Altered functional connectivity of the language network in ASD: Role of classical language areas and cerebellum☆

PubMed Central

Verly, Marjolein; Verhoeven, Judith; Zink, Inge; Mantini, Dante; Peeters, Ronald; Deprez, Sabine; Emsell, Louise; Boets, Bart; Noens, Ilse; Steyaert, Jean; Lagae, Lieven; De Cock, Paul; Rommel, Nathalie; Sunaert, Stefan

2014-01-01

The development of language, social interaction and communicative skills is remarkably different in the child with autism spectrum disorder (ASD). Atypical brain connectivity has frequently been reported in this patient population. However, the neural correlates underlying their disrupted language development and functioning are still poorly understood. Using resting state fMRI, we investigated the functional connectivity properties of the language network in a group of ASD patients with clear comorbid language impairment (ASD-LI; N = 19) and compared them to the language related connectivity properties of 23 age-matched typically developing children. A verb generation task was used to determine language components commonly active in both groups. Eight joint language components were identified and subsequently used as seeds in a resting state analysis. Interestingly, both the interregional and the seed-based whole brain connectivity analysis showed preserved connectivity between the classical intrahemispheric language centers, Wernicke's and Broca's areas. In contrast however, a marked loss of functional connectivity was found between the right cerebellar region and the supratentorial regulatory language areas. Also, the connectivity between the interhemispheric Broca regions and modulatory control dorsolateral prefrontal region was found to be decreased. This disruption of normal modulatory control and automation function by the cerebellum may underlie the abnormal language function in children with ASD-LI. PMID:24567909

Auditory Resting-State Network Connectivity in Tinnitus: A Functional MRI Study

PubMed Central

Maudoux, Audrey; Lefebvre, Philippe; Cabay, Jean-Evrard; Demertzi, Athena; Vanhaudenhuyse, Audrey; Laureys, Steven; Soddu, Andrea

2012-01-01

The underlying functional neuroanatomy of tinnitus remains poorly understood. Few studies have focused on functional cerebral connectivity changes in tinnitus patients. The aim of this study was to test if functional MRI “resting-state” connectivity patterns in auditory network differ between tinnitus patients and normal controls. Thirteen chronic tinnitus subjects and fifteen age-matched healthy controls were studied on a 3 tesla MRI. Connectivity was investigated using independent component analysis and an automated component selection approach taking into account the spatial and temporal properties of each component. Connectivity in extra-auditory regions such as brainstem, basal ganglia/NAc, cerebellum, parahippocampal, right prefrontal, parietal, and sensorimotor areas was found to be increased in tinnitus subjects. The right primary auditory cortex, left prefrontal, left fusiform gyrus, and bilateral occipital regions showed a decreased connectivity in tinnitus. These results show that there is a modification of cortical and subcortical functional connectivity in tinnitus encompassing attentional, mnemonic, and emotional networks. Our data corroborate the hypothesized implication of non-auditory regions in tinnitus physiopathology and suggest that various regions of the brain seem involved in the persistent awareness of the phenomenon as well as in the development of the associated distress leading to disabling chronic tinnitus. PMID:22574141

Dynamic GSCA (Generalized Structured Component Analysis) with Applications to the Analysis of Effective Connectivity in Functional Neuroimaging Data

ERIC Educational Resources Information Center

Jung, Kwanghee; Takane, Yoshio; Hwang, Heungsun; Woodward, Todd S.

2012-01-01

We propose a new method of structural equation modeling (SEM) for longitudinal and time series data, named Dynamic GSCA (Generalized Structured Component Analysis). The proposed method extends the original GSCA by incorporating a multivariate autoregressive model to account for the dynamic nature of data taken over time. Dynamic GSCA also…

Long-term intensive gymnastic training induced changes in intra- and inter-network functional connectivity: an independent component analysis.

PubMed

Huang, Huiyuan; Wang, Junjing; Seger, Carol; Lu, Min; Deng, Feng; Wu, Xiaoyan; He, Yuan; Niu, Chen; Wang, Jun; Huang, Ruiwang

2018-01-01

Long-term intensive gymnastic training can induce brain structural and functional reorganization. Previous studies have identified structural and functional network differences between world class gymnasts (WCGs) and non-athletes at the whole-brain level. However, it is still unclear how interactions within and between functional networks are affected by long-term intensive gymnastic training. We examined both intra- and inter-network functional connectivity of gymnasts relative to non-athletes using resting-state fMRI (R-fMRI). R-fMRI data were acquired from 13 WCGs and 14 non-athlete controls. Group-independent component analysis (ICA) was adopted to decompose the R-fMRI data into spatial independent components and associated time courses. An automatic component identification method was used to identify components of interest associated with resting-state networks (RSNs). We identified nine RSNs, the basal ganglia network (BG), sensorimotor network (SMN), cerebellum (CB), anterior and posterior default mode networks (aDMN/pDMN), left and right fronto-parietal networks (lFPN/rFPN), primary visual network (PVN), and extrastriate visual network (EVN). Statistical analyses revealed that the intra-network functional connectivity was significantly decreased within the BG, aDMN, lFPN, and rFPN, but increased within the EVN in the WCGs compared to the controls. In addition, the WCGs showed uniformly decreased inter-network functional connectivity between SMN and BG, CB, and PVN, BG and PVN, and pDMN and rFPN compared to the controls. We interpret this generally weaker intra- and inter-network functional connectivity in WCGs during the resting state as a result of greater efficiency in the WCGs' brain associated with long-term motor skill training.

A method for functional network connectivity among spatially independent resting-state components in schizophrenia.

PubMed

Jafri, Madiha J; Pearlson, Godfrey D; Stevens, Michael; Calhoun, Vince D

2008-02-15

Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in healthy individuals as well as in patients with brain disorders. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subject's ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients.

A Method for Functional Network Connectivity Among Spatially Independent Resting-State Components in Schizophrenia

PubMed Central

Jafri, Madiha J; Pearlson, Godfrey D; Stevens, Michael; Calhoun, Vince D

2011-01-01

Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in patients versus controls. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subject’s ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients. PMID:18082428

Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia.

PubMed

Damaraju, E; Allen, E A; Belger, A; Ford, J M; McEwen, S; Mathalon, D H; Mueller, B A; Pearlson, G D; Potkin, S G; Preda, A; Turner, J A; Vaidya, J G; van Erp, T G; Calhoun, V D

2014-01-01

Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale thalamo-cortical connectivity as being disrupted in patients. However, observed connectivity differences in schizophrenia have been inconsistent between studies, with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on a multi-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4 minutes in length), and a dynamic sense, computed using sliding windows (44 s in length) and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual), as well as reduced connectivity (hypoconnectivity) between sensory networks from all modalities. Dynamic analysis suggests that (1), on average, schizophrenia patients spend much less time than healthy controls in states typified by strong, large-scale connectivity, and (2), that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical-subcortical antagonism (anti-correlations) and strong positive connectivity between sensory networks are those that show the group differences of thalamic hyperconnectivity and sensory hypoconnectivity. Group differences are weak or absent during other connectivity states. Dynamic analysis also revealed hypoconnectivity between the putamen and sensory networks during the same states of thalamic hyperconnectivity; notably, this finding cannot be observed in the static connectivity analysis. Finally, in post-hoc analyses we observed that the relationships between sub-cortical low frequency power and connectivity with sensory networks is altered in patients, suggesting different functional interactions between sub-cortical nuclei and sensorimotor cortex during specific connectivity states. While important differences between patients with schizophrenia and healthy controls have been identified, one should interpret the results with caution given the history of medication in patients. Taken together, our results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences.

Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia

PubMed Central

Damaraju, E.; Allen, E.A.; Belger, A.; Ford, J.M.; McEwen, S.; Mathalon, D.H.; Mueller, B.A.; Pearlson, G.D.; Potkin, S.G.; Preda, A.; Turner, J.A.; Vaidya, J.G.; van Erp, T.G.; Calhoun, V.D.

2014-01-01

Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale thalamo-cortical connectivity as being disrupted in patients. However, observed connectivity differences in schizophrenia have been inconsistent between studies, with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on a multi-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4 minutes in length), and a dynamic sense, computed using sliding windows (44 s in length) and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual), as well as reduced connectivity (hypoconnectivity) between sensory networks from all modalities. Dynamic analysis suggests that (1), on average, schizophrenia patients spend much less time than healthy controls in states typified by strong, large-scale connectivity, and (2), that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical–subcortical antagonism (anti-correlations) and strong positive connectivity between sensory networks are those that show the group differences of thalamic hyperconnectivity and sensory hypoconnectivity. Group differences are weak or absent during other connectivity states. Dynamic analysis also revealed hypoconnectivity between the putamen and sensory networks during the same states of thalamic hyperconnectivity; notably, this finding cannot be observed in the static connectivity analysis. Finally, in post-hoc analyses we observed that the relationships between sub-cortical low frequency power and connectivity with sensory networks is altered in patients, suggesting different functional interactions between sub-cortical nuclei and sensorimotor cortex during specific connectivity states. While important differences between patients with schizophrenia and healthy controls have been identified, one should interpret the results with caution given the history of medication in patients. Taken together, our results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences. PMID:25161896

Reliability, Convergent Validity and Time Invariance of Default Mode Network Deviations in Early Adult Major Depressive Disorder.

PubMed

Bessette, Katie L; Jenkins, Lisanne M; Skerrett, Kristy A; Gowins, Jennifer R; DelDonno, Sophie R; Zubieta, Jon-Kar; McInnis, Melvin G; Jacobs, Rachel H; Ajilore, Olusola; Langenecker, Scott A

2018-01-01

There is substantial variability across studies of default mode network (DMN) connectivity in major depressive disorder, and reliability and time-invariance are not reported. This study evaluates whether DMN dysconnectivity in remitted depression (rMDD) is reliable over time and symptom-independent, and explores convergent relationships with cognitive features of depression. A longitudinal study was conducted with 82 young adults free of psychotropic medications (47 rMDD, 35 healthy controls) who completed clinical structured interviews, neuropsychological assessments, and 2 resting-state fMRI scans across 2 study sites. Functional connectivity analyses from bilateral posterior cingulate and anterior hippocampal formation seeds in DMN were conducted at both time points within a repeated-measures analysis of variance to compare groups and evaluate reliability of group-level connectivity findings. Eleven hyper- (from posterior cingulate) and 6 hypo- (from hippocampal formation) connectivity clusters in rMDD were obtained with moderate to adequate reliability in all but one cluster (ICC's range = 0.50 to 0.76 for 16 of 17). The significant clusters were reduced with a principle component analysis (5 components obtained) to explore these connectivity components, and were then correlated with cognitive features (rumination, cognitive control, learning and memory, and explicit emotion identification). At the exploratory level, for convergent validity, components consisting of posterior cingulate with cognitive control network hyperconnectivity in rMDD were related to cognitive control (inverse) and rumination (positive). Components consisting of anterior hippocampal formation with social emotional network and DMN hypoconnectivity were related to memory (inverse) and happy emotion identification (positive). Thus, time-invariant DMN connectivity differences exist early in the lifespan course of depression and are reliable. The nuanced results suggest a ventral within-network hypoconnectivity associated with poor memory and a dorsal cross-network hyperconnectivity linked to poorer cognitive control and elevated rumination. Study of early course remitted depression with attention to reliability and symptom independence could lead to more readily translatable clinical assessment tools for biomarkers.

Design Choices for Thermofluid Flow Components and Systems that are Exported as Functional Mockup Units

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

Wetter, Michael; Fuchs, Marcus; Nouidui, Thierry

This paper discusses design decisions for exporting Modelica thermofluid flow components as Functional Mockup Units. The purpose is to provide guidelines that will allow building energy simulation programs and HVAC equipment manufacturers to effectively use FMUs for modeling of HVAC components and systems. We provide an analysis for direct input-output dependencies of such components and discuss how these dependencies can lead to algebraic loops that are formed when connecting thermofluid flow components. Based on this analysis, we provide recommendations that increase the computing efficiency of such components and systems that are formed by connecting multiple components. We explain what codemore » optimizations are lost when providing thermofluid flow components as FMUs rather than Modelica code. We present an implementation of a package for FMU export of such components, explain the rationale for selecting the connector variables of the FMUs and finally provide computing benchmarks for different design choices. It turns out that selecting temperature rather than specific enthalpy as input and output signals does not lead to a measurable increase in computing time, but selecting nine small FMUs rather than a large FMU increases computing time by 70%.« less

Functional connectivity analysis of the neural bases of emotion regulation: A comparison of independent component method with density-based k-means clustering method.

PubMed

Zou, Ling; Guo, Qian; Xu, Yi; Yang, Biao; Jiao, Zhuqing; Xiang, Jianbo

2016-04-29

Functional magnetic resonance imaging (fMRI) is an important tool in neuroscience for assessing connectivity and interactions between distant areas of the brain. To find and characterize the coherent patterns of brain activity as a means of identifying brain systems for the cognitive reappraisal of the emotion task, both density-based k-means clustering and independent component analysis (ICA) methods can be applied to characterize the interactions between brain regions involved in cognitive reappraisal of emotion. Our results reveal that compared with the ICA method, the density-based k-means clustering method provides a higher sensitivity of polymerization. In addition, it is more sensitive to those relatively weak functional connection regions. Thus, the study concludes that in the process of receiving emotional stimuli, the relatively obvious activation areas are mainly distributed in the frontal lobe, cingulum and near the hypothalamus. Furthermore, density-based k-means clustering method creates a more reliable method for follow-up studies of brain functional connectivity.

Intrinsic Resting-State Functional Connectivity in the Human Spinal Cord at 3.0 T.

PubMed

San Emeterio Nateras, Oscar; Yu, Fang; Muir, Eric R; Bazan, Carlos; Franklin, Crystal G; Li, Wei; Li, Jinqi; Lancaster, Jack L; Duong, Timothy Q

2016-04-01

To apply resting-state functional magnetic resonance (MR) imaging to map functional connectivity of the human spinal cord. Studies were performed in nine self-declared healthy volunteers with informed consent and institutional review board approval. Resting-state functional MR imaging was performed to map functional connectivity of the human cervical spinal cord from C1 to C4 at 1 × 1 × 3-mm resolution with a 3.0-T clinical MR imaging unit. Independent component analysis (ICA) was performed to derive resting-state functional MR imaging z-score maps rendered on two-dimensional and three-dimensional images. Seed-based analysis was performed for cross validation with ICA networks by using Pearson correlation. Reproducibility analysis of resting-state functional MR imaging maps from four repeated trials in a single participant yielded a mean z score of 6 ± 1 (P < .0001). The centroid coordinates across the four trials deviated by 2 in-plane voxels ± 2 mm (standard deviation) and up to one adjacent image section ± 3 mm. ICA of group resting-state functional MR imaging data revealed prominent functional connectivity patterns within the spinal cord gray matter. There were statistically significant (z score > 3, P < .001) bilateral, unilateral, and intersegmental correlations in the ventral horns, dorsal horns, and central spinal cord gray matter. Three-dimensional surface rendering provided visualization of these components along the length of the spinal cord. Seed-based analysis showed that many ICA components exhibited strong and significant (P < .05) correlations, corroborating the ICA results. Resting-state functional MR imaging connectivity networks are qualitatively consistent with known neuroanatomic and functional structures in the spinal cord. Resting-state functional MR imaging of the human cervical spinal cord with a 3.0-T clinical MR imaging unit and standard MR imaging protocols and hardware reveals prominent functional connectivity patterns within the spinal cord gray matter, consistent with known functional and anatomic layouts of the spinal cord.

Visual target modulation of functional connectivity networks revealed by self-organizing group ICA.

PubMed

van de Ven, Vincent; Bledowski, Christoph; Prvulovic, David; Goebel, Rainer; Formisano, Elia; Di Salle, Francesco; Linden, David E J; Esposito, Fabrizio

2008-12-01

We applied a data-driven analysis based on self-organizing group independent component analysis (sogICA) to fMRI data from a three-stimulus visual oddball task. SogICA is particularly suited to the investigation of the underlying functional connectivity and does not rely on a predefined model of the experiment, which overcomes some of the limitations of hypothesis-driven analysis. Unlike most previous applications of ICA in functional imaging, our approach allows the analysis of the data at the group level, which is of particular interest in high order cognitive studies. SogICA is based on the hierarchical clustering of spatially similar independent components, derived from single subject decompositions. We identified four main clusters of components, centered on the posterior cingulate, bilateral insula, bilateral prefrontal cortex, and right posterior parietal and prefrontal cortex, consistently across all participants. Post hoc comparison of time courses revealed that insula, prefrontal cortex and right fronto-parietal components showed higher activity for targets than for distractors. Activation for distractors was higher in the posterior cingulate cortex, where deactivation was observed for targets. While our results conform to previous neuroimaging studies, they also complement conventional results by showing functional connectivity networks with unique contributions to the task that were consistent across subjects. SogICA can thus be used to probe functional networks of active cognitive tasks at the group-level and can provide additional insights to generate new hypotheses for further study. Copyright 2007 Wiley-Liss, Inc.

Cross-modal integration of polyphonic characters in Chinese audio-visual sentences: a MVPA study based on functional connectivity.

PubMed

Zhang, Zhengyi; Zhang, Gaoyan; Zhang, Yuanyuan; Liu, Hong; Xu, Junhai; Liu, Baolin

2017-12-01

This study aimed to investigate the functional connectivity in the brain during the cross-modal integration of polyphonic characters in Chinese audio-visual sentences. The visual sentences were all semantically reasonable and the audible pronunciations of the polyphonic characters in corresponding sentences contexts varied in four conditions. To measure the functional connectivity, correlation, coherence and phase synchronization index (PSI) were used, and then multivariate pattern analysis was performed to detect the consensus functional connectivity patterns. These analyses were confined in the time windows of three event-related potential components of P200, N400 and late positive shift (LPS) to investigate the dynamic changes of the connectivity patterns at different cognitive stages. We found that when differentiating the polyphonic characters with abnormal pronunciations from that with the appreciate ones in audio-visual sentences, significant classification results were obtained based on the coherence in the time window of the P200 component, the correlation in the time window of the N400 component and the coherence and PSI in the time window the LPS component. Moreover, the spatial distributions in these time windows were also different, with the recruitment of frontal sites in the time window of the P200 component, the frontal-central-parietal regions in the time window of the N400 component and the central-parietal sites in the time window of the LPS component. These findings demonstrate that the functional interaction mechanisms are different at different stages of audio-visual integration of polyphonic characters.

Locality preserving non-negative basis learning with graph embedding.

PubMed

Ghanbari, Yasser; Herrington, John; Gur, Ruben C; Schultz, Robert T; Verma, Ragini

2013-01-01

The high dimensionality of connectivity networks necessitates the development of methods identifying the connectivity building blocks that not only characterize the patterns of brain pathology but also reveal representative population patterns. In this paper, we present a non-negative component analysis framework for learning localized and sparse sub-network patterns of connectivity matrices by decomposing them into two sets of discriminative and reconstructive bases. In order to obtain components that are designed towards extracting population differences, we exploit the geometry of the population by using a graphtheoretical scheme that imposes locality-preserving properties as well as maintaining the underlying distance between distant nodes in the original and the projected space. The effectiveness of the proposed framework is demonstrated by applying it to two clinical studies using connectivity matrices derived from DTI to study a population of subjects with ASD, as well as a developmental study of structural brain connectivity that extracts gender differences.

Highly efficient codec based on significance-linked connected-component analysis of wavelet coefficients

NASA Astrophysics Data System (ADS)

Chai, Bing-Bing; Vass, Jozsef; Zhuang, Xinhua

1997-04-01

Recent success in wavelet coding is mainly attributed to the recognition of importance of data organization. There has been several very competitive wavelet codecs developed, namely, Shapiro's Embedded Zerotree Wavelets (EZW), Servetto et. al.'s Morphological Representation of Wavelet Data (MRWD), and Said and Pearlman's Set Partitioning in Hierarchical Trees (SPIHT). In this paper, we propose a new image compression algorithm called Significant-Linked Connected Component Analysis (SLCCA) of wavelet coefficients. SLCCA exploits both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. A so-called significant link between connected components is designed to reduce the positional overhead of MRWD. In addition, the significant coefficients' magnitude are encoded in bit plane order to match the probability model of the adaptive arithmetic coder. Experiments show that SLCCA outperforms both EZW and MRWD, and is tied with SPIHT. Furthermore, it is observed that SLCCA generally has the best performance on images with large portion of texture. When applied to fingerprint image compression, it outperforms FBI's wavelet scalar quantization by about 1 dB.

A New Approach to Investigate the Association between Brain Functional Connectivity and Disease Characteristics of Attention-Deficit/Hyperactivity Disorder: Topological Neuroimaging Data Analysis.

PubMed

Kyeong, Sunghyon; Park, Seonjeong; Cheon, Keun-Ah; Kim, Jae-Jin; Song, Dong-Ho; Kim, Eunjoo

2015-01-01

Attention-deficit/hyperactivity disorder (ADHD) is currently diagnosed by a diagnostic interview, mainly based on subjective reports from parents or teachers. It is necessary to develop methods that rely on objectively measureable neurobiological data to assess brain-behavior relationship in patients with ADHD. We investigated the application of a topological data analysis tool, Mapper, to analyze the brain functional connectivity data from ADHD patients. To quantify the disease severity using the neuroimaging data, the decomposition of individual functional networks into normal and disease components by the healthy state model (HSM) was performed, and the magnitude of the disease component (MDC) was computed. Topological data analysis using Mapper was performed to distinguish children with ADHD (n = 196) from typically developing controls (TDC) (n = 214). In the topological data analysis, the partial clustering results of patients with ADHD and normal subjects were shown in a chain-like graph. In the correlation analysis, the MDC showed a significant increase with lower intelligence scores in TDC. We also found that the rates of comorbidity in ADHD significantly increased when the deviation of the functional connectivity from HSM was large. In addition, a significant correlation between ADHD symptom severity and MDC was found in part of the dataset. The application of HSM and topological data analysis methods in assessing the brain functional connectivity seem to be promising tools to quantify ADHD symptom severity and to reveal the hidden relationship between clinical phenotypic variables and brain connectivity.

Independent Component Analysis of Resting-State Functional Magnetic Resonance Imaging in Pedophiles.

PubMed

Cantor, J M; Lafaille, S J; Hannah, J; Kucyi, A; Soh, D W; Girard, T A; Mikulis, D J

2016-10-01

Neuroimaging and other studies have changed the common view that pedophilia is a result of childhood sexual abuse and instead is a neurologic phenomenon with prenatal origins. Previous research has identified differences in the structural connectivity of the brain in pedophilia. To identify analogous differences in functional connectivity. Functional magnetic resonance images were recorded from three groups of participants while they were at rest: pedophilic men with a history of sexual offenses against children (n = 37) and two control groups: non-pedophilic men who committed non-sexual offenses (n = 28) and non-pedophilic men with no criminal history (n = 39). Functional magnetic resonance imaging data were subjected to independent component analysis to identify known functional networks of the brain, and groups were compared to identify differences in connectivity with those networks (or "components"). The pedophilic group demonstrated wide-ranging increases in functional connectivity with the default mode network compared with controls and regional differences (increases and decreases) with the frontoparietal network. Of these brain regions (total = 23), 20 have been identified by meta-analytic studies to respond to sexually relevant stimuli. Conversely, of the brain areas known to be those that respond to sexual stimuli, nearly all emerged in the present data as significantly different in pedophiles. This study confirms the presence of significant differences in the functional connectivity of the brain in pedophilia consistent with previously reported differences in structural connectivity. The connectivity differences detected here and elsewhere are opposite in direction from those associated with anti-sociality, arguing against anti-sociality and for pedophilia as the source of the neuroanatomic differences detected. Copyright © 2016 International Society for Sexual Medicine. Published by Elsevier Inc. All rights reserved.

Facilitating Neuronal Connectivity Analysis of Evoked Responses by Exposing Local Activity with Principal Component Analysis Preprocessing: Simulation of Evoked MEG

PubMed Central

Gao, Lin; Zhang, Tongsheng; Wang, Jue; Stephen, Julia

2014-01-01

When connectivity analysis is carried out for event related EEG and MEG, the presence of strong spatial correlations from spontaneous activity in background may mask the local neuronal evoked activity and lead to spurious connections. In this paper, we hypothesized PCA decomposition could be used to diminish the background activity and further improve the performance of connectivity analysis in event related experiments. The idea was tested using simulation, where we found that for the 306-channel Elekta Neuromag system, the first 4 PCs represent the dominant background activity, and the source connectivity pattern after preprocessing is consistent with the true connectivity pattern designed in the simulation. Improving signal to noise of the evoked responses by discarding the first few PCs demonstrates increased coherences at major physiological frequency bands when removing the first few PCs. Furthermore, the evoked information was maintained after PCA preprocessing. In conclusion, it is demonstrated that the first few PCs represent background activity, and PCA decomposition can be employed to remove it to expose the evoked activity for the channels under investigation. Therefore, PCA can be applied as a preprocessing approach to improve neuronal connectivity analysis for event related data. PMID:22918837

Facilitating neuronal connectivity analysis of evoked responses by exposing local activity with principal component analysis preprocessing: simulation of evoked MEG.

PubMed

Gao, Lin; Zhang, Tongsheng; Wang, Jue; Stephen, Julia

2013-04-01

When connectivity analysis is carried out for event related EEG and MEG, the presence of strong spatial correlations from spontaneous activity in background may mask the local neuronal evoked activity and lead to spurious connections. In this paper, we hypothesized PCA decomposition could be used to diminish the background activity and further improve the performance of connectivity analysis in event related experiments. The idea was tested using simulation, where we found that for the 306-channel Elekta Neuromag system, the first 4 PCs represent the dominant background activity, and the source connectivity pattern after preprocessing is consistent with the true connectivity pattern designed in the simulation. Improving signal to noise of the evoked responses by discarding the first few PCs demonstrates increased coherences at major physiological frequency bands when removing the first few PCs. Furthermore, the evoked information was maintained after PCA preprocessing. In conclusion, it is demonstrated that the first few PCs represent background activity, and PCA decomposition can be employed to remove it to expose the evoked activity for the channels under investigation. Therefore, PCA can be applied as a preprocessing approach to improve neuronal connectivity analysis for event related data.

Modular thought in the circuit analysis

NASA Astrophysics Data System (ADS)

Wang, Feng

2018-04-01

Applied to solve the problem of modular thought, provides a whole for simplification's method, the complex problems have become of, and the study of circuit is similar to the above problems: the complex connection between components, make the whole circuit topic solution seems to be more complex, and actually components the connection between the have rules to follow, this article mainly tells the story of study on the application of the circuit modular thought. First of all, this paper introduces the definition of two-terminal network and the concept of two-terminal network equivalent conversion, then summarizes the common source resistance hybrid network modular approach, containing controlled source network modular processing method, lists the common module, typical examples analysis.

Dynamic analysis of a flexible spacecraft with rotating components. Volume 1: Analytical developments

NASA Technical Reports Server (NTRS)

Bodley, C. S.; Devers, A. D.; Park, A. C.

1975-01-01

Analytical procedures and digital computer code are presented for the dynamic analysis of a flexible spacecraft with rotating components. Topics, considered include: (1) nonlinear response in the time domain, and (2) linear response in the frequency domain. The spacecraft is assumed to consist of an assembly of connected rigid or flexible subassemblies. The total system is not restricted to a topological connection arrangement and may be acting under the influence of passive or active control systems and external environments. The analytics and associated digital code provide the user with the capability to establish spacecraft system nonlinear total response for specified initial conditions, linear perturbation response about a calculated or specified nominal motion, general frequency response and graphical display, and spacecraft system stability analysis.

Specialization and integration of functional thalamocortical connectivity in the human infant.

PubMed

Toulmin, Hilary; Beckmann, Christian F; O'Muircheartaigh, Jonathan; Ball, Gareth; Nongena, Pumza; Makropoulos, Antonios; Ederies, Ashraf; Counsell, Serena J; Kennea, Nigel; Arichi, Tomoki; Tusor, Nora; Rutherford, Mary A; Azzopardi, Denis; Gonzalez-Cinca, Nuria; Hajnal, Joseph V; Edwards, A David

2015-05-19

Connections between the thalamus and cortex develop rapidly before birth, and aberrant cerebral maturation during this period may underlie a number of neurodevelopmental disorders. To define functional thalamocortical connectivity at the normal time of birth, we used functional MRI (fMRI) to measure blood oxygen level-dependent (BOLD) signals in 66 infants, 47 of whom were at high risk of neurocognitive impairment because of birth before 33 wk of gestation and 19 of whom were term infants. We segmented the thalamus based on correlation with functionally defined cortical components using independent component analysis (ICA) and seed-based correlations. After parcellating the cortex using ICA and segmenting the thalamus based on dominant connections with cortical parcellations, we observed a near-facsimile of the adult functional parcellation. Additional analysis revealed that BOLD signal in heteromodal association cortex typically had more widespread and overlapping thalamic representations than primary sensory cortex. Notably, more extreme prematurity was associated with increased functional connectivity between thalamus and lateral primary sensory cortex but reduced connectivity between thalamus and cortex in the prefrontal, insular and anterior cingulate regions. This work suggests that, in early infancy, functional integration through thalamocortical connections depends on significant functional overlap in the topographic organization of the thalamus and that the experience of premature extrauterine life modulates network development, altering the maturation of networks thought to support salience, executive, integrative, and cognitive functions.

Specialization and integration of functional thalamocortical connectivity in the human infant

PubMed Central

Toulmin, Hilary; Beckmann, Christian F.; O'Muircheartaigh, Jonathan; Ball, Gareth; Nongena, Pumza; Makropoulos, Antonios; Ederies, Ashraf; Counsell, Serena J.; Kennea, Nigel; Arichi, Tomoki; Tusor, Nora; Rutherford, Mary A.; Azzopardi, Denis; Gonzalez-Cinca, Nuria; Hajnal, Joseph V.; Edwards, A. David

2015-01-01

Connections between the thalamus and cortex develop rapidly before birth, and aberrant cerebral maturation during this period may underlie a number of neurodevelopmental disorders. To define functional thalamocortical connectivity at the normal time of birth, we used functional MRI (fMRI) to measure blood oxygen level-dependent (BOLD) signals in 66 infants, 47 of whom were at high risk of neurocognitive impairment because of birth before 33 wk of gestation and 19 of whom were term infants. We segmented the thalamus based on correlation with functionally defined cortical components using independent component analysis (ICA) and seed-based correlations. After parcellating the cortex using ICA and segmenting the thalamus based on dominant connections with cortical parcellations, we observed a near-facsimile of the adult functional parcellation. Additional analysis revealed that BOLD signal in heteromodal association cortex typically had more widespread and overlapping thalamic representations than primary sensory cortex. Notably, more extreme prematurity was associated with increased functional connectivity between thalamus and lateral primary sensory cortex but reduced connectivity between thalamus and cortex in the prefrontal, insular and anterior cingulate regions. This work suggests that, in early infancy, functional integration through thalamocortical connections depends on significant functional overlap in the topographic organization of the thalamus and that the experience of premature extrauterine life modulates network development, altering the maturation of networks thought to support salience, executive, integrative, and cognitive functions. PMID:25941391

Abnormal functional network connectivity among resting-state networks in children with frontal lobe epilepsy.

PubMed

Widjaja, E; Zamyadi, M; Raybaud, C; Snead, O C; Smith, M L

2013-12-01

Epilepsy is considered a disorder of neural networks. The aims of this study were to assess functional connectivity within resting-state networks and functional network connectivity across resting-state networks by use of resting-state fMRI in children with frontal lobe epilepsy and to relate changes in resting-state networks with neuropsychological function. Fifteen patients with frontal lobe epilepsy and normal MR imaging and 14 healthy control subjects were recruited. Spatial independent component analysis was used to identify the resting-state networks, including frontal, attention, default mode network, sensorimotor, visual, and auditory networks. The Z-maps of resting-state networks were compared between patients and control subjects. The relation between abnormal connectivity and neuropsychological function was assessed. Correlations from all pair-wise combinations of independent components were performed for each group and compared between groups. The frontal network was the only network that showed reduced connectivity in patients relative to control subjects. The remaining 5 networks demonstrated both reduced and increased functional connectivity within resting-state networks in patients. There was a weak association between connectivity in frontal network and executive function (P = .029) and a significant association between sensorimotor network and fine motor function (P = .004). Control subjects had 79 pair-wise independent components that showed significant temporal coherence across all resting-state networks except for default mode network-auditory network. Patients had 66 pairs of independent components that showed significant temporal coherence across all resting-state networks. Group comparison showed reduced functional network connectivity between default mode network-attention, frontal-sensorimotor, and frontal-visual networks and increased functional network connectivity between frontal-attention, default mode network-sensorimotor, and frontal-visual networks in patients relative to control subjects. We found abnormal functional connectivity within and across resting-state networks in children with frontal lobe epilepsy. Impairment in functional connectivity was associated with impaired neuropsychological function.

How multi segmental patterns deviate in spastic diplegia from typical developed.

PubMed

Zago, Matteo; Sforza, Chiarella; Bona, Alessia; Cimolin, Veronica; Costici, Pier Francesco; Condoluci, Claudia; Galli, Manuela

2017-10-01

The relationship between gait features and coordination in children with Cerebral Palsy is not sufficiently analyzed yet. Principal Component Analysis can help in understanding motion patterns decomposing movement into its fundamental components (Principal Movements). This study aims at quantitatively characterizing the functional connections between multi-joint gait patterns in Cerebral Palsy. 65 children with spastic diplegia aged 10.6 (SD 3.7) years participated in standardized gait analysis trials; 31 typically developing adolescents aged 13.6 (4.4) years were also tested. To determine if posture affects gait patterns, patients were split into Crouch and knee Hyperextension group according to knee flexion angle at standing. 3D coordinates of hips, knees, ankles, metatarsal joints, pelvis and shoulders were submitted to Principal Component Analysis. Four Principal Movements accounted for 99% of global variance; components 1-3 explained major sagittal patterns, components 4-5 referred to movements on frontal plane and component 6 to additional movement refinements. Dimensionality was higher in patients than in controls (p<0.01), and the Crouch group significantly differed from controls in the application of components 1 and 4-6 (p<0.05), while the knee Hyperextension group in components 1-2 and 5 (p<0.05). Compensatory strategies of children with Cerebral Palsy (interactions between main and secondary movement patterns), were objectively determined. Principal Movements can reduce the effort in interpreting gait reports, providing an immediate and quantitative picture of the connections between movement components. Copyright © 2017 Elsevier Ltd. All rights reserved.

Increased resting-state functional connectivity of visual- and cognitive-control brain networks after training in children with reading difficulties

PubMed Central

Horowitz-Kraus, Tzipi; DiFrancesco, Mark; Kay, Benjamin; Wang, Yingying; Holland, Scott K.

2015-01-01

The Reading Acceleration Program, a computerized reading-training program, increases activation in neural circuits related to reading. We examined the effect of the training on the functional connectivity between independent components related to visual processing, executive functions, attention, memory, and language during rest after the training. Children 8–12 years old with reading difficulties and typical readers participated in the study. Behavioral testing and functional magnetic resonance imaging were performed before and after the training. Imaging data were analyzed using an independent component analysis approach. After training, both reading groups showed increased single-word contextual reading and reading comprehension scores. Greater positive correlations between the visual-processing component and the executive functions, attention, memory, or language components were found after training in children with reading difficulties. Training-related increases in connectivity between the visual and attention components and between the visual and executive function components were positively correlated with increased word reading and reading comprehension, respectively. Our findings suggest that the effect of the Reading Acceleration Program on basic cognitive domains can be detected even in the absence of an ongoing reading task. PMID:26199874

Increased resting-state functional connectivity of visual- and cognitive-control brain networks after training in children with reading difficulties.

PubMed

Horowitz-Kraus, Tzipi; DiFrancesco, Mark; Kay, Benjamin; Wang, Yingying; Holland, Scott K

2015-01-01

The Reading Acceleration Program, a computerized reading-training program, increases activation in neural circuits related to reading. We examined the effect of the training on the functional connectivity between independent components related to visual processing, executive functions, attention, memory, and language during rest after the training. Children 8-12 years old with reading difficulties and typical readers participated in the study. Behavioral testing and functional magnetic resonance imaging were performed before and after the training. Imaging data were analyzed using an independent component analysis approach. After training, both reading groups showed increased single-word contextual reading and reading comprehension scores. Greater positive correlations between the visual-processing component and the executive functions, attention, memory, or language components were found after training in children with reading difficulties. Training-related increases in connectivity between the visual and attention components and between the visual and executive function components were positively correlated with increased word reading and reading comprehension, respectively. Our findings suggest that the effect of the Reading Acceleration Program on basic cognitive domains can be detected even in the absence of an ongoing reading task.

Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools.

PubMed

Siettos, Constantinos; Starke, Jens

2016-09-01

The extreme complexity of the brain naturally requires mathematical modeling approaches on a large variety of scales; the spectrum ranges from single neuron dynamics over the behavior of groups of neurons to neuronal network activity. Thus, the connection between the microscopic scale (single neuron activity) to macroscopic behavior (emergent behavior of the collective dynamics) and vice versa is a key to understand the brain in its complexity. In this work, we attempt a review of a wide range of approaches, ranging from the modeling of single neuron dynamics to machine learning. The models include biophysical as well as data-driven phenomenological models. The discussed models include Hodgkin-Huxley, FitzHugh-Nagumo, coupled oscillators (Kuramoto oscillators, Rössler oscillators, and the Hindmarsh-Rose neuron), Integrate and Fire, networks of neurons, and neural field equations. In addition to the mathematical models, important mathematical methods in multiscale modeling and reconstruction of the causal connectivity are sketched. The methods include linear and nonlinear tools from statistics, data analysis, and time series analysis up to differential equations, dynamical systems, and bifurcation theory, including Granger causal connectivity analysis, phase synchronization connectivity analysis, principal component analysis (PCA), independent component analysis (ICA), and manifold learning algorithms such as ISOMAP, and diffusion maps and equation-free techniques. WIREs Syst Biol Med 2016, 8:438-458. doi: 10.1002/wsbm.1348 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.

Population connectivity of the plating coral Agaricia lamarcki from southwest Puerto Rico

NASA Astrophysics Data System (ADS)

Hammerman, Nicholas M.; Rivera-Vicens, Ramon E.; Galaska, Matthew P.; Weil, Ernesto; Appledoorn, Richard S.; Alfaro, Monica; Schizas, Nikolaos V.

2018-03-01

Identifying genetic connectivity and discrete population boundaries is an important objective for management of declining Caribbean reef-building corals. A double digest restriction-associated DNA sequencing protocol was utilized to generate 321 single nucleotide polymorphisms to estimate patterns of horizontal and vertical gene flow in the brooding Caribbean plate coral, Agaricia lamarcki. Individual colonies ( n = 59) were sampled from eight locations throughout southwestern Puerto Rico from six shallow ( 10-20 m) and two mesophotic habitats ( 30-40 m). Descriptive summary statistics (fixation index, F ST), analysis of molecular variance, and analysis through landscape and ecological associations and discriminant analysis of principal components estimated high population connectivity with subtle subpopulation structure among all sampling localities.

Multilevel Dynamic Generalized Structured Component Analysis for Brain Connectivity Analysis in Functional Neuroimaging Data.

PubMed

Jung, Kwanghee; Takane, Yoshio; Hwang, Heungsun; Woodward, Todd S

2016-06-01

We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.

Connected component analysis of review-SEM images for sub-10nm node process verification

NASA Astrophysics Data System (ADS)

Halder, Sandip; Leray, Philippe; Sah, Kaushik; Cross, Andrew; Parisi, Paolo

2017-03-01

Analysis of hotspots is becoming more and more critical as we scale from node to node. To define true process windows at sub-14 nm technology nodes, often defect inspections are being included to weed out design weak spots (often referred to as hotspots). Defect inspection sub 28 nm nodes is a two pass process. Defect locations identified by optical inspection tools need to be reviewed by review-SEM's to understand exactly which feature is failing in the region flagged by the optical tool. The images grabbed by the review-SEM tool are used for classification but rarely for quantification. The goal of this paper is to see if the thousands of review-SEM images which are existing can be used for quantification and further analysis. More specifically we address the SEM quantification problem with connected component analysis.

Aberrant functional connectivity of default-mode network in type 2 diabetes patients.

PubMed

Cui, Ying; Jiao, Yun; Chen, Hua-Jun; Ding, Jie; Luo, Bing; Peng, Cheng-Yu; Ju, Sheng-Hong; Teng, Gao-Jun

2015-11-01

Type 2 diabetes mellitus is associated with increased risk for dementia. Patients with impaired cognition often show default-mode network disruption. We aimed to investigate the integrity of a default-mode network in diabetic patients by using independent component analysis, and to explore the relationship between network abnormalities, neurocognitive performance and diabetic variables. Forty-two patients with type 2 diabetes and 42 well-matched healthy controls were included and underwent resting-state functional MRI in a 3 Tesla unit. Independent component analysis was adopted to extract the default-mode network, including its anterior and posterior components. Z-maps of both sub-networks were compared between the two groups and correlated with each clinical variable. Patients showed increased connectivity around the medial prefrontal cortex in the anterior sub-network, but decreased connectivity around the posterior cingulate cortex in the posterior sub-network. The decreased connectivity in the posterior part was significantly correlated with the score on Complex Figure Test-delay recall test (r = 0.359, p = 0.020), the time spent on Trail-Making Test-part B (r = -0.346, p = 0.025) and the insulin resistance level (r = -0.404, p = 0.024). Dissociation pattern in the default-mode network was found in diabetic patients, which might provide powerful new insights into the neural mechanisms that underlie the diabetes-related cognitive decline. • Type 2 diabetes mellitus is associated with impaired cognition • Default- mode network plays a central role in maintaining normal cognition • Network connectivity within the default mode was disrupted in type 2 diabetes patients • Decreased network connectivity was correlated with cognitive performance and insulin resistance level • Disrupted default-mode network might explain the impaired cognition in diabetic population.

Assessment of Change in Green Infrastructure Components Using Morphological Spatial Pattern Analysis for the Conterminous United States

EPA Science Inventory

Green infrastructure is a widely used framework for conservation planning in the United States and elsewhere. The main components of green infrastructure are hubs and corridors. Hubs are large areas of natural vegetation, and corridors are linear features that connect hubs. W...

Connectedness to Nature and Life Satisfaction among College Outdoor Program Staff

ERIC Educational Resources Information Center

Frauman, Eric; Shaffer, Francesca

2017-01-01

Ecologists have long theorized about humans' psychological relationship to the natural world. The importance of feeling connected to nature is a theme in the writing of ecologists (Leopold, 1949; Orr, 1994; Roszak, 1995). They have argued that this connection to nature is a key component of fostering ecological behavior. In a meta-analysis of the…

Statistical analysis of major ion and trace element geochemistry of water, 1986-2006, at seven wells transecting the freshwater/saline-water interface of the Edwards Aquifer, San Antonio, Texas

USGS Publications Warehouse

Mahler, Barbara J.

2008-01-01

The statistical analyses taken together indicate that the geochemistry at the freshwater-zone wells is more variable than that at the transition-zone wells. The geochemical variability at the freshwater-zone wells might result from dilution of ground water by meteoric water. This is indicated by relatively constant major ion molar ratios; a preponderance of positive correlations between SC, major ions, and trace elements; and a principal components analysis in which the major ions are strongly loaded on the first principal component. Much of the variability at three of the four transition-zone wells might result from the use of different laboratory analytical methods or reporting procedures during the period of sampling. This is reflected by a lack of correlation between SC and major ion concentrations at the transition-zone wells and by a principal components analysis in which the variability is fairly evenly distributed across several principal components. The statistical analyses further indicate that, although the transition-zone wells are less well connected to surficial hydrologic conditions than the freshwater-zone wells, there is some connection but the response time is longer. 

Resting State Functional Connectivity in Mild Traumatic Brain Injury at the Acute Stage: Independent Component and Seed-Based Analyses

PubMed Central

Iraji, Armin; Benson, Randall R.; Welch, Robert D.; O'Neil, Brian J.; Woodard, John L.; Imran Ayaz, Syed; Kulek, Andrew; Mika, Valerie; Medado, Patrick; Soltanian-Zadeh, Hamid; Liu, Tianming; Haacke, E. Mark

2015-01-01

Abstract Mild traumatic brain injury (mTBI) accounts for more than 1 million emergency visits each year. Most of the injured stay in the emergency department for a few hours and are discharged home without a specific follow-up plan because of their negative clinical structural imaging. Advanced magnetic resonance imaging (MRI), particularly functional MRI (fMRI), has been reported as being sensitive to functional disturbances after brain injury. In this study, a cohort of 12 patients with mTBI were prospectively recruited from the emergency department of our local Level-1 trauma center for an advanced MRI scan at the acute stage. Sixteen age- and sex-matched controls were also recruited for comparison. Both group-based and individual-based independent component analysis of resting-state fMRI (rsfMRI) demonstrated reduced functional connectivity in both posterior cingulate cortex (PCC) and precuneus regions in comparison with controls, which is part of the default mode network (DMN). Further seed-based analysis confirmed reduced functional connectivity in these two regions and also demonstrated increased connectivity between these regions and other regions of the brain in mTBI. Seed-based analysis using the thalamus, hippocampus, and amygdala regions further demonstrated increased functional connectivity between these regions and other regions of the brain, particularly in the frontal lobe, in mTBI. Our data demonstrate alterations of multiple brain networks at the resting state, particularly increased functional connectivity in the frontal lobe, in response to brain concussion at the acute stage. Resting-state functional connectivity of the DMN could serve as a potential biomarker for improved detection of mTBI in the acute setting. PMID:25285363

Progressive Disintegration of Brain Networking from Normal Aging to Alzheimer Disease: Analysis of Independent Components of 18F-FDG PET Data.

PubMed

Pagani, Marco; Giuliani, Alessandro; Öberg, Johanna; De Carli, Fabrizio; Morbelli, Silvia; Girtler, Nicola; Arnaldi, Dario; Accardo, Jennifer; Bauckneht, Matteo; Bongioanni, Francesca; Chincarini, Andrea; Sambuceti, Gianmario; Jonsson, Cathrine; Nobili, Flavio

2017-07-01

Brain connectivity has been assessed in several neurodegenerative disorders investigating the mutual correlations between predetermined regions or nodes. Selective breakdown of brain networks during progression from normal aging to Alzheimer disease dementia (AD) has also been observed. Methods: We implemented independent-component analysis of 18 F-FDG PET data in 5 groups of subjects with cognitive states ranging from normal aging to AD-including mild cognitive impairment (MCI) not converting or converting to AD-to disclose the spatial distribution of the independent components in each cognitive state and their accuracy in discriminating the groups. Results: We could identify spatially distinct independent components in each group, with generation of local circuits increasing proportionally to the severity of the disease. AD-specific independent components first appeared in the late-MCI stage and could discriminate converting MCI and AD from nonconverting MCI with an accuracy of 83.5%. Progressive disintegration of the intrinsic networks from normal aging to MCI to AD was inversely proportional to the conversion time. Conclusion: Independent-component analysis of 18 F-FDG PET data showed a gradual disruption of functional brain connectivity with progression of cognitive decline in AD. This information might be useful as a prognostic aid for individual patients and as a surrogate biomarker in intervention trials. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

The Value of Distributed Solar Electric Generation to San Antonio

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

Jones, Nic; Norris, Ben; Meyer, Lisa

2013-02-14

This report presents an analysis of value provided by grid-connected, distributed PV in San Antonio from a utility perspective. The study quantified six value components, summarized in Table ES- 1. These components represent the benefits that accrue to the utility, CPS Energy, in accepting solar onto the grid. This analysis does not treat the compensation of value, policy objectives, or cost-effectiveness from the retail consumer perspective.

Registering Cortical Surfaces Based on Whole-Brain Structural Connectivity and Continuous Connectivity Analysis

PubMed Central

Gutman, Boris; Leonardo, Cassandra; Jahanshad, Neda; Hibar, Derrek; Eschen-burg, Kristian; Nir, Talia; Villalon, Julio; Thompson, Paul

2014-01-01

We present a framework for registering cortical surfaces based on tractography-informed structural connectivity. We define connectivity as a continuous kernel on the product space of the cortex, and develop a method for estimating this kernel from tractography fiber models. Next, we formulate the kernel registration problem, and present a means to non-linearly register two brains’ continuous connectivity profiles. We apply theoretical results from operator theory to develop an algorithm for decomposing the connectome into its shared and individual components. Lastly, we extend two discrete connectivity measures to the continuous case, and apply our framework to 98 Alzheimer’s patients and controls. Our measures show significant differences between the two groups. PMID:25320795

Stochastic modular analysis for gene circuits: interplay among retroactivity, nonlinearity, and stochasticity.

PubMed

Kim, Kyung Hyuk; Sauro, Herbert M

2015-01-01

This chapter introduces a computational analysis method for analyzing gene circuit dynamics in terms of modules while taking into account stochasticity, system nonlinearity, and retroactivity. (1) ANALOG ELECTRICAL CIRCUIT REPRESENTATION FOR GENE CIRCUITS: A connection between two gene circuit components is often mediated by a transcription factor (TF) and the connection signal is described by the TF concentration. The TF is sequestered to its specific binding site (promoter region) and regulates downstream transcription. This sequestration has been known to affect the dynamics of the TF by increasing its response time. The downstream effect-retroactivity-has been shown to be explicitly described in an electrical circuit representation, as an input capacitance increase. We provide a brief review on this topic. (2) MODULAR DESCRIPTION OF NOISE PROPAGATION: Gene circuit signals are noisy due to the random nature of biological reactions. The noisy fluctuations in TF concentrations affect downstream regulation. Thus, noise can propagate throughout the connected system components. This can cause different circuit components to behave in a statistically dependent manner, hampering a modular analysis. Here, we show that the modular analysis is still possible at the linear noise approximation level. (3) NOISE EFFECT ON MODULE INPUT-OUTPUT RESPONSE: We investigate how to deal with a module input-output response and its noise dependency. Noise-induced phenotypes are described as an interplay between system nonlinearity and signal noise. Lastly, we provide the comprehensive approach incorporating the above three analysis methods, which we call "stochastic modular analysis." This method can provide an analysis framework for gene circuit dynamics when the nontrivial effects of retroactivity, stochasticity, and nonlinearity need to be taken into account.

Downhole tool

DOEpatents

Hall, David R.; Muradov, Andrei; Pixton, David S.; Dahlgren, Scott Steven; Briscoe, Michael A.

2007-03-20

A double shouldered downhole tool connection comprises box and pin connections having mating threads intermediate mating primary and secondary shoulders. The connection further comprises a secondary shoulder component retained in the box connection intermediate a floating component and the primary shoulders. The secondary shoulder component and the pin connection cooperate to transfer a portion of makeup load to the box connection. The downhole tool may be selected from the group consisting of drill pipe, drill collars, production pipe, and reamers. The floating component may be selected from the group consisting of electronics modules, generators, gyroscopes, power sources, and stators. The secondary shoulder component may comprises an interface to the box connection selected from the group consisting of radial grooves, axial grooves, tapered grooves, radial protrusions, axial protrusions, tapered protrusions, shoulders, and threads.

Reduced brain resting-state network specificity in infants compared with adults.

PubMed

Wylie, Korey P; Rojas, Donald C; Ross, Randal G; Hunter, Sharon K; Maharajh, Keeran; Cornier, Marc-Andre; Tregellas, Jason R

2014-01-01

Infant resting-state networks do not exhibit the same connectivity patterns as those of young children and adults. Current theories of brain development emphasize developmental progression in regional and network specialization. We compared infant and adult functional connectivity, predicting that infants would exhibit less regional specificity and greater internetwork communication compared with adults. Functional magnetic resonance imaging at rest was acquired in 12 healthy, term infants and 17 adults. Resting-state networks were extracted, using independent components analysis, and the resulting components were then compared between the adult and infant groups. Adults exhibited stronger connectivity in the posterior cingulate cortex node of the default mode network, but infants had higher connectivity in medial prefrontal cortex/anterior cingulate cortex than adults. Adult connectivity was typically higher than infant connectivity within structures previously associated with the various networks, whereas infant connectivity was frequently higher outside of these structures. Internetwork communication was significantly higher in infants than in adults. We interpret these findings as consistent with evidence suggesting that resting-state network development is associated with increasing spatial specificity, possibly reflecting the corresponding functional specialization of regions and their interconnections through experience.

Resting State Network Topology of the Ferret Brain

PubMed Central

Zhou, Zhe Charles; Salzwedel, Andrew P.; Radtke-Schuller, Susanne; Li, Yuhui; Sellers, Kristin K.; Gilmore, John H.; Shih, Yen-Yu Ian; Fröhlich, Flavio; Gao, Wei

2016-01-01

Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4 tesla MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function. PMID:27596024

Cradle-to-Gate Impact Assessment of a High-Pressure Die-Casting Safety-Relevant Automotive Component

NASA Astrophysics Data System (ADS)

Cecchel, Silvia; Cornacchia, Giovanna; Panvini, Andrea

2016-09-01

The mass of automotive components has a direct influence on several aspects of vehicle performance, including both fuel consumption and tailpipe emissions, but the real environmental benefit has to be evaluated considering the entire life of the products with a proper life cycle assessment. In this context, the present paper analyzes the environmental burden connected to the production of a safety-relevant aluminum high-pressure die-casting component for commercial vehicles (a suspension cross-beam) considering all the phases connected to its manufacture. The focus on aluminum high-pressure die casting reflects the current trend of the industry and its high energy consumption. This work shows a new method that deeply analyzes every single step of the component's production through the implementation of a wide database of primary data collected thanks to collaborations of some automotive supplier companies. This energy analysis shows significant environmental benefits of aluminum recycling.

Detection of resting state functional connectivity using partial correlation analysis: A study using multi-distance and whole-head probe near-infrared spectroscopy.

PubMed

Sakakibara, Eisuke; Homae, Fumitaka; Kawasaki, Shingo; Nishimura, Yukika; Takizawa, Ryu; Koike, Shinsuke; Kinoshita, Akihide; Sakurada, Hanako; Yamagishi, Mika; Nishimura, Fumichika; Yoshikawa, Akane; Inai, Aya; Nishioka, Masaki; Eriguchi, Yosuke; Matsuoka, Jun; Satomura, Yoshihiro; Okada, Naohiro; Kakiuchi, Chihiro; Araki, Tsuyoshi; Kan, Chiemi; Umeda, Maki; Shimazu, Akihito; Uga, Minako; Dan, Ippeita; Hashimoto, Hideki; Kawakami, Norito; Kasai, Kiyoto

2016-11-15

Multichannel near-infrared spectroscopy (NIRS) is a functional neuroimaging modality that enables easy-to-use and noninvasive measurement of changes in blood oxygenation levels. We developed a clinically-applicable method for estimating resting state functional connectivity (RSFC) with NIRS using a partial correlation analysis to reduce the influence of extraneural components. Using a multi-distance probe arrangement NIRS, we measured resting state brain activity for 8min in 17 healthy participants. Independent component analysis was used to extract shallow and deep signals from the original NIRS data. Pearson's correlation calculated from original signals was significantly higher than that calculated from deep signals, while partial correlation calculated from original signals was comparable to that calculated from deep (cerebral-tissue) signals alone. To further test the validity of our method, we also measured 8min of resting state brain activity using a whole-head NIRS arrangement consisting of 17 cortical regions in 80 healthy participants. Significant RSFC between neighboring, interhemispheric homologous, and some distant ipsilateral brain region pairs was revealed. Additionally, females exhibited higher RSFC between interhemispheric occipital region-pairs, in addition to higher connectivity between some ipsilateral pairs in the left hemisphere, when compared to males. The combined results of the two component experiments indicate that partial correlation analysis is effective in reducing the influence of extracerebral signals, and that NIRS is able to detect well-described resting state networks and sex-related differences in RSFC. Copyright © 2016 Elsevier Inc. All rights reserved.

Abnormal synchrony and effective connectivity in patients with schizophrenia and auditory hallucinations

PubMed Central

de la Iglesia-Vaya, Maria; Escartí, Maria José; Molina-Mateo, Jose; Martí-Bonmatí, Luis; Gadea, Marien; Castellanos, Francisco Xavier; Aguilar García-Iturrospe, Eduardo J.; Robles, Montserrat; Biswal, Bharat B.; Sanjuan, Julio

2014-01-01

Auditory hallucinations (AH) are the most frequent positive symptoms in patients with schizophrenia. Hallucinations have been related to emotional processing disturbances, altered functional connectivity and effective connectivity deficits. Previously, we observed that, compared to healthy controls, the limbic network responses of patients with auditory hallucinations differed when the subjects were listening to emotionally charged words. We aimed to compare the synchrony patterns and effective connectivity of task-related networks between schizophrenia patients with and without AH and healthy controls. Schizophrenia patients with AH (n = 27) and without AH (n = 14) were compared with healthy participants (n = 31). We examined functional connectivity by analyzing correlations and cross-correlations among previously detected independent component analysis time courses. Granger causality was used to infer the information flow direction in the brain regions. The results demonstrate that the patterns of cortico-cortical functional synchrony differentiated the patients with AH from the patients without AH and from the healthy participants. Additionally, Granger-causal relationships between the networks clearly differentiated the groups. In the patients with AH, the principal causal source was an occipital–cerebellar component, versus a temporal component in the patients without AH and the healthy controls. These data indicate that an anomalous process of neural connectivity exists when patients with AH process emotional auditory stimuli. Additionally, a central role is suggested for the cerebellum in processing emotional stimuli in patients with persistent AH. PMID:25379429

Topological image texture analysis for quality assessment

NASA Astrophysics Data System (ADS)

Asaad, Aras T.; Rashid, Rasber Dh.; Jassim, Sabah A.

2017-05-01

Image quality is a major factor influencing pattern recognition accuracy and help detect image tampering for forensics. We are concerned with investigating topological image texture analysis techniques to assess different type of degradation. We use Local Binary Pattern (LBP) as a texture feature descriptor. For any image construct simplicial complexes for selected groups of uniform LBP bins and calculate persistent homology invariants (e.g. number of connected components). We investigated image quality discriminating characteristics of these simplicial complexes by computing these models for a large dataset of face images that are affected by the presence of shadows as a result of variation in illumination conditions. Our tests demonstrate that for specific uniform LBP patterns, the number of connected component not only distinguish between different levels of shadow effects but also help detect the infected regions as well.

Connectivity patterns in cognitive control networks predict naturalistic multitasking ability.

PubMed

Wen, Tanya; Liu, De-Cyuan; Hsieh, Shulan

2018-06-01

Multitasking is a fundamental aspect of everyday life activities. To achieve a complex, multi-component goal, the tasks must be subdivided into sub-tasks and component steps, a critical function of prefrontal networks. The prefrontal cortex is considered to be organized in a cascade of executive processes from the sensorimotor to anterior prefrontal cortex, which includes execution of specific goal-directed action, to encoding and maintaining task rules, and finally monitoring distal goals. In the current study, we used a virtual multitasking paradigm to tap into real-world performance and relate it to each individual's resting-state functional connectivity in fMRI. While did not find any correlation between global connectivity of any of the major networks with multitasking ability, global connectivity of the lateral prefrontal cortex (LPFC) was predictive of multitasking ability. Further analysis showed that multivariate connectivity patterns within the sensorimotor network (SMN), and between-network connectivity of the frontoparietal network (FPN) and dorsal attention network (DAN), predicted individual multitasking ability and could be generalized to novel individuals. Together, these results support previous research that prefrontal networks underlie multitasking abilities and show that connectivity patterns in the cascade of prefrontal networks may explain individual differences in performance. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Association between heart rate variability and fluctuations in resting-state functional connectivity

PubMed Central

Chang, Catie; Metzger, Coraline D.; Glover, Gary H.; Duyn, Jeff H.; Heinze, Hans-Jochen; Walter, Martin

2012-01-01

Functional connectivity has been observed to fluctuate across the course of a resting state scan, though the origins and functional relevance of this phenomenon remain to be shown. The present study explores the link between endogenous dynamics of functional connectivity and autonomic state in an eyes-closed resting condition. Using a sliding window analysis on resting state fMRI data from 35 young, healthy male subjects, we examined how heart rate variability (HRV) covaries with temporal changes in whole-brain functional connectivity with seed regions previously described to mediate effects of vigilance and arousal (amygdala and dorsal anterior cingulate cortex; dACC). We identified a set of regions, including brainstem, thalamus, putamen, and dorsolateral prefrontal cortex, that became more strongly coupled with the dACC and amygdala seeds during states of elevated HRV. Effects differed between high and low frequency components of HRV, suggesting specific contributions of parasympathetic and sympathetic tone on individual connections. Furthermore, dynamics of functional connectivity could be separated from those primarily related to BOLD signal fluctuations. The present results contribute novel information about the neural basis of transient changes of autonomic nervous system states, and suggest physiological and psychological components of the recently observed non-stationarity in resting state functional connectivity. PMID:23246859

Development and Plasticity of Cortical Processing Architectures

NASA Astrophysics Data System (ADS)

Singer, Wolf

1995-11-01

One of the basic functions of the cerebral cortex is the analysis and representation of relations among the components of sensory and motor patterns. It is proposed that the cortex applies two complementary strategies to cope with the combinatorial problem posed by the astronomical number of possible relations: (i) the analysis and representation of frequently occurring, behaviorally relevant relations by groups of cells with fixed but broadly tuned response properties; and (ii) the dynamic association of these cells into functionally coherent assemblies. Feedforward connections and reciprocal associative connections, respectively, are thought to underlie these two operations. The architectures of both types of connections are susceptible to experience-dependent modifications during development, but they become fixed in the adult. As development proceeds, feedforward connections also appear to lose much of their functional plasticity, whereas the synapses of the associative connections retain a high susceptibility to use-dependent modifications. The reduced plasticity of feedforward connections is probably responsible for the invariance of cognitive categories acquired early in development. The persistent adaptivity of reciprocal connections is a likely substrate for the ability to generate representations for new perceptual objects and motor patterns throughout life.

Characterization of functional brain activity and connectivity using EEG and fMRI in patients with sickle cell disease.

PubMed

Case, Michelle; Zhang, Huishi; Mundahl, John; Datta, Yvonne; Nelson, Stephen; Gupta, Kalpna; He, Bin

2017-01-01

Sickle cell disease (SCD) is a red blood cell disorder that causes many complications including life-long pain. Treatment of pain remains challenging due to a poor understanding of the mechanisms and limitations to characterize and quantify pain. In the present study, we examined simultaneously recording functional MRI (fMRI) and electroencephalogram (EEG) to better understand neural connectivity as a consequence of chronic pain in SCD patients. We performed independent component analysis and seed-based connectivity on fMRI data. Spontaneous power and microstate analysis was performed on EEG-fMRI data. ICA analysis showed that patients lacked activity in the default mode network (DMN) and executive control network compared to controls. EEG-fMRI data revealed that the insula cortex's role in salience increases with age in patients. EEG microstate analysis showed patients had increased activity in pain processing regions. The cerebellum in patients showed a stronger connection to the periaqueductal gray matter (involved in pain inhibition), and negative connections to pain processing areas. These results suggest that patients have reduced activity of DMN and increased activity in pain processing regions during rest. The present findings suggest resting state connectivity differences between patients and controls can be used as novel biomarkers of SCD pain.

Structural Analysis of the Redesigned Ice/Frost Ramp Bracket

NASA Technical Reports Server (NTRS)

Phillips, D. R.; Dawicke, D. S.; Gentz, S. J.; Roberts, P. W.; Raju, I. S.

2007-01-01

This paper describes the interim structural analysis of a redesigned Ice/Frost Ramp bracket for the Space Shuttle External Tank (ET). The proposed redesigned bracket consists of mounts for attachment to the ET wall, supports for the electronic/instrument cables and propellant repressurization lines that run along the ET, an upper plate, a lower plate, and complex bolted connections. The eight nominal bolted connections are considered critical in the summarized structural analysis. Each bolted connection contains a bolt, a nut, four washers, and a non-metallic spacer and block that are designed for thermal insulation. A three-dimensional (3D) finite element model of the bracket is developed using solid 10-node tetrahedral elements. The loading provided by the ET Project is used in the analysis. Because of the complexities associated with accurately modeling the bolted connections in the bracket, the analysis is performed using a global/local analysis procedure. The finite element analysis of the bracket identifies one of the eight bolted connections as having high stress concentrations. A local area of the bracket surrounding this bolted connection is extracted from the global model and used as a local model. Within the local model, the various components of the bolted connection are refined, and contact is introduced along the appropriate interfaces determined by the analysts. The deformations from the global model are applied as boundary conditions to the local model. The results from the global/local analysis show that while the stresses in the bolts are well within yield, the spacers fail due to compression. The primary objective of the interim structural analysis is to show concept viability for static thermal testing. The proposed design concept would undergo continued design optimization to address the identified analytical assumptions and concept shortcomings, assuming successful thermal testing.

A Two-Step Approach to Analyze Satisfaction Data

ERIC Educational Resources Information Center

Ferrari, Pier Alda; Pagani, Laura; Fiorio, Carlo V.

2011-01-01

In this paper a two-step procedure based on Nonlinear Principal Component Analysis (NLPCA) and Multilevel models (MLM) for the analysis of satisfaction data is proposed. The basic hypothesis is that observed ordinal variables describe different aspects of a latent continuous variable, which depends on covariates connected with individual and…

Resting state network topology of the ferret brain.

PubMed

Zhou, Zhe Charles; Salzwedel, Andrew P; Radtke-Schuller, Susanne; Li, Yuhui; Sellers, Kristin K; Gilmore, John H; Shih, Yen-Yu Ian; Fröhlich, Flavio; Gao, Wei

2016-12-01

Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4T MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function. Copyright © 2016 Elsevier Inc. All rights reserved.

Receiver System Analysis and Optimization

DTIC Science & Technology

2013-01-01

designers to make best use of advanced silicon processes (scale, fast devices) while minimizing the disadvantages (low-Q passives, low transimpedance ). A...components such as amplifiers , filters, mixers, oscillators, etc. Specifications for the components are then passed on to design teams. The digital and...cascade connection of an LNA, Mixer, Voltage Controlled Oscillator [VCO], Amplifier and Analog to Digital Converter [ADC] as well as appropriate

Preliminary differences in resting state MEG functional connectivity pre- and post-ketamine in major depressive disorder.

PubMed

Nugent, Allison C; Robinson, Stephen E; Coppola, Richard; Zarate, Carlos A

2016-08-30

Functional neuroimaging techniques including magnetoencephalography (MEG) have demonstrated that the brain is organized into networks displaying correlated activity. Group connectivity differences between healthy controls and participants with major depressive disorder (MDD) can be detected using temporal independent components analysis (ICA) on beta-bandpass filtered Hilbert envelope MEG data. However, the response of these networks to treatment is unknown. Ketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist, exerts rapid antidepressant effects. We obtained MEG recordings before and after open-label infusion of 0.5mg/kg ketamine in MDD subjects (N=13) and examined networks previously shown to differ between healthy individuals and those with MDD. Connectivity between the amygdala and an insulo-temporal component decreased post-ketamine in MDD subjects towards that observed in control subjects at baseline. Decreased baseline connectivity of the subgenual anterior cingulate cortex (sgACC) with a bilateral precentral network had previously been observed in MDD compared to healthy controls, and the change in connectivity post-ketamine was proportional to the change in sgACC glucose metabolism in a subset (N=8) of subjects receiving [11F]FDG-PET imaging. Ketamine appeared to reduce connectivity, regardless of whether connectivity was abnormally high or low compared to controls at baseline. These preliminary findings suggest that sgACC connectivity may be directly related to glutamate levels. Published by Elsevier Ireland Ltd.

Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data

PubMed Central

Havlicek, Martin; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D.

2015-01-01

Increasing interest in understanding dynamic interactions of brain neural networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the functional networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover, this metric provides identification of causal relation in frequency domain, which allows one to distinguish the frequency components related to the experimental paradigm. The procedure of evaluating Granger causality via dynamic MAR was demonstrated on simulated time series as well as on two sets of group fMRI data collected during an auditory sensorimotor (SM) or auditory oddball discrimination (AOD) tasks. Finally, a comparison with the results obtained from a standard time-invariant MAR model was provided. PMID:20561919

Multimodal and Multi-tissue Measures of Connectivity Revealed by Joint Independent Component Analysis.

PubMed

Franco, Alexandre R; Ling, Josef; Caprihan, Arvind; Calhoun, Vince D; Jung, Rex E; Heileman, Gregory L; Mayer, Andrew R

2008-12-01

The human brain functions as an efficient system where signals arising from gray matter are transported via white matter tracts to other regions of the brain to facilitate human behavior. However, with a few exceptions, functional and structural neuroimaging data are typically optimized to maximize the quantification of signals arising from a single source. For example, functional magnetic resonance imaging (FMRI) is typically used as an index of gray matter functioning whereas diffusion tensor imaging (DTI) is typically used to determine white matter properties. While it is likely that these signals arising from different tissue sources contain complementary information, the signal processing algorithms necessary for the fusion of neuroimaging data across imaging modalities are still in a nascent stage. In the current paper we present a data-driven method for combining measures of functional connectivity arising from gray matter sources (FMRI resting state data) with different measures of white matter connectivity (DTI). Specifically, a joint independent component analysis (J-ICA) was used to combine these measures of functional connectivity following intensive signal processing and feature extraction within each of the individual modalities. Our results indicate that one of the most predominantly used measures of functional connectivity (activity in the default mode network) is highly dependent on the integrity of white matter connections between the two hemispheres (corpus callosum) and within the cingulate bundles. Importantly, the discovery of this complex relationship of connectivity was entirely facilitated by the signal processing and fusion techniques presented herein and could not have been revealed through separate analyses of both data types as is typically performed in the majority of neuroimaging experiments. We conclude by discussing future applications of this technique to other areas of neuroimaging and examining potential limitations of the methods.

Macroscopic and microscopic evaluation of a new implant design supporting immediately loaded full arch rehabilitation

PubMed Central

Tetè, Stefano; Zizzari, Vincenzo; De Carlo, Alessandro; Sinjari, Bruna; Gherlone, Enrico

2012-01-01

Summary The purpose of this study is to evaluate macroscopic and microscopic appearance of a new implant design, with particular emphasis given to the type of prosthesis connection. Two dental implants of the same type (Torque Type®, WinSix®, BioSAFin. S.r.l. - Ancona, Italy), with sandblasted and acid etched surfaces (Micro Rough Surface®), but differing from each other for the prosthesis connection system, were examined by scanning electron microscope (SEM) analysis at different magnifications: TTI implant, with a hexagonal internal connection, and TTX implant, with a hexagonal external connection. SEM analysis showed that the Torque Type® implant is characterized by a truncated cone shape with tapered tips. The implant body showed a double loop thread and double pitch with blunt tips. For both types of connection, the implant neck was 0.7 mm in height with a 3% taper. This implant design may be able to guarantee osteotomic properties at the time of insertion in a surgical site suitably prepared, a facilitated screwing, thanks to the thread pitch and to the broad and deep draining grooves, thereby ensuring a good primary stability. The different connection design appears defined and precise, in order to ensure a good interface between the fixture and the prosthetic components. Therefore, this design appears to be particularly suitable in cases where a good primary stability is necessary and a precise coupling between endosseous and prosthetic components, as it allows an easy insertion of the fixture even in conditions of reduced bone availability, and in cases of immediately loaded full-arch rehabilitations. PMID:23087785

Macroscopic and microscopic evaluation of a new implant design supporting immediately loaded full arch rehabilitation.

PubMed

Tetè, Stefano; Zizzari, Vincenzo; De Carlo, Alessandro; Sinjari, Bruna; Gherlone, Enrico

2012-04-01

The purpose of this study is to evaluate macroscopic and microscopic appearance of a new implant design, with particular emphasis given to the type of prosthesis connection. Two dental implants of the same type (Torque Type(®), WinSix(®), BioSAFin. S.r.l. - Ancona, Italy), with sandblasted and acid etched surfaces (Micro Rough Surface(®)), but differing from each other for the prosthesis connection system, were examined by scanning electron microscope (SEM) analysis at different magnifications: TTI implant, with a hexagonal internal connection, and TTX implant, with a hexagonal external connection. SEM analysis showed that the Torque Type(®) implant is characterized by a truncated cone shape with tapered tips. The implant body showed a double loop thread and double pitch with blunt tips. For both types of connection, the implant neck was 0.7 mm in height with a 3% taper. This implant design may be able to guarantee osteotomic properties at the time of insertion in a surgical site suitably prepared, a facilitated screwing, thanks to the thread pitch and to the broad and deep draining grooves, thereby ensuring a good primary stability. The different connection design appears defined and precise, in order to ensure a good interface between the fixture and the prosthetic components. Therefore, this design appears to be particularly suitable in cases where a good primary stability is necessary and a precise coupling between endosseous and prosthetic components, as it allows an easy insertion of the fixture even in conditions of reduced bone availability, and in cases of immediately loaded full-arch rehabilitations.

Alterations of white matter structural networks in patients with non-neuropsychiatric systemic lupus erythematosus identified by probabilistic tractography and connectivity-based analyses.

PubMed

Xu, Man; Tan, Xiangliang; Zhang, Xinyuan; Guo, Yihao; Mei, Yingjie; Feng, Qianjin; Xu, Yikai; Feng, Yanqiu

2017-01-01

Systemic lupus erythematosus (SLE) is a chronic inflammatory female-predominant autoimmune disease that can affect the central nervous system and exhibit neuropsychiatric symptoms. In SLE patients without neuropsychiatric symptoms (non-NPSLE), recent diffusion tensor imaging studies showed white matter abnormalities in their brains. The present study investigated the entire brain white matter structural connectivity in non-NPSLE patients by using probabilistic tractography and connectivity-based analyses. Whole-brain structural networks of 29 non-NPSLE patients and 29 healthy controls (HCs) were examined. The structural networks were constructed with interregional probabilistic connectivity. Graph theory analysis was performed to investigate the topological properties, and network-based statistic was employed to assess the alterations of the interregional connections among non-NPSLE patients and controls. Compared with HCs, non-NPSLE patients demonstrated significantly decreased global and local network efficiencies and showed increased characteristic path length. This finding suggests that the global integration and local specialization were impaired. Moreover, the regional properties (nodal efficiency and degree) in the frontal, occipital, and cingulum regions of the non-NPSLE patients were significantly changed and negatively correlated with the disease activity index. The distribution pattern of the hubs measured by nodal degree was altered in the patient group. Finally, the non-NPSLE group exhibited decreased structural connectivity in the left median cingulate-centered component and increased connectivity in the left precuneus-centered component and right middle temporal lobe-centered component. This study reveals an altered topological organization of white matter networks in non-NPSLE patients. Furthermore, this research provides new insights into the structural disruptions underlying the functional and neurocognitive deficits in non-NPSLE patients.

Limbic hyperconnectivity in the vegetative state.

PubMed

Di Perri, Carol; Bastianello, Stefano; Bartsch, Andreas J; Pistarini, Caterina; Maggioni, Giorgio; Magrassi, Lorenzo; Imberti, Roberto; Pichiecchio, Anna; Vitali, Paolo; Laureys, Steven; Di Salle, Francesco

2013-10-15

To investigate functional connectivity between the default mode network (DMN) and other networks in disorders of consciousness. We analyzed MRI data from 11 patients in a vegetative state and 7 patients in a minimally conscious state along with age- and sex-matched healthy control subjects. MRI data analysis included nonlinear spatial normalization to compensate for disease-related anatomical distortions. We studied brain connectivity data from resting-state MRI temporal series, combining noninferential (independent component analysis) and inferential (seed-based general linear model) methods. In DMN hypoconnectivity conditions, a patient's DMN functional connectivity shifts and paradoxically increases in limbic structures, including the orbitofrontal cortex, insula, hypothalamus, and the ventral tegmental area. Concurrently with DMN hypoconnectivity, we report limbic hyperconnectivity in patients in vegetative and minimally conscious states. This hyperconnectivity may reflect the persistent engagement of residual neural activity in self-reinforcing neural loops, which, in turn, could disrupt normal patterns of connectivity.

Learning Gene Expression Through Modelling and Argumentation. A Case Study Exploring the Connections Between the Worlds of Knowledge

NASA Astrophysics Data System (ADS)

Puig, Blanca; Ageitos, Noa; Jiménez-Aleixandre, María Pilar

2017-12-01

There is emerging interest on the interactions between modelling and argumentation in specific contexts, such as genetics learning. It has been suggested that modelling might help students understand and argue on genetics. We propose modelling gene expression as a way to learn molecular genetics and diseases with a genetic component. The study is framed in Tiberghien's (2000) two worlds of knowledge, the world of "theories & models" and the world of "objects & events", adding a third component, the world of representations. We seek to examine how modelling and argumentation interact and connect the three worlds of knowledge while modelling gene expression. It is a case study of 10th graders learning about diseases with a genetic component. The research questions are as follows: (1) What argumentative and modelling operations do students enact in the process of modelling gene expression? Specifically, which operations allow connecting the three worlds of knowledge? (2) What are the interactions between modelling and argumentation in modelling gene expression? To what extent do these interactions help students connect the three worlds of knowledge and modelling gene expression? The argumentative operation of using evidence helps students to relate the three worlds of knowledge, enacted in all the connections. It seems to be a relationship among the number of interactions between modelling and argumentation, the connections between world of knowledge and students' capacity to develop a more sophisticated representation. Despite this is a case study, this approach of analysis reveals potentialities for a deeper understanding of learning genetics though scientific practices.

Brain functional network abnormality extends beyond the sensorimotor network in brachial plexus injury patients.

PubMed

Feng, Jun-Tao; Liu, Han-Qiu; Hua, Xu-Yun; Gu, Yu-Dong; Xu, Jian-Guang; Xu, Wen-Dong

2016-12-01

Brachial plexus injury (BPI) is a type of severe peripheral nerve trauma that leads to central remodeling in the brain, as revealed by functional MRI analysis. However, previously reported remodeling is mostly restricted to sensorimotor areas of the brain. Whether this disturbance in the sensorimotor network leads to larger-scale functional remodeling remains unknown. We sought to explore the higher-level brain functional abnormality pattern of BPI patients from a large-scale network function connectivity dimension in 15 right-handed BPI patients. Resting-state functional MRI data were collected and analyzed using independent component analysis methods. Five components of interest were recognized and compared between patients and healthy subjects. Patients showed significantly altered brain local functional activities in the bilateral fronto-parietal network (FPN), sensorimotor network (SMN), and executive-control network (ECN) compared with healthy subjects. Moreover, functional connectivity between SMN and ECN were significantly less in patients compared with healthy subjects, and connectivity strength between ECN and SMN was negatively correlated with patients' residual function of the affected limb. Functional connectivity between SMN and right FPN were also significantly less than in controls, although connectivity between ECN and default mode network (DMN) was greater than in controls. These data suggested that brain functional disturbance in BPI patients extends beyond the sensorimotor network and cascades serial remodeling in the brain, which significantly correlates with residual hand function of the paralyzed limb. Furthermore, functional remodeling in these higher-level functional networks may lead to cognitive alterations in complex tasks.

Flexible connector

DOEpatents

Savage, Mark E.; Simpson, Walter W.

1999-01-01

An electrical connector accommodates high current, is not labor intensive to assemble and disassemble, and allows a wide range of motion to accommodate mechanical variations and movement of connected components. The connector comprises several parts with joints therebetween, wherein each joint provides electrical connection between and allows relative motion of the joined parts. The combination of parts and joints maintains electrical connection between two electrical components even if the components are misaligned or move after connection.

Altered Brain Functional Connectivity in Betel Quid-Dependent Chewers.

PubMed

Huang, Xiaojun; Pu, Weidan; Liu, Haihong; Li, Xinmin; Greenshaw, Andrew J; Dursun, Serdar M; Xue, Zhimin; Liu, Zhening

2017-01-01

Betel quid (BQ) is a common psychoactive substance worldwide with particularly high usage in many Asian countries. This study aimed to explore the effect of BQ use on functional connectivity by comparing global functional brain networks and their subset between BQ chewers and healthy controls (HCs). Resting-state functional magnetic resonance imaging (fMRI) was obtained from 24 betel quid-dependent (BQD) male chewers and 27 healthy male individuals on a 3.0T scanner. We used independent component analysis (ICA) to determine components that represent the brain's functional networks and their spatial aspects of functional connectivity. Two sample t -tests were used to identify the functional connectivity differences in each network between these two groups. Seventeen networks were identified by ICA. Nine of them showed connectivity differences between BQD and HCs (two sample t -tests, p  < 0.001 uncorrected). We found increased functional connectivity in the orbitofrontal, bilateral frontoparietal, frontotemporal, occipital/parietal, frontotemporal/cerebellum, and temporal/limbic networks, and decreased connectivity in the parietal and medial frontal/anterior cingulate networks in the BQD compared to the HCs. The betel quid dependence scale scores were positively related to the increased functional connectivity in the orbitofrontal ( r  = 0.39, p  = 0.03) while negatively related to the decreased functional connectivity in medial frontal/anterior cingulate networks ( r  = -0.35, p  = 0.02). Our findings provide further evidence that BQ chewing may lead to brain functional connectivity changes, which may play a key role in the psychological and physiological effects of BQ.

Flexible connector

DOEpatents

Savage, M.E.; Simpson, W.W.

1999-07-27

An electrical connector accommodates high current, is not labor intensive to assemble and disassemble, and allows a wide range of motion to accommodate mechanical variations and movement of connected components. The connector comprises several parts with joints therebetween, wherein each joint provides electrical connection between and allows relative motion of the joined parts. The combination of parts and joints maintains electrical connection between two electrical components even if the components are misaligned or move after connection. 6 figs.

Association of familial risk for schizophrenia with thalamic and medial prefrontal functional connectivity during attentional control.

PubMed

Antonucci, Linda A; Taurisano, Paolo; Fazio, Leonardo; Gelao, Barbara; Romano, Raffaella; Quarto, Tiziana; Porcelli, Annamaria; Mancini, Marina; Di Giorgio, Annabella; Caforio, Grazia; Pergola, Giulio; Popolizio, Teresa; Bertolino, Alessandro; Blasi, Giuseppe

2016-05-01

Anomalies in behavioral correlates of attentional processing and related brain activity are crucial correlates of schizophrenia and associated with familial risk for this brain disorder. However, it is not clear how brain functional connectivity during attentional processes is key for schizophrenia and linked with trait vs. state related variables. To address this issue, we investigated patterns of functional connections during attentional control in healthy siblings of patients with schizophrenia, who share with probands genetic features but not variables related to the state of the disorder. 356 controls, 55 patients with schizophrenia on stable treatment with antipsychotics and 40 healthy siblings of patients with this brain disorder underwent the Variable Attentional Control (VAC) task during fMRI. Independent Component Analysis (ICA) is allowed to identify independent components (IC) of BOLD signal recorded during task performance. Results indicated reduced connectivity strength in patients with schizophrenia as well as in their healthy siblings in left thalamus within an attentional control component and greater connectivity in right medial prefrontal cortex (PFC) within the so-called Default Mode Network (DMN) compared to healthy individuals. These results suggest a relationship between familial risk for schizophrenia and brain functional networks during attentional control, such that this biological phenotype may be considered a useful intermediate phenotype in order to link genes effects to aspects of the pathophysiology of this brain disorder. Copyright © 2016 Elsevier B.V. All rights reserved.

Hybrid ICA-Bayesian network approach reveals distinct effective connectivity differences in schizophrenia.

PubMed

Kim, D; Burge, J; Lane, T; Pearlson, G D; Kiehl, K A; Calhoun, V D

2008-10-01

We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge, J., Lane, T., Link, H., Qiu, S., Clark, V.P., 2007. Discrete dynamic Bayesian network analysis of fMRI data. Hum Brain Mapp.) to determine differences in brain regions between patients with schizophrenia and healthy controls on a measure of effective connectivity, termed the approximate conditional likelihood score (ACL) (Burge, J., Lane, T., 2005. Learning Class-Discriminative Dynamic Bayesian Networks. Proceedings of the International Conference on Machine Learning, Bonn, Germany, pp. 97-104.). The ACL score represents a class-discriminative measure of effective connectivity by measuring the relative likelihood of the correlation between brain regions in one group versus another. The algorithm is capable of finding non-linear relationships between brain regions because it uses discrete rather than continuous values and attempts to model temporal relationships with a first-order Markov and stationary assumption constraint (Papoulis, A., 1991. Probability, random variables, and stochastic processes. McGraw-Hill, New York.). Since Bayesian networks are overly sensitive to noisy data, we introduced an independent component analysis (ICA) filtering approach that attempted to reduce the noise found in fMRI data by unmixing the raw datasets into a set of independent spatial component maps. Components that represented noise were removed and the remaining components reconstructed into the dimensions of the original fMRI datasets. We applied the dDBN algorithm to a group of 35 patients with schizophrenia and 35 matched healthy controls using an ICA filtered and unfiltered approach. We determined that filtering the data significantly improved the magnitude of the ACL score. Patients showed the greatest ACL scores in several regions, most markedly the cerebellar vermis and hemispheres. Our findings suggest that schizophrenia patients exhibit weaker connectivity than healthy controls in multiple regions, including bilateral temporal, frontal, and cerebellar regions during an auditory paradigm.

Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients

PubMed Central

Vanhaudenhuyse, Audrey; Noirhomme, Quentin; Tshibanda, Luaba J.-F.; Bruno, Marie-Aurelie; Boveroux, Pierre; Schnakers, Caroline; Soddu, Andrea; Perlbarg, Vincent; Ledoux, Didier; Brichant, Jean-François; Moonen, Gustave; Maquet, Pierre; Greicius, Michael D.

2010-01-01

The ‘default network’ is defined as a set of areas, encompassing posterior-cingulate/precuneus, anterior cingulate/mesiofrontal cortex and temporo-parietal junctions, that show more activity at rest than during attention-demanding tasks. Recent studies have shown that it is possible to reliably identify this network in the absence of any task, by resting state functional magnetic resonance imaging connectivity analyses in healthy volunteers. However, the functional significance of these spontaneous brain activity fluctuations remains unclear. The aim of this study was to test if the integrity of this resting-state connectivity pattern in the default network would differ in different pathological alterations of consciousness. Fourteen non-communicative brain-damaged patients and 14 healthy controls participated in the study. Connectivity was investigated using probabilistic independent component analysis, and an automated template-matching component selection approach. Connectivity in all default network areas was found to be negatively correlated with the degree of clinical consciousness impairment, ranging from healthy controls and locked-in syndrome to minimally conscious, vegetative then coma patients. Furthermore, precuneus connectivity was found to be significantly stronger in minimally conscious patients as compared with unconscious patients. Locked-in syndrome patient’s default network connectivity was not significantly different from controls. Our results show that default network connectivity is decreased in severely brain-damaged patients, in proportion to their degree of consciousness impairment. Future prospective studies in a larger patient population are needed in order to evaluate the prognostic value of the presented methodology. PMID:20034928

Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia

PubMed Central

Yu, Qingbao; Erhardt, Erik B.; Sui, Jing; Du, Yuhui; He, Hao; Hjelm, Devon; Cetin, Mustafa S.; Rachakonda, Srinivas; Miller, Robyn L.; Pearlson, Godfrey; Calhoun, Vince D.

2014-01-01

Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia which may underscore the abnormal brain performance in this mental illness. PMID:25514514

A Procedure for Modeling Structural Component/Attachment Failure Using Transient Finite Element Analysis

NASA Technical Reports Server (NTRS)

Lovejoy, Andrew E.; Jegley, Dawn C. (Technical Monitor)

2007-01-01

Structures often comprise smaller substructures that are connected to each other or attached to the ground by a set of finite connections. Under static loading one or more of these connections may exceed allowable limits and be deemed to fail. Of particular interest is the structural response when a connection is severed (failed) while the structure is under static load. A transient failure analysis procedure was developed by which it is possible to examine the dynamic effects that result from introducing a discrete failure while a structure is under static load. The failure is introduced by replacing a connection load history by a time-dependent load set that removes the connection load at the time of failure. The subsequent transient response is examined to determine the importance of the dynamic effects by comparing the structural response with the appropriate allowables. Additionally, this procedure utilizes a standard finite element transient analysis that is readily available in most commercial software, permitting the study of dynamic failures without the need to purchase software specifically for this purpose. The procedure is developed and explained, demonstrated on a simple cantilever box example, and finally demonstrated on a real-world example, the American Airlines Flight 587 (AA587) vertical tail plane (VTP).

Efficient computational nonlinear dynamic analysis using modal modification response technique

NASA Astrophysics Data System (ADS)

Marinone, Timothy; Avitabile, Peter; Foley, Jason; Wolfson, Janet

2012-08-01

Generally, structural systems contain nonlinear characteristics in many cases. These nonlinear systems require significant computational resources for solution of the equations of motion. Much of the model, however, is linear where the nonlinearity results from discrete local elements connecting different components together. Using a component mode synthesis approach, a nonlinear model can be developed by interconnecting these linear components with highly nonlinear connection elements. The approach presented in this paper, the Modal Modification Response Technique (MMRT), is a very efficient technique that has been created to address this specific class of nonlinear problem. By utilizing a Structural Dynamics Modification (SDM) approach in conjunction with mode superposition, a significantly smaller set of matrices are required for use in the direct integration of the equations of motion. The approach will be compared to traditional analytical approaches to make evident the usefulness of the technique for a variety of test cases.

Semi-blind Bayesian inference of CMB map and power spectrum

NASA Astrophysics Data System (ADS)

Vansyngel, Flavien; Wandelt, Benjamin D.; Cardoso, Jean-François; Benabed, Karim

2016-04-01

We present a new blind formulation of the cosmic microwave background (CMB) inference problem. The approach relies on a phenomenological model of the multifrequency microwave sky without the need for physical models of the individual components. For all-sky and high resolution data, it unifies parts of the analysis that had previously been treated separately such as component separation and power spectrum inference. We describe an efficient sampling scheme that fully explores the component separation uncertainties on the inferred CMB products such as maps and/or power spectra. External information about individual components can be incorporated as a prior giving a flexible way to progressively and continuously introduce physical component separation from a maximally blind approach. We connect our Bayesian formalism to existing approaches such as Commander, spectral mismatch independent component analysis (SMICA), and internal linear combination (ILC), and discuss possible future extensions.

Altered cortical and subcortical connectivity due to infrasound administered near the hearing threshold - Evidence from fMRI.

PubMed

Weichenberger, Markus; Bauer, Martin; Kühler, Robert; Hensel, Johannes; Forlim, Caroline Garcia; Ihlenfeld, Albrecht; Ittermann, Bernd; Gallinat, Jürgen; Koch, Christian; Kühn, Simone

2017-01-01

In the present study, the brain's response towards near- and supra-threshold infrasound (IS) stimulation (sound frequency < 20 Hz) was investigated under resting-state fMRI conditions. The study involved two consecutive sessions. In the first session, 14 healthy participants underwent a hearing threshold-as well as a categorical loudness scaling measurement in which the individual loudness perception for IS was assessed across different sound pressure levels (SPL). In the second session, these participants underwent three resting-state acquisitions, one without auditory stimulation (no-tone), one with a monaurally presented 12-Hz IS tone (near-threshold) and one with a similar tone above the individual hearing threshold corresponding to a 'medium loud' hearing sensation (supra-threshold). Data analysis mainly focused on local connectivity measures by means of regional homogeneity (ReHo), but also involved independent component analysis (ICA) to investigate inter-regional connectivity. ReHo analysis revealed significantly higher local connectivity in right superior temporal gyrus (STG) adjacent to primary auditory cortex, in anterior cingulate cortex (ACC) and, when allowing smaller cluster sizes, also in the right amygdala (rAmyg) during the near-threshold, compared to both the supra-threshold and the no-tone condition. Additional independent component analysis (ICA) revealed large-scale changes of functional connectivity, reflected in a stronger activation of the right amygdala (rAmyg) in the opposite contrast (no-tone > near-threshold) as well as the right superior frontal gyrus (rSFG) during the near-threshold condition. In summary, this study is the first to demonstrate that infrasound near the hearing threshold may induce changes of neural activity across several brain regions, some of which are known to be involved in auditory processing, while others are regarded as keyplayers in emotional and autonomic control. These findings thus allow us to speculate on how continuous exposure to (sub-)liminal IS could exert a pathogenic influence on the organism, yet further (especially longitudinal) studies are required in order to substantialize these findings.

Brain resting-state networks in adolescents with high-functioning autism: Analysis of spatial connectivity and temporal neurodynamics.

PubMed

Bernas, Antoine; Barendse, Evelien M; Aldenkamp, Albert P; Backes, Walter H; Hofman, Paul A M; Hendriks, Marc P H; Kessels, Roy P C; Willems, Frans M J; de With, Peter H N; Zinger, Svitlana; Jansen, Jacobus F A

2018-02-01

Autism spectrum disorder (ASD) is mainly characterized by functional and communication impairments as well as restrictive and repetitive behavior. The leading hypothesis for the neural basis of autism postulates globally abnormal brain connectivity, which can be assessed using functional magnetic resonance imaging (fMRI). Even in the absence of a task, the brain exhibits a high degree of functional connectivity, known as intrinsic, or resting-state, connectivity. Global default connectivity in individuals with autism versus controls is not well characterized, especially for a high-functioning young population. The aim of this study is to test whether high-functioning adolescents with ASD (HFA) have an abnormal resting-state functional connectivity. We performed spatial and temporal analyses on resting-state networks (RSNs) in 13 HFA adolescents and 13 IQ- and age-matched controls. For the spatial analysis, we used probabilistic independent component analysis (ICA) and a permutation statistical method to reveal the RSN differences between the groups. For the temporal analysis, we applied Granger causality to find differences in temporal neurodynamics. Controls and HFA display very similar patterns and strengths of resting-state connectivity. We do not find any significant differences between HFA adolescents and controls in the spatial resting-state connectivity. However, in the temporal dynamics of this connectivity, we did find differences in the causal effect properties of RSNs originating in temporal and prefrontal cortices. The results show a difference between HFA and controls in the temporal neurodynamics from the ventral attention network to the salience-executive network: a pathway involving cognitive, executive, and emotion-related cortices. We hypothesized that this weaker dynamic pathway is due to a subtle trigger challenging the cognitive state prior to the resting state.

Dynamics of Rotating Multi-component Turbomachinery Systems

NASA Technical Reports Server (NTRS)

Lawrence, Charles

1993-01-01

The ultimate objective of turbomachinery vibration analysis is to predict both the overall, as well as component dynamic response. To accomplish this objective requires complete engine structural models, including multistages of bladed disk assemblies, flexible rotor shafts and bearings, and engine support structures and casings. In the present approach each component is analyzed as a separate structure and boundary information is exchanged at the inter-component connections. The advantage of this tactic is that even though readily available detailed component models are utilized, accurate and comprehensive system response information may be obtained. Sample problems, which include a fixed base rotating blade and a blade on a flexible rotor, are presented.

Vehicle Concept Model Abstractions For Integrated Geometric, Inertial ,Rigid Body, Powertrain and FE Analysis

DTIC Science & Technology

2011-06-17

structure through quantitative assessment of stiffness and modal parameter changes resulting from modifications to the beam geometries and positions...power transmission assembly. If the power limit at a wheel exceeds the traction limit, then depending on the type of differential placed on the axle ...components with appropriate model connectivity instead to determine the free modal response of powertrain type components, without abstraction

Clustering analysis of water distribution systems: identifying critical components and community impacts.

PubMed

Diao, K; Farmani, R; Fu, G; Astaraie-Imani, M; Ward, S; Butler, D

2014-01-01

Large water distribution systems (WDSs) are networks with both topological and behavioural complexity. Thereby, it is usually difficult to identify the key features of the properties of the system, and subsequently all the critical components within the system for a given purpose of design or control. One way is, however, to more explicitly visualize the network structure and interactions between components by dividing a WDS into a number of clusters (subsystems). Accordingly, this paper introduces a clustering strategy that decomposes WDSs into clusters with stronger internal connections than external connections. The detected cluster layout is very similar to the community structure of the served urban area. As WDSs may expand along with urban development in a community-by-community manner, the correspondingly formed distribution clusters may reveal some crucial configurations of WDSs. For verification, the method is applied to identify all the critical links during firefighting for the vulnerability analysis of a real-world WDS. Moreover, both the most critical pipes and clusters are addressed, given the consequences of pipe failure. Compared with the enumeration method, the method used in this study identifies the same group of the most critical components, and provides similar criticality prioritizations of them in a more computationally efficient time.

Temporal Sequence of Hemispheric Network Activation during Semantic Processing: A Functional Network Connectivity Analysis

ERIC Educational Resources Information Center

Assaf, Michal; Jagannathan, Kanchana; Calhoun, Vince; Kraut, Michael; Hart, John, Jr.; Pearlson, Godfrey

2009-01-01

To explore the temporal sequence of, and the relationship between, the left and right hemispheres (LH and RH) during semantic memory (SM) processing we identified the neural networks involved in the performance of functional MRI semantic object retrieval task (SORT) using group independent component analysis (ICA) in 47 healthy individuals. SORT…

Two efficient label-equivalence-based connected-component labeling algorithms for 3-D binary images.

PubMed

He, Lifeng; Chao, Yuyan; Suzuki, Kenji

2011-08-01

Whenever one wants to distinguish, recognize, and/or measure objects (connected components) in binary images, labeling is required. This paper presents two efficient label-equivalence-based connected-component labeling algorithms for 3-D binary images. One is voxel based and the other is run based. For the voxel-based one, we present an efficient method of deciding the order for checking voxels in the mask. For the run-based one, instead of assigning each foreground voxel, we assign each run a provisional label. Moreover, we use run data to label foreground voxels without scanning any background voxel in the second scan. Experimental results have demonstrated that our voxel-based algorithm is efficient for 3-D binary images with complicated connected components, that our run-based one is efficient for those with simple connected components, and that both are much more efficient than conventional 3-D labeling algorithms.

Sample injector for high pressure liquid chromatography

DOEpatents

Paul, Phillip H.; Arnold, Don W.; Neyer, David W.

2001-01-01

Apparatus and method for driving a sample, having a well-defined volume, under pressure into a chromatography column. A conventional high pressure sampling valve is replaced by a sample injector composed of a pair of injector components connected in series to a common junction. The injector components are containers of porous dielectric material constructed so as to provide for electroosmotic flow of a sample into the junction. At an appropriate time, a pressure pulse from a high pressure source, that can be an electrokinetic pump, connected to the common junction, drives a portion of the sample, whose size is determined by the dead volume of the common junction, into the chromatographic column for subsequent separation and analysis. The apparatus can be fabricated on a substrate for microanalytical applications.

Modality-Spanning Deficits in Attention-Deficit/Hyperactivity Disorder in Functional Networks, Gray Matter, and White Matter

PubMed Central

Kessler, Daniel; Angstadt, Michael; Welsh, Robert C.

2014-01-01

Previous neuroimaging investigations in attention-deficit/hyperactivity disorder (ADHD) have separately identified distributed structural and functional deficits, but interconnections between these deficits have not been explored. To unite these modalities in a common model, we used joint independent component analysis, a multivariate, multimodal method that identifies cohesive components that span modalities. Based on recent network models of ADHD, we hypothesized that altered relationships between large-scale networks, in particular, default mode network (DMN) and task-positive networks (TPNs), would co-occur with structural abnormalities in cognitive regulation regions. For 756 human participants in the ADHD-200 sample, we produced gray and white matter volume maps with voxel-based morphometry, as well as whole-brain functional connectomes. Joint independent component analysis was performed, and the resulting transmodal components were tested for differential expression in ADHD versus healthy controls. Four components showed greater expression in ADHD. Consistent with our a priori hypothesis, we observed reduced DMN-TPN segregation co-occurring with structural abnormalities in dorsolateral prefrontal cortex and anterior cingulate cortex, two important cognitive control regions. We also observed altered intranetwork connectivity in DMN, dorsal attention network, and visual network, with co-occurring distributed structural deficits. There was strong evidence of spatial correspondence across modalities: For all four components, the impact of the respective component on gray matter at a region strongly predicted the impact on functional connectivity at that region. Overall, our results demonstrate that ADHD involves multiple, cohesive modality spanning deficits, each one of which exhibits strong spatial overlap in the pattern of structural and functional alterations. PMID:25505309

System diagnostics using qualitative analysis and component functional classification

DOEpatents

Reifman, J.; Wei, T.Y.C.

1993-11-23

A method for detecting and identifying faulty component candidates during off-normal operations of nuclear power plants involves the qualitative analysis of macroscopic imbalances in the conservation equations of mass, energy and momentum in thermal-hydraulic control volumes associated with one or more plant components and the functional classification of components. The qualitative analysis of mass and energy is performed through the associated equations of state, while imbalances in momentum are obtained by tracking mass flow rates which are incorporated into a first knowledge base. The plant components are functionally classified, according to their type, as sources or sinks of mass, energy and momentum, depending upon which of the three balance equations is most strongly affected by a faulty component which is incorporated into a second knowledge base. Information describing the connections among the components of the system forms a third knowledge base. The method is particularly adapted for use in a diagnostic expert system to detect and identify faulty component candidates in the presence of component failures and is not limited to use in a nuclear power plant, but may be used with virtually any type of thermal-hydraulic operating system. 5 figures.

System diagnostics using qualitative analysis and component functional classification

DOEpatents

Reifman, Jaques; Wei, Thomas Y. C.

1993-01-01

A method for detecting and identifying faulty component candidates during off-normal operations of nuclear power plants involves the qualitative analysis of macroscopic imbalances in the conservation equations of mass, energy and momentum in thermal-hydraulic control volumes associated with one or more plant components and the functional classification of components. The qualitative analysis of mass and energy is performed through the associated equations of state, while imbalances in momentum are obtained by tracking mass flow rates which are incorporated into a first knowledge base. The plant components are functionally classified, according to their type, as sources or sinks of mass, energy and momentum, depending upon which of the three balance equations is most strongly affected by a faulty component which is incorporated into a second knowledge base. Information describing the connections among the components of the system forms a third knowledge base. The method is particularly adapted for use in a diagnostic expert system to detect and identify faulty component candidates in the presence of component failures and is not limited to use in a nuclear power plant, but may be used with virtually any type of thermal-hydraulic operating system.

Development and Validation of a Portable Platform for Deploying Decision-Support Algorithms in Prehospital Settings

PubMed Central

Reisner, A. T.; Khitrov, M. Y.; Chen, L.; Blood, A.; Wilkins, K.; Doyle, W.; Wilcox, S.; Denison, T.; Reifman, J.

2013-01-01

Summary Background Advanced decision-support capabilities for prehospital trauma care may prove effective at improving patient care. Such functionality would be possible if an analysis platform were connected to a transport vital-signs monitor. In practice, there are technical challenges to implementing such a system. Not only must each individual component be reliable, but, in addition, the connectivity between components must be reliable. Objective We describe the development, validation, and deployment of the Automated Processing of Physiologic Registry for Assessment of Injury Severity (APPRAISE) platform, intended to serve as a test bed to help evaluate the performance of decision-support algorithms in a prehospital environment. Methods We describe the hardware selected and the software implemented, and the procedures used for laboratory and field testing. Results The APPRAISE platform met performance goals in both laboratory testing (using a vital-sign data simulator) and initial field testing. After its field testing, the platform has been in use on Boston MedFlight air ambulances since February of 2010. Conclusion These experiences may prove informative to other technology developers and to healthcare stakeholders seeking to invest in connected electronic systems for prehospital as well as in-hospital use. Our experiences illustrate two sets of important questions: are the individual components reliable (e.g., physical integrity, power, core functionality, and end-user interaction) and is the connectivity between components reliable (e.g., communication protocols and the metadata necessary for data interpretation)? While all potential operational issues cannot be fully anticipated and eliminated during development, thoughtful design and phased testing steps can reduce, if not eliminate, technical surprises. PMID:24155791

Hybrid ICA-Bayesian Network approach reveals distinct effective connectivity differences in schizophrenia

PubMed Central

Kim, D.; Burge, J.; Lane, T.; Pearlson, G. D; Kiehl, K. A; Calhoun, V. D.

2008-01-01

We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge et al., 2007) to determine differences in brain regions between patients with schizophrenia and healthy controls on a measure of effective connectivity, termed the approximate conditional likelihood score (ACL) (Burge and Lane, 2005). The ACL score represents a class-discriminative measure of effective connectivity by measuring the relative likelihood of the correlation between brain regions in one group versus another. The algorithm is capable of finding non-linear relationships between brain regions because it uses discrete rather than continuous values and attempts to model temporal relationships with a first-order Markov and stationary assumption constraint (Papoulis, 1991). Since Bayesian networks are overly sensitive to noisy data, we introduced an independent component analysis (ICA) filtering approach that attempted to reduce the noise found in fMRI data by unmixing the raw datasets into a set of independent spatial component maps. Components that represented noise were removed and the remaining components reconstructed into the dimensions of the original fMRI datasets. We applied the dDBN algorithm to a group of 35 patients with schizophrenia and 35 matched healthy controls using an ICA filtered and unfiltered approach. We determined that filtering the data significantly improved the magnitude of the ACL score. Patients showed the greatest ACL scores in several regions, most markedly the cerebellar vermis and hemispheres. Our findings suggest that schizophrenia patients exhibit weaker connectivity than healthy controls in multiple regions, including bilateral temporal and frontal cortices, plus cerebellum during an auditory paradigm. PMID:18602482

A method for independent component graph analysis of resting-state fMRI.

PubMed

Ribeiro de Paula, Demetrius; Ziegler, Erik; Abeyasinghe, Pubuditha M; Das, Tushar K; Cavaliere, Carlo; Aiello, Marco; Heine, Lizette; di Perri, Carol; Demertzi, Athena; Noirhomme, Quentin; Charland-Verville, Vanessa; Vanhaudenhuyse, Audrey; Stender, Johan; Gomez, Francisco; Tshibanda, Jean-Flory L; Laureys, Steven; Owen, Adrian M; Soddu, Andrea

2017-03-01

Independent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data. Here, we detail a graph building technique that allows these ICNs to be analyzed with graph theory. First, ICA was performed at the single-subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple-template matching procedure and a subsequent component classification based on the network "neuronal" properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between-node functional connectivity was established by building edge weights for each networks. Group-level graph analysis was finally performed for each network and compared to the classical network. Network graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small-worldness. This novel approach permits us to take advantage of the well-recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well-established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.

Intrinsic Connectivity Provides the Baseline Framework for Variability in Motor Performance: A Multivariate Fusion Analysis of Low- and High-Frequency Resting-State Oscillations and Antisaccade Performance.

PubMed

Jamadar, Sharna D; Egan, Gary F; Calhoun, Vince D; Johnson, Beth; Fielding, Joanne

2016-07-01

Intrinsic brain activity provides the functional framework for the brain's full repertoire of behavioral responses; that is, a common mechanism underlies intrinsic and extrinsic neural activity, with extrinsic activity building upon the underlying baseline intrinsic activity. The generation of a motor movement in response to sensory stimulation is one of the most fundamental functions of the central nervous system. Since saccadic eye movements are among our most stereotyped motor responses, we hypothesized that individual variability in the ability to inhibit a prepotent saccade and make a voluntary antisaccade would be related to individual variability in intrinsic connectivity. Twenty-three individuals completed the antisaccade task and resting-state functional magnetic resonance imaging (fMRI). A multivariate analysis of covariance identified relationships between fMRI oscillations (0.01-0.2 Hz) of resting-state networks determined using high-dimensional independent component analysis and antisaccade performance (latency, error rate). Significant multivariate relationships between antisaccade latency and directional error rate were obtained in independent components across the entire brain. Some of the relationships were obtained in components that overlapped substantially with the task; however, many were obtained in components that showed little overlap with the task. The current results demonstrate that even in the absence of a task, spectral power in regions showing little overlap with task activity predicts an individual's performance on a saccade task.

Quick-Turn Finite Element Analysis for Plug-and-Play Satellite Structures

DTIC Science & Technology

2007-03-01

produced from 0.375 inch round stock and turned on a machine lathe to achieve the shoulder feature and drilled to make it hollow. Figure 3.1...component, a linear taper was machined from the connection shoulder to the solar panel connecting fork. The part was then turned using the machine lathe ...utilizing a modern five-axis Computer Numerical Code ( CNC ) machine mill, the process time could be reduced by as much as seventy-five percent and the

Effect of electrocardiogram interference on cortico-cortical connectivity analysis and a possible solution.

PubMed

Govindan, R B; Kota, Srinivas; Al-Shargabi, Tareq; Massaro, An N; Chang, Taeun; du Plessis, Adre

2016-09-01

Electroencephalogram (EEG) signals are often contaminated by the electrocardiogram (ECG) interference, which affects quantitative characterization of EEG. We propose null-coherence, a frequency-based approach, to attenuate the ECG interference in EEG using simultaneously recorded ECG as a reference signal. After validating the proposed approach using numerically simulated data, we apply this approach to EEG recorded from six newborns receiving therapeutic hypothermia for neonatal encephalopathy. We compare our approach with an independent component analysis (ICA), a previously proposed approach to attenuate ECG artifacts in the EEG signal. The power spectrum and the cortico-cortical connectivity of the ECG attenuated EEG was compared against the power spectrum and the cortico-cortical connectivity of the raw EEG. The null-coherence approach attenuated the ECG contamination without leaving any residual of the ECG in the EEG. We show that the null-coherence approach performs better than ICA in attenuating the ECG contamination without enhancing cortico-cortical connectivity. Our analysis suggests that using ICA to remove ECG contamination from the EEG suffers from redistribution problems, whereas the null-coherence approach does not. We show that both the null-coherence and ICA approaches attenuate the ECG contamination. However, the EEG obtained after ICA cleaning displayed higher cortico-cortical connectivity compared with that obtained using the null-coherence approach. This suggests that null-coherence is superior to ICA in attenuating the ECG interference in EEG for cortico-cortical connectivity analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

Piston and connecting rod assembly

NASA Technical Reports Server (NTRS)

Brogdon, James William (Inventor); Gill, David Keith (Inventor); Chatten, John K. (Inventor)

2001-01-01

A piston and connecting rod assembly includes a piston crown, a piston skirt, a connecting rod, and a bearing insert. The piston skirt is a component separate from the piston crown and is connected to the piston crown to provide a piston body. The bearing insert is a component separate from the piston crown and the piston skirt and is fixedly disposed within the piston body. A bearing surface of a connecting rod contacts the bearing insert to thereby movably associate the connecting rod and the piston body.

Altered effective connectivity of default model brain network underlying amnestic MCI

NASA Astrophysics Data System (ADS)

Yan, Hao; Wang, Yonghui; Tian, Jie

2012-02-01

Mild cognitive impairment (MCI) is the transitional, heterogeneous continuum from healthy elderly to Alzheimer's disease (AD). Previous studies have shown that brain functional activity in the default mode network (DMN) is impaired in MCI patients. However, the altered effective connectivity of the DMN in MCI patients remains largely unknown. The present study combined an independent component analysis (ICA) approach with Granger causality analysis (mGCA) to investigate the effective connectivity within the DMN in 12 amnestic MCI patients and 12 age-matched healthy elderly. Compared to the healthy control, the MCI exhibited decreased functional activity in the posterior DMN regions, as well as a trend towards activity increases in anterior DMN regions. Results from mGCA further supported this conclusion that the causal influence projecting to the precuneus/PCC became much weaker in MCI, while stronger interregional interactions emerged within the frontal-parietal cortices. These findings suggested that abnormal effective connectivity within the DMN may elucidate the dysfunctional and compensatory processes in MCI brain networks.

The connectivity structure, giant strong component and centrality of metabolic networks.

PubMed

Ma, Hong-Wu; Zeng, An-Ping

2003-07-22

Structural and functional analysis of genome-based large-scale metabolic networks is important for understanding the design principles and regulation of the metabolism at a system level. The metabolic network is conventionally considered to be highly integrated and very complex. A rational reduction of the metabolic network to its core structure and a deeper understanding of its functional modules are important. In this work, we show that the metabolites in a metabolic network are far from fully connected. A connectivity structure consisting of four major subsets of metabolites and reactions, i.e. a fully connected sub-network, a substrate subset, a product subset and an isolated subset is found to exist in metabolic networks of 65 fully sequenced organisms. The largest fully connected part of a metabolic network, called 'the giant strong component (GSC)', represents the most complicated part and the core of the network and has the feature of scale-free networks. The average path length of the whole network is primarily determined by that of the GSC. For most of the organisms, GSC normally contains less than one-third of the nodes of the network. This connectivity structure is very similar to the 'bow-tie' structure of World Wide Web. Our results indicate that the bow-tie structure may be common for large-scale directed networks. More importantly, the uncovered structure feature makes a structural and functional analysis of large-scale metabolic network more amenable. As shown in this work, comparing the closeness centrality of the nodes in the GSC can identify the most central metabolites of a metabolic network. To quantitatively characterize the overall connection structure of the GSC we introduced the term 'overall closeness centralization index (OCCI)'. OCCI correlates well with the average path length of the GSC and is a useful parameter for a system-level comparison of metabolic networks of different organisms. http://genome.gbf.de/bioinformatics/

Stability of a giant connected component in a complex network

NASA Astrophysics Data System (ADS)

Kitsak, Maksim; Ganin, Alexander A.; Eisenberg, Daniel A.; Krapivsky, Pavel L.; Krioukov, Dmitri; Alderson, David L.; Linkov, Igor

2018-01-01

We analyze the stability of the network's giant connected component under impact of adverse events, which we model through the link percolation. Specifically, we quantify the extent to which the largest connected component of a network consists of the same nodes, regardless of the specific set of deactivated links. Our results are intuitive in the case of single-layered systems: the presence of large degree nodes in a single-layered network ensures both its robustness and stability. In contrast, we find that interdependent networks that are robust to adverse events have unstable connected components. Our results bring novel insights to the design of resilient network topologies and the reinforcement of existing networked systems.

ATLAS tile calorimeter cesium calibration control and analysis software

NASA Astrophysics Data System (ADS)

Solovyanov, O.; Solodkov, A.; Starchenko, E.; Karyukhin, A.; Isaev, A.; Shalanda, N.

2008-07-01

An online control system to calibrate and monitor ATLAS Barrel hadronic calorimeter (TileCal) with a movable radioactive source, driven by liquid flow, is described. To read out and control the system an online software has been developed, using ATLAS TDAQ components like DVS (Diagnostic and Verification System) to verify the hardware before running, IS (Information Server) for data and status exchange between networked computers, and other components like DDC (DCS to DAQ Connection), to connect to PVSS-based slow control systems of Tile Calorimeter, high voltage and low voltage. A system of scripting facilities, based on Python language, is used to handle all the calibration and monitoring processes from hardware perspective to final data storage, including various abnormal situations. A QT based graphical user interface to display the status of the calibration system during the cesium source scan is described. The software for analysis of the detector response, using online data, is discussed. Performance of the system and first experience from the ATLAS pit are presented.

The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery.

PubMed

Calhoun, Vince D; Miller, Robyn; Pearlson, Godfrey; Adalı, Tulay

2014-10-22

Recent years have witnessed a rapid growth of interest in moving functional magnetic resonance imaging (fMRI) beyond simple scan-length averages and into approaches that capture time-varying properties of connectivity. In this Perspective we use the term "chronnectome" to describe metrics that allow a dynamic view of coupling. In the chronnectome, coupling refers to possibly time-varying levels of correlated or mutually informed activity between brain regions whose spatial properties may also be temporally evolving. We primarily focus on multivariate approaches developed in our group and review a number of approaches with an emphasis on matrix decompositions such as principle component analysis and independent component analysis. We also discuss the potential these approaches offer to improve characterization and understanding of brain function. There are a number of methodological directions that need to be developed further, but chronnectome approaches already show great promise for the study of both the healthy and the diseased brain.

Risperidone Effects on Brain Dynamic Connectivity-A Prospective Resting-State fMRI Study in Schizophrenia.

PubMed

Lottman, Kristin K; Kraguljac, Nina V; White, David M; Morgan, Charity J; Calhoun, Vince D; Butt, Allison; Lahti, Adrienne C

2017-01-01

Resting-state functional connectivity studies in schizophrenia evaluating average connectivity over the entire experiment have reported aberrant network integration, but findings are variable. Examining time-varying (dynamic) functional connectivity may help explain some inconsistencies. We assessed dynamic network connectivity using resting-state functional MRI in patients with schizophrenia, while unmedicated ( n  = 34), after 1 week ( n  = 29) and 6 weeks of treatment with risperidone ( n  = 24), as well as matched controls at baseline ( n  = 35) and after 6 weeks ( n  = 19). After identifying 41 independent components (ICs) comprising resting-state networks, sliding window analysis was performed on IC timecourses using an optimal window size validated with linear support vector machines. Windowed correlation matrices were then clustered into three discrete connectivity states (a relatively sparsely connected state, a relatively abundantly connected state, and an intermediately connected state). In unmedicated patients, static connectivity was increased between five pairs of ICs and decreased between two pairs of ICs when compared to controls, dynamic connectivity showed increased connectivity between the thalamus and somatomotor network in one of the three states. State statistics indicated that, in comparison to controls, unmedicated patients had shorter mean dwell times and fraction of time spent in the sparsely connected state, and longer dwell times and fraction of time spent in the intermediately connected state. Risperidone appeared to normalize mean dwell times after 6 weeks, but not fraction of time. Results suggest that static connectivity abnormalities in schizophrenia may partly be related to altered brain network temporal dynamics rather than consistent dysconnectivity within and between functional networks and demonstrate the importance of implementing complementary data analysis techniques.

Characterization of structural connections for multicomponent systems

NASA Technical Reports Server (NTRS)

Lawrence, Charles; Huckelbridge, Arthur A.

1988-01-01

This study explores combining Component Mode Synthesis methods for coupling structural components with Parameter Identification procedures for improving the analytical modeling of the connections. Improvements in the connection stiffness and damping properties are computed in terms of physical parameters so that the physical characteristics of the connections can be better understood, in addition to providing improved input for the system model.

Using graph theory to analyze biological networks

PubMed Central

2011-01-01

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system. PMID:21527005

Hydrodynamic modeling of hydrologic surface connectivity within a coastal river-floodplain system

NASA Astrophysics Data System (ADS)

Castillo, C. R.; Guneralp, I.

2017-12-01

Hydrologic surface connectivity (HSC) within river-floodplain environments is a useful indicator of the overall health of riparian habitats because it allows connections amongst components/landforms of the riverine landscape system to be quantified. Overbank flows have traditionally been the focus for analyses concerned with river-floodplain connectivity, but recent works have identified the large significance from sub-bankfull streamflows. Through the use of morphometric analysis and a digital elevation model that is relative to the river water surface, we previously determined that >50% of the floodplain for Mission River on the Coastal Bend of Texas becomes connected to the river at streamflows well-below bankfull conditions. Guided by streamflow records, field-based inundation data, and morphometric analysis; we develop a two-dimensional hydrodynamic model for lower portions of Mission River Floodplain system. This model not only allows us to analyze connections induced by surface water inundation, but also other aspects of the hydrologic connectivity concept such as exchanges of sediment and energy between the river and its floodplain. We also aggregate hydrodynamic model outputs to an object/landform level in order to analyze HSC and associated attributes using measures from graph/network theory. Combining physically-based hydrodynamic models with object-based and graph theoretical analyses allow river-floodplain connectivity to be quantified in a consistent manner with measures/indicators commonly used in landscape analysis. Analyzes similar to ours build towards the establishment of a formal framework for analyzing river-floodplain interaction that will ultimately serve to inform the management of riverine/floodplain environments.

Ictal connectivity in childhood absence epilepsy: Associations with outcome.

PubMed

Tenney, Jeffrey R; Kadis, Darren S; Agler, William; Rozhkov, Leonid; Altaye, Mekibib; Xiang, Jing; Vannest, Jennifer; Glauser, Tracy A

2018-05-01

The understanding of childhood absence epilepsy (CAE) has been revolutionized over the past decade, but the biological mechanisms responsible for variable treatment outcomes are unknown. Our purpose in this prospective observational study was to determine how pretreatment ictal network pathways, defined using a combined electroencephalography (EEG)-functional magnetic resonance imaging (EEG-fMRI) and magnetoencephalography (MEG) effective connectivity analysis, were related to treatment response. Sixteen children with newly diagnosed and drug-naive CAE had 31 typical absence seizures during EEG-fMRI and 74 during MEG. The spatial extent of the pretreatment ictal network was defined using fMRI hemodynamic response with an event-related independent component analysis (eICA). This spatially defined pretreatment ictal network supplied prior information for MEG-effective connectivity analysis calculated using phase slope index (PSI). Treatment outcome was assessed 2 years following diagnosis and dichotomized to ethosuximide (ETX)-treatment responders (N = 11) or nonresponders (N = 5). Effective connectivity of the pretreatment ictal network was compared to the treatment response. Patterns of pretreatment connectivity demonstrated strongest connections in the thalamus and posterior brain regions (parietal, posterior cingulate, angular gyrus, precuneus, and occipital) at delta frequencies and the frontal cortices at gamma frequencies (P < .05). ETX treatment nonresponders had pretreatment connectivity, which was decreased in the precuneus region and increased in the frontal cortex compared to ETX responders (P < .05). Pretreatment ictal connectivity differences in children with CAE were associated with response to antiepileptic treatment. This is a possible mechanism for the variable treatment response seen in patients sharing the same epilepsy syndrome. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

Role of Graph Architecture in Controlling Dynamical Networks with Applications to Neural Systems.

PubMed

Kim, Jason Z; Soffer, Jonathan M; Kahn, Ari E; Vettel, Jean M; Pasqualetti, Fabio; Bassett, Danielle S

2018-01-01

Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviors such as synchronization. While descriptions of these behaviors are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behavior. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain's control performance by removing single edges in the network. Generally, our results ground the expectation of a control system's behavior in its network architecture, and directly inspire new directions in network analysis and design via distributed control.

Role of graph architecture in controlling dynamical networks with applications to neural systems

NASA Astrophysics Data System (ADS)

Kim, Jason Z.; Soffer, Jonathan M.; Kahn, Ari E.; Vettel, Jean M.; Pasqualetti, Fabio; Bassett, Danielle S.

2018-01-01

Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviours such as synchronization. Although descriptions of these behaviours are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behaviour. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain's control performance by removing single edges in the network. Generally, our results ground the expectation of a control system's behaviour in its network architecture, and directly inspire new directions in network analysis and design via distributed control.

Effects of non-neuronal components for functional connectivity analysis from resting-state functional MRI toward automated diagnosis of schizophrenia

NASA Astrophysics Data System (ADS)

Kim, Junghoe; Lee, Jong-Hwan

2014-03-01

A functional connectivity (FC) analysis from resting-state functional MRI (rsfMRI) is gaining its popularity toward the clinical application such as diagnosis of neuropsychiatric disease. To delineate the brain networks from rsfMRI data, non-neuronal components including head motions and physiological artifacts mainly observed in cerebrospinal fluid (CSF), white matter (WM) along with a global brain signal have been regarded as nuisance variables in calculating the FC level. However, it is still unclear how the non-neuronal components can affect the performance toward diagnosis of neuropsychiatric disease. In this study, a systematic comparison of classification performance of schizophrenia patients was provided employing the partial correlation coefficients (CCs) as feature elements. Pair-wise partial CCs were calculated between brain regions, in which six combinatorial sets of nuisance variables were considered. The partial CCs were used as candidate feature elements followed by feature selection based on the statistical significance test between two groups in the training set. Once a linear support vector machine was trained using the selected features from the training set, the classification performance was evaluated using the features from the test set (i.e. leaveone- out cross validation scheme). From the results, the error rate using all non-neuronal components as nuisance variables (12.4%) was significantly lower than those using remaining combination of non-neuronal components as nuisance variables (13.8 ~ 20.0%). In conclusion, the non-neuronal components substantially degraded the automated diagnosis performance, which supports our hypothesis that the non-neuronal components are crucial in controlling the automated diagnosis performance of the neuropsychiatric disease using an fMRI modality.

Disrupted functional brain connectivity in partial epilepsy: a resting-state fMRI study.

PubMed

Luo, Cheng; Qiu, Chuan; Guo, Zhiwei; Fang, Jiajia; Li, Qifu; Lei, Xu; Xia, Yang; Lai, Yongxiu; Gong, Qiyong; Zhou, Dong; Yao, Dezhong

2011-01-01

Examining the spontaneous activity to understand the neural mechanism of brain disorder is a focus in recent resting-state fMRI. In the current study, to investigate the alteration of brain functional connectivity in partial epilepsy in a systematical way, two levels of analyses (functional connectivity analysis within resting state networks (RSNs) and functional network connectivity (FNC) analysis) were carried out on resting-state fMRI data acquired from the 30 participants including 14 healthy controls(HC) and 16 partial epilepsy patients. According to the etiology, all patients are subdivided into temporal lobe epilepsy group (TLE, included 7 patients) and mixed partial epilepsy group (MPE, 9 patients). Using group independent component analysis, eight RSNs were identified, and selected to evaluate functional connectivity and FNC between groups. Compared with the controls, decreased functional connectivity within all RSNs was found in both TLE and MPE. However, dissociating patterns were observed within the 8 RSNs between two patient groups, i.e, compared with TLE, we found decreased functional connectivity in 5 RSNs increased functional connectivity in 1 RSN, and no difference in the other 2 RSNs in MPE. Furthermore, the hierarchical disconnections of FNC was found in two patient groups, in which the intra-system connections were preserved for all three subsystems while the lost connections were confined to intersystem connections in patients with partial epilepsy. These findings may suggest that decreased resting state functional connectivity and disconnection of FNC are two remarkable characteristics of partial epilepsy. The selective impairment of FNC implicated that it is unsuitable to understand the partial epilepsy only from global or local perspective. We presumed that studying epilepsy in the multi-perspective based on RSNs may be a valuable means to assess the functional changes corresponding to specific RSN and may contribute to the understanding of the neuro-pathophysiological mechanism of epilepsy.

Voxel-wise motion artifacts in population-level whole-brain connectivity analysis of resting-state FMRI.

PubMed

Spisák, Tamás; Jakab, András; Kis, Sándor A; Opposits, Gábor; Aranyi, Csaba; Berényi, Ervin; Emri, Miklós

2014-01-01

Functional Magnetic Resonance Imaging (fMRI) based brain connectivity analysis maps the functional networks of the brain by estimating the degree of synchronous neuronal activity between brain regions. Recent studies have demonstrated that "resting-state" fMRI-based brain connectivity conclusions may be erroneous when motion artifacts have a differential effect on fMRI BOLD signals for between group comparisons. A potential explanation could be that in-scanner displacement, due to rotational components, is not spatially constant in the whole brain. However, this localized nature of motion artifacts is poorly understood and is rarely considered in brain connectivity studies. In this study, we initially demonstrate the local correspondence between head displacement and the changes in the resting-state fMRI BOLD signal. Than, we investigate how connectivity strength is affected by the population-level variation in the spatial pattern of regional displacement. We introduce Regional Displacement Interaction (RDI), a new covariate parameter set for second-level connectivity analysis and demonstrate its effectiveness in reducing motion related confounds in comparisons of groups with different voxel-vise displacement pattern and preprocessed using various nuisance regression methods. The effect of using RDI as second-level covariate is than demonstrated in autism-related group comparisons. The relationship between the proposed method and some of the prevailing subject-level nuisance regression techniques is evaluated. Our results show that, depending on experimental design, treating in-scanner head motion as a global confound may not be appropriate. The degree of displacement is highly variable among various brain regions, both within and between subjects. These regional differences bias correlation-based measures of brain connectivity. The inclusion of the proposed second-level covariate into the analysis successfully reduces artifactual motion-related group differences and preserves real neuronal differences, as demonstrated by the autism-related comparisons.

Building Change Detection from LIDAR Point Cloud Data Based on Connected Component Analysis

NASA Astrophysics Data System (ADS)

Awrangjeb, M.; Fraser, C. S.; Lu, G.

2015-08-01

Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessary for many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updated to ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many building change detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in order to avoid under segmentation errors. Then, a new building change detection technique from LIDAR point cloud data is proposed. Buildings which are totally new or demolished are directly added to the change detection output. However, for demolished or extended building parts, a connected component analysis algorithm is applied and for each connected component its area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building part. Finally, a graphical user interface (GUI) has been developed to update detected changes to the existing building map. Experimental results show that the improved building detection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of undersegmentation errors as compared to its original counterpart. The proposed change detection technique produces no omission errors and thus it can be exploited for enhanced automated building information updating within a topographic database. Using the developed GUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks.

Acupuncture Modulates Resting State Connectivity in Default and Sensorimotor Brain Networks

PubMed Central

Dhond, Rupali P.; Yeh, Calvin; Park, Kyungmo; Kettner, Norman; Napadow, Vitaly

2008-01-01

Previous studies have defined low-frequency, spatially consistent networks in resting fMRI data which may reflect functional connectivity. We sought to explore how a complex somatosensory stimulation, acupuncture, influences intrinsic connectivity in two of these networks: the default mode network (DMN) and sensorimotor network (SMN). We analyzed resting fMRI data taken before and after verum and sham acupuncture. Electrocardiography data was used to infer autonomic modulation through measures of heart rate variability (HRV). Probabilistic independent component analysis was used to separate resting fMRI data into DMN and SMN components. Following verum, but not sham, acupuncture there was increased DMN connectivity with pain (anterior cingulate cortex (ACC), periaqueductal gray), affective (amygdala, ACC), and memory (hippocampal formation, middle temporal gyrus) related brain regions. Furthermore, increased DMN connectivity with the hippocampal formation, a region known to support memory and interconnected with autonomic brain regions, was negatively correlated with acupuncture-induced increase in a sympathetic related HRV metric (LFu), and positively correlated with a parasympathetic related metric (HFu). Following verum, but not sham, acupuncture there was also increased SMN connectivity with pain related brain regions (ACC, cerebellum). We attribute differences between verum and sham acupuncture to more varied and stronger sensations evoked by verum acupuncture. Our results demonstrate for the first time that acupuncture can enhance the post-stimulation spatial extent of resting brain networks to include anti-nociceptive, memory, and affective brain regions. This modulation and sympathovagal response may relate to acupuncture analgesia and other potential therapeutic effects. PMID:18337009

The Balanced Leadership Framework: Connecting Vision with Action

ERIC Educational Resources Information Center

Waters, Tim; Cameron, Greg

2007-01-01

This handbook discusses the components of McREL's Balanced Leadership Framework, which describes the 21 responsibilities of effective leaders that McREL identified in its meta-analysis of research on leadership (published through ASCD as "School Leadership That Works"). The authors also describe the concept of "balanced leadership," noting that…

The International Space Station Comparative Maintenance Analysis(CMAM)

DTIC Science & Technology

2004-09-01

External Component • Entire ORU Database 2. Database Connectivity The CMAM ORU database consists of three tables: an ORU master parts list , an ISS...Flight table, and an ISS Subsystem table. The ORU master parts list and the ISS Flight table can be updated or modified from the CMAM user interface

Extension of RCC Topological Relations for 3d Complex Objects Components Extracted from 3d LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

Xing, Xu-Feng; Abolfazl Mostafavia, Mir; Wang, Chen

2016-06-01

Topological relations are fundamental for qualitative description, querying and analysis of a 3D scene. Although topological relations for 2D objects have been extensively studied and implemented in GIS applications, their direct extension to 3D is very challenging and they cannot be directly applied to represent relations between components of complex 3D objects represented by 3D B-Rep models in R3. Herein we present an extended Region Connection Calculus (RCC) model to express and formalize topological relations between planar regions for creating 3D model represented by Boundary Representation model in R3. We proposed a new dimension extended 9-Intersection model to represent the basic relations among components of a complex object, including disjoint, meet and intersect. The last element in 3*3 matrix records the details of connection through the common parts of two regions and the intersecting line of two planes. Additionally, this model can deal with the case of planar regions with holes. Finally, the geometric information is transformed into a list of strings consisting of topological relations between two planar regions and detailed connection information. The experiments show that the proposed approach helps to identify topological relations of planar segments of point cloud automatically.

Fronto-temporal connectivity predicts cognitive empathy deficits and experiential negative symptoms in schizophrenia.

PubMed

Abram, Samantha V; Wisner, Krista M; Fox, Jaclyn M; Barch, Deanna M; Wang, Lei; Csernansky, John G; MacDonald, Angus W; Smith, Matthew J

2017-03-01

Impaired cognitive empathy is a core social cognitive deficit in schizophrenia associated with negative symptoms and social functioning. Cognitive empathy and negative symptoms have also been linked to medial prefrontal and temporal brain networks. While shared behavioral and neural underpinnings are suspected for cognitive empathy and negative symptoms, research is needed to test these hypotheses. In two studies, we evaluated whether resting-state functional connectivity between data-driven networks, or components (referred to as, inter-component connectivity), predicted cognitive empathy and experiential and expressive negative symptoms in schizophrenia subjects. Study 1: We examined associations between cognitive empathy and medial prefrontal and temporal inter-component connectivity at rest using a group-matched schizophrenia and control sample. We then assessed whether inter-component connectivity metrics associated with cognitive empathy were also related to negative symptoms. Study 2: We sought to replicate the connectivity-symptom associations observed in Study 1 using an independent schizophrenia sample. Study 1 results revealed that while the groups did not differ in average inter-component connectivity, a medial-fronto-temporal metric and an orbito-fronto-temporal metric were related to cognitive empathy. Moreover, the medial-fronto-temporal metric was associated with experiential negative symptoms in both schizophrenia samples. These findings support recent models that link social cognition and negative symptoms in schizophrenia. Hum Brain Mapp 38:1111-1124, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Topographical Organization of Attentional, Social, and Memory Processes in the Human Temporoparietal Cortex123

PubMed Central

Webb, Taylor W.; Kelly, Yin T.; Graziano, Michael S. A.

2016-01-01

Abstract The temporoparietal junction (TPJ) is activated in association with a large range of functions, including social cognition, episodic memory retrieval, and attentional reorienting. An ongoing debate is whether the TPJ performs an overarching, domain-general computation, or whether functions reside in domain-specific subdivisions. We scanned subjects with fMRI during five tasks known to activate the TPJ, probing social, attentional, and memory functions, and used data-driven parcellation (independent component analysis) to isolate task-related functional processes in the bilateral TPJ. We found that one dorsal component in the right TPJ, which was connected with the frontoparietal control network, was activated in all of the tasks. Other TPJ subregions were specific for attentional reorienting, oddball target detection, or social attribution of belief. The TPJ components that participated in attentional reorienting and oddball target detection appeared spatially separated, but both were connected with the ventral attention network. The TPJ component that participated in the theory-of-mind task was part of the default-mode network. Further, we found that the BOLD response in the domain-general dorsal component had a longer latency than responses in the domain-specific components, suggesting an involvement in distinct, perhaps postperceptual, computations. These findings suggest that the TPJ performs both domain-general and domain-specific computations that reside within spatially distinct functional components. PMID:27280153

Altered cortical and subcortical connectivity due to infrasound administered near the hearing threshold – Evidence from fMRI

PubMed Central

Weichenberger, Markus; Bauer, Martin; Kühler, Robert; Hensel, Johannes; Forlim, Caroline Garcia; Ihlenfeld, Albrecht; Ittermann, Bernd; Gallinat, Jürgen; Koch, Christian; Kühn, Simone

2017-01-01

In the present study, the brain’s response towards near- and supra-threshold infrasound (IS) stimulation (sound frequency < 20 Hz) was investigated under resting-state fMRI conditions. The study involved two consecutive sessions. In the first session, 14 healthy participants underwent a hearing threshold—as well as a categorical loudness scaling measurement in which the individual loudness perception for IS was assessed across different sound pressure levels (SPL). In the second session, these participants underwent three resting-state acquisitions, one without auditory stimulation (no-tone), one with a monaurally presented 12-Hz IS tone (near-threshold) and one with a similar tone above the individual hearing threshold corresponding to a ‘medium loud’ hearing sensation (supra-threshold). Data analysis mainly focused on local connectivity measures by means of regional homogeneity (ReHo), but also involved independent component analysis (ICA) to investigate inter-regional connectivity. ReHo analysis revealed significantly higher local connectivity in right superior temporal gyrus (STG) adjacent to primary auditory cortex, in anterior cingulate cortex (ACC) and, when allowing smaller cluster sizes, also in the right amygdala (rAmyg) during the near-threshold, compared to both the supra-threshold and the no-tone condition. Additional independent component analysis (ICA) revealed large-scale changes of functional connectivity, reflected in a stronger activation of the right amygdala (rAmyg) in the opposite contrast (no-tone > near-threshold) as well as the right superior frontal gyrus (rSFG) during the near-threshold condition. In summary, this study is the first to demonstrate that infrasound near the hearing threshold may induce changes of neural activity across several brain regions, some of which are known to be involved in auditory processing, while others are regarded as keyplayers in emotional and autonomic control. These findings thus allow us to speculate on how continuous exposure to (sub-)liminal IS could exert a pathogenic influence on the organism, yet further (especially longitudinal) studies are required in order to substantialize these findings. PMID:28403175

Multiple brain networks underpinning word learning from fluent speech revealed by independent component analysis.

PubMed

López-Barroso, Diana; Ripollés, Pablo; Marco-Pallarés, Josep; Mohammadi, Bahram; Münte, Thomas F; Bachoud-Lévi, Anne-Catherine; Rodriguez-Fornells, Antoni; de Diego-Balaguer, Ruth

2015-04-15

Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging (fMRI) have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown. Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis (ICA) to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: (i) a dorsal Auditory-Premotor network; (ii) a dorsal Sensory-Motor network; (iii) a dorsal Fronto-Parietal network; and (iv) a ventral Fronto-Temporal network. The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: (i) word learning relies on segregated connectivity patterns involving dorsal and ventral networks; and (ii) specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance. Copyright © 2015 Elsevier Inc. All rights reserved.

Neuroanatomic connectivity of the human ascending arousal system critical to consciousness and its disorders.

PubMed

Edlow, Brian L; Takahashi, Emi; Wu, Ona; Benner, Thomas; Dai, Guangping; Bu, Lihong; Grant, Patricia Ellen; Greer, David M; Greenberg, Steven M; Kinney, Hannah C; Folkerth, Rebecca D

2012-06-01

The ascending reticular activating system (ARAS) mediates arousal, an essential component of human consciousness. Lesions of the ARAS cause coma, the most severe disorder of consciousness. Because of current methodological limitations, including of postmortem tissue analysis, the neuroanatomic connectivity of the human ARAS is poorly understood. We applied the advanced imaging technique of high angular resolution diffusion imaging (HARDI) to elucidate the structural connectivity of the ARAS in 3 adult human brains, 2 of which were imaged postmortem. High angular resolution diffusion imaging tractography identified the ARAS connectivity previously described in animals and also revealed novel human pathways connecting the brainstem to the thalamus, the hypothalamus, and the basal forebrain. Each pathway contained different distributions of fiber tracts from known neurotransmitter-specific ARAS nuclei in the brainstem. The histologically guided tractography findings reported here provide initial evidence for human-specific pathways of the ARAS. The unique composition of neurotransmitter-specific fiber tracts within each ARAS pathway suggests structural specializations that subserve the different functional characteristics of human arousal. This ARAS connectivity analysis provides proof of principle that HARDI tractography may affect the study of human consciousness and its disorders, including in neuropathologic studies of patients dying in coma and the persistent vegetative state.

Normalization of Intrinsic Neural Circuits Governing Tourette's Syndrome Using Cranial Electrotherapy Stimulation.

PubMed

Qiao, Jianping; Weng, Shenhong; Wang, Pengwei; Long, Jun; Wang, Zhishun

2015-05-01

The aim of this study was to investigate the normalization of the intrinsic functional activity and connectivity of TS adolescents before and after the cranial electrotherapy stimulation (CES) with alpha stim device. We performed resting-state functional magnetic resonance imaging on eight adolescents before and after CES with mean age of about nine-years old who had Tourette's syndrome with moderate to severe tics symptom. Independent component analysis (ICA) with hierarchical partner matching method was used to examine the functional connectivity between regions within cortico-striato-thalamo-cortical (CSTC) circuit. Granger causality was used to investigate effective connectivity among these regions detected by ICA. We then performed pattern classification on independent components with significant group differences that served as endophenotype markers to distinguish the adolescents between TS and the normalized ones after CES. Results showed that TS adolescents after CES treatment had stronger functional activity and connectivity in anterior cingulate cortex (ACC), caudate and posterior cingulate cortex while had weaker activity in supplementary motor area within the motor pathway compared with TS before CES. The results suggest that the functional activity and connectivity in motor pathway was suppressed while activities in the control portions within CSTC loop including ACC and caudate were increased in TS adolescents after CES compared with adolescents before CES. The normalization of the balance between motor and control portions of the CSTC circuit may result in the recovery of TS adolescents.

Compensatory Hyperconnectivity in Developing Brains of Young Children With Type 1 Diabetes.

PubMed

Saggar, Manish; Tsalikian, Eva; Mauras, Nelly; Mazaika, Paul; White, Neil H; Weinzimer, Stuart; Buckingham, Bruce; Hershey, Tamara; Reiss, Allan L

2017-03-01

Sustained dysregulation of blood glucose (hyper- or hypoglycemia) associated with type 1 diabetes (T1D) has been linked to cognitive deficits and altered brain anatomy and connectivity. However, a significant gap remains with respect to how T1D affects spontaneous at-rest connectivity in young developing brains. Here, using a large multisite study, resting-state functional MRI data were examined in young children with T1D ( n = 57; mean age = 7.88 years; 27 females) as compared with age-matched control subjects without diabetes ( n = 26; mean age = 7.43 years; 14 females). Using both model-driven seed-based analysis and model-free independent component analysis and controlling for age, data acquisition site, and sex, converging results were obtained, suggesting increased connectivity in young children with T1D as compared with control subjects without diabetes. Further, increased connectivity in children with T1D was observed to be positively associated with cognitive functioning. The observed positive association of connectivity with cognitive functioning in T1D, without overall group differences in cognitive function, suggests a putative compensatory role of hyperintrinsic connectivity in the brain in children with this condition. Altogether, our study attempts to fill a critical gap in knowledge regarding how dysglycemia in T1D might affect the brain's intrinsic connectivity at very young ages. © 2017 by the American Diabetes Association.

Characterizing Individual Differences in Functional Connectivity Using Dual-Regression and Seed-Based Approaches

PubMed Central

Smith, David V.; Utevsky, Amanda V.; Bland, Amy R.; Clement, Nathan; Clithero, John A.; Harsch, Anne E. W.; Carter, R. McKell; Huettel, Scott A.

2014-01-01

A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent components analysis (ICA). We estimated voxelwise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal-parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust—yet frequently ignored—neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity. PMID:24662574

Analysis of turbine-grid interaction of grid-connected wind turbine using HHT

NASA Astrophysics Data System (ADS)

Chen, A.; Wu, W.; Miao, J.; Xie, D.

2018-05-01

This paper processes the output power of the grid-connected wind turbine with the denoising and extracting method based on Hilbert Huang transform (HHT) to discuss the turbine-grid interaction. At first, the detailed Empirical Mode Decomposition (EMD) and the Hilbert Transform (HT) are introduced. Then, on the premise of decomposing the output power of the grid-connected wind turbine into a series of Intrinsic Mode Functions (IMFs), energy ratio and power volatility are calculated to detect the unessential components. Meanwhile, combined with vibration function of turbine-grid interaction, data fitting of instantaneous amplitude and phase of each IMF is implemented to extract characteristic parameters of different interactions. Finally, utilizing measured data of actual parallel-operated wind turbines in China, this work accurately obtains the characteristic parameters of turbine-grid interaction of grid-connected wind turbine.

Visuomotor coordination and cortical connectivity of modular motor learning.

PubMed

Burgos, Pablo I; Mariman, Juan J; Makeig, Scott; Rivera-Lillo, Gonzalo; Maldonado, Pedro E

2018-05-15

The ability to transfer sensorimotor skill components to new actions and the capacity to use skill components from whole actions are characteristic of the adaptability of the human sensorimotor system. However, behavioral evidence suggests complex limitations for transfer after combined or modular learning of motor adaptations. Also, to date, only behavioral analysis of the consequences of the modular learning has been reported, with little understanding of the sensorimotor mechanisms of control and the interaction between cortical areas. We programmed a video game with distorted kinematic and dynamic features to test the ability to combine sensorimotor skill components learned modularly (composition) and the capacity to use separate sensorimotor skill components learned in combination (decomposition). We examined motor performance, eye-hand coordination, and EEG connectivity. When tested for integrated learning, we found that combined practice initially performed better than separated practice, but differences disappeared after integrated practice. Separate learning promotes fewer anticipatory control mechanisms (depending more on feedback control), evidenced in a lower gaze leading behavior and in higher connectivity between visual and premotor domains, in comparison with the combined practice. The sensorimotor system can acquire motor modules in a separated or integrated manner. However, the system appears to require integrated practice to coordinate the adaptations with the skill learning and the networks involved in the integrated behavior. This integration seems to be related to the acquisition of anticipatory mechanism of control and with the decrement of feedback control. © 2018 Wiley Periodicals, Inc.

Frequency distribution of causal connectivity in rat sensorimotor network: resting-state fMRI analyses.

PubMed

Shim, Woo H; Baek, Kwangyeol; Kim, Jeong Kon; Chae, Yongwook; Suh, Ji-Yeon; Rosen, Bruce R; Jeong, Jaeseung; Kim, Young R

2013-01-01

Resting-state functional MRI (fMRI) has emerged as an important method for assessing neural networks, enabling extensive connectivity analyses between multiple brain regions. Among the analysis techniques proposed, partial directed coherence (PDC) provides a promising tool to unveil causal connectivity networks in the frequency domain. Using the MRI time series obtained from the rat sensorimotor system, we applied PDC analysis to determine the frequency-dependent causality networks. In particular, we compared in vivo and postmortem conditions to establish the statistical significance of directional PDC values. Our results demonstrate that two distinctive frequency populations drive the causality networks in rat; significant, high-frequency causal connections clustered in the range of 0.2-0.4 Hz, and the frequently documented low-frequency connections <0.15 Hz. Frequency-dependence and directionality of the causal connection are characteristic between sensorimotor regions, implying the functional role of frequency bands to transport specific resting-state signals. In particular, whereas both intra- and interhemispheric causal connections between heterologous sensorimotor regions are robust over all frequency levels, the bilaterally homologous regions are interhemispherically linked mostly via low-frequency components. We also discovered a significant, frequency-independent, unidirectional connection from motor cortex to thalamus, indicating dominant cortical inputs to the thalamus in the absence of external stimuli. Additionally, to address factors underlying the measurement error, we performed signal simulations and revealed that the interactive MRI system noise alone is a likely source of the inaccurate PDC values. This work demonstrates technical basis for the PDC analysis of resting-state fMRI time series and the presence of frequency-dependent causality networks in the sensorimotor system.

One-year test-retest reliability of intrinsic connectivity network fMRI in older adults

PubMed Central

Guo, Cong C.; Kurth, Florian; Zhou, Juan; Mayer, Emeran A.; Eickhoff, Simon B; Kramer, Joel H.; Seeley, William W.

2014-01-01

“Resting-state” or task-free fMRI can assess intrinsic connectivity network (ICN) integrity in health and disease, suggesting a potential for use of these methods as disease-monitoring biomarkers. Numerous analytical options are available, including model-driven ROI-based correlation analysis and model-free, independent component analysis (ICA). High test-retest reliability will be a necessary feature of a successful ICN biomarker, yet available reliability data remains limited. Here, we examined ICN fMRI test-retest reliability in 24 healthy older subjects scanned roughly one year apart. We focused on the salience network, a disease-relevant ICN not previously subjected to reliability analysis. Most ICN analytical methods proved reliable (intraclass coefficients > 0.4) and could be further improved by wavelet analysis. Seed-based ROI correlation analysis showed high map-wise reliability, whereas graph theoretical measures and temporal concatenation group ICA produced the most reliable individual unit-wise outcomes. Including global signal regression in ROI-based correlation analyses reduced reliability. Our study provides a direct comparison between the most commonly used ICN fMRI methods and potential guidelines for measuring intrinsic connectivity in aging control and patient populations over time. PMID:22446491

Downhole data transmission system

DOEpatents

Hall, David R.; Hall, Jr., H. Tracy; Pixton, David S; Dahlgren, Scott; Fox, Joe

2006-06-20

A system for transmitting data through a string of downhole components. In one aspect, the system includes first and second magnetically conductive, electrically insulating elements at both ends of the component. Each element includes a first U-shaped trough with a bottom, first and second sides and an opening between the two sides. Electrically conducting coils are located in each trough. An electrical conductor connects the coils in each component. In operation, a varying current applied to a first coil in one component generates a varying magnetic field in the first magnetically conductive, electrically insulating element, which varying magnetic field is conducted to and thereby produces a varying magnetic field in the second magnetically conductive, electrically insulating element of a connected component, which magnetic field thereby generates a varying electrical current in the second coil in the connected component.

Downhole Data Transmission System

DOEpatents

Hall, David R.; Hall, Jr., H. Tracy; Pixton, David; Dahlgren, Scott; Fox, Joe

2003-12-30

A system for transmitting data through a string of downhole components. In one aspect, the system includes first and second magnetically conductive, electrically insulating elements at both ends of the component. Each element includes a first U-shaped trough with a bottom, first and second sides and an opening between the two sides. Electrically conducting coils are located in each trough. An electrical conductor connects the coils in each component. In operation, a varying current applied to a first coil in one component generates a varying magnetic field in the first magnetically conductive, electrically insulating element, which varying magnetic field is conducted to and thereby produces a varying magnetic field in the second magnetically conductive, electrically insulating element of a connected component, which magnetic field thereby generates a varying electrical current in the second coil in the connected component.

Correlation between lifetime heterogeneity and kinetics heterogeneity during chlorophyll fluorescence induction in leaves: 2. Multi-frequency phase and modulation analysis evidences a loosely connected PSII pigment-protein complex.

PubMed

Moise, Nicolae; Moya, Ismaël

2004-06-28

We report the first direct decomposition of the fluorescence lifetime heterogeneity during multiphasic fluorescence induction in dark-adapted leaves by multi-frequency phase and modulation fluorometry (PMF). A very fast component, assigned to photosystem I (PSI), was found to be constant in lifetime and yield, whereas the two slow components, which are strongly affected by the closure of the reaction centers by light, were assigned to PSII. Based on a modified "reversible radical pair" kinetic model with three compartments, we showed that a loosely connected pigment complex, which is assumed to be the CP47 complex, plays a specific role with respect to the structure and function of the PSII: (i) it explains the heterogeneity of PSII fluorescence lifetime as a compartmentation of excitation energy in the antenna, (ii) it is the site of a conformational change in the first second of illumination, and (iii) it is involved in the mechanisms of nonphotochemical quenching (NPQ). On the basis of the multi-frequency PMF analysis, we reconciled two apparently antagonistic aspects of chlorophyll a fluorescence in vivo: it is heterogeneous with respect to the kinetic structure (several lifetime components) and homogeneous with respect to average quantities (quasi-linear mean tau-Phi relationship).

Network-Based Comparative Analysis of Arabidopsis Immune Responses to Golovinomyces orontii and Botrytis cinerea Infections.

PubMed

Jiang, Zhenhong; Dong, Xiaobao; Zhang, Ziding

2016-01-11

A comprehensive exploration of common and specific plant responses to biotrophs and necrotrophs is necessary for a better understanding of plant immunity. Here, we compared the Arabidopsis defense responses evoked by the biotrophic fungus Golovinomyces orontii and the necrotrophic fungus Botrytis cinerea through integrative network analysis. Two time-course transcriptional datasets were integrated with an Arabidopsis protein-protein interaction (PPI) network to construct a G. orontii conditional PPI sub-network (gCPIN) and a B. cinerea conditional PPI sub-network (bCPIN). We found that hubs in gCPIN and bCPIN played important roles in disease resistance. Hubs in bCPIN evolved faster than hubs in gCPIN, indicating the different selection pressures imposed on plants by different pathogens. By analyzing the common network from gCPIN and bCPIN, we identified two network components in which the genes were heavily involved in defense and development, respectively. The co-expression relationships between interacting proteins connecting the two components were different under G. orontii and B. cinerea infection conditions. Closer inspection revealed that auxin-related genes were overrepresented in the interactions connecting these two components, suggesting a critical role of auxin signaling in regulating the different co-expression relationships. Our work may provide new insights into plant defense responses against pathogens with different lifestyles.

Thermodynamic stability of nanosized multicomponent bubbles/droplets: the square gradient theory and the capillary approach.

PubMed

Wilhelmsen, Øivind; Bedeaux, Dick; Kjelstrup, Signe; Reguera, David

2014-01-14

Formation of nanosized droplets/bubbles from a metastable bulk phase is connected to many unresolved scientific questions. We analyze the properties and stability of multicomponent droplets and bubbles in the canonical ensemble, and compare with single-component systems. The bubbles/droplets are described on the mesoscopic level by square gradient theory. Furthermore, we compare the results to a capillary model which gives a macroscopic description. Remarkably, the solutions of the square gradient model, representing bubbles and droplets, are accurately reproduced by the capillary model except in the vicinity of the spinodals. The solutions of the square gradient model form closed loops, which shows the inherent symmetry and connected nature of bubbles and droplets. A thermodynamic stability analysis is carried out, where the second variation of the square gradient description is compared to the eigenvalues of the Hessian matrix in the capillary description. The analysis shows that it is impossible to stabilize arbitrarily small bubbles or droplets in closed systems and gives insight into metastable regions close to the minimum bubble/droplet radii. Despite the large difference in complexity, the square gradient and the capillary model predict the same finite threshold sizes and very similar stability limits for bubbles and droplets, both for single-component and two-component systems.

Thermodynamic stability of nanosized multicomponent bubbles/droplets: The square gradient theory and the capillary approach

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

Wilhelmsen, Øivind, E-mail: oivind.wilhelmsen@ntnu.no; Bedeaux, Dick; Kjelstrup, Signe

Formation of nanosized droplets/bubbles from a metastable bulk phase is connected to many unresolved scientific questions. We analyze the properties and stability of multicomponent droplets and bubbles in the canonical ensemble, and compare with single-component systems. The bubbles/droplets are described on the mesoscopic level by square gradient theory. Furthermore, we compare the results to a capillary model which gives a macroscopic description. Remarkably, the solutions of the square gradient model, representing bubbles and droplets, are accurately reproduced by the capillary model except in the vicinity of the spinodals. The solutions of the square gradient model form closed loops, which showsmore » the inherent symmetry and connected nature of bubbles and droplets. A thermodynamic stability analysis is carried out, where the second variation of the square gradient description is compared to the eigenvalues of the Hessian matrix in the capillary description. The analysis shows that it is impossible to stabilize arbitrarily small bubbles or droplets in closed systems and gives insight into metastable regions close to the minimum bubble/droplet radii. Despite the large difference in complexity, the square gradient and the capillary model predict the same finite threshold sizes and very similar stability limits for bubbles and droplets, both for single-component and two-component systems.« less

Finding the ‘lost years’ in green turtles: insights from ocean circulation models and genetic analysis

PubMed Central

Putman, Nathan F.; Naro-Maciel, Eugenia

2013-01-01

Organismal movement is an essential component of ecological processes and connectivity among ecosystems. However, estimating connectivity and identifying corridors of movement are challenging in oceanic organisms such as young turtles that disperse into the open sea and remain largely unobserved during a period known as ‘the lost years’. Using predictions of transport within an ocean circulation model and data from published genetic analysis, we present to our knowledge, the first basin-scale hypothesis of distribution and connectivity among major rookeries and foraging grounds (FGs) of green turtles (Chelonia mydas) during their ‘lost years’. Simulations indicate that transatlantic dispersal is likely to be common and that recurrent connectivity between the southwestern Indian Ocean and the South Atlantic is possible. The predicted distribution of pelagic juvenile turtles suggests that many ‘lost years hotspots’ are presently unstudied and located outside protected areas. These models, therefore, provide new information on possible dispersal pathways that link nesting beaches with FGs. These pathways may be of exceptional conservation concern owing to their importance for sea turtles during a critical developmental period. PMID:23945687

Dimensional change card sort performance associated with age-related differences in functional connectivity of lateral prefrontal cortex.

PubMed

Ezekiel, Fredrick; Bosma, Rachael; Morton, J Bruce

2013-07-01

The Dimensional Change Card Sort (DCCS) is a standard procedure for assessing executive functioning early in development. In the task, participants switch from sorting cards one way (e.g., by color) to sorting them a different way (e.g., by shape). Traditional accounts associate age-related changes in DCCS performance with circumscribed changes in lateral prefrontal cortex (lPFC) functioning, but evidence of age-related differences in the modulation of lPFC activity by switching is mixed. The current study therefore tested for possible age-related differences in functional connectivity of lPFC with regions that comprise a larger cognitive control network. Functional magnetic resonance imaging (fMRI) data collected from children and adults performing the DCCS were analyzed by means of independent components analysis (ICA). The analysis revealed several important age-related differences in functional connectivity of lPFC. In particular, lPFC was more strongly connected with the anterior cingulate, inferior parietal cortex, and the ventral tegmental area in adults than in children. Theoretical implications are discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.

Nu-Way Snaps and Snap Leads: an Important Connection in the History of Behavior Analysis.

PubMed

Escobar, Rogelio; Lattal, Kennon A

2014-10-01

Beginning in the early 1950s, the snap lead became an integral and ubiquitous component of the programming of electromechanical modules used in behavioral experiments. It was composed of a Nu-Way snap connector on either end of a colored electrical wire. Snap leads were used to connect the modules to one another, thereby creating the programs that controlled contingencies, arranged reinforcers, and recorded behavior in laboratory experiments. These snap leads populated operant conditioning laboratories from their inception until the turn of the twenty-first century. They allowed quick and flexible programming because of the ease with which they could be connected, stacked, and removed. Thus, the snap lead was integral to the research activity that constituted the experimental analysis of behavior for more than five decades. This review traces the history of the snap lead from the origins of the snap connector in Birmingham, England, in the late eighteenth century, through the use of snaps connected to wires during the Second World War, to its adoption in operant laboratories, and finally to its demise in the digital age.

Bayesian network analysis reveals alterations to default mode network connectivity in individuals at risk for Alzheimer's disease.

PubMed

Li, Rui; Yu, Jing; Zhang, Shouzi; Bao, Feng; Wang, Pengyun; Huang, Xin; Li, Juan

2013-01-01

Alzheimer's disease (AD) is associated with abnormal functioning of the default mode network (DMN). Functional connectivity (FC) changes to the DMN have been found in patients with amnestic mild cognitive impairment (aMCI), which is the prodromal stage of AD. However, whether or not aMCI also alters the effective connectivity (EC) of the DMN remains unknown. We employed a combined group independent component analysis (ICA) and Bayesian network (BN) learning approach to resting-state functional MRI (fMRI) data from 17 aMCI patients and 17 controls, in order to establish the EC pattern of DMN, and to evaluate changes occurring in aMCI. BN analysis demonstrated heterogeneous regional convergence degree across DMN regions, which were organized into two closely interacting subsystems. Compared to controls, the aMCI group showed altered directed connectivity weights between DMN regions in the fronto-parietal, temporo-frontal, and temporo-parietal pathways. The aMCI group also exhibited altered regional convergence degree in the right inferior parietal lobule. Moreover, we found EC changes in DMN regions in aMCI were correlated with regional FC levels, and the connectivity metrics were associated with patients' cognitive performance. This study provides novel sights into our understanding of the functional architecture of the DMN and adds to a growing body of work demonstrating the importance of the DMN as a mechanism of aMCI.

Principle of maximum entropy for reliability analysis in the design of machine components

NASA Astrophysics Data System (ADS)

Zhang, Yimin

2018-03-01

We studied the reliability of machine components with parameters that follow an arbitrary statistical distribution using the principle of maximum entropy (PME). We used PME to select the statistical distribution that best fits the available information. We also established a probability density function (PDF) and a failure probability model for the parameters of mechanical components using the concept of entropy and the PME. We obtained the first four moments of the state function for reliability analysis and design. Furthermore, we attained an estimate of the PDF with the fewest human bias factors using the PME. This function was used to calculate the reliability of the machine components, including a connecting rod, a vehicle half-shaft, a front axle, a rear axle housing, and a leaf spring, which have parameters that typically follow a non-normal distribution. Simulations were conducted for comparison. This study provides a design methodology for the reliability of mechanical components for practical engineering projects.

Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging

PubMed Central

Tsvetanov, Kamen A.; Cam‐CAN; Henson, Richard N.

2017-01-01

Abstract Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre‐processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience (www.cam-can.com). This dataset contained two sessions of resting‐state fMRI from 214 adults aged 18–88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between‐participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high‐pass filtering, instead of band‐pass filtering, produced stronger and more reliable age‐effects. Head motion was correlated with gray‐matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp 38:4125–4156, 2017. © 2017 Wiley Periodicals, Inc. PMID:28544076

Connected components of irreducible maps and 1D quantum phases

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

Szehr, Oleg, E-mail: oleg.szehr@posteo.de; Wolf, Michael M., E-mail: wolf@ma.tum.de

We investigate elementary topological properties of sets of completely positive (CP) maps that arise in quantum Perron-Frobenius theory. We prove that the set of primitive CP maps of fixed Kraus rank is path-connected and we provide a complete classification of the connected components of irreducible CP maps at given Kraus rank and fixed peripheral spectrum in terms of a multiplicity index. These findings are then applied to analyse 1D quantum phases by studying equivalence classes of translational invariant matrix product states that correspond to the connected components of the respective CP maps. Our results extend the previously obtained picture inmore » that they do not require blocking of physical sites, they lead to analytic paths, and they allow us to decompose into ergodic components and to study the breaking of translational symmetry.« less

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

Aceves, Salvador M.; Ledesma-Orozco, Elias Rigoberto; Espinosa-Loza, Francisco

A pressure vessel apparatus for cryogenic capable storage of hydrogen or other cryogenic gases at high pressure includes an insert with a parallel inlet duct, a perpendicular inlet duct connected to the parallel inlet. The perpendicular inlet duct and the parallel inlet duct connect the interior cavity with the external components. The insert also includes a parallel outlet duct and a perpendicular outlet duct connected to the parallel outlet duct. The perpendicular outlet duct and the parallel outlet duct connect the interior cavity with the external components.

Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses

PubMed Central

Nickerson, Lisa D.; Smith, Stephen M.; Öngür, Döst; Beckmann, Christian F.

2017-01-01

Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise. ICA and other regression techniques, on the other hand, retain magnitude information and therefore can be sensitive to both changes in the spatially distributed nature of correlations (differences in the spatial pattern or “shape”) as well as the amplitude of the network activity. Furthermore, motion can mimic amplitude effects so it is crucial to use a technique that retains such information to ensure that connectivity differences are accurately localized. In this work, we investigate the dual regression approach that is frequently applied with group ICA to assess group differences in resting state functional connectivity of brain networks. We show how ignoring amplitude effects and how excessive motion corrupts connectivity maps and results in spurious connectivity differences. We also show how to implement the dual regression to retain amplitude information and how to use dual regression outputs to identify potential motion effects. Two key findings are that using a technique that retains magnitude information, e.g., dual regression, and using strict motion criteria are crucial for controlling both network amplitude and motion-related amplitude effects, respectively, in resting state connectivity analyses. We illustrate these concepts using realistic simulated resting state FMRI data and in vivo data acquired in healthy subjects and patients with bipolar disorder and schizophrenia. PMID:28348512

"Maintaining connections but wanting more": the continuity of familial relationships among assisted-living residents.

PubMed

Tompkins, Catherine J; Ihara, Emily S; Cusick, Alison; Park, Nan Sook

2012-01-01

Social support is a key component of well-being for older adults, particularly for those who have moved from independent living to assisted living involving a transformation of roles, relationships, and responsibilities. Twenty-nine assisted-living facility residents were interviewed to understand the perceived continuity of relationships with family and friends. An inductive approach to thematic analysis revealed 1 main theme and 3 subthemes. The main theme that emerged was: maintaining connections but wanting more. Residents appreciated maintaining connections with family and friends, but often expressed feelings of discontentment with the continuity of former relationships. The subthemes included: appreciating family and friends, waiting for more, and losing control. Implications for research and practice are discussed.

JGrass-NewAge hydrological system: an open-source platform for the replicability of science.

NASA Astrophysics Data System (ADS)

Bancheri, Marialaura; Serafin, Francesco; Formetta, Giuseppe; Rigon, Riccardo; David, Olaf

2017-04-01

JGrass-NewAge is an open source semi-distributed hydrological modelling system. It is based on the object modelling framework (OMS version 3), on the JGrasstools and on the Geotools. OMS3 allows to create independent packages of software which can be connected at run-time in a working modelling solution. These components are available as library/dependency or as repository to fork in order to add further features. Different tools are adopted to make easier the integration, the interoperability and the use of each package. Most of the components are Gradle integrated, since it represents the state-of-art of the building systems, especially for Java projects. The continuous integration is a further layer between local source code (client-side) and remote repository (server-side) and ensures the building and the testing of the source code at each commit. Finally, the use of Zenodo makes the code hosted in GitHub unique, citable and traceable, with a defined DOI. Following the previous standards, each part of the hydrological cycle is implemented in JGrass-NewAge as a component that can be selected, adopted, and connected to obtain a user "customized" hydrological model. A variety of modelling solutions are possible, allowing a complete hydrological analysis. Moreover, thanks to the JGrasstools and the Geotools, the visualization of the data and of the results using a selected GIS is possible. After the geomorphological analysis of the watershed, the spatial interpolation of the meteorological inputs can be performed using both deterministic (IDW) and geostatistic (Kriging) algorithms. For the radiation balance, the shortwave and longwave radiation can be estimated, which are, in turn, inputs for the simulation of the evapotranspiration, according to Priestly-Taylor and Penman-Monteith formulas. Three degree-day models are implemented for the snow melting and SWE. The runoff production can be simulated using two different components, "Adige" and "Embedded Reservoirs". The travel time theory has recently been integrated for a coupled analysis of the solute transport. Eventually, each component can be connected to the different calibration tools such as LUCA and PSO. Further information about the actual implementation can be found at (https://github.com/geoframecomponents), while the OMS projects with the examples, data and results are available at (https://github.com/GEOframeOMSProjects).

Spoken Word Recognition and Serial Recall of Words from Components in the Phonological Network

ERIC Educational Resources Information Center

Siew, Cynthia S. Q.; Vitevitch, Michael S.

2016-01-01

Network science uses mathematical techniques to study complex systems such as the phonological lexicon (Vitevitch, 2008). The phonological network consists of a "giant component" (the largest connected component of the network) and "lexical islands" (smaller groups of words that are connected to each other, but not to the giant…

Sustainable Energy for University Science Majors: Developing Guidelines for Educators

ERIC Educational Resources Information Center

Langbeheim, Elon; Rez, Peter

2017-01-01

This paper describes the basic tenets of a sustainable energy course for university science majors. First, it outlines the three core components of the course: (1) The scientific evidence for the connection between climate change and energy usage; (2) An analysis of the capacity and environmental impact of various renewable and traditional energy…

Whole brain resting-state analysis reveals decreased functional connectivity in major depression.

PubMed

Veer, Ilya M; Beckmann, Christian F; van Tol, Marie-José; Ferrarini, Luca; Milles, Julien; Veltman, Dick J; Aleman, André; van Buchem, Mark A; van der Wee, Nic J; Rombouts, Serge A R B

2010-01-01

Recently, both increases and decreases in resting-state functional connectivity have been found in major depression. However, these studies only assessed functional connectivity within a specific network or between a few regions of interest, while comorbidity and use of medication was not always controlled for. Therefore, the aim of the current study was to investigate whole-brain functional connectivity, unbiased by a priori definition of regions or networks of interest, in medication-free depressive patients without comorbidity. We analyzed resting-state fMRI data of 19 medication-free patients with a recent diagnosis of major depression (within 6 months before inclusion) and no comorbidity, and 19 age- and gender-matched controls. Independent component analysis was employed on the concatenated data sets of all participants. Thirteen functionally relevant networks were identified, describing the entire study sample. Next, individual representations of the networks were created using a dual regression method. Statistical inference was subsequently done on these spatial maps using voxel-wise permutation tests. Abnormal functional connectivity was found within three resting-state networks in depression: (1) decreased bilateral amygdala and left anterior insula connectivity in an affective network, (2) reduced connectivity of the left frontal pole in a network associated with attention and working memory, and (3) decreased bilateral lingual gyrus connectivity within ventromedial visual regions. None of these effects were associated with symptom severity or gray matter density. We found abnormal resting-state functional connectivity not previously associated with major depression, which might relate to abnormal affect regulation and mild cognitive deficits, both associated with the symptomatology of the disorder.

Whole Brain Resting-State Analysis Reveals Decreased Functional Connectivity in Major Depression

PubMed Central

Veer, Ilya M.; Beckmann, Christian F.; van Tol, Marie-José; Ferrarini, Luca; Milles, Julien; Veltman, Dick J.; Aleman, André; van Buchem, Mark A.; van der Wee, Nic J.; Rombouts, Serge A.R.B.

2010-01-01

Recently, both increases and decreases in resting-state functional connectivity have been found in major depression. However, these studies only assessed functional connectivity within a specific network or between a few regions of interest, while comorbidity and use of medication was not always controlled for. Therefore, the aim of the current study was to investigate whole-brain functional connectivity, unbiased by a priori definition of regions or networks of interest, in medication-free depressive patients without comorbidity. We analyzed resting-state fMRI data of 19 medication-free patients with a recent diagnosis of major depression (within 6 months before inclusion) and no comorbidity, and 19 age- and gender-matched controls. Independent component analysis was employed on the concatenated data sets of all participants. Thirteen functionally relevant networks were identified, describing the entire study sample. Next, individual representations of the networks were created using a dual regression method. Statistical inference was subsequently done on these spatial maps using voxel-wise permutation tests. Abnormal functional connectivity was found within three resting-state networks in depression: (1) decreased bilateral amygdala and left anterior insula connectivity in an affective network, (2) reduced connectivity of the left frontal pole in a network associated with attention and working memory, and (3) decreased bilateral lingual gyrus connectivity within ventromedial visual regions. None of these effects were associated with symptom severity or gray matter density. We found abnormal resting-state functional connectivity not previously associated with major depression, which might relate to abnormal affect regulation and mild cognitive deficits, both associated with the symptomatology of the disorder. PMID:20941370

Decreased functional connectivity to posterior cingulate cortex in major depressive disorder.

PubMed

Yang, Rui; Gao, Chengge; Wu, Xiaoping; Yang, Junle; Li, Shengbin; Cheng, Hu

2016-09-30

The default mode network (DMN) and its interaction with other key networks such as the salience network and executive network are keys to understand psychiatric and neurological disorders including major depressive disorder (MDD). In this study, we combined independent component analysis and seed based connectivity analysis to study the posterior default mode network between 20 patients with MDD and 25 normal controls, as well as pre-treatment and post-treatment conditions of the patients. Both correlated and anti-correlated networks centered at the posterior cingulate cortex (PCC) were examined (PCC+ and PCC-). Our results showed aberrant functional connectivity of the PCC+ and PCC- networks between patients and normal controls. Specifically, normal controls exhibited significantly higher connectivity between the PCC and frontal/temporal regions for the PCC+ network and stronger connectivity strength between the PCC and the insula/middle frontal cortex for the PCC- network. The overall connectivity strength of the PCC+ and PCC- networks was also significantly lower in MDD. Because the PCC is a hub in the DMN that interacts with other networks, our result suggested a stronger interaction between the DMN and the salience network but a weak interaction between the DMN and the executive network in MDD. The treatment using sertraline did increase the functional connectivity strength, especially in the PCC+ network. Despite a large inter-subject variability in the overall connectivity strengths and change of the PCC network in response to the treatment, a high correlation between change of connectivity strength and the Hamilton depression score was observed for both the PCC+ and PCC- network. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Nicotine restores functional connectivity of the ventral attention network in schizophrenia.

PubMed

Smucny, Jason; Olincy, Ann; Tregellas, Jason R

2016-09-01

While previous work has suggested that nicotine may transiently improve attention deficits in schizophrenia, the neuronal mechanisms are poorly understood. This study is the first to examine the effects of nicotine on connectivity within the ventral attention network (VAN) during a selective attention task in schizophrenia. Using a crossover design, 17 nonsmoking patients with schizophrenia and 20 age/gender-matched nonsmoking healthy controls performed a go/no-go task with environmental noise distractors during application of a 7 mg nicotine or placebo patch. Psychophysiological interaction analysis was performed to analyze task-associated changes in connectivity between a ventral parietal cortex (VPC) seed and the inferior frontal gyrus (IFG), key components of the human VAN. Effects of nicotine on resting state VAN connectivity were also examined. A significant diagnosis × drug interaction was observed on task-associated connectivity between the VPC seed and the left IFG (F(1,35) = 8.03, p < 0.01). This effect was driven by decreased connectivity after placebo in patients and greater connectivity after nicotine. Resting state connectivity analysis showed a significant main effect of diagnosis between the seed and right IFG (F = 4.25, p = 0.023) due to increased connectivity in patients during placebo, but no drug × diagnosis interactions or main effects of drug. This study is the first to demonstrate that 1) the VAN is disconnected in schizophrenia during selective attention, and 2) nicotine may normalize this pathological state. Copyright © 2016 Elsevier Ltd. All rights reserved.

Intrinsic Brain Connectivity in Chronic Pain: A Resting-State fMRI Study in Patients with Rheumatoid Arthritis

PubMed Central

Flodin, Pär; Martinsen, Sofia; Altawil, Reem; Waldheim, Eva; Lampa, Jon; Kosek, Eva; Fransson, Peter

2016-01-01

Background: Rheumatoid arthritis (RA) is commonly accompanied by pain that is discordant with the degree of peripheral pathology. Very little is known about the cerebral processes involved in pain processing in RA. Here we investigated resting-state brain connectivity associated with prolonged pain in RA. Methods: 24 RA subjects and 19 matched controls were compared with regard to both behavioral measures of pain perception and resting-resting state fMRI data acquired subsequently to fMRI sessions involving pain stimuli. The resting-state fMRI brain connectivity was investigated using 159 seed regions located in cardinal pain processing brain regions. Additional principal component based multivariate pattern analysis of the whole brain connectivity pattern was carried out in a data driven analysis to localize group differences in functional connectivity. Results: When RA patients were compared to controls, we observed significantly lower pain resilience for pressure on the affected finger joints (i.e., P50-joint) and an overall heightened level of perceived global pain in RA patients. Relative to controls, RA patients displayed increased brain connectivity predominately for the supplementary motor areas, mid-cingulate cortex, and the primary sensorimotor cortex. Additionally, we observed an increase in brain connectivity between the insula and prefrontal cortex as well as between anterior cingulate cortex and occipital areas for RA patients. None of the group differences in brain connectivity were significantly correlated with behavioral parameters. Conclusion: Our study provides experimental evidence of increased connectivity between frontal midline regions that are implicated in affective pain processing and bilateral sensorimotor regions in RA patients. PMID:27014038

Decreased intrinsic brain connectivity is associated with reduced clinical pain in fibromyalgia.

PubMed

Napadow, Vitaly; Kim, Jieun; Clauw, Daniel J; Harris, Richard E

2012-07-01

A major impediment to the development of novel treatment strategies for fibromyalgia (FM) is the lack of an objective marker that reflects spontaneously reported clinical pain in patients with FM. Studies of resting-state intrinsic brain connectivity in FM have demonstrated increased insular connectivity to the default mode network (DMN), a network whose activity is increased during nontask states. Moreover, increased insular connectivity to the DMN was associated with increased spontaneous pain levels. However, as these analyses were cross-sectional in nature, they provided no insight into dynamic changes in connectivity or their relationship to variations in self-reported clinical pain. The purpose of this study was to evaluate longitudinal changes in the intrinsic brain connectivity of FM patients treated with nonpharmacologic interventions known to modulate pain levels in this patient population, and to test the hypothesis that the reduction of DMN-insula connectivity following therapy would correlate with diminished pain. Seventeen FM patients underwent resting-state functional magnetic resonance imaging at baseline and following 4 weeks of a nonpharmacologic intervention to diminish pain. Intrinsic DMN connectivity was evaluated using probabilistic independent components analysis. Longitudinal changes in intrinsic DMN connectivity were evaluated by paired analysis, and correlations between longitudinal changes in clinical pain and changes in intrinsic DMN connectivity were investigated by multiple linear regression analysis. Changes in clinical pain were assessed with the short form of the McGill Pain Questionnaire (SF-MPQ). Clinical pain as assessed using the sensory scale of the SF-MPQ was reduced following therapy (P=0.02). Intrinsic DMN connectivity to the insula was reduced, and this reduction correlated with reductions in pain (corrected P<0.05). Our findings suggest that intrinsic brain connectivity can be used as a candidate objective marker that reflects changes in spontaneous chronic pain within individual FM patients. We propose that intrinsic connectivity measures could potentially be used in either research or clinical settings as a complementary, more objective outcome measure for use in FM. Copyright © 2012 by the American College of Rheumatology.

Resting state connectivity of the medial prefrontal cortex covaries with individual differences in high-frequency heart rate variability.

PubMed

Jennings, J Richard; Sheu, Lei K; Kuan, Dora C-H; Manuck, Stephen B; Gianaros, Peter J

2016-04-01

Resting high-frequency heart rate variability (HF-HRV) relates to cardiac vagal control and predicts individual differences in health and longevity, but its functional neural correlates are not well defined. The medial prefrontal cortex (mPFC) encompasses visceral control regions that are components of intrinsic networks of the brain, particularly the default mode network (DMN) and the salience network (SN). Might individual differences in resting HF-HRV covary with resting state neural activity in the DMN and SN, particularly within the mPFC? This question was addressed using fMRI data from an eyes-open, 5-min rest period during which echoplanar brain imaging yielded BOLD time series. Independent component analysis yielded functional connectivity estimates defining the DMN and SN. HF-HRV was measured in a rest period outside of the scanner. Midlife (52% female) adults were assessed in two studies (Study 1, N = 107; Study 2, N = 112). Neither overall DMN nor SN connectivity strength was related to HF-HRV. However, HF-HRV related to connectivity of one region within mPFC shared by the DMN and SN, namely, the perigenual anterior cingulate cortex, an area with connectivity to other regions involved in autonomic control. In sum, HF-HRV does not seem directly related to global resting state activity of intrinsic brain networks, but rather to more localized connectivity. A mPFC region was of particular interest as connectivity related to HF-HRV was shared by the DMN and SN. These findings may indicate a functional basis for the coordination of autonomic cardiac control with engagement and disengagement from the environment. © 2015 Society for Psychophysiological Research.

The Relationship of Social Engagement and Social Support With Sense of Community.

PubMed

Tang, Fengyan; Chi, Iris; Dong, Xinqi

2017-07-01

We aimed to investigate the relationship of engagement in social and cognitive activities and social support with the sense of community (SOC) and its components among older Chinese Americans. The Sense of Community Index (SCI) was used to measure SOC and its four component factors: membership, influence, needs fulfillment, and emotional connection. Social engagement was assessed with 16 questions. Social support included positive support and negative strain. Principal component analysis was used to identify the SCI components. Linear regression analysis was used to detect the contribution of social engagement and social support to SOC and its components. After controlling for sociodemographics and self-rated health, social activity engagement and positive social support were positively related to SOC and its components. This study points to the importance of social activity engagement and positive support from family and friends in increasing the sense of community. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A Baseline for the Multivariate Comparison of Resting-State Networks

PubMed Central

Allen, Elena A.; Erhardt, Erik B.; Damaraju, Eswar; Gruner, William; Segall, Judith M.; Silva, Rogers F.; Havlicek, Martin; Rachakonda, Srinivas; Fries, Jill; Kalyanam, Ravi; Michael, Andrew M.; Caprihan, Arvind; Turner, Jessica A.; Eichele, Tom; Adelsheim, Steven; Bryan, Angela D.; Bustillo, Juan; Clark, Vincent P.; Feldstein Ewing, Sarah W.; Filbey, Francesca; Ford, Corey C.; Hutchison, Kent; Jung, Rex E.; Kiehl, Kent A.; Kodituwakku, Piyadasa; Komesu, Yuko M.; Mayer, Andrew R.; Pearlson, Godfrey D.; Phillips, John P.; Sadek, Joseph R.; Stevens, Michael; Teuscher, Ursina; Thoma, Robert J.; Calhoun, Vince D.

2011-01-01

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease. PMID:21442040

Impaired Long Distance Functional Connectivity and Weighted Network Architecture in Alzheimer's Disease

PubMed Central

Liu, Yong; Yu, Chunshui; Zhang, Xinqing; Liu, Jieqiong; Duan, Yunyun; Alexander-Bloch, Aaron F.; Liu, Bing; Jiang, Tianzi; Bullmore, Ed

2014-01-01

Alzheimer's disease (AD) is increasingly recognized as a disconnection syndrome, which leads to cognitive impairment due to the disruption of functional activity across large networks or systems of interconnected brain regions. We explored abnormal functional magnetic resonance imaging (fMRI) resting-state dynamics, functional connectivity, and weighted functional networks, in a sample of patients with severe AD (N = 18) and age-matched healthy volunteers (N = 21). We found that patients had reduced amplitude and regional homogeneity of low-frequency fMRI oscillations, and reduced the strength of functional connectivity, in several regions previously described as components of the default mode network, for example, medial posterior parietal cortex and dorsal medial prefrontal cortex. In patients with severe AD, functional connectivity was particularly attenuated between regions that were separated by a greater physical distance; and loss of long distance connectivity was associated with less efficient global and nodal network topology. This profile of functional abnormality in severe AD was consistent with the results of a comparable analysis of data on 2 additional groups of patients with mild AD (N = 17) and amnestic mild cognitive impairment (MCI; N = 18). A greater degree of cognitive impairment, measured by the mini-mental state examination across all patient groups, was correlated with greater attenuation of functional connectivity, particularly over long connection distances, for example, between anterior and posterior components of the default mode network, and greater reduction of global and nodal network efficiency. These results indicate that neurodegenerative disruption of fMRI oscillations and connectivity in AD affects long-distance connections to hub nodes, with the consequent loss of network efficiency. This profile was evident also to a lesser degree in the patients with less severe cognitive impairment, indicating that the potential of resting-state fMRI measures as biomarkers or predictors of disease progression in AD. PMID:23314940

Decreased triple network connectivity in patients with post-traumatic stress disorder

NASA Astrophysics Data System (ADS)

Liu, Yang; Li, Liang; Li, Baojuan; Zhang, Xi; Lu, Hongbing

2017-03-01

The triple network model provides a common framework for understanding affective and neurocognitive dysfunctions across multiple disorders, including central executive network (CEN), default mode network (DMN), and salience network (SN). Considering the effect of traumatic experience on post-traumatic stress disorder (PTSD), this study aims to explore the alteration of triple network connectivity in a specific PTSD induced by a single prolonged trauma exposure. With arterial spin labeling sequence, three networks were identified using independent component analysis in 10 PTSD patients and 10 healthy survivors, who experienced the same coal mining flood disaster. In PTSD patients, decreased connectivity was identified in left middle frontal gyrus of CEN, left precuneus and bilateral superior frontal gyrus of DMN, and right anterior insula of SN. The decreased connectivity in left middle frontal gyrus was identified to associate with clinical severity. These results indicated the decreased triple network connectivity, which not only supported the proposal of the triple network model, but also prompted possible neurobiology mechanism of cognitive dysfunction for this kind of PTSD.

Predicting the cumulative effect of multiple disturbances on seagrass connectivity.

PubMed

Grech, Alana; Hanert, Emmanuel; McKenzie, Len; Rasheed, Michael; Thomas, Christopher; Tol, Samantha; Wang, Mingzhu; Waycott, Michelle; Wolter, Jolan; Coles, Rob

2018-03-15

The rate of exchange, or connectivity, among populations effects their ability to recover after disturbance events. However, there is limited information on the extent to which populations are connected or how multiple disturbances affect connectivity, especially in coastal and marine ecosystems. We used network analysis and the outputs of a biophysical model to measure potential functional connectivity and predict the impact of multiple disturbances on seagrasses in the central Great Barrier Reef World Heritage Area (GBRWHA), Australia. The seagrass networks were densely connected, indicating that seagrasses are resilient to the random loss of meadows. Our analysis identified discrete meadows that are important sources of seagrass propagules and that serve as stepping stones connecting various different parts of the network. Several of these meadows were close to urban areas or ports and likely to be at risk from coastal development. Deep water meadows were highly connected to coastal meadows and may function as a refuge, but only for non-foundation species. We evaluated changes to the structure and functioning of the seagrass networks when one or more discrete meadows were removed due to multiple disturbance events. The scale of disturbance required to disconnect the seagrass networks into two or more components was on average >245 km, about half the length of the metapopulation. The densely connected seagrass meadows of the central GBRWHA are not limited by the supply of propagules; therefore, management should focus on improving environmental conditions that support natural seagrass recruitment and recovery processes. Our study provides a new framework for assessing the impact of global change on the connectivity and persistence of coastal and marine ecosystems. Without this knowledge, management actions, including coastal restoration, may prove unnecessary and be unsuccessful. © 2018 John Wiley & Sons Ltd.

The Great Whoosh: Connecting an Online Personal Health Narrative and Communication Privacy Management.

PubMed

Smith, Stephanie A; Brunner, Steven R

2016-01-01

This research study examined Bud Goodall's online health narrative as a case study through the use of a thematic analysis to investigate the presence of communication privacy management (CPM) theory. Emergent themes of humor as a privacy management strategy, legitimization of co-owners, shifting privacy rules at end of life, and metaphors as privacy protection were used to recount Goodall's cancer experience on his personal blog, connecting to the components of CPM. The themes the authors analyzed represent the push-pull dialectical tension experienced to reveal and conceal information, conceptualization of private information, shared boundaries, and boundary linkages.

GUIDOS: tools for the assessment of pattern, connectivity, and fragmentation

NASA Astrophysics Data System (ADS)

Vogt, Peter

2013-04-01

Pattern, connectivity, and fragmentation can be considered as pillars for a quantitative analysis of digital landscape images. The free software toolbox GUIDOS (http://forest.jrc.ec.europa.eu/download/software/guidos) includes a variety of dedicated methodologies for the quantitative assessment of these features. Amongst others, Morphological Spatial Pattern Analysis (MSPA) is used for an intuitive description of image pattern structures and the automatic detection of connectivity pathways. GUIDOS includes tools for the detection and quantitative assessment of key nodes and links as well as to define connectedness in raster images and to setup appropriate input files for an enhanced network analysis using Conefor Sensinode. Finally, fragmentation is usually defined from a species point of view but a generic and quantifiable indicator is needed to measure fragmentation and its changes. Some preliminary results for different conceptual approaches will be shown for a sample dataset. Complemented by pre- and post-processing routines and a complete GIS environment the portable GUIDOS Toolbox may facilitate a holistic assessment in risk assessment studies, landscape planning, and conservation/restoration policies. Alternatively, individual analysis components may contribute to or enhance studies conducted with other software packages in landscape ecology.

On the lightweighting of automobile engine components : forming sheet metal connecting rod

NASA Astrophysics Data System (ADS)

Date, P. P.; Kasture, R. N.; Kore, A. S.

2017-09-01

Reducing the inertia of the reciprocating engine components can lead to significant savings on fuel. A lighter connecting rod (for the same functionality and performance) with a lower material input would be an advantage to the user (customer) and the manufacturer alike. Light materials will make the connecting rod much more expensive compared to those made from steel. Non-ferrous metals are amenable to cold forging of engine components to achieve lightweighting. Alternately, one can make a hollow connecting rod formed from steel sheet, thereby making it lighter, and with many advantages over the conventionally hot forged product. The present paper describes the process of forming a connecting rod from sheet metal. Cold forming (as opposed to high energy needs, lower tool life and the need for greater number of operations and finishing processes in hot forming) would be expected to reduce the cost of manufacture by cold forming. Work hardening during forming is also expected to enhance the in-service performance of the connecting rod.

Trade-off of cerebello-cortical and cortico-cortical functional networks for planning in 6-year-old children.

PubMed

Kipping, Judy A; Margulies, Daniel S; Eickhoff, Simon B; Lee, Annie; Qiu, Anqi

2018-08-01

Childhood is a critical period for the development of cognitive planning. There is a lack of knowledge on its neural mechanisms in children. This study aimed to examine cerebello-cortical and cortico-cortical functional connectivity in association with planning skills in 6-year-olds (n = 76). We identified the cerebello-cortical and cortico-cortical functional networks related to cognitive planning using activation likelihood estimation (ALE) meta-analysis on existing functional imaging studies on spatial planning, and data-driven independent component analysis (ICA) of children's resting-state functional MRI (rs-fMRI). We investigated associations of cerebello-cortical and cortico-cortical functional connectivity with planning ability in 6-year-olds, as assessed using the Stockings of Cambridge task. Long-range functional connectivity of two cerebellar networks (lobules VI and lateral VIIa) with the prefrontal and premotor cortex were greater in children with poorer planning ability. In contrast, cortico-cortical association networks were not associated with the performance of planning in children. These results highlighted the key contribution of the lateral cerebello-frontal functional connectivity, but not cortico-cortical association functional connectivity, for planning ability in 6-year-olds. Our results suggested that brain adaptation to the acquisition of planning ability during childhood is partially achieved through the engagement of the cerebello-cortical functional connectivity. Copyright © 2018 Elsevier Inc. All rights reserved.

Whole-brain functional connectivity during acquisition of novel grammar: Distinct functional networks depend on language learning abilities.

PubMed

Kepinska, Olga; de Rover, Mischa; Caspers, Johanneke; Schiller, Niels O

2017-03-01

In an effort to advance the understanding of brain function and organisation accompanying second language learning, we investigate the neural substrates of novel grammar learning in a group of healthy adults, consisting of participants with high and average language analytical abilities (LAA). By means of an Independent Components Analysis, a data-driven approach to functional connectivity of the brain, the fMRI data collected during a grammar-learning task were decomposed into maps representing separate cognitive processes. These included the default mode, task-positive, working memory, visual, cerebellar and emotional networks. We further tested for differences within the components, representing individual differences between the High and Average LAA learners. We found high analytical abilities to be coupled with stronger contributions to the task-positive network from areas adjacent to bilateral Broca's region, stronger connectivity within the working memory network and within the emotional network. Average LAA participants displayed stronger engagement within the task-positive network from areas adjacent to the right-hemisphere homologue of Broca's region and typical to lower level processing (visual word recognition), and increased connectivity within the default mode network. The significance of each of the identified networks for the grammar learning process is presented next to a discussion on the established markers of inter-individual learners' differences. We conclude that in terms of functional connectivity, the engagement of brain's networks during grammar acquisition is coupled with one's language learning abilities. Copyright © 2016 Elsevier B.V. All rights reserved.

Exploring Vietnamese co-authorship patterns in social sciences with basic network measures of 2008-2017 Scopus data.

PubMed

Ho, Tung Manh; Nguyen, Ha Viet; Vuong, Thu-Trang; Dam, Quang-Minh; Pham, Hiep-Hung; Vuong, Quan-Hoang

2017-01-01

Background: Collaboration is a common occurrence among Vietnamese scientists; however, insights into Vietnamese scientific collaborations have been scarce. On the other hand, the application of social network analysis in studying science collaboration has gained much attention all over the world. The technique could be employed to explore Vietnam's scientific community. Methods: This paper employs network theory to explore characteristics of a network of 412 Vietnamese social scientists whose papers can be found indexed in the Scopus database. Two basic network measures, density and clustering coefficient, were taken, and the entire network was studied in comparison with two of its largest components. Results: The networks connections are very sparse, with a density of only 0.47%, while the clustering coefficient is very high (58.64%). This suggests an inefficient dissemination of information, knowledge, and expertise in the network. Secondly, the disparity in levels of connection among individuals indicates that the network would easily fall apart if a few highly-connected nodes are removed. Finally, the two largest components of the network were found to differ from the entire networks in terms of measures and were both led by the most productive and well-connected researchers. Conclusions: High clustering and low density seems to be tied to inefficient dissemination of expertise among Vietnamese social scientists, and consequently low scientific output. Also low in robustness, the network shows the potential of an intellectual elite composed of well-connected, productive, and socially significant individuals.

Exploring Vietnamese co-authorship patterns in social sciences with basic network measures of 2008-2017 Scopus data

PubMed Central

Ho, Tung Manh; Nguyen, Ha Viet; Vuong, Thu-Trang; Dam, Quang-Minh; Pham, Hiep-Hung; Vuong, Quan-Hoang

2017-01-01

Background: Collaboration is a common occurrence among Vietnamese scientists; however, insights into Vietnamese scientific collaborations have been scarce. On the other hand, the application of social network analysis in studying science collaboration has gained much attention all over the world. The technique could be employed to explore Vietnam’s scientific community. Methods: This paper employs network theory to explore characteristics of a network of 412 Vietnamese social scientists whose papers can be found indexed in the Scopus database. Two basic network measures, density and clustering coefficient, were taken, and the entire network was studied in comparison with two of its largest components. Results: The networks connections are very sparse, with a density of only 0.47%, while the clustering coefficient is very high (58.64%). This suggests an inefficient dissemination of information, knowledge, and expertise in the network. Secondly, the disparity in levels of connection among individuals indicates that the network would easily fall apart if a few highly-connected nodes are removed. Finally, the two largest components of the network were found to differ from the entire networks in terms of measures and were both led by the most productive and well-connected researchers. Conclusions: High clustering and low density seems to be tied to inefficient dissemination of expertise among Vietnamese social scientists, and consequently low scientific output. Also low in robustness, the network shows the potential of an intellectual elite composed of well-connected, productive, and socially significant individuals. PMID:28928958

Multivariate pattern dependence

PubMed Central

Saxe, Rebecca

2017-01-01

When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD): a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS) and to the fusiform face area (FFA), using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity. PMID:29155809

Mapping white-matter functional organization at rest and during naturalistic visual perception.

PubMed

Marussich, Lauren; Lu, Kun-Han; Wen, Haiguang; Liu, Zhongming

2017-02-01

Despite the wide applications of functional magnetic resonance imaging (fMRI) to mapping brain activation and connectivity in cortical gray matter, it has rarely been utilized to study white-matter functions. In this study, we investigated the spatiotemporal characteristics of fMRI data within the white matter acquired from humans both in the resting state and while watching a naturalistic movie. By using independent component analysis and hierarchical clustering, resting-state fMRI data in the white matter were de-noised and decomposed into spatially independent components, which were further assembled into hierarchically organized axonal fiber bundles. Interestingly, such components were partly reorganized during natural vision. Relative to resting state, the visual task specifically induced a stronger degree of temporal coherence within the optic radiations, as well as significant correlations between the optic radiations and multiple cortical visual networks. Therefore, fMRI contains rich functional information about the activity and connectivity within white matter at rest and during tasks, challenging the conventional practice of taking white-matter signals as noise or artifacts. Copyright © 2016 Elsevier Inc. All rights reserved.

Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches.

PubMed

Smith, David V; Utevsky, Amanda V; Bland, Amy R; Clement, Nathan; Clithero, John A; Harsch, Anne E W; McKell Carter, R; Huettel, Scott A

2014-07-15

A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent component analysis (ICA). We estimated voxel-wise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal-parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust-yet frequently ignored-neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity. Copyright © 2014 Elsevier Inc. All rights reserved.

A SVM-based quantitative fMRI method for resting-state functional network detection.

PubMed

Song, Xiaomu; Chen, Nan-kuei

2014-09-01

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies. Copyright © 2014 Elsevier Inc. All rights reserved.

Altered Functional Connectivity of the Default Mode Network in Low-Empathy Subjects

PubMed Central

Kim, Seung Jun; Kim, Sung-Eun; Kim, Hyo Eun; Han, Kiwan; Jeong, Bumseok; Kim, Jae-Jin; Namkoong, Kee

2017-01-01

Empathy is the ability to identify with or make a vicariously experience of another person's feelings or thoughts based on memory and/or self-referential mental simulation. The default mode network in particular is related to self-referential empathy. In order to elucidate the possible neural mechanisms underlying empathy, we investigated the functional connectivity of the default mode network in subjects from a general population. Resting state functional magnetic resonance imaging data were acquired from 19 low-empathy subjects and 18 medium-empathy subjects. An independent component analysis was used to identify the default mode network, and differences in functional connectivity strength were compared between the two groups. The low-empathy group showed lower functional connectivity of the medial prefrontal cortex and anterior cingulate cortex (Brodmann areas 9 and 32) within the default mode network, compared to the medium-empathy group. The results of the present study suggest that empathy is related to functional connectivity of the medial prefrontal cortex/anterior cingulate cortex within the default mode network. Functional decreases in connectivity among low-empathy subjects may reflect an impairment of self-referential mental simulation. PMID:28792155

Strengthening of Top-Down Frontal Cognitive Control Networks Underlying the Development of Inhibitory Control: An fMRI Effective Connectivity Study

PubMed Central

Hwang, Kai; Velanova, Katerina; Luna, Beatriz

2010-01-01

The ability to voluntarily inhibit responses to task irrelevant stimuli, which is a fundamental component of cognitive control, has a protracted development through adolescence. Prior human developmental imaging studies have found immaturities in localized brain activity in children and adolescents. However, little is known about how these regions integrate with age to form the distributed networks known to support cognitive control. In the present study, we used Granger Causality analysis to characterize developmental changes in effective connectivity underlying inhibitory control (antisaccade task) compared to reflexive responses (prosaccade task) in human participants. By childhood few top-down connectivity were evident with increased parietal interconnectivity. By adolescence connections from prefrontal cortex increased and parietal interconnectivity decreased in number. From adolescence to adulthood there was evidence of increased number and strength of frontal connections to cortical regions as well as subcortical regions. Taken together, results suggest that developmental improvements in inhibitory control may be supported by age related enhancements in top-down effective connectivity between frontal, oculomotor and subcortical regions. PMID:21084608

Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter?

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2016-07-01

The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer’s Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals.

High-frequency seismic noise: Results of investigation in Kamchatka

NASA Astrophysics Data System (ADS)

Saltykov, V.; Chebrov, V.; Kugaenko, Yu.; Sinitsyn, V.

The investigation of seismic noise in Kamchatka is carried out for the control of the medium stress condition and search of the strong earthquakes precursors. The main directions of this research are modulation of high-frequency seismic noise (HFSN, frequency range of the first tens of Hz, amplitudes about 10 -9-10 -12 m) by the Earth tides and temporal variations of HFSN parameters connected with the strong earthquake preparation. For reception of the statistically significant characteristics of HFSN and tides connection it was necessary to carry out long-term HFSN observations in points free from anthropogenous influence as far as possible. The station of HFSN observation was organized in the settlement Nachiky. The sensor is a narrow-band ( Q = 100) piezoelectric seismometer, tuned to frequency 30 Hz. Signal envelope is recorded and analyzed. The continuous HFSN registration was begun in 1990 and proceeds till now. In 2000 the second station was established in the complex geophysical observatory “Karymshina”. The HFSN sensor is set up in the borehole at the depth of 30 m. The research of HFSN structure gave the opportunity to allocate HFSN components connected with the Earth tides. Besides it was revealed that the tidal response is not stable in time: the intervals of the tidal component existence are replaced by intervals of its absence, correlation between tide and HFSN varies in time, while tides have constant parameters. We propose a hypothesis about the connection of variations of the tidal components in HFSN data with the tectonic conditions in region, and consequently, about an opportunity to use this phenomenon for the prediction of strong earthquakes. The phase of the HFSN component connected with a tidal wave O1 ( T = 25.8 h) was chosen as a parameter. The choice of wave O1 is connected with its greatest hindrance-immunity. It was shown that the stabilization of this phase is observed before earthquakes with M > 6.0, occurred at distances up to 250 km from the HFSN registration point, within time from several weeks to several months. Since 1996 such an analysis of the HFSN response to tides is conducted in an operative mode, and only in 1 case out of 19 the large earthquake precursor was not shown in any way.

Reduced functional connectivity within and between ‘social’ resting state networks in autism spectrum conditions

PubMed Central

Stoyanova, Raliza S.; Baron-Cohen, Simon; Calder, Andrew J.

2013-01-01

Individuals with Autism Spectrum Conditions (ASC) have difficulties in social interaction and communication, which is reflected in hypoactivation of brain regions engaged in social processing, such as medial prefrontal cortex (mPFC), amygdala and insula. Resting state studies in ASC have identified reduced connectivity of the default mode network (DMN), which includes mPFC, suggesting that other resting state networks incorporating ‘social’ brain regions may also be abnormal. Using Seed-based Connectivity and Group Independent Component Analysis (ICA) approaches, we looked at resting functional connectivity in ASC between specific ‘social’ brain regions, as well as within and between whole networks incorporating these regions. We found reduced functional connectivity within the DMN in individuals with ASC, using both ICA and seed-based approaches. Two further networks identified by ICA, the salience network, incorporating the insula and a medial temporal lobe network, incorporating the amygdala, showed reduced inter-network connectivity. This was underlined by reduced seed-based connectivity between the insula and amygdala. The results demonstrate significantly reduced functional connectivity within and between resting state networks incorporating ‘social’ brain regions. This reduced connectivity may result in difficulties in communication and integration of information across these networks, which could contribute to the impaired processing of social signals in ASC. PMID:22563003

Intrinsic connectivity networks within cerebellum and beyond in eating disorders.

PubMed

Amianto, F; D'Agata, F; Lavagnino, L; Caroppo, P; Abbate-Daga, G; Righi, D; Scarone, S; Bergui, M; Mortara, P; Fassino, S

2013-10-01

Cerebellum seems to have a role both in feeding behavior and emotion regulation; therefore, it is a region that warrants further neuroimaging studies in eating disorders, severe conditions that determine a significant impairment in the physical and psychological domain. The aim of this study was to examine the cerebellum intrinsic connectivity during functional magnetic resonance imaging resting state in anorexia nervosa (AN), bulimia nervosa (BN), and healthy controls (CN). Resting state brain activity was decomposed into intrinsic connectivity networks (ICNs) using group spatial independent component analysis on the resting blood oxygenation level dependent time courses of 12 AN, 12 BN, and 10 CN. We extracted the cerebellar ICN and compared it between groups. Intrinsic connectivity within the cerebellar network showed some common alterations in eating disordered compared to healthy subjects (e.g., a greater connectivity with insulae, vermis, and paravermis and a lesser connectivity with parietal lobe); AN and BN patients were characterized by some peculiar alterations in connectivity patterns (e.g., greater connectivity with the insulae in AN compared to BN, greater connectivity with anterior cingulate cortex in BN compared to AN). Our data are consistent with the presence of different alterations in the cerebellar network in AN and BN patients that could be related to psychopathologic dimensions of eating disorders.

Verbal working memory-related functional connectivity alterations in boys with attention-deficit/hyperactivity disorder and the effects of methylphenidate.

PubMed

Wu, Zhao-Min; Bralten, Janita; An, Li; Cao, Qing-Jiu; Cao, Xiao-Hua; Sun, Li; Liu, Lu; Yang, Li; Mennes, Maarten; Zang, Yu-Feng; Franke, Barbara; Hoogman, Martine; Wang, Yu-Feng

2017-08-01

Few studies have investigated verbal working memory-related functional connectivity patterns in participants with attention-deficit/hyperactivity disorder (ADHD). Thus, we aimed to compare working memory-related functional connectivity patterns in healthy children and those with ADHD, and study effects of methylphenidate (MPH). Twenty-two boys with ADHD were scanned twice, under either MPH (single dose, 10 mg) or placebo, in a randomised, cross-over, counterbalanced placebo-controlled design. Thirty healthy boys were scanned once. We used fMRI during a numerical n-back task to examine functional connectivity patterns in case-control and MPH-placebo comparisons, using independent component analysis. There was no significant difference in behavioural performance between children with ADHD, treated with MPH or placebo, and healthy controls. Compared with controls, participants with ADHD under placebo showed increased functional connectivity within fronto-parietal and auditory networks, and decreased functional connectivity within the executive control network. MPH normalized the altered functional connectivity pattern and significantly enhanced functional connectivity within the executive control network, though in non-overlapping areas. Our study contributes to the identification of the neural substrates of working memory. Single dose of MPH normalized the altered brain functional connectivity network, but had no enhancing effect on (non-impaired) behavioural performance.

Identifying dynamic functional connectivity biomarkers using GIG-ICA: application to schizophrenia, schizoaffective disorder and psychotic bipolar disorder

PubMed Central

Du, Yuhui; Pearlson, Godfrey D; Lin, Dongdong; Sui, Jing; Chen, Jiayu; Salman, Mustafa; Tamminga, Carol A.; Ivleva, Elena I.; Sweeney, John A.; Keshavan, Matcheri S.; Clementz, Brett A.; Bustillo, Juan; Calhoun, Vince D.

2017-01-01

Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here we aim to explore DFC across a spectrum of symptomatically-related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD) and schizophrenia (SZ). We introduce a group information guided independent component analysis (GIG-ICA) procedure to estimate both group-level and subject-specific connectivity states from DFC. Using resting-state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD and 113 SZ patients, we identified measures differentiating groups from the whole-brain DFC and traditional static functional connectivity (SFC), separately. Results show that DFC provided more informative measures than SFC. Diagnosis-related connectivity states were evident using DFC analysis. For the dominant state consistent across groups, we found 22 instances of hypoconnectivity (with decreasing trends from HC to BPP to SAD to SZ) mainly involving post-central, frontal and cerebellar cortices as well as 34 examples of hyperconnectivity (with increasing trends HC through SZ) primarily involving thalamus and temporal cortices. Hypoconnectivities/hyperconnectivities also showed negative/positive correlations, respectively, with clinical symptom scores. Specifically, hypoconnectivities linking postcentral and frontal gyri were significantly negatively correlated with the PANSS positive/negative scores. For frontal connectivities, BPP resembled HC while SAD and SZ were more similar. Three connectivities involving the left cerebellar crus differentiated SZ from other groups and one connection linking frontal and fusiform cortices showed a SAD-unique change. In summary, our method is promising for assessing DFC and may yield imaging biomarkers for quantifying the dimension of psychosis. PMID:28294459

A Selective Review of Simulated Driving Studies: Combining Naturalistic and Hybrid Paradigms, Analysis Approaches, and Future Directions

PubMed Central

Calhoun, V. D.; Pearlson, G. D.

2011-01-01

Naturalistic paradigms such as movie watching or simulated driving that mimic closely real-world complex activities are becoming more widely used in functional magnetic resonance imaging (fMRI) studies both because of their ability to robustly stimulate brain connectivity and the availability of analysis methods which are able to capitalize on connectivity within and among intrinsic brain networks identified both during a task and in resting fMRI data. In this paper we review over a decade of work from our group and others on the use of simulated driving paradigms to study both the healthy brain as well as the effects of acute alcohol administration on functional connectivity during such paradigms. We briefly review our initial work focused on the configuration of the driving simulator and the analysis strategies. We then describe in more detail several recent studies from our group including a hybrid study examining distracted driving and compare resulting data with those from a separate visual oddball task. The analysis of these data were performed primarily using a combination of group independent component analysis (ICA) and the general linear model (GLM) and in the various studies we highlight novel findings which result from an analysis of either 1) within-network connectivity, 2) inter-network connectivity, also called functional network connectivity, or 3) the degree to which the modulation of the various intrinsic networks were associated with the alcohol administration and the task context. Despite the fact that the behavioral effects of alcohol intoxication are relatively well known, there is still much to discover on how acute alcohol exposure modulates brain function in a selective manner, associated with behavioral alterations. Through the above studies, we have learned more regarding the impact of acute alcohol intoxication on organization of the brain’s intrinsic connectivity networks during performance of a complex, real-world cognitive operation. Lessons learned from the above studies have broader applicability to designing ecologically valid, complex, functional MRI cognitive paradigms and incorporating pharmacologic challenges into such studies. Overall, the use of hybrid driving studies is a particularly promising area of neuroscience investigation. PMID:21718791

The Mediating Role of Intercultural Wonderment: Connecting Programmatic Components to Global Outcomes in Study Abroad

ERIC Educational Resources Information Center

Engberg, Mark E.; Jourian, T. J.; Davidson, Lisa M.

2016-01-01

This study examines the mediating role of intercultural wonderment in relation to students' development of a global perspective. We utilize both confirmatory factor analysis and structural equation modeling to validate the intercultural wonderment construct and test the direct and indirect effects of the structural pathways in the model,…

Spatial patterns of soil moisture connected to monthly-seasonal precipitation variability in a monsoon region

Treesearch

Yongqiang Liu

2003-01-01

The relations between monthly-seasonal soil moisture and precipitation variability are investigated by identifying the coupled patterns of the two hydrological fields using singular value decomposition (SVD). SVD is a technique of principal component analysis similar to empirical orthogonal knctions (EOF). However, it is applied to two variables simultaneously and is...

Down hole transmission system

DOEpatents

Hall, David R [Provo, UT; Hall, Jr., H. Tracy

2007-07-24

A transmission system in a downhole component comprises a data transmission element in both ends of the downhole component. Each data transmission element houses an electrically conducting coil in a MCEI circular trough. The electrically conducting coil comprises at least two generally fractional loops. In the preferred embodiment, the transmission elements are connected by an electrical conductor. Preferably, the electrical conductor is a coaxial cable. Preferably, the MCEI trough comprises ferrite. In the preferred embodiment, the fractional loops are connected by a connecting cable. In one aspect of the present invention, the connecting cable is a pair of twisted wires. In one embodiment the connecting cable is a shielded pair of twisted wires. In another aspect of the present invention, the connecting cable is a coaxial cable. The connecting cable may be disposed outside of the MCEI circular trough.

Fusion of LIDAR Data and Multispectral Imagery for Effective Building Detection Based on Graph and Connected Component Analysis

NASA Astrophysics Data System (ADS)

Gilani, S. A. N.; Awrangjeb, M.; Lu, G.

2015-03-01

Building detection in complex scenes is a non-trivial exercise due to building shape variability, irregular terrain, shadows, and occlusion by highly dense vegetation. In this research, we present a graph based algorithm, which combines multispectral imagery and airborne LiDAR information to completely delineate the building boundaries in urban and densely vegetated area. In the first phase, LiDAR data is divided into two groups: ground and non-ground data, using ground height from a bare-earth DEM. A mask, known as the primary building mask, is generated from the non-ground LiDAR points where the black region represents the elevated area (buildings and trees), while the white region describes the ground (earth). The second phase begins with the process of Connected Component Analysis (CCA) where the number of objects present in the test scene are identified followed by initial boundary detection and labelling. Additionally, a graph from the connected components is generated, where each black pixel corresponds to a node. An edge of a unit distance is defined between a black pixel and a neighbouring black pixel, if any. An edge does not exist from a black pixel to a neighbouring white pixel, if any. This phenomenon produces a disconnected components graph, where each component represents a prospective building or a dense vegetation (a contiguous block of black pixels from the primary mask). In the third phase, a clustering process clusters the segmented lines, extracted from multispectral imagery, around the graph components, if possible. In the fourth step, NDVI, image entropy, and LiDAR data are utilised to discriminate between vegetation, buildings, and isolated building's occluded parts. Finally, the initially extracted building boundary is extended pixel-wise using NDVI, entropy, and LiDAR data to completely delineate the building and to maximise the boundary reach towards building edges. The proposed technique is evaluated using two Australian data sets: Aitkenvale and Hervey Bay, for object-based and pixel-based completeness, correctness, and quality. The proposed technique detects buildings larger than 50 m2 and 10 m2 in the Aitkenvale site with 100% and 91% accuracy, respectively, while in the Hervey Bay site it performs better with 100% accuracy for buildings larger than 10 m2 in area.

Connecting Symbolic Integrals to Physical Meaning in Introductory Physics

NASA Astrophysics Data System (ADS)

Amos, Nathaniel R.

This dissertation presents a series of studies pertaining to introductory physics students' abilities to derive physical meaning from symbolic integrals (e.g., the integral of vdt) and their components, namely differentials and differential products (e.g., dt and vdt, respectively). Our studies focus on physical meaning in the form of interpretations (e.g., "the total displacement of an object") and units (e.g., "meters"). Our first pair of studies independently attempted to identify introductory-level mechanics students' common conceptual difficulties with and unproductive interpretations of physics integrals and their components, as well as to estimate the frequencies of these difficulties. Our results confirmed some previously-observed incorrect interpretations, such as the notion that differentials are physically meaningless; however, we also uncovered two new conceptualizations of differentials, the "rate" (differentials are "rates" or "derivatives") and "instantaneous value" (differentials are values of physical variables "at an instant") interpretations, which were exhibited by more than half of our participants at least once. Our next study used linear regression analysis to estimate the strengths of the inter-connections between the abilities to derive physical meaning from each of differentials, differential products, and integrals in both first- and second-semester, calculus-based introductory physics. As part of this study, we also developed a highly reliable, multiple choice assessment designed to measure students' abilities to connect symbolic differentials, differential products, and integrals with their physical interpretations and units. Findings from this study were consistent with statistical mediation via differential products. In particular, students' abilities to extract physical meaning from differentials were seen to be strongly related to their abilities to derive physical meaning from differential products, and similarly differential products to integrals; there was seen to be almost no direct connection between the abilities to derive physical meaning from differentials and the abilities to derive physical meaning from integrals. Our final pair of studies intended to implement and quantitatively assess the efficacy of specially-designed instructional tutorials in controlled experiments (with several treatment factors that may impact performance, most notably the effect of feedback during training) for the purpose of promoting better connection between symbolic differentials, differential products, and integrals with their corresponding physical meaning. Results from both experiments consistently and conclusively demonstrated that the ability to connect verbal and symbolic representations of integrals and their components is greatly improved by the provision of electronic feedback during training. We believe that these results signify the first instance of a large, controlled experiment involving introductory physics students that has yielded significantly stronger connection of physics integrals and their components to physical meaning, compared to untrained peers.

Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT)

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

Williams, Dean N.; Silva, Claudio

2013-09-30

For the past three years, a large analysis and visualization effort—funded by the Department of Energy’s Office of Biological and Environmental Research (BER), the National Aeronautics and Space Administration (NASA), and the National Oceanic and Atmospheric Administration (NOAA)—has brought together a wide variety of industry-standard scientific computing libraries and applications to create Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT) to serve the global climate simulation and observational research communities. To support interactive analysis and visualization, all components connect through a provenance application–programming interface to capture meaningful history and workflow. Components can be loosely coupled into the framework for fast integrationmore » or tightly coupled for greater system functionality and communication with other components. The overarching goal of UV-CDAT is to provide a new paradigm for access to and analysis of massive, distributed scientific data collections by leveraging distributed data architectures located throughout the world. The UV-CDAT framework addresses challenges in analysis and visualization and incorporates new opportunities, including parallelism for better efficiency, higher speed, and more accurate scientific inferences. Today, it provides more than 600 users access to more analysis and visualization products than any other single source.« less

Pressure activated interconnection of micro transfer printed components

NASA Astrophysics Data System (ADS)

Prevatte, Carl; Guven, Ibrahim; Ghosal, Kanchan; Gomez, David; Moore, Tanya; Bonafede, Salvatore; Raymond, Brook; Trindade, António Jose; Fecioru, Alin; Kneeburg, David; Meitl, Matthew A.; Bower, Christopher A.

2016-05-01

Micro transfer printing and other forms of micro assembly deterministically produce heterogeneously integrated systems of miniaturized components on non-native substrates. Most micro assembled systems include electrical interconnections to the miniaturized components, typically accomplished by metal wires formed on the non-native substrate after the assembly operation. An alternative scheme establishing interconnections during the assembly operation is a cost-effective manufacturing method for producing heterogeneous microsystems, and facilitates the repair of integrated microsystems, such as displays, by ex post facto addition of components to correct defects after system-level tests. This letter describes pressure-concentrating conductor structures formed on silicon (1 0 0) wafers to establish connections to preexisting conductive traces on glass and plastic substrates during micro transfer printing with an elastomer stamp. The pressure concentrators penetrate a polymer layer to form the connection, and reflow of the polymer layer bonds the components securely to the target substrate. The experimental yield of series-connected test systems with >1000 electrical connections demonstrates the suitability of the process for manufacturing, and robustness of the test systems against exposure to thermal shock, damp heat, and mechanical flexure shows reliability of the resulting bonds.

Correlation between resting state fMRI total neuronal activity and PET metabolism in healthy controls and patients with disorders of consciousness.

PubMed

Soddu, Andrea; Gómez, Francisco; Heine, Lizette; Di Perri, Carol; Bahri, Mohamed Ali; Voss, Henning U; Bruno, Marie-Aurélie; Vanhaudenhuyse, Audrey; Phillips, Christophe; Demertzi, Athena; Chatelle, Camille; Schrouff, Jessica; Thibaut, Aurore; Charland-Verville, Vanessa; Noirhomme, Quentin; Salmon, Eric; Tshibanda, Jean-Flory Luaba; Schiff, Nicholas D; Laureys, Steven

2016-01-01

The mildly invasive 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established imaging technique to measure 'resting state' cerebral metabolism. This technique made it possible to assess changes in metabolic activity in clinical applications, such as the study of severe brain injury and disorders of consciousness. We assessed the possibility of creating functional MRI activity maps, which could estimate the relative levels of activity in FDG-PET cerebral metabolic maps. If no metabolic absolute measures can be extracted, our approach may still be of clinical use in centers without access to FDG-PET. It also overcomes the problem of recognizing individual networks of independent component selection in functional magnetic resonance imaging (fMRI) resting state analysis. We extracted resting state fMRI functional connectivity maps using independent component analysis and combined only components of neuronal origin. To assess neuronality of components a classification based on support vector machine (SVM) was used. We compared the generated maps with the FDG-PET maps in 16 healthy controls, 11 vegetative state/unresponsive wakefulness syndrome patients and four locked-in patients. The results show a significant similarity with ρ = 0.75 ± 0.05 for healthy controls and ρ = 0.58 ± 0.09 for vegetative state/unresponsive wakefulness syndrome patients between the FDG-PET and the fMRI based maps. FDG-PET, fMRI neuronal maps, and the conjunction analysis show decreases in frontoparietal and medial regions in vegetative patients with respect to controls. Subsequent analysis in locked-in syndrome patients produced also consistent maps with healthy controls. The constructed resting state fMRI functional connectivity map points toward the possibility for fMRI resting state to estimate relative levels of activity in a metabolic map.

Using Separable Nonnegative Matrix Factorization Techniques for the Analysis of Time-Resolved Raman Spectra

NASA Astrophysics Data System (ADS)

Luce, R.; Hildebrandt, P.; Kuhlmann, U.; Liesen, J.

2016-09-01

The key challenge of time-resolved Raman spectroscopy is the identification of the constituent species and the analysis of the kinetics of the underlying reaction network. In this work we present an integral approach that allows for determining both the component spectra and the rate constants simultaneously from a series of vibrational spectra. It is based on an algorithm for non-negative matrix factorization which is applied to the experimental data set following a few pre-processing steps. As a prerequisite for physically unambiguous solutions, each component spectrum must include one vibrational band that does not significantly interfere with vibrational bands of other species. The approach is applied to synthetic "experimental" spectra derived from model systems comprising a set of species with component spectra differing with respect to their degree of spectral interferences and signal-to-noise ratios. In each case, the species involved are connected via monomolecular reaction pathways. The potential and limitations of the approach for recovering the respective rate constants and component spectra are discussed.

Brain Functional Connectivity in Small Cell Lung Cancer Population after Chemotherapy Treatment: an ICA fMRI Study

NASA Astrophysics Data System (ADS)

Bromis, K.; Kakkos, I.; Gkiatis, K.; Karanasiou, I. S.; Matsopoulos, G. K.

2017-11-01

Previous neurocognitive assessments in Small Cell Lung Cancer (SCLC) population, highlight the presence of neurocognitive impairments (mainly in attention processing and executive functioning) in this type of cancer. The majority of these studies, associate these deficits with the Prophylactic Cranial Irradiation (PCI) that patients undergo in order to avoid brain metastasis. However, there is not much evidence exploring cognitive impairments induced by chemotherapy in SCLC patients. For this reason, we aimed to investigate the underlying processes that may potentially affect cognition by examining brain functional connectivity in nineteen SCLC patients after chemotherapy treatment, while additionally including fourteen healthy participants as control group. Independent Component Analysis (ICA) is a functional connectivity measure aiming to unravel the temporal correlation between brain regions, which are called brain networks. We focused on two brain networks related to the aforementioned cognitive functions, the Default Mode Network (DMN) and the Task-Positive Network (TPN). Permutation tests were performed between the two groups to assess the differences and control for familywise errors in the statistical parametric maps. ICA analysis showed functional connectivity disruptions within both of the investigated networks. These results, propose a detrimental effect of chemotherapy on brain functioning in the SCLC population.

Analysis of the Spatial Organization of Pastures as a Contact Network, Implications for Potential Disease Spread and Biosecurity in Livestock, France, 2010.

PubMed

Palisson, Aurore; Courcoul, Aurélie; Durand, Benoit

2017-01-01

The use of pastures is part of common herd management practices for livestock animals, but contagion between animals located on neighbouring pastures is one of the major modes of infectious disease transmission between herds. At the population level, this transmission is strongly constrained by the spatial organization of pastures. The aim of this study was to answer two questions: (i) is the spatial configuration of pastures favourable to the spread of infectious diseases in France? (ii) would biosecurity measures allow decreasing this vulnerability? Based on GIS data, the spatial organization of pastures was represented using networks. Nodes were the 3,159,787 pastures reported in 2010 by the French breeders to claim the Common Agricultural Policy subsidies. Links connected pastures when the distance between them was below a predefined threshold. Premises networks were obtained by aggregating into a single node all the pastures under the same ownership. Although the pastures network was very fragmented when the distance threshold was short (1.5 meters, relevant for a directly-transmitted disease), it was not the case when the distance threshold was larger (500 m, relevant for a vector-borne disease: 97% of the nodes in the largest connected component). The premises network was highly connected as the largest connected component always included more than 83% of the nodes, whatever the distance threshold. Percolation analyses were performed to model the population-level efficacy of biosecurity measures. Percolation thresholds varied according to the modelled biosecurity measures and to the distance threshold. They were globally high (e.g. >17% of nodes had to be removed, mimicking the confinement of animals inside farm buildings, to obtain the disappearance of the large connected component). The network of pastures thus appeared vulnerable to the spread of diseases in France. Only a large acceptance of biosecurity measures by breeders would allow reducing this structural risk.

Analysis of the Spatial Organization of Pastures as a Contact Network, Implications for Potential Disease Spread and Biosecurity in Livestock, France, 2010

PubMed Central

Palisson, Aurore; Courcoul, Aurélie; Durand, Benoit

2017-01-01

The use of pastures is part of common herd management practices for livestock animals, but contagion between animals located on neighbouring pastures is one of the major modes of infectious disease transmission between herds. At the population level, this transmission is strongly constrained by the spatial organization of pastures. The aim of this study was to answer two questions: (i) is the spatial configuration of pastures favourable to the spread of infectious diseases in France? (ii) would biosecurity measures allow decreasing this vulnerability? Based on GIS data, the spatial organization of pastures was represented using networks. Nodes were the 3,159,787 pastures reported in 2010 by the French breeders to claim the Common Agricultural Policy subsidies. Links connected pastures when the distance between them was below a predefined threshold. Premises networks were obtained by aggregating into a single node all the pastures under the same ownership. Although the pastures network was very fragmented when the distance threshold was short (1.5 meters, relevant for a directly-transmitted disease), it was not the case when the distance threshold was larger (500 m, relevant for a vector-borne disease: 97% of the nodes in the largest connected component). The premises network was highly connected as the largest connected component always included more than 83% of the nodes, whatever the distance threshold. Percolation analyses were performed to model the population-level efficacy of biosecurity measures. Percolation thresholds varied according to the modelled biosecurity measures and to the distance threshold. They were globally high (e.g. >17% of nodes had to be removed, mimicking the confinement of animals inside farm buildings, to obtain the disappearance of the large connected component). The network of pastures thus appeared vulnerable to the spread of diseases in France. Only a large acceptance of biosecurity measures by breeders would allow reducing this structural risk. PMID:28060913

Identification and apportionment of hazardous elements in the sediments in the Yangtze River estuary.

PubMed

Wang, Jiawei; Liu, Ruimin; Wang, Haotian; Yu, Wenwen; Xu, Fei; Shen, Zhenyao

2015-12-01

In this study, positive matrix factorization (PMF) and principal components analysis (PCA) were combined to identify and apportion pollution-based sources of hazardous elements in the surface sediments in the Yangtze River estuary (YRE). Source identification analysis indicated that PC1, including Al, Fe, Mn, Cr, Ni, As, Cu, and Zn, can be defined as a sewage component; PC2, including Pb and Sb, can be considered as an atmospheric deposition component; and PC3, containing Cd and Hg, can be considered as an agricultural nonpoint component. To better identify the sources and quantitatively apportion the concentrations to their sources, eight sources were identified with PMF: agricultural/industrial sewage mixed (18.6 %), mining wastewater (15.9 %), agricultural fertilizer (14.5 %), atmospheric deposition (12.8 %), agricultural nonpoint (10.6 %), industrial wastewater (9.8 %), marine activity (9.0 %), and nickel plating industry (8.8 %). Overall, the hazardous element content seems to be more connected to anthropogenic activity instead of natural sources. The PCA results laid the foundation for the PMF analysis by providing a general classification of sources. PMF resolves more factors with a higher explained variance than PCA; PMF provided both the internal analysis and the quantitative analysis. The combination of the two methods can provide more reasonable and reliable results.

Frontal–Occipital Connectivity During Visual Search

PubMed Central

Pantazatos, Spiro P.; Yanagihara, Ted K.; Zhang, Xian; Meitzler, Thomas

2012-01-01

Abstract Although expectation- and attention-related interactions between ventral and medial prefrontal cortex and stimulus category-selective visual regions have been identified during visual detection and discrimination, it is not known if similar neural mechanisms apply to other tasks such as visual search. The current work tested the hypothesis that high-level frontal regions, previously implicated in expectation and visual imagery of object categories, interact with visual regions associated with object recognition during visual search. Using functional magnetic resonance imaging, subjects searched for a specific object that varied in size and location within a complex natural scene. A model-free, spatial-independent component analysis isolated multiple task-related components, one of which included visual cortex, as well as a cluster within ventromedial prefrontal cortex (vmPFC), consistent with the engagement of both top-down and bottom-up processes. Analyses of psychophysiological interactions showed increased functional connectivity between vmPFC and object-sensitive lateral occipital cortex (LOC), and results from dynamic causal modeling and Bayesian Model Selection suggested bidirectional connections between vmPFC and LOC that were positively modulated by the task. Using image-guided diffusion-tensor imaging, functionally seeded, probabilistic white-matter tracts between vmPFC and LOC, which presumably underlie this effective interconnectivity, were also observed. These connectivity findings extend previous models of visual search processes to include specific frontal–occipital neuronal interactions during a natural and complex search task. PMID:22708993

Breaking Functional Connectivity into Components: A Novel Approach Using an Individual-Based Model, and First Outcomes

PubMed Central

Pe'er, Guy; Henle, Klaus; Dislich, Claudia; Frank, Karin

2011-01-01

Landscape connectivity is a key factor determining the viability of populations in fragmented landscapes. Predicting ‘functional connectivity’, namely whether a patch or a landscape functions as connected from the perspective of a focal species, poses various challenges. First, empirical data on the movement behaviour of species is often scarce. Second, animal-landscape interactions are bound to yield complex patterns. Lastly, functional connectivity involves various components that are rarely assessed separately. We introduce the spatially explicit, individual-based model FunCon as means to distinguish between components of functional connectivity and to assess how each of them affects the sensitivity of species and communities to landscape structures. We then present the results of exploratory simulations over six landscapes of different fragmentation levels and across a range of hypothetical bird species that differ in their response to habitat edges. i) Our results demonstrate that estimations of functional connectivity depend not only on the response of species to edges (avoidance versus penetration into the matrix), the movement mode investigated (home range movements versus dispersal), and the way in which the matrix is being crossed (random walk versus gap crossing), but also on the choice of connectivity measure (in this case, the model output examined). ii) We further show a strong effect of the mortality scenario applied, indicating that movement decisions that do not fully match the mortality risks are likely to reduce connectivity and enhance sensitivity to fragmentation. iii) Despite these complexities, some consistent patterns emerged. For instance, the ranking order of landscapes in terms of functional connectivity was mostly consistent across the entire range of hypothetical species, indicating that simple landscape indices can potentially serve as valuable surrogates for functional connectivity. Yet such simplifications must be carefully evaluated in terms of the components of functional connectivity they actually predict. PMID:21829617

Cortical networks involved in visual awareness independent of visual attention.

PubMed

Webb, Taylor W; Igelström, Kajsa M; Schurger, Aaron; Graziano, Michael S A

2016-11-29

It is now well established that visual attention, as measured with standard spatial attention tasks, and visual awareness, as measured by report, can be dissociated. It is possible to attend to a stimulus with no reported awareness of the stimulus. We used a behavioral paradigm in which people were aware of a stimulus in one condition and unaware of it in another condition, but the stimulus drew a similar amount of spatial attention in both conditions. The paradigm allowed us to test for brain regions active in association with awareness independent of level of attention. Participants performed the task in an MRI scanner. We looked for brain regions that were more active in the aware than the unaware trials. The largest cluster of activity was obtained in the temporoparietal junction (TPJ) bilaterally. Local independent component analysis (ICA) revealed that this activity contained three distinct, but overlapping, components: a bilateral, anterior component; a left dorsal component; and a right dorsal component. These components had brain-wide functional connectivity that partially overlapped the ventral attention network and the frontoparietal control network. In contrast, no significant activity in association with awareness was found in the banks of the intraparietal sulcus, a region connected to the dorsal attention network and traditionally associated with attention control. These results show the importance of separating awareness and attention when testing for cortical substrates. They are also consistent with a recent proposal that awareness is associated with ventral attention areas, especially in the TPJ.

Localized reductions in resting-state functional connectivity in children with prenatal alcohol exposure.

PubMed

Fan, Jia; Taylor, Paul A; Jacobson, Sandra W; Molteno, Christopher D; Gohel, Suril; Biswal, Bharat B; Jacobson, Joseph L; Meintjes, Ernesta M

2017-10-01

Fetal alcohol spectrum disorders (FASD) are characterized by impairment in cognitive function that may or may not be accompanied by craniofacial anomalies, microcephaly, and/or growth retardation. Resting-state functional MRI (rs-fMRI), which examines the low-frequency component of the blood oxygen level dependent (BOLD) signal in the absence of an explicit task, provides an efficient and powerful mechanism for studying functional brain networks even in low-functioning and young subjects. Studies using independent component analysis (ICA) have identified a set of resting-state networks (RSNs) that have been linked to distinct domains of cognitive and perceptual function, which are believed to reflect the intrinsic functional architecture of the brain. This study is the first to examine resting-state functional connectivity within these RSNs in FASD. Rs-fMRI scans were performed on 38 children with FASD (19 with either full fetal alcohol syndrome (FAS) or partial FAS (PFAS), 19 nonsyndromal heavily exposed (HE)), and 19 controls, mean age 11.3 ± 0.9 years, from the Cape Town Longitudinal Cohort. Nine resting-state networks were generated by ICA. Voxelwise group comparison between a combined FAS/PFAS group and controls revealed localized dose-dependent functional connectivity reductions in five regions in separate networks: anterior default mode, salience, ventral and dorsal attention, and R executive control. The former three also showed lower connectivity in the HE group. Gray matter connectivity deficits in four of the five networks appear to be related to deficits in white matter tracts that provide intra-RSN connections. Hum Brain Mapp 38:5217-5233, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Insight and Evidence Motivating the Simplification of Dual-Analysis Hybrid Systems into Single-Analysis Hybrid Systems

NASA Technical Reports Server (NTRS)

Todling, Ricardo; Diniz, F. L. R.; Takacs, L. L.; Suarez, M. J.

2018-01-01

Many hybrid data assimilation systems currently used for NWP employ some form of dual-analysis system approach. Typically a hybrid variational analysis is responsible for creating initial conditions for high-resolution forecasts, and an ensemble analysis system is responsible for creating sample perturbations used to form the flow-dependent part of the background error covariance required in the hybrid analysis component. In many of these, the two analysis components employ different methodologies, e.g., variational and ensemble Kalman filter. In such cases, it is not uncommon to have observations treated rather differently between the two analyses components; recentering of the ensemble analysis around the hybrid analysis is used to compensated for such differences. Furthermore, in many cases, the hybrid variational high-resolution system implements some type of four-dimensional approach, whereas the underlying ensemble system relies on a three-dimensional approach, which again introduces discrepancies in the overall system. Connected to these is the expectation that one can reliably estimate observation impact on forecasts issued from hybrid analyses by using an ensemble approach based on the underlying ensemble strategy of dual-analysis systems. Just the realization that the ensemble analysis makes substantially different use of observations as compared to their hybrid counterpart should serve as enough evidence of the implausibility of such expectation. This presentation assembles numerous anecdotal evidence to illustrate the fact that hybrid dual-analysis systems must, at the very minimum, strive for consistent use of the observations in both analysis sub-components. Simpler than that, this work suggests that hybrid systems can reliably be constructed without the need to employ a dual-analysis approach. In practice, the idea of relying on a single analysis system is appealing from a cost-maintenance perspective. More generally, single-analysis systems avoid contradictions such as having to choose one sub-component to generate performance diagnostics to another, possibly not fully consistent, component.

Intrinsic connectivity networks from childhood to late adolescence: Effects of age and sex.

PubMed

Solé-Padullés, Cristina; Castro-Fornieles, Josefina; de la Serna, Elena; Calvo, Rosa; Baeza, Inmaculada; Moya, Jaime; Lázaro, Luisa; Rosa, Mireia; Bargalló, Nuria; Sugranyes, Gisela

2016-02-01

There is limited evidence on the effects of age and sex on intrinsic connectivity of networks underlying cognition during childhood and adolescence. Independent component analysis was conducted in 113 subjects aged 7-18; the default mode, executive control, anterior salience, basal ganglia, language and visuospatial networks were identified. The effect of age was examined with multiple regression, while sex and 'age × sex' interactions were assessed by dividing the sample according to age (7-12 and 13-18 years). As age increased, connectivity in the dorsal and ventral default mode network became more anterior and posterior, respectively, while in the executive control network, connectivity increased within frontoparietal regions. The basal ganglia network showed increased engagement of striatum, thalami and precuneus. The anterior salience network showed greater connectivity in frontal areas and anterior cingulate, and less connectivity of orbitofrontal, middle cingulate and temporoparietal regions. The language network presented increased connectivity of inferior frontal and decreased connectivity within the right middle frontal and left inferior parietal cortices. The visuospatial network showed greater engagement of inferior parietal and frontal cortices. No effect of sex, nor age by sex interactions was observed. These findings provide evidence of strengthening of cortico-cortical and cortico-subcortical networks across childhood and adolescence. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics

NASA Astrophysics Data System (ADS)

Lamoš, Martin; Mareček, Radek; Slavíček, Tomáš; Mikl, Michal; Rektor, Ivan; Jan, Jiří

2018-06-01

Objective. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. Approach. The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. Main results. We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. Significance. Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.

Dynamic Neural Networks Supporting Memory Retrieval

PubMed Central

St. Jacques, Peggy L.; Kragel, Philip A.; Rubin, David C.

2011-01-01

How do separate neural networks interact to support complex cognitive processes such as remembrance of the personal past? Autobiographical memory (AM) retrieval recruits a consistent pattern of activation that potentially comprises multiple neural networks. However, it is unclear how such large-scale neural networks interact and are modulated by properties of the memory retrieval process. In the present functional MRI (fMRI) study, we combined independent component analysis (ICA) and dynamic causal modeling (DCM) to understand the neural networks supporting AM retrieval. ICA revealed four task-related components consistent with the previous literature: 1) Medial Prefrontal Cortex (PFC) Network, associated with self-referential processes, 2) Medial Temporal Lobe (MTL) Network, associated with memory, 3) Frontoparietal Network, associated with strategic search, and 4) Cingulooperculum Network, associated with goal maintenance. DCM analysis revealed that the medial PFC network drove activation within the system, consistent with the importance of this network to AM retrieval. Additionally, memory accessibility and recollection uniquely altered connectivity between these neural networks. Recollection modulated the influence of the medial PFC on the MTL network during elaboration, suggesting that greater connectivity among subsystems of the default network supports greater re-experience. In contrast, memory accessibility modulated the influence of frontoparietal and MTL networks on the medial PFC network, suggesting that ease of retrieval involves greater fluency among the multiple networks contributing to AM. These results show the integration between neural networks supporting AM retrieval and the modulation of network connectivity by behavior. PMID:21550407

Semantic retrieval during overt picture description: Left anterior temporal or the parietal lobe?

PubMed

Geranmayeh, Fatemeh; Leech, Robert; Wise, Richard J S

2015-09-01

Retrieval of semantic representations is a central process during overt speech production. There is an increasing consensus that an amodal semantic 'hub' must exist that draws together modality-specific representations of concepts. Based on the distribution of atrophy and the behavioral deficit of patients with the semantic variant of fronto-temporal lobar degeneration, it has been proposed that this hub is localized within both anterior temporal lobes (ATL), and is functionally connected with verbal 'output' systems via the left ATL. An alternative view, dating from Geschwind's proposal in 1965, is that the angular gyrus (AG) is central to object-based semantic representations. In this fMRI study we examined the connectivity of the left ATL and parietal lobe (PL) with whole brain networks known to be activated during overt picture description. We decomposed each of these two brain volumes into 15 regions of interest (ROIs), using independent component analysis. A dual regression analysis was used to establish the connectivity of each ROI with whole brain-networks. An ROI within the left anterior superior temporal sulcus (antSTS) was functionally connected to other parts of the left ATL, including anterior ventromedial left temporal cortex (partially attenuated by signal loss due to susceptibility artifact), a large left dorsolateral prefrontal region (including 'classic' Broca's area), extensive bilateral sensory-motor cortices, and the length of both superior temporal gyri. The time-course of this functionally connected network was associated with picture description but not with non-semantic baseline tasks. This system has the distribution expected for the production of overt speech with appropriate semantic content, and the auditory monitoring of the overt speech output. In contrast, the only left PL ROI that showed connectivity with brain systems most strongly activated by the picture-description task, was in the superior parietal lobe (supPL). This region showed connectivity with predominantly posterior cortical regions required for the visual processing of the pictorial stimuli, with additional connectivity to the dorsal left AG and a small component of the left inferior frontal gyrus. None of the other PL ROIs that included part of the left AG were activated by Speech alone. The best interpretation of these results is that the left antSTS connects the proposed semantic hub (specifically localized to ventral anterior temporal cortex based on clinical neuropsychological studies) to posterior frontal regions and sensory-motor cortices responsible for the overt production of speech. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects

NASA Astrophysics Data System (ADS)

Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

2017-08-01

Objective. Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. Approach. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. Main results. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. Significance. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects.

PubMed

Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

2017-08-01

Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

Islanding detection technique using wavelet energy in grid-connected PV system

NASA Astrophysics Data System (ADS)

Kim, Il Song

2016-08-01

This paper proposes a new islanding detection method using wavelet energy in a grid-connected photovoltaic system. The method detects spectral changes in the higher-frequency components of the point of common coupling voltage and obtains wavelet coefficients by multilevel wavelet analysis. The autocorrelation of the wavelet coefficients can clearly identify islanding detection, even in the variations of the grid voltage harmonics during normal operating conditions. The advantage of the proposed method is that it can detect islanding condition the conventional under voltage/over voltage/under frequency/over frequency methods fail to detect. The theoretical method to obtain wavelet energies is evolved and verified by the experimental result.

Accounting for Poverty in Infrastructure Reform: Learning from Latin America's Experience. WBI Development Studies.

ERIC Educational Resources Information Center

Estache, Antonio; Foster, Vivien; Wodon, Quentin

This book explores the connections between infrastructure reform and poverty alleviation in Latin America based on a detailed analysis of the effects of a decade of reforms. The book demonstrates that because the access to, and affordability of, basic services is still a major problem, infrastructure investment will be a core component of poverty…

An Analysis of Program Issues Perceived by Cooperative Education Coordinators in Pennsylvania Secondary Schools and Career and Technology Centers

ERIC Educational Resources Information Center

Richard, Elizabeth D.; Clark, Robert W.; Welch, Steven M.

2011-01-01

Cooperative education has been a long-standing component of career and technical education. The practice embodies many established theories of learning and is a premier delivery model for the school-to-work connections espoused by modern legislation. Yet in this era of high-stakes testing and academic accountability, allocated time for cooperative…

A conceptual model for quantifying connectivity using graph theory and cellular (per-pixel) approach

NASA Astrophysics Data System (ADS)

Singh, Manudeo; Sinha, Rajiv; Tandon, Sampat K.

2016-04-01

The concept of connectivity is being increasingly used for understanding hydro-geomorphic processes at all spatio-temporal scales. Connectivity is defined as the potential for energy and material flux (water, sediments, nutrients, heat, etc.) to navigate within or between the landscape systems and has two components, structural connectivity and dynamic connectivity. Structural connectivity is defined by the spatially connected features (physical linkages) through which energy and materials flow. Dynamic connectivity is a process defined connectivity component. These two connectivity components also interact with each other by forming a feedback system. This study attempts to explore a method to quantify structural and dynamic connectivity. In fluvial transport systems, sediment and water can flow in either a diffused manner or in a channelized way. At all the scales, hydrological and sediment fluxes can be tracked using a cellular (per-pixel) approach and can be quantified using graphical approach. The material flux, slope and LULC (Land Use Land Cover) weightage factors of a pixel together determine if it will contribute towards connectivity of the landscape/system. In a graphical approach, all the contributing pixels will form a node at their centroid and this node will be connected to the next 'down-node' via a directed edge with 'least cost path'. The length of the edge will depend on the desired spatial scale and its path direction will depend on the traversed pixel's slope and the LULC (weightage) factors. The weightage factors will lie in-between 0 to 1. This value approaches 1 for the LULC factors which promote connectivity. For example, in terms of sediment connectivity, the weightage could be RUSLE (Revised Universal Soil Loss Equation) C-factors with bare unconsolidated surfaces having values close to 1. This method is best suited for areas with low slopes, where LULC can be a deciding as well as dominating factor. The degree of connectivity and its pathways will show changes under different LULC conditions even if the slope remains the same. The graphical approach provides the statistics of connected and disconnected graph elements (edges, nodes) and graph components, thereby allowing the quantification of structural connectivity. This approach also quantifies the dynamic connectivity by allowing the measurement of the fluxes (e.g. via hydrographs or sedimentographs) at any node as well as at any system outlet. The contribution of any sub-system can be understood by removing the remaining sub-systems which can be conveniently achieved by masking associated graph elements.

Alterations of Brain Functional Architecture Associated with Psychopathic Traits in Male Adolescents with Conduct Disorder.

PubMed

Pu, Weidan; Luo, Qiang; Jiang, Yali; Gao, Yidian; Ming, Qingsen; Yao, Shuqiao

2017-09-12

Psychopathic traits of conduct disorder (CD) have a core callous-unemotional (CU) component and an impulsive-antisocial component. Previous task-driven fMRI studies have suggested that psychopathic traits are associated with dysfunction of several brain areas involved in different cognitive functions (e.g., empathy, reward, and response inhibition etc.), but the relationship between psychopathic traits and intrinsic brain functional architecture has not yet been explored in CD. Using a holistic brain-wide functional connectivity analysis, this study delineated the alterations in brain functional networks in patients with conduct disorder. Compared with matched healthy controls, we found decreased anti-synchronization between the fronto-parietal network (FPN) and default mode network (DMN), and increased intra-network synchronization within the frontothalamic-basal ganglia, right frontoparietal, and temporal/limbic/visual networks in CD patients. Correlation analysis showed that the weakened FPN-DMN interaction was associated with CU traits, while the heightened intra-network functional connectivity was related to impulsivity traits in CD patients. Our findings suggest that decoupling of cognitive control (FPN) with social understanding of others (DMN) is associated with the CU traits, and hyper-functions of the reward and motor inhibition systems elevate impulsiveness in CD.

[Muscles and connective tissue: histology].

PubMed

Delage, J-P

2012-10-01

Here, we give some comments about the DVD movies "Muscle Attitudes" from Endovivo productions, the movies up lighting some loss in the attention given to studies on the connective tissue, and especially them into muscles. The main characteristics of the different components in the intra-muscular connective tissue (perimysium, endomysium, epimysium) are shown here with special references to their ordered architecture and special references to their spatial distributions. This connective tissue is abundant into the muscles and is in continuity with the muscles in vicinity, with their tendons and their sheath, sticking the whole on skin. This connective tissue has also very abundant connections on the muscles fibres. It is then assumed that the connective tissue sticks every organs or cells of the locomotion system. Considering the elastic properties of the collagen fibres which are the most abundant component of connective tissue, it is possible to up light a panel of connective tissue associated functions such as the transmission of muscle contractions or the regulation of protein and energetic muscles metabolism. Copyright © 2012. Published by Elsevier SAS.

Hemispheric Connectivity and the Visual-Spatial Divergent-Thinking Component of Creativity

ERIC Educational Resources Information Center

Moore, Dana W.; Bhadelia, Rafeeque A.; Billings, Rebecca L.; Fulwiler, Carl; Heilman, Kenneth M.; Rood, Kenneth M. J.; Gansler, David A.

2009-01-01

Background/hypothesis: Divergent thinking is an important measurable component of creativity. This study tested the postulate that divergent thinking depends on large distributed inter- and intra-hemispheric networks. Although preliminary evidence supports increased brain connectivity during divergent thinking, the neural correlates of this…

Abnormal network connectivity in frontotemporal dementia: evidence for prefrontal isolation.

PubMed

Farb, Norman A S; Grady, Cheryl L; Strother, Stephen; Tang-Wai, David F; Masellis, Mario; Black, Sandra; Freedman, Morris; Pollock, Bruce G; Campbell, Karen L; Hasher, Lynn; Chow, Tiffany W

2013-01-01

Degraded social function, disinhibition, and stereotypy are defining characteristics of frontotemporal dementia (FTD), manifesting in both the behavioral variant of frontotemporal dementia (bvFTD) and semantic dementia (SD) subtypes. Recent neuroimaging research also associates FTD with alterations in the brain's intrinsic connectivity networks. The present study explored the relationship between neural network connectivity and specific behavioral symptoms in FTD. Resting-state functional magnetic resonance imaging was employed to investigate neural network changes in bvFTD and SD. We used independent components analysis (ICA) to examine changes in frontolimbic network connectivity, as well as several metrics of local network strength, such as the fractional amplitude of low-frequency fluctuations, regional homogeneity, and seed-based functional connectivity. For each analysis, we compared each FTD subgroup to healthy controls, characterizing general and subtype-unique network changes. The relationship between abnormal connectivity in FTD and behavior disturbances was explored. Across multiple analytic approaches, both bvFTD and SD were associated with disrupted frontolimbic connectivity and elevated local connectivity within the prefrontal cortex. Even after controlling for structural atrophy, prefrontal hyperconnectivity was robustly associated with apathy scores. Frontolimbic disconnection was associated with lower disinhibition scores, suggesting that abnormal frontolimbic connectivity contributes to positive symptoms in dementia. Unique to bvFTD, stereotypy was associated with elevated default network connectivity in the right angular gyrus. The behavioral variant was also associated with marginally higher apathy scores and a more diffuse pattern of prefrontal hyperconnectivity than SD. The present findings support a theory of FTD as a disorder of frontolimbic disconnection leading to unconstrained prefrontal connectivity. Prefrontal hyperconnectivity may represent a compensatory response to the absence of affective feedback during the planning and execution of behavior. Increased reliance upon prefrontal processes in isolation from subcortical structures appears to be maladaptive and may drive behavioral withdrawal that is commonly observed in later phases of neurodegeneration. Copyright © 2012 Elsevier Ltd. All rights reserved.

Functional Connectivity with Distinct Neural Networks Tracks Fluctuations in Gain/Loss Framing Susceptibility

PubMed Central

Smith, David V.; Sip, Kamila E.; Delgado, Mauricio R.

2016-01-01

Multiple large-scale neural networks orchestrate a wide range of cognitive processes. For example, interoceptive processes related to self-referential thinking have been linked to the default-mode network (DMN); whereas exteroceptive processes related to cognitive control have been linked to the executive-control network (ECN). Although the DMN and ECN have been postulated to exert opposing effects on cognition, it remains unclear how connectivity with these spatially overlapping networks contribute to fluctuations in behavior. While previous work has suggested the medial prefrontal cortex (MPFC) is involved in behavioral change following feedback, these observations could be linked to interoceptive processes tied to DMN or exteroceptive processes tied to ECN because MPFC is positioned in both networks. To address this problem, we employed independent component analysis combined with dual-regression functional connectivity analysis. Participants made a series of financial decisions framed as monetary gains or losses. In some sessions, participants received feedback from a peer observing their choices; in other sessions, feedback was not provided. Following feedback, framing susceptibility—indexed as the increase in gambling behavior in loss frames compared to gain frames—was heightened in some participants and diminished in others. We examined whether these individual differences were linked to differences in connectivity by contrasting sessions containing feedback against those that did not contain feedback. We found two key results. As framing susceptibility increased, the MPFC increased connectivity with DMN; in contrast, temporal-parietal junction decreased connectivity with the ECN. Our results highlight how functional connectivity patterns with distinct neural networks contribute to idiosyncratic behavioral changes. PMID:25858445

Functional connectivity with distinct neural networks tracks fluctuations in gain/loss framing susceptibility.

PubMed

Smith, David V; Sip, Kamila E; Delgado, Mauricio R

2015-07-01

Multiple large-scale neural networks orchestrate a wide range of cognitive processes. For example, interoceptive processes related to self-referential thinking have been linked to the default-mode network (DMN); whereas exteroceptive processes related to cognitive control have been linked to the executive-control network (ECN). Although the DMN and ECN have been postulated to exert opposing effects on cognition, it remains unclear how connectivity with these spatially overlapping networks contribute to fluctuations in behavior. While previous work has suggested the medial-prefrontal cortex (MPFC) is involved in behavioral change following feedback, these observations could be linked to interoceptive processes tied to DMN or exteroceptive processes tied to ECN because MPFC is positioned in both networks. To address this problem, we employed independent component analysis combined with dual-regression functional connectivity analysis. Participants made a series of financial decisions framed as monetary gains or losses. In some sessions, participants received feedback from a peer observing their choices; in other sessions, feedback was not provided. Following feedback, framing susceptibility-indexed as the increase in gambling behavior in loss frames compared to gain frames-was heightened in some participants and diminished in others. We examined whether these individual differences were linked to differences in connectivity by contrasting sessions containing feedback against those that did not contain feedback. We found two key results. As framing susceptibility increased, the MPFC increased connectivity with DMN; in contrast, temporal-parietal junction decreased connectivity with the ECN. Our results highlight how functional connectivity patterns with distinct neural networks contribute to idiosyncratic behavioral changes. © 2015 Wiley Periodicals, Inc.

A New Analysis of Resting State Connectivity and Graph Theory Reveals Distinctive Short-Term Modulations due to Whisker Stimulation in Rats.

PubMed

Kreitz, Silke; de Celis Alonso, Benito; Uder, Michael; Hess, Andreas

2018-01-01

Resting state (RS) connectivity has been increasingly studied in healthy and diseased brains in humans and animals. This paper presents a new method to analyze RS data from fMRI that combines multiple seed correlation analysis with graph-theory (MSRA). We characterize and evaluate this new method in relation to two other graph-theoretical methods and ICA. The graph-theoretical methods calculate cross-correlations of regional average time-courses, one using seed regions of the same size (SRCC) and the other using whole brain structure regions (RCCA). We evaluated the reproducibility, power, and capacity of these methods to characterize short-term RS modulation to unilateral physiological whisker stimulation in rats. Graph-theoretical networks found with the MSRA approach were highly reproducible, and their communities showed large overlaps with ICA components. Additionally, MSRA was the only one of all tested methods that had the power to detect significant RS modulations induced by whisker stimulation that are controlled by family-wise error rate (FWE). Compared to the reduced resting state network connectivity during task performance, these modulations implied decreased connectivity strength in the bilateral sensorimotor and entorhinal cortex. Additionally, the contralateral ventromedial thalamus (part of the barrel field related lemniscal pathway) and the hypothalamus showed reduced connectivity. Enhanced connectivity was observed in the amygdala, especially the contralateral basolateral amygdala (involved in emotional learning processes). In conclusion, MSRA is a powerful analytical approach that can reliably detect tiny modulations of RS connectivity. It shows a great promise as a method for studying RS dynamics in healthy and pathological conditions.

A New Analysis of Resting State Connectivity and Graph Theory Reveals Distinctive Short-Term Modulations due to Whisker Stimulation in Rats

PubMed Central

Kreitz, Silke; de Celis Alonso, Benito; Uder, Michael; Hess, Andreas

2018-01-01

Resting state (RS) connectivity has been increasingly studied in healthy and diseased brains in humans and animals. This paper presents a new method to analyze RS data from fMRI that combines multiple seed correlation analysis with graph-theory (MSRA). We characterize and evaluate this new method in relation to two other graph-theoretical methods and ICA. The graph-theoretical methods calculate cross-correlations of regional average time-courses, one using seed regions of the same size (SRCC) and the other using whole brain structure regions (RCCA). We evaluated the reproducibility, power, and capacity of these methods to characterize short-term RS modulation to unilateral physiological whisker stimulation in rats. Graph-theoretical networks found with the MSRA approach were highly reproducible, and their communities showed large overlaps with ICA components. Additionally, MSRA was the only one of all tested methods that had the power to detect significant RS modulations induced by whisker stimulation that are controlled by family-wise error rate (FWE). Compared to the reduced resting state network connectivity during task performance, these modulations implied decreased connectivity strength in the bilateral sensorimotor and entorhinal cortex. Additionally, the contralateral ventromedial thalamus (part of the barrel field related lemniscal pathway) and the hypothalamus showed reduced connectivity. Enhanced connectivity was observed in the amygdala, especially the contralateral basolateral amygdala (involved in emotional learning processes). In conclusion, MSRA is a powerful analytical approach that can reliably detect tiny modulations of RS connectivity. It shows a great promise as a method for studying RS dynamics in healthy and pathological conditions. PMID:29875622

Multiple Resting-State Networks Are Associated With Tremors and Cognitive Features in Essential Tremor.

PubMed

Fang, Weidong; Chen, Huiyue; Wang, Hansheng; Zhang, Han; Liu, Mengqi; Puneet, Munankami; Lv, Fajin; Cheng, Oumei; Wang, Xuefeng; Lu, Xiurong; Luo, Tianyou

2015-12-01

The heterogeneous clinical features of essential tremor indicate that the dysfunctions of this syndrome are not confined to motor networks, but extend to nonmotor networks. Currently, these neural network dysfunctions in essential tremor remain unclear. In this study, independent component analysis of resting-state functional MRI was used to study these neural network mechanisms. Thirty-five essential tremor patients and 35 matched healthy controls with clinical and neuropsychological tests were included, and eight resting-state networks were identified. After considering the structure and head-motion factors and testing the reliability of the selected resting-state networks, we assessed the functional connectivity changes within or between resting-state networks. Finally, image-behavior correlation analysis was performed. Compared to healthy controls, essential tremor patients displayed increased functional connectivity in the sensorimotor and salience networks and decreased functional connectivity in the cerebellum network. Additionally, increased functional network connectivity was observed between anterior and posterior default mode networks, and a decreased functional network connectivity was noted between the cerebellum network and the sensorimotor and posterior default mode networks. Importantly, the functional connectivity changes within and between these resting-state networks were correlated with the tremor severity and total cognitive scores of essential tremor patients. The findings of this study provide the first evidence that functional connectivity changes within and between multiple resting-state networks are associated with tremors and cognitive features of essential tremor, and this work demonstrates a potential approach for identifying the underlying neural network mechanisms of this syndrome. © 2015 International Parkinson and Movement Disorder Society.

Resting state fMRI reveals a default mode dissociation between retrosplenial and medial prefrontal subnetworks in ASD despite motion scrubbing.

PubMed

Starck, Tuomo; Nikkinen, Juha; Rahko, Jukka; Remes, Jukka; Hurtig, Tuula; Haapsamo, Helena; Jussila, Katja; Kuusikko-Gauffin, Sanna; Mattila, Marja-Leena; Jansson-Verkasalo, Eira; Pauls, David L; Ebeling, Hanna; Moilanen, Irma; Tervonen, Osmo; Kiviniemi, Vesa J

2013-01-01

In resting state functional magnetic resonance imaging (fMRI) studies of autism spectrum disorders (ASDs) decreased frontal-posterior functional connectivity is a persistent finding. However, the picture of the default mode network (DMN) hypoconnectivity remains incomplete. In addition, the functional connectivity analyses have been shown to be susceptible even to subtle motion. DMN hypoconnectivity in ASD has been specifically called for re-evaluation with stringent motion correction, which we aimed to conduct by so-called scrubbing. A rich set of default mode subnetworks can be obtained with high dimensional group independent component analysis (ICA) which can potentially provide more detailed view of the connectivity alterations. We compared the DMN connectivity in high-functioning adolescents with ASDs to typically developing controls using ICA dual-regression with decompositions from typical to high dimensionality. Dual-regression analysis within DMN subnetworks did not reveal alterations but connectivity between anterior and posterior DMN subnetworks was decreased in ASD. The results were very similar with and without motion scrubbing thus indicating the efficacy of the conventional motion correction methods combined with ICA dual-regression. Specific dissociation between DMN subnetworks was revealed on high ICA dimensionality, where networks centered at the medial prefrontal cortex and retrosplenial cortex showed weakened coupling in adolescents with ASDs compared to typically developing control participants. Generally the results speak for disruption in the anterior-posterior DMN interplay on the network level whereas local functional connectivity in DMN seems relatively unaltered.

Role of design complexity in technology improvement.

PubMed

McNerney, James; Farmer, J Doyne; Redner, Sidney; Trancik, Jessika E

2011-05-31

We study a simple model for the evolution of the cost (or more generally the performance) of a technology or production process. The technology can be decomposed into n components, each of which interacts with a cluster of d - 1 other components. Innovation occurs through a series of trial-and-error events, each of which consists of randomly changing the cost of each component in a cluster, and accepting the changes only if the total cost of the cluster is lowered. We show that the relationship between the cost of the whole technology and the number of innovation attempts is asymptotically a power law, matching the functional form often observed for empirical data. The exponent α of the power law depends on the intrinsic difficulty of finding better components, and on what we term the design complexity: the more complex the design, the slower the rate of improvement. Letting d as defined above be the connectivity, in the special case in which the connectivity is constant, the design complexity is simply the connectivity. When the connectivity varies, bottlenecks can arise in which a few components limit progress. In this case the design complexity depends on the details of the design. The number of bottlenecks also determines whether progress is steady, or whether there are periods of stasis punctuated by occasional large changes. Our model connects the engineering properties of a design to historical studies of technology improvement.

Development of automated extraction method of biliary tract from abdominal CT volumes based on local intensity structure analysis

NASA Astrophysics Data System (ADS)

Koga, Kusuto; Hayashi, Yuichiro; Hirose, Tomoaki; Oda, Masahiro; Kitasaka, Takayuki; Igami, Tsuyoshi; Nagino, Masato; Mori, Kensaku

2014-03-01

In this paper, we propose an automated biliary tract extraction method from abdominal CT volumes. The biliary tract is the path by which bile is transported from liver to the duodenum. No extraction method have been reported for the automated extraction of the biliary tract from common contrast CT volumes. Our method consists of three steps including: (1) extraction of extrahepatic bile duct (EHBD) candidate regions, (2) extraction of intrahepatic bile duct (IHBD) candidate regions, and (3) combination of these candidate regions. The IHBD has linear structures and intensities of the IHBD are low in CT volumes. We use a dark linear structure enhancement (DLSE) filter based on a local intensity structure analysis method using the eigenvalues of the Hessian matrix for the IHBD candidate region extraction. The EHBD region is extracted using a thresholding process and a connected component analysis. In the combination process, we connect the IHBD candidate regions to each EHBD candidate region and select a bile duct region from the connected candidate regions. We applied the proposed method to 22 cases of CT volumes. An average Dice coefficient of extraction result was 66.7%.

Automatic quadrature control and measuring system. [using optical coupling circuitry

NASA Technical Reports Server (NTRS)

Hamlet, J. F. (Inventor)

1974-01-01

A quadrature component cancellation and measuring system comprising a detection system for detecting the quadrature component from a primary signal, including reference circuitry to define the phase of the quadrature component for detection is described. A Raysistor optical coupling control device connects an output from the detection system to a circuit driven by a signal based upon the primary signal. Combining circuitry connects the primary signal and the circuit controlled by the Raysistor device to subtract quadrature components. A known current through the optically sensitive element produces a signal defining the magnitude of the quadrature component.

High performance, rapid thermal/UV curing epoxy resin for additive manufacturing of short and continuous carbon fiber epoxy composites

DOEpatents

Lewicki, James

2018-04-17

An additive manufacturing resin system including an additive manufacturing print head; a continuous carbon fiber or short carbon fibers operatively connected to the additive manufacturing print head; and a tailored resin operatively connected to the print head, wherein the tailored resin has a resin mass and wherein the tailored resin includes an epoxy component, a filler component, a catalyst component, and a chain extender component; wherein the epoxy component is 70-95% of the resin mass, wherein the filler component is 1-20% of the resin mass, wherein the catalyst component is 0.1-10% of the resin mass, and wherein the chain extender component is 0-50% of the resin mass.

Evolutionary tradeoffs in cellular composition across diverse bacteria

PubMed Central

Kempes, Christopher P; Wang, Lawrence; Amend, Jan P; Doyle, John; Hoehler, Tori

2016-01-01

One of the most important classic and contemporary interests in biology is the connection between cellular composition and physiological function. Decades of research have allowed us to understand the detailed relationship between various cellular components and processes for individual species, and have uncovered common functionality across diverse species. However, there still remains the need for frameworks that can mechanistically predict the tradeoffs between cellular functions and elucidate and interpret average trends across species. Here we provide a comprehensive analysis of how cellular composition changes across the diversity of bacteria as connected with physiological function and metabolism, spanning five orders of magnitude in body size. We present an analysis of the trends with cell volume that covers shifts in genomic, protein, cellular envelope, RNA and ribosomal content. We show that trends in protein content are more complex than a simple proportionality with the overall genome size, and that the number of ribosomes is simply explained by cross-species shifts in biosynthesis requirements. Furthermore, we show that the largest and smallest bacteria are limited by physical space requirements. At the lower end of size, cell volume is dominated by DNA and protein content—the requirement for which predicts a lower limit on cell size that is in good agreement with the smallest observed bacteria. At the upper end of bacterial size, we have identified a point at which the number of ribosomes required for biosynthesis exceeds available cell volume. Between these limits we are able to discuss systematic and dramatic shifts in cellular composition. Much of our analysis is connected with the basic energetics of cells where we show that the scaling of metabolic rate is surprisingly superlinear with all cellular components. PMID:27046336

Tools for the Conceptual Design and Engineering Analysis of Micro Air Vehicles

DTIC Science & Technology

2009-03-01

problem with two DC motors with propellers, mounted on each wing tip and oriented such that the thrust vectors had an angular separation of 180...ElectriCalc or MotoCalc Database • Script Program (MC) In determination of the components to be integrated into MC, the R/C world was explored since the tools...Excel, ProE, QuickWrap and Script . Importing outside applications can be achieved by direct interaction with MC or through analysis server connections [11

Meeting the Challenge of Installing Canes During New Ship Construction on LPD 28

DTIC Science & Technology

2015-03-01

components making up the SWAN are enclosed in Raytheon-designed hardware systems that meet Military Specification MIL-S-901D Grade B shock...Connected Connected Connected, NRE Connected, Engr Control Sys (ECS), Integrated to segreg; tte NREto Condition Assmt Sys (I CAS) SWAN develop HM&E...Hull Mecllanical & Electrical (HM&E) - COA Connected NA Connected Connected, NRE Connected, Damage Control Action Management Dependent to segreg; tte

Identifying dynamic functional connectivity biomarkers using GIG-ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder.

PubMed

Du, Yuhui; Pearlson, Godfrey D; Lin, Dongdong; Sui, Jing; Chen, Jiayu; Salman, Mustafa; Tamminga, Carol A; Ivleva, Elena I; Sweeney, John A; Keshavan, Matcheri S; Clementz, Brett A; Bustillo, Juan; Calhoun, Vince D

2017-05-01

Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here, we aim to explore DFC across a spectrum of symptomatically-related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD), and schizophrenia (SZ). We introduce a group information guided independent component analysis procedure to estimate both group-level and subject-specific connectivity states from DFC. Using resting-state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD, and 113 SZ patients, we identified measures differentiating groups from the whole-brain DFC and traditional static functional connectivity (SFC), separately. Results show that DFC provided more informative measures than SFC. Diagnosis-related connectivity states were evident using DFC analysis. For the dominant state consistent across groups, we found 22 instances of hypoconnectivity (with decreasing trends from HC to BPP to SAD to SZ) mainly involving post-central, frontal, and cerebellar cortices as well as 34 examples of hyperconnectivity (with increasing trends HC through SZ) primarily involving thalamus and temporal cortices. Hypoconnectivities/hyperconnectivities also showed negative/positive correlations, respectively, with clinical symptom scores. Specifically, hypoconnectivities linking postcentral and frontal gyri were significantly negatively correlated with the PANSS positive/negative scores. For frontal connectivities, BPP resembled HC while SAD and SZ were more similar. Three connectivities involving the left cerebellar crus differentiated SZ from other groups and one connection linking frontal and fusiform cortices showed a SAD-unique change. In summary, our method is promising for assessing DFC and may yield imaging biomarkers for quantifying the dimension of psychosis. Hum Brain Mapp 38:2683-2708, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Beyond the word and image: II- Structural and functional connectivity of a common semantic system.

PubMed

Jouen, A L; Ellmore, T M; Madden-Lombardi, C J; Pallier, C; Dominey, P F; Ventre-Dominey, J

2018-02-01

Understanding events requires interplaying cognitive processes arising in neural networks whose organisation and connectivity remain subjects of controversy in humans. In the present study, by combining diffusion tensor imaging and functional interaction analysis, we aim to provide new insights on the organisation of the structural and functional pathways connecting the multiple nodes of the identified semantic system -shared by vision and language (Jouen et al., 2015). We investigated a group of 19 healthy human subjects during experimental tasks of reading sentences or seeing pictures. The structural connectivity was realised by deterministic tractography using an algorithm to extract white matter fibers terminating in the selected regions of interest (ROIs) and the functional connectivity by independent component analysis to measure correlated activities among these ROIs. The major connections link ventral neural stuctures including the parietal and temporal cortices through inferior and middle longitudinal fasciculi, the retrosplenial and parahippocampal cortices through the cingulate bundle, and the temporal and prefrontal structures through the uncinate fasciculus. The imageability score provided when the subject was reading a sentence was significantly correlated with the factor of anisotropy of the left parieto-temporal connections of the middle longitudinal fasciculus. A large part of this ventrally localised structural connectivity corresponds to functional interactions between the main parietal, temporal and frontal nodes. More precisely, the strong coactivation both in the anterior temporal pole and in the region of the temporo-parietal cortex suggests dual and cooperating roles for these areas within the semantic system. These findings are discussed in terms of two semantics-related sub-systems responsible for conceptual representation. Copyright © 2017 Elsevier Inc. All rights reserved.

Exploring connectivity with large-scale Granger causality on resting-state functional MRI.

PubMed

DSouza, Adora M; Abidin, Anas Z; Leistritz, Lutz; Wismüller, Axel

2017-08-01

Large-scale Granger causality (lsGC) is a recently developed, resting-state functional MRI (fMRI) connectivity analysis approach that estimates multivariate voxel-resolution connectivity. Unlike most commonly used multivariate approaches, which establish coarse-resolution connectivity by aggregating voxel time-series avoiding an underdetermined problem, lsGC estimates voxel-resolution, fine-grained connectivity by incorporating an embedded dimension reduction. We investigate application of lsGC on realistic fMRI simulations, modeling smoothing of neuronal activity by the hemodynamic response function and repetition time (TR), and empirical resting-state fMRI data. Subsequently, functional subnetworks are extracted from lsGC connectivity measures for both datasets and validated quantitatively. We also provide guidelines to select lsGC free parameters. Results indicate that lsGC reliably recovers underlying network structure with area under receiver operator characteristic curve (AUC) of 0.93 at TR=1.5s for a 10-min session of fMRI simulations. Furthermore, subnetworks of closely interacting modules are recovered from the aforementioned lsGC networks. Results on empirical resting-state fMRI data demonstrate recovery of visual and motor cortex in close agreement with spatial maps obtained from (i) visuo-motor fMRI stimulation task-sequence (Accuracy=0.76) and (ii) independent component analysis (ICA) of resting-state fMRI (Accuracy=0.86). Compared with conventional Granger causality approach (AUC=0.75), lsGC produces better network recovery on fMRI simulations. Furthermore, it cannot recover functional subnetworks from empirical fMRI data, since quantifying voxel-resolution connectivity is not possible as consequence of encountering an underdetermined problem. Functional network recovery from fMRI data suggests that lsGC gives useful insight into connectivity patterns from resting-state fMRI at a multivariate voxel-resolution. Copyright © 2017 Elsevier B.V. All rights reserved.

Coactivation of cognitive control networks during task switching.

PubMed

Yin, Shouhang; Deák, Gedeon; Chen, Antao

2018-01-01

The ability to flexibly switch between tasks is considered an important component of cognitive control that involves frontal and parietal cortical areas. The present study was designed to characterize network dynamics across multiple brain regions during task switching. Functional magnetic resonance images (fMRI) were captured during a standard rule-switching task to identify switching-related brain regions. Multiregional psychophysiological interaction (PPI) analysis was used to examine effective connectivity between these regions. During switching trials, behavioral performance declined and activation of a generic cognitive control network increased. Concurrently, task-related connectivity increased within and between cingulo-opercular and fronto-parietal cognitive control networks. Notably, the left inferior frontal junction (IFJ) was most consistently coactivated with the 2 cognitive control networks. Furthermore, switching-dependent effective connectivity was negatively correlated with behavioral switch costs. The strength of effective connectivity between left IFJ and other regions in the networks predicted individual differences in switch costs. Task switching was supported by coactivated connections within cognitive control networks, with left IFJ potentially acting as a key hub between the fronto-parietal and cingulo-opercular networks. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Vulnerability of animal trade networks to the spread of infectious diseases: a methodological approach applied to evaluation and emergency control strategies in cattle, France, 2005.

PubMed

Rautureau, S; Dufour, B; Durand, B

2011-04-01

Besides farming, trade of livestock is a major component of agricultural economy. However, the networks generated by live animal movements are the major support for the propagation of infectious agents between farms, and their structure strongly affects how fast a disease may spread. Structural characteristics may thus be indicators of network vulnerability to the spread of infectious disease. The method proposed here is based upon the analysis of specific subnetworks: the giant strongly connected components (GSCs). Their existence, size and geographic extent are used to assess network vulnerability. Their disappearance when targeted nodes are removed allows studying how network vulnerability may be controlled under emergency conditions. The method was applied to the cattle trade network in France, 2005. Giant strongly connected components were present and widely spread all over the country in yearly, monthly and weekly networks. Among several tested approaches, the most efficient way to make GSCs disappear was based on the ranking of nodes by decreasing betweenness centrality (the proportion of shortest paths between nodes on which a specific node lies). Giant strongly connected components disappearance was obtained after removal of <1% of network nodes. Under emergency conditions, suspending animal trade activities in a small subset of holdings may thus allow to control the spread of an infectious disease through the animal trade network. Nodes representing markets and dealers were widely affected by these simulated control measures. This confirms their importance as 'hubs' for infectious diseases spread. Besides emergency conditions, specific sensitization and preventive measures should be dedicated to this population. © 2010 Blackwell Verlag GmbH.

Function-specific and Enhanced Brain Structural Connectivity Mapping via Joint Modeling of Diffusion and Functional MRI.

PubMed

Chu, Shu-Hsien; Parhi, Keshab K; Lenglet, Christophe

2018-03-16

A joint structural-functional brain network model is presented, which enables the discovery of function-specific brain circuits, and recovers structural connections that are under-estimated by diffusion MRI (dMRI). Incorporating information from functional MRI (fMRI) into diffusion MRI to estimate brain circuits is a challenging task. Usually, seed regions for tractography are selected from fMRI activation maps to extract the white matter pathways of interest. The proposed method jointly analyzes whole brain dMRI and fMRI data, allowing the estimation of complete function-specific structural networks instead of interactively investigating the connectivity of individual cortical/sub-cortical areas. Additionally, tractography techniques are prone to limitations, which can result in erroneous pathways. The proposed framework explicitly models the interactions between structural and functional connectivity measures thereby improving anatomical circuit estimation. Results on Human Connectome Project (HCP) data demonstrate the benefits of the approach by successfully identifying function-specific anatomical circuits, such as the language and resting-state networks. In contrast to correlation-based or independent component analysis (ICA) functional connectivity mapping, detailed anatomical connectivity patterns are revealed for each functional module. Results on a phantom (Fibercup) also indicate improvements in structural connectivity mapping by rejecting false-positive connections with insufficient support from fMRI, and enhancing under-estimated connectivity with strong functional correlation.

46 CFR 111.30-3 - Accessibility of switchboard components and connections.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 46 Shipping 4 2014-10-01 2014-10-01 false Accessibility of switchboard components and connections. 111.30-3 Section 111.30-3 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) ELECTRICAL ENGINEERING ELECTRIC SYSTEMS-GENERAL REQUIREMENTS Switchboards § 111.30-3 Accessibility of switchboard...

46 CFR 111.30-3 - Accessibility of switchboard components and connections.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 4 2010-10-01 2010-10-01 false Accessibility of switchboard components and connections. 111.30-3 Section 111.30-3 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) ELECTRICAL ENGINEERING ELECTRIC SYSTEMS-GENERAL REQUIREMENTS Switchboards § 111.30-3 Accessibility of switchboard...

46 CFR 111.30-3 - Accessibility of switchboard components and connections.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 4 2011-10-01 2011-10-01 false Accessibility of switchboard components and connections. 111.30-3 Section 111.30-3 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) ELECTRICAL ENGINEERING ELECTRIC SYSTEMS-GENERAL REQUIREMENTS Switchboards § 111.30-3 Accessibility of switchboard...

46 CFR 111.30-3 - Accessibility of switchboard components and connections.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 46 Shipping 4 2013-10-01 2013-10-01 false Accessibility of switchboard components and connections. 111.30-3 Section 111.30-3 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) ELECTRICAL ENGINEERING ELECTRIC SYSTEMS-GENERAL REQUIREMENTS Switchboards § 111.30-3 Accessibility of switchboard...

46 CFR 111.30-3 - Accessibility of switchboard components and connections.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 46 Shipping 4 2012-10-01 2012-10-01 false Accessibility of switchboard components and connections. 111.30-3 Section 111.30-3 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) ELECTRICAL ENGINEERING ELECTRIC SYSTEMS-GENERAL REQUIREMENTS Switchboards § 111.30-3 Accessibility of switchboard...

Resting-state test-retest reliability of a priori defined canonical networks over different preprocessing steps.

PubMed

Varikuti, Deepthi P; Hoffstaedter, Felix; Genon, Sarah; Schwender, Holger; Reid, Andrew T; Eickhoff, Simon B

2017-04-01

Resting-state functional connectivity analysis has become a widely used method for the investigation of human brain connectivity and pathology. The measurement of neuronal activity by functional MRI, however, is impeded by various nuisance signals that reduce the stability of functional connectivity. Several methods exist to address this predicament, but little consensus has yet been reached on the most appropriate approach. Given the crucial importance of reliability for the development of clinical applications, we here investigated the effect of various confound removal approaches on the test-retest reliability of functional-connectivity estimates in two previously defined functional brain networks. Our results showed that gray matter masking improved the reliability of connectivity estimates, whereas denoising based on principal components analysis reduced it. We additionally observed that refraining from using any correction for global signals provided the best test-retest reliability, but failed to reproduce anti-correlations between what have been previously described as antagonistic networks. This suggests that improved reliability can come at the expense of potentially poorer biological validity. Consistent with this, we observed that reliability was proportional to the retained variance, which presumably included structured noise, such as reliable nuisance signals (for instance, noise induced by cardiac processes). We conclude that compromises are necessary between maximizing test-retest reliability and removing variance that may be attributable to non-neuronal sources.

Resting-state test-retest reliability of a priori defined canonical networks over different preprocessing steps

PubMed Central

Varikuti, Deepthi P.; Hoffstaedter, Felix; Genon, Sarah; Schwender, Holger; Reid, Andrew T.; Eickhoff, Simon B.

2016-01-01

Resting-state functional connectivity analysis has become a widely used method for the investigation of human brain connectivity and pathology. The measurement of neuronal activity by functional MRI, however, is impeded by various nuisance signals that reduce the stability of functional connectivity. Several methods exist to address this predicament, but little consensus has yet been reached on the most appropriate approach. Given the crucial importance of reliability for the development of clinical applications, we here investigated the effect of various confound removal approaches on the test-retest reliability of functional-connectivity estimates in two previously defined functional brain networks. Our results showed that grey matter masking improved the reliability of connectivity estimates, whereas de-noising based on principal components analysis reduced it. We additionally observed that refraining from using any correction for global signals provided the best test-retest reliability, but failed to reproduce anti-correlations between what have been previously described as antagonistic networks. This suggests that improved reliability can come at the expense of potentially poorer biological validity. Consistent with this, we observed that reliability was proportional to the retained variance, which presumably included structured noise, such as reliable nuisance signals (for instance, noise induced by cardiac processes). We conclude that compromises are necessary between maximizing test-retest reliability and removing variance that may be attributable to non-neuronal sources. PMID:27550015

Altered Functional Connectivity of the Default Mode Network in Low-Empathy Subjects.

PubMed

Kim, Seung Jun; Kim, Sung Eun; Kim, Hyo Eun; Han, Kiwan; Jeong, Bumseok; Kim, Jae Jin; Namkoong, Kee; Kim, Ji Woong

2017-09-01

Empathy is the ability to identify with or make a vicariously experience of another person's feelings or thoughts based on memory and/or self-referential mental simulation. The default mode network in particular is related to self-referential empathy. In order to elucidate the possible neural mechanisms underlying empathy, we investigated the functional connectivity of the default mode network in subjects from a general population. Resting state functional magnetic resonance imaging data were acquired from 19 low-empathy subjects and 18 medium-empathy subjects. An independent component analysis was used to identify the default mode network, and differences in functional connectivity strength were compared between the two groups. The low-empathy group showed lower functional connectivity of the medial prefrontal cortex and anterior cingulate cortex (Brodmann areas 9 and 32) within the default mode network, compared to the medium-empathy group. The results of the present study suggest that empathy is related to functional connectivity of the medial prefrontal cortex/anterior cingulate cortex within the default mode network. Functional decreases in connectivity among low-empathy subjects may reflect an impairment of self-referential mental simulation. © Copyright: Yonsei University College of Medicine 2017.

Artifact suppression and analysis of brain activities with electroencephalography signals.

PubMed

Rashed-Al-Mahfuz, Md; Islam, Md Rabiul; Hirose, Keikichi; Molla, Md Khademul Islam

2013-06-05

Brain-computer interface is a communication system that connects the brain with computer (or other devices) but is not dependent on the normal output of the brain (i.e., peripheral nerve and muscle). Electro-oculogram is a dominant artifact which has a significant negative influence on further analysis of real electroencephalography data. This paper presented a data adaptive technique for artifact suppression and brain wave extraction from electroencephalography signals to detect regional brain activities. Empirical mode decomposition based adaptive thresholding approach was employed here to suppress the electro-oculogram artifact. Fractional Gaussian noise was used to determine the threshold level derived from the analysis data without any training. The purified electroencephalography signal was composed of the brain waves also called rhythmic components which represent the brain activities. The rhythmic components were extracted from each electroencephalography channel using adaptive wiener filter with the original scale. The regional brain activities were mapped on the basis of the spatial distribution of rhythmic components, and the results showed that different regions of the brain are activated in response to different stimuli. This research analyzed the activities of a single rhythmic component, alpha with respect to different motor imaginations. The experimental results showed that the proposed method is very efficient in artifact suppression and identifying individual motor imagery based on the activities of alpha component.

Research concerning the evaluation of the connection forces in the joints of the sucker rod pumping units mechanism

NASA Astrophysics Data System (ADS)

Bădoiu, D.; Petrescu, M. G.; Antonescu, N. N.; Toma, G.

2018-01-01

At present, the sucker rod pumping installations are the most used in the case of the wells in production, when an eruptive exploitation is not possible. The practice has demonstrated that an important role in increasing safety in the operation of the pumping units has the design of the various component bearings because of the extremely high values of the connection forces to which they are loaded. That is why it is necessary to establish as accurately as possible the values of these connecting forces. In the paper is analyzed the dynamics of a conventional pumping unit mechanism. The dynamic study which allows establishing the values of the connecting forces in the joints is performed within the Assur structural groups. The dynamic analysis was implemented into a computer program using Maple programming environment and finally it has been presented some simulation results in the case of a C-320D-256-100 pumping unit.

Modulating Intrinsic Connectivity: Adjacent Subregions within Supplementary Motor Cortex, Dorsolateral Prefrontal Cortex, and Parietal Cortex Connect to Separate Functional Networks during Task and Also Connect during Rest

PubMed Central

Roth, Jennifer K.; Johnson, Marcia K.; Tokoglu, Fuyuze; Murphy, Isabella; Constable, R. Todd

2014-01-01

Supplementary motor area (SMA), the inferior frontal junction (IFJ), superior frontal junction (SFJ) and parietal cortex are active in many cognitive tasks. In a previous study, we found that subregions of each of these major areas were differentially active in component processes of executive function during working memory tasks. In the present study, each of these subregions was used as a seed in a whole brain functional connectivity analysis of working memory and resting state data. These regions show functional connectivity to different networks, thus supporting the parcellation of these major regions into functional subregions. Many regions showing significant connectivity during the working memory residual data (with task events regressed from the data) were also significantly connected during rest suggesting that these network connections to subregions within major regions of cortex are intrinsic. For some of these connections, task demands modulate activity in these intrinsic networks. Approximately half of the connections significant during task were significant during rest, indicating that some of the connections are intrinsic while others are recruited only in the service of the task. Furthermore, the network connections to traditional ‘task positive’ and ‘task negative’ (a.k.a ‘default mode’) regions shift from positive connectivity to negative connectivity depending on task demands. These findings demonstrate that such task-identified subregions are part of distinct networks, and that these networks have different patterns of connectivity for task as they do during rest, engaging connections both to task positive and task negative regions. These results have implications for understanding the parcellation of commonly active regions into more specific functional networks. PMID:24637793

Global Analysis Reveals the Complexity of the Human Glomerular Extracellular Matrix

PubMed Central

Byron, Adam; Humphries, Jonathan D.; Randles, Michael J.; Carisey, Alex; Murphy, Stephanie; Knight, David; Brenchley, Paul E.; Zent, Roy; Humphries, Martin J.

2014-01-01

The glomerulus contains unique cellular and extracellular matrix (ECM) components, which are required for intact barrier function. Studies of the cellular components have helped to build understanding of glomerular disease; however, the full composition and regulation of glomerular ECM remains poorly understood. We used mass spectrometry-based proteomics of enriched ECM extracts for a global analysis of human glomerular ECM in vivo and identified a tissue-specific proteome of 144 structural and regulatory ECM proteins. This catalog includes all previously identified glomerular components plus many new and abundant components. Relative protein quantification showed a dominance of collagen IV, collagen I, and laminin isoforms in the glomerular ECM together with abundant collagen VI and TINAGL1. Protein network analysis enabled the creation of a glomerular ECM interactome, which revealed a core of highly connected structural components. More than one half of the glomerular ECM proteome was validated using colocalization studies and data from the Human Protein Atlas. This study yields the greatest number of ECM proteins relative to previous investigations of whole glomerular extracts, highlighting the importance of sample enrichment. It also shows that the composition of glomerular ECM is far more complex than previously appreciated and suggests that many more ECM components may contribute to glomerular development and disease processes. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD000456. PMID:24436468

The Coplane Analysis Technique for Three-Dimensional Wind Retrieval Using the HIWRAP Airborne Doppler Radar

NASA Technical Reports Server (NTRS)

Didlake, Anthony C., Jr.; Heymsfield, Gerald M.; Tian, Lin; Guimond, Stephen R.

2015-01-01

The coplane analysis technique for mapping the three-dimensional wind field of precipitating systems is applied to the NASA High Altitude Wind and Rain Airborne Profiler (HIWRAP). HIWRAP is a dual-frequency Doppler radar system with two downward pointing and conically scanning beams. The coplane technique interpolates radar measurements to a natural coordinate frame, directly solves for two wind components, and integrates the mass continuity equation to retrieve the unobserved third wind component. This technique is tested using a model simulation of a hurricane and compared to a global optimization retrieval. The coplane method produced lower errors for the cross-track and vertical wind components, while the global optimization method produced lower errors for the along-track wind component. Cross-track and vertical wind errors were dependent upon the accuracy of the estimated boundary condition winds near the surface and at nadir, which were derived by making certain assumptions about the vertical velocity field. The coplane technique was then applied successfully to HIWRAP observations of Hurricane Ingrid (2013). Unlike the global optimization method, the coplane analysis allows for a transparent connection between the radar observations and specific analysis results. With this ability, small-scale features can be analyzed more adequately and erroneous radar measurements can be identified more easily.

Functional connectivity in the prefrontal cortex measured by near-infrared spectroscopy during ultrarapid object recognition

NASA Astrophysics Data System (ADS)

Medvedev, Andrei V.; Kainerstorfer, Jana M.; Borisov, Sergey V.; Vanmeter, John

2011-01-01

Near-infrared spectroscopy (NIRS) is a developing technology for low-cost noninvasive functional brain imaging. With multichannel optical instruments, it becomes possible to measure not only local changes in hemoglobin concentrations but also temporal correlations of those changes in different brain regions which gives an optical analog of functional connectivity traditionally measured by fMRI. We recorded hemodynamic activity during the Go-NoGo task from 11 right-handed subjects with probes placed bilaterally over prefrontal areas. Subjects were detecting animals as targets in natural scenes pressing a mouse button. Data were low-pass filtered <1 Hz and cardiac/respiration/superficial layers artifacts were removed using Independent Component Analysis. Fisher's transformed correlations of poststimulus responses (30 s) were averaged over groups of channels unilaterally in each hemisphere (intrahemispheric connectivity) and the corresponding channels between hemispheres (interhemispheric connectivity). The hemodynamic response showed task-related activation (an increase/decrease in oxygenated/deoxygenated hemoglobin, respectively) greater in the right versus left hemisphere. Intra- and interhemispheric functional connectivity was also significantly stronger during the task compared to baseline. Functional connectivity between the inferior and the middle frontal regions was significantly stronger in the right hemisphere. Our results demonstrate that optical methods can be used to detect transient changes in functional connectivity during rapid cognitive processes.

Altered caudate connectivity is associated with executive dysfunction after traumatic brain injury

PubMed Central

De Simoni, Sara; Jenkins, Peter O; Bourke, Niall J; Fleminger, Jessica J; Jolly, Amy E; Patel, Maneesh C; Leech, Robert; Sharp, David J

2018-01-01

Abstract Traumatic brain injury often produces executive dysfunction. This characteristic cognitive impairment often causes long-term problems with behaviour and personality. Frontal lobe injuries are associated with executive dysfunction, but it is unclear how these injuries relate to corticostriatal interactions that are known to play an important role in behavioural control. We hypothesized that executive dysfunction after traumatic brain injury would be associated with abnormal corticostriatal interactions, a question that has not previously been investigated. We used structural and functional MRI measures of connectivity to investigate this. Corticostriatal functional connectivity in healthy individuals was initially defined using a data-driven approach. A constrained independent component analysis approach was applied in 100 healthy adult dataset from the Human Connectome Project. Diffusion tractography was also performed to generate white matter tracts. The output of this analysis was used to compare corticostriatal functional connectivity and structural integrity between groups of 42 patients with traumatic brain injury and 21 age-matched controls. Subdivisions of the caudate and putamen had distinct patterns of functional connectivity. Traumatic brain injury patients showed disruption to functional connectivity between the caudate and a distributed set of cortical regions, including the anterior cingulate cortex. Cognitive impairments in the patients were mainly seen in processing speed and executive function, as well as increased levels of apathy and fatigue. Abnormalities of caudate functional connectivity correlated with these cognitive impairments, with reductions in right caudate connectivity associated with increased executive dysfunction, information processing speed and memory impairment. Structural connectivity, measured using diffusion tensor imaging between the caudate and anterior cingulate cortex was impaired and this also correlated with measures of executive dysfunction. We show for the first time that altered subcortical connectivity is associated with large-scale network disruption in traumatic brain injury and that this disruption is related to the cognitive impairments seen in these patients. PMID:29186356

Thermal protection apparatus

DOEpatents

Bennett, Gloria A.; Moore, Troy K.

1988-01-01

An apparatus for thermally protecting heat sensitive components of tools. The apparatus comprises a Dewar for holding the heat sensitive components. The Dewar has spaced-apart inside and outside walls, an open top end and a bottom end. An insulating plug is located in the top end. The inside wall has portions defining an inside wall aperture located at the bottom of the Dewar and the outside wall has portions defining an outside wall aperture located at the bottom of the Dewar. A bottom connector has inside and outside components. The inside component sealably engages the inside wall aperture and the outside component sealably engages the outside wall aperture. The inside component is operatively connected to the heat sensitive components and to the outside component. The connections can be made with optical fibers or with electrically conducting wires.

Radiation-hardened fast acquisition/weak signal tracking system and method

NASA Technical Reports Server (NTRS)

Winternitz, Luke (Inventor); Boegner, Gregory J. (Inventor); Sirotzky, Steve (Inventor)

2009-01-01

A global positioning system (GPS) receiver and method of acquiring and tracking GPS signals comprises an antenna adapted to receive GPS signals; an analog radio frequency device operatively connected to the antenna and adapted to convert the GPS signals from an analog format to a digital format; a plurality of GPS signal tracking correlators operatively connected to the analog RF device; a GPS signal acquisition component operatively connected to the analog RF device and the plurality of GPS signal tracking correlators, wherein the GPS signal acquisition component is adapted to calculate a maximum vector on a databit correlation grid; and a microprocessor operatively connected to the plurality of GPS signal tracking correlators and the GPS signal acquisition component, wherein the microprocessor is adapted to compare the maximum vector with a predetermined correlation threshold to allow the GPS signal to be fully acquired and tracked.

Turbine engine component with cooling passages

DOEpatents

Arrell, Douglas J [Oviedo, FL; James, Allister W [Orlando, FL

2012-01-17

A component for use in a turbine engine including a first member and a second member associated with the first member. The second member includes a plurality of connecting elements extending therefrom. The connecting elements include securing portions at ends thereof that are received in corresponding cavities formed in the first member to attach the second member to the first member. The connecting elements are constructed to space apart a first surface of the second member from a first surface of the first member such that at least one cooling passage is formed between adjacent connecting elements and the first surface of the second member and the first surface of the first member.

Long and short term changes in the forests of the Cumberland Plateau and Mountains using large scale forest inventory data

Treesearch

Christopher M. Oswalt; Andrew J. Hartsell

2012-01-01

The Cumberland Plateau and Mountains (CPM) are a significant component of the eastern deciduous forest with biological and cultural resources strongly connected to and dependent upon the forest resources of the region. As a result, continuous inventory and monitoring is critical. The USDA Forest Service Forest Inventory and Analysis (FIA) program has been collecting...

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI

PubMed Central

Xu, Tingting; Cullen, Kathryn R.; Mueller, Bryon; Schreiner, Mindy W.; Lim, Kelvin O.; Schulz, S. Charles; Parhi, Keshab K.

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03–0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03–0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works. PMID:26977400

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI.

PubMed

Xu, Tingting; Cullen, Kathryn R; Mueller, Bryon; Schreiner, Mindy W; Lim, Kelvin O; Schulz, S Charles; Parhi, Keshab K

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03-0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03-0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works.

Corolla Is a Novel Protein That Contributes to the Architecture of the Synaptonemal Complex of Drosophila

PubMed Central

Collins, Kimberly A.; Unruh, Jay R.; Slaughter, Brian D.; Yu, Zulin; Lake, Cathleen M.; Nielsen, Rachel J.; Box, Kimberly S.; Miller, Danny E.; Blumenstiel, Justin P.; Perera, Anoja G.; Malanowski, Kathryn E.; Hawley, R. Scott

2014-01-01

In most organisms the synaptonemal complex (SC) connects paired homologs along their entire length during much of meiotic prophase. To better understand the structure of the SC, we aim to identify its components and to determine how each of these components contributes to SC function. Here, we report the identification of a novel SC component in Drosophila melanogaster female oocytes, which we have named Corolla. Using structured illumination microscopy, we demonstrate that Corolla is a component of the central region of the SC. Consistent with its localization, we show by yeast two-hybrid analysis that Corolla strongly interacts with Cona, a central element protein, demonstrating the first direct interaction between two inner-synaptonemal complex proteins in Drosophila. These observations help provide a more complete model of SC structure and function in Drosophila females. PMID:24913682

Attribution of emotions to body postures: an independent component analysis study of functional connectivity in autism.

PubMed

Libero, Lauren E; Stevens, Carl E; Kana, Rajesh K

2014-10-01

The ability to interpret others' body language is a vital skill that helps us infer their thoughts and emotions. However, individuals with autism spectrum disorder (ASD) have been found to have difficulty in understanding the meaning of people's body language, perhaps leading to an overarching deficit in processing emotions. The current fMRI study investigates the functional connectivity underlying emotion and action judgment in the context of processing body language in high-functioning adolescents and young adults with autism, using an independent components analysis (ICA) of the fMRI time series. While there were no reliable group differences in brain activity, the ICA revealed significant involvement of occipital and parietal regions in processing body actions; and inferior frontal gyrus, superior medial prefrontal cortex, and occipital cortex in body expressions of emotions. In a between-group analysis, participants with autism, relative to typical controls, demonstrated significantly reduced temporal coherence in left ventral premotor cortex and right superior parietal lobule while processing emotions. Participants with ASD, on the other hand, showed increased temporal coherence in left fusiform gyrus while inferring emotions from body postures. Finally, a positive predictive relationship was found between empathizing ability and the brain areas underlying emotion processing in ASD participants. These results underscore the differential role of frontal and parietal brain regions in processing emotional body language in autism. Copyright © 2014 Wiley Periodicals, Inc.

Can Social Network Analysis Help Address the High Rates of Bacterial Sexually Transmitted Infections in Saskatchewan?

PubMed

Trecker, Molly A; Dillon, Jo-Anne R; Lloyd, Kathy; Hennink, Maurice; Jolly, Ann; Waldner, Cheryl

2017-06-01

Saskatchewan has one of the highest rates of gonorrhea among the Canadian provinces-more than double the national rate. In light of these high rates, and the growing threat of untreatable infections, improved understanding of gonorrhea transmission dynamics in the province and evaluation of the current system and tools for disease control are important. We extracted data from a cross-sectional sample of laboratory-confirmed gonorrhea cases between 2003 and 2012 from the notifiable disease files of the Regina Qu'Appelle Health Region. The database was stratified by calendar year, and social network analysis combined with statistical modeling was used to identify associations between measures of connection within the network and the odds of repeat gonorrhea and risk of coinfection with chlamydia at the time of diagnosis. Networks were highly fragmented. Younger age and component size were positively associated with being coinfected with chlamydia. Being coinfected, reporting sex trade involvement, and component size were all positively associated with repeat infection. This is the first study to apply social network analysis to gonorrhea transmission in Saskatchewan and contributes important information about the relationship of network connections to gonorrhea/chlamydia coinfection and repeat gonorrhea. This study also suggests several areas for change of systems-related factors that could greatly increase understanding of social networks and enhance the potential for bacterial sexually transmitted infection control in Saskatchewan.

Track-weighted functional connectivity (TW-FC): a tool for characterizing the structural-functional connections in the brain.

PubMed

Calamante, Fernando; Masterton, Richard A J; Tournier, Jacques-Donald; Smith, Robert E; Willats, Lisa; Raffelt, David; Connelly, Alan

2013-04-15

MRI provides a powerful tool for studying the functional and structural connections in the brain non-invasively. The technique of functional connectivity (FC) exploits the intrinsic temporal correlations of slow spontaneous signal fluctuations to characterise brain functional networks. In addition, diffusion MRI fibre-tracking can be used to study the white matter structural connections. In recent years, there has been considerable interest in combining these two techniques to provide an overall structural-functional description of the brain. In this work we applied the recently proposed super-resolution track-weighted imaging (TWI) methodology to demonstrate how whole-brain fibre-tracking data can be combined with FC data to generate a track-weighted (TW) FC map of FC networks. The method was applied to data from 8 healthy volunteers, and illustrated with (i) FC networks obtained using a seeded connectivity-based analysis (seeding in the precuneus/posterior cingulate cortex, PCC, known to be part of the default mode network), and (ii) with FC networks generated using independent component analysis (in particular, the default mode, attention, visual, and sensory-motor networks). TW-FC maps showed high intensity in white matter structures connecting the nodes of the FC networks. For example, the cingulum bundles show the strongest TW-FC values in the PCC seeded-based analysis, due to their major role in the connection between medial frontal cortex and precuneus/posterior cingulate cortex; similarly the superior longitudinal fasciculus was well represented in the attention network, the optic radiations in the visual network, and the corticospinal tract and corpus callosum in the sensory-motor network. The TW-FC maps highlight the white matter connections associated with a given FC network, and their intensity in a given voxel reflects the functional connectivity of the part of the nodes of the network linked by the structural connections traversing that voxel. They therefore contain a different (and novel) image contrast from that of the images used to generate them. The results shown in this study illustrate the potential of the TW-FC approach for the fusion of structural and functional data into a single quantitative image. This technique could therefore have important applications in neuroscience and neurology, such as for voxel-based comparison studies. Copyright © 2012 Elsevier Inc. All rights reserved.

Network rhythms influence the relationship between spike-triggered local field potential and functional connectivity

PubMed Central

Maunsell, John H.R.

2012-01-01

Characterizing the functional connectivity between neurons is key for understanding brain function. We recorded spikes and local field potentials (LFP) from multi-electrode arrays implanted in monkey visual cortex to test the hypotheses that spikes generated outward traveling LFP waves and the strength of functional connectivity depended on stimulus contrast, as described recently. These hypotheses were proposed based on the observation that the latency of the peak negativity of the spike-triggered LFP average (STA) increased with distance between the spike and LFP electrodes, and the magnitude of the STA negativity and the distance over which it was observed decreased with increasing stimulus contrast. Detailed analysis of the shape of the STA, however, revealed contributions from two distinct sources – a transient negativity in the LFP locked to the spike (∼0 ms) that attenuated rapidly with distance, and a low frequency rhythm with peak negativity ∼25 ms after the spike that attenuated slowly with distance. The overall negative peak of the LFP, which combined both these components, shifted from ∼0 to ∼25 ms going from electrodes near the spike to electrodes far from the spike, giving an impression of a traveling wave, although the shift was fully explained by changing contributions from the two fixed components. The low frequency rhythm was attenuated during stimulus presentations, decreasing the overall magnitude of the STA. These results highlight the importance of accounting for the network activity while using STAs to determine functional connectivity. PMID:21880928

Connectomic markers of symptom severity in sport-related concussion: Whole-brain analysis of resting-state fMRI.

PubMed

Churchill, Nathan W; Hutchison, Michael G; Graham, Simon J; Schweizer, Tom A

2018-01-01

Concussion is associated with significant adverse effects within the first week post-injury, including physical complaints and altered cognition, sleep and mood. It is currently unknown whether these subjective disturbances have reliable functional brain correlates. Resting-state functional magnetic resonance imaging (rs-fMRI) has been used to measure functional connectivity of individuals after traumatic brain injury, but less is known about the relationship between functional connectivity and symptom assessments after a sport concussion. In this study, rs-fMRI was used to evaluate whole-brain functional connectivity for seventy (70) university-level athletes, including 35 with acute concussion and 35 healthy matched controls. Univariate analyses showed that greater symptom severity was mainly associated with lower pairwise connectivity in frontal, temporal and insular regions, along with higher connectivity in a sparser set of cerebellar regions. A novel multivariate approach also extracted two components that showed reliable covariation with symptom severity: (1) a network of frontal, temporal and insular regions where connectivity was negatively correlated with symptom severity (replicating the univariate findings); and (2) a network with anti-correlated elements of the default-mode network and sensorimotor system, where connectivity was positively correlated with symptom severity. These findings support the presence of connectomic signatures of symptom complaints following a sport-related concussion, including both increased and decreased functional connectivity within distinct functional brain networks.

Isolation and connectivity: Relationships between periodic connection to the ocean and environmental variables in intermittently closed estuaries

NASA Astrophysics Data System (ADS)

Lill, Adrian Wilfred Thomas; Schallenberg, Marc; Lal, Aparna; Savage, Candida; Closs, Gerard Patrick

2013-08-01

Morphometric and physicochemical variables are key determinants of biotic community structure in estuaries and are influenced by changes to estuary mouth state (open/closed). This study examined and compared the consequences of intermittent connection to the ocean on environmental gradients among estuaries; specifically, how estuary morphology and hydrology relate to physical connection to the sea, and the influence of this relationship on the physicochemical environment. By sampling 20 estuaries across New Zealand and using historical aerial photographs, a continuous index of estuarine connection to the ocean was developed and independently validated using berm elevation derived from Airborne Laser Scanning (ALS) data. Using published literature, this index was compared to equivalent indices in South Africa and Australia. A clear relationship between connections to the ocean, freshwater flow and productivity indices underlie the environmental differences between permanently open and intermittently closed estuaries. Consistent patterns across the Southern Hemisphere, albeit with regional variations in estuarine characteristics, suggest that remote sensing is useful for predicting the physicochemical environment of small estuaries across regions. Principal components analysis for Otago estuaries showed that 40% of measured variation in the environment could be attributed to the gradient of relative connectivity (EOI), or isolation (berm elevation) to the ocean. Evaluating these relationships is central to understanding how global and local environmental changes may affect estuarine connectivity regimes and, ultimately, the functioning of estuarine ecosystems.

High performance semantic factoring of giga-scale semantic graph databases.

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

al-Saffar, Sinan; Adolf, Bob; Haglin, David

2010-10-01

As semantic graph database technology grows to address components ranging from extant large triple stores to SPARQL endpoints over SQL-structured relational databases, it will become increasingly important to be able to bring high performance computational resources to bear on their analysis, interpretation, and visualization, especially with respect to their innate semantic structure. Our research group built a novel high performance hybrid system comprising computational capability for semantic graph database processing utilizing the large multithreaded architecture of the Cray XMT platform, conventional clusters, and large data stores. In this paper we describe that architecture, and present the results of our deployingmore » that for the analysis of the Billion Triple dataset with respect to its semantic factors, including basic properties, connected components, namespace interaction, and typed paths.« less

Analysis Insights, August 2015: Sustainable Transportation; NREL (National Renewable Energy Laboratory)

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

None

2015-08-01

NREL Analysis Insights mines our body of analysis work to synthesize topical insights and key findings. In this issue, we examine transportation systems, alternative fuels, and implications of increasing electrification of transit. Moving people and goods from point A to B has never been easier, but our current transportation systems also take a toll on our environment. Transportation currently accounts for 71% of total U.S. petroleum use and 33% of the nation’s total carbon emissions. With new technology, can we make our transportation system cleaner and more cost effective? NREL is applying its analytical expertise and imagination to do justmore » that. Solutions start with systems thinking. Connecting the dots between physical components - vehicles, fueling stations, and highways - and institutional components - traffic laws, regulations, and vehicle standards - helps illuminate solutions that address the needs of the transportation system's many stakeholders.« less

30 CFR 18.49 - Connection boxes on machines.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Connection boxes on machines. 18.49 Section 18... Design Requirements § 18.49 Connection boxes on machines. Connection boxes used to facilitate replacement of cables or machine components shall be explosion-proof. Portable-cable terminals on cable reels...

Template based rotation: A method for functional connectivity analysis with a priori templates☆

PubMed Central

Schultz, Aaron P.; Chhatwal, Jasmeer P.; Huijbers, Willem; Hedden, Trey; van Dijk, Koene R.A.; McLaren, Donald G.; Ward, Andrew M.; Wigman, Sarah; Sperling, Reisa A.

2014-01-01

Functional connectivity magnetic resonance imaging (fcMRI) is a powerful tool for understanding the network level organization of the brain in research settings and is increasingly being used to study large-scale neuronal network degeneration in clinical trial settings. Presently, a variety of techniques, including seed-based correlation analysis and group independent components analysis (with either dual regression or back projection) are commonly employed to compute functional connectivity metrics. In the present report, we introduce template based rotation,1 a novel analytic approach optimized for use with a priori network parcellations, which may be particularly useful in clinical trial settings. Template based rotation was designed to leverage the stable spatial patterns of intrinsic connectivity derived from out-of-sample datasets by mapping data from novel sessions onto the previously defined a priori templates. We first demonstrate the feasibility of using previously defined a priori templates in connectivity analyses, and then compare the performance of template based rotation to seed based and dual regression methods by applying these analytic approaches to an fMRI dataset of normal young and elderly subjects. We observed that template based rotation and dual regression are approximately equivalent in detecting fcMRI differences between young and old subjects, demonstrating similar effect sizes for group differences and similar reliability metrics across 12 cortical networks. Both template based rotation and dual-regression demonstrated larger effect sizes and comparable reliabilities as compared to seed based correlation analysis, though all three methods yielded similar patterns of network differences. When performing inter-network and sub-network connectivity analyses, we observed that template based rotation offered greater flexibility, larger group differences, and more stable connectivity estimates as compared to dual regression and seed based analyses. This flexibility owes to the reduced spatial and temporal orthogonality constraints of template based rotation as compared to dual regression. These results suggest that template based rotation can provide a useful alternative to existing fcMRI analytic methods, particularly in clinical trial settings where predefined outcome measures and conserved network descriptions across groups are at a premium. PMID:25150630

Disrupted global metastability and static and dynamic brain connectivity across individuals in the Alzheimer’s disease continuum

NASA Astrophysics Data System (ADS)

Córdova-Palomera, Aldo; Kaufmann, Tobias; Persson, Karin; Alnæs, Dag; Doan, Nhat Trung; Moberget, Torgeir; Lund, Martina Jonette; Barca, Maria Lage; Engvig, Andreas; Brækhus, Anne; Engedal, Knut; Andreassen, Ole A.; Selbæk, Geir; Westlye, Lars T.

2017-01-01

As findings on the neuropathological and behavioral components of Alzheimer’s disease (AD) continue to accrue, converging evidence suggests that macroscale brain functional disruptions may mediate their association. Recent developments on theoretical neuroscience indicate that instantaneous patterns of brain connectivity and metastability may be a key mechanism in neural communication underlying cognitive performance. However, the potential significance of these patterns across the AD spectrum remains virtually unexplored. We assessed the clinical sensitivity of static and dynamic functional brain disruptions across the AD spectrum using resting-state fMRI in a sample consisting of AD patients (n = 80) and subjects with either mild (n = 44) or subjective (n = 26) cognitive impairment (MCI, SCI). Spatial maps constituting the nodes in the functional brain network and their associated time-series were estimated using spatial group independent component analysis and dual regression, and whole-brain oscillatory activity was analyzed both globally (metastability) and locally (static and dynamic connectivity). Instantaneous phase metrics showed functional coupling alterations in AD compared to MCI and SCI, both static (putamen, dorsal and default-mode) and dynamic (temporal, frontal-superior and default-mode), along with decreased global metastability. The results suggest that brains of AD patients display altered oscillatory patterns, in agreement with theoretical premises on cognitive dynamics.

Using Separable Nonnegative Matrix Factorization Techniques for the Analysis of Time-Resolved Raman Spectra.

PubMed

Luce, Robert; Hildebrandt, Peter; Kuhlmann, Uwe; Liesen, Jörg

2016-09-01

The key challenge of time-resolved Raman spectroscopy is the identification of the constituent species and the analysis of the kinetics of the underlying reaction network. In this work we present an integral approach that allows for determining both the component spectra and the rate constants simultaneously from a series of vibrational spectra. It is based on an algorithm for nonnegative matrix factorization that is applied to the experimental data set following a few pre-processing steps. As a prerequisite for physically unambiguous solutions, each component spectrum must include one vibrational band that does not significantly interfere with the vibrational bands of other species. The approach is applied to synthetic "experimental" spectra derived from model systems comprising a set of species with component spectra differing with respect to their degree of spectral interferences and signal-to-noise ratios. In each case, the species involved are connected via monomolecular reaction pathways. The potential and limitations of the approach for recovering the respective rate constants and component spectra are discussed. © The Author(s) 2016.

Concurrent white matter bundles and grey matter networks using independent component analysis.

PubMed

O'Muircheartaigh, Jonathan; Jbabdi, Saad

2018-04-15

Developments in non-invasive diffusion MRI tractography techniques have permitted the investigation of both the anatomy of white matter pathways connecting grey matter regions and their structural integrity. In parallel, there has been an expansion in automated techniques aimed at parcellating grey matter into distinct regions based on functional imaging. Here we apply independent component analysis to whole-brain tractography data to automatically extract brain networks based on their associated white matter pathways. This method decomposes the tractography data into components that consist of paired grey matter 'nodes' and white matter 'edges', and automatically separates major white matter bundles, including known cortico-cortical and cortico-subcortical tracts. We show how this framework can be used to investigate individual variations in brain networks (in terms of both nodes and edges) as well as their associations with individual differences in behaviour and anatomy. Finally, we investigate correspondences between tractography-based brain components and several canonical resting-state networks derived from functional MRI. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Inter-vender and test-retest reliabilities of resting-state functional magnetic resonance imaging: Implications for multi-center imaging studies.

PubMed

An, Hyeong Su; Moon, Won-Jin; Ryu, Jae-Kyun; Park, Ju Yeon; Yun, Won Sung; Choi, Jin Woo; Jahng, Geon-Ho; Park, Jang-Yeon

2017-12-01

This prospective multi-center study aimed to evaluate the inter-vendor and test-retest reliabilities of resting-state functional magnetic resonance imaging (RS-fMRI) by assessing the temporal signal-to-noise ratio (tSNR) and functional connectivity. Study included 10 healthy subjects and each subject was scanned using three 3T MR scanners (GE Signa HDxt, Siemens Skyra, and Philips Achieva) in two sessions. The tSNR was calculated from the time course data. Inter-vendor and test-retest reliabilities were assessed with intra-class correlation coefficients (ICCs) derived from variant component analysis. Independent component analysis was performed to identify the connectivity of the default-mode network (DMN). In result, the tSNR for the DMN was not significantly different among the GE, Philips, and Siemens scanners (P=0.638). In terms of vendor differences, the inter-vendor reliability was good (ICC=0.774). Regarding the test-retest reliability, the GE scanner showed excellent correlation (ICC=0.961), while the Philips (ICC=0.671) and Siemens (ICC=0.726) scanners showed relatively good correlation. The DMN pattern of the subjects between the two sessions for each scanner and between three scanners showed the identical patterns of functional connectivity. The inter-vendor and test-retest reliabilities of RS-fMRI using different 3T MR scanners are good. Thus, we suggest that RS-fMRI could be used in multicenter imaging studies as a reliable imaging marker. Copyright © 2017 Elsevier Inc. All rights reserved.

Metabolomics analysis: Finding out metabolic building blocks

PubMed Central

2017-01-01

In this paper we propose a new methodology for the analysis of metabolic networks. We use the notion of strongly connected components of a graph, called in this context metabolic building blocks. Every strongly connected component is contracted to a single node in such a way that the resulting graph is a directed acyclic graph, called a metabolic DAG, with a considerably reduced number of nodes. The property of being a directed acyclic graph brings out a background graph topology that reveals the connectivity of the metabolic network, as well as bridges, isolated nodes and cut nodes. Altogether, it becomes a key information for the discovery of functional metabolic relations. Our methodology has been applied to the glycolysis and the purine metabolic pathways for all organisms in the KEGG database, although it is general enough to work on any database. As expected, using the metabolic DAGs formalism, a considerable reduction on the size of the metabolic networks has been obtained, specially in the case of the purine pathway due to its relative larger size. As a proof of concept, from the information captured by a metabolic DAG and its corresponding metabolic building blocks, we obtain the core of the glycolysis pathway and the core of the purine metabolism pathway and detect some essential metabolic building blocks that reveal the key reactions in both pathways. Finally, the application of our methodology to the glycolysis pathway and the purine metabolism pathway reproduce the tree of life for the whole set of the organisms represented in the KEGG database which supports the utility of this research. PMID:28493998

Abnormal structural connectivity between the basal ganglia, thalamus, and frontal cortex in patients with disorders of consciousness.

PubMed

Weng, Ling; Xie, Qiuyou; Zhao, Ling; Zhang, Ruibin; Ma, Qing; Wang, Junjing; Jiang, Wenjie; He, Yanbin; Chen, Yan; Li, Changhong; Ni, Xiaoxiao; Xu, Qin; Yu, Ronghao; Huang, Ruiwang

2017-05-01

Consciousness loss in patients with severe brain injuries is associated with reduced functional connectivity of the default mode network (DMN), fronto-parietal network, and thalamo-cortical network. However, it is still unclear if the brain white matter connectivity between the above mentioned networks is changed in patients with disorders of consciousness (DOC). In this study, we collected diffusion tensor imaging (DTI) data from 13 patients and 17 healthy controls, constructed whole-brain white matter (WM) structural networks with probabilistic tractography. Afterward, we estimated and compared topological properties, and revealed an altered structural organization in the patients. We found a disturbance in the normal balance between segregation and integration in brain structural networks and detected significantly decreased nodal centralities primarily in the basal ganglia and thalamus in the patients. A network-based statistical analysis detected a subnetwork with uniformly significantly decreased structural connections between the basal ganglia, thalamus, and frontal cortex in the patients. Further analysis indicated that along the WM fiber tracts linking the basal ganglia, thalamus, and frontal cortex, the fractional anisotropy was decreased and the radial diffusivity was increased in the patients compared to the controls. Finally, using the receiver operating characteristic method, we found that the structural connections within the NBS-derived component that showed differences between the groups demonstrated high sensitivity and specificity (>90%). Our results suggested that major consciousness deficits in DOC patients may be related to the altered WM connections between the basal ganglia, thalamus, and frontal cortex. Copyright © 2017 Elsevier Ltd. All rights reserved.

Increased functional connectivity with puberty in the mentalising network involved in social emotion processing

PubMed Central

Klapwijk, Eduard T.; Goddings, Anne-Lise; Heyes, Stephanie Burnett; Bird, Geoffrey; Viner, Russell M.; Blakemore, Sarah-Jayne

2015-01-01

There is increasing evidence that puberty plays an important role in the structural and functional brain development seen in adolescence, but little is known of the pubertal influence on changes in functional connectivity. We explored how pubertal indicators (salivary concentrations of testosterone, oestradiol and DHEA; pubertal stage; menarcheal status) relate to functional connectivity between components of a mentalising network identified to be engaged in social emotion processing by our prior work, using psychophysiological interaction (PPI) analysis. Female adolescents aged 11 to 13 years were scanned whilst silently reading scenarios designed to evoke either social emotions (guilt and embarrassment) or basic emotions (disgust and fear), of which only social compared to basic emotions require the representation of another person’s mental states. Pubertal stage and menarcheal status were used to assign participants to pre/early or mid/late puberty groups. We found increased functional connectivity between the dorsomedial prefrontal cortex (DMPFC) and the right posterior superior temporal sulcus (pSTS) and right temporo-parietal junction (TPJ) during social relative to basic emotion processing. Moreover, increasing oestradiol concentrations were associated with increased functional connectivity between the DMPFC and the right TPJ during social relative to basic emotion processing, independent of age. Our analysis of the PPI data by phenotypic pubertal status showed that more advanced puberty stage was associated with enhanced functional connectivity between the DMPFC and the left anterior temporal cortex (ATC) during social relative to basic emotion processing, also independent of age. Our results suggest increased functional maturation of the social brain network with the advancement of puberty in girls. PMID:23998674

Resting-state connectivity of pre-motor cortex reflects disability in multiple sclerosis.

PubMed

Dogonowski, A-M; Siebner, H R; Soelberg Sørensen, P; Paulson, O B; Dyrby, T B; Blinkenberg, M; Madsen, K H

2013-11-01

To characterize the relationship between motor resting-state connectivity of the dorsal pre-motor cortex (PMd) and clinical disability in patients with multiple sclerosis (MS). A total of 27 patients with relapsing-remitting MS (RR-MS) and 15 patients with secondary progressive MS (SP-MS) underwent functional resting-state magnetic resonance imaging. Clinical disability was assessed using the Expanded Disability Status Scale (EDSS). Independent component analysis was used to characterize motor resting-state connectivity. Multiple regression analysis was performed in SPM8 between the individual expression of motor resting-state connectivity in PMd and EDSS scores including age as covariate. Separate post hoc analyses were performed for patients with RR-MS and SP-MS. The EDSS scores ranged from 0 to 7 with a median score of 4.3. Motor resting-state connectivity of left PMd showed a positive linear relation with clinical disability in patients with MS. This effect was stronger when considering the group of patients with RR-MS alone, whereas patients with SP-MS showed no increase in coupling strength between left PMd and the motor resting-state network with increasing clinical disability. No significant relation between motor resting-state connectivity of the right PMd and clinical disability was detected in MS. The increase in functional coupling between left PMd and the motor resting-state network with increasing clinical disability can be interpreted as adaptive reorganization of the motor system to maintain motor function, which appears to be limited to the relapsing-remitting stage of the disease. © 2013 John Wiley & Sons A/S.

Functional brain networks in schizophrenia: a review.

PubMed

Calhoun, Vince D; Eichele, Tom; Pearlson, Godfrey

2009-01-01

Functional magnetic resonance imaging (fMRI) has become a major technique for studying cognitive function and its disruption in mental illness, including schizophrenia. The major proportion of imaging studies focused primarily upon identifying regions which hemodynamic response amplitudes covary with particular stimuli and differentiate between patient and control groups. In addition to such amplitude based comparisons, one can estimate temporal correlations and compute maps of functional connectivity between regions which include the variance associated with event-related responses as well as intrinsic fluctuations of hemodynamic activity. Functional connectivity maps can be computed by correlating all voxels with a seed region when a spatial prior is available. An alternative are multivariate decompositions such as independent component analysis (ICA) which extract multiple components, each of which is a spatially distinct map of voxels with a common time course. Recent work has shown that these networks are pervasive in relaxed resting and during task performance and hence provide robust measures of intact and disturbed brain activity. This in turn bears the prospect of yielding biomarkers for schizophrenia, which can be described both in terms of disrupted local processing as well as altered global connectivity between large-scale networks. In this review we will summarize functional connectivity measures with a focus upon work with ICA and discuss the meaning of intrinsic fluctuations. In addition, examples of how brain networks have been used for classification of disease will be shown. We present work with functional network connectivity, an approach that enables the evaluation of the interplay between multiple networks and how they are affected in disease. We conclude by discussing new variants of ICA for extracting maximally group discriminative networks from data. In summary, it is clear that identification of brain networks and their inter-relationships with fMRI has great potential to improve our understanding of schizophrenia.

Electrophysiological signatures of atypical intrinsic brain connectivity networks in autism

NASA Astrophysics Data System (ADS)

Shou, Guofa; Mosconi, Matthew W.; Wang, Jun; Ethridge, Lauren E.; Sweeney, John A.; Ding, Lei

2017-08-01

Objective. Abnormal local and long-range brain connectivity have been widely reported in autism spectrum disorder (ASD), yet the nature of these abnormalities and their functional relevance at distinct cortical rhythms remains unknown. Investigations of intrinsic connectivity networks (ICNs) and their coherence across whole brain networks hold promise for determining whether patterns of functional connectivity abnormalities vary across frequencies and networks in ASD. In the present study, we aimed to probe atypical intrinsic brain connectivity networks in ASD from resting-state electroencephalography (EEG) data via characterizing the whole brain network. Approach. Connectivity within individual ICNs (measured by spectral power) and between ICNs (measured by coherence) were examined at four canonical frequency bands via a time-frequency independent component analysis on high-density EEG, which were recorded from 20 ASD and 20 typical developing (TD) subjects during an eyes-closed resting state. Main results. Among twelve identified electrophysiological ICNs, individuals with ASD showed hyper-connectivity in individual ICNs and hypo-connectivity between ICNs. Functional connectivity alterations in ASD were more severe in the frontal lobe and the default mode network (DMN) and at low frequency bands. These functional connectivity measures also showed abnormal age-related associations in ICNs related to frontal, temporal and motor regions in ASD. Significance. Our findings suggest that ASD is characterized by the opposite directions of abnormalities (i.e. hypo- and hyper-connectivity) in the hierarchical structure of the whole brain network, with more impairments in the frontal lobe and the DMN at low frequency bands, which are critical for top-down control of sensory systems, as well as for both cognition and social skills.

Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects.

PubMed

Rashid, Barnaly; Damaraju, Eswar; Pearlson, Godfrey D; Calhoun, Vince D

2014-01-01

Schizophrenia (SZ) and bipolar disorder (BP) share significant overlap in clinical symptoms, brain characteristics, and risk genes, and both are associated with dysconnectivity among large-scale brain networks. Resting state functional magnetic resonance imaging (rsfMRI) data facilitates studying macroscopic connectivity among distant brain regions. Standard approaches to identifying such connectivity include seed-based correlation and data-driven clustering methods such as independent component analysis (ICA) but typically focus on average connectivity. In this study, we utilize ICA on rsfMRI data to obtain intrinsic connectivity networks (ICNs) in cohorts of healthy controls (HCs) and age matched SZ and BP patients. Subsequently, we investigated difference in functional network connectivity, defined as pairwise correlations among the timecourses of ICNs, between HCs and patients. We quantified differences in both static (average) and dynamic (windowed) connectivity during the entire scan duration. Disease-specific differences were identified in connectivity within different dynamic states. Notably, results suggest that patients make fewer transitions to some states (states 1, 2, and 4) compared to HCs, with most such differences confined to a single state. SZ patients showed more differences from healthy subjects than did bipolars, including both hyper and hypo connectivity in one common connectivity state (dynamic state 3). Also group differences between SZ and bipolar patients were identified in patterns (states) of connectivity involving the frontal (dynamic state 1) and frontal-parietal regions (dynamic state 3). Our results provide new information about these illnesses and strongly suggest that state-based analyses are critical to avoid averaging together important factors that can help distinguish these clinical groups.

A Social Network Analysis of a Coalition Initiative to Prevent Underage Drinking in Los Angeles County

PubMed Central

Chu, Kar-Hai; Hoeppner, Elena; Valente, Thomas; Rohrbach, Luanne

2016-01-01

In 2011, the Los Angeles County Department of Public Health began a prevention services initiative to address problems dealing with alcohol and other drugs across the County. A major component of the strategy included the formation of eight coalitions. Defined by geographic borders, each coalition consisted of multiple service provider organizations, and were mandated to implement customized plans that would focus on preventing underage drinking by addressing availability and accessibility of alcohol. In this study, we collect survey data and observe coalition meetings to study the interactions within and between coalitions. We are informed by network tie strength theories to supplement our view of how organizations communicate. We apply social network analysis to learn how the multi-coalition network is functioning, and identify important unrealized connections. Our findings suggest there are many potential connections between coalitions that are not being leveraged. PMID:27899879

Attention without intention: explicit processing and implicit goal-setting in family medicine residents' written reflections.

PubMed

Shaughnessy, Allen F; Allen, Lucas; Duggan, Ashley

2017-05-01

Reflection, a process of self-analysis to promote learning through better understanding of one's experiences, is often used to assess learners' metacognitive ability. However, writing reflective exercises, not submitted for assessment, may allow learners to explore their experiences and indicate learning and professional growth without explicitly connecting to intentional sense-making. To identify core components of learning about medicine or medical education from family medicine residents' written reflections. Family medicine residents' wrote reflections about their experiences throughout an academic year. Qualitative thematic analysis to identify core components in 767 reflections written by 33 residents. We identified four themes of learning: 'Elaborated reporting' and 'metacognitive monitoring' represent explicit, purposeful self-analysis that typically would be characterised as reflective learning about medicine. 'Simple reporting' and 'goal setting' signal an analysis of experience that indicates learning and professional growth but that is overlooked as a component of learning. Identified themes elucidate the explicit and implicit forms of written reflection as sense-making and learning. An expanded theoretical understanding of reflection as inclusive of conscious sense-making as well as implicit discovery better enables the art of physician self-development.

Exploratory factor analysis of the Clinical Learning Environment, Supervision and Nurse Teacher Scale (CLES+T).

PubMed

Watson, Paul Barry; Seaton, Philippa; Sims, Deborah; Jamieson, Isabel; Mountier, Jane; Whittle, Rose; Saarikoski, Mikko

2014-01-01

The Clinical Learning Environment, Supervision and Nurse Teacher (CLES+T) scale measures student nurses' perceptions of clinical learning environments. This study evaluates the construct validity and internal reliability of the CLES+T in hospital settings in New Zealand. Comparisons are made between New Zealand and Finnish data. The CLES+T scale was completed by 416 Bachelor of Nursing students following hospital clinical placements between October 2008 and December 2009. Construct validity and internal reliability were assessed using exploratory factor analysis and Cronbach's alpha. Exploratory factor analysis supports 4 factors. Cronbach's alpha ranged from .82 to .93. All items except 1 loaded on the same factors found in unpublished Finnish data. The first factor combined 2 previous components from the published Finnish component analysis and was renamed: connecting with, and learning in, communities of clinical practice. The remaining 3 factors (Nurse teacher, Supervisory relationship, and Leadership style of the manager) corresponded to previous components and their conceptualizations. The CLES+T has good internal reliability and a consistent factor structure across samples. The consistency across international samples supports faculties and hospitals using the CLES+T to benchmark the quality of clinical learning environments provided to students.

Kernel PLS-SVC for Linear and Nonlinear Discrimination

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Trejo, Leonard J.; Matthews, Bryan

2003-01-01

A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.

Brain-controlled body movement assistance devices and methods

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

Leuthardt, Eric C.; Love, Lonnie J.; Coker, Rob

Methods, devices, systems, and apparatus, including computer programs encoded on a computer storage medium, for brain-controlled body movement assistance devices. In one aspect, a device includes a brain-controlled body movement assistance device with a brain-computer interface (BCI) component adapted to be mounted to a user, a body movement assistance component operably connected to the BCI component and adapted to be worn by the user, and a feedback mechanism provided in connection with at least one of the BCI component and the body movement assistance component, the feedback mechanism being configured to output information relating to a usage session of themore » brain-controlled body movement assistance device.« less

Auditory neural networks involved in attention modulation prefer biologically significant sounds and exhibit sexual dimorphism in anurans.

PubMed

Xue, Fei; Yue, Xizi; Fan, Yanzhu; Cui, Jianguo; Brauth, Steven E; Tang, Yezhong; Fang, Guangzhan

2018-03-09

Allocating attention to biologically relevant stimuli in a complex environment is critically important for survival and reproductive success. In humans, attention modulation is regulated by the frontal cortex, and is often reflected by changes in specific components of the event-related potential (ERP). Although brain networks for attention modulation have been widely studied in primates and avian species, little is known about attention modulation in amphibians. The present study aimed to investigate the attention modulation networks in an anuran species, the Emei music frog ( Babina daunchina ). Male music frogs produce advertisement calls from within underground nest burrows that modify the acoustic features of the calls, and both males and females prefer calls produced from inside burrows. We broadcast call stimuli to male and female music frogs while simultaneously recording electroencephalographic (EEG) signals from the telencephalon and mesencephalon. Granger causal connectivity analysis was used to elucidate functional brain networks within the time window of ERP components. The results show that calls produced from inside nests which are highly sexually attractive result in the strongest brain connections; both ascending and descending connections involving the left telencephalon were stronger in males while those in females were stronger with the right telencephalon. Our findings indicate that the frog brain allocates neural attention resources to highly attractive sounds within the window of early components of ERP, and that such processing is sexually dimorphic, presumably reflecting the different reproductive strategies of males and females. © 2018. Published by The Company of Biologists Ltd.

Stimulation artifact correction method for estimation of early cortico-cortical evoked potentials.

PubMed

Trebaul, Lena; Rudrauf, David; Job, Anne-Sophie; Mălîia, Mihai Dragos; Popa, Irina; Barborica, Andrei; Minotti, Lorella; Mîndruţă, Ioana; Kahane, Philippe; David, Olivier

2016-05-01

Effective connectivity can be explored using direct electrical stimulations in patients suffering from drug-resistant focal epilepsies and investigated with intracranial electrodes. Responses to brief electrical pulses mimic the physiological propagation of signals and manifest as cortico-cortical evoked potentials (CCEP). The first CCEP component is believed to reflect direct connectivity with the stimulated region but the stimulation artifact, a sharp deflection occurring during a few milliseconds, frequently contaminates it. In order to recover the characteristics of early CCEP responses, we developed an artifact correction method based on electrical modeling of the electrode-tissue interface. The biophysically motivated artifact templates are then regressed out of the recorded data as in any classical template-matching removal artifact methods. Our approach is able to make the distinction between the physiological responses time-locked to the stimulation pulses and the non-physiological component. We tested the correction on simulated CCEP data in order to quantify its efficiency for different stimulation and recording parameters. We demonstrated the efficiency of the new correction method on simulations of single trial recordings for early responses contaminated with the stimulation artifact. The results highlight the importance of sampling frequency for an accurate analysis of CCEP. We then applied the approach to experimental data. The model-based template removal was compared to a correction based on the subtraction of the averaged artifact. This new correction method of stimulation artifact will enable investigators to better analyze early CCEP components and infer direct effective connectivity in future CCEP studies. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

The Future Compatible Campus. Planning, Designing, and Implementing Information Technology in the Academy.

ERIC Educational Resources Information Center

Oblinger, Diana G., Ed.; Rush, Sean C., Ed.

This collection of 16 monographs centers around the theme the "future compatible campus," which is based on the premise that higher education will become a "connected campus" in a technology-enabled environment consisting of three components: connected learning, connected service to the community; and connected management. In…

Fast orthogonal transforms and generation of Brownian paths

PubMed Central

Leobacher, Gunther

2012-01-01

We present a number of fast constructions of discrete Brownian paths that can be used as alternatives to principal component analysis and Brownian bridge for stratified Monte Carlo and quasi-Monte Carlo. By fast we mean that a path of length n can be generated in O(nlog(n)) floating point operations. We highlight some of the connections between the different constructions and we provide some numerical examples. PMID:23471545

Vehicle Concept Model Abstractions for Integrated Geometric, Inertial, Rigid Body, Powertrain, and FE Analysis

DTIC Science & Technology

2011-01-01

refinement of the vehicle body structure through quantitative assessment of stiffness and modal parameter changes resulting from modifications to the beam...differential placed on the axle , adjustment of the torque output to the opposite wheel may be required to obtain the correct solution. Thus...represented by simple inertial components with appropriate model connectivity instead to determine the free modal response of powertrain type

System-level Analysis of Chilled Water Systems Aboard Naval Ships

DTIC Science & Technology

2015-06-24

developed one-dimensional partial differen- tial equation models that simulate time-dependent hy- drodynamics and heat transport in a piping network...Thermal zone extents. 2) Piping path and diameter. 3) Specifications and locations of chillers, heat ex- changers, pumps and valves. The framework of the... pipes and provides boundary conditions for the end of the connecting pipes . Pumps, valves, bends and heat exchangers are such components. These

EGRINs (Environmental Gene Regulatory Influence Networks) in Rice That Function in the Response to Water Deficit, High Temperature, and Agricultural Environments[OPEN

PubMed Central

Hafemeister, Christoph; Nicotra, Adrienne B.; Jagadish, S.V. Krishna; Bonneau, Richard; Purugganan, Michael

2016-01-01

Environmental gene regulatory influence networks (EGRINs) coordinate the timing and rate of gene expression in response to environmental signals. EGRINs encompass many layers of regulation, which culminate in changes in accumulated transcript levels. Here, we inferred EGRINs for the response of five tropical Asian rice (Oryza sativa) cultivars to high temperatures, water deficit, and agricultural field conditions by systematically integrating time-series transcriptome data, patterns of nucleosome-free chromatin, and the occurrence of known cis-regulatory elements. First, we identified 5447 putative target genes for 445 transcription factors (TFs) by connecting TFs with genes harboring known cis-regulatory motifs in nucleosome-free regions proximal to their transcriptional start sites. We then used network component analysis to estimate the regulatory activity for each TF based on the expression of its putative target genes. Finally, we inferred an EGRIN using the estimated transcription factor activity (TFA) as the regulator. The EGRINs include regulatory interactions between 4052 target genes regulated by 113 TFs. We resolved distinct regulatory roles for members of the heat shock factor family, including a putative regulatory connection between abiotic stress and the circadian clock. TFA estimation using network component analysis is an effective way of incorporating multiple genome-scale measurements into network inference. PMID:27655842

Contextual Classification of Point Cloud Data by Exploiting Individual 3d Neigbourhoods

NASA Astrophysics Data System (ADS)

Weinmann, M.; Schmidt, A.; Mallet, C.; Hinz, S.; Rottensteiner, F.; Jutzi, B.

2015-03-01

The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification.

N1 Magnitude of Auditory Evoked Potentials and Spontaneous Functional Connectivity Between Bilateral Heschl's Gyrus Are Coupled at Interindividual Level.

PubMed

Tan, Ao; Hu, Li; Tu, Yiheng; Chen, Rui; Hung, Yeung Sam; Zhang, Zhiguo

2016-07-01

N1 component of auditory evoked potentials is extensively used to investigate the propagation and processing of auditory inputs. However, the substantial interindividual variability of N1 could be a possible confounding factor when comparing different individuals or groups. Therefore, identifying the neuronal mechanism and origin of the interindividual variability of N1 is crucial in basic research and clinical applications. This study is aimed to use simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data to investigate the coupling between N1 and spontaneous functional connectivity (FC). EEG and fMRI data were simultaneously collected from a group of healthy individuals during a pure-tone listening task. Spontaneous FC was estimated from spontaneous blood oxygenation level-dependent (BOLD) signals that were isolated by regressing out task evoked BOLD signals from raw BOLD signals and then was correlated to N1 magnitude across individuals. It was observed that spontaneous FC between bilateral Heschl's gyrus was significantly and positively correlated with N1 magnitude across individuals (Spearman's R = 0.829, p < 0.001). The specificity of this observation was further confirmed by two whole-brain voxelwise analyses (voxel-mirrored homotopic connectivity analysis and seed-based connectivity analysis). These results enriched our understanding of the functional significance of the coupling between event-related brain responses and spontaneous brain connectivity, and hold the potential to increase the applicability of brain responses as a probe to the mechanism underlying pathophysiological conditions.

Risk seeking for losses modulates the functional connectivity of the default mode and left frontoparietal networks in young males.

PubMed

Deza Araujo, Yacila I; Nebe, Stephan; Neukam, Philipp T; Pooseh, Shakoor; Sebold, Miriam; Garbusow, Maria; Heinz, Andreas; Smolka, Michael N

2018-06-01

Value-based decision making (VBDM) is a principle that states that humans and other species adapt their behavior according to the dynamic subjective values of the chosen or unchosen options. The neural bases of this process have been extensively investigated using task-based fMRI and lesion studies. However, the growing field of resting-state functional connectivity (RSFC) may shed light on the organization and function of brain connections across different decision-making domains. With this aim, we used independent component analysis to study the brain network dynamics in a large cohort of young males (N = 145) and the relationship of these dynamics with VBDM. Participants completed a battery of behavioral tests that evaluated delay aversion, risk seeking for losses, risk aversion for gains, and loss aversion, followed by an RSFC scan session. We identified a set of large-scale brain networks and conducted our analysis only on the default mode network (DMN) and networks comprising cognitive control, appetitive-driven, and reward-processing regions. Higher risk seeking for losses was associated with increased connectivity between medial temporal regions, frontal regions, and the DMN. Higher risk seeking for losses was also associated with increased coupling between the left frontoparietal network and occipital cortices. These associations illustrate the participation of brain regions involved in prospective thinking, affective decision making, and visual processing in participants who are greater risk-seekers, and they demonstrate the sensitivity of RSFC to detect brain connectivity differences associated with distinct VBDM parameters.

A foundational methodology for determining system static complexity using notional lunar oxygen production processes

NASA Astrophysics Data System (ADS)

Long, Nicholas James

This thesis serves to develop a preliminary foundational methodology for evaluating the static complexity of future lunar oxygen production systems when extensive information is not yet available about the various systems under consideration. Evaluating static complexity, as part of a overall system complexity analysis, is an important consideration in ultimately selecting a process to be used in a lunar base. When system complexity is higher, there is generally an overall increase in risk which could impact the safety of astronauts and the economic performance of the mission. To evaluate static complexity in lunar oxygen production, static complexity is simplified and defined into its essential components. First, three essential dimensions of static complexity are investigated, including interconnective complexity, strength of connections, and complexity in variety. Then a set of methods is developed upon which to separately evaluate each dimension. Q-connectivity analysis is proposed as a means to evaluate interconnective complexity and strength of connections. The law of requisite variety originating from cybernetic theory is suggested to interpret complexity in variety. Secondly, a means to aggregate the results of each analysis is proposed to create holistic measurement for static complexity using the Single Multi-Attribute Ranking Technique (SMART). Each method of static complexity analysis and the aggregation technique is demonstrated using notional data for four lunar oxygen production processes.

Spinal Cord Stimulation (SCS) and Functional Magnetic Resonance Imaging (fMRI): Modulation of Cortical Connectivity With Therapeutic SCS.

PubMed

Deogaonkar, Milind; Sharma, Mayur; Oluigbo, Chima; Nielson, Dylan M; Yang, Xiangyu; Vera-Portocarrero, Louis; Molnar, Gregory F; Abduljalil, Amir; Sederberg, Per B; Knopp, Michael; Rezai, Ali R

2016-02-01

The neurophysiological basis of pain relief due to spinal cord stimulation (SCS) and the related cortical processing of sensory information are not completely understood. The aim of this study was to use resting state functional magnetic resonance imaging (rs-fMRI) to detect changes in cortical networks and cortical processing related to the stimulator-induced pain relief. Ten patients with complex regional pain syndrome (CRPS) or neuropathic leg pain underwent thoracic epidural spinal cord stimulator implantation. Stimulation parameters associated with "optimal" pain reduction were evaluated prior to imaging studies. Rs-fMRI was obtained on a 3 Tesla, Philips Achieva MRI. Rs-fMRI was performed with stimulator off (300TRs) and stimulator at optimum (Opt, 300 TRs) pain relief settings. Seed-based analysis of the resting state functional connectivity was conducted using seeds in regions established as participating in pain networks or in the default mode network (DMN) in addition to the network analysis. NCUT (normalized cut) parcellation was used to generate 98 cortical and subcortical regions of interest in order to expand our analysis of changes in functional connections to the entire brain. We corrected for multiple comparisons by limiting the false discovery rate to 5%. Significant differences in resting state connectivity between SCS off and optimal state were seen between several regions related to pain perception, including the left frontal insula, right primary and secondary somatosensory cortices, as well as in regions involved in the DMN, such as the precuneus. In examining changes in connectivity across the entire brain, we found decreased connection strength between somatosensory and limbic areas and increased connection strength between somatosensory and DMN with optimal SCS resulting in pain relief. This suggests that pain relief from SCS may be reducing negative emotional processing associated with pain, allowing somatosensory areas to become more integrated into default mode activity. SCS reduces the affective component of pain resulting in optimal pain relief. Study shows a decreased connectivity between somatosensory and limbic areas associated with optimal pain relief due to SCS. © 2015 International Neuromodulation Society.

Pattern classification of fMRI data: applications for analysis of spatially distributed cortical networks.

PubMed

Yourganov, Grigori; Schmah, Tanya; Churchill, Nathan W; Berman, Marc G; Grady, Cheryl L; Strother, Stephen C

2014-08-01

The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI. Copyright © 2014 Elsevier Inc. All rights reserved.

Effect of phase-encoding direction on group analysis of resting-state functional magnetic resonance imaging.

PubMed

Mori, Yasuo; Miyata, Jun; Isobe, Masanori; Son, Shuraku; Yoshihara, Yujiro; Aso, Toshihiko; Kouchiyama, Takanori; Murai, Toshiya; Takahashi, Hidehiko

2018-05-17

Echo-planar imaging is a common technique used in functional magnetic resonance imaging (fMRI), however it suffers from image distortion and signal loss because of large susceptibility effects that are related to the phase-encoding direction of the scan. Despite this relationship, the majority of neuroimaging studies have not considered the influence of phase-encoding direction. Here, we aimed to clarify how phase-encoding direction can affect the outcome of an fMRI connectivity study of schizophrenia. Resting-state fMRI using anterior to posterior (A-P) and posterior to anterior (P-A) directions was used to examine 25 patients with schizophrenia (SC) and 37 matched healthy controls (HC). We conducted a functional connectivity analysis using independent component analysis and performed three group comparisons: A-P vs. P-A (all participants), SC vs. HC for the A-P and P-A datasets, and the interaction between phase-encoding direction and participant group. The estimated functional connectivity differed between the two phase-encoding directions in areas that were more extensive than those where signal loss has been reported. Although functional connectivity in the SC group was lower than that in the HC group for both directions, the A-P and P-A conditions did not exhibit the same specific pattern of differences. Further, we observed an interaction between participant group and the phase-encoding direction in the left temporo-parietal junction and left fusiform gyrus. Phase-encoding direction can influence the results of functional connectivity studies. Thus, appropriate selection and documentation of phase-encoding direction will be important in future resting-state fMRI studies. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Functional connectivity among multi-channel EEGs when working memory load reaches the capacity.

PubMed

Zhang, Dan; Zhao, Huipo; Bai, Wenwen; Tian, Xin

2016-01-15

Evidence from behavioral studies has suggested a capacity existed in working memory. As the concept of functional connectivity has been introduced into neuroscience research in the recent years, the aim of this study is to investigate the functional connectivity in the brain when working memory load reaches the capacity. 32-channel electroencephalographs (EEGs) were recorded for 16 healthy subjects, while they performed a visual working memory task with load 1-6. Individual working memory capacity was calculated according to behavioral results. Short-time Fourier transform was used to determine the principal frequency band (theta band) related to working memory. The functional connectivity among EEGs was measured by the directed transform function (DTF) via spectral Granger causal analysis. The capacity was 4 calculated from the behavioral results. The power was focused in the frontal midline region. The strongest connectivity strengths of EEG theta components from load 1 to 6 distributed in the frontal midline region. The curve of DTF values vs load numbers showed that DTF increased from load 1 to 4, peaked at load 4, then decreased after load 4. This study finds that the functional connectivity between EEGs, described quantitatively by DTF, became less strong when working memory load exceeded the capacity. Copyright © 2015 Elsevier B.V. All rights reserved.

Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding

PubMed Central

Ghanbari, Yasser; Smith, Alex R.; Schultz, Robert T.; Verma, Ragini

2014-01-01

Diffusion tensor imaging (DTI) offers rich insights into the physical characteristics of white matter (WM) fiber tracts and their development in the brain, facilitating a network representation of brain’s traffic pathways. Such a network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. The high dimensionality of these connectivity networks necessitates the development of methods that identify the connectivity building blocks or sub-network components that characterize the underlying variation in the population. In addition, the projection of the subject networks into the basis set provides a low dimensional representation of it, that teases apart different sources of variation in the sample, facilitating variation-specific statistical analysis. We propose a unified framework of non-negative matrix factorization and graph embedding for learning sub-network patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis sets representing variational sources in the population like age and pathology. The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism that shows localized sparse sub-networks which mostly capture the changes related to pathology and developmental variations. PMID:25037933

Dysfunctional Default Mode Network in Methadone Treated Patients Who Have a Higher Heroin Relapse Risk.

PubMed

Li, Wei; Li, Qiang; Wang, Defeng; Xiao, Wei; Liu, Kai; Shi, Lin; Zhu, Jia; Li, Yongbin; Yan, Xuejiao; Chen, Jiajie; Ye, Jianjun; Li, Zhe; Wang, Yarong; Wang, Wei

2015-10-15

The purpose of this study was to identify whether heroin relapse is associated with changes in the functional connectivity of the default mode network (DMN) during methadone maintenance treatment (MMT). Resting-state functional magnetic resonance imaging (fMRI) data of chronic heroin relapsers (HR) (12 males, 1 female, age: 36.1 ± 6.9 years) and abstainers (HA) (11males, 2 female; age: 42.1 ± 8.1 years) were investigated with an independent component analysis to address the functional connectivity of their DMN. Group comparison was then performed between the relapsers and abstainers. Our study found that the left inferior temporal gyrus and the right superior occipital gyrus associated with DMN showed decreased functional connectivity in HR when compared with HA, while the left precuneus and the right middle cingulum had increased functional connectivity. Mean intensity signal, extracted from left inferior temporal gyrus of HR patients, showed a significant negative correlation corresponding to the degree of heroin relapse. These findings suggest that altered functional connectivity of DMN may contribute to the potential neurobiological mechanism(s) of heroin relapse and have a predictive value concerning heroin relapse under MMT.

White Matter Connectivity of the Thalamus Delineates the Functional Architecture of Competing Thalamocortical Systems

PubMed Central

O'Muircheartaigh, Jonathan; Keller, Simon S.; Barker, Gareth J.; Richardson, Mark P.

2015-01-01

There is an increasing awareness of the involvement of thalamic connectivity on higher level cortical functioning in the human brain. This is reflected by the influence of thalamic stimulation on cortical activity and behavior as well as apparently cortical lesion syndromes occurring as a function of small thalamic insults. Here, we attempt to noninvasively test the correspondence of structural and functional connectivity of the human thalamus using diffusion-weighted and resting-state functional MRI. Using a large sample of 102 adults, we apply tensor independent component analysis to diffusion MRI tractography data to blindly parcellate bilateral thalamus according to diffusion tractography-defined structural connectivity. Using resting-state functional MRI collected in the same subjects, we show that the resulting structurally defined thalamic regions map to spatially distinct, and anatomically predictable, whole-brain functional networks in the same subjects. Although there was significant variability in the functional connectivity patterns, the resulting 51 structural and functional patterns could broadly be reduced to a subset of 7 similar core network types. These networks were distinct from typical cortical resting-state networks. Importantly, these networks were distributed across the brain and, in a subset, map extremely well to known thalamocortico-basal-ganglial loops. PMID:25899706

Lightning Threat Analysis for the Space Shuttle Launch Pad and the Payload Changeout Room Using Finite Difference Methods

NASA Technical Reports Server (NTRS)

Collier, Richard S.

1997-01-01

This report describes finite difference computer calculations for the Space Shuttle Launch Pad which predict lightning induced electric currents and electric and magnetic fields caused by a lightning strike to the Lightning Protection System caternary wire. Description of possible lightning threats to Shuttle Payload components together with specifications for protection of these components, result from the calculation of lightning induced electric and magnetic fields inside and outside the during a lightning event. These fields also induce currents and voltages on cables and circuits which may be connected to, or a part of, shuttle payload components. These currents and voltages are also calculated. These threat levels are intended as a guide for designers of payload equipment to specify any shielding and/or lightning protection mitigation which may be required for payload components which are in the process of preparation or being transferred into the Shuttle Orbiter.

Independent component analysis-based source-level hyperlink analysis for two-person neuroscience studies

NASA Astrophysics Data System (ADS)

Zhao, Yang; Dai, Rui-Na; Xiao, Xiang; Zhang, Zong; Duan, Lian; Li, Zheng; Zhu, Chao-Zhe

2017-02-01

Two-person neuroscience, a perspective in understanding human social cognition and interaction, involves designing immersive social interaction experiments as well as simultaneously recording brain activity of two or more subjects, a process termed "hyperscanning." Using newly developed imaging techniques, the interbrain connectivity or hyperlink of various types of social interaction has been revealed. Functional near-infrared spectroscopy (fNIRS)-hyperscanning provides a more naturalistic environment for experimental paradigms of social interaction and has recently drawn much attention. However, most fNIRS-hyperscanning studies have computed hyperlinks using sensor data directly while ignoring the fact that the sensor-level signals contain confounding noises, which may lead to a loss of sensitivity and specificity in hyperlink analysis. In this study, on the basis of independent component analysis (ICA), a source-level analysis framework is proposed to investigate the hyperlinks in a fNIRS two-person neuroscience study. The performance of five widely used ICA algorithms in extracting sources of interaction was compared in simulative datasets, and increased sensitivity and specificity of hyperlink analysis by our proposed method were demonstrated in both simulative and real two-person experiments.

Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage.

PubMed

Mejia, Amanda F; Nebel, Mary Beth; Barber, Anita D; Choe, Ann S; Pekar, James J; Caffo, Brian S; Lindquist, Martin A

2018-05-15

Reliability of subject-level resting-state functional connectivity (FC) is determined in part by the statistical techniques employed in its estimation. Methods that pool information across subjects to inform estimation of subject-level effects (e.g., Bayesian approaches) have been shown to enhance reliability of subject-level FC. However, fully Bayesian approaches are computationally demanding, while empirical Bayesian approaches typically rely on using repeated measures to estimate the variance components in the model. Here, we avoid the need for repeated measures by proposing a novel measurement error model for FC describing the different sources of variance and error, which we use to perform empirical Bayes shrinkage of subject-level FC towards the group average. In addition, since the traditional intra-class correlation coefficient (ICC) is inappropriate for biased estimates, we propose a new reliability measure denoted the mean squared error intra-class correlation coefficient (ICC MSE ) to properly assess the reliability of the resulting (biased) estimates. We apply the proposed techniques to test-retest resting-state fMRI data on 461 subjects from the Human Connectome Project to estimate connectivity between 100 regions identified through independent components analysis (ICA). We consider both correlation and partial correlation as the measure of FC and assess the benefit of shrinkage for each measure, as well as the effects of scan duration. We find that shrinkage estimates of subject-level FC exhibit substantially greater reliability than traditional estimates across various scan durations, even for the most reliable connections and regardless of connectivity measure. Additionally, we find partial correlation reliability to be highly sensitive to the choice of penalty term, and to be generally worse than that of full correlations except for certain connections and a narrow range of penalty values. This suggests that the penalty needs to be chosen carefully when using partial correlations. Copyright © 2018. Published by Elsevier Inc.

OPTIMAL NETWORK TOPOLOGY DESIGN

NASA Technical Reports Server (NTRS)

Yuen, J. H.

1994-01-01

This program was developed as part of a research study on the topology design and performance analysis for the Space Station Information System (SSIS) network. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. It is intended that this new design technique consider all important performance measures explicitly and take into account the constraints due to various technical feasibilities. In the current program, technical constraints are taken care of by the user properly forming the starting set of candidate components (e.g. nonfeasible links are not included). As subsets are generated, they are tested to see if they form an acceptable network by checking that all requirements are satisfied. Thus the first acceptable subset encountered gives the cost-optimal topology satisfying all given constraints. The user must sort the set of "feasible" link elements in increasing order of their costs. The program prompts the user for the following information for each link: 1) cost, 2) connectivity (number of stations connected by the link), and 3) the stations connected by that link. Unless instructed to stop, the program generates all possible acceptable networks in increasing order of their total costs. The program is written only to generate topologies that are simply connected. Tests on reliability, delay, and other performance measures are discussed in the documentation, but have not been incorporated into the program. This program is written in PASCAL for interactive execution and has been implemented on an IBM PC series computer operating under PC DOS. The disk contains source code only. This program was developed in 1985.

Altered default mode, fronto-parietal and salience networks in adolescents with Internet addiction.

PubMed

Wang, Lubin; Shen, Hui; Lei, Yu; Zeng, Ling-Li; Cao, Fenglin; Su, Linyan; Yang, Zheng; Yao, Shuqiao; Hu, Dewen

2017-07-01

Internet addiction (IA) is a condition characterized by loss of control over Internet use, leading to a variety of negative psychosocial consequences. Recent neuroimaging studies have begun to identify IA-related changes in specific brain regions and connections. However, whether and how the interactions within and between the large-scale brain networks are disrupted in individuals with IA remain largely unexplored. Using group independent component analysis, we extracted five intrinsic connectivity networks (ICNs) from the resting-state fMRI data of 26 adolescents with IA and 43 controls, including the anterior and posterior default mode network (DMN), left and right fronto-parietal network (FPN), and salience network (SN). We then examined the possible group differences in the functional connectivity within each ICN and between the ICNs. We found that, compared with controls, IA subjects showed: (1) reduced inter-hemispheric functional connectivity of the right FPN, whereas increased intra-hemispheric functional connectivity of the left FPN; (2) reduced functional connectivity in the dorsal medial prefrontal cortex (mPFC) of the anterior DMN; (3) reduced functional connectivity between the SN and anterior DMN. Our findings suggest that IA is associated with imbalanced interactions among the DMN, FPN and SN, which may serve as system-level neural underpinnings for the uncontrollable Internet-using behaviors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Intrinsic Connectivity Networks in post-traumatic stress disorder during sub- and supraliminal processing of threat-related stimuli.

PubMed

Rabellino, D; Tursich, M; Frewen, P A; Daniels, J K; Densmore, M; Théberge, J; Lanius, R A

2015-11-01

To investigate the functional connectivity of large-scale intrinsic connectivity networks (ICNs) in post-traumatic stress disorder (PTSD) during subliminal and supraliminal presentation of threat-related stimuli. Group independent component analysis was utilized to study functional connectivity within the ICNs most correlated with the Default-mode Network (DMN), Salience Network (SN), and Central Executive Network (CEN) in PTSD participants (n = 26) as compared to healthy controls (n = 20) during sub- and supraliminal processing of threat-related stimuli. Comparing patients with PTSD with healthy participants, prefrontal and anterior cingulate cortex involved in top-down regulation showed increased integration during subliminal threat processing within the CEN and SN and during supraliminal threat processing within the DMN. The right amygdala showed increased connectivity with the DMN during subliminal processing in PTSD as compared to controls. Brain regions associated with self-awareness and consciousness exhibited decreased connectivity during subliminal threat processing in PTSD as compared to controls: the claustrum within the SN and the precuneus within the DMN. Key nodes of the ICNs showed altered functional connectivity in PTSD as compared to controls, and differential results characterized sub- and supraliminal processing of threat-related stimuli. These findings enhance our understanding of ICNs underlying PTSD at different levels of conscious threat perception. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Visualization Component of Vehicle Health Decision Support System

NASA Technical Reports Server (NTRS)

Jacob, Joseph; Turmon, Michael; Stough, Timothy; Siegel, Herbert; Walter, patrick; Kurt, Cindy

2008-01-01

The visualization front-end of a Decision Support System (DSS) also includes an analysis engine linked to vehicle telemetry, and a database of learned models for known behaviors. Because the display is graphical rather than text-based, the summarization it provides has a greater information density on one screen for evaluation by a flight controller.This tool provides a system-level visualization of the state of a vehicle, and drill-down capability for more details and interfaces to separate analysis algorithms and sensor data streams. The system-level view is a 3D rendering of the vehicle, with sensors represented as icons, tied to appropriate positions within the vehicle body and colored to indicate sensor state (e.g., normal, warning, anomalous state, etc.). The sensor data is received via an Information Sharing Protocol (ISP) client that connects to an external server for real-time telemetry. Users can interactively pan, zoom, and rotate this 3D view, as well as select sensors for a detail plot of the associated time series data. Subsets of the plotted data can be selected and sent to an external analysis engine to either search for a similar time series in an historical database, or to detect anomalous events. The system overview and plotting capabilities are completely general in that they can be applied to any vehicle instrumented with a collection of sensors. This visualization component can interface with the ISP for data streams used by NASA s Mission Control Center at Johnson Space Center. In addition, it can connect to, and display results from, separate analysis engine components that identify anomalies or that search for past instances of similar behavior. This software supports NASA's Software, Intelligent Systems, and Modeling element in the Exploration Systems Research and Technology Program by augmenting the capability of human flight controllers to make correct decisions, thus increasing safety and reliability. It was designed specifically as a tool for NASA's flight controllers to monitor the International Space Station and a future Crew Exploration Vehicle.

Evolution of optical fibre cabling components at CERN: Performance and technology trends analysis

NASA Astrophysics Data System (ADS)

Shoaie, Mohammad Amin; Meroli, Stefano; Machado, Simao; Ricci, Daniel

2018-05-01

CERN optical fibre infrastructure has been growing constantly over the past decade due to ever increasing connectivity demands. The provisioning plan and fibre installation of this vast laboratory is performed by Fibre Optics and Cabling Section at Engineering Department. In this paper we analyze the procurement data for essential fibre cabling components during a five-year interval to extract the existing trends and anticipate future directions. The analysis predicts high contribution of LC connector and an increasing usage of multi-fibre connectors. It is foreseen that single-mode fibres become the main fibre type for mid and long-range installations while air blowing would be the major installation technique. Performance assessment of various connectors shows that the expanded beam ferrule is favored for emerging on-board optical interconnections thanks to its scalable density and stable return-loss.

Identifying Dynamic Functional Connectivity Changes in Dementia with Lewy Bodies Based on Product Hidden Markov Models.

PubMed

Sourty, Marion; Thoraval, Laurent; Roquet, Daniel; Armspach, Jean-Paul; Foucher, Jack; Blanc, Frédéric

2016-01-01

Exploring time-varying connectivity networks in neurodegenerative disorders is a recent field of research in functional MRI. Dementia with Lewy bodies (DLB) represents 20% of the neurodegenerative forms of dementia. Fluctuations of cognition and vigilance are the key symptoms of DLB. To date, no dynamic functional connectivity (DFC) investigations of this disorder have been performed. In this paper, we refer to the concept of connectivity state as a piecewise stationary configuration of functional connectivity between brain networks. From this concept, we propose a new method for group-level as well as for subject-level studies to compare and characterize connectivity state changes between a set of resting-state networks (RSNs). Dynamic Bayesian networks, statistical and graph theory-based models, enable one to learn dependencies between interacting state-based processes. Product hidden Markov models (PHMM), an instance of dynamic Bayesian networks, are introduced here to capture both statistical and temporal aspects of DFC of a set of RSNs. This analysis was based on sliding-window cross-correlations between seven RSNs extracted from a group independent component analysis performed on 20 healthy elderly subjects and 16 patients with DLB. Statistical models of DFC differed in patients compared to healthy subjects for the occipito-parieto-frontal network, the medial occipital network and the right fronto-parietal network. In addition, pairwise comparisons of DFC of RSNs revealed a decrease of dependency between these two visual networks (occipito-parieto-frontal and medial occipital networks) and the right fronto-parietal control network. The analysis of DFC state changes thus pointed out networks related to the cognitive functions that are known to be impaired in DLB: visual processing as well as attentional and executive functions. Besides this context, product HMM applied to RSNs cross-correlations offers a promising new approach to investigate structural and temporal aspects of brain DFC.

Neural Systems Underlying Individual Differences in Intertemporal Decision-making.

PubMed

Elton, Amanda; Smith, Christopher T; Parrish, Michael H; Boettiger, Charlotte A

2017-03-01

Excessively choosing immediate over larger future rewards, or delay discounting (DD), associates with multiple clinical conditions. Individual differences in DD likely depend on variations in the activation of and functional interactions between networks, representing possible endophenotypes for associated disorders, including alcohol use disorders (AUDs). Numerous fMRI studies have probed the neural bases of DD, but investigations of large-scale networks remain scant. We addressed this gap by testing whether activation within large-scale networks during Now/Later decision-making predicts individual differences in DD. To do so, we scanned 95 social drinkers (18-40 years old; 50 women) using fMRI during hypothetical choices between small monetary amounts available "today" or larger amounts available later. We identified neural networks engaged during Now/Later choice using independent component analysis and tested the relationship between component activation and degree of DD. The activity of two components during Now/Later choice correlated with individual DD rates: A temporal lobe network positively correlated with DD, whereas a frontoparietal-striatal network negatively correlated with DD. Activation differences between these networks predicted individual differences in DD, and their negative correlation during Now/Later choice suggests functional competition. A generalized psychophysiological interactions analysis confirmed a decrease in their functional connectivity during decision-making. The functional connectivity of these two networks negatively correlates with alcohol-related harm, potentially implicating these networks in AUDs. These findings provide novel insight into the neural underpinnings of individual differences in impulsive decision-making with potential implications for addiction and related disorders in which impulsivity is a defining feature.

Sex Commonalities and Differences in Obesity-Related Alterations in Intrinsic Brain Activity and Connectivity.

PubMed

Gupta, Arpana; Mayer, Emeran A; Labus, Jennifer S; Bhatt, Ravi R; Ju, Tiffany; Love, Aubrey; Bal, Amanat; Tillisch, Kirsten; Naliboff, Bruce; Sanmiguel, Claudia P; Kilpatrick, Lisa A

2018-02-01

This study aimed to characterize obesity-related sex differences in the intrinsic activity and connectivity of the brain's reward networks. Eighty-six women (n = 43) and men (n = 43) completed a 10-minute resting functional magnetic resonance imaging scan. Sex differences and commonalities in BMI-related frequency power distribution and reward seed-based connectivity were investigated by using partial least squares analysis. For whole-brain activity in both men and women, increased BMI was associated with increased slow-5 activity in the left globus pallidus (GP) and substantia nigra. In women only, increased BMI was associated with increased slow-4 activity in the right GP and bilateral putamen. For seed-based connectivity in women, increased BMI was associated with reduced slow-5 connectivity between the left GP and putamen and the emotion and cortical regulation regions, but in men, increased BMI was associated with increased connectivity with the medial frontal cortex. In both men and women, increased BMI was associated with increased slow-4 connectivity between the right GP and bilateral putamen and the emotion regulation and sensorimotor-related regions. The stronger relationship between increased BMI and decreased connectivity of core reward network components with cortical and emotion regulation regions in women may be related to the greater prevalence of emotional eating. The present findings suggest the importance of personalized treatments for obesity that consider the sex of the affected individual. © 2017 The Obesity Society.

Design and verification for front mirror-body structure of on-axis three mirror anastigmatic space camera

NASA Astrophysics Data System (ADS)

Wang, Xiaoyong; Guo, Chongling; Hu, Yongli; He, Hongyan

2017-11-01

The primary and secondary mirrors of onaxis three mirror anastigmatic (TMA) space camera are connected and supported by its front mirror-body structure, which affects both imaging performance and stability of the camera. In this paper, the carbon fiber reinforced plastics (CFRP) thin-walled cylinder and titanium alloy connecting rod have been used for the front mirror-body opto-mechanical structure of the long-focus on-axis and TMA space camera optical system. The front mirror-body component structure has then been optimized by finite element analysis (FEA) computing. Each performance of the front mirror-body structure has been tested by mechanics and vacuum experiments in order to verify the validity of such structure engineering design.

Critical behavior of the contact process in a multiscale network

NASA Astrophysics Data System (ADS)

Ferreira, Silvio C.; Martins, Marcelo L.

2007-09-01

Inspired by dengue and yellow fever epidemics, we investigated the contact process (CP) in a multiscale network constituted by one-dimensional chains connected through a Barabási-Albert scale-free network. In addition to the CP dynamics inside the chains, the exchange of individuals between connected chains (travels) occurs at a constant rate. A finite epidemic threshold and an epidemic mean lifetime diverging exponentially in the subcritical phase, concomitantly with a power law divergence of the outbreak’s duration, were found. A generalized scaling function involving both regular and SF components was proposed for the quasistationary analysis and the associated critical exponents determined, demonstrating that the CP on this hybrid network and nonvanishing travel rates establishes a new universality class.

Fabrication method for miniature plastic gripper

DOEpatents

Benett, William J.; Krulevitch, Peter A.; Lee, Abraham P.; Northrup, Milton A.; Folta, James A.

1998-01-01

A miniature plastic gripper actuated by inflation of a miniature balloon and method of fabricating same. The gripper is constructed of either heat-shrinkable or heat-expandable plastic tubing and is formed around a mandrel, then cut to form gripper prongs or jaws and the mandrel removed. The gripper is connected at one end with a catheter or tube having an actuating balloon at its tip, whereby the gripper is opened or dosed by inflation or deflation of the balloon. The gripper is designed to removably retain a member to which is connected a quantity or medicine, plugs, or micro-components. The miniature plastic gripper is inexpensive to fabricate and can be used for various applications, such as gripping, sorting, or placing of micron-scale particles for analysis.

Miniature plastic gripper and fabrication method

DOEpatents

Benett, William J.; Krulevitch, Peter A.; Lee, Abraham P.; Northrup, Milton A.; Folta, James A.

1997-01-01

A miniature plastic gripper actuated by inflation of a miniature balloon and method of fabricating same. The gripper is constructed of either heat-shrinkable or heat-expandable plastic tubing and is formed around a mandrel, then cut to form gripper prongs or jaws and the mandrel removed. The gripper is connected at one end with a catheter or tube having an actuating balloon at its tip, whereby the gripper is opened or closed by inflation or deflation of the balloon. The gripper is designed to removably retain a member to which is connected a quantity or medicine, plugs, or micro-components. The miniature plastic gripper is inexpensive to fabricate and can be used for various applications, such as gripping, sorting, or placing of micron-scale particles for analysis.

The neurogenetic frontier--lessons from misbehaving zebrafish.

PubMed

Burgess, Harold A; Granato, Michael

2008-11-01

One of the central questions in neuroscience is how refined patterns of connectivity in the brain generate and monitor behavior. Genetic mutations can influence neural circuits by disrupting differentiation or maintenance of component neuronal cells or by altering functional patterns of nervous system connectivity. Mutagenesis screens therefore have the potential to reveal not only the molecular underpinnings of brain development and function, but to illuminate the cellular basis of behavior. Practical considerations make the zebrafish an organism of choice for undertaking forward genetic analysis of behavior. The powerful array of experimental tools at the disposal of the zebrafish researcher makes it possible to link molecular function to neuronal properties that underlie behavior. This review focuses on specific challenges to isolating and analyzing behavioral mutants in zebrafish.

The neurogenetic frontier—lessons from misbehaving zebrafish

PubMed Central

Granato, Michael

2008-01-01

One of the central questions in neuroscience is how refined patterns of connectivity in the brain generate and monitor behavior. Genetic mutations can influence neural circuits by disrupting differentiation or maintenance of component neuronal cells or by altering functional patterns of nervous system connectivity. Mutagenesis screens therefore have the potential to reveal not only the molecular underpinnings of brain development and function, but to illuminate the cellular basis of behavior. Practical considerations make the zebrafish an organism of choice for undertaking forward genetic analysis of behavior. The powerful array of experimental tools at the disposal of the zebrafish researcher makes it possible to link molecular function to neuronal properties that underlie behavior. This review focuses on specific challenges to isolating and analyzing behavioral mutants in zebrafish. PMID:18836206

Regional Technical Exchange Centers Connect Fuel Cell Technology Suppliers,

Science.gov Websites

Manufacturers | News | NREL Regional Technical Exchange Centers Connect Fuel Cell Technology Suppliers, Manufacturers Regional Technical Exchange Centers Connect Fuel Cell Technology Suppliers fuel cell and hydrogen components and systems and improve U.S. manufacturing competitiveness. The

A Novel Characterization of Amalgamated Networks in Natural Systems

PubMed Central

Barranca, Victor J.; Zhou, Douglas; Cai, David

2015-01-01

Densely-connected networks are prominent among natural systems, exhibiting structural characteristics often optimized for biological function. To reveal such features in highly-connected networks, we introduce a new network characterization determined by a decomposition of network-connectivity into low-rank and sparse components. Based on these components, we discover a new class of networks we define as amalgamated networks, which exhibit large functional groups and dense connectivity. Analyzing recent experimental findings on cerebral cortex, food-web, and gene regulatory networks, we establish the unique importance of amalgamated networks in fostering biologically advantageous properties, including rapid communication among nodes, structural stability under attacks, and separation of network activity into distinct functional modules. We further observe that our network characterization is scalable with network size and connectivity, thereby identifying robust features significant to diverse physical systems, which are typically undetectable by conventional characterizations of connectivity. We expect that studying the amalgamation properties of biological networks may offer new insights into understanding their structure-function relationships. PMID:26035066

Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders.

PubMed

He, Hao; Sui, Jing; Du, Yuhui; Yu, Qingbao; Lin, Dongdong; Drevets, Wayne C; Savitz, Jonathan B; Yang, Jian; Victor, Teresa A; Calhoun, Vince D

2017-12-01

Bipolar disorder (BD) and major depressive disorder (MDD) share similar clinical characteristics that often obscure the diagnostic distinctions between their depressive conditions. Both functional and structural brain abnormalities have been reported in these two disorders. However, the direct link between altered functioning and structure in these two diseases is unknown. To elucidate this relationship, we conducted a multimodal fusion analysis on the functional network connectivity (FNC) and gray matter density from MRI data from 13 BD, 40 MDD, and 33 matched healthy controls (HC). A data-driven fusion method called mCCA+jICA was used to identify the co-altered FNC and gray matter components. Comparing to HC, BD exhibited reduced gray matter density in the parietal and occipital cortices, which correlated with attenuated functional connectivity within sensory and motor networks, as well as hyper-connectivity in regions that are putatively engaged in cognitive control. In addition, lower gray matter density was found in MDD in the amygdala and cerebellum. High accuracy in discriminating across groups was also achieved by trained classification models, implying that features extracted from the fusion analysis hold the potential to ultimately serve as diagnostic biomarkers for mood disorders.

Functional Connectivity of Child and Adolescent Attention Deficit Hyperactivity Disorder Patients: Correlation with IQ.

PubMed

Park, Bo-Yong; Hong, Jisu; Lee, Seung-Hak; Park, Hyunjin

2016-01-01

Attention deficit hyperactivity disorder (ADHD) is a pervasive neuropsychological disorder that affects both children and adolescents. Child and adolescent ADHD patients exhibit different behavioral symptoms such as hyperactivity and impulsivity, but not much connectivity research exists to help explain these differences. We analyzed openly accessible resting-state functional magnetic resonance imaging (rs-fMRI) data on 112 patients (28 child ADHD, 28 adolescent ADHD, 28 child normal control (NC), and 28 adolescent NC). We used group independent component analysis (ICA) and weighted degree values to identify interaction effects of age (child and adolescent) and symptom (ADHD and NC) in brain networks. The frontoparietal network showed significant interaction effects ( p = 0.0068). The frontoparietal network is known to be related to hyperactive and impulsive behaviors. Intelligence quotient (IQ) is an important factor in ADHD, and we predicted IQ scores using the results of our connectivity analysis. IQ was predicted using degree centrality values of networks with significant interaction effects of age and symptom. Actual and predicted IQ scores demonstrated significant correlation values, with an error of about 10%. Our study might provide imaging biomarkers for future ADHD and intelligence studies.

Non-Stationarity in the “Resting Brain’s” Modular Architecture

PubMed Central

Jones, David T.; Vemuri, Prashanthi; Murphy, Matthew C.; Gunter, Jeffrey L.; Senjem, Matthew L.; Machulda, Mary M.; Przybelski, Scott A.; Gregg, Brian E.; Kantarci, Kejal; Knopman, David S.; Boeve, Bradley F.; Petersen, Ronald C.; Jack, Clifford R.

2012-01-01

Task-free functional magnetic resonance imaging (TF-fMRI) has great potential for advancing the understanding and treatment of neurologic illness. However, as with all measures of neural activity, variability is a hallmark of intrinsic connectivity networks (ICNs) identified by TF-fMRI. This variability has hampered efforts to define a robust metric of connectivity suitable as a biomarker for neurologic illness. We hypothesized that some of this variability rather than representing noise in the measurement process, is related to a fundamental feature of connectivity within ICNs, which is their non-stationary nature. To test this hypothesis, we used a large (n = 892) population-based sample of older subjects to construct a well characterized atlas of 68 functional regions, which were categorized based on independent component analysis network of origin, anatomical locations, and a functional meta-analysis. These regions were then used to construct dynamic graphical representations of brain connectivity within a sliding time window for each subject. This allowed us to demonstrate the non-stationary nature of the brain’s modular organization and assign each region to a “meta-modular” group. Using this grouping, we then compared dwell time in strong sub-network configurations of the default mode network (DMN) between 28 subjects with Alzheimer’s dementia and 56 cognitively normal elderly subjects matched 1∶2 on age, gender, and education. We found that differences in connectivity we and others have previously observed in Alzheimer’s disease can be explained by differences in dwell time in DMN sub-network configurations, rather than steady state connectivity magnitude. DMN dwell time in specific modular configurations may also underlie the TF-fMRI findings that have been described in mild cognitive impairment and cognitively normal subjects who are at risk for Alzheimer’s dementia. PMID:22761880

An Encryption Scheme for Communication Internet SCADA Components

NASA Astrophysics Data System (ADS)

Robles, Rosslin John; Kim, Tai-Hoon

The trend in most systems is that they are connected through the Internet. Traditional Supervisory Control and Data Acquisition Systems (SCADA) is connected only in a limited private network. SCADA is considered a critical infrastructure, and connecting to the internet is putting the society on jeopardy, some operators hold back on connecting it to the internet. But since the internet Supervisory Control and Data Acquisition Systems (SCADA) facility has brought a lot of advantages in terms of control, data viewing and generation. Along with these advantages, are security issues regarding web SCADA, operators are pushed to connect Supervisory Control and Data Acquisition Systems (SCADA) through the internet. Because of this, many issues regarding security surfaced. In this paper, we discuss web SCADA and the issues regarding security. As a countermeasure, a web SCADA security solution using crossed-crypto-scheme is proposed to be used in the communication of SCADA components.

Experimental Evaluation of Beam to Diamond Box Column Connection with Through Plate in Moment Frames

NASA Astrophysics Data System (ADS)

Keshavarzi, Farhad; Mirghaderi, Rasoul; Torabian, Shahabeddin; Imanpour, Ali

2008-07-01

Moment resisting frames with built up section have very enhanced features due to high bending stiffness and strength characteristics in two principal axes and access to column faces for beam to column easy connections. But due to proper transfer of beam stresses to column faces there were always some specific controvertibly issues that how to make the load transfer through and in plane manner in order to mobilize the forces in column faces. Using diamond column instead of box column provide possibility to mobilize the load transfer mechanism in column faces. This section as a column has considerable benefit such as high plastic to elastic section modulus ratio which is an effective factor for force controlled components. Typical connection has no chance to be applied with diamond column. This paper elucidates the seismic behavior of through-plates moment connections to diamond box columns for use in steel moment resisting frames. This connection has a lot of economical benefits such as no need to horizontal continuity plates and satisfying the weak beam—strong column criteria in the connection region. They might serve as panel zone plates as well. According to high shear demand in panel zone of beam to column joint one should use the doublers plates in order to decrease the shear strength demand in this sensitive part of structure but these plates have no possibility to mobilize the load transfer mechanism in column web and transfer them to column flanges. In this type of connection, column faces have effective role in order to decrease the demands on through plate and they are impressive factors for improving the performance of the connection. Experimental analysis was conducted to elucidate the seismic behavior of this connection. The results of Experimental analysis established the effectiveness of the through plate in mitigating local stress concentrations and forming the plastic hinge zone in the beam away from the beam to column interface. The moment-rotation graphs form sub-assemblage show a desirable seismic performance of this connection

Graph processing platforms at scale: practices and experiences

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

Lim, Seung-Hwan; Lee, Sangkeun; Brown, Tyler C

2015-01-01

Graph analysis unveils hidden associations of data in many phenomena and artifacts, such as road network, social networks, genomic information, and scientific collaboration. Unfortunately, a wide diversity in the characteristics of graphs and graph operations make it challenging to find a right combination of tools and implementation of algorithms to discover desired knowledge from the target data set. This study presents an extensive empirical study of three representative graph processing platforms: Pegasus, GraphX, and Urika. Each system represents a combination of options in data model, processing paradigm, and infrastructure. We benchmarked each platform using three popular graph operations, degree distribution,more » connected components, and PageRank over a variety of real-world graphs. Our experiments show that each graph processing platform shows different strength, depending the type of graph operations. While Urika performs the best in non-iterative operations like degree distribution, GraphX outputforms iterative operations like connected components and PageRank. In addition, we discuss challenges to optimize the performance of each platform over large scale real world graphs.« less

Microfluidic chip for peptide analysis with an integrated HPLC column, sample enrichment column, and nanoelectrospray tip.

PubMed

Yin, Hongfeng; Killeen, Kevin; Brennen, Reid; Sobek, Dan; Werlich, Mark; van de Goor, Tom

2005-01-15

Current nano-LC/MS systems require the use of an enrichment column, a separation column, a nanospray tip, and the fittings needed to connect these parts together. In this paper, we present a microfabricated approach to nano-LC, which integrates these components on a single LC chip, eliminating the need for conventional LC connections. The chip was fabricated by laminating polyimide films with laser-ablated channels, ports, and frit structures. The enrichment and separation columns were packed using conventional reversed-phase chromatography particles. A face-seal rotary valve provided a means for switching between sample loading and separation configurations with minimum dead and delay volumes while allowing high-pressure operation. The LC chip and valve assembly were mounted within a custom electrospray source on an ion-trap mass spectrometer. The overall system performance was demonstrated through reversed-phase gradient separations of tryptic protein digests at flow rates between 100 and 400 nL/min. Microfluidic integration of the nano-LC components enabled separations with subfemtomole detection sensitivity, minimal carryover, and robust and stable electrospray throughout the LC solvent gradient.

Electroconvulsive Therapy Response in Major Depressive Disorder: A Pilot Functional Network Connectivity Resting State fMRI Investigation

PubMed Central

Abbott, Christopher C.; Lemke, Nicholas T.; Gopal, Shruti; Thoma, Robert J.; Bustillo, Juan; Calhoun, Vince D.; Turner, Jessica A.

2013-01-01

Major depressive disorder (MDD) is associated with increased functional connectivity in specific neural networks. Electroconvulsive therapy (ECT), the gold-standard treatment for acute, treatment-resistant MDD, but temporal dependencies between networks associated with ECT response have yet to be investigated. In the present longitudinal, case–control investigation, we used independent component analysis to identify distinct networks of brain regions with temporally coherent hemodynamic signal change and functional network connectivity (FNC) to assess component time course correlations across these networks. MDD subjects completed imaging and clinical assessments immediately prior to the ECT series and a minimum of 5 days after the last ECT treatment. We focused our analysis on four networks affected in MDD: the subcallosal cingulate gyrus, default mode, dorsal lateral prefrontal cortex, and dorsal medial prefrontal cortex (DMPFC). In an older sample of ECT subjects (n = 12) with MDD, remission associated with the ECT series reverses the relationship from negative to positive between the posterior default mode (p_DM) and two other networks: the DMPFC and left dorsal lateral prefrontal cortex (l_DLPFC). Relative to demographically healthy subjects (n = 12), the FNC between the p_DM areas and the DMPFC normalizes with ECT response. The FNC changes following treatment did not correlate with symptom improvement; however, a direct comparison between ECT remitters and non-remitters showed the pattern of increased FNC between the p_DM and l_DLPFC following ECT to be specific to those who responded to the treatment. The differences between ECT remitters and non-remitters suggest that this increased FNC between p_DM areas and the left dorsolateral prefrontal cortex is a neural correlate and potential biomarker of recovery from a depressed episode. PMID:23459749

Electroconvulsive therapy response in major depressive disorder: a pilot functional network connectivity resting state FMRI investigation.

PubMed

Abbott, Christopher C; Lemke, Nicholas T; Gopal, Shruti; Thoma, Robert J; Bustillo, Juan; Calhoun, Vince D; Turner, Jessica A

2013-01-01

Major depressive disorder (MDD) is associated with increased functional connectivity in specific neural networks. Electroconvulsive therapy (ECT), the gold-standard treatment for acute, treatment-resistant MDD, but temporal dependencies between networks associated with ECT response have yet to be investigated. In the present longitudinal, case-control investigation, we used independent component analysis to identify distinct networks of brain regions with temporally coherent hemodynamic signal change and functional network connectivity (FNC) to assess component time course correlations across these networks. MDD subjects completed imaging and clinical assessments immediately prior to the ECT series and a minimum of 5 days after the last ECT treatment. We focused our analysis on four networks affected in MDD: the subcallosal cingulate gyrus, default mode, dorsal lateral prefrontal cortex, and dorsal medial prefrontal cortex (DMPFC). In an older sample of ECT subjects (n = 12) with MDD, remission associated with the ECT series reverses the relationship from negative to positive between the posterior default mode (p_DM) and two other networks: the DMPFC and left dorsal lateral prefrontal cortex (l_DLPFC). Relative to demographically healthy subjects (n = 12), the FNC between the p_DM areas and the DMPFC normalizes with ECT response. The FNC changes following treatment did not correlate with symptom improvement; however, a direct comparison between ECT remitters and non-remitters showed the pattern of increased FNC between the p_DM and l_DLPFC following ECT to be specific to those who responded to the treatment. The differences between ECT remitters and non-remitters suggest that this increased FNC between p_DM areas and the left dorsolateral prefrontal cortex is a neural correlate and potential biomarker of recovery from a depressed episode.

Understanding linkages between global climate indices and terrestrial water storage changes over Africa using GRACE products.

PubMed

Anyah, R O; Forootan, E; Awange, J L; Khaki, M

2018-09-01

Africa, a continent endowed with huge water resources that sustain its agricultural activities is increasingly coming under threat from impacts of climate extremes (droughts and floods), which puts the very precious water resource into jeopardy. Understanding the relationship between climate variability and water storage over the continent, therefore, is paramount in order to inform future water management strategies. This study employs Gravity Recovery And Climate Experiment (GRACE) satellite data and the higher order (fourth order cumulant) statistical independent component analysis (ICA) method to study the relationship between terrestrial water storage (TWS) changes and five global climate-teleconnection indices; El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Madden-Julian Oscillation (MJO), Quasi-Biennial Oscillation (QBO) and the Indian Ocean Dipole (IOD) over Africa for the period 2003-2014. Pearson correlation analysis is applied to extract the connections between these climate indices (CIs) and TWS, from which some known strong CI-rainfall relationships (e.g., over equatorial eastern Africa) are found. Results indicate unique linear-relationships and regions that exhibit strong linkages between CIs and TWS. Moreover, unique regions having strong CI-TWS connections that are completely different from the typical ENSO-rainfall connections over eastern and southern Africa are also identified. Furthermore, the results indicate that the first dominant independent components (IC) of the CIs are linked to NAO, and are characterized by significant reductions of TWS over southern Africa. The second dominant ICs are associated with IOD and are characterized by significant increases in TWS over equatorial eastern Africa, while the combined ENSO and MJO are apparently linked to the third ICs, which are also associated with significant increase in TWS changes over both southern Africa, as well as equatorial eastern Africa. Copyright © 2018 Elsevier B.V. All rights reserved.

A conditional Granger causality model approach for group analysis in functional MRI

PubMed Central

Zhou, Zhenyu; Wang, Xunheng; Klahr, Nelson J.; Liu, Wei; Arias, Diana; Liu, Hongzhi; von Deneen, Karen M.; Wen, Ying; Lu, Zuhong; Xu, Dongrong; Liu, Yijun

2011-01-01

Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed for identifying effective connectivity in the human brain with functional MR imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pairwise GCM has commonly been applied based on single voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of an fMRI data with GCM. To compare the effectiveness of our approach with traditional pairwise GCM models, we applied a well-established conditional GCM to pre-selected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis (ICA) of an fMRI dataset in the temporal domain. Datasets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM detected brain activation regions in the emotion related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state dataset, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network (DMN) that can be characterized as both afferent and efferent influences on the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive (MVAR) model can achieve greater accuracy in detecting network connectivity than the widely used pairwise GCM, and this group analysis methodology can be quite useful to extend the information obtainable in fMRI. PMID:21232892

How to Build a Functional Connectomic Biomarker for Mild Cognitive Impairment From Source Reconstructed MEG Resting-State Activity: The Combination of ROI Representation and Connectivity Estimator Matters.

PubMed

Dimitriadis, Stavros I; López, María E; Bruña, Ricardo; Cuesta, Pablo; Marcos, Alberto; Maestú, Fernando; Pereda, Ernesto

2018-01-01

Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv). Both intra and cross-frequency interactions (CFC) have been estimated with the three connectivity estimators within the seven frequency bands (intra-frequency) and in pairs (CFC), correspondingly. We demonstrated how different versions of functional connectivity graphs single-layer (SL-FCG) and multi-layer (ML-FCG) can give us a different view of the functional interactions across the brain areas. Finally, we applied machine learning techniques with main scope to build a reliable connectomic biomarker by analyzing both SL-FCG and ML-FCG in two different options: as a whole unit using a tensorial extraction algorithm and as single pair-wise coupling estimations. We concluded that edge-weighed feature selection strategy outperformed the tensorial treatment of SL-FCG and ML-FCG. The highest classification performance was obtained with the centroid ROI representation and edge-weighted analysis of the SL-FCG reaching the 98% for the CorrEnv in α 1 :α 2 and 94% for the iPLV in α 2 . Classification performance based on the multi-layer participation coefficient, a multiplexity index reached 52% for iPLV and 52% for CorrEnv. Selected functional connections that build the multivariate connectomic biomarker in the edge-weighted scenario are located in default-mode, fronto-parietal, and cingulo-opercular network. Our analysis supports the notion of analyzing FCG simultaneously in intra and cross-frequency whole brain interactions with various connectivity estimators in beamformed recordings.

The Rationale for Joint Mobilization.

ERIC Educational Resources Information Center

Burkhardt, Sandy

This paper presents an overview of the functions of connective tissue and the mechanisms of joint injury and contracture formation in relation to therapeutic exercise. The components of connective tissue operation are explained, including fibroblasts, macrophages, plasma cells, and collagen. An examination of the histology of connective tissue as…

Vibration energy harvesting based on integrated piezoelectric components operating in different modes.

PubMed

Hu, Junhui; Jong, Januar; Zhao, Chunsheng

2010-01-01

To increase the vibration energy-harvesting capability of the piezoelectric generator based on a cantilever beam, we have proposed a piezoelectric generator that not only uses the strain change of piezoelectric components bonded on a cantilever beam, but also employs the weights at the tip of the cantilever beam to hit piezoelectric components located on the 2 sides of weights. A prototype of the piezoelectric generator has been fabricated and its characteristics have been measured and analyzed. The experimental results show that the piezoelectric components operating in the hit mode can substantially enhance the energy harvesting of the piezoelectric generator on a cantilever beam. Two methods are used and compared in the management of rectified output voltages from different groups of piezoelectric components. In one of them, the DC voltages from rectifiers are connected in series, and then the total DC voltage is applied to a capacitor. In another connection, the DC voltage from each group is applied to different capacitors. It is found that 22.3% of the harvested energy is wasted due to the series connection. The total output electric energy of our piezoelectric generator at nonresonance could be up to 43 nJ for one vibration excitation applied by spring, with initial vibration amplitude (0-p) of 18 mm and frequency of 18.5 Hz, when the rectified voltages from different groups of piezoelectric components are connected to their individual capacitors. In addition, the motion and impact of the weights at the tip of the cantilever beam are theoretically analyzed, which well explains the experimental phenomena and suggests the measures to improve the generator.

Design and performance analysis of generalised integrator-based controller for grid connected PV system

NASA Astrophysics Data System (ADS)

Saxena, Hemant; Singh, Alka; Rai, J. N.

2018-07-01

This article discusses the design and control of a single-phase grid-connected photovoltaic (PV) system. A 5-kW PV system is designed and integrated at the DC link of an H-bridge voltage source converter (VSC). The control of the VSC and switching logic is modelled using a generalised integrator (GI). The use of GI or its variants such as second-order GI have recently evolved for synchronisation and are being used as phase locked loop (PLL) circuits for grid integration. Design of PLL circuits and the use of transformations such as Park's and Clarke's are much easier in three-phase systems. But obtaining in-phase and quadrature components becomes an important and challenging issue in single-phase systems. This article addresses this issue and discusses an altogether different application of GI for the design of compensator based on the extraction of in-phase and quadrature components. GI is frequently used as a PLL; however, in this article, it is not used for synchronisation purposes. A new controller has been designed for a single-phase grid-connected PV system working as a single-phase active compensator. Extensive simulation results are shown for the working of integrated PV system under different atmospheric and operating conditions during daytime as well as night conditions. Experimental results showing the proposed control approach are presented and discussed for the hardware set-up developed in the laboratory.

The Distressed Brain: A Group Blind Source Separation Analysis on Tinnitus

PubMed Central

De Ridder, Dirk; Vanneste, Sven; Congedo, Marco

2011-01-01

Background Tinnitus, the perception of a sound without an external sound source, can lead to variable amounts of distress. Methodology In a group of tinnitus patients with variable amounts of tinnitus related distress, as measured by the Tinnitus Questionnaire (TQ), an electroencephalography (EEG) is performed, evaluating the patients' resting state electrical brain activity. This resting state electrical activity is compared with a control group and between patients with low (N = 30) and high distress (N = 25). The groups are homogeneous for tinnitus type, tinnitus duration or tinnitus laterality. A group blind source separation (BSS) analysis is performed using a large normative sample (N = 84), generating seven normative components to which high and low tinnitus patients are compared. A correlation analysis of the obtained normative components' relative power and distress is performed. Furthermore, the functional connectivity as reflected by lagged phase synchronization is analyzed between the brain areas defined by the components. Finally, a group BSS analysis on the Tinnitus group as a whole is performed. Conclusions Tinnitus can be characterized by at least four BSS components, two of which are posterior cingulate based, one based on the subgenual anterior cingulate and one based on the parahippocampus. Only the subgenual component correlates with distress. When performed on a normative sample, group BSS reveals that distress is characterized by two anterior cingulate based components. Spectral analysis of these components demonstrates that distress in tinnitus is related to alpha and beta changes in a network consisting of the subgenual anterior cingulate cortex extending to the pregenual and dorsal anterior cingulate cortex as well as the ventromedial prefrontal cortex/orbitofrontal cortex, insula, and parahippocampus. This network overlaps partially with brain areas implicated in distress in patients suffering from pain, functional somatic syndromes and posttraumatic stress disorder, and might therefore represent a specific distress network. PMID:21998628

A custom-made temporomandibular joint prosthesis for fabrication by selective laser melting: Finite element analysis.

PubMed

Xu, Xiangliang; Luo, Danmei; Guo, Chuanbin; Rong, Qiguo

2017-08-01

A novel and custom-made selective laser melting (SLM) 3D-printed alloplastic temporomandibular joint (TMJ) prosthesis is proposed. The titanium-6aluminium-4vanadium (Ti-6Al-4V) condyle component and ultra-high molecular weight polyethylene (UHMWPE) fossa component comprised the total alloplastic TMJ replacement prosthesis. For the condyle component, an optimized tetrahedral open-porous scaffold with combined connection structures, i.e. an inlay rod and an onlay plate, between the prosthesis and remaining mandible was designed. The trajectory of movement of the intact condyle was assessed via kinematic analysis to facilitate the design of the fossa component. The behaviours of the intact mandible and mandible with the prosthesis were compared. The biomechanical behaviour was analysed by assessing the stress distribution on the prosthesis and strain distribution on the mandible. After muscle force was applied, the magnitude of the compressive strain on the condyle neck of the mandible with the prosthesis was lower than that on the condyle neck of the intact mandible, with the exception of the area about the screws; additionally, the magnitude of the strain at the scaffold-bone interface was relatively high. Copyright © 2017. Published by Elsevier Ltd.

Dynamics of the CMZ - Giant Magnetic Loops Connection in the Galactic Center

NASA Astrophysics Data System (ADS)

Langer, William

2012-10-01

Understanding the mass transfer and dynamics among the Galactic Center, the disk, and the halo of the Milky Way is fundamental to the study of the evolution of galaxies and star formation. Several giant molecular loops (GML), detected in CO maps of the Galactic Center, are likely the result of the magnetic Parker instability. We have new evidence of a possible dynamical connection between these loops and the Central Molecular Zone (CMZ) from a sparse [CII] sampling from our Herschel Open Time Key Project GOT C+. The CMZ-GML region is dynamically active and is likely to have a significant ionized component. However, we have no information on the distribution and dynamics of the ionized gas. The fine-structure lines of [NII] are key probes of the warm ionized medium (WIM) and along with the [CII] can isolate the different ionization components. We have a Herschel OT2 Priority 1 program to map the GML and the CMZ-GML connection in [CII] in more detail. However, we did not propose needed [NII] observations due to an incomplete analysis of our limited GOT C+ data at the time. Here we propose to observe with the SOFIA/GREAT instrument, [NII] in the CMZ-GML interface region using the L1b band, and serendipitously CO (16-15) using band L2. With this data, combined with our Herschel HIFI [CII], Mopra 12CO (1-0) and 13CO (1-0), and HI, we will characterize these important ISM components and their motions in these Galactic Center features. These observations of the nearest such regions of galactic center activity, also have bearing on the dynamics of other galactic nuclei.

Functional modular architecture underlying attentional control in aging.

PubMed

Monge, Zachary A; Geib, Benjamin R; Siciliano, Rachel E; Packard, Lauren E; Tallman, Catherine W; Madden, David J

2017-07-15

Previous research suggests that age-related differences in attention reflect the interaction of top-down and bottom-up processes, but the cognitive and neural mechanisms underlying this interaction remain an active area of research. Here, within a sample of community-dwelling adults 19-78 years of age, we used diffusion reaction time (RT) modeling and multivariate functional connectivity to investigate the behavioral components and whole-brain functional networks, respectively, underlying bottom-up and top-down attentional processes during conjunction visual search. During functional MRI scanning, participants completed a conjunction visual search task in which each display contained one item that was larger than the other items (i.e., a size singleton) but was not informative regarding target identity. This design allowed us to examine in the RT components and functional network measures the influence of (a) additional bottom-up guidance when the target served as the size singleton, relative to when the distractor served as the size singleton (i.e., size singleton effect) and (b) top-down processes during target detection (i.e., target detection effect; target present vs. absent trials). We found that the size singleton effect (i.e., increased bottom-up guidance) was associated with RT components related to decision and nondecision processes, but these effects did not vary with age. Also, a modularity analysis revealed that frontoparietal module connectivity was important for both the size singleton and target detection effects, but this module became central to the networks through different mechanisms for each effect. Lastly, participants 42 years of age and older, in service of the target detection effect, relied more on between-frontoparietal module connections. Our results further elucidate mechanisms through which frontoparietal regions support attentional control and how these mechanisms vary in relation to adult age. Copyright © 2017 Elsevier Inc. All rights reserved.

Dynamic analysis of Space Shuttle/RMS configuration using continuum approach

NASA Technical Reports Server (NTRS)

Ramakrishnan, Jayant; Taylor, Lawrence W., Jr.

1994-01-01

The initial assembly of Space Station Freedom involves the Space Shuttle, its Remote Manipulation System (RMS) and the evolving Space Station Freedom. The dynamics of this coupled system involves both the structural and the control system dynamics of each of these components. The modeling and analysis of such an assembly is made even more formidable by kinematic and joint nonlinearities. The current practice of modeling such flexible structures is to use finite element modeling in which the mass and interior dynamics is ignored between thousands of nodes, for each major component. The model characteristics of only tens of modes are kept out of thousands which are calculated. The components are then connected by approximating the boundary conditions and inserting the control system dynamics. In this paper continuum models are used instead of finite element models because of the improved accuracy, reduced number of model parameters, the avoidance of model order reduction, and the ability to represent the structural and control system dynamics in the same system of equations. Dynamic analysis of linear versions of the model is performed and compared with finite element model results. Additionally, the transfer matrix to continuum modeling is presented.

Portable rapid and quiet drill

NASA Technical Reports Server (NTRS)

Badescu, Mireca (Inventor); Chang, Zenshea (Inventor); Sherrit, Stewart (Inventor); Bar-Cohen, Yoseph (Inventor); Bao, Xiaoqi (Inventor)

2010-01-01

A hand-held drilling device, and method for drilling using the device, has a housing, a transducer within the housing, with the transducer effectively operating at ultrasonic frequencies, a rotating motor component within the housing and rigid cutting end-effector rotationally connected to the rotating motor component and vibrationally connected to the transducer. The hand-held drilling device of the present invention operates at a noise level of from about 50 decibels or less.

Defense strategies for cloud computing multi-site server infrastructures

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

Rao, Nageswara S.; Ma, Chris Y. T.; He, Fei

We consider cloud computing server infrastructures for big data applications, which consist of multiple server sites connected over a wide-area network. The sites house a number of servers, network elements and local-area connections, and the wide-area network plays a critical, asymmetric role of providing vital connectivity between them. We model this infrastructure as a system of systems, wherein the sites and wide-area network are represented by their cyber and physical components. These components can be disabled by cyber and physical attacks, and also can be protected against them using component reinforcements. The effects of attacks propagate within the systems, andmore » also beyond them via the wide-area network.We characterize these effects using correlations at two levels using: (a) aggregate failure correlation function that specifies the infrastructure failure probability given the failure of an individual site or network, and (b) first-order differential conditions on system survival probabilities that characterize the component-level correlations within individual systems. We formulate a game between an attacker and a provider using utility functions composed of survival probability and cost terms. At Nash Equilibrium, we derive expressions for the expected capacity of the infrastructure given by the number of operational servers connected to the network for sum-form, product-form and composite utility functions.« less

On chemical distances and shape theorems in percolation models with long-range correlations

NASA Astrophysics Data System (ADS)

Drewitz, Alexander; Ráth, Balázs; Sapozhnikov, Artëm

2014-08-01

In this paper, we provide general conditions on a one parameter family of random infinite subsets of {{Z}}^d to contain a unique infinite connected component for which the chemical distances are comparable to the Euclidean distance. In addition, we show that these conditions also imply a shape theorem for the corresponding infinite connected component. By verifying these conditions for specific models, we obtain novel results about the structure of the infinite connected component of the vacant set of random interlacements and the level sets of the Gaussian free field. As a byproduct, we obtain alternative proofs to the corresponding results for random interlacements in the work of Černý and Popov ["On the internal distance in the interlacement set," Electron. J. Probab. 17(29), 1-25 (2012)], and while our main interest is in percolation models with long-range correlations, we also recover results in the spirit of the work of Antal and Pisztora ["On the chemical distance for supercritical Bernoulli percolation," Ann Probab. 24(2), 1036-1048 (1996)] for Bernoulli percolation. Finally, as a corollary, we derive new results about the (chemical) diameter of the largest connected component in the complement of the trace of the random walk on the torus.

Measurement of Scenic Spots Sustainable Capacity Based on PCA-Entropy TOPSIS: A Case Study from 30 Provinces, China

PubMed Central

Liang, Xuedong; Liu, Canmian; Li, Zhi

2017-01-01

In connection with the sustainable development of scenic spots, this paper, with consideration of resource conditions, economic benefits, auxiliary industry scale and ecological environment, establishes a comprehensive measurement model of the sustainable capacity of scenic spots; optimizes the index system by principal components analysis to extract principal components; assigns the weight of principal components by entropy method; analyzes the sustainable capacity of scenic spots in each province of China comprehensively in combination with TOPSIS method and finally puts forward suggestions aid decision-making. According to the study, this method provides an effective reference for the study of the sustainable development of scenic spots and is very significant for considering the sustainable development of scenic spots and auxiliary industries to establish specific and scientific countermeasures for improvement. PMID:29271947

Measurement of Scenic Spots Sustainable Capacity Based on PCA-Entropy TOPSIS: A Case Study from 30 Provinces, China.

PubMed

Liang, Xuedong; Liu, Canmian; Li, Zhi

2017-12-22

In connection with the sustainable development of scenic spots, this paper, with consideration of resource conditions, economic benefits, auxiliary industry scale and ecological environment, establishes a comprehensive measurement model of the sustainable capacity of scenic spots; optimizes the index system by principal components analysis to extract principal components; assigns the weight of principal components by entropy method; analyzes the sustainable capacity of scenic spots in each province of China comprehensively in combination with TOPSIS method and finally puts forward suggestions aid decision-making. According to the study, this method provides an effective reference for the study of the sustainable development of scenic spots and is very significant for considering the sustainable development of scenic spots and auxiliary industries to establish specific and scientific countermeasures for improvement.

Wavelet analysis applied to the IRAS cirrus

NASA Technical Reports Server (NTRS)

Langer, William D.; Wilson, Robert W.; Anderson, Charles H.

1994-01-01

The structure of infrared cirrus clouds is analyzed with Laplacian pyramid transforms, a form of non-orthogonal wavelets. Pyramid and wavelet transforms provide a means to decompose images into their spatial frequency components such that all spatial scales are treated in an equivalent manner. The multiscale transform analysis is applied to IRAS 100 micrometer maps of cirrus emission in the north Galactic pole region to extract features on different scales. In the maps we identify filaments, fragments and clumps by separating all connected regions. These structures are analyzed with respect to their Hausdorff dimension for evidence of the scaling relationships in the cirrus clouds.

Software and database for the analysis of mutations in the human FBN1 gene.

PubMed Central

Collod, G; Béroud, C; Soussi, T; Junien, C; Boileau, C

1996-01-01

Fibrillin is the major component of extracellular microfibrils. Mutations in the fibrillin gene on chromosome 15 (FBN1) were described at first in the heritable connective tissue disorder, Marfan syndrome (MFS). More recently, FBN1 has also been shown to harbor mutations related to a spectrum of conditions phenotypically related to MFS and many mutations will have to be accumulated before genotype/phenotype relationships emerge. To facilitate mutational analysis of the FBN1 gene, a software package along with a computerized database (currently listing 63 entries) have been created. PMID:8594563

An automated method for identifying artifact in independent component analysis of resting-state FMRI.

PubMed

Bhaganagarapu, Kaushik; Jackson, Graeme D; Abbott, David F

2013-01-01

An enduring issue with data-driven analysis and filtering methods is the interpretation of results. To assist, we present an automatic method for identification of artifact in independent components (ICs) derived from functional MRI (fMRI). The method was designed with the following features: does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin; can be applied to resting-state fMRI; is automated, requiring minimal or no human intervention. We applied the method to a MELODIC probabilistic ICA of resting-state functional connectivity data acquired in 50 healthy control subjects, and compared the results to a blinded expert manual classification. The method identified between 26 and 72% of the components as artifact (mean 55%). About 0.3% of components identified as artifact were discordant with the manual classification; retrospective examination of these ICs suggested the automated method had correctly identified these as artifact. We have developed an effective automated method which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data. Source code of our implementation of the method is available.

An Automated Method for Identifying Artifact in Independent Component Analysis of Resting-State fMRI

PubMed Central

Bhaganagarapu, Kaushik; Jackson, Graeme D.; Abbott, David F.

2013-01-01

An enduring issue with data-driven analysis and filtering methods is the interpretation of results. To assist, we present an automatic method for identification of artifact in independent components (ICs) derived from functional MRI (fMRI). The method was designed with the following features: does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin; can be applied to resting-state fMRI; is automated, requiring minimal or no human intervention. We applied the method to a MELODIC probabilistic ICA of resting-state functional connectivity data acquired in 50 healthy control subjects, and compared the results to a blinded expert manual classification. The method identified between 26 and 72% of the components as artifact (mean 55%). About 0.3% of components identified as artifact were discordant with the manual classification; retrospective examination of these ICs suggested the automated method had correctly identified these as artifact. We have developed an effective automated method which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data. Source code of our implementation of the method is available. PMID:23847511

Global Qualitative Flow-Path Modeling for Local State Determination in Simulation and Analysis

NASA Technical Reports Server (NTRS)

Malin, Jane T. (Inventor); Fleming, Land D. (Inventor)

1998-01-01

For qualitative modeling and analysis, a general qualitative abstraction of power transmission variables (flow and effort) for elements of flow paths includes information on resistance, net flow, permissible directions of flow, and qualitative potential is discussed. Each type of component model has flow-related variables and an associated internal flow map, connected into an overall flow network of the system. For storage devices, the implicit power transfer to the environment is represented by "virtual" circuits that include an environmental junction. A heterogeneous aggregation method simplifies the path structure. A method determines global flow-path changes during dynamic simulation and analysis, and identifies corresponding local flow state changes that are effects of global configuration changes. Flow-path determination is triggered by any change in a flow-related device variable in a simulation or analysis. Components (path elements) that may be affected are identified, and flow-related attributes favoring flow in the two possible directions are collected for each of them. Next, flow-related attributes are determined for each affected path element, based on possibly conflicting indications of flow direction. Spurious qualitative ambiguities are minimized by using relative magnitudes and permissible directions of flow, and by favoring flow sources over effort sources when comparing flow tendencies. The results are output to local flow states of affected components.

Altered amygdalar resting-state connectivity in depression is explained by both genes and environment.

PubMed

Córdova-Palomera, Aldo; Tornador, Cristian; Falcón, Carles; Bargalló, Nuria; Nenadic, Igor; Deco, Gustavo; Fañanás, Lourdes

2015-10-01

Recent findings indicate that alterations of the amygdalar resting-state fMRI connectivity play an important role in the etiology of depression. While both depression and resting-state brain activity are shaped by genes and environment, the relative contribution of genetic and environmental factors mediating the relationship between amygdalar resting-state connectivity and depression remain largely unexplored. Likewise, novel neuroimaging research indicates that different mathematical representations of resting-state fMRI activity patterns are able to embed distinct information relevant to brain health and disease. The present study analyzed the influence of genes and environment on amygdalar resting-state fMRI connectivity, in relation to depression risk. High-resolution resting-state fMRI scans were analyzed to estimate functional connectivity patterns in a sample of 48 twins (24 monozygotic pairs) informative for depressive psychopathology (6 concordant, 8 discordant and 10 healthy control pairs). A graph-theoretical framework was employed to construct brain networks using two methods: (i) the conventional approach of filtered BOLD fMRI time-series and (ii) analytic components of this fMRI activity. Results using both methods indicate that depression risk is increased by environmental factors altering amygdalar connectivity. When analyzing the analytic components of the BOLD fMRI time-series, genetic factors altering the amygdala neural activity at rest show an important contribution to depression risk. Overall, these findings show that both genes and environment modify different patterns the amygdala resting-state connectivity to increase depression risk. The genetic relationship between amygdalar connectivity and depression may be better elicited by examining analytic components of the brain resting-state BOLD fMRI signals. © 2015 Wiley Periodicals, Inc.

High Performance Descriptive Semantic Analysis of Semantic Graph Databases

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

Joslyn, Cliff A.; Adolf, Robert D.; al-Saffar, Sinan

As semantic graph database technology grows to address components ranging from extant large triple stores to SPARQL endpoints over SQL-structured relational databases, it will become increasingly important to be able to understand their inherent semantic structure, whether codified in explicit ontologies or not. Our group is researching novel methods for what we call descriptive semantic analysis of RDF triplestores, to serve purposes of analysis, interpretation, visualization, and optimization. But data size and computational complexity makes it increasingly necessary to bring high performance computational resources to bear on this task. Our research group built a novel high performance hybrid system comprisingmore » computational capability for semantic graph database processing utilizing the large multi-threaded architecture of the Cray XMT platform, conventional servers, and large data stores. In this paper we describe that architecture and our methods, and present the results of our analyses of basic properties, connected components, namespace interaction, and typed paths such for the Billion Triple Challenge 2010 dataset.« less

High pressure and temperature optical flow cell for near-infra-red spectroscopic analysis of gas mixtures.

PubMed

Norton, C G; Suedmeyer, J; Oderkerk, B; Fieback, T M

2014-05-01

A new optical flow cell with a new optical arrangement adapted for high pressures and temperatures using glass fibres to connect light source, cell, and spectrometer has been developed, as part of a larger project comprising new methods for in situ analysis of bio and hydrogen gas mixtures in high pressure and temperature applications. The analysis is based on measurements of optical, thermo-physical, and electromagnetic properties in gas mixtures with newly developed high pressure property sensors, which are mounted in a new apparatus which can generate gas mixtures with up to six components with an uncertainty of composition of as little as 0.1 mol. %. Measurements of several pure components of natural gases and biogases to a pressure of 20 MPa were performed on two isotherms, and with binary mixtures of the same pure gases at pressures to 17.5 MPa. Thereby a new method of analyzing the obtained spectra based on the partial density of methane was investigated.

A New Principle of Sound Frequency Analysis

NASA Technical Reports Server (NTRS)

Theodorsen, Theodore

1932-01-01

In connection with the study of aircraft and propeller noises, the National Advisory Committee for Aeronautics has developed an instrument for sound-frequency analysis which differs fundamentally from previous types, and which, owing to its simplicity of principle, construction, and operation, has proved to be of value in this investigation. The method is based on the well-known fact that the Ohmic loss in an electrical resistance is equal to the sum of the losses of the harmonic components of a complex wave, except for the case in which any two components approach or attain vectorial identity, in which case the Ohmic loss is increased by a definite amount. The principle of frequency analysis has been presented mathematically and a number of distinct advantages relative to previous methods have been pointed out. An automatic recording instrument embodying this principle is described in detail. It employs a beat-frequency oscillator as a source of variable frequency. A large number of experiments have verified the predicted superiority of the method. A number of representative records are presented.

Method and apparatus for fine tuning an orifice pulse tube refrigerator

DOEpatents

Swift, Gregory W.; Wollan, John J.

2003-12-23

An orifice pulse tube refrigerator uses flow resistance, compliance, and inertance components connected to a pulse tube for establishing a phase relationship between oscillating pressure and oscillating velocity in the pulse tube. A temperature regulating system heats or cools a working gas in at least one of the flow resistance and inertance components. A temperature control system is connected to the temperature regulating system for controlling the temperature of the working gas in the at least one of the flow resistance and inertance components and maintains a control temperature that is indicative of a desired temporal phase relationship.

Functional Disconnectivity during Inter-Task Resting State in Dementia with Lewy Bodies.

PubMed

Chabran, Eléna; Roquet, Daniel; Gounot, Daniel; Sourty, Marion; Armspach, Jean-Paul; Blanc, Frédéric

2018-01-01

Limited research has been done on the functional connectivity in visuoperceptual regions in dementia with Lewy bodies (DLB) patients. This study aimed to investigate the functional connectivity differences between a task condition and an inter-task resting state condition within a visuoperceptual paradigm, in DLB patients compared with Alzheimer disease (AD) patients and healthy elderly control subjects. Twenty-six DLB, 29 AD, and 22 healthy subjects underwent a detailed clinical and neuropsychological examination along with a functional MRI during the different conditions of a visuoperceptual paradigm. Functional images were analyzed using group-level spatial independent component analysis and seed-based connectivity analyses. While the DLB patients scored well and did not differ from the control and AD groups in terms of functional activity and connectivity during the task conditions, they showed decreased functional connectivity in visuoperceptual regions during the resting state condition, along with a temporal impairment of the default-mode network activity. Functional connectivity disturbances were also found within two attentional-executive networks and between these networks and visuoperceptual regions. We found a specific functional profile in the switching between task and resting state conditions in DLB patients. This result could help better characterize functional impairments in DLB and their contribution to several core symptoms of this pathology such as visual hallucinations and cognitive fluctuations. © 2018 S. Karger AG, Basel.

Altered resting-state connectivity within default mode network associated with late chronotype.

PubMed

Horne, Charlotte Mary; Norbury, Ray

2018-04-20

Current evidence suggests late chronotype individuals have an increased risk of developing depression. However, the underlying neural mechanisms of this association are not fully understood. Forty-six healthy, right-handed individuals free of current or previous diagnosis of depression, family history of depression or sleep disorder underwent resting-state functional Magnetic Resonance Imaging (rsFMRI). Using an Independent Component Analysis (ICA) approach, the Default Mode Network (DMN) was identified based on a well validated template. Linear effects of chronotype on DMN connectivity were tested for significance using non-parametric permutation tests (applying 5000 permutations). Sleep quality, age, gender, measures of mood and anxiety, time of scan and cortical grey matter volume were included as covariates in the regression model. A significant positive correlation between chronotype and functional connectivity within nodes of the DMN was observed, including; bilateral PCC and precuneus, such that later chronotype (participants with lower rMEQ scores) was associated with decreased connectivity within these regions. The current results appear consistent with altered DMN connectivity in depressed patients and weighted evidence towards reduced DMN connectivity in other at-risk populations which may, in part, explain the increased vulnerability for depression in late chronotype individuals. The effect may be driven by self-critical thoughts associated with late chronotype although future studies are needed to directly investigate this. Copyright © 2018 Elsevier Ltd. All rights reserved.

Using Spatial Multiple Regression to Identify Intrinsic Connectivity Networks Involved in Working Memory Performance

PubMed Central

Gordon, Evan M.; Stollstorff, Melanie; Vaidya, Chandan J.

2012-01-01

Many researchers have noted that the functional architecture of the human brain is relatively invariant during task performance and the resting state. Indeed, intrinsic connectivity networks (ICNs) revealed by resting-state functional connectivity analyses are spatially similar to regions activated during cognitive tasks. This suggests that patterns of task-related activation in individual subjects may result from the engagement of one or more of these ICNs; however, this has not been tested. We used a novel analysis, spatial multiple regression, to test whether the patterns of activation during an N-back working memory task could be well described by a linear combination of ICNs delineated using Independent Components Analysis at rest. We found that across subjects, the cingulo-opercular Set Maintenance ICN, as well as right and left Frontoparietal Control ICNs, were reliably activated during working memory, while Default Mode and Visual ICNs were reliably deactivated. Further, involvement of Set Maintenance, Frontoparietal Control, and Dorsal Attention ICNs was sensitive to varying working memory load. Finally, the degree of left Frontoparietal Control network activation predicted response speed, while activation in both left Frontoparietal Control and Dorsal Attention networks predicted task accuracy. These results suggest that a close relationship between resting-state networks and task-evoked activation is functionally relevant for behavior, and that spatial multiple regression analysis is a suitable method for revealing that relationship. PMID:21761505

The wave-based substructuring approach for the efficient description of interface dynamics in substructuring

NASA Astrophysics Data System (ADS)

Donders, S.; Pluymers, B.; Ragnarsson, P.; Hadjit, R.; Desmet, W.

2010-04-01

In the vehicle design process, design decisions are more and more based on virtual prototypes. Due to competitive and regulatory pressure, vehicle manufacturers are forced to improve product quality, to reduce time-to-market and to launch an increasing number of design variants on the global market. To speed up the design iteration process, substructuring and component mode synthesis (CMS) methods are commonly used, involving the analysis of substructure models and the synthesis of the substructure analysis results. Substructuring and CMS enable efficient decentralized collaboration across departments and allow to benefit from the availability of parallel computing environments. However, traditional CMS methods become prohibitively inefficient when substructures are coupled along large interfaces, i.e. with a large number of degrees of freedom (DOFs) at the interface between substructures. The reason is that the analysis of substructures involves the calculation of a number of enrichment vectors, one for each interface degree of freedom (DOF). Since large interfaces are common in vehicles (e.g. the continuous line connections to connect the body with the windshield, roof or floor), this interface bottleneck poses a clear limitation in the vehicle noise, vibration and harshness (NVH) design process. Therefore there is a need to describe the interface dynamics more efficiently. This paper presents a wave-based substructuring (WBS) approach, which allows reducing the interface representation between substructures in an assembly by expressing the interface DOFs in terms of a limited set of basis functions ("waves"). As the number of basis functions can be much lower than the number of interface DOFs, this greatly facilitates the substructure analysis procedure and results in faster design predictions. The waves are calculated once from a full nominal assembly analysis, but these nominal waves can be re-used for the assembly of modified components. The WBS approach thus enables efficient structural modification predictions of the global modes, so that efficient vibro-acoustic design modification, optimization and robust design become possible. The results show that wave-based substructuring offers a clear benefit for vehicle design modifications, by improving both the speed of component reduction processes and the efficiency and accuracy of design iteration predictions, as compared to conventional substructuring approaches.

Functional brain connectivity is predictable from anatomic network's Laplacian eigen-structure.

PubMed

Abdelnour, Farras; Dayan, Michael; Devinsky, Orrin; Thesen, Thomas; Raj, Ashish

2018-05-15

How structural connectivity (SC) gives rise to functional connectivity (FC) is not fully understood. Here we mathematically derive a simple relationship between SC measured from diffusion tensor imaging, and FC from resting state fMRI. We establish that SC and FC are related via (structural) Laplacian spectra, whereby FC and SC share eigenvectors and their eigenvalues are exponentially related. This gives, for the first time, a simple and analytical relationship between the graph spectra of structural and functional networks. Laplacian eigenvectors are shown to be good predictors of functional eigenvectors and networks based on independent component analysis of functional time series. A small number of Laplacian eigenmodes are shown to be sufficient to reconstruct FC matrices, serving as basis functions. This approach is fast, and requires no time-consuming simulations. It was tested on two empirical SC/FC datasets, and was found to significantly outperform generative model simulations of coupled neural masses. Copyright © 2018. Published by Elsevier Inc.

Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network

PubMed Central

Daianu, Madelaine; Jahanshad, Neda; Nir, Talia M.; Jack, Clifford R.; Weiner, Michael W.; Bernstein, Matthew; Thompson, Paul M.

2015-01-01

Diffusion imaging can assess the white matter connections within the brain, revealing how neural pathways break down in Alzheimer's disease (AD). We analyzed 3-Tesla whole-brain diffusion-weighted images from 202 participants scanned by the Alzheimer's Disease Neuroimaging Initiative – 50 healthy controls, 110 with mild cognitive impairment (MCI) and 42 AD patients. From whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We tested whether AD disrupts the ‘rich-club’ – a network property where high-degree network nodes are more interconnected than expected by chance. We calculated the rich-club properties at a range of degree thresholds, as well as other network topology measures including global degree, clustering coefficient, path length and efficiency. Network disruptions predominated in the low-degree regions of the connectome in patients, relative to controls. The other metrics also showed alterations, suggesting a distinctive pattern of disruption in AD, less pronounced in MCI, targeting global brain connectivity, and focusing on more remotely connected nodes rather than the central core of the network. AD involves severely reduced structural connectivity; our step-wise rich club coefficients analyze points to disruptions predominantly in the peripheral network components; other modalities of data are needed to know if this indicates impaired communication among non rich-club regions. The highly connected core was relatively preserved, offering new evidence on the neural basis of progressive risk for cognitive decline. PMID:26037224

Discussion about effecting of stiffener in four bolts in a row end plate connection for long span and heavy load steel structures in Vietnam

NASA Astrophysics Data System (ADS)

Huong, Khang T.; Nguyen, Cung H.

2018-04-01

Nowadays, steel structure industry in Vietnam is in strong development. The construction of steel structure becomes larger span and heavier load. The issue spawned a number of issues arise from optimizing connections. Typical of steel connections in prefabricated steel structure that is an end plate (face plate) bolted connection. When the connection carried a heavy load, then the number of bolts is required much more. Increasing the number of rows bolts will less effective because can still not enough strength requirements, the bolts in row near rotational center will level arm reduction, then it cannot carry heavy loads. The current solution is doing multiple bolts in a row. Current standards such as EN [1], AISC [2] are no specific guidelines for calculating the connection four bolts in a row that primarily assumes the way works like a T-stub of the two bolts a row. Some articles studied T-stub four bolts in a row [3], [4], [5], [6] by component method but it has some components which weren’t considered. In this paper, in order to provide a contribution to improve the T-stub four bolts in a row, the stiffener component in T-stub will be added and compared with T-stub without stiffener by the finite element model to demonstrate effectiveness in reducing stress and displacement of T-stub. It gives ideas for the economic design of four bolts in a row end plate connection in Vietnam for future.

A flow study in radial inflow turbine scroll-nozzle assembly

NASA Technical Reports Server (NTRS)

Hamed, A.; Baskharone, E.; Tabakoff, W.

1978-01-01

The present analysis describes the flow behavior in the combined scroll-nozzle assembly of a radial inflow turbine. This model was chosen to provide a better understanding of the mutual interaction effects of these two components on the flow. The finite element method is used in the solution of the flow field in this multiply connected domain. The mass flow rates in the different nozzle channels is not presumed constant, but is determined from the solution.

Cooperative terrain model acquisition by a team of two or three point-robots

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

Rao, N.S.V.; Protopopescu, V.; Manickam, N.

1996-04-01

We address the model acquisition problem for an unknown planar terrain by a team of two or three robots. The terrain is cluttered by a finite number of polygonal obstacles whose shapes and positions are unknown. The robots are point-sized and equipped with visual sensors which acquire all visible parts of the terrain by scan operations executed from their locations. The robots communicate with each other via wireless connection. The performance is measured by the number of the sensor (scan) operations which are assumed to be the most time-consuming of all the robot operations. We employ the restricted visibility graphmore » methods in a hierarchical setup. For terrains with convex obstacles and for teams of n(= 2, 3) robots, we prove that the sensing time is reduced by a factor of 1/n. For terrains with concave corners, the performance of the algorithm depends on the number of concave regions and their depths. A hierarchical decomposition of the restricted visibility graph into n-connected and (n - 1)-or-less connected components is considered. The performance for the n(= 2, 3) robot team is expressed in terms of the sizes of n-connected components, and the sizes and diameters of (n - 1)-or-less connected components.« less

Maximum covariance analysis to identify intraseasonal oscillations over tropical Brazil

NASA Astrophysics Data System (ADS)

Barreto, Naurinete J. C.; Mesquita, Michel d. S.; Mendes, David; Spyrides, Maria H. C.; Pedra, George U.; Lucio, Paulo S.

2017-09-01

A reliable prognosis of extreme precipitation events in the tropics is arguably challenging to obtain due to the interaction of meteorological systems at various time scales. A pivotal component of the global climate variability is the so-called intraseasonal oscillations, phenomena that occur between 20 and 100 days. The Madden-Julian Oscillation (MJO), which is directly related to the modulation of convective precipitation in the equatorial belt, is considered the primary oscillation in the tropical region. The aim of this study is to diagnose the connection between the MJO signal and the regional intraseasonal rainfall variability over tropical Brazil. This is achieved through the development of an index called Multivariate Intraseasonal Index for Tropical Brazil (MITB). This index is based on Maximum Covariance Analysis (MCA) applied to the filtered daily anomalies of rainfall data over tropical Brazil against a group of covariates consisting of: outgoing longwave radiation and the zonal component u of the wind at 850 and 200 hPa. The first two MCA modes, which were used to create the { MITB}_1 and { MITB}_2 indices, represent 65 and 16 % of the explained variance, respectively. The combined multivariate index was able to satisfactorily represent the pattern of intraseasonal variability over tropical Brazil, showing that there are periods of activation and inhibition of precipitation connected with the pattern of MJO propagation. The MITB index could potentially be used as a diagnostic tool for intraseasonal forecasting.

Resting-state functional connectivity of the default mode network associated with happiness

PubMed Central

Luo, Yangmei; Kong, Feng; Qi, Senqing; You, Xuqun

2016-01-01

Happiness refers to people’s cognitive and affective evaluation of their life. Why are some people happier than others? One reason might be that unhappy people are prone to ruminate more than happy people. The default mode network (DMN) is normally active during rest and is implicated in rumination. We hypothesized that unhappiness may be associated with increased default-mode functional connectivity during rest, including the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC) and inferior parietal lobule (IPL). The hyperconnectivity of these areas may be associated with higher levels of rumination. One hundred forty-eight healthy participants underwent a resting-state fMRI scan. A group-independent component analysis identified the DMNs. Results indicated increased functional connectivity in the DMN was associated with lower levels of happiness. Specifically, relative to happy people, unhappy people exhibited greater functional connectivity in the anterior medial cortex (bilateral MPFC), posterior medial cortex regions (bilateral PCC) and posterior parietal cortex (left IPL). Moreover, the increased functional connectivity of the MPFC, PCC and IPL, correlated positively with the inclination to ruminate. These results highlight the important role of the DMN in the neural correlates of happiness, and suggest that rumination may play an important role in people’s perceived happiness. PMID:26500289

A social network typology and sexual risk-taking among men who have sex with men in Cape Town and Port Elizabeth, South Africa

PubMed Central

de Voux, Alex; Baral, Stefan; Bekker, Linda-Gail; Beyrer, Chris; Phaswana-Mafuya, Nancy; Siegler, Aaron; Sullivan, Patrick; Winskell, Kate; Stephenson, Rob

2016-01-01

Despite the high prevalence of HIV among men who have sex with men in South Africa, very little is known about their lived realities, including their social and sexual networks. Given the influence of social network structure on sexual risk behaviours, a better understanding of the social contexts of men who have sex with men is essential for informing the design of HIV programming and messaging. This study explored social network connectivity, an understudied network attribute, examining self-reported connectivity between friends, family and sex partners. Data were collected in Cape Town and Port Elizabeth, South Africa from 78 men who have sex with men who participated in in-depth interviews which included a social network mapping component. Five social network types emerged from the content analysis of these social network maps based on the level of connectivity between family, friends and sex partners, and ranged from disconnected to densely connected networks. The ways in which participants reported sexual risk-taking differed across the five network types revealing diversity in social network profiles. HIV programming and messaging for this population can greatly benefit from recognising the diversity in lived realities and social connections between men who have sex with men. PMID:26569376

A social network typology and sexual risk-taking among men who have sex with men in Cape Town and Port Elizabeth, South Africa.

PubMed

de Voux, Alex; Baral, Stefan D; Bekker, Linda-Gail; Beyrer, Chris; Phaswana-Mafuya, Nancy; Siegler, Aaron J; Sullivan, Patrick S; Winskell, Kate; Stephenson, Rob

2016-01-01

Despite the high prevalence of HIV among men who have sex with men in South Africa, very little is known about their lived realities, including their social and sexual networks. Given the influence of social network structure on sexual risk behaviours, a better understanding of the social contexts of men who have sex with men is essential for informing the design of HIV programming and messaging. This study explored social network connectivity, an understudied network attribute, examining self-reported connectivity between friends, family and sex partners. Data were collected in Cape Town and Port Elizabeth, South Africa, from 78 men who have sex with men who participated in in-depth interviews that included a social network mapping component. Five social network types emerged from the content analysis of these social network maps based on the level of connectivity between family, friends and sex partners, and ranged from disconnected to densely connected networks. The ways in which participants reported sexual risk-taking differed across the five network types, revealing diversity in social network profiles. HIV programming and messaging for this population can greatly benefit from recognising the diversity in lived realities and social connections between men who have sex with men.

Comparison of functional network connectivity for passive-listening and active-response narrative comprehension in adolescents.

PubMed

Wang, Yingying; Holland, Scott K

2014-05-01

Comprehension of narrative stories plays an important role in the development of language skills. In this study, we compared brain activity elicited by a passive-listening version and an active-response (AR) version of a narrative comprehension task by using independent component (IC) analysis on functional magnetic resonance imaging data from 21 adolescents (ages 14-18 years). Furthermore, we explored differences in functional network connectivity engaged by two versions of the task and investigated the relationship between the online response time and the strength of connectivity between each pair of ICs. Despite similar brain region involvements in auditory, temporoparietal, and frontoparietal language networks for both versions, the AR version engages some additional network elements including the left dorsolateral prefrontal, anterior cingulate, and sensorimotor networks. These additional involvements are likely associated with working memory and maintenance of attention, which can be attributed to the differences in cognitive strategic aspects of the two versions. We found significant positive correlation between the online response time and the strength of connectivity between an IC in left inferior frontal region and an IC in sensorimotor region. An explanation for this finding is that longer reaction time indicates stronger connection between the frontal and sensorimotor networks caused by increased activation in adolescents who require more effort to complete the task.

Revealing the Structural Complexity of Component Interactions of Topic-Specific PCK when Planning to Teach

NASA Astrophysics Data System (ADS)

Mavhunga, Elizabeth

2018-04-01

Teaching pedagogical content knowledge (PCK) at a topic-specific level requires clarity on the content-specific nature of the components employed, as well as the specific features that bring about the desirable depth in teacher explanations. Such understanding is often hazy; yet, it influences the nature of teacher tasks and learning opportunities afforded to pre-service teachers in a teaching program. The purpose of this study was twofold: firstly, to illuminate the emerging complexity when content-specific components of PCK interact when planning to teach a chemistry topic; and secondly, to identify the kinds of teacher tasks that promote the emergence of such complexity. Data collected were content representations (CoRes) in chemical equilibrium accompanied by expanded lesson outlines from 15 pre-service teachers in their final year of study towards a first degree in teaching (B Ed). The analysis involved extraction of episodes that exhibited component interaction by using a qualitative in-depth analysis method. The results revealed the structure in which the components of PCK in a topic interact among each other to be linear, interwoven, or a combination of the two. The interwoven interactions contained multiple components that connected explanations on different aspects of a concept, all working in a complementary manner. The most sophisticated component interactions emerged from teacher tasks on descriptions of a lesson sequence and a summary of a lesson. Recommendations in this study highlight core practices for making pedagogical transformation of topic content knowledge more accessible.

Global and system-specific resting-state fMRI fluctuations are uncorrelated: principal component analysis reveals anti-correlated networks.

PubMed

Carbonell, Felix; Bellec, Pierre; Shmuel, Amir

2011-01-01

The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)-based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations between resting-state fluctuations in the default-mode and the task-positive networks. We conclude that resting-state global fluctuations and network-specific fluctuations are uncorrelated, supporting a Resting-State Linear-Additive Model. In addition, we conclude that the network-specific resting-state fluctuations of the default-mode and task-positive networks show artifact-free anti-correlations.

Differential recruitment of theory of mind brain network across three tasks: An independent component analysis.

PubMed

Thye, Melissa D; Ammons, Carla J; Murdaugh, Donna L; Kana, Rajesh K

2018-07-16

Social neuroscience research has focused on an identified network of brain regions primarily associated with processing Theory of Mind (ToM). However, ToM is a broad cognitive process, which encompasses several sub-processes, such as mental state detection and intentional attribution, and the connectivity of brain regions underlying the broader ToM network in response to paradigms assessing these sub-processes requires further characterization. Standard fMRI analyses which focus only on brain activity cannot capture information about ToM processing at a network level. An alternative method, independent component analysis (ICA), is a data-driven technique used to isolate intrinsic connectivity networks, and this approach provides insight into network-level regional recruitment. In this fMRI study, three complementary, but distinct ToM tasks assessing mental state detection (e.g. RMIE: Reading the Mind in the Eyes; RMIV: Reading the Mind in the Voice) and intentional attribution (Causality task) were each analyzed using ICA in order to separately characterize the recruitment and functional connectivity of core nodes in the ToM network in response to the sub-processes of ToM. Based on visual comparison of the derived networks for each task, the spatiotemporal network patterns were similar between the RMIE and RMIV tasks, which elicited mentalizing about the mental states of others, and these networks differed from the network derived for the Causality task, which elicited mentalizing about goal-directed actions. The medial prefrontal cortex, precuneus, and right inferior frontal gyrus were seen in the components with the highest correlation with the task condition for each of the tasks highlighting the role of these regions in general ToM processing. Using a data-driven approach, the current study captured the differences in task-related brain response to ToM in three distinct ToM paradigms. The findings of this study further elucidate the neural mechanisms associated with mental state detection and causal attribution, which represent possible sub-processes of the complex construct of ToM processing. Published by Elsevier B.V.

Comparative Evaluation of Preprocessing Freeware on Chromatography/Mass Spectrometry Data for Signature Discovery

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

Coble, Jamie B.; Fraga, Carlos G.

2014-07-07

Preprocessing software is crucial for the discovery of chemical signatures in metabolomics, chemical forensics, and other signature-focused disciplines that involve analyzing large data sets from chemical instruments. Here, four freely available and published preprocessing tools known as metAlign, MZmine, SpectConnect, and XCMS were evaluated for impurity profiling using nominal mass GC/MS data and accurate mass LC/MS data. Both data sets were previously collected from the analysis of replicate samples from multiple stocks of a nerve-agent precursor. Each of the four tools had their parameters set for the untargeted detection of chromatographic peaks from impurities present in the stocks. The peakmore » table generated by each preprocessing tool was analyzed to determine the number of impurity components detected in all replicate samples per stock. A cumulative set of impurity components was then generated using all available peak tables and used as a reference to calculate the percent of component detections for each tool, in which 100% indicated the detection of every component. For the nominal mass GC/MS data, metAlign performed the best followed by MZmine, SpectConnect, and XCMS with detection percentages of 83, 60, 47, and 42%, respectively. For the accurate mass LC/MS data, the order was metAlign, XCMS, and MZmine with detection percentages of 80, 45, and 35%, respectively. SpectConnect did not function for the accurate mass LC/MS data. Larger detection percentages were obtained by combining the top performer with at least one of the other tools such as 96% by combining metAlign with MZmine for the GC/MS data and 93% by combining metAlign with XCMS for the LC/MS data. In terms of quantitative performance, the reported peak intensities had average absolute biases of 41, 4.4, 1.3 and 1.3% for SpectConnect, metAlign, XCMS, and MZmine, respectively, for the GC/MS data. For the LC/MS data, the average absolute biases were 22, 4.5, and 3.1% for metAlign, MZmine, and XCMS, respectively. In summary, metAlign performed the best in terms of peak discovery; however, more than one preprocessing tool should be considered to avoid missing potential chemical signatures.« less

Connected motorcycle system performance.

DOT National Transportation Integrated Search

2016-01-15

This project characterized the performance of Connected Vehicle Systems (CVS) on motorcycles based on two key components: global positioning and wireless communication systems. Considering that Global Positioning System (GPS) and 5.9 GHz Dedicated Sh...

Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis

PubMed Central

Dordek, Yedidyah; Soudry, Daniel; Meir, Ron; Derdikman, Dori

2016-01-01

Many recent models study the downstream projection from grid cells to place cells, while recent data have pointed out the importance of the feedback projection. We thus asked how grid cells are affected by the nature of the input from the place cells. We propose a single-layer neural network with feedforward weights connecting place-like input cells to grid cell outputs. Place-to-grid weights are learned via a generalized Hebbian rule. The architecture of this network highly resembles neural networks used to perform Principal Component Analysis (PCA). Both numerical results and analytic considerations indicate that if the components of the feedforward neural network are non-negative, the output converges to a hexagonal lattice. Without the non-negativity constraint, the output converges to a square lattice. Consistent with experiments, grid spacing ratio between the first two consecutive modules is −1.4. Our results express a possible linkage between place cell to grid cell interactions and PCA. DOI: http://dx.doi.org/10.7554/eLife.10094.001 PMID:26952211

Flux transfer events: Reconnection without separators. [magnetopause

NASA Technical Reports Server (NTRS)

Hesse, M.; Birn, J.; Schindler, K.

1989-01-01

A topological analysis of a simple model magnetic field of a perturbation at the magnetopause modeling an apparent flux transfer event is presented. It is shown that a localized perturbation at the magnetopause can in principle open a closed magnetosphere by establishing magnetic connections across the magnetopause. Although the model field exhibits neutral points, these are not involved in the magnetic connection of the flux tubes. The topological substructure of a localized perturbation is analyzed in a simpler configuration. The presence of both signs of the magnetic field component normal to the magnetopause leads to a linkage of topologically different flux tubes, described as a flux knot, and a filamentary substructure of field lines of different topological types which becomes increasingly complicated for decreasing magnetic shear at the magnetopause.

Miniature plastic gripper and fabrication method

DOEpatents

Benett, W.J.; Krulevitch, P.A.; Lee, A.P.; Northrup, M.A.; Folta, J.A.

1997-03-11

A miniature plastic gripper actuated by inflation of a miniature balloon and method of fabricating same are disclosed. The gripper is constructed of either heat-shrinkable or heat-expandable plastic tubing and is formed around a mandrel, then cut to form gripper prongs or jaws and the mandrel removed. The gripper is connected at one end with a catheter or tube having an actuating balloon at its tip, whereby the gripper is opened or closed by inflation or deflation of the balloon. The gripper is designed to removably retain a member to which is connected a quantity or medicine, plugs, or micro-components. The miniature plastic gripper is inexpensive to fabricate and can be used for various applications, such as gripping, sorting, or placing of micron-scale particles for analysis. 8 figs.

Fabrication method for miniature plastic gripper

DOEpatents

Benett, W.J.; Krulevitch, P.A.; Lee, A.P.; Northrup, M.A.; Folta, J.A.

1998-07-21

A miniature plastic gripper is described actuated by inflation of a miniature balloon and method of fabricating same. The gripper is constructed of either heat-shrinkable or heat-expandable plastic tubing and is formed around a mandrel, then cut to form gripper prongs or jaws and the mandrel removed. The gripper is connected at one end with a catheter or tube having an actuating balloon at its tip, whereby the gripper is opened or dosed by inflation or deflation of the balloon. The gripper is designed to removably retain a member to which is connected a quantity or medicine, plugs, or micro-components. The miniature plastic gripper is inexpensive to fabricate and can be used for various applications, such as gripping, sorting, or placing of micron-scale particles for analysis. 8 figs.

Understanding the mind of a worm: hierarchical network structure underlying nervous system function in C. elegans.

PubMed

Chatterjee, Nivedita; Sinha, Sitabhra

2008-01-01

The nervous system of the nematode C. elegans provides a unique opportunity to understand how behavior ('mind') emerges from activity in the nervous system ('brain') of an organism. The hermaphrodite worm has only 302 neurons, all of whose connections (synaptic and gap junctional) are known. Recently, many of the functional circuits that make up its behavioral repertoire have begun to be identified. In this paper, we investigate the hierarchical structure of the nervous system through k-core decomposition and find it to be intimately related to the set of all known functional circuits. Our analysis also suggests a vital role for the lateral ganglion in processing information, providing an essential connection between the sensory and motor components of the C. elegans nervous system.

ICA-based artefact and accelerated fMRI acquisition for improved Resting State Network imaging

PubMed Central

Griffanti, Ludovica; Salimi-Khorshidi, Gholamreza; Beckmann, Christian F.; Auerbach, Edward J.; Douaud, Gwenaëlle; Sexton, Claire E.; Zsoldos, Enikő; Ebmeier, Klaus P; Filippini, Nicola; Mackay, Clare E.; Moeller, Steen; Xu, Junqian; Yacoub, Essa; Baselli, Giuseppe; Ugurbil, Kamil; Miller, Karla L.; Smith, Stephen M.

2014-01-01

The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB’s ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures were assessed using timeseries (amplitude and spectra), network matrix and spatial map analyses. For timeseries and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses. PMID:24657355

ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging.

PubMed

Griffanti, Ludovica; Salimi-Khorshidi, Gholamreza; Beckmann, Christian F; Auerbach, Edward J; Douaud, Gwenaëlle; Sexton, Claire E; Zsoldos, Enikő; Ebmeier, Klaus P; Filippini, Nicola; Mackay, Clare E; Moeller, Steen; Xu, Junqian; Yacoub, Essa; Baselli, Giuseppe; Ugurbil, Kamil; Miller, Karla L; Smith, Stephen M

2014-07-15

The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses. Copyright © 2014 Elsevier Inc. All rights reserved.

Structural and Functional Connectivity from Unmanned-Aerial System Data

NASA Astrophysics Data System (ADS)

Masselink, Rens; Heckmann, Tobias; Casalí, Javier; Giménez, Rafael; Cerdá, Artemi; Keesstra, Saskia

2017-04-01

Over the past decade there has been an increase in both connectivity research and research involving Unmanned-Aerial systems (UASs). In some studies, UASs were successfully used for the assessment of connectivity, but not yet to their full potential. We present several ways to use data obtained from UASs to measure variables related to connectivity, and use these to assess both structural and functional connectivity. These assessments of connectivity can aid us in obtaining a better understanding of the dynamics of e.g. sediment and nutrient transport. We identify three sources of data obtained from a consumer camera mounted on a fixed-wing UAS, which can be used separately or combined: Visual and near-infrared imagery, point clouds, and digital elevation models (DEMs). Imagery (or: orthophotos) can be used for (automatic) mapping of connectivity features like rills, gullies and soil and water conservation measures using supervised or unsupervised classification methods with e.g. Object-Based Image Analysis. Furthermore, patterns of soil moisture in the top layers can be extracted from visual and near-infrared imagery. Point clouds can be analysed for vegetation height and density, and soil surface roughness. Lastly, DEMs can be used in combination with imagery for a number of tasks, including raster-based (e.g. DEM derivatives) and object-based (e.g., feature detection) analysis: Flow routing algorithms can be used to analyse potential pathways of surface runoff and sediment transport. This allows for the assessment of structural connectivity through indices that are based, for example, on morphometric and other properties of surfaces, contributing areas, and pathways. Third, erosion and deposition can be measured by calculating elevation changes from repeat surveys. From these "intermediate" variables like roughness, vegetation density and soil moisture, structural connectivity and functional connectivity can be assessed by combining them into a dynamic index of connectivity, use them in connectivity modelling (Masselink et al., 2016b) or be combined with measured data of water and sediment fluxes (Masselink et al., 2016a). References Masselink, R.J.H., Heckmann, T., Temme, A.J.A.M., Anders, N.S., Gooren, H.P.A., Keesstra, S.D., 2016a. A network theory approach for a better understanding of overland flow connectivity. Hydrol. Process. doi:10.1002/hyp.10993 Masselink, R.J.H., Keesstra, S.D., Temme, A.J.A.M., Seeger, M., Giménez, R., Casalí, J., 2016b. Modelling Discharge and Sediment Yield at Catchment Scale Using Connectivity Components. Land Degrad. Dev. 27, 933-945. doi:10.1002/ldr.2512

A Comprehensive Plan for the Long-Term Calibration and Validation of Oceanic Biogeochemical Satellite Data

NASA Technical Reports Server (NTRS)

Hooker, Stanford B.; McClain, Charles R.; Mannino, Antonio

2007-01-01

The primary objective of this planning document is to establish a long-term capability and validating oceanic biogeochemical satellite data. It is a pragmatic solution to a practical problem based primarily o the lessons learned from prior satellite missions. All of the plan's elements are seen to be interdependent, so a horizontal organizational scheme is anticipated wherein the overall leadership comes from the NASA Ocean Biology and Biogeochemistry (OBB) Program Manager and the entire enterprise is split into two components of equal sature: calibration and validation plus satellite data processing. The detailed elements of the activity are based on the basic tasks of the two main components plus the current objectives of the Carbon Cycle and Ecosystems Roadmap. The former is distinguished by an internal core set of responsibilities and the latter is facilitated through an external connecting-core ring of competed or contracted activities. The core elements for the calibration and validation component include a) publish protocols and performance metrics; b) verify uncertainty budgets; c) manage the development and evaluation of instrumentation; and d) coordinate international partnerships. The core elements for the satellite data processing component are e) process and reprocess multisensor data; f) acquire, distribute, and archive data products; and g) implement new data products. Both components have shared responsibilities for initializing and temporally monitoring satellite calibration. Connecting-core elements include (but are not restricted to) atmospheric correction and characterization, standards and traceability, instrument and analysis round robins, field campaigns and vicarious calibration sites, in situ database, bio-optical algorithm (and product) validation, satellite characterization and vicarious calibration, and image processing software. The plan also includes an accountability process, creating a Calibration and Validation Team (to help manage the activity), and a discussion of issues associated with the plan's scientific focus.

The Invasive Species Forecasting System

NASA Technical Reports Server (NTRS)

Schnase, John; Most, Neal; Gill, Roger; Ma, Peter

2011-01-01

The Invasive Species Forecasting System (ISFS) provides computational support for the generic work processes found in many regional-scale ecosystem modeling applications. Decision support tools built using ISFS allow a user to load point occurrence field sample data for a plant species of interest and quickly generate habitat suitability maps for geographic regions of management concern, such as a national park, monument, forest, or refuge. This type of decision product helps resource managers plan invasive species protection, monitoring, and control strategies for the lands they manage. Until now, scientists and resource managers have lacked the data-assembly and computing capabilities to produce these maps quickly and cost efficiently. ISFS focuses on regional-scale habitat suitability modeling for invasive terrestrial plants. ISFS s component architecture emphasizes simplicity and adaptability. Its core services can be easily adapted to produce model-based decision support tools tailored to particular parks, monuments, forests, refuges, and related management units. ISFS can be used to build standalone run-time tools that require no connection to the Internet, as well as fully Internet-based decision support applications. ISFS provides the core data structures, operating system interfaces, network interfaces, and inter-component constraints comprising the canonical workflow for habitat suitability modeling. The predictors, analysis methods, and geographic extents involved in any particular model run are elements of the user space and arbitrarily configurable by the user. ISFS provides small, lightweight, readily hardened core components of general utility. These components can be adapted to unanticipated uses, are tailorable, and require at most a loosely coupled, nonproprietary connection to the Web. Users can invoke capabilities from a command line; programmers can integrate ISFS's core components into more complex systems and services. Taken together, these features enable a degree of decentralization and distributed ownership that have helped other types of scientific information services succeed in recent years.

The NASA/industry design analysis methods for vibrations (DAMVIBS) program - Accomplishments and contributions

NASA Technical Reports Server (NTRS)

Kvaternik, Raymond G.

1991-01-01

A NASA Langley-sponsored rotorcraft structural dynamics program, known as Design Analysis Methods for VIBrationS (DAMVIBS), has been under development since 1984. The objective of this program was to establish the technology base needed by the industry to develop an advanced finite-element-based dynamics design analysis capability for vibrations. Under the program, teams from the four major helicopter manufacturers have formed finite-element models, conducted ground vibration tests, made test/analysis comparisons of both metal and composite airframes, performed 'difficult components' studies on airframes to identify components which need more complete finite-element representation for improved correlation, and evaluated industry codes for computing coupled rotor-airframe vibrations. Studies aimed at establishing the role that structural optimization can play in airframe vibrations design work have also been initiated. Five government/industry meetings were held in connection with these activities during the course of the program. Because the DAMVIBS Program is coming to an end, the fifth meeting included a brief assessment of the program and its benefits to the industry.

The NASA/industry design analysis methods for vibrations (DAMVIBS) program: Accomplishments and contributions

NASA Technical Reports Server (NTRS)

Kvaternik, Raymond G.

1991-01-01

A NASA Langley-sponsored rotorcraft structural dynamics program, known as Design Analysis Methods for VIBrationS (DAMVIBS), has been under development since 1984. The objective of this program was to establish the technology base needed by the industry to develop an advanced finite-element-based dynamics design analysis capability for vibrations. Under the program, teams from the four major helicopter manufacturers have formed finite-element models, conducted ground vibration tests, made test/analysis comparisons of both metal and composite airframes, performed 'difficult components' studies on airframes to identify components which need more complete finite-element representation for improved correlation, and evaluated industry codes for computing coupled rotor-airframe vibrations. Studies aimed at establishing the role that structural optimization can play in airframe vibrations design work have also been initiated. Five government/industry meetings were held in connection with these activities during the course of the program. Because the DAMVIBS Program is coming to an end, the fifth meeting included a brief assessment of the program and its benefits to the industry.

New Representation of Bearings in LS-DYNA

NASA Technical Reports Server (NTRS)

Carney, Kelly S.; Howard, Samuel A.; Miller, Brad A.; Benson, David J.

2014-01-01

Non-linear, dynamic, finite element analysis is used in various engineering disciplines to evaluate high-speed, dynamic impact and vibration events. Some of these applications require connecting rotating to stationary components. For example, bird impacts on rotating aircraft engine fan blades are a common analysis performed using this type of analysis tool. Traditionally, rotating machines utilize some type of bearing to allow rotation in one degree of freedom while offering constraints in the other degrees of freedom. Most times, bearings are modeled simply as linear springs with rotation. This is a simplification that is not necessarily accurate under the conditions of high-velocity, high-energy, dynamic events such as impact problems. For this reason, it is desirable to utilize a more realistic non-linear force-deflection characteristic of real bearings to model the interaction between rotating and non-rotating components during dynamic events. The present work describes a rolling element bearing model developed for use in non-linear, dynamic finite element analysis. This rolling element bearing model has been implemented in LS-DYNA as a new element, *ELEMENT_BEARING.

Brain network of semantic integration in sentence reading: insights from independent component analysis and graph theoretical analysis.

PubMed

Ye, Zheng; Doñamayor, Nuria; Münte, Thomas F

2014-02-01

A set of cortical and sub-cortical brain structures has been linked with sentence-level semantic processes. However, it remains unclear how these brain regions are organized to support the semantic integration of a word into sentential context. To look into this issue, we conducted a functional magnetic resonance imaging (fMRI) study that required participants to silently read sentences with semantically congruent or incongruent endings and analyzed the network properties of the brain with two approaches, independent component analysis (ICA) and graph theoretical analysis (GTA). The GTA suggested that the whole-brain network is topologically stable across conditions. The ICA revealed a network comprising the supplementary motor area (SMA), left inferior frontal gyrus, left middle temporal gyrus, left caudate nucleus, and left angular gyrus, which was modulated by the incongruity of sentence ending. Furthermore, the GTA specified that the connections between the left SMA and left caudate nucleus as well as that between the left caudate nucleus and right thalamus were stronger in response to incongruent vs. congruent endings. Copyright © 2012 Wiley Periodicals, Inc.

Delta connected resonant snubber circuit

DOEpatents

Lai, J.S.; Peng, F.Z.; Young, R.W. Sr.; Ott, G.W. Jr.

1998-01-20

A delta connected, resonant snubber-based, soft switching, inverter circuit achieves lossless switching during dc-to-ac power conversion and power conditioning with minimum component count and size. Current is supplied to the resonant snubber branches solely by the dc supply voltage through the main inverter switches and the auxiliary switches. Component count and size are reduced by use of a single semiconductor switch in the resonant snubber branches. Component count is also reduced by maximizing the use of stray capacitances of the main switches as parallel resonant capacitors. Resonance charging and discharging of the parallel capacitances allows lossless, zero voltage switching. In one embodiment, circuit component size and count are minimized while achieving lossless, zero voltage switching within a three-phase inverter. 36 figs.

Delta connected resonant snubber circuit

DOEpatents

Lai, Jih-Sheng; Peng, Fang Zheng; Young, Sr., Robert W.; Ott, Jr., George W.

1998-01-01

A delta connected, resonant snubber-based, soft switching, inverter circuit achieves lossless switching during dc-to-ac power conversion and power conditioning with minimum component count and size. Current is supplied to the resonant snubber branches solely by the dc supply voltage through the main inverter switches and the auxiliary switches. Component count and size are reduced by use of a single semiconductor switch in the resonant snubber branches. Component count is also reduced by maximizing the use of stray capacitances of the main switches as parallel resonant capacitors. Resonance charging and discharging of the parallel capacitances allows lossless, zero voltage switching. In one embodiment, circuit component size and count are minimized while achieving lossless, zero voltage switching within a three-phase inverter.

The Connection between Different Tracers of the Diffuse Interstellar Medium: Kinematics

NASA Astrophysics Data System (ADS)

Rice, Johnathan S.; Federman, S. R.; Flagey, Nicolas; Goldsmith, Paul F.; Langer, William D.; Pineda, Jorge L.; Lambert, D. L.

2018-05-01

Using visible, radio, microwave, and submillimeter data, we study several lines of sight toward stars generally closer than 1 kpc on a component-by-component basis. We derive the component structure seen in absorption at visible wavelengths from Ca II, Ca I, K I, CH, CH+, and CN and compare it to emission from H I, CO and its isotopologues, and C+ from the GOT C+ survey. The correspondence between components in emission and absorption helps create a more unified picture of diffuse atomic and molecular gas in the interstellar medium. We also discuss how these tracers are related to the CO-dark H2 gas probed by C+ emission and discuss the kinematic connections among the species observed.

Striatal functional connectivity changes following specific balance training in elderly people: MRI results of a randomized controlled pilot study.

PubMed

Magon, Stefano; Donath, Lars; Gaetano, Laura; Thoeni, Alain; Radue, Ernst-Wilhelm; Faude, Oliver; Sprenger, Till

2016-09-01

Practice-induced effects of specific balance training on brain structure and activity in elderly people are largely unknown. In the present study, we investigated morphological and functional brain changes following slacking training (balancing over nylon ribbons) in a group of elderly people. Twenty-eight healthy volunteers were recruited and randomly assigned to the intervention (mean age: 62.3±5.4years) or control group (mean age: 61.8±5.3years). The intervention group completed six-weeks of slackline training. Brain morphological changes were investigated using voxel-based morphometry and functional connectivity changes were computed via independent component analysis and seed-based analyses. All analyses were applied to the whole sample and to a subgroup of participants who improved in slackline performance. The repeated measures analysis of variance showed a significant interaction effect between groups and sessions. Specifically, the Tukey post-hoc analysis revealed a significantly improved slackline standing performance after training for the left leg stance time (pre: 4.5±3.6s vs. 26.0±30.0s, p<0.038) as well as for tandem stance time (pre: 1.4±0.6s vs. post: 4.5±4.0s, p=0.003) in the intervention group. No significant changes in balance performance were observed in the control group. The MRI analysis did not reveal morphological or functional connectivity differences before or after the training between the intervention and control groups (whole sample). However, subsequent analysis in subjects with improved slackline performance showed a decrease of connectivity between the striatum and other brain areas during the training period. These preliminary results suggest that improved balance performance with slackline training goes along with an increased efficiency of the striatal network. Copyright © 2016 Elsevier B.V. All rights reserved.

Circulating heat exchangers for oscillating wave engines and refrigerators

DOEpatents

Swift, Gregory W.; Backhaus, Scott N.

2003-10-28

An oscillating-wave engine or refrigerator having a regenerator or a stack in which oscillating flow of a working gas occurs in a direction defined by an axis of a trunk of the engine or refrigerator, incorporates an improved heat exchanger. First and second connections branch from the trunk at locations along the axis in selected proximity to one end of the regenerator or stack, where the trunk extends in two directions from the locations of the connections. A circulating heat exchanger loop is connected to the first and second connections. At least one fluidic diode within the circulating heat exchanger loop produces a superimposed steady flow component and oscillating flow component of the working gas within the circulating heat exchanger loop. A local process fluid is in thermal contact with an outside portion of the circulating heat exchanger loop.

Development of ISO connection-oriented/correctionless gateways

NASA Technical Reports Server (NTRS)

Landweber, Lawrence H.

1991-01-01

The project had two goals, establishment of a gateway between French and U.S. academic networks and studies of issues related to the development of ISO connection-oriented/connectionless (CO/CL) gateways. The first component involved installation of a 56K bps line between Princeton Univ. and INRIA in France. The end-points of these lines were connected by Vitalink link level bridges. The Princeton end was then connected to the NSFNET via the John Von Neumann Supercomputer Center. The French end was connected to Transpac, the French X.25 public data network and to the French IP research internet. U.S. users may communicate with users of the French internet by e-mail and may access computational and data resources in France by use of remote login and file transfer. The connection to Transpac enables U.S. users to access the SIMBAD astronomical database outside of Paris. Access to this database from the U.S. can be via TCP/IP or DECNET (via a DECNET to TCP/IP gateway) protocols utilizing a TCP/IP to X.25 gateway developed and operated by INRIA. The second component of the project involved experiments aimed at understanding the issues involved is ISO CO/CL gateways. An experimental gateway was developed at Wisconsin and a preliminary report was prepared. Because of the need to devote most resources to the first component of the project, work in this area did not go beyond development of a prototype gateway.

Will Systems Biology Deliver Its Promise and Contribute to the Development of New or Improved Vaccines? What Really Constitutes the Study of "Systems Biology" and How Might Such an Approach Facilitate Vaccine Design.

PubMed

Germain, Ronald N

2017-10-16

A dichotomy exists in the field of vaccinology about the promise versus the hype associated with application of "systems biology" approaches to rational vaccine design. Some feel it is the only way to efficiently uncover currently unknown parameters controlling desired immune responses or discover what elements actually mediate these responses. Others feel that traditional experimental, often reductionist, methods for incrementally unraveling complex biology provide a more solid way forward, and that "systems" approaches are costly ways to collect data without gaining true insight. Here I argue that both views are inaccurate. This is largely because of confusion about what can be gained from classical experimentation versus statistical analysis of large data sets (bioinformatics) versus methods that quantitatively explain emergent properties of complex assemblies of biological components, with the latter reflecting what was previously called "physiology." Reductionist studies will remain essential for generating detailed insight into the functional attributes of specific elements of biological systems, but such analyses lack the power to provide a quantitative and predictive understanding of global system behavior. But by employing (1) large-scale screening methods for discovery of unknown components and connections in the immune system ( omics ), (2) statistical analysis of large data sets ( bioinformatics ), and (3) the capacity of quantitative computational methods to translate these individual components and connections into models of emergent behavior ( systems biology ), we will be able to better understand how the overall immune system functions and to determine with greater precision how to manipulate it to produce desired protective responses. Copyright © 2017 Cold Spring Harbor Laboratory Press; all rights reserved.

Description of real-time Ada software implementation of a power system monitor for the Space Station Freedom PMAD DC testbed

NASA Technical Reports Server (NTRS)

Ludwig, Kimberly; Mackin, Michael; Wright, Theodore

1991-01-01

The Ada language software development to perform the electrical system monitoring functions for the NASA Lewis Research Center's Power Management and Distribution (PMAD) DC testbed is described. The results of the effort to implement this monitor are presented. The PMAD DC testbed is a reduced-scale prototype of the electrical power system to be used in the Space Station Freedom. The power is controlled by smart switches known as power control components (or switchgear). The power control components are currently coordinated by five Compaq 382/20e computers connected through an 802.4 local area network. One of these computers is designated as the control node with the other four acting as subsidiary controllers. The subsidiary controllers are connected to the power control components with a Mil-Std-1553 network. An operator interface is supplied by adding a sixth computer. The power system monitor algorithm is comprised of several functions including: periodic data acquisition, data smoothing, system performance analysis, and status reporting. Data is collected from the switchgear sensors every 100 milliseconds, then passed through a 2 Hz digital filter. System performance analysis includes power interruption and overcurrent detection. The reporting mechanism notifies an operator of any abnormalities in the system. Once per second, the system monitor provides data to the control node for further processing, such as state estimation. The system monitor required a hardware time interrupt to activate the data acquisition function. The execution time of the code was optimized using an assembly language routine. The routine allows direct vectoring of the processor to Ada language procedures that perform periodic control activities. A summary of the advantages and side effects of this technique are discussed.

Catechol-O-methyltransferase (COMT) influences the connectivity of the prefrontal cortex at rest

PubMed Central

Tunbridge, Elizabeth M.; Farrell, Sarah M.; Harrison, Paul J.; Mackay, Clare E.

2013-01-01

Catechol-O-methyltransferase (COMT) modulates dopamine in the prefrontal cortex (PFC) and influences PFC dopamine-dependent cognitive task performance. A human COMT polymorphism (Val158Met) alters enzyme activity and is associated with both the activation and functional connectivity of the PFC during task performance, particularly working memory. Here, we used functional magnetic resonance imaging and a data-driven, independent components analysis (ICA) approach to compare resting state functional connectivity within the executive control network (ECN) between young, male COMT Val158 (n = 27) and Met158 (n = 28) homozygotes. COMT genotype effects on grey matter were assessed using voxel-based morphometry. COMT genotype significantly modulated functional connectivity within the ECN, which included the head of the caudate, and anterior cingulate and frontal cortical regions. Val158 homozygotes showed greater functional connectivity between a cluster within the left ventrolateral PFC and the rest of the ECN (using a threshold of Z > 2.3 and a family-wise error cluster significance level of p < 0.05). This difference occurred in the absence of any alterations in grey matter. Our data show that COMT Val158Met affects the functional connectivity of the PFC at rest, complementing its prominent role in the activation and functional connectivity of this region during cognitive task performance. The results suggest that genotype-related differences in prefrontal dopaminergic tone result in neuroadaptive changes in basal functional connectivity, potentially including subtle COMT genotype-dependent differences in the relative coupling of task-positive and task-negative regions, which could in turn contribute to its effects on brain activation, connectivity, and behaviour. PMID:23228511

Genetic Interaction Is Associated with Lower Metabolic Connectivity and Memory Impairment in Clinically Mild Alzheimer's Disease.

PubMed

Chang, Ya-Ting; Huang, Chi-Wei; Huang, Shu-Hua; Hsu, Shih-Wei; Chang, Wen-Neng; Lee, Jun-Jun; Chang, Chiung-Chih

2018-06-08

Metabolic connectivity as revealed by [18F] fluorodeoxyglucose positron emission tomography reflects neuronal connectivity. The aim of this study was to investigate the genetic impact on metabolic connectivity in default mode subnetworks and its clinical-pathological relationships in patients with Alzheimer's disease. We separately investigated the modulation of two default mode subnetworks, as identified with independent component analysis, by comparing APOE-ε4 carriers to non-carriers with Alzheimer's disease. We further analyzed the interaction effects of APOE (APOE-ε4 carriers versus non-carriers) with PICALM (rs3851179-GG versus rs3851179-A-allele carriers) on episodic memory deficits, reduction in cerebral metabolic rate for glucose, and decreased metabolic connectivity in default mode subnetworks. The metabolic connectivity in the ventral default mode network was positively correlated with episodic memory scores (β= 0.441, p< 0.001). The APOE-ε4 carriers had significantly lower metabolic connectivity in the ventral default mode network than the APOE-ε4 carriers (t(96)= -2.233, P= 0.028). There was an effect of the APOE-PICALM (rs3851179) interactions on reduced cerebral metabolic rate for glucose in regions of ventral default mode network (p< 0.001), and on memory deficits (F3,93= 5.568, p= 0.020). This study identified that PICALM may modulates memory deficits, reduced cerebral metabolic rate for glucose, and decreased metabolic connectivity in the ventral default mode network in APOE-ε4 carriers. [18F] fluorodeoxyglucose positron emission tomography-based metabolic connectivity may serve a useful tool to elucidate the neural networks underlying clinical-pathological relationships in Alzheimer's disease. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Brain dynamics during natural viewing conditions--a new guide for mapping connectivity in vivo.

PubMed

Bartels, Andreas; Zeki, Semir

2005-01-15

We describe here a new way of obtaining maps of connectivity in the human brain based on interregional correlations of blood oxygen level-dependent (BOLD) signal during natural viewing conditions. We propose that anatomical connections are reflected in BOLD signal correlations during natural brain dynamics. This may provide a powerful approach to chart connectivity, more so than that based on the 'resting state' of the human brain, and it may complement diffusion tensor imaging. Our approach relies on natural brain dynamics and is therefore experimentally unbiased and independent of hypothesis-driven, specialized stimuli. It has the advantage that natural viewing leads to considerably stronger cortical activity than rest, thus facilitating detection of weaker connections. To validate our technique, we used functional magnetic resonance imaging (fMRI) to record BOLD signal while volunteers freely viewed a movie that was interrupted by resting periods. We used independent component analysis (ICA) to segregate cortical areas before characterizing the dynamics of their BOLD signal during free viewing and rest. Natural viewing and rest each revealed highly specific correlation maps, which reflected known anatomical connections. Examples are homologous regions in visual and auditory cortices in the two hemispheres and the language network consisting of Wernicke's area, Broca's area, and a premotor region. Correlations between regions known to be directly connected were always substantially higher than between nonconnected regions. Furthermore, compared to rest, natural viewing specifically increased correlations between anatomically connected regions while it decreased correlations between nonconnected regions. Our findings therefore demonstrate that natural viewing conditions lead to particularly specific interregional correlations and thus provide a powerful environment to reveal anatomical connectivity in vivo.

Intrinsic brain connectivity predicts impulse control disorders in patients with Parkinson's disease.

PubMed

Tessitore, Alessandro; De Micco, Rosa; Giordano, Alfonso; di Nardo, Federica; Caiazzo, Giuseppina; Siciliano, Mattia; De Stefano, Manuela; Russo, Antonio; Esposito, Fabrizio; Tedeschi, Gioacchino

2017-12-01

Impulse control disorders can be triggered by dopamine replacement therapies in patients with PD. Using resting-state functional MRI, we investigated the intrinsic brain network connectivity at baseline in a cohort of drug-naive PD patients who successively developed impulse control disorders over a 36-month follow-up period compared with patients who did not. Baseline 3-Tesla MRI images of 30 drug-naive PD patients and 20 matched healthy controls were analyzed. The impulse control disorders' presence and severity at follow-up were assessed by the Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease Rating Scale. Single-subject and group-level independent component analysis was used to investigate functional connectivity differences within the major resting-state networks. We also compared internetwork connectivity between patients. Finally, a multivariate Cox regression model was used to investigate baseline predictors of impulse control disorder development. At baseline, decreased connectivity in the default-mode and right central executive networks and increased connectivity in the salience network were detected in PD patients with impulse control disorders at follow-up compared with those without. Increased default-mode/central executive internetwork connectivity was significantly associated with impulse control disorders development (P < 0.05). Our findings demonstrated that abnormal brain connectivity in the three large-scale networks characterizes drug-naive PD patients who will eventually develop impulse control disorders while on dopaminergic treatment. We hypothesize that these divergent cognitive and limbic network connectivity changes could represent a potential biomarker and an additional risk factor for the emergence of impulse control disorders. © 2017 International Parkinson and Movement Disorder Society. © 2017 International Parkinson and Movement Disorder Society.

Capattery double layer capacitor life performance

NASA Astrophysics Data System (ADS)

Evans, David A.; Clark, Nancy H.; Baca, W. E.; Miller, John R.; Barker, Thomas B.

Double layer capacitors (DLCs) have received increased use in computer memory backup applications for consumer products during the past ten years. Their extraordinarily high capacitance density along with their maintenance-free operation makes them particularly suited for these products. These same features also make DLCs very attractive in military type applications. Unfortunately, lifetime performance data has not been reported in the literature for any DLC component. Our objective in this study was to investigate the effects that voltage and temperature have on the properties and performance of single and series-connected DLCs as a function of time. Evans model RE110474, 0.47-farad, 11.0-volt Capatteries were evaluated. These components have a tantalum package, use welded construction, and contain a glass-to-metal seal, all incorporated to circumvent the typical DLC failure modes of electrolyte loss and container corrosion. A five-level, two-factor Central Composite Design was used in the study. Single and series-connected Capatteries rated at 85 C, 11.0-volts operation were subjected to test temperatures between 25 and 95 C, and voltages between 0 and 12.9 volts (9 test conditions). Measured responses included capacitance, equivalent series resistance, and discharge time. Data were analyzed using a regression analysis to obtain response functions relating DLC properties to their voltage, temperature, and test time history. These results are described and should aid system and component engineers in using DLCs in critical applications.

Investigation of relationships between fMRI brain networks in the spectral domain using ICA and Granger causality reveals distinct differences between schizophrenia patients and healthy controls

PubMed Central

Demirci, Oguz; Stevens, Michael C.; Andreasen, Nancy C.; Michael, Andrew; Liu, Jingyu; White, Tonya; Pearlson, Godfrey D.; Clark, Vincent P.; Calhoun, Vince D.

2009-01-01

Functional network connectivity (FNC) is an approach that examines the relationships between brain networks (as opposed to functional connectivity (FC) that focuses upon the relationships between single voxels). FNC may help explain the complex relationships between distributed cerebral sites in the brain and possibly provide new understanding of neurological and psychiatric disorders such as schizophrenia. In this paper, we use independent component analysis (ICA) to extract the time courses of spatially independent components and then use these in Granger causality test (GCT) to investigate causal relationships between brain activation networks. We present results using both simulations and fMRI data of 155 subjects obtained during two different tasks. Unlike previous research, causal relationships are presented over different portions of the frequency spectrum in order to differentiate high and low frequency effects and not merged in a scalar. The results obtained using Sternberg item recognition paradigm (SIRP) and auditory oddball (AOD) tasks showed FNC differentiations between schizophrenia and control groups, and explained how the two groups differed during these tasks. During the SIRP task, secondary visual and cerebellum activation networks served as hubs and included most complex relationships between the activated regions. Secondary visual and temporal lobe activations replaced these components during the AOD task. PMID:19245841

Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis.

PubMed

Lee, Young-Beom; Lee, Jeonghyeon; Tak, Sungho; Lee, Kangjoo; Na, Duk L; Seo, Sang Won; Jeong, Yong; Ye, Jong Chul

2016-01-15

Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimer's disease (AD). However, there is not yet a consensus on the preferred method for resting-state analysis. Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold. Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM)-type analysis method based on a sparse graph model where temporal dynamics at each voxel position are described as a sparse combination of global brain dynamics. In particular, a new concept of a spatially adaptive design matrix has been proposed to represent local connectivity that shares the same temporal dynamics. If we further assume that local network structures within a group are similar, the estimation problem of global and local dynamics can be solved using sparse dictionary learning for the concatenated temporal data across subjects. Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics. Using an extensive resting fMRI data set obtained from normal, mild cognitive impairment (MCI), and Alzheimer's disease patient groups, we demonstrated that the changes in the default mode network extracted by the proposed method are more closely correlated with the progression of Alzheimer's disease. Copyright © 2015 Elsevier Inc. All rights reserved.

Altered Effective Connectivity of Hippocampus-Dependent Episodic Memory Network in mTBI Survivors

PubMed Central

2016-01-01

Traumatic brain injuries (TBIs) are generally recognized to affect episodic memory. However, less is known regarding how external force altered the way functionally connected brain structures of the episodic memory system interact. To address this issue, we adopted an effective connectivity based analysis, namely, multivariate Granger causality approach, to explore causal interactions within the brain network of interest. Results presented that TBI induced increased bilateral and decreased ipsilateral effective connectivity in the episodic memory network in comparison with that of normal controls. Moreover, the left anterior superior temporal gyrus (aSTG, the concept forming hub), left hippocampus (the personal experience binding hub), and left parahippocampal gyrus (the contextual association hub) were no longer network hubs in TBI survivors, who compensated for hippocampal deficits by relying more on the right hippocampus (underlying perceptual memory) and the right medial frontal gyrus (MeFG) in the anterior prefrontal cortex (PFC). We postulated that the overrecruitment of the right anterior PFC caused dysfunction of the strategic component of episodic memory, which caused deteriorating episodic memory in mTBI survivors. Our findings also suggested that the pattern of brain network changes in TBI survivors presented similar functional consequences to normal aging. PMID:28074162

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

Wu, Chase

A number of Department of Energy (DOE) science applications, involving exascale computing systems and large experimental facilities, are expected to generate large volumes of data, in the range of petabytes to exabytes, which will be transported over wide-area networks for the purpose of storage, visualization, and analysis. The objectives of this proposal are to (1) develop and test the component technologies and their synthesis methods to achieve source-to-sink high-performance flows, and (2) develop tools that provide these capabilities through simple interfaces to users and applications. In terms of the former, we propose to develop (1) optimization methods that align andmore » transition multiple storage flows to multiple network flows on multicore, multibus hosts; and (2) edge and long-haul network path realization and maintenance using advanced provisioning methods including OSCARS and OpenFlow. We also propose synthesis methods that combine these individual technologies to compose high-performance flows using a collection of constituent storage-network flows, and realize them across the storage and local network connections as well as long-haul connections. We propose to develop automated user tools that profile the hosts, storage systems, and network connections; compose the source-to-sink complex flows; and set up and maintain the needed network connections.« less

X-Chromosome Effects on Attention Networks: Insights from Imaging Resting-State Networks in Turner Syndrome.

PubMed

Green, Tamar; Saggar, Manish; Ishak, Alexandra; Hong, David S; Reiss, Allan L

2017-07-18

Attention deficit hyperactivity disorder (ADHD) is strongly affected by sex, but sex chromosomes' effect on brain attention networks and cognition are difficult to examine in humans. This is due to significant etiologic heterogeneity among diagnosed individuals. In contrast, individuals with Turner syndrome (TS), who have substantially increased risk for ADHD symptoms, share a common genetic risk factor related to the absence of the X-chromosome, thus serving as a more homogeneous genetic model. Resting-state functional MRI was employed to examine differences in attention networks between girls with TS (n = 40) and age- sex- and Tanner-matched controls (n = 33). We compared groups on resting-state functional connectivity measures from data-driven independent components analysis (ICA) and hypothesis-based seed analysis. Using ICA, reduced connectivity was observed in both frontoparietal and dorsal attention networks. Similarly, using seeds in the bilateral intraparietal sulcus (IPS), reduced connectivity was observed between IPS and frontal and cerebellar regions. Finally, we observed a brain-behavior correlation between IPS-cerebellar connectivity and cognitive attention measures. These findings indicate that X-monosomy contributes affects to attention networks and cognitive dysfunction that might increase risk for ADHD. Our findings not only have clinical relevance for girls with TS, but might also serve as a biological marker in future research examining the effects of the intervention that targets attention skills. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Composition and Realization of Source-to-Sink High-Performance Flows: File Systems, Storage, Hosts, LAN and WAN

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

Wu, Chase Qishi

A number of Department of Energy (DOE) science applications, involving exascale computing systems and large experimental facilities, are expected to generate large volumes of data, in the range of petabytes to exabytes, which will be transported over wide-area networks for the purpose of storage, visualization, and analysis. To support such capabilities, significant progress has been made in various components including the deployment of 100 Gbps networks with future 1 Tbps bandwidth, increases in end-host capabilities with multiple cores and buses, capacity improvements in large disk arrays, and deployment of parallel file systems such as Lustre and GPFS. High-performance source-to-sink datamore » flows must be composed of these component systems, which requires significant optimizations of the storage-to-host data and execution paths to match the edge and long-haul network connections. In particular, end systems are currently supported by 10-40 Gbps Network Interface Cards (NIC) and 8-32 Gbps storage Host Channel Adapters (HCAs), which carry the individual flows that collectively must reach network speeds of 100 Gbps and higher. Indeed, such data flows must be synthesized using multicore, multibus hosts connected to high-performance storage systems on one side and to the network on the other side. Current experimental results show that the constituent flows must be optimally composed and preserved from storage systems, across the hosts and the networks with minimal interference. Furthermore, such a capability must be made available transparently to the science users without placing undue demands on them to account for the details of underlying systems and networks. And, this task is expected to become even more complex in the future due to the increasing sophistication of hosts, storage systems, and networks that constitute the high-performance flows. The objectives of this proposal are to (1) develop and test the component technologies and their synthesis methods to achieve source-to-sink high-performance flows, and (2) develop tools that provide these capabilities through simple interfaces to users and applications. In terms of the former, we propose to develop (1) optimization methods that align and transition multiple storage flows to multiple network flows on multicore, multibus hosts; and (2) edge and long-haul network path realization and maintenance using advanced provisioning methods including OSCARS and OpenFlow. We also propose synthesis methods that combine these individual technologies to compose high-performance flows using a collection of constituent storage-network flows, and realize them across the storage and local network connections as well as long-haul connections. We propose to develop automated user tools that profile the hosts, storage systems, and network connections; compose the source-to-sink complex flows; and set up and maintain the needed network connections. These solutions will be tested using (1) 100 Gbps connection(s) between Oak Ridge National Laboratory (ORNL) and Argonne National Laboratory (ANL) with storage systems supported by Lustre and GPFS file systems with an asymmetric connection to University of Memphis (UM); (2) ORNL testbed with multicore and multibus hosts, switches with OpenFlow capabilities, and network emulators; and (3) 100 Gbps connections from ESnet and their Openflow testbed, and other experimental connections. This proposal brings together the expertise and facilities of the two national laboratories, ORNL and ANL, and UM. It also represents a collaboration between DOE and the Department of Defense (DOD) projects at ORNL by sharing technical expertise and personnel costs, and leveraging the existing DOD Extreme Scale Systems Center (ESSC) facilities at ORNL.« less

Finding Strong Bridges and Strong Articulation Points in Linear Time

NASA Astrophysics Data System (ADS)

Italiano, Giuseppe F.; Laura, Luigi; Santaroni, Federico

Given a directed graph G, an edge is a strong bridge if its removal increases the number of strongly connected components of G. Similarly, we say that a vertex is a strong articulation point if its removal increases the number of strongly connected components of G. In this paper, we present linear-time algorithms for computing all the strong bridges and all the strong articulation points of directed graphs, solving an open problem posed in [2].

String resistance detector

NASA Technical Reports Server (NTRS)

Hall, A. Daniel (Inventor); Davies, Francis J. (Inventor)

2007-01-01

Method and system are disclosed for determining individual string resistance in a network of strings when the current through a parallel connected string is unknown and when the voltage across a series connected string is unknown. The method/system of the invention involves connecting one or more frequency-varying impedance components with known electrical characteristics to each string and applying a frequency-varying input signal to the network of strings. The frequency-varying impedance components may be one or more capacitors, inductors, or both, and are selected so that each string is uniquely identifiable in the output signal resulting from the frequency-varying input signal. Numerical methods, such as non-linear regression, may then be used to resolve the resistance associated with each string.

The component-based architecture of the HELIOS medical software engineering environment.

PubMed

Degoulet, P; Jean, F C; Engelmann, U; Meinzer, H P; Baud, R; Sandblad, B; Wigertz, O; Le Meur, R; Jagermann, C

1994-12-01

The constitution of highly integrated health information networks and the growth of multimedia technologies raise new challenges for the development of medical applications. We describe in this paper the general architecture of the HELIOS medical software engineering environment devoted to the development and maintenance of multimedia distributed medical applications. HELIOS is made of a set of software components, federated by a communication channel called the HELIOS Unification Bus. The HELIOS kernel includes three main components, the Analysis-Design and Environment, the Object Information System and the Interface Manager. HELIOS services consist in a collection of toolkits providing the necessary facilities to medical application developers. They include Image Related services, a Natural Language Processor, a Decision Support System and Connection services. The project gives special attention to both object-oriented approaches and software re-usability that are considered crucial steps towards the development of more reliable, coherent and integrated applications.

Behavior of wet precast beam column connections under progressive collapse scenario: an experimental study

NASA Astrophysics Data System (ADS)

Nimse, Rohit B.; Joshi, Digesh D.; Patel, Paresh V.

2014-12-01

Progressive collapse denotes a failure of a major portion of a structure that has been initiated by failure of a relatively small part of the structure such as failure of any vertical load carrying element (typically columns). Failure of large part of any structure will results into substantial loss of human lives and natural resources. Therefore, it is important to prevent progressive collapse which is also known as disproportionate collapse. Nowadays, there is an increasing trend toward construction of buildings using precast concrete. In precast concrete construction, all the components of structures are produced in controlled environment and they are being transported to the site. At site such individual components are connected appropriately. Connections are the most critical elements of any precast structure, because in past major collapse of precast structure took place because of connection failure. In this study, behavior of three different 1/3rd scaled wet precast beam column connections under progressive collapse scenario are studied and its performance is compared with monolithic connection. Precast connections are constructed by adopting different connection detailing at the junction by considering reinforced concrete corbel for two specimens and steel billet for one specimen. Performance of specimen is evaluated on the basis of ultimate load carrying capacity, maximum deflection and deflection measured along the span of the beam. From the results, it is observed that load carrying capacity and ductility of precast connections considered in this study are more than that of monolithic connections.

Functional subdivision of group-ICA results of fMRI data collected during cinema viewing.

PubMed

Pamilo, Siina; Malinen, Sanna; Hlushchuk, Yevhen; Seppä, Mika; Tikka, Pia; Hari, Riitta

2012-01-01

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film ("At land" by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative.

Altered affective, executive and sensorimotor resting state networks in patients with pediatric mania

PubMed Central

Wu, Minjie; Lu, Lisa H.; Passarotti, Alessandra M.; Wegbreit, Ezra; Fitzgerald, Jacklynn; Pavuluri, Mani N.

2013-01-01

Background The aim of the present study was to map the pathophysiology of resting state functional connectivity accompanying structural and functional abnormalities in children with bipolar disorder. Methods Children with bipolar disorder and demographically matched healthy controls underwent resting-state functional magnetic resonance imaging. A model-free independent component analysis was performed to identify intrinsically interconnected networks. Results We included 34 children with bipolar disorder and 40 controls in our analysis. Three distinct resting state networks corresponding to affective, executive and sensorimotor functions emerged as being significantly different between the pediatric bipolar disorder (PBD) and control groups. All 3 networks showed hyperconnectivity in the PBD relative to the control group. Specifically, the connectivity of the dorsal anterior cingulate cortex (ACC) differentiated the PBD from the control group in both the affective and the executive networks. Exploratory analysis suggests that greater connectivity of the right amygdala within the affective network is associated with better executive function in children with bipolar disorder, but not in controls. Limitations Unique clinical characteristics of the study sample allowed us to evaluate the pathophysiology of resting state connectivity at an early state of PBD, which led to the lack of generalizability in terms of comorbid disorders existing in a typical PBD population. Conclusion Abnormally engaged resting state affective, executive and sensorimotor networks observed in children with bipolar disorder may reflect a biological context in which abnormal task-based brain activity can occur. Dual engagement of the dorsal ACC in affective and executive networks supports the neuroanatomical interface of these networks, and the amygdala’s engagement in moderating executive function illustrates the intricate interplay of these neural operations at rest. PMID:23735583

Systems Level Analysis of Systemic Sclerosis Shows a Network of Immune and Profibrotic Pathways Connected with Genetic Polymorphisms

PubMed Central

Mahoney, J. Matthew; Taroni, Jaclyn; Martyanov, Viktor; Wood, Tammara A.; Greene, Casey S.; Pioli, Patricia A.; Hinchcliff, Monique E.; Whitfield, Michael L.

2015-01-01

Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited) are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6–12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets. To identify gene expression modules common to three independent datasets from three different clinical centers, we developed a consensus clustering procedure based on mutual information of partitions, an information theory concept, and performed a meta-analysis of these genome-wide gene expression datasets. We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms. The network is composed of distinct, but interconnected, components related to interferon activation, M2 macrophages, adaptive immunity, extracellular matrix remodeling, and cell proliferation. The network shows extensive connections between the inflammatory- and fibroproliferative-specific genes. The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA) genes. Our analyses suggest that the gene expression changes underlying the SSc subsets may be long-lived, but mechanistically interconnected and related to a patients underlying genetic risk. PMID:25569146

Non-Inferential Multi-Subject Study of Functional Connectivity during Visual Stimulation.

PubMed

Esposito, F; Cirillo, M; Aragri, A; Caranci, F; Cirillo, L; Di Salle, F; Cirillo, S

2007-01-31

Independent component analysis (ICA) is a powerful technique for the multivariate, non-inferential, data-driven analysis of functional magnetic resonance imaging (fMRI) data-sets. The non-inferential nature of ICA makes this a suitable technique for the study of complex mental states whose temporal evolution would be difficult to describe analytically in terms of classical statistical regressors. Taking advantage of this feature, ICA can extract a number of functional connectivity patterns regardless of the task executed by the subject. The technique is so powerful that functional connectivity patterns can be derived even when the subject is just resting in the scanner, opening the opportunity for functional investigation of the human mind at its basal "default" state, which has been proposed to be altered in several brain disorders. However, one major drawback of ICA consists in the difficulty of managing its results, which are not represented by a single functional image as in inferential studies. This produces the need for a classification of ICA results and exacerbates the difficulty of obtaining group "averaged" functional connectivity patterns, while preserving the interpretation of individual differences. Addressing the subject-level variability in the very same framework of "grouping" appears to be a favourable approach towards the clinical evaluation and application of ICA-based methodologies. Here we present a novel strategy for group-level ICA analyses, namely the self-organizing group-level ICA (sog-ICA), which is used on visual activation fMRI data from a block-design experiment repeated on six subjects. We propose the sog-ICA as a multi-subject analysis tool for grouping ICA data while assessing the similarity and variability of the fMRI results of individual subject decompositions.

High-Speed Real-Time Resting-State fMRI Using Multi-Slab Echo-Volumar Imaging

PubMed Central

Posse, Stefan; Ackley, Elena; Mutihac, Radu; Zhang, Tongsheng; Hummatov, Ruslan; Akhtari, Massoud; Chohan, Muhammad; Fisch, Bruce; Yonas, Howard

2013-01-01

We recently demonstrated that ultra-high-speed real-time fMRI using multi-slab echo-volumar imaging (MEVI) significantly increases sensitivity for mapping task-related activation and resting-state networks (RSNs) compared to echo-planar imaging (Posse et al., 2012). In the present study we characterize the sensitivity of MEVI for mapping RSN connectivity dynamics, comparing independent component analysis (ICA) and a novel seed-based connectivity analysis (SBCA) that combines sliding-window correlation analysis with meta-statistics. This SBCA approach is shown to minimize the effects of confounds, such as movement, and CSF and white matter signal changes, and enables real-time monitoring of RSN dynamics at time scales of tens of seconds. We demonstrate highly sensitive mapping of eloquent cortex in the vicinity of brain tumors and arterio-venous malformations, and detection of abnormal resting-state connectivity in epilepsy. In patients with motor impairment, resting-state fMRI provided focal localization of sensorimotor cortex compared with more diffuse activation in task-based fMRI. The fast acquisition speed of MEVI enabled segregation of cardiac-related signal pulsation using ICA, which revealed distinct regional differences in pulsation amplitude and waveform, elevated signal pulsation in patients with arterio-venous malformations and a trend toward reduced pulsatility in gray matter of patients compared with healthy controls. Mapping cardiac pulsation in cortical gray matter may carry important functional information that distinguishes healthy from diseased tissue vasculature. This novel fMRI methodology is particularly promising for mapping eloquent cortex in patients with neurological disease, having variable degree of cooperation in task-based fMRI. In conclusion, ultra-high-real-time speed fMRI enhances the sensitivity of mapping the dynamics of resting-state connectivity and cerebro-vascular pulsatility for clinical and neuroscience research applications. PMID:23986677

Brain networks engaged in audiovisual integration during speech perception revealed by persistent homology-based network filtration.

PubMed

Kim, Heejung; Hahm, Jarang; Lee, Hyekyoung; Kang, Eunjoo; Kang, Hyejin; Lee, Dong Soo

2015-05-01

The human brain naturally integrates audiovisual information to improve speech perception. However, in noisy environments, understanding speech is difficult and may require much effort. Although the brain network is supposed to be engaged in speech perception, it is unclear how speech-related brain regions are connected during natural bimodal audiovisual or unimodal speech perception with counterpart irrelevant noise. To investigate the topological changes of speech-related brain networks at all possible thresholds, we used a persistent homological framework through hierarchical clustering, such as single linkage distance, to analyze the connected component of the functional network during speech perception using functional magnetic resonance imaging. For speech perception, bimodal (audio-visual speech cue) or unimodal speech cues with counterpart irrelevant noise (auditory white-noise or visual gum-chewing) were delivered to 15 subjects. In terms of positive relationship, similar connected components were observed in bimodal and unimodal speech conditions during filtration. However, during speech perception by congruent audiovisual stimuli, the tighter couplings of left anterior temporal gyrus-anterior insula component and right premotor-visual components were observed than auditory or visual speech cue conditions, respectively. Interestingly, visual speech is perceived under white noise by tight negative coupling in the left inferior frontal region-right anterior cingulate, left anterior insula, and bilateral visual regions, including right middle temporal gyrus, right fusiform components. In conclusion, the speech brain network is tightly positively or negatively connected, and can reflect efficient or effortful processes during natural audiovisual integration or lip-reading, respectively, in speech perception.

A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping.

PubMed

Wen, Qing; Kim, Chang-Sik; Hamilton, Peter W; Zhang, Shu-Dong

2016-05-11

Gene expression connectivity mapping has gained much popularity recently with a number of successful applications in biomedical research testifying its utility and promise. Previously methodological research in connectivity mapping mainly focused on two of the key components in the framework, namely, the reference gene expression profiles and the connectivity mapping algorithms. The other key component in this framework, the query gene signature, has been left to users to construct without much consensus on how this should be done, albeit it has been an issue most relevant to end users. As a key input to the connectivity mapping process, gene signature is crucially important in returning biologically meaningful and relevant results. This paper intends to formulate a standardized procedure for constructing high quality gene signatures from a user's perspective. We describe a two-stage process for making quality gene signatures using gene expression data as initial inputs. First, a differential gene expression analysis comparing two distinct biological states; only the genes that have passed stringent statistical criteria are considered in the second stage of the process, which involves ranking genes based on statistical as well as biological significance. We introduce a "gene signature progression" method as a standard procedure in connectivity mapping. Starting from the highest ranked gene, we progressively determine the minimum length of the gene signature that allows connections to the reference profiles (drugs) being established with a preset target false discovery rate. We use a lung cancer dataset and a breast cancer dataset as two case studies to demonstrate how this standardized procedure works, and we show that highly relevant and interesting biological connections are returned. Of particular note is gefitinib, identified as among the candidate therapeutics in our lung cancer case study. Our gene signature was based on gene expression data from Taiwan female non-smoker lung cancer patients, while there is evidence from independent studies that gefitinib is highly effective in treating women, non-smoker or former light smoker, advanced non-small cell lung cancer patients of Asian origin. In summary, we introduced a gene signature progression method into connectivity mapping, which enables a standardized procedure for constructing high quality gene signatures. This progression method is particularly useful when the number of differentially expressed genes identified is large, and when there is a need to prioritize them to be included in the query signature. The results from two case studies demonstrate that the approach we have developed is capable of obtaining pertinent candidate drugs with high precision.

Exploring the underlying structure of mental disorders: cross-diagnostic differences and similarities from a network perspective using both a top-down and a bottom-up approach.

PubMed

Wigman, J T W; van Os, J; Borsboom, D; Wardenaar, K J; Epskamp, S; Klippel, A; Viechtbauer, W; Myin-Germeys, I; Wichers, M

2015-08-01

It has been suggested that the structure of psychopathology is best described as a complex network of components that interact in dynamic ways. The goal of the present paper was to examine the concept of psychopathology from a network perspective, combining complementary top-down and bottom-up approaches using momentary assessment techniques. A pooled Experience Sampling Method (ESM) dataset of three groups (individuals with a diagnosis of depression, psychotic disorder or no diagnosis) was used (pooled N = 599). The top-down approach explored the network structure of mental states across different diagnostic categories. For this purpose, networks of five momentary mental states ('cheerful', 'content', 'down', 'insecure' and 'suspicious') were compared between the three groups. The complementary bottom-up approach used principal component analysis to explore whether empirically derived network structures yield meaningful higher order clusters. Individuals with a clinical diagnosis had more strongly connected moment-to-moment network structures, especially the depressed group. This group also showed more interconnections specifically between positive and negative mental states than the psychotic group. In the bottom-up approach, all possible connections between mental states were clustered into seven main components that together captured the main characteristics of the network dynamics. Our combination of (i) comparing network structure of mental states across three diagnostically different groups and (ii) searching for trans-diagnostic network components across all pooled individuals showed that these two approaches yield different, complementary perspectives in the field of psychopathology. The network paradigm therefore may be useful to map transdiagnostic processes.

Network analysis of pig movements: Loyalty patterns and contact chains of different holding types in Denmark

PubMed Central

Boklund, Anette; Halasa, Tariq H. B.; Toft, Nils; Lentz, Hartmut H. K.

2017-01-01

Understanding animal movements is an important factor for the development of meaningful surveillance and control programs, but also for the development of disease spread models. We analysed the Danish pig movement network using static and temporal network analysis tools to provide deeper insight in the connection between holdings dealing with pigs, such as breeding and multiplier herds, production herds, slaughterhouses or traders. Pig movements, which occurred between 1st January 2006 and 31st December 2015 in Denmark, were summarized to investigate temporal trends such as the number of active holdings, the number of registered movements and the number of pigs moved. To identify holdings and holding types with potentially higher risk for introduction or spread of diseases via pig movements, we determined loyalty patterns, annual network components and contact chains for the 24 registered holding types. The total number of active holdings as well as the number of pig movements decreased during the study period while the holding sizes increased. Around 60–90% of connections between two pig holdings were present in two consecutive years and around one third of the connections persisted within the considered time period. Weaner herds showed the highest level of in-loyalty, whereas we observed an intermediate level of in-loyalty for all breeding sites and for production herds. Boar stations, production herds and trade herds showed a high level of out-loyalty. Production herds constituted the highest proportion of holdings in the largest strongly connected component. All production sites showed low levels of in-going contact chains and we observed a high level of out-going contact chain for breeding and multiplier herds. Except for livestock auctions, all transit sites also showed low levels of out-going contact chains. Our results reflect the pyramidal structure of the underlying network. Based on the considered disease, the time frame for the calculation of network measurements needs to be adapted. Using these adapted values for loyalty and contact chains might help to identify holdings with high potential of spreading diseases and thus limit the outbreak size or support control or eradication of the considered pathogen. PMID:28662077

Network analysis of pig movements: Loyalty patterns and contact chains of different holding types in Denmark.

PubMed

Schulz, Jana; Boklund, Anette; Halasa, Tariq H B; Toft, Nils; Lentz, Hartmut H K

2017-01-01

Understanding animal movements is an important factor for the development of meaningful surveillance and control programs, but also for the development of disease spread models. We analysed the Danish pig movement network using static and temporal network analysis tools to provide deeper insight in the connection between holdings dealing with pigs, such as breeding and multiplier herds, production herds, slaughterhouses or traders. Pig movements, which occurred between 1st January 2006 and 31st December 2015 in Denmark, were summarized to investigate temporal trends such as the number of active holdings, the number of registered movements and the number of pigs moved. To identify holdings and holding types with potentially higher risk for introduction or spread of diseases via pig movements, we determined loyalty patterns, annual network components and contact chains for the 24 registered holding types. The total number of active holdings as well as the number of pig movements decreased during the study period while the holding sizes increased. Around 60-90% of connections between two pig holdings were present in two consecutive years and around one third of the connections persisted within the considered time period. Weaner herds showed the highest level of in-loyalty, whereas we observed an intermediate level of in-loyalty for all breeding sites and for production herds. Boar stations, production herds and trade herds showed a high level of out-loyalty. Production herds constituted the highest proportion of holdings in the largest strongly connected component. All production sites showed low levels of in-going contact chains and we observed a high level of out-going contact chain for breeding and multiplier herds. Except for livestock auctions, all transit sites also showed low levels of out-going contact chains. Our results reflect the pyramidal structure of the underlying network. Based on the considered disease, the time frame for the calculation of network measurements needs to be adapted. Using these adapted values for loyalty and contact chains might help to identify holdings with high potential of spreading diseases and thus limit the outbreak size or support control or eradication of the considered pathogen.

River-spring connectivity and hydrogeochemical interactions in a shallow fractured rock formation. The case study of Fuensanta river valley (Southern Spain)

NASA Astrophysics Data System (ADS)

Barberá, J. A.; Andreo, B.

2017-04-01

In upland catchments, the hydrology and hydrochemistry of streams are largely influenced by groundwater inflows, at both regional and local scale. However, reverse conditions (groundwater dynamics conditioned by surface water interferences), although less described, may also occur. In this research, the local river-spring connectivity and induced hydrogeochemical interactions in intensely folded, fractured and layered Cretaceous marls and marly-limestones (Fuensanta river valley, S Spain) are discussed based on field observations, tracer tests and hydrodynamic and hydrochemical data. The differential flow measurements and tracing experiments performed in the Fuensanta river permitted us to quantify the surface water losses and to verify its direct hydraulic connection with the Fuensanta spring. The numerical simulations of tracer breakthrough curves suggest the existence of a groundwater flow system through well-connected master and tributary fractures, with fast and multi-source flow components. Furthermore, the multivariate statistical analysis conducted using chemical data from the sampled waters, the geochemical study of water-rock interactions and the proposed water mixing approach allowed the spatial characterization of the chemistry of the springs and river/stream waters draining low permeable Cretaceous formations. Results corroborated that the mixing of surface waters, as well as calcite dissolution and CO2 dissolution/exsolution, are the main geochemical processes constraining Fuensanta spring hydrochemistry. The estimated contribution of the tributary surface waters to the spring flow during the research period was approximately 26-53% (Fuensanta river) and 47-74% (Convento stream), being predominant the first component during high flow and the second one during the dry season. The identification of secondary geochemical processes (dolomite and gypsum dissolution and dedolomitization) in Fuensanta spring waters evidences the induced hydrogeochemical changes resulting from the allogenic recharge. This research highlights the usefulness of an integrated approach based on river and spring flow examination, dye tracing interpretation and regression and multivariate statistical analysis using hydrochemical data for surface water-groundwater interaction assessment in fractured complex environments worldwide, whose implementation becomes critical for an appropriate groundwater policy.

A Component-Based Study of the Effect of Diameter on Bond and Anchorage Characteristics of Blind-Bolted Connections

PubMed Central

Amin, Muhammad Nasir; Zaheer, Salman; Alazba, Abdulrahman Ali; Saleem, Muhammad Umair; Niazi, Muhammad Umar Khan; Khurram, Nauman; Amin, Muhammad Tahir

2016-01-01

Structural hollow sections are gaining worldwide importance due to their structural and architectural advantages over open steel sections. The only obstacle to their use is their connection with other structural members. To overcome the obstacle of tightening the bolt from one side has given birth to the concept of blind bolts. Blind bolts, being the practical solution to the connection hindrance for the use of hollow and concrete filled hollow sections play a vital role. Flowdrill, the Huck High Strength Blind Bolt and the Lindapter Hollobolt are the well-known commercially available blind bolts. Although the development of blind bolts has largely resolved this issue, the use of structural hollow sections remains limited to shear resistance. Therefore, a new modified version of the blind bolt, known as the “Extended Hollo-Bolt” (EHB) due to its enhanced capacity for bonding with concrete, can overcome the issue of low moment resistance capacity associated with blind-bolted connections. The load transfer mechanism of this recently developed blind bolt remains unclear, however. This study uses a parametric approach to characterising the EHB, using diameter as the variable parameter. Stiffness and load-carrying capacity were evaluated at two different bolt sizes. To investigate the load transfer mechanism, a component-based study of the bond and anchorage characteristics was performed by breaking down the EHB into its components. The results of the study provide insight into the load transfer mechanism of the blind bolt in question. The proposed component-based model was validated by a spring model, through which the stiffness of the EHB was compared to that of its components combined. The combined stiffness of the components was found to be roughly equivalent to that of the EHB as a whole, validating the use of this component-based approach. PMID:26901866

A Component-Based Study of the Effect of Diameter on Bond and Anchorage Characteristics of Blind-Bolted Connections.

PubMed

Amin, Muhammad Nasir; Zaheer, Salman; Alazba, Abdulrahman Ali; Saleem, Muhammad Umair; Niazi, Muhammad Umar Khan; Khurram, Nauman; Amin, Muhammad Tahir

2016-01-01

Structural hollow sections are gaining worldwide importance due to their structural and architectural advantages over open steel sections. The only obstacle to their use is their connection with other structural members. To overcome the obstacle of tightening the bolt from one side has given birth to the concept of blind bolts. Blind bolts, being the practical solution to the connection hindrance for the use of hollow and concrete filled hollow sections play a vital role. Flowdrill, the Huck High Strength Blind Bolt and the Lindapter Hollobolt are the well-known commercially available blind bolts. Although the development of blind bolts has largely resolved this issue, the use of structural hollow sections remains limited to shear resistance. Therefore, a new modified version of the blind bolt, known as the "Extended Hollo-Bolt" (EHB) due to its enhanced capacity for bonding with concrete, can overcome the issue of low moment resistance capacity associated with blind-bolted connections. The load transfer mechanism of this recently developed blind bolt remains unclear, however. This study uses a parametric approach to characterising the EHB, using diameter as the variable parameter. Stiffness and load-carrying capacity were evaluated at two different bolt sizes. To investigate the load transfer mechanism, a component-based study of the bond and anchorage characteristics was performed by breaking down the EHB into its components. The results of the study provide insight into the load transfer mechanism of the blind bolt in question. The proposed component-based model was validated by a spring model, through which the stiffness of the EHB was compared to that of its components combined. The combined stiffness of the components was found to be roughly equivalent to that of the EHB as a whole, validating the use of this component-based approach.

Mapleson's Breathing Systems.

PubMed

Kaul, Tej K; Mittal, Geeta

2013-09-01

Mapleson breathing systems are used for delivering oxygen and anaesthetic agents and to eliminate carbon dioxide during anaesthesia. They consist of different components: Fresh gas flow, reservoir bag, breathing tubes, expiratory valve, and patient connection. There are five basic types of Mapleson system: A, B, C, D and E depending upon the different arrangements of these components. Mapleson F was added later. For adults, Mapleson A is the circuit of choice for spontaneous respiration where as Mapleson D and its Bains modifications are best available circuits for controlled ventilation. For neonates and paediatric patients Mapleson E and F (Jackson Rees modification) are the best circuits. In this review article, we will discuss the structure of the circuits and functional analysis of various types of Mapleson systems and their advantages and disadvantages.

Modulation of the SSTA decadal variation on ENSO events and relationships of SSTA With LOD,SOI, etc

NASA Astrophysics Data System (ADS)

Liao, D. C.; Zhou, Y. H.; Liao, X. H.

2007-01-01

Interannual and decadal components of the length of day (LOD), Southern Oscillation Index (SOI) and Sea Surface Temperature anomaly (SSTA) in Nino regions are extracted by band-pass filtering, and used for research of the modulation of the SSTA on the ENSO events. Results show that besides the interannual components, the decadal components in SSTA have strong impacts on monitoring and representing of the ENSO events. When the ENSO events are strong, the modulation of the decadal components of the SSTA tends to prolong the life-time of the events and enlarge the extreme anomalies of the SST, while the ENSO events, which are so weak that they can not be detected by the interannual components of the SSTA, can also be detected with the help of the modulation of the SSTA decadal components. The study further draws attention to the relationships of the SSTA interannual and decadal components with those of LOD, SOI, both of the sea level pressure anomalies (SLPA) and the trade wind anomalies (TWA) in tropic Pacific, and also with those of the axial components of the atmospheric angular momentum (AAM) and oceanic angular momentum (OAM). Results of the squared coherence and coherent phases among them reveal close connections with the SSTA and almost all of the parameters mentioned above on the interannual time scales, while on the decadal time scale significant connections are among the SSTA and SOI, SLPA, TWA, ?3w and ?3w+v as well, and slight weaker connections between the SSTA and LOD, ?3pib and ?3bp

Revealing the microstructure of the giant component in random graph ensembles

NASA Astrophysics Data System (ADS)

Tishby, Ido; Biham, Ofer; Katzav, Eytan; Kühn, Reimer

2018-04-01

The microstructure of the giant component of the Erdős-Rényi network and other configuration model networks is analyzed using generating function methods. While configuration model networks are uncorrelated, the giant component exhibits a degree distribution which is different from the overall degree distribution of the network and includes degree-degree correlations of all orders. We present exact analytical results for the degree distributions as well as higher-order degree-degree correlations on the giant components of configuration model networks. We show that the degree-degree correlations are essential for the integrity of the giant component, in the sense that the degree distribution alone cannot guarantee that it will consist of a single connected component. To demonstrate the importance and broad applicability of these results, we apply them to the study of the distribution of shortest path lengths on the giant component, percolation on the giant component, and spectra of sparse matrices defined on the giant component. We show that by using the degree distribution on the giant component one obtains high quality results for these properties, which can be further improved by taking the degree-degree correlations into account. This suggests that many existing methods, currently used for the analysis of the whole network, can be adapted in a straightforward fashion to yield results conditioned on the giant component.

Drivers and Effects of Groundwater-Surface Water Interaction in the Karstic Lower Flint River Basin, Southwestern Georgia, USA

NASA Astrophysics Data System (ADS)

Rugel, K.; Golladay, S. W.; Jackson, C. R.; Rasmussen, T. C.; Dowd, J. F.; Mcdowell, R. J.

2017-12-01

Groundwater provides the majority of global water resources for domestic and agricultural usage while contributing vital surface water baseflows which support healthy aquatic ecosystems. Understanding the extent and magnitude of hydrologic connectivity between groundwater and surface water components in karst watersheds is essential to the prudent management of these hydraulically-interactive systems. We examined groundwater and surface water connectivity between the Upper Floridan Aquifer (UFA) and streams in the Lower Flint River Basin (LFRB) in southwestern Georgia where development of agricultural irrigation intensified over the past 30 years. An analysis of USGS streamflow data for the pre- and post-irrigation period showed summer baseflows in some Lower Flint River tributaries were reduced by an order of magnitude in the post-irrigation period, reiterating the strong hydraulic connection between these streams and the underlying aquifer. Large and fine-scale monitoring of calcium, nitrate, specific conductance and stable isotopes (δ18O and δD) on 50 km of Ichawaynochaway Creek, a major tributary of the Lower Flint, detected discrete groundwater-surface water flow paths which accounted for 42% of total groundwater contributions in the 50 km study reach. This presentation will highlight a new analysis using the metadata EPA Reach File (1) and comparing stream reach and instream bedrock joint azimuths with stream geochemical results from previous field study. Our findings suggested that reaches with NNW bearing may be more likely to display enhanced groundwater-surface water connectivity. Our results show that local heterogeneity can significantly affect water budgets and quality within these watersheds, making the use of geomorphological stream attributes a valuable tool to water resource management for the prediction and protection of vulnerable regions of hydrologic connectivity in karst catchments.

Dynamic functional connectivity and individual differences in emotions during social stress.

PubMed

Tobia, Michael J; Hayashi, Koby; Ballard, Grey; Gotlib, Ian H; Waugh, Christian E

2017-12-01

Exposure to acute stress induces multiple emotional responses, each with their own unique temporal dynamics. Dynamic functional connectivity (dFC) measures the temporal variability of network synchrony and captures individual differences in network neurodynamics. This study investigated the relationship between dFC and individual differences in emotions induced by an acute psychosocial stressor. Sixteen healthy adult women underwent fMRI scanning during a social evaluative threat (SET) task, and retrospectively completed questionnaires that assessed individual differences in subjectively experienced positive and negative emotions about stress and stress relief during the task. Group dFC was decomposed with parallel factor analysis (PARAFAC) into 10 components, each with a temporal signature, spatial network of functionally connected regions, and vector of participant loadings that captures individual differences in dFC. Participant loadings of two networks were positively correlated with stress-related emotions, indicating the existence of networks for positive and negative emotions. The emotion-related networks involved the ventromedial prefrontal cortex, cingulate cortex, anterior insula, and amygdala, among other distributed brain regions, and time signatures for these emotion-related networks were uncorrelated. These findings demonstrate that individual differences in stress-induced positive and negative emotions are each uniquely associated with large-scale brain networks, and suggest that dFC is a mechanism that generates individual differences in the emotional components of the stress response. Hum Brain Mapp 38:6185-6205, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Comparative histology of mouse, rat, and human pelvic ligaments.

PubMed

Iwanaga, Ritsuko; Orlicky, David J; Arnett, Jameson; Guess, Marsha K; Hurt, K Joseph; Connell, Kathleen A

2016-11-01

The uterosacral (USL) and cardinal ligaments (CL) provide support to the uterus and pelvic organs, and the round ligaments (RL) maintain their position in the pelvis. In women with pelvic organ prolapse (POP), the connective tissue, smooth muscle, vasculature, and innervation of the pelvic support structures are altered. Rodents are commonly used animal models for POP research. However, the pelvic ligaments have not been defined in these animals. In this study, we hypothesized that the gross anatomy and histological composition of pelvic ligaments in rodents and humans are similar. We performed an extensive literature search for anatomical and histological descriptions of the pelvic support ligaments in rodents. We also performed anatomical dissections of the pelvis to define anatomical landmarks in relation to the ligaments. In addition, we identified the histological components of the pelvic ligaments and performed quantitative analysis of the smooth muscle bundles and connective tissue of the USL and RL. The anatomy of the USL, CL, and RL and their anatomical landmarks are similar in mice, rats, and humans. All species contain the same cellular components and have similar histological architecture. However, the cervical portion of the mouse USL and RL contain more smooth muscle and less connective tissue compared with rat and human ligaments. The pelvic support structures of rats and mice are anatomically and histologically similar to those of humans. We propose that both mice and rats are appropriate, cost-effective models for directed studies in POP research.

Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging.

PubMed

Schouten, Tijn M; Koini, Marisa; Vos, Frank de; Seiler, Stephan; Rooij, Mark de; Lechner, Anita; Schmidt, Reinhold; Heuvel, Martijn van den; Grond, Jeroen van der; Rombouts, Serge A R B

2017-05-15

Diffusion magnetic resonance imaging (MRI) is a powerful non-invasive method to study white matter integrity, and is sensitive to detect differences in Alzheimer's disease (AD) patients. Diffusion MRI may be able to contribute towards reliable diagnosis of AD. We used diffusion MRI to classify AD patients (N=77), and controls (N=173). We use different methods to extract information from the diffusion MRI data. First, we use the voxel-wise diffusion tensor measures that have been skeletonised using tract based spatial statistics. Second, we clustered the voxel-wise diffusion measures with independent component analysis (ICA), and extracted the mixing weights. Third, we determined structural connectivity between Harvard Oxford atlas regions with probabilistic tractography, as well as graph measures based on these structural connectivity graphs. Classification performance for voxel-wise measures ranged between an AUC of 0.888, and 0.902. The ICA-clustered measures ranged between an AUC of 0.893, and 0.920. The AUC for the structural connectivity graph was 0.900, while graph measures based upon this graph ranged between an AUC of 0.531, and 0.840. All measures combined with a sparse group lasso resulted in an AUC of 0.896. Overall, fractional anisotropy clustered into ICA components was the best performing measure. These findings may be useful for future incorporation of diffusion MRI into protocols for AD classification, or as a starting point for early detection of AD using diffusion MRI. Copyright © 2017 Elsevier Inc. All rights reserved.

Variational-based segmentation of bio-pores in tomographic images

NASA Astrophysics Data System (ADS)

Bauer, Benjamin; Cai, Xiaohao; Peth, Stephan; Schladitz, Katja; Steidl, Gabriele

2017-01-01

X-ray computed tomography (CT) combined with a quantitative analysis of the resulting volume images is a fruitful technique in soil science. However, the variations in X-ray attenuation due to different soil components keep the segmentation of single components within these highly heterogeneous samples a challenging problem. Particularly demanding are bio-pores due to their elongated shape and the low gray value difference to the surrounding soil structure. Recently, variational models in connection with algorithms from convex optimization were successfully applied for image segmentation. In this paper we apply these methods for the first time for the segmentation of bio-pores in CT images of soil samples. We introduce a novel convex model which enforces smooth boundaries of bio-pores and takes the varying attenuation values in the depth into account. Segmentation results are reported for different real-world 3D data sets as well as for simulated data. These results are compared with two gray value thresholding methods, namely indicator kriging and a global thresholding procedure, and with a morphological approach. Pros and cons of the methods are assessed by considering geometric features of the segmented bio-pore systems. The variational approach features well-connected smooth pores while not detecting smaller or shallower pores. This is an advantage in cases where the main bio-pores network is of interest and where infillings, e.g., excrements of earthworms, would result in losing pore connections as observed for the other thresholding methods.

A Component-Based Diffusion Model With Structural Diversity for Social Networks.

PubMed

Qing Bao; Cheung, William K; Yu Zhang; Jiming Liu

2017-04-01

Diffusion on social networks refers to the process where opinions are spread via the connected nodes. Given a set of observed information cascades, one can infer the underlying diffusion process for social network analysis. The independent cascade model (IC model) is a widely adopted diffusion model where a node is assumed to be activated independently by any one of its neighbors. In reality, how a node will be activated also depends on how its neighbors are connected and activated. For instance, the opinions from the neighbors of the same social group are often similar and thus redundant. In this paper, we extend the IC model by considering that: 1) the information coming from the connected neighbors are similar and 2) the underlying redundancy can be modeled using a dynamic structural diversity measure of the neighbors. Our proposed model assumes each node to be activated independently by different communities (or components) of its parent nodes, each weighted by its effective size. An expectation maximization algorithm is derived to infer the model parameters. We compare the performance of the proposed model with the basic IC model and its variants using both synthetic data sets and a real-world data set containing news stories and Web blogs. Our empirical results show that incorporating the community structure of neighbors and the structural diversity measure into the diffusion model significantly improves the accuracy of the model, at the expense of only a reasonable increase in run-time.

Implementing Connected Component Labeling as a User Defined Operator for SciDB

NASA Technical Reports Server (NTRS)

Oloso, Amidu; Kuo, Kwo-Sen; Clune, Thomas; Brown, Paul; Poliakov, Alex; Yu, Hongfeng

2016-01-01

We have implemented a flexible User Defined Operator (UDO) for labeling connected components of a binary mask expressed as an array in SciDB, a parallel distributed database management system based on the array data model. This UDO is able to process very large multidimensional arrays by exploiting SciDB's memory management mechanism that efficiently manipulates arrays whose memory requirements far exceed available physical memory. The UDO takes as primary inputs a binary mask array and a binary stencil array that specifies the connectivity of a given cell to its neighbors. The UDO returns an array of the same shape as the input mask array with each foreground cell containing the label of the component it belongs to. By default, dimensions are treated as non-periodic, but the UDO also accepts optional input parameters to specify periodicity in any of the array dimensions. The UDO requires four stages to completely label connected components. In the first stage, labels are computed for each subarray or chunk of the mask array in parallel across SciDB instances using the weighted quick union (WQU) with half-path compression algorithm. In the second stage, labels around chunk boundaries from the first stage are stored in a temporary SciDB array that is then replicated across all SciDB instances. Equivalences are resolved by again applying the WQU algorithm to these boundary labels. In the third stage, relabeling is done for each chunk using the resolved equivalences. In the fourth stage, the resolved labels, which so far are "flattened" coordinates of the original binary mask array, are renamed with sequential integers for legibility. The UDO is demonstrated on a 3-D mask of O(1011) elements, with O(108) foreground cells and O(106) connected components. The operator completes in 19 minutes using 84 SciDB instances.

Hybrid solar lighting systems and components

DOEpatents

Muhs, Jeffrey D [Lenoir City, TN; Earl, Dennis D [Knoxville, TN; Beshears, David L [Knoxville, TN; Maxey, Lonnie C [Powell, TN; Jordan, John K [Oak Ridge, TN; Lind, Randall F [Lenoir City, TN

2007-06-12

A hybrid solar lighting system and components having at least one hybrid solar concentrator, at least one fiber receiver, at least one hybrid luminaire, and a light distribution system operably connected to each hybrid solar concentrator and each hybrid luminaire. A controller operates each component.

Connecting Different Data Sources to Assess the Interconnections between Biosecurity, Health, Welfare, and Performance in Commercial Pig Farms in Great Britain.

PubMed

Pandolfi, Fanny; Edwards, Sandra A; Maes, Dominiek; Kyriazakis, Ilias

2018-01-01

This study aimed to provide an overview of the interconnections between biosecurity, health, welfare, and performance in commercial pig farms in Great Britain. We collected on-farm data about the level of biosecurity and animal performance in 40 fattening pig farms and 28 breeding pig farms between 2015 and 2016. We identified interconnections between these data, slaughterhouse health indicators, and welfare indicator records in fattening pig farms. After achieving the connections between databases, a secondary data analysis was performed to assess the interconnections between biosecurity, health, welfare, and performance using correlation analysis, principal component analysis, and hierarchical clustering. Although we could connect the different data sources the final sample size was limited, suggesting room for improvement in database connection to conduct secondary data analyses. The farm biosecurity scores ranged from 40 to 90 out of 100, with internal biosecurity scores being lower than external biosecurity scores. Our analysis suggested several interconnections between health, welfare, and performance. The initial correlation analysis showed that the prevalence of lameness and severe tail lesions was associated with the prevalence of enzootic pneumonia-like lesions and pyaemia, and the prevalence of severe body marks was associated with several disease indicators, including peritonitis and milk spots ( r  > 0.3; P  < 0.05). Higher average daily weight gain (ADG) was associated with lower prevalence of pleurisy ( r  > 0.3; P  < 0.05), but no connection was identified between mortality and health indicators. A subsequent cluster analysis enabled identification of patterns which considered concurrently indicators of health, welfare, and performance. Farms from cluster 1 had lower biosecurity scores, lower ADG, and higher prevalence of several disease and welfare indicators. Farms from cluster 2 had higher biosecurity scores than cluster 1, but a higher prevalence of pigs requiring hospitalization and lameness which confirmed the correlation between biosecurity and the prevalence of pigs requiring hospitalization ( r  > 0.3; P  < 0.05). Farms from cluster 3 had higher biosecurity, higher ADG, and lower prevalence for some disease and welfare indicators. The study suggests a smaller impact of biosecurity on issues such as mortality, prevalence of lameness, and pig requiring hospitalization. The correlations and the identified clusters suggested the importance of animal welfare for the pig industry.

Hemispheric connectivity and the visual-spatial divergent-thinking component of creativity.

PubMed

Moore, Dana W; Bhadelia, Rafeeque A; Billings, Rebecca L; Fulwiler, Carl; Heilman, Kenneth M; Rood, Kenneth M J; Gansler, David A

2009-08-01

Divergent thinking is an important measurable component of creativity. This study tested the postulate that divergent thinking depends on large distributed inter- and intra-hemispheric networks. Although preliminary evidence supports increased brain connectivity during divergent thinking, the neural correlates of this characteristic have not been entirely specified. It was predicted that visuospatial divergent thinking would correlate with right hemisphere white matter volume (WMV) and with the size of the corpus callosum (CC). Volumetric magnetic resonance imaging (MRI) analyses and the Torrance Tests of Creative Thinking (TTCT) were completed among 21 normal right-handed adult males. TTCT scores correlated negatively with the size of the CC and were not correlated with right or, incidentally, left WMV. Although these results were not predicted, perhaps, as suggested by Bogen and Bogen (1988), decreased callosal connectivity enhances hemispheric specialization, which benefits the incubation of ideas that are critical for the divergent-thinking component of creativity, and it is the momentary inhibition of this hemispheric independence that accounts for the illumination that is part of the innovative stage of creativity. Alternatively, decreased CC size may reflect more selective developmental pruning, thereby facilitating efficient functional connectivity.

Quantum algorithms for topological and geometric analysis of data

PubMed Central

Lloyd, Seth; Garnerone, Silvano; Zanardi, Paolo

2016-01-01

Extracting useful information from large data sets can be a daunting task. Topological methods for analysing data sets provide a powerful technique for extracting such information. Persistent homology is a sophisticated tool for identifying topological features and for determining how such features persist as the data is viewed at different scales. Here we present quantum machine learning algorithms for calculating Betti numbers—the numbers of connected components, holes and voids—in persistent homology, and for finding eigenvectors and eigenvalues of the combinatorial Laplacian. The algorithms provide an exponential speed-up over the best currently known classical algorithms for topological data analysis. PMID:26806491

Finding text in color images

NASA Astrophysics Data System (ADS)

Zhou, Jiangying; Lopresti, Daniel P.; Tasdizen, Tolga

1998-04-01

In this paper, we consider the problem of locating and extracting text from WWW images. A previous algorithm based on color clustering and connected components analysis works well as long as the color of each character is relatively uniform and the typography is fairly simple. It breaks down quickly, however, when these assumptions are violated. In this paper, we describe more robust techniques for dealing with this challenging problem. We present an improved color clustering algorithm that measures similarity based on both RGB and spatial proximity. Layout analysis is also incorporated to handle more complex typography. THese changes significantly enhance the performance of our text detection procedure.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

PubMed

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

PubMed Central

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization. PMID:28912803

Analyzing brain networks with PCA and conditional Granger causality.

PubMed

Zhou, Zhenyu; Chen, Yonghong; Ding, Mingzhou; Wright, Paul; Lu, Zuhong; Liu, Yijun

2009-07-01

Identifying directional influences in anatomical and functional circuits presents one of the greatest challenges for understanding neural computations in the brain. Granger causality mapping (GCM) derived from vector autoregressive models of data has been employed for this purpose, revealing complex temporal and spatial dynamics underlying cognitive processes. However, the traditional GCM methods are computationally expensive, as signals from thousands of voxels within selected regions of interest (ROIs) are individually processed, and being based on pairwise Granger causality, they lack the ability to distinguish direct from indirect connectivity among brain regions. In this work a new algorithm called PCA based conditional GCM is proposed to overcome these problems. The algorithm implements the following two procedures: (i) dimensionality reduction in ROIs of interest with principle component analysis (PCA), and (ii) estimation of the direct causal influences in local brain networks, using conditional Granger causality. Our results show that the proposed method achieves greater accuracy in detecting network connectivity than the commonly used pairwise Granger causality method. Furthermore, the use of PCA components in conjunction with conditional GCM greatly reduces the computational cost relative to the use of individual voxel time series. Copyright 2009 Wiley-Liss, Inc

Network-Centric Interventions to Contain the Syphilis Epidemic in San Francisco.

PubMed

Juher, David; Saldaña, Joan; Kohn, Robert; Bernstein, Kyle; Scoglio, Caterina

2017-07-25

The number of reported early syphilis cases in San Francisco has increased steadily since 2005. It is not yet clear what factors are responsible for such an increase. A recent analysis of the sexual contact network of men who have sex with men with syphilis in San Francisco has discovered a large connected component, members of which have a significantly higher chance of syphilis and HIV compared to non-member individuals. This study investigates whether it is possible to exploit the existence of the largest connected component to design new notification strategies that can potentially contribute to reducing the number of cases. We develop a model capable of incorporating multiple types of notification strategies and compare the corresponding incidence of syphilis. Through extensive simulations, we show that notifying the community of the infection state of few central nodes appears to be the most effective approach, balancing the cost of notification and the reduction of syphilis incidence. Additionally, among the different measures of centrality, the eigenvector centrality reveals to be the best to reduce the incidence in the long term as long as the number of missing links (non-disclosed contacts) is not very large.

Convergence of an iterative procedure for large-scale static analysis of structural components

NASA Technical Reports Server (NTRS)

Austin, F.; Ojalvo, I. U.

1976-01-01

The paper proves convergence of an iterative procedure for calculating the deflections of built-up component structures which can be represented as consisting of a dominant, relatively stiff primary structure and a less stiff secondary structure, which may be composed of one or more substructures that are not connected to one another but are all connected to the primary structure. The iteration consists in estimating the deformation of the primary structure in the absence of the secondary structure on the assumption that all mechanical loads are applied directly to the primary structure. The j-th iterate primary structure deflections at the interface are imposed on the secondary structure, and the boundary loads required to produce these deflections are computed. The cycle is completed by applying the interface reaction to the primary structure and computing its updated deflections. It is shown that the mathematical condition for convergence of this procedure is that the maximum eigenvalue of the equation relating primary-structure deflection to imposed secondary-structure deflection be less than unity, which is shown to correspond with the physical requirement that the secondary structure be more flexible at the interface boundary.

Rotor component displacement measurement system

DOEpatents

Mercer, Gary D.; Li, Ming C.; Baum, Charles R.

2003-05-27

A measuring system for measuring axial displacement of a tube relative to an axially stationary component in a rotating rotor assembly includes at least one displacement sensor adapted to be located normal to a longitudinal axis of the tube; an insulated cable system adapted for passage through the rotor assembly; a rotatable proximitor module located axially beyond the rotor assembly to which the cables are connected; and a telemetry system operatively connected to the proximitor module for sampling signals from the proximitor module and forwarding data to a ground station.

Dynamic reconfiguration of human brain functional networks through neurofeedback.

PubMed

Haller, Sven; Kopel, Rotem; Jhooti, Permi; Haas, Tanja; Scharnowski, Frank; Lovblad, Karl-Olof; Scheffler, Klaus; Van De Ville, Dimitri

2013-11-01

Recent fMRI studies demonstrated that functional connectivity is altered following cognitive tasks (e.g., learning) or due to various neurological disorders. We tested whether real-time fMRI-based neurofeedback can be a tool to voluntarily reconfigure brain network interactions. To disentangle learning-related from regulation-related effects, we first trained participants to voluntarily regulate activity in the auditory cortex (training phase) and subsequently asked participants to exert learned voluntary self-regulation in the absence of feedback (transfer phase without learning). Using independent component analysis (ICA), we found network reconfigurations (increases in functional network connectivity) during the neurofeedback training phase between the auditory target region and (1) the auditory pathway; (2) visual regions related to visual feedback processing; (3) insula related to introspection and self-regulation and (4) working memory and high-level visual attention areas related to cognitive effort. Interestingly, the auditory target region was identified as the hub of the reconfigured functional networks without a-priori assumptions. During the transfer phase, we again found specific functional connectivity reconfiguration between auditory and attention network confirming the specific effect of self-regulation on functional connectivity. Functional connectivity to working memory related networks was no longer altered consistent with the absent demand on working memory. We demonstrate that neurofeedback learning is mediated by widespread changes in functional connectivity. In contrast, applying learned self-regulation involves more limited and specific network changes in an auditory setup intended as a model for tinnitus. Hence, neurofeedback training might be used to promote recovery from neurological disorders that are linked to abnormal patterns of brain connectivity. Copyright © 2013 Elsevier Inc. All rights reserved.

Differential Effects of Left and Right Prefrontal High-Frequency Repetitive Transcranial Magnetic Stimulation on Resting-State Functional Magnetic Resonance Imaging in Healthy Individuals.

PubMed

Schluter, Renée S; Jansen, Jochem M; van Holst, Ruth J; van den Brink, Wim; Goudriaan, Anna E

2018-03-01

High-frequency repetitive transcranial magnetic stimulation (HF-rTMS) has gained great interest in multiple clinical and research fields and is believed to accomplish its effect by influencing neuronal networks. The dorsolateral prefrontal cortex (dlPFC) is frequently chosen as the cortical target for HF-rTMS. However, very little is known about the differential effect of HF-rTMS over the left and right dlPFC on intrinsic functional connectivity networks in patients or in healthy individuals. The current study assessed the differential effects of left or right HF-rTMS (corrected for sham) on intrinsic independent component analysis (ICA)-defined functional connectivity networks in a sample of 45 healthy individuals. All subjects had a first scanning session in which baseline functional connectivity was assessed. During the second session, individuals received one session of left, right, or sham dlPFC HF-rTMS (60 5-sec trains of 10 Hz at 110% motor threshold). The sham condition was used to correct for time and placebo effects. ICAs were performed to assess baseline differences and stimulation effects on within- and between-network functional connectivity. Stimulation of the left dlPFC resulted in decreased functional connectivity in the salience network, whereas right dlPFC stimulation resulted in increased functional connectivity within this network. No differences between left or right dlPFC stimulation were found in between-network connectivity. These results suggest that left and right HF-rTMS may have differential effects, and more research is needed on the clinical consequences.

Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity.

PubMed

Napadow, Vitaly; LaCount, Lauren; Park, Kyungmo; As-Sanie, Sawsan; Clauw, Daniel J; Harris, Richard E

2010-08-01

Fibromyalgia (FM) is considered to be the prototypical central chronic pain syndrome and is associated with widespread pain that fluctuates spontaneously. Multiple studies have demonstrated altered brain activity in these patients. The objective of this study was to investigate the degree of connectivity between multiple brain networks in patients with FM, as well as how activity in these networks correlates with the level of spontaneous pain. Resting-state functional magnetic resonance imaging (FMRI) data from 18 patients with FM and 18 age-matched healthy control subjects were analyzed using dual-regression independent components analysis, which is a data-driven approach for the identification of independent brain networks. Intrinsic, or resting-state, connectivity was evaluated in multiple brain networks: the default mode network (DMN), the executive attention network (EAN), and the medial visual network (MVN), with the MVN serving as a negative control. Spontaneous pain levels were also analyzed for covariance with intrinsic connectivity. Patients with FM had greater connectivity within the DMN and right EAN (corrected P [P(corr)] < 0.05 versus controls), and greater connectivity between the DMN and the insular cortex, which is a brain region known to process evoked pain. Furthermore, greater intensity of spontaneous pain at the time of the FMRI scan correlated with greater intrinsic connectivity between the insula and both the DMN and right EAN (P(corr) < 0.05). These findings indicate that resting brain activity within multiple networks is associated with spontaneous clinical pain in patients with FM. These findings may also have broader implications for how subjective experiences such as pain arise from a complex interplay among multiple brain networks.

Intrinsic Brain Connectivity in Fibromyalgia is Associated with Chronic Pain Intensity

PubMed Central

Napadow, Vitaly; LaCount, Lauren; Park, Kyungmo; As-Sanie, Suzie; Clauw, Daniel J; Harris, Richard E

2010-01-01

OBJECTIVE Fibromyalgia (FM) is considered to be the prototypical central chronic pain syndrome and is associated with widespread pain that fluctuates spontaneously. Multiple studies have demonstrated altered brain activity in these patients. Our objective was to investigate the degree of connectivity between multiple brain networks in FM, as well as how activity in these networks correlates with spontaneous pain. METHODS Resting functional magnetic resonance imaging (fMRI) data in FM patients (n=18) and age-matched healthy controls (HC, n=18) were analyzed using dual regression independent component analysis (ICA) - a data driven approach used to identify independent brain networks. We evaluated intrinsic, or resting, connectivity in multiple brain networks: the default mode network (DMN), the executive attention network (EAN), and the medial visual network (MVN), with the MVN serving as a negative control. Spontaneous pain levels were also covaried with intrinsic connectivity. RESULTS We found that FM patients had greater connectivity within the DMN and right EAN (rEAN; p<0.05, corrected), and greater connectivity between the DMN and the insular cortex – a brain region known to process evoked pain. Furthermore, greater spontaneous pain at the time of the scan correlated with greater intrinsic connectivity between the insula and both the DMN and rEAN (p<0.05, corrected). CONCLUSION Our findings indicate that resting brain activity within multiple networks is associated with spontaneous clinical pain in FM. These findings may also have broader implications for how subjective experiences such as pain arise from a complex interplay amongst multiple brain networks. PMID:20506181

Modulation of Brain Resting-State Networks by Sad Mood Induction

PubMed Central

Harrison, Ben J.; Pujol, Jesus; Ortiz, Hector; Fornito, Alex; Pantelis, Christos; Yücel, Murat

2008-01-01

Background There is growing interest in the nature of slow variations of the blood oxygen level-dependent (BOLD) signal observed in functional MRI resting-state studies. In humans, these slow BOLD variations are thought to reflect an underlying or intrinsic form of brain functional connectivity in discrete neuroanatomical systems. While these ‘resting-state networks’ may be relatively enduring phenomena, other evidence suggest that dynamic changes in their functional connectivity may also emerge depending on the brain state of subjects during scanning. Methodology/Principal Findings In this study, we examined healthy subjects (n = 24) with a mood induction paradigm during two continuous fMRI recordings to assess the effects of a change in self-generated mood state (neutral to sad) on the functional connectivity of these resting-state networks (n = 24). Using independent component analysis, we identified five networks that were common to both experimental states, each showing dominant signal fluctuations in the very low frequency domain (∼0.04 Hz). Between the two states, we observed apparent increases and decreases in the overall functional connectivity of these networks. Primary findings included increased connectivity strength of a paralimbic network involving the dorsal anterior cingulate and anterior insula cortices with subjects' increasing sadness and decreased functional connectivity of the ‘default mode network’. Conclusions/Significance These findings support recent studies that suggest the functional connectivity of certain resting-state networks may, in part, reflect a dynamic image of the current brain state. In our study, this was linked to changes in subjective mood. PMID:18350136

Connective tissue fibroblasts and Tcf4 regulate myogenesis

PubMed Central

Mathew, Sam J.; Hansen, Jody M.; Merrell, Allyson J.; Murphy, Malea M.; Lawson, Jennifer A.; Hutcheson, David A.; Hansen, Mark S.; Angus-Hill, Melinda; Kardon, Gabrielle

2011-01-01

Muscle and its connective tissue are intimately linked in the embryo and in the adult, suggesting that interactions between these tissues are crucial for their development. However, the study of muscle connective tissue has been hindered by the lack of molecular markers and genetic reagents to label connective tissue fibroblasts. Here, we show that the transcription factor Tcf4 (transcription factor 7-like 2; Tcf7l2) is strongly expressed in connective tissue fibroblasts and that Tcf4GFPCre mice allow genetic manipulation of these fibroblasts. Using this new reagent, we find that connective tissue fibroblasts critically regulate two aspects of myogenesis: muscle fiber type development and maturation. Fibroblasts promote (via Tcf4-dependent signals) slow myogenesis by stimulating the expression of slow myosin heavy chain. Also, fibroblasts promote the switch from fetal to adult muscle by repressing (via Tcf4-dependent signals) the expression of developmental embryonic myosin and promoting (via a Tcf4-independent mechanism) the formation of large multinucleate myofibers. In addition, our analysis of Tcf4 function unexpectedly reveals a novel mechanism of intrinsic regulation of muscle fiber type development. Unlike other intrinsic regulators of fiber type, low levels of Tcf4 in myogenic cells promote both slow and fast myogenesis, thereby promoting overall maturation of muscle fiber type. Thus, we have identified novel extrinsic and intrinsic mechanisms regulating myogenesis. Most significantly, our data demonstrate for the first time that connective tissue is important not only for adult muscle structure and function, but is a vital component of the niche within which muscle progenitors reside and is a critical regulator of myogenesis. PMID:21177349

Blind colour separation of H&E stained histological images by linearly transforming the colour space.

PubMed

Celis, R; Romo, D; Romero, E

2015-12-01

Blind source separation methods aim to split information into the original sources. In histology, each dye component attempts to specifically characterize different microscopic structures. In the case of the hematoxylin-eosin stain, universally used for routine examination, quantitative analysis may often require the inspection of different morphological signatures related mainly to nuclei patterns, but also to stroma distribution. Stain separation is usually a preprocessing operation that is transversal to different applications. This paper presents a novel colour separation method that finds the hematoxylin and eosin clusters by projecting the whole (r,g,b) space to a folded surface connecting the distributions of a series of [(r-b),g] planes that divide the cloud of H&E tones. The proposed method produces density maps closer to those obtained with the colour mixing matrices set by an expert, when comparing with the density maps obtained using nonnegative matrix factorization (NMF), independent component analysis (ICA) and a state-of-the-art method. The method has outperformed three baseline methods, NMF, Macenko and ICA, in about 8%, 12% and 52% for the eosin component, whereas this was about 4%, 8% and 26% for the hematoxylin component. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.

Static and Dynamic Characteristics of Cerebral Blood Flow During the Resting State in Schizophrenia

PubMed Central

Kindler, Jochen; Jann, Kay; Homan, Philipp; Hauf, Martinus; Walther, Sebastian; Strik, Werner; Dierks, Thomas; Hubl, Daniela

2015-01-01

Background: The cerebral network that is active during rest and is deactivated during goal-oriented activity is called the default mode network (DMN). It appears to be involved in self-referential mental activity. Atypical functional connectivity in the DMN has been observed in schizophrenia. One hypothesis suggests that pathologically increased DMN connectivity in schizophrenia is linked with a main symptom of psychosis, namely, misattribution of thoughts. Methods: A resting-state pseudocontinuous arterial spin labeling (ASL) study was conducted to measure absolute cerebral blood flow (CBF) in 34 schizophrenia patients and 27 healthy controls. Using independent component analysis (ICA), the DMN was extracted from ASL data. Mean CBF and DMN connectivity were compared between groups using a 2-sample t test. Results: Schizophrenia patients showed decreased mean CBF in the frontal and temporal regions (P < .001). ICA demonstrated significantly increased DMN connectivity in the precuneus (x/y/z = −16/−64/38) in patients than in controls (P < .001). CBF was not elevated in the respective regions. DMN connectivity in the precuneus was significantly correlated with the Positive and Negative Syndrome Scale scores (P < .01). Conclusions: In schizophrenia patients, the posterior hub—which is considered the strongest part of the DMN—showed increased DMN connectivity. We hypothesize that this increase hinders the deactivation of the DMN and, thus, the translation of cognitive processes from an internal to an external focus. This might explain symptoms related to defective self-monitoring, such as auditory verbal hallucinations or ego disturbances. PMID:24327756

On-line multiple component analysis for efficient quantitative bioprocess development.

PubMed

Dietzsch, Christian; Spadiut, Oliver; Herwig, Christoph

2013-02-20

On-line monitoring devices for the precise determination of a multitude of components are a prerequisite for fast bioprocess quantification. On-line measured values have to be checked for quality and consistency, in order to extract quantitative information from these data. In the present study we characterized a novel on-line sampling and analysis device comprising an automatic photometric robot. We connected this on-line device to a bioreactor and concomitantly measured six components (i.e. glucose, glycerol, ethanol, acetate, phosphate and ammonium) during different batch cultivations of Pichia pastoris. The on-line measured data did not show significant deviations from off-line taken samples and were consequently used for incremental rate and yield calculations. In this respect we highlighted the importance of data quality and discussed the phenomenon of error propagation. On-line calculated rates and yields depicted the physiological responses of the P. pastoris cells in unlimited and limited cultures. A more detailed analysis of the physiological state was possible by considering the off-line determined biomass dry weight and the calculation of specific rates. Here we present a novel device for on-line monitoring of bioprocesses, which ensures high data quality in real-time and therefore refers to a valuable tool for Process Analytical Technology (PAT). Copyright © 2012 Elsevier B.V. All rights reserved.

Neuroanatomical basis of paroxysmal sympathetic hyperactivity: A diffusion tensor imaging analysis

PubMed Central

Hinson, Holly E.; Puybasset, Louis; Weiss, Nicolas; Perlbarg, Vincent; Benali, Habib; Galanaud, Damien; Lasarev, Mike; Stevens, Robert D.

2015-01-01

Primary objective Paroxysmal sympathetic hyperactivity (PSH) is observed in a sub-set of patients with moderate-to-severe traumatic brain injury (TBI). The neuroanatomical basis of PSH is poorly understood. It is hypothesized that PSH is linked to changes in connectivity within the central autonomic network. Research design Retrospective analysis in a sub-set of patients from a multi-centre, prospective cohort study Methods and procedures Adult patients who were <3 weeks after severe TBI were enrolled and screened for PSH using a standard definition. Patients underwent multimodal MRI, which included quantitative diffusion tensor imaging. Main outcomes and results Principal component analysis (PCA) was used to resolve the set of tracts into components. Ability to predict PSH was evaluated via area under the receiver operating characteristic (AUROC) and tree-based classification analyses. Among 102 enrolled patients, 16 met criteria for PSH. The first principle component was significantly associated (p = 0.024, AUROC = 0.867) with PSH status even after controlling for age and admission GCS. In a classification tree analysis, age, GCS and decreased FA in the splenium of the corpus callosum and in the right posterior limb of the internal capsule discriminated PSH vs no PSH with an AUROC of 0.933. Conclusions Disconnection involving the posterior corpus callosum and of the posterior limb of the internal capsule may play a role in the pathogenesis or expression of PSH. PMID:25565392

Hybrid solar lighting distribution systems and components

DOEpatents

Muhs, Jeffrey D [Lenoir City, TN; Earl, Dennis D [Knoxville, TN; Beshears, David L [Knoxville, TN; Maxey, Lonnie C [Powell, TN; Jordan, John K [Oak Ridge, TN; Lind, Randall F [Lenoir City, TN

2011-07-05

A hybrid solar lighting distribution system and components having at least one hybrid solar concentrator, at least one fiber receiver, at least one hybrid luminaire, and a light distribution system operably connected to each hybrid solar concentrator and each hybrid luminaire. A controller operates all components.

Teaching Software Componentization: A Bar Chart Java Bean

ERIC Educational Resources Information Center

Mitri, Michel

2010-01-01

In the current object-oriented paradigm, software construction increasingly involves creating and utilizing "software components". These components can serve a variety of functions, from common algorithmic processes to database connectivity to graphical interfaces. The advantage of component architectures is that programmers can use pre-existing…

Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients.

PubMed

Zhu, Xueling; Wang, Xiang; Xiao, Jin; Liao, Jian; Zhong, Mingtian; Wang, Wei; Yao, Shuqiao

2012-04-01

Imaging studies have shown that major depressive disorder (MDD) is associated with altered activity patterns of the default mode network (DMN). However, the neural correlates of the resting-state DMN and MDD-related pathopsychological characteristics, such as depressive rumination and overgeneral autobiographical memory (OGM) phenomena, still remain unclear. Using independent component analysis, we analyzed resting-state functional magnetic resonance imaging data obtained from 35 first-episode, treatment-naive young adults with MDD and from 35 matched healthy control subjects. Patients with MDD exhibited higher levels of rumination and OGM than did the control subjects. We observed increased functional connectivity in the anterior medial cortex regions (especially the medial prefrontal cortex and anterior cingulate cortex) and decreased functional connectivity in the posterior medial cortex regions (especially the posterior cingulate cortex/precuneus) in MDD patients compared with control subjects. In the depressed group, the increased functional connectivity in the anterior medial cortex correlated positively with rumination score, while the decreased functional connectivity in the posterior medial cortex correlated negatively with OGM score. We report dissociation between anterior and posterior functional connectivity in resting-state DMNs of first-episode, treatment-naive young adults with MDD. Increased functional connectivity in anterior medial regions of the resting-state DMN was associated with rumination, whereas decreased functional connectivity in posterior medial regions was associated with OGM. These results provide new evidence for the importance of the DMN in the pathophysiology of MDD and suggest that abnormal DMN activity may be an MDD trait. Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Top-Down Network Effective Connectivity in Abstinent Substance Dependent Individuals

PubMed Central

Regner, Michael F.; Saenz, Naomi; Maharajh, Keeran; Yamamoto, Dorothy J.; Mohl, Brianne; Wylie, Korey; Tregellas, Jason; Tanabe, Jody

2016-01-01

Objective We hypothesized that compared to healthy controls, long-term abstinent substance dependent individuals (SDI) will differ in their effective connectivity between large-scale brain networks and demonstrate increased directional information from executive control to interoception-, reward-, and habit-related networks. In addition, using graph theory to compare network efficiencies we predicted decreased small-worldness in SDI compared to controls. Methods 50 SDI and 50 controls of similar sex and age completed psychological surveys and resting state fMRI. fMRI results were analyzed using group independent component analysis; 14 networks-of-interest (NOI) were selected using template matching to a canonical set of resting state networks. The number, direction, and strength of connections between NOI were analyzed with Granger Causality. Within-group thresholds were p<0.005 using a bootstrap permutation. Between group thresholds were p<0.05, FDR-corrected for multiple comparisons. NOI were correlated with behavioral measures, and group-level graph theory measures were compared. Results Compared to controls, SDI showed significantly greater Granger causal connectivity from right executive control network (RECN) to dorsal default mode network (dDMN) and from dDMN to basal ganglia network (BGN). RECN was negatively correlated with impulsivity, behavioral approach, and negative affect; dDMN was positively correlated with impulsivity. Among the 14 NOI, SDI showed greater bidirectional connectivity; controls showed more unidirectional connectivity. SDI demonstrated greater global efficiency and lower local efficiency. Conclusions Increased effective connectivity in long-term abstinent drug users may reflect improved cognitive control over habit and reward processes. Higher global and lower local efficiency across all networks in SDI compared to controls may reflect connectivity changes associated with drug dependence or remission and requires future, longitudinal studies to confirm. PMID:27776135

Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia.

PubMed

Cassidy, Clifford M; Van Snellenberg, Jared X; Benavides, Caridad; Slifstein, Mark; Wang, Zhishun; Moore, Holly; Abi-Dargham, Anissa; Horga, Guillermo

2016-04-13

Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during ann-back working-memory task) and positron emission tomography using the radiotracer [(11)C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. It is unclear how communication between brain networks responds to changing environmental demands during complex cognitive processes. Also, unknown in regard to these network dynamics is the role of neuromodulators, such as dopamine, and whether their dysregulation could underlie cognitive deficits in neuropsychiatric illness. We found that connectivity between brain networks changes with working-memory load and greater increases predict better working memory performance; however, it was not related to capacity for dopamine release in the cortex. Patients with schizophrenia did show dynamic internetwork connectivity; however, this was more weakly associated with successful performance in patients compared with healthy individuals. Our findings indicate that dynamic interactions between brain networks may support the type of flexible adaptations essential to goal-directed behavior. Copyright © 2016 the authors 0270-6474/16/364378-12$15.00/0.

Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia

PubMed Central

Van Snellenberg, Jared X.; Benavides, Caridad; Slifstein, Mark; Wang, Zhishun; Moore, Holly; Abi-Dargham, Anissa

2016-01-01

Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during an n-back working-memory task) and positron emission tomography using the radiotracer [11C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. SIGNIFICANCE STATEMENT It is unclear how communication between brain networks responds to changing environmental demands during complex cognitive processes. Also, unknown in regard to these network dynamics is the role of neuromodulators, such as dopamine, and whether their dysregulation could underlie cognitive deficits in neuropsychiatric illness. We found that connectivity between brain networks changes with working-memory load and greater increases predict better working memory performance; however, it was not related to capacity for dopamine release in the cortex. Patients with schizophrenia did show dynamic internetwork connectivity; however, this was more weakly associated with successful performance in patients compared with healthy individuals. Our findings indicate that dynamic interactions between brain networks may support the type of flexible adaptations essential to goal-directed behavior. PMID:27076432

76 FR 70706 - Frontseating Service Valves From the People's Republic of China: Final Results of the 2008-2010...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-15

... more of the following components: A valve body, field connection tube, factory connection tube or valve charge port. The valve body is a rectangular block, or brass forging, machined to be hollow in the interior, with a generally square shaped seat (bottom of body). The field connection tube and factory...

Enabling Community Through Social Media

PubMed Central

Haythornthwaite, Caroline

2013-01-01

Background Social network analysis provides a perspective and method for inquiring into the structures that comprise online groups and communities. Traces from interaction via social media provide the opportunity for understanding how a community is formed and maintained online. Objective The paper aims to demonstrate how social network analysis provides a vocabulary and set of techniques for examining interaction patterns via social media. Using the case of the #hcsmca online discussion forum, this paper highlights what has been and can be gained by approaching online community from a social network perspective, as well as providing an inside look at the structure of the #hcsmca community. Methods Social network analysis was used to examine structures in a 1-month sample of Twitter messages with the hashtag #hcsmca (3871 tweets, 486 unique posters), which is the tag associated with the social media–supported group Health Care Social Media Canada. Network connections were considered present if the individual was mentioned, replied to, or had a post retweeted. Results Network analyses revealed patterns of interaction that characterized the community as comprising one component, with a set of core participants prominent in the network due to their connections with others. Analysis showed the social media health content providers were the most influential group based on in-degree centrality. However, there was no preferential attachment among people in the same professional group, indicating that the formation of connections among community members was not constrained by professional status. Conclusions Network analysis and visualizations provide techniques and a vocabulary for understanding online interaction, as well as insights that can help in understanding what, and who, comprises and sustains a network, and whether community emerges from a network of online interactions. PMID:24176835

Social network analysis of child and adult interorganizational connections.

PubMed

Davis, Maryann; Koroloff, Nancy; Johnsen, Matthew

2012-01-01

Because most programs serve either children and their families or adults, a critical component of service and treatment continuity in mental health and related services for individuals transitioning into adulthood (ages 14-25) is coordination across programs on either side of the adult age divide. This study was conducted in Clark County, Washington, a community that had received a Partnership for Youth Transition grant from the Federal Center for Mental Health Services. Social Network Analysis methodology was used to describe the strength and direction of each organization's relationship to other organizations in the transition network. Interviews were conducted before grant implementation (n=103) and again four years later (n=99). The findings of the study revealed significant changes in the nature of relationships between organizations over time. While the overall density of the transition service network remained stable, specific ways of connecting did change. Some activities became more decentralized while others became more inclusive as evidenced by the increase in size of the largest K-core. This was particularly true for the activity of "receiving referrals." These changes reflected more direct contact between child and adult serving organizations. The two separate child and adult systems identified at baseline appeared more integrated by the end of the grant period. Having greater connectivity among all organizations regardless of ages served should benefit youth and young adults of transition age. This study provides further evidence that Social Network Analysis is a useful method for measuring change in service system integration over time.

In vitro fatigue tests and in silico finite element analysis of dental implants with different fixture/abutment joint types using computer-aided design models.

PubMed

Yamaguchi, Satoshi; Yamanishi, Yasufumi; Machado, Lucas S; Matsumoto, Shuji; Tovar, Nick; Coelho, Paulo G; Thompson, Van P; Imazato, Satoshi

2018-01-01

The aim of this study was to evaluate fatigue resistance of dental fixtures with two different fixture-abutment connections by in vitro fatigue testing and in silico three-dimensional finite element analysis (3D FEA) using original computer-aided design (CAD) models. Dental implant fixtures with external connection (EX) or internal connection (IN) abutments were fabricated from original CAD models using grade IV titanium and step-stress accelerated life testing was performed. Fatigue cycles and loads were assessed by Weibull analysis, and fatigue cracking was observed by micro-computed tomography and a stereomicroscope with high dynamic range software. Using the same CAD models, displacement vectors of implant components were also analyzed by 3D FEA. Angles of the fractured line occurring at fixture platforms in vitro and of displacement vectors corresponding to the fractured line in silico were compared by two-way ANOVA. Fatigue testing showed significantly greater reliability for IN than EX (p<0.001). Fatigue crack initiation was primarily observed at implant fixture platforms. FEA demonstrated that crack lines of both implant systems in vitro were observed in the same direction as displacement vectors of the implant fixtures in silico. In silico displacement vectors in the implant fixture are insightful for geometric development of dental implants to reduce complex interactions leading to fatigue failure. Copyright © 2017 Japan Prosthodontic Society. Published by Elsevier Ltd. All rights reserved.

Computer Drawing Method for Operating Characteristic Curve of PV Power Plant Array Unit

NASA Astrophysics Data System (ADS)

Tan, Jianbin

2018-02-01

According to the engineering design of large-scale grid-connected photovoltaic power stations and the research and development of many simulation and analysis systems, it is necessary to draw a good computer graphics of the operating characteristic curves of photovoltaic array elements and to propose a good segmentation non-linear interpolation algorithm. In the calculation method, Component performance parameters as the main design basis, the computer can get 5 PV module performances. At the same time, combined with the PV array series and parallel connection, the computer drawing of the performance curve of the PV array unit can be realized. At the same time, the specific data onto the module of PV development software can be calculated, and the good operation of PV array unit can be improved on practical application.

Maintenance method and its critical issues for a fast-ignition laser fusion reactor based on a dry wall chamber

NASA Astrophysics Data System (ADS)

Someya, Y.; Matsumoto, T.; Okano, K.; Asaoka, Y.; Hiwatari, R.; Goto, T.; Ogawa, Y.

2008-05-01

The neutronics analysis has been carried out for feasibility study of the FALCON-D concept by Monte Carlo N-paticle transport code (MCNP), in order to inspect the cooling performance of in-vessel and ex-vessel components, and a connection pipe between Vacuum Vessel and reactor room. The nuclear heating rate in the Vacuum Vessel was at the same level as that of NBI duct of the ITER. The temperature of the connection pipe was found to be 345·, ·which was smaller than the melting point of structure materials (F82H). Moreover, the radiation damage of the final optics was also investigated. We propose a sliding changer concept for replacement. This method could be adapted for the replacement of one FPY cycle in the final optics system.

Abnormal resting-state connectivity of motor and cognitive networks in early manifest Huntington's disease.

PubMed

Wolf, R C; Sambataro, F; Vasic, N; Depping, M S; Thomann, P A; Landwehrmeyer, G B; Süssmuth, S D; Orth, M

2014-11-01

Functional magnetic resonance imaging (fMRI) of multiple neural networks during the brain's 'resting state' could facilitate biomarker development in patients with Huntington's disease (HD) and may provide new insights into the relationship between neural dysfunction and clinical symptoms. To date, however, very few studies have examined the functional integrity of multiple resting state networks (RSNs) in manifest HD, and even less is known about whether concomitant brain atrophy affects neural activity in patients. Using MRI, we investigated brain structure and RSN function in patients with early HD (n = 20) and healthy controls (n = 20). For resting-state fMRI data a group-independent component analysis identified spatiotemporally distinct patterns of motor and prefrontal RSNs of interest. We used voxel-based morphometry to assess regional brain atrophy, and 'biological parametric mapping' analyses to investigate the impact of atrophy on neural activity. Compared with controls, patients showed connectivity changes within distinct neural systems including lateral prefrontal, supplementary motor, thalamic, cingulate, temporal and parietal regions. In patients, supplementary motor area and cingulate cortex connectivity indices were associated with measures of motor function, whereas lateral prefrontal connectivity was associated with cognition. This study provides evidence for aberrant connectivity of RSNs associated with motor function and cognition in early manifest HD when controlling for brain atrophy. This suggests clinically relevant changes of RSN activity in the presence of HD-associated cortical and subcortical structural abnormalities.

Resting-state functional connectivity of the default mode network associated with happiness.

PubMed

Luo, Yangmei; Kong, Feng; Qi, Senqing; You, Xuqun; Huang, Xiting

2016-03-01

Happiness refers to people's cognitive and affective evaluation of their life. Why are some people happier than others? One reason might be that unhappy people are prone to ruminate more than happy people. The default mode network (DMN) is normally active during rest and is implicated in rumination. We hypothesized that unhappiness may be associated with increased default-mode functional connectivity during rest, including the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC) and inferior parietal lobule (IPL). The hyperconnectivity of these areas may be associated with higher levels of rumination. One hundred forty-eight healthy participants underwent a resting-state fMRI scan. A group-independent component analysis identified the DMNs. Results indicated increased functional connectivity in the DMN was associated with lower levels of happiness. Specifically, relative to happy people, unhappy people exhibited greater functional connectivity in the anterior medial cortex (bilateral MPFC), posterior medial cortex regions (bilateral PCC) and posterior parietal cortex (left IPL). Moreover, the increased functional connectivity of the MPFC, PCC and IPL, correlated positively with the inclination to ruminate. These results highlight the important role of the DMN in the neural correlates of happiness, and suggest that rumination may play an important role in people's perceived happiness. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Treatment with olanzapine is associated with modulation of the default mode network in patients with Schizophrenia.

PubMed

Sambataro, Fabio; Blasi, Giuseppe; Fazio, Leonardo; Caforio, Grazia; Taurisano, Paolo; Romano, Raffaella; Di Giorgio, Annabella; Gelao, Barbara; Lo Bianco, Luciana; Papazacharias, Apostolos; Popolizio, Teresa; Nardini, Marcello; Bertolino, Alessandro

2010-03-01

Earlier studies have shown widespread alterations of functional connectivity of various brain networks in schizophrenia, including the default mode network (DMN). The DMN has also an important role in the performance of cognitive tasks. Furthermore, treatment with second-generation antipsychotic drugs may ameliorate to some degree working memory (WM) deficits and related brain activity. The aim of this study was to evaluate the effects of treatment with olanzapine monotherapy on functional connectivity among brain regions of the DMN during WM. Seventeen patients underwent an 8-week prospective study and completed two functional magnetic resonance imaging (fMRI) scans at 4 and 8 weeks of treatment during the performance of the N-back WM task. To control for potential repetition effects, 19 healthy controls also underwent two fMRI scans at a similar time interval. We used spatial group-independent component analysis (ICA) to analyze fMRI data. Relative to controls, patients with schizophrenia had reduced connectivity strength within the DMN in posterior cingulate, whereas it was greater in precuneus and inferior parietal lobule. Treatment with olanzapine was associated with increases in DMN connectivity with ventromedial prefrontal cortex, but not in posterior regions of DMN. These results suggest that treatment with olanzapine is associated with the modulation of DMN connectivity in schizophrenia. In addition, our findings suggest critical functional differences in the regions of DMN.

Treatment with Olanzapine is Associated with Modulation of the Default Mode Network in Patients with Schizophrenia

PubMed Central

Sambataro, Fabio; Blasi, Giuseppe; Fazio, Leonardo; Caforio, Grazia; Taurisano, Paolo; Romano, Raffaella; Di Giorgio, Annabella; Gelao, Barbara; Lo Bianco, Luciana; Papazacharias, Apostolos; Popolizio, Teresa; Nardini, Marcello; Bertolino, Alessandro

2010-01-01

Earlier studies have shown widespread alterations of functional connectivity of various brain networks in schizophrenia, including the default mode network (DMN). The DMN has also an important role in the performance of cognitive tasks. Furthermore, treatment with second-generation antipsychotic drugs may ameliorate to some degree working memory (WM) deficits and related brain activity. The aim of this study was to evaluate the effects of treatment with olanzapine monotherapy on functional connectivity among brain regions of the DMN during WM. Seventeen patients underwent an 8-week prospective study and completed two functional magnetic resonance imaging (fMRI) scans at 4 and 8 weeks of treatment during the performance of the N-back WM task. To control for potential repetition effects, 19 healthy controls also underwent two fMRI scans at a similar time interval. We used spatial group-independent component analysis (ICA) to analyze fMRI data. Relative to controls, patients with schizophrenia had reduced connectivity strength within the DMN in posterior cingulate, whereas it was greater in precuneus and inferior parietal lobule. Treatment with olanzapine was associated with increases in DMN connectivity with ventromedial prefrontal cortex, but not in posterior regions of DMN. These results suggest that treatment with olanzapine is associated with the modulation of DMN connectivity in schizophrenia. In addition, our findings suggest critical functional differences in the regions of DMN. PMID:19956088

Comparison of Functional Network Connectivity for Passive-Listening and Active-Response Narrative Comprehension in Adolescents

PubMed Central

Holland, Scott K.

2014-01-01

Abstract Comprehension of narrative stories plays an important role in the development of language skills. In this study, we compared brain activity elicited by a passive-listening version and an active-response (AR) version of a narrative comprehension task by using independent component (IC) analysis on functional magnetic resonance imaging data from 21 adolescents (ages 14–18 years). Furthermore, we explored differences in functional network connectivity engaged by two versions of the task and investigated the relationship between the online response time and the strength of connectivity between each pair of ICs. Despite similar brain region involvements in auditory, temporoparietal, and frontoparietal language networks for both versions, the AR version engages some additional network elements including the left dorsolateral prefrontal, anterior cingulate, and sensorimotor networks. These additional involvements are likely associated with working memory and maintenance of attention, which can be attributed to the differences in cognitive strategic aspects of the two versions. We found significant positive correlation between the online response time and the strength of connectivity between an IC in left inferior frontal region and an IC in sensorimotor region. An explanation for this finding is that longer reaction time indicates stronger connection between the frontal and sensorimotor networks caused by increased activation in adolescents who require more effort to complete the task. PMID:24689887

The theoretical background to BS7167: 1990 - specification for Bordeaux connections

NASA Astrophysics Data System (ADS)

Gorley, T. A. E.

1992-03-01

The theoretical background to the specification of Bordeaux connections (components of lifting gear used to join together wire rope and a chain, or two lengths of wire rope, where the joined lengths have to run over a sheave as in the case of grabbing cranes) is documented. Decisions taken in the drafting of earlier specifications are not documented. The design criteria for BS7167:1990 are addressed. The various parts of the Bordeaux connection specified in the standard are discussed in turn: the link portion, the rope portion, and the grab shackle. Some tests on the new design undertaken by the Health and Safety Executive confirm the new design criteria to be adequate for the strengths of rope to be used with this component.

Temperature detection in a gas turbine

DOEpatents

Lacy, Benjamin; Kraemer, Gilbert; Stevenson, Christian

2012-12-18

A temperature detector includes a first metal and a second metal different from the first metal. The first metal includes a plurality of wires and the second metal includes a wire. The plurality of wires of the first metal are connected to the wire of the second metal in parallel junctions. Another temperature detector includes a plurality of resistance temperature detectors. The plurality of resistance temperature detectors are connected at a plurality of junctions. A method of detecting a temperature change of a component of a turbine includes providing a temperature detector include ing a first metal and a second metal different from the first metal connected to each other at a plurality of junctions in contact with the component; and detecting any voltage change at any junction.

Profile of mathematical reasoning ability of 8th grade students seen from communicational ability, basic skills, connection, and logical thinking

NASA Astrophysics Data System (ADS)

Sumarsih; Budiyono; Indriati, D.

2018-04-01

This research aims to understand the students’ weaknesses in mathematical reasoning ability in junior secondary school. A set of multiple choice tests were used to measure this ability involve components mathematical communication, basic skills, connection, and logical thinking. A total of 259 respondents were determined by stratified cluster random sampling. Data were analyzed using one-way Anova test with Fobs = 109.5760 and F = 3.0000. The results show that students’ ability from schools with high National Exam in mathematics category was the best and followed by medium and low category. Mathematical connection is the most difficult component performed by students. In addition, most students also have difficulty in expressing ideas and developing logical arguments.

A Method to Capture Macroslip at Bolted Interfaces

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

Hopkins, Ronald Neil; Heitman, Lili Anne Akin

2015-10-01

Relative motion at bolted connections can occur for large shock loads as the internal shear force in the bolted connection overcomes the frictional resistive force. This macroslip in a structure dissipates energy and reduces the response of the components above the bolted connection. There is a need to be able to capture macroslip behavior in a structural dynamics model. A linear model and many nonlinear models are not able to predict marcoslip effectively. The proposed method to capture macroslip is to use the multi-body dynamics code ADAMS to model joints with 3-D contact at the bolted interfaces. This model includesmore » both static and dynamic friction. The joints are preloaded and the pinning effect when a bolt shank impacts a through hole inside diameter is captured. Substructure representations of the components are included to account for component flexibility and dynamics. This method was applied to a simplified model of an aerospace structure and validation experiments were performed to test the adequacy of the method.« less

A Method to Capture Macroslip at Bolted Interfaces [PowerPoint

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

Hopkins, Ronald Neil; Heitman, Lili Anne Akin

2016-01-01

Relative motion at bolted connections can occur for large shock loads as the internal shear force in the bolted connection overcomes the frictional resistive force. This macroslip in a structure dissipates energy and reduces the response of the components above the bolted connection. There is a need to be able to capture macroslip behavior in a structural dynamics model. A linear model and many nonlinear models are not able to predict marcoslip effectively. The proposed method to capture macroslip is to use the multi-body dynamics code ADAMS to model joints with 3-D contact at the bolted interfaces. This model includesmore » both static and dynamic friction. The joints are preloaded and the pinning effect when a bolt shank impacts a through hole inside diameter is captured. Substructure representations of the components are included to account for component flexibility and dynamics. This method was applied to a simplified model of an aerospace structure and validation experiments were performed to test the adequacy of the method.« less

Research Status on Reinforcement Connection Form of Precast Concrete Shear Wall Structure

NASA Astrophysics Data System (ADS)

Zhang, Zhuangnan; Zhang, Yan

2018-03-01

With the rapid development of Chinese economy and the speeding up the process of urbanization, housing industrialization has been paid more and more attention. And the fabricated structure has been widely used in China. The key of precast concrete shear wall structure is the connection of precast components. The reinforcement connection can directly affect the entirety performance and seismic behavior of the structure. Different reinforcement connections have a great impact on the overall behavior of the structure. By studying the characteristics of the reinforcement connection forms used in the vertical connection and horizontal connection of precast concrete shear wall, it can provide reference for the research and development of the reinforcement connection forms in the future.

Whole-brain MEG connectivity-based analyses reveals critical hubs in childhood absence epilepsy.

PubMed

Youssofzadeh, Vahab; Agler, William; Tenney, Jeffrey R; Kadis, Darren S

2018-06-04

Absence seizures are thought to be linked to abnormal interplays between regions of a thalamocortical network. However, the complexity of this widespread network makes characterizing the functional interactions among various brain regions challenging. Using whole-brain functional connectivity and network analysis of magnetoencephalography (MEG) data, we explored pre-treatment brain hubs ("highly connected nodes") of patients aged 6 to 12 years with childhood absence epilepsy. We analyzed ictal MEG data of 74 seizures from 16 patients. We employed a time-domain beamformer technique to estimate MEG sources in broadband (1-40 Hz) where the greatest power changes between ictal and preictal periods were identified. A phase synchrony measure, phase locking value, and a graph theory metric, eigenvector centrality (EVC), were utilized to quantify voxel-level connectivity and network hubs of ictal > preictal periods, respectively. A volumetric atlas containing 116 regions of interests (ROIs) was utilized to summarize the network measures. ROIs with EVC (z-score) > 1.96 were reported as critical hubs. ROIs analysis revealed functional-anatomical hubs in a widespread network containing bilateral precuneus (right/left, z = 2.39, 2.18), left thalamus (z = 2.28), and three anterior cerebellar subunits of lobule "IV-V" (z = 3.9), vermis "IV-V" (z = 3.57), and lobule "III" (z = 2.03). Findings suggest that highly connected brain areas or hubs are present in focal cortical, subcortical, and cerebellar regions during absence seizures. Hubs in thalami, precuneus and cingulate cortex generally support a theory of rapidly engaging and bilaterally distributed networks of cortical and subcortical regions responsible for seizures generation, whereas hubs in anterior cerebellar regions may be linked to terminating motor automatisms frequently seen during typical absence seizures. Whole-brain network connectivity is a powerful analytic tool to reveal focal components of absence seizures in MEG. Our investigations can lead to a better understanding of the pathophysiology of CAE. Copyright © 2018 Elsevier B.V. All rights reserved.

The MOD-OA 200 kilowatt wind turbine generator design and analysis report

NASA Astrophysics Data System (ADS)

Andersen, T. S.; Bodenschatz, C. A.; Eggers, A. G.; Hughes, P. S.; Lampe, R. F.; Lipner, M. H.; Schornhorst, J. R.

1980-08-01

The project requirements, approach, system description, design requirements, design, analysis, system tests, installation safety considerations, failure modes and effects analysis, data acquisition, and initial performance for the MOD-OA 200 kw wind turbine generator are discussed. The components, the rotor, driven train, nacelle equipment, yaw drive mechanism and brake, tower, foundation, electrical system, and control systems are presented. The rotor includes the blades, hub and pitch change mechanism. The drive train includes the low speed shaft, speed increaser, high speed shaft, and rotor brake. The electrical system includes the generator, switchgear, transformer, and utility connection. The control systems are the blade pitch, yaw, and generator control, and the safety system. Manual, automatic, and remote control and Dynamic loads and fatigue are analyzed.

The MOD-OA 200 kilowatt wind turbine generator design and analysis report

NASA Technical Reports Server (NTRS)

Andersen, T. S.; Bodenschatz, C. A.; Eggers, A. G.; Hughes, P. S.; Lampe, R. F.; Lipner, M. H.; Schornhorst, J. R.

1980-01-01

The project requirements, approach, system description, design requirements, design, analysis, system tests, installation safety considerations, failure modes and effects analysis, data acquisition, and initial performance for the MOD-OA 200 kw wind turbine generator are discussed. The components, the rotor, driven train, nacelle equipment, yaw drive mechanism and brake, tower, foundation, electrical system, and control systems are presented. The rotor includes the blades, hub and pitch change mechanism. The drive train includes the low speed shaft, speed increaser, high speed shaft, and rotor brake. The electrical system includes the generator, switchgear, transformer, and utility connection. The control systems are the blade pitch, yaw, and generator control, and the safety system. Manual, automatic, and remote control and Dynamic loads and fatigue are analyzed.

Measuring Multiple Resistances Using Single-Point Excitation

NASA Technical Reports Server (NTRS)

Hall, Dan; Davies, Frank

2009-01-01

In a proposed method of determining the resistances of individual DC electrical devices connected in a series or parallel string, no attempt would be made to perform direct measurements on individual devices. Instead, (1) the devices would be instrumented by connecting reactive circuit components in parallel and/or in series with the devices, as appropriate; (2) a pulse or AC voltage excitation would be applied at a single point on the string; and (3) the transient or AC steady-state current response of the string would be measured at that point only. Each reactive component(s) associated with each device would be distinct in order to associate a unique time-dependent response with that device.

Characterization of structural connections using free and forced response test data

NASA Technical Reports Server (NTRS)

Lawrence, Charles; Huckelbridge, Arthur A.

1989-01-01

The accurate prediction of system dynamic response often has been limited by deficiencies in existing capabilities to characterize connections adequately. Connections between structural components often are complex mechanically, and difficult to accurately model analytically. Improved analytical models for connections are needed to improve system dynamic preditions. A procedure for identifying physical connection properties from free and forced response test data is developed, then verified utilizing a system having both a linear and nonlinear connection. Connection properties are computed in terms of physical parameters so that the physical characteristics of the connections can better be understood, in addition to providing improved input for the system model. The identification procedure is applicable to multi-degree of freedom systems, and does not require that the test data be measured directly at the connection locations.

29 CFR 1919.32 - Specially designed blocks and components.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 29 Labor 7 2010-07-01 2010-07-01 false Specially designed blocks and components. 1919.32 Section 1919.32 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION... Treatment; Competent Persons § 1919.32 Specially designed blocks and components. (a) Blocks and connecting...

"Tactic": Traffic Aware Cloud for Tiered Infrastructure Consolidation

ERIC Educational Resources Information Center

Sangpetch, Akkarit

2013-01-01

Large-scale enterprise applications are deployed as distributed applications. These applications consist of many inter-connected components with heterogeneous roles and complex dependencies. Each component typically consumes 5-15% of the server capacity. Deploying each component as a separate virtual machine (VM) allows us to consolidate the…

49 CFR 192.153 - Components fabricated by welding.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 3 2010-10-01 2010-10-01 false Components fabricated by welding. 192.153 Section....153 Components fabricated by welding. (a) Except for branch connections and assemblies of standard... welding, whose strength cannot be determined, must be established in accordance with paragraph UG-101 of...

49 CFR 192.153 - Components fabricated by welding.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 3 2014-10-01 2014-10-01 false Components fabricated by welding. 192.153 Section....153 Components fabricated by welding. (a) Except for branch connections and assemblies of standard... welding, whose strength cannot be determined, must be established in accordance with paragraph UG-101 of...

49 CFR 192.153 - Components fabricated by welding.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 3 2011-10-01 2011-10-01 false Components fabricated by welding. 192.153 Section....153 Components fabricated by welding. (a) Except for branch connections and assemblies of standard... welding, whose strength cannot be determined, must be established in accordance with paragraph UG-101 of...