synthesis of different gonadal steroidogenic enzymes. In view of the feedback role of sex steroids upon the synthesis and release of GnRH and GtHs in fish, it is possible that the inhibition observed at brain and pituitary levels in treated fish is consequence of the sharp decrease in plasma steroids levels. Results of in vitro studies on the inhibition of pejerrey gonad aromatase expression by high temperature corroborate that ovary functions are directly disrupted by high temperature independently of the brain-pituitary axis. For the reproductive responses obtained in laboratory fish studies, it is plausible to predict changes in the timing and magnitude of reproductive activity or even the total failure of spawning season may occur in warm years, reducing annual reproductive output and affecting future populations. Copyright © 2013 Elsevier Inc. All rights reserved.

Do acute phase markers explain body temperature and brain temperature after ischemic stroke?

PubMed Central

Whiteley, William N.; Thomas, Ralph; Lowe, Gordon; Rumley, Ann; Karaszewski, Bartosz; Armitage, Paul; Marshall, Ian; Lymer, Katherine; Dennis, Martin

2012-01-01

Objective: Both brain and body temperature rise after stroke but the cause of each is uncertain. We investigated the relationship between circulating markers of inflammation with brain and body temperature after stroke. Methods: We recruited patients with acute ischemic stroke and measured brain temperature at hospital admission and 5 days after stroke with multivoxel magnetic resonance spectroscopic imaging in normal brain and the acute ischemic lesion (defined by diffusion-weighted imaging [DWI]). We measured body temperature with digital aural thermometers 4-hourly and drew blood daily to measure interleukin-6, C-reactive protein, and fibrinogen, for 5 days after stroke. Results: In 44 stroke patients, the mean temperature in DWI-ischemic brain soon after admission was 38.4°C (95% confidence interval [CI] 38.2–38.6), in DWI-normal brain was 37.7°C (95% CI 37.6–37.7), and mean body temperature was 36.6°C (95% CI 36.3–37.0). Higher mean levels of interleukin-6, C-reactive protein, and fibrinogen were associated with higher temperature in DWI-normal brain at admission and 5 days, and higher overall mean body temperature, but only with higher temperature in DWI-ischemic brain on admission. Conclusions: Systemic inflammation after stroke is associated with elevated temperature in normal brain and the body but not with later ischemic brain temperature. Elevated brain temperature is a potential mechanism for the poorer outcome observed in stroke patients with higher levels of circulating inflammatory markers. PMID:22744672

TU-G-210-01: Modeling for Breast and Brain HIFU Treatment Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Christensen, D.

Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such asmore » the liver or kidney. Douglas Christensen – Modeling for Breast and Brain HIFU Treatment Planning Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Learning Objectives: Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments. Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies. Determine how to obtain appropriate acoustic modeling parameters from clinical images. Understand the separate role of absorption and scattering in energy delivery to tissues. See how organ motion can be compensated for in ultrasound therapies. Compare simulated data with clinical temperature measurements in transcranial applications. Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007-2013) under 270186 (FUSIMO) and 611889 (TRANS-FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV)« less

The contribution of carotid rete variability to brain temperature variability in sheep in a thermoneutral environment.

PubMed

Maloney, Shane K; Mitchell, Duncan; Blache, Dominique

2007-03-01

The degree of variability in the temperature difference between the brain and carotid arterial blood is greater than expected from the presumed tight coupling between brain heat production and brain blood flow. In animals with a carotid rete, some of that variability arises in the rete. Using thermometric data loggers in five sheep, we have measured the temperature of arterial blood before it enters the carotid rete and after it has perfused the carotid rete, as well as hypothalamic temperature, every 2 min for between 6 and 12 days. The sheep were conscious, unrestrained, and maintained at an ambient temperature of 20-22 degrees C. On average, carotid arterial blood and brain temperatures were the same, with a decrease in blood temperature of 0.35 degrees C across the rete and then an increase in temperature of the same magnitude between blood leaving the rete and the brain. Rete cooling of arterial blood took place at temperatures below the threshold for selective brain cooling. All of the variability in the temperature difference between carotid artery and brain was attributable statistically to variability in the temperature difference across the rete. The temperature difference between arterial blood leaving the rete and the brain varied from -0.1 to 0.9 degrees C. Some of this variability was related to a thermal inertia of the brain, but the majority we attribute to instability in the relationship between brain blood flow and brain heat production.

Non-invasive measurement of brain temperature with microwave radiometry: demonstration in a head phantom and clinical case.

PubMed

Stauffer, Paul R; Snow, Brent W; Rodrigues, Dario B; Salahi, Sara; Oliveira, Tiago R; Reudink, Doug; Maccarini, Paolo F

2014-02-01

This study characterizes the sensitivity and accuracy of a non-invasive microwave radiometric thermometer intended for monitoring body core temperature directly in brain to assist rapid recovery from hypothermia such as occurs during surgical procedures. To study this approach, a human head model was constructed with separate brain and scalp regions consisting of tissue equivalent liquids circulating at independent temperatures on either side of intact skull. This test setup provided differential surface/deep tissue temperatures for quantifying sensitivity to change in brain temperature independent of scalp and surrounding environment. A single band radiometer was calibrated and tested in a multilayer model of the human head with differential scalp and brain temperature. Following calibration of a 500MHz bandwidth microwave radiometer in the head model, feasibility of clinical monitoring was assessed in a pediatric patient during a 2-hour surgery. The results of phantom testing showed that calculated radiometric equivalent brain temperature agreed within 0.4°C of measured temperature when the brain phantom was lowered 10°C and returned to original temperature (37°C), while scalp was maintained constant over a 4.6-hour experiment. The intended clinical use of this system was demonstrated by monitoring brain temperature during surgery of a pediatric patient. Over the 2-hour surgery, the radiometrically measured brain temperature tracked within 1-2°C of rectal and nasopharynx temperatures, except during rapid cooldown and heatup periods when brain temperature deviated 2-4°C from slower responding core temperature surrogates. In summary, the radiometer demonstrated long term stability, accuracy and sensitivity sufficient for clinical monitoring of deep brain temperature during surgery.

TU-G-210-03: Acoustic Simulations in Transcranial MRgFUS: Treatment Prediction and Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vyas, U.

Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such asmore » the liver or kidney. Douglas Christensen – Modeling for Breast and Brain HIFU Treatment Planning Tobias Preusser – TRANS-FUSIMO - An Integrative Approach to Model-Based Treatment Planning of Liver FUS Urvi Vyas – Acoustic Simulations in Transcranial MRgFUS: Treatment Prediction and Analysis Learning Objectives: Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments. Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies. Determine how to obtain appropriate acoustic modeling parameters from clinical images. Understand the separate role of absorption and scattering in energy delivery to tissues. See how organ motion can be compensated for in ultrasound therapies. Compare simulated data with clinical temperature measurements in transcranial applications. Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007-2013) under 270186 (FUSIMO) and 611889 (TRANS-FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV)« less

Monitoring brain temperature by time-resolved near-infrared spectroscopy: pilot study

NASA Astrophysics Data System (ADS)

Bakhsheshi, Mohammad Fazel; Diop, Mamadou; St. Lawrence, Keith; Lee, Ting-Yim

2014-05-01

Mild hypothermia (HT) is an effective neuroprotective strategy for a variety of acute brain injuries. However, the wide clinical adaptation of HT has been hampered by the lack of a reliable noninvasive method for measuring brain temperature, since core measurements have been shown to not always reflect brain temperature. The goal of this work was to develop a noninvasive optical technique for measuring brain temperature that exploits both the temperature dependency of water absorption and the high concentration of water in brain (80%-90%). Specifically, we demonstrate the potential of time-resolved near-infrared spectroscopy (TR-NIRS) to measure temperature in tissue-mimicking phantoms (in vitro) and deep brain tissue (in vivo) during heating and cooling, respectively. For deep brain tissue temperature monitoring, experiments were conducted on newborn piglets wherein hypothermia was induced by gradual whole body cooling. Brain temperature was concomitantly measured by TR-NIRS and a thermocouple probe implanted in the brain. Our proposed TR-NIRS method was able to measure the temperature of tissue-mimicking phantoms and brain tissues with a correlation of 0.82 and 0.66 to temperature measured with a thermometer, respectively. The mean difference between the TR-NIRS and thermometer measurements was 0.15°C±1.1°C for the in vitro experiments and 0.5°C±1.6°C for the in vivo measurements.

Modeling the spatiotemporal dynamics of light and heat propagation for in vivo optogenetics

PubMed Central

Stujenske, Joseph M.; Spellman, Timothy; Gordon, Joshua A.

2015-01-01

Summary Despite the increasing use of optogenetics in vivo, the effects of direct light exposure to brain tissue are understudied. Of particular concern is the potential for heat induced by prolonged optical stimulation. We demonstrate that high intensity light, delivered through an optical fiber, is capable of elevating firing rate locally, even in the absence of opsin expression. Predicting the severity and spatial extent of any temperature increase during optogenetic stimulation is therefore of considerable importance. Here we describe a realistic model that simulates light and heat propagation during optogenetic experiments. We validated the model by comparing predicted and measured temperature changes in vivo. We further demonstrate the utility of this model by comparing predictions for various wavelengths of light and fiber sizes, as well as testing methods for reducing heat effects on neural targets in vivo. PMID:26166563

Reliability issues in human brain temperature measurement

PubMed Central

2009-01-01

Introduction The influence of brain temperature on clinical outcome after severe brain trauma is currently poorly understood. When brain temperature is measured directly, different values between the inside and outside of the head can occur. It is not yet clear if these differences are 'real' or due to measurement error. Methods The aim of this study was to assess the performance and measurement uncertainty of body and brain temperature sensors currently in use in neurocritical care. Two organic fixed-point, ultra stable temperature sources were used as the temperature references. Two different types of brain sensor (brain type 1 and brain type 2) and one body type sensor were tested under rigorous laboratory conditions and at the bedside. Measurement uncertainty was calculated using internationally recognised methods. Results Average differences between the 26°C reference temperature source and the clinical temperature sensors were +0.11°C (brain type 1), +0.24°C (brain type 2) and -0.15°C (body type), respectively. For the 36°C temperature reference source, average differences between the reference source and clinical thermometers were -0.02°C, +0.09°C and -0.03°C for brain type 1, brain type 2 and body type sensor, respectively. Repeat calibrations the following day confirmed that these results were within the calculated uncertainties. The results of the immersion tests revealed that the reading of the body type sensor was sensitive to position, with differences in temperature of -0.5°C to -1.4°C observed on withdrawing the thermometer from the base of the isothermal environment by 4 cm and 8 cm, respectively. Taking into account all the factors tested during the calibration experiments, the measurement uncertainty of the clinical sensors against the (nominal) 26°C and 36°C temperature reference sources for the brain type 1, brain type 2 and body type sensors were ± 0.18°C, ± 0.10°C and ± 0.12°C respectively. Conclusions The results show that brain temperature sensors are fundamentally accurate and the measurements are precise to within 0.1 to 0.2°C. Subtle dissociation between brain and body temperature in excess of 0.1 to 0.2°C is likely to be real. Body temperature sensors need to be secured in position to ensure that measurements are reliable. PMID:19573241

Brain temperature and its fundamental properties: a review for clinical neuroscientists

PubMed Central

Wang, Huan; Wang, Bonnie; Normoyle, Kieran P.; Jackson, Kevin; Spitler, Kevin; Sharrock, Matthew F.; Miller, Claire M.; Best, Catherine; Llano, Daniel; Du, Rose

2014-01-01

Brain temperature, as an independent therapeutic target variable, has received increasingly intense clinical attention. To date, brain hypothermia represents the most potent neuroprotectant in laboratory studies. Although the impact of brain temperature is prevalent in a number of common human diseases including: head trauma, stroke, multiple sclerosis, epilepsy, mood disorders, headaches, and neurodegenerative disorders, it is evident and well recognized that the therapeutic application of induced hypothermia is limited to a few highly selected clinical conditions such as cardiac arrest and hypoxic ischemic neonatal encephalopathy. Efforts to understand the fundamental aspects of brain temperature regulation are therefore critical for the development of safe, effective, and pragmatic clinical treatments for patients with brain injuries. Although centrally-mediated mechanisms to maintain a stable body temperature are relatively well established, very little is clinically known about brain temperature's spatial and temporal distribution, its physiological and pathological fluctuations, and the mechanism underlying brain thermal homeostasis. The human brain, a metabolically “expensive” organ with intense heat production, is sensitive to fluctuations in temperature with regards to its functional activity and energy efficiency. In this review, we discuss several critical aspects concerning the fundamental properties of brain temperature from a clinical perspective. PMID:25339859

Brain surface temperature under a craniotomy

PubMed Central

Kalmbach, Abigail S.

2012-01-01

Many neuroscientists access surface brain structures via a small cranial window, opened in the bone above the brain region of interest. Unfortunately this methodology has the potential to perturb the structure and function of the underlying brain tissue. One potential perturbation is heat loss from the brain surface, which may result in local dysregulation of brain temperature. Here, we demonstrate that heat loss is a significant problem in a cranial window preparation in common use for electrical recording and imaging studies in mice. In the absence of corrective measures, the exposed surface of the neocortex was at ∼28°C, ∼10°C below core body temperature, and a standing temperature gradient existed, with tissue below the core temperature even several millimeters into the brain. Cooling affected cellular and network function in neocortex and resulted principally from increased heat loss due to convection and radiation through the skull and cranial window. We demonstrate that constant perfusion of solution, warmed to 37°C, over the brain surface readily corrects the brain temperature, resulting in a stable temperature of 36–38°C at all depths. Our results indicate that temperature dysregulation may be common in cranial window preparations that are in widespread use in neuroscience, underlining the need to take measures to maintain the brain temperature in many physiology experiments. PMID:22972953

Kalman filtered MR temperature imaging for laser induced thermal therapies.

PubMed

Fuentes, D; Yung, J; Hazle, J D; Weinberg, J S; Stafford, R J

2012-04-01

The feasibility of using a stochastic form of Pennes bioheat model within a 3-D finite element based Kalman filter (KF) algorithm is critically evaluated for the ability to provide temperature field estimates in the event of magnetic resonance temperature imaging (MRTI) data loss during laser induced thermal therapy (LITT). The ability to recover missing MRTI data was analyzed by systematically removing spatiotemporal information from a clinical MR-guided LITT procedure in human brain and comparing predictions in these regions to the original measurements. Performance was quantitatively evaluated in terms of a dimensionless L(2) (RMS) norm of the temperature error weighted by acquisition uncertainty. During periods of no data corruption, observed error histories demonstrate that the Kalman algorithm does not alter the high quality temperature measurement provided by MR thermal imaging. The KF-MRTI implementation considered is seen to predict the bioheat transfer with RMS error < 4 for a short period of time, ∆t < 10 s, until the data corruption subsides. In its present form, the KF-MRTI method currently fails to compensate for consecutive for consecutive time periods of data loss ∆t > 10 sec.

Relationships between brain and body temperature, clinical and imaging outcomes after ischemic stroke

PubMed Central

Karaszewski, Bartosz; Carpenter, Trevor K; Thomas, Ralph G R; Armitage, Paul A; Lymer, Georgina Katherine S; Marshall, Ian; Dennis, Martin S; Wardlaw, Joanna M

2013-01-01

Pyrexia soon after stroke is associated with severe stroke and poor functional outcome. Few studies have assessed brain temperature after stroke in patients, so little is known of its associations with body temperature, stroke severity, or outcome. We measured temperatures in ischemic and normal-appearing brain using 1H-magnetic resonance spectroscopy and its correlations with body (tympanic) temperature measured four-hourly, infarct growth by 5 days, early neurologic (National Institute of Health Stroke Scale, NIHSS) and late functional outcome (death or dependency). Among 40 patients (mean age 73 years, median NIHSS 7, imaged at median 17 hours), temperature in ischemic brain was higher than in normal-appearing brain on admission (38.6°C-core, 37.9°C-contralateral hemisphere, P=0.03) but both were equally elevated by 5 days; both were higher than tympanic temperature. Ischemic lesion temperature was not associated with NIHSS or 3-month functional outcome; in contrast, higher contralateral normal-appearing brain temperature was associated with worse NIHSS, infarct expansion and poor functional outcome, similar to associations for tympanic temperature. We conclude that brain temperature is higher than body temperature; that elevated temperature in ischemic brain reflects a local tissue response to ischemia, whereas pyrexia reflects the systemic response to stroke, occurs later, and is associated with adverse outcomes. PMID:23571281

Dual role of cerebral blood flow in regional brain temperature control in the healthy newborn infant.

PubMed

Iwata, Sachiko; Tachtsidis, Ilias; Takashima, Sachio; Matsuishi, Toyojiro; Robertson, Nicola J; Iwata, Osuke

2014-10-01

Small shifts in brain temperature after hypoxia-ischaemia affect cell viability. The main determinants of brain temperature are cerebral metabolism, which contributes to local heat production, and brain perfusion, which removes heat. However, few studies have addressed the effect of cerebral metabolism and perfusion on regional brain temperature in human neonates because of the lack of non-invasive cot-side monitors. This study aimed (i) to determine non-invasive monitoring tools of cerebral metabolism and perfusion by combining near-infrared spectroscopy and echocardiography, and (ii) to investigate the dependence of brain temperature on cerebral metabolism and perfusion in unsedated newborn infants. Thirty-two healthy newborn infants were recruited. They were studied with cerebral near-infrared spectroscopy, echocardiography, and a zero-heat flux tissue thermometer. A surrogate of cerebral blood flow (CBF) was measured using superior vena cava flow adjusted for cerebral volume (rSVC flow). The tissue oxygenation index, fractional oxygen extraction (FOE), and the cerebral metabolic rate of oxygen relative to rSVC flow (CMRO₂ index) were also estimated. A greater rSVC flow was positively associated with higher brain temperatures, particularly for superficial structures. The CMRO₂ index and rSVC flow were positively coupled. However, brain temperature was independent of FOE and the CMRO₂ index. A cooler ambient temperature was associated with a greater temperature gradient between the scalp surface and the body core. Cerebral oxygen metabolism and perfusion were monitored in newborn infants without using tracers. In these healthy newborn infants, cerebral perfusion and ambient temperature were significant independent variables of brain temperature. CBF has primarily been associated with heat removal from the brain. However, our results suggest that CBF is likely to deliver heat specifically to the superficial brain. Further studies are required to assess the effect of cerebral metabolism and perfusion on regional brain temperature in low-cardiac output conditions, fever, and with therapeutic hypothermia. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Evaluation of data transformations used with the square root and schoolfield models for predicting bacterial growth rate.

PubMed Central

Alber, S A; Schaffner, D W

1992-01-01

A comparison was made between mathematical variations of the square root and Schoolfield models for predicting growth rate as a function of temperature. The statistical consequences of square root and natural logarithm transformations of growth rate use in several variations of the Schoolfield and square root models were examined. Growth rate variances of Yersinia enterocolitica in brain heart infusion broth increased as a function of temperature. The ability of the two data transformations to correct for the heterogeneity of variance was evaluated. A natural logarithm transformation of growth rate was more effective than a square root transformation at correcting for the heterogeneity of variance. The square root model was more accurate than the Schoolfield model when both models used natural logarithm transformation. PMID:1444367

The hidden side of drug action: Brain temperature changes induced by neuroactive drugs

PubMed Central

Kiyatkin, Eugene A.

2013-01-01

Rationale Most neuroactive drugs affect brain metabolism as well as systemic and cerebral blood flow, thus altering brain temperature. Although this aspect of drug action usually remains in the shadows, drug-induced alterations in brain temperature reflect their metabolic neural effects and affect neural activity and neural functions. Objectives Here, I review brain temperature changes induced by neuroactive drugs, which are used therapeutically (general anesthetics), as a research tool (dopamine agonists and antagonists), and self-administered to induce desired psychic effects (cocaine, methamphetamine, ecstasy). I consider the mechanisms underlying these temperature fluctuations and their influence on neural, physiological, and behavioral effects of these drugs. Results By interacting with neural mechanisms regulating metabolic activity and heat exchange between the brain and the rest of the body, neuroactive drugs either increase or decrease brain temperatures both within (35-39°C) and exceeding the range of physiological fluctuations. These temperature effects differ drastically depending upon the environmental conditions and activity state during drug administration. This state-dependence is especially important for drugs of abuse that are usually taken by humans during psycho-physiological activation and in environments that prevent proper heat dissipation from the brain. Under these conditions, amphetamine-like stimulants induce pathological brain hyperthermia (>40°C) associated with leakage of the blood-brain barrier and structural abnormalities of brain cells. Conclusions The knowledge on brain temperature fluctuations induced by neuroactive drugs provides new information to understand how they influence metabolic neural activity, why their effects depend upon the behavioral context of administration, and the mechanisms underlying adverse drug effects including neurotoxicity PMID:23274506

The Effect of Temperature on Photoluminescence Enhancement of Quantum Dots in Brain Slices.

PubMed

Zhao, Fei; Kim, Jongsung

2017-04-01

In this paper, we investigated the effect of temperature on photoluminescence of quantum dots immobilized on the surface of an optical fiber in a rat brain slice. The optical fiber was silanized with 3-aminopropyl trimethoxysilane (APTMS), following which quantum dots with carboxyl functional group were immobilized on the optical fiber via amide bond formation. The effect of temperature on the fluorescence intensity of the quantum dots in rat brain slices was studied. This report shows that the fluorescence intensity of quantum dots increases with the increase of temperature of the brain slice. The fluorescence enhancement phenomenon appears to take place via electron transfer related to pH increase. With the gradual increase of temperature, the fluorescence intensity of quantum dots in solution decreased, while that in the brain slice increased. This enhanced thermal performance of QDs in brain slice makes suggestion for the study of QDs-based brain temperature sensors.

Body and brain temperature coupling: the critical role of cerebral blood flow

PubMed Central

Ackerman, Joseph J. H.; Yablonskiy, Dmitriy A.

2010-01-01

Direct measurements of deep-brain and body-core temperature were performed on rats to determine the influence of cerebral blood flow (CBF) on brain temperature regulation under static and dynamic conditions. Static changes of CBF were achieved using different anesthetics (chloral hydrate, CH; α-chloralose, αCS; and isoflurane, IF) with αCS causing larger decreases in CBF than CH and IF; dynamic changes were achieved by inducing transient hypercapnia (5% CO2 in 40% O2 and 55% N2). Initial deep-brain/body-core temperature differentials were anesthetic-type dependent with the largest differential observed with rats under αCS anesthesia (ca. 2°C). Hypercapnia induction raised rat brain temperature under all three anesthesia regimes, but by different anesthetic-dependent amounts correlated with the initial differentials—αCS anesthesia resulted in the largest brain temperature increase (0.32 ± 0.08°C), while CH and IF anesthesia lead to smaller increases (0.12 ± 0.03 and 0.16 ± 0.05°C, respectively). The characteristic temperature transition time for the hypercapnia-induced temperature increase was 2–3 min under CH and IF anesthesia and ~4 min under αCS anesthesia. We conclude that both, the deep-brain/body-core temperature differential and the characteristic temperature transition time correlate with CBF: a lower CBF promotes higher deep-brain/body-core temperature differentials and, upon hypercapnia challenge, longer characteristic transition times to increased temperatures. PMID:19277681

Body and brain temperature coupling: the critical role of cerebral blood flow.

PubMed

Zhu, Mingming; Ackerman, Joseph J H; Yablonskiy, Dmitriy A

2009-08-01

Direct measurements of deep-brain and body-core temperature were performed on rats to determine the influence of cerebral blood flow (CBF) on brain temperature regulation under static and dynamic conditions. Static changes of CBF were achieved using different anesthetics (chloral hydrate, CH; alpha-chloralose, alphaCS; and isoflurane, IF) with alphaCS causing larger decreases in CBF than CH and IF; dynamic changes were achieved by inducing transient hypercapnia (5% CO(2) in 40% O(2) and 55% N(2)). Initial deep-brain/body-core temperature differentials were anesthetic-type dependent with the largest differential observed with rats under alphaCS anesthesia (ca. 2 degrees C). Hypercapnia induction raised rat brain temperature under all three anesthesia regimes, but by different anesthetic-dependent amounts correlated with the initial differentials--alphaCS anesthesia resulted in the largest brain temperature increase (0.32 +/- 0.08 degrees C), while CH and IF anesthesia lead to smaller increases (0.12 +/- 0.03 and 0.16 +/- 0.05 degrees C, respectively). The characteristic temperature transition time for the hypercapnia-induced temperature increase was 2-3 min under CH and IF anesthesia and approximately 4 min under alphaCS anesthesia. We conclude that both, the deep-brain/body-core temperature differential and the characteristic temperature transition time correlate with CBF: a lower CBF promotes higher deep-brain/body-core temperature differentials and, upon hypercapnia challenge, longer characteristic transition times to increased temperatures.

Effect of operating microscope light on brain temperature during craniotomy.

PubMed

Gayatri, Parthasarathi; Menon, Girish G; Suneel, Puthuvassery R

2013-07-01

Operating microscopes used during neurosurgery are fitted with xenon light. Burn injuries have been reported because of xenon microscope lighting as the intensity of xenon light is 300 W. We designed this study to find out if the light of operating microscope causes an increase in temperature of the brain tissue, which is exposed underneath. Twenty-one adult patients scheduled for elective craniotomies were enrolled. Distal esophageal temperature (T Eso), brain temperature under the microscope light (T Brain), and brain temperature under dura mater (T Dura) were measured continuously at 15-minute intervals during microscope use. The irrigation fluid temperature, room temperature, intensity of the microscope light, and the distance of the microscope from the brain surface were kept constant. The average age of the patients was 44±15 years (18 males and 3 females). The mean duration of microscope use was 140±39 minutes. There were no significant changes in T Brain and T Dura and T Eso over time. T Dura was significantly lower than T Brain both at time 0 and 60 minutes but not at 90 minutes. T Brain was significantly lower than T Eso both at time 0 and 60 minutes but not at 90 minutes. The T Dura remained significantly lower than T Eso at 0, 60, and 90 minutes. Our study shows that there is no significant rise in brain temperature under xenon microscope light up to 120 minutes duration, at intensity of 60% to 70%, from a distance of 20 to 25 cm from the brain surface.

Temperature-dependent elastic properties of brain tissues measured with the shear wave elastography method.

PubMed

Liu, Yan-Lin; Li, Guo-Yang; He, Ping; Mao, Ze-Qi; Cao, Yanping

2017-01-01

Determining the mechanical properties of brain tissues is essential in such cases as the surgery planning and surgical training using virtual reality based simulators, trauma research and the diagnosis of some diseases that alter the elastic properties of brain tissues. Here, we suggest a protocol to measure the temperature-dependent elastic properties of brain tissues in physiological saline using the shear wave elastography method. Experiments have been conducted on six porcine brains. Our results show that the shear moduli of brain tissues decrease approximately linearly with a slope of -0.041±0.006kPa/°C when the temperature T increases from room temperature (~23°C) to body temperature (~37°C). A case study has been further conducted which shows that the shear moduli are insensitive to the temperature variation when T is in the range of 37 to 43°C and will increase when T is higher than 43°C. With the present experimental setup, temperature-dependent elastic properties of brain tissues can be measured in a simulated physiological environment and a non-destructive manner. Thus the method suggested here offers a unique tool for the mechanical characterization of brain tissues with potential applications in brain biomechanics research. Copyright © 2016 Elsevier Ltd. All rights reserved.

Kalman Filtered MR Temperature Imaging for Laser Induced Thermal Therapies

PubMed Central

Fuentes, D.; Yung, J.; Hazle, J. D.; Weinberg, J. S.; Stafford, R. J.

2013-01-01

The feasibility of using a stochastic form of Pennes bioheat model within a 3D finite element based Kalman filter (KF) algorithm is critically evaluated for the ability to provide temperature field estimates in the event of magnetic resonance temperature imaging (MRTI) data loss during laser induced thermal therapy (LITT). The ability to recover missing MRTI data was analyzed by systematically removing spatiotemporal information from a clinical MR-guided LITT procedure in human brain and comparing predictions in these regions to the original measurements. Performance was quantitatively evaluated in terms of a dimensionless L2 (RMS) norm of the temperature error weighted by acquisition uncertainty. During periods of no data corruption, observed error histories demonstrate that the Kalman algorithm does not alter the high quality temperature measurement provided by MR thermal imaging. The KF-MRTI implementation considered is seen to predict the bioheat transfer with RMS error < 4 for a short period of time, Δt < 10sec, until the data corruption subsides. In its present form, the KF-MRTI method currently fails to compensate for consecutive for consecutive time periods of data loss Δt > 10sec. PMID:22203706

Study on Control of Brain Temperature for Brain Hypothermia Treatment

NASA Astrophysics Data System (ADS)

Gaohua, Lu; Wakamatsu, Hidetoshi

The brain hypothermia treatment is an attractive therapy for the neurologist because of its neuroprotection in hypoxic-ischemic encephalopathy patients. The present paper deals with the possibility of controlling the brain and other viscera in different temperatures from the viewpoint of system control. It is theoretically attempted to realize the special brain hypothermia treatment to cool only the head but to warm the body by using the simple apparatus such as the cooling cap, muffler and warming blanket. For this purpose, a biothermal system concerning the temperature difference between the brain and the other thoracico-abdominal viscus is synthesized from the biothermal model of hypothermic patient. The output controllability and the asymptotic stability of the system are examined on the basis of its structure. Then, the maximum temperature difference to be realized is shown dependent on the temperature range of the apparatus and also on the maximum gain determined from the coefficient matrices A, B and C of the biothermal system. Its theoretical analysis shows the realization of difference of about 2.5°C, if there is absolutely no constraint of the temperatures of the cooling cap, muffler and blanket. It is, however, physically unavailable. Those are shown by simulation example of the optimal brain temperature regulation using a standard adult database. It is thus concluded that the surface cooling and warming apparatus do no make it possible to realize the special brain hypothermia treatment, because the brain temperature cannot be cooled lower than those of other viscera in an appropriate temperature environment. This study shows that the ever-proposed good method of clinical treatment is in principle impossible in the actual brain hypothermia treatment.

Enhanced upper respiratory tract airflow and head fanning reduce brain temperature in brain-injured, mechanically ventilated patients: a randomized, crossover, factorial trial.

PubMed

Harris, B A; Andrews, P J D; Murray, G D

2007-01-01

Heat loss from the upper airways and through the skull are physiological mechanisms of brain cooling which have not been fully explored clinically. This randomized, crossover, factorial trial in 12 brain-injured, orally intubated patients investigated the effect of enhanced nasal airflow (high flow unhumidified air with 20 p.p.m. nitric oxide gas) and bilateral head fanning on frontal lobe brain temperature and selective brain cooling. After a 30 min baseline, each patient received the four possible combinations of the interventions--airflow, fanning, both together, no intervention--in randomized order. Each combination was delivered for 30 min and followed by a 30 min washout, the last 5 min of which provided the baseline for the next intervention. The difference in mean brain temperature over the last 5 min of the preceding washout minus the mean over the last 5 min of intervention, was 0.15 degrees C with nasal airflow (P=0.001, 95% CI 0.06-0.23 degrees C) and 0.26 degrees C with head fanning (P<0.001, 95% CI 0.17-0.34 degrees C). The estimate of the combined effect of airflow and fanning on brain temperature was 0.41 degrees C. Selective brain cooling did not occur. Physiologically, this study demonstrates that heat loss through the upper airways and through the skull can reduce parenchymal brain temperature in brain-injured humans and the onset of temperature reduction is rapid. Clinically, in ischaemic stroke, a temperature decrease of 0.27 degrees C may reduce the relative risk of poor outcome by 10-20%. Head fanning may have the potential to achieve a temperature decrease of this order.

Biothermal Model of Patient and Automatic Control System of Brain Temperature for Brain Hypothermia Treatment

NASA Astrophysics Data System (ADS)

Wakamatsu, Hidetoshi; Gaohua, Lu

Various surface-cooling apparatus such as the cooling cap, muffler and blankets have been commonly used for the cooling of the brain to provide hypothermic neuro-protection for patients of hypoxic-ischemic encephalopathy. The present paper is aimed at the brain temperature regulation from the viewpoint of automatic system control, in order to help clinicians decide an optimal temperature of the cooling fluid provided for these three types of apparatus. At first, a biothermal model characterized by dynamic ambient temperatures is constructed for adult patient, especially on account of the clinical practice of hypothermia and anesthesia in the brain hypothermia treatment. Secondly, the model is represented by the state equation as a lumped parameter linear dynamic system. The biothermal model is justified from their various responses corresponding to clinical phenomena and treatment. Finally, the optimal regulator is tentatively designed to give clinicians some suggestions on the optimal temperature regulation of the patient’s brain. It suggests the patient’s brain temperature could be optimally controlled to follow-up the temperature process prescribed by the clinicians. This study benefits us a great clinical possibility for the automatic hypothermia treatment.

Selective Brain Cooling Reduces Water Turnover in Dehydrated Sheep

PubMed Central

Strauss, W. Maartin; Hetem, Robyn S.; Mitchell, Duncan; Maloney, Shane K.; Meyer, Leith C. R.; Fuller, Andrea

2015-01-01

In artiodactyls, arterial blood destined for the brain can be cooled through counter-current heat exchange within the cavernous sinus via a process called selective brain cooling. We test the hypothesis that selective brain cooling, which results in lowered hypothalamic temperature, contributes to water conservation in sheep. Nine Dorper sheep, instrumented to provide measurements of carotid blood and brain temperature, were dosed with deuterium oxide (D2O), exposed to heat for 8 days (40◦C for 6-h per day) and deprived of water for the last five days (days 3 to 8). Plasma osmolality increased and the body water fraction decreased over the five days of water deprivation, with the sheep losing 16.7% of their body mass. Following water deprivation, both the mean 24h carotid blood temperature and the mean 24h brain temperature increased, but carotid blood temperature increased more than did brain temperature resulting in increased selective brain cooling. There was considerable inter-individual variation in the degree to which individual sheep used selective brain cooling. In general, sheep spent more time using selective brain cooling, and it was of greater magnitude, when dehydrated compared to when they were euhydrated. We found a significant positive correlation between selective brain cooling magnitude and osmolality (an index of hydration state). Both the magnitude of selective brain cooling and the proportion of time that sheep spent selective brain cooling were negatively correlated with water turnover. Sheep that used selective brain cooling more frequently, and with greater magnitude, lost less water than did conspecifics using selective brain cooling less efficiently. Our results show that a 50kg sheep can save 2.6L of water per day (~60% of daily water intake) when it employs selective brain cooling for 50% of the day during heat exposure. We conclude that selective brain cooling has a water conservation function in artiodactyls. PMID:25675092

Design and optimization of an ultra wideband and compact microwave antenna for radiometric monitoring of brain temperature.

PubMed

Rodrigues, Dario B; Maccarini, Paolo F; Salahi, Sara; Oliveira, Tiago R; Pereira, Pedro J S; Limao-Vieira, Paulo; Snow, Brent W; Reudink, Doug; Stauffer, Paul R

2014-07-01

We present the modeling efforts on antenna design and frequency selection to monitor brain temperature during prolonged surgery using noninvasive microwave radiometry. A tapered log-spiral antenna design is chosen for its wideband characteristics that allow higher power collection from deep brain. Parametric analysis with the software HFSS is used to optimize antenna performance for deep brain temperature sensing. Radiometric antenna efficiency (η) is evaluated in terms of the ratio of power collected from brain to total power received by the antenna. Anatomical information extracted from several adult computed tomography scans is used to establish design parameters for constructing an accurate layered 3-D tissue phantom. This head phantom includes separate brain and scalp regions, with tissue equivalent liquids circulating at independent temperatures on either side of an intact skull. The optimized frequency band is 1.1-1.6 GHz producing an average antenna efficiency of 50.3% from a two turn log-spiral antenna. The entire sensor package is contained in a lightweight and low-profile 2.8 cm diameter by 1.5 cm high assembly that can be held in place over the skin with an electromagnetic interference shielding adhesive patch. The calculated radiometric equivalent brain temperature tracks within 0.4 °C of the measured brain phantom temperature when the brain phantom is lowered 10 °C and then returned to the original temperature (37 °C) over a 4.6-h experiment. The numerical and experimental results demonstrate that the optimized 2.5-cm log-spiral antenna is well suited for the noninvasive radiometric sensing of deep brain temperature.

On the same wavelength: predictable language enhances speaker-listener brain-to-brain synchrony in posterior superior temporal gyrus.

PubMed

Dikker, Suzanne; Silbert, Lauren J; Hasson, Uri; Zevin, Jason D

2014-04-30

Recent research has shown that the degree to which speakers and listeners exhibit similar brain activity patterns during human linguistic interaction is correlated with communicative success. Here, we used an intersubject correlation approach in fMRI to test the hypothesis that a listener's ability to predict a speaker's utterance increases such neural coupling between speakers and listeners. Nine subjects listened to recordings of a speaker describing visual scenes that varied in the degree to which they permitted specific linguistic predictions. In line with our hypothesis, the temporal profile of listeners' brain activity was significantly more synchronous with the speaker's brain activity for highly predictive contexts in left posterior superior temporal gyrus (pSTG), an area previously associated with predictive auditory language processing. In this region, predictability differentially affected the temporal profiles of brain responses in the speaker and listeners respectively, in turn affecting correlated activity between the two: whereas pSTG activation increased with predictability in the speaker, listeners' pSTG activity instead decreased for more predictable sentences. Listeners additionally showed stronger BOLD responses for predictive images before sentence onset, suggesting that highly predictable contexts lead comprehenders to preactivate predicted words.

Forced convective head cooling device reduces human cross-sectional brain temperature measured by magnetic resonance: a non-randomized healthy volunteer pilot study.

PubMed

Harris, B A; Andrews, P J D; Marshall, I; Robinson, T M; Murray, G D

2008-03-01

This pilot study in five healthy adult humans forms the pre-clinical assessment of the effect of a forced convective head cooling device on intracranial temperature, measured non-invasively by magnetic resonance spectroscopy (MRS). After a 10 min baseline with no cooling, subjects received 30 min of head cooling followed by 30 min of head and neck cooling via a hood and neck collar delivering 14.5 degrees C air at 42.5 litre s(-1). Over baseline and at the end of both cooling periods, MRS was performed, using chemical shift imaging, to measure brain temperature simultaneously across a single slice of brain at the level of the basal ganglia. Oesophageal temperature was measured continuously using a fluoroptic thermometer. MRS brain temperature was calculated for baseline and the last 10 min of each cooling period. The net brain temperature reduction with head cooling was 0.45 degrees C (SD 0.23 degrees C, P=0.01, 95% CI 0.17-0.74 degrees C) and with head and neck cooling was 0.37 degrees C (SD 0.30 degrees C, P=0.049, 95% CI 0.00-0.74 degrees C). The equivalent net reductions in oesophageal temperature were 0.16 degrees C (SD 0.04 degrees C) and 0.36 degrees C (SD 0.12 degrees C). Baseline-corrected brain temperature gradients from outer through intermediate to core voxels were not significant for either head cooling (P=0.43) or head and neck cooling (P=0.07), indicating that there was not a significant reduction in cooling with progressive depth into the brain. Convective head cooling reduced MRS brain temperature and core brain was cooled.

Increased brain-predicted aging in treated HIV disease

PubMed Central

Underwood, Jonathan; Caan, Matthan W.A.; De Francesco, Davide; van Zoest, Rosan A.; Leech, Robert; Wit, Ferdinand W.N.M.; Portegies, Peter; Geurtsen, Gert J.; Schmand, Ben A.; Schim van der Loeff, Maarten F.; Franceschi, Claudio; Sabin, Caroline A.; Majoie, Charles B.L.M.; Winston, Alan; Reiss, Peter; Sharp, David J.

2017-01-01

Objective: To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters. Methods: A large sample of virologically suppressed HIV-positive adults (n = 162, age 45–82 years) and highly comparable HIV-negative controls (n = 105) were recruited as part of the Comorbidity in Relation to AIDS (COBRA) collaboration. Using T1-weighted MRI scans, a machine-learning model of healthy brain aging was defined in an independent cohort (n = 2,001, aged 18–90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age − chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out. Results: HIV-positive individuals had greater brain-PAD score (mean ± SD 2.15 ± 7.79 years) compared to HIV-negative individuals (−0.87 ± 8.40 years; b = 3.48, p < 0.01). Increased brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV infection, or other HIV-related measures. Conclusion: Increased apparent brain aging, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain aging related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV infection, suggesting that HIV disease may accentuate rather than accelerate brain aging. PMID:28258081

Increased brain-predicted aging in treated HIV disease.

PubMed

Cole, James H; Underwood, Jonathan; Caan, Matthan W A; De Francesco, Davide; van Zoest, Rosan A; Leech, Robert; Wit, Ferdinand W N M; Portegies, Peter; Geurtsen, Gert J; Schmand, Ben A; Schim van der Loeff, Maarten F; Franceschi, Claudio; Sabin, Caroline A; Majoie, Charles B L M; Winston, Alan; Reiss, Peter; Sharp, David J

2017-04-04

To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters. A large sample of virologically suppressed HIV-positive adults (n = 162, age 45-82 years) and highly comparable HIV-negative controls (n = 105) were recruited as part of the Comorbidity in Relation to AIDS (COBRA) collaboration. Using T1-weighted MRI scans, a machine-learning model of healthy brain aging was defined in an independent cohort (n = 2,001, aged 18-90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age - chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out. HIV-positive individuals had greater brain-PAD score (mean ± SD 2.15 ± 7.79 years) compared to HIV-negative individuals (-0.87 ± 8.40 years; b = 3.48, p < 0.01). Increased brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV infection, or other HIV-related measures. Increased apparent brain aging, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain aging related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV infection, suggesting that HIV disease may accentuate rather than accelerate brain aging. Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

PubMed

Kawahara, Jeremy; Brown, Colin J; Miller, Steven P; Booth, Brian G; Chau, Vann; Grunau, Ruth E; Zwicker, Jill G; Hamarneh, Ghassan

2017-02-01

We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain. Copyright © 2016 Elsevier Inc. All rights reserved.

Regulation of body temperature in the blue-tongued lizard.

PubMed

Hammel, H T; Caldwell, F T; Abrams, R M

1967-06-02

Lizards (Tiliqua scincoides) regulated their internal body temperature by moving back and forth between 15 degrees and 45 degrees C environments to maintain colonic and brain temperatures between 30 degrees and 37 degrees C. A pair of thermodes were implanted across the preoptic region of the brain stem, and a reentrant tube for a thermocouple was implanted in the brain stem. Heating the brain stem to 41 degrees C activated the exit response from the hot environment at a colonic temperature 1 degrees to 2 degrees C lower than normal, whereas cooling the brain stem to 25 degrees C delayed the exit from the hot environment until the colonic temperature was 1 degrees to 2 degrees C higher than normal. The behavioral thermoregulatory responses of this ectotherm appear to be activated by a combination of hypothalamic and other body temperatures.

Language and reading development in the brain today: neuromarkers and the case for prediction.

PubMed

Buchweitz, Augusto

2016-01-01

The goal of this article is to provide an account of language development in the brain using the new information about brain function gleaned from cognitive neuroscience. This account goes beyond describing the association between language and specific brain areas to advocate the possibility of predicting language outcomes using brain-imaging data. The goal is to address the current evidence about language development in the brain and prediction of language outcomes. Recent studies will be discussed in the light of the evidence generated for predicting language outcomes and using new methods of analysis of brain data. The present account of brain behavior will address: (1) the development of a hardwired brain circuit for spoken language; (2) the neural adaptation that follows reading instruction and fosters the "grafting" of visual processing areas of the brain onto the hardwired circuit of spoken language; and (3) the prediction of language development and the possibility of translational neuroscience. Brain imaging has allowed for the identification of neural indices (neuromarkers) that reflect typical and atypical language development; the possibility of predicting risk for language disorders has emerged. A mandate to develop a bridge between neuroscience and health and cognition-related outcomes may pave the way for translational neuroscience. Copyright © 2016 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

Alterations in brain temperatures as a possible cause of migraine headache.

PubMed

Horváth, Csilla

2014-05-01

Migraine is a debilitating disease with a recurring generally unilateral headache and concomitant symptoms of nausea, vomiting and photo- and/or phonophobia that affects some 11-18% of the population. Most of the mechanisms previously put forward to explain the attacks have been questioned or give an explanation only some of the symptoms. Moreover, the best drugs for treatment are still the 20-year-old triptans, which have serious limitations as regards both efficacy and tolerability. As the dura and some cranial vessels are the only intracranial structures capable of pain sensations, a vascular theory of migraine emerged, but has been debated. Recent theories identified the hyperexcitability of structures involved in pain transmission, such as the trigeminal system or the cortex, or an abnormal modulatory function of the brainstem. However, there is ongoing scientific debate concerning these theories, neither of which is fully capable of explaining the occurrence of a migraine attack. The present article puts forward a hypothesis of the possibility of abnormal temperature regulation in certain regions or the overall brain in migraineurs, the attack being a defense mechanism to prevent neuronal damage. Few examinations have been made of temperature regulation in the human brain. It lacks the carotid rete, a vascular heat exchanger that serves in many animals to provide constant brain temperature. The human brain contains a high density of neurons with a considerable energy demand that is converted to heat. The human brain has a higher temperature than other parts of the body and needs continuous cooling. Recent studies revealed unexpectedly great variations in temperature of various structures of the brain and considerable changes in response to functional activation. There is various evidence in support of the hypothesis that accumulated heat in some structure or the overall brain may be behind the symptoms observed, such as a platelet abnormality, a decreased serotonin content, and dural "inflammation" including vasodilation and brainstem activation. The hypothesis postulates that a migraine attack serves to restore the brain temperature. Abnormally low temperatures in the brain can also result in headache. Surprisingly, no systematic examination of brain temperature changes in migraineurs has been published. Certain case reports support the present hypothesis. Various noninvasive technologies (e.g. MR) capable of monitoring brain temperature are available. If a systematic examination of local brain temperature revealed abnormalities in structures presumed to be involved in migraine, that would increase our understanding of the disease and trigger the development of improved treatment. Copyright © 2014 Elsevier Ltd. All rights reserved.

A new prospect in magnetic nanoparticle-based cancer therapy: Taking credit from mathematical tissue-mimicking phantom brain models.

PubMed

Saeedi, Mostafa; Vahidi, Omid; Goodarzi, Vahabodin; Saeb, Mohammad Reza; Izadi, Leila; Mozafari, Masoud

2017-11-01

Distribution patterns/performance of magnetic nanoparticles (MNPs) was visualized by computer simulation and experimental validation on agarose gel tissue-mimicking phantom (AGTMP) models. The geometry of a complex three-dimensional mathematical phantom model of a cancer tumor was examined by tomography imaging. The capability of mathematical model to predict distribution patterns/performance in AGTMP model was captured. The temperature profile vs. hyperthermia duration was obtained by solving bio-heat equations for four different MNPs distribution patterns and correlated with cell death rate. The outcomes indicated that bio-heat model was able to predict temperature profile throughout the tissue model with a reasonable precision, to be applied for complex tissue geometries. The simulation results on the cancer tumor model shed light on the effectiveness of the studied parameters. Copyright © 2017 Elsevier Inc. All rights reserved.

Cold Blooded: Evaluating Brain Temperature by MRI During Surface Cooling of Human Subjects.

PubMed

Curran, Eric J; Wolfson, Daniel L; Watts, Richard; Freeman, Kalev

2017-10-01

Targeted temperature management (TTM) confers neurological and survival benefits for post-cardiac arrest patients with return of spontaneous circulation (ROSC) who remain comatose. Specialized equipment for induction of hypothermia is not available in the prehospital setting, and there are no reliable methods for emergency medical services personnel to initiate TTM. We hypothesized that the application of surface cooling elements to the neck will decrease brain temperature and act as initiators of TTM. Magnetic resonance (MR) spectroscopy was used to evaluate the effect of a carotid surface cooling element on brain temperature in healthy adults. Six individuals completed this study. We measured a temperature drop of 0.69 ± 0.38 °C (95% CI) in the cortex of the brain following the application of the cooling element. Application of a room temperature element also caused a measurable decrease in brain temperature of 0.66 ± 0.41 °C (95% CI) which may be attributable to baroreceptor activation. The application of surface cooling elements to the neck decreased brain temperature and may serve as a method to initiate TTM in the prehospital setting.

Brain hyperthermia and temperature fluctuations during sexual interaction in female rats.

PubMed

Mitchum, Robert D; Kiyatkin, Eugene A

2004-03-12

Since the metabolic activity of neural cells is accompanied by heat release, brain temperature monitoring provides insight into behavior-associated changes in neural activity. In the present study, local temperatures were continuously recorded in several brain structures (nucleus accumbens, medial-preoptic hypothalamus and hippocampus) and a non-locomotor head muscle (musculus temporalis) in a receptive female rat during sexually arousing stimulation and subsequent copulatory behavior with an experienced male. Placement of the male into a neighboring compartment increased the female's temperature (approximately 0.8 degrees C) and additional, transient increases (approximately 0.2 degrees C) occurred when the rats were allowed to see and smell each other through a transparent barrier. Temperatures gradually increased further as the male repeatedly mounted and achieved intromissions, peaked 2-3 min after male's ejaculation (0.2-0.4 degrees C), and abruptly dropped until the male initiated a new copulatory cycle. Similar biphasic fluctuations accompanied subsequent copulatory cycles. Although both arousal-related temperature increases and biphasic fluctuations associated with copulatory cycles were evident in each recording location, brain sites showed consistently faster and stronger increases than the muscle, suggesting metabolic brain activation as the primary source of brain temperature fluctuations and a force behind associated changes in brain temperature. Robust brain hyperthermia and the generally similar pattern of phasic temperature fluctuations associated with individual events of sexual interaction found in males and females suggest widespread neural activation (motivational arousal) as a driving force underlying this cooperative motivated behavior in animals of both sexes. Females, however, showed different temperature changes in association with the initial (first mount or intromission) and final (ejaculation) events of each copulatory cycle, suggesting sex-specific differences in neural activity associated with the initiation and regulation of sexual behavior.

Predictive modeling of neuroanatomic structures for brain atrophy detection

NASA Astrophysics Data System (ADS)

Hu, Xintao; Guo, Lei; Nie, Jingxin; Li, Kaiming; Liu, Tianming

2010-03-01

In this paper, we present an approach of predictive modeling of neuroanatomic structures for the detection of brain atrophy based on cross-sectional MRI image. The underlying premise of applying predictive modeling for atrophy detection is that brain atrophy is defined as significant deviation of part of the anatomy from what the remaining normal anatomy predicts for that part. The steps of predictive modeling are as follows. The central cortical surface under consideration is reconstructed from brain tissue map and Regions of Interests (ROI) on it are predicted from other reliable anatomies. The vertex pair-wise distance between the predicted vertex and the true one within the abnormal region is expected to be larger than that of the vertex in normal brain region. Change of white matter/gray matter ratio within a spherical region is used to identify the direction of vertex displacement. In this way, the severity of brain atrophy can be defined quantitatively by the displacements of those vertices. The proposed predictive modeling method has been evaluated by using both simulated atrophies and MRI images of Alzheimer's disease.

MemBrain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

NASA Astrophysics Data System (ADS)

Yin, Xi; Yang, Jing; Xiao, Feng; Yang, Yang; Shen, Hong-Bin

2018-03-01

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels, transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments, accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called MemBrain, whose input is the amino acid sequence. MemBrain consists of specialized modules for predicting transmembrane helices, residue-residue contacts and relative accessible surface area of α-helical membrane proteins. MemBrain achieves a prediction accuracy of 97.9% of A TMH, 87.1% of A P, 3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. MemBrain-Contact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction, respectively. And MemBrain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of 13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins. MemBrain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/MemBrain/. [Figure not available: see fulltext.

TU-G-210-00: Treatment Planning Strategies, Modeling, Control

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such asmore » the liver or kidney. Douglas Christensen – Modeling for Breast and Brain HIFU Treatment Planning Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Learning Objectives: Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments. Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies. Determine how to obtain appropriate acoustic modeling parameters from clinical images. Understand the separate role of absorption and scattering in energy delivery to tissues. See how organ motion can be compensated for in ultrasound therapies. Compare simulated data with clinical temperature measurements in transcranial applications. Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007-2013) under 270186 (FUSIMO) and 611889 (TRANS-FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV)« less

EFFECT OF WEARING AN N95 FILTERING FACEPIECE RESPIRATOR ON SUPEROMEDIAL ORBITAL INFRARED INDIRECT BRAIN TEMPERATURE MEASUREMENTS

PubMed Central

DiLeo, Travis; Roberge, Raymond J.; Kim, Jung-Hyun

2016-01-01

Purpose To determine any effect of wearing a filtering facepiece respirator on brain temperature. Methods Subjects (n=18) wore a filtering facepiece respirator (FFR) for 1h at rest while undergoing infrared thermography measurements of the superomedial periobital region of the eye, a non-invasive indirect method of brain temperature measurements we termed the superomedial orbital infrared indirect brain temperature (SOIIBT) measurement. Temperature of the facial skin covered by the FFR, infrared temperature measurements of the tympanic membrane and superficial temporal artery region were concurrently measured, and subjective impressions of thermal comfort obtained simultaneously. Results The temperature of the skin under the FFR and subjective impressions of thermal discomfort both increased significantly. The mean tympanic membrane temperature did not increase, and the superficial temporal artery region temperature decreased significantly. The SOIIBT values did not change significantly, but subjects who switched from nasal to oronasal breathing during the study (n=5) experienced a slight increase in the SOIIBT measurements. Conclusions Wearing a FFR for 1h at rest does not have a significant effect on brain temperatures, as evaluated by the SOIIBT measurements, but a change in the route of breathing may impact these measurements. These findings suggest that subjective impressions of thermal discomfort from wearing a FFR under the study conditions are more likely the result of local dermal sensations rather than brain warming. PMID:26759336

A non invasive wearable sensor for the measurement of brain temperature.

PubMed

Dittmar, A; Gehin, C; Delhomme, G; Boivin, D; Dumont, G; Mott, C

2006-01-01

As the thermoregulation centres are deep in the brain, the cerebral temperature is one of the most important markers of fever, circadian rhythms physical and mental activities. However due to a lack of accessibility, the brain temperature is not easily measured. The axillary, buccal, tympanic and rectal temperatures do not reflect exactly the cerebral temperature. Nevertheless the rectal temperature is used as probably the most reliable indicator of the core body temperature. The brain temperature can be measured using NMR spectroscopy, microwave radiometry, near infrared spectroscopy, ultra-sound thermometry. However none of those methods are amenable to long term ambulatory use outside of the laboratory or of the hospital during normal daily activities, sport, etc. The brain core temperature "BCT" sensor, developed by the Biomedical Microsensors dpt of LPM at INSA-Lyon is a flexible active sensor using "zero-heat-flow" principle. The sensor has been used for experimental measurement: brain temperature during mental activity, and in hospital for the study of circadian rhythms. The results are in agreement with the measurement by the rectal probe. There are 2 versions of this sensor: a non ambulatory for the use in hospitals, and an ambulatory version using teletransmission. We are working for improving the autonomy of the ambulatory version up to several days. This wearable biomedical sensor (WBS) can be used for circadian assessment for chronobiology studies and in medical therapies.

Brain age predicts mortality

PubMed Central

Cole, J H; Ritchie, S J; Bastin, M E; Valdés Hernández, M C; Muñoz Maniega, S; Royle, N; Corley, J; Pattie, A; Harris, S E; Zhang, Q; Wray, N R; Redmond, P; Marioni, R E; Starr, J M; Cox, S R; Wardlaw, J M; Sharp, D J; Deary, I J

2018-01-01

Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, ‘brain-predicted age’, derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N=2001), then tested in the Lothian Birth Cohort 1936 (N=669), to determine relationships with age-associated functional measures and mortality. Having a brain-predicted age indicative of an older-appearing brain was associated with: weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load and increased mortality risk. Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted age and DNA-methylation-predicted age did. This indicates that neuroimaging and epigenetics measures of ageing can provide complementary data regarding health outcomes. Our study introduces a clinically-relevant neuroimaging ageing biomarker and demonstrates that combining distinct measurements of biological ageing further helps to determine risk of age-related deterioration and death. PMID:28439103

Optical imaging characterizing brain response to thermal insult in injured rodent

NASA Astrophysics Data System (ADS)

Abookasis, David; Shaul, Oren; Meitav, Omri; Pinhasi, Gadi A.

2018-02-01

We used spatially modulated optical imaging system to assess the effect of temperature elevation on intact brain tissue in a mouse heatstress model. Heatstress or heatstroke is a medical emergency defined by abnormally elevated body temperature that causes biochemical, physiological and hematological changes. During experiments, brain temperature was measured concurrently with a thermal camera while core body temperature was monitored with rectal thermocouple probe. Changes in a battery of macroscopic brain physiological parameters, such as hemoglobin oxygen saturation level, cerebral water content, as well as intrinsic tissue optical properties were monitored during temperature elevation. These concurrent changes reflect the pathophysiology of the brain during heatstress and demonstrate successful monitoring of thermoregulation mechanisms. In addition, the variation of tissue refractive index was calculated showing a monotonous decrease with increasing wavelength. We found increased temperature to greatly affect both the scattering properties and refractive index which represent cellular and subcellular swelling indicative of neuronal damage. The overall trends detected in brain tissue parameters were consistent with previous observations using conventional medical devices and optical modalities.

MR Guidance, Monitoring and Control of Brain HIFU Therapy in Small Animals: In Vivo Demonstration in Rats

NASA Astrophysics Data System (ADS)

Larrat, B.; Pernot, M.; Dervishi, E.; Souilah, A.; Seilhean, D.; Marie, Y.; Boch, A. L.; Aubry, J. F.; Fink, M.; Tanter, M.

2010-03-01

In the framework of HIFU transcranial brain therapy, it is mandatory to develop techniques capable of assessing the focusing quality and location before the treatment. Monitoring heat deposition in real time and verifying the extension of the treated area are also important steps. In this study, an imaging protocol is proposed to:1/ locate the US radiation force induced displacement in tissues and quantify the acoustic pressure at focus prior to HIFU; 2/ monitor the temperature rise during HIFU; and 3/ assess the changes in elasticity in the treated area. A 7T MRI scanner was equipped with a home-made stereotactic frame for rats and a US focused transducer working at 1.5 MHz. Such a tool is key for the evaluation of the biological effects of HIFU on brain tissue and tumors. The proposed protocol was successfully tested on 12 rats with and without injected tumors. The accurate localization of the focal point prior to HIFU was demonstrated in vivo. Furthermore, the pressure estimation in situ allowed to accurately simulate the heat deposition at focus and to plan the treatment (electrical power, duration). The temperature measurements were in good accordance with the predicted curves. The elasticity maps showed significant changes after treatment in some cases.

Regulation of brain temperature in winter-acclimatized reindeer under heat stress.

PubMed

Blix, Arnoldus Schytte; Walløe, Lars; Folkow, Lars P

2011-11-15

Reindeer (Rangifer tarandus) are protected against the Arctic winter cold by thick fur of prime insulating capacity and hence have few avenues of heat loss during work. We have investigated how these animals regulate brain temperature under heavy heat loads. Animals were instrumented for measurements of blood flow, tissue temperatures and respiratory frequency (f) under full anaesthesia, whereas measurements were also made in fully conscious animals while in a climatic chamber or running on a treadmill. At rest, brain temperature (T(brain)) rose from 38.5±0.1°C at 10°C to 39.5±0.2°C at 50°C, while f increased from ×7 to ×250 breaths min(-1), with a change to open-mouth panting (OMP) at T(brain) 39.0±0.1°C, and carotid and sublingual arterial flows increased by 160% and 500%, respectively. OMP caused jugular venous and carotid arterial temperatures to drop, presumably owing to a much increased respiratory evaporative heat loss. Angular oculi vein (AOV) flow was negligible until T(brain) reached 38.9±0.1°C, but it increased to 0.81 ml min(-1) kg(-1) at T(brain) 39.2±0.2°C. Bilateral occlusion of both AOVs induced OMP and a rise in T(brain) and f at T(brain) >38.8°C. We propose that reindeer regulate body and, particularly, brain temperature under heavy heat loads by a combination of panting, at first through the nose, but later, when the heat load and the minute volume requirements increase due to exercise, primarily through the mouth and that they eventually resort to selective brain cooling.

Numerical simulation of high intensity focused ultrasound temperature distribution for transcranial brain therapy

NASA Astrophysics Data System (ADS)

Zhang, Qian; Wang, Yizhe; Zhou, Wenzheng; Zhang, Ji; Jian, Xiqi

2017-03-01

To provide a reference for the HIFU clinical therapeutic planning, the temperature distribution and lesion volume are analyzed by the numerical simulation. The adopted numerical simulation is based on a transcranial ultrasound therapy model, including an 8 annular-element curved phased array transducer. The acoustic pressure and temperature elevation are calculated by using the approximation of Westervelt Formula and the Pennes Heat Transfer Equation. In addition, the Time Reversal theory and eliminating hot spot technique are combined to optimize the temperature distribution. With different input powers and exposure times, the lesion volume is evaluated based on temperature threshold theory. The lesion region could be restored at the expected location by the time reversal theory. Although the lesion volume reduces after eliminating the peak temperature in the skull and more input power and exposure time is required, the injury of normal tissue around skull could be reduced during the HIFU therapy. The prediction of thermal deposition in the skull and the lesion region could provide a reference for clinical therapeutic dose.

Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images.

PubMed

Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S; Lin, Weili; Shen, Dinggang

2015-09-01

Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient's exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [(18)F]FDG PET image by using a low-dose brain [(18)F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. The authors employ a regression forest for predicting the standard-dose brain [(18)F]FDG PET image by low-dose brain [(18)F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [(18)F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [(18)F]FDG PET image and substantially enhanced image quality of low-dose brain [(18)F]FDG PET image. In this paper, the authors propose a framework to generate standard-dose brain [(18)F]FDG PET image using low-dose brain [(18)F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [(18)F]FDG PET can be well-predicted using MRI and low-dose brain [(18)F]FDG PET.

Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images

PubMed Central

Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S.; Lin, Weili; Shen, Dinggang

2015-01-01

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [18F]FDG PET image by using a low-dose brain [18F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [18F]FDG PET image by low-dose brain [18F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [18F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [18F]FDG PET image and substantially enhanced image quality of low-dose brain [18F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [18F]FDG PET image using low-dose brain [18F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [18F]FDG PET can be well-predicted using MRI and low-dose brain [18F]FDG PET. PMID:26328979

Prediction of standard-dose brain PET image by using MRI and low-dose brain [{sup 18}F]FDG PET images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kang, Jiayin; Gao, Yaozong; Shi, Feng

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. Asmore » yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [{sup 18}F]FDG PET image by using a low-dose brain [{sup 18}F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [{sup 18}F]FDG PET image by low-dose brain [{sup 18}F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [{sup 18}F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [{sup 18}F]FDG PET image and substantially enhanced image quality of low-dose brain [{sup 18}F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [{sup 18}F]FDG PET image using low-dose brain [{sup 18}F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [{sup 18}F]FDG PET can be well-predicted using MRI and low-dose brain [{sup 18}F]FDG PET.« less

Simultaneous in vivo recording of local brain temperature and electrophysiological signals with a novel neural probe

NASA Astrophysics Data System (ADS)

Fekete, Z.; Csernai, M.; Kocsis, K.; Horváth, Á. C.; Pongrácz, A.; Barthó, P.

2017-06-01

Objective. Temperature is an important factor for neural function both in normal and pathological states, nevertheless, simultaneous monitoring of local brain temperature and neuronal activity has not yet been undertaken. Approach. In our work, we propose an implantable, calibrated multimodal biosensor that facilitates the complex investigation of thermal changes in both cortical and deep brain regions, which records multiunit activity of neuronal populations in mice. The fabricated neural probe contains four electrical recording sites and a platinum temperature sensor filament integrated on the same probe shaft within a distance of 30 µm from the closest recording site. The feasibility of the simultaneous functionality is presented in in vivo studies. The probe was tested in the thalamus of anesthetized mice while manipulating the core temperature of the animals. Main results. We obtained multiunit and local field recordings along with measurement of local brain temperature with accuracy of 0.14 °C. Brain temperature generally followed core body temperature, but also showed superimposed fluctuations corresponding to epochs of increased local neural activity. With the application of higher currents, we increased the local temperature by several degrees without observable tissue damage between 34-39 °C. Significance. The proposed multifunctional tool is envisioned to broaden our knowledge on the role of the thermal modulation of neuronal activity in both cortical and deeper brain regions.

From blood oxygenation level dependent (BOLD) signals to brain temperature maps.

PubMed

Sotero, Roberto C; Iturria-Medina, Yasser

2011-11-01

A theoretical framework is presented for converting Blood Oxygenation Level Dependent (BOLD) images to brain temperature maps, based on the idea that disproportional local changes in cerebral blood flow (CBF) as compared with cerebral metabolic rate of oxygen consumption (CMRO₂) during functional brain activity, lead to both brain temperature changes and the BOLD effect. Using an oxygen limitation model and a BOLD signal model, we obtain a transcendental equation relating CBF and CMRO₂ changes with the corresponding BOLD signal, which is solved in terms of the Lambert W function. Inserting this result in the dynamic bioheat equation describing the rate of temperature changes in the brain, we obtain a nonautonomous ordinary differential equation that depends on the BOLD response, which is solved numerically for each brain voxel. Temperature maps obtained from a real BOLD dataset registered in an attention to visual motion experiment were calculated, obtaining temperature variations in the range: (-0.15, 0.1) which is consistent with experimental results. The statistical analysis revealed that significant temperature activations have a similar distribution pattern than BOLD activations. An interesting difference was the activation of the precuneus in temperature maps, a region involved in visuospatial processing, an effect that was not observed on BOLD maps. Furthermore, temperature maps were more localized to gray matter regions than the original BOLD maps, showing less activated voxels in white matter and cerebrospinal fluid.

Relationship between temperature variability and brain injury on magnetic resonance imaging in cooled newborn infants after perinatal asphyxia.

PubMed

Brotschi, B; Gunny, R; Rethmann, C; Held, U; Latal, B; Hagmann, C

2017-09-01

The objective of the study was whether temperature management during therapeutic hypothermia correlates with the severity of brain injury assessed on magnetic resonance imaging in term infants with hypoxic-ischemic encephalopathy. Prospectively collected register data from the National Asphyxia and Cooling Register of Switzerland were analyzed. Fifty-five newborn infants were cooled for 72 h with a target temperature range of 33 to 34 °C. Individual temperature variability (odds ratio (OR) 40.17 (95% confidence interval (CI) 1.37 to 1037.67)) and percentage of temperatures within the target range (OR 0.95 (95% CI 0.90 to 0.98)) were associated with the severity of brain injury seen on magnetic resonance imaging (MRI). Neither the percentage of measured temperatures above (OR 1.08 (95% CI 0.96 to 1.21)) nor below (OR 0.99 (95% CI 0.92 to 1.07) the target range was associated with the severity of brain injury seen on MRI. In a national perinatal asphyxia cohort, temperature variability and percentage of temperatures within the target temperature range were associated with the severity of brain injury.

Prediction of individual brain maturity using fMRI.

PubMed

Dosenbach, Nico U F; Nardos, Binyam; Cohen, Alexander L; Fair, Damien A; Power, Jonathan D; Church, Jessica A; Nelson, Steven M; Wig, Gagan S; Vogel, Alecia C; Lessov-Schlaggar, Christina N; Barnes, Kelly Anne; Dubis, Joseph W; Feczko, Eric; Coalson, Rebecca S; Pruett, John R; Barch, Deanna M; Petersen, Steven E; Schlaggar, Bradley L

2010-09-10

Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals' brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55% of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain's major functional networks.

Physiological complexity of acute traumatic brain injury in patients treated with a brain oxygen protocol: utility of symbolic regression in predictive modeling of a dynamical system.

PubMed

Narotam, Pradeep K; Morrison, John F; Schmidt, Michael D; Nathoo, Narendra

2014-04-01

Predictive modeling of emergent behavior, inherent to complex physiological systems, requires the analysis of large complex clinical data streams currently being generated in the intensive care unit. Brain tissue oxygen protocols have yielded outcome benefits in traumatic brain injury (TBI), but the critical physiological thresholds for low brain oxygen have not been established for a dynamical patho-physiological system. High frequency, multi-modal clinical data sets from 29 patients with severe TBI who underwent multi-modality neuro-clinical care monitoring and treatment with a brain oxygen protocol were analyzed. The inter-relationship between acute physiological parameters was determined using symbolic regression (SR) as the computational framework. The mean patient age was 44.4±15 with a mean admission GCS of 6.6±3.9. Sixty-three percent sustained motor vehicle accidents and the most common pathology was intra-cerebral hemorrhage (50%). Hospital discharge mortality was 21%, poor outcome occurred in 24% of patients, and good outcome occurred in 56% of patients. Criticality for low brain oxygen was intracranial pressure (ICP) ≥22.8 mm Hg, for mortality at ICP≥37.1 mm Hg. The upper therapeutic threshold for cerebral perfusion pressure (CPP) was 75 mm Hg. Eubaric hyperoxia significantly impacted partial pressure of oxygen in brain tissue (PbtO2) at all ICP levels. Optimal brain temperature (Tbr) was 34-35°C, with an adverse effect when Tbr≥38°C. Survivors clustered at [Formula: see text] Hg vs. non-survivors [Formula: see text] 18 mm Hg. There were two mortality clusters for ICP: High ICP/low PbtO2 and low ICP/low PbtO2. Survivors maintained PbtO2 at all ranges of mean arterial pressure in contrast to non-survivors. The final SR equation for cerebral oxygenation is: [Formula: see text]. The SR-model of acute TBI advances new physiological thresholds or boundary conditions for acute TBI management: PbtO2≥25 mmHg; ICP≤22 mmHg; CPP≈60-75 mmHg; and Tbr≈34-37°C. SR is congruous with the emerging field of complexity science in the modeling of dynamical physiological systems, especially during pathophysiological states. The SR model of TBI is generalizable to known physical laws. This increase in entropy reduces uncertainty and improves predictive capacity. SR is an appropriate computational framework to enable future smart monitoring devices.

Far-field brainstem responses evoked by vestibular and auditory stimuli exhibit increases in interpeak latency as brain temperature is decreased

NASA Technical Reports Server (NTRS)

Hoffman, L. F.; Horowitz, J. M.

1984-01-01

The effect of decreasing of brain temperature on the brainstem auditory evoked response (BAER) in rats was investigated. Voltage pulses, applied to a piezoelectric crystal attached to the skull, were used to evoke stimuli in the auditory system by means of bone-conducted vibrations. The responses were recorded at 37 C and 34 C brain temperatures. The peaks of the BAER recorded at 34 C were delayed in comparison with the peaks from the 37 C wave, and the later peaks were more delayed than the earlier peaks. These results indicate that an increase in the interpeak latency occurs as the brain temperature is decreased. Preliminary experiments, in which responses to brief angular acceleration were used to measure the brainstem vestibular evoked response (BVER), have also indicated increases in the interpeak latency in response to the lowering of brain temperature.

The critical limiting temperature and selective brain cooling: neuroprotection during exercise?

PubMed

Marino, Frank E

2011-01-01

There is wide consensus that long duration exercise in the heat is impaired compared with cooler conditions. A common observation when examining exercise tolerance in the heat in laboratory studies is the critical limiting core temperature (CLT) and the apparent attenuation in central nervous system (CNS) drive leading to premature fatigue. Selective brain cooling (SBC) purportedly confers neuroprotection during exercise heat stress by attenuating the increase in brain temperature. As the CLT is dependent on heating to invoke a reduction in efferent drive, it is thus not compatible with SBC which supposedly attenuates the rise in brain temperature. Therefore, the CLT and SBC hypotheses cannot be complimentary if the goal is to confer neuroprotection from thermal insult as it is counter-intuitive to selectively cool the brain if the purpose of rising brain temperature is to down-regulate skeletal muscle recruitment. This presents a circular model for which there is no apparent end to the ultimate physiological outcome; a 'hot brain' selectively cooled in order to reduce the CNS drive to skeletal muscle. This review will examine the postulates of the CLT and SBC with their relationship to the avoidance of a 'hot brain' which together argue for a theoretical position against neuroprotection as the key physiological strategy in exercise-induced hyperthermia.

Brain stem sites mediating specific and non-specific temperature effects on thermoregulation in the pekin duck.

PubMed Central

Martin, R; Simon, E; Simon-Oppermann, C

1981-01-01

1. Thermodes were chronically implanted into various levels of the brain stem of sixteen Pekin ducks. The effects of local thermal stimulation on metabolic heat production, core temperature, peripheral skin temperature and respiratory frequency were investigated. 2. Four areas of thermode positions were determined according to the responses observed and were histologically identified at the end of the investigation. 3. Thermal stimulation of the lower mid-brain/upper pontine brain stem (Pos. III) elicited an increase in metabolic heat production, cutaneous vasoconstriction and rises in core temperature in response to cooling at thermoneutral and cold ambient conditions and, further, inhibition of panting by cooling and activation of panting by heating at warm ambient conditions. The metabolic response to cooling this brain stem section amounted to -0.1 W/kg. degrees C as compared with -7 W/kg. degrees C in response to total body cooling. 4. Cooling of the anterior and middle hypothalamus (Pos. II) caused vasodilatation in the skin and did not elicit shivering. The resulting drop in core temperature at a given degree of cooling was greater than the rise in core temperature in response to equivalent cooling of the lower mid-brain/upper pontine brain stem. 5. Cooling of the preoptic forebrain (Pos. I) and of the myelencephalon (Pos. IV) did not elicit thermoregulatory reactions. 6. It is concluded that the duck's brain stem contains thermoreceptive structures in the lower mid-brain/upper pontine section. However, the brain stem as a whole appears to contribute little to cold defence during general hypothermia because of the inhibitory effects originating in the anterior and middle hypothalamus. Cold defence in the duck, which is comparable in strength to that in mammals, has to rely on extracerebral thermosensory structures. PMID:7310688

Temperature dependence and GABA modulation of (TH)triazolam binding in the rat brain

DOE Office of Scientific and Technical Information (OSTI.GOV)

Earle, M.E.; Concas, A.; Wamsley, J.K.

1987-07-27

The hypnotic triazolam (TZ), a triazolobenzodiazepine displays a short physiological half life and has been used for the treatment of insomnia related to anxiety states. The authors major objectives were the direct measurement of the temperature dependence and the gamma-aminobutyric acid (GABA) effect of (TH)TZ binding in the rat brain. Saturation studies showed a shift to lower affinity with increasing temperatures (K/sub d/ = 0.27 +/- 08 nM at 0C; K/sub d/ = 1.96 +/- 0.85 nM at 37C) while the B/sub max/ values remained unchanged (1220 +/- 176 fmoles/mg protein at 0C and 1160 +/- 383 fmoles/mg protein atmore » 37C). Saturation studies of (TH)TZ binding in the presence or absence of GABA (100 M) showed a GABA-shift. At 0C the K/sub d/ values were (K/sub d/ = 0.24 +/- 0.03 nM/-GABA; K/sub d/ = 0.16 +/- 0.04/+GABA) and at 37C the K/sub d/ values were (K/sub d/ = 1.84 +/- 0.44 nM/-GABA; K/sub d/ = 0.95 +/- 0.29 nM/+GABA). In contrast to reported literature, the authors findings show that TZ interacts with benzodiazepine receptors with a temperature dependence and GABA-shift consistent with predicted behavior for benzodiazepine agonists. 20 references, 3 tables.« less

Clinical safety of 3-T brain magnetic resonance imaging in newborns.

PubMed

Fumagalli, Monica; Cinnante, Claudia Maria; Calloni, Sonia Francesca; Sorrentino, Gabriele; Gorla, Ilaria; Plevani, Laura; Pesenti, Nicola; Sirgiovanni, Ida; Mosca, Fabio; Triulzi, Fabio

2018-03-29

The effects and potential hazards of brain magnetic resonance imaging (MRI) at 3 T in newborns are debated. Assess the impact of 3-T MRI in newborns on body temperature and physiological parameters. Forty-nine newborns, born preterm and at term, underwent 3-T brain MRI at term-corrected age. Rectal and skin temperature, oxygen saturation and heart rate were recorded before, during and after the scan. A statistically significant increase in skin temperature of 0.6 °C was observed at the end of the MRI scan (P<0.01). There was no significant changes in rectal temperature, heart rate or oxygen saturation. Core temperature, heart rate and oxygen saturation in newborns were not affected by 3-T brain MR scanning.

Brain size and thermoregulation during the evolution of the genus Homo.

PubMed

Naya, Daniel E; Naya, Hugo; Lessa, Enrique P

2016-01-01

Several hypotheses have been proposed to explain the evolution of an energetically costly brain in the genus Homo. Some of these hypotheses are based on the correlation between climatic factors and brain size recorded for this genus during the last millions of years. In this study, we propose a complementary climatic hypothesis that is based on the mechanistic connection between temperature, thermoregulation, and size of internal organs in endothermic species. We hypothesized that global cooling during the last 3.2 my may have imposed an increased energy expenditure for thermoregulation, which in the case of hominids could represent a driver for the evolution of an expanded brain, or at least, it could imply the relaxation of a negative selection pressure acting upon this costly organ. To test this idea, here we (1) assess variation in the energetic costs of thermoregulation and brain maintenance for the last 3.2 my, and (2) evaluate the relationship between Earth temperature and brain maintenance cost for the same period, taking into account the effects of body mass and fossil age. We found that: (1) the energetic cost associated with brain enlargement represents an important fraction (between 47.5% and 82.5%) of the increase in energy needed for thermoregulation; (2) fossil age is a better predictor of brain maintenance cost than Earth temperature, suggesting that (at least) another factor correlated with time was more relevant than ambient temperature in brain size evolution; and (3) there is a significant negative correlation between the energetic cost of brain and Earth temperature, even after accounting for the effect of body mass and fossil age. Thus, our results expand the current energetic framework for the study of brain size evolution in our lineage by suggesting that a fall in Earth temperature during the last millions of years may have facilitated brain enlargement. Copyright © 2015 Elsevier Inc. All rights reserved.

Temperature monitoring during cardiopulmonary bypass--do we undercool or overheat the brain?

PubMed

Kaukuntla, Hemanth; Harrington, Deborah; Bilkoo, Inderaj; Clutton-Brock, Tom; Jones, Timothy; Bonser, Robert S

2004-09-01

Brain cooling is an essential component of aortic surgery requiring circulatory arrest and inadequate cooling may lead to brain injury. Similarly, brain hyperthermia during the rewarming phase of cardiopulmonary bypass may also lead to neurological injury. Conventional temperature monitoring sites may not reflect the core brain temperature (Tdegrees). We compared jugular bulb venous temperatures (JB) during deep hypothermic circulatory arrest and normothermic bypass with Nasopharyngeal (NP), Arterial inflow (AI), Oesophageal (O), Venous return (VR), Bladder (B) and Orbital skin (OS) temperatures. 18 patients undergoing deep hypothermia (DH) and 8 patients undergoing normothermic bypass (mean bladder Tdegrees-36.29 degreesC) were studied. For DH, cooling was continued to 15 degreesC NP (mean cooling time-66 min). At pre-determined arterial inflow Tdegrees, NP, JB and O Tdegree's were measured. A 6-channel recorder continuously recorded all Tdegree's using calibrated thermocouples. During the cooling phase of DH, NP lagged behind AI and JB Tdegree's. All these equilibrated at 15 degreesC. During rewarming, JB and NP lagged behind AI and JB was higher than NP at any time point. During normothermic bypass, although NP was reflective of the AI and JB Tdegrees trends, it underestimated peak JB Tdegrees (P=0.001). Towards the end of bypass, peak JB was greater than the arterial inflow Tdegrees (P=0.023). If brain venous outflow Tdegrees (JB) accurately reflects brain Tdegrees, NP Tdegrees is a safe surrogate indicator of cooling. During rewarming, all peripheral sites underestimate brain temperature and caution is required to avoid hyperthermic arterial inflow, which may inadvertently, result in brain hyperthermia.

Physiological Fluctuations in Brain Temperature as a Factor Affecting Electrochemical Evaluations of Extracellular Glutamate and Glucose in Behavioral Experiments

PubMed Central

2013-01-01

The rate of any chemical reaction or process occurring in the brain depends on temperature. While it is commonly believed that brain temperature is a stable, tightly regulated homeostatic parameter, it fluctuates within 1–4 °C following exposure to salient arousing stimuli and neuroactive drugs, and during different behaviors. These temperature fluctuations should affect neural activity and neural functions, but the extent of this influence on neurochemical measurements in brain tissue of freely moving animals remains unclear. In this Review, we present the results of amperometric evaluations of extracellular glutamate and glucose in awake, behaving rats and discuss how naturally occurring fluctuations in brain temperature affect these measurements. While this temperature contribution appears to be insignificant for glucose because its extracellular concentrations are large, it is a serious factor for electrochemical evaluations of glutamate, which is present in brain tissue at much lower levels, showing smaller phasic fluctuations. We further discuss experimental strategies for controlling the nonspecific chemical and physical contributions to electrochemical currents detected by enzyme-based biosensors to provide greater selectivity and reliability of neurochemical measurements in behaving animals. PMID:23448428

Automatic Incubator-type Temperature Control System for Brain Hypothermia Treatment

NASA Astrophysics Data System (ADS)

Gaohua, Lu; Wakamatsu, Hidetoshi

An automatic air-cooling incubator is proposed to replace the manual water-cooling blanket to control the brain tissue temperature for brain hypothermia treatment. Its feasibility is theoretically discussed as follows: First, an adult patient with the cooling incubator is modeled as a linear dynamical patient-incubator biothermal system. The patient is represented by an 18-compartment structure and described by its state equations. The air-cooling incubator provides almost same cooling effect as the water-cooling blanket, if a light breeze of speed around 3 m/s is circulated in the incubator. Then, in order to control the brain temperature automatically, an adaptive-optimal control algorithm is adopted, while the patient-blanket therapeutic system is considered as a reference model. Finally, the brain temperature of the patient-incubator biothermal system is controlled to follow up the given reference temperature course, in which an adaptive algorithm is confirmed useful for unknown environmental change and/or metabolic rate change of the patient in the incubating system. Thus, the present work ensures the development of the automatic air-cooling incubator for a better temperature regulation of the brain hypothermia treatment in ICU.

Brain temperature in volunteers subjected to intranasal cooling.

PubMed

Covaciu, L; Weis, J; Bengtsson, C; Allers, M; Lunderquist, A; Ahlström, H; Rubertsson, S

2011-08-01

Intranasal cooling can be used to initiate therapeutic hypothermia. However, direct measurement of brain temperature is difficult and the intra-cerebral distribution of temperature changes with cooling is unknown. The purpose of this study was to measure the brain temperature of human volunteers subjected to intranasal cooling using non-invasive magnetic resonance (MR) methods. Intranasal balloons catheters circulated with saline at 20°C were applied for 60 min in ten awake volunteers. No sedation was used. Brain temperature changes were measured and mapped using MR spectroscopic imaging (MRSI) and phase-mapping techniques. Heart rate and blood pressure were monitored throughout the experiment. Rectal temperature was measured before and after the cooling. Mini Mental State Examination (MMSE) test and nasal inspection were done before and after the cooling. Questionnaires about the subjects' personal experience were completed after the experiment. Brain temperature decrease measured by MRSI was -1.7 ± 0.8°C and by phase-mapping -1.8 ± 0.9°C (n = 9) at the end of cooling. Spatial distribution of temperature changes was relatively uniform. Rectal temperature decreased by -0.5 ± 0.3°C (n = 5). The physiological parameters were stable and no shivering was reported. The volunteers remained alert during cooling and no cognitive dysfunctions were apparent in the MMSE test. Postcooling nasal examination detected increased nasal secretion in nine of the ten volunteers. Volunteers' acceptance of the method was good. Both MR techniques revealed brain temperature reductions after 60 min of intranasal cooling with balloons circulated with saline at 20°C in awake, unsedated volunteers.

The relevance of MRI for patient modeling in head and neck hyperthermia treatment planning: a comparison of CT and CT-MRI based tissue segmentation on simulated temperature.

PubMed

Verhaart, René F; Fortunati, Valerio; Verduijn, Gerda M; van der Lugt, Aad; van Walsum, Theo; Veenland, Jifke F; Paulides, Margarethus M

2014-12-01

In current clinical practice, head and neck (H&N) hyperthermia treatment planning (HTP) is solely based on computed tomography (CT) images. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast over CT. The purpose of the authors' study is to investigate the relevance of using MRI in addition to CT for patient modeling in H&N HTP. CT and MRI scans were acquired for 11 patients in an immobilization mask. Three observers manually segmented on CT, MRI T1 weighted (MRI-T1w), and MRI T2 weighted (MRI-T2w) images the following thermo-sensitive tissues: cerebrum, cerebellum, brainstem, myelum, sclera, lens, vitreous humor, and the optical nerve. For these tissues that are used for patient modeling in H&N HTP, the interobserver variation of manual tissue segmentation in CT and MRI was quantified with the mean surface distance (MSD). Next, the authors compared the impact of CT and CT and MRI based patient models on the predicted temperatures. For each tissue, the modality was selected that led to the lowest observer variation and inserted this in the combined CT and MRI based patient model (CT and MRI), after a deformable image registration. In addition, a patient model with a detailed segmentation of brain tissues (including white matter, gray matter, and cerebrospinal fluid) was created (CT and MRIdb). To quantify the relevance of MRI based segmentation for H&N HTP, the authors compared the predicted maximum temperatures in the segmented tissues (Tmax) and the corresponding specific absorption rate (SAR) of the patient models based on (1) CT, (2) CT and MRI, and (3) CT and MRIdb. In MRI, a similar or reduced interobserver variation was found compared to CT (maximum of median MSD in CT: 0.93 mm, MRI-T1w: 0.72 mm, MRI-T2w: 0.66 mm). Only for the optical nerve the interobserver variation is significantly lower in CT compared to MRI (median MSD in CT: 0.58 mm, MRI-T1w: 1.27 mm, MRI-T2w: 1.40 mm). Patient models based on CT (Tmax: 38.0 °C) and CT and MRI (Tmax: 38.1 °C) result in similar simulated temperatures, while CT and MRIdb (Tmax: 38.5 °C) resulted in significantly higher temperatures. The SAR corresponding to these temperatures did not differ significantly. Although MR imaging reduces the interobserver variation in most tissues, it does not affect simulated local tissue temperatures. However, the improved soft-tissue contrast provided by MRI allows generating a detailed brain segmentation, which has a strong impact on the predicted local temperatures and hence may improve simulation guided hyperthermia.

Using connectome-based predictive modeling to predict individual behavior from brain connectivity

PubMed Central

Shen, Xilin; Finn, Emily S.; Scheinost, Dustin; Rosenberg, Monica D.; Chun, Marvin M.; Papademetris, Xenophon; Constable, R Todd

2017-01-01

Neuroimaging is a fast developing research area where anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale datasets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: 1) feature selection, 2) feature summarization, 3) model building, and 4) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a significant amount of the variance in these measures. It has been demonstrated that the CPM protocol performs equivalently or better than most of the existing approaches in brain-behavior prediction. However, because CPM focuses on linear modeling and a purely data-driven driven approach, neuroscientists with limited or no experience in machine learning or optimization would find it easy to implement the protocols. Depending on the volume of data to be processed, the protocol can take 10–100 minutes for model building, 1–48 hours for permutation testing, and 10–20 minutes for visualization of results. PMID:28182017

TU-G-210-02: TRANS-FUSIMO - An Integrative Approach to Model-Based Treatment Planning of Liver FUS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Preusser, T.

Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such asmore » the liver or kidney. Douglas Christensen – Modeling for Breast and Brain HIFU Treatment Planning Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Learning Objectives: Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments. Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies. Determine how to obtain appropriate acoustic modeling parameters from clinical images. Understand the separate role of absorption and scattering in energy delivery to tissues. See how organ motion can be compensated for in ultrasound therapies. Compare simulated data with clinical temperature measurements in transcranial applications. Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007-2013) under 270186 (FUSIMO) and 611889 (TRANS-FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV)« less

Ischemic Brain Injury Leads to Brain Edema via Hyperthermia-Induced TRPV4 Activation.

PubMed

Hoshi, Yutaka; Okabe, Kohki; Shibasaki, Koji; Funatsu, Takashi; Matsuki, Norio; Ikegaya, Yuji; Koyama, Ryuta

2018-06-20

Brain edema is characterized by an increase in net brain water content, which results in an increase in brain volume. Although brain edema is associated with a high fatality rate, the cellular and molecular processes of edema remain largely unclear. Here, we developed an in vitro model of ischemic stroke-induced edema in which male mouse brain slices were treated with oxygen-glucose deprivation (OGD) to mimic ischemia. We continuously measured the cross-sectional area of the brain slice for 150 min under macroscopic microscopy, finding that OGD induces swelling of brain slices. OGD-induced swelling was prevented by pharmacologically blocking or genetically knocking out the transient receptor potential vanilloid 4 (TRPV4), a member of the thermosensitive TRP channel family. Because TRPV4 is activated at around body temperature and its activation is enhanced by heating, we next elevated the temperature of the perfusate in the recording chamber, finding that hyperthermia induces swelling via TRPV4 activation. Furthermore, using the temperature-dependent fluorescence lifetime of a fluorescent-thermosensitive probe, we confirmed that OGD treatment increases the temperature of brain slices through the activation of glutamate receptors. Finally, we found that brain edema following traumatic brain injury was suppressed in TRPV4-deficient male mice in vivo Thus, our study proposes a novel mechanism: hyperthermia activates TRPV4 and induces brain edema after ischemia. SIGNIFICANCE STATEMENT Brain edema is characterized by an increase in net brain water content, which results in an increase in brain volume. Although brain edema is associated with a high fatality rate, the cellular and molecular processes of edema remain unclear. Here, we developed an in vitro model of ischemic stroke-induced edema in which mouse brain slices were treated with oxygen-glucose deprivation. Using this system, we showed that the increase in brain temperature and the following activation of the thermosensitive cation channel TRPV4 (transient receptor potential vanilloid 4) are involved in the pathology of edema. Finally, we confirmed that TRPV4 is involved in brain edema in vivo using TRPV4-deficient mice, concluding that hyperthermia activates TRPV4 and induces brain edema after ischemia. Copyright © 2018 the authors 0270-6474/18/385700-10$15.00/0.

Post-anoxic quantitative MRI changes may predict emergence from coma and functional outcomes at discharge.

PubMed

Reynolds, Alexandra S; Guo, Xiaotao; Matthews, Elizabeth; Brodie, Daniel; Rabbani, Leroy E; Roh, David J; Park, Soojin; Claassen, Jan; Elkind, Mitchell S V; Zhao, Binsheng; Agarwal, Sachin

2017-08-01

Traditional predictors of neurological prognosis after cardiac arrest are unreliable after targeted temperature management. Absence of pupillary reflexes remains a reliable predictor of poor outcome. Diffusion-weighted imaging has emerged as a potential predictor of recovery, and here we compare imaging characteristics to pupillary exam. We identified 69 patients who had MRIs within seven days of arrest and used a semi-automated algorithm to perform quantitative volumetric analysis of apparent diffusion coefficient (ADC) sequences at various thresholds. Area under receiver operating characteristic curves (ROC-AUC) were estimated to compare predictive values of quantitative MRI with pupillary exam at days 3, 5 and 7 post-arrest, for persistence of coma and functional outcomes at discharge. Cerebral Performance Category scores of 3-4 were considered poor outcome. Excluding patients where life support was withdrawn, ≥2.8% diffusion restriction of the entire brain at an ADC of ≤650×10 -6 m 2 /s was 100% specific and 68% sensitive for failure to wake up from coma before discharge. The ROC-AUC of ADC changes at ≤450×10 -6 mm 2 /s and ≤650×10 -6 mm 2 /s were significantly superior in predicting failure to wake up from coma compared to bilateral absence of pupillary reflexes. Among survivors, >0.01% of diffusion restriction of the entire brain at an ADC ≤450×10 -6 m 2 /s was 100% specific and 46% sensitive for poor functional outcome at discharge. The ROC curve predicting poor functional outcome at ADC ≤450×10 -6 mm 2 /s had an AUC of 0.737 (0.574-0.899, p=0.04). Post-anoxic diffusion changes using quantitative brain MRI may aid in predicting persistent coma and poor functional outcomes at hospital discharge. Copyright © 2017 Elsevier B.V. All rights reserved.

The Human Burst Suppression Electroencephalogram of Deep Hypothermia

PubMed Central

Kumaraswamy, Vishakhadatta M.; Akeju, Seun Oluwaseun; Pierce, Eric; Cash, Sydney S.; Kilbride, Ronan; Brown, Emery N.; Purdon, Patrick L.

2015-01-01

Objective Deep hypothermia induces ‘burst suppression’ (BS), an electroencephalogram pattern with low-voltage ‘suppressions’ alternating with high-voltage ‘bursts’. Current understanding of BS comes mainly from anesthesia studies, while hypothermia-induced BS has received little study. We set out to investigate the electroencephalogram changes induced by cooling the human brain through increasing depths of BS through isoelectricity. Methods We recorded scalp electroencephalograms from eleven patients undergoing deep hypothermia during cardiac surgery with complete circulatory arrest, and analyzed these using methods of spectral analysis. Results Within patients, the depth of BS systematically depends on the depth of hypothermia, though responses vary between patients except at temperature extremes. With decreasing temperature, burst lengths increase, and burst amplitudes and lengths decrease, while the spectral content of bursts remains constant. Conclusions These findings support an existing theoretical model in which the common mechanism of burst suppression across diverse etiologies is the cyclical diffuse depletion of metabolic resources, and suggest the new hypothesis of local micro-network dropout to explain decreasing burst amplitudes at lower temperatures. Significance These results pave the way for accurate noninvasive tracking of brain metabolic state during surgical procedures under deep hypothermia, and suggest new testable predictions about the network mechanisms underlying burst suppression. PMID:25649968

The human burst suppression electroencephalogram of deep hypothermia.

PubMed

Westover, M Brandon; Ching, Shinung; Kumaraswamy, Vishakhadatta M; Akeju, Seun Oluwaseun; Pierce, Eric; Cash, Sydney S; Kilbride, Ronan; Brown, Emery N; Purdon, Patrick L

2015-10-01

Deep hypothermia induces 'burst suppression' (BS), an electroencephalogram pattern with low-voltage 'suppressions' alternating with high-voltage 'bursts'. Current understanding of BS comes mainly from anesthesia studies, while hypothermia-induced BS has received little study. We set out to investigate the electroencephalogram changes induced by cooling the human brain through increasing depths of BS through isoelectricity. We recorded scalp electroencephalograms from eleven patients undergoing deep hypothermia during cardiac surgery with complete circulatory arrest, and analyzed these using methods of spectral analysis. Within patients, the depth of BS systematically depends on the depth of hypothermia, though responses vary between patients except at temperature extremes. With decreasing temperature, burst lengths increase, and burst amplitudes and lengths decrease, while the spectral content of bursts remains constant. These findings support an existing theoretical model in which the common mechanism of burst suppression across diverse etiologies is the cyclical diffuse depletion of metabolic resources, and suggest the new hypothesis of local micro-network dropout to explain decreasing burst amplitudes at lower temperatures. These results pave the way for accurate noninvasive tracking of brain metabolic state during surgical procedures under deep hypothermia, and suggest new testable predictions about the network mechanisms underlying burst suppression. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

A simulation model for predicting the temperature during the application of MR-guided focused ultrasound for stroke treatment using pulsed ultrasound

NASA Astrophysics Data System (ADS)

Hadjisavvas, V.; Damianou, C.

2011-09-01

In this paper a simulation model for predicting the temperature during the application of MR-guided focused ultrasound for stroke treatment using pulsed ultrasound is presented. A single element spherically focused transducer of 5 cm diameter, focusing at 10 cm and operating at either 0.5 MHz or 1 MHz was considered. The power field was estimated using the KZK model. The temperature was estimated using the bioheat equation. The goal was to extract the acoustic parameters (power, pulse duration, duty factor and pulse repetition frequency) that maintain a temperature increase of less than 1 °C during the application of a pulse ultrasound protocol. It was found that the temperature change increases linearly with duty factor. The higher the power, the lower the duty factor needed to keep the temperature change to the safe limit of 1 °C. The higher the frequency the lower the duty factor needed to keep the temperature change to the safe limit of 1 °C. Finally, the deeper the target, the higher the duty factor needed to keep the temperature change to the safe limit of 1 °C. The simulation model was tested in brain tissue during the application of pulse ultrasound and the measured temperature was in close agreement with the simulated temperature. This simulation model is considered to be very useful tool for providing acoustic parameters (frequency, power, duty factor, pulse repetition frequency) during the application of pulsed ultrasound at various depths in tissue so that a safe temperature is maintained during the treatment. This model could be tested soon during stroke clinical trials.

Influence of the carotid rete on brain temperature in cats exposed to hot environments.

PubMed

Baker, M A

1972-02-01

1. Thermocouples were chronically implanted in various intracranial and extracranial structures in adult cats. Temperature of arterial blood on the proximal and distal sides of the carotid rete was determined by measuring temperature in the aortic arch and at the anterior cerebral arteries. Temperatures of brain stem regions supplied by the carotid rete and by the vertebral-basilar system were determined by measuring temperature in the anterior hypothalamus and the caudal medulla. Nasal mucosal temperature was measured with a thermocouple implanted in the nasal cavity.2. In a cool environment (25 degrees C), the temperature of anterior cerebral arterial blood was lower than aortic arterial temperature. Anterior cerebral temperature showed shifts which were not present in central (aortic) arterial blood and which were clearly associated with changes in heat loss from the nasal mucosa and with the behaviour of the animal. When the cats were relaxed or in e.e.g. slow-wave sleep, the nasal mucosal temperature was high and the temperature at the anterior cerebral arteries was as much as 0.30 degrees C less than aortic temperature. During behavioural arousal and paradoxical sleep, the nasal mucosal temperature fell and the anterior cerebral arterial temperature rose toward central arterial temperature. Shifts in hypothalamic temperature followed the changes in anterior cerebral arterial temperature. Medullary temperature was higher than aortic temperature and showed shifts which suggested that blood from the rostral circle of Willis mixed with vertebral blood in the basilar artery.3. When the ambient temperature was raised to 40-45 degrees C the cooling of cerebral arterial blood and brain increased as the rate of thermal panting increased. Respiratory rate increased tenfold and aortic temperature rose by 2.0-2.5 degrees C. Anterior cerebral arterial temperature fell below aortic temperature by as much as 1 degrees C, hypothalamic temperature dropping in parallel with cerebral arterial temperature. Medullary temperature cooled below aortic temperature during heat exposure, but the temperature drop in the medulla was not as high as in the rostral brain stem.4. Blowing air into the nasal cavity of anaesthetized cats produced a large, rapid temperature drop at the anterior cerebral arteries and in the hypothalamus, with little effect on central arterial temperature. The same experiments in a dead animal cooled the brain after a longer period of time, suggesting that an active process is involved in the brain cooling observed in living animals.5. It is concluded that the cooling of the rostral cerebral arterial blood and brain which occurs in cats in a cool environment and is accelerated during thermal panting, is a result of countercurrent heat exchange between arterial blood in the carotid rete and venous blood draining the evaporative surfaces of the upper respiratory passages. Such direct brain cooling during thermal panting has now been demonstrated in the cat, the sheep and the gazelle, and probably explains the high heat tolerance of the carnivores and hoofed mammals in which a rete is present.

Prediction of brain deformations and risk of traumatic brain injury due to closed-head impact: quantitative analysis of the effects of boundary conditions and brain tissue constitutive model.

PubMed

Wang, Fang; Han, Yong; Wang, Bingyu; Peng, Qian; Huang, Xiaoqun; Miller, Karol; Wittek, Adam

2018-05-12

In this study, we investigate the effects of modelling choices for the brain-skull interface (layers of tissues between the brain and skull that determine boundary conditions for the brain) and the constitutive model of brain parenchyma on the brain responses under violent impact as predicted using computational biomechanics model. We used the head/brain model from Total HUman Model for Safety (THUMS)-extensively validated finite element model of the human body that has been applied in numerous injury biomechanics studies. The computations were conducted using a well-established nonlinear explicit dynamics finite element code LS-DYNA. We employed four approaches for modelling the brain-skull interface and four constitutive models for the brain tissue in the numerical simulations of the experiments on post-mortem human subjects exposed to violent impacts reported in the literature. The brain-skull interface models included direct representation of the brain meninges and cerebrospinal fluid, outer brain surface rigidly attached to the skull, frictionless sliding contact between the brain and skull, and a layer of spring-type cohesive elements between the brain and skull. We considered Ogden hyperviscoelastic, Mooney-Rivlin hyperviscoelastic, neo-Hookean hyperviscoelastic and linear viscoelastic constitutive models of the brain tissue. Our study indicates that the predicted deformations within the brain and related brain injury criteria are strongly affected by both the approach of modelling the brain-skull interface and the constitutive model of the brain parenchyma tissues. The results suggest that accurate prediction of deformations within the brain and risk of brain injury due to violent impact using computational biomechanics models may require representation of the meninges and subarachnoidal space with cerebrospinal fluid in the model and application of hyperviscoelastic (preferably Ogden-type) constitutive model for the brain tissue.

Effects of tissue susceptibility on brain temperature mapping.

PubMed

Maudsley, Andrew A; Goryawala, Mohammed Z; Sheriff, Sulaiman

2017-02-01

A method for mapping of temperature over a large volume of the brain using volumetric proton MR spectroscopic imaging has been implemented and applied to 150 normal subjects. Magnetic susceptibility-induced frequency shifts in gray- and white-matter regions were measured and included as a correction in the temperature mapping calculation. Additional sources of magnetic susceptibility variations of the individual metabolite resonance frequencies were also observed that reflect the cellular-level organization of the brain metabolites, with the most notable differences being attributed to changes of the N-Acetylaspartate resonance frequency that reflect the intra-axonal distribution and orientation of the white-matter tracts with respect to the applied magnetic field. These metabolite-specific susceptibility effects are also shown to change with age. Results indicate no change of apparent brain temperature with age from 18 to 84 years old, with a trend for increased brain temperature throughout the cerebrum in females relative for males on the order of 0.1°C; slightly increased temperatures in the left hemisphere relative to the right; and a lower temperature of 0.3°C in the cerebellum relative to that of cerebral white-matter. This study presents a novel acquisition method for noninvasive measurement of brain temperature that is of potential value for diagnostic purposes and treatment monitoring, while also demonstrating limitations of the measurement due to the confounding effects of tissue susceptibility variations. Copyright © 2016 Elsevier Inc. All rights reserved.

Predicting Intracranial Pressure and Brain Tissue Oxygen Crises in Patients With Severe Traumatic Brain Injury.

PubMed

Myers, Risa B; Lazaridis, Christos; Jermaine, Christopher M; Robertson, Claudia S; Rusin, Craig G

2016-09-01

To develop computer algorithms that can recognize physiologic patterns in traumatic brain injury patients that occur in advance of intracranial pressure and partial brain tissue oxygenation crises. The automated early detection of crisis precursors can provide clinicians with time to intervene in order to prevent or mitigate secondary brain injury. A retrospective study was conducted from prospectively collected physiologic data. intracranial pressure, and partial brain tissue oxygenation crisis events were defined as intracranial pressure of greater than or equal to 20 mm Hg lasting at least 15 minutes and partial brain tissue oxygenation value of less than 10 mm Hg for at least 10 minutes, respectively. The physiologic data preceding each crisis event were used to identify precursors associated with crisis onset. Multivariate classification models were applied to recorded data in 30-minute epochs of time to predict crises between 15 and 360 minutes in the future. The neurosurgical unit of Ben Taub Hospital (Houston, TX). Our cohort consisted of 817 subjects with severe traumatic brain injury. Our algorithm can predict the onset of intracranial pressure crises with 30-minute advance warning with an area under the receiver operating characteristic curve of 0.86 using only intracranial pressure measurements and time since last crisis. An analogous algorithm can predict the start of partial brain tissue oxygenation crises with 30-minute advanced warning with an area under the receiver operating characteristic curve of 0.91. Our algorithms provide accurate and timely predictions of intracranial hypertension and tissue hypoxia crises in patients with severe traumatic brain injury. Almost all of the information needed to predict the onset of these events is contained within the signal of interest and the time since last crisis.

Human Brain Modeling with Its Anatomical Structure and Realistic Material Properties for Brain Injury Prediction.

PubMed

Atsumi, Noritoshi; Nakahira, Yuko; Tanaka, Eiichi; Iwamoto, Masami

2018-05-01

Impairments of executive brain function after traumatic brain injury (TBI) due to head impacts in traffic accidents need to be obviated. Finite element (FE) analyses with a human brain model facilitate understanding of the TBI mechanisms. However, conventional brain FE models do not suitably describe the anatomical structure in the deep brain, which is a critical region for executive brain function, and the material properties of brain parenchyma. In this study, for better TBI prediction, a novel brain FE model with anatomical structure in the deep brain was developed. The developed model comprises a constitutive model of brain parenchyma considering anisotropy and strain rate dependency. Validation was performed against postmortem human subject test data associated with brain deformation during head impact. Brain injury analyses were performed using head acceleration curves obtained from reconstruction analysis of rear-end collision with a human whole-body FE model. The difference in structure was found to affect the regions of strain concentration, while the difference in material model contributed to the peak strain value. The injury prediction result by the proposed model was consistent with the characteristics in the neuroimaging data of TBI patients due to traffic accidents.

MR brain volumetric measurements are predictive of neurobehavioral impairment in the HIV-1 transgenic rat.

PubMed

Casas, Rafael; Muthusamy, Siva; Wakim, Paul G; Sinharay, Sanhita; Lentz, Margaret R; Reid, William C; Hammoud, Dima A

2018-01-01

HIV infection is known to be associated with brain volume loss, even in optimally treated patients. In this study, we assessed whether dynamic brain volume changes over time are predictive of neurobehavorial performance in the HIV-1 transgenic (Tg) rat, a model of treated HIV-positive patients. Cross-sectional brain MRI imaging was first performed comparing Tg and wild type (WT) rats at 3 and 19 months of age. Longitudinal MRI and neurobehavioral testing of another group of Tg and WT rats was then performed from 5 to 23 weeks of age. Whole brain and subregional image segmentation was used to assess the rate of brain growth over time. We used repeated-measures mixed models to assess differences in brain volumes and to establish how predictive the volume differences are of specific neurobehavioral deficits. Cross-sectional imaging showed smaller whole brain volumes in Tg compared to WT rats at 3 and at 19 months of age. Longitudinally, Tg brain volumes were smaller than age-matched WT rats at all time points, starting as early as 5 weeks of age. The Tg striatal growth rate delay between 5 and 9 weeks of age was greater than that of the whole brain. Striatal volume in combination with genotype was the most predictive of rota-rod scores and in combination with genotype and age was the most predictive of total exploratory activity scores in the Tg rats. The disproportionately delayed striatal growth compared to whole brain between 5 and 9 weeks of age and the role of striatal volume in predicting neurobehavioral deficits suggest an important role of the dopaminergic system in HIV associated neuropathology. This might explain problems with motor coordination and executive decisions in this animal model. Smaller brain and subregional volumes and neurobehavioral deficits were seen as early as 5 weeks of age, suggesting an early brain insult in the Tg rat. Neuroprotective therapy testing in this model should thus target this early stage of development, before brain damage becomes irreversible.

The influence of the nasal mucosa and the carotid rete upon hypothalamic temperature in sheep

PubMed Central

Baker, Mary Ann; Hayward, James N.

1968-01-01

1. In chronically-prepared sheep, intracranial temperatures were measured in the cavernous sinus among the vessels of the carotid rete and at the circle of Willis extravascularly, and in the preoptic area and in other brain stem regions. Extracranial temperatures were measured intravascularly in the carotid or internal maxillary arteries and on the nasal mucosa and the skin of the ear. 2. At 20° C ambient temperature, shifts in temperature of the hypothalamus and of other brain sites paralleled temperature shifts in the cerebral arterial blood which was cooler than central arterial blood. During periods of arousal and of paradoxical sleep, vasoconstriction of the nasal mucosa and the ear skin occurred and temperatures at the cerebral arteries and in the brain rose without a comparable rise in central arterial blood temperature. 3. Anaesthetic doses of barbiturate abolished the temperature oscillations in the cerebral arterial blood and the brain. When air was blown rapidly over the nasal mucosa in anaesthetized animals, temperatures dropped precipitously in the cavernous sinus, at the cerebral arteries, and in the brain, while central arterial temperature fell only slightly. Injections of latex into the facial venous system demonstrated a venous pathway from the nasal mucosa to the cavernous sinus. 4. When sheep were exposed to 45-50° C ambient temperature, respiratory rate increased 5-10 times and the temperature gradient between central and cerebral arterial blood widened. 5. It is concluded that venous blood returning from the nasal mucosa and the skin of the head to the cavernous sinus cools the central arterial blood in the carotid rete. This is an important factor in the maintenance of hypothalamic temperature in the wool-covered, long-nosed, panting sheep and undoubtedly affects hypothalamic thermoreceptors and temperature regulation in artiodactyls. PMID:5685288

Thermal Index Evaluation of Local SAR in MRI-Based Head Models of Adult and Children for Portable Telephones

NASA Astrophysics Data System (ADS)

Fujiwara, Osamu; Miyamoto, Kayoko; Wang, Jianqing

Biological hazards due to radio-frequency (RF) waves result mainly from the temperature rise in tissue. It should be, therefore, clarified to what extent the RF waves of portable telephones increase the temperature-rise in human brain that includes the central part governing the body-temperature regulation function. In this paper, we calculated both the specific absorption rate (SAR) and the resultant temperature-rise for 900 MHz and 2 GHz portable telephones using the finite-difference time-domain (FDTD) method for three typical use positions, i.e., the vertical position, cheek position and tilt position. As a result, we found that there was an increase for median and 1% value of the cumulative distribution of temperature-rise in children’s brains for any use positions of the portable telephones compared to that in the adult’s brain, and also that the increasing trend in children’s brains for temperature-rise is identical to the temperature-rise trend in children’s hypothalamus. In addition, we found that the ten-gram averaged peak SAR among the adult and children heads had the same trend as that of the 0.1% value of the relatively cumulative distribution of temperature-rise, which shows that the ten-gram averaged peak SAR reflects only the localized temperature-rise in the brain surface.

Cortical and subcortical predictive dynamics and learning during perception, cognition, emotion and action

PubMed Central

Grossberg, Stephen

2009-01-01

An intimate link exists between the predictive and learning processes in the brain. Perceptual/cognitive and spatial/motor processes use complementary predictive mechanisms to learn, recognize, attend and plan about objects in the world, determine their current value, and act upon them. Recent neural models clarify these mechanisms and how they interact in cortical and subcortical brain regions. The present paper reviews and synthesizes data and models of these processes, and outlines a unified theory of predictive brain processing. PMID:19528003

The new Licox combined brain tissue oxygen and brain temperature monitor: assessment of in vitro accuracy and clinical experience in severe traumatic brain injury.

PubMed

Stewart, Campbell; Haitsma, Iain; Zador, Zsolt; Hemphill, J Claude; Morabito, Diane; Manley, Geoffrey; Rosenthal, Guy

2008-12-01

Monitoring of brain tissue oxygen tension is increasingly being used to monitor patients after severe traumatic brain injury and to guide therapies aimed at maintaining brain tissue oxygen tension above threshold levels. The new Licox PMO combined oxygen and temperature catheter (Integra LifeSciences, Plainsboro, NJ) combines measurements of oxygen tension and temperature in a single probe inserted through a bolt mechanism. In this study, we sought to evaluate the accuracy of the new Licox PMO probe under controlled laboratory conditions and to assess the accuracy of oxygen tension and temperature measurements and the new automated card calibration system. We also describe our clinical experience with the Licox PMO probe. Oxygen tension was measured in a 2-chambered apparatus at different oxygen tensions and temperatures. The new card calibration system was compared with a manually calibrated system. Rates of hematoma, infection, and dislodgement in our clinical experience were recorded. The new Licox PMO probe accurately measures oxygen tension over a wide range of oxygen concentrations and physiological temperatures, but it does have a small tendency to underestimate oxygen tension (mean error, -3.8 +/- 3.5%) that is more pronounced between the temperatures of 33 and 39 degrees C. The thermistor of the PMO probe also has a tendency to underestimate temperature when compared with a resistance thermometer (mean error, -0.67 +/- 0.22 degrees C). The card calibration system was also found to introduce some variability in measurements of oxygen tension when compared with a manually calibrated system. Clinical experience with the new probe indicates good placement within the white matter using the improved bolt system and low rates of hematoma (2.9%), infection (0%), and dislodgement (5.9%). The new Licox PMO probe is accurate but has a small, consistent tendency to under-read oxygen tension that is more pronounced at higher temperatures. The probe tends to under-read temperature by 0.5 to 0.8 degrees C across temperatures, suggesting that caution should be used when brain temperature is measured with the Licox PMO probe and used to guide temperature-directed treatment strategies. The Licox PMO probe improves upon previous models in allowing consistent and accurate placement in the white matter and obviating the need for placement of 2 separate probes to measure oxygen tension and temperature.

Top-down predictions in the cognitive brain

PubMed Central

Kveraga, Kestutis; Ghuman, Avniel S.; Bar, Moshe

2007-01-01

The human brain is not a passive organ simply waiting to be activated by external stimuli. Instead, it is proposed tat the brain continuously employs memory of past experiences to interpret sensory information and predict the immediately relevant future. This review concentrates on visual recognition as the model system for developing and testing ideas about the role and mechanisms of top-down predictions in the brain. We cover relevant behavioral, computational and neural aspects. These ideas are then extended to other domains. The basic elements of this proposal include analogical mapping, associative representations and the generation of predictions. Connections to a host of cognitive processes will be made and implications to several mental disorders will be proposed. PMID:17923222

Monitoring of deep brain temperature in infants using multi-frequency microwave radiometry and thermal modelling.

PubMed

Han, J W; Van Leeuwen, G M; Mizushina, S; Van de Kamer, J B; Maruyama, K; Sugiura, T; Azzopardi, D V; Edwards, A D

2001-07-01

In this study we present a design for a multi-frequency microwave radiometer aimed at prolonged monitoring of deep brain temperature in newborn infants and suitable for use during hypothermic neural rescue therapy. We identify appropriate hardware to measure brightness temperature and evaluate the accuracy of the measurements. We describe a method to estimate the tissue temperature distribution from measured brightness temperatures which uses the results of numerical simulations of the tissue temperature as well as the propagation of the microwaves in a realistic detailed three-dimensional infant head model. The temperature retrieval method is then used to evaluate how the statistical fluctuations in the measured brightness temperatures limit the confidence interval for the estimated temperature: for an 18 degrees C temperature differential between cooled surface and deep brain we found a standard error in the estimated central brain temperature of 0.75 degrees C. Evaluation of the systematic errors arising from inaccuracies in model parameters showed that realistic deviations in tissue parameters have little impact compared to uncertainty in the thickness of the bolus between the receiving antenna and the infant's head or in the skull thickness. This highlights the need to pay particular attention to these latter parameters in future practical implementation of the technique.

Prediction complements explanation in understanding the developing brain.

PubMed

Rosenberg, Monica D; Casey, B J; Holmes, Avram J

2018-02-21

A central aim of human neuroscience is understanding the neurobiology of cognition and behavior. Although we have made significant progress towards this goal, reliance on group-level studies of the developed adult brain has limited our ability to explain population variability and developmental changes in neural circuitry and behavior. In this review, we suggest that predictive modeling, a method for predicting individual differences in behavior from brain features, can complement descriptive approaches and provide new ways to account for this variability. Highlighting the outsized scientific and clinical benefits of prediction in developmental populations including adolescence, we show that predictive brain-based models are already providing new insights on adolescent-specific risk-related behaviors. Together with large-scale developmental neuroimaging datasets and complementary analytic approaches, predictive modeling affords us the opportunity and obligation to identify novel treatment targets and individually tailor the course of interventions for developmental psychopathologies that impact so many young people today.

Phase-difference and spectroscopic imaging for monitoring of human brain temperature during cooling.

PubMed

Weis, Jan; Covaciu, Lucian; Rubertsson, Sten; Allers, Mats; Lunderquist, Anders; Ortiz-Nieto, Francisco; Ahlström, Håkan

2012-12-01

Decrease of the human brain temperature was induced by intranasal cooling. The main purpose of this study was to compare the two magnetic resonance methods for monitoring brain temperature changes during cooling: phase-difference and magnetic resonance spectroscopic imaging (MRSI) with high spatial resolution. Ten healthy volunteers were measured. Selective brain cooling was performed through nasal cavities using saline-cooled balloon catheters. MRSI was based on a radiofrequency spoiled gradient echo sequence. The spectral information was encoded by incrementing the echo time of the subsequent eight image records. Reconstructed voxel size was 1×1×5 mm(3). Relative brain temperature was computed from the positions of water spectral lines. Phase maps were obtained from the first image record of the MRSI sequence. Mild hypothermia was achieved in 15-20 min. Mean brain temperature reduction varied in the interval <-3.0; -0.6>°C and <-2.7; -0.7>°C as measured by the MRSI and phase-difference methods, respectively. Very good correlation was found in all locations between the temperatures measured by both techniques except in the frontal lobe. Measurements in the transversal slices were more robust to the movement artifacts than those in the sagittal planes. Good agreement was found between the MRSI and phase-difference techniques. Copyright © 2012 Elsevier Inc. All rights reserved.

Computational analysis of transcranial magnetic stimulation in the presence of deep brain stimulation probes

NASA Astrophysics Data System (ADS)

Syeda, F.; Holloway, K.; El-Gendy, A. A.; Hadimani, R. L.

2017-05-01

Transcranial Magnetic Stimulation is an emerging non-invasive treatment for depression, Parkinson's disease, and a variety of other neurological disorders. Many Parkinson's patients receive the treatment known as Deep Brain Stimulation, but often require additional therapy for speech and swallowing impairment. Transcranial Magnetic Stimulation has been explored as a possible treatment by stimulating the mouth motor area of the brain. We have calculated induced electric field, magnetic field, and temperature distributions in the brain using finite element analysis and anatomically realistic heterogeneous head models fitted with Deep Brain Stimulation leads. A Figure of 8 coil, current of 5000 A, and frequency of 2.5 kHz are used as simulation parameters. Results suggest that Deep Brain Stimulation leads cause surrounding tissues to experience slightly increased E-field (Δ Emax =30 V/m), but not exceeding the nominal values induced in brain tissue by Transcranial Magnetic Stimulation without leads (215 V/m). The maximum temperature in the brain tissues surrounding leads did not change significantly from the normal human body temperature of 37 °C. Therefore, we ascertain that Transcranial Magnetic Stimulation in the mouth motor area may stimulate brain tissue surrounding Deep Brain Stimulation leads, but will not cause tissue damage.

Brain size predicts problem-solving ability in mammalian carnivores

PubMed Central

Benson-Amram, Sarah; Dantzer, Ben; Stricker, Gregory; Swanson, Eli M.; Holekamp, Kay E.

2016-01-01

Despite considerable interest in the forces shaping the relationship between brain size and cognitive abilities, it remains controversial whether larger-brained animals are, indeed, better problem-solvers. Recently, several comparative studies have revealed correlations between brain size and traits thought to require advanced cognitive abilities, such as innovation, behavioral flexibility, invasion success, and self-control. However, the general assumption that animals with larger brains have superior cognitive abilities has been heavily criticized, primarily because of the lack of experimental support for it. Here, we designed an experiment to inquire whether specific neuroanatomical or socioecological measures predict success at solving a novel technical problem among species in the mammalian order Carnivora. We presented puzzle boxes, baited with food and scaled to accommodate body size, to members of 39 carnivore species from nine families housed in multiple North American zoos. We found that species with larger brains relative to their body mass were more successful at opening the boxes. In a subset of species, we also used virtual brain endocasts to measure volumes of four gross brain regions and show that some of these regions improve model prediction of success at opening the boxes when included with total brain size and body mass. Socioecological variables, including measures of social complexity and manual dexterity, failed to predict success at opening the boxes. Our results, thus, fail to support the social brain hypothesis but provide important empirical support for the relationship between relative brain size and the ability to solve this novel technical problem. PMID:26811470

Brain size predicts problem-solving ability in mammalian carnivores.

PubMed

Benson-Amram, Sarah; Dantzer, Ben; Stricker, Gregory; Swanson, Eli M; Holekamp, Kay E

2016-03-01

Despite considerable interest in the forces shaping the relationship between brain size and cognitive abilities, it remains controversial whether larger-brained animals are, indeed, better problem-solvers. Recently, several comparative studies have revealed correlations between brain size and traits thought to require advanced cognitive abilities, such as innovation, behavioral flexibility, invasion success, and self-control. However, the general assumption that animals with larger brains have superior cognitive abilities has been heavily criticized, primarily because of the lack of experimental support for it. Here, we designed an experiment to inquire whether specific neuroanatomical or socioecological measures predict success at solving a novel technical problem among species in the mammalian order Carnivora. We presented puzzle boxes, baited with food and scaled to accommodate body size, to members of 39 carnivore species from nine families housed in multiple North American zoos. We found that species with larger brains relative to their body mass were more successful at opening the boxes. In a subset of species, we also used virtual brain endocasts to measure volumes of four gross brain regions and show that some of these regions improve model prediction of success at opening the boxes when included with total brain size and body mass. Socioecological variables, including measures of social complexity and manual dexterity, failed to predict success at opening the boxes. Our results, thus, fail to support the social brain hypothesis but provide important empirical support for the relationship between relative brain size and the ability to solve this novel technical problem.

Effects of incubation temperature and estrogen exposure on aromatase activity in the brain and gonads of embryonic alligators.

PubMed Central

Milnes, Matthew R; Roberts, Robert N; Guillette, Louis J

2002-01-01

During embryogenesis, incubation temperature and the hormonal environment influence gonadal differentiation of some reptiles, including all crocodilians. Current evidence suggests that aromatase, the enzyme that converts androgens to estrogens, has a role in sexual differentiation of species that exhibit temperature-dependent sex determination (TSD). During the temperature-sensitive period (TSP) of sex determination, we compared aromatase activity in the brain and gonads of putative male and female alligator embryos to determine if aromatase activity in the embryonic brain could provide the hormonal environment necessary for ovarian development in a TSD species. In addition, we assessed the pattern of aromatase activity in the brain and gonads of embryos treated with estradiol-17beta (E(2)) and incubated at male-producing temperatures to compare enzyme activity in E(2) sex-reversed females to control males and females. This has particular significance regarding wildlife species living in areas contaminated with suspected environmental estrogens. Gonadal aromatase activity remained low during the early stages of the TSP in both sexes and increased late in the TSP only in females. Aromatase activity in the brain increased prior to gonadal differentiation in both sexes. These results suggest that aromatase activity in the brain is not directly responsible for mediating differentiation of the gonad. E(2) exposure at male-producing temperatures resulted in sex-reversed females that had intermediate gonad function and masculinized brain activity. This study indicates the need to examine multiple end points and to determine the persistence of developmental alterations in contaminant-exposed wildlife populations. PMID:12060834

Transpulmonary hypothermia: a novel method of rapid brain cooling through augmented heat extraction from the lungs.

PubMed

Kumar, Matthew M; Goldberg, Andrew D; Kashiouris, Markos; Keenan, Lawrence R; Rabinstein, Alejandro A; Afessa, Bekele; Johnson, Larry D; Atkinson, John L D; Nayagam, Vedha

2014-10-01

Delay in instituting neuroprotective measures after cardiac arrest increases death and decreases neuronal recovery. Current hypothermia methods are slow, ineffective, unreliable, or highly invasive. We report the feasibility of rapid hypothermia induction in swine through augmented heat extraction from the lungs. Twenty-four domestic crossbred pigs (weight, 50-55kg) were ventilated with room air. Intraparenchymal brain temperature and core temperatures from pulmonary artery, lower esophagus, bladder, rectum, nasopharynx, and tympanum were recorded. In eight animals, ventilation was switched to cooled helium-oxygen mixture (heliox) and perfluorocarbon (PFC) aerosol and continued for 90min or until target brain temperature of 32°C was reached. Eight animals received body-surface cooling with water-circulating blankets; eight control animals continued to be ventilated with room air. Brain and core temperatures declined rapidly with cooled heliox-PFC ventilation. The brain reached target temperature within the study period (mean [SD], 66 [7.6]min) in only the transpulmonary cooling group. Cardiopulmonary functions and poststudy histopathological examination of the lungs were normal. Transpulmonary cooling is novel, rapid, minimally invasive, and an effective technique to induce therapeutic hypothermia. High thermal conductivity of helium and vaporization of PFC produces rapid cooling of alveolar gases. The thinness and large surface area of alveolar membrane facilitate rapid cooling of the pulmonary circulation. Because of differences in thermogenesis, blood flow, insulation, and exposure to the external environment, the brain cools at a different rate than other organs. Transpulmonary hypothermia was significantly faster than body surface cooling in reaching target brain temperature. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Translational Modeling in Schizophrenia: Predicting Human Dopamine D2 Receptor Occupancy.

PubMed

Johnson, Martin; Kozielska, Magdalena; Pilla Reddy, Venkatesh; Vermeulen, An; Barton, Hugh A; Grimwood, Sarah; de Greef, Rik; Groothuis, Geny M M; Danhof, Meindert; Proost, Johannes H

2016-04-01

To assess the ability of a previously developed hybrid physiology-based pharmacokinetic-pharmacodynamic (PBPKPD) model in rats to predict the dopamine D2 receptor occupancy (D2RO) in human striatum following administration of antipsychotic drugs. A hybrid PBPKPD model, previously developed using information on plasma concentrations, brain exposure and D2RO in rats, was used as the basis for the prediction of D2RO in human. The rat pharmacokinetic and brain physiology parameters were substituted with human population pharmacokinetic parameters and human physiological information. To predict the passive transport across the human blood-brain barrier, apparent permeability values were scaled based on rat and human brain endothelial surface area. Active efflux clearance in brain was scaled from rat to human using both human brain endothelial surface area and MDR1 expression. Binding constants at the D2 receptor were scaled based on the differences between in vitro and in vivo systems of the same species. The predictive power of this physiology-based approach was determined by comparing the D2RO predictions with the observed human D2RO of six antipsychotics at clinically relevant doses. Predicted human D2RO was in good agreement with clinically observed D2RO for five antipsychotics. Models using in vitro information predicted human D2RO well for most of the compounds evaluated in this analysis. However, human D2RO was under-predicted for haloperidol. The rat hybrid PBPKPD model structure, integrated with in vitro information and human pharmacokinetic and physiological information, constitutes a scientific basis to predict the time course of D2RO in man.

Blood-brain barrier alteration after microwave-induced hyperthermia is purely a thermal effect: I. Temperature and power measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moriyama, E.; Salcman, M.; Broadwell, R.D.

The effect of microwave-induced hyperthermia on the blood-brain barrier was studied in 21 Sprague-Dawley rats. Under sodium pentobarbital anesthesia, animals were place in a stereotactic frame, and an interstitial microwave antenna operating at 2450 MHz was inserted in a bony groove drilled parallel to the sagittal suture. Some antennae were equipped with an external cooling jacket. Temperature measurements were made lateral to the antenna by fluoroptical thermometry, and power was calculated from the time-temperature profile. Five minutes prior to termination of microwave irradiation, horseradish peroxidase (1 mg/20 g body weight) was injected intravenously. Extravasation of horseradish peroxidase was observed inmore » brain tissue heated above 44.3 degrees C for 30 minutes and at 42.5 degrees C for 60 minutes. Microwave irradiation failed to open the blood-brain barrier when brain temperatures were sustained below 40.3 degrees C by the cooling system. Extravasation of blood-borne peroxidase occurred at sites of maximal temperature elevation, even when these did not coincide with the site of maximum power density. The data suggest that microwave-induced hyperthermia is an effective means for opening the blood-brain barrier and that the mechanism is not related to the nonthermal effect of microwaves.« less

Brain Activity in Self- and Value-Related Regions in Response to Online Antismoking Messages Predicts Behavior Change

PubMed Central

Cooper, Nicole; Tompson, Steve; O’Donnell, Matthew Brook; Falk, Emily B.

2017-01-01

In this study, we combined approaches from media psychology and neuroscience to ask whether brain activity in response to online antismoking messages can predict smoking behavior change. In particular, we examined activity in subregions of the medial prefrontal cortex linked to self- and value-related processing, to test whether these neurocognitive processes play a role in message-consistent behavior change. We observed significant relationships between activity in both brain regions of interest and behavior change (such that higher activity predicted a larger reduction in smoking). Furthermore, activity in these brain regions predicted variance independent of traditional, theory-driven self-report metrics such as intention, self-efficacy, and risk perceptions. We propose that valuation is an additional cognitive process that should be investigated further as we search for a mechanistic explanation of the relationship between brain activity and media effects relevant to health behavior change. PMID:29057013

Inferring deep-brain activity from cortical activity using functional near-infrared spectroscopy

PubMed Central

Liu, Ning; Cui, Xu; Bryant, Daniel M.; Glover, Gary H.; Reiss, Allan L.

2015-01-01

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular technology for studying brain function because it is non-invasive, non-irradiating and relatively inexpensive. Further, fNIRS potentially allows measurement of hemodynamic activity with high temporal resolution (milliseconds) and in naturalistic settings. However, in comparison with other imaging modalities, namely fMRI, fNIRS has a significant drawback: limited sensitivity to hemodynamic changes in deep-brain regions. To overcome this limitation, we developed a computational method to infer deep-brain activity using fNIRS measurements of cortical activity. Using simultaneous fNIRS and fMRI, we measured brain activity in 17 participants as they completed three cognitive tasks. A support vector regression (SVR) learning algorithm was used to predict activity in twelve deep-brain regions using information from surface fNIRS measurements. We compared these predictions against actual fMRI-measured activity using Pearson’s correlation to quantify prediction performance. To provide a benchmark for comparison, we also used fMRI measurements of cortical activity to infer deep-brain activity. When using fMRI-measured activity from the entire cortex, we were able to predict deep-brain activity in the fusiform cortex with an average correlation coefficient of 0.80 and in all deep-brain regions with an average correlation coefficient of 0.67. The top 15% of predictions using fNIRS signal achieved an accuracy of 0.7. To our knowledge, this study is the first to investigate the feasibility of using cortical activity to infer deep-brain activity. This new method has the potential to extend fNIRS applications in cognitive and clinical neuroscience research. PMID:25798327

Changes in event-related potential functional networks predict traumatic brain injury in piglets.

PubMed

Atlan, Lorre S; Lan, Ingrid S; Smith, Colin; Margulies, Susan S

2018-06-01

Traumatic brain injury is a leading cause of cognitive and behavioral deficits in children in the US each year. None of the current diagnostic tools, such as quantitative cognitive and balance tests, have been validated to identify mild traumatic brain injury in infants, adults and animals. In this preliminary study, we report a novel, quantitative tool that has the potential to quickly and reliably diagnose traumatic brain injury and which can track the state of the brain during recovery across multiple ages and species. Using 32 scalp electrodes, we recorded involuntary auditory event-related potentials from 22 awake four-week-old piglets one day before and one, four, and seven days after two different injury types (diffuse and focal) or sham. From these recordings, we generated event-related potential functional networks and assessed whether the patterns of the observed changes in these networks could distinguish brain-injured piglets from non-injured. Piglet brains exhibited significant changes after injury, as evaluated by five network metrics. The injury prediction algorithm developed from our analysis of the changes in the event-related potentials functional networks ultimately produced a tool with 82% predictive accuracy. This novel approach is the first application of auditory event-related potential functional networks to the prediction of traumatic brain injury. The resulting tool is a robust, objective and predictive method that offers promise for detecting mild traumatic brain injury, in particular because collecting event-related potentials data is noninvasive and inexpensive. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Brain Basics: Understanding Sleep

MedlinePlus

... slow, and muscles relax even further. Your body temperature drops and eye movements stop. Brain wave activity ... functions from daily fluctuations in wakefulness to body temperature, metabolism, and the release of hormones. They control ...

Regional pressure and temperature variations across the injured human brain: comparisons between paired intraparenchymal and ventricular measurements.

PubMed

Childs, Charmaine; Shen, Liang

2015-06-23

Intraparenchymal, multimodality sensors are commonly used in the management of patients with severe traumatic brain injury (TBI). The 'gold standard', based on accuracy, reliability and cost for intracranial pressure (ICP) monitoring is within the cerebral ventricle (external strain gauge). There are no standards yet for intracerebral temperature monitoring and little is known of temperature differences between brain tissue and ventricle. The aim of the study therefore was to determine pressure and temperature differences at intraparenchymal and ventricular sites during five days of continuous neuromonitoring. Patients with severe TBI requiring emergency surgery. patients who required ICP monitoring were eligible for recruitment. Two intracerebral probe types were used: a) intraventricular, dual parameter sensor (measuring pressure, temperature) with inbuilt catheter for CSF drainage: b) multiparameter intraparenchymal sensor measuring pressure, temperature and oxygen partial pressure. All sensors were inserted during surgery and under aseptic conditions. Seventeen patients, 12 undergoing neurosurgery (decompressive craniectomy n = 8, craniotomy n = 4) aged 21-78 years were studied. Agreement of measures for 9540 brain tissue-ventricular temperature 'pairs' and 10,291 brain tissue-ventricular pressure 'pairs' were determined using mixed model to compare mean temperature and pressure for longitudinal data. There was no significant overall difference for mean temperature (p = 0.92) or mean pressure readings (p = 0.379) between tissue and ventricular sites. With 95.8 % of paired temperature readings within 2SD (-0.4 to 0.4 °C) differences in temperature between brain tissue and ventricle were clinically insignificant. For pressure, 93.5 % of readings pairs fell within the 2SD range (-9.4756 to 7.8112 mmHg). However, for individual patients, agreement for mean tissue-ventricular pressure differences was poor on occasions. There is good overall agreement between paired temperature measurements obtained from deep white matter and brain ventricle in patients with and without early neurosurgery. For paired ICP measurements, 93.5 % of readings were within 2SD of mean difference. Whilst the majority of paired readings were comparable (within 10 mmHg) clinically relevant tissue-ventricular dissociations were noted. Further work is required to unravel the events responsible for short intervals of pressure dissociation before tissue pressure readings can be definitively accepted as a reliable surrogate for ventricular pressure.

Brain temperature effects of intravenous heroin: State dependency, environmental modulation, and the effects of dose.

PubMed

Bola, R Aaron; Kiyatkin, Eugene A

2017-11-01

Here we examined how intravenous heroin at a dose that maintains self-administration (0.1 mg/kg) affects brain temperature homeostasis in freely moving rats under conditions that seek to mimic some aspects of human drug use. When administered under standard laboratory conditions (quiet rest at 22 °C ambient temperature), heroin induced moderate temperature increases (1.0-1.5 °C) in the nucleus accumbens (NAc), a critical structure of the brain motivation-reinforcement circuit. By simultaneously recording temperatures in the temporal muscle and skin, we demonstrate that the hyperthermic effects of heroin results primarily from inhibition of heat loss due to strong and prolonged skin vasoconstriction. Heroin-induced brain temperature increases were enhanced during behavioral activation (i.e., social interaction) and in a moderately warm environment (29 °C). By calculating the "net" effects of the drug in these two conditions, we found that this enhancement results from the summation of the hyperthermic effects of heroin with similar effects induced by either social interaction or a warmer environment. When the dose of heroin was increased (to 0.2, 0.4, 0.8, 1.6, 3.2, and 6.4 mg/kg), brain temperature showed a biphasic down-up response. The initial temperature decrease was dose-dependent and resulted from a transient inhibition of intra-brain heat production coupled with increased heat loss via skin surfaces-the effects typically induced by general anesthetics. These initial inhibitory effects induced by large-dose heroin injections could be related to profound CNS depression-the most serious health complications typical of heroin overdose in humans. Published by Elsevier Ltd.

Top-Down Predictions in the Cognitive Brain

ERIC Educational Resources Information Center

Kveraga, Kestutis; Ghuman, Avniel S.; Bar, Moshe

2007-01-01

The human brain is not a passive organ simply waiting to be activated by external stimuli. Instead, we propose that the brain continuously employs memory of past experiences to interpret sensory information and predict the immediately relevant future. The basic elements of this proposal include analogical mapping, associative representations and…

Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers.

PubMed

Cole, James H; Franke, Katja

2017-12-01

The brain changes as we age and these changes are associated with functional deterioration and neurodegenerative disease. It is vital that we better understand individual differences in the brain ageing process; hence, techniques for making individualised predictions of brain ageing have been developed. We present evidence supporting the use of neuroimaging-based 'brain age' as a biomarker of an individual's brain health. Increasingly, research is showing how brain disease or poor physical health negatively impacts brain age. Importantly, recent evidence shows that having an 'older'-appearing brain relates to advanced physiological and cognitive ageing and the risk of mortality. We discuss controversies surrounding brain age and highlight emerging trends such as the use of multimodality neuroimaging and the employment of 'deep learning' methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Finding influential nodes for integration in brain networks using optimal percolation theory.

PubMed

Del Ferraro, Gino; Moreno, Andrea; Min, Byungjoon; Morone, Flaviano; Pérez-Ramírez, Úrsula; Pérez-Cervera, Laura; Parra, Lucas C; Holodny, Andrei; Canals, Santiago; Makse, Hernán A

2018-06-11

Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here, we apply optimal percolation theory and pharmacogenetic interventions in vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function.

Evaluating Temperature Changes of Brain Tissue Due to Induced Heating of Cell Phone Waves.

PubMed

Forouharmajd, Farhad; Pourabdian, Siamak; Ebrahimi, Hossein

2018-01-01

Worries have recently been increased in the absorption of radiofrequency waves and their destructing effects on human health by increasing use of cell phones (mobile phones). This study performed to determine the thermal changes due to mobile phone radio frequency waves in gray and white brain tissue. This study is an empirical study, where the thermal changes of electromagnetic waves resulted from cell phones (900 MHZ, specific absorption rate for head 1.18 w/kg) on the 15 brain tissue of a cow were analyzed in a compartment with three different thickness of 2 mm, 12 mm, and 22 mm, for 15 min. The Lutron thermometer (model: MT-917) with 0.01°C precision was used for measuring the tissue temperature. For each thickness was measured three times. Data analysis is done by Lutron and MATLAB software packages. In confronting of the tissue with the cell phone, the temperature was increased by 0.53°C in the 2 mm thickness that is the gray matter of the brain, increased by 0.99°C in the 12 mm thickness, and also increased by 0.92°C in the 22 mm thickness. Brain temperature showed higher rates than the base temperature after 15 min of confrontation with cell phone waves in all the three thicknesses. Cell phone radiated radio frequency waves were effective on increasing brain tissue temperature, and this temperature increase has cumulative effect on the tissue, being higher, for some time after the confrontation than the time with no confrontation.

Evaluating Temperature Changes of Brain Tissue Due to Induced Heating of Cell Phone Waves

PubMed Central

Forouharmajd, Farhad; Pourabdian, Siamak; Ebrahimi, Hossein

2018-01-01

Background: Worries have recently been increased in the absorption of radiofrequency waves and their destructing effects on human health by increasing use of cell phones (mobile phones). This study performed to determine the thermal changes due to mobile phone radio frequency waves in gray and white brain tissue. Methods: This study is an empirical study, where the thermal changes of electromagnetic waves resulted from cell phones (900 MHZ, specific absorption rate for head 1.18 w/kg) on the 15 brain tissue of a cow were analyzed in a compartment with three different thickness of 2 mm, 12 mm, and 22 mm, for 15 min. The Lutron thermometer (model: MT-917) with 0.01°C precision was used for measuring the tissue temperature. For each thickness was measured three times. Data analysis is done by Lutron and MATLAB software packages. Results: In confronting of the tissue with the cell phone, the temperature was increased by 0.53°C in the 2 mm thickness that is the gray matter of the brain, increased by 0.99°C in the 12 mm thickness, and also increased by 0.92°C in the 22 mm thickness. Brain temperature showed higher rates than the base temperature after 15 min of confrontation with cell phone waves in all the three thicknesses. Conclusions: Cell phone radiated radio frequency waves were effective on increasing brain tissue temperature, and this temperature increase has cumulative effect on the tissue, being higher, for some time after the confrontation than the time with no confrontation. PMID:29861880

Neural substrates of updating the prediction through prediction error during decision making.

PubMed

Wang, Ying; Ma, Ning; He, Xiaosong; Li, Nan; Wei, Zhengde; Yang, Lizhuang; Zha, Rujing; Han, Long; Li, Xiaoming; Zhang, Daren; Liu, Ying; Zhang, Xiaochu

2017-08-15

Learning of prediction error (PE), including reward PE and risk PE, is crucial for updating the prediction in reinforcement learning (RL). Neurobiological and computational models of RL have reported extensive brain activations related to PE. However, the occurrence of PE does not necessarily predict updating the prediction, e.g., in a probability-known event. Therefore, the brain regions specifically engaged in updating the prediction remain unknown. Here, we conducted two functional magnetic resonance imaging (fMRI) experiments, the probability-unknown Iowa Gambling Task (IGT) and the probability-known risk decision task (RDT). Behavioral analyses confirmed that PEs occurred in both tasks but were only used for updating the prediction in the IGT. By comparing PE-related brain activations between the two tasks, we found that the rostral anterior cingulate cortex/ventral medial prefrontal cortex (rACC/vmPFC) and the posterior cingulate cortex (PCC) activated only during the IGT and were related to both reward and risk PE. Moreover, the responses in the rACC/vmPFC and the PCC were modulated by uncertainty and were associated with reward prediction-related brain regions. Electric brain stimulation over these regions lowered the performance in the IGT but not in the RDT. Our findings of a distributed neural circuit of PE processing suggest that the rACC/vmPFC and the PCC play a key role in updating the prediction through PE processing during decision making. Copyright © 2017 Elsevier Inc. All rights reserved.

The relevance of MRI for patient modeling in head and neck hyperthermia treatment planning: A comparison of CT and CT-MRI based tissue segmentation on simulated temperature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Verhaart, René F., E-mail: r.f.verhaart@erasmusmc.nl; Paulides, Margarethus M.; Fortunati, Valerio

Purpose: In current clinical practice, head and neck (H and N) hyperthermia treatment planning (HTP) is solely based on computed tomography (CT) images. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast over CT. The purpose of the authors’ study is to investigate the relevance of using MRI in addition to CT for patient modeling in H and N HTP. Methods: CT and MRI scans were acquired for 11 patients in an immobilization mask. Three observers manually segmented on CT, MRI T1 weighted (MRI-T1w), and MRI T2 weighted (MRI-T2w) images the following thermo-sensitive tissues: cerebrum, cerebellum, brainstem, myelum, sclera, lens, vitreousmore » humor, and the optical nerve. For these tissues that are used for patient modeling in H and N HTP, the interobserver variation of manual tissue segmentation in CT and MRI was quantified with the mean surface distance (MSD). Next, the authors compared the impact of CT and CT and MRI based patient models on the predicted temperatures. For each tissue, the modality was selected that led to the lowest observer variation and inserted this in the combined CT and MRI based patient model (CT and MRI), after a deformable image registration. In addition, a patient model with a detailed segmentation of brain tissues (including white matter, gray matter, and cerebrospinal fluid) was created (CT and MRI{sub db}). To quantify the relevance of MRI based segmentation for H and N HTP, the authors compared the predicted maximum temperatures in the segmented tissues (T{sub max}) and the corresponding specific absorption rate (SAR) of the patient models based on (1) CT, (2) CT and MRI, and (3) CT and MRI{sub db}. Results: In MRI, a similar or reduced interobserver variation was found compared to CT (maximum of median MSD in CT: 0.93 mm, MRI-T1w: 0.72 mm, MRI-T2w: 0.66 mm). Only for the optical nerve the interobserver variation is significantly lower in CT compared to MRI (median MSD in CT: 0.58 mm, MRI-T1w: 1.27 mm, MRI-T2w: 1.40 mm). Patient models based on CT (T{sub max}: 38.0 °C) and CT and MRI (T{sub max}: 38.1 °C) result in similar simulated temperatures, while CT and MRI{sub db} (T{sub max}: 38.5 °C) resulted in significantly higher temperatures. The SAR corresponding to these temperatures did not differ significantly. Conclusions: Although MR imaging reduces the interobserver variation in most tissues, it does not affect simulated local tissue temperatures. However, the improved soft-tissue contrast provided by MRI allows generating a detailed brain segmentation, which has a strong impact on the predicted local temperatures and hence may improve simulation guided hyperthermia.« less

Brown adipose tissue thermogenesis, the basic rest-activity cycle, meal initiation, and bodily homeostasis in rats.

PubMed

Blessing, William; Mohammed, Mazher; Ootsuka, Youichirou

2013-09-10

Laboratory rats alternate between behaviorally active and inactive states every 1-2h throughout the 24hour day, the ultradian basic rest-activity cycle (BRAC). During the behaviorally active phases of the BRAC, brown adipose tissue (BAT) temperature, body and brain temperature, and arterial pressure and heart rate increase in an integrated manner. Since the BAT temperature increases are substantially greater than the corresponding body and brain temperature increases, BAT thermogenesis contributes to the body and brain temperature increases. When food is available ad libitum, eating commences approximately 15min after the onset of an episodic increase in BAT temperature, and not at other times. If no food is available, the rat still disturbs the empty food container approximately 15min after the onset of an episodic increase in BAT temperature, and not at other times. The increase in brain temperature that precedes eating may facilitate the cognitive processing that occurs during the search for food, when the rat engages with the external environment. Rather than being triggered by changes in levels of body fuels or other meal-associated factors, in sedentary laboratory rats with ad libitum access to food, meal initiation normally occurs as part of the centrally-programmed ultradian BRAC. BRAC-associated BAT temperature increases occur in a thermoneutral environment and they are not preceded by falls in body or brain temperature, so they are not homeostatic thermoregulatory responses. The pattern of integrated behaviors and physiological functions associated with the BRAC presumably reflects Darwinian natural selection, and homeostatic thermoregulatory explanations of the BRAC-associated changes in temperature should be considered in this context. Copyright © 2013 Elsevier Inc. All rights reserved.

Refining a measure of brain injury sequelae to predict postacute rehabilitation outcome: rating scale analysis of the Mayo-Portland Adaptability Inventory.

PubMed

Malec, J F; Moessner, A M; Kragness, M; Lezak, M D

2000-02-01

Evaluate the psychometric properties of the Mayo-Portland Adaptability Inventory (MPAI). Rating scale (Rasch) analysis of MPAI and principal component analysis of residuals; the predictive validity of the MPAI measures and raw scores was assessed in a sample from a day rehabilitation program. Outpatient brain injury rehabilitation. 305 persons with brain injury. A 22-item scale reflecting severity of sequelae of brain injury that contained a mix of indicators of impairment, activity, and participation was identified. Scores and measures for MPAI scales were strongly correlated and their predictive validities were comparable. Impairment, activity, and participation define a single dimension of brain injury sequelae. The MPAI shows promise as a measure of this construct.

Temporal and spatial localization of prediction-error signals in the visual brain.

PubMed

Johnston, Patrick; Robinson, Jonathan; Kokkinakis, Athanasios; Ridgeway, Samuel; Simpson, Michael; Johnson, Sam; Kaufman, Jordy; Young, Andrew W

2017-04-01

It has been suggested that the brain pre-empts changes in the environment through generating predictions, although real-time electrophysiological evidence of prediction violations in the domain of visual perception remain elusive. In a series of experiments we showed participants sequences of images that followed a predictable implied sequence or whose final image violated the implied sequence. Through careful design we were able to use the same final image transitions across predictable and unpredictable conditions, ensuring that any differences in neural responses were due only to preceding context and not to the images themselves. EEG and MEG recordings showed that early (N170) and mid-latency (N300) visual evoked potentials were robustly modulated by images that violated the implied sequence across a range of types of image change (expression deformations, rigid-rotations and visual field location). This modulation occurred irrespective of stimulus object category. Although the stimuli were static images, MEG source reconstruction of the early latency signal (N/M170) localized expectancy violation signals to brain areas associated with motion perception. Our findings suggest that the N/M170 can index mismatches between predicted and actual visual inputs in a system that predicts trajectories based on ongoing context. More generally we suggest that the N/M170 may reflect a "family" of brain signals generated across widespread regions of the visual brain indexing the resolution of top-down influences and incoming sensory data. This has important implications for understanding the N/M170 and investigating how the brain represents context to generate perceptual predictions. Copyright © 2017 Elsevier B.V. All rights reserved.

[Post mortem temperature equilibration of the structures of the head. I. Thermometric techniques and principal investigations (author's transl)].

PubMed

Brinkmann, B; May, D; Riemann, U

1976-06-30

Special thin and flexible thermometric probes showing a diameter of 1 mm and a sharp end were used for post mortem (p.m.) thermometric studies in several tissues. Brain temperatures were measured by inserting a double probe through the superior orbital fissura thus allowing to record the central and the peripheral brain regions separately. Another probe was inserted into the galea and a fourth into the liver. Temperature changes were recorded simultaneously. Many variables of the human head were measured. Sixteen corpses were investigated. The results were as follows: 1. Of all temperature curves registered those of the central brain regions showed the smallest variance. 2. The p.m. temperature curve of the brain shows a sigmoid shape with a rather short "plateau" in the beginning. 3. In the early p.m. phase there is an increasing difference of temperatures between central and peripheral brain regions amounting to 2-4, 6 degrees C in the time period between 78th and 128th minute. 4. The insertion of the thin probes does not cause visible damages. Thus it should be considered for use in forensic practice. 5. Some artificial "head models" were constructed and temperature decrease recorded after warming. The curves showed the same type of sigmoid shape as those obtained from the corpses. 6. Of the possible variables measured that could influence the temperature decrease only the density of the hair seems to be of interest.

Skull and cerebrospinal fluid effects on microwave radiation propagation in human brain

NASA Astrophysics Data System (ADS)

Ansari, M. A.; Zarei, M.; Akhlaghipour, N.; Niknam, A. R.

2017-12-01

The determination of microwave absorption distribution in the human brain is necessary for the detection of brain tumors using thermo-acoustic imaging and for removing them using hyperthermia treatment. In contrast to ionizing radiation, hyperthermia treatment can be applied to remove tumors inside the brain without the concern of including secondary malignancies, which typically form from the neuronal cells of the septum pellucidum. The aim of this study is to determine the microwave absorption distribution in an adult human brain and to study the effects of skull and cerebrospinal fluid on the propagation of microwave radiation inside the brain. To this end, we simulate the microwave absorption distribution in a realistic adult brain model (Colin 27) using the mesh-based Monte Carlo (MMC) method. This is because in spite of there being other numerical methods, the MMC does not require a large memory, even for complicated geometries, and its algorithm is simple and easy to implement with low computational cost. The brain model is constructed using high-resolution (1 mm isotropic voxel) and low noise magnetic resonance imaging (MRI) scans and its volume contains 181×217×181 voxels, covering the brain completely. Using the MMC method, the radiative transport equation is solved and the absorbed microwave energy distribution in different brain regions is obtained without any fracture or anomaly. The simulation results show that the skull and cerebrospinal fluid guide the microwave radiation and suppress its penetration through deep brain compartments as a shielding factor. These results reveal that the MMC can be used to predict the amount of required energy to increase the temperature inside the tumour during hyperthermia treatment. Our results also show why a deep tumour inside an adult human brain cannot be efficiently treated using hyperthermia treatment. Finally, the accuracy of the presented numerical method is verified using the signal flow graph technique.

Differences between chronological and brain age are related to education and self-reported physical activity.

PubMed

Steffener, Jason; Habeck, Christian; O'Shea, Deirdre; Razlighi, Qolamreza; Bherer, Louis; Stern, Yaakov

2016-04-01

This study investigated the relationship between education and physical activity and the difference between a physiological prediction of age and chronological age (CA). Cortical and subcortical gray matter regional volumes were calculated from 331 healthy adults (range: 19-79 years). Multivariate analyses identified a covariance pattern of brain volumes best predicting CA (R(2) = 47%). Individual expression of this brain pattern served as a physiologic measure of brain age (BA). The difference between CA and BA was predicted by education and self-report measures of physical activity. Education and the daily number of flights of stairs climbed (FOSC) were the only 2 significant predictors of decreased BA. Effect sizes demonstrated that BA decreased by 0.95 years for each year of education and by 0.58 years for 1 additional FOSC daily. Effects of education and FOSC on regional brain volume were largely driven by temporal and subcortical volumes. These results demonstrate that higher levels of education and daily FOSC are related to larger brain volume than predicted by CA which supports the utility of regional gray matter volume as a biomarker of healthy brain aging. Copyright © 2016 Elsevier Inc. All rights reserved.

Development of a Physiologically-Based Pharmacokinetic Model of the Rat Central Nervous System

PubMed Central

Badhan, Raj K. Singh; Chenel, Marylore; Penny, Jeffrey I.

2014-01-01

Central nervous system (CNS) drug disposition is dictated by a drug’s physicochemical properties and its ability to permeate physiological barriers. The blood–brain barrier (BBB), blood-cerebrospinal fluid barrier and centrally located drug transporter proteins influence drug disposition within the central nervous system. Attainment of adequate brain-to-plasma and cerebrospinal fluid-to-plasma partitioning is important in determining the efficacy of centrally acting therapeutics. We have developed a physiologically-based pharmacokinetic model of the rat CNS which incorporates brain interstitial fluid (ISF), choroidal epithelial and total cerebrospinal fluid (CSF) compartments and accurately predicts CNS pharmacokinetics. The model yielded reasonable predictions of unbound brain-to-plasma partition ratio (Kpuu,brain) and CSF:plasma ratio (CSF:Plasmau) using a series of in vitro permeability and unbound fraction parameters. When using in vitro permeability data obtained from L-mdr1a cells to estimate rat in vivo permeability, the model successfully predicted, to within 4-fold, Kpuu,brain and CSF:Plasmau for 81.5% of compounds simulated. The model presented allows for simultaneous simulation and analysis of both brain biophase and CSF to accurately predict CNS pharmacokinetics from preclinical drug parameters routinely available during discovery and development pathways. PMID:24647103

Retardation of brain growth of guinea pigs by hyperthermia: effect of varying intervals between successive exposures.

PubMed

Edwards, M J; Gray, C H; Beatson, J

1984-04-01

Guinea pigs were exposed to a temperature of 42.5-43.5 degrees C on three occasions between days 20 and 23 of pregnancy. In the first experiment, groups of mothers were exposed at intervals of 18-30 hr. Each exposure ended when the deep rectal temperature had been over 43 degrees C for 6 min and mean temperatures were 43.2-43.4 degrees C. Micrencephaly was found in 78% of heated newborn offspring, the mean brain weights of all groups being significantly less than controls. In the heated groups, the brain weights were reduced significantly as the interval between exposures decreased. Abnormalities other than micrencephaly were found in 10% of heated offspring and included exomphalos, clubfoot, and hypodactyly. In the second experiment, groups of mothers were exposed for 1 hour at intervals of 6-20 hr. The mean temperatures of heated groups were 42.6-42.9 degrees C. The mean brain weights of all groups of heated newborn were significantly reduced and micrencephaly was found in 61% of newborn. Brain weights were reduced significantly as mean maternal temperature increased. There was a significant interaction between the level of temperature elevation and the interval between exposures.(ABSTRACT TRUNCATED AT 250 WORDS)

What cues do nurses use to predict aggression in people with acquired brain injury?

PubMed

Pryor, Julie

2005-04-01

There is a paucity of research on the frequent and repeated episodes of aggression and violence experienced by nurses when working with people who have an acquired brain injury. The purpose of this study was to bring this issue into focus by identifying the cues nurses use to predict aggression in people with acquired brain injury. Twenty-eight nurses from 10 different inpatient brain injury rehabilitation units in Australia participated in the study. Participants were interviewed using the Critical Decision Method on a one to one basis for up to one and one half hours on two consecutive days. Transcripts of the interviews were analysed using thematic analysis. Results revealed that nurses identified five groups of cues that predict aggression in a patient: (1) what a patient is saying; (2) changes in a patient's voice; (3) changes in a patient's face; (4) changes in a patient's behavior; and (5) a patient's emotions. Nurses reported using multiple cues to predict aggression and highlighted the importance of personal knowledge of the patient in conjunction with identified cues when predicting aggression. Nurses caring for patients with acquired brain injury can predict many episodes of aggression, though not all, by identifying cues from the patient.

Brain Network Theory Can Predict Whether Neuropsychological Outcomes Will Differ from Clinical Expectations.

PubMed

Warren, David E; Denburg, Natalie L; Power, Jonathan D; Bruss, Joel; Waldron, Eric J; Sun, Haoxin; Petersen, Steve E; Tranel, Daniel

2017-02-01

Theories of brain-network organization based on neuroimaging data have burgeoned in recent years, but the predictive power of such theories for cognition and behavior has only rarely been examined. Here, predictions from clinical neuropsychologists about the cognitive profiles of patients with focal brain lesions were used to evaluate a brain-network theory (Warren et al., 2014). Neuropsychologists made predictions regarding the neuropsychological profiles of a neurological patient sample (N = 30) based on lesion location. The neuropsychologists then rated the congruence of their predictions with observed neuropsychological outcomes, in regard to the "severity" of neuropsychological deficits and the "focality" of neuropsychological deficits. Based on the network theory, two types of lesion locations were identified: "target" locations (putative hubs in a brain-wide network) and "control" locations (hypothesized to play limited roles in network function). We found that patients with lesions of target locations (N = 19) had deficits of greater than expected severity that were more widespread than expected, whereas patients with lesions of control locations (N = 11) showed milder, circumscribed deficits that were more congruent with expectations. The findings for the target brain locations suggest that prevailing views of brain-behavior relationships may be sharpened and refined by integrating recently proposed network-oriented perspectives. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Cerebral oxygenation in traumatic brain injury; Can a non-invasive frequency domain near-infrared spectroscopy device detect changes in brain tissue oxygen tension as well as the established invasive monitor?

PubMed

Davies, David James; Clancy, Michael; Dehghani, Hamid; Lucas, Samuel John Edwin; Forcione, Mario; Yakoub, Kamal Makram; Belli, Antonio

2018-06-07

The cost and highly invasive nature of brain monitoring modality in traumatic brain injury patients currently restrict its utility to specialist neurological intensive care settings. We aim to test the abilities of a frequency domain near-infrared spectroscopy (FD-NIRS) device in predicting changes in invasively measured brain tissue oxygen tension. Individuals admitted to a United Kingdom specialist major trauma centre were contemporaneously monitored with an FD-NIRS device and invasively measured brain tissue oxygen tension probe. Area under the curve receiver operating characteristic (AUROC) statistical analysis was utilised to assess the predictive power of FD-NIRS in detecting both moderate and severe hypoxia (20 and 10 mmHg, respectively), as measured invasively. 16 individuals were prospectively recruited to the investigation. Severe hypoxic episodes were detected in 9 of these individuals, with the NIRS demonstrating a broad range of predictive abilities (AUROC 0.68-0.88) from relatively poor to good. Moderate hypoxic episodes were detected in seven individuals with similar predictive performance (AUROC 0.576 - 0.905). A variable performance in the predictive powers of this FD-NIRS device to detect changes in brain tissue oxygen was demonstrated. Consequently, this enhanced NIRS technology has not demonstrated sufficient ability to replace the established invasive measurement.

Seasonal acclimatization of brain lipidome in a eurythermal fish (Carassius carassius) is mainly determined by temperature.

PubMed

Käkelä, Reijo; Mattila, Minja; Hermansson, Martin; Haimi, Perttu; Uphoff, Andreas; Paajanen, Vesa; Somerharju, Pentti; Vornanen, Matti

2008-05-01

Crucian carp (Carassius carassius) is an excellent vertebrate model for studies on temperature adaptation in biological excitable membranes, since the species can tolerate temperatures from 0 to +36 degrees C. To determine how temperature affects the lipid composition of brain, the fish were acclimated for 4 wk at +30, +16, or +4 degrees C in the laboratory, or seasonally acclimatized individuals were captured from the wild throughout the year (temperature = +1 to +23 degrees C), and the brain glycerophospholipid and sphingolipid compositions were analyzed in detail by electrospray-ionization mass spectrometry. Numerous significant temperature-related changes were found in the molecular species composition of the membrane lipids. The most notable and novel finding was a large (approximately 3-fold) increase of the di-22:6n-3 phosphatidylserine and phosphatidylethanolamine species in the cold. Since the increase of 22:6n-3 in the total fatty acyl pool of the brain was small, the formation of di-22:6n-3 aminophospholipid species appears to be a specific adaptation to low temperature. Such highly unsaturated species could be needed to maintain adequate membrane fluidity in the vicinity of transporters and other integral membrane proteins. Plasmalogens increased somewhat at higher temperatures, possibly to protect membranes against oxidation. The modifications of brain lipidome during the 4-wk laboratory acclimation were, in many respects, similar to those found in the wild, which indicates that the seasonal changes observed in the wild are temperature dependent rather than induced by other environmental factors.

Relative value of diverse brain MRI and blood-based biomarkers for predicting cognitive decline in the elderly

NASA Astrophysics Data System (ADS)

Madsen, Sarah K.; Ver Steeg, Greg; Daianu, Madelaine; Mezher, Adam; Jahanshad, Neda; Nir, Talia M.; Hua, Xue; Gutman, Boris A.; Galstyan, Aram; Thompson, Paul M.

2016-03-01

Cognitive decline accompanies many debilitating illnesses, including Alzheimer's disease (AD). In old age, brain tissue loss also occurs along with cognitive decline. Although blood tests are easier to perform than brain MRI, few studies compare brain scans to standard blood tests to see which kinds of information best predict future decline. In 504 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we first used linear regression to assess the relative value of different types of data to predict cognitive decline, including 196 blood panel biomarkers, 249 MRI biomarkers obtained from the FreeSurfer software, demographics, and the AD-risk gene APOE. A subset of MRI biomarkers was the strongest predictor. There was no specific blood marker that increased predictive accuracy on its own, we found that a novel unsupervised learning method, CorEx, captured weak correlations among blood markers, and the resulting clusters offered unique predictive power.

The possibility of application of spiral brain computed tomography to traumatic brain injury.

PubMed

Lim, Daesung; Lee, Soo Hoon; Kim, Dong Hoon; Choi, Dae Seub; Hong, Hoon Pyo; Kang, Changwoo; Jeong, Jin Hee; Kim, Seong Chun; Kang, Tae-Sin

2014-09-01

The spiral computed tomography (CT) with the advantage of low radiation dose, shorter test time required, and its multidimensional reconstruction is accepted as an essential diagnostic method for evaluating the degree of injury in severe trauma patients and establishment of therapeutic plans. However, conventional sequential CT is preferred for the evaluation of traumatic brain injury (TBI) over spiral CT due to image noise and artifact. We aimed to compare the diagnostic power of spiral facial CT for TBI to that of conventional sequential brain CT. We evaluated retrospectively the images of 315 traumatized patients who underwent both brain CT and facial CT simultaneously. The hemorrhagic traumatic brain injuries such as epidural hemorrhage, subdural hemorrhage, subarachnoid hemorrhage, and contusional hemorrhage were evaluated in both images. Statistics were performed using Cohen's κ to compare the agreement between 2 imaging modalities and sensitivity, specificity, positive predictive value, and negative predictive value of spiral facial CT to conventional sequential brain CT. Almost perfect agreement was noted regarding hemorrhagic traumatic brain injuries between spiral facial CT and conventional sequential brain CT (Cohen's κ coefficient, 0.912). To conventional sequential brain CT, sensitivity, specificity, positive predictive value, and negative predictive value of spiral facial CT were 92.2%, 98.1%, 95.9%, and 96.3%, respectively. In TBI, the diagnostic power of spiral facial CT was equal to that of conventional sequential brain CT. Therefore, expanded spiral facial CT covering whole frontal lobe can be applied to evaluate TBI in the future. Copyright © 2014 Elsevier Inc. All rights reserved.

Rapid Induction of Therapeutic Hypothermia Using Transnasal High Flow Dry Air

PubMed Central

Chava, Raghuram; Raghavan, Madhavan Srinivas; Halperin, Henry; Maqbool, Farhan; Geocadin, Romergryko; Quinones-Hinojosa, Alfredo; Kolandaivelu, Aravindan; Rosen, Benjamin A.

2017-01-01

Early induction of therapeutic hypothermia (TH) is recommended in out-of-hospital cardiac arrest (CA); however, currently no reliable methods exist to initiate cooling. We investigated the effect of high flow transnasal dry air on brain and body temperatures in adult porcine animals. Adult porcine animals (n = 23) under general anesthesia were subject to high flow of transnasal dry air. Mouth was kept open to create a unidirectional airflow, in through the nostrils and out through the mouth. Brain, internal jugular, and aortic temperatures were recorded. The effect of varying airflow rate and the air humidity (0% or 100%) on the temperature profiles were recorded. The degree of brain cooling was measured as the differential temperature from baseline. A 10-minute exposure of high flow dry air caused rapid cooling of brain and gradual cooling of the jugular and the aortic temperatures in all animals. The degree of brain cooling was flow dependent and significantly higher at higher airflow rates (0.8°C ± 0.3°C, 1.03°C ± 0.6°C, and 1.3°C ± 0.7°C for 20, 40, and 80 L, respectively, p < 0.05 for all comparisons). Air temperature had minimal effect on the brain cooling over 10 minutes with similar decrease in temperature at 4°C and 30°C. At a constant flow rate (40 LPM) and temperature, the degree of cooling over 10 minutes during dry air exposure was significantly higher compared to humid air (100% saturation) (1.22°C ± 0.35°C vs. 0.21°C ± 0.12°C, p < 0.001). High flow transnasal dry air causes flow dependent cooling of the brain and the core temperatures in intubated porcine animals. The mechanism of cooling appears to be evaporation of nasal mucus as cooling is mitigated by humidifying the air. This mechanism may be exploited to initiate TH in CA. PMID:27635468

Rapid Induction of Therapeutic Hypothermia Using Transnasal High Flow Dry Air.

PubMed

Chava, Raghuram; Zviman, Menekhem; Raghavan, Madhavan Srinivas; Halperin, Henry; Maqbool, Farhan; Geocadin, Romergryko; Quinones-Hinojosa, Alfredo; Kolandaivelu, Aravindan; Rosen, Benjamin A; Tandri, Harikrishna

2017-03-01

Early induction of therapeutic hypothermia (TH) is recommended in out-of-hospital cardiac arrest (CA); however, currently no reliable methods exist to initiate cooling. We investigated the effect of high flow transnasal dry air on brain and body temperatures in adult porcine animals. Adult porcine animals (n = 23) under general anesthesia were subject to high flow of transnasal dry air. Mouth was kept open to create a unidirectional airflow, in through the nostrils and out through the mouth. Brain, internal jugular, and aortic temperatures were recorded. The effect of varying airflow rate and the air humidity (0% or 100%) on the temperature profiles were recorded. The degree of brain cooling was measured as the differential temperature from baseline. A 10-minute exposure of high flow dry air caused rapid cooling of brain and gradual cooling of the jugular and the aortic temperatures in all animals. The degree of brain cooling was flow dependent and significantly higher at higher airflow rates (0.8°C ± 0.3°C, 1.03°C ± 0.6°C, and 1.3°C ± 0.7°C for 20, 40, and 80 L, respectively, p < 0.05 for all comparisons). Air temperature had minimal effect on the brain cooling over 10 minutes with similar decrease in temperature at 4°C and 30°C. At a constant flow rate (40 LPM) and temperature, the degree of cooling over 10 minutes during dry air exposure was significantly higher compared to humid air (100% saturation) (1.22°C ± 0.35°C vs. 0.21°C ± 0.12°C, p < 0.001). High flow transnasal dry air causes flow dependent cooling of the brain and the core temperatures in intubated porcine animals. The mechanism of cooling appears to be evaporation of nasal mucus as cooling is mitigated by humidifying the air. This mechanism may be exploited to initiate TH in CA.

Regional Differences in Brain Volume Predict the Acquisition of Skill in a Complex Real-Time Strategy Videogame

PubMed Central

Basak, Chandramallika; Voss, Michelle W.; Erickson, Kirk I.; Boot, Walter R.; Kramer, Arthur F.

2015-01-01

Previous studies have found that differences in brain volume among older adults predict performance in laboratory tasks of executive control, memory, and motor learning. In the present study we asked whether regional differences in brain volume as assessed by the application of a voxel-based morphometry technique on high resolution MRI would also be useful in predicting the acquisition of skill in complex tasks, such as strategy-based video games. Twenty older adults were trained for over 20 hours to play Rise of Nations, a complex real-time strategy game. These adults showed substantial improvements over the training period in game performance. MRI scans obtained prior to training revealed that the volume of a number of brain regions, which have been previously associated with subsets of the trained skills, predicted a substantial amount of variance in learning on the complex game. Thus, regional differences in brain volume can predict learning in complex tasks that entail the use of a variety of perceptual, cognitive and motor processes. PMID:21546146

Regional differences in brain volume predict the acquisition of skill in a complex real-time strategy videogame.

PubMed

Basak, Chandramallika; Voss, Michelle W; Erickson, Kirk I; Boot, Walter R; Kramer, Arthur F

2011-08-01

Previous studies have found that differences in brain volume among older adults predict performance in laboratory tasks of executive control, memory, and motor learning. In the present study we asked whether regional differences in brain volume as assessed by the application of a voxel-based morphometry technique on high resolution MRI would also be useful in predicting the acquisition of skill in complex tasks, such as strategy-based video games. Twenty older adults were trained for over 20 h to play Rise of Nations, a complex real-time strategy game. These adults showed substantial improvements over the training period in game performance. MRI scans obtained prior to training revealed that the volume of a number of brain regions, which have been previously associated with subsets of the trained skills, predicted a substantial amount of variance in learning on the complex game. Thus, regional differences in brain volume can predict learning in complex tasks that entail the use of a variety of perceptual, cognitive and motor processes. Copyright © 2011 Elsevier Inc. All rights reserved.

Outcome prediction in home- and community-based brain injury rehabilitation using the Mayo-Portland Adaptability Inventory.

PubMed

Malec, James F; Parrot, Devan; Altman, Irwin M; Swick, Shannon

2015-01-01

The objective of the study was to develop statistical formulas to predict levels of community participation on discharge from post-hospital brain injury rehabilitation using retrospective data analysis. Data were collected from seven geographically distinct programmes in a home- and community-based brain injury rehabilitation provider network. Participants were 642 individuals with post-traumatic brain injury. Interventions consisted of home- and community-based brain injury rehabilitation. The main outcome measure was the Mayo-Portland Adaptability Inventory (MPAI-4) Participation Index. Linear discriminant models using admission MPAI-4 Participation Index score and log chronicity correctly predicted excellent (no to minimal participation limitations), very good (very mild participation limitations), good (mild participation limitations), and limited (significant participation limitations) outcome levels at discharge. Predicting broad outcome categories for post-hospital rehabilitation programmes based on admission assessment data appears feasible and valid. Equations to provide patients and families with probability statements on admission about expected levels of outcome are provided. It is unknown to what degree these prediction equations can be reliably applied and valid in other settings.

Dynamic Filtering Improves Attentional State Prediction with fNIRS

NASA Technical Reports Server (NTRS)

Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.

2016-01-01

Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% +/- 6% versus 72% +/- 15%).

A nomogram to predict brain metastasis as the first relapse in curatively resected non-small cell lung cancer patients.

PubMed

Won, Young-Woong; Joo, Jungnam; Yun, Tak; Lee, Geon-Kook; Han, Ji-Youn; Kim, Heung Tae; Lee, Jin Soo; Kim, Moon Soo; Lee, Jong Mog; Lee, Hyun-Sung; Zo, Jae Ill; Kim, Sohee

2015-05-01

Development of brain metastasis results in a significant reduction in overall survival. However, there is no an effective tool to predict brain metastasis in non-small cell lung cancer (NSCLC) patients. We conducted this study to develop a feasible nomogram that can predict metastasis to the brain as the first relapse site in patients with curatively resected NSCLC. A retrospective review of NSCLC patients who had received curative surgery at National Cancer Center (Goyang, South Korea) between 2001 and 2008 was performed. We chose metastasis to the brain as the first relapse site after curative surgery as the primary endpoint of the study. A nomogram was modeled using logistic regression. Among 1218 patients, brain metastasis as the first relapse developed in 87 patients (7.14%) during the median follow-up of 43.6 months. Occurrence rates of brain metastasis were higher in patients with adenocarcinoma or those with a high pT and pN stage. Younger age appeared to be associated with brain metastasis, but this result was not statistically significant. The final prediction model included histology, smoking status, pT stage, and the interaction between adenocarcinoma and pN stage. The model showed fairly good discriminatory ability with a C-statistic of 69.3% and 69.8% for predicting brain metastasis within 2 years and 5 years, respectively. Internal validation using 2000 bootstrap samples resulted in C-statistics of 67.0% and 67.4% which still indicated good discriminatory performances. The nomogram presented here provides the individual risk estimate of developing metastasis to the brain as the first relapse site in patients with NSCLC who have undergone curative surgery. Surveillance programs or preventive treatment strategies for brain metastasis could be established based on this nomogram. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

An Investigation of Individual Variability in Brain Activity During Episodic Encoding and Retrieval

DTIC Science & Technology

2008-12-01

variability in mnemonic strategy use is, at least in part, related to the extensive variability observed in brain activity patterns. While a number of...1 AN INVESTIGATION OF INDIVIDUAL VARIABILITY IN BRAIN ACTIVITY DURING EPISODIC ENCODING AND RETRIEVAL C.L. Donovan*, and M.B. Miller Department of...strategy measures for predicting differences in brain activity patterns during a learning and memory task and to compare their predictive value to other

Reliability of temperatures measured at standard monitoring sites as an index of brain temperature during deep hypothermic cardiopulmonary bypass conducted for thoracic aortic reconstruction.

PubMed

Akata, Takashi; Setoguchi, Hidekazu; Shirozu, Kazuhiro; Yoshino, Jun

2007-06-01

It is essential to estimate the brain temperature of patients during deliberate deep hypothermia. Using jugular bulb temperature as a standard for brain temperature, we evaluated the accuracy and precision of 5 standard temperature monitoring sites (ie, pulmonary artery, nasopharynx, forehead deep-tissue, urinary bladder, and fingertip skin-surface tissue) during deep hypothermic cardiopulmonary bypass conducted for thoracic aortic reconstruction. In 20 adult patients with thoracic aortic aneurysms, the 5 temperature monitoring sites were recorded every 1 minute during deep hypothermic (<20 degrees C) cardiopulmonary bypass. The accuracy was evaluated by the difference from jugular bulb temperature, and the precision was evaluated by its standard deviation, as well as by the correlation with jugular bulb temperature. Pulmonary artery temperature and jugular bulb temperature began to change immediately after the start of cooling or rewarming, closely matching each other, and the other temperatures lagged behind these two temperatures. During either situation, the accuracy of pulmonary artery temperature measurement (0.3 degrees C-0.5 degrees C) was much superior to the other measurements, and its precision (standard deviation of the difference from jugular bulb temperature = 1.5 degrees C-1.8 degrees C; correlation coefficient = 0.94-0.95) was also best among the measurements, with its rank order being pulmonary artery > or = nasopharynx > forehead > bladder > fingertip. However, the accuracy and precision of pulmonary artery temperature measurement was significantly impaired during and for several minutes after infusion of cold cardioplegic solution. Pulmonary artery temperature measurement is recommended to estimate brain temperature during deep hypothermic cardiopulmonary bypass, even if it is conducted with the sternum opened; however, caution needs to be exercised in interpreting its measurements during periods of the cardioplegic solution infusion.

Focal epidural cooling reduces the infarction volume of permanent middle cerebral artery occlusion in swine.

PubMed

Zhang, Lihua; Cheng, Huilin; Shi, Jixin; Chen, Jun

2007-02-01

The protective effect against ischemic stroke by systemic hypothermia is limited by the cooling rate and it has severe complications. This study was designed to evaluate the effect of SBH induced by epidural cooling on infarction volume in a swine model of PMCAO. Permanent middle cerebral artery occlusion was performed in 12 domestic swine assigned to groups A and B. In group A, the cranial and rectal temperatures were maintained at normal range (37 degrees C-39 degrees C) for 6 hours after PMCAO. In group B, cranial temperature was reduced to moderate (deep brain, <30 degrees C) and deep (brain surface, <20 degrees C) temperature and maintained at that level for 5 hours after 1 hour after PMCAO, by the epidural cooling method. All animals were euthanized 6 hours after MCAO; their brains were sectioned and stained with 2,3,5-triphenyltetrazolium chloride and their infarct volumes were calculated. The moderate and deep brain temperature (at deep brain and brain surface) can be induced by rapid epidural cooling, whereas the rectal temperature was maintained within normal range. The infarction volume after PMCAO was significantly reduced by epidural cooling compared with controls (13.73% +/- 1.82% vs 5.62% +/- 2.57%, P < .05). The present study has demonstrated, with histologic confirmation, that epidural cooling may be a useful strategy for reducing infarct volume after the onset of ischemia.

Task relevance modulates the behavioural and neural effects of sensory predictions

PubMed Central

Friston, Karl J.; Nobre, Anna C.

2017-01-01

The brain is thought to generate internal predictions to optimize behaviour. However, it is unclear whether predictions signalling is an automatic brain function or depends on task demands. Here, we manipulated the spatial/temporal predictability of visual targets, and the relevance of spatial/temporal information provided by auditory cues. We used magnetoencephalography (MEG) to measure participants’ brain activity during task performance. Task relevance modulated the influence of predictions on behaviour: spatial/temporal predictability improved spatial/temporal discrimination accuracy, but not vice versa. To explain these effects, we used behavioural responses to estimate subjective predictions under an ideal-observer model. Model-based time-series of predictions and prediction errors (PEs) were associated with dissociable neural responses: predictions correlated with cue-induced beta-band activity in auditory regions and alpha-band activity in visual regions, while stimulus-bound PEs correlated with gamma-band activity in posterior regions. Crucially, task relevance modulated these spectral correlates, suggesting that current goals influence PE and prediction signalling. PMID:29206225

Brain aromatase (Cyp19A2) and estrogen receptors, in larvae and adult pejerrey fish Odontesthes bonariensis: Neuroanatomical and functional relations

USGS Publications Warehouse

Strobl-Mazzulla, P. H.; Lethimonier, C.; Gueguen, M.M.; Karube, M.; Fernandino, J.I.; Yoshizaki, G.; Patino, R.; Strussmann, C.A.; Kah, O.; Somoza, G.M.

2008-01-01

Although estrogens exert many functions on vertebrate brains, there is little information on the relationship between brain aromatase and estrogen receptors. Here, we report the cloning and characterization of two estrogen receptors, ?? and ??, in pejerrey. Both receptors' mRNAs largely overlap and were predominantly expressed in the brain, pituitary, liver, and gonads. Also brain aromatase and estrogen receptors were up-regulated in the brain of estradiol-treated males. In situ hybridization was performed to study in more detail, the distribution of the two receptors in comparison with brain aromatase mRNA in the brain of adult pejerrey. The estrogen receptors' mRNAs exhibited distinct but partially overlapping patterns of expression in the preoptic area and the mediobasal hypothalamus, as well as in the pituitary gland. Moreover, the estrogen receptor ??, but not ??, were found to be expressed in cells lining the preoptic recess, similarly as observed for brain aromatase. Finally, it was shown that the onset expression of brain aromatase and both estrogen receptors in the head of larvae preceded the morphological differentiation of the gonads. Because pejerrey sex differentiation is strongly influenced by temperature, brain aromatase expression was measured during the temperature-sensitive window and was found to be significantly higher at male-promoting temperature. Taken together these results suggest close neuroanatomical and functional relationships between brain aromatase and estrogen receptors, probably involved in the sexual differentiation of the brain and raising interesting questions on the origin (central or peripheral) of the brain aromatase substrate. ?? 2008 Elsevier Inc.

Predicting Treatment Response in Social Anxiety Disorder From Functional Magnetic Resonance Imaging

PubMed Central

Doehrmann, Oliver; Ghosh, Satrajit S.; Polli, Frida E.; Reynolds, Gretchen O.; Horn, Franziska; Keshavan, Anisha; Triantafyllou, Christina; Saygin, Zeynep M.; Whitfield-Gabrieli, Susan; Hofmann, Stefan G.; Pollack, Mark; Gabrieli, John D.

2013-01-01

Context Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD. Objective To measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT). Design Functional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli. Setting Patients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology. Patients Thirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD. Interventions Brain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT. Main Outcome Measures Whole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure. Results Pretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline. Conclusions The results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient. PMID:22945462

Robust prediction of individual creative ability from brain functional connectivity.

PubMed

Beaty, Roger E; Kenett, Yoed N; Christensen, Alexander P; Rosenberg, Monica D; Benedek, Mathias; Chen, Qunlin; Fink, Andreas; Qiu, Jiang; Kwapil, Thomas R; Kane, Michael J; Silvia, Paul J

2018-01-30

People's ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis-connectome-based predictive modeling-to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. At the behavioral level, we found a strong correlation between creative thinking ability and self-reported creative behavior and accomplishment in the arts and sciences ( r = 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems-intrinsic functional networks that tend to work in opposition-suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks.

Utilizing multiple scale models to improve predictions of extra-axial hemorrhage in the immature piglet.

PubMed

Scott, Gregory G; Margulies, Susan S; Coats, Brittany

2016-10-01

Traumatic brain injury (TBI) is a leading cause of death and disability in the USA. To help understand and better predict TBI, researchers have developed complex finite element (FE) models of the head which incorporate many biological structures such as scalp, skull, meninges, brain (with gray/white matter differentiation), and vasculature. However, most models drastically simplify the membranes and substructures between the pia and arachnoid membranes. We hypothesize that substructures in the pia-arachnoid complex (PAC) contribute substantially to brain deformation following head rotation, and that when included in FE models accuracy of extra-axial hemorrhage prediction improves. To test these hypotheses, microscale FE models of the PAC were developed to span the variability of PAC substructure anatomy and regional density. The constitutive response of these models were then integrated into an existing macroscale FE model of the immature piglet brain to identify changes in cortical stress distribution and predictions of extra-axial hemorrhage (EAH). Incorporating regional variability of PAC substructures substantially altered the distribution of principal stress on the cortical surface of the brain compared to a uniform representation of the PAC. Simulations of 24 non-impact rapid head rotations in an immature piglet animal model resulted in improved accuracy of EAH prediction (to 94 % sensitivity, 100 % specificity), as well as a high accuracy in regional hemorrhage prediction (to 82-100 % sensitivity, 100 % specificity). We conclude that including a biofidelic PAC substructure variability in FE models of the head is essential for improved predictions of hemorrhage at the brain/skull interface.

Brain Injury in Neonates with Complex Congenital Heart Disease: What Is the Predictive Value of MRI in the Fetal Period?

PubMed

Brossard-Racine, M; du Plessis, A; Vezina, G; Robertson, R; Donofrio, M; Tworetzky, W; Limperopoulos, C

2016-07-01

Brain injury in neonates with congenital heart disease is an important predictor of adverse neurodevelopmental outcome. Impaired brain development in congenital heart disease may have a prenatal origin, but the sensitivity and specificity of fetal brain MR imaging for predicting neonatal brain lesions are currently unknown. We sought to determine the value of conventional fetal MR imaging for predicting abnormal findings on neonatal preoperative MR imaging in neonates with complex congenital heart disease. MR imaging studies were performed in 103 fetuses with confirmed congenital heart disease (mean gestational age, 31.57 ± 3.86 weeks) and were repeated postnatally before cardiac surgery (mean age, 6.8 ± 12.2 days). Each MR imaging study was read by a pediatric neuroradiologist. Brain abnormalities were detected in 17/103 (16%) fetuses by fetal MR imaging and in 33/103 (32%) neonates by neonatal MR imaging. Only 9/33 studies with abnormal neonatal findings were preceded by abnormal findings on fetal MR imaging. The sensitivity and specificity of conventional fetal brain MR imaging for predicting neonatal brain abnormalities were 27% and 89%, respectively. Brain abnormalities detected by in utero MR imaging in fetuses with congenital heart disease are associated with higher risk of postnatal preoperative brain injury. However, a substantial proportion of anomalies on postnatal MR imaging were not present on fetal MR imaging; this result is likely due to the limitations of conventional fetal MR imaging and the emergence of new lesions that occurred after the fetal studies. Postnatal brain MR imaging studies are needed to confirm the presence of injury before open heart surgery. © 2016 by American Journal of Neuroradiology.

Critical Care Management Focused on Optimizing Brain Function After Cardiac Arrest.

PubMed

Nakashima, Ryuta; Hifumi, Toru; Kawakita, Kenya; Okazaki, Tomoya; Egawa, Satoshi; Inoue, Akihiko; Seo, Ryutaro; Inagaki, Nobuhiro; Kuroda, Yasuhiro

2017-03-24

The discussion of neurocritical care management in post-cardiac arrest syndrome (PCAS) has generally focused on target values used for targeted temperature management (TTM). There has been less attention paid to target values for systemic and cerebral parameters to minimize secondary brain damage in PCAS. And the neurologic indications for TTM to produce a favorable neurologic outcome remain to be determined. Critical care management of PCAS patients is fundamental and essential for both cardiologists and general intensivists to improve neurologic outcome, because definitive therapy of PCAS includes both special management of the cause of cardiac arrest, such as coronary intervention to ischemic heart disease, and intensive management of the results of cardiac arrest, such as ventilation strategies to avoid brain ischemia. We reviewed the literature and the latest research about the following issues and propose practical care recommendations. Issues are (1) prediction of TTM candidate on admission, (2) cerebral blood flow and metabolism and target value of them, (3) seizure management using continuous electroencephalography, (4) target value of hemodynamic stabilization and its method, (5) management and analysis of respiration, (6) sedation and its monitoring, (7) shivering control and its monitoring, and (8) glucose management. We hope to establish standards of neurocritical care to optimize brain function and produce a favorable neurologic outcome.

A new microcontroller-based human brain hypothermia system.

PubMed

Kapidere, Metin; Ahiska, Raşit; Güler, Inan

2005-10-01

Many studies show that artificial hypothermia of brain in conditions of anesthesia with the rectal temperature lowered down to 33 degrees C produces pronounced prophylactic effect protecting the brain from anoxia. Out of the methods employed now in clinical practice for reducing the oxygen consumption by the cerebral tissue, the most efficacious is craniocerebral hypothermia (CCH). It is finding even more extensive application in cardiovascular surgery, neurosurgery, neurorenimatology and many other fields of medical practice. In this study, a microcontroller-based designed human brain hypothermia system (HBHS) is designed and constructed. The system is intended for cooling and heating the brain. HBHS consists of a thermoelectric hypothermic helmet, a control and a power unit. Helmet temperature is controlled by 8-bit PIC16F877 microcontroller which is programmed using MPLAB editor. Temperature is converted to 10-bit digital and is controlled automatically by the preset values which have been already entered in the microcontroller. Calibration is controlled and the working range is tested. Temperature of helmet is controlled between -5 and +46 degrees C by microcontroller, with the accuracy of +/-0.5 degrees C.

Spontaneous brain activity predicts learning ability of foreign sounds.

PubMed

Ventura-Campos, Noelia; Sanjuán, Ana; González, Julio; Palomar-García, María-Ángeles; Rodríguez-Pujadas, Aina; Sebastián-Gallés, Núria; Deco, Gustavo; Ávila, César

2013-05-29

Can learning capacity of the human brain be predicted from initial spontaneous functional connectivity (FC) between brain areas involved in a task? We combined task-related functional magnetic resonance imaging (fMRI) and resting-state fMRI (rs-fMRI) before and after training with a Hindi dental-retroflex nonnative contrast. Previous fMRI results were replicated, demonstrating that this learning recruited the left insula/frontal operculum and the left superior parietal lobe, among other areas of the brain. Crucially, resting-state FC (rs-FC) between these two areas at pretraining predicted individual differences in learning outcomes after distributed (Experiment 1) and intensive training (Experiment 2). Furthermore, this rs-FC was reduced at posttraining, a change that may also account for learning. Finally, resting-state network analyses showed that the mechanism underlying this reduction of rs-FC was mainly a transfer in intrinsic activity of the left frontal operculum/anterior insula from the left frontoparietal network to the salience network. Thus, rs-FC may contribute to predict learning ability and to understand how learning modifies the functioning of the brain. The discovery of this correspondence between initial spontaneous brain activity in task-related areas and posttraining performance opens new avenues to find predictors of learning capacities in the brain using task-related fMRI and rs-fMRI combined.

The brain, self and society: a social-neuroscience model of predictive processing.

PubMed

Kelly, Michael P; Kriznik, Natasha M; Kinmonth, Ann Louise; Fletcher, Paul C

2018-05-10

This paper presents a hypothesis about how social interactions shape and influence predictive processing in the brain. The paper integrates concepts from neuroscience and sociology where a gulf presently exists between the ways that each describe the same phenomenon - how the social world is engaged with by thinking humans. We combine the concepts of predictive processing models (also called predictive coding models in the neuroscience literature) with ideal types, typifications and social practice - concepts from the sociological literature. This generates a unified hypothetical framework integrating the social world and hypothesised brain processes. The hypothesis combines aspects of neuroscience and psychology with social theory to show how social behaviors may be "mapped" onto brain processes. It outlines a conceptual framework that connects the two disciplines and that may enable creative dialogue and potential future research.

Dynamic filtering improves attentional state prediction with fNIRS

PubMed Central

Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.

2016-01-01

Brain activity can predict a person’s level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise – thereby increasing such state prediction accuracy – remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% ± 6% versus 72% ± 15%). PMID:27231602

Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

PubMed Central

Meng, Xing; Jiang, Rongtao; Lin, Dongdong; Bustillo, Juan; Jones, Thomas; Chen, Jiayu; Yu, Qingbao; Du, Yuhui; Zhang, Yu; Jiang, Tianzi; Sui, Jing; Calhoun, Vince D.

2016-01-01

Neuroimaging techniques have greatly enhanced the understanding of neurodiversity (human brain variation across individuals) in both health and disease. The ultimate goal of using brain imaging biomarkers is to perform individualized predictions. Here we proposed a generalized framework that can predict explicit values of the targeted measures by taking advantage of joint information from multiple modalities. This framework also enables whole brain voxel-wise searching by combining multivariate techniques such as ReliefF, clustering, correlation-based feature selection and multiple regression models, which is more flexible and can achieve better prediction performance than alternative atlas-based methods. For 50 healthy controls and 47 schizophrenia patients, three kinds of features derived from resting-state fMRI (fALFF), sMRI (gray matter) and DTI (fractional anisotropy) were extracted and fed into a regression model, achieving high prediction for both cognitive scores (MCCB composite r = 0.7033, MCCB social cognition r = 0.7084) and symptomatic scores (positive and negative syndrome scale [PANSS] positive r = 0.7785, PANSS negative r = 0.7804). Moreover, the brain areas likely responsible for cognitive deficits of schizophrenia, including middle temporal gyrus, dorsolateral prefrontal cortex, striatum, cuneus and cerebellum, were located with different weights, as well as regions predicting PANSS symptoms, including thalamus, striatum and inferior parietal lobule, pinpointing the potential neuromarkers. Finally, compared to a single modality, multimodal combination achieves higher prediction accuracy and enables individualized prediction on multiple clinical measures. There is more work to be done, but the current results highlight the potential utility of multimodal brain imaging biomarkers to eventually inform clinical decision-making. PMID:27177764

Brief anesthesia, but not voluntary locomotion, significantly alters cortical temperature

PubMed Central

Shirey, Michael J.; Kudlik, D'Anne E.; Huo, Bing-Xing; Greene, Stephanie E.; Drew, Patrick J.

2015-01-01

Changes in brain temperature can alter electrical properties of neurons and cause changes in behavior. However, it is not well understood how behaviors, like locomotion, or experimental manipulations, like anesthesia, alter brain temperature. We implanted thermocouples in sensorimotor cortex of mice to understand how cortical temperature was affected by locomotion, as well as by brief and prolonged anesthesia. Voluntary locomotion induced small (∼0.1°C) but reliable increases in cortical temperature that could be described using a linear convolution model. In contrast, brief (90-s) exposure to isoflurane anesthesia depressed cortical temperature by ∼2°C, which lasted for up to 30 min after the cessation of anesthesia. Cortical temperature decreases were not accompanied by a concomitant decrease in the γ-band local field potential power, multiunit firing rate, or locomotion behavior, which all returned to baseline within a few minutes after the cessation of anesthesia. In anesthetized animals where core body temperature was kept constant, cortical temperature was still >1°C lower than in the awake animal. Thermocouples implanted in the subcortex showed similar temperature changes under anesthesia, suggesting these responses occur throughout the brain. Two-photon microscopy of individual blood vessel dynamics following brief isoflurane exposure revealed a large increase in vessel diameter that ceased before the brain temperature significantly decreased, indicating cerebral heat loss was not due to increased cerebral blood vessel dilation. These data should be considered in experimental designs recording in anesthetized preparations, computational models relating temperature and neural activity, and awake-behaving methods that require brief anesthesia before experimental procedures. PMID:25972579

Regional Differences in Brain Volume Predict the Acquisition of Skill in a Complex Real-Time Strategy Videogame

ERIC Educational Resources Information Center

Basak, Chandramallika; Voss, Michelle W.; Erickson, Kirk I.; Boot, Walter R.; Kramer, Arthur F.

2011-01-01

Previous studies have found that differences in brain volume among older adults predict performance in laboratory tasks of executive control, memory, and motor learning. In the present study we asked whether regional differences in brain volume as assessed by the application of a voxel-based morphometry technique on high resolution MRI would also…

Predicting Story Goodness Performance from Cognitive Measures Following Traumatic Brain Injury

ERIC Educational Resources Information Center

Le, Karen; Coelho, Carl; Mozeiko, Jennifer; Krueger, Frank; Grafman, Jordan

2012-01-01

Purpose: This study examined the prediction of performance on measures of the Story Goodness Index (SGI; Le, Coelho, Mozeiko, & Grafman, 2011) from executive function (EF) and memory measures following traumatic brain injury (TBI). It was hypothesized that EF and memory measures would significantly predict SGI outcomes. Method: One hundred…

Brain mechanisms in religion and spirituality: An integrative predictive processing framework.

PubMed

van Elk, Michiel; Aleman, André

2017-02-01

We present the theory of predictive processing as a unifying framework to account for the neurocognitive basis of religion and spirituality. Our model is substantiated by discussing four different brain mechanisms that play a key role in religion and spirituality: temporal brain areas are associated with religious visions and ecstatic experiences; multisensory brain areas and the default mode network are involved in self-transcendent experiences; the Theory of Mind-network is associated with prayer experiences and over attribution of intentionality; top-down mechanisms instantiated in the anterior cingulate cortex and the medial prefrontal cortex could be involved in acquiring and maintaining intuitive supernatural beliefs. We compare the predictive processing model with two-systems accounts of religion and spirituality, by highlighting the central role of prediction error monitoring. We conclude by presenting novel predictions for future research and by discussing the philosophical and theological implications of neuroscientific research on religion and spirituality. Copyright © 2016 Elsevier Ltd. All rights reserved.

In vivo monitoring of neuronal loss in traumatic brain injury: a microdialysis study

PubMed Central

Tisdall, Martin M.; Girbes, Armand R.; Martinian, Lillian; Thom, Maria; Kitchen, Neil; Smith, Martin

2011-01-01

Traumatic brain injury causes diffuse axonal injury and loss of cortical neurons. These features are well recognized histologically, but their in vivo monitoring remains challenging. In vivo cortical microdialysis samples the extracellular fluid adjacent to neurons and axons. Here, we describe a novel neuronal proteolytic pathway and demonstrate the exclusive neuro-axonal expression of Pavlov’s enterokinase. Enterokinase is membrane bound and cleaves the neurofilament heavy chain at positions 476 and 986. Using a 100 kDa microdialysis cut-off membrane the two proteolytic breakdown products, extracellular fluid neurofilament heavy chains NfH476−986 and NfH476−1026, can be quantified with a relative recovery of 20%. In a prospective clinical in vivo study, we included 10 patients with traumatic brain injury with a median Glasgow Coma Score of 9, providing 640 cortical extracellular fluid samples for longitudinal data analysis. Following high-velocity impact traumatic brain injury, microdialysate extracellular fluid neurofilament heavy chain levels were significantly higher (6.18 ± 2.94 ng/ml) and detectable for longer (>4 days) compared with traumatic brain injury secondary to falls (0.84 ± 1.77 ng/ml, <2 days). During the initial 16 h following traumatic brain injury, strong correlations were found between extracellular fluid neurofilament heavy chain levels and physiological parameters (systemic blood pressure, anaerobic cerebral metabolism, excessive brain tissue oxygenation, elevated brain temperature). Finally, extracellular fluid neurofilament heavy chain levels were of prognostic value, predicting mortality with an odds ratio of 7.68 (confidence interval 2.15–27.46, P = 0.001). In conclusion, this study describes the discovery of Pavlov’s enterokinase in the human brain, a novel neuronal proteolytic pathway that gives rise to specific protein biomarkers (NfH476−986 and NfH476−1026) applicable to in vivo monitoring of diffuse axonal injury and neuronal loss in traumatic brain injury. PMID:21278408

Glutamate Excitoxicity Is the Key Molecular Mechanism Which Is Influenced by Body Temperature during the Acute Phase of Brain Stroke

PubMed Central

Campos, Francisco; Pérez-Mato, María; Agulla, Jesús; Blanco, Miguel; Barral, David; Almeida, Ángeles; Brea, David; Waeber, Christian; Castillo, José; Ramos-Cabrer, Pedro

2012-01-01

Glutamate excitotoxicity, metabolic rate and inflammatory response have been associated to the deleterious effects of temperature during the acute phase of stroke. So far, the association of temperature with these mechanisms has been studied individually. However, the simultaneous study of the influence of temperature on these mechanisms is necessary to clarify their contributions to temperature-mediated ischemic damage. We used non-invasive Magnetic Resonance Spectroscopy to simultaneously measure temperature, glutamate excitotoxicity and metabolic rate in the brain in animal models of ischemia. The immune response to ischemia was measured through molecular serum markers in peripheral blood. We submitted groups of animals to different experimental conditions (hypothermia at 33°C, normothermia at 37°C and hyperthermia at 39°C), and combined these conditions with pharmacological modulation of glutamate levels in the brain through systemic injections of glutamate and oxaloacetate. We show that pharmacological modulation of glutamate levels can neutralize the deleterious effects of hyperthermia and the beneficial effects of hypothermia, however the analysis of the inflammatory response and metabolic rate, demonstrated that their effects on ischemic damage are less critical than glutamate excitotoxity. We conclude that glutamate excitotoxicity is the key molecular mechanism which is influenced by body temperature during the acute phase of brain stroke. PMID:22952923

Glutamate excitoxicity is the key molecular mechanism which is influenced by body temperature during the acute phase of brain stroke.

PubMed

Campos, Francisco; Pérez-Mato, María; Agulla, Jesús; Blanco, Miguel; Barral, David; Almeida, Angeles; Brea, David; Waeber, Christian; Castillo, José; Ramos-Cabrer, Pedro

2012-01-01

Glutamate excitotoxicity, metabolic rate and inflammatory response have been associated to the deleterious effects of temperature during the acute phase of stroke. So far, the association of temperature with these mechanisms has been studied individually. However, the simultaneous study of the influence of temperature on these mechanisms is necessary to clarify their contributions to temperature-mediated ischemic damage. We used non-invasive Magnetic Resonance Spectroscopy to simultaneously measure temperature, glutamate excitotoxicity and metabolic rate in the brain in animal models of ischemia. The immune response to ischemia was measured through molecular serum markers in peripheral blood. We submitted groups of animals to different experimental conditions (hypothermia at 33°C, normothermia at 37°C and hyperthermia at 39°C), and combined these conditions with pharmacological modulation of glutamate levels in the brain through systemic injections of glutamate and oxaloacetate. We show that pharmacological modulation of glutamate levels can neutralize the deleterious effects of hyperthermia and the beneficial effects of hypothermia, however the analysis of the inflammatory response and metabolic rate, demonstrated that their effects on ischemic damage are less critical than glutamate excitotoxity. We conclude that glutamate excitotoxicity is the key molecular mechanism which is influenced by body temperature during the acute phase of brain stroke.

A nomogram for predicting distant brain failure in patients treated with gamma knife stereotactic radiosurgery without whole brain radiotherapy

PubMed Central

Ayala-Peacock, Diandra N.; Peiffer, Ann M.; Lucas, John T.; Isom, Scott; Kuremsky, J. Griff; Urbanic, James J.; Bourland, J. Daniel; Laxton, Adrian W.; Tatter, Stephen B.; Shaw, Edward G.; Chan, Michael D.

2014-01-01

Background We review our single institution experience to determine predictive factors for early and delayed distant brain failure (DBF) after radiosurgery without whole brain radiotherapy (WBRT) for brain metastases. Materials and methods Between January 2000 and December 2010, a total of 464 patients were treated with Gamma Knife stereotactic radiosurgery (SRS) without WBRT for primary management of newly diagnosed brain metastases. Histology, systemic disease, RPA class, and number of metastases were evaluated as possible predictors of DBF rate. DBF rates were determined by serial MRI. Kaplan–Meier method was used to estimate rate of DBF. Multivariate analysis was performed using Cox Proportional Hazard regression. Results Median number of lesions treated was 1 (range 1–13). Median time to DBF was 4.9 months. Twenty-seven percent of patients ultimately required WBRT with median time to WBRT of 5.6 months. Progressive systemic disease (χ2= 16.748, P < .001), number of metastases at SRS (χ2 = 27.216, P < .001), discovery of new metastases at time of SRS (χ2 = 9.197, P < .01), and histology (χ2 = 12.819, P < .07) were factors that predicted for earlier time to distant failure. High risk histologic subtypes (melanoma, her2 negative breast, χ2 = 11.020, P < .001) and low risk subtypes (her2 + breast, χ2 = 11.343, P < .001) were identified. Progressive systemic disease (χ2 = 9.549, P < .01), number of brain metastases (χ2 = 16.953, P < .001), minimum SRS dose (χ2 = 21.609, P < .001), and widespread metastatic disease (χ2 = 29.396, P < .001) were predictive of shorter time to WBRT. Conclusion Systemic disease, number of metastases, and histology are factors that predict distant failure rate after primary radiosurgical management of brain metastases. PMID:24558022

Model of brain activation predicts the neural collective influence map of the brain

PubMed Central

Morone, Flaviano; Roth, Kevin; Min, Byungjoon; Makse, Hernán A.

2017-01-01

Efficient complex systems have a modular structure, but modularity does not guarantee robustness, because efficiency also requires an ingenious interplay of the interacting modular components. The human brain is the elemental paradigm of an efficient robust modular system interconnected as a network of networks (NoN). Understanding the emergence of robustness in such modular architectures from the interconnections of its parts is a longstanding challenge that has concerned many scientists. Current models of dependencies in NoN inspired by the power grid express interactions among modules with fragile couplings that amplify even small shocks, thus preventing functionality. Therefore, we introduce a model of NoN to shape the pattern of brain activations to form a modular environment that is robust. The model predicts the map of neural collective influencers (NCIs) in the brain, through the optimization of the influence of the minimal set of essential nodes responsible for broadcasting information to the whole-brain NoN. Our results suggest intervention protocols to control brain activity by targeting influential neural nodes predicted by network theory. PMID:28351973

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain.

PubMed

Aerts, Hannelore; Schirner, Michael; Jeurissen, Ben; Van Roost, Dirk; Achten, Eric; Ritter, Petra; Marinazzo, Daniele

2018-01-01

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

A thermocouple thermode for small animals

NASA Technical Reports Server (NTRS)

Williams, B. A.

1972-01-01

Thermode composed of two thin-walled stainless steel hypodermic needles and cooper-constantan thermocouple or small thermistor to indicate temperature at point of perfusion is used to measure brain temperature in animals. Because of relatively small size of thermode, structural damage to brain is minimized.

Estimation of brain network ictogenicity predicts outcome from epilepsy surgery

NASA Astrophysics Data System (ADS)

Goodfellow, M.; Rummel, C.; Abela, E.; Richardson, M. P.; Schindler, K.; Terry, J. R.

2016-07-01

Surgery is a valuable option for pharmacologically intractable epilepsy. However, significant post-operative improvements are not always attained. This is due in part to our incomplete understanding of the seizure generating (ictogenic) capabilities of brain networks. Here we introduce an in silico, model-based framework to study the effects of surgery within ictogenic brain networks. We find that factors conventionally determining the region of tissue to resect, such as the location of focal brain lesions or the presence of epileptiform rhythms, do not necessarily predict the best resection strategy. We validate our framework by analysing electrocorticogram (ECoG) recordings from patients who have undergone epilepsy surgery. We find that when post-operative outcome is good, model predictions for optimal strategies align better with the actual surgery undertaken than when post-operative outcome is poor. Crucially, this allows the prediction of optimal surgical strategies and the provision of quantitative prognoses for patients undergoing epilepsy surgery.

Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression

PubMed Central

Shimizu, Yu; Yoshimoto, Junichiro; Takamura, Masahiro; Okada, Go; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

In diagnostic applications of statistical machine learning methods to brain imaging data, common problems include data high-dimensionality and co-linearity, which often cause over-fitting and instability. To overcome these problems, we applied partial least squares (PLS) regression to resting-state functional magnetic resonance imaging (rs-fMRI) data, creating a low-dimensional representation that relates symptoms to brain activity and that predicts clinical measures. Our experimental results, based upon data from clinically depressed patients and healthy controls, demonstrated that PLS and its kernel variants provided significantly better prediction of clinical measures than ordinary linear regression. Subsequent classification using predicted clinical scores distinguished depressed patients from healthy controls with 80% accuracy. Moreover, loading vectors for latent variables enabled us to identify brain regions relevant to depression, including the default mode network, the right superior frontal gyrus, and the superior motor area. PMID:28700672

Spatial Attention, Motor Intention, and Bayesian Cue Predictability in the Human Brain.

PubMed

Kuhns, Anna B; Dombert, Pascasie L; Mengotti, Paola; Fink, Gereon R; Vossel, Simone

2017-05-24

Predictions about upcoming events influence how we perceive and respond to our environment. There is increasing evidence that predictions may be generated based upon previous observations following Bayesian principles, but little is known about the underlying cortical mechanisms and their specificity for different cognitive subsystems. The present study aimed at identifying common and distinct neural signatures of predictive processing in the spatial attentional and motor intentional system. Twenty-three female and male healthy human volunteers performed two probabilistic cueing tasks with either spatial or motor cues while lying in the fMRI scanner. In these tasks, the percentage of cue validity changed unpredictably over time. Trialwise estimates of cue predictability were derived from a Bayesian observer model of behavioral responses. These estimates were included as parametric regressors for analyzing the BOLD time series. Parametric effects of cue predictability in valid and invalid trials were considered to reflect belief updating by precision-weighted prediction errors. The brain areas exhibiting predictability-dependent effects dissociated between the spatial attention and motor intention task, with the right temporoparietal cortex being involved during spatial attention and the left angular gyrus and anterior cingulate cortex during motor intention. Connectivity analyses revealed that all three areas showed predictability-dependent coupling with the right hippocampus. These results suggest that precision-weighted prediction errors of stimulus locations and motor responses are encoded in distinct brain regions, but that crosstalk with the hippocampus may be necessary to integrate new trialwise outcomes in both cognitive systems. SIGNIFICANCE STATEMENT The brain is able to infer the environments' statistical structure and responds strongly to expectancy violations. In the spatial attentional domain, it has been shown that parts of the attentional networks are sensitive to the predictability of stimuli. It remains unknown, however, whether these effects are ubiquitous or if they are specific for different cognitive systems. The present study compared the influence of model-derived cue predictability on brain activity in the spatial attentional and motor intentional system. We identified areas with distinct predictability-dependent activation for spatial attention and motor intention, but also common connectivity changes of these regions with the hippocampus. These findings provide novel insights into the generality and specificity of predictive processing signatures in the human brain. Copyright © 2017 the authors 0270-6474/17/375334-11$15.00/0.

Modeling the germination kinetics of clostridium botulinum 56A spores as affected by temperature, pH, and sodium chloride.

PubMed

Chea, F P; Chen, Y; Montville, T J; Schaffner, D W

2000-08-01

The germination kinetics of proteolytic Clostridium botulinum 56A spores were modeled as a function of temperature (15, 22, 30 degrees C), pH (5.5, 6.0, 6.5), and sodium chloride (0.5, 2.0, 4.0%). Germination in brain heart infusion (BHI) broth was followed with phase-contrast microscopy. Data collected were used to develop the mathematical models. The germination kinetics expressed as cumulated fraction of germinated spores over time at each environmental condition were best described by an exponential distribution. Quadratic polynomial models were developed by regression analysis to describe the exponential parameter (time to 63% germination) (r2 = 0.982) and the germination extent (r2 = 0.867) as a function of temperature, pH, and sodium chloride. Validation experiments in BHI broth (pH: 5.75, 6.25; NaCl: 1.0, 3.0%; temperature: 18, 26 degrees C) confirmed that the model's predictions were within an acceptable range compared to the experimental results and were fail-safe in most cases.

Rapid and selective brain cooling method using vortex tube: A feasibility study.

PubMed

Bakhsheshi, Mohammad Fazel; Keenliside, Lynn; Lee, Ting-Yim

2016-05-01

Vortex tubes are simple mechanical devices to produce cold air from a stream of compressed air without any moving parts. The primary focus of the current study is to investigate the feasibility and efficiency of nasopharyngeal brain cooling method using a vortex tube. Experiments were conducted on 5 juvenile pigs. Nasopharygeal brain cooling was achieved by directing cooled air via a catheter in each nostril into the nasal cavities. A vortex tube was used to generate cold air using various sources of compressed air: (I) hospital medical air outlet (n = 1); (II) medical air cylinders (n = 3); and (III) scuba (diving) cylinders (n = 1). By using compressed air from a hospital medical air outlet at fixed inlet pressure of 50 PSI, maximum brain-rectal temperature gradient of -2°C was reached about 45-60 minutes by setting the flow rate of 25 L/min and temperature of -7°C at the cold air outlet. Similarly, by using medical air cylinders at fill-pressure of 2265 PSI and down regulate the inlet pressure to the vortex tube to 50 PSI, brain temperature could be reduced more rapidly by blowing -22°C ± 2°C air at a flow rate of 50 L/min; brain-body temperature gradient of -8°C was obtained about 30 minutes. Furthermore, we examined scuba cylinders as a portable source of compressed gas supply to the vortex tube. Likewise, by setting up the vortex tube to have an inlet pressure of 25 PSI and 50 L/min and -3°C at the cold air outlet, brain temperature decreased 4.5°C within 10-20 min. Copyright © 2016 Elsevier Inc. All rights reserved.

Engineers have more sons, nurses have more daughters: an evolutionary psychological extension of Baron-Cohen's extreme male brain theory of autism.

PubMed

Kanazawa, Satoshi; Vandermassen, Griet

2005-04-21

In his extreme male brain theory of autism, Baron-Cohen postulates that having a typically male brain was adaptive for ancestral men and having a typically female brain was adaptive for ancestral women. He also suggests that brain types are substantially heritable. These postulates, combined with the insight from the Trivers-Willard hypothesis regarding parental ability to vary offspring sex ratio, lead to the prediction that people who have strong male brains should have more sons than daughters, and people who have strong female brains should have more daughters than sons. The analysis of the 1994 US General Social Survey data provides support for this prediction. Our results suggest potentially fruitful extensions of both Baron-Cohen's theory and the Trivers-Willard hypothesis.

Absence of circannual toxicity of parathion to starlings

USGS Publications Warehouse

Rattner, B.A.; Grue, C.E.

1990-01-01

Ambient temperature and season have been observed to influence the toxicity of several environmental pollutants in homeotherms. The circannual toxicity of ethyl parathion (EP) was examined in adult European starlings (Sturnus vulgaris). Groups of birds housed in outdoor pens received oral doses of EP (20-150 mg/kg body weight) in fall, winter, spring and summer (temperature range -3.3 to 36.7?C). The median lethal dosage (LD50), and brain and plasma cholinesterase inhibition, were found to be quite similar among seasons. There was some suggestion that EP may have been more toxic during hot weather (winter versus summer LD50 estimate [95% confidence interval]:160 [114-225] vs. 118 [102-136] mg/kg; P<0.10). In view of previous reports in which ambient temperature extremes and harsh weather have enhanced organophosphorus insecticide toxicity to birds, it is concluded that circannual toxicity studies should include measures of sensitivity (acute oral exposure) and vulnerability (dietary exposure) to better predict responses of free-ranging birds

Brain Structural Integrity and Intrinsic Functional Connectivity Forecast 6 Year Longitudinal Growth in Children's Numerical Abilities.

PubMed

Evans, Tanya M; Kochalka, John; Ngoon, Tricia J; Wu, Sarah S; Qin, Shaozheng; Battista, Christian; Menon, Vinod

2015-08-19

Early numerical proficiency lays the foundation for acquiring quantitative skills essential in today's technological society. Identification of cognitive and brain markers associated with long-term growth of children's basic numerical computation abilities is therefore of utmost importance. Previous attempts to relate brain structure and function to numerical competency have focused on behavioral measures from a single time point. Thus, little is known about the brain predictors of individual differences in growth trajectories of numerical abilities. Using a longitudinal design, with multimodal imaging and machine-learning algorithms, we investigated whether brain structure and intrinsic connectivity in early childhood are predictive of 6 year outcomes in numerical abilities spanning childhood and adolescence. Gray matter volume at age 8 in distributed brain regions, including the ventrotemporal occipital cortex (VTOC), the posterior parietal cortex, and the prefrontal cortex, predicted longitudinal gains in numerical, but not reading, abilities. Remarkably, intrinsic connectivity analysis revealed that the strength of functional coupling among these regions also predicted gains in numerical abilities, providing novel evidence for a network of brain regions that works in concert to promote numerical skill acquisition. VTOC connectivity with posterior parietal, anterior temporal, and dorsolateral prefrontal cortices emerged as the most extensive network predicting individual gains in numerical abilities. Crucially, behavioral measures of mathematics, IQ, working memory, and reading did not predict children's gains in numerical abilities. Our study identifies, for the first time, functional circuits in the human brain that scaffold the development of numerical skills, and highlights potential biomarkers for identifying children at risk for learning difficulties. Children show substantial individual differences in math abilities and ease of math learning. Early numerical abilities provide the foundation for future academic and professional success in an increasingly technological society. Understanding the early identification of poor math skills has therefore taken on great significance. This work provides important new insights into brain structure and connectivity measures that can predict longitudinal growth of children's math skills over a 6 year period, and may eventually aid in the early identification of children who might benefit from targeted interventions. Copyright © 2015 the authors 0270-6474/15/3511743-08$15.00/0.

Altered predictive capability of the brain network EEG model in schizophrenia during cognition.

PubMed

Gomez-Pilar, Javier; Poza, Jesús; Gómez, Carlos; Northoff, Georg; Lubeiro, Alba; Cea-Cañas, Benjamín B; Molina, Vicente; Hornero, Roberto

2018-05-12

The study of the mechanisms involved in cognition is of paramount importance for the understanding of the neurobiological substrates in psychiatric disorders. Hence, this research is aimed at exploring the brain network dynamics during a cognitive task. Specifically, we analyze the predictive capability of the pre-stimulus theta activity to ascertain the functional brain dynamics during cognition in both healthy and schizophrenia subjects. Firstly, EEG recordings were acquired during a three-tone oddball task from fifty-one healthy subjects and thirty-five schizophrenia patients. Secondly, phase-based coupling measures were used to generate the time-varying functional network for each subject. Finally, pre-stimulus network connections were iteratively modified according to different models of network reorganization. This adjustment was applied by minimizing the prediction error through recurrent iterations, following the predictive coding approach. Both controls and schizophrenia patients follow a reinforcement of the secondary neural pathways (i.e., pathways between cortical brain regions weakly connected during pre-stimulus) for most of the subjects, though the ratio of controls that exhibited this behavior was statistically significant higher than for patients. These findings suggest that schizophrenia is associated with an impaired ability to modify brain network configuration during cognition. Furthermore, we provide direct evidence that the changes in phase-based brain network parameters from pre-stimulus to cognitive response in the theta band are closely related to the performance in important cognitive domains. Our findings not only contribute to the understanding of healthy brain dynamics, but also shed light on the altered predictive neuronal substrates in schizophrenia. Copyright © 2018 Elsevier B.V. All rights reserved.

Reliability of MRSI brain temperature mapping at 1.5 and 3 T.

PubMed

Thrippleton, Michael J; Parikh, Jehill; Harris, Bridget A; Hammer, Steven J; Semple, Scott I K; Andrews, Peter J D; Wardlaw, Joanna M; Marshall, Ian

2014-02-01

MRSI permits the non-invasive mapping of brain temperature in vivo, but information regarding its reliability is lacking. We obtained MRSI data from 31 healthy male volunteers [age range, 22-40 years; mean ± standard deviation (SD), 30.5 ± 5.0 years]. Eleven subjects (age range, 23-40 years; mean ± SD, 30.5 ± 5.2 years) were invited to receive four point-resolved spectroscopy MRSI scans on each of 3 days in both 1.5-T (TR/TE = 1000/144 ms) and 3-T (TR/TE = 1700/144 ms) clinical scanners; a further 20 subjects (age range, 22-40 years; mean ± SD, 30.5 ± 4.9 years) were scanned on a single occasion at 3 T. Data were fitted in the time domain to determine the water-N-acetylaspartate chemical shift difference, from which the temperature was estimated. Temperature data were analysed using a linear mixed effects model to determine variance components and systematic temperature changes during the scanning sessions. To characterise the effects of instrumental drift on apparent MRSI brain temperature, a temperature-controlled phantom was constructed and scanned on multiple occasions. Components of apparent in vivo temperature variability at 1.5 T/3 T caused by inter-subject (0.18/0.17 °C), inter-session (0.18/0.15 °C) and within-session (0.36/0.14 °C) effects, as well as voxel-to-voxel variation (0.59/0.54 °C), were determined. There was a brain cooling effect during in vivo MRSI of 0.10 °C [95% confidence interval (CI): -0.110, -0.094 °C; p < 0.001] and 0.051 °C (95% CI: -0.054, -0.048 °C; p < 0.001) per scan at 1.5 T and 3 T, respectively, whereas phantom measurements revealed minimal drift in apparent MRSI temperature relative to fibre-optic temperature measurements. The mean brain temperature at 3 T was weakly associated with aural (R = 0.55, p = 0.002) and oral (R = 0.62, p < 0.001) measurements of head temperature. In conclusion, the variability associated with MRSI brain temperature mapping was quantified. Repeatability was somewhat higher at 3 T than at 1.5 T, although subtle spatial and temporal variations in apparent temperature were demonstrated at both field strengths. Such data should assist in the efficient design of future clinical studies. © 2013 The Authors. NMR in Biomedicine published by John Wiley & Sons, Ltd.

Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease

PubMed Central

Plant, Claudia; Teipel, Stefan J.; Oswald, Annahita; Böhm, Christian; Meindl, Thomas; Mourao-Miranda, Janaina; Bokde, Arun W.; Hampel, Harald; Ewers, Michael

2010-01-01

Subjects with mild cognitive impairment (MCI) have an increased risk to develop Alzheimer's disease (AD). Voxel-based MRI studies have demonstrated that widely distributed cortical and subcortical brain areas show atrophic changes in MCI, preceding the onset of AD-type dementia. Here we developed a novel data mining framework in combination with three different classifiers including support vector machine (SVM), Bayes statistics, and voting feature intervals (VFI) to derive a quantitative index of pattern matching for the prediction of the conversion from MCI to AD. MRI was collected in 32 AD patients, 24 MCI subjects and 18 healthy controls (HC). Nine out of 24 MCI subjects converted to AD after an average follow-up interval of 2.5 years. Using feature selection algorithms, brain regions showing the highest accuracy for the discrimination between AD and HC were identified, reaching a classification accuracy of up to 92%. The extracted AD clusters were used as a search region to extract those brain areas that are predictive of conversion to AD within MCI subjects. The most predictive brain areas included the anterior cingulate gyrus and orbitofrontal cortex. The best prediction accuracy, which was cross-validated via train-and-test, was 75% for the prediction of the conversion from MCI to AD. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD. PMID:19961938

Assessment of MRI-Based Automated Fetal Cerebral Cortical Folding Measures in Prediction of Gestational Age in the Third Trimester.

PubMed

Wu, J; Awate, S P; Licht, D J; Clouchoux, C; du Plessis, A J; Avants, B B; Vossough, A; Gee, J C; Limperopoulos, C

2015-07-01

Traditional methods of dating a pregnancy based on history or sonographic assessment have a large variation in the third trimester. We aimed to assess the ability of various quantitative measures of brain cortical folding on MR imaging in determining fetal gestational age in the third trimester. We evaluated 8 different quantitative cortical folding measures to predict gestational age in 33 healthy fetuses by using T2-weighted fetal MR imaging. We compared the accuracy of the prediction of gestational age by these cortical folding measures with the accuracy of prediction by brain volume measurement and by a previously reported semiquantitative visual scale of brain maturity. Regression models were constructed, and measurement biases and variances were determined via a cross-validation procedure. The cortical folding measures are accurate in the estimation and prediction of gestational age (mean of the absolute error, 0.43 ± 0.45 weeks) and perform better than (P = .024) brain volume (mean of the absolute error, 0.72 ± 0.61 weeks) or sonography measures (SDs approximately 1.5 weeks, as reported in literature). Prediction accuracy is comparable with that of the semiquantitative visual assessment score (mean, 0.57 ± 0.41 weeks). Quantitative cortical folding measures such as global average curvedness can be an accurate and reliable estimator of gestational age and brain maturity for healthy fetuses in the third trimester and have the potential to be an indicator of brain-growth delays for at-risk fetuses and preterm neonates. © 2015 by American Journal of Neuroradiology.

Magnetic resonance spectroscopy metabolite profiles predict survival in paediatric brain tumours.

PubMed

Wilson, Martin; Cummins, Carole L; Macpherson, Lesley; Sun, Yu; Natarajan, Kal; Grundy, Richard G; Arvanitis, Theodoros N; Kauppinen, Risto A; Peet, Andrew C

2013-01-01

Brain tumours cause the highest mortality and morbidity rate of all childhood tumour groups and new methods are required to improve clinical management. (1)H magnetic resonance spectroscopy (MRS) allows non-invasive concentration measurements of small molecules present in tumour tissue, providing clinically useful imaging biomarkers. The primary aim of this study was to investigate whether MRS detectable molecules can predict the survival of paediatric brain tumour patients. Short echo time (30ms) single voxel (1)H MRS was performed on children attending Birmingham Children's Hospital with a suspected brain tumour and 115 patients were included in the survival analysis. Patients were followed-up for a median period of 35 months and Cox-Regression was used to establish the prognostic value of individual MRS detectable molecules. A multivariate model of survival was also investigated to improve prognostic power. Lipids and scyllo-inositol predicted poor survival whilst glutamine and N-acetyl aspartate predicted improved survival (p<0.05). A multivariate model of survival based on three MRS biomarkers predicted survival with a similar accuracy to histologic grading (p<5e-5). A negative correlation between lipids and glutamine was found, suggesting a functional link between these molecules. MRS detectable biomolecules have been identified that predict survival of paediatric brain tumour patients across a range of tumour types. The evaluation of these biomarkers in large prospective studies of specific tumour types should be undertaken. The correlation between lipids and glutamine provides new insight into paediatric brain tumour metabolism that may present novel targets for therapy. Copyright © 2012 Elsevier Ltd. All rights reserved.

Diffuse near-infrared reflectance spectroscopy during heatstroke in a mouse model: pilot study.

PubMed

Abookasis, David; Zafrir, Elad; Nesher, Elimelech; Pinhasov, Albert; Sternklar, Shmuel; Mathews, Marlon S

2012-10-01

Heatstroke, a form of hyperthermia, is a life-threatening condition characterized by an elevated core body temperature that rises above 40°C (104°F) and central nervous system dysfunction that results in delirium, convulsions, or coma. Without emergency treatment, the victim lapses into a coma and death soon follows. The study presented was conducted with a diffuse reflectance spectroscopy (DRS) setup to assess the effects of brain dysfunction that occurred during heatstroke in mice model (n=6). It was hypothesized that DRS can be utilized in small animal studies to monitor change in internal brain tissue temperature during heatstroke injury since it induces a sequence of pathologic changes that change the tissue composition and structure. Heatstroke was induced by exposure of the mice body under general anesthesia, to a high ambient temperature. A type of DRS in which the brain tissue was illuminated through the intact scalp with a broadband light source and diffuse reflected spectra was employed, taking in the spectral region between 650 and 1000 nm and acquired at an angle of 90 deg at a position on the scalp ∼12  mm from the illumination site. The temperature at the onset of the experiment was ∼34°C (rectal temperature) with increasing intervals of 1°C until mouse death. The increase in temperature caused optical scattering signal changes consistent with a structural alteration of brain tissue, ultimately resulting in death. We have found that the peak absorbance intensity and its second derivative at specific wavelengths correlate well with temperature with an exponential dependence. Based on these findings, in order to estimate the influence of temperature on the internal brain tissue a reflectance-temperature index was established and was seen to correlate as well with measured temperature. Overall, results indicate variations in neural tissue properties during heatstroke and the feasibility to monitor and assess internal temperature variations using DRS. Although several approaches have described the rise in temperature and its impact on tissue, to the best of our knowledge no information is available describing the ability to monitor temperature during heatstroke with DRS. The motivation of this study was to successfully describe this ability.

Circadian rhythms in Macaca mulatta monkeys during Bion 11 flight

NASA Technical Reports Server (NTRS)

Alpatov, A. M.; Hoban-Higgins, T. M.; Klimovitsky, V. Y.; Tumurova, E. G.; Fuller, C. A.

2000-01-01

Circadian rhythms of primate brain temperature, head and ankle skin temperature, motor activity, and heart rate were studied during spaceflight and on the ground. In space, the circadian rhythms of all the parameters were synchronized with diurnal Zeitgebers. However, in space the brain temperature rhythm showed a significantly more delayed phase angle, which may be ascribed to an increase of the endogenous circadian period.

Phasic and tonic fluctuations in brain, muscle, and skin temperatures during motivated drinking behavior in rats: physiological correlates of motivation and reward.

PubMed

Smirnov, Michael S; Kiyatkin, Eugene A

2010-01-15

Since brain metabolism is accompanied by heat production, measurement of brain temperature offers a method for assessing global alterations in metabolic neural activity. This approach, high-resolution (5-s bin) temperature recording from the nucleus accumbens (NAcc), temporal muscle, and facial skin, was used to study motivated drinking behavior in rats. Experienced animals were presented with a cup containing 5-ml of Coca-Cola(R) (Coke) beverage that resulted, within certain latencies, in initiation of a continuous chain of licking until all liquid was fully consumed. While cup presentation induced rapid, gradual NAcc temperature increase peaking at the start of drinking, temperatures slowly decreased during Coke consumption, but phasically increased again in the post-consumption period when rats were hyperactive, showing multiple interactions with an empty cup. Muscle temperatures followed a similar pattern, but the changes were weaker and delayed compared to those in the brain. Skin temperature rapidly dropped after cup presentation, steadily maintained at low levels during consumption, and slowly restored during the post-consumption period. Substitution of the expected Coke with either sugar-free Diet Coke(R) or water resulted in numerous drinking attempts but ultimately no consumption. During these tests, locomotor activation was much greater and more prolonged, brain and muscle temperatures increased monophasically, and their elevation was significantly greater than that with regular Coke tests. Food deprivation decreased drinking latencies, did not change the pattern of temperature fluctuations during Coke consumption, but temperature elevations were greater than in controls. Our data suggest sustained neural activation triggered by appetitive stimuli and associated with activational (seeking) aspects of appetitive motivated behavior. This seeking-related activation is rapidly ceased following consumption, suggesting this change as a neural correlate of reward. In contrast, inability to obtain an expected reward maintains neural activation and seeking behavior, resulting in larger deviations in physiological parameters. Published by Elsevier B.V.

Phasic and tonic fluctuations in brain, muscle and skin temperatures during motivated drinking behavior in rats: physiological correlates of motivation and reward

PubMed Central

Smirnov, Michael S.; Kiyatkin, Eugene A.

2009-01-01

Since brain metabolism is accompanied by heat production, measurement of brain temperature offers a method for assessing global alterations in metabolic neural activity. This approach, high-resolution (5-s bin) temperature recording from the nucleus accumbens (NAcc), temporal muscle, and facial skin, was used to study motivated drinking behavior in rats. Experienced animals were presented with a cup containing 5-ml of Coca-Cola® (Coke) beverage that resulted, within certain latencies, in initiation of a continuous chain of licking until all liquid was fully consumed. While cup presentation induced rapid, gradual NAcc temperature increase peaking at the start of drinking, temperatures slowly decreased during Coke consumption, but phasically increased again in the post-consumption period when rats were hyperactive, showing multiple interactions with an empty cup. Muscle temperatures followed a similar pattern, but the changes were weaker and delayed compared to those in the brain. Skin temperature rapidly dropped after cup presentation, steadily maintained at low levels during consumption, and slowly restored during the post-consumption period. Substitution of the expected Coke with either sugar-free Diet Coke® or water resulted in numerous drinking attempts but ultimately no consumption. During these tests, locomotor activation was much greater and more prolonged, brain and muscle temperatures increased monophasically, and their elevation was significantly greater than that with regular Coke tests. Food deprivation decreased drinking latencies, did not change the pattern of temperature fluctuations during Coke consumption, but temperature elevations were greater than in controls. Our data suggest sustained neural activation triggered by appetitive stimuli and associated with activational (seeking) aspects of appetitive motivated behavior. This seeking-related activation is rapidly ceased following consumption, suggesting this change as a neural correlate of reward. In contrast, inability to obtain an expected reward maintains neural activation and seeking behavior, resulting in larger deviations in physiological parameters. PMID:19932691

Diffusion-weighted imaging score of the brain stem: A predictor of outcome in acute basilar artery occlusion treated with the Solitaire FR device.

PubMed

Mourand, I; Machi, P; Nogué, E; Arquizan, C; Costalat, V; Picot, M-C; Bonafé, A; Milhaud, D

2014-06-01

The prognosis for ischemic stroke due to acute basilar artery occlusion is very poor: Early recanalization remains the main factor that can improve outcomes. The baseline extent of brain stem ischemic damage can also influence outcomes. We evaluated the validity of an easy-to-use DWI score to predict clinical outcome in patients with acute basilar artery occlusion treated by mechanical thrombectomy. We analyzed the baseline clinical and DWI parameters of 31 patients with acute basilar artery occlusion, treated within 24 hours of symptom onset by using a Solitaire FR device. The DWI score of the brain stem was assessed with a 12-point semiquantitative score that separately considered each side of the medulla, pons, and midbrain. Clinical outcome was assessed at 180 days by using the mRS. According to receiver operating characteristic analyses, the cutoff score determined the optimal positive predictive value for outcome. The Spearman rank correlation coefficient assessed the correlation between the DWI brain stem score and baseline characteristics. Successful recanalization (Thrombolysis in Cerebral Infarction 3-2b) was achieved in 23 patients (74%). A favorable outcome (mRS ≤ 2) was observed in 11 patients (35%). An optimal DWI brain stem score of <3 predicted a favorable outcome. The probability of a very poor outcome (mRS ≥ 5) if the DWI brain stem score was ≥5 reached 80% (positive predictive value) and 100% if this score was ≥6. Interobserver reliability of the DWI brain stem score was excellent, with an intraclass correlation coefficient of 0.97 (95% CI, 0.96-0.99). The DWI brain stem score was significantly associated with baseline tetraplegia (P = .001) and coma (P = .005). In patients with acute basilar artery occlusion treated by mechanical thrombectomy, the baseline DWI brain lesion score seems to predict clinical outcome. © 2014 by American Journal of Neuroradiology.

Natural Changes in Brain Temperature Underlie Variations in Song Tempo during a Mating Behavior

PubMed Central

Aronov, Dmitriy; Fee, Michale S.

2012-01-01

The song of a male zebra finch is a stereotyped motor sequence whose tempo varies with social context – whether or not the song is directed at a female bird – as well as with the time of day. The neural mechanisms underlying these changes in tempo are unknown. Here we show that brain temperature recorded in freely behaving male finches exhibits a global increase in response to the presentation of a female bird. This increase strongly correlates with, and largely explains, the faster tempo of songs directed at a female compared to songs produced in social isolation. Furthermore, we find that the observed diurnal variations in song tempo are also explained by natural variations in brain temperature. Our findings suggest that brain temperature is an important variable that can influence the dynamics of activity in neural circuits, as well as the temporal features of behaviors that some of these circuits generate. PMID:23112858

Five-band microwave radiometer system for noninvasive brain temperature measurement in newborn babies: Phantom experiment and confidence interval

NASA Astrophysics Data System (ADS)

Sugiura, T.; Hirata, H.; Hand, J. W.; van Leeuwen, J. M. J.; Mizushina, S.

2011-10-01

Clinical trials of hypothermic brain treatment for newborn babies are currently hindered by the difficulty in measuring deep brain temperatures. As one of the possible methods for noninvasive and continuous temperature monitoring that is completely passive and inherently safe is passive microwave radiometry (MWR). We have developed a five-band microwave radiometer system with a single dual-polarized, rectangular waveguide antenna operating within the 1-4 GHz range and a method for retrieving the temperature profile from five radiometric brightness temperatures. This paper addresses (1) the temperature calibration for five microwave receivers, (2) the measurement experiment using a phantom model that mimics the temperature profile in a newborn baby, and (3) the feasibility for noninvasive monitoring of deep brain temperatures. Temperature resolutions were 0.103, 0.129, 0.138, 0.105 and 0.111 K for 1.2, 1.65, 2.3, 3.0 and 3.6 GHz receivers, respectively. The precision of temperature estimation (2σ confidence interval) was about 0.7°C at a 5-cm depth from the phantom surface. Accuracy, which is the difference between the estimated temperature using this system and the measured temperature by a thermocouple at a depth of 5 cm, was about 2°C. The current result is not satisfactory for clinical application because the clinical requirement for accuracy must be better than 1°C for both precision and accuracy at a depth of 5 cm. Since a couple of possible causes for this inaccuracy have been identified, we believe that the system can take a step closer to the clinical application of MWR for hypothermic rescue treatment.

Insular dwarfism in hippos and a model for brain size reduction in Homo floresiensis.

PubMed

Weston, Eleanor M; Lister, Adrian M

2009-05-07

Body size reduction in mammals is usually associated with only moderate brain size reduction, because the brain and sensory organs complete their growth before the rest of the body during ontogeny. On this basis, 'phyletic dwarfs' are predicted to have a greater relative brain size than 'phyletic giants'. However, this trend has been questioned in the special case of dwarfism of mammals on islands. Here we show that the endocranial capacities of extinct dwarf species of hippopotamus from Madagascar are up to 30% smaller than those of a mainland African ancestor scaled to equivalent body mass. These results show that brain size reduction is much greater than predicted from an intraspecific 'late ontogenetic' model of dwarfism in which brain size scales to body size with an exponent of 0.35. The nature of the proportional change or grade shift observed here indicates that selective pressures on brain size are potentially independent of those on body size. This study demonstrates empirically that it is mechanistically possible for dwarf mammals on islands to evolve significantly smaller brains than would be predicted from a model of dwarfing based on the intraspecific scaling of the mainland ancestor. Our findings challenge current understanding of brain-body allometric relationships in mammals and suggest that the process of dwarfism could in principle explain small brain size, a factor relevant to the interpretation of the small-brained hominin found on the Island of Flores, Indonesia.

Insular dwarfism in hippos and a model for brain size reduction in Homo floresiensis

PubMed Central

Weston, Eleanor M.; Lister, Adrian M.

2009-01-01

Body size reduction in mammals is usually associated with only moderate brain size reduction as the brain and sensory organs complete their growth before the rest of the body during ontogeny1,2. On this basis “phyletic dwarfs” are predicted to have a higher relative brain size than “phyletic giants”1,3. This trend has been questioned, however, in the special case of dwarfism of mammals on islands4. Here we show that the endocranial capacities of extinct dwarf species of hippopotamus from Madagascar are up to 30% smaller than those of a mainland African ancestor scaled to equivalent body mass. These results show brain size reduction is much greater than predicted from an intraspecific ‘late ontogenetic’ model of dwarfism where brain size scales to body size with an exponent of 0.35. The nature of the proportional change or grade shift2,5 observed here indicates that selective pressures upon brain size are potentially independent from those on body size. This study demonstrates empirically that it is mechanistically possible for dwarf mammals on islands to evolve significantly smaller brains than would be predicted from a model of dwarfing based on the intraspecific scaling of the mainland ancestor. Our findings challenge our understanding of brain-body allometric relationships in mammals and suggest that the process of dwarfism could in principle explain small brain size, a factor relevant to the interpretation of the small-brained hominin found on the Island of Flores, Indonesia6. PMID:19424156

Brain shift computation using a fully nonlinear biomechanical model.

PubMed

Wittek, Adam; Kikinis, Ron; Warfield, Simon K; Miller, Karol

2005-01-01

In the present study, fully nonlinear (i.e. accounting for both geometric and material nonlinearities) patient specific finite element brain model was applied to predict deformation field within the brain during the craniotomy-induced brain shift. Deformation of brain surface was used as displacement boundary conditions. Application of the computed deformation field to align (i.e. register) the preoperative images with the intraoperative ones indicated that the model very accurately predicts the displacements of gravity centers of the lateral ventricles and tumor even for very limited information about the brain surface deformation. These results are sufficient to suggest that nonlinear biomechanical models can be regarded as one possible way of complementing medical image processing techniques when conducting nonrigid registration. Important advantage of such models over the linear ones is that they do not require unrealistic assumptions that brain deformations are infinitesimally small and brain tissue stress-strain relationship is linear.

Brain size and visual environment predict species differences in paper wasp sensory processing brain regions (hymenoptera: vespidae, polistinae).

PubMed

O'Donnell, Sean; Clifford, Marie R; DeLeon, Sara; Papa, Christopher; Zahedi, Nazaneen; Bulova, Susan J

2013-01-01

The mosaic brain evolution hypothesis predicts that the relative volumes of functionally distinct brain regions will vary independently and correlate with species' ecology. Paper wasp species (Hymenoptera: Vespidae, Polistinae) differ in light exposure: they construct open versus enclosed nests and one genus (Apoica) is nocturnal. We asked whether light environments were related to species differences in the size of antennal and optic processing brain tissues. Paper wasp brains have anatomically distinct peripheral and central regions that process antennal and optic sensory inputs. We measured the volumes of 4 sensory processing brain regions in paper wasp species from 13 Neotropical genera including open and enclosed nesters, and diurnal and nocturnal species. Species differed in sensory region volumes, but there was no evidence for trade-offs among sensory modalities. All sensory region volumes correlated with brain size. However, peripheral optic processing investment increased with brain size at a higher rate than peripheral antennal processing investment. Our data suggest that mosaic and concerted (size-constrained) brain evolution are not exclusive alternatives. When brain regions increase with brain size at different rates, these distinct allometries can allow for differential investment among sensory modalities. As predicted by mosaic evolution, species ecology was associated with some aspects of brain region investment. Nest architecture variation was not associated with brain investment differences, but the nocturnal genus Apoica had the largest antennal:optic volume ratio in its peripheral sensory lobes. Investment in central processing tissues was not related to nocturnality, a pattern also noted in mammals. The plasticity of neural connections in central regions may accommodate evolutionary shifts in input from the periphery with relatively minor changes in volume. © 2013 S. Karger AG, Basel.

Rapid Morphological Brain Abnormalities during Acute Methamphetamine Intoxication in the Rat. An Experimental study using Light and Electron Microscopy

PubMed Central

Sharma, Hari S.; Kiyatkin, Eugene A.

2009-01-01

This study describes morphological abnormalities of brain cells during acute methamphetamine (METH) intoxication in the rat and demonstrates the role of hyperthermia, disruption of the blood-brain barrier (BBB) and edema in their development. Rats with chronically implanted brain, muscle and skin temperature probes and an intravenous (iv) catheter were exposed to METH (9 mg/kg) at standard (23°C) and warm (29°C) ambient temperatures, allowing for the observation of hyperthermia ranging from mild to pathological levels (38–42°C). When brain temperature peaked or reached a level suggestive of possible lethality (>41.5°C), rats were injected with Evans blue (EB), rapidly anesthetized, perfused, and their brains were taken for further analyses. Four brain areas (cortex, hippocampus, thalamus and hypothalamus) were analyzed for EB extravasation, water and electrolyte (Na+, K+, Cl−) contents, immunostained for albumin and glial fibrillary acidic protein, and examined for neuronal, glial and axonal alterations using standard light and electron microscopy. These examinations revealed profound abnormalities in neuronal, glial, and endothelial cells, which were stronger with METH administered at 29°C than 23°C and tightly correlated with brain and body hyperthermia. These changes had some structural specificity, but in each structure they tightly correlated with increases in EB levels, the numbers of albumin-positive cells, and water and ion contents, suggesting leakage of the BBB, acutely developing brain edema, and serious shifts in brain ion homeostasis as leading factors underlying brain abnormalities. While most of these acute structural and functional abnormalities appear to be reversible, they could trigger subsequent cellular alterations in the brain and accelerate neurodegeneration—the most dangerous complication of chronic amphetamine-like drug abuse. PMID:18773954

Cold Environment Exacerbates Brain Pathology and Oxidative Stress Following Traumatic Brain Injuries: Potential Therapeutic Effects of Nanowired Antioxidant Compound H-290/51.

PubMed

Sharma, Aruna; Muresanu, Dafin F; Lafuente, José Vicente; Sjöquist, Per-Ove; Patnaik, Ranjana; Ryan Tian, Z; Ozkizilcik, Asya; Sharma, Hari S

2018-01-01

The possibility that traumatic brain injury (TBI) occurring in a cold environment exacerbates brain pathology and oxidative stress was examined in our rat model. TBI was inflicted by making a longitudinal incision into the right parietal cerebral cortex (2 mm deep and 4 mm long) in cold-acclimatized rats (5 °C for 3 h daily for 5 weeks) or animals at room temperature under Equithesin anesthesia. TBI in cold-exposed rats exhibited pronounced increase in brain lucigenin (LCG), luminol (LUM), and malondialdehyde (MDA) and marked pronounced decrease in glutathione (GTH) as compared to identical TBI at room temperature. The magnitude and intensity of BBB breakdown to radioiodine and Evans blue albumin, edema formation, and neuronal injuries were also exacerbated in cold-exposed rats after injury as compared to room temperature. Nanowired delivery of H-290/51 (50 mg/kg) 6 and 8 h after injury in cold-exposed group significantly thwarted brain pathology and oxidative stress whereas normal delivery of H-290/51 was neuroprotective after TBI at room temperature only. These observations are the first to demonstrate that (i) cold aggravates the pathophysiology of TBI possibly due to an enhanced production of oxidative stress, (ii) and in such conditions, nanodelivery of antioxidant compound has superior neuroprotective effects, not reported earlier.

Temperatures Achieved in Human and Canine Neocortex During Intraoperative Passive or Active Focal Cooling

PubMed Central

Han, Rowland H.; Yarbrough, Chester K.; Patterson, Edward E.; Yang, Xiao-Feng; Miller, John W.; Rothman, Steven M.; D'Ambrosio, Raimondo

2015-01-01

Focal cortical cooling inhibits seizures and prevents acquired epileptogenesis in rodents. To investigate the potential clinical utility of this treatment modality, we examined the thermal characteristics of canine and human brain undergoing active and passive surface cooling in intraoperative settings. Four patients with intractable epilepsy were treated in a standard manner. Before the resection of a neocortical epileptogenic focus, multiple intraoperative studies of active (custom-made cooled irrigation-perfused grid) and passive (stainless steel probe) cooling were performed. We also actively cooled the neocortices of two dogs with perfused grids implanted for 2 hours. Focal surface cooling of the human brain causes predictable depth-dependent cooling of the underlying brain tissue. Cooling of 0.6–2°C was achieved both actively and passively to a depth of 10–15 mm from the cortical surface. The perfused grid permitted comparable and persistent cooling of canine neocortex when the craniotomy was closed. Thus, the human cortex can easily be cooled with the use of simple devices such as a cooling grid or a small passive probe. These techniques provide pilot data for the design of a permanently implantable device to control intractable epilepsy. PMID:25902001

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing.

PubMed

Grisoni, Luigi; Miller, Tally McCormick; Pulvermüller, Friedemann

2017-05-03

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system-in dorsolateral hand motor areas for expected hand-related words (e.g., "write"), but in ventral motor cortex for face-related words ("talk"). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before they appear. The granularity of the semantic predictions was so fine grained that the cortical sources in sensorimotor and medial prefrontal cortex even distinguished between predicted face- or hand-related action words (e.g., the words "lick" or "pick") and between affirmative and negated sentence meanings. Copyright © 2017 Grisoni et al.

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing

PubMed Central

2017-01-01

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system—in dorsolateral hand motor areas for expected hand-related words (e.g., “write”), but in ventral motor cortex for face-related words (“talk”). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before they appear. The granularity of the semantic predictions was so fine grained that the cortical sources in sensorimotor and medial prefrontal cortex even distinguished between predicted face- or hand-related action words (e.g., the words “lick” or “pick”) and between affirmative and negated sentence meanings. PMID:28411271

Neural underpinnings of music: the polyrhythmic brain.

PubMed

Vuust, Peter; Gebauer, Line K; Witek, Maria A G

2014-01-01

Musical rhythm, consisting of apparently abstract intervals of accented temporal events, has the remarkable ability to move our minds and bodies. Why do certain rhythms make us want to tap our feet, bop our heads or even get up and dance? And how does the brain process rhythmically complex rhythms during our experiences of music? In this chapter, we describe some common forms of rhythmic complexity in music and propose that the theory of predictive coding can explain how rhythm and rhythmic complexity are processed in the brain. We also consider how this theory may reveal why we feel so compelled by rhythmic tension in music. First, musical-theoretical and neuroscientific frameworks of rhythm are presented, in which rhythm perception is conceptualized as an interaction between what is heard ('rhythm') and the brain's anticipatory structuring of music ('the meter'). Second, three different examples of tension between rhythm and meter in music are described: syncopation, polyrhythm and groove. Third, we present the theory of predictive coding of music, which posits a hierarchical organization of brain responses reflecting fundamental, survival-related mechanisms associated with predicting future events. According to this theory, perception and learning is manifested through the brain's Bayesian minimization of the error between the input to the brain and the brain's prior expectations. Fourth, empirical studies of neural and behavioral effects of syncopation, polyrhythm and groove will be reported, and we propose how these studies can be seen as special cases of the predictive coding theory. Finally, we argue that musical rhythm exploits the brain's general principles of anticipation and propose that pleasure from musical rhythm may be a result of such anticipatory mechanisms.

Early Fever As a Predictor of Paroxysmal Sympathetic Hyperactivity in Traumatic Brain Injury.

PubMed

Hinson, Holly E; Schreiber, Martin A; Laurie, Amber L; Baguley, Ian J; Bourdette, Dennis; Ling, Geoffrey S F

Paroxysmal sympathetic hyperactivity (PSH) is characterized by episodic, hyperadrenergic alterations in vital signs after traumatic brain injury (TBI). We sought to apply an objective scale to the vital sign alterations of PSH in order to determine whether 1 element might be predictive of developing PSH. We conducted an observational study of consecutive TBI patients (Glasgow Coma Scale score ≤12) and monitored the cohort for clinical evidence of PSH. PSH was defined as a paroxysm of 3 or more of the following characteristics: (1) tachycardia, (2) tachypnea, (3) hypertension, (4) fever, (5) dystonia (rigidity or decerebrate posturing), and (6) diaphoresis, with no other obvious causation (ie, alcohol withdrawal, sepsis). The Modified Clinical Feature Severity Scale (mCFSS) was applied to each participant once daily for the first 5 days of hospitalization. Nineteen (11%) of the 167 patients met criteria for PSH. Patients with PSH had a higher 5-day cumulative mCFSS score than those without PSH (median [interquartile range] = 36 [29-42] vs 29 [22-35], P = .01). Of the 4 components of the mCFSS, elevated temperature appeared to be most predictive of the development of PSH, especially during the first 24 hours (odds ratio = 1.95; 95% confidence interval, 1.12-3.40). Early fever after TBI may signal impending autonomic dysfunction.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion.

PubMed

Zafar, Raheel; Dass, Sarat C; Malik, Aamir Saeed

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.

Radiation-induced brain structural and functional abnormalities in presymptomatic phase and outcome prediction.

PubMed

Ding, Zhongxiang; Zhang, Han; Lv, Xiao-Fei; Xie, Fei; Liu, Lizhi; Qiu, Shijun; Li, Li; Shen, Dinggang

2018-01-01

Radiation therapy, a major method of treatment for brain cancer, may cause severe brain injuries after many years. We used a rare and unique cohort of nasopharyngeal carcinoma patients with normal-appearing brains to study possible early irradiation injury in its presymptomatic phase before severe, irreversible necrosis happens. The aim is to detect any structural or functional imaging biomarker that is sensitive to early irradiation injury, and to understand the recovery and progression of irradiation injury that can shed light on outcome prediction for early clinical intervention. We found an acute increase in local brain activity that is followed by extensive reductions in such activity in the temporal lobe and significant loss of functional connectivity in a distributed, large-scale, high-level cognitive function-related brain network. Intriguingly, these radiosensitive functional alterations were found to be fully or partially recoverable. In contrast, progressive late disruptions to the integrity of the related far-end white matter structure began to be significant after one year. Importantly, early increased local brain functional activity was predictive of severe later temporal lobe necrosis. Based on these findings, we proposed a dynamic, multifactorial model for radiation injury and another preventive model for timely clinical intervention. Hum Brain Mapp 39:407-427, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

FDTD analysis of a noninvasive hyperthermia system for brain tumors.

PubMed

Yacoob, Sulafa M; Hassan, Noha S

2012-08-14

Hyperthermia is considered one of the new therapeutic modalities for cancer treatment and is based on the difference in thermal sensitivity between healthy tissues and tumors. During hyperthermia treatment, the temperature of the tumor is raised to 40-45°C for a definite period resulting in the destruction of cancer cells. This paper investigates design, modeling and simulation of a new non-invasive hyperthermia applicator system capable of effectively heating deep seated as well as superficial brain tumors using inexpensive, simple, and easy to fabricate components without harming surrounding healthy brain tissues. The proposed hyperthermia applicator system is composed of an air filled partial half ellipsoidal chamber, a patch antenna, and a head model with an embedded tumor at an arbitrary location. The irradiating antenna is placed at one of the foci of the hyperthermia chamber while the center of the brain tumor is placed at the other focus. The finite difference time domain (FDTD) method is used to compute both the SAR patterns and the temperature distribution in three different head models due to two different patch antennas at a frequency of 915 MHz. The obtained results suggest that by using the proposed noninvasive hyperthermia system it is feasible to achieve sufficient and focused energy deposition and temperature rise to therapeutic values in deep seated as well as superficial brain tumors without harming surrounding healthy tissue. The proposed noninvasive hyperthermia system proved suitable for raising the temperature in tumors embedded in the brain to therapeutic values by carefully selecting the systems components. The operator of the system only needs to place the center of the brain tumor at a pre-specified location and excite the antenna at a single frequency of 915 MHz. Our study may provide a basis for a clinical applicator prototype capable of heating brain tumors.

Sparse network-based models for patient classification using fMRI

PubMed Central

Rosa, Maria J.; Portugal, Liana; Hahn, Tim; Fallgatter, Andreas J.; Garrido, Marta I.; Shawe-Taylor, John; Mourao-Miranda, Janaina

2015-01-01

Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces. PMID:25463459

Prediction of brain maturity based on cortical thickness at different spatial resolutions.

PubMed

Khundrakpam, Budhachandra S; Tohka, Jussi; Evans, Alan C

2015-05-01

Several studies using magnetic resonance imaging (MRI) scans have shown developmental trajectories of cortical thickness. Cognitive milestones happen concurrently with these structural changes, and a delay in such changes has been implicated in developmental disorders such as attention-deficit/hyperactivity disorder (ADHD). Accurate estimation of individuals' brain maturity, therefore, is critical in establishing a baseline for normal brain development against which neurodevelopmental disorders can be assessed. In this study, cortical thickness derived from structural magnetic resonance imaging (MRI) scans of a large longitudinal dataset of normally growing children and adolescents (n=308), were used to build a highly accurate predictive model for estimating chronological age (cross-validated correlation up to R=0.84). Unlike previous studies which used kernelized approach in building prediction models, we used an elastic net penalized linear regression model capable of producing a spatially sparse, yet accurate predictive model of chronological age. Upon investigating different scales of cortical parcellation from 78 to 10,240 brain parcels, we observed that the accuracy in estimated age improved with increased spatial scale of brain parcellation, with the best estimations obtained for spatial resolutions consisting of 2560 and 10,240 brain parcels. The top predictors of brain maturity were found in highly localized sensorimotor and association areas. The results of our study demonstrate that cortical thickness can be used to estimate individuals' brain maturity with high accuracy, and the estimated ages relate to functional and behavioural measures, underscoring the relevance and scope of the study in the understanding of biological maturity. Copyright © 2015 Elsevier Inc. All rights reserved.

Fluctuations in central and peripheral temperatures induced by intravenous nicotine: central and peripheral contributions

PubMed Central

Tang, Jeremy; Kiyatkin, Eugene A.

2011-01-01

Nicotine (NIC) is a highly addictive substance that interacts with different subtypes of nicotinic acetylcholine receptors widely distributed in the central and peripheral nervous systems. While the direct action of NIC on central neurons appears to be essential for its reinforcing properties, the role of peripheral actions of this drug remains a matter of controversy. In this study, we examined changes in locomotor activity and temperature fluctuations in the brain (nucleus accumbens and ventral tegmental area), temporal muscle, and skin induced by intravenous (iv) NIC at low human-relevant doses (10 and 30 μg/kg) in freely moving rats. These effects were compared to those induced by social interaction, an arousing procedure that induces behavioral activation and temperature responses via pure neural mechanism procedure, and iv injections of a peripherally acting NIC analogue, NIC pyrrolidine methiodide (NIC-PM) used at equimolar doses. We found that NIC at 30 μg/kg induces a modest locomotor activation, rapid and strong decrease in skin temperature, and weak increases in brain and muscle temperature. While these effects were qualitatively similar to those induced by social interaction, they were much weaker and showed a tendency to increase with repeated drug administrations. In contrast, NIC-PM did not affect locomotion and induced much weaker than NIC increases in brain and muscle temperatures and decreases in skin temperature; these effects showed a tendency to be weaker with repeated drug administrations. Our data indicate that NIC's actions in the brain are essential to induce locomotor activation and brain and body hyperthermic responses. However, rapid peripheral action of NIC on sensory afferents could be an important factor in triggering its central effects, contributing to neural and physiological activation following repeated drug use. PMID:21295014

Short-Term Memory: The "Storage" Component of Human Brain Responses Predicts Recall.

ERIC Educational Resources Information Center

Chapman, Robert M.; And Others

1978-01-01

Presents electrophysiological and behavioral evidence for a neural process related to storage in short-term memory. Predicting recall performance on the basis of the storage component of brain responses is presented. A list of references is also included. (HM)

Learning Temporal Statistics for Sensory Predictions in Aging.

PubMed

Luft, Caroline Di Bernardi; Baker, Rosalind; Goldstone, Aimee; Zhang, Yang; Kourtzi, Zoe

2016-03-01

Predicting future events based on previous knowledge about the environment is critical for successful everyday interactions. Here, we ask which brain regions support our ability to predict the future based on implicit knowledge about the past in young and older age. Combining behavioral and fMRI measurements, we test whether training on structured temporal sequences improves the ability to predict upcoming sensory events; we then compare brain regions involved in learning predictive structures between young and older adults. Our behavioral results demonstrate that exposure to temporal sequences without feedback facilitates the ability of young and older adults to predict the orientation of an upcoming stimulus. Our fMRI results provide evidence for the involvement of corticostriatal regions in learning predictive structures in both young and older learners. In particular, we showed learning-dependent fMRI responses for structured sequences in frontoparietal regions and the striatum (putamen) for young adults. However, for older adults, learning-dependent activations were observed mainly in subcortical (putamen, thalamus) regions but were weaker in frontoparietal regions. Significant correlations of learning-dependent behavioral and fMRI changes in these regions suggest a strong link between brain activations and behavioral improvement rather than general overactivation. Thus, our findings suggest that predicting future events based on knowledge of temporal statistics engages brain regions involved in implicit learning in both young and older adults.

Association of Elevated Reward Prediction Error Response With Weight Gain in Adolescent Anorexia Nervosa.

PubMed

DeGuzman, Marisa; Shott, Megan E; Yang, Tony T; Riederer, Justin; Frank, Guido K W

2017-06-01

Anorexia nervosa is a psychiatric disorder of unknown etiology. Understanding associations between behavior and neurobiology is important in treatment development. Using a novel monetary reward task during functional magnetic resonance brain imaging, the authors tested how brain reward learning in adolescent anorexia nervosa changes with weight restoration. Female adolescents with anorexia nervosa (N=21; mean age, 16.4 years [SD=1.9]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy female control adolescents (N=21; mean age, 15.2 years [SD=2.4]) underwent fMRI on two occasions. Brain function was tested using the reward prediction error construct, a computational model for reward receipt and omission related to motivation and neural dopamine responsiveness. Compared with the control group, the anorexia nervosa group exhibited greater brain response 1) for prediction error regression within the caudate, ventral caudate/nucleus accumbens, and anterior and posterior insula, 2) to unexpected reward receipt in the anterior and posterior insula, and 3) to unexpected reward omission in the caudate body. Prediction error and unexpected reward omission response tended to normalize with treatment, while unexpected reward receipt response remained significantly elevated. Greater caudate prediction error response when underweight was associated with lower weight gain during treatment. Punishment sensitivity correlated positively with ventral caudate prediction error response. Reward system responsiveness is elevated in adolescent anorexia nervosa when underweight and after weight restoration. Heightened prediction error activity in brain reward regions may represent a phenotype of adolescent anorexia nervosa that does not respond well to treatment. Prediction error response could be a neurobiological marker of illness severity that can indicate individual treatment needs.

Association of Elevated Reward Prediction Error Response With Weight Gain in Adolescent Anorexia Nervosa

PubMed Central

DeGuzman, Marisa; Shott, Megan E.; Yang, Tony T.; Riederer, Justin; Frank, Guido K.W.

2017-01-01

Objective Anorexia nervosa is a psychiatric disorder of unknown etiology. Understanding associations between behavior and neurobiology is important in treatment development. Using a novel monetary reward task during functional magnetic resonance brain imaging, the authors tested how brain reward learning in adolescent anorexia nervosa changes with weight restoration. Method Female adolescents with anorexia nervosa (N=21; mean age, 15.2 years [SD=2.4]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy female control adolescents (N=21; mean age, 16.4 years [SD=1.9]) underwent fMRI on two occasions. Brain function was tested using the reward prediction error construct, a computational model for reward receipt and omission related to motivation and neural dopamine responsiveness. Results Compared with the control group, the anorexia nervosa group exhibited greater brain response 1) for prediction error regression within the caudate, ventral caudate/nucleus accumbens, and anterior and posterior insula, 2) to unexpected reward receipt in the anterior and posterior insula, and 3) to unexpected reward omission in the caudate body. Prediction error and unexpected reward omission response tended to normalize with treatment, while unexpected reward receipt response remained significantly elevated. Greater caudate prediction error response when underweight was associated with lower weight gain during treatment. Punishment sensitivity correlated positively with ventral caudate prediction error response. Conclusions Reward system responsiveness is elevated in adolescent anorexia nervosa when underweight and after weight restoration. Heightened prediction error activity in brain reward regions may represent a phenotype of adolescent anorexia nervosa that does not respond well to treatment. Prediction error response could be a neurobiological marker of illness severity that can indicate individual treatment needs. PMID:28231717

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach.

PubMed

Xu, Nan; Spreng, R Nathan; Doerschuk, Peter C

2017-01-01

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the "common driver" problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain.

Gray Matter-White Matter De-Differentiation on Brain Computed Tomography Predicts Brain Death Occurrence.

PubMed

Vigneron, C; Labeye, V; Cour, M; Hannoun, S; Grember, A; Rampon, F; Cotton, F

2016-01-01

Previous studies have shown that a loss of distinction between gray matter (GM) and white matter (WM) on unenhanced CT scans was predictive of poor outcome after cardiac arrest. The aim of this study was to identify a marker/predictor of imminent brain death. In this retrospective study, 15 brain-dead patients after anoxia and cardiac arrest were included. Patients were paired (1:1) with normal control subjects. Only patients' unenhanced CT scans performed before brain death and during the 24 hours after initial signs were analyzed. WM and GM densities were measured in predefined regions of interest (basal ganglia level, centrum semi-ovale level, high convexity level, brainstem level). At each level, GM and WM density and GM/WM ratio for brain-dead patients and normal control subjects were compared using the Wilcoxon signed-rank test. At each level, a lower GM/WM ratio and decreased GM and WM densities were observed in brain-dead patients' CT scans when compared with normal control subject CT scans. A cut-off value of 1.21 at the basal ganglia level was identified, below which brain death systematically occurred. GM/WM dedifferentiation on unenhanced CT scan is measurable before the occurrence of brain death, highlighting its importance in brain death prediction. The mechanism of GM/WM differentiation loss could be explained by the lack of oxygen caused by ischemia initially affecting the mitochondrial system. Copyright © 2016 Elsevier Inc. All rights reserved.

A simple brain atrophy measure improves the prediction of malignant middle cerebral artery infarction by acute DWI lesion volume.

PubMed

Beck, Christoph; Kruetzelmann, Anna; Forkert, Nils D; Juettler, Eric; Singer, Oliver C; Köhrmann, Martin; Kersten, Jan F; Sobesky, Jan; Gerloff, Christian; Fiehler, Jens; Schellinger, Peter D; Röther, Joachim; Thomalla, Götz

2014-06-01

In patients with malignant middle cerebral artery infarction (MMI) decompressive surgery within 48 h improves functional outcome. In this respect, early identification of patients at risk of developing MMI is crucial. While the acute diffusion weighted imaging (DWI) lesion volume was found to predict MMI with high predictive values, the potential impact of preexisting brain atrophy on the course of space-occupying middle cerebral artery (MCA) infarction and the development of MMI remains unclear. We tested the hypothesis that the combination of the acute DWI lesion volume with simple measures of brain atrophy improves the early prediction of MMI. Data from a prospective, multicenter, observational study, which included patients with acute middle cerebral artery main stem occlusion studied by MRI within 6 h of symptom onset, was analyzed retrospectively. The development of MMI was defined according to the European randomized controlled trials of decompressive surgery. Acute DWI lesion volume, as well as brain and cerebrospinal fluid volume (CSF) were delineated. The intercaudate distance (ICD) was assessed as a linear brain atrophy marker by measuring the hemi-ICD of the intact hemisphere to account for local brain swelling. Binary logistic regression analysis was used to identify significant predictors of MMI. Cut-off values were determined by Classification and Regression Trees analysis. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the resulting models were calculated. Twenty-one (18 %) of 116 patients developed a MMI. Malignant middle cerebral artery infarctions patients had higher National Institutes of Health Stroke Scale scores on admission and presented more often with combined occlusion of the internal carotid artery and MCA. There were no differences in brain and CSF volume between the two groups. Diffusion weighted imaging lesion volume was larger (p < 0.001), while hemi-ICD was smaller (p = 0.029) in MMI patients. Inclusion of hemi-ICD improved the prediction of MMI. Best cut-off values to predict the development of MMI were DWI lesion volume > 87 ml and hemi-ICD ≤ 9.4 mm. The addition of hemi-ICD to the decision tree strongly increased PPV (0.93 vs. 0.70) resulting in a reduction of false positive findings from 7/23 (30 %) to 1/15 (7 %), while there were only slight changes in specificity, sensitivity and NPV. The absolute number of correct classifications increased by 4 (3.4 %). The integration of hemi-ICD as a linear marker of brain atrophy, that can easily be assessed in an emergency setting, may improve the prediction of MMI by lesion volume based predictive models.

Distribution of temperature changes and neurovascular coupling in rat brain following 3,4-methylenedioxymethamphetamine (MDMA, "ecstasy") exposure.

PubMed

Coman, Daniel; Sanganahalli, Basavaraju G; Jiang, Lihong; Hyder, Fahmeed; Behar, Kevin L

2015-10-01

(+/-)3,4-methylenedioxymethamphetamine (MDMA, "ecstasy") is an abused psychostimulant that produces strong monoaminergic stimulation and whole-body hyperthermia. MDMA-induced thermogenesis involves activation of uncoupling proteins (UCPs), primarily a type specific to skeletal muscle (UCP-3) and absent from the brain, although other UCP types are expressed in the brain (e.g. thalamus) and might contribute to thermogenesis. Since neuroimaging of brain temperature could provide insights into MDMA action, we measured spatial distributions of systemically administered MDMA-induced temperature changes and dynamics in rat cortex and subcortex using a novel magnetic resonance method, Biosensor Imaging of Redundant Deviation in Shifts (BIRDS), with an exogenous temperature-sensitive probe (thulium ion and macrocyclic chelate 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetramethyl-1,4,7,10-tetraacetate (DOTMA(4-))). The MDMA-induced temperature rise was greater in the cortex than in the subcortex (1.6 ± 0.4 °C versus 1.3 ± 0.4 °C) and occurred more rapidly (2.0 ± 0.2 °C/h versus 1.5 ± 0.2 °C/h). MDMA-induced temperature changes and dynamics in the cortex and body were correlated, although the body temperature exceeded the cortex temperature before and after MDMA. Temperature, neuronal activity, and blood flow (CBF) were measured simultaneously in the cortex and subcortex (i.e. thalamus) to investigate possible differences of MDMA-induced warming across brain regions. MDMA-induced warming correlated with increases in neuronal activity and blood flow in the cortex, suggesting that the normal neurovascular response to increased neural activity was maintained. In contrast to the cortex, a biphasic relationship was seen in the subcortex (i.e. thalamus), with a decline in CBF as temperature and neural activity rose, transitioning to a rise in CBF for temperature above 37 °C, suggesting that MDMA affected CBF and neurovascular coupling differently in subcortical regions. Considering that MDMA effects on CBF and heat dissipation (as well as potential heat generation) may vary regionally, neuroprotection may require different cooling strategies. Copyright © 2015 John Wiley & Sons, Ltd.

Metastatic brain cancer: prediction of response to whole-brain helical tomotherapy with simultaneous intralesional boost for metastatic disease using quantitative MR imaging features

NASA Astrophysics Data System (ADS)

Sharma, Harish; Bauman, Glenn; Rodrigues, George; Bartha, Robert; Ward, Aaron

2014-03-01

The sequential application of whole brain radiotherapy (WBRT) and more targeted stereotactic radiosurgery (SRS) is frequently used to treat metastatic brain tumors. However, SRS has side effects related to necrosis and edema, and requires separate and relatively invasive localization procedures. Helical tomotherapy (HT) allows for a SRS-type simultaneous infield boost (SIB) of multiple brain metastases, synchronously with WBRT and without separate stereotactic procedures. However, some patients' tumors may not respond to HT+SIB, and would be more appropriately treated with radiosurgery or conventional surgery despite the additional risks and side effects. As a first step toward a broader objective of developing a means for response prediction to HT+SIB, the goal of this study was to investigate whether quantitative measurements of tumor size and appearance (including first- and second-order texture features) on a magnetic resonance imaging (MRI) scan acquired prior to treatment could be used to differentiate responder and nonresponder patient groups after HT+SIB treatment of metastatic disease of the brain. Our results demonstrated that smaller lesions may respond better to this form of therapy; measures of appearance provided limited added value over measures of size for response prediction. With further validation on a larger data set, this approach may lead to a means for prediction of individual patient response based on pre-treatment MRI, supporting appropriate therapy selection for patients with metastatic brain cancer.

Brain temperature profiles during epidural cooling with the ChillerPad in a monkey model of traumatic brain injury.

PubMed

King, Christopher; Robinson, Timothy; Dixon, C Edward; Rao, Gutti R; Larnard, Donald; Nemoto, C Edwin M

2010-10-01

Therapeutic hypothermia remains a promising treatment for patients with severe traumatic brain injury (TBI). Multiple animal studies have suggested that hypothermia is neuroprotective after TBI, but clinical trials have been inconclusive. Systemic hypothermia, the method used in almost all major clinical trials, is limited by the time to target temperature, the depth of hypothermia, and complications, problems that may be solved by selective brain cooling. We evaluated the effects on brain temperature of a cooling device called the ChillerPad,™ which is applied to the dura in a non-human primate TBI model using controlled cortical impact (CCI). The cortical surface was rapidly cooled to approximately 15°C and maintained at that level for 24 h, followed by rewarming over about 10 h. Brain temperatures fell to 34-35°C at a depth of 15 mm at the cortical gray/white matter interface, and to 28-32°C at 10 mm deep. Intracranial pressure was mildly elevated (8-12 mm Hg) after cooling and rewarming, likely due to TBI. Other physiological variables were unchanged. Cooling was rapidly diminished at points distant from the cooling pad. The ChillerPad may be useful for highly localized cooling of the brain in circumstances in which a craniotomy is clinically indicated. However, because of the delay required by the craniotomy, other methods that are more readily available for inducing hypothermia may be used as a bridge between the time of injury to placement of the ChillerPad.

Intellectual enrichment lessens the effect of brain atrophy on learning and memory in multiple sclerosis.

PubMed

Sumowski, James F; Wylie, Glenn R; Chiaravalloti, Nancy; DeLuca, John

2010-06-15

Learning and memory impairments are prevalent among persons with multiple sclerosis (MS); however, such deficits are only weakly associated with MS disease severity (brain atrophy). The cognitive reserve hypothesis states that greater lifetime intellectual enrichment lessens the negative impact of brain disease on cognition, thereby helping to explain the incomplete relationship between brain disease and cognitive status in neurologic populations. The literature on cognitive reserve has focused mainly on Alzheimer disease. The current research examines whether greater intellectual enrichment lessens the negative effect of brain atrophy on learning and memory in patients with MS. Forty-four persons with MS completed neuropsychological measures of verbal learning and memory, and a vocabulary-based estimate of lifetime intellectual enrichment. Brain atrophy was estimated with third ventricle width measured from 3-T magnetization-prepared rapid gradient echo MRIs. Hierarchical regression was used to predict learning and memory with brain atrophy, intellectual enrichment, and the interaction between brain atrophy and intellectual enrichment. Brain atrophy predicted worse learning and memory, and intellectual enrichment predicted better learning; however, these effects were moderated by interactions between brain atrophy and intellectual enrichment. Specifically, higher intellectual enrichment lessened the negative impact of brain atrophy on both learning and memory. These findings help to explain the incomplete relationship between multiple sclerosis disease severity and cognition, as the effect of disease on cognition is attenuated among patients with higher intellectual enrichment. As such, intellectual enrichment is supported as a protective factor against disease-related cognitive impairment in persons with multiple sclerosis.

Reliability of MRSI brain temperature mapping at 1.5 and 3 T

PubMed Central

Thrippleton, Michael J; Parikh, Jehill; Harris, Bridget A; Hammer, Steven J; Semple, Scott I K; Andrews, Peter J D; Wardlaw, Joanna M; Marshall, Ian

2014-01-01

MRSI permits the non-invasive mapping of brain temperature in vivo, but information regarding its reliability is lacking. We obtained MRSI data from 31 healthy male volunteers [age range, 22–40 years; mean ± standard deviation (SD), 30.5 ± 5.0 years]. Eleven subjects (age range, 23–40 years; mean ± SD, 30.5 ± 5.2 years) were invited to receive four point-resolved spectroscopy MRSI scans on each of 3 days in both 1.5-T (TR/TE = 1000/144 ms) and 3-T (TR/TE = 1700/144 ms) clinical scanners; a further 20 subjects (age range, 22–40 years; mean ± SD, 30.5 ± 4.9 years) were scanned on a single occasion at 3 T. Data were fitted in the time domain to determine the water–N-acetylaspartate chemical shift difference, from which the temperature was estimated. Temperature data were analysed using a linear mixed effects model to determine variance components and systematic temperature changes during the scanning sessions. To characterise the effects of instrumental drift on apparent MRSI brain temperature, a temperature-controlled phantom was constructed and scanned on multiple occasions. Components of apparent in vivo temperature variability at 1.5 T/3 T caused by inter-subject (0.18/0.17 °C), inter-session (0.18/0.15 °C) and within-session (0.36/0.14 °C) effects, as well as voxel-to-voxel variation (0.59/0.54 °C), were determined. There was a brain cooling effect during in vivo MRSI of 0.10 °C [95% confidence interval (CI): –0.110, –0.094 °C; p < 0.001] and 0.051 °C (95% CI: –0.054, –0.048 °C; p < 0.001) per scan at 1.5 T and 3 T, respectively, whereas phantom measurements revealed minimal drift in apparent MRSI temperature relative to fibre-optic temperature measurements. The mean brain temperature at 3 T was weakly associated with aural (R = 0.55, p = 0.002) and oral (R = 0.62, p < 0.001) measurements of head temperature. In conclusion, the variability associated with MRSI brain temperature mapping was quantified. Repeatability was somewhat higher at 3 T than at 1.5 T, although subtle spatial and temporal variations in apparent temperature were demonstrated at both field strengths. Such data should assist in the efficient design of future clinical studies. © 2013 The Authors. NMR in Biomedicine published by John Wiley & Sons, Ltd. PMID:24273188

Prediction of human errors by maladaptive changes in event-related brain networks.

PubMed

Eichele, Tom; Debener, Stefan; Calhoun, Vince D; Specht, Karsten; Engel, Andreas K; Hugdahl, Kenneth; von Cramon, D Yves; Ullsperger, Markus

2008-04-22

Humans engaged in monotonous tasks are susceptible to occasional errors that may lead to serious consequences, but little is known about brain activity patterns preceding errors. Using functional MRI and applying independent component analysis followed by deconvolution of hemodynamic responses, we studied error preceding brain activity on a trial-by-trial basis. We found a set of brain regions in which the temporal evolution of activation predicted performance errors. These maladaptive brain activity changes started to evolve approximately 30 sec before the error. In particular, a coincident decrease of deactivation in default mode regions of the brain, together with a decline of activation in regions associated with maintaining task effort, raised the probability of future errors. Our findings provide insights into the brain network dynamics preceding human performance errors and suggest that monitoring of the identified precursor states may help in avoiding human errors in critical real-world situations.

Prediction of human errors by maladaptive changes in event-related brain networks

PubMed Central

Eichele, Tom; Debener, Stefan; Calhoun, Vince D.; Specht, Karsten; Engel, Andreas K.; Hugdahl, Kenneth; von Cramon, D. Yves; Ullsperger, Markus

2008-01-01

Humans engaged in monotonous tasks are susceptible to occasional errors that may lead to serious consequences, but little is known about brain activity patterns preceding errors. Using functional MRI and applying independent component analysis followed by deconvolution of hemodynamic responses, we studied error preceding brain activity on a trial-by-trial basis. We found a set of brain regions in which the temporal evolution of activation predicted performance errors. These maladaptive brain activity changes started to evolve ≈30 sec before the error. In particular, a coincident decrease of deactivation in default mode regions of the brain, together with a decline of activation in regions associated with maintaining task effort, raised the probability of future errors. Our findings provide insights into the brain network dynamics preceding human performance errors and suggest that monitoring of the identified precursor states may help in avoiding human errors in critical real-world situations. PMID:18427123

Tissue oxidative metabolism can increase the difference between local temperature and arterial blood temperature by up to 1.3oC: Implications for brain, brown adipose tissue, and muscle physiology.

PubMed

Zaretsky, Dmitry V; Romanovsky, Andrej A; Zaretskaia, Maria V; Molkov, Yaroslav I

2018-01-01

Tissue temperature increases, when oxidative metabolism is boosted. The source of nutrients and oxygen for this metabolism is the blood. The blood also cools down the tissue, and this is the only cooling mechanism, when direct dissipation of heat from the tissue to the environment is insignificant, e.g. , in the brain. While this concept is relatively simple, it has not been described quantitatively. The purpose of the present work was to answer two questions: 1) to what extent can oxidative metabolism make the organ tissue warmer than the body core, and, 2) how quickly are changes in the local metabolism reflected in the temperature of the tissue? Our theoretical analysis demonstrates that, at equilibrium, given that heat exchange with the organ is provided by the blood, the temperature difference between the organ tissue and the arterial blood is proportional to the arteriovenous difference in oxygen content, does not depend on the blood flow, and cannot exceed 1.3 o C. Unlike the equilibrium temperature difference, the rate of change of the local temperature, with respect to time, does depend on the blood flow. In organs with high perfusion rates, such as the brain and muscles, temperature changes occur on a time scale of a few minutes. In organs with low perfusion rates, such changes may have characteristic time constants of tens or hundreds of minutes. Our analysis explains, why arterial blood temperature is the main determinant of the temperature of tissues with limited heat exchange, such as the brain.

Analytical modelling of temperature effects on an AMPA-type synapse.

PubMed

Kufel, Dominik S; Wojcik, Grzegorz M

2018-05-11

It was previously reported, that temperature may significantly influence neural dynamics on the different levels of brain function. Thus, in computational neuroscience, it would be useful to make models scalable for a wide range of various brain temperatures. However, lack of experimental data and an absence of temperature-dependent analytical models of synaptic conductance does not allow to include temperature effects at the multi-neuron modeling level. In this paper, we propose a first step to deal with this problem: A new analytical model of AMPA-type synaptic conductance, which is able to incorporate temperature effects in low-frequency stimulations. It was constructed based on Markov model description of AMPA receptor kinetics using the set of coupled ODEs. The closed-form solution for the set of differential equations was found using uncoupling assumption (introduced in the paper) with few simplifications motivated both from experimental data and from Monte Carlo simulation of synaptic transmission. The model may be used for computationally efficient and biologically accurate implementation of temperature effects on AMPA receptor conductance in large-scale neural network simulations. As a result, it may open a wide range of new possibilities for researching the influence of temperature on certain aspects of brain functioning.

Brain atrophy and lesion load measures over 1 year relate to clinical status after 6 years in patients with clinically isolated syndromes.

PubMed

Di Filippo, M; Anderson, V M; Altmann, D R; Swanton, J K; Plant, G T; Thompson, A J; Miller, D H

2010-02-01

Conventional MRI lesion measures modestly predict long term disability in some clinically isolated syndrome (CIS) studies. Brain atrophy suggests neuroaxonal loss in multiple sclerosis (MS) with the potential to reflect disease progression to a greater extent than lesion measures. To investigate whether brain atrophy and lesion load, during the first year in patients presenting with CIS, independently predict clinical outcome (development of MS and disability at 6 years). 99 patients presenting with CIS were included in the study. T1 gadolinium enhanced and T2 weighted brain MRI was acquired at baseline and approximately 1 year later. Percentage brain atrophy rate between baseline and follow-up scans was analysed using SIENA. Mean annual brain atrophy rates were -0.38% for all patients, -0.50% in patients who had developed MS at 6 years and -0.26% in those who had not. Brain atrophy rate (p = 0.005) and baseline T2 lesion load (p<0.001) were independent predictors of clinically definite MS. While brain atrophy rate was a predictor of Expanded Disability Status Scale (EDSS) score in a univariate analysis, only 1 year T2 lesion load change (p = 0.007) and baseline gadolinium enhancing lesion number (p = 0.03) were independent predictors of EDSS score at the 6 year follow-up. T1 lesion load was the only MRI parameter which predicted Multiple Sclerosis Functional Composite score at the 6 year follow-up. The findings confirm that brain atrophy occurs during the earliest phases of MS and suggest that 1 year longitudinal measures of MRI change, if considered together with baseline MRI variables, might help to predict clinical status 6 years after the first demyelinating event in CIS patients, better than measurements such as lesion or brain volumes on baseline MRI alone.

Identification of brain regions predicting epileptogenesis by serial [18F]GE-180 positron emission tomography imaging of neuroinflammation in a rat model of temporal lobe epilepsy.

PubMed

Russmann, Vera; Brendel, Matthias; Mille, Erik; Helm-Vicidomini, Angela; Beck, Roswitha; Günther, Lisa; Lindner, Simon; Rominger, Axel; Keck, Michael; Salvamoser, Josephine D; Albert, Nathalie L; Bartenstein, Peter; Potschka, Heidrun

2017-01-01

Excessive activation of inflammatory signaling pathways seems to be a hallmark of epileptogenesis. Positron emission tomography (PET) allows in vivo detection of brain inflammation with spatial information and opportunities for longitudinal follow-up scanning protocols. Here, we assessed whether molecular imaging of the 18 kDa translocator protein (TSPO) can serve as a biomarker for the development of epilepsy. Therefore, brain uptake of [ 18 F]GE-180, a highly selective radioligand of TSPO, was investigated in a longitudinal PET study in a chronic rat model of temporal lobe epilepsy. Analyses revealed that the influence of the epileptogenic insult on [ 18 F]GE-180 brain uptake was most pronounced in the earlier phase of epileptogenesis. Differences were evident in various brain regions during earlier phases of epileptogenesis with [ 18 F]GE-180 standardized uptake value enhanced by 2.1 to 2.7fold. In contrast, brain regions exhibiting differences seemed to be more restricted with less pronounced increases of tracer uptake by 1.8-2.5fold four weeks following status epilepticus and by 1.5-1.8fold in the chronic phase. Based on correlation analysis, we were able to identify regions with a predictive value showing a correlation with seizure development. These regions include the amygdala as well as a cluster of brain areas. This cluster comprises parts of different brain regions, e.g. the hippocampus, parietal cortex, thalamus, and somatosensory cortex. In conclusion, the data provide evidence that [ 18 F]GE-180 PET brain imaging can serve as a biomarker of epileptogenesis. The identification of brain regions with predictive value might facilitate the development of preventive concepts as well as the early assessment of the interventional success. Future studies are necessary to further confirm the predictivity of the approach.

EEG as an Indicator of Cerebral Functioning in Postanoxic Coma.

PubMed

Juan, Elsa; Kaplan, Peter W; Oddo, Mauro; Rossetti, Andrea O

2015-12-01

Postanoxic coma after cardiac arrest is one of the most serious acute cerebral conditions and a frequent cause of admission to critical care units. Given substantial improvement of outcome over the recent years, a reliable and timely assessment of clinical evolution and prognosis is essential in this context, but may be challenging. In addition to the classic neurologic examination, EEG is increasingly emerging as an important tool to assess cerebral functions noninvasively. Although targeted temperature management and related sedation may delay clinical assessment, EEG provides accurate prognostic information in the early phase of coma. Here, the most frequently encountered EEG patterns in postanoxic coma are summarized and their relations with outcome prediction are discussed. This article also addresses the influence of targeted temperature management on brain signals and the implication of the evolution of EEG patterns over time. Finally, the article ends with a view of the future prospects for EEG in postanoxic management and prognostication.

Longitudinal Changes in Total Brain Volume in Schizophrenia: Relation to Symptom Severity, Cognition and Antipsychotic Medication

PubMed Central

Veijola, Juha; Guo, Joyce Y.; Moilanen, Jani S.; Jääskeläinen, Erika; Miettunen, Jouko; Kyllönen, Merja; Haapea, Marianne; Huhtaniska, Sanna; Alaräisänen, Antti; Mäki, Pirjo; Kiviniemi, Vesa; Nikkinen, Juha; Starck, Tuomo; Remes, Jukka J.; Tanskanen, Päivikki; Tervonen, Osmo; Wink, Alle-Meije; Kehagia, Angie; Suckling, John; Kobayashi, Hiroyuki; Barnett, Jennifer H.; Barnes, Anna; Koponen, Hannu J.; Jones, Peter B.; Isohanni, Matti; Murray, Graham K.

2014-01-01

Studies show evidence of longitudinal brain volume decreases in schizophrenia. We studied brain volume changes and their relation to symptom severity, level of function, cognition, and antipsychotic medication in participants with schizophrenia and control participants from a general population based birth cohort sample in a relatively long follow-up period of almost a decade. All members of the Northern Finland Birth Cohort 1966 with any psychotic disorder and a random sample not having psychosis were invited for a MRI brain scan, and clinical and cognitive assessment during 1999–2001 at the age of 33–35 years. A follow-up was conducted 9 years later during 2008–2010. Brain scans at both time points were obtained from 33 participants with schizophrenia and 71 control participants. Regression models were used to examine whether brain volume changes predicted clinical and cognitive changes over time, and whether antipsychotic medication predicted brain volume changes. The mean annual whole brain volume reduction was 0.69% in schizophrenia, and 0.49% in controls (p = 0.003, adjusted for gender, educational level, alcohol use and weight gain). The brain volume reduction in schizophrenia patients was found especially in the temporal lobe and periventricular area. Symptom severity, functioning level, and decline in cognition were not associated with brain volume reduction in schizophrenia. The amount of antipsychotic medication (dose years of equivalent to 100 mg daily chlorpromazine) over the follow-up period predicted brain volume loss (p = 0.003 adjusted for symptom level, alcohol use and weight gain). In this population based sample, brain volume reduction continues in schizophrenia patients after the onset of illness, and antipsychotic medications may contribute to these reductions. PMID:25036617

Thermodynamic laws apply to brain function.

PubMed

Salerian, Alen J

2010-02-01

Thermodynamic laws and complex system dynamics govern brain function. Thus, any change in brain homeostasis by an alteration in brain temperature, neurotransmission or content may cause region-specific brain dysfunction. This is the premise for the Salerian Theory of Brain built upon a new paradigm for neuropsychiatric disorders: the governing influence of neuroanatomy, neurophysiology, thermodynamic laws. The principles of region-specific brain function thermodynamics are reviewed. The clinical and supporting evidence including the paradoxical effects of various agents that alter brain homeostasis is demonstrated.

Brain Cooling With Ventilation of Cold Air Over Respiratory Tract in Newborn Piglets: An Experimental and Numerical Study

PubMed Central

Bakhsheshi, Mohammad Fazel; Moradi, Hadi Vafadar; Stewart, Errol E.; Keenliside, Lynn; Lee, Ting-Yim

2015-01-01

We investigate thermal effects of pulmonary cooling which was induced by cold air through an endotracheal tube via a ventilator on newborn piglets. A mathematical model was initially employed to compare the thermal impact of two different gas mixtures, O2-medical air (1:2) and O2-Xe (1:2), across the respiratory tract and within the brain. Following mathematical simulations, we examined the theoretical predictions with O2-medical air condition on nine anesthetized piglets which were randomized to two treatment groups: 1) control group (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$n = 4$ \\end{document}) and 2) pulmonary cooling group (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$n = 5$ \\end{document}). Numerical and experimental results using O2-medical air mixture show that brain temperature fell from 38.5 °C and 38.3 °C ± 0.3 °C to 35.7 °C ± 0.9 °C and 36.5 °C ± 0.6 °C during 3 h cooling which corresponded to a mean cooling rate of 0.9 °C/h ± 0.2 °C/h and 0.6 °C/h ± 0.1 °C/h, respectively. According to the numerical results, decreasing the metabolic rate and increasing air velocity are helpful to maximize the cooling effect. We demonstrated that pulmonary cooling by cooling of inhalation gases immediately before they enter the trachea can slowly reduce brain and core body temperature of newborn piglets. Numerical simulations show no significant differences between two different inhaled conditions, i.e., O2-medical air (1:2) and O2-Xe (1:2) with respect to cooling rate. PMID:27170888

Apparent diffusion coefficient mapping in medulloblastoma predicts non-infiltrative surgical planes.

PubMed

Marupudi, Neena I; Altinok, Deniz; Goncalves, Luis; Ham, Steven D; Sood, Sandeep

2016-11-01

An appropriate surgical approach for posterior fossa lesions is to start tumor removal from areas with a defined plane to where tumor is infiltrating the brainstem or peduncles. This surgical approach minimizes risk of damage to eloquent areas. Although magnetic resonance imaging (MRI) is the current standard preoperative imaging obtained for diagnosis and surgical planning of pediatric posterior fossa tumors, it offers limited information on the infiltrative planes between tumor and normal structures in patients with medulloblastomas. Because medulloblastomas demonstrate diffusion restriction on apparent diffusion coefficient map (ADC map) sequences, we investigated the role of ADC map in predicting infiltrative and non-infiltrative planes along the brain stem and/or cerebellar peduncles by medulloblastomas prior to surgery. Thirty-four pediatric patients with pathologically confirmed medulloblastomas underwent surgical resection at our facility from 2004 to 2012. An experienced pediatric neuroradiologist reviewed the brain MRIs/ADC map, assessing the planes between the tumor and cerebellar peduncles/brain stem. An independent evaluator documented surgical findings from operative reports for comparison to the radiographic findings. The radiographic findings were statistically compared to the documented intraoperative findings to determine predictive value of the test in identifying tumor infiltration of the brain stem cerebellar peduncles. Twenty-six patients had preoperative ADC mapping completed and thereby, met inclusion criteria. Mean age at time of surgery was 8.3 ± 4.6 years. Positive predictive value of ADC maps to predict tumor invasion of the brain stem and cerebellar peduncles ranged from 69 to 88 %; negative predictive values ranged from 70 to 89 %. Sensitivity approached 93 % while specificity approached 78 %. ADC maps are valuable in predicting the infiltrative and non-infiltrative planes along the tumor and brain stem interface in medulloblastomas. Inclusion and evaluation of ADC maps in preoperative evaluation can assist in surgical resection planning in patients with medulloblastoma.

Fast and precise thermoregulation system in physiological brain slice experiment

NASA Astrophysics Data System (ADS)

Sheu, Y. H.; Young, M. S.

1995-12-01

We have developed a fast and precise thermoregulation system incorporated within a physiological experiment on a brain slice. The thermoregulation system is used to control the temperature of a recording chamber in which the brain slice is placed. It consists of a single-chip microcomputer, a set command module, a display module, and an FLC module. A fuzzy control algorithm was developed and a fuzzy logic controller then designed for achieving fast, smooth thermostatic performance and providing precise temperature control with accuracy to 0.1 °C, from room temperature through 42 °C (experimental temperature range). The fuzzy logic controller is implemented by microcomputer software and related peripheral hardware circuits. Six operating modes of thermoregulation are offered with the system and this can be further extended according to experimental needs. The test results of this study demonstrate that the fuzzy control method is easily implemented by a microcomputer and also verifies that this method provides a simple way to achieve fast and precise high-performance control of a nonlinear thermoregulation system in a physiological brain slice experiment.

A prospective, observational clinical trial of fever reduction to reduce systemic oxygen consumption in the setting of acute brain injury.

PubMed

Hata, J Steven; Shelsky, Constance R; Hindman, Bradley J; Smith, Thomas C; Simmons, Jonathan S; Todd, Michael M

2008-01-01

Fever after acute brain injury appears to be a detrimental factor, associated with impaired neurological outcomes. This study assessed physiological changes in systemic oxygen consumption (VO2) during cutaneous cooling after severe brain injury. This prospective, observational, clinical study evaluated ten, critically ill, brain-injured patients requiring mechanical ventilation with a core body temperature of greater or equal to 38 degrees C. Febrile patients failing to defervesce after acetaminophen underwent indirect calorimetry for a 1-hour baseline period followed by a 4 h cooling period. The Arctic Sun(R) Temperature Management System (Medivance) directed core temperature to a goal of 36 degrees C. The patients had a mean age of 32 years (95% CI 23, 40), Glasgow Coma Scale of 6 (95% CI 5,7), and APACHE 2 score of 19 (95% CI 15, 22), with 8 of 10 patients suffering traumatic brain injuries. The baseline 1-h core temperature was significantly reduced from 38.6 degrees +/- 0.9 to 36.3 degrees +/- 1.2 degrees C (P < 0.0001) over 4 h. Two cohorts were identified based upon the presence or absence of shivering. Within the non-shivering cohort, systemic VO2 was significantly reduced from 415 +/- 123 to 308 +/- 115 ml/min (-27 +/- 18%) (P < 0.05). In contrast, those with shivering showed no significant reduction in VO2, despite significantly decreasing core temperature. The overall percentage change of VCO2 correlated with VO2 (r (2) = 0.91). Fever reduction in acute brain injury appears to significantly reduce systemic VO2, but is highly dependent on shivering control.

The proactive brain: memory for predictions

PubMed Central

Bar, Moshe

2009-01-01

It is proposed that the human brain is proactive in that it continuously generates predictions that anticipate the relevant future. In this proposal, analogies are derived from elementary information that is extracted rapidly from the input, to link that input with the representations that exist in memory. Finding an analogical link results in the generation of focused predictions via associative activation of representations that are relevant to this analogy, in the given context. Predictions in complex circumstances, such as social interactions, combine multiple analogies. Such predictions need not be created afresh in new situations, but rather rely on existing scripts in memory, which are the result of real as well as of previously imagined experiences. This cognitive neuroscience framework provides a new hypothesis with which to consider the purpose of memory, and can help explain a variety of phenomena, ranging from recognition to first impressions, and from the brain's ‘default mode’ to a host of mental disorders. PMID:19528004

Predictions penetrate perception: Converging insights from brain, behaviour and disorder

PubMed Central

O’Callaghan, Claire; Kveraga, Kestutis; Shine, James M; Adams, Reginald B.; Bar, Moshe

2018-01-01

It is argued that during ongoing visual perception, the brain is generating top-down predictions to facilitate, guide and constrain the processing of incoming sensory input. Here we demonstrate that these predictions are drawn from a diverse range of cognitive processes, in order to generate the richest and most informative prediction signals. This is consistent with a central role for cognitive penetrability in visual perception. We review behavioural and mechanistic evidence that indicate a wide spectrum of domains—including object recognition, contextual associations, cognitive biases and affective state—that can directly influence visual perception. We combine these insights from the healthy brain with novel observations from neuropsychiatric disorders involving visual hallucinations, which highlight the consequences of imbalance between top-down signals and incoming sensory information. Together, these lines of evidence converge to indicate that predictive penetration, be it cognitive, social or emotional, should be considered a fundamental framework that supports visual perception. PMID:27222169

Thermal effects of diagnostic ultrasound in an anthropomorphic skull model.

PubMed

Vyskocil, E; Pfaffenberger, S; Kollmann, C; Gleiss, A; Nawratil, G; Kastl, S; Unger, E; Aumayr, K; Schuhfried, O; Huber, K; Wojta, J; Gottsauner-Wolf, M

2012-12-01

Exposure to diagnostic ultrasound (US) can significantly heat biological tissue although conventional routine examinations are regarded as safe. The risk of unwanted thermal effects increases with a high absorption coefficient and extended insonation time. Certain applications of transcranial diagnostic US (TC-US) require prolonged exposure. An anthropomorphic skull model (ASM) was developed to evaluate thermal effects induced by TC-US of different modalities. The objective was to determine whether prolonged continuous TC-US application results in potentially harmful temperature increases. The ASM consists of a human skull with tissue mimicking material and exhibits acoustic and anatomical characteristics of the human skull and brain. Experiments are performed with a diagnostic US device testing four different US modalities: Duplex PW (pulsed wave) Doppler, PW Doppler, color flow Doppler and B-mode. Temperature changes are recorded during 180 minutes of insonation. All measurements revealed significant temperature increases during insonation independent of the US modality. The maximum temperature elevation of + 5.25° C (p < 0.001) was observed on the surface of the skull exposed to duplex PW Doppler. At the bone-brain border a maximum temperature increae of + 2.01 °C (p < 0.001) was noted. Temperature increases within the brain were < 1.23 °C (p = 0.001). The highest values were registered using the duplex PW Doppler modality. TC-US induces significant local heating effects in an ASM. An application duration that extends routine clinical periods causes potentially harmful heating especially in tissue close to bone. TC-US elevates the temperature in the brain mimicking tissue but is not capable of producing harmful temperature increases during routine examinations. However, the risk of thermal injury in brain tissue increases significantly after an exposure time of > 2 hours. © Georg Thieme Verlag KG Stuttgart · New York.

NMR imaging of cell phone radiation absorption in brain tissue

PubMed Central

Gultekin, David H.; Moeller, Lothar

2013-01-01

A method is described for measuring absorbed electromagnetic energy radiated from cell phone antennae into ex vivo brain tissue. NMR images the 3D thermal dynamics inside ex vivo bovine brain tissue and equivalent gel under exposure to power and irradiation time-varying radio frequency (RF) fields. The absorbed RF energy in brain tissue converts into Joule heat and affects the nuclear magnetic shielding and the Larmor precession. The resultant temperature increase is measured by the resonance frequency shift of hydrogen protons in brain tissue. This proposed application of NMR thermometry offers sufficient spatial and temporal resolution to characterize the hot spots from absorbed cell phone radiation in aqueous media and biological tissues. Specific absorption rate measurements averaged over 1 mg and 10 s in the brain tissue cover the total absorption volume. Reference measurements with fiber optic temperature sensors confirm the accuracy of the NMR thermometry. PMID:23248293

NMR imaging of cell phone radiation absorption in brain tissue.

PubMed

Gultekin, David H; Moeller, Lothar

2013-01-02

A method is described for measuring absorbed electromagnetic energy radiated from cell phone antennae into ex vivo brain tissue. NMR images the 3D thermal dynamics inside ex vivo bovine brain tissue and equivalent gel under exposure to power and irradiation time-varying radio frequency (RF) fields. The absorbed RF energy in brain tissue converts into Joule heat and affects the nuclear magnetic shielding and the Larmor precession. The resultant temperature increase is measured by the resonance frequency shift of hydrogen protons in brain tissue. This proposed application of NMR thermometry offers sufficient spatial and temporal resolution to characterize the hot spots from absorbed cell phone radiation in aqueous media and biological tissues. Specific absorption rate measurements averaged over 1 mg and 10 s in the brain tissue cover the total absorption volume. Reference measurements with fiber optic temperature sensors confirm the accuracy of the NMR thermometry.

Characteristics of voxel prediction power in full-brain Granger causality analysis of fMRI data

NASA Astrophysics Data System (ADS)

Garg, Rahul; Cecchi, Guillermo A.; Rao, A. Ravishankar

2011-03-01

Functional neuroimaging research is moving from the study of "activations" to the study of "interactions" among brain regions. Granger causality analysis provides a powerful technique to model spatio-temporal interactions among brain regions. We apply this technique to full-brain fMRI data without aggregating any voxel data into regions of interest (ROIs). We circumvent the problem of dimensionality using sparse regression from machine learning. On a simple finger-tapping experiment we found that (1) a small number of voxels in the brain have very high prediction power, explaining the future time course of other voxels in the brain; (2) these voxels occur in small sized clusters (of size 1-4 voxels) distributed throughout the brain; (3) albeit small, these clusters overlap with most of the clusters identified with the non-temporal General Linear Model (GLM); and (4) the method identifies clusters which, while not determined by the task and not detectable by GLM, still influence brain activity.

Structure Shapes Dynamics and Directionality in Diverse Brain Networks: Mathematical Principles and Empirical Confirmation in Three Species

NASA Astrophysics Data System (ADS)

Moon, Joon-Young; Kim, Junhyeok; Ko, Tae-Wook; Kim, Minkyung; Iturria-Medina, Yasser; Choi, Jee-Hyun; Lee, Joseph; Mashour, George A.; Lee, Uncheol

2017-04-01

Identifying how spatially distributed information becomes integrated in the brain is essential to understanding higher cognitive functions. Previous computational and empirical studies suggest a significant influence of brain network structure on brain network function. However, there have been few analytical approaches to explain the role of network structure in shaping regional activities and directionality patterns. In this study, analytical methods are applied to a coupled oscillator model implemented in inhomogeneous networks. We first derive a mathematical principle that explains the emergence of directionality from the underlying brain network structure. We then apply the analytical methods to the anatomical brain networks of human, macaque, and mouse, successfully predicting simulation and empirical electroencephalographic data. The results demonstrate that the global directionality patterns in resting state brain networks can be predicted solely by their unique network structures. This study forms a foundation for a more comprehensive understanding of how neural information is directed and integrated in complex brain networks.

Transtemporal ultrasound application potentially elevates brain temperature: results of an anthropomorphic skull model.

PubMed

Pfaffenberger, S; Vyskocil, E; Kollmann, C; Unger, E; Kaun, C; Kastl, S; Woeber, C; Nawratil, G; Huber, K; Maurer, G; Gottsauner-Wolf, M; Wojta, J

2013-02-01

Transtemporal sonothrombolysis is a tool for a more effective treatment in acute stroke patients. However, some reports revealed side effects, which might be potentially connected to temperature elevation. To gain better insight into cerebral temperature changes during transtemporal sonication, diagnostic and therapeutic ultrasound (US) applications were evaluated using an anthropomorphic skull model. The impact of diagnostic (PW-Doppler, 1.8-MHz, 0.11 W/cm², TIC 1.2) and therapeutic (1-MHz and 3-MHz, 0.07 - 0.71 W/cm², continuous and pulsed mode) US application on temperature changes was evaluated at the level of muscle/temporal bone (TB), TB/brain, brain and at the middle cerebral artery (MCA) using 4 miniature thermocouples along the US beam. Sonication lasted 120 minutes. Diagnostic ultrasound revealed a maximum temperature increase of 1.45°/0.60°/0.39°/0.41°C (muscle/TB, TB/brain, brain, MCA) after 120 minutes. Therapeutic-1-MHz ultrasound raised temperature by 4.33°/2.02°/1.05 °C/0.81°C (pulsed 1:20) and by 10.38°/4.95°/2.43°/2.08°C (pulsed 1:5) over 120 minutes. Therapeutic-3-MHz US raised temperature by 4.89°/2.56°/1.24/1.25°C (pulsed 1:20) and by 14.77°/6.59°/3.56°/2.86°C (pulsed 1:5) over 120 minutes, respectively. Continuous application of therapeutic US (1-MHz and 3-MHz) led to a temperature increase of 13.86°/3.63°/1.66°/1.48°C and 17.09°/4.28°/1.38/0.99°C within 3 minutes. Diagnostic PW-Doppler showed only a moderate temperature increase and can be considered as safe. Therapeutic sonication is very powerful in delivering energy so that even pulsed application modes resulted in significant and potentially harmful temperature increases. © Georg Thieme Verlag KG Stuttgart · New York.

Distribution of temperature changes and neurovascular coupling in rat brain following 3,4-methylenedioxymethamphetamine (MDMA,‘ecstasy’) exposure

PubMed Central

Coman, Daniel; Sanganahalli, Basavaraju G.; Jiang, Lihong; Hyder, Fahmeed; Behar, Kevin L.

2015-01-01

(+/−)3,4-methylenedioxymethamphetamine (MDMA, ‘ecstasy’) is an abused psychostimulant producing strong monoaminergic stimulation and whole-body hyperthermia. MDMA-induced thermogenesis involves activation of uncoupling proteins (UCP), primarily a type specific to skeletal muscle (UCP-3) and which is absent in brain, although other UCP types are expressed in brain (e.g., thalamus) and might contribute to thermogenesis. Since neuroimaging of brain temperature could provide insights of MDMA action, we measured spatial distributions of systemically-administered MDMA-induced temperature changes and dynamics in rat cortex and subcortex using a novel magnetic resonance method, Biosensor Imaging of Redundant Deviation of Shifts (BIRDS), with an exogenous temperature-sensitive probe (thulium ion and macrocyclic chelate 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetramethyl-1,4,7,10-tetraacetate (DOTMA4−)). The MDMA-induced temperature rise in cortex was greater than in subcortex (1.6±0.4°C vs. 1.3±0.4°C) and occurred more rapidly (2.0±0.2°C/h vs. 1.5±0.2°C/h). MDMA-induced temperature changes and dynamics in cortex and body were correlated, although body temperature exceeded cortex before and after MDMA. Temperature, neuronal activity, and blood flow (CBF) were measured simultaneously in cortex and subcortex (i.e., thalamus) to investigate possible differences of MDMA-induced warming across brain regions. MDMA-induced warming correlated with increases in neuronal activity and blood flow in cortex, suggesting that the normal neurovascular response to increased neural activity was maintained. In contrast to cortex, a biphasic relationship was seen in subcortex (i.e., thalamus), with a decline in CBF as temperature and neural activity rose, transitioning to a rise in CBF for temperature >37°C, suggesting that MDMA affected CBF and neurovascular coupling differently in subcortical regions. Considering that MDMA effects on CBF and heat dissipation (as well as potential heat generation) may vary regionally, neuroprotection may require different cooling strategies. PMID:26286889

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands.

PubMed

Deligianni, Fani; Centeno, Maria; Carmichael, David W; Clayden, Jonathan D

2014-01-01

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity.

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands

PubMed Central

Deligianni, Fani; Centeno, Maria; Carmichael, David W.; Clayden, Jonathan D.

2014-01-01

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity. PMID:25221467

The problem with brain GUTs: conflation of different senses of "prediction" threatens metaphysical disaster.

PubMed

Anderson, Michael L; Chemero, Tony

2013-06-01

Clark appears to be moving toward epistemic internalism, which he once rightly rejected. This results from a double over-interpretation of predictive coding's significance. First, Clark argues that predictive coding offers a Grand Unified Theory (GUT) of brain function. Second, he over-reads its epistemic import, perhaps even conflating causal and epistemic mediators. We argue instead for a plurality of neurofunctional principles.

Attention and prediction in human audition: a lesson from cognitive psychophysiology

PubMed Central

Schröger, Erich; Marzecová, Anna; SanMiguel, Iria

2015-01-01

Attention is a hypothetical mechanism in the service of perception that facilitates the processing of relevant information and inhibits the processing of irrelevant information. Prediction is a hypothetical mechanism in the service of perception that considers prior information when interpreting the sensorial input. Although both (attention and prediction) aid perception, they are rarely considered together. Auditory attention typically yields enhanced brain activity, whereas auditory prediction often results in attenuated brain responses. However, when strongly predicted sounds are omitted, brain responses to silence resemble those elicited by sounds. Studies jointly investigating attention and prediction revealed that these different mechanisms may interact, e.g. attention may magnify the processing differences between predicted and unpredicted sounds. Following the predictive coding theory, we suggest that prediction relates to predictions sent down from predictive models housed in higher levels of the processing hierarchy to lower levels and attention refers to gain modulation of the prediction error signal sent up to the higher level. As predictions encode contents and confidence in the sensory data, and as gain can be modulated by the intention of the listener and by the predictability of the input, various possibilities for interactions between attention and prediction can be unfolded. From this perspective, the traditional distinction between bottom-up/exogenous and top-down/endogenous driven attention can be revisited and the classic concepts of attentional gain and attentional trace can be integrated. PMID:25728182

3D brain tumor localization and parameter estimation using thermographic approach on GPU.

PubMed

Bousselham, Abdelmajid; Bouattane, Omar; Youssfi, Mohamed; Raihani, Abdelhadi

2018-01-01

The aim of this paper is to present a GPU parallel algorithm for brain tumor detection to estimate its size and location from surface temperature distribution obtained by thermography. The normal brain tissue is modeled as a rectangular cube including spherical tumor. The temperature distribution is calculated using forward three dimensional Pennes bioheat transfer equation, it's solved using massively parallel Finite Difference Method (FDM) and implemented on Graphics Processing Unit (GPU). Genetic Algorithm (GA) was used to solve the inverse problem and estimate the tumor size and location by minimizing an objective function involving measured temperature on the surface to those obtained by numerical simulation. The parallel implementation of Finite Difference Method reduces significantly the time of bioheat transfer and greatly accelerates the inverse identification of brain tumor thermophysical and geometrical properties. Experimental results show significant gains in the computational speed on GPU and achieve a speedup of around 41 compared to the CPU. The analysis performance of the estimation based on tumor size inside brain tissue also presented. Copyright © 2017 Elsevier Ltd. All rights reserved.

Thermosensory perception regulates speed of movement in response to temperature changes in Drosophila melanogaster.

PubMed

Soto-Padilla, Andrea; Ruijsink, Rick; Sibon, Ody C M; van Rijn, Hedderik; Billeter, Jean-Christophe

2018-04-12

Temperature influences physiology and behavior of all organisms. For ectotherms, which lack central temperature regulation, temperature adaptation requires sheltering from or moving to a heat source. As temperature constrains the rate of metabolic reactions, it can directly affect ectotherm physiology and thus behavioral performance. This direct effect is particularly relevant for insects whose small body readily equilibrates with ambient temperature. In fact, models of enzyme kinetics applied to insect behavior predict performance at different temperatures, suggesting that thermal physiology governs behavior. However, insects also possess thermosensory neurons critical for locating preferred temperatures, showing cognitive control. This suggests that temperature-related behavior can emerge directly from a physiological effect, indirectly as consequence of thermosensory processing, or through both. To separate the roles of thermal physiology and cognitive control, we developed an arena that allows fast temperature changes in time and space, and in which animals' movements are automatically quantified. We exposed wild-type and thermosensory receptor mutants Drosophila melanogaster to a dynamic temperature environment and tracked their movements. The locomotor speed of wild-type flies closely matched models of enzyme kinetics, but the behavior of thermosensory mutants did not. Mutations in thermosensory receptor dTrpA1 ( Transient receptor potential ) expressed in the brain resulted in a complete lack of response to temperature changes, while mutation in peripheral thermosensory receptor Gr28b(D) resulted in diminished response. We conclude that flies react to temperature through cognitive control, informed by interactions between various thermosensory neurons, whose behavioral output resembles that of enzyme kinetics. © 2018. Published by The Company of Biologists Ltd.

Dynamic causal modelling of brain-behaviour relationships.

PubMed

Rigoux, L; Daunizeau, J

2015-08-15

In this work, we expose a mathematical treatment of brain-behaviour relationships, which we coin behavioural Dynamic Causal Modelling or bDCM. This approach aims at decomposing the brain's transformation of stimuli into behavioural outcomes, in terms of the relative contribution of brain regions and their connections. In brief, bDCM places the brain at the interplay between stimulus and behaviour: behavioural outcomes arise from coordinated activity in (hidden) neural networks, whose dynamics are driven by experimental inputs. Estimating neural parameters that control network connectivity and plasticity effectively performs a neurobiologically-constrained approximation to the brain's input-outcome transform. In other words, neuroimaging data essentially serves to enforce the realism of bDCM's decomposition of input-output relationships. In addition, post-hoc artificial lesions analyses allow us to predict induced behavioural deficits and quantify the importance of network features for funnelling input-output relationships. This is important, because this enables one to bridge the gap with neuropsychological studies of brain-damaged patients. We demonstrate the face validity of the approach using Monte-Carlo simulations, and its predictive validity using empirical fMRI/behavioural data from an inhibitory control task. Lastly, we discuss promising applications of this work, including the assessment of functional degeneracy (in the healthy brain) and the prediction of functional recovery after lesions (in neurological patients). Copyright © 2015 Elsevier Inc. All rights reserved.

Brain entropy and human intelligence: A resting-state fMRI study

PubMed Central

Calderone, Daniel; Morales, Leah J.

2018-01-01

Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns. PMID:29432427

Brain entropy and human intelligence: A resting-state fMRI study.

PubMed

Saxe, Glenn N; Calderone, Daniel; Morales, Leah J

2018-01-01

Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns.

Prefrontal Brain Activity Predicts Temporally Extended Decision-Making Behavior

ERIC Educational Resources Information Center

Yarkoni, Tal; Braver, Todd S.; Gray, Jeremy R.; Green, Leonard

2005-01-01

Although functional neuroimaging studies of human decision-making processes are increasingly common, most of the research in this area has relied on passive tasks that generate little individual variability. Relatively little attention has been paid to the ability of brain activity to predict overt behavior. Using functional magnetic resonance…

Brain age and other bodily 'ages': implications for neuropsychiatry.

PubMed

Cole, James H; Marioni, Riccardo E; Harris, Sarah E; Deary, Ian J

2018-06-11

As our brains age, we tend to experience cognitive decline and are at greater risk of neurodegenerative disease and dementia. Symptoms of chronic neuropsychiatric diseases are also exacerbated during ageing. However, the ageing process does not affect people uniformly; nor, in fact, does the ageing process appear to be uniform even within an individual. Here, we outline recent neuroimaging research into brain ageing and the use of other bodily ageing biomarkers, including telomere length, the epigenetic clock, and grip strength. Some of these techniques, using statistical approaches, have the ability to predict chronological age in healthy people. Moreover, they are now being applied to neurological and psychiatric disease groups to provide insights into how these diseases interact with the ageing process and to deliver individualised predictions about future brain and body health. We discuss the importance of integrating different types of biological measurements, from both the brain and the rest of the body, to build more comprehensive models of the biological ageing process. Finally, we propose seven steps for the field of brain-ageing research to take in coming years. This will help us reach the long-term goal of developing clinically applicable statistical models of biological processes to measure, track and predict brain and body health in ageing and disease.

Respiratory cooling and thermoregulatory coupling in reptiles.

PubMed

Tattersall, Glenn J; Cadena, Viviana; Skinner, Matthew C

2006-11-01

Comparative physiological research on reptiles has focused primarily on the understanding of mechanisms of the control of breathing as they relate to respiratory gases or temperature itself. Comparatively less research has been done on the possible link between breathing and thermoregulation. Reptiles possess remarkable thermoregulatory capabilities, making use of behavioural and physiological mechanisms to regulate body temperature. The presence of thermal panting and gaping in numerous reptiles, coupled with the existence of head-body temperature differences, suggests that head temperature may be the primary regulated variable rather than body temperature. This review examines the preponderance of head and body temperature differences in reptiles, the occurrence of breathing patterns that possess putative thermoregulatory roles, and the propensity for head and brain temperature to be controlled by reptiles, particularly at higher temperatures. The available evidence suggests that these thermoregulatory breathing patterns are indeed present, though primarily in arid-dwelling reptiles. More importantly, however, it appears that the respiratory mechanisms that have the capacity to cool evolved initially in reptiles, perhaps as regulatory mechanisms for preventing overheating of the brain. Examining the control of these breathing patterns and their efficacy at regulating head or brain temperature may shed light on the evolution of thermoregulatory mechanisms in other vertebrates, namely the endothermic mammals and birds.

Cortical oscillations and sensory predictions.

PubMed

Arnal, Luc H; Giraud, Anne-Lise

2012-07-01

Many theories of perception are anchored in the central notion that the brain continuously updates an internal model of the world to infer the probable causes of sensory events. In this framework, the brain needs not only to predict the causes of sensory input, but also when they are most likely to happen. In this article, we review the neurophysiological bases of sensory predictions of "what' (predictive coding) and 'when' (predictive timing), with an emphasis on low-level oscillatory mechanisms. We argue that neural rhythms offer distinct and adapted computational solutions to predicting 'what' is going to happen in the sensory environment and 'when'. Copyright © 2012 Elsevier Ltd. All rights reserved.

Childhood Brain Insult: Can Age at Insult Help Us Predict Outcome?

ERIC Educational Resources Information Center

Anderson, Vicki; Spencer-Smith, Megan; Leventer, Rick; Coleman, Lee; Anderson, Peter; Williams, Jackie; Greenham, Mardee; Jacobs, Rani

2009-01-01

Until recently, the impact of early brain insult (EBI) has been considered to be less significant than for later brain injuries, consistent with the notion that the young brain is more flexible and able to reorganize in the context of brain insult. This study aimed to evaluate this notion by comparing cognitive and behavioural outcomes for…

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach

PubMed Central

Xu, Nan; Spreng, R. Nathan; Doerschuk, Peter C.

2017-01-01

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the “common driver” problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain. PMID:28559793

Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints.

PubMed

Yuan, Yaxia; Zheng, Fang; Zhan, Chang-Guo

2018-03-21

Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.

Multivariate Brain Prediction of Heart Rate and Skin Conductance Responses to Social Threat.

PubMed

Eisenbarth, Hedwig; Chang, Luke J; Wager, Tor D

2016-11-23

Psychosocial stressors induce autonomic nervous system (ANS) responses in multiple body systems that are linked to health risks. Much work has focused on the common effects of stress, but ANS responses in different body systems are dissociable and may result from distinct patterns of cortical-subcortical interactions. Here, we used machine learning to develop multivariate patterns of fMRI activity predictive of heart rate (HR) and skin conductance level (SCL) responses during social threat in humans (N = 18). Overall, brain patterns predicted both HR and SCL in cross-validated analyses successfully (r HR = 0.54, r SCL = 0.58, both p < 0.0001). These patterns partly reflected central stress mechanisms common to both responses because each pattern predicted the other signal to some degree (r HR→SCL = 0.21 and r SCL→HR = 0.22, both p < 0.01), but they were largely physiological response specific. Both patterns included positive predictive weights in dorsal anterior cingulate and cerebellum and negative weights in ventromedial PFC and local pattern similarity analyses within these regions suggested that they encode common central stress mechanisms. However, the predictive maps and searchlight analysis suggested that the patterns predictive of HR and SCL were substantially different across most of the brain, including significant differences in ventromedial PFC, insula, lateral PFC, pre-SMA, and dmPFC. Overall, the results indicate that specific patterns of cerebral activity track threat-induced autonomic responses in specific body systems. Physiological measures of threat are not interchangeable, but rather reflect specific interactions among brain systems. We show that threat-induced increases in heart rate and skin conductance share some common representations in the brain, located mainly in the vmPFC, temporal and parahippocampal cortices, thalamus, and brainstem. However, despite these similarities, the brain patterns that predict these two autonomic responses are largely distinct. This evidence for largely output-measure-specific regulation of autonomic responses argues against a common system hypothesis and provides evidence that different autonomic measures reflect distinct, measurable patterns of cortical-subcortical interactions. Copyright © 2016 the authors 0270-6474/16/3611987-12$15.00/0.

Simultaneous measurement of brain tissue oxygen partial pressure, temperature, and global oxygen consumption during hibernation, arousal, and euthermy in non-sedated and non-anesthetized Arctic ground squirrels.

PubMed

Ma, Yilong; Wu, Shufen

2008-09-30

This study reports an online temperature correction method for determining tissue oxygen partial pressure P(tO2) in the striatum and a novel simultaneous measurement of brain P(tO2) and temperature (T(brain)) in conjunction with global oxygen consumption V(O2) in non-sedated and non-anesthetized freely moving Arctic ground squirrels (AGS, Spermophilus parryii). This method fills an important research gap-the lack of a suitable method for physiologic studies of tissue P(O2) in hibernating or other cool-blooded species. P(tO2) in AGS brain during euthermy (21.22+/-2.06 mmHg) is significantly higher (P=0.016) than during hibernation (13.21+/-0.46 mmHg) suggests brain oxygenation in the striatum is normoxic during euthermy and hypoxic during hibernation. These results in P(tO2) are different from blood oxygen partial pressure P(aO2) in AGS, which are significantly lower during euthermy than during hibernation and are actually hypoxic during euthermy and normoxic during hibernation in our previous study. This intriguing difference between the P(O2) of brain tissue and blood during these two physiological states suggests that regional mechanisms in the brain play a role in maintaining tissue oxygenation and protect against hypoxia during hibernation.

Critical Role of Peripheral Vasoconstriction in Fatal Brain Hyperthermia Induced by MDMA (Ecstasy) under Conditions That Mimic Human Drug Use

PubMed Central

Kim, Albert H.; Wakabayashi, Ken T.; Baumann, Michael H.; Shaham, Yavin

2014-01-01

MDMA (Ecstasy) is an illicit drug used by young adults at hot, crowed “rave” parties, yet the data on potential health hazards of its abuse remain controversial. Here, we examined the effect of MDMA on temperature homeostasis in male rats under standard laboratory conditions and under conditions that simulate drug use in humans. We chronically implanted thermocouple microsensors in the nucleus accumbens (a brain reward area), temporal muscle, and facial skin to measure temperature continuously from freely moving rats. While focusing on brain hyperthermia, temperature monitoring from the two peripheral locations allowed us to evaluate the physiological mechanisms (i.e., intracerebral heat production and heat loss via skin surfaces) that underlie MDMA-induced brain temperature responses. Our data confirm previous reports on high individual variability and relatively weak brain hyperthermic effects of MDMA under standard control conditions (quiet rest, 22−23°C), but demonstrate dramatic enhancements of drug-induced brain hyperthermia during social interaction (exposure to male conspecific) and in warm environments (29°C). Importantly, we identified peripheral vasoconstriction as a critical mechanism underlying the activity- and state-dependent potentiation of MDMA-induced brain hyperthermia. Through this mechanism, which prevents proper heat dissipation to the external environment, MDMA at a moderate nontoxic dose (9 mg/kg or ∼1/5 of LD50 in rats) can cause fatal hyperthermia under environmental conditions commonly encountered by humans. Our results demonstrate that doses of MDMA that are nontoxic under cool, quiet conditions can become highly dangerous under conditions that mimic recreational use of MDMA at rave parties or other hot, crowded venues. PMID:24899699

Intellectual enrichment lessens the effect of brain atrophy on learning and memory in multiple sclerosis

PubMed Central

Sumowski, James F.; Wylie, Glenn R.; Chiaravalloti, Nancy; DeLuca, John

2010-01-01

Objective: Learning and memory impairments are prevalent among persons with multiple sclerosis (MS); however, such deficits are only weakly associated with MS disease severity (brain atrophy). The cognitive reserve hypothesis states that greater lifetime intellectual enrichment lessens the negative impact of brain disease on cognition, thereby helping to explain the incomplete relationship between brain disease and cognitive status in neurologic populations. The literature on cognitive reserve has focused mainly on Alzheimer disease. The current research examines whether greater intellectual enrichment lessens the negative effect of brain atrophy on learning and memory in patients with MS. Methods: Forty-four persons with MS completed neuropsychological measures of verbal learning and memory, and a vocabulary-based estimate of lifetime intellectual enrichment. Brain atrophy was estimated with third ventricle width measured from 3-T magnetization-prepared rapid gradient echo MRIs. Hierarchical regression was used to predict learning and memory with brain atrophy, intellectual enrichment, and the interaction between brain atrophy and intellectual enrichment. Results: Brain atrophy predicted worse learning and memory, and intellectual enrichment predicted better learning; however, these effects were moderated by interactions between brain atrophy and intellectual enrichment. Specifically, higher intellectual enrichment lessened the negative impact of brain atrophy on both learning and memory. Conclusion: These findings help to explain the incomplete relationship between multiple sclerosis disease severity and cognition, as the effect of disease on cognition is attenuated among patients with higher intellectual enrichment. As such, intellectual enrichment is supported as a protective factor against disease-related cognitive impairment in persons with multiple sclerosis. GLOSSARY AD = Alzheimer disease; ANOVA = analysis of variance; MPRAGE = magnetization-prepared rapid gradient echo; MS = multiple sclerosis; SRT = Selective Reminding Test; TVW = third ventricle width; WASI = Wechsler Abbreviated Scale of Intelligence. PMID:20548040

Development of Automatic Controller of Brain Temperature Based on the Conditions of Clinical Use

NASA Astrophysics Data System (ADS)

Utsuki, Tomohiko; Wakamatsu, Hidetoshi

A new automatic controller of brain temperature was developed based on the inevitable conditions of its clinical use from the viewpoint of various kinds of feasibility, in particular, electric power consumption of less than 1,500W in ICU. The adaptive algorithm was employed to cope with individual time-varying characteristic change of patients. The controller under water-surface cooling hypothermia requires much power for the frequent regulation of the water temperature of cooling blankets. Thus, in this study, the power consumption of the controller was checked by several kinds of examinations involving the control simulation of brain temperature using a mannequin with thermal characteristics similar to that of adult patients. The required accuracy of therapeutic brain hypothermia, i.e. control deviation within ±0.1C was experimentally confirmed using “root mean square of the control error”, despite the present controller consumes less energy comparing with the one in the case of our conventional controller, where it can still keeps remaining power margin more than 300W even in the full operation. Thereby, the clinically required water temperature was also confirmed within the limit of power supply, thus its practical application is highly expected with less physical burden of medical staff inclusive of more usability and more medical cost performance.

Application of brain cholinesterase reactivation to differentiate between organophosphorus and carbamate pesticide exposure in wild birds

USGS Publications Warehouse

Smith, M.R.; Thomas, N.J.; Hulse, C.

1995-01-01

Brain cholinesterase activity was measured to evaluate pesticide exposure in wild birds. Thermal reactivation of brain cholinesterase was used to differentiate between carbamate and organophosphorus pesticide exposure. Brain cholinesterase activity was compared with gas chromatography and mass spectrometry of stomach contents. Pesticides were identified and confirmed in 86 of 102 incidents of mortality from 29 states within the USA from 1986 through 1991. Thermal reactivation of cholinesterase activity was used to correctly predict carbamates in 22 incidents and organophosphates in 59 incidents. Agreement (P < 0.001) between predictions based on cholinesterase activities and GC/MS results was significant.

Longitudinal connectome-based predictive modeling for REM sleep behavior disorder from structural brain connectivity

NASA Astrophysics Data System (ADS)

Giancardo, Luca; Ellmore, Timothy M.; Suescun, Jessika; Ocasio, Laura; Kamali, Arash; Riascos-Castaneda, Roy; Schiess, Mya C.

2018-02-01

Methods to identify neuroplasticity patterns in human brains are of the utmost importance in understanding and potentially treating neurodegenerative diseases. Parkinson disease (PD) research will greatly benefit and advance from the discovery of biomarkers to quantify brain changes in the early stages of the disease, a prodromal period when subjects show no obvious clinical symptoms. Diffusion tensor imaging (DTI) allows for an in-vivo estimation of the structural connectome inside the brain and may serve to quantify the degenerative process before the appearance of clinical symptoms. In this work, we introduce a novel strategy to compute longitudinal structural connectomes in the context of a whole-brain data-driven pipeline. In these initial tests, we show that our predictive models are able to distinguish controls from asymptomatic subjects at high risk of developing PD (REM sleep behavior disorder, RBD) with an area under the receiving operating characteristic curve of 0.90 (p<0.001) and a longitudinal dataset of 46 subjects part of the Parkinson's Progression Markers Initiative. By analyzing the brain connections most relevant for the predictive ability of the best performing model, we find connections that are biologically relevant to the disease.

Structural brain MRI trait polygenic score prediction of cognitive abilities

PubMed Central

Luciano, Michelle; Marioni, Riccardo E; Hernández, Maria Valdés; Maniega, Susana Munoz; Hamilton, Iona F; Royle, Natalie A.; Scotland, Generation; Chauhan, Ganesh; Bis, Joshua C.; Debette, Stephanie; DeCarli, Charles; Fornage, Myriam; Schmidt, Reinhold; Ikram, M. Arfan; Launer, Lenore J.; Seshadri, Sudha; Bastin, Mark E.; Porteous, David J.; Wardlaw, Joanna; Deary, Ian J

2016-01-01

Structural brain magnetic resonance imaging (MRI) traits share part of their genetic variance with cognitive traits. Here, we use genetic association results from large meta-analytic studies of genome-wide association for brain infarcts, white matter hyperintensities, intracranial, hippocampal and total brain volumes to estimate polygenic scores for these traits in three Scottish samples: Generation Scotland: Scottish Family Health Study (GS:SFHS), and the Lothian Birth Cohorts of 1936 (LBC1936) and 1921 (LBC1921). These five brain MRI trait polygenic scores were then used to 1) predict corresponding MRI traits in the LBC1936 (numbers ranged 573 to 630 across traits) and 2) predict cognitive traits in all three cohorts (in 8,115 to 8,250 persons). In the LBC1936, all MRI phenotypic traits were correlated with at least one cognitive measure; and polygenic prediction of MRI traits was observed for intracranial volume. Meta-analysis of the correlations between MRI polygenic scores and cognitive traits revealed a significant negative correlation (maximal r=0.08) between the hippocampal volume polygenic score and measures of global cognitive ability collected in childhood and in old age in the Lothian Birth Cohorts. The lack of association to a related general cognitive measure when including the GS:SFHS points to either type 1 error or the importance of using prediction samples that closely match the demographics of the genome-wide association samples from which prediction is based. Ideally, these analyses should be repeated in larger samples with data on both MRI and cognition, and using MRI GWA results from even larger meta-analysis studies. PMID:26427786

A Natural Language Processing-based Model to Automate MRI Brain Protocol Selection and Prioritization.

PubMed

Brown, Andrew D; Marotta, Thomas R

2017-02-01

Incorrect imaging protocol selection can contribute to increased healthcare cost and waste. To help healthcare providers improve the quality and safety of medical imaging services, we developed and evaluated three natural language processing (NLP) models to determine whether NLP techniques could be employed to aid in clinical decision support for protocoling and prioritization of magnetic resonance imaging (MRI) brain examinations. To test the feasibility of using an NLP model to support clinical decision making for MRI brain examinations, we designed three different medical imaging prediction tasks, each with a unique outcome: selecting an examination protocol, evaluating the need for contrast administration, and determining priority. We created three models for each prediction task, each using a different classification algorithm-random forest, support vector machine, or k-nearest neighbor-to predict outcomes based on the narrative clinical indications and demographic data associated with 13,982 MRI brain examinations performed from January 1, 2013 to June 30, 2015. Test datasets were used to calculate the accuracy, sensitivity and specificity, predictive values, and the area under the curve. Our optimal results show an accuracy of 82.9%, 83.0%, and 88.2% for the protocol selection, contrast administration, and prioritization tasks, respectively, demonstrating that predictive algorithms can be used to aid in clinical decision support for examination protocoling. NLP models developed from the narrative clinical information provided by referring clinicians and demographic data are feasible methods to predict the protocol and priority of MRI brain examinations. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

First steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage

PubMed Central

Clark, Ian A.; Niehaus, Katherine E.; Duff, Eugene P.; Di Simplicio, Martina C.; Clifford, Gari D.; Smith, Stephen M.; Mackay, Clare E.; Woolrich, Mark W.; Holmes, Emily A.

2014-01-01

After psychological trauma, why do some only some parts of the traumatic event return as intrusive memories while others do not? Intrusive memories are key to cognitive behavioural treatment for post-traumatic stress disorder, and an aetiological understanding is warranted. We present here analyses using multivariate pattern analysis (MVPA) and a machine learning classifier to investigate whether peri-traumatic brain activation was able to predict later intrusive memories (i.e. before they had happened). To provide a methodological basis for understanding the context of the current results, we first show how functional magnetic resonance imaging (fMRI) during an experimental analogue of trauma (a trauma film) via a prospective event-related design was able to capture an individual's later intrusive memories. Results showed widespread increases in brain activation at encoding when viewing a scene in the scanner that would later return as an intrusive memory in the real world. These fMRI results were replicated in a second study. While traditional mass univariate regression analysis highlighted an association between brain processing and symptomatology, this is not the same as prediction. Using MVPA and a machine learning classifier, it was possible to predict later intrusive memories across participants with 68% accuracy, and within a participant with 97% accuracy; i.e. the classifier could identify out of multiple scenes those that would later return as an intrusive memory. We also report here brain networks key in intrusive memory prediction. MVPA opens the possibility of decoding brain activity to reconstruct idiosyncratic cognitive events with relevance to understanding and predicting mental health symptoms. PMID:25151915

Remote Associates Test and Alpha Brain Waves

ERIC Educational Resources Information Center

Haarmann, Henk J.; George, Timothy; Smaliy, Alexei; Dien, Joseph

2012-01-01

Previous studies found that performance on the remote associates test (RAT) improves after a period of incubation and that increased alpha brain waves over the right posterior brain predict the emergence of RAT insight solutions. We report an experiment that tested whether increased alpha brain waves during incubation improve RAT performance.…

Ultrasound effects on brain-targeting mannosylated liposomes: in vitro and blood-brain barrier transport investigations.

PubMed

Zidan, Ahmed S; Aldawsari, Hibah

2015-01-01

Delivering drugs to intracerebral regions can be accomplished by improving the capacity of transport through blood-brain barrier. Using sertraline as model drug for brain targeting, the current study aimed at modifying its liposomal vesicles with mannopyranoside. Box-Behnken design was employed to statistically optimize the ultrasound parameters, namely ultrasound amplitude, time, and temperature, for maximum mannosylation capacity, sertraline entrapment, and surface charge while minimizing vesicular size. Moreover, in vitro blood-brain barrier transport model was established to assess the transendothelial capacity of the optimized mannosylated vesicles. Results showed a dependence of vesicular size, mannosylation capacity, and sertraline entrapment on cavitation and bubble implosion events that were related to ultrasound power amplitude, temperature. However, short ultrasound duration was required to achieve >90% mannosylation with nanosized vesicles (<200 nm) of narrow size distribution. Optimized ultrasound parameters of 65°C, 27%, and 59 seconds for ultrasound temperature, amplitude, and time were elucidated to produce 81.1%, 46.6 nm, and 77.6% sertraline entrapment, vesicular size, and mannosylation capacity, respectively. Moreover, the transendothelial ability was significantly increased by 2.5-fold by mannosylation through binding with glucose transporters. Hence, mannosylated liposomes processed by ultrasound could be a promising approach for manufacturing and scale-up of brain-targeting liposomes.

Imagine All the People: How the Brain Creates and Uses Personality Models to Predict Behavior

PubMed Central

Hassabis, Demis; Spreng, R. Nathan; Rusu, Andrei A.; Robbins, Clifford A.; Mar, Raymond A.; Schacter, Daniel L.

2014-01-01

The behaviors of other people are often central to envisioning the future. The ability to accurately predict the thoughts and actions of others is essential for successful social interactions, with far-reaching consequences. Despite its importance, little is known about how the brain represents people in order to predict behavior. In this functional magnetic resonance imaging study, participants learned the unique personality of 4 protagonists and imagined how each would behave in different scenarios. The protagonists' personalities were composed of 2 traits: Agreeableness and Extraversion. Which protagonist was being imagined was accurately inferred based solely on activity patterns in the medial prefrontal cortex using multivariate pattern classification, providing novel evidence that brain activity can reveal whom someone is thinking about. Lateral temporal and posterior cingulate cortex discriminated between different degrees of agreeableness and extraversion, respectively. Functional connectivity analysis confirmed that regions associated with trait-processing and individual identities were functionally coupled. Activity during the imagination task, and revealed by functional connectivity, was consistent with the default network. Our results suggest that distinct regions code for personality traits, and that the brain combines these traits to represent individuals. The brain then uses this “personality model” to predict the behavior of others in novel situations. PMID:23463340

Effects of variation in cerebral haemodynamics during aneurysm surgery on brain tissue oxygen and metabolism.

PubMed

Kett-White, R; Hutchinson, P J; Czosnyka, M; al-Rawi, P; Gupta, A; Pickard, J D; Kirkpatrick, P J

2002-01-01

This study explores the sensitivities of multiparameter tissue gas sensors and microdialysis to variations in blood pressure, CSF drainage and to well-defined periods of ischaemia accompanying aneurysm surgery, and their predictive value for infarction. A Neurotrend sensor [brain tissue partial pressure of oxygen (PBO2), carbon dioxide (PBCO2), brain pH (pHB) and temperature] and microdialysis catheter were inserted into the appropriate vascular territory prior to craniotomy. Baseline data showed a clear correlation between PBO2 and mean arterial pressure (MAP) below a threshold of 80 mmHg. PBO2 improved with CSF drainage in 20 out of 28 (Wilcoxon: P < 0.05) cases where data was available. In 26 patients the effects of temporary vascular clipping (TC) (mean duration 16 minutes) were assessed. 2 patients subsequently declared infarction in the region of the probes. PBO2 fell from a mean 3.2 (95% CI 2.4-4.1) kPa to a minimum of 1.5 (95% CI 1.0-2.0) kPa in the non-infarct group. There was a lower baseline PBO2 (mean 0.8 kPa) in the patients who infarcted. PBCO2 mirrored PBO2 changes, whereas pHB did not change significantly in either group. Microdialysis changes associated with decreased PBO2 included a delayed increase in lactate, a raised lactate/pyruvate ratio and more rarely an increased glutamate. These changes were seen in 11 patients but were not predictive of infarction. Hypotension during aneurysm surgery is associated with a low PBO2. Multiparameter sensors can be sensitive to acute ischaemia. Microdialysis shows potential in the detection of metabolic changes during tissue hypoxia.

c-Fos expression predicts long-term social memory retrieval in mice.

PubMed

Lüscher Dias, Thomaz; Fernandes Golino, Hudson; Moura de Oliveira, Vinícius Elias; Dutra Moraes, Márcio Flávio; Schenatto Pereira, Grace

2016-10-15

The way the rodent brain generally processes socially relevant information is rather well understood. How social information is stored into long-term social memory, however, is still under debate. Here, brain c-Fos expression was measured after adult mice were exposed to familiar or novel juveniles and expression was compared in several memory and socially relevant brain areas. Machine Learning algorithm Random Forest was then used to predict the social interaction category of adult mice based on c-Fos expression in these areas. Interaction with a familiar co-specific altered brain activation in the olfactory bulb, amygdala, hippocampus, lateral septum and medial prefrontal cortex. Remarkably, Random Forest was able to predict interaction with a familiar juvenile with 100% accuracy. Activity in the olfactory bulb, amygdala, hippocampus and the medial prefrontal cortex were crucial to this prediction. From our results, we suggest long-term social memory depends on initial social olfactory processing in the medial amygdala and its output connections synergistically with non-social contextual integration by the hippocampus and medial prefrontal cortex top-down modulation of primary olfactory structures. Copyright © 2016 Elsevier B.V. All rights reserved.

Accelerated Changes in Cortical Thickness Measurements with Age in Military Service Members with Traumatic Brain Injury.

PubMed

Savjani, Ricky R; Taylor, Brian A; Acion, Laura; Wilde, Elisabeth A; Jorge, Ricardo E

2017-11-15

Finding objective and quantifiable imaging markers of mild traumatic brain injury (TBI) has proven challenging, especially in the military population. Changes in cortical thickness after injury have been reported in animals and in humans, but it is unclear how these alterations manifest in the chronic phase, and it is difficult to characterize accurately with imaging. We used cortical thickness measures derived from Advanced Normalization Tools (ANTs) to predict a continuous demographic variable: age. We trained four different regression models (linear regression, support vector regression, Gaussian process regression, and random forests) to predict age from healthy control brains from publicly available datasets (n = 762). We then used these models to predict brain age in military Service Members with TBI (n = 92) and military Service Members without TBI (n = 34). Our results show that all four models overpredicted age in Service Members with TBI, and the predicted age difference was significantly greater compared with military controls. These data extend previous civilian findings and show that cortical thickness measures may reveal an association of accelerated changes over time with military TBI.

Individual differences in intrinsic brain connectivity predict decision strategy.

PubMed

Barnes, Kelly Anne; Anderson, Kevin M; Plitt, Mark; Martin, Alex

2014-10-15

When humans are provided with ample time to make a decision, individual differences in strategy emerge. Using an adaptation of a well-studied decision making paradigm, motion direction discrimination, we probed the neural basis of individual differences in strategy. We tested whether strategies emerged from moment-to-moment reconfiguration of functional brain networks involved in decision making with task-evoked functional MRI (fMRI) and whether intrinsic properties of functional brain networks, measured at rest with functional connectivity MRI (fcMRI), were associated with strategy use. We found that human participants reliably selected one of two strategies across 2 days of task performance, either continuously accumulating evidence or waiting for task difficulty to decrease. Individual differences in decision strategy were predicted both by the degree of task-evoked activation of decision-related brain regions and by the strength of pretask correlated spontaneous brain activity. These results suggest that spontaneous brain activity constrains strategy selection on perceptual decisions.

Two Dimensional Finite Element Analysis for the Effect of a Pressure Wave in the Human Brain

NASA Astrophysics Data System (ADS)

Ponce L., Ernesto; Ponce S., Daniel

2008-11-01

Brain injuries in people of all ages is a serious, world-wide health problem, with consequences as varied as attention or memory deficits, difficulties in problem-solving, aggressive social behavior, and neuro degenerative diseases such as Alzheimer's and Parkinson's. Brain injuries can be the result of a direct impact, but also pressure waves and direct impulses. The aim of this work is to develop a predictive method to calculate the stress generated in the human brain by pressure waves such as high power sounds. The finite element method is used, combined with elastic wave theory. The predictions of the generated stress levels are compared with the resistance of the arterioles that pervade the brain. The problem was focused to the Chilean mining where there are some accidents happen by detonations and high sound level. There are not formal medical investigation, however these pressure waves could produce human brain damage.

T2 Relaxometry MRI Predicts Cerebral Palsy in Preterm Infants.

PubMed

Chen, L-W; Wang, S-T; Huang, C-C; Tu, Y-F; Tsai, Y-S

2018-01-18

T2-relaxometry brain MR imaging enables objective measurement of brain maturation based on the water-macromolecule ratio in white matter, but the outcome correlation is not established in preterm infants. Our study aimed to predict neurodevelopment with T2-relaxation values of brain MR imaging among preterm infants. From January 1, 2012, to May 31, 2015, preterm infants who underwent both T2-relaxometry brain MR imaging and neurodevelopmental follow-up were retrospectively reviewed. T2-relaxation values were measured over the periventricular white matter, including sections through the frontal horns, midbody of the lateral ventricles, and centrum semiovale. Periventricular T2 relaxometry in relation to corrected age was analyzed with restricted cubic spline regression. Prediction of cerebral palsy was examined with the receiver operating characteristic curve. Thirty-eight preterm infants were enrolled for analysis. Twenty patients (52.6%) had neurodevelopmental abnormalities, including 8 (21%) with developmental delay without cerebral palsy and 12 (31.6%) with cerebral palsy. The periventricular T2-relaxation values in relation to age were curvilinear in preterm infants with normal development, linear in those with developmental delay without cerebral palsy, and flat in those with cerebral palsy. When MR imaging was performed at >1 month corrected age, cerebral palsy could be predicted with T2 relaxometry of the periventricular white matter on sections through the midbody of the lateral ventricles (area under the receiver operating characteristic curve = 0.738; cutoff value of >217.4 with 63.6% sensitivity and 100.0% specificity). T2-relaxometry brain MR imaging could provide prognostic prediction of neurodevelopmental outcomes in premature infants. Age-dependent and area-selective interpretation in preterm brains should be emphasized. © 2018 by American Journal of Neuroradiology.

Brain Regional Blood Flow and Working Memory Performance Predict Change in Blood Pressure Over 2 Years.

PubMed

Jennings, J Richard; Heim, Alicia F; Sheu, Lei K; Muldoon, Matthew F; Ryan, Christopher; Gach, H Michael; Schirda, Claudiu; Gianaros, Peter J

2017-12-01

Hypertension is a presumptive risk factor for premature cognitive decline. However, lowering blood pressure (BP) does not uniformly reverse cognitive decline, suggesting that high BP per se may not cause cognitive decline. We hypothesized that essential hypertension has initial effects on the brain that, over time, manifest as cognitive dysfunction in conjunction with both brain vascular abnormalities and systemic BP elevation. Accordingly, we tested whether neuropsychological function and brain blood flow responses to cognitive challenges among prehypertensive individuals would predict subsequent progression of BP. Midlife adults (n=154; mean age, 49; 45% men) with prehypertensive BP underwent neuropsychological testing and assessment of regional cerebral blood flow (rCBF) response to cognitive challenges. Neuropsychological performance measures were derived for verbal and logical memory (memory), executive function, working memory, mental efficiency, and attention. A pseudo-continuous arterial spin labeling magnetic resonance imaging sequence compared rCBF responses with control and active phases of cognitive challenges. Brain areas previously associated with BP were grouped into composites for frontoparietal, frontostriatal, and insular-subcortical rCBF areas. Multiple regression models tested whether BP after 2 years was predicted by initial BP, initial neuropsychological scores, and initial rCBF responses to cognitive challenge. The neuropsychological composite of working memory (standardized beta, -0.276; se=0.116; P =0.02) and the frontostriatal rCBF response to cognitive challenge (standardized beta, 0.234; se=0.108; P =0.03) significantly predicted follow-up BP. Initial BP failed to significantly predict subsequent cognitive performance or rCBF. Changes in brain function may precede or co-occur with progression of BP toward hypertensive levels in midlife. © 2017 American Heart Association, Inc.

Predicting human age using regional morphometry and inter-regional morphological similarity

NASA Astrophysics Data System (ADS)

Wang, Xun-Heng; Li, Lihua

2016-03-01

The goal of this study is predicting human age using neuro-metrics derived from structural MRI, as well as investigating the relationships between age and predictive neuro-metrics. To this end, a cohort of healthy subjects were recruited from 1000 Functional Connectomes Project. The ages of the participations were ranging from 7 to 83 (36.17+/-20.46). The structural MRI for each subject was preprocessed using FreeSurfer, resulting in regional cortical thickness, mean curvature, regional volume and regional surface area for 148 anatomical parcellations. The individual age was predicted from the combination of regional and inter-regional neuro-metrics. The prediction accuracy is r = 0.835, p < 0.00001, evaluated by Pearson correlation coefficient between predicted ages and actual ages. Moreover, the LASSO linear regression also found certain predictive features, most of which were inter-regional features. The turning-point of the developmental trajectories in human brain was around 40 years old based on regional cortical thickness. In conclusion, structural MRI could be potential biomarkers for the aging in human brain. The human age could be successfully predicted from the combination of regional morphometry and inter-regional morphological similarity. The inter-regional measures could be beneficial to investigating human brain connectome.

Inferencing Processes After Right Hemisphere Brain Damage: Effects of Contextual Bias

PubMed Central

Blake, Margaret Lehman

2009-01-01

Purpose Comprehension deficits associated with right hemisphere brain damage (RHD) have been attributed to an inability to use context, but there is little direct evidence to support the claim. This study evaluated the effect of varying contextual bias on predictive inferencing by adults with RHD. Method Fourteen adults with no brain damage (NBD) and 14 with RHD read stories constructed with either high predictability or low predictability of a specific outcome. Reading time for a sentence that disconfirmed the target outcome was measured and compared with a control story context. Results Adults with RHD evidenced activation of predictive inferences only for highly predictive conditions, whereas NBD adults generated inferences in both high- and low-predictability stories. Adults with RHD were more likely than those with NBD to require additional time to integrate inferences in high-predictability conditions. The latter finding was related to working memory for the RHD group. Results are interpreted in light of previous findings obtained using the same stimuli. Conclusions RHD does not abolish the ability to use context. Evidence of predictive inferencing is influenced by task and strength of inference activation. Treatment considerations and cautions regarding interpreting results from one methodology are discussed. PMID:19252126

Predictions interact with missing sensory evidence in semantic processing areas.

PubMed

Scharinger, Mathias; Bendixen, Alexandra; Herrmann, Björn; Henry, Molly J; Mildner, Toralf; Obleser, Jonas

2016-02-01

Human brain function draws on predictive mechanisms that exploit higher-level context during lower-level perception. These mechanisms are particularly relevant for situations in which sensory information is compromised or incomplete, as for example in natural speech where speech segments may be omitted due to sluggish articulation. Here, we investigate which brain areas support the processing of incomplete words that were predictable from semantic context, compared with incomplete words that were unpredictable. During functional magnetic resonance imaging (fMRI), participants heard sentences that orthogonally varied in predictability (semantically predictable vs. unpredictable) and completeness (complete vs. incomplete, i.e. missing their final consonant cluster). The effects of predictability and completeness interacted in heteromodal semantic processing areas, including left angular gyrus and left precuneus, where activity did not differ between complete and incomplete words when they were predictable. The same regions showed stronger activity for incomplete than for complete words when they were unpredictable. The interaction pattern suggests that for highly predictable words, the speech signal does not need to be complete for neural processing in semantic processing areas. Hum Brain Mapp 37:704-716, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

MDMA, Methylone, and MDPV: Drug-Induced Brain Hyperthermia and Its Modulation by Activity State and Environment.

PubMed

Kiyatkin, Eugene A; Ren, Suelynn E

2017-01-01

Psychomotor stimulants are frequently used by humans to intensify the subjective experience of different types of social interactions. Since psychomotor stimulants enhance metabolism and increase body temperatures, their use under conditions of physiological activation and in warm humid environments could result in pathological hyperthermia, a life-threatening symptom of acute drug intoxication. Here, we will describe the brain hyperthermic effects of MDMA, MDPV, and methylone, three structurally related recreational drugs commonly used by young adults during raves and other forms of social gatherings. After a short introduction on brain temperature and basic mechanisms underlying its physiological fluctuations, we will consider how MDMA, MDPV, and methylone affect brain and body temperatures in awake freely moving rats. Here, we will discuss the role of drug-induced heat production in the brain due to metabolic brain activation and diminished heat dissipation due to peripheral vasoconstriction as two primary contributors to the hyperthermic effects of these drugs. Then, we will consider how the hyperthermic effects of these drugs are modulated under conditions that model human drug use (social interaction and warm ambient temperature). Since social interaction results in brain and body heat production, coupled with skin vasoconstriction that impairs heat loss to the external environment, these physiological changes interact with drug-induced changes in heat production and loss, resulting in distinct changes in the hyperthermic effects of each tested drug. Finally, we present our recent data, in which we compared the efficacy of different pharmacological strategies for reversing MDMA-induced hyperthermia in both the brain and body. Specifically, we demonstrate increased efficacy of the centrally acting atypical neuroleptic compound clozapine over the peripherally acting vasodilator drug, carvedilol. These data could be important for understanding the potential dangers of MDMA in humans and the development of pharmacological tools to alleviate drug-induced hyperthermia - potentially saving the lives of highly intoxicated individuals.

FDTD analysis of a noninvasive hyperthermia system for brain tumors

PubMed Central

2012-01-01

Background Hyperthermia is considered one of the new therapeutic modalities for cancer treatment and is based on the difference in thermal sensitivity between healthy tissues and tumors. During hyperthermia treatment, the temperature of the tumor is raised to 40–45°C for a definite period resulting in the destruction of cancer cells. This paper investigates design, modeling and simulation of a new non-invasive hyperthermia applicator system capable of effectively heating deep seated as well as superficial brain tumors using inexpensive, simple, and easy to fabricate components without harming surrounding healthy brain tissues. Methods The proposed hyperthermia applicator system is composed of an air filled partial half ellipsoidal chamber, a patch antenna, and a head model with an embedded tumor at an arbitrary location. The irradiating antenna is placed at one of the foci of the hyperthermia chamber while the center of the brain tumor is placed at the other focus. The finite difference time domain (FDTD) method is used to compute both the SAR patterns and the temperature distribution in three different head models due to two different patch antennas at a frequency of 915 MHz. Results The obtained results suggest that by using the proposed noninvasive hyperthermia system it is feasible to achieve sufficient and focused energy deposition and temperature rise to therapeutic values in deep seated as well as superficial brain tumors without harming surrounding healthy tissue. Conclusions The proposed noninvasive hyperthermia system proved suitable for raising the temperature in tumors embedded in the brain to therapeutic values by carefully selecting the systems components. The operator of the system only needs to place the center of the brain tumor at a pre-specified location and excite the antenna at a single frequency of 915 MHz. Our study may provide a basis for a clinical applicator prototype capable of heating brain tumors. PMID:22891953

Decoding Spontaneous Emotional States in the Human Brain

PubMed Central

Kragel, Philip A.; Knodt, Annchen R.; Hariri, Ahmad R.; LaBar, Kevin S.

2016-01-01

Pattern classification of human brain activity provides unique insight into the neural underpinnings of diverse mental states. These multivariate tools have recently been used within the field of affective neuroscience to classify distributed patterns of brain activation evoked during emotion induction procedures. Here we assess whether neural models developed to discriminate among distinct emotion categories exhibit predictive validity in the absence of exteroceptive emotional stimulation. In two experiments, we show that spontaneous fluctuations in human resting-state brain activity can be decoded into categories of experience delineating unique emotional states that exhibit spatiotemporal coherence, covary with individual differences in mood and personality traits, and predict on-line, self-reported feelings. These findings validate objective, brain-based models of emotion and show how emotional states dynamically emerge from the activity of separable neural systems. PMID:27627738

Neural predictors of chocolate intake following chocolate exposure.

PubMed

Frankort, Astrid; Roefs, Anne; Siep, Nicolette; Roebroeck, Alard; Havermans, Remco; Jansen, Anita

2015-04-01

Previous studies have shown that one's brain response to high-calorie food cues can predict long-term weight gain or weight loss. The neural correlates that predict food intake in the short term have, however, hardly been investigated. This study examined which brain regions' activation predicts chocolate intake after participants had been either exposed to real chocolate or to control stimuli during approximately one hour, with interruptions for fMRI measurements. Further we investigated whether the variance in chocolate intake could be better explained by activated brain regions than by self-reported craving. In total, five brain regions correlated with subsequent chocolate intake. The activation of two reward regions (the right caudate and the left frontopolar cortex) correlated positively with intake in the exposure group. The activation of two regions associated with cognitive control (the left dorsolateral and left mid-dorsolateral PFC) correlated negatively with intake in the control group. When the regression analysis was conducted with the exposure and the control group together, an additional region's activation (the right anterior PFC) correlated positively with chocolate intake. In all analyses, the intake variance explained by neural correlates was above and beyond the variance explained by self-reported craving. These results are in line with neuroimaging research showing that brain responses are a better predictor of subsequent intake than self-reported craving. Therefore, our findings might provide for a missing link by associating brain activation, previously shown to predict weight change, with short-term intake. Copyright © 2014 Elsevier Ltd. All rights reserved.

Disruption to functional networks in neonates with perinatal brain injury predicts motor skills at 8 months.

PubMed

Linke, Annika C; Wild, Conor; Zubiaurre-Elorza, Leire; Herzmann, Charlotte; Duffy, Hester; Han, Victor K; Lee, David S C; Cusack, Rhodri

2018-01-01

Functional connectivity magnetic resonance imaging (fcMRI) of neonates with perinatal brain injury could improve prediction of motor impairment before symptoms manifest, and establish how early brain organization relates to subsequent development. This cohort study is the first to describe and quantitatively assess functional brain networks and their relation to later motor skills in neonates with a diverse range of perinatal brain injuries. Infants ( n  = 65, included in final analyses: n  = 53) were recruited from the neonatal intensive care unit (NICU) and were stratified based on their age at birth (premature vs. term), and on whether neuropathology was diagnosed from structural MRI. Functional brain networks and a measure of disruption to functional connectivity were obtained from 14 min of fcMRI acquired during natural sleep at term-equivalent age. Disruption to connectivity of the somatomotor and frontoparietal executive networks predicted motor impairment at 4 and 8 months. This disruption in functional connectivity was not found to be driven by differences between clinical groups, or by any of the specific measures we captured to describe the clinical course. fcMRI was predictive over and above other clinical measures available at discharge from the NICU, including structural MRI. Motor learning was affected by disruption to somatomotor networks, but also frontoparietal executive networks, which supports the functional importance of these networks in early development. Disruption to these two networks might be best addressed by distinct intervention strategies.

[CT scans in children with head/brain injury: five years after the revision of the guideline on "mild traumatic head/brain injury"].

PubMed

Hageman, G Gerard

2015-01-01

In 2010 the guideline on mild traumatic head/ brain injury for both adults and children was revised under the supervision of the Dutch Neurology Society. The revised guideline endorsed rules for decisions on whether to carry out diagnostic imaging investigations (brain CT scanning) and formulates indications for admission. Unfortunately, 5 years after its introduction, it is clear that the guideline rules result in excessive brain CT scanning, in which no more serious head injury is diagnosed. Brain injury may be present in (small) children even if symptoms are absent at first presentation. Also, clinical signs do not predict intracranial complications. This was nicely demonstrated in a study by Tilma, Bekhof and Brand of 410 children with mTBI: no clinical symptom or sign reliably predicted the risk of intracranial bleeding. They advise hospitalisation for observation instead of brain CT scanning. It may be necessary to review part of the Dutch guideline on mTBI.

Virtual reality and consciousness inference in dreaming.

PubMed

Hobson, J Allan; Hong, Charles C-H; Friston, Karl J

2014-01-01

This article explores the notion that the brain is genetically endowed with an innate virtual reality generator that - through experience-dependent plasticity - becomes a generative or predictive model of the world. This model, which is most clearly revealed in rapid eye movement (REM) sleep dreaming, may provide the theater for conscious experience. Functional neuroimaging evidence for brain activations that are time-locked to rapid eye movements (REMs) endorses the view that waking consciousness emerges from REM sleep - and dreaming lays the foundations for waking perception. In this view, the brain is equipped with a virtual model of the world that generates predictions of its sensations. This model is continually updated and entrained by sensory prediction errors in wakefulness to ensure veridical perception, but not in dreaming. In contrast, dreaming plays an essential role in maintaining and enhancing the capacity to model the world by minimizing model complexity and thereby maximizing both statistical and thermodynamic efficiency. This perspective suggests that consciousness corresponds to the embodied process of inference, realized through the generation of virtual realities (in both sleep and wakefulness). In short, our premise or hypothesis is that the waking brain engages with the world to predict the causes of sensations, while in sleep the brain's generative model is actively refined so that it generates more efficient predictions during waking. We review the evidence in support of this hypothesis - evidence that grounds consciousness in biophysical computations whose neuronal and neurochemical infrastructure has been disclosed by sleep research.

Timing of activities of daily life is jaggy: How episodic ultradian changes in body and brain temperature are integrated into this process.

PubMed

Blessing, William; Ootsuka, Youichirou

2016-01-01

Charles Darwin noted that natural selection applies even to the hourly organization of daily life. Indeed, in many species, the day is segmented into active periods when the animal searches for food, and inactive periods when the animal digests and rests. This episodic temporal patterning is conventionally referred to as ultradian (<24 hours) rhythmicity. The average time between ultradian events is approximately 1-2 hours, but the interval is highly variable. The ultradian pattern is stochastic, jaggy rather than smooth, so that although the next event is likely to occur within 1-2 hours, it is not possible to predict the precise timing. When models of circadian timing are applied to the ultradian temporal pattern, the underlying assumption of true periodicity (stationarity) has distorted the analyses, so that the ultradian pattern is frequently averaged away and ignored. Each active ultradian episode commences with an increase in hippocampal theta rhythm, indicating the switch of attention to the external environment. During each active episode, behavioral and physiological processes, including changes in body and brain temperature, occur in an integrated temporal order, confirming organization by programs endogenous to the central nervous system. We describe methods for analyzing episodic ultradian events, including the use of wavelet mathematics to determine their timing and amplitude, and the use of fractal-based procedures to determine their complexity.

Timing of activities of daily life is jaggy: How episodic ultradian changes in body and brain temperature are integrated into this process

PubMed Central

Blessing, William; Ootsuka, Youichirou

2016-01-01

ABSTRACT Charles Darwin noted that natural selection applies even to the hourly organization of daily life. Indeed, in many species, the day is segmented into active periods when the animal searches for food, and inactive periods when the animal digests and rests. This episodic temporal patterning is conventionally referred to as ultradian (<24 hours) rhythmicity. The average time between ultradian events is approximately 1–2 hours, but the interval is highly variable. The ultradian pattern is stochastic, jaggy rather than smooth, so that although the next event is likely to occur within 1–2 hours, it is not possible to predict the precise timing. When models of circadian timing are applied to the ultradian temporal pattern, the underlying assumption of true periodicity (stationarity) has distorted the analyses, so that the ultradian pattern is frequently averaged away and ignored. Each active ultradian episode commences with an increase in hippocampal theta rhythm, indicating the switch of attention to the external environment. During each active episode, behavioral and physiological processes, including changes in body and brain temperature, occur in an integrated temporal order, confirming organization by programs endogenous to the central nervous system. We describe methods for analyzing episodic ultradian events, including the use of wavelet mathematics to determine their timing and amplitude, and the use of fractal-based procedures to determine their complexity. PMID:28349079

Spatial analysis and high resolution mapping of the human whole-brain transcriptome for integrative analysis in neuroimaging.

PubMed

Gryglewski, Gregor; Seiger, René; James, Gregory Miles; Godbersen, Godber Mathis; Komorowski, Arkadiusz; Unterholzner, Jakob; Michenthaler, Paul; Hahn, Andreas; Wadsak, Wolfgang; Mitterhauser, Markus; Kasper, Siegfried; Lanzenberger, Rupert

2018-08-01

The quantification of big pools of diverse molecules provides important insights on brain function, but is often restricted to a limited number of observations, which impairs integration with other modalities. To resolve this issue, a method allowing for the prediction of mRNA expression in the entire brain based on microarray data provided in the Allen Human Brain Atlas was developed. Microarray data of 3702 samples from 6 brain donors was registered to MNI and cortical surface space using FreeSurfer. For each of 18,686 genes, spatial dependence of transcription was assessed using variogram modelling. Variogram models were employed in Gaussian process regression to calculate best linear unbiased predictions for gene expression at all locations represented in well-established imaging atlases for cortex, subcortical structures and cerebellum. For validation, predicted whole-brain transcription of the HTR1A gene was correlated with [carbonyl- 11 C]WAY-100635 positron emission tomography data collected from 30 healthy subjects. Prediction results showed minimal bias ranging within ±0.016 (cortical surface), ±0.12 (subcortical regions) and ±0.14 (cerebellum) in units of log2 expression intensity for all genes. Across genes, the correlation of predicted and observed mRNA expression in leave-one-out cross-validation correlated with the strength of spatial dependence (cortical surface: r = 0.91, subcortical regions: r = 0.85, cerebellum: r = 0.84). 816 out of 18,686 genes exhibited a high spatial dependence accounting for more than 50% of variance in the difference of gene expression on the cortical surface. In subcortical regions and cerebellum, different sets of genes were implicated by high spatially structured variability. For the serotonin 1A receptor, correlation between PET binding potentials and predicted comprehensive mRNA expression was markedly higher (Spearman ρ = 0.72 for cortical surface, ρ = 0.84 for subcortical regions) than correlation of PET and discrete samples only (ρ = 0.55 and ρ = 0.63, respectively). Prediction of mRNA expression in the entire human brain allows for intuitive visualization of gene transcription and seamless integration in multimodal analysis without bias arising from non-uniform distribution of available samples. Extension of this methodology promises to facilitate translation of omics research and enable investigation of human brain function at a systems level. Copyright © 2018 Elsevier Inc. All rights reserved.

Investigation of blast-induced traumatic brain injury.

PubMed

Taylor, Paul A; Ludwigsen, John S; Ford, Corey C

2014-01-01

Many troops deployed in Iraq and Afghanistan have sustained blast-related, closed-head injuries from being within non-lethal distance of detonated explosive devices. Little is known, however, about the mechanisms associated with blast exposure that give rise to traumatic brain injury (TBI). This study attempts to identify the precise conditions of focused stress wave energy within the brain, resulting from blast exposure, which will correlate with a threshold for persistent brain injury. This study developed and validated a set of modelling tools to simulate blast loading to the human head. Using these tools, the blast-induced, early-time intracranial wave motions that lead to focal brain damage were simulated. The simulations predict the deposition of three distinct wave energy components, two of which can be related to injury-inducing mechanisms, namely cavitation and shear. Furthermore, the results suggest that the spatial distributions of these damaging energy components are independent of blast direction. The predictions reported herein will simplify efforts to correlate simulation predictions with clinical measures of TBI and aid in the development of protective headwear.

Investigation of blast-induced traumatic brain injury

PubMed Central

Ludwigsen, John S.; Ford, Corey C.

2014-01-01

Objective Many troops deployed in Iraq and Afghanistan have sustained blast-related, closed-head injuries from being within non-lethal distance of detonated explosive devices. Little is known, however, about the mechanisms associated with blast exposure that give rise to traumatic brain injury (TBI). This study attempts to identify the precise conditions of focused stress wave energy within the brain, resulting from blast exposure, which will correlate with a threshold for persistent brain injury. Methods This study developed and validated a set of modelling tools to simulate blast loading to the human head. Using these tools, the blast-induced, early-time intracranial wave motions that lead to focal brain damage were simulated. Results The simulations predict the deposition of three distinct wave energy components, two of which can be related to injury-inducing mechanisms, namely cavitation and shear. Furthermore, the results suggest that the spatial distributions of these damaging energy components are independent of blast direction. Conclusions The predictions reported herein will simplify efforts to correlate simulation predictions with clinical measures of TBI and aid in the development of protective headwear. PMID:24766453

Prospective Design of Anti‐Transferrin Receptor Bispecific Antibodies for Optimal Delivery into the Human Brain

PubMed Central

Kanodia, JS; Gadkar, K; Bumbaca, D; Zhang, Y; Tong, RK; Luk, W; Hoyte, K; Lu, Y; Wildsmith, KR; Couch, JA; Watts, RJ; Dennis, MS; Ernst, JA; Scearce‐Levie, K; Atwal, JK; Joseph, S

2016-01-01

Anti‐transferrin receptor (TfR)‐based bispecific antibodies have shown promise for boosting antibody uptake in the brain. Nevertheless, there are limited data on the molecular properties, including affinity required for successful development of TfR‐based therapeutics. A complex nonmonotonic relationship exists between affinity of the anti‐TfR arm and brain uptake at therapeutically relevant doses. However, the quantitative nature of this relationship and its translatability to humans is heretofore unexplored. Therefore, we developed a mechanistic pharmacokinetic‐pharmacodynamic (PK‐PD) model for bispecific anti‐TfR/BACE1 antibodies that accounts for antibody‐TfR interactions at the blood‐brain barrier (BBB) as well as the pharmacodynamic (PD) effect of anti‐BACE1 arm. The calibrated model correctly predicted the optimal anti‐TfR affinity required to maximize brain exposure of therapeutic antibodies in the cynomolgus monkey and was scaled to predict the optimal affinity of anti‐TfR bispecifics in humans. Thus, this model provides a framework for testing critical translational predictions for anti‐TfR bispecific antibodies, including choice of candidate molecule for clinical development. PMID:27299941

From Vivaldi to Beatles and back: predicting lateralized brain responses to music.

PubMed

Alluri, Vinoo; Toiviainen, Petri; Lund, Torben E; Wallentin, Mikkel; Vuust, Peter; Nandi, Asoke K; Ristaniemi, Tapani; Brattico, Elvira

2013-12-01

We aimed at predicting the temporal evolution of brain activity in naturalistic music listening conditions using a combination of neuroimaging and acoustic feature extraction. Participants were scanned using functional Magnetic Resonance Imaging (fMRI) while listening to two musical medleys, including pieces from various genres with and without lyrics. Regression models were built to predict voxel-wise brain activations which were then tested in a cross-validation setting in order to evaluate the robustness of the hence created models across stimuli. To further assess the generalizability of the models we extended the cross-validation procedure by including another dataset, which comprised continuous fMRI responses of musically trained participants to an Argentinean tango. Individual models for the two musical medleys revealed that activations in several areas in the brain belonging to the auditory, limbic, and motor regions could be predicted. Notably, activations in the medial orbitofrontal region and the anterior cingulate cortex, relevant for self-referential appraisal and aesthetic judgments, could be predicted successfully. Cross-validation across musical stimuli and participant pools helped identify a region of the right superior temporal gyrus, encompassing the planum polare and the Heschl's gyrus, as the core structure that processed complex acoustic features of musical pieces from various genres, with or without lyrics. Models based on purely instrumental music were able to predict activation in the bilateral auditory cortices, parietal, somatosensory, and left hemispheric primary and supplementary motor areas. The presence of lyrics on the other hand weakened the prediction of activations in the left superior temporal gyrus. Our results suggest spontaneous emotion-related processing during naturalistic listening to music and provide supportive evidence for the hemispheric specialization for categorical sounds with realistic stimuli. We herewith introduce a powerful means to predict brain responses to music, speech, or soundscapes across a large variety of contexts. © 2013.

Single Subject Prediction of Brain Disorders in Neuroimaging: Promises and Pitfalls

PubMed Central

Arbabshirani, Mohammad R.; Plis, Sergey; Sui, Jing; Calhoun, Vince D.

2016-01-01

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there are extensive evidences showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead. PMID:27012503

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

PubMed

Arbabshirani, Mohammad R; Plis, Sergey; Sui, Jing; Calhoun, Vince D

2017-01-15

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead. Copyright © 2016 Elsevier Inc. All rights reserved.

Brain Temperature in Spontaneously Hypertensive Rats during Physical Exercise in Temperate and Warm Environments.

PubMed

Drummond, Lucas Rios; Kunstetter, Ana Cançado; Vaz, Filipe Ferreira; Campos, Helton Oliveira; Andrade, André Gustavo Pereira de; Coimbra, Cândido Celso; Natali, Antônio José; Wanner, Samuel Penna; Prímola-Gomes, Thales Nicolau

2016-01-01

This study aimed to evaluate brain temperature (Tbrain) changes in spontaneously hypertensive rats (SHRs) subjected to two different physical exercise protocols in temperate or warm environments. We also investigated whether hypertension affects the kinetics of exercise-induced increases in Tbrain relative to the kinetics of abdominal temperature (Tabd) increases. Male 16-week-old normotensive Wistar rats (NWRs) and SHRs were implanted with an abdominal temperature sensor and a guide cannula in the frontal cortex to enable the insertion of a thermistor to measure Tbrain. Next, the animals were subjected to incremental-speed (initial speed of 10 m/min; speed was increased by 1 m/min every 3 min) or constant-speed (60% of the maximum speed) treadmill running until they were fatigued in a temperate (25°C) or warm (32°C) environment. Tbrain, Tabd and tail skin temperature were measured every min throughout the exercise trials. During incremental and constant exercise at 25°C and 32°C, the SHR group exhibited greater increases in Tbrain and Tabd relative to the NWR group. Irrespective of the environment, the heat loss threshold was attained at higher temperatures (either Tbrain or Tabd) in the SHRs. Moreover, the brain-abdominal temperature differential was lower at 32°C in the SHRs than in the NWRs during treadmill running. Overall, we conclude that SHRs exhibit enhanced brain hyperthermia during exercise and that hypertension influences the kinetics of the Tbrain relative to the Tabd increases, particularly during exercise in a warm environment.

Brain Temperature in Spontaneously Hypertensive Rats during Physical Exercise in Temperate and Warm Environments

PubMed Central

Drummond, Lucas Rios; Kunstetter, Ana Cançado; Vaz, Filipe Ferreira; Campos, Helton Oliveira; de Andrade, André Gustavo Pereira; Coimbra, Cândido Celso; Natali, Antônio José

2016-01-01

This study aimed to evaluate brain temperature (Tbrain) changes in spontaneously hypertensive rats (SHRs) subjected to two different physical exercise protocols in temperate or warm environments. We also investigated whether hypertension affects the kinetics of exercise-induced increases in Tbrain relative to the kinetics of abdominal temperature (Tabd) increases. Male 16-week-old normotensive Wistar rats (NWRs) and SHRs were implanted with an abdominal temperature sensor and a guide cannula in the frontal cortex to enable the insertion of a thermistor to measure Tbrain. Next, the animals were subjected to incremental-speed (initial speed of 10 m/min; speed was increased by 1 m/min every 3 min) or constant-speed (60% of the maximum speed) treadmill running until they were fatigued in a temperate (25°C) or warm (32°C) environment. Tbrain, Tabd and tail skin temperature were measured every min throughout the exercise trials. During incremental and constant exercise at 25°C and 32°C, the SHR group exhibited greater increases in Tbrain and Tabd relative to the NWR group. Irrespective of the environment, the heat loss threshold was attained at higher temperatures (either Tbrain or Tabd) in the SHRs. Moreover, the brain-abdominal temperature differential was lower at 32°C in the SHRs than in the NWRs during treadmill running. Overall, we conclude that SHRs exhibit enhanced brain hyperthermia during exercise and that hypertension influences the kinetics of the Tbrain relative to the Tabd increases, particularly during exercise in a warm environment. PMID:27214497

Joint Prediction of Longitudinal Development of Cortical Surfaces and White Matter Fibers from Neonatal MRI

PubMed Central

Rekik, Islem; Li, Gang; Yap, Pew-Thian; Chen, Geng; Lin, Weili; Shen, Dinggang

2017-01-01

The human brain can be modeled as multiple interrelated shapes (or a multishape), each for characterizing one aspect of the brain, such as the cortex and white matter pathways. Predicting the developing multishape is a very challenging task due to the contrasting nature of the developmental trajectories of the constituent shapes: smooth for the cortical surface and non-smooth for white matter tracts due to changes such as bifurcation. We recently addressed this problem and proposed an approach for predicting the multishape developmental spatiotemporal trajectories of infant brains based only on neonatal MRI data using a set of geometric, dynamic, and fiber-to-surface connectivity features. In this paper, we propose two key innovations to further improve the prediction of multishape evolution. First, for a more accurate cortical surface prediction, instead of simply relying on one neonatal atlas to guide the prediction of the multishape, we propose to use multiple neonatal atlases to build a spatially heterogeneous atlas using the multidirectional varifold representation. This individualizes the atlas by locally maximizing its similarity to the testing baseline cortical shape for each cortical region, thereby better representing the baseline testing cortical surface, which founds the multishape prediction process. Second, for temporally consistent fiber prediction, we propose to reliably estimate spatiotemporal connectivity features using low-rank tensor completion, thereby capturing the variability and richness of the temporal development of fibers. Experimental results confirm that the proposed variants significantly improve the prediction performance of our original multishape prediction framework for both cortical surfaces and fiber tracts shape at 3, 6, and 9 months of age. Our pioneering model will pave the way for learning how to predict the evolution of anatomical shapes with abnormal changes. Ultimately, devising accurate shape evolution prediction models that can help quantify and predict the severity of a brain disorder as it progresses will be of great aid in individualized treatment planning. PMID:28284800

Joint prediction of longitudinal development of cortical surfaces and white matter fibers from neonatal MRI.

PubMed

Rekik, Islem; Li, Gang; Yap, Pew-Thian; Chen, Geng; Lin, Weili; Shen, Dinggang

2017-05-15

The human brain can be modeled as multiple interrelated shapes (or a multishape), each for characterizing one aspect of the brain, such as the cortex and white matter pathways. Predicting the developing multishape is a very challenging task due to the contrasting nature of the developmental trajectories of the constituent shapes: smooth for the cortical surface and non-smooth for white matter tracts due to changes such as bifurcation. We recently addressed this problem and proposed an approach for predicting the multishape developmental spatiotemporal trajectories of infant brains based only on neonatal MRI data using a set of geometric, dynamic, and fiber-to-surface connectivity features. In this paper, we propose two key innovations to further improve the prediction of multishape evolution. First, for a more accurate cortical surface prediction, instead of simply relying on one neonatal atlas to guide the prediction of the multishape, we propose to use multiple neonatal atlases to build a spatially heterogeneous atlas using the multidirectional varifold representation. This individualizes the atlas by locally maximizing its similarity to the testing baseline cortical shape for each cortical region, thereby better representing the baseline testing cortical surface, which founds the multishape prediction process. Second, for temporally consistent fiber prediction, we propose to reliably estimate spatiotemporal connectivity features using low-rank tensor completion, thereby capturing the variability and richness of the temporal development of fibers. Experimental results confirm that the proposed variants significantly improve the prediction performance of our original multishape prediction framework for both cortical surfaces and fiber tracts shape at 3, 6, and 9 months of age. Our pioneering model will pave the way for learning how to predict the evolution of anatomical shapes with abnormal changes. Ultimately, devising accurate shape evolution prediction models that can help quantify and predict the severity of a brain disorder as it progresses will be of great aid in individualized treatment planning. Copyright © 2017 Elsevier Inc. All rights reserved.

How the brain attunes to sentence processing: Relating behavior, structure, and function

PubMed Central

Fengler, Anja; Meyer, Lars; Friederici, Angela D.

2016-01-01

Unlike other aspects of language comprehension, the ability to process complex sentences develops rather late in life. Brain maturation as well as verbal working memory (vWM) expansion have been discussed as possible reasons. To determine the factors contributing to this functional development, we assessed three aspects in different age-groups (5–6 years, 7–8 years, and adults): first, functional brain activity during the processing of increasingly complex sentences; second, brain structure in language-related ROIs; and third, the behavioral comprehension performance on complex sentences and the performance on an independent vWM test. At the whole-brain level, brain functional data revealed a qualitatively similar neural network in children and adults including the left pars opercularis (PO), the left inferior parietal lobe together with the posterior superior temporal gyrus (IPL/pSTG), the supplementary motor area, and the cerebellum. While functional activation of the language-related ROIs PO and IPL/pSTG predicted sentence comprehension performance for all age-groups, only adults showed a functional selectivity in these brain regions with increased activation for more complex sentences. The attunement of both the PO and IPL/pSTG toward a functional selectivity for complex sentences is predicted by region-specific gray matter reduction while that of the IPL/pSTG is additionally predicted by vWM span. Thus, both structural brain maturation and vWM expansion provide the basis for the emergence of functional selectivity in language-related brain regions leading to more efficient sentence processing during development. PMID:26777477

Effect of bulk modulus on deformation of the brain under rotational accelerations

NASA Astrophysics Data System (ADS)

Ganpule, S.; Daphalapurkar, N. P.; Cetingul, M. P.; Ramesh, K. T.

2018-01-01

Traumatic brain injury such as that developed as a consequence of blast is a complex injury with a broad range of symptoms and disabilities. Computational models of brain biomechanics hold promise for illuminating the mechanics of traumatic brain injury and for developing preventive devices. However, reliable material parameters are needed for models to be predictive. Unfortunately, the properties of human brain tissue are difficult to measure, and the bulk modulus of brain tissue in particular is not well characterized. Thus, a wide range of bulk modulus values are used in computational models of brain biomechanics, spanning up to three orders of magnitude in the differences between values. However, the sensitivity of these variations on computational predictions is not known. In this work, we study the sensitivity of a 3D computational human head model to various bulk modulus values. A subject-specific human head model was constructed from T1-weighted MRI images at 2-mm3 voxel resolution. Diffusion tensor imaging provided data on spatial distribution and orientation of axonal fiber bundles for modeling white matter anisotropy. Non-injurious, full-field brain deformations in a human volunteer were used to assess the simulated predictions. The comparison suggests that a bulk modulus value on the order of GPa gives the best agreement with experimentally measured in vivo deformations in the human brain. Further, simulations of injurious loading suggest that bulk modulus values on the order of GPa provide the closest match with the clinical findings in terms of predicated injured regions and extent of injury.

Magnetic resonance spectroscopy and brain volumetry in mild cognitive impairment. A prospective study.

PubMed

Fayed, Nicolás; Modrego, Pedro J; García-Martí, Gracián; Sanz-Requena, Roberto; Marti-Bonmatí, Luis

2017-05-01

To assess the accuracy of magnetic resonance spectroscopy (1H-MRS) and brain volumetry in mild cognitive impairment (MCI) to predict conversion to probable Alzheimer's disease (AD). Forty-eight patients fulfilling the criteria of amnestic MCI who underwent a conventional magnetic resonance imaging (MRI) followed by MRS, and T1-3D on 1.5 Tesla MR unit. At baseline the patients underwent neuropsychological examination. 1H-MRS of the brain was carried out by exploring the left medial occipital lobe and ventral posterior cingulated cortex (vPCC) using the LCModel software. A high resolution T1-3D sequence was acquired to carry out the volumetric measurement. A cortical and subcortical parcellation strategy was used to obtain the volumes of each area within the brain. The patients were followed up to detect conversion to probable AD. After a 3-year follow-up, 15 (31.2%) patients converted to AD. The myo-inositol in the occipital cortex and glutamate+glutamine (Glx) in the posterior cingulate cortex predicted conversion to probable AD at 46.1% sensitivity and 90.6% specificity. The positive predictive value was 66.7%, and the negative predictive value was 80.6%, with an overall cross-validated classification accuracy of 77.8%. The volume of the third ventricle, the total white matter and entorhinal cortex predict conversion to probable AD at 46.7% sensitivity and 90.9% specificity. The positive predictive value was 70%, and the negative predictive value was 78.9%, with an overall cross-validated classification accuracy of 77.1%. Combining volumetric measures in addition to the MRS measures the prediction to probable AD has a 38.5% sensitivity and 87.5% specificity, with a positive predictive value of 55.6%, a negative predictive value of 77.8% and an overall accuracy of 73.3%. Either MRS or brain volumetric measures are markers separately of cognitive decline and may serve as a noninvasive tool to monitor cognitive changes and progression to dementia in patients with amnestic MCI, but the results do not support the routine use in the clinical settings. Copyright © 2016 Elsevier Inc. All rights reserved.

Brain Metastasis Velocity: A Novel Prognostic Metric Predictive of Overall Survival and Freedom From Whole-Brain Radiation Therapy After Distant Brain Failure Following Upfront Radiosurgery Alone

DOE Office of Scientific and Technical Information (OSTI.GOV)

Farris, Michael, E-mail: mfarris@wakehealth.edu; McTyre, Emory R.; Cramer, Christina K.

Purpose: Prior statistical models attempted to identify risk factors for time to distant brain failure (DBF) or time to salvage whole-brain radiation therapy (WBRT) to predict the benefit of early WBRT versus stereotactic radiosurgery (SRS) alone. We introduce a novel clinical metric, brain metastasis velocity (BMV), for predicting clinical outcomes after initial DBF following upfront SRS alone. Methods and Materials: BMV was defined as the cumulative number of new brain metastases that developed over time since first SRS in years. Patients were classified by BMV into low-, intermediate-, and high-risk groups, consisting of <4, 4 to 13, and >13 newmore » metastases per year, respectively. Histology, number of metastases at the time of first SRS, and systemic disease status were assessed for effect on BMV. Results: Of 737 patients treated at our institution with upfront SRS without WBRT, 286 had ≥1 DBF event. A lower BMV predicted for improved overall survival (OS) following initial DBF (log-rank P<.0001). Median OS for the low, intermediate, and high BMV groups was 12.4 months (95% confidence interval [CI], 10.4-16.9 months), 8.2 months (95% CI, 5.0-9.7 months), and 4.3 months (95% CI, 2.6-6.7 months), respectively. Multivariate analysis showed that BMV remained the dominant predictor of OS, with a hazard ratio of 2.75 for the high BMV group (95% CI, 1.94-3.89; P<.0001) and a hazard ratio of 1.65 for the intermediate BMV group (95% CI, 1.18-2.30; P<.004). A lower BMV was associated with decreased rates of salvage WBRT (P=.02) and neurologic death (P=.008). Factors predictive for a higher BMV included ≥2 initial brain metastases (P=.004) and melanoma histology (P=.008). Conclusions: BMV is a novel metric associated with OS, neurologic death, and need for salvage WBRT after initial DBF following upfront SRS alone.« less

Assessment of brain core temperature using MR DWI-thermometry in Alzheimer disease patients compared to healthy subjects.

PubMed

Sparacia, Gianvincenzo; Sakai, Koji; Yamada, Kei; Giordano, Giovanna; Coppola, Rosalia; Midiri, Massimo; Grimaldi, Luigi Maria

2017-04-01

To assess the brain core temperature of Alzheimer disease (AD) patients in comparison with healthy volunteers using diffusion-weighted thermometry. Fourteen AD patients (3 men, 11 women; age range 60-81 years, mean age 73.8 ± 6.1 years) and 14 healthy volunteers, age and sex-matched (mean age 70.1 ± 6.9 years; range 62-84 years; 5 men, 9 women) underwent MR examination between February 2014 and March 2016. MR imaging studies were performed with a 1.5-T MR scanner. Brain core temperature (T: °C) was calculated using the following equation from the diffusion coefficient (D) in the lateral ventricular (LV) cerebrospinal fluid: T = 2256.74/ln (4.39221/D) - 273.15 using a standard DWI single-shot echo-planar pulse sequence (b value 1000 s/mm 2 ). Statistical analysis was performed using a nonparametric Wilcoxon rank-sum test to compare the patient and control groups regarding LV temperatures. There was no significant difference (P = 0.1937) in LV temperature between patients (mean 37.9 ± 1.1 °C, range 35.8-39.2 °C) and control group (38.7 ± 1.4 °C, range 36.9-42.7 °C). Brain core temperature in AD patients showed no significant alterations compared to healthy volunteers.

Risk and protective factors for structural brain ageing in the eighth decade of life.

PubMed

Ritchie, Stuart J; Tucker-Drob, Elliot M; Cox, Simon R; Dickie, David Alexander; Del C Valdés Hernández, Maria; Corley, Janie; Royle, Natalie A; Redmond, Paul; Muñoz Maniega, Susana; Pattie, Alison; Aribisala, Benjamin S; Taylor, Adele M; Clarke, Toni-Kim; Gow, Alan J; Starr, John M; Bastin, Mark E; Wardlaw, Joanna M; Deary, Ian J

2017-11-01

Individuals differ markedly in brain structure, and in how this structure degenerates during ageing. In a large sample of human participants (baseline n = 731 at age 73 years; follow-up n = 488 at age 76 years), we estimated the magnitude of mean change and variability in changes in MRI measures of brain macrostructure (grey matter, white matter, and white matter hyperintensity volumes) and microstructure (fractional anisotropy and mean diffusivity from diffusion tensor MRI). All indices showed significant average change with age, with considerable heterogeneity in those changes. We then tested eleven socioeconomic, physical, health, cognitive, allostatic (inflammatory and metabolic), and genetic variables for their value in predicting these differences in changes. Many of these variables were significantly correlated with baseline brain structure, but few could account for significant portions of the heterogeneity in subsequent brain change. Physical fitness was an exception, being correlated both with brain level and changes. The results suggest that only a subset of correlates of brain structure are also predictive of differences in brain ageing.

It's OK if 'my brain made me do it': people's intuitions about free will and neuroscientific prediction.

PubMed

Nahmias, Eddy; Shepard, Jason; Reuter, Shane

2014-11-01

In recent years, a number of prominent scientists have argued that free will is an illusion, appealing to evidence demonstrating that information about brain activity can be used to predict behavior before people are aware of having made a decision. These scientists claim that the possibility of perfect prediction based on neural information challenges the ordinary understanding of free will. In this paper we provide evidence suggesting that most people do not view the possibility of neuro-prediction as a threat to free will unless it also raises concerns about manipulation of the agent's behavior. In Experiment 1 two scenarios described future brain imaging technology that allows perfect prediction of decisions and actions based on earlier neural activity, and this possibility did not undermine most people's attributions of free will or responsibility, except in the scenario that also allowed manipulation. In Experiment 2 the scenarios increased the salience of the physicalist implications of neuro-prediction, while in Experiment 3 the scenarios suggested dualism, with perfect prediction by mindreaders. The patterns of results for these two experiments were similar to the results in Experiment 1, suggesting that participants do not understand free will to require specific metaphysical conditions regarding the mind-body relation. Most people seem to understand free will in a way that is not threatened by perfect prediction based on neural information, suggesting that they believe that just because "my brain made me do it," that does not mean that I didn't do it of my own free will. Copyright © 2014 Elsevier B.V. All rights reserved.

Fetal brain hypometabolism during prolonged hypoxaemia in the llama

PubMed Central

Ebensperger, Germán; Ebensperger, Renato; Herrera, Emilio A; Riquelme, Raquel A; Sanhueza, Emilia M; Lesage, Florian; Marengo, Juan J; Tejo, Rodrigo I; Llanos, Aníbal J; Reyes, Roberto V

2005-01-01

In this study we looked for additional evidence to support the hypothesis that fetal llama reacts to hypoxaemia with adaptive brain hypometabolism. We determined fetal llama brain temperature, Na+ and K+ channel density and Na+–K+-ATPase activity. Additionally, we looked to see whether there were signs of cell death in the brain cortex of llama fetuses submitted to prolonged hypoxaemia. Ten fetal llamas were instrumented under general anaesthesia to measure pH, arterial blood gases, mean arterial pressure, heart rate, and brain and core temperatures. Measurements were made 1 h before and every hour during 24 h of hypoxaemia (n = 5), which was imposed by reducing maternal inspired oxygen fraction to reach a fetal arterial partial pressure of oxygen (Pa,O2) of about 12 mmHg. A normoxaemic group was the control (n = 5). After 24 h of hypoxaemia, we determined brain cortex Na+–K+-ATPase activity, ouabain binding, and the expression of NaV1.1, NaV1.2, NaV1.3, NaV1.6, TREK1, TRAAK and KATP channels. The lack of brain cortex damage was assessed as poly ADP-ribose polymerase (PARP) proteolysis. We found a mean decrease of 0.56°C in brain cortex temperature during prolonged hypoxaemia, which was accompanied by a 51% decrease in brain cortex Na+–K+-ATPase activity, and by a 44% decrease in protein content of NaV1.1, a voltage-gated Na+ channel. These changes occurred in absence of changes in PARP protein degradation, suggesting that the cell death of the brain was not enhanced in the fetal llama during hypoxaemia. Taken together, these results provide further evidence to support the hypothesis that the fetal llama responds to prolonged hypoxaemia with adaptive brain hypometabolism, partly mediated by decreases in Na+–K+-ATPase activity and expression of NaV channels. PMID:16037083

Robust Brain Hyperglycemia during General Anesthesia: Relationships with Metabolic Brain Inhibition and Vasodilation

PubMed Central

Bola, R. Aaron; Kiyatkin, Eugene A.

2016-01-01

Glucose is the main energetic substrate for the metabolic activity of brain cells and its proper delivery into the extracellular space is essential for maintaining normal neural functions. Under physiological conditions, glucose continuously enters the extracellular space from arterial blood via gradient-dependent facilitated diffusion governed by the GLUT-1 transporters. Due to this gradient-dependent mechanism, glucose levels rise in the brain after consumption of glucose-containing foods and drinks. Glucose entry is also accelerated due to local neuronal activation and neuro-vascular coupling, resulting in transient hyperglycemia to prevent any metabolic deficit. Here, we explored another mechanism that is activated during general anesthesia and results in significant brain hyperglycemia. By using enzyme-based glucose biosensors we demonstrate that glucose levels in the nucleus accumbens (NAc) strongly increase after iv injection of Equthesin, a mixture of chloral hydrate and sodium pentobarbital, which is often used for general anesthesia in rats. By combining electrochemical recordings with brain, muscle, and skin temperature monitoring, we show that the gradual increase in brain glucose occurring during the development of general anesthesia tightly correlate with decreases in brain-muscle temperature differentials, suggesting that this rise in glucose is related to metabolic inhibition. While the decreased consumption of glucose by brain cells could contribute to the development of hyperglycemia, an exceptionally strong positive correlation (r = 0.99) between glucose rise and increases in skin-muscle temperature differentials was also found, suggesting the strong vasodilation of cerebral vessels as the primary mechanism for accelerated entry of glucose into brain tissue. Our present data could explain drastic differences in basal glucose levels found in awake and anesthetized animal preparations. They also suggest that glucose entry into brain tissue could be strongly modulated by pharmacological drugs via drug-induced changes in metabolic activity and the tone of cerebral vessels. PMID:26913008

cAMP signalling in mushroom bodies modulates temperature preference behaviour in Drosophila.

PubMed

Hong, Sung-Tae; Bang, Sunhoe; Hyun, Seogang; Kang, Jongkyun; Jeong, Kyunghwa; Paik, Donggi; Chung, Jongkyeong; Kim, Jaeseob

2008-08-07

Homoiotherms, for example mammals, regulate their body temperature with physiological responses such as a change of metabolic rate and sweating. In contrast, the body temperature of poikilotherms, for example Drosophila, is the result of heat exchange with the surrounding environment as a result of the large ratio of surface area to volume of their bodies. Accordingly, these animals must instinctively move to places with an environmental temperature as close as possible to their genetically determined desired temperature. The temperature that Drosophila instinctively prefers has a function equivalent to the 'set point' temperature in mammals. Although various temperature-gated TRP channels have been discovered, molecular and cellular components in Drosophila brain responsible for determining the desired temperature remain unknown. We identified these components by performing a large-scale genetic screen of temperature preference behaviour (TPB) in Drosophila. In parallel, we mapped areas of the Drosophila brain controlling TPB by targeted inactivation of neurons with tetanus toxin and a potassium channel (Kir2.1) driven with various brain-specific GAL4s. Here we show that mushroom bodies (MBs) and the cyclic AMP-cAMP-dependent protein kinase A (cAMP-PKA) pathway are essential for controlling TPB. Furthermore, targeted expression of cAMP-PKA pathway components in only the MB was sufficient to rescue abnormal TPB of the corresponding mutants. Preferred temperatures were affected by the level of cAMP and PKA activity in the MBs in various PKA pathway mutants.

Brain temperature measurement: A study of in vitro accuracy and stability of smart catheter temperature sensors.

PubMed

Li, Chunyan; Wu, Pei-Ming; Wu, Zhizhen; Ahn, Chong H; LeDoux, David; Shutter, Lori A; Hartings, Jed A; Narayan, Raj K

2012-02-01

The injured brain is vulnerable to increases in temperature after severe head injury. Therefore, accurate and reliable measurement of brain temperature is important to optimize patient outcome. In this work, we have fabricated, optimized and characterized temperature sensors for use with a micromachined smart catheter for multimodal intracranial monitoring. Developed temperature sensors have resistance of 100.79 ± 1.19Ω and sensitivity of 67.95 mV/°C in the operating range from15-50°C, and time constant of 180 ms. Under the optimized excitation current of 500 μA, adequate signal-to-noise ratio was achieved without causing self-heating, and changes in immersion depth did not introduce clinically significant errors of measurements (<0.01°C). We evaluated the accuracy and long-term drift (5 days) of twenty temperature sensors in comparison to two types of commercial temperature probes (USB Reference Thermometer, NIST-traceable bulk probe with 0.05°C accuracy; and IT-21, type T type clinical microprobe with guaranteed 0.1°C accuracy) under controlled laboratory conditions. These in vitro experimental data showed that the temperature measurement performance of our sensors was accurate and reliable over the course of 5 days. The smart catheter temperature sensors provided accuracy and long-term stability comparable to those of commercial tissue-implantable microprobes, and therefore provide a means for temperature measurement in a microfabricated, multimodal cerebral monitoring device.

Prediction of a Therapeutic Dose for Buagafuran, a Potent Anxiolytic Agent by Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling Starting from Pharmacokinetics in Rats and Human.

PubMed

Yang, Fen; Wang, Baolian; Liu, Zhihao; Xia, Xuejun; Wang, Weijun; Yin, Dali; Sheng, Li; Li, Yan

2017-01-01

Physiologically based pharmacokinetic (PBPK)/pharmacodynamic (PD) models can contribute to animal-to-human extrapolation and therapeutic dose predictions. Buagafuran is a novel anxiolytic agent and phase I clinical trials of buagafuran have been completed. In this paper, a potentially effective dose for buagafuran of 30 mg t.i.d. in human was estimated based on the human brain concentration predicted by a PBPK/PD modeling. The software GastroPlus TM was used to build the PBPK/PD model for buagafuran in rat which related the brain tissue concentrations of buagafuran and the times of animals entering the open arms in the pharmacological model of elevated plus-maze. Buagafuran concentrations in human plasma were fitted and brain tissue concentrations were predicted by using a human PBPK model in which the predicted plasma profiles were in good agreement with observations. The results provided supportive data for the rational use of buagafuran in clinic.

Observed parenting behaviors interact with a polymorphism of the brain-derived neurotrophic factor gene to predict the emergence of oppositional defiant and callous-unemotional behaviors at age 3 years.

PubMed

Willoughby, Michael T; Mills-Koonce, Roger; Propper, Cathi B; Waschbusch, Daniel A

2013-11-01

Using the Durham Child Health and Development Study, this study (N = 171) tested whether observed parenting behaviors in infancy (6 and 12 months) and toddlerhood/preschool (24 and 36 months) interacted with a child polymorphism of the brain-derived neurotrophic factor gene to predict oppositional defiant disorder (ODD) and callous-unemotional (CU) behaviors at age 3 years. Child genotype interacted with observed harsh and intrusive (but not sensitive) parenting to predict ODD and CU behaviors. Harsh-intrusive parenting was more strongly associated with ODD and CU for children with a methionine allele of the brain-derived neurotrophic factor gene. CU behaviors were uniquely predicted by harsh-intrusive parenting in infancy, whereas ODD behaviors were predicted by harsh-intrusive parenting in both infancy and toddlerhood/preschool. The results are discussed from the perspective of the contributions of caregiving behaviors as contributing to distinct aspects of early onset disruptive behavior.

Influence of temperature on viral hemorrhagic septicemia (Genogroup IVa) in Pacific herring, Clupea pallasii Valenciennes

USGS Publications Warehouse

Hershberger, P.K.; Purcell, M.K.; Hart, L.M.; Gregg, J.L.; Thompson, R.L.; Garver, K.A.; Winton, J.R.

2013-01-01

An inverse relationship between water temperature and susceptibility of Pacific herring (Clupea pallasii) to viral hemorrhagic septicemia, genogroup IVa (VHS) was indicated by controlled exposure studies where cumulative mortalities, viral shedding rates, and viral persistence in survivors were greatest at the coolest exposure temperatures. Among groups of specific pathogen-free (SPF) Pacific herring maintained at 8, 11, and 15 °C, cumulative mortalities after waterborne exposure to viral hemorrhagic septicemia virus (VHSV) were 78%, 40%, and 13%, respectively. The prevalence of survivors with VHSV-positive tissues 25 d post-exposure was 64%, 16%, and 0% (at 8, 11 and 15 °C, respectively) with viral prevalence typically higher in brain tissues than in kidney/spleen tissue pools at each temperature. Similarly, geometric mean viral titers in brain tissues and kidney/spleen tissue pools decreased at higher temperatures, and kidney/spleen titers were generally 10-fold lower than those in brain tissues at each temperature. This inverse relationship between temperature and VHS severity was likely mediated by an enhanced immune response at the warmer temperatures, where a robust type I interferon response was indicated by rapid and significant upregulation of the herring Mx gene. The effect of relatively small temperature differences on the susceptibility of a natural host to VHS provides insights into conditions that preface periodic VHSV epizootics in wild populations throughout the NE Pacific.

Information spreading by a combination of MEG source estimation and multivariate pattern classification.

PubMed

Sato, Masashi; Yamashita, Okito; Sato, Masa-Aki; Miyawaki, Yoichi

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of "information spreading" may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined.

Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time: application to non-rigid neuroimage registration.

PubMed

Wittek, Adam; Joldes, Grand; Couton, Mathieu; Warfield, Simon K; Miller, Karol

2010-12-01

Long computation times of non-linear (i.e. accounting for geometric and material non-linearity) biomechanical models have been regarded as one of the key factors preventing application of such models in predicting organ deformation for image-guided surgery. This contribution presents real-time patient-specific computation of the deformation field within the brain for six cases of brain shift induced by craniotomy (i.e. surgical opening of the skull) using specialised non-linear finite element procedures implemented on a graphics processing unit (GPU). In contrast to commercial finite element codes that rely on an updated Lagrangian formulation and implicit integration in time domain for steady state solutions, our procedures utilise the total Lagrangian formulation with explicit time stepping and dynamic relaxation. We used patient-specific finite element meshes consisting of hexahedral and non-locking tetrahedral elements, together with realistic material properties for the brain tissue and appropriate contact conditions at the boundaries. The loading was defined by prescribing deformations on the brain surface under the craniotomy. Application of the computed deformation fields to register (i.e. align) the preoperative and intraoperative images indicated that the models very accurately predict the intraoperative deformations within the brain. For each case, computing the brain deformation field took less than 4 s using an NVIDIA Tesla C870 GPU, which is two orders of magnitude reduction in computation time in comparison to our previous study in which the brain deformation was predicted using a commercial finite element solver executed on a personal computer. Copyright © 2010 Elsevier Ltd. All rights reserved.

Information spreading by a combination of MEG source estimation and multivariate pattern classification

PubMed Central

Sato, Masashi; Yamashita, Okito; Sato, Masa-aki

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of “information spreading” may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined. PMID:29912968

Post-Activation Brain Warming: A 1-H MRS Thermometry Study

PubMed Central

Rango, Mario; Bonifati, Cristiana; Bresolin, Nereo

2015-01-01

Purpose Temperature plays a fundamental role for the proper functioning of the brain. However, there are only fragmentary data on brain temperature (Tbr) and its regulation under different physiological conditions. Methods We studied Tbr in the visual cortex of 20 normal subjects serially with a wide temporal window under different states including rest, activation and recovery by a visual stimulation-Magnetic Resonance Spectroscopy Thermometry combined approach. We also studied Tbr in a control region, the centrum semiovale, under the same conditions. Results Visual cortex mean baseline Tbr was higher than mean body temperature (37.38 vs 36.60, P<0.001). During activation Tbr remained unchanged at first and then showed a small decrease (-0.20 C°) around the baseline value. After the end of activation Tbr increased consistently (+0.60 C°) and then returned to baseline values after some minutes. Centrum semiovale Tbr remained unchanged through rest, visual stimulation and recovery. Conclusion These findings have several implications, among them that neuronal firing itself is not a major source of heat release in the brain and that there is an aftermath of brain activation that lasts minutes before returning to baseline conditions. PMID:26011731

Regional rat brain noradrenaline turnover in response to restraint stress.

PubMed

Glavin, G B; Tanaka, M; Tsuda, A; Kohno, Y; Hoaki, Y; Nagasaki, N

1983-08-01

Male Wistar rats were starved for 12 hr and then subjected to either 2 hr of wire mesh "envelope" restraint at room temperature; 2 hr of supine restraint in a specially constructed harness at room temperature or were not restrained. Eight brain regions were examined for NA level and the level of its major metabolite, MHPG-SO4. Plasma corticosterone and gastric ulcer incidence were also measured. All restrained rats displayed marked elevations in MHPG-SO4 levels in most brain regions. In addition, several brain regions in restrained animals showed a reduction in NA level. All restrained rats showed elevated plasma corticosterone levels and evidence of gastric lesions. In general, supine restraint produced greater alterations in regional brain NA turnover, greater evidence of ulcer disease, and higher plasma corticosterone levels than did wire mesh restraint. These data suggest that acute but intense stress in the form of restraint causes markedly altered brain NA activity--a possible neurochemical mechanism underlying the phenomenon of stress-induced disease.

Analyzing the dynamics of brain circuits with temperature: design and implementation of a miniature thermoelectric device.

PubMed

Aronov, Dmitriy; Fee, Michale S

2011-04-15

Traditional lesion or inactivation methods are useful for determining if a given brain area is involved in the generation of a behavior, but not for determining if circuit dynamics in that area control the timing of the behavior. In contrast, localized mild cooling or heating of a brain area alters the speed of neuronal and circuit dynamics and can reveal the role of that area in the control of timing. It has been shown that miniaturized solid-state heat pumps based on the Peltier effect can be useful for analyzing brain dynamics in small freely behaving animals (Long and Fee, 2008). Here we present a theoretical analysis of these devices and a procedure for optimizing their design. We describe the construction and implementation of one device for cooling surface brain areas, such as cortex, and another device for cooling deep brain regions. We also present measurements of the magnitude and localization of the brain temperature changes produced by these two devices. Copyright © 2011 Elsevier B.V. All rights reserved.

Individual differences in the dominance of interhemispheric connections predict cognitive ability beyond sex and brain size.

PubMed

Martínez, Kenia; Janssen, Joost; Pineda-Pardo, José Ángel; Carmona, Susanna; Román, Francisco Javier; Alemán-Gómez, Yasser; Garcia-Garcia, David; Escorial, Sergio; Quiroga, María Ángeles; Santarnecchi, Emiliano; Navas-Sánchez, Francisco Javier; Desco, Manuel; Arango, Celso; Colom, Roberto

2017-07-15

Global structural brain connectivity has been reported to be sex-dependent with women having increased interhemispheric connectivity (InterHc) and men having greater intrahemispheric connectivity (IntraHc). However, (a) smaller brains show greater InterHc, (b) larger brains show greater IntraHc, and (c) women have, on average, smaller brains than men. Therefore, sex differences in brain size may modulate sex differences in global brain connectivity. At the behavioural level, sex-dependent differences in connectivity are thought to contribute to men-women differences in spatial and verbal abilities. But this has never been tested at the individual level. The current study assessed whether individual differences in global structural connectome measures (InterHc, IntraHc and the ratio of InterHc relative to IntraHc) predict spatial and verbal ability while accounting for the effect of sex and brain size. The sample included forty men and forty women, who did neither differ in age nor in verbal and spatial latent components defined by a broad battery of tests and tasks. High-resolution T 1 -weighted and diffusion-weighted images were obtained for computing brain size and reconstructing the structural connectome. Results showed that men had higher IntraHc than women, while women had an increased ratio InterHc/IntraHc. However, these sex differences were modulated by brain size. Increased InterHc relative to IntraHc predicted higher spatial and verbal ability irrespective of sex and brain size. The positive correlations between the ratio InterHc/IntraHc and the spatial and verbal abilities were confirmed in 1000 random samples generated by bootstrapping. Therefore, sex differences in global structural connectome connectivity were modulated by brain size and did not underlie sex differences in verbal and spatial abilities. Rather, the level of dominance of InterHc over IntraHc may be associated with individual differences in verbal and spatial abilities in both men and women. Copyright © 2017 Elsevier Inc. All rights reserved.

Acute Brain Dysfunction: Development and Validation of a Daily Prediction Model.

PubMed

Marra, Annachiara; Pandharipande, Pratik P; Shotwell, Matthew S; Chandrasekhar, Rameela; Girard, Timothy D; Shintani, Ayumi K; Peelen, Linda M; Moons, Karl G M; Dittus, Robert S; Ely, E Wesley; Vasilevskis, Eduard E

2018-03-24

The goal of this study was to develop and validate a dynamic risk model to predict daily changes in acute brain dysfunction (ie, delirium and coma), discharge, and mortality in ICU patients. Using data from a multicenter prospective ICU cohort, a daily acute brain dysfunction-prediction model (ABD-pm) was developed by using multinomial logistic regression that estimated 15 transition probabilities (from one of three brain function states [normal, delirious, or comatose] to one of five possible outcomes [normal, delirious, comatose, ICU discharge, or died]) using baseline and daily risk factors. Model discrimination was assessed by using predictive characteristics such as negative predictive value (NPV). Calibration was assessed by plotting empirical vs model-estimated probabilities. Internal validation was performed by using a bootstrap procedure. Data were analyzed from 810 patients (6,711 daily transitions). The ABD-pm included individual risk factors: mental status, age, preexisting cognitive impairment, baseline and daily severity of illness, and daily administration of sedatives. The model yielded very high NPVs for "next day" delirium (NPV: 0.823), coma (NPV: 0.892), normal cognitive state (NPV: 0.875), ICU discharge (NPV: 0.905), and mortality (NPV: 0.981). The model demonstrated outstanding calibration when predicting the total number of patients expected to be in any given state across predicted risk. We developed and internally validated a dynamic risk model that predicts the daily risk for one of three cognitive states, ICU discharge, or mortality. The ABD-pm may be useful for predicting the proportion of patients for each outcome state across entire ICU populations to guide quality, safety, and care delivery activities. Copyright © 2018 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction.

PubMed

Patel, Meenal J; Andreescu, Carmen; Price, Julie C; Edelman, Kathryn L; Reynolds, Charles F; Aizenstein, Howard J

2015-10-01

Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate accurate prediction models for late-life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi-modal imaging and non-imaging whole brain and network-based features. Late-life depression patients (medicated post-recruitment) (n = 33) and older non-depressed individuals (n = 35) were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi-modal magnetic resonance imaging pretreatment. Linear and nonlinear learning methods were tested for estimating accurate prediction models. A learning method called alternating decision trees estimated the most accurate prediction models for late-life depression diagnosis (87.27% accuracy) and treatment response (89.47% accuracy). The diagnosis model included measures of age, Mini-mental state examination score, and structural imaging (e.g. whole brain atrophy and global white mater hyperintensity burden). The treatment response model included measures of structural and functional connectivity. Combinations of multi-modal imaging and/or non-imaging measures may help better predict late-life depression diagnosis and treatment response. As a preliminary observation, we speculate that the results may also suggest that different underlying brain characteristics defined by multi-modal imaging measures-rather than region-based differences-are associated with depression versus depression recovery because to our knowledge this is the first depression study to accurately predict both using the same approach. These findings may help better understand late-life depression and identify preliminary steps toward personalized late-life depression treatment. Copyright © 2015 John Wiley & Sons, Ltd.

Does Brain Reserve Protect Older Women from Vascular Depression?

PubMed Central

2014-01-01

Objectives. Brain reserve theory, typically discussed in relation to dementia, was examined with regard to late-life depression symptomatology and cerebrovascular burden (CVB) in older-old women. Method. It was predicted that in a 6-year longitudinal sample (Health and Retirement Study) of 1,355 stroke-free women aged 80 years and older, higher levels of depressive symptomatology (8-item Center for Epidemiologic Studies-Depression score) would be predicted by high CVB, less educational attainment, and the education × CVB interaction after controlling for age and cognitive functioning (Telephone Interview for Cognitive Status). A latent growth curve model was used to identify differences in depression symptomatology at baseline and over time. Logistic regression analyses were used to predict clinically significant depressive symptomatology at each wave based on CVB, education, and the education × CVB interaction. Results. Results indicate that among older women, greater educational attainment predicted fewer depression symptoms at baseline, but this advantage was partially eroded over time. The education × CVB interaction predicted clinically significant depressive symptoms at baseline when the benefits of education were most robust. Discussion. Brain reserve, characterized by educational attainment, may counterbalance the effect of high CVB with respect to depressive symptoms, thereby preserving mood in late life. These findings support the application of brain reserve theory to late-life depression. PMID:23448867

Molecular determinants of blood-brain barrier permeation.

PubMed

Geldenhuys, Werner J; Mohammad, Afroz S; Adkins, Chris E; Lockman, Paul R

2015-01-01

The blood-brain barrier (BBB) is a microvascular unit which selectively regulates the permeability of drugs to the brain. With the rise in CNS drug targets and diseases, there is a need to be able to accurately predict a priori which compounds in a company database should be pursued for favorable properties. In this review, we will explore the different computational tools available today, as well as underpin these to the experimental methods used to determine BBB permeability. These include in vitro models and the in vivo models that yield the dataset we use to generate predictive models. Understanding of how these models were experimentally derived determines our accurate and predicted use for determining a balance between activity and BBB distribution.

Molecular determinants of blood–brain barrier permeation

PubMed Central

Geldenhuys, Werner J; Mohammad, Afroz S; Adkins, Chris E; Lockman, Paul R

2015-01-01

The blood–brain barrier (BBB) is a microvascular unit which selectively regulates the permeability of drugs to the brain. With the rise in CNS drug targets and diseases, there is a need to be able to accurately predict a priori which compounds in a company database should be pursued for favorable properties. In this review, we will explore the different computational tools available today, as well as underpin these to the experimental methods used to determine BBB permeability. These include in vitro models and the in vivo models that yield the dataset we use to generate predictive models. Understanding of how these models were experimentally derived determines our accurate and predicted use for determining a balance between activity and BBB distribution. PMID:26305616

Laminar fMRI and computational theories of brain function.

PubMed

Stephan, K E; Petzschner, F H; Kasper, L; Bayer, J; Wellstein, K V; Stefanics, G; Pruessmann, K P; Heinzle, J

2017-11-02

Recently developed methods for functional MRI at the resolution of cortical layers (laminar fMRI) offer a novel window into neurophysiological mechanisms of cortical activity. Beyond physiology, laminar fMRI also offers an unprecedented opportunity to test influential theories of brain function. Specifically, hierarchical Bayesian theories of brain function, such as predictive coding, assign specific computational roles to different cortical layers. Combined with computational models, laminar fMRI offers a unique opportunity to test these proposals noninvasively in humans. This review provides a brief overview of predictive coding and related hierarchical Bayesian theories, summarises their predictions with regard to layered cortical computations, examines how these predictions could be tested by laminar fMRI, and considers methodological challenges. We conclude by discussing the potential of laminar fMRI for clinically useful computational assays of layer-specific information processing. Copyright © 2017 Elsevier Inc. All rights reserved.

Predictive Coding Strategies for Developmental Neurorobotics

PubMed Central

Park, Jun-Cheol; Lim, Jae Hyun; Choi, Hansol; Kim, Dae-Shik

2012-01-01

In recent years, predictive coding strategies have been proposed as a possible means by which the brain might make sense of the truly overwhelming amount of sensory data available to the brain at any given moment of time. Instead of the raw data, the brain is hypothesized to guide its actions by assigning causal beliefs to the observed error between what it expects to happen and what actually happens. In this paper, we present a variety of developmental neurorobotics experiments in which minimalist prediction error-based encoding strategies are utilize to elucidate the emergence of infant-like behavior in humanoid robotic platforms. Our approaches will be first naively Piagian, then move onto more Vygotskian ideas. More specifically, we will investigate how simple forms of infant learning, such as motor sequence generation, object permanence, and imitation learning may arise if minimizing prediction errors are used as objective functions. PMID:22586416

Modelling Cerebral Blood Flow and Temperature Using a Vascular Porous Model

NASA Astrophysics Data System (ADS)

Blowers, Stephen; Thrippleton, Michael; Marshall, Ian; Harris, Bridget; Andrews, Peter; Valluri, Prashant

2016-11-01

Macro-modelling of cerebral blood flow can assist in determining the impact of temperature intervention to reduce permanent tissue damage during instances of brain trauma. Here we present a 3D two phase fluid-porous model for simulating blood flow through the capillary region linked to intersecting 1D arterial and venous vessel trees. This combined vasculature porous (VaPor) model simulates both flow and energy balances, including heat from metabolism, using a vasculature extracted from MRI data which are expanded upon using a tree generation algorithm. Validation of temperature balance has been achieved using rodent brain data. Direct flow validation is not as straight forward due to the method used in determining regional cerebral blood flow (rCBF). In-vivo measurements are achieved using a tracer, which disagree with direct measurements of simulated flow. However, by modelling a virtual tracer, rCBF values are obtained that agree with those found in literature. Temperature profiles generated with the VaPor model show a reduction in core brain temperature after cooling the scalp not seen previously in other models.

The role of mechanics during brain development

NASA Astrophysics Data System (ADS)

Budday, Silvia; Steinmann, Paul; Kuhl, Ellen

2014-12-01

Convolutions are a classical hallmark of most mammalian brains. Brain surface morphology is often associated with intelligence and closely correlated with neurological dysfunction. Yet, we know surprisingly little about the underlying mechanisms of cortical folding. Here we identify the role of the key anatomic players during the folding process: cortical thickness, stiffness, and growth. To establish estimates for the critical time, pressure, and the wavelength at the onset of folding, we derive an analytical model using the Föppl-von Kármán theory. Analytical modeling provides a quick first insight into the critical conditions at the onset of folding, yet it fails to predict the evolution of complex instability patterns in the post-critical regime. To predict realistic surface morphologies, we establish a computational model using the continuum theory of finite growth. Computational modeling not only confirms our analytical estimates, but is also capable of predicting the formation of complex surface morphologies with asymmetric patterns and secondary folds. Taken together, our analytical and computational models explain why larger mammalian brains tend to be more convoluted than smaller brains. Both models provide mechanistic interpretations of the classical malformations of lissencephaly and polymicrogyria. Understanding the process of cortical folding in the mammalian brain has direct implications on the diagnostics of neurological disorders including severe retardation, epilepsy, schizophrenia, and autism.

The role of mechanics during brain development

PubMed Central

Budday, Silvia; Steinmann, Paul; Kuhl, Ellen

2014-01-01

Convolutions are a classical hallmark of most mammalian brains. Brain surface morphology is often associated with intelligence and closely correlated to neurological dysfunction. Yet, we know surprisingly little about the underlying mechanisms of cortical folding. Here we identify the role of the key anatomic players during the folding process: cortical thickness, stiffness, and growth. To establish estimates for the critical time, pressure, and the wavelength at the onset of folding, we derive an analytical model using the Föppl-von-Kármán theory. Analytical modeling provides a quick first insight into the critical conditions at the onset of folding, yet it fails to predict the evolution of complex instability patterns in the post-critical regime. To predict realistic surface morphologies, we establish a computational model using the continuum theory of finite growth. Computational modeling not only confirms our analytical estimates, but is also capable of predicting the formation of complex surface morphologies with asymmetric patterns and secondary folds. Taken together, our analytical and computational models explain why larger mammalian brains tend to be more convoluted than smaller brains. Both models provide mechanistic interpretations of the classical malformations of lissencephaly and polymicrogyria. Understanding the process of cortical folding in the mammalian brain has direct implications on the diagnostics of neurological disorders including severe retardation, epilepsy, schizophrenia, and autism. PMID:25202162

Biofidelic white matter heterogeneity decreases computational model predictions of white matter strains during rapid head rotations.

PubMed

Maltese, Matthew R; Margulies, Susan S

2016-11-01

The finite element (FE) brain model is used increasingly as a design tool for developing technology to mitigate traumatic brain injury. We developed an ultra high-definition FE brain model (>4 million elements) from CT and MRI scans of a 2-month-old pre-adolescent piglet brain, and simulated rapid head rotations. Strain distributions in the thalamus, coronal radiata, corpus callosum, cerebral cortex gray matter, brainstem and cerebellum were evaluated to determine the influence of employing homogeneous brain moduli, or distinct experimentally derived gray and white matter property representations, where some white matter regions are stiffer and others less stiff than gray matter. We find that constitutive heterogeneity significantly lowers white matter deformations in all regions compared with homogeneous properties, and should be incorporated in FE model injury prediction.

Individual brain structure and modelling predict seizure propagation

PubMed Central

Proix, Timothée; Bartolomei, Fabrice; Guye, Maxime; Jirsa, Viktor K.

2017-01-01

Abstract See Lytton (doi:10.1093/awx018) for a scientific commentary on this article. Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions. PMID:28364550

Inference of ecological and social drivers of human brain-size evolution.

PubMed

González-Forero, Mauricio; Gardner, Andy

2018-05-01

The human brain is unusually large. It has tripled in size from Australopithecines to modern humans 1 and has become almost six times larger than expected for a placental mammal of human size 2 . Brains incur high metabolic costs 3 and accordingly a long-standing question is why the large human brain has evolved 4 . The leading hypotheses propose benefits of improved cognition for overcoming ecological 5-7 , social 8-10 or cultural 11-14 challenges. However, these hypotheses are typically assessed using correlative analyses, and establishing causes for brain-size evolution remains difficult 15,16 . Here we introduce a metabolic approach that enables causal assessment of social hypotheses for brain-size evolution. Our approach yields quantitative predictions for brain and body size from formalized social hypotheses given empirical estimates of the metabolic costs of the brain. Our model predicts the evolution of adult Homo sapiens-sized brains and bodies when individuals face a combination of 60% ecological, 30% cooperative and 10% between-group competitive challenges, and suggests that between-individual competition has been unimportant for driving human brain-size evolution. Moreover, our model indicates that brain expansion in Homo was driven by ecological rather than social challenges, and was perhaps strongly promoted by culture. Our metabolic approach thus enables causal assessments that refine, refute and unify hypotheses of brain-size evolution.

Warm Body Temperature Facilitates Energy Efficient Cortical Action Potentials

PubMed Central

Yu, Yuguo; Hill, Adam P.; McCormick, David A.

2012-01-01

The energy efficiency of neural signal transmission is important not only as a limiting factor in brain architecture, but it also influences the interpretation of functional brain imaging signals. Action potential generation in mammalian, versus invertebrate, axons is remarkably energy efficient. Here we demonstrate that this increase in energy efficiency is due largely to a warmer body temperature. Increases in temperature result in an exponential increase in energy efficiency for single action potentials by increasing the rate of Na+ channel inactivation, resulting in a marked reduction in overlap of the inward Na+, and outward K+, currents and a shortening of action potential duration. This increase in single spike efficiency is, however, counterbalanced by a temperature-dependent decrease in the amplitude and duration of the spike afterhyperpolarization, resulting in a nonlinear increase in the spike firing rate, particularly at temperatures above approximately 35°C. Interestingly, the total energy cost, as measured by the multiplication of total Na+ entry per spike and average firing rate in response to a constant input, reaches a global minimum between 37–42°C. Our results indicate that increases in temperature result in an unexpected increase in energy efficiency, especially near normal body temperature, thus allowing the brain to utilize an energy efficient neural code. PMID:22511855

Fish with thermolabile sex determination (TSD) as models to study brain sex differentiation.

PubMed

Blázquez, Mercedes; Somoza, Gustavo M

2010-05-01

As fish are ectothermic animals, water temperature can affect their basic biological processes such as larval development, growth and reproduction. Similar to reptiles, the incubation temperature during early phases of development is capable to modify sex ratios in a large number of fish species. This phenomenon, known as thermolabile sex determination (TSD) was first reported in Menidia menidia, a species belonging to the family Atherinopsidae. Since then, an increasing number of fish have also been found to exhibit TSD. Traditionally, likewise in reptiles, several TSD patterns have been described in fish, however it has been recently postulated that only one, females at low temperatures and males at high temperatures, may represent the "real" or "true" TSD. Many studies regarding the influence of temperature on the final sex ratios have been focused on the expression and activity of gonadal aromatase, the enzyme involved in the conversion of androgens into estrogens and encoded by the cyp19a1a gene. In this regard, teleost fish, may be due to a whole genome duplication event, produce another aromatase enzyme, commonly named brain aromatase, encoded by the cyp19a1b gene. Contrary to what has been described in other vertebrates, fish exhibit very high levels of aromatase activity in the brain and therefore they synthesize high amounts of neuroestrogens. However, its biological significance is still not understood. In addition, the mechanism whereby temperature can induce the development of a testis or an ovary still remains elusive. In this context the present review is aimed to discuss several theories about the possible role of brain aromatase using fish as models. The relevance of brain aromatase and therefore of neuroestrogens as the possible cue for gonadal differentiation is raised. In addition, the possible role of brain aromatase as the way to keep the high levels of neurogenesis in fish is also considered. Several key examples of how teleosts and aromatase regulation can offer more insight into basic mechanisms of TSD are also reviewed. Copyright 2009 Elsevier Inc. All rights reserved.

What does brain response to neutral faces tell us about major depression? evidence from machine learning and fMRI.

PubMed

Oliveira, Leticia; Ladouceur, Cecile D; Phillips, Mary L; Brammer, Michael; Mourao-Miranda, Janaina

2013-01-01

A considerable number of previous studies have shown abnormalities in the processing of emotional faces in major depression. Fewer studies, however, have focused specifically on abnormal processing of neutral faces despite evidence that depressed patients are slow and less accurate at recognizing neutral expressions in comparison with healthy controls. The current study aimed to investigate whether this misclassification described behaviourally for neutral faces also occurred when classifying patterns of brain activation to neutral faces for these patients. TWO INDEPENDENT DEPRESSED SAMPLES: (1) Nineteen medication-free patients with depression and 19 healthy volunteers and (2) Eighteen depressed individuals and 18 age and gender-ratio-matched healthy volunteers viewed emotional faces (sad/neutral; happy/neutral) during an fMRI experiment. We used a new pattern recognition framework: first, we trained the classifier to discriminate between two brain states (e.g. viewing happy faces vs. viewing neutral faces) using data only from healthy controls (HC). Second, we tested the classifier using patterns of brain activation of a patient and a healthy control for the same stimuli. Finally, we tested if the classifier's predictions (predictive probabilities) for emotional and neutral face classification were different for healthy controls and depressed patients. Predictive probabilities to patterns of brain activation to neutral faces in both groups of patients were significantly lower in comparison to the healthy controls. This difference was specific to neutral faces. There were no significant differences in predictive probabilities to patterns of brain activation to sad faces (sample 1) and happy faces (samples 2) between depressed patients and healthy controls. Our results suggest that the pattern of brain activation to neutral faces in depressed patients is not consistent with the pattern observed in healthy controls subject to the same stimuli. This difference in brain activation might underlie the behavioural misinterpretation of the neutral faces content by the depressed patients.

Alterations of LKB1 and KRAS and risk of brain metastasis: comprehensive characterization by mutation analysis, copy number, and gene expression in non-small-cell lung carcinoma.

PubMed

Zhao, Ni; Wilkerson, Matthew D; Shah, Usman; Yin, Xiaoying; Wang, Anyou; Hayward, Michele C; Roberts, Patrick; Lee, Carrie B; Parsons, Alden M; Thorne, Leigh B; Haithcock, Benjamin E; Grilley-Olson, Juneko E; Stinchcombe, Thomas E; Funkhouser, William K; Wong, Kwok-Kin; Sharpless, Norman E; Hayes, D Neil

2014-11-01

Brain metastases are one of the most malignant complications of lung cancer and constitute a significant cause of cancer related morbidity and mortality worldwide. Recent years of investigation suggested a role of LKB1 in NSCLC development and progression, in synergy with KRAS alteration. In this study, we systematically analyzed how LKB1 and KRAS alteration, measured by mutation, gene expression (GE) and copy number (CN), are associated with brain metastasis in NSCLC. Patients treated at University of North Carolina Hospital from 1990 to 2009 with NSCLC provided frozen, surgically extracted tumors for analysis. GE was measured using Agilent 44,000 custom-designed arrays, CN was assessed by Affymetrix GeneChip Human Mapping 250K Sty Array or the Genome-Wide Human SNP Array 6.0 and gene mutation was detected using ABI sequencing. Integrated analysis was conducted to assess the relationship between these genetic markers and brain metastasis. A model was proposed for brain metastasis prediction using these genetic measurements. 17 of the 174 patients developed brain metastasis. LKB1 wild type tumors had significantly higher LKB1 CN (p<0.001) and GE (p=0.002) than the LKB1 mutant group. KRAS wild type tumors had significantly lower KRAS GE (p<0.001) and lower CN, although the latter failed to be significant (p=0.295). Lower LKB1 CN (p=0.039) and KRAS mutation (p=0.007) were significantly associated with more brain metastasis. The predictive model based on nodal (N) stage, patient age, LKB1 CN and KRAS mutation had a good prediction accuracy, with area under the ROC curve of 0.832 (p<0.001). LKB1 CN in combination with KRAS mutation predicted brain metastasis in NSCLC. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Divergent effects of postmortem ambient temperature on organophosphorus- and carbamate-inhibited brain cholinesterase activity in birds

USGS Publications Warehouse

Hill, E.F.

1989-01-01

Time- and temperature-dependent postmortem changes in inhibited brain cholinesterase (ChE) activity may confound diagnosis of field poisoning of wildlife by anticholinesterase pesticide. Carbamate-inhibited ChE activity may return to normal within 1 to 2 days of exposure of intact carcass to moderate ambient temperature (18-32C). Organophosphorus-inhibited ChE activity becomes more depressed over the same time. Uninhibited ChE activity was resilient to above freezing temperature to 32C for 1 day and 25C for 3 days. Carbamate- and organophosphorus-inhibited ChE can be separated by incubation of homogenate for 1 hour at physiological temperatures; carbamylated ChE can be readily reactivated while phosphorylated ChE cannot.

Brain volumetric changes and cognitive ageing during the eighth decade of life

PubMed Central

Dickie, David Alexander; Cox, Simon R.; Valdes Hernandez, Maria del C.; Corley, Janie; Royle, Natalie A.; Pattie, Alison; Aribisala, Benjamin S.; Redmond, Paul; Muñoz Maniega, Susana; Taylor, Adele M.; Sibbett, Ruth; Gow, Alan J.; Starr, John M.; Bastin, Mark E.; Wardlaw, Joanna M.; Deary, Ian J.

2015-01-01

Abstract Later‐life changes in brain tissue volumes—decreases in the volume of healthy grey and white matter and increases in the volume of white matter hyperintensities (WMH)—are strong candidates to explain some of the variation in ageing‐related cognitive decline. We assessed fluid intelligence, memory, processing speed, and brain volumes (from structural MRI) at mean age 73 years, and at mean age 76 in a narrow‐age sample of older individuals (n = 657 with brain volumetric data at the initial wave, n = 465 at follow‐up). We used latent variable modeling to extract error‐free cognitive levels and slopes. Initial levels of cognitive ability were predictive of subsequent brain tissue volume changes. Initial brain volumes were not predictive of subsequent cognitive changes. Brain volume changes, especially increases in WMH, were associated with declines in each of the cognitive abilities. All statistically significant results were modest in size (absolute r‐values ranged from 0.114 to 0.334). These results build a comprehensive picture of macrostructural brain volume changes and declines in important cognitive faculties during the eighth decade of life. Hum Brain Mapp 36:4910–4925, 2015. © 2015 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc PMID:26769551

Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors

PubMed Central

Vilar, Santiago; Chakrabarti, Mayukh; Costanzi, Stefano

2010-01-01

The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of log BB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (log P), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental log BB data had been determined in vivo. In particular, since molecules with log BB > 0.3 cross the blood-brain barrier (BBB) readily while molecules with log BB < −1 are poorly distributed to the brain, on the basis of these thresholds we derived two distinct models, both of which show a percentage of good classification of about 80%. Notably, the predictive power of our models was confirmed by the analysis of a large external dataset of compounds with reported activity on the central nervous system (CNS) or lack thereof. The calculation of straightforward physicochemical descriptors is the only requirement for the prediction of the log BB of novel compounds through our models, which can be conveniently applied in conjunction with drug design and virtual screenings. PMID:20427217

Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors.

PubMed

Vilar, Santiago; Chakrabarti, Mayukh; Costanzi, Stefano

2010-06-01

The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of logBB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (logP), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental logBB data had been determined in vivo. In particular, since molecules with logBB>0.3 cross the blood-brain barrier (BBB) readily while molecules with logBB<-1 are poorly distributed to the brain, on the basis of these thresholds we derived two distinct models, both of which show a percentage of good classification of about 80%. Notably, the predictive power of our models was confirmed by the analysis of a large external dataset of compounds with reported activity on the central nervous system (CNS) or lack thereof. The calculation of straightforward physicochemical descriptors is the only requirement for the prediction of the logBB of novel compounds through our models, which can be conveniently applied in conjunction with drug design and virtual screenings. Published by Elsevier Inc.

Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction

PubMed Central

Lancaster, Jenessa; Lorenz, Romy; Leech, Rob; Cole, James H.

2018-01-01

Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimization framework to the new dataset, out-performing the parameters optimized for the initial training dataset. Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimization to derive case-specific pre-processing parameters. Our results suggest that different pre-processing parameters are selected when optimization is conducted in specific contexts. This potentially motivates use of optimization techniques at many different points during the experimental process, which may improve statistical sensitivity and reduce opportunities for experimenter-led bias. PMID:29483870

Biothermal Model of Patient for Brain Hypothermia Treatment

NASA Astrophysics Data System (ADS)

Wakamatsu, Hidetoshi; Gaohua, Lu

A biothermal model of patient is proposed and verified for the brain hypothermia treatment, since the conventionally applied biothermal models are inappropriate for their unprecedented application. The model is constructed on the basis of the clinical practice of the pertinent therapy and characterized by the mathematical relation with variable ambient temperatures, in consideration of the clinical treatments such as the vital cardiopulmonary regulation. It has geometrically clear representation of multi-segmental core-shell structure, database of physiological and physical parameters with a systemic state equation setting the initial temperature of each compartment. Its step response gives the time constant about 3 hours in agreement with clinical knowledge. As for the essential property of the model, the dynamic temperature of its face-core compartment is realized, which corresponds to the tympanic membrane temperature measured under the practical anesthesia. From the various simulations consistent with the phenomena of clinical practice, it is concluded that the proposed model is appropriate for the theoretical analysis and clinical application to the brain hypothermia treatment.

LSD-induced entropic brain activity predicts subsequent personality change.

PubMed

Lebedev, A V; Kaelen, M; Lövdén, M; Nilsson, J; Feilding, A; Nutt, D J; Carhart-Harris, R L

2016-09-01

Personality is known to be relatively stable throughout adulthood. Nevertheless, it has been shown that major life events with high personal significance, including experiences engendered by psychedelic drugs, can have an enduring impact on some core facets of personality. In the present, balanced-order, placebo-controlled study, we investigated biological predictors of post-lysergic acid diethylamide (LSD) changes in personality. Nineteen healthy adults underwent resting state functional MRI scans under LSD (75µg, I.V.) and placebo (saline I.V.). The Revised NEO Personality Inventory (NEO-PI-R) was completed at screening and 2 weeks after LSD/placebo. Scanning sessions consisted of three 7.5-min eyes-closed resting-state scans, one of which involved music listening. A standardized preprocessing pipeline was used to extract measures of sample entropy, which characterizes the predictability of an fMRI time-series. Mixed-effects models were used to evaluate drug-induced shifts in brain entropy and their relationship with the observed increases in the personality trait openness at the 2-week follow-up. Overall, LSD had a pronounced global effect on brain entropy, increasing it in both sensory and hierarchically higher networks across multiple time scales. These shifts predicted enduring increases in trait openness. Moreover, the predictive power of the entropy increases was greatest for the music-listening scans and when "ego-dissolution" was reported during the acute experience. These results shed new light on how LSD-induced shifts in brain dynamics and concomitant subjective experience can be predictive of lasting changes in personality. Hum Brain Mapp 37:3203-3213, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Learning-based prediction of gestational age from ultrasound images of the fetal brain.

PubMed

Namburete, Ana I L; Stebbing, Richard V; Kemp, Bryn; Yaqub, Mohammad; Papageorghiou, Aris T; Alison Noble, J

2015-04-01

We propose an automated framework for predicting gestational age (GA) and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain image appearance. Our method capitalizes on age-related sonographic image patterns in conjunction with clinical measurements to develop, for the first time, a predictive age model which improves on the GA-prediction potential of US images. The framework benefits from a manifold surface representation of the fetal head which delineates the inner skull boundary and serves as a common coordinate system based on cranial position. This allows for fast and efficient sampling of anatomically-corresponding brain regions to achieve like-for-like structural comparison of different developmental stages. We develop bespoke features which capture neurosonographic patterns in 3D images, and using a regression forest classifier, we characterize structural brain development both spatially and temporally to capture the natural variation existing in a healthy population (N=447) over an age range of active brain maturation (18-34weeks). On a routine clinical dataset (N=187) our age prediction results strongly correlate with true GA (r=0.98,accurate within±6.10days), confirming the link between maturational progression and neurosonographic activity observable across gestation. Our model also outperforms current clinical methods by ±4.57 days in the third trimester-a period complicated by biological variations in the fetal population. Through feature selection, the model successfully identified the most age-discriminating anatomies over this age range as being the Sylvian fissure, cingulate, and callosal sulci. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Transcripts with in silico predicted RNA structure are enriched everywhere in the mouse brain

PubMed Central

2012-01-01

Background Post-transcriptional control of gene expression is mostly conducted by specific elements in untranslated regions (UTRs) of mRNAs, in collaboration with specific binding proteins and RNAs. In several well characterized cases, these RNA elements are known to form stable secondary structures. RNA secondary structures also may have major functional implications for long noncoding RNAs (lncRNAs). Recent transcriptional data has indicated the importance of lncRNAs in brain development and function. However, no methodical efforts to investigate this have been undertaken. Here, we aim to systematically analyze the potential for RNA structure in brain-expressed transcripts. Results By comprehensive spatial expression analysis of the adult mouse in situ hybridization data of the Allen Mouse Brain Atlas, we show that transcripts (coding as well as non-coding) associated with in silico predicted structured probes are highly and significantly enriched in almost all analyzed brain regions. Functional implications of these RNA structures and their role in the brain are discussed in detail along with specific examples. We observe that mRNAs with a structure prediction in their UTRs are enriched for binding, transport and localization gene ontology categories. In addition, after manual examination we observe agreement between RNA binding protein interaction sites near the 3’ UTR structures and correlated expression patterns. Conclusions Our results show a potential use for RNA structures in expressed coding as well as noncoding transcripts in the adult mouse brain, and describe the role of structured RNAs in the context of intracellular signaling pathways and regulatory networks. Based on this data we hypothesize that RNA structure is widely involved in transcriptional and translational regulatory mechanisms in the brain and ultimately plays a role in brain function. PMID:22651826

Prediction of movement intention using connectivity within motor-related network: An electrocorticography study.

PubMed

Kang, Byeong Keun; Kim, June Sic; Ryun, Seokyun; Chung, Chun Kee

2018-01-01

Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.

Fluid and flexible minds: Intelligence reflects synchrony in the brain’s intrinsic network architecture

PubMed Central

Ferguson, Michael A.; Anderson, Jeffrey S.; Spreng, R. Nathan

2017-01-01

Human intelligence has been conceptualized as a complex system of dissociable cognitive processes, yet studies investigating the neural basis of intelligence have typically emphasized the contributions of discrete brain regions or, more recently, of specific networks of functionally connected regions. Here we take a broader, systems perspective in order to investigate whether intelligence is an emergent property of synchrony within the brain’s intrinsic network architecture. Using a large sample of resting-state fMRI and cognitive data (n = 830), we report that the synchrony of functional interactions within and across distributed brain networks reliably predicts fluid and flexible intellectual functioning. By adopting a whole-brain, systems-level approach, we were able to reliably predict individual differences in human intelligence by characterizing features of the brain’s intrinsic network architecture. These findings hold promise for the eventual development of neural markers to predict changes in intellectual function that are associated with neurodevelopment, normal aging, and brain disease.

Reducing Brain Signal Noise in the Prediction of Economic Choices: A Case Study in Neuroeconomics

PubMed Central

Sundararajan, Raanju R.; Palma, Marco A.; Pourahmadi, Mohsen

2017-01-01

In order to reduce the noise of brain signals, neuroeconomic experiments typically aggregate data from hundreds of trials collected from a few individuals. This contrasts with the principle of simple and controlled designs in experimental and behavioral economics. We use a frequency domain variant of the stationary subspace analysis (SSA) technique, denoted as DSSA, to filter out the noise (nonstationary sources) in EEG brain signals. The nonstationary sources in the brain signal are associated with variations in the mental state that are unrelated to the experimental task. DSSA is a powerful tool for reducing the number of trials needed from each participant in neuroeconomic experiments and also for improving the prediction performance of an economic choice task. For a single trial, when DSSA is used as a noise reduction technique, the prediction model in a food snack choice experiment has an increase in overall accuracy by around 10% and in sensitivity and specificity by around 20% and in AUC by around 30%, respectively. PMID:29311784

Reducing Brain Signal Noise in the Prediction of Economic Choices: A Case Study in Neuroeconomics.

PubMed

Sundararajan, Raanju R; Palma, Marco A; Pourahmadi, Mohsen

2017-01-01

In order to reduce the noise of brain signals, neuroeconomic experiments typically aggregate data from hundreds of trials collected from a few individuals. This contrasts with the principle of simple and controlled designs in experimental and behavioral economics. We use a frequency domain variant of the stationary subspace analysis (SSA) technique, denoted as DSSA, to filter out the noise (nonstationary sources) in EEG brain signals. The nonstationary sources in the brain signal are associated with variations in the mental state that are unrelated to the experimental task. DSSA is a powerful tool for reducing the number of trials needed from each participant in neuroeconomic experiments and also for improving the prediction performance of an economic choice task. For a single trial, when DSSA is used as a noise reduction technique, the prediction model in a food snack choice experiment has an increase in overall accuracy by around 10% and in sensitivity and specificity by around 20% and in AUC by around 30%, respectively.

Predicting novel histopathological microlesions in human epileptic brain through transcriptional clustering.

PubMed

Dachet, Fabien; Bagla, Shruti; Keren-Aviram, Gal; Morton, Andrew; Balan, Karina; Saadat, Laleh; Valyi-Nagy, Tibor; Kupsky, William; Song, Fei; Dratz, Edward; Loeb, Jeffrey A

2015-02-01

Although epilepsy is associated with a variety of abnormalities, exactly why some brain regions produce seizures and others do not is not known. We developed a method to identify cellular changes in human epileptic neocortex using transcriptional clustering. A paired analysis of high and low spiking tissues recorded in vivo from 15 patients predicted 11 cell-specific changes together with their 'cellular interactome'. These predictions were validated histologically revealing millimetre-sized 'microlesions' together with a global increase in vascularity and microglia. Microlesions were easily identified in deeper cortical layers using the neuronal marker NeuN, showed a marked reduction in neuronal processes, and were associated with nearby activation of MAPK/CREB signalling, a marker of epileptic activity, in superficial layers. Microlesions constitute a common, undiscovered layer-specific abnormality of neuronal connectivity in human neocortex that may be responsible for many 'non-lesional' forms of epilepsy. The transcriptional clustering approach used here could be applied more broadly to predict cellular differences in other brain and complex tissue disorders. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Connectivity Predicts Deep Brain Stimulation Outcome in Parkinson Disease

PubMed Central

Horn, Andreas; Reich, Martin; Vorwerk, Johannes; Li, Ningfei; Wenzel, Gregor; Fang, Qianqian; Schmitz-Hübsch, Tanja; Nickl, Robert; Kupsch, Andreas; Volkmann, Jens; Kühn, Andrea A.; Fox, Michael D.

2018-01-01

Objective The benefit of deep brain stimulation (DBS) for Parkinson disease (PD) may depend on connectivity between the stimulation site and other brain regions, but which regions and whether connectivity can predict outcome in patients remain unknown. Here, we identify the structural and functional connectivity profile of effective DBS to the subthalamic nucleus (STN) and test its ability to predict outcome in an independent cohort. Methods A training dataset of 51 PD patients with STN DBS was combined with publicly available human connectome data (diffusion tractography and resting state functional connectivity) to identify connections reliably associated with clinical improvement (motor score of the Unified Parkinson Disease Rating Scale [UPDRS]). This connectivity profile was then used to predict outcome in an independent cohort of 44 patients from a different center. Results In the training dataset, connectivity between the DBS electrode and a distributed network of brain regions correlated with clinical response including structural connectivity to supplementary motor area and functional anticorrelation to primary motor cortex (p<0.001). This same connectivity profile predicted response in an independent patient cohort (p<0.01). Structural and functional connectivity were independent predictors of clinical improvement (p<0.001) and estimated response in individual patients with an average error of 15% UPDRS improvement. Results were similar using connectome data from normal subjects or a connectome age, sex, and disease matched to our DBS patients. Interpretation Effective STN DBS for PD is associated with a specific connectivity profile that can predict clinical outcome across independent cohorts. This prediction does not require specialized imaging in PD patients themselves. PMID:28586141

Functional network architecture predicts psychologically mediated analgesia related to treatment in chronic knee pain patients.

PubMed

Hashmi, Javeria Ali; Kong, Jian; Spaeth, Rosa; Khan, Sheraz; Kaptchuk, Ted J; Gollub, Randy L

2014-03-12

Placebo analgesia is an indicator of how efficiently the brain translates psychological signals conveyed by a treatment procedure into pain relief. It has been demonstrated that functional connectivity between distributed brain regions predicts placebo analgesia in chronic back pain patients. Greater network efficiency in baseline brain networks may allow better information transfer and facilitate adaptive physiological responses to psychological aspects of treatment. Here, we theorized that topological network alignments in resting state scans predict psychologically conditioned analgesic responses to acupuncture treatment in chronic knee osteoarthritis pain patients (n = 45). Analgesia was induced by building positive expectations toward acupuncture treatment with verbal suggestion and heat pain conditioning on a test site of the arm. This procedure induced significantly more analgesia after sham or real acupuncture on the test site than in a control site. The psychologically conditioned analgesia was invariant to sham versus real treatment. Efficiency of information transfer within local networks calculated with graph-theoretic measures (local efficiency and clustering coefficients) significantly predicted conditioned analgesia. Clustering coefficients in regions associated with memory, motivation, and pain modulation were closely involved in predicting analgesia. Moreover, women showed higher clustering coefficients and marginally greater pain reduction than men. Overall, analgesic response to placebo cues can be predicted from a priori resting state data by observing local network topology. Such low-cost synchronizations may represent preparatory resources that facilitate subsequent performance of brain circuits in responding to adaptive environmental cues. This suggests a potential utility of network measures in predicting placebo response for clinical use.

Individual brain structure and modelling predict seizure propagation.

PubMed

Proix, Timothée; Bartolomei, Fabrice; Guye, Maxime; Jirsa, Viktor K

2017-03-01

See Lytton (doi:10.1093/awx018) for a scientific commentary on this article.Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain.

Medial-Frontal Stimulation Enhances Learning in Schizophrenia by Restoring Prediction Error Signaling.

PubMed

Reinhart, Robert M G; Zhu, Julia; Park, Sohee; Woodman, Geoffrey F

2015-09-02

Posterror learning, associated with medial-frontal cortical recruitment in healthy subjects, is compromised in neuropsychiatric disorders. Here we report novel evidence for the mechanisms underlying learning dysfunctions in schizophrenia. We show that, by noninvasively passing direct current through human medial-frontal cortex, we could enhance the event-related potential related to learning from mistakes (i.e., the error-related negativity), a putative index of prediction error signaling in the brain. Following this causal manipulation of brain activity, the patients learned a new task at a rate that was indistinguishable from healthy individuals. Moreover, the severity of delusions interacted with the efficacy of the stimulation to improve learning. Our results demonstrate a causal link between disrupted prediction error signaling and inefficient learning in schizophrenia. These findings also demonstrate the feasibility of nonpharmacological interventions to address cognitive deficits in neuropsychiatric disorders. When there is a difference between what we expect to happen and what we actually experience, our brains generate a prediction error signal, so that we can map stimuli to responses and predict outcomes accurately. Theories of schizophrenia implicate abnormal prediction error signaling in the cognitive deficits of the disorder. Here, we combine noninvasive brain stimulation with large-scale electrophysiological recordings to establish a causal link between faulty prediction error signaling and learning deficits in schizophrenia. We show that it is possible to improve learning rate, as well as the neural signature of prediction error signaling, in patients to a level quantitatively indistinguishable from that of healthy subjects. The results provide mechanistic insight into schizophrenia pathophysiology and suggest a future therapy for this condition. Copyright © 2015 the authors 0270-6474/15/3512232-09$15.00/0.

Effect of shivering on brain tissue oxygenation during induced normothermia in patients with severe brain injury.

PubMed

Oddo, Mauro; Frangos, Suzanne; Maloney-Wilensky, Eileen; Andrew Kofke, W; Le Roux, Peter D; Levine, Joshua M

2010-02-01

We analyzed the impact of shivering on brain tissue oxygenation (PbtO(2)) during induced normothermia in patients with severe brain injury. We studied patients with severe brain injury who developed shivering during induced normothermia. Induced normothermia was applied to treat refractory fever (body temperature [BT] > or =38.3 degrees C, refractory to conventional treatment) using a surface cooling device with computerized adjustment of patient BT target to 37 +/- 0.5 degrees C. PbtO(2), intracranial pressure, mean arterial pressure, cerebral perfusion pressure, and BT were monitored continuously. Circulating water temperature of the device system was measured to assess the intensity of cooling. Fifteen patients (10 with severe traumatic brain injury, 5 with aneurysmal subarachnoid hemorrhage) were treated with induced normothermia for an average of 5 +/- 2 days. Shivering caused a significant decrease in PbtO(2) levels both in SAH and TBI patients. Compared to baseline, shivering was associated with an overall reduction of PbtO(2) from 34.1 +/- 7.3 to 24.4 +/- 5.5 mmHg (P < 0.001). A significant correlation was found between the magnitude of shivering-associated decrease of PbtO(2) (DeltaPbtO(2)) and circulating water temperature (R = 0.82, P < 0.001). In patients with severe brain injury treated with induced normothermia, shivering was associated with a significant decrease of PbtO(2), which correlated with the intensity of cooling. Monitoring of therapeutic cooling with computerized thermoregulatory systems may help prevent shivering and optimize the management of induced normothermia. The clinical significance of shivering-induced decrease in brain tissue oxygenation remains to be determined.

Depletion of serotonin synthesis with p-CPA pretreatment alters EEG in urethane anesthetized rats under whole body hyperthermia.

PubMed

Sinha, Rakesh Kumar; Aggarwal, Yogender

2007-01-01

Serotonin is believed as an important factor in brain function. The role of serotonin in cerebral psycho-patho-physiology has already been well established. However, the function of serotonin antagonist in anesthetized subjects under hyperthermia has not been studied properly. Experiments were performed in three groups of urethane-anesthetized rats, such as: (i) control group, (ii) whole body hyperthermia group and (iii) p-CPA (para-Chlorophenylalanine) pretreated hyperthermia group. Hyperthermia was produced by subjecting the rats to high ambient temperature of 38 +/- 1 degrees C (relative humidity 45-50%). Each group was divided for EEG (electroencephalogram) study and for determination of edematous swelling in the brain. Urethane anesthetized rats under hyperthermia show highly significant reduction in their survival time. The body temperature recorded during the hyperthermia was observed with significant and linear rise with marked increase in brain water content, which was analyzed just after the death of the subjects. The results of the electroencephalographic study in urethane-anesthetized rats recorded before death indicate that brain function varies in systematic manner during hyperthermia as sequential changes in EEG patterns were observed. However, a serotonin antagonist, p-CPA pretreatment increases the survival time with significant reduction in edematous swelling in brain but it does not affect the relationship between the core body temperature and the brain cortical potentials as observed in urethane anesthetized subjects exposed to whole body hyperthermia. The core body temperature in p-CPA pretreated rats show non-linear relationship with respect to the exposure time as it was observed in drug untreated subjects. The findings of the present study indicate that although pretreatment of p-CPA in rats has a marked correlation between the extravasations of the blood-brain barrier under hyperthermia but shows minimum effect on the EEG in a model of hyperthermia under irreversible anesthesia.

The influence of meteorological and geomagnetic factors on acute myocardial infarction and brain stroke in Moscow, Russia.

PubMed

Shaposhnikov, Dmitry; Revich, Boris; Gurfinkel, Yuri; Naumova, Elena

2014-07-01

Evidence of the impact of air temperature and pressure on cardiovascular morbidity is still quite limited and controversial, and even less is known about the potential influence of geomagnetic activity. The objective of this study was to assess impacts of air temperature, barometric pressure and geomagnetic activity on hospitalizations with myocardial infarctions and brain strokes. We studied 2,833 myocardial infarctions and 1,096 brain strokes registered in two Moscow hospitals between 1992 and 2005. Daily event rates were linked with meteorological and geomagnetic conditions, using generalized linear model with controls for day of the week, seasonal and long-term trends. The number of myocardial infarctions decreased with temperature, displayed a U-shaped relationship with pressure and variations in pressure, and increased with geomagnetic activity. The number of strokes increased with temperature, daily temperature range and geomagnetic activity. Detrimental effects on strokes of low pressure and falling pressure were observed. Relative risks of infarctions and strokes during geomagnetic storms were 1.29 (95% CI 1.19-1.40) and 1.25 (1.10-1.42), respectively. The number of strokes doubled during cold spells. The influence of barometric pressure on hospitalizations was relatively greater than the influence of geomagnetic activity, and the influence of temperature was greater than the influence of pressure. Brain strokes were more sensitive to inclement weather than myocardial infarctions. This paper provides quantitative estimates of the expected increases in hospital admissions on the worst days and can help to develop preventive health plans for cardiovascular diseases.

Proton MRS in acute traumatic brain injury: role for glutamate/glutamine and choline for outcome prediction.

PubMed

Shutter, Lori; Tong, Karen A; Holshouser, Barbara A

2004-12-01

Proton magnetic resonance spectroscopy (MRS) is being used to evaluate individuals with acute traumatic brain injury and several studies have shown that changes in certain brain metabolites (N-acetylaspartate, choline) are associated with poor neurologic outcomes. The majority of previous MRS studies have been obtained relatively late after injury and none have examined the role of glutamate/ glutamine (Glx). We conducted a prospective MRS study of 42 severely injured adults to measure quantitative metabolite changes early (7 days) after injury in normal appearing brain. We used these findings to predict long-term neurologic outcome and to determine if MRS data alone or in combination with clinical outcome variables provided better prediction of long-term outcomes. We found that glutamate/glutamine (Glx) and choline (Cho) were significantly elevated in occipital gray and parietal white matter early after injury in patients with poor long-term (6-12-month) outcomes. Glx and Cho ratios predicted long-term outcome with 94% accuracy and when combined with the motor Glasgow Coma Scale score provided the highest predictive accuracy (97%). Somatosensory evoked potentials were not as accurate as MRS data in predicting outcome. Elevated Glx and Cho are more sensitive indicators of injury and predictors of poor outcome when spectroscopy is done early after injury. This may be a reflection of early excitotoxic injury (i.e., elevated Glx) and of injury associated with membrane disruption (i.e., increased Cho) secondary to diffuse axonal injury.

GABA-A receptors in mPOAH simultaneously regulate sleep and body temperature in freely moving rats.

PubMed

Jha, S K; Yadav, V; Mallick, B N

2001-09-01

Sleep-wakefulness and body temperature are two circadian rhythmic biological phenomena. The role of GABAergic inputs in the medial preoptico-anterior hypothalamus (mPOAH) on simultaneous regulation of those phenomena was investigated in freely moving normally behaving rats. The GABA-A receptors were blocked by microinjecting picrotoxin, and the effects on electrophysiological parameters signifying sleep-wakefulness, rectal temperature and brain temperature were recorded simultaneously. The results suggest that, normally, GABA in the medial preoptic area acts through GABA-A receptor that induces sleep and prevents an excessive rise in body temperature. However, the results do not allow us to comment on the cause and effect relationship, if any, between changes in sleep-wakefulness and body temperature. The changes in brain and rectal temperatures showed a positive correlation, however, the former varied within a narrower range than that of the latter.

The Predictive Brain State: Timing Deficiency in Traumatic Brain Injury?

PubMed Central

Ghajar, Jamshid; Ivry, Richard B.

2015-01-01

Attention and memory deficits observed in traumatic brain injury (TBI) are postulated to result from the shearing of white matter connections between the prefrontal cortex, parietal lobe, and cerebellum that are critical in the generation, maintenance, and precise timing of anticipatory neural activity. These fiber tracts are part of a neural network that generates predictions of future states and events, processes that are required for optimal performance on attention and working memory tasks. The authors discuss the role of this anticipatory neural system for understanding the varied symptoms and potential rehabilitation interventions for TBI. Preparatory neural activity normally allows the efficient integration of sensory information with goal-based representations. It is postulated that an impairment in the generation of this activity in traumatic brain injury (TBI) leads to performance variability as the brain shifts from a predictive to reactive mode. This dysfunction may constitute a fundamental defect in TBI as well as other attention disorders, causing working memory deficits, distractibility, a loss of goal-oriented behavior, and decreased awareness. “The future is not what is coming to meet us, but what we are moving forward to meet.” —Jean-Marie Guyau1 PMID:18460693

Distributed Patterns of Reactivation Predict Vividness of Recollection.

PubMed

St-Laurent, Marie; Abdi, Hervé; Buchsbaum, Bradley R

2015-10-01

According to the principle of reactivation, memory retrieval evokes patterns of brain activity that resemble those instantiated when an event was first experienced. Intuitively, one would expect neural reactivation to contribute to recollection (i.e., the vivid impression of reliving past events), but evidence of a direct relationship between the subjective quality of recollection and multiregional reactivation of item-specific neural patterns is lacking. The current study assessed this relationship using fMRI to measure brain activity as participants viewed and mentally replayed a set of short videos. We used multivoxel pattern analysis to train a classifier to identify individual videos based on brain activity evoked during perception and tested how accurately the classifier could distinguish among videos during mental replay. Classification accuracy correlated positively with memory vividness, indicating that the specificity of multivariate brain patterns observed during memory retrieval was related to the subjective quality of a memory. In addition, we identified a set of brain regions whose univariate activity during retrieval predicted both memory vividness and the strength of the classifier's prediction irrespective of the particular video that was retrieved. Our results establish distributed patterns of neural reactivation as a valid and objective marker of the quality of recollection.

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Copyright © 2017. Published by Elsevier Inc.

Predicting individual brain functional connectivity using a Bayesian hierarchical model.

PubMed

Dai, Tian; Guo, Ying

2017-02-15

Network-oriented analysis of functional magnetic resonance imaging (fMRI), especially resting-state fMRI, has revealed important association between abnormal connectivity and brain disorders such as schizophrenia, major depression and Alzheimer's disease. Imaging-based brain connectivity measures have become a useful tool for investigating the pathophysiology, progression and treatment response of psychiatric disorders and neurodegenerative diseases. Recent studies have started to explore the possibility of using functional neuroimaging to help predict disease progression and guide treatment selection for individual patients. These studies provide the impetus to develop statistical methodology that would help provide predictive information on disease progression-related or treatment-related changes in neural connectivity. To this end, we propose a prediction method based on Bayesian hierarchical model that uses individual's baseline fMRI scans, coupled with relevant subject characteristics, to predict the individual's future functional connectivity. A key advantage of the proposed method is that it can improve the accuracy of individualized prediction of connectivity by combining information from both group-level connectivity patterns that are common to subjects with similar characteristics as well as individual-level connectivity features that are particular to the specific subject. Furthermore, our method also offers statistical inference tools such as predictive intervals that help quantify the uncertainty or variability of the predicted outcomes. The proposed prediction method could be a useful approach to predict the changes in individual patient's brain connectivity with the progression of a disease. It can also be used to predict a patient's post-treatment brain connectivity after a specified treatment regimen. Another utility of the proposed method is that it can be applied to test-retest imaging data to develop a more reliable estimator for individual functional connectivity. We show there exists a nice connection between our proposed estimator and a recently developed shrinkage estimator of connectivity measures in the neuroimaging community. We develop an expectation-maximization (EM) algorithm for estimation of the proposed Bayesian hierarchical model. Simulations studies are performed to evaluate the accuracy of our proposed prediction methods. We illustrate the application of the methods with two data examples: the longitudinal resting-state fMRI from ADNI2 study and the test-retest fMRI data from Kirby21 study. In both the simulation studies and the fMRI data applications, we demonstrate that the proposed methods provide more accurate prediction and more reliable estimation of individual functional connectivity as compared with alternative methods. Copyright © 2017 Elsevier Inc. All rights reserved.

Brain properties predict proximity to symptom onset in sporadic Alzheimer's disease.

PubMed

Vogel, Jacob W; Vachon-Presseau, Etienne; Pichet Binette, Alexa; Tam, Angela; Orban, Pierre; La Joie, Renaud; Savard, Mélissa; Picard, Cynthia; Poirier, Judes; Bellec, Pierre; Breitner, John C S; Villeneuve, Sylvia

2018-06-01

See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article.Alzheimer's disease is preceded by a lengthy 'preclinical' stage spanning many years, during which subtle brain changes occur in the absence of overt cognitive symptoms. Predicting when the onset of disease symptoms will occur is an unsolved challenge in individuals with sporadic Alzheimer's disease. In individuals with autosomal dominant genetic Alzheimer's disease, the age of symptom onset is similar across generations, allowing the prediction of individual onset times with some accuracy. We extend this concept to persons with a parental history of sporadic Alzheimer's disease to test whether an individual's symptom onset age can be informed by the onset age of their affected parent, and whether this estimated onset age can be predicted using only MRI. Structural and functional MRIs were acquired from 255 ageing cognitively healthy subjects with a parental history of sporadic Alzheimer's disease from the PREVENT-AD cohort. Years to estimated symptom onset was calculated as participant age minus age of parental symptom onset. Grey matter volume was extracted from T1-weighted images and whole-brain resting state functional connectivity was evaluated using degree count. Both modalities were summarized using a 444-region cortical-subcortical atlas. The entire sample was divided into training (n = 138) and testing (n = 68) sets. Within the training set, individuals closer to or beyond their parent's symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer's disease. Machine learning was used to identify a weighted set of imaging features trained to predict years to estimated symptom onset. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. We next tested if these brain properties could be generalized to predict time to clinical progression in a subgroup of 26 individuals from the Alzheimer's Disease Neuroimaging Initiative, who eventually converted either to mild cognitive impairment or to Alzheimer's dementia. The feature set trained on years to estimated symptom onset in the PREVENT-AD predicted variance in time to clinical conversion in this separate longitudinal dataset. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals.

Predicting consumer behavior: using novel mind-reading approaches.

PubMed

Calvert, Gemma A; Brammer, Michael J

2012-01-01

Advances in machine learning as applied to functional magnetic resonance imaging (fMRI) data offer the possibility of pretesting and classifying marketing communications using unbiased pattern recognition algorithms. By using these algorithms to analyze brain responses to brands, products, or existing marketing communications that either failed or succeeded in the marketplace and identifying the patterns of brain activity that characterize success or failure, future planned campaigns or new products can now be pretested to determine how well the resulting brain responses match the desired (successful) pattern of brain activity without the need for verbal feedback. This major advance in signal processing is poised to revolutionize the application of these brain-imaging techniques in the marketing sector by offering greater accuracy of prediction in terms of consumer acceptance of new brands, products, and campaigns at a speed that makes them accessible as routine pretesting tools that will clearly demonstrate return on investment.

Ultrasound-induced temperature increase in guinea-pig fetal brain in utero: third-trimester gestation.

PubMed

Horder, M M; Barnett, S B; Vella, G J; Edwards, M J; Wood, A K

1998-11-01

Temperature increase was measured at various depths in the brain of living fetal guinea pigs during in utero exposure to unscanned pulsed ultrasound at ISPTA 2.8 W/cm2. Mean temperature increases of 4.9 degrees C close to parietal bone and 1.2 degrees C in the midbrain were recorded after 2-min exposures. When exposures were repeated on the same sites in each fetus after death, the corresponding mean temperature increases were 4.9 degrees C and 1.3 degrees C, respectively. Cerebral blood perfusion had little cooling effect on ultrasound-induced heating in the guinea pig fetus of 57-61 days gestational age.

Intraoperative application of thermal camera for the assessment of during surgical resection or biopsy of human's brain tumors

NASA Astrophysics Data System (ADS)

Kastek, M.; Piatkowski, T.; Polakowski, H.; Kaczmarska, K.; Czernicki, Z.; Bogucki, J.; Zebala, M.

2014-05-01

Motivation to undertake research on brain surface temperature in clinical practice is based on a strong conviction that the enormous progress in thermal imaging techniques and camera design has a great application potential. Intraoperative imaging of pathological changes and functionally important areas of the brain is not yet fully resolved in neurosurgery and remains a challenge. A study of temperature changes across cerebral cortex was performed for five patients with brain tumors (previously diagnosed using magnetic resonance or computed tomography) during surgical resection or biopsy of tumors. Taking into account their origin and histology the tumors can be divided into the following types: gliomas, with different degrees of malignancy (G2 to G4), with different metabolic activity and various temperatures depending on the malignancy level (3 patients), hypervascular tumor associated with meninges (meningioma), metastatic tumor - lung cancer with a large cyst and noticeable edema. In the case of metastatic tumor with large edema and a liquid-filled space different temperature of a cerebral cortex were recorded depending on metabolic activity. Measurements have shown that the temperature on the surface of the cyst was on average 2.6 K below the temperature of surrounding areas. It has been also observed that during devascularization of a tumor, i.e. cutting off its blood vessels, the tumor temperature lowers significantly in spite of using bipolar coagulation, which causes additional heat emission in the tissue. The results of the measurements taken intra-operatively confirm the capability of a thermal camera to perform noninvasive temperature monitoring of a cerebral cortex. As expected surface temperature of tumors is different from surface temperature of tissues free from pathological changes. The magnitude of this difference depends on histology and the origin of the tumor. These conclusions lead to taking on further experimental research, implementation and further verification of the thermal imaging method and its usefulness in clinical practice. In particular the research will be undertaken on intraoperative temperature changes of active cerebral cortex areas in post-anesthetic recovery.

Behavioral, Brain Imaging and Genomic Measures to Predict Functional Outcomes Post-Bed Rest and Space Flight

NASA Technical Reports Server (NTRS)

Mulavara, A. P.; Peters, B.; De Dios, Y. E.; Gadd, N. E.; Caldwell, E. E.; Batson, C. D.; Goel, R.; Oddsson, L.; Kreutzberg, G.; Zanello, S.;

Prefrontal-hippocampal-fusiform activity during encoding predicts intraindividual differences in free recall ability: an event-related functional-anatomic MRI study.

PubMed

Dickerson, B C; Miller, S L; Greve, D N; Dale, A M; Albert, M S; Schacter, D L; Sperling, R A

2007-01-01

The ability to spontaneously recall recently learned information is a fundamental mnemonic activity of daily life, but has received little study using functional neuroimaging. We developed a functional MRI (fMRI) paradigm to study regional brain activity during encoding that predicts free recall. In this event-related fMRI study, ten lists of fourteen pictures of common objects were shown to healthy young individuals and regional brain activity during encoding was analyzed based on subsequent free recall performance. Free recall of items was predicted by activity during encoding in hippocampal, fusiform, and inferior prefrontal cortical regions. Within-subject variance in free recall performance for the ten lists was predicted by a linear combination of condition-specific inferior prefrontal, hippocampal, and fusiform activity. Recall performance was better for lists in which prefrontal activity was greater for all items of the list and hippocampal and fusiform activity were greater specifically for items that were recalled from the list. Thus, the activity of medial temporal, fusiform, and prefrontal brain regions during the learning of new information is important for the subsequent free recall of this information. These fronto-temporal brain regions act together as a large-scale memory-related network, the components of which make distinct yet interacting contributions during encoding that predict subsequent successful free recall performance.

Prefrontal-Hippocampal-Fusiform Activity During Encoding Predicts Intraindividual Differences in Free Recall Ability: An Event-Related Functional-Anatomic MRI Study

PubMed Central

Dickerson, B.C.; Miller, S.L.; Greve, D.N.; Dale, A.M.; Albert, M.S.; Schacter, D.L.; Sperling, R.A.

2009-01-01

The ability to spontaneously recall recently learned information is a fundamental mnemonic activity of daily life, but has received little study using functional neuroimaging. We developed a functional MRI (fMRI) paradigm to study regional brain activity during encoding that predicts free recall. In this event-related fMRI study, ten lists of fourteen pictures of common objects were shown to healthy young individuals and regional brain activity during encoding was analyzed based on subsequent free recall performance. Free recall of items was predicted by activity during encoding in hippocampal, fusiform, and inferior prefrontal cortical regions. Within-subject variance in free recall performance for the ten lists was predicted by a linear combination of condition-specific inferior prefrontal, hippocampal, and fusiform activity. Recall performance was better for lists in which pre-frontal activity was greater for all items of the list and hippocampal and fusi-form activity were greater specifically for items that were recalled from the list. Thus, the activity of medial temporal, fusiform, and prefrontal brain regions during the learning of new information is important for the subsequent free recall of this information. These fronto-temporal brain regions act together as a large-scale memory-related network, the components of which make distinct yet interacting contributions during encoding that predict subsequent successful free recall performance. PMID:17604356

Design of a superconducting volume coil for magnetic resonance microscopy of the mouse brain

NASA Astrophysics Data System (ADS)

Nouls, John C.; Izenson, Michael G.; Greeley, Harold P.; Johnson, G. Allan

2008-04-01

We present the design process of a superconducting volume coil for magnetic resonance microscopy of the mouse brain at 9.4 T. The yttrium barium copper oxide coil has been designed through an iterative process of three-dimensional finite-element simulations and validation against room temperature copper coils. Compared to previous designs, the Helmholtz pair provides substantially higher B1 homogeneity over an extended volume of interest sufficiently large to image biologically relevant specimens. A custom-built cryogenic cooling system maintains the superconducting probe at 60 ± 0.1 K. Specimen loading and probe retuning can be carried out interactively with the coil at operating temperature, enabling much higher through-put. The operation of the probe is a routine, consistent procedure. Signal-to-noise ratio in a mouse brain increased by a factor ranging from 1.1 to 2.9 as compared to a room-temperature solenoid coil optimized for mouse brain microscopy. We demonstrate images encoded at 10 × 10 × 20 μm for an entire mouse brain specimen with signal-to-noise ratio of 18 and a total acquisition time of 16.5 h, revealing neuroanatomy unseen at lower resolution. Phantom measurements show an effective spatial resolution better than 20 μm.

Design of a superconducting volume coil for magnetic resonance microscopy of the mouse brain.

PubMed

Nouls, John C; Izenson, Michael G; Greeley, Harold P; Johnson, G Allan

2008-04-01

We present the design process of a superconducting volume coil for magnetic resonance microscopy of the mouse brain at 9.4T. The yttrium barium copper oxide coil has been designed through an iterative process of three-dimensional finite-element simulations and validation against room temperature copper coils. Compared to previous designs, the Helmholtz pair provides substantially higher B(1) homogeneity over an extended volume of interest sufficiently large to image biologically relevant specimens. A custom-built cryogenic cooling system maintains the superconducting probe at 60+/-0.1K. Specimen loading and probe retuning can be carried out interactively with the coil at operating temperature, enabling much higher through-put. The operation of the probe is a routine, consistent procedure. Signal-to-noise ratio in a mouse brain increased by a factor ranging from 1.1 to 2.9 as compared to a room-temperature solenoid coil optimized for mouse brain microscopy. We demonstrate images encoded at 10x10x20mum for an entire mouse brain specimen with signal-to-noise ratio of 18 and a total acquisition time of 16.5h, revealing neuroanatomy unseen at lower resolution. Phantom measurements show an effective spatial resolution better than 20mum.

Market mechanisms protect the vulnerable brain.

PubMed

Ramchandran, Kanchna; Nayakankuppam, Dhananjay; Berg, Joyce; Tranel, Daniel; Denburg, Natalie L

2011-07-01

Markets are mechanisms of social exchange, intended to facilitate trading. However, the question remains as to whether markets would help or hurt individuals with decision-makings deficits, as is frequently encountered in the case of cognitive aging. Essential for predicting future gains and losses in monetary and social domains, the striatal nuclei in the brain undergo structural, neurochemical, and functional decline with age. We correlated the efficacy of market mechanisms with dorsal striatal decline in an aging population, by using market based trading in the context of the 2008 U.S. Presidential Elections (primary cycle). Impaired decision-makers displayed higher prediction error (difference between their prediction and actual outcome). Lower in vivo caudate volume was also associated with higher prediction error. Importantly, market-based trading protected older adults with lower caudate volume to a greater extent from their own poorly calibrated predictions. Counterintuitive to the traditional public perception of the market as a fickle, risky proposition where vulnerable traders are most surely to be burned, we suggest that market-based mechanisms protect individuals with brain-based decision-making vulnerabilities. Copyright © 2011 Elsevier Ltd. All rights reserved.

Market mechanisms protect the vulnerable brain

PubMed Central

Ramchandran, Kanchna; Nayakankuppam, Dhananjay; Berg, Joyce; Tranel, Daniel

2011-01-01

Markets are mechanisms of social exchange, intended to facilitate trading. However, the question remains as to whether markets would help or hurt individuals with decision-makings deficits, as is frequently encountered in the case of cognitive aging. Essential for predicting future gains and losses in monetary and social domains, the striatal nuclei in the brain undergo structural, neurochemical, and functional decline with age. We correlated the efficacy of market mechanisms with dorsal striatal decline in an aging population, by using market based trading in the context of the 2008 U.S Presidential Elections (primary cycle). Impaired decision-makers displayed higher prediction error (difference between their prediction and actual outcome). Lower in vivo caudate volume was also associated with higher prediction error. Importantly, market-based trading protected older adults with lower caudate volume to a greater extent from their own poorly calibrated predictions. Counterintuitive to the traditional public perception of the market as a fickle, risky proposition where vulnerable traders are most surely to be burned, we suggest that market-based mechanisms protect individuals with brain-based decision-making vulnerabilities. PMID:21600226

Post-injury personality in the prediction of outcome following severe acquired brain injury.

PubMed

Cattran, Charlotte Jane; Oddy, Michael; Wood, Rodger Llewellyn; Moir, Jane Frances

2011-01-01

The aim of the study was to examine the utility of five measures of non-cognitive neurobehavioural (NCNB) changes that often occur following acquired brain injury, in predicting outcome (measured in terms of participation and social adaptation) at 1-year follow-up. The study employed a longitudinal, correlational design. Multiple regression was employed to investigate the value of five new NCNB measures of social perception, emotional regulation, motivation, impulsivity and disinhibition in the prediction of outcome as measured by the Mayo-Portland Adaptability Inventory (MPAI). Two NCNB measures (motivation and emotional regulation) were found to significantly predict outcome at 1-year follow-up, accounting for 53% of the variance in MPAI total scores. These measures provide a method of quantifying the extent of NCNB changes following brain injury. The predictive value of the measures indicates that they may represent a useful tool which could aid clinicians in identifying early-on those whose symptoms are likely to persist and who may require ongoing intervention. This could facilitate the planning of rehabilitation programmes.

fMRI Reactivity to High-Calorie Food Pictures Predicts Short- and Long-Term Outcome in a Weight-Loss Program

PubMed Central

Murdaugh, Donna L.; Cox, James E.; Cook, Edwin W.; Weller, Rosalyn E.

2011-01-01

Behavioral studies have suggested that food cues have stronger motivating effects in obese than in normal-weight individuals, which may be a risk factor underlying obesity. Previous cross-sectional neuroimaging studies have suggested that this difference is mediated by increased reactivity to food cues in parts of the reward system in obese individuals. To date, however, only a few prospective neuroimaging studies have been conducted to examine whether individual differences in brain activation elicited by food cues can predict differences in weight change. We used functional magnetic resonance imaging (fMRI) to investigate activation in reward-system as well as other brain regions in response to viewing high-calorie food vs. control pictures in 25 obese individuals before and after a 12-week psychosocial weight-loss treatment and at 9-mo follow-up. In those obese individuals who were least successful in losing weight during the treatment, we found greater pre-treatment activation to high-calorie food vs. control pictures in brain regions implicated in reward-system processes, such as the nucleus accumbens, anterior cingulate, and insula. We found similar correlations with weight loss in brain regions implicated by other studies in vision and attention, such as superior occipital cortex, inferior and superior parietal lobule, and prefrontal cortex. Furthermore, less successful weight maintenance at 9-mo follow-up was predicted by greater post-treatment activation in such brain regions as insula, ventral tegmental area, putamen, and fusiform gyrus. In summary, we found that greater activation in brain regions mediating motivational and attentional salience of food cues in obese individuals at the start of a weight-loss program was predictive of less success in the program and that such activation following the program predicted poorer weight control over a 9-mo follow-up period. PMID:22332246

Biospheric Cooling and the Emergence of Intelligence

NASA Astrophysics Data System (ADS)

Schwartzman, David; Middendorf, George

The long-term cooling history of the Earth's biosphere implies a temperature constraint on the timing of major events in biologic evolution, e.g., emergence of cyanobacteria, eucaryotes and Metazoa apparently occurred at times when temperatures were near their upper growth limits. Could biospheric cooling also have been a necessary condition for the emergence of veterbrates and their encephalization? The upper temperature limit for vertebrate growth is about 10 degrees below the limit for Metazoa (50 degrees C). Heterothermy followed by full homeothermy was likely a necessary condition for greater encephalization because of the energy requirement of larger brains. The temperature differential between an animal and a cooler environment, all other factors equal, will increase the efficiency of heat loss from the brain, but too large a differential will shift metabolic energy away from the brain to the procurement of food. Encephalization has also entailed the evolution of internal cooling mechanisms to avoid overheating the brain. The two periods of pronounced Phanerozoic cooling, the PermoCarboniferous and late Cenozoic, corresponded to the emergence of mammal-like reptiles and hominids respectively, with a variety of explanations offered for the apparent link. The origin of highly encephalized whales, dolphins and porpoises occurred with the drop in ocean temperatures 25-30 mya. Of course, other possible paths to encephalization are conceivable, with radically different solutions to the problem of heat dissipation. But the intrinsic requirements for information processing capacity necessary for intelligence suggest our terrestrial pattern may resemble those of alien biospheres given similar histories.

Brain-computer interface analysis of a dynamic visuo-motor task.

PubMed

Logar, Vito; Belič, Aleš

2011-01-01

The area of brain-computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's "thoughts" to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain-computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data. In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could, therefore, be further used for the development of a closed-loop, non-invasive, brain-computer interface. For the case of this study two types of measurements were performed, i.e., the electroencephalographic (EEG) signals and the wrist movements were measured simultaneously, during the subject's performance of a dynamic visuo-motor task. Wrist-movement predictions were computed by using the EEG data-processing methodology of double brain-rhythm filtering, double phase demodulation and double principal component analyses (PCA), each with a separate set of parameters. For the movement-prediction model a fuzzy inference system was used. The results have shown that the EEG signals measured during the dVM tasks carry enough information about the subjects' wrist movements for them to be successfully decoded using the presented methodology. Reasonably high values of the correlation coefficients suggest that the validation of the proposed approach is satisfactory. Moreover, since the causality of the rhythm filtering and the PCA transformation has been achieved, we have shown that these methods can also be used in a real-time, brain-computer interface. The study revealed that using non-causal, optimized methods yields better prediction results in comparison with the causal, non-optimized methodology; however, taking into account that the causality of these methods allows real-time processing, the minor decrease in prediction quality is acceptable. The study suggests that the methodology that was proposed in our previous studies is also valid for identifying the EEG-coded content during dVM tasks, albeit with various modifications, which allow better prediction results and real-time data processing. The results have shown that wrist movements can be predicted in simulated or real time; however, the results of the non-causal, optimized methodology (simulated) are slightly better. Nevertheless, the study has revealed that these methods should be suitable for use in the development of a non-invasive, brain-computer interface. Copyright © 2010 Elsevier B.V. All rights reserved.

Epileptic seizure prediction by non-linear methods

DOEpatents

Hively, Lee M.; Clapp, Ned E.; Daw, C. Stuart; Lawkins, William F.

1999-01-01

Methods and apparatus for automatically predicting epileptic seizures monitor and analyze brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis tools; obtaining time serial trends in the nonlinear measures; comparison of the trend to known seizure predictors; and providing notification that a seizure is forthcoming.

Brain State Is a Major Factor in Preseizure Hippocampal Network Activity and Influences Success of Seizure Intervention

PubMed Central

Ewell, Laura A.; Liang, Liang; Armstrong, Caren; Soltész, Ivan; Leutgeb, Stefan

2015-01-01

Neural dynamics preceding seizures are of interest because they may shed light on mechanisms of seizure generation and could be predictive. In healthy animals, hippocampal network activity is shaped by behavioral brain state and, in epilepsy, seizures selectively emerge during specific brain states. To determine the degree to which changes in network dynamics before seizure are pathological or reflect ongoing fluctuations in brain state, dorsal hippocampal neurons were recorded during spontaneous seizures in a rat model of temporal lobe epilepsy. Seizures emerged from all brain states, but with a greater likelihood after REM sleep, potentially due to an observed increase in baseline excitability during periods of REM compared with other brains states also characterized by sustained theta oscillations. When comparing the firing patterns of the same neurons across brain states associated with and without seizures, activity dynamics before seizures followed patterns typical of the ongoing brain state, or brain state transitions, and did not differ until the onset of the electrographic seizure. Next, we tested whether disparate activity patterns during distinct brain states would influence the effectiveness of optogenetic curtailment of hippocampal seizures in a mouse model of temporal lobe epilepsy. Optogenetic curtailment was significantly more effective for seizures preceded by non-theta states compared with seizures that emerged from theta states. Our results indicate that consideration of behavioral brain state preceding a seizure is important for the appropriate interpretation of network dynamics leading up to a seizure and for designing effective seizure intervention. SIGNIFICANCE STATEMENT Hippocampal single-unit activity is strongly shaped by behavioral brain state, yet this relationship has been largely ignored when studying activity dynamics before spontaneous seizures in medial temporal lobe epilepsy. In light of the increased attention on using single-unit activity for the prediction of seizure onset and closed-loop seizure intervention, we show a need for monitoring brain state to interpret correctly whether changes in neural activity before seizure onset is pathological or normal. Moreover, we also find that the brain state preceding a seizure determines the success of therapeutic interventions to curtail seizure duration. Together, these findings suggest that seizure prediction and intervention will be more successful if tailored for the specific brain states from which seizures emerge. PMID:26609157

Methylphenidate Modulates Functional Network Connectivity to Enhance Attention

PubMed Central

Zhang, Sheng; Hsu, Wei-Ting; Scheinost, Dustin; Finn, Emily S.; Shen, Xilin; Constable, R. Todd; Li, Chiang-Shan R.; Chun, Marvin M.

2016-01-01

Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention. SIGNIFICANCE STATEMENT Recent work identified a promising neuromarker of sustained attention based on whole-brain functional connectivity networks. To investigate the causal role of these networks in attention, we examined their response to a dose of methylphenidate, a common and effective treatment for attention-deficit/hyperactivity disorder, in healthy adults. As predicted, individuals on methylphenidate showed connectivity signatures of better sustained attention: higher high-attention and lower low-attention network strength than controls. These results suggest that methylphenidate acts by modulating strength in functional brain networks related to attention, and that changing whole-brain connectivity patterns may improve attention. PMID:27629707

Methylphenidate Modulates Functional Network Connectivity to Enhance Attention.

PubMed

Rosenberg, Monica D; Zhang, Sheng; Hsu, Wei-Ting; Scheinost, Dustin; Finn, Emily S; Shen, Xilin; Constable, R Todd; Li, Chiang-Shan R; Chun, Marvin M

2016-09-14

Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention. Recent work identified a promising neuromarker of sustained attention based on whole-brain functional connectivity networks. To investigate the causal role of these networks in attention, we examined their response to a dose of methylphenidate, a common and effective treatment for attention-deficit/hyperactivity disorder, in healthy adults. As predicted, individuals on methylphenidate showed connectivity signatures of better sustained attention: higher high-attention and lower low-attention network strength than controls. These results suggest that methylphenidate acts by modulating strength in functional brain networks related to attention, and that changing whole-brain connectivity patterns may improve attention. Copyright © 2016 the authors 0270-6474/16/369547-11$15.00/0.

In Silico Prediction for Intestinal Absorption and Brain Penetration of Chemical Pesticides in Humans.

PubMed

Chedik, Lisa; Mias-Lucquin, Dominique; Bruyere, Arnaud; Fardel, Olivier

2017-06-30

Intestinal absorption and brain permeation constitute key parameters of toxicokinetics for pesticides, conditioning their toxicity, including neurotoxicity. However, they remain poorly characterized in humans. The present study was therefore designed to evaluate human intestine and brain permeation for a large set of pesticides ( n = 338) belonging to various chemical classes, using an in silico graphical BOILED-Egg/SwissADME online method based on lipophilicity and polarity that was initially developed for drugs. A high percentage of the pesticides (81.4%) was predicted to exhibit high intestinal absorption, with a high accuracy (96%), whereas a lower, but substantial, percentage (38.5%) displayed brain permeation. Among the pesticide classes, organochlorines ( n = 30) constitute the class with the lowest percentage of intestine-permeant members (40%), whereas that of the organophosphorus compounds ( n = 99) has the lowest percentage of brain-permeant chemicals (9%). The predictions of the permeations for the pesticides were additionally shown to be significantly associated with various molecular descriptors well-known to discriminate between permeant and non-permeant drugs. Overall, our in silico data suggest that human exposure to pesticides through the oral way is likely to result in an intake of these dietary contaminants for most of them and brain permeation for some of them, thus supporting the idea that they have toxic effects on human health, including neurotoxic effects.

In Silico Prediction for Intestinal Absorption and Brain Penetration of Chemical Pesticides in Humans

PubMed Central

Chedik, Lisa; Mias-Lucquin, Dominique; Bruyere, Arnaud; Fardel, Olivier

2017-01-01

Intestinal absorption and brain permeation constitute key parameters of toxicokinetics for pesticides, conditioning their toxicity, including neurotoxicity. However, they remain poorly characterized in humans. The present study was therefore designed to evaluate human intestine and brain permeation for a large set of pesticides (n = 338) belonging to various chemical classes, using an in silico graphical BOILED-Egg/SwissADME online method based on lipophilicity and polarity that was initially developed for drugs. A high percentage of the pesticides (81.4%) was predicted to exhibit high intestinal absorption, with a high accuracy (96%), whereas a lower, but substantial, percentage (38.5%) displayed brain permeation. Among the pesticide classes, organochlorines (n = 30) constitute the class with the lowest percentage of intestine-permeant members (40%), whereas that of the organophosphorus compounds (n = 99) has the lowest percentage of brain-permeant chemicals (9%). The predictions of the permeations for the pesticides were additionally shown to be significantly associated with various molecular descriptors well-known to discriminate between permeant and non-permeant drugs. Overall, our in silico data suggest that human exposure to pesticides through the oral way is likely to result in an intake of these dietary contaminants for most of them and brain permeation for some of them, thus supporting the idea that they have toxic effects on human health, including neurotoxic effects. PMID:28665355

Unique semantic space in the brain of each beholder predicts perceived similarity

PubMed Central

Charest, Ian; Kievit, Rogier A.; Schmitz, Taylor W.; Deca, Diana; Kriegeskorte, Nikolaus

2014-01-01

The unique way in which each of us perceives the world must arise from our brain representations. If brain imaging could reveal an individual’s unique mental representation, it could help us understand the biological substrate of our individual experiential worlds in mental health and disease. However, imaging studies of object vision have focused on commonalities between individuals rather than individual differences and on category averages rather than representations of particular objects. Here we investigate the individually unique component of brain representations of particular objects with functional MRI (fMRI). Subjects were presented with unfamiliar and personally meaningful object images while we measured their brain activity on two separate days. We characterized the representational geometry by the dissimilarity matrix of activity patterns elicited by particular object images. The representational geometry remained stable across scanning days and was unique in each individual in early visual cortex and human inferior temporal cortex (hIT). The hIT representation predicted perceived similarity as reflected in dissimilarity judgments. Importantly, hIT predicted the individually unique component of the judgments when the objects were personally meaningful. Our results suggest that hIT brain representational idiosyncrasies accessible to fMRI are expressed in an individual's perceptual judgments. The unique way each of us perceives the world thus might reflect the individually unique representation in high-level visual areas. PMID:25246586

Fluctuations in central and peripheral temperatures associated with feeding behavior in rats

PubMed Central

Smirnov, Michael S.; Kiyatkin, Eugene A.

2008-01-01

We examined the pattern of temperature fluctuations in the nucleus accumbens (NAcc), temporal muscle, and skin, along with locomotion in food-deprived and nondeprived rats following the presentation of an open or closed food container and during subsequent eating or food-seeking behavior without eating. Although rats in food-deprived, quiet resting conditions had more than twofold lower spontaneous locomotion and lower temperature values than in nondeprived conditions, after presentation of a container, they consistently displayed food-seeking behavior, showing much larger and longer temperature changes. When the container was open, rats rapidly retrieved food and consumed it. Food consumption was preceded and accompanied by gradual increases in brain and muscle temperatures (∼1.5°C) and a weaker, delayed increase in skin temperature (∼0.8°C). All temperatures began to rapidly fall immediately after eating was completed, but NAcc and muscle temperatures returned to baseline after ∼35 min. When the container was closed and rats were unable to obtain food, they continued food-seeking activity during the entire period of presentation. Similar to eating, this activity was preceded and accompanied by gradual temperature increases in the brain and muscle, which were somewhat smaller than those during eating (∼1.2°C), with no changes in skin temperature. In contrast to trials with eating, NAcc and muscle temperatures continued to increase for ∼10 min after the container was removed from the cage and the rat continued food-seeking behavior, with a return to baselines after ∼50 min. These temperature fluctuations are discussed with respect to alterations in metabolic brain activity associated with feeding behavior, depending upon deprivation state and food availability. PMID:18799633

Temperature influences neuronal activity and CO2/pH sensitivity of locus coeruleus neurons in the bullfrog, Lithobates catesbeianus.

PubMed

Santin, Joseph M; Watters, Kayla C; Putnam, Robert W; Hartzler, Lynn K

2013-12-15

The locus coeruleus (LC) is a chemoreceptive brain stem region in anuran amphibians and contains neurons sensitive to physiological changes in CO2/pH. The ventilatory and central sensitivity to CO2/pH is proportional to the temperature in amphibians, i.e., sensitivity increases with increasing temperature. We hypothesized that LC neurons from bullfrogs, Lithobates catesbeianus, would increase CO2/pH sensitivity with increasing temperature and decrease CO2/pH sensitivity with decreasing temperature. Further, we hypothesized that cooling would decrease, while warming would increase, normocapnic firing rates of LC neurons. To test these hypotheses, we used whole cell patch-clamp electrophysiology to measure firing rate, membrane potential (V(m)), and input resistance (R(in)) in LC neurons in brain stem slices from adult bullfrogs over a physiological range of temperatures during normocapnia and hypercapnia. We found that cooling reduced chemosensitive responses of LC neurons as temperature decreased until elimination of CO2/pH sensitivity at 10°C. Chemosensitive responses increased at elevated temperatures. Surprisingly, chemosensitive LC neurons increased normocapnic firing rate and underwent membrane depolarization when cooled and decreased normocapnic firing rate and underwent membrane hyperpolarization when warmed. These responses to temperature were not observed in nonchemosensitive LC neurons or neurons in a brain stem slice 500 μm rostral to the LC. Our results indicate that modulation of cellular chemosensitivity within the LC during temperature changes may influence temperature-dependent respiratory drive during acid-base disturbances in amphibians. Additionally, cold-activated/warm-inhibited LC neurons introduce paradoxical temperature sensitivity in respiratory control neurons of amphibians.

Temperature differentially regulates the two kisspeptin systems in the brain of zebrafish.

PubMed

Shahjahan, Md; Kitahashi, Takashi; Ogawa, Satoshi; Parhar, Ishwar S

2013-11-01

Kisspeptins encoded by the kiss1 and kiss2 genes play an important role in reproduction through the stimulation of gonadotropin-releasing hormone (GnRH) secretion by activating their receptors (KissR1 EU047918 and KissR2 EU047917). To understand the mechanism through which temperature affects reproduction, we examined kiss1 and kiss2 and their respective receptor (kissr1 and kissr2) gene expression in the brain of male zebrafish exposed to a low temperature (15°C), normal temperature (27°C), and high temperature (35°C) for 7-days. kiss1 mRNA levels in the brain were significantly increased (2.9-fold) in the low temperature compared to the control (27°C), while no noticeable change was observed in the high temperature conditions. Similarly, kissr1 mRNA levels were significantly increased (1.5-2.2-folds) in the low temperature conditions in the habenula, the nucleus of the medial longitudinal fascicle, oculomotor nucleus, and the interpeduncular nucleus. kiss2 mRNA levels were significantly decreased (0.5-fold) in the low and high temperature conditions, concomitant with kissr2 mRNA levels (0.5-fold) in the caudal zone of the periventricular hypothalamus and the posterior tuberal nucleus. gnrh3 but not gnrh2 mRNA levels were also decreased (0.5-fold) in the low and high temperature conditions. These findings suggest that while the kiss1/kissr1 system is sensitive to low temperature, the kiss2/kissr2 system is sensitive to both extremes of temperature, which leads to failure in reproduction. Copyright © 2013. Published by Elsevier Inc.

Ocean circulation and biogeochemistry moderate interannual and decadal surface water pH changes in the Sargasso Sea

USGS Publications Warehouse

Nathalie F. Goodkin,; Bo-Shian Wang,; Chen-Feng You,; Konrad Hughen,; Prouty, Nancy G.; Bates, Nicholas; Scott Doney,

2015-01-01

The oceans absorb anthropogenic CO2 from the atmosphere, lowering surface ocean pH, a concern for calcifying marine organisms. The impact of ocean acidification is challenging to predict as each species appears to respond differently and because our knowledge of natural changes to ocean pH is limited in both time and space. Here we reconstruct 222 years of biennial seawater pH variability in the Sargasso Sea from a brain coral, Diploria labyrinthiformis. Using hydrographic data from the Bermuda Atlantic Time-series Study and the coral-derived pH record, we are able to differentiate pH changes due to surface temperature versus those from ocean circulation and biogeochemical changes. We find that ocean pH does not simply reflect atmospheric CO2 trends but rather that circulation/biogeochemical changes account for >90% of pH variability in the Sargasso Sea and more variability in the last century than would be predicted from anthropogenic uptake of CO2 alone.

Predicting risky choices from brain activity patterns

PubMed Central

Helfinstein, Sarah M.; Schonberg, Tom; Congdon, Eliza; Karlsgodt, Katherine H.; Mumford, Jeanette A.; Sabb, Fred W.; Cannon, Tyrone D.; London, Edythe D.; Bilder, Robert M.; Poldrack, Russell A.

2014-01-01

Previous research has implicated a large network of brain regions in the processing of risk during decision making. However, it has not yet been determined if activity in these regions is predictive of choices on future risky decisions. Here, we examined functional MRI data from a large sample of healthy subjects performing a naturalistic risk-taking task and used a classification analysis approach to predict whether individuals would choose risky or safe options on upcoming trials. We were able to predict choice category successfully in 71.8% of cases. Searchlight analysis revealed a network of brain regions where activity patterns were reliably predictive of subsequent risk-taking behavior, including a number of regions known to play a role in control processes. Searchlights with significant predictive accuracy were primarily located in regions more active when preparing to avoid a risk than when preparing to engage in one, suggesting that risk taking may be due, in part, to a failure of the control systems necessary to initiate a safe choice. Additional analyses revealed that subject choice can be successfully predicted with minimal decrements in accuracy using highly condensed data, suggesting that information relevant for risky choice behavior is encoded in coarse global patterns of activation as well as within highly local activation within searchlights. PMID:24550270

Model-free and model-based reward prediction errors in EEG.

PubMed

Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy

2018-05-24

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

Improved calibration technique for in vivo proton MRS thermometry for brain temperature measurement.

PubMed

Zhu, M; Bashir, A; Ackerman, J J; Yablonskiy, D A

2008-09-01

The most common MR-based approach to noninvasively measure brain temperature relies on the linear relationship between the (1)H MR resonance frequency of tissue water and the tissue's temperature. Herein we provide the most accurate in vivo assessment existing thus far of such a relationship. It was derived by acquiring in vivo MR spectra from a rat brain using a high field (11.74 Tesla [T]) MRI scanner and a single-voxel MR spectroscopy technique based on a LASER pulse sequence. Data were analyzed using three different methods to estimate the (1)H resonance frequencies of water and the metabolites NAA, Cho, and Cr, which are used as temperature-independent internal (frequency) references. Standard modeling of frequency-domain data as composed of resonances characterized by Lorentzian line shapes gave the tightest resonance-frequency versus temperature correlation. An analysis of the uncertainty in temperature estimation has shown that the major limiting factor is an error in estimating the metabolite frequency. For example, for a metabolite resonance linewidth of 8 Hz, signal sampling rate of 2 Hz and SNR of 5, an accuracy of approximately 0.5 degrees C can be achieved at a magnetic field of 3T. For comparison, in the current study conducted at 11.74T, the temperature estimation error was approximately 0.1 degrees C.

Perception of social synchrony induces mother-child gamma coupling in the social brain.

PubMed

Levy, Jonathan; Goldstein, Abraham; Feldman, Ruth

2017-07-01

The recent call to move from focus on one brain's functioning to two-brain communication initiated a search for mechanisms that enable two humans to coordinate brain response during social interactions. Here, we utilized the mother-child context as a developmentally salient setting to study two-brain coupling. Mothers and their 9-year-old children were videotaped at home in positive and conflictual interactions. Positive interactions were microcoded for social synchrony and conflicts for overall dialogical style. Following, mother and child underwent magnetoencephalography while observing the positive vignettes. Episodes of behavioral synchrony, compared to non-synchrony, increased gamma-band power in the superior temporal sulcus (STS), hub of social cognition, mirroring and mentalizing. This neural pattern was coupled between mother and child. Brain-to-brain coordination was anchored in behavioral synchrony; only during episodes of behavioral synchrony, but not during non-synchronous moments, mother's and child's STS gamma power was coupled. Importantly, neural synchrony was not found during observation of unfamiliar mother-child interaction Maternal empathic/dialogical conflict style predicted mothers' STS activations whereas child withdrawal predicted attenuated STS response in both partners. Results define a novel neural marker for brain-to-brain synchrony, highlight the role of rapid bottom-up oscillatory mechanisms for neural coupling and indicate that behavior-based processes may drive synchrony between two brains during social interactions. © The Author (2017). Published by Oxford University Press.

A physical multifield model predicts the development of volume and structure in the human brain

NASA Astrophysics Data System (ADS)

Rooij, Rijk de; Kuhl, Ellen

2018-03-01

The prenatal development of the human brain is characterized by a rapid increase in brain volume and a development of a highly folded cortex. At the cellular level, these events are enabled by symmetric and asymmetric cell division in the ventricular regions of the brain followed by an outwards cell migration towards the peripheral regions. The role of mechanics during brain development has been suggested and acknowledged in past decades, but remains insufficiently understood. Here we propose a mechanistic model that couples cell division, cell migration, and brain volume growth to accurately model the developing brain between weeks 10 and 29 of gestation. Our model accurately predicts a 160-fold volume increase from 1.5 cm3 at week 10 to 235 cm3 at week 29 of gestation. In agreement with human brain development, the cortex begins to form around week 22 and accounts for about 30% of the total brain volume at week 29. Our results show that cell division and coupling between cell density and volume growth are essential to accurately model brain volume development, whereas cell migration and diffusion contribute mainly to the development of the cortex. We demonstrate that complex folding patterns, including sinusoidal folds and creases, emerge naturally as the cortex develops, even for low stiffness contrasts between the cortex and subcortex.

Frontal top-down signals increase coupling of auditory low-frequency oscillations to continuous speech in human listeners.

PubMed

Park, Hyojin; Ince, Robin A A; Schyns, Philippe G; Thut, Gregor; Gross, Joachim

2015-06-15

Humans show a remarkable ability to understand continuous speech even under adverse listening conditions. This ability critically relies on dynamically updated predictions of incoming sensory information, but exactly how top-down predictions improve speech processing is still unclear. Brain oscillations are a likely mechanism for these top-down predictions [1, 2]. Quasi-rhythmic components in speech are known to entrain low-frequency oscillations in auditory areas [3, 4], and this entrainment increases with intelligibility [5]. We hypothesize that top-down signals from frontal brain areas causally modulate the phase of brain oscillations in auditory cortex. We use magnetoencephalography (MEG) to monitor brain oscillations in 22 participants during continuous speech perception. We characterize prominent spectral components of speech-brain coupling in auditory cortex and use causal connectivity analysis (transfer entropy) to identify the top-down signals driving this coupling more strongly during intelligible speech than during unintelligible speech. We report three main findings. First, frontal and motor cortices significantly modulate the phase of speech-coupled low-frequency oscillations in auditory cortex, and this effect depends on intelligibility of speech. Second, top-down signals are significantly stronger for left auditory cortex than for right auditory cortex. Third, speech-auditory cortex coupling is enhanced as a function of stronger top-down signals. Together, our results suggest that low-frequency brain oscillations play a role in implementing predictive top-down control during continuous speech perception and that top-down control is largely directed at left auditory cortex. This suggests a close relationship between (left-lateralized) speech production areas and the implementation of top-down control in continuous speech perception. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Frontal Top-Down Signals Increase Coupling of Auditory Low-Frequency Oscillations to Continuous Speech in Human Listeners

PubMed Central

Park, Hyojin; Ince, Robin A.A.; Schyns, Philippe G.; Thut, Gregor; Gross, Joachim

2015-01-01

Summary Humans show a remarkable ability to understand continuous speech even under adverse listening conditions. This ability critically relies on dynamically updated predictions of incoming sensory information, but exactly how top-down predictions improve speech processing is still unclear. Brain oscillations are a likely mechanism for these top-down predictions [1, 2]. Quasi-rhythmic components in speech are known to entrain low-frequency oscillations in auditory areas [3, 4], and this entrainment increases with intelligibility [5]. We hypothesize that top-down signals from frontal brain areas causally modulate the phase of brain oscillations in auditory cortex. We use magnetoencephalography (MEG) to monitor brain oscillations in 22 participants during continuous speech perception. We characterize prominent spectral components of speech-brain coupling in auditory cortex and use causal connectivity analysis (transfer entropy) to identify the top-down signals driving this coupling more strongly during intelligible speech than during unintelligible speech. We report three main findings. First, frontal and motor cortices significantly modulate the phase of speech-coupled low-frequency oscillations in auditory cortex, and this effect depends on intelligibility of speech. Second, top-down signals are significantly stronger for left auditory cortex than for right auditory cortex. Third, speech-auditory cortex coupling is enhanced as a function of stronger top-down signals. Together, our results suggest that low-frequency brain oscillations play a role in implementing predictive top-down control during continuous speech perception and that top-down control is largely directed at left auditory cortex. This suggests a close relationship between (left-lateralized) speech production areas and the implementation of top-down control in continuous speech perception. PMID:26028433

Prediction of brain maturity in infants using machine-learning algorithms.

PubMed

Smyser, Christopher D; Dosenbach, Nico U F; Smyser, Tara A; Snyder, Abraham Z; Rogers, Cynthia E; Inder, Terrie E; Schlaggar, Bradley L; Neil, Jeffrey J

2016-08-01

Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23-29weeks of gestation and without moderate-severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p<0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants. Copyright © 2016 Elsevier Inc. All rights reserved.

Prediction of brain maturity in infants using machine-learning algorithms

PubMed Central

Smyser, Christopher D.; Dosenbach, Nico U.F.; Smyser, Tara A.; Snyder, Abraham Z.; Rogers, Cynthia E.; Inder, Terrie E.; Schlaggar, Bradley L.; Neil, Jeffrey J.

2016-01-01

Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23–29 weeks of gestation and without moderate–severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p < 0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants. PMID:27179605

Anterior cingulate taste activation predicts ad libitum intake of sweet and savory drinks in healthy, normal-weight men.

PubMed

Spetter, Maartje S; de Graaf, Cees; Viergever, Max A; Smeets, Paul A M

2012-04-01

After food consumption, the motivation to eat (wanting) decreases and associated brain reward responses change. Wanting-related brain responses and how these are affected by consumption of specific foods are ill documented. Moreover, the predictive value of food-induced brain responses for subsequent consumption has not been assessed. We aimed to determine the effects of consumption of sweet and savory foods on taste activation in the brain and to assess how far taste activation can predict subsequent ad libitum intake. Fifteen healthy men (age: 27 ± 2 y, BMI: 22.0 ± 1.5 kg/m2) participated in a randomized crossover trial. After a >3-h fast, participants were scanned with the use of functional MRI before and after consumption of a sweet or savory preload (0.35 L fruit or tomato juice) on two occasions. After the scans, the preload juice was consumed ad libitum. During scanning, participants tasted the juices and rated their pleasantness. Striatal taste activation decreased after juice consumption, independent of pleasantness. Sweet and savory taste activation were not differentially affected by consumption. Anterior cingulate taste activation predicted subsequent ad libitum intake of sweet (r = -0.78; P < 0.001(uncorrected)) as well as savory juice (r = -0.70; P < 0.001(uncorrected)). In conclusion, we showed how taste activation of brain reward areas changes following food consumption. These changes may be associated with the food's physiological relevance. Further, the results suggest that anterior cingulate taste activation reflects food-specific satiety. This extends our understanding of the representation of food specific-appetite in the brain and shows that neuroimaging may provide objective and more accurate measures of food motivation than self-report measures.

EKG-based detection of deep brain stimulation in fMRI studies.

PubMed

Fiveland, Eric; Madhavan, Radhika; Prusik, Julia; Linton, Renee; Dimarzio, Marisa; Ashe, Jeffrey; Pilitsis, Julie; Hancu, Ileana

2018-04-01

To assess the impact of synchronization errors between the assumed functional MRI paradigm timing and the deep brain stimulation (DBS) on/off cycling using a custom electrocardiogram-based triggering system METHODS: A detector for measuring and predicting the on/off state of cycling deep brain stimulation was developed and tested in six patients in office visits. Three-electrode electrocardiogram measurements, amplified by a commercial bio-amplifier, were used as input for a custom electronics box (e-box). The e-box transformed the deep brain stimulation waveforms into transistor-transistor logic pulses, recorded their timing, and propagated it in time. The e-box was used to trigger task-based deep brain stimulation functional MRI scans in 5 additional subjects; the impact of timing accuracy on t-test values was investigated in a simulation study using the functional MRI data. Following locking to each patient's individual waveform, the e-box was shown to predict stimulation onset with an average absolute error of 112 ± 148 ms, 30 min after disconnecting from the patients. The subsecond accuracy of the e-box in predicting timing onset is more than adequate for our slow varying, 30-/30-s on/off stimulation paradigm. Conversely, the experimental deep brain stimulation onset prediction accuracy in the absence of the e-box, which could be off by as much as 4 to 6 s, could significantly decrease activation strength. Using this detector, stimulation can be accurately synchronized to functional MRI acquisitions, without adding any additional hardware in the MRI environment. Magn Reson Med 79:2432-2439, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Bio-heat transfer model of deep brain stimulation-induced temperature changes

NASA Astrophysics Data System (ADS)

Elwassif, Maged M.; Kong, Qingjun; Vazquez, Maribel; Bikson, Marom

2006-12-01

There is a growing interest in the use of chronic deep brain stimulation (DBS) for the treatment of medically refractory movement disorders and other neurological and psychiatric conditions. Fundamental questions remain about the physiologic effects of DBS. Previous basic research studies have focused on the direct polarization of neuronal membranes by electrical stimulation. The goal of this paper is to provide information on the thermal effects of DBS using finite element models to investigate the magnitude and spatial distribution of DBS-induced temperature changes. The parameters investigated include stimulation waveform, lead selection, brain tissue electrical and thermal conductivities, blood perfusion, metabolic heat generation during the stimulation and lead thermal conductivity/heat dissipation through the electrode. Our results show that clinical DBS protocols will increase the temperature of surrounding tissue by up to 0.8 °C depending on stimulation/tissue parameters.

Motor prediction in Brain-Computer Interfaces for controlling mobile robots.

PubMed

Geng, Tao; Gan, John Q

2008-01-01

EEG-based Brain-Computer Interface (BCI) can be regarded as a new channel for motor control except that it does not involve muscles. Normal neuromuscular motor control has two fundamental components: (1) to control the body, and (2) to predict the consequences of the control command, which is called motor prediction. In this study, after training with a specially designed BCI paradigm based on motor imagery, two subjects learnt to predict the time course of some features of the EEG signals. It is shown that, with this newly-obtained motor prediction skill, subjects can use motor imagery of feet to directly control a mobile robot to avoid obstacles and reach a small target in a time-critical scenario.

"Neural overlap of L1 and L2 semantic representations across visual and auditory modalities: a decoding approach".

PubMed

Van de Putte, Eowyn; De Baene, Wouter; Price, Cathy J; Duyck, Wouter

2018-05-01

This study investigated whether brain activity in Dutch-French bilinguals during semantic access to concepts from one language could be used to predict neural activation during access to the same concepts from another language, in different language modalities/tasks. This was tested using multi-voxel pattern analysis (MVPA), within and across language comprehension (word listening and word reading) and production (picture naming). It was possible to identify the picture or word named, read or heard in one language (e.g. maan, meaning moon) based on the brain activity in a distributed bilateral brain network while, respectively, naming, reading or listening to the picture or word in the other language (e.g. lune). The brain regions identified differed across tasks. During picture naming, brain activation in the occipital and temporal regions allowed concepts to be predicted across languages. During word listening and word reading, across-language predictions were observed in the rolandic operculum and several motor-related areas (pre- and postcentral, the cerebellum). In addition, across-language predictions during reading were identified in regions typically associated with semantic processing (left inferior frontal, middle temporal cortex, right cerebellum and precuneus) and visual processing (inferior and middle occipital regions and calcarine sulcus). Furthermore, across modalities and languages, the left lingual gyrus showed semantic overlap across production and word reading. These findings support the idea of at least partially language- and modality-independent semantic neural representations. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures

PubMed Central

Ye, Zheng; Rae, Charlotte L.; Nombela, Cristina; Ham, Timothy; Rittman, Timothy; Jones, Peter Simon; Rodríguez, Patricia Vázquez; Coyle‐Gilchrist, Ian; Regenthal, Ralf; Altena, Ellemarije; Housden, Charlotte R.; Maxwell, Helen; Sahakian, Barbara J.; Barker, Roger A.; Robbins, Trevor W.

2016-01-01

Abstract Recent studies indicate that selective noradrenergic (atomoxetine) and serotonergic (citalopram) reuptake inhibitors may improve response inhibition in selected patients with Parkinson's disease, restoring behavioral performance and brain activity. We reassessed the behavioral efficacy of these drugs in a larger cohort and developed predictive models to identify patient responders. We used a double‐blind randomized three‐way crossover design to investigate stopping efficiency in 34 patients with idiopathic Parkinson's disease after 40 mg atomoxetine, 30 mg citalopram, or placebo. Diffusion‐weighted and functional imaging measured microstructural properties and regional brain activations, respectively. We confirmed that Parkinson's disease impairs response inhibition. Overall, drug effects on response inhibition varied substantially across patients at both behavioral and brain activity levels. We therefore built binary classifiers with leave‐one‐out cross‐validation (LOOCV) to predict patients’ responses in terms of improved stopping efficiency. We identified two optimal models: (1) a “clinical” model that predicted the response of an individual patient with 77–79% accuracy for atomoxetine and citalopram, using clinically available information including age, cognitive status, and levodopa equivalent dose, and a simple diffusion‐weighted imaging scan; and (2) a “mechanistic” model that explained the behavioral response with 85% accuracy for each drug, using drug‐induced changes of brain activations in the striatum and presupplementary motor area from functional imaging. These data support growing evidence for the role of noradrenaline and serotonin in inhibitory control. Although noradrenergic and serotonergic drugs have highly variable effects in patients with Parkinson's disease, the individual patient's response to each drug can be predicted using a pattern of clinical and neuroimaging features. Hum Brain Mapp 37:1026–1037, 2016. © 2016 Wiley Periodicals, Inc. PMID:26757216

Brain-behavioral adaptability predicts response to cognitive behavioral therapy for emotional disorders: A person-centered event-related potential study.

PubMed

Stange, Jonathan P; MacNamara, Annmarie; Kennedy, Amy E; Hajcak, Greg; Phan, K Luan; Klumpp, Heide

2017-06-23

Single-trial-level analyses afford the ability to link neural indices of elaborative attention (such as the late positive potential [LPP], an event-related potential) with downstream markers of attentional processing (such as reaction time [RT]). This approach can provide useful information about individual differences in information processing, such as the ability to adapt behavior based on attentional demands ("brain-behavioral adaptability"). Anxiety and depression are associated with maladaptive information processing implicating aberrant cognition-emotion interactions, but whether brain-behavioral adaptability predicts response to psychotherapy is not known. We used a novel person-centered, trial-level analysis approach to link neural indices of stimulus processing to behavioral responses and to predict treatment outcome. Thirty-nine patients with anxiety and/or depression received 12 weeks of cognitive behavioral therapy (CBT). Prior to treatment, patients performed a speeded reaction-time task involving briefly-presented pairs of aversive and neutral pictures while electroencephalography was recorded. Multilevel modeling demonstrated that larger LPPs predicted slower responses on subsequent trials, suggesting that increased attention to the task-irrelevant nature of pictures interfered with reaction time on subsequent trials. Whereas using LPP and RT averages did not distinguish CBT responders from nonresponders, in trial-level analyses individuals who demonstrated greater ability to benefit behaviorally (i.e., faster RT) from smaller LPPs on the previous trial (greater brain-behavioral adaptability) were more likely to respond to treatment and showed greater improvements in depressive symptoms. These results highlight the utility of trial-level analyses to elucidate variability in within-subjects, brain-behavioral attentional coupling in the context of emotion processing, in predicting response to CBT for emotional disorders. Copyright © 2017 Elsevier Ltd. All rights reserved.

Current State and Future Perspectives in QSAR Models to Predict Blood- Brain Barrier Penetration in Central Nervous System Drug R&D.

PubMed

Morales, Juan F; Montoto, Sebastian Scioli; Fagiolino, Pietro; Ruiz, Maria E

2017-01-01

The Blood-Brain Barrier (BBB) is a physical and biochemical barrier that restricts the entry of certain drugs to the Central Nervous System (CNS), while allowing the passage of others. The ability to predict the permeability of a given molecule through the BBB is a key aspect in CNS drug discovery and development, since neurotherapeutic agents with molecular targets in the CNS should be able to cross the BBB, whereas peripherally acting agents should not, to minimize the risk of CNS adverse effects. In this review we examine and discuss QSAR approaches and current availability of experimental data for the construction of BBB permeability predictive models, focusing on the modeling of the biorelevant parameter unbound partitioning coefficient (Kp,uu). Emphasis is made on two possible strategies to overcome the current limitations of in silico models: considering the prediction of brain penetration as a multifactorial problem, and increasing experimental datasets through accurate and standardized experimental techniques.

Appearance Matters: Neural Correlates of Food Choice and Packaging Aesthetics

PubMed Central

Van der Laan, Laura N.; De Ridder, Denise T. D.; Viergever, Max A.; Smeets, Paul A. M.

2012-01-01

Neuro-imaging holds great potential for predicting choice behavior from brain responses. In this study we used both traditional mass-univariate and state-of-the-art multivariate pattern analysis to establish which brain regions respond to preferred packages and to what extent neural activation patterns can predict realistic low-involvement consumer choices. More specifically, this was assessed in the context of package-induced binary food choices. Mass-univariate analyses showed that several regions, among which the bilateral striatum, were more strongly activated in response to preferred food packages. Food choices could be predicted with an accuracy of up to 61.2% by activation patterns in brain regions previously found to be involved in healthy food choices (superior frontal gyrus) and visual processing (middle occipital gyrus). In conclusion, this study shows that mass-univariate analysis can detect small package-induced differences in product preference and that MVPA can successfully predict realistic low-involvement consumer choices from functional MRI data. PMID:22848586

Brain volumetry and self-regulation of brain activity relevant for neurofeedback.

PubMed

Ninaus, M; Kober, S E; Witte, M; Koschutnig, K; Neuper, C; Wood, G

2015-09-01

Neurofeedback is a technique to learn to control brain signals by means of real time feedback. In the present study, the individual ability to learn two EEG neurofeedback protocols - sensorimotor rhythm and gamma rhythm - was related to structural properties of the brain. The volumes in the anterior insula bilaterally, left thalamus, right frontal operculum, right putamen, right middle frontal gyrus, and right lingual gyrus predicted the outcomes of sensorimotor rhythm training. Gray matter volumes in the supplementary motor area and left middle frontal gyrus predicted the outcomes of gamma rhythm training. These findings combined with further evidence from the literature are compatible with the existence of a more general self-control network, which through self-referential and self-control processes regulates neurofeedback learning. Copyright © 2015 Elsevier B.V. All rights reserved.

Specific regions of the brain are capable of fructose metabolism.

PubMed

Oppelt, Sarah A; Zhang, Wanming; Tolan, Dean R

2017-02-15

High fructose consumption in the Western diet correlates with disease states such as obesity and metabolic syndrome complications, including type II diabetes, chronic kidney disease, and non-alcoholic fatty acid liver disease. Liver and kidneys are responsible for metabolism of 40-60% of ingested fructose, while the physiological fate of the remaining fructose remains poorly understood. The primary metabolic pathway for fructose includes the fructose-transporting solute-like carrier transport proteins 2a (SLC2a or GLUT), including GLUT5 and GLUT9, ketohexokinase (KHK), and aldolase. Bioinformatic analysis of gene expression encoding these proteins (glut5, glut9, khk, and aldoC, respectively) identifies other organs capable of this fructose metabolism. This analysis predicts brain, lymphoreticular tissue, placenta, and reproductive tissues as possible additional organs for fructose metabolism. While expression of these genes is highest in liver, the brain is predicted to have expression levels of these genes similar to kidney. RNA in situ hybridization of coronal slices of adult mouse brains validate the in silico expression of glut5, glut9, khk, and aldoC, and show expression across many regions of the brain, with the most notable expression in the cerebellum, hippocampus, cortex, and olfactory bulb. Dissected samples of these brain regions show KHK and aldolase enzyme activity 5-10 times the concentration of that in liver. Furthermore, rates of fructose oxidation in these brain regions are 15-150 times that of liver slices, confirming the bioinformatics prediction and in situ hybridization data. This suggests that previously unappreciated regions across the brain can use fructose, in addition to glucose, for energy production. Copyright © 2016 Elsevier B.V. All rights reserved.

Specific regions of the brain are capable of fructose metabolism

PubMed Central

Oppelt, Sarah A.; Zhang, Wanming; Tolan, Dean R.

2017-01-01

High fructose consumption in the Western diet correlates with disease states such as obesity and metabolic syndrome complications, including type II diabetes, chronic kidney disease, and nonalcoholic fatty acid liver disease. Liver and kidneys are responsible for metabolism of 40–60% of ingested fructose, while the physiological fate of the remaining fructose remains poorly understood. The primary metabolic pathway for fructose includes the fructose-transporting solute-like carrier transport proteins 2a (SLC2a or GLUT), including GLUT5 and GLUT9, ketohexokinase (KHK), and aldolase. Bioinformatic analysis of gene expression encoding these proteins (glut5, glut9, khk, and aldoC, respectively) identifies other organs capable of this fructose metabolism. This analysis predicts brain, lymphoreticular tissue, placenta, and reproductive tissues as possible additional organs for fructose metabolism. While expression of these genes is highest in liver, the brain is predicted to have expression levels of these genes similar to kidney. RNA in situ hybridization of coronal slices of adult mouse brains validate the in silico expression of glut5, glut9, khk, and aldoC, and show expression across many regions of the brain, with the most notable expression in the cerebellum, hippocampus, cortex, and olfactory bulb. Dissected samples of these brain regions show KHK and aldolase enzyme activity 5–10 times the concentration of that in liver. Furthermore, rates of fructose oxidation in these brain regions are 15–150 times that of liver slices, confirming the bioinformatics prediction and in situ hybridization data. This suggests that previously unappreciated regions across the brain can use fructose, in addition to glucose, for energy production. PMID:28034722

Cadmium and high temperature effects on brain and behaviour of Lymantria dispar L. caterpillars originating from polluted and less-polluted forests.

PubMed

Perić-Mataruga, Vesna; Petković, Branka; Ilijin, Larisa; Mrdaković, Marija; Dronjak Čučaković, Slađana; Todorović, Dajana; Vlahović, Milena

2017-10-01

Insects brain as a part of nervous system is the first-line of fast stress response that integrate stress signals to regulate all aspects of insect physiology and behaviour. The cadmium (Cd) bioaccumulation factor (BF), activity of the neurotoxicity biomarker acetylcholinesterase (AChE), dopamine content, expression and amount of Hsp70 in the brain and locomotor activity were evaluated in the 4th instar of Lymantria dispar L. caterpillars fed a Cd supplemented diet and reared in an optimal temperature regime (23 °C) and/or exposed to high temperature (28 °C). The insects originated from two forests, one close to "Nikola Tesla" thermoelectric power plant, Obrenovac (polluted population), and the other Kosmaj mountain (less-polluted population, far from any industrial region). The Cd BF was higher in the less-polluted than in the polluted population especially at the high ambient temperature. AChE activity and dopamine content were changed in the brains of L. dispar from both populations in the same manner. Hsp70 concentration in caterpillar brains showed opposite trends, a decrease in the less-polluted and an increase in the polluted population. Locomotor activity was modified in both Lymantria dispar populations, but the pattern of changes depended on the stressors and their combined effect. ACh activity and dopamine content are sensitive parameters to Cd exposure, regardless of pollutant experience, and might be promising biomarkers in monitoring forest ecosystems. Copyright © 2017 Elsevier Ltd. All rights reserved.

A mathematical model of endovascular heat transfer for human brain cooling

NASA Astrophysics Data System (ADS)

Salsac, Anne-Virginie; Lasheras, Juan Carlos; Yon, Steven; Magers, Mike; Dobak, John

2000-11-01

Selective cooling of the brain has been shown to exhibit protective effects in cerebral ischemia, trauma, and spinal injury/ischemia. A multi-compartment, unsteady thermal model of the response of the human brain to endovascular cooling is discussed and its results compared to recent experimental data conducted with sheep and other mammals. The model formulation is based on the extension of the bioheat equation, originally proposed by Pennes(1) and later modified by Wissler(2), Stolwijk(3) and Werner and Webb(4). The temporal response of the brain temperature and that of the various body compartments to the cooling of the blood flowing through the common carotid artery is calculated under various scenarios. The effect of the boundary conditions as well as the closure assumptions used in the model, i.e. perfusion rate, metabolism heat production, etc. on the cooling rate of the brain are systematically investigated. (1) Pennes H. H., “Analysis of tissue and arterial blood temperature in the resting forearm.” J. Appl. Physiol. 1: 93-122, 1948. (2) Wissler E. H., “Steady-state temperature distribution in man”, J. Appl. Physiol., 16: 764-740, 1961. (3) Stolwick J. A. J., “Mathematical model of thermoregulation” in “Physiological and behavioral temperature regulation”, edited by J. D. Hardy, A. P. Gagge and A. J. Stolwijk, Charles C. Thomas Publisher, Springfiels, Ill., 703-721, 1971. (4) Werner J., Webb P., “A six-cylinder model of human thermoregulation for general use on personal computers”, Ann. Physiol. Anthrop., 12(3): 123-134, 1993.

The proactive brain and the fate of dead hypotheses

PubMed Central

Tal, Amir; Bar, Moshe

2014-01-01

A substantial portion of information flow in the brain is directed top-down, from high processing areas downwards. Signals of this sort are regarded as conveying prior expectations, biasing the processing and eventual perception of incoming stimuli. In this perspective we describe a framework of top-down processing in the visual system in which predictions on the identity of objects in sight aid in their recognition. Focus is placed, in particular, on a relatively uncharted ramification of this framework, that of the fate of initial predictions that are eventually rejected during the process of selection. We propose that such predictions are rapidly inhibited in the brain after a competing option has been selected. Empirical support, along with behavioral, neuronal and computational aspects of this proposal are discussed, and future directions for related research are offered. PMID:25408645

How Prediction Errors Shape Perception, Attention, and Motivation

PubMed Central

den Ouden, Hanneke E. M.; Kok, Peter; de Lange, Floris P.

2012-01-01

Prediction errors (PE) are a central notion in theoretical models of reinforcement learning, perceptual inference, decision-making and cognition, and prediction error signals have been reported across a wide range of brain regions and experimental paradigms. Here, we will make an attempt to see the forest for the trees and consider the commonalities and differences of reported PE signals in light of recent suggestions that the computation of PE forms a fundamental mode of brain function. We discuss where different types of PE are encoded, how they are generated, and the different functional roles they fulfill. We suggest that while encoding of PE is a common computation across brain regions, the content and function of these error signals can be very different and are determined by the afferent and efferent connections within the neural circuitry in which they arise. PMID:23248610

The proactive brain and the fate of dead hypotheses.

PubMed

Tal, Amir; Bar, Moshe

2014-01-01

A substantial portion of information flow in the brain is directed top-down, from high processing areas downwards. Signals of this sort are regarded as conveying prior expectations, biasing the processing and eventual perception of incoming stimuli. In this perspective we describe a framework of top-down processing in the visual system in which predictions on the identity of objects in sight aid in their recognition. Focus is placed, in particular, on a relatively uncharted ramification of this framework, that of the fate of initial predictions that are eventually rejected during the process of selection. We propose that such predictions are rapidly inhibited in the brain after a competing option has been selected. Empirical support, along with behavioral, neuronal and computational aspects of this proposal are discussed, and future directions for related research are offered.

Biomarkers and Stimulation Algorithms for Adaptive Brain Stimulation

PubMed Central

Hoang, Kimberly B.; Cassar, Isaac R.; Grill, Warren M.; Turner, Dennis A.

2017-01-01

The goal of this review is to describe in what ways feedback or adaptive stimulation may be delivered and adjusted based on relevant biomarkers. Specific treatment mechanisms underlying therapeutic brain stimulation remain unclear, in spite of the demonstrated efficacy in a number of nervous system diseases. Brain stimulation appears to exert widespread influence over specific neural networks that are relevant to specific disease entities. In awake patients, activation or suppression of these neural networks can be assessed by either symptom alleviation (i.e., tremor, rigidity, seizures) or physiological criteria, which may be predictive of expected symptomatic treatment. Secondary verification of network activation through specific biomarkers that are linked to symptomatic disease improvement may be useful for several reasons. For example, these biomarkers could aid optimal intraoperative localization, possibly improve efficacy or efficiency (i.e., reduced power needs), and provide long-term adaptive automatic adjustment of stimulation parameters. Possible biomarkers for use in portable or implanted devices span from ongoing physiological brain activity, evoked local field potentials (LFPs), and intermittent pathological activity, to wearable devices, biochemical, blood flow, optical, or magnetic resonance imaging (MRI) changes, temperature changes, or optogenetic signals. First, however, potential biomarkers must be correlated directly with symptom or disease treatment and network activation. Although numerous biomarkers are under consideration for a variety of stimulation indications the feasibility of these approaches has yet to be fully determined. Particularly, there are critical questions whether the use of adaptive systems can improve efficacy over continuous stimulation, facilitate adjustment of stimulation interventions and improve our understanding of the role of abnormal network function in disease mechanisms. PMID:29066947

Using the brain's fight-or-flight response for predicting mental illness on the human space flight program

NASA Astrophysics Data System (ADS)

Losik, L.

A predictive medicine program allows disease and illness including mental illness to be predicted using tools created to identify the presence of accelerated aging (a.k.a. disease) in electrical and mechanical equipment. When illness and disease can be predicted, actions can be taken so that the illness and disease can be prevented and eliminated. A predictive medicine program uses the same tools and practices from a prognostic and health management program to process biological and engineering diagnostic data provided in analog telemetry during prelaunch readiness and space exploration missions. The biological and engineering diagnostic data necessary to predict illness and disease is collected from the pre-launch spaceflight readiness activities and during space flight for the ground crew to perform a prognostic analysis on the results from a diagnostic analysis. The diagnostic, biological data provided in telemetry is converted to prognostic (predictive) data using the predictive algorithms. Predictive algorithms demodulate telemetry behavior. They illustrate the presence of accelerated aging/disease in normal appearing systems that function normally. Mental illness can predicted using biological diagnostic measurements provided in CCSDS telemetry from a spacecraft such as the ISS or from a manned spacecraft in deep space. The measurements used to predict mental illness include biological and engineering data from an astronaut's circadian and ultranian rhythms. This data originates deep in the brain that is also damaged from the long-term exposure to cortisol and adrenaline anytime the body's fight or flight response is activated. This paper defines the brain's FOFR; the diagnostic, biological and engineering measurements needed to predict mental illness, identifies the predictive algorithms necessary to process the behavior in CCSDS analog telemetry to predict and thus prevent mental illness from occurring on human spaceflight missions.

Recent Advancement of the Molecular Diagnosis in Pediatric Brain Tumor.

PubMed

Bae, Jeong-Mo; Won, Jae-Kyung; Park, Sung-Hye

2018-05-01

Recent discoveries of brain tumor-related genes and fast advances in genomic testing technologies have led to the era of molecular diagnosis of brain tumor. Molecular profiling of brain tumor became the significant step in the diagnosis, the prediction of prognosis and the treatment of brain tumor. Because traditional molecular testing methods have limitations in time and cost for multiple gene tests, next-generation sequencing technologies are rapidly introduced into clinical practice. Targeted sequencing panels using these technologies have been developed for brain tumors. In this article, focused on pediatric brain tumor, key discoveries of brain tumor-related genes are reviewed and cancer panels used in the molecular profiling of brain tumor are discussed.

Recent Advancement of the Molecular Diagnosis in Pediatric Brain Tumor

PubMed Central

Bae, Jeong-Mo; Won, Jae-Kyung; Park, Sung-Hye

2018-01-01

Recent discoveries of brain tumor-related genes and fast advances in genomic testing technologies have led to the era of molecular diagnosis of brain tumor. Molecular profiling of brain tumor became the significant step in the diagnosis, the prediction of prognosis and the treatment of brain tumor. Because traditional molecular testing methods have limitations in time and cost for multiple gene tests, next-generation sequencing technologies are rapidly introduced into clinical practice. Targeted sequencing panels using these technologies have been developed for brain tumors. In this article, focused on pediatric brain tumor, key discoveries of brain tumor-related genes are reviewed and cancer panels used in the molecular profiling of brain tumor are discussed. PMID:29742887

Differential expression analysis of genes involved in high-temperature induced sex differentiation in Nile tilapia.

PubMed

Li, Chun Ge; Wang, Hui; Chen, Hong Ju; Zhao, Yan; Fu, Pei Sheng; Ji, Xiang Shan

2014-01-01

Nowadays, high temperature effects on the molecular pathways during sex differentiation in teleosts need to be deciphered. In this study, a systematic differential expression analysis of genes involved in high temperature-induced sex differentiation was done in the Nile tilapia gonad and brain. Our results showed that high temperature caused significant down-regulation of CYP19A1A in the gonad of both sexes in induction group, and FOXL2 in the ovary of the induction group. The expressions of GTHα, LHβ and ERα were also significantly down-regulated in the brain of both sexes in the induction and recovery groups. On the contrary, the expression of CYP11B2 was significantly up-regulated in the ovary, but not in the testis in both groups. Spearman rank correlation analysis showed that there are significant correlations between the expressions of CYP19A1A, FOXL2, or DMRT1 in the gonads and the expression of some genes in the brain. Another result in this study showed that high temperature up-regulated the expression level of DNMT1 in the testis of the induction group, and DNMT1 and DNMT3A in the female brain of both groups. The expression and correlation analysis of HSPs showed that high temperature action on tilapia HSPs might indirectly induce the expression changes of sex differentiation genes in the gonads. These findings provide new insights on TSD and suggest that sex differentiation related genes, heat shock proteins, and DNA methylation genes are new candidates for studying TSD in fish species. Copyright © 2014 Elsevier Inc. All rights reserved.

[Application of artificial neural networks on the prediction of surface ozone concentrations].

PubMed

Shen, Lu-Lu; Wang, Yu-Xuan; Duan, Lei

2011-08-01

Ozone is an important secondary air pollutant in the lower atmosphere. In order to predict the hourly maximum ozone one day in advance based on the meteorological variables for the Wanqingsha site in Guangzhou, Guangdong province, a neural network model (Multi-Layer Perceptron) and a multiple linear regression model were used and compared. Model inputs are meteorological parameters (wind speed, wind direction, air temperature, relative humidity, barometric pressure and solar radiation) of the next day and hourly maximum ozone concentration of the previous day. The OBS (optimal brain surgeon) was adopted to prune the neutral work, to reduce its complexity and to improve its generalization ability. We find that the pruned neural network has the capacity to predict the peak ozone, with an agreement index of 92.3%, the root mean square error of 0.0428 mg/m3, the R-square of 0.737 and the success index of threshold exceedance 77.0% (the threshold O3 mixing ratio of 0.20 mg/m3). When the neural classifier was added to the neural network model, the success index of threshold exceedance increased to 83.6%. Through comparison of the performance indices between the multiple linear regression model and the neural network model, we conclud that that neural network is a better choice to predict peak ozone from meteorological forecast, which may be applied to practical prediction of ozone concentration.

In Silico Prediction and Validation of Gfap as an miR-3099 Target in Mouse Brain.

PubMed

Abidin, Shahidee Zainal; Leong, Jia-Wen; Mahmoudi, Marzieh; Nordin, Norshariza; Abdullah, Syahril; Cheah, Pike-See; Ling, King-Hwa

2017-08-01

MicroRNAs are small non-coding RNAs that play crucial roles in the regulation of gene expression and protein synthesis during brain development. MiR-3099 is highly expressed throughout embryogenesis, especially in the developing central nervous system. Moreover, miR-3099 is also expressed at a higher level in differentiating neurons in vitro, suggesting that it is a potential regulator during neuronal cell development. This study aimed to predict the target genes of miR-3099 via in-silico analysis using four independent prediction algorithms (miRDB, miRanda, TargetScan, and DIANA-micro-T-CDS) with emphasis on target genes related to brain development and function. Based on the analysis, a total of 3,174 miR-3099 target genes were predicted. Those predicted by at least three algorithms (324 genes) were subjected to DAVID bioinformatics analysis to understand their overall functional themes and representation. The analysis revealed that nearly 70% of the target genes were expressed in the nervous system and a significant proportion were associated with transcriptional regulation and protein ubiquitination mechanisms. Comparison of in situ hybridization (ISH) expression patterns of miR-3099 in both published and in-house-generated ISH sections with the ISH sections of target genes from the Allen Brain Atlas identified 7 target genes (Dnmt3a, Gabpa, Gfap, Itga4, Lxn, Smad7, and Tbx18) having expression patterns complementary to miR-3099 in the developing and adult mouse brain samples. Of these, we validated Gfap as a direct downstream target of miR-3099 using the luciferase reporter gene system. In conclusion, we report the successful prediction and validation of Gfap as an miR-3099 target gene using a combination of bioinformatics resources with enrichment of annotations based on functional ontologies and a spatio-temporal expression dataset.

Learning Predictive Statistics: Strategies and Brain Mechanisms.

PubMed

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-08-30

When immersed in a new environment, we are challenged to decipher initially incomprehensible streams of sensory information. However, quite rapidly, the brain finds structure and meaning in these incoming signals, helping us to predict and prepare ourselves for future actions. This skill relies on extracting the statistics of event streams in the environment that contain regularities of variable complexity from simple repetitive patterns to complex probabilistic combinations. Here, we test the brain mechanisms that mediate our ability to adapt to the environment's statistics and predict upcoming events. By combining behavioral training and multisession fMRI in human participants (male and female), we track the corticostriatal mechanisms that mediate learning of temporal sequences as they change in structure complexity. We show that learning of predictive structures relates to individual decision strategy; that is, selecting the most probable outcome in a given context (maximizing) versus matching the exact sequence statistics. These strategies engage distinct human brain regions: maximizing engages dorsolateral prefrontal, cingulate, sensory-motor regions, and basal ganglia (dorsal caudate, putamen), whereas matching engages occipitotemporal regions (including the hippocampus) and basal ganglia (ventral caudate). Our findings provide evidence for distinct corticostriatal mechanisms that facilitate our ability to extract behaviorally relevant statistics to make predictions. SIGNIFICANCE STATEMENT Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. Past work has studied how humans identify repetitive patterns and associative pairings. However, the natural environment contains regularities that vary in complexity from simple repetition to complex probabilistic combinations. Here, we combine behavior and multisession fMRI to track the brain mechanisms that mediate our ability to adapt to changes in the environment's statistics. We provide evidence for an alternate route for learning complex temporal statistics: extracting the most probable outcome in a given context is implemented by interactions between executive and motor corticostriatal mechanisms compared with visual corticostriatal circuits (including hippocampal cortex) that support learning of the exact temporal statistics. Copyright © 2017 Wang et al.

Whole-brain grey matter density predicts balance stability irrespective of age and protects older adults from falling.

PubMed

Boisgontier, Matthieu P; Cheval, Boris; van Ruitenbeek, Peter; Levin, Oron; Renaud, Olivier; Chanal, Julien; Swinnen, Stephan P

2016-03-01

Functional and structural imaging studies have demonstrated the involvement of the brain in balance control. Nevertheless, how decisive grey matter density and white matter microstructural organisation are in predicting balance stability, and especially when linked to the effects of ageing, remains unclear. Standing balance was tested on a platform moving at different frequencies and amplitudes in 30 young and 30 older adults, with eyes open and with eyes closed. Centre of pressure variance was used as an indicator of balance instability. The mean density of grey matter and mean white matter microstructural organisation were measured using voxel-based morphometry and diffusion tensor imaging, respectively. Mixed-effects models were built to analyse the extent to which age, grey matter density, and white matter microstructural organisation predicted balance instability. Results showed that both grey matter density and age independently predicted balance instability. These predictions were reinforced when the level of difficulty of the conditions increased. Furthermore, grey matter predicted balance instability beyond age and at least as consistently as age across conditions. In other words, for balance stability, the level of whole-brain grey matter density is at least as decisive as being young or old. Finally, brain grey matter appeared to be protective against falls in older adults as age increased the probability of losing balance in older adults with low, but not moderate or high grey matter density. No such results were observed for white matter microstructural organisation, thereby reinforcing the specificity of our grey matter findings. Copyright © 2016 Elsevier B.V. All rights reserved.

Epidemiology of Mild Traumatic Brain Injury with Intracranial Hemorrhage: Focusing Predictive Models for Neurosurgical Intervention.

PubMed

Orlando, Alessandro; Levy, A Stewart; Carrick, Matthew M; Tanner, Allen; Mains, Charles W; Bar-Or, David

2017-11-01

To outline differences in neurosurgical intervention (NI) rates between intracranial hemorrhage (ICH) types in mild traumatic brain injuries and help identify which ICH types are most likely to benefit from creation of predictive models for NI. A multicenter retrospective study of adult patients spanning 3 years at 4 U.S. trauma centers was performed. Patients were included if they presented with mild traumatic brain injury (Glasgow Coma Scale score 13-15) with head CT scan positive for ICH. Patients were excluded for skull fractures, "unspecified hemorrhage," or coagulopathy. Primary outcome was NI. Stepwise multivariable logistic regression models were built to analyze the independent association between ICH variables and outcome measures. The study comprised 1876 patients. NI rate was 6.7%. There was a significant difference in rate of NI by ICH type. Subdural hematomas had the highest rate of NI (15.5%) and accounted for 78% of all NIs. Isolated subarachnoid hemorrhages had the lowest, nonzero, NI rate (0.19%). Logistic regression models identified ICH type as the most influential independent variable when examining NI. A model predicting NI for isolated subarachnoid hemorrhages would require 26,928 patients, but a model predicting NI for isolated subdural hematomas would require only 328 patients. This study highlighted disparate NI rates among ICH types in patients with mild traumatic brain injury and identified mild, isolated subdural hematomas as most appropriate for construction of predictive NI models. Increased health care efficiency will be driven by accurate understanding of risk, which can come only from accurate predictive models. Copyright © 2017 Elsevier Inc. All rights reserved.

Minimal Brain Dysfunction in Childhood: II. Late Outcome in Relation to Initial Presentation. III. Predictive Factors in Relation to Late Outcome.

ERIC Educational Resources Information Center

Milman, Doris H.

Two studies explore the late outcome of minimal brain dysfunction in 73 patients in relation to their initial presentation and predictive factors. Both studies followed the patients for a period of 10 to 20 years. Findings from the first study of initial presentation in relation to adult outcome showed that there was a strong positive correlation…

Epileptic seizure prediction by non-linear methods

DOEpatents

Hively, L.M.; Clapp, N.E.; Day, C.S.; Lawkins, W.F.

1999-01-12

This research discloses methods and apparatus for automatically predicting epileptic seizures monitor and analyze brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis tools; obtaining time serial trends in the nonlinear measures; comparison of the trend to known seizure predictors; and providing notification that a seizure is forthcoming. 76 figs.

Predictors of neurologic and nonneurologic death in patients with brain metastasis initially treated with upfront stereotactic radiosurgery without whole-brain radiation therapy

PubMed Central

Johnson, Adam G.; Ruiz, Jimmy; Isom, Scott; Lucas, John T.; Hinson, William H.; Watabe, Kounosuke; Laxton, Adrian W.; Tatter, Stephen B.; Chan, Michael D.

2017-01-01

Abstract Background. In this study we attempted to discern the factors predictive of neurologic death in patients with brain metastasis treated with upfront stereotactic radiosurgery (SRS) without whole brain radiation therapy (WBRT) while accounting for the competing risk of nonneurologic death. Methods. We performed a retrospective single-institution analysis of patients with brain metastasis treated with upfront SRS without WBRT. Competing risks analysis was performed to estimate the subdistribution hazard ratios (HRs) for neurologic and nonneurologic death for predictor variables of interest. Results. Of 738 patients treated with upfront SRS alone, neurologic death occurred in 226 (30.6%), while nonneurologic death occurred in 309 (41.9%). Multivariate competing risks analysis identified an increased hazard of neurologic death associated with diagnosis-specific graded prognostic assessment (DS-GPA) ≤ 2 (P = .005), melanoma histology (P = .009), and increased number of brain metastases (P<.001), while there was a decreased hazard associated with higher SRS dose (P = .004). Targeted agents were associated with a decreased HR of neurologic death in the first 1.5 years (P = .04) but not afterwards. An increased hazard of nonneurologic death was seen with increasing age (P =.03), nonmelanoma histology (P<.001), presence of extracranial disease (P<.001), and progressive systemic disease (P =.004). Conclusions. Melanoma, DS-GPA, number of brain metastases, and SRS dose are predictive of neurologic death, while age, nonmelanoma histology, and more advanced systemic disease are predictive of nonneurologic death. Targeted agents appear to delay neurologic death. PMID:27571883

A simple physiologically based pharmacokinetic model evaluating the effect of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saylor, Kyle, E-mail: saylor@vt.edu; Zhang, Chenmi

Physiologically based pharmacokinetic (PBPK) modeling was applied to investigate the effects of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans. Successful construction of both rat and human models was achieved by fitting model outputs to published nicotine concentration time course data in the blood and in the brain. Key parameters presumed to have the most effect on the ability of these antibodies to prevent nicotine from entering the brain were selected for investigation using the human model. These parameters, which included antibody affinity for nicotine, antibody cross-reactivity with cotinine, and antibody concentration, were broken down intomore » different, clinically-derived in silico treatment levels and fed into the human PBPK model. Model predictions suggested that all three parameters, in addition to smoking status, have a sizable impact on anti-nicotine antibodies' ability to prevent nicotine from entering the brain and that the antibodies elicited by current human vaccines do not have sufficient binding characteristics to reduce brain nicotine concentrations. If the antibody binding characteristics achieved in animal studies can similarly be achieved in human studies, however, nicotine vaccine efficacy in terms of brain nicotine concentration reduction is predicted to meet threshold values for alleviating nicotine dependence. - Highlights: • Modelling of nicotine disposition in the presence of anti-nicotine antibodies • Key vaccine efficacy factors are evaluated in silico in rats and in humans. • Model predicts insufficient antibody binding in past human nicotine vaccines. • Improving immunogenicity and antibody specificity may lead to vaccine success.« less

A decade of changes in brain volume and cognition.

PubMed

Aljondi, Rowa; Szoeke, Cassandra; Steward, Chris; Yates, Paul; Desmond, Patricia

2018-05-09

Brain atrophy can occur several decades prior to onset of cognitive impairments. However, few longitudinal studies have examined the relationship between brain volume changes and cognition over a long follow-up period in healthy elderly women. In the present study we investigate the relationship between whole brain and hippocampal atrophy rates and longitudinal changes in cognition, including verbal episodic memory and executive function, in older women. We also examine whether baseline brain volume predicts subsequent changes in cognitive performance over a 10-year period. A total of 60 individuals from the population-based Women's Healthy Ageing Project with a mean age at baseline of 59 years underwent 3T MRI. Of these, 40 women completed follow-up cognitive assessments, 23 of whom had follow-up MRI scans. Linear regression analysis was used to examine the relationship between brain atrophy and changes in verbal episodic memory and executive function over a 10-year period. The results show that baseline measurements of frontal and temporal grey matter volumes predict changes in verbal episodic memory performance, whereas hippocampal volume at baseline is associated with changes in executive function performance over a 10-year period of follow-ups. In addition, higher whole brain and hippocampal atrophy rates are correlated with a decline in verbal episodic memory. These findings indicate that in addition to atrophy rate, smaller regional grey matter volumes even 10 years prior is associated with increased rates of cognitive decline. This study suggests useful neuroimaging biomarkers for the prediction of cognitive decline in healthy elderly women.

Brain Regions Associated With Internalizing and Externalizing Psychiatric Symptoms in Patients With Penetrating Traumatic Brain Injury.

PubMed

Huey, Edward D; Lee, Seonjoo; Lieberman, Jeffrey A; Devanand, D P; Brickman, Adam M; Raymont, Vanessa; Krueger, Frank; Grafman, Jordan

2016-01-01

A factor structure underlying DSM-IV diagnoses has been previously reported in neurologically intact patients. The authors determined the brain regions associated with factors underlying DSM-IV diagnoses and compared the ability of DSM-IV diagnoses, factor scores, and self-report measures to account for the neuroanatomical findings in patients with penetrating brain injuries. This prospective cohort study included 254 Vietnam War veterans: 199 with penetrating brain injuries and 55 matched control participants. Measures include DSM-IV diagnoses (from a Structured Clinical Interview for DSM), self-report measures of depression and anxiety, and CT scans. Factors underlying DSM-IV diagnoses were determined using an exploratory factor analysis and correlated with percent of brain regions affected. The ability of the factor scores, DSM-IV diagnoses, and the self-report psychiatric measures to account for the anatomical variance was compared with multiple regressions. Internalizing and externalizing factors were identified in these brain-injured patients. Damage to the left amygdala and bilateral basal ganglia was associated with lower internalizing factor scores, and damage to the left medial orbitofrontal cortex (OFC) with higher, and bilateral hippocampi with lower, externalizing factor scores. Factor scores best predicted left amygdala and bilateral hippocampal involvement, whereas DSM-IV diagnoses best predicted bilateral basal ganglia and left OFC involvement. Damage to the limbic areas involved in the processing of emotional and reward information, including structures involved in the National Institute of Mental Health's Research Domain Criteria Negative Valence Domain, influences the development of internalizing and externalizing psychiatric symptoms. Self-report measures underperformed DSM-IV and factor scores in predicting neuroanatomical findings.

Longitudinal study of neonatal brain tissue volumes in preterm infants and their ability to predict neurodevelopmental outcome.

PubMed

Gui, L; Loukas, S; Lazeyras, F; Hüppi, P S; Meskaldji, D E; Borradori Tolsa, C

2018-06-14

Premature birth has been associated with poor neurodevelopmental outcomes. However, the relation between such outcomes and brain growth in the neonatal period has not yet been fully elucidated. This study investigates longitudinal brain development between birth and term-equivalent age (TEA) by quantitative imaging in a cohort of premature infants born between 26 and 36 weeks gestational age (GA), to provide insight into the relation of brain growth with later neurodevelopmental outcomes. Longitudinal T2-weighted magnetic resonance images (MRI) of 84 prematurely born infants acquired shortly after birth and TEA were automatically segmented into cortical gray matter (CGM), unmyelinated white matter (UWM), subcortical gray matter (SGM), cerebellum (CB) and cerebrospinal fluid (CSF). General linear models and correlation analysis were used to study the relation between brain volumes and their growth, and perinatal variables. To investigate the ability of the brain volumes to predict children's neurodevelopmental outcome at 18-24 months and at 5 years of age, a linear discriminant analysis classifier was tested and several general linear models were fitted and compared by statistical tests. From birth to TEA, relative volumes of CGM, CB and CSF with respect to total intracranial volume increased, while relative volumes of UWM and SGM decreased. The fastest growing tissues between birth and TEA were found to be the CB and the CGM. Lower GA at birth was associated with lower growth rates of CGM, CB and total tissue. Among perinatal factors, persistent ductus arteriosus was associated with lower SGM, CB and IC growth rates, while sepsis was associated with lower CSF and intracranial volume growth rates. Model comparisons showed that brain tissue volumes at birth and at TEA contributed to the prediction of motor outcomes at 18-24 months, while volumes at TEA and volume growth rates contributed to the prediction of cognitive scores at 5 years of age. The family socio-economic status (SES) was not correlated with brain volumes at birth or at TEA, but was strongly associated with the cognitive outcomes at 18-24 months and 5 years of age. This study provides information about brain growth between birth and TEA in premature children with no focal brain lesions, and investigates their association with subsequent neurodevelopmental outcome. Parental SES was found to be a major determinant of neurodevelopmental outcome, unrelated to brain growth. However, further research is necessary in order to fully explain the variability of neurodevelopmental outcomes in this population. Copyright © 2018. Published by Elsevier Inc.

Baseline Gray- and White Matter Volume Predict Successful Weight Loss in the Elderly

PubMed Central

Mokhtari, Fatemeh; Paolini, Brielle M.; Burdette, Jonathan H.; Marsh, Anthony P.; Rejeski, W. Jack; Laurienti, Paul J.

2016-01-01

Objective The purpose of this study is to investigate if structural brain phenotypes can be used to predict weight loss success following behavioral interventions in older adults that are overweight or obese and have cardiometabolic dysfunction. Methods A support vector machine (SVM) with a repeated random subsampling validation approach was used to classify participants into the upper and lower halves of the weight loss distribution following 18 months of a weight loss intervention. Predictions were based on baseline brain gray matter (GM) and white matter (WM) volume from 52 individuals that completed the intervention and a magnetic resonance imaging session. Results The SVM resulted in an average classification accuracy of 72.62 % based on GM and WM volume. A receiver operating characteristic analysis indicated that classification performance was robust based on an area under the curve of 0.82. Conclusions Our findings suggest that baseline brain structure is able to predict weight loss success following 18 months of treatment. The identification of brain structure as a predictor of successful weight loss is an innovative approach to identifying phenotypes for responsiveness to intensive lifestyle interventions. This phenotype could prove useful in future research focusing on the tailoring of treatment for weight loss. PMID:27804273

Resting lateralized activity predicts the cortical response and appraisal of emotions: an fNIRS study.

PubMed

Balconi, Michela; Grippa, Elisabetta; Vanutelli, Maria Elide

2015-12-01

This study explored the effect of lateralized left-right resting brain activity on prefrontal cortical responsiveness to emotional cues and on the explicit appraisal (stimulus evaluation) of emotions based on their valence. Indeed subjective responses to different emotional stimuli should be predicted by brain resting activity and should be lateralized and valence-related (positive vs negative valence). A hemodynamic measure was considered (functional near-infrared spectroscopy). Indeed hemodynamic resting activity and brain response to emotional cues were registered when subjects (N = 19) viewed emotional positive vs negative stimuli (IAPS). Lateralized index response during resting state, LI (lateralized index) during emotional processing and self-assessment manikin rating were considered. Regression analysis showed the significant predictive effect of resting activity (more left or right lateralized) on both brain response and appraisal of emotional cues based on stimuli valence. Moreover, significant effects were found as a function of valence (more right response to negative stimuli; more left response to positive stimuli) during emotion processing. Therefore, resting state may be considered a predictive marker of the successive cortical responsiveness to emotions. The significance of resting condition for emotional behavior was discussed. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

In silico investigation of blast-induced intracranial fluid cavitation as it potentially leads to traumatic brain injury

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haniff, S.; Taylor, P. A.

In this paper, we conducted computational macroscale simulations predicting blast-induced intracranial fluid cavitation possibly leading to brain injury. To further understanding of this problem, we developed microscale models investigating the effects of blast-induced cavitation bubble collapse within white matter axonal fiber bundles of the brain. We model fiber tracks of myelinated axons whose diameters are statistically representative of white matter. Nodes of Ranvier are modeled as unmyelinated sections of axon. Extracellular matrix envelops the axon fiber bundle, and gray matter is placed adjacent to the bundle. Cavitation bubbles are initially placed assuming an intracranial wave has already produced them. Pressuremore » pulses, of varied strengths, are applied to the upper boundary of the gray matter and propagate through the model, inducing bubble collapse. Simulations, conducted using the shock wave physics code CTH, predict an increase in pressure and von Mises stress in axons downstream of the bubbles after collapse. This appears to be the result of hydrodynamic jetting produced during bubble collapse. Interestingly, results predict axon cores suffer significantly lower shear stresses from proximal bubble collapse than does their myelin sheathing. Finally, simulations also predict damage to myelin sheathing, which, if true, degrades axonal electrical transmissibility and general health of the white matter structures in the brain.« less

In silico investigation of blast-induced intracranial fluid cavitation as it potentially leads to traumatic brain injury

DOE PAGES

Haniff, S.; Taylor, P. A.

2017-10-17

In this paper, we conducted computational macroscale simulations predicting blast-induced intracranial fluid cavitation possibly leading to brain injury. To further understanding of this problem, we developed microscale models investigating the effects of blast-induced cavitation bubble collapse within white matter axonal fiber bundles of the brain. We model fiber tracks of myelinated axons whose diameters are statistically representative of white matter. Nodes of Ranvier are modeled as unmyelinated sections of axon. Extracellular matrix envelops the axon fiber bundle, and gray matter is placed adjacent to the bundle. Cavitation bubbles are initially placed assuming an intracranial wave has already produced them. Pressuremore » pulses, of varied strengths, are applied to the upper boundary of the gray matter and propagate through the model, inducing bubble collapse. Simulations, conducted using the shock wave physics code CTH, predict an increase in pressure and von Mises stress in axons downstream of the bubbles after collapse. This appears to be the result of hydrodynamic jetting produced during bubble collapse. Interestingly, results predict axon cores suffer significantly lower shear stresses from proximal bubble collapse than does their myelin sheathing. Finally, simulations also predict damage to myelin sheathing, which, if true, degrades axonal electrical transmissibility and general health of the white matter structures in the brain.« less

Improving the modelling of irradiation-induced brain activation for in vivo PET verification of proton therapy.

PubMed

Bauer, Julia; Chen, Wenjing; Nischwitz, Sebastian; Liebl, Jakob; Rieken, Stefan; Welzel, Thomas; Debus, Juergen; Parodi, Katia

2018-04-24

A reliable Monte Carlo prediction of proton-induced brain tissue activation used for comparison to particle therapy positron-emission-tomography (PT-PET) measurements is crucial for in vivo treatment verification. Major limitations of current approaches to overcome include the CT-based patient model and the description of activity washout due to tissue perfusion. Two approaches were studied to improve the activity prediction for brain irradiation: (i) a refined patient model using tissue classification based on MR information and (ii) a PT-PET data-driven refinement of washout model parameters. Improvements of the activity predictions compared to post-treatment PT-PET measurements were assessed in terms of activity profile similarity for six patients treated with a single or two almost parallel fields delivered by active proton beam scanning. The refined patient model yields a generally higher similarity for most of the patients, except in highly pathological areas leading to tissue misclassification. Using washout model parameters deduced from clinical patient data could considerably improve the activity profile similarity for all patients. Current methods used to predict proton-induced brain tissue activation can be improved with MR-based tissue classification and data-driven washout parameters, thus providing a more reliable basis for PT-PET verification. Copyright © 2018 Elsevier B.V. All rights reserved.

Global brain dynamics during social exclusion predict subsequent behavioral conformity

PubMed Central

Wasylyshyn, Nick; Hemenway Falk, Brett; Garcia, Javier O; Cascio, Christopher N; O’Donnell, Matthew Brook; Bingham, C Raymond; Simons-Morton, Bruce; Vettel, Jean M; Falk, Emily B

2018-01-01

Abstract Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (n = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (dorsal anterior cingulate cortex, anterior insula). Using predictive modeling, this measure of global connectivity during exclusion predicted the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences. PMID:29529310

In silico investigation of blast-induced intracranial fluid cavitation as it potentially leads to traumatic brain injury

NASA Astrophysics Data System (ADS)

Haniff, S.; Taylor, P. A.

2017-11-01

We conducted computational macroscale simulations predicting blast-induced intracranial fluid cavitation possibly leading to brain injury. To further understanding of this problem, we developed microscale models investigating the effects of blast-induced cavitation bubble collapse within white matter axonal fiber bundles of the brain. We model fiber tracks of myelinated axons whose diameters are statistically representative of white matter. Nodes of Ranvier are modeled as unmyelinated sections of axon. Extracellular matrix envelops the axon fiber bundle, and gray matter is placed adjacent to the bundle. Cavitation bubbles are initially placed assuming an intracranial wave has already produced them. Pressure pulses, of varied strengths, are applied to the upper boundary of the gray matter and propagate through the model, inducing bubble collapse. Simulations, conducted using the shock wave physics code CTH, predict an increase in pressure and von Mises stress in axons downstream of the bubbles after collapse. This appears to be the result of hydrodynamic jetting produced during bubble collapse. Interestingly, results predict axon cores suffer significantly lower shear stresses from proximal bubble collapse than does their myelin sheathing. Simulations also predict damage to myelin sheathing, which, if true, degrades axonal electrical transmissibility and general health of the white matter structures in the brain.

Predictable information in neural signals during resting state is reduced in autism spectrum disorder.

PubMed

Brodski-Guerniero, Alla; Naumer, Marcus J; Moliadze, Vera; Chan, Jason; Althen, Heike; Ferreira-Santos, Fernando; Lizier, Joseph T; Schlitt, Sabine; Kitzerow, Janina; Schütz, Magdalena; Langer, Anne; Kaiser, Jochen; Freitag, Christine M; Wibral, Michael

2018-04-04

The neurophysiological underpinnings of the nonsocial symptoms of autism spectrum disorder (ASD) which include sensory and perceptual atypicalities remain poorly understood. Well-known accounts of less dominant top-down influences and more dominant bottom-up processes compete to explain these characteristics. These accounts have been recently embedded in the popular framework of predictive coding theory. To differentiate between competing accounts, we studied altered information dynamics in ASD by quantifying predictable information in neural signals. Predictable information in neural signals measures the amount of stored information that is used for the next time step of a neural process. Thus, predictable information limits the (prior) information which might be available for other brain areas, for example, to build predictions for upcoming sensory information. We studied predictable information in neural signals based on resting-state magnetoencephalography (MEG) recordings of 19 ASD patients and 19 neurotypical controls aged between 14 and 27 years. Using whole-brain beamformer source analysis, we found reduced predictable information in ASD patients across the whole brain, but in particular in posterior regions of the default mode network. In these regions, epoch-by-epoch predictable information was positively correlated with source power in the alpha and beta frequency range as well as autocorrelation decay time. Predictable information in precuneus and cerebellum was negatively associated with nonsocial symptom severity, indicating a relevance of the analysis of predictable information for clinical research in ASD. Our findings are compatible with the assumption that use or precision of prior knowledge is reduced in ASD patients. © 2018 Wiley Periodicals, Inc.

Predicting Individual Differences in Placebo Analgesia: Contributions of Brain Activity during Anticipation and Pain Experience

PubMed Central

Wager, Tor D.; Atlas, Lauren Y.; Leotti, Lauren A.; Rilling, James K.

2012-01-01

Recent studies have identified brain correlates of placebo analgesia, but none have assessed how accurately patterns of brain activity can predict individual differences in placebo responses. We reanalyzed data from two fMRI studies of placebo analgesia (N = 47), using patterns of fMRI activity during the anticipation and experience of pain to predict new subjects’ scores on placebo analgesia and placebo-induced changes in pain processing. We used a cross-validated regression procedure, LASSO-PCR, which provided both unbiased estimates of predictive accuracy and interpretable maps of which regions are most important for prediction. Increased anticipatory activity in a frontoparietal network and decreases in a posterior insular/temporal network predicted placebo analgesia. Patterns of anticipatory activity across the cortex predicted a moderate amount of variance in the placebo response (~12% overall, ~40% for study 2 alone), which is substantial considering the multiple likely contributing factors. The most predictive regions were those associated with emotional appraisal, rather than cognitive control or pain processing. During pain, decreases in limbic and paralimbic regions most strongly predicted placebo analgesia. Responses within canonical pain-processing regions explained significant variance in placebo analgesia, but the pattern of effects was inconsistent with widespread decreases in nociceptive processing. Together, the findings suggest that engagement of emotional appraisal circuits drives individual variation in placebo analgesia, rather than early suppression of nociceptive processing. This approach provides a framework that will allow prediction accuracy to increase as new studies provide more precise information for future predictive models. PMID:21228154

Neural representations of the concepts in simple sentences: Concept activation prediction and context effects.

PubMed

Just, Marcel Adam; Wang, Jing; Cherkassky, Vladimir L

2017-08-15

Although it has been possible to identify individual concepts from a concept's brain activation pattern, there have been significant obstacles to identifying a proposition from its fMRI signature. Here we demonstrate the ability to decode individual prototype sentences from readers' brain activation patterns, by using theory-driven regions of interest and semantic properties. It is possible to predict the fMRI brain activation patterns evoked by propositions and words which are entirely new to the model with reliably above-chance rank accuracy. The two core components implemented in the model that reflect the theory were the choice of intermediate semantic features and the brain regions associated with the neurosemantic dimensions. This approach also predicts the neural representation of object nouns across participants, studies, and sentence contexts. Moreover, we find that the neural representation of an agent-verb-object proto-sentence is more accurately characterized by the neural signatures of its components as they occur in a similar context than by the neural signatures of these components as they occur in isolation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Implications of polymorphonuclear neutrophils for ischemic stroke and intracerebral hemorrhage: Predictive value, pathophysiological consequences and utility as therapeutic target.

PubMed

Hermann, Dirk M; Kleinschnitz, Christoph; Gunzer, Matthias

2018-04-24

Polymorphonuclear neutrophil granulocytes (PMN) orchestrate the removal of cell debris in ischemic stroke and intracerebral hemorrhage. In both pathologies, high neutrophil to lymphocyte ratios in peripheral blood are predictive of poor outcome in human stroke patients. Following earlier studies indicating that the cerebral microvasculature forms an efficient barrier that impedes neutrophil brain entry, intravital microscopy and immunohistochemistry in the meantime unequivocally revealed the accumulation of PMN in the ischemic and hemorrhagic brain parenchyma. These studies provide definite evidence that PMN contribute to the degradation of the blood-brain barrier, predisposing the brain to secondary injury, edema, hemorrhage formation, hemorrhage growth and poor neurological recovery. Recent studies demonstrated the role of pro-inflammatory N1 neutrophils in brain edema and neurotoxicity, whereas anti-inflammatory N2 neutrophils were found to limit this excessive immune response, promoting neuronal survival and successful brain remodeling. In view of the recent failure of anti-inflammatory immunotherapies in clinical trials, strategies specifically modulating the brain accumulation, differentiation and action of PMN may open promising perspectives for stroke treatment. Copyright © 2018 Elsevier B.V. All rights reserved.

Predictive models for pressure-driven fluid infusions into brain parenchyma

NASA Astrophysics Data System (ADS)

Raghavan, Raghu; Brady, Martin

2011-10-01

Direct infusions into brain parenchyma of biological therapeutics for serious brain diseases have been, and are being, considered. However, individual brains, as well as distinct cytoarchitectural regions within brains, vary in their response to fluid flow and pressure. Further, the tissue responds dynamically to these stimuli, requiring a nonlinear treatment of equations that would describe fluid flow and drug transport in brain. We here report in detail on an individual-specific model and a comparison of its prediction with simulations for living porcine brains. Two critical features we introduced into our model—absent from previous ones, but requirements for any useful simulation—are the infusion-induced interstitial expansion and the backflow. These are significant determinants of the flow. Another feature of our treatment is the use of cross-property relations to obtain individual-specific parameters that are coefficients in the equations. The quantitative results are at least encouraging, showing a high fraction of overlap between the computed and measured volumes of distribution of a tracer molecule and are potentially clinically useful. Several improvements are called for; principally a treatment of the interstitial expansion more fundamentally based on poroelasticity and a better delineation of the diffusion tensor of a particle confined to the interstitial spaces.

Inferencing Processes after Right Hemisphere Brain Damage: Effects of Contextual Bias

ERIC Educational Resources Information Center

Blake, Margaret Lehman

2009-01-01

Purpose: Comprehension deficits associated with right hemisphere brain damage (RHD) have been attributed to an inability to use context, but there is little direct evidence to support the claim. This study evaluated the effect of varying contextual bias on predictive inferencing by adults with RHD. Method: Fourteen adults with no brain damage…

Identify the dominant variables to predict stream water temperature

NASA Astrophysics Data System (ADS)

Chien, H.; Flagler, J.

2016-12-01

Stream water temperature is a critical variable controlling water quality and the health of aquatic ecosystems. Accurate prediction of water temperature and the assessment of the impacts of environmental variables on water temperature variation are critical for water resources management, particularly in the context of water quality and aquatic ecosystem sustainability. The objective of this study is to measure stream water temperature and air temperature and to examine the importance of streamflow on stream water temperature prediction. The measured stream water temperature and air temperature will be used to test two hypotheses: 1) streamflow is a relatively more important factor than air temperature in regulating water temperature, and 2) by combining air temperature and streamflow data stream water temperature can be more accurately estimated. Water and air temperature data loggers are placed at two USGS stream gauge stations #01362357and #01362370, located in the upper Esopus Creek watershed in Phonecia, NY. The ARIMA (autoregressive integrated moving average) time series model is used to analyze the measured water temperature data, identify the dominant environmental variables, and predict the water temperature with identified dominant variable. The preliminary results show that streamflow is not a significant variable in predicting stream water temperature at both USGS gauge stations. Daily mean air temperature is sufficient to predict stream water temperature at this site scale.

Relative Brain and Brain Part Sizes Provide Only Limited Evidence that Machiavellian Behaviour in Cleaner Wrasse Is Cognitively Demanding

PubMed Central

Chojnacka, Dominika; Isler, Karin; Barski, Jaroslaw Jerzy; Bshary, Redouan

2015-01-01

It is currently widely accepted that the complexity of a species’ social life is a major determinant of its brain complexity, as predicted by the social brain hypothesis. However, it remains a challenge to explain what social complexity exactly is and what the best corresponding measures of brain anatomy are. Absolute and relative size of the brain and of the neocortex have often been used as a proxy to predict cognitive performance. Here, we apply the logic of the social brain hypothesis to marine cleaning mutualism involving the genus Labroides. These wrasses remove ectoparasites from ‘client’ reef fish. Conflict occurs as wrasse prefer client mucus over ectoparasites, where mucus feeding constitutes cheating. As a result of this conflict, cleaner wrasse show remarkable Machiavellian-like behaviour. Using own data as well as available data from the literature, we investigated whether the general brain anatomy of Labroides provides any indication that their Machiavellian behaviour is associated with a more complex brain. Neither data set provided evidence for an increased encephalisation index compared to other wrasse species. Published data on relative sizes of brain parts in 25 species of the order Perciformes suggests that only the diencephalon is relatively enlarged in Labroides dimidiatus. This part contains various nuclei of the social decision making network. In conclusion, gross brain anatomy yields little evidence for the hypothesis that strategic behaviour in cleaning selects for larger brains, while future research should focus on more detailed aspects like the sizes of specific nuclei as well as their cryoarchitectonic structure and connectivity. PMID:26263490

Relative Brain and Brain Part Sizes Provide Only Limited Evidence that Machiavellian Behaviour in Cleaner Wrasse Is Cognitively Demanding.

PubMed

Chojnacka, Dominika; Isler, Karin; Barski, Jaroslaw Jerzy; Bshary, Redouan

2015-01-01

It is currently widely accepted that the complexity of a species' social life is a major determinant of its brain complexity, as predicted by the social brain hypothesis. However, it remains a challenge to explain what social complexity exactly is and what the best corresponding measures of brain anatomy are. Absolute and relative size of the brain and of the neocortex have often been used as a proxy to predict cognitive performance. Here, we apply the logic of the social brain hypothesis to marine cleaning mutualism involving the genus Labroides. These wrasses remove ectoparasites from 'client' reef fish. Conflict occurs as wrasse prefer client mucus over ectoparasites, where mucus feeding constitutes cheating. As a result of this conflict, cleaner wrasse show remarkable Machiavellian-like behaviour. Using own data as well as available data from the literature, we investigated whether the general brain anatomy of Labroides provides any indication that their Machiavellian behaviour is associated with a more complex brain. Neither data set provided evidence for an increased encephalisation index compared to other wrasse species. Published data on relative sizes of brain parts in 25 species of the order Perciformes suggests that only the diencephalon is relatively enlarged in Labroides dimidiatus. This part contains various nuclei of the social decision making network. In conclusion, gross brain anatomy yields little evidence for the hypothesis that strategic behaviour in cleaning selects for larger brains, while future research should focus on more detailed aspects like the sizes of specific nuclei as well as their cryoarchitectonic structure and connectivity.

Oxidative damage and brain concentrations of free amino acid in chicks exposed to high ambient temperature.

PubMed

Chowdhury, Vishwajit S; Tomonaga, Shozo; Ikegami, Taro; Erwan, Edi; Ito, Kentaro; Cockrem, John F; Furuse, Mitsuhiro

2014-03-01

High ambient temperatures (HT) reduce food intake and body weight in young chickens, and HT can cause increased expression of hypothalamic neuropeptides. The mechanisms by which HT act, and the effects of HT on cellular homeostasis in the brain, are however not well understood. In the current study lipid peroxidation and amino acid metabolism were measured in the brains of 14 d old chicks exposed to HT (35 °C for 24- or 48-h) or to control thermoneutral temperature (CT; 30 °C). Malondialdehyde (MDA) was measured in the brain to determine the degree of oxidative damage. HT increased body temperature and reduced food intake and body weight gain. HT also increased diencephalic oxidative damage after 48 h, and altered some free amino acid concentrations in the diencephalon. Diencephalic MDA concentrations were increased by HT and time, with the effect of HT more prominent with increasing time. HT altered cystathionine, serine, tyrosine and isoleucine concentrations. Cystathionine was lower in HT birds compared with CT birds at 24h, whilst serine, tyrosine and isoleucine were higher at 48 h in HT birds. An increase in oxidative damage and alterations in amino acid concentrations in the diencephalon may contribute to the physiological, behavioral and thermoregulatory responses of heat-exposed chicks. Copyright © 2013 Elsevier Inc. All rights reserved.

Perception of social synchrony induces mother–child gamma coupling in the social brain

PubMed Central

Levy, Jonathan; Goldstein, Abraham

2017-01-01

Abstract The recent call to move from focus on one brain’s functioning to two-brain communication initiated a search for mechanisms that enable two humans to coordinate brain response during social interactions. Here, we utilized the mother–child context as a developmentally salient setting to study two-brain coupling. Mothers and their 9-year-old children were videotaped at home in positive and conflictual interactions. Positive interactions were microcoded for social synchrony and conflicts for overall dialogical style. Following, mother and child underwent magnetoencephalography while observing the positive vignettes. Episodes of behavioral synchrony, compared to non-synchrony, increased gamma-band power in the superior temporal sulcus (STS), hub of social cognition, mirroring and mentalizing. This neural pattern was coupled between mother and child. Brain-to-brain coordination was anchored in behavioral synchrony; only during episodes of behavioral synchrony, but not during non-synchronous moments, mother’s and child's STS gamma power was coupled. Importantly, neural synchrony was not found during observation of unfamiliar mother-child interaction Maternal empathic/dialogical conflict style predicted mothers’ STS activations whereas child withdrawal predicted attenuated STS response in both partners. Results define a novel neural marker for brain-to-brain synchrony, highlight the role of rapid bottom-up oscillatory mechanisms for neural coupling and indicate that behavior-based processes may drive synchrony between two brains during social interactions. PMID:28402479

Network measures predict neuropsychological outcome after brain injury

PubMed Central

Warren, David E.; Power, Jonathan D.; Bruss, Joel; Denburg, Natalie L.; Waldron, Eric J.; Sun, Haoxin; Petersen, Steven E.; Tranel, Daniel

2014-01-01

Hubs are network components that hold positions of high importance for network function. Previous research has identified hubs in human brain networks derived from neuroimaging data; however, there is little consensus on the localization of such hubs. Moreover, direct evidence regarding the role of various proposed hubs in network function (e.g., cognition) is scarce. Regions of the default mode network (DMN) have been frequently identified as “cortical hubs” of brain networks. On theoretical grounds, we have argued against some of the methods used to identify these hubs and have advocated alternative approaches that identify different regions of cortex as hubs. Our framework predicts that our proposed hub locations may play influential roles in multiple aspects of cognition, and, in contrast, that hubs identified via other methods (including salient regions in the DMN) might not exert such broad influence. Here we used a neuropsychological approach to directly test these predictions by studying long-term cognitive and behavioral outcomes in 30 patients, 19 with focal lesions to six “target” hubs identified by our approaches (high system density and participation coefficient) and 11 with focal lesions to two “control” hubs (high degree centrality). In support of our predictions, we found that damage to target locations produced severe and widespread cognitive deficits, whereas damage to control locations produced more circumscribed deficits. These findings support our interpretation of how neuroimaging-derived network measures relate to cognition and augment classic neuroanatomically based predictions about cognitive and behavioral outcomes after focal brain injury. PMID:25225403

Virtual reality and consciousness inference in dreaming

PubMed Central

Hobson, J. Allan; Hong, Charles C.-H.; Friston, Karl J.

2014-01-01

This article explores the notion that the brain is genetically endowed with an innate virtual reality generator that – through experience-dependent plasticity – becomes a generative or predictive model of the world. This model, which is most clearly revealed in rapid eye movement (REM) sleep dreaming, may provide the theater for conscious experience. Functional neuroimaging evidence for brain activations that are time-locked to rapid eye movements (REMs) endorses the view that waking consciousness emerges from REM sleep – and dreaming lays the foundations for waking perception. In this view, the brain is equipped with a virtual model of the world that generates predictions of its sensations. This model is continually updated and entrained by sensory prediction errors in wakefulness to ensure veridical perception, but not in dreaming. In contrast, dreaming plays an essential role in maintaining and enhancing the capacity to model the world by minimizing model complexity and thereby maximizing both statistical and thermodynamic efficiency. This perspective suggests that consciousness corresponds to the embodied process of inference, realized through the generation of virtual realities (in both sleep and wakefulness). In short, our premise or hypothesis is that the waking brain engages with the world to predict the causes of sensations, while in sleep the brain’s generative model is actively refined so that it generates more efficient predictions during waking. We review the evidence in support of this hypothesis – evidence that grounds consciousness in biophysical computations whose neuronal and neurochemical infrastructure has been disclosed by sleep research. PMID:25346710

ADME evaluation in drug discovery. 1. Applications of genetic algorithms to the prediction of blood-brain partitioning of a large set of drugs.

PubMed

Hou, Tingjun; Xu, Xiaojie

2002-12-01

In this study, the relationships between the brain-blood concentration ratio of 96 structurally diverse compounds with a large number of structurally derived descriptors were investigated. The linear models were based on molecular descriptors that can be calculated for any compound simply from a knowledge of its molecular structure. The linear correlation coefficients of the models were optimized by genetic algorithms (GAs), and the descriptors used in the linear models were automatically selected from 27 structurally derived descriptors. The GA optimizations resulted in a group of linear models with three or four molecular descriptors with good statistical significance. The change of descriptor use as the evolution proceeds demonstrates that the octane/water partition coefficient and the partial negative solvent-accessible surface area multiplied by the negative charge are crucial to brain-blood barrier permeability. Moreover, we found that the predictions using multiple QSPR models from GA optimization gave quite good results in spite of the diversity of structures, which was better than the predictions using the best single model. The predictions for the two external sets with 37 diverse compounds using multiple QSPR models indicate that the best linear models with four descriptors are sufficiently effective for predictive use. Considering the ease of computation of the descriptors, the linear models may be used as general utilities to screen the blood-brain barrier partitioning of drugs in a high-throughput fashion.

Word Memory Test Predicts Recovery in Claimants With Work-Related Head Injury.

PubMed

Colangelo, Annette; Abada, Abigail; Haws, Calvin; Park, Joanne; Niemeläinen, Riikka; Gross, Douglas P

2016-05-01

To investigate the predictive validity of the Word Memory Test (WMT), a verbal memory neuropsychological test developed as a performance validity measure to assess memory, effort, and performance consistency. Cohort study with 1-year follow-up. Workers' compensation rehabilitation facility. Participants included workers' compensation claimants with work-related head injury (N=188; mean age, 44y; 161 men [85.6%]). Not applicable. Outcome measures for determining predictive validity included days to suspension of wage replacement benefits during the 1-year follow-up and work status at discharge in claimants undergoing rehabilitation. Analysis included multivariable Cox and logistic regression. Better WMT performance was significantly but weakly correlated with younger age (r=-.30), documented brain abnormality (r=.28), and loss of consciousness at the time of injury (r=.25). Claimants with documented brain abnormalities on diagnostic imaging scans performed better (∼9%) on the WMT than those without brain abnormalities. The WMT predicted days receiving benefits (adjusted hazard ratio, 1.13; 95% confidence interval, 1.04-1.24) and work status outcome at program discharge (adjusted odds ratio, 1.62; 95% confidence interval, 1.13-2.34). Our results provide evidence for the predictive validity of the WMT in workers' compensation claimants. Younger claimants and those with more severe brain injuries performed better on the WMT. It may be that financial incentives or other factors related to the compensation claim affected the performance. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

N-terminal pro-brain natriuretic peptide in acute Kawasaki disease correlates with coronary artery involvement.

PubMed

Adjagba, Philippe M; Desjardins, Laurent; Fournier, Anne; Spigelblatt, Linda; Montigny, Martine; Dahdah, Nagib

2015-10-01

We have lately documented the importance of N-terminal pro-brain natriuretic peptide in aiding the diagnosis of Kawasaki disease. We sought to investigate the potential value of N-terminal pro-brain natriuretic peptide pertaining to the prediction of coronary artery dilatation (Z-score>2.5) and/or of resistance to intravenous immunoglobulin therapy. We hypothesised that increased serum N-terminal pro-brain natriuretic peptide level correlates with increased coronary artery dilatation and/or resistance to intravenous immunoglobulin. We carried out a prospective study involving newly diagnosed patients treated with 2 g/kg intravenous immunoglobulin within 5-10 days of onset of fever. Echocardiography was performed in all patients at onset, then weekly for 3 weeks, then at month 2, and month 3. Coronary arteries were measured at each visit, and coronary artery Z-score was calculated. All the patients had N-terminal pro-brain natriuretic peptide serum level measured at onset, and the Z-score calculated. There were 109 patients enrolled at 6.58±2.82 days of fever, age 3.79±2.92 years. High N-terminal pro-brain natriuretic peptide level was associated with coronary artery dilatation at onset in 22.2 versus 5.6% for normal N-terminal pro-brain natriuretic peptide levels (odds ratio 4.8 [95% confidence interval 1.05-22.4]; p=0.031). This was predictive of cumulative coronary artery dilatation for the first 3 months (p=0.04-0.02), but not during convalescence at 2-3 months (odds ratio 1.28 [95% confidence interval 0.23-7.3]; p=non-significant). Elevated N-terminal pro-brain natriuretic peptide levels did not predict intravenous immunoglobulin resistance, 15.3 versus 13.5% (p=1). Elevated N-terminal pro-brain natriuretic peptide level correlates with acute coronary artery dilatation in treated Kawasaki disease, but not with intravenous immunoglobulin resistance.

Acute post-traumatic stress symptoms and age predict outcome in military blast concussion.

PubMed

Mac Donald, Christine L; Adam, Octavian R; Johnson, Ann M; Nelson, Elliot C; Werner, Nicole J; Rivet, Dennis J; Brody, David L

2015-05-01

High rates of adverse outcomes have been reported following blast-related concussive traumatic brain injury in US military personnel, but the extent to which such adverse outcomes can be predicted acutely after injury is unknown. We performed a prospective, observational study of US military personnel with blast-related concussive traumatic brain injury (n = 38) and controls (n = 34) enrolled between March and September 2012. Importantly all subjects returned to duty and did not require evacuation. Subjects were evaluated acutely 0-7 days after injury at two sites in Afghanistan and again 6-12 months later in the United States. Acute assessments revealed heightened post-concussive, post-traumatic stress, and depressive symptoms along with worse cognitive performance in subjects with traumatic brain injury. At 6-12 months follow-up, 63% of subjects with traumatic brain injury and 20% of controls had moderate overall disability. Subjects with traumatic brain injury showed more severe neurobehavioural, post-traumatic stress and depression symptoms along with more frequent cognitive performance deficits and more substantial headache impairment than control subjects. Logistic regression modelling using only acute measures identified that a diagnosis of traumatic brain injury, older age, and more severe post-traumatic stress symptoms provided a good prediction of later adverse global outcomes (area under the receiver-operating characteristic curve = 0.84). Thus, US military personnel with concussive blast-related traumatic brain injury in Afghanistan who returned to duty still fared quite poorly on many clinical outcome measures 6-12 months after injury. Poor global outcome seems to be largely driven by psychological health measures, age, and traumatic brain injury status. The effects of early interventions and longer term implications of these findings are unknown. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Diagnosis of anticholinesterase poisoning in birds: Effects of environmental temperature and underfeeding on cholinesterase activity

USGS Publications Warehouse

Rattner, B.A.

1982-01-01

Brain cholinesterase (ChE) activity has been used extensively to monitor exposure to organophosphorus (OP) and carbamate (CB) insecticides in wild birds. A series of factorial experiments was conducted to assess the extent to which noncontaminant-related environmental conditions might affect brain ChE activity and thereby confound the diagnosis of OP and CB intoxication. Underfeeding (restricting intake to 50% of control for 21 d or fasting for 1-3 d) or exposure to elevated temperature (36 + 1?C for 1 d) caused only slight reductions (10-17%) in brain AChE activity in adult male Japanese quail (Coturnix coturnix japonica). This degree of 'reduction' in brain AChE activity is considerably less than the 50% 'inhibition' criterion employed in the diagnosis of insecticide-induced mortality, but nevertheless approaches the 20% 'inhibition' level used as a conservative estimate of sublethal exposure to a known insecticide application.

Melatonin affects the order, dynamics and hydration of brain membrane lipids

NASA Astrophysics Data System (ADS)

Akkas, Sara B.; Inci, Servet; Zorlu, Faruk; Severcan, Feride

2007-05-01

The brain is especially susceptible to free radical attack since it is rich in polyunsaturated fatty acids and consumes very high amounts of oxygen. Melatonin is a non-enzymatic amphiphilic antioxidant hormone that is widely used in medicine for protective and treatment purposes in cases of oxidative stress. In the present work, the effects of the clinically used dose of melatonin (a single intraperitoneal dose of 100 mg/kg) on rat brain homogenate were investigated as a function of temperature using Fourier transform infrared spectroscopy. The results showed that the lipid to protein ratio decreases in the melatonin treated brain samples. Moreover, it is revealed that melatonin disorders and decreases the dynamics of lipids and induces a strengthening in the hydrogen bonding between the functional groups of both melatonin and the polar parts of lipids and/or water at physiological temperatures.

Brain properties predict proximity to symptom onset in sporadic Alzheimer’s disease

PubMed Central

Vogel, Jacob W; Vachon-Presseau, Etienne; Pichet Binette, Alexa; Tam, Angela; Orban, Pierre; La Joie, Renaud; Savard, Mélissa; Picard, Cynthia; Poirier, Judes; Bellec, Pierre; Breitner, John C S; Villeneuve, Sylvia

2018-01-01

Abstract See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article. Alzheimer’s disease is preceded by a lengthy ‘preclinical’ stage spanning many years, during which subtle brain changes occur in the absence of overt cognitive symptoms. Predicting when the onset of disease symptoms will occur is an unsolved challenge in individuals with sporadic Alzheimer’s disease. In individuals with autosomal dominant genetic Alzheimer’s disease, the age of symptom onset is similar across generations, allowing the prediction of individual onset times with some accuracy. We extend this concept to persons with a parental history of sporadic Alzheimer’s disease to test whether an individual’s symptom onset age can be informed by the onset age of their affected parent, and whether this estimated onset age can be predicted using only MRI. Structural and functional MRIs were acquired from 255 ageing cognitively healthy subjects with a parental history of sporadic Alzheimer’s disease from the PREVENT-AD cohort. Years to estimated symptom onset was calculated as participant age minus age of parental symptom onset. Grey matter volume was extracted from T1-weighted images and whole-brain resting state functional connectivity was evaluated using degree count. Both modalities were summarized using a 444-region cortical-subcortical atlas. The entire sample was divided into training (n = 138) and testing (n = 68) sets. Within the training set, individuals closer to or beyond their parent’s symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer’s disease. Machine learning was used to identify a weighted set of imaging features trained to predict years to estimated symptom onset. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. We next tested if these brain properties could be generalized to predict time to clinical progression in a subgroup of 26 individuals from the Alzheimer’s Disease Neuroimaging Initiative, who eventually converted either to mild cognitive impairment or to Alzheimer’s dementia. The feature set trained on years to estimated symptom onset in the PREVENT-AD predicted variance in time to clinical conversion in this separate longitudinal dataset. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals. PMID:29688388

Estimation of State Transition Probabilities: A Neural Network Model

NASA Astrophysics Data System (ADS)

Saito, Hiroshi; Takiyama, Ken; Okada, Masato

2015-12-01

Humans and animals can predict future states on the basis of acquired knowledge. This prediction of the state transition is important for choosing the best action, and the prediction is only possible if the state transition probability has already been learned. However, how our brains learn the state transition probability is unknown. Here, we propose a simple algorithm for estimating the state transition probability by utilizing the state prediction error. We analytically and numerically confirmed that our algorithm is able to learn the probability completely with an appropriate learning rate. Furthermore, our learning rule reproduced experimentally reported psychometric functions and neural activities in the lateral intraparietal area in a decision-making task. Thus, our algorithm might describe the manner in which our brains learn state transition probabilities and predict future states.

Caspase inhibitors increase the rate of recovery of neural stem/progenitor cells from post-mortem rat brains stored at room temperature.

PubMed

Hasegawa, Atsuko; Yamada, Chikako; Tani, Miho; Hirano, Shun-ichiro; Tokumoto, Yasuhito; Miyake, Jun

2009-06-01

To match the demand of regenerative medicine for nerve system, collection of stem cells from the post-mortem body is one of the most practical ways. In this study, the storage condition of the post-mortem body was examined. We prepared neural stem/progenitor cells (NSPCs) from post-mortem rat brains stored at different temperatures. When brains were stored at 4 degrees C, for one week, we were able to obtain neurospheres (a spheroid body containing NSPCs) by stimulation of cells with epidermal growth factor (EGF). Incremental increases in storage temperature decreased the rate of appearance of neurospheres. Within 48 h at 15 degrees C, 24 h at 25 degrees C, in both condition, we were able to recover NSPCs from post-mortem rat brains. At 15 degrees C, 90% of neurosphere-forming activity was lost within 24 h. However, even after 24 h at 25 degrees C, 2% neurosphere-forming activity remained. After 6 h of death, there was very little difference between the rates of NSPC recovery at 4 degrees C and 25 degrees C. Addition of caspase inhibitors to both the rat brain storage solution and the NSPC culture medium increased the rate of neurosphere-forming activity. In particular, an inhibitor of caspase-8 activity increased the NSPC recovery rate approximately three-fold, with no accompanying detrimental effects on neural differentiation in vitro.

Pupillometry in brain death: differences in pupillary diameter between paediatric and adult subjects.

PubMed

Olgun, Gokhan; Newey, Christopher R; Ardelt, Agnieszka

2015-11-01

The determination of brain death in neonates, infants, children and adults relies on a clinical diagnosis based on the absence of neurological function with a known irreversible cause of brain injury. Evaluation of pupil size and non-reactivity is a requisite for determination of brain death. There are no studies in the literature that quantitatively assess pupil size in brain dead children and adults. Infants, children and adults diagnosed with brain death were included in the study. Pupils were measured with a quantitative pupillometer (Forsite; Neuroptics, Irvine, CA, USA). Median, minimum and maximum pupil sizes were documented and the results were adjudicated for age, vasopressor use and temperature. Median right and left pupil sizes were 5.01 ± 0.85 mm and 5.12 ± 0.87 mm, respectively, with a range between 3.69 and 7.34 mm. Paediatric pupils were larger than adult pupils (right pupil 5.53 vs 4.73 mm p: 0.018; left pupil 5.87 vs 4.77 mm P: 0.03), and there was no correlation of pupil size with temperature or increasing number of vasopressors. This is the first study in the literature objectively evaluating pupil sizes in infants, children and adults diagnosed with brain death. We observed variation between observed pupil size and that expected based on brain death determination guidelines.

Magnetite pollution nanoparticles in the human brain

NASA Astrophysics Data System (ADS)

Maher, Barbara A.; Ahmed, Imad A. M.; Karloukovski, Vassil; MacLaren, Donald A.; Foulds, Penelope G.; Allsop, David; Mann, David M. A.; Torres-Jardón, Ricardo; Calderon-Garciduenas, Lilian

2016-09-01

Biologically formed nanoparticles of the strongly magnetic mineral, magnetite, were first detected in the human brain over 20 y ago [Kirschvink JL, Kobayashi-Kirschvink A, Woodford BJ (1992) Proc Natl Acad Sci USA 89(16):7683-7687]. Magnetite can have potentially large impacts on the brain due to its unique combination of redox activity, surface charge, and strongly magnetic behavior. We used magnetic analyses and electron microscopy to identify the abundant presence in the brain of magnetite nanoparticles that are consistent with high-temperature formation, suggesting, therefore, an external, not internal, source. Comprising a separate nanoparticle population from the euhedral particles ascribed to endogenous sources, these brain magnetites are often found with other transition metal nanoparticles, and they display rounded crystal morphologies and fused surface textures, reflecting crystallization upon cooling from an initially heated, iron-bearing source material. Such high-temperature magnetite nanospheres are ubiquitous and abundant in airborne particulate matter pollution. They arise as combustion-derived, iron-rich particles, often associated with other transition metal particles, which condense and/or oxidize upon airborne release. Those magnetite pollutant particles which are <˜200 nm in diameter can enter the brain directly via the olfactory bulb. Their presence proves that externally sourced iron-bearing nanoparticles, rather than their soluble compounds, can be transported directly into the brain, where they may pose hazard to human health.

Magnetite pollution nanoparticles in the human brain.

PubMed

Maher, Barbara A; Ahmed, Imad A M; Karloukovski, Vassil; MacLaren, Donald A; Foulds, Penelope G; Allsop, David; Mann, David M A; Torres-Jardón, Ricardo; Calderon-Garciduenas, Lilian

2016-09-27

Biologically formed nanoparticles of the strongly magnetic mineral, magnetite, were first detected in the human brain over 20 y ago [Kirschvink JL, Kobayashi-Kirschvink A, Woodford BJ (1992) Proc Natl Acad Sci USA 89(16):7683-7687]. Magnetite can have potentially large impacts on the brain due to its unique combination of redox activity, surface charge, and strongly magnetic behavior. We used magnetic analyses and electron microscopy to identify the abundant presence in the brain of magnetite nanoparticles that are consistent with high-temperature formation, suggesting, therefore, an external, not internal, source. Comprising a separate nanoparticle population from the euhedral particles ascribed to endogenous sources, these brain magnetites are often found with other transition metal nanoparticles, and they display rounded crystal morphologies and fused surface textures, reflecting crystallization upon cooling from an initially heated, iron-bearing source material. Such high-temperature magnetite nanospheres are ubiquitous and abundant in airborne particulate matter pollution. They arise as combustion-derived, iron-rich particles, often associated with other transition metal particles, which condense and/or oxidize upon airborne release. Those magnetite pollutant particles which are <∼200 nm in diameter can enter the brain directly via the olfactory bulb. Their presence proves that externally sourced iron-bearing nanoparticles, rather than their soluble compounds, can be transported directly into the brain, where they may pose hazard to human health.

The neural encoding of guesses in the human brain.

PubMed

Bode, Stefan; Bogler, Carsten; Soon, Chun Siong; Haynes, John-Dylan

2012-01-16

Human perception depends heavily on the quality of sensory information. When objects are hard to see we often believe ourselves to be purely guessing. Here we investigated whether such guesses use brain networks involved in perceptual decision making or independent networks. We used a combination of fMRI and pattern classification to test how visibility affects the signals, which determine choices. We found that decisions regarding clearly visible objects are predicted by signals in sensory brain regions, whereas different regions in parietal cortex became predictive when subjects were shown invisible objects and believed themselves to be purely guessing. This parietal network was highly overlapping with regions, which have previously been shown to encode free decisions. Thus, the brain might use a dedicated network for determining choices when insufficient sensory information is available. Copyright © 2011 Elsevier Inc. All rights reserved.

Brain and cognitive-behavioural development after asphyxia at term birth.

PubMed

de Haan, Michelle; Wyatt, John S; Roth, Simon; Vargha-Khadem, Faraneh; Gadian, David; Mishkin, Mortimer

2006-07-01

Perinatal asphyxia occurs in approximately 1-6 per 1000 live full-term births. Different patterns of brain damage can result, though the relation of these patterns to long-term cognitive-behavioural outcome remains under investigation. The hippocampus is one brain region that can be damaged (typically not in isolation), and this site of damage has been implicated in two different long-term outcomes, cognitive memory impairment and the psychiatric disorder schizophrenia. Factors in addition to the acute episode of asphyxia likely contribute to these specific outcomes, making prediction difficult. Future studies that better document long-term cognitive-behavioural outcome, quantitatively identify patterns of brain injury over development and consider additional variables that may modulate the impact of asphyxia on cognitive and behavioural function will forward the goals of predicting long-term outcome and understanding the mechanisms by which it unfolds.

In vivo brain electrophoresis - a novel method for chemotherapy of CNS diseases.

PubMed

Ammirati, Mario; Lamki, Tariq; Chitnis, Girish; Yang, Xiangyu; Russell, Duncan; Coble, Dondrae; Kaur, Balveen; Knopp, Michael; Moore, Sarah; Ziaie, Babak

2015-05-01

The blood-brain barrier (BBB) is a protective mechanism that does its job superbly. So much so, that hitherto, brain chemotherapy has been limited by it. In fact, very few agents are effective against brain disease due to the inherent difficulties of penetrating the BBB. We describe a novel, extremely focused method for delivering drugs to specific diseased areas. This innovative method directly delivers putative substances to the pathological area, bypassing the BBB. Treatment of brain diseases could be improved by targeted, controlled delivery of therapeutic substances to diseased cerebral areas. Our described novel method - in vivo electrophoresis - achieves this. This technique was evaluated in beagles after craniotomy was performed and a custom-designed plate with electrodes inserted. The delivery of charged substances to selected areas with predictably guided movement was achieved via a created electrical field. Gadolinium, a compound unable to cross the BBB, was injected intracerebrally whereas an electrical field was created using the implanted electrodes surrounding the injection area. The electrical field-guided Gadolinium movement was evaluated using MRI. Gadolinium was moved predictably using the created electrical field without complications. The experiment successfully demonstrated controlled movement of the substance. This technique can significantly change treatment of brain diseases because substances: i) may be moved in a controlled, predictable way - exponentially increasing therapeutic interactions with the target; and ii) no longer need to conform to constraints dictated by the BBB (molecular mass < 500 d; lipophilic), thereby increasing potential number of usable substances.

Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states

PubMed Central

Chandrasekaran, Sriram; Ament, Seth A.; Eddy, James A.; Rodriguez-Zas, Sandra L.; Schatz, Bruce R.; Price, Nathan D.; Robinson, Gene E.

2011-01-01

Using brain transcriptomic profiles from 853 individual honey bees exhibiting 48 distinct behavioral phenotypes in naturalistic contexts, we report that behavior-specific neurogenomic states can be inferred from the coordinated action of transcription factors (TFs) and their predicted target genes. Unsupervised hierarchical clustering of these transcriptomic profiles showed three clusters that correspond to three ecologically important behavioral categories: aggression, maturation, and foraging. To explore the genetic influences potentially regulating these behavior-specific neurogenomic states, we reconstructed a brain transcriptional regulatory network (TRN) model. This brain TRN quantitatively predicts with high accuracy gene expression changes of more than 2,000 genes involved in behavior, even for behavioral phenotypes on which it was not trained, suggesting that there is a core set of TFs that regulates behavior-specific gene expression in the bee brain, and other TFs more specific to particular categories. TFs playing key roles in the TRN include well-known regulators of neural and behavioral plasticity, e.g., Creb, as well as TFs better known in other biological contexts, e.g., NF-κB (immunity). Our results reveal three insights concerning the relationship between genes and behavior. First, distinct behaviors are subserved by distinct neurogenomic states in the brain. Second, the neurogenomic states underlying different behaviors rely upon both shared and distinct transcriptional modules. Third, despite the complexity of the brain, simple linear relationships between TFs and their putative target genes are a surprisingly prominent feature of the networks underlying behavior. PMID:21960440

A new threshold of apparent diffusion coefficient values in white matter after successful tissue plasminogen activator treatment for acute brain ischemia.

PubMed

Sato, Atsushi; Shimizu, Yusaku; Koyama, Junichi; Hongo, Kazuhiro

2017-06-01

Tissue plasminogen activator (tPA) is effective for the treatment of acute brain ischemia, but may trigger fatal brain edema or hemorrhage if the brain ischemia results in a large infarct. Herein, we attempted to predict the extent of infarcts by determining the optimal threshold of ADC values on DWI that predictively distinguishes between infarct and reversible areas, and by reconstructing color-coded images based on this threshold. The study subjects consisted of 36 patients with acute brain ischemia in whom MRA had confirmed reopening of the occluded arteries in a short time (mean: 99min) after tPA treatment. We measured the apparetnt diffusion coefficient (ADC) values in several small regions of interest over the white matter within high-intensity areas on the initial diffusion weighted image (DWI); then, by comparing the findings to the follow-up images, we obtained the optimal threshold of ADC values using receiver-operating characteristic analysis. The threshold obtained (583×10 -6 m 2 /s) was lower than those previously reported; this threshold could distinguish between infarct and reversible areas with considerable accuracy (sensitivity: 0.87, specificity: 0.94). The threshold obtained and the reconstructed images were predictive of the final radiological result of tPA treatment, and this threshold may be helpful in determining the appropriate management of patients with acute brain ischemia. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Application of spatially modulated near-infrared structured light to study changes in optical properties of mouse brain tissue during heatstress.

PubMed

Shaul, Oren; Fanrazi-Kahana, Michal; Meitav, Omri; Pinhasi, Gad A; Abookasis, David

2017-11-10

Heat stress (HS) is a medical emergency defined by abnormally elevated body temperature that causes biochemical, physiological, and hematological changes. The goal of the present research was to detect variations in optical properties (absorption, reduced scattering, and refractive index coefficients) of mouse brain tissue during HS by using near-infrared (NIR) spatial light modulation. NIR spatial patterns with different spatial phases were used to differentiate the effects of tissue scattering from those of absorption. Decoupling optical scattering from absorption enabled the quantification of a tissue's chemical constituents (related to light absorption) and structural properties (related to light scattering). Technically, structured light patterns at low and high spatial frequencies of six wavelengths ranging between 690 and 970 nm were projected onto the mouse scalp surface while diffuse reflected light was recorded by a CCD camera positioned perpendicular to the mouse scalp. Concurrently to pattern projection, brain temperature was measured with a thermal camera positioned slightly off angle from the mouse head while core body temperature was monitored by thermocouple probe. Data analysis demonstrated variations from baseline measurements in a battery of intrinsic brain properties following HS.

Cold-induced ependymin expression in zebrafish and carp brain: implications for cold acclimation.

PubMed

Tang, S J; Sun, K H; Sun, G H; Lin, G; Lin, W W; Chuang, M J

1999-10-01

Cold acclimation has been suggested to be mediated by alternations in the gene expression pattern in the cold-adapted fish. To investigate the mechanism of cold acclimation in fish brain at the molecular level, relevant subsets of differentially expressed genes of interest were identified and cloned by the PCR-based subtraction suppression hybridization. Characterization of the selected cold-induced cDNA clones revealed one encoding ependymin. This gene was shown to be brain-specific. The expression of ependymin was induced by a temperature shift from 25 degrees C to 6 degrees C in Cyprinus carpio or 12 degrees C in Danio rerio. Activation of ependymin was detected 2 h after cold exposure and peaked at more than 10-fold at 12 h. This peak level remains unchanged until the temperature returns to 25 degrees C. Although the amount of soluble ependymin protein in brain was not changed by cold treatment, its level in the fibrous insoluble polymers increased 2-fold after exposure to low temperature. These findings indicate that the increase in ependymin expression is an early event that may play an important role in the cold acclimation of fish.

Six Degree-of-Freedom Measurements of Human Mild Traumatic Brain Injury.

PubMed

Hernandez, Fidel; Wu, Lyndia C; Yip, Michael C; Laksari, Kaveh; Hoffman, Andrew R; Lopez, Jaime R; Grant, Gerald A; Kleiven, Svein; Camarillo, David B

2015-08-01

This preliminary study investigated whether direct measurement of head rotation improves prediction of mild traumatic brain injury (mTBI). Although many studies have implicated rotation as a primary cause of mTBI, regulatory safety standards use 3 degree-of-freedom (3DOF) translation-only kinematic criteria to predict injury. Direct 6DOF measurements of human head rotation (3DOF) and translation (3DOF) have not been previously available to examine whether additional DOFs improve injury prediction. We measured head impacts in American football, boxing, and mixed martial arts using 6DOF instrumented mouthguards, and predicted clinician-diagnosed injury using 12 existing kinematic criteria and 6 existing brain finite element (FE) criteria. Among 513 measured impacts were the first two 6DOF measurements of clinically diagnosed mTBI. For this dataset, 6DOF criteria were the most predictive of injury, more than 3DOF translation-only and 3DOF rotation-only criteria. Peak principal strain in the corpus callosum, a 6DOF FE criteria, was the strongest predictor, followed by two criteria that included rotation measurements, peak rotational acceleration magnitude and Head Impact Power (HIP). These results suggest head rotation measurements may improve injury prediction. However, more 6DOF data is needed to confirm this evaluation of existing injury criteria, and to develop new criteria that considers directional sensitivity to injury.

Functional brain imaging predicts public health campaign success

PubMed Central

O’Donnell, Matthew Brook; Tompson, Steven; Gonzalez, Richard; Dal Cin, Sonya; Strecher, Victor; Cummings, Kenneth Michael; An, Lawrence

2016-01-01

Mass media can powerfully affect health decision-making. Pre-testing through focus groups or surveys is a standard, though inconsistent, predictor of effectiveness. Converging evidence demonstrates that activity within brain systems associated with self-related processing can predict individual behavior in response to health messages. Preliminary evidence also suggests that neural activity in small groups can forecast population-level campaign outcomes. Less is known about the psychological processes that link neural activity and population-level outcomes, or how these predictions are affected by message content. We exposed 50 smokers to antismoking messages and used their aggregated neural activity within a ‘self-localizer’ defined region of medial prefrontal cortex to predict the success of the same campaign messages at the population level (n = 400 000 emails). Results demonstrate that: (i) independently localized neural activity during health message exposure complements existing self-report data in predicting population-level campaign responses (model combined R2 up to 0.65) and (ii) this relationship depends on message content—self-related neural processing predicts outcomes in response to strong negative arguments against smoking and not in response to compositionally similar neutral images. These data advance understanding of the psychological link between brain and large-scale behavior and may aid the construction of more effective media health campaigns. PMID:26400858

Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals.

PubMed

Kauppi, Jukka-Pekka; Kandemir, Melih; Saarinen, Veli-Matti; Hirvenkari, Lotta; Parkkonen, Lauri; Klami, Arto; Hari, Riitta; Kaski, Samuel

2015-05-15

We hypothesize that brain activity can be used to control future information retrieval systems. To this end, we conducted a feasibility study on predicting the relevance of visual objects from brain activity. We analyze both magnetoencephalographic (MEG) and gaze signals from nine subjects who were viewing image collages, a subset of which was relevant to a predetermined task. We report three findings: i) the relevance of an image a subject looks at can be decoded from MEG signals with performance significantly better than chance, ii) fusion of gaze-based and MEG-based classifiers significantly improves the prediction performance compared to using either signal alone, and iii) non-linear classification of the MEG signals using Gaussian process classifiers outperforms linear classification. These findings break new ground for building brain-activity-based interactive image retrieval systems, as well as for systems utilizing feedback both from brain activity and eye movements. Copyright © 2015 Elsevier Inc. All rights reserved.

Estimating direction in brain-behavior interactions: Proactive and reactive brain states in driving.

PubMed

Garcia, Javier O; Brooks, Justin; Kerick, Scott; Johnson, Tony; Mullen, Tim R; Vettel, Jean M

2017-04-15

Conventional neuroimaging analyses have ascribed function to particular brain regions, exploiting the power of the subtraction technique in fMRI and event-related potential analyses in EEG. Moving beyond this convention, many researchers have begun exploring network-based neurodynamics and coordination between brain regions as a function of behavioral parameters or environmental statistics; however, most approaches average evoked activity across the experimental session to study task-dependent networks. Here, we examined on-going oscillatory activity as measured with EEG and use a methodology to estimate directionality in brain-behavior interactions. After source reconstruction, activity within specific frequency bands (delta: 2-3Hz; theta: 4-7Hz; alpha: 8-12Hz; beta: 13-25Hz) in a priori regions of interest was linked to continuous behavioral measurements, and we used a predictive filtering scheme to estimate the asymmetry between brain-to-behavior and behavior-to-brain prediction using a variant of Granger causality. We applied this approach to a simulated driving task and examined directed relationships between brain activity and continuous driving performance (steering behavior or vehicle heading error). Our results indicated that two neuro-behavioral states may be explored with this methodology: a Proactive brain state that actively plans the response to the sensory information and is characterized by delta-beta activity, and a Reactive brain state that processes incoming information and reacts to environmental statistics primarily within the alpha band. Published by Elsevier Inc.

A prediction model for cognitive performance in health ageing using diffusion tensor imaging with graph theory.

PubMed

Yun, Ruijuan; Lin, Chung-Chih; Wu, Shuicai; Huang, Chu-Chung; Lin, Ching-Po; Chao, Yi-Ping

2013-01-01

In this study, we employed diffusion tensor imaging (DTI) to construct brain structural network and then derive the connection matrices from 96 healthy elderly subjects. The correlation analysis between these topological properties of network based on graph theory and the Cognitive Abilities Screening Instrument (CASI) index were processed to extract the significant network characteristics. These characteristics were then integrated to estimate the models by various machine-learning algorithms to predict user's cognitive performance. From the results, linear regression model and Gaussian processes model showed presented better abilities with lower mean absolute errors of 5.8120 and 6.25 to predict the cognitive performance respectively. Moreover, these extracted topological properties of brain structural network derived from DTI also could be regarded as the bio-signatures for further evaluation of brain degeneration in healthy aged and early diagnosis of mild cognitive impairment (MCI).

A probabilistic framework to infer brain functional connectivity from anatomical connections.

PubMed

Deligianni, Fani; Varoquaux, Gael; Thirion, Bertrand; Robinson, Emma; Sharp, David J; Edwards, A David; Rueckert, Daniel

2011-01-01

We present a novel probabilistic framework to learn across several subjects a mapping from brain anatomical connectivity to functional connectivity, i.e. the covariance structure of brain activity. This prediction problem must be formulated as a structured-output learning task, as the predicted parameters are strongly correlated. We introduce a model selection framework based on cross-validation with a parametrization-independent loss function suitable to the manifold of covariance matrices. Our model is based on constraining the conditional independence structure of functional activity by the anatomical connectivity. Subsequently, we learn a linear predictor of a stationary multivariate autoregressive model. This natural parameterization of functional connectivity also enforces the positive-definiteness of the predicted covariance and thus matches the structure of the output space. Our results show that functional connectivity can be explained by anatomical connectivity on a rigorous statistical basis, and that a proper model of functional connectivity is essential to assess this link.

Prestimulus brain activity predicts primacy in list learning

PubMed Central

Galli, Giulia; Choy, Tsee Leng; Otten, Leun J.

2012-01-01

Brain activity immediately before an event can predict whether the event will later be remembered. This indicates that memory formation is influenced by anticipatory mechanisms engaged ahead of stimulus presentation. Here, we asked whether anticipatory processes affect the learning of short word lists, and whether such activity varies as a function of serial position. Participants memorized lists of intermixed visual and auditory words with either an elaborative or rote rehearsal strategy. At the end of each list, a distraction task was performed followed by free recall. Recall performance was better for words in initial list positions and following elaborative rehearsal. Electrical brain activity before auditory words predicted later recall in the elaborative rehearsal condition. Crucially, anticipatory activity only affected recall when words occurred in initial list positions. This indicates that anticipatory processes, possibly related to general semantic preparation, contribute to primacy effects. PMID:22888370

A PC-based system for predicting movement from deep brain signals in Parkinson's disease.

PubMed

Loukas, Constantinos; Brown, Peter

2012-07-01

There is much current interest in deep brain stimulation (DBS) of the subthalamic nucleus (STN) for the treatment of Parkinson's disease (PD). This type of surgery has enabled unprecedented access to deep brain signals in the awake human. In this paper we present an easy-to-use computer based system for recording, displaying, archiving, and processing electrophysiological signals from the STN. The system was developed for predicting self-paced hand-movements in real-time via the online processing of the electrophysiological activity of the STN. It is hoped that such a computerised system might have clinical and experimental applications. For example, those sites within the STN most relevant to the processing of voluntary movement could be identified through the predictive value of their activities with respect to the timing of future movement. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Predicting the knowledge–recklessness distinction in the human brain

PubMed Central

Vilares, Iris; Wesley, Michael J.; Ahn, Woo-Young; Bonnie, Richard J.; Hoffman, Morris; Jones, Owen D.; Morse, Stephen J.; Yaffe, Gideon; Lohrenz, Terry; Montague, P. Read

2017-01-01

Criminal convictions require proof that a prohibited act was performed in a statutorily specified mental state. Different legal consequences, including greater punishments, are mandated for those who act in a state of knowledge, compared with a state of recklessness. Existing research, however, suggests people have trouble classifying defendants as knowing, rather than reckless, even when instructed on the relevant legal criteria. We used a machine-learning technique on brain imaging data to predict, with high accuracy, which mental state our participants were in. This predictive ability depended on both the magnitude of the risks and the amount of information about those risks possessed by the participants. Our results provide neural evidence of a detectable difference in the mental state of knowledge in contrast to recklessness and suggest, as a proof of principle, the possibility of inferring from brain data in which legally relevant category a person belongs. Some potential legal implications of this result are discussed. PMID:28289225

Sepsis-induced morbidity in mice: effects on body temperature, body weight, cage activity, social behavior and cytokines in brain

PubMed Central

Granger, Jill I.; Ratti, Pietro-Luca; Datta, Subhash C.; Raymond, Richard M.; Opp, Mark R.

2012-01-01

Infection negatively impacts mental health, as evidenced by the lethargy, malaise, and cognitive deficits experienced during illness. These changes in central nervous system processes, collectively termed sickness behavior, have been shown in animal models to be mediated primarily by the actions of cytokines in brain. Most studies of sickness behavior to date have used bolus injection of bacterial lipopolysaccharide (LPS) or selective administration of the proinflammatory cytokines interleukin-1β (IL-1β) or IL-6 as the immune challenge. Such models, although useful for determining mechanisms responsible for acute changes in physiology and behavior, do not adequately represent the more complex effects on central nervous system (CNS) processes of a true infection with replicating pathogens. In the present study, we used the cecal ligation and puncture (CLP) model to quantify sepsis-induced alterations in several facets of physiology and behavior of mice. We determined the impact of sepsis on cage activity, body temperature, food and water consumption and body weights of mice. Because cytokines are critical mediators of changes in behavior and temperature regulation during immune challenge, we also quantified sepsis-induced alterations in cytokine mRNA and protein in brain during the acute period of sepsis onset. We now report that cage activity and temperature regulation in mice that survive are altered for up to 23 days after sepsis induction. Food and water consumption are transiently reduced, and body weight is lost during sepsis. Furthermore, sepsis decreases social interactions for 24 – 48 hours. Finally, mRNA and protein for IL-1β, IL-6, and tumor necrosis factor-α (TNFα) are upregulated in the hypothalamus, hippocampus, and brain stem during sepsis onset, from 6–72 hour post sepsis induction. Collectively, these data indicate that sepsis not only acutely alters physiology, behavior and cytokine profiles in brain, but that some brain functions are impaired for long periods in animals that survive. PMID:23146654

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

PubMed Central

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain–computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method. PMID:28558002

Predictive Technologies: Can Smart Tools Augment the Brain's Predictive Abilities?

PubMed Central

Pezzulo, Giovanni; D'Ausilio, Alessandro; Gaggioli, Andrea

2016-01-01

The ability of “looking into the future”—namely, the capacity of anticipating future states of the environment or of the body—represents a fundamental function of human (and animal) brains. A goalkeeper who tries to guess the ball's direction; a chess player who attempts to anticipate the opponent's next move; or a man-in-love who tries to calculate what are the chances of her saying yes—in all these cases, people are simulating possible future states of the world, in order to maximize the success of their decisions or actions. Research in neuroscience is showing that our ability to predict the behavior of physical or social phenomena is largely dependent on the brain's ability to integrate current and past information to generate (probabilistic) simulations of the future. But could predictive processing be augmented using advanced technologies? In this contribution, we discuss how computational technologies may be used to support, facilitate or enhance the prediction of future events, by considering exemplificative scenarios across different domains, from simpler sensorimotor decisions to more complex cognitive tasks. We also examine the key scientific and technical challenges that must be faced to turn this vision into reality. PMID:27199648

Neurophysiology of Reward-Guided Behavior: Correlates Related to Predictions, Value, Motivation, Errors, Attention, and Action.

PubMed

Bissonette, Gregory B; Roesch, Matthew R

2016-01-01

Many brain areas are activated by the possibility and receipt of reward. Are all of these brain areas reporting the same information about reward? Or are these signals related to other functions that accompany reward-guided learning and decision-making? Through carefully controlled behavioral studies, it has been shown that reward-related activity can represent reward expectations related to future outcomes, errors in those expectations, motivation, and signals related to goal- and habit-driven behaviors. These dissociations have been accomplished by manipulating the predictability of positively and negatively valued events. Here, we review single neuron recordings in behaving animals that have addressed this issue. We describe data showing that several brain areas, including orbitofrontal cortex, anterior cingulate, and basolateral amygdala signal reward prediction. In addition, anterior cingulate, basolateral amygdala, and dopamine neurons also signal errors in reward prediction, but in different ways. For these areas, we will describe how unexpected manipulations of positive and negative value can dissociate signed from unsigned reward prediction errors. All of these signals feed into striatum to modify signals that motivate behavior in ventral striatum and guide responding via associative encoding in dorsolateral striatum.

Neurophysiology of Reward-Guided Behavior: Correlates Related to Predictions, Value, Motivation, Errors, Attention, and Action

PubMed Central

Roesch, Matthew R.

2017-01-01

Many brain areas are activated by the possibility and receipt of reward. Are all of these brain areas reporting the same information about reward? Or are these signals related to other functions that accompany reward-guided learning and decision-making? Through carefully controlled behavioral studies, it has been shown that reward-related activity can represent reward expectations related to future outcomes, errors in those expectations, motivation, and signals related to goal- and habit-driven behaviors. These dissociations have been accomplished by manipulating the predictability of positively and negatively valued events. Here, we review single neuron recordings in behaving animals that have addressed this issue. We describe data showing that several brain areas, including orbitofrontal cortex, anterior cingulate, and basolateral amygdala signal reward prediction. In addition, anterior cingulate, basolateral amygdala, and dopamine neurons also signal errors in reward prediction, but in different ways. For these areas, we will describe how unexpected manipulations of positive and negative value can dissociate signed from unsigned reward prediction errors. All of these signals feed into striatum to modify signals that motivate behavior in ventral striatum and guide responding via associative encoding in dorsolateral striatum. PMID:26276036

Changing body temperature affects the T2* signal in the rat brain and reveals hypothalamic activity.

PubMed

Vanhoutte, G; Verhoye, M; Van der Linden, A

2006-05-01

This study was designed to determine brain activity in the hypothalamus-in particular the thermoregulatory function of the hypothalamic preoptic area (PO). We experimentally changed the body temperature in rats within the physiological range (37-39 degrees C) and monitored changes in blood oxygenation level-dependent (BOLD) MR signal. To explore PO activity we had to deal with general signal changes caused by temperature-dependent alterations in the affinity of oxygen for hemoglobin, which contributes to BOLD contrast because it is partly sensitive to the amount of paramagnetic deoxyhemoglobin in the voxel. To reduce these overall temperature-induced effects, we corrected the BOLD data using brain-specific correction algorithms. The results showed activity of the PO during body warming from 38 degrees C to 39 degrees C, supported by an increased BOLD signal after correction. This is the first fMRI study on the autonomous nervous system in which hypothalamic activity elicited by changes in the internal environment (body temperature) was monitored. In this study we also demonstrate 1) that any fMRI study of anesthetized small animals should guard against background BOLD signal drift, since animals are vulnerable to body temperature fluctuations; and 2) the existence of a link between PO activity and the sympathetically-mediated opening of the arteriovenous anastomoses in a parallel study on the rat tail, a peripheral thermoregulatory organ.

Pituitary disorders as a predictor of apathy and executive dysfunction in adult survivors of childhood brain tumors.

PubMed

Fox, Michelle E; King, Tricia Z

2016-11-01

The relationship between apathy and endocrine dysfunction, both frequent outcomes of neurological insult, has not yet been investigated in brain tumor survivors. The present study aimed to assess the relationship between pituitary disorders and apathy and other facets of executive function in long-term adult survivors of childhood brain tumors and to differentiate between apathy and depression in this population. Seventy-six adult survivors of childhood brain tumors at least 5 years past diagnosis participated. An informant completed the Frontal Systems Behavior Scale (FrSBe), and 75 of the 76 participants completed a Structured Clinical Interview for the DSM-IV-TR (SCID). Information on neuroendocrine dysfunction was obtained through medical chart review. Clinically significant levels of apathy on the FrSBe were identified in 41% of survivors. Pituitary dysfunction significantly explained 9% of the variance in apathy scores and affected whether an individual presented with clinical levels of apathy. Pituitary dysfunction predicted higher levels of executive dysfunction but did not impact whether a participant reached clinical levels of executive dysfunction. A past major depressive episode (MDE) significantly predicted current apathy but showed no relationship with pituitary disorders. Radiation treatment predicted pituitary dysfunction but not the differences in apathy or executive functions. Apathy and executive dysfunction in survivors of childhood brain tumors are strongly predicted by pituitary dysfunction, and individuals with pituitary dysfunction are more likely to present with clinical levels of apathy as adults. Clinical levels of apathy may present absent of current depression, and pituitary dysfunction impacts apathy uniquely. © 2016 Wiley Periodicals, Inc.

Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach.

PubMed

Sartori, Juliana M; Reckziegel, Ramiro; Passos, Ives Cavalcante; Czepielewski, Leticia S; Fijtman, Adam; Sodré, Leonardo A; Massuda, Raffael; Goi, Pedro D; Vianna-Sulzbach, Miréia; Cardoso, Taiane de Azevedo; Kapczinski, Flávio; Mwangi, Benson; Gama, Clarissa S

2018-08-01

Neuroimaging studies have been steadily explored in Bipolar Disorder (BD) in the last decades. Neuroanatomical changes tend to be more pronounced in patients with repeated episodes. Although the role of such changes in cognition and memory is well established, daily-life functioning impairments bulge among the consequences of the proposed progression. The objective of this study was to analyze MRI volumetric modifications in BD and healthy controls (HC) as possible predictors of daily-life functioning through a machine learning approach. Ninety-four participants (35 DSM-IV BD type I and 59 HC) underwent clinical and functioning assessments, and structural MRI. Functioning was assessed using the Functioning Assessment Short Test (FAST). The machine learning analysis was used to identify possible candidates of regional brain volumes that could predict functioning status, through a support vector regression algorithm. Patients with BD and HC did not differ in age, education and marital status. There were significant differences between groups in gender, BMI, FAST score, and employment status. There was significant correlation between observed and predicted FAST score for patients with BD, but not for controls. According to the model, the brain structures volumes that could predict FAST scores were: left superior frontal cortex, left rostral medial frontal cortex, right white matter total volume and right lateral ventricle volume. The machine learning approach demonstrated that brain volume changes in MRI were predictors of FAST score in patients with BD and could identify specific brain areas related to functioning impairment. Copyright © 2018 Elsevier Ltd. All rights reserved.

Expression of aromatase in the embryonic brain of the olive ridley sea turtle (Lepidochelys olivacea), and the effect of bisphenol-A in sexually differentiated embryos.

PubMed

Gómez-Picos, Patsy; Sifuentes-Romero, Itzel; Merchant-Larios, Horacio; Hernández-Cornejo, Rubí; Díaz-Hernández, Verónica; García-Gasca, Alejandra

2014-01-01

Brain aromatase participates in several biological processes, such as regulation of the reproductive-endocrine axis, memory, stress, sexual differentiation of the nervous system, male sexual behavior, and brain repair. Here we report the isolation and expression of brain aromatase in olive ridley sea turtle (Lepidochelys olivacea) embryos incubated at male- and female-promoting temperatures (MPT and FPT, respectively), at the thermosensitive period (TSP) and the sex-differentiated period. Also, aromatase expression was assessed in differentiated embryos exposed to bisphenol-A (BPA) during the TSP. BPA is a monomer of polycarbonate plastics and is considered an endocrine-disrupting compound. Normal aromatase expression was measured in both forebrain and hindbrain, showing higher expression levels in the forebrain of differentiated embryos at both incubation temperatures. Although no significant differences were detected in the hindbrain, expression was slightly higher at MPT. BPA did not affect aromatase expression neither in forebrains or hindbrains from embryos incubated at MPT, whereas at FPT an inverted U-shape curve was observed in forebrains with significant differences at lower concentrations, whereas in hindbrains a non-significant increment was observed at higher concentrations. Our data indicate that both incubation temperature and developmental stage are critical factors affecting aromatase expression in the forebrain. Because of the timing and location of aromatase expression in the brain, we suggest that brain aromatase may participate in the imprinting of sexual trends related to reproduction and sexual behavior at the onset of sex differentiation, and BPA exposure may impair aromatase function in the female forebrain.

Ubiquitous and temperature-dependent neural plasticity in hibernators.

PubMed

von der Ohe, Christina G; Darian-Smith, Corinna; Garner, Craig C; Heller, H Craig

2006-10-11

Hibernating mammals are remarkable for surviving near-freezing brain temperatures and near cessation of neural activity for a week or more at a time. This extreme physiological state is associated with dendritic and synaptic changes in hippocampal neurons. Here, we investigate whether these changes are a ubiquitous phenomenon throughout the brain that is driven by temperature. We iontophoretically injected Lucifer yellow into several types of neurons in fixed slices from hibernating ground squirrels. We analyzed neuronal microstructure from animals at several stages of torpor at two different ambient temperatures, and during the summer. We show that neuronal cell bodies, dendrites, and spines from several cell types in hibernating ground squirrels retract on entry into torpor, change little over the course of several days, and then regrow during the 2 h return to euthermia. Similar structural changes take place in neurons from the hippocampus, cortex, and thalamus, suggesting a global phenomenon. Investigation of neural microstructure from groups of animals hibernating at different ambient temperatures revealed that there is a linear relationship between neural retraction and minimum body temperature. Despite significant temperature-dependent differences in extent of retraction during torpor, recovery reaches the same final values of cell body area, dendritic arbor complexity, and spine density. This study demonstrates large-scale and seemingly ubiquitous neural plasticity in the ground squirrel brain during torpor. It also defines a temperature-driven model of dramatic neural plasticity, which provides a unique opportunity to explore mechanisms of large-scale regrowth in adult mammals, and the effects of remodeling on learning and memory.

Neuroimaging in Pediatric Traumatic Brain Injury: Current and Future Predictors of Functional Outcome

ERIC Educational Resources Information Center

Suskauer, Stacy J.; Huisman, Thierry A. G. M.

2009-01-01

Although neuroimaging has long played a role in the acute management of pediatric traumatic brain injury (TBI), until recently, its use as a tool for understanding and predicting long-term brain-behavior relationships after TBI has been limited by the relatively poor sensitivity of routine clinical imaging for detecting diffuse axonal injury…