Ranking and averaging independent component analysis by reproducibility (RAICAR).

PubMed

Yang, Zhi; LaConte, Stephen; Weng, Xuchu; Hu, Xiaoping

2008-06-01

Independent component analysis (ICA) is a data-driven approach that has exhibited great utility for functional magnetic resonance imaging (fMRI). Standard ICA implementations, however, do not provide the number and relative importance of the resulting components. In addition, ICA algorithms utilizing gradient-based optimization give decompositions that are dependent on initialization values, which can lead to dramatically different results. In this work, a new method, RAICAR (Ranking and Averaging Independent Component Analysis by Reproducibility), is introduced to address these issues for spatial ICA applied to fMRI. RAICAR utilizes repeated ICA realizations and relies on the reproducibility between them to rank and select components. Different realizations are aligned based on correlations, leading to aligned components. Each component is ranked and thresholded based on between-realization correlations. Furthermore, different realizations of each aligned component are selectively averaged to generate the final estimate of the given component. Reliability and accuracy of this method are demonstrated with both simulated and experimental fMRI data. Copyright 2007 Wiley-Liss, Inc.

Spatial independent component analysis of functional MRI time-series: to what extent do results depend on the algorithm used?

PubMed

Esposito, Fabrizio; Formisano, Elia; Seifritz, Erich; Goebel, Rainer; Morrone, Renato; Tedeschi, Gioacchino; Di Salle, Francesco

2002-07-01

Independent component analysis (ICA) has been successfully employed to decompose functional MRI (fMRI) time-series into sets of activation maps and associated time-courses. Several ICA algorithms have been proposed in the neural network literature. Applied to fMRI, these algorithms might lead to different spatial or temporal readouts of brain activation. We compared the two ICA algorithms that have been used so far for spatial ICA (sICA) of fMRI time-series: the Infomax (Bell and Sejnowski [1995]: Neural Comput 7:1004-1034) and the Fixed-Point (Hyvärinen [1999]: Adv Neural Inf Proc Syst 10:273-279) algorithms. We evaluated the Infomax- and Fixed Point-based sICA decompositions of simulated motor, and real motor and visual activation fMRI time-series using an ensemble of measures. Log-likelihood (McKeown et al. [1998]: Hum Brain Mapp 6:160-188) was used as a measure of how significantly the estimated independent sources fit the statistical structure of the data; receiver operating characteristics (ROC) and linear correlation analyses were used to evaluate the algorithms' accuracy of estimating the spatial layout and the temporal dynamics of simulated and real activations; cluster sizing calculations and an estimation of a residual gaussian noise term within the components were used to examine the anatomic structure of ICA components and for the assessment of noise reduction capabilities. Whereas both algorithms produced highly accurate results, the Fixed-Point outperformed the Infomax in terms of spatial and temporal accuracy as long as inferential statistics were employed as benchmarks. Conversely, the Infomax sICA was superior in terms of global estimation of the ICA model and noise reduction capabilities. Because of its adaptive nature, the Infomax approach appears to be better suited to investigate activation phenomena that are not predictable or adequately modelled by inferential techniques. Copyright 2002 Wiley-Liss, Inc.

Extracting functional components of neural dynamics with Independent Component Analysis and inverse Current Source Density.

PubMed

Lęski, Szymon; Kublik, Ewa; Swiejkowski, Daniel A; Wróbel, Andrzej; Wójcik, Daniel K

2010-12-01

Local field potentials have good temporal resolution but are blurred due to the slow spatial decay of the electric field. For simultaneous recordings on regular grids one can reconstruct efficiently the current sources (CSD) using the inverse Current Source Density method (iCSD). It is possible to decompose the resultant spatiotemporal information about the current dynamics into functional components using Independent Component Analysis (ICA). We show on test data modeling recordings of evoked potentials on a grid of 4 × 5 × 7 points that meaningful results are obtained with spatial ICA decomposition of reconstructed CSD. The components obtained through decomposition of CSD are better defined and allow easier physiological interpretation than the results of similar analysis of corresponding evoked potentials in the thalamus. We show that spatiotemporal ICA decompositions can perform better for certain types of sources but it does not seem to be the case for the experimental data studied. Having found the appropriate approach to decomposing neural dynamics into functional components we use the technique to study the somatosensory evoked potentials recorded on a grid spanning a large part of the forebrain. We discuss two example components associated with the first waves of activation of the somatosensory thalamus. We show that the proposed method brings up new, more detailed information on the time and spatial location of specific activity conveyed through various parts of the somatosensory thalamus in the rat.

Importance of spatial autocorrelation in modeling bird distributions at a continental scale

USGS Publications Warehouse

Bahn, V.; O'Connor, R.J.; Krohn, W.B.

2006-01-01

Spatial autocorrelation in species' distributions has been recognized as inflating the probability of a type I error in hypotheses tests, causing biases in variable selection, and violating the assumption of independence of error terms in models such as correlation or regression. However, it remains unclear whether these problems occur at all spatial resolutions and extents, and under which conditions spatially explicit modeling techniques are superior. Our goal was to determine whether spatial models were superior at large extents and across many different species. In addition, we investigated the importance of purely spatial effects in distribution patterns relative to the variation that could be explained through environmental conditions. We studied distribution patterns of 108 bird species in the conterminous United States using ten years of data from the Breeding Bird Survey. We compared the performance of spatially explicit regression models with non-spatial regression models using Akaike's information criterion. In addition, we partitioned the variance in species distributions into an environmental, a pure spatial and a shared component. The spatially-explicit conditional autoregressive regression models strongly outperformed the ordinary least squares regression models. In addition, partialling out the spatial component underlying the species' distributions showed that an average of 17% of the explained variation could be attributed to purely spatial effects independent of the spatial autocorrelation induced by the underlying environmental variables. We concluded that location in the range and neighborhood play an important role in the distribution of species. Spatially explicit models are expected to yield better predictions especially for mobile species such as birds, even in coarse-grained models with a large extent. ?? Ecography.

A hybrid spatial-spectral denoising method for infrared hyperspectral images using 2DPCA

NASA Astrophysics Data System (ADS)

Huang, Jun; Ma, Yong; Mei, Xiaoguang; Fan, Fan

2016-11-01

The traditional noise reduction methods for 3-D infrared hyperspectral images typically operate independently in either the spatial or spectral domain, and such methods overlook the relationship between the two domains. To address this issue, we propose a hybrid spatial-spectral method in this paper to link both domains. First, principal component analysis and bivariate wavelet shrinkage are performed in the 2-D spatial domain. Second, 2-D principal component analysis transformation is conducted in the 1-D spectral domain to separate the basic components from detail ones. The energy distribution of noise is unaffected by orthogonal transformation; therefore, the signal-to-noise ratio of each component is used as a criterion to determine whether a component should be protected from over-denoising or denoised with certain 1-D denoising methods. This study implements the 1-D wavelet shrinking threshold method based on Stein's unbiased risk estimator, and the quantitative results on publicly available datasets demonstrate that our method can improve denoising performance more effectively than other state-of-the-art methods can.

Classification of independent components of EEG into multiple artifact classes.

PubMed

Frølich, Laura; Andersen, Tobias S; Mørup, Morten

2015-01-01

In this study, we aim to automatically identify multiple artifact types in EEG. We used multinomial regression to classify independent components of EEG data, selecting from 65 spatial, spectral, and temporal features of independent components using forward selection. The classifier identified neural and five nonneural types of components. Between subjects within studies, high classification performances were obtained. Between studies, however, classification was more difficult. For neural versus nonneural classifications, performance was on par with previous results obtained by others. We found that automatic separation of multiple artifact classes is possible with a small feature set. Our method can reduce manual workload and allow for the selective removal of artifact classes. Identifying artifacts during EEG recording may be used to instruct subjects to refrain from activity causing them. Copyright © 2014 Society for Psychophysiological Research.

Spatiotemporal patterns of ERP based on combined ICA-LORETA analysis

NASA Astrophysics Data System (ADS)

Zhang, Jiacai; Guo, Taomei; Xu, Yaqin; Zhao, Xiaojie; Yao, Li

2007-03-01

In contrast to the FMRI methods widely used up to now, this method try to understand more profoundly how the brain systems work under sentence processing task map accurately the spatiotemporal patterns of activity of the large neuronal populations in the human brain from the analysis of ERP data recorded on the brain scalp. In this study, an event-related brain potential (ERP) paradigm to record the on-line responses to the processing of sentences is chosen as an example. In order to give attention to both utilizing the ERPs' temporal resolution of milliseconds and overcoming the insensibility of cerebral location ERP sources, we separate these sources in space and time based on a combined method of independent component analysis (ICA) and low-resolution tomography (LORETA) algorithms. ICA blindly separate the input ERP data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. And then the spatial maps associated with each ICA component are analyzed, with use of LORETA to uniquely locate its cerebral sources throughout the full brain according to the assumption that neighboring neurons are simultaneously and synchronously activated. Our results show that the cerebral computation mechanism underlies content words reading is mediated by the orchestrated activity of several spatially distributed brain sources located in the temporal, frontal, and parietal areas, and activate at distinct time intervals and are grouped into different statistically independent components. Thus ICA-LORETA analysis provides an encouraging and effective method to study brain dynamics from ERP.

A novel spatial performance metric for robust pattern optimization of distributed hydrological models

NASA Astrophysics Data System (ADS)

Stisen, S.; Demirel, C.; Koch, J.

2017-12-01

Evaluation of performance is an integral part of model development and calibration as well as it is of paramount importance when communicating modelling results to stakeholders and the scientific community. There exists a comprehensive and well tested toolbox of metrics to assess temporal model performance in the hydrological modelling community. On the contrary, the experience to evaluate spatial performance is not corresponding to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study aims at making a contribution towards advancing spatial pattern oriented model evaluation for distributed hydrological models. This is achieved by introducing a novel spatial performance metric which provides robust pattern performance during model calibration. The promoted SPAtial EFficiency (spaef) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multi-component approach is necessary in order to adequately compare spatial patterns. spaef, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are tested in a spatial pattern oriented model calibration of a catchment model in Denmark. The calibration is constrained by a remote sensing based spatial pattern of evapotranspiration and discharge timeseries at two stations. Our results stress that stand-alone metrics tend to fail to provide holistic pattern information to the optimizer which underlines the importance of multi-component metrics. The three spaef components are independent which allows them to complement each other in a meaningful way. This study promotes the use of bias insensitive metrics which allow comparing variables which are related but may differ in unit in order to optimally exploit spatial observations made available by remote sensing platforms. We see great potential of spaef across environmental disciplines dealing with spatially distributed modelling.

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.

Electromagnetic potential vectors and the Lagrangian of a charged particle

NASA Technical Reports Server (NTRS)

Shebalin, John V.

1992-01-01

Maxwell's equations can be shown to imply the existence of two independent three-dimensional potential vectors. A comparison between the potential vectors and the electric and magnetic field vectors, using a spatial Fourier transformation, reveals six independent potential components but only four independent electromagnetic field components for each mode. Although the electromagnetic fields determined by Maxwell's equations give a complete description of all possible classical electromagnetic phenomena, potential vectors contains more information and allow for a description of such quantum mechanical phenomena as the Aharonov-Bohm effect. A new result is that a charged particle Lagrangian written in terms of potential vectors automatically contains a 'spontaneous symmetry breaking' potential.

Independence and interaction of luminance and chromatic contributions to spatial hyperacuity performance.

PubMed

Cooper, Bonnie; Lee, Barry B

2014-04-01

Here we test interactions of luminance and chromatic input to spatial hyperacuity mechanisms. First, we tested alignment of luminance and chromatic gratings matched or mismatched in contrast polarity or grating type. Thresholds with matched gratings were low while all mismatched pairs were elevated. Second, we determined alignment acuity as a function of luminance or chromatic contrast alone or in the presence of constant contrast components of the other type. For in-phase components, performance followed the envelope of the more sensitive mechanism. However, polarity reversals revealed an asymmetric effect for luminance and chromatic conditions, which suggested that luminance can override chromatic mechanisms in hyperacuity; we interpret these findings in the context of spatial mechanisms.

Children's construction task performance and spatial ability: controlling task complexity and predicting mathematics performance.

PubMed

Richardson, Miles; Hunt, Thomas E; Richardson, Cassandra

2014-12-01

This paper presents a methodology to control construction task complexity and examined the relationships between construction performance and spatial and mathematical abilities in children. The study included three groups of children (N = 96); ages 7-8, 10-11, and 13-14 years. Each group constructed seven pre-specified objects. The study replicated and extended previous findings that indicated that the extent of component symmetry and variety, and the number of components for each object and available for selection, significantly predicted construction task difficulty. Results showed that this methodology is a valid and reliable technique for assessing and predicting construction play task difficulty. Furthermore, construction play performance predicted mathematical attainment independently of spatial ability.

Nonlinear Extraction of Independent Components of Natural Images Using Radial Gaussianization

PubMed Central

Lyu, Siwei; Simoncelli, Eero P.

2011-01-01

We consider the problem of efficiently encoding a signal by transforming it to a new representation whose components are statistically independent. A widely studied linear solution, known as independent component analysis (ICA), exists for the case when the signal is generated as a linear transformation of independent nongaussian sources. Here, we examine a complementary case, in which the source is nongaussian and elliptically symmetric. In this case, no invertible linear transform suffices to decompose the signal into independent components, but we show that a simple nonlinear transformation, which we call radial gaussianization (RG), is able to remove all dependencies. We then examine this methodology in the context of natural image statistics. We first show that distributions of spatially proximal bandpass filter responses are better described as elliptical than as linearly transformed independent sources. Consistent with this, we demonstrate that the reduction in dependency achieved by applying RG to either nearby pairs or blocks of bandpass filter responses is significantly greater than that achieved by ICA. Finally, we show that the RG transformation may be closely approximated by divisive normalization, which has been used to model the nonlinear response properties of visual neurons. PMID:19191599

Translation of EEG Spatial Filters from Resting to Motor Imagery Using Independent Component Analysis

PubMed Central

Wang, Yijun; Wang, Yu-Te; Jung, Tzyy-Ping

2012-01-01

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often use spatial filters to improve signal-to-noise ratio of task-related EEG activities. To obtain robust spatial filters, large amounts of labeled data, which are often expensive and labor-intensive to obtain, need to be collected in a training procedure before online BCI control. Several studies have recently developed zero-training methods using a session-to-session scenario in order to alleviate this problem. To our knowledge, a state-to-state translation, which applies spatial filters derived from one state to another, has never been reported. This study proposes a state-to-state, zero-training method to construct spatial filters for extracting EEG changes induced by motor imagery. Independent component analysis (ICA) was separately applied to the multi-channel EEG in the resting and the motor imagery states to obtain motor-related spatial filters. The resultant spatial filters were then applied to single-trial EEG to differentiate left- and right-hand imagery movements. On a motor imagery dataset collected from nine subjects, comparable classification accuracies were obtained by using ICA-based spatial filters derived from the two states (motor imagery: 87.0%, resting: 85.9%), which were both significantly higher than the accuracy achieved by using monopolar scalp EEG data (80.4%). The proposed method considerably increases the practicality of BCI systems in real-world environments because it is less sensitive to electrode misalignment across different sessions or days and does not require annotated pilot data to derive spatial filters. PMID:22666377

Preferential sampling and Bayesian geostatistics: Statistical modeling and examples.

PubMed

Cecconi, Lorenzo; Grisotto, Laura; Catelan, Dolores; Lagazio, Corrado; Berrocal, Veronica; Biggeri, Annibale

2016-08-01

Preferential sampling refers to any situation in which the spatial process and the sampling locations are not stochastically independent. In this paper, we present two examples of geostatistical analysis in which the usual assumption of stochastic independence between the point process and the measurement process is violated. To account for preferential sampling, we specify a flexible and general Bayesian geostatistical model that includes a shared spatial random component. We apply the proposed model to two different case studies that allow us to highlight three different modeling and inferential aspects of geostatistical modeling under preferential sampling: (1) continuous or finite spatial sampling frame; (2) underlying causal model and relevant covariates; and (3) inferential goals related to mean prediction surface or prediction uncertainty. © The Author(s) 2016.

Classification of fMRI resting-state maps using machine learning techniques: A comparative study

NASA Astrophysics Data System (ADS)

Gallos, Ioannis; Siettos, Constantinos

2017-11-01

We compare the efficiency of Principal Component Analysis (PCA) and nonlinear learning manifold algorithms (ISOMAP and Diffusion maps) for classifying brain maps between groups of schizophrenia patients and healthy from fMRI scans during a resting-state experiment. After a standard pre-processing pipeline, we applied spatial Independent component analysis (ICA) to reduce (a) noise and (b) spatial-temporal dimensionality of fMRI maps. On the cross-correlation matrix of the ICA components, we applied PCA, ISOMAP and Diffusion Maps to find an embedded low-dimensional space. Finally, support-vector-machines (SVM) and k-NN algorithms were used to evaluate the performance of the algorithms in classifying between the two groups.

Psychophysical and physiological responses to gratings with luminance and chromatic components of different spatial frequencies.

PubMed

Cooper, Bonnie; Sun, Hao; Lee, Barry B

2012-02-01

Gratings that contain luminance and chromatic components of different spatial frequencies were used to study the segregation of signals in luminance and chromatic pathways. Psychophysical detection and discrimination thresholds to these compound gratings, with luminance and chromatic components of the one either half or double the spatial frequency of the other, were measured in human observers. Spatial frequency tuning curves for detection of compound gratings followed the envelope of those for luminance and chromatic gratings. Different grating types were discriminable at detection threshold. Fourier analysis of physiological responses of macaque retinal ganglion cells to compound waveforms showed chromatic information to be restricted to the parvocellular pathway and luminance information to the magnocellular pathway. Taken together, the human psychophysical and macaque physiological data support the strict segregation of luminance and chromatic information in independent channels, with the magnocellular and parvocellular pathways, respectively, serving as likely the physiological substrates. © 2012 Optical Society of America

Multiple foci of spatial attention in multimodal working memory.

PubMed

Katus, Tobias; Eimer, Martin

2016-11-15

The maintenance of sensory information in working memory (WM) is mediated by the attentional activation of stimulus representations that are stored in perceptual brain regions. Using event-related potentials (ERPs), we measured tactile and visual contralateral delay activity (tCDA/CDA components) in a bimodal WM task to concurrently track the attention-based maintenance of information stored in anatomically segregated (somatosensory and visual) brain areas. Participants received tactile and visual sample stimuli on both sides, and in different blocks, memorized these samples on the same side or on opposite sides. After a retention delay, memory was unpredictably tested for touch or vision. In the same side blocks, tCDA and CDA components simultaneously emerged over the same hemisphere, contralateral to the memorized tactile/visual sample set. In opposite side blocks, these two components emerged over different hemispheres, but had the same sizes and onset latencies as in the same side condition. Our results reveal distinct foci of tactile and visual spatial attention that were concurrently maintained on task-relevant stimulus representations in WM. The independence of spatially-specific biasing mechanisms for tactile and visual WM content suggests that multimodal information is stored in distributed perceptual brain areas that are activated through modality-specific processes that can operate simultaneously and largely independently of each other. Copyright © 2016 Elsevier Inc. All rights reserved.

Detection of independent functional networks during music listening using electroencephalogram and sLORETA-ICA.

PubMed

Jäncke, Lutz; Alahmadi, Nsreen

2016-04-13

The measurement of brain activation during music listening is a topic that is attracting increased attention from many researchers. Because of their high spatial accuracy, functional MRI measurements are often used for measuring brain activation in the context of music listening. However, this technique faces the issues of contaminating scanner noise and an uncomfortable experimental environment. Electroencephalogram (EEG), however, is a neural registration technique that allows the measurement of neurophysiological activation in silent and more comfortable experimental environments. Thus, it is optimal for recording brain activations during pleasant music stimulation. Using a new mathematical approach to calculate intracortical independent components (sLORETA-IC) on the basis of scalp-recorded EEG, we identified specific intracortical independent components during listening of a musical piece and scales, which differ substantially from intracortical independent components calculated from the resting state EEG. Most intracortical independent components are located bilaterally in perisylvian brain areas known to be involved in auditory processing and specifically in music perception. Some intracortical independent components differ between the music and scale listening conditions. The most prominent difference is found in the anterior part of the perisylvian brain region, with stronger activations seen in the left-sided anterior perisylvian regions during music listening, most likely indicating semantic processing during music listening. A further finding is that the intracortical independent components obtained for the music and scale listening are most prominent in higher frequency bands (e.g. beta-2 and beta-3), whereas the resting state intracortical independent components are active in lower frequency bands (alpha-1 and theta). This new technique for calculating intracortical independent components is able to differentiate independent neural networks associated with music and scale listening. Thus, this tool offers new opportunities for studying neural activations during music listening using the silent and more convenient EEG technology.

A K-means multivariate approach for clustering independent components from magnetoencephalographic data.

PubMed

Spadone, Sara; de Pasquale, Francesco; Mantini, Dante; Della Penna, Stefania

2012-09-01

Independent component analysis (ICA) is typically applied on functional magnetic resonance imaging, electroencephalographic and magnetoencephalographic (MEG) data due to its data-driven nature. In these applications, ICA needs to be extended from single to multi-session and multi-subject studies for interpreting and assigning a statistical significance at the group level. Here a novel strategy for analyzing MEG independent components (ICs) is presented, Multivariate Algorithm for Grouping MEG Independent Components K-means based (MAGMICK). The proposed approach is able to capture spatio-temporal dynamics of brain activity in MEG studies by running ICA at subject level and then clustering the ICs across sessions and subjects. Distinctive features of MAGMICK are: i) the implementation of an efficient set of "MEG fingerprints" designed to summarize properties of MEG ICs as they are built on spatial, temporal and spectral parameters; ii) the implementation of a modified version of the standard K-means procedure to improve its data-driven character. This algorithm groups the obtained ICs automatically estimating the number of clusters through an adaptive weighting of the parameters and a constraint on the ICs independence, i.e. components coming from the same session (at subject level) or subject (at group level) cannot be grouped together. The performances of MAGMICK are illustrated by analyzing two sets of MEG data obtained during a finger tapping task and median nerve stimulation. The results demonstrate that the method can extract consistent patterns of spatial topography and spectral properties across sessions and subjects that are in good agreement with the literature. In addition, these results are compared to those from a modified version of affinity propagation clustering method. The comparison, evaluated in terms of different clustering validity indices, shows that our methodology often outperforms the clustering algorithm. Eventually, these results are confirmed by a comparison with a MEG tailored version of the self-organizing group ICA, which is largely used for fMRI IC clustering. Copyright © 2012 Elsevier Inc. All rights reserved.

Modality-specificity of Selective Attention Networks.

PubMed

Stewart, Hannah J; Amitay, Sygal

2015-01-01

To establish the modality specificity and generality of selective attention networks. Forty-eight young adults completed a battery of four auditory and visual selective attention tests based upon the Attention Network framework: the visual and auditory Attention Network Tests (vANT, aANT), the Test of Everyday Attention (TEA), and the Test of Attention in Listening (TAiL). These provided independent measures for auditory and visual alerting, orienting, and conflict resolution networks. The measures were subjected to an exploratory factor analysis to assess underlying attention constructs. The analysis yielded a four-component solution. The first component comprised of a range of measures from the TEA and was labeled "general attention." The third component was labeled "auditory attention," as it only contained measures from the TAiL using pitch as the attended stimulus feature. The second and fourth components were labeled as "spatial orienting" and "spatial conflict," respectively-they were comprised of orienting and conflict resolution measures from the vANT, aANT, and TAiL attend-location task-all tasks based upon spatial judgments (e.g., the direction of a target arrow or sound location). These results do not support our a-priori hypothesis that attention networks are either modality specific or supramodal. Auditory attention separated into selectively attending to spatial and non-spatial features, with the auditory spatial attention loading onto the same factor as visual spatial attention, suggesting spatial attention is supramodal. However, since our study did not include a non-spatial measure of visual attention, further research will be required to ascertain whether non-spatial attention is modality-specific.

Local and Widely Distributed EEG Activity in Schizophrenia With Prevalence of Negative Symptoms.

PubMed

Grin-Yatsenko, Vera A; Ponomarev, Valery A; Pronina, Marina V; Poliakov, Yury I; Plotnikova, Irina V; Kropotov, Juri D

2017-09-01

We evaluated EEG frequency abnormalities in resting state (eyes closed and eyes open) EEG in a group of chronic schizophrenia patients as compared with healthy subjects. The study included 3 methods of analysis of deviation of EEG characteristics: genuine EEG, current source density (CSD), and group independent component (gIC). All 3 methods have shown that the EEG in schizophrenia patients is characterized by enhanced low-frequency (delta and theta) and high-frequency (beta) activity in comparison with the control group. However, the spatial pattern of differences was dependent on the type of method used. Comparative analysis has shown that increased EEG power in schizophrenia patients apparently concerns both widely spatially distributed components and local components of signal. Furthermore, the observed differences in the delta and theta range can be described mainly by the local components, and those in the beta range mostly by spatially widely distributed ones. The possible nature of the widely distributed activity is discussed.

The relation between working memory and language comprehension in signers and speakers.

PubMed

Emmorey, Karen; Giezen, Marcel R; Petrich, Jennifer A F; Spurgeon, Erin; O'Grady Farnady, Lucinda

2017-06-01

This study investigated the relation between linguistic and spatial working memory (WM) resources and language comprehension for signed compared to spoken language. Sign languages are both linguistic and visual-spatial, and therefore provide a unique window on modality-specific versus modality-independent contributions of WM resources to language processing. Deaf users of American Sign Language (ASL), hearing monolingual English speakers, and hearing ASL-English bilinguals completed several spatial and linguistic serial recall tasks. Additionally, their comprehension of spatial and non-spatial information in ASL and spoken English narratives was assessed. Results from the linguistic serial recall tasks revealed that the often reported advantage for speakers on linguistic short-term memory tasks does not extend to complex WM tasks with a serial recall component. For English, linguistic WM predicted retention of non-spatial information, and both linguistic and spatial WM predicted retention of spatial information. For ASL, spatial WM predicted retention of spatial (but not non-spatial) information, and linguistic WM did not predict retention of either spatial or non-spatial information. Overall, our findings argue against strong assumptions of independent domain-specific subsystems for the storage and processing of linguistic and spatial information and furthermore suggest a less important role for serial encoding in signed than spoken language comprehension. Copyright © 2017 Elsevier B.V. All rights reserved.

SCGICAR: Spatial concatenation based group ICA with reference for fMRI data analysis.

PubMed

Shi, Yuhu; Zeng, Weiming; Wang, Nizhuan

2017-09-01

With the rapid development of big data, the functional magnetic resonance imaging (fMRI) data analysis of multi-subject is becoming more and more important. As a kind of blind source separation technique, group independent component analysis (GICA) has been widely applied for the multi-subject fMRI data analysis. However, spatial concatenated GICA is rarely used compared with temporal concatenated GICA due to its disadvantages. In this paper, in order to overcome these issues and to consider that the ability of GICA for fMRI data analysis can be improved by adding a priori information, we propose a novel spatial concatenation based GICA with reference (SCGICAR) method to take advantage of the priori information extracted from the group subjects, and then the multi-objective optimization strategy is used to implement this method. Finally, the post-processing means of principal component analysis and anti-reconstruction are used to obtain group spatial component and individual temporal component in the group, respectively. The experimental results show that the proposed SCGICAR method has a better performance on both single-subject and multi-subject fMRI data analysis compared with classical methods. It not only can detect more accurate spatial and temporal component for each subject of the group, but also can obtain a better group component on both temporal and spatial domains. These results demonstrate that the proposed SCGICAR method has its own advantages in comparison with classical methods, and it can better reflect the commonness of subjects in the group. Copyright © 2017 Elsevier B.V. All rights reserved.

Analysis on unevenness of skin color using the melanin and hemoglobin components separated by independent component analysis of skin color image

NASA Astrophysics Data System (ADS)

Ojima, Nobutoshi; Fujiwara, Izumi; Inoue, Yayoi; Tsumura, Norimichi; Nakaguchi, Toshiya; Iwata, Kayoko

2011-03-01

Uneven distribution of skin color is one of the biggest concerns about facial skin appearance. Recently several techniques to analyze skin color have been introduced by separating skin color information into chromophore components, such as melanin and hemoglobin. However, there are not many reports on quantitative analysis of unevenness of skin color by considering type of chromophore, clusters of different sizes and concentration of the each chromophore. We propose a new image analysis and simulation method based on chromophore analysis and spatial frequency analysis. This method is mainly composed of three techniques: independent component analysis (ICA) to extract hemoglobin and melanin chromophores from a single skin color image, an image pyramid technique which decomposes each chromophore into multi-resolution images, which can be used for identifying different sizes of clusters or spatial frequencies, and analysis of the histogram obtained from each multi-resolution image to extract unevenness parameters. As the application of the method, we also introduce an image processing technique to change unevenness of melanin component. As the result, the method showed high capabilities to analyze unevenness of each skin chromophore: 1) Vague unevenness on skin could be discriminated from noticeable pigmentation such as freckles or acne. 2) By analyzing the unevenness parameters obtained from each multi-resolution image for Japanese ladies, agerelated changes were observed in the parameters of middle spatial frequency. 3) An image processing system modulating the parameters was proposed to change unevenness of skin images along the axis of the obtained age-related change in real time.

Keys and seats: Spatial response coding underlying the joint spatial compatibility effect.

PubMed

Dittrich, Kerstin; Dolk, Thomas; Rothe-Wulf, Annelie; Klauer, Karl Christoph; Prinz, Wolfgang

2013-11-01

Spatial compatibility effects (SCEs) are typically observed when participants have to execute spatially defined responses to nonspatial stimulus features (e.g., the color red or green) that randomly appear to the left and the right. Whereas a spatial correspondence of stimulus and response features facilitates response execution, a noncorrespondence impairs task performance. Interestingly, the SCE is drastically reduced when a single participant responds to one stimulus feature (e.g., green) by operating only one response key (individual go/no-go task), whereas a full-blown SCE is observed when the task is distributed between two participants (joint go/no-go task). This joint SCE (a.k.a. the social Simon effect) has previously been explained by action/task co-representation, whereas alternative accounts ascribe joint SCEs to spatial components inherent in joint go/no-go tasks that allow participants to code their responses spatially. Although increasing evidence supports the idea that spatial rather than social aspects are responsible for joint SCEs emerging, it is still unclear to which component(s) the spatial coding refers to: the spatial orientation of response keys, the spatial orientation of responding agents, or both. By varying the spatial orientation of the responding agents (Exp. 1) and of the response keys (Exp. 2), independent of the spatial orientation of the stimuli, in the present study we found joint SCEs only when both the seating and the response key alignment matched the stimulus alignment. These results provide evidence that spatial response coding refers not only to the response key arrangement, but also to the-often neglected-spatial orientation of the responding agents.

How does spatial extent of fMRI datasets affect independent component analysis decomposition?

PubMed

Aragri, Adriana; Scarabino, Tommaso; Seifritz, Erich; Comani, Silvia; Cirillo, Sossio; Tedeschi, Gioacchino; Esposito, Fabrizio; Di Salle, Francesco

2006-09-01

Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to different spatial or temporal readouts of brain activation phenomena. By reducing and increasing a volume of interest (VOI), we applied sICA to different datasets from real activation experiments with multislice acquisition and single or multiple sensory-motor task-induced blood oxygenation level-dependent (BOLD) signal sources with different spatial and temporal structure. Using receiver operating characteristics (ROC) methodology for accuracy evaluation and multiple regression analysis as benchmark, we compared sICA decompositions of reduced and increased VOI fMRI time-series containing auditory, motor and hemifield visual activation occurring separately or simultaneously in time. Both approaches yielded valid results; however, the results of the increased VOI approach were spatially more accurate compared to the results of the decreased VOI approach. This is consistent with the capability of sICA to take advantage of extended samples of statistical observations and suggests that sICA is more powerful with extended rather than reduced VOI datasets to delineate brain activity. (c) 2006 Wiley-Liss, Inc.

Fourier Domain Sensing

NASA Technical Reports Server (NTRS)

Feldkhun, Daniel (Inventor); Wagner, Kelvin H. (Inventor)

2013-01-01

Methods and systems are disclosed of sensing an object. A first radiation is spatially modulated to generate a structured second radiation. The object is illuminated with the structured second radiation such that the object produces a third radiation in response. Apart from any spatially dependent delay, a time variation of the third radiation is spatially independent. With a single-element detector, a portion of the third radiation is detected from locations on the object simultaneously. At least one characteristic of a sinusoidal spatial Fourier-transform component of the object is estimated from a time-varying signal from the detected portion of the third radiation.

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

Efficient use of bit planes in the generation of motion stimuli

NASA Technical Reports Server (NTRS)

Mulligan, Jeffrey B.; Stone, Leland S.

1988-01-01

The production of animated motion sequences on computer-controlled display systems presents a technical problem because large images cannot be transferred from disk storage to image memory at conventional frame rates. A technique is described in which a single base image can be used to generate a broad class of motion stimuli without the need for such memory transfers. This technique was applied to the generation of drifting sine-wave gratings (and by extension, sine wave plaids). For each drifting grating, sine and cosine spatial phase components are first reduced to 1 bit/pixel using a digital halftoning technique. The resulting pairs of 1-bit images are then loaded into pairs of bit planes of the display memory. To animate the patterns, the display hardware's color lookup table is modified on a frame-by-frame basis; for each frame the lookup table is set to display a weighted sum of the spatial sine and cosine phase components. Because the contrasts and temporal frequencies of the various components are mutually independent in each frame, the sine and cosine components can be counterphase modulated in temporal quadrature, yielding a single drifting grating. Using additional bit planes, multiple drifting gratings can be combined to form sine-wave plaid patterns. A large number of resultant plaid motions can be produced from a single image file because the temporal frequencies of all the components can be varied independently. For a graphics device having 8 bits/pixel, up to four drifting gratings may be combined, each having independently variable contrast and speed.

Equivalent water height extracted from GRACE gravity field model with robust independent component analysis

NASA Astrophysics Data System (ADS)

Guo, Jinyun; Mu, Dapeng; Liu, Xin; Yan, Haoming; Dai, Honglei

2014-08-01

The Level-2 monthly GRACE gravity field models issued by Center for Space Research (CSR), GeoForschungs Zentrum (GFZ), and Jet Propulsion Laboratory (JPL) are treated as observations used to extract the equivalent water height (EWH) with the robust independent component analysis (RICA). The smoothing radii of 300, 400, and 500 km are tested, respectively, in the Gaussian smoothing kernel function to reduce the observation Gaussianity. Three independent components are obtained by RICA in the spatial domain; the first component matches the geophysical signal, and the other two match the north-south strip and the other noises. The first mode is used to estimate EWHs of CSR, JPL, and GFZ, and compared with the classical empirical decorrelation method (EDM). The EWH STDs for 12 months in 2010 extracted by RICA and EDM show the obvious fluctuation. The results indicate that the sharp EWH changes in some areas have an important global effect, like in Amazon, Mekong, and Zambezi basins.

Dual-energy x-ray image decomposition by independent component analysis

NASA Astrophysics Data System (ADS)

Jiang, Yifeng; Jiang, Dazong; Zhang, Feng; Zhang, Dengfu; Lin, Gang

2001-09-01

The spatial distributions of bone and soft tissue in human body are separated by independent component analysis (ICA) of dual-energy x-ray images. It is because of the dual energy imaging modelí-s conformity to the ICA model that we can apply this method: (1) the absorption in body is mainly caused by photoelectric absorption and Compton scattering; (2) they take place simultaneously but are mutually independent; and (3) for monochromatic x-ray sources the total attenuation is achieved by linear combination of these two absorption. Compared with the conventional method, the proposed one needs no priori information about the accurate x-ray energy magnitude for imaging, while the results of the separation agree well with the conventional one.

Spatially distributed effects of mental exhaustion on resting-state FMRI networks.

PubMed

Esposito, Fabrizio; Otto, Tobias; Zijlstra, Fred R H; Goebel, Rainer

2014-01-01

Brain activity during rest is spatially coherent over functional connectivity networks called resting-state networks. In resting-state functional magnetic resonance imaging, independent component analysis yields spatially distributed network representations reflecting distinct mental processes, such as intrinsic (default) or extrinsic (executive) attention, and sensory inhibition or excitation. These aspects can be related to different treatments or subjective experiences. Among these, exhaustion is a common psychological state induced by prolonged mental performance. Using repeated functional magnetic resonance imaging sessions and spatial independent component analysis, we explored the effect of several hours of sustained cognitive performances on the resting human brain. Resting-state functional magnetic resonance imaging was performed on the same healthy volunteers in two days, with and without, and before, during and after, an intensive psychological treatment (skill training and sustained practice with a flight simulator). After each scan, subjects rated their level of exhaustion and performed an N-back task to evaluate eventual decrease in cognitive performance. Spatial maps of selected resting-state network components were statistically evaluated across time points to detect possible changes induced by the sustained mental performance. The intensive treatment had a significant effect on exhaustion and effort ratings, but no effects on N-back performances. Significant changes in the most exhausted state were observed in the early visual processing and the anterior default mode networks (enhancement) and in the fronto-parietal executive networks (suppression), suggesting that mental exhaustion is associated with a more idling brain state and that internal attention processes are facilitated to the detriment of more extrinsic processes. The described application may inspire future indicators of the level of fatigue in the neural attention system.

Modality-specificity of Selective Attention Networks

PubMed Central

Stewart, Hannah J.; Amitay, Sygal

2015-01-01

Objective: To establish the modality specificity and generality of selective attention networks. Method: Forty-eight young adults completed a battery of four auditory and visual selective attention tests based upon the Attention Network framework: the visual and auditory Attention Network Tests (vANT, aANT), the Test of Everyday Attention (TEA), and the Test of Attention in Listening (TAiL). These provided independent measures for auditory and visual alerting, orienting, and conflict resolution networks. The measures were subjected to an exploratory factor analysis to assess underlying attention constructs. Results: The analysis yielded a four-component solution. The first component comprised of a range of measures from the TEA and was labeled “general attention.” The third component was labeled “auditory attention,” as it only contained measures from the TAiL using pitch as the attended stimulus feature. The second and fourth components were labeled as “spatial orienting” and “spatial conflict,” respectively—they were comprised of orienting and conflict resolution measures from the vANT, aANT, and TAiL attend-location task—all tasks based upon spatial judgments (e.g., the direction of a target arrow or sound location). Conclusions: These results do not support our a-priori hypothesis that attention networks are either modality specific or supramodal. Auditory attention separated into selectively attending to spatial and non-spatial features, with the auditory spatial attention loading onto the same factor as visual spatial attention, suggesting spatial attention is supramodal. However, since our study did not include a non-spatial measure of visual attention, further research will be required to ascertain whether non-spatial attention is modality-specific. PMID:26635709

Texture-dependent motion signals in primate middle temporal area

PubMed Central

Gharaei, Saba; Tailby, Chris; Solomon, Selina S; Solomon, Samuel G

2013-01-01

Neurons in the middle temporal (MT) area of primate cortex provide an important stage in the analysis of visual motion. For simple stimuli such as bars and plaids some neurons in area MT – pattern cells – seem to signal motion independent of contour orientation, but many neurons – component cells – do not. Why area MT supports both types of receptive field is unclear. To address this we made extracellular recordings from single units in area MT of anaesthetised marmoset monkeys and examined responses to two-dimensional images with a large range of orientations and spatial frequencies. Component and pattern cell response remained distinct during presentation of these complex spatial textures. Direction tuning curves were sharpest in component cells when a texture contained a narrow range of orientations, but were similar across all neurons for textures containing all orientations. Response magnitude of pattern cells, but not component cells, increased with the spatial bandwidth of the texture. In addition, response variability in all neurons was reduced when the stimulus was rich in spatial texture. Fisher information analysis showed that component cells provide more informative responses than pattern cells when a texture contains a narrow range of orientations, but pattern cells had more informative responses for broadband textures. Component cells and pattern cells may therefore coexist because they provide complementary and parallel motion signals. PMID:24000175

Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.

PubMed

Xie, Jianwen; Douglas, Pamela K; Wu, Ying Nian; Brody, Arthur L; Anderson, Ariana E

2017-04-15

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (p<0.001) for predicting the task being performed within each scan using artifact-cleaned components. The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy compared to the ICA and sparse coding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p<0.001). Lower classification accuracy occurred when the extracted spatial maps contained more CSF regions (p<0.001). The success of sparse coding algorithms suggests that algorithms which enforce sparsity, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA. Negative BOLD signal may capture task-related activations. Copyright © 2017 Elsevier B.V. All rights reserved.

Sustained attention is favored by progesterone during early luteal phase and visuo-spatial memory by estrogens during ovulatory phase in young women.

PubMed

Solís-Ortiz, S; Corsi-Cabrera, M

2008-08-01

Studies examining the influence of the menstrual cycle on cognitive function have been highly contradictory. The maintenance of attention is key to successful information processing, however how it co-vary with other cognitive functions and mood in function of phases of the menstrual cycle is not well know. Therefore, neuropsychological performance of nine healthy women with regular menstrual cycles was assessed during ovulation (OVU), early luteal (EL), late luteal (LL) and menstrual (MEN) phases. Neuropsychological test scores of sustained attention, executive functions, manual coordination, visuo-spatial memory, verbal fluency, spatial ability, anxiety and depression were obtained and submitted to a principal components analysis (PCA). Five eigenvectors that accounted the 68.31% of the total variance were identified. Performance of the sustained attention was grouped in an independent eigenvector (component 1), and the scores on verbal fluency and visuo-spatial memory were grouped together in an eigenvector (component 5), which explained 17.69% and 12.03% of the total variance, respectively. The component 1 (p<0.034) and the component 5 (p<0.003) showed significant variations during the menstrual cycle. Sustained attention showed an increase in the EL phase, when the progesterone is high. Visuo-spatial memory was increased, while that verbal fluency was decreased during the OVU phase, when the estrogens levels are high. These results indicate that sustained attention is favored by early luteal phase progesterone and do not covaried with any other neuropsychological variables studied. The influence of the estrogens on visuo-spatial memory was corroborated, and covaried inversely with verbal fluency.

Aberrant temporal and spatial brain activity during rest in patients with chronic pain

PubMed Central

Malinen, Sanna; Vartiainen, Nuutti; Hlushchuk, Yevhen; Koskinen, Miika; Ramkumar, Pavan; Forss, Nina; Kalso, Eija; Hari, Riitta

2010-01-01

In the absence of external stimuli, human hemodynamic brain activity displays slow intrinsic variations. To find out whether such fluctuations would be altered by persistent pain, we asked 10 patients with unrelenting chronic pain of different etiologies and 10 sex- and age-matched control subjects to rest with eyes open during 3-T functional MRI. Independent component analysis was used to identify functionally coupled brain networks. Time courses of an independent component comprising the insular cortices of both hemispheres showed stronger spectral power at 0.12 to 0.25 Hz in patients than in control subjects, with the largest difference at 0.16 Hz. A similar but weaker effect was seen in the anterior cingulate cortex, whereas activity of the precuneus and early visual cortex, used as a control site, did not differ between the groups. In the patient group, seed point-based correlation analysis revealed altered spatial connectivity between insulae and anterior cingulate cortex. The results imply both temporally and spatially aberrant activity of the affective pain-processing areas in patients suffering from chronic pain. The accentuated 0.12- to 0.25-Hz fluctuations in the patient group might be related to altered activity of the autonomic nervous system. PMID:20308545

Three-dimensional localization and optical imaging of objects in turbid media with independent component analysis.

PubMed

Xu, M; Alrubaiee, M; Gayen, S K; Alfano, R R

2005-04-01

A new approach for optical imaging and localization of objects in turbid media that makes use of the independent component analysis (ICA) from information theory is demonstrated. Experimental arrangement realizes a multisource illumination of a turbid medium with embedded objects and a multidetector acquisition of transmitted light on the medium boundary. The resulting spatial diversity and multiple angular observations provide robust data for three-dimensional localization and characterization of absorbing and scattering inhomogeneities embedded in a turbid medium. ICA of the perturbations in the spatial intensity distribution on the medium boundary sorts out the embedded objects, and their locations are obtained from Green's function analysis based on any appropriate light propagation model. Imaging experiments were carried out on two highly scattering samples of thickness approximately 50 times the transport mean-free path of the respective medium. One turbid medium had two embedded absorptive objects, and the other had four scattering objects. An independent component separation of the signal, in conjunction with diffusive photon migration theory, was used to locate the embedded inhomogeneities. In both cases, improved lateral and axial localizations of the objects over the result obtained by use of common photon migration reconstruction algorithms were achieved. The approach is applicable to different medium geometries, can be used with any suitable photon propagation model, and is amenable to near-real-time imaging applications.

Failure criterion for materials with spatially correlated mechanical properties

NASA Astrophysics Data System (ADS)

Faillettaz, J.; Or, D.

2015-03-01

The role of spatially correlated mechanical elements in the failure behavior of heterogeneous materials represented by fiber bundle models (FBMs) was evaluated systematically for different load redistribution rules. Increasing the range of spatial correlation for FBMs with local load sharing is marked by a transition from ductilelike failure characteristics into brittlelike failure. The study identified a global failure criterion based on macroscopic properties (external load and cumulative damage) that is independent of spatial correlation or load redistribution rules. This general metric could be applied to assess the mechanical stability of complex and heterogeneous systems and thus provide an important component for early warning of a class of geophysical ruptures.

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.

Electron beam control for barely separated beams

DOEpatents

Douglas, David R.; Ament, Lucas J. P.

2017-04-18

A method for achieving independent control of multiple beams in close proximity to one another, such as in a multi-pass accelerator where coaxial beams are at different energies, but moving on a common axis, and need to be split into spatially separated beams for efficient recirculation transport. The method for independent control includes placing a magnet arrangement in the path of the barely separated beams with the magnet arrangement including at least two multipole magnets spaced closely together and having a multipole distribution including at least one odd multipole and one even multipole. The magnetic fields are then tuned to cancel out for a first of the barely separated beams to allow independent control of the second beam with common magnets. The magnetic fields may be tuned to cancel out either the dipole component or tuned to cancel out the quadrupole component in order to independently control the separate beams.

Analysis and visualization of single-trial event-related potentials

NASA Technical Reports Server (NTRS)

Jung, T. P.; Makeig, S.; Westerfield, M.; Townsend, J.; Courchesne, E.; Sejnowski, T. J.

2001-01-01

In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Copyright 2001 Wiley-Liss, Inc.

Structural covariance networks across healthy young adults and their consistency.

PubMed

Guo, Xiaojuan; Wang, Yan; Guo, Taomei; Chen, Kewei; Zhang, Jiacai; Li, Ke; Jin, Zhen; Yao, Li

2015-08-01

To investigate structural covariance networks (SCNs) as measured by regional gray matter volumes with structural magnetic resonance imaging (MRI) from healthy young adults, and to examine their consistency and stability. Two independent cohorts were included in this study: Group 1 (82 healthy subjects aged 18-28 years) and Group 2 (109 healthy subjects aged 20-28 years). Structural MRI data were acquired at 3.0T and 1.5T using a magnetization prepared rapid-acquisition gradient echo sequence for these two groups, respectively. We applied independent component analysis (ICA) to construct SCNs and further applied the spatial overlap ratio and correlation coefficient to evaluate the spatial consistency of the SCNs between these two datasets. Seven and six independent components were identified for Group 1 and Group 2, respectively. Moreover, six SCNs including the posterior default mode network, the visual and auditory networks consistently existed across the two datasets. The overlap ratios and correlation coefficients of the visual network reached the maximums of 72% and 0.71. This study demonstrates the existence of consistent SCNs corresponding to general functional networks. These structural covariance findings may provide insight into the underlying organizational principles of brain anatomy. © 2014 Wiley Periodicals, Inc.

Image processing analysis of traditional Gestalt vision experiments

NASA Astrophysics Data System (ADS)

McCann, John J.

2002-06-01

In the late 19th century, the Gestalt Psychology rebelled against the popular new science of Psychophysics. The Gestalt revolution used many fascinating visual examples to illustrate that the whole is greater than the sum of all the parts. Color constancy was an important example. The physical interpretation of sensations and their quantification by JNDs and Weber fractions were met with innumerable examples in which two 'identical' physical stimuli did not look the same. The fact that large changes in the color of the illumination failed to change color appearance in real scenes demanded something more than quantifying the psychophysical response of a single pixel. The debates continues today with proponents of both physical, pixel-based colorimetry and perceptual, image- based cognitive interpretations. Modern instrumentation has made colorimetric pixel measurement universal. As well, new examples of unconscious inference continue to be reported in the literature. Image processing provides a new way of analyzing familiar Gestalt displays. Since the pioneering experiments by Fergus Campbell and Land, we know that human vision has independent spatial channels and independent color channels. Color matching data from color constancy experiments agrees with spatial comparison analysis. In this analysis, simple spatial processes can explain the different appearances of 'identical' stimuli by analyzing the multiresolution spatial properties of their surrounds. Benary's Cross, White's Effect, the Checkerboard Illusion and the Dungeon Illusion can all be understood by the analysis of their low-spatial-frequency components. Just as with color constancy, these Gestalt images are most simply described by the analysis of spatial components. Simple spatial mechanisms account for the appearance of 'identical' stimuli in complex scenes. It does not require complex, cognitive processes to calculate appearances in familiar Gestalt experiments.

Applying independent component analysis to detect silent speech in magnetic resonance imaging signals.

PubMed

Abe, Kazuhiro; Takahashi, Toshimitsu; Takikawa, Yoriko; Arai, Hajime; Kitazawa, Shigeru

2011-10-01

Independent component analysis (ICA) can be usefully applied to functional imaging studies to evaluate the spatial extent and temporal profile of task-related brain activity. It requires no a priori assumptions about the anatomical areas that are activated or the temporal profile of the activity. We applied spatial ICA to detect a voluntary but hidden response of silent speech. To validate the method against a standard model-based approach, we used the silent speech of a tongue twister as a 'Yes' response to single questions that were delivered at given times. In the first task, we attempted to estimate one number that was chosen by a participant from 10 possibilities. In the second task, we increased the possibilities to 1000. In both tasks, spatial ICA was as effective as the model-based method for determining the number in the subject's mind (80-90% correct per digit), but spatial ICA outperformed the model-based method in terms of time, especially in the 1000-possibility task. In the model-based method, calculation time increased by 30-fold, to 15 h, because of the necessity of testing 1000 possibilities. In contrast, the calculation time for spatial ICA remained as short as 30 min. In addition, spatial ICA detected an unexpected response that occurred by mistake. This advantage was validated in a third task, with 13 500 possibilities, in which participants had the freedom to choose when to make one of four responses. We conclude that spatial ICA is effective for detecting the onset of silent speech, especially when it occurs unexpectedly. © 2011 The Authors. European Journal of Neuroscience © 2011 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Discriminant analysis of resting-state functional connectivity patterns on the Grassmann manifold

NASA Astrophysics Data System (ADS)

Fan, Yong; Liu, Yong; Jiang, Tianzi; Liu, Zhening; Hao, Yihui; Liu, Haihong

2010-03-01

The functional networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive functions and neurological diseases. In this paper, we propose a novel algorithm for discriminant analysis of functional networks encoded by spatial independent components. The functional networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of temporal signals of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based subspace distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional networks that are informative for schizophrenia diagnosis.

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

Evidence for modality-independent order coding in working memory.

PubMed

Depoorter, Ann; Vandierendonck, André

2009-03-01

The aim of the present study was to investigate the representation of serial order in working memory, more specifically whether serial order is coded by means of a modality-dependent or a modality-independent order code. This was investigated by means of a series of four experiments based on a dual-task methodology in which one short-term memory task was embedded between the presentation and recall of another short-term memory task. Two aspects were varied in these memory tasks--namely, the modality of the stimulus materials (verbal or visuo-spatial) and the presence of an order component in the task (an order or an item memory task). The results of this study showed impaired primary-task recognition performance when both the primary and the embedded task included an order component, irrespective of the modality of the stimulus materials. If one or both of the tasks did not contain an order component, less interference was found. The results of this study support the existence of a modality-independent order code.

Residential expansion as a continental threat to U.S. coastal ecosystems

Treesearch

J.G. Bartlett; D.M. Mageean; R.J. O' Connor

2000-01-01

Spatially extensive analysis of satellite, climate, and census data reveals human-environment interactions of regional or continental concern in the United States. A grid-based principal components analysis of Bureau of Census variables revealed two independent demographic phenomena, a-settlement reflecting traditional human settlement patterns and p-settlement...

A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface.

PubMed

Zhou, Bangyan; Wu, Xiaopei; Lv, Zhao; Zhang, Lei; Guo, Xiaojin

2016-01-01

Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address this issue by combining the single-trial-based ICA filter with zero-training classifier. We developed a two-round data selection method to identify automatically the badly corrupted EEG trials in the training set. The "high quality" training trials were utilized to optimize the ICA filter. In addition, we proposed an accuracy-matrix method to locate the artifact data segments within a single trial and investigated which types of artifacts can influence the performance of the ICA-based MIBCIs. Twenty-six EEG datasets of three-class motor imagery were used to validate the proposed methods, and the classification accuracies were compared with that obtained by frequently used common spatial pattern (CSP) spatial filtering algorithm. The experimental results demonstrated that the proposed optimizing strategy could effectively improve the stability, practicality and classification performance of ICA-based MIBCI. The study revealed that rational use of ICA method may be crucial in building a practical ICA-based MIBCI system.

Spatial eigenmodes and synchronous oscillation: co-incidence detection in simulated cerebral cortex.

PubMed

Chapman, Clare L; Wright, James J; Bourke, Paul D

2002-07-01

Zero-lag synchronisation arises between points on the cerebral cortex receiving concurrent independent inputs; an observation generally ascribed to nonlinear mechanisms. Using simulations of cerebral cortex and Principal Component Analysis (PCA) we show patterns of zero-lag synchronisation (associated with empirically realistic spectral content) can arise from both linear and nonlinear mechanisms. For low levels of activation, we show the synchronous field is described by the eigenmodes of the resultant damped wave activity. The first and second spatial eigenmodes (which capture most of the signal variance) arise from the even and odd components of the independent input signals. The pattern of zero-lag synchronisation can be accounted for by the relative dominance of the first mode over the second, in the near-field of the inputs. The simulated cortical surface can act as a few millisecond response coincidence detector for concurrent, but uncorrelated, inputs. As cortical activation levels are increased, local damped oscillations in the gamma band undergo a transition to highly nonlinear undamped activity with 40 Hz dominant frequency. This is associated with "locking" between active sites and spatially segregated phase patterns. The damped wave synchronisation and the locked nonlinear oscillations may combine to permit fast representation of multiple patterns of activity within the same field of neurons.

Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis.

PubMed

Cong, Fengyu; Puoliväli, Tuomas; Alluri, Vinoo; Sipola, Tuomo; Burunat, Iballa; Toiviainen, Petri; Nandi, Asoke K; Brattico, Elvira; Ristaniemi, Tapani

2014-02-15

Independent component analysis (ICA) has been often used to decompose fMRI data mostly for the resting-state, block and event-related designs due to its outstanding advantage. For fMRI data during free-listening experiences, only a few exploratory studies applied ICA. For processing the fMRI data elicited by 512-s modern tango, a FFT based band-pass filter was used to further pre-process the fMRI data to remove sources of no interest and noise. Then, a fast model order selection method was applied to estimate the number of sources. Next, both individual ICA and group ICA were performed. Subsequently, ICA components whose temporal courses were significantly correlated with musical features were selected. Finally, for individual ICA, common components across majority of participants were found by diffusion map and spectral clustering. The extracted spatial maps (by the new ICA approach) common across most participants evidenced slightly right-lateralized activity within and surrounding the auditory cortices. Meanwhile, they were found associated with the musical features. Compared with the conventional ICA approach, more participants were found to have the common spatial maps extracted by the new ICA approach. Conventional model order selection methods underestimated the true number of sources in the conventionally pre-processed fMRI data for the individual ICA. Pre-processing the fMRI data by using a reasonable band-pass digital filter can greatly benefit the following model order selection and ICA with fMRI data by naturalistic paradigms. Diffusion map and spectral clustering are straightforward tools to find common ICA spatial maps. Copyright © 2013 Elsevier B.V. All rights reserved.

Common mode error in Antarctic GPS coordinate time series on its effect on bedrock-uplift estimates

NASA Astrophysics Data System (ADS)

Liu, Bin; King, Matt; Dai, Wujiao

2018-05-01

Spatially-correlated common mode error always exists in regional, or-larger, GPS networks. We applied independent component analysis (ICA) to GPS vertical coordinate time series in Antarctica from 2010 to 2014 and made a comparison with the principal component analysis (PCA). Using PCA/ICA, the time series can be decomposed into a set of temporal components and their spatial responses. We assume the components with common spatial responses are common mode error (CME). An average reduction of ˜40% about the RMS values was achieved in both PCA and ICA filtering. However, the common mode components obtained from the two approaches have different spatial and temporal features. ICA time series present interesting correlations with modeled atmospheric and non-tidal ocean loading displacements. A white noise (WN) plus power law noise (PL) model was adopted in the GPS velocity estimation using maximum likelihood estimation (MLE) analysis, with ˜55% reduction of the velocity uncertainties after filtering using ICA. Meanwhile, spatiotemporal filtering reduces the amplitude of PL and periodic terms in the GPS time series. Finally, we compare the GPS uplift velocities, after correction for elastic effects, with recent models of glacial isostatic adjustment (GIA). The agreements of the GPS observed velocities and four GIA models are generally improved after the spatiotemporal filtering, with a mean reduction of ˜0.9 mm/yr of the WRMS values, possibly allowing for more confident separation of various GIA model predictions.

Multimodal integration of fMRI and EEG data for high spatial and temporal resolution analysis of brain networks

PubMed Central

Mantini, D.; Marzetti, L.; Corbetta, M.; Romani, G.L.; Del Gratta, C.

2017-01-01

Two major non-invasive brain mapping techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. We propose an approach based on the integration of EEG and fMRI, enabling the EEG temporal dynamics of information processing to be characterized within spatially well-defined fMRI large-scale networks. First, the fMRI data are decomposed into networks by means of spatial independent component analysis (sICA), and those associated with intrinsic activity and/or responding to task performance are selected using information from the related time-courses. Next, the EEG data over all sensors are averaged with respect to event timing, thus calculating event-related potentials (ERPs). The ERPs are subjected to temporal ICA (tICA), and the resulting components are localized with the weighted minimum norm (WMNLS) algorithm using the task-related fMRI networks as priors. Finally, the temporal contribution of each ERP component in the areas belonging to the fMRI large-scale networks is estimated. The proposed approach has been evaluated on visual target detection data. Our results confirm that two different components, commonly observed in EEG when presenting novel and salient stimuli respectively, are related to the neuronal activation in large-scale networks, operating at different latencies and associated with different functional processes. PMID:20052528

Improved separability of dipole sources by tripolar versus conventional disk electrodes: a modeling study using independent component analysis.

PubMed

Cao, H; Besio, W; Jones, S; Medvedev, A

2009-01-01

Tripolar electrodes have been shown to have less mutual information and higher spatial resolution than disc electrodes. In this work, a four-layer anisotropic concentric spherical head computer model was programmed, then four configurations of time-varying dipole signals were used to generate the scalp surface signals that would be obtained with tripolar and disc electrodes, and four important EEG artifacts were tested: eye blinking, cheek movements, jaw movements, and talking. Finally, a fast fixed-point algorithm was used for signal independent component analysis (ICA). The results show that signals from tripolar electrodes generated better ICA separation results than from disc electrodes for EEG signals with these four types of artifacts.

Patterns of white matter damage are non-random and associated with cognitive function in secondary progressive multiple sclerosis.

PubMed

Meijer, K A; Cercignani, M; Muhlert, N; Sethi, V; Chard, D; Geurts, J J G; Ciccarelli, O

2016-01-01

In multiple sclerosis (MS), white matter damage is thought to contribute to cognitive dysfunction, which is especially prominent in secondary progressive MS (SPMS). While studies in healthy subjects have revealed patterns of correlated fractional anisotropy (FA) across white matter tracts, little is known about the underlying patterns of white matter damage in MS. In the present study, we aimed to map the SPMS-related covariance patterns of microstructural white matter changes, and investigated whether or not these patterns were associated with cognitive dysfunction. Diffusion MRI was acquired from 30 SPMS patients and 32 healthy controls (HC). A tensor model was fitted and FA maps were processed using tract-based spatial statistics (TBSS) in order to obtain a skeletonised map for each subject. The skeletonised FA maps of patients only were decomposed into 18 spatially independent components (ICs) using independent component analysis. Comprehensive cognitive assessment was conducted to evaluate five cognitive domains. Correlations between cognitive performance and (1) severity of FA abnormalities of the extracted ICs (i.e. z-scores relative to FA values of HC) and (2) IC load (i.e. FA covariance of a particular IC) were examined. SPMS patients showed lower FA values of all examined patterns of correlated FA (i.e. spatially independent components) than HC (p < 0.01). Tracts visually assigned to the supratentorial commissural class were most severely damaged (z = - 3.54; p < 0.001). Reduced FA was significantly correlated with reduced IC load (i.e. FA covariance) (r = 0.441; p < 0.05). Lower mean FA and component load of the supratentorial projection tracts and limbic association tracts classes were associated with worse cognitive function, including executive function, working memory and verbal memory. Despite the presence of white matter damage, it was possible to reveal patterns of FA covariance across SPMS patients. This could indicate that white matter tracts belonging to the same cluster, and thus with similar characteristics, tend to follow similar trends during neurodegeneration. Furthermore, these underlying FA patterns might help to explain cognitive dysfunction in SPMS.

Digital visual communications using a Perceptual Components Architecture

NASA Technical Reports Server (NTRS)

Watson, Andrew B.

1991-01-01

The next era of space exploration will generate extraordinary volumes of image data, and management of this image data is beyond current technical capabilities. We propose a strategy for coding visual information that exploits the known properties of early human vision. This Perceptual Components Architecture codes images and image sequences in terms of discrete samples from limited bands of color, spatial frequency, orientation, and temporal frequency. This spatiotemporal pyramid offers efficiency (low bit rate), variable resolution, device independence, error-tolerance, and extensibility.

Unsupervised Neural Network Quantifies the Cost of Visual Information Processing.

PubMed

Orbán, Levente L; Chartier, Sylvain

2015-01-01

Untrained, "flower-naïve" bumblebees display behavioural preferences when presented with visual properties such as colour, symmetry, spatial frequency and others. Two unsupervised neural networks were implemented to understand the extent to which these models capture elements of bumblebees' unlearned visual preferences towards flower-like visual properties. The computational models, which are variants of Independent Component Analysis and Feature-Extracting Bidirectional Associative Memory, use images of test-patterns that are identical to ones used in behavioural studies. Each model works by decomposing images of floral patterns into meaningful underlying factors. We reconstruct the original floral image using the components and compare the quality of the reconstructed image to the original image. Independent Component Analysis matches behavioural results substantially better across several visual properties. These results are interpreted to support a hypothesis that the temporal and energetic costs of information processing by pollinators served as a selective pressure on floral displays: flowers adapted to pollinators' cognitive constraints.

Spatiotemporal Patterns of Precipitation-Modulated Landslide Deformation From Independent Component Analysis of InSAR Time Series

NASA Astrophysics Data System (ADS)

Cohen-Waeber, J.; Bürgmann, R.; Chaussard, E.; Giannico, C.; Ferretti, A.

2018-02-01

Long-term landslide deformation is disruptive and costly in urbanized environments. We rely on TerraSAR-X satellite images (2009-2014) and an improved data processing algorithm (SqueeSAR™) to produce an exceptionally dense Interferometric Synthetic Aperture Radar ground deformation time series for the San Francisco East Bay Hills. Independent and principal component analyses of the time series reveal four distinct spatial and temporal surface deformation patterns in the area around Blakemont landslide, which we relate to different geomechanical processes. Two components of time-dependent landslide deformation isolate continuous motion and motion driven by precipitation-modulated pore pressure changes controlled by annual seasonal cycles and multiyear drought conditions. Two components capturing more widespread seasonal deformation separate precipitation-modulated soil swelling from annual cycles that may be related to groundwater level changes and thermal expansion of buildings. High-resolution characterization of landslide response to precipitation is a first step toward improved hazard forecasting.

Modeling spatiotemporal covariance for magnetoencephalography or electroencephalography source analysis.

PubMed

Plis, Sergey M; George, J S; Jun, S C; Paré-Blagoev, J; Ranken, D M; Wood, C C; Schmidt, D M

2007-01-01

We propose a new model to approximate spatiotemporal noise covariance for use in neural electromagnetic source analysis, which better captures temporal variability in background activity. As with other existing formalisms, our model employs a Kronecker product of matrices representing temporal and spatial covariance. In our model, spatial components are allowed to have differing temporal covariances. Variability is represented as a series of Kronecker products of spatial component covariances and corresponding temporal covariances. Unlike previous attempts to model covariance through a sum of Kronecker products, our model is designed to have a computationally manageable inverse. Despite increased descriptive power, inversion of the model is fast, making it useful in source analysis. We have explored two versions of the model. One is estimated based on the assumption that spatial components of background noise have uncorrelated time courses. Another version, which gives closer approximation, is based on the assumption that time courses are statistically independent. The accuracy of the structural approximation is compared to an existing model, based on a single Kronecker product, using both Frobenius norm of the difference between spatiotemporal sample covariance and a model, and scatter plots. Performance of ours and previous models is compared in source analysis of a large number of single dipole problems with simulated time courses and with background from authentic magnetoencephalography data.

Independent components of neural activity carry information on individual populations.

PubMed

Głąbska, Helena; Potworowski, Jan; Łęski, Szymon; Wójcik, Daniel K

2014-01-01

Local field potential (LFP), the low-frequency part of the potential recorded extracellularly in the brain, reflects neural activity at the population level. The interpretation of LFP is complicated because it can mix activity from remote cells, on the order of millimeters from the electrode. To understand better the relation between the recordings and the local activity of cells we used a large-scale network thalamocortical model to compute simultaneous LFP, transmembrane currents, and spiking activity. We used this model to study the information contained in independent components obtained from the reconstructed Current Source Density (CSD), which smooths transmembrane currents, decomposed further with Independent Component Analysis (ICA). We found that the three most robust components matched well the activity of two dominating cell populations: superior pyramidal cells in layer 2/3 (rhythmic spiking) and tufted pyramids from layer 5 (intrinsically bursting). The pyramidal population from layer 2/3 could not be well described as a product of spatial profile and temporal activation, but by a sum of two such products which we recovered in two of the ICA components in our analysis, which correspond to the two first principal components of PCA decomposition of layer 2/3 population activity. At low noise one more cell population could be discerned but it is unlikely that it could be recovered in experiment given typical noise ranges.

Independent Components of Neural Activity Carry Information on Individual Populations

PubMed Central

Głąbska, Helena; Potworowski, Jan; Łęski, Szymon; Wójcik, Daniel K.

2014-01-01

Local field potential (LFP), the low-frequency part of the potential recorded extracellularly in the brain, reflects neural activity at the population level. The interpretation of LFP is complicated because it can mix activity from remote cells, on the order of millimeters from the electrode. To understand better the relation between the recordings and the local activity of cells we used a large-scale network thalamocortical model to compute simultaneous LFP, transmembrane currents, and spiking activity. We used this model to study the information contained in independent components obtained from the reconstructed Current Source Density (CSD), which smooths transmembrane currents, decomposed further with Independent Component Analysis (ICA). We found that the three most robust components matched well the activity of two dominating cell populations: superior pyramidal cells in layer 2/3 (rhythmic spiking) and tufted pyramids from layer 5 (intrinsically bursting). The pyramidal population from layer 2/3 could not be well described as a product of spatial profile and temporal activation, but by a sum of two such products which we recovered in two of the ICA components in our analysis, which correspond to the two first principal components of PCA decomposition of layer 2/3 population activity. At low noise one more cell population could be discerned but it is unlikely that it could be recovered in experiment given typical noise ranges. PMID:25153730

Spatial learning and psychomotor performance of C57BL/6 mice: age sensitivity and reliability of individual differences.

PubMed

de Fiebre, Nancyellen C; Sumien, Nathalie; Forster, Michael J; de Fiebre, Christopher M

2006-09-01

Two tests often used in aging research, the elevated path test and the Morris water maze test, were examined for their application to the study of brain aging in a large sample of C57BL/6JNia mice. Specifically, these studies assessed: (1) sensitivity to age and the degree of interrelatedness among different behavioral measures derived from these tests, (2) the effect of age on variation in the measurements, and (3) the reliability of individual differences in performance on the tests. Both tests detected age-related deficits in group performance that occurred independently of each other. However, analysis of data obtained on the Morris water maze test revealed three relatively independent components of cognitive performance. Performance in initial acquisition of spatial learning in the Morris maze was not highly correlated with performance during reversal learning (when mice were required to learn a new spatial location), whereas performance in both of those phases was independent of spatial performance assessed during a single probe trial administered at the end of acquisition training. Moreover, impaired performance during initial acquisition could be detected at an earlier age than impairments in reversal learning. There were modest but significant age-related increases in the variance of both elevated path test scores and in several measures of learning in the Morris maze test. Analysis of test scores of mice across repeated testing sessions confirmed reliability of the measurements obtained for cognitive and psychomotor function. Power calculations confirmed that there are sufficiently large age-related differences in elevated path test performance, relative to within age variability, to render this test useful for studies into the ability of an intervention to prevent or reverse age-related deficits in psychomotor performance. Power calculations indicated a need for larger sample sizes for detection of intervention effects on cognitive components of the Morris water maze test, at least when implemented at the ages tested in this study. Variability among old mice in both tests, including each of the various independent measures in the Morris maze, may be useful for elucidating the biological bases of different aspects of dysfunctional brain aging.

Finding Coefficients of the Full Array of Motion-Independent N-Body Potentials of Metric Gravity from Gravity's Exterior and Interior Effacement Algebra

NASA Astrophysics Data System (ADS)

Nordtvedt, Kenneth

2018-01-01

In the author's previous publications, a recursive linear algebraic method was introduced for obtaining (without gravitational radiation) the full potential expansions for the gravitational metric field components and the Lagrangian for a general N-body system. Two apparent properties of gravity— Exterior Effacement and Interior Effacement—were defined and fully enforced to obtain the recursive algebra, especially for the motion-independent potential expansions of the general N-body situation. The linear algebraic equations of this method determine the potential coefficients at any order n of the expansions in terms of the lower-order coefficients. Then, enforcing Exterior and Interior Effacement on a selecedt few potential series of the full motion-independent potential expansions, the complete exterior metric field for a single, spherically-symmetric mass source was obtained, producing the Schwarzschild metric field of general relativity. In this fourth paper of this series, the complete spatial metric's motion-independent potentials for N bodies are obtained using enforcement of Interior Effacement and knowledge of the Schwarzschild potentials. From the full spatial metric, the complete set of temporal metric potentials and Lagrangian potentials in the motion-independent case can then be found by transfer equations among the coefficients κ( n, α) → λ( n, ɛ) → ξ( n, α) with κ( n, α), λ( n, ɛ), ξ( n, α) being the numerical coefficients in the spatial metric, the Lagrangian, and the temporal metric potential expansions, respectively.

Anorexia nervosa and body dysmorphic disorder are associated with abnormalities in processing visual information.

PubMed

Li, W; Lai, T M; Bohon, C; Loo, S K; McCurdy, D; Strober, M; Bookheimer, S; Feusner, J

2015-07-01

Anorexia nervosa (AN) and body dysmorphic disorder (BDD) are characterized by distorted body image and are frequently co-morbid with each other, although their relationship remains little studied. While there is evidence of abnormalities in visual and visuospatial processing in both disorders, no study has directly compared the two. We used two complementary modalities--event-related potentials (ERPs) and functional magnetic resonance imaging (fMRI)--to test for abnormal activity associated with early visual signaling. We acquired fMRI and ERP data in separate sessions from 15 unmedicated individuals in each of three groups (weight-restored AN, BDD, and healthy controls) while they viewed images of faces and houses of different spatial frequencies. We used joint independent component analyses to compare activity in visual systems. AN and BDD groups demonstrated similar hypoactivity in early secondary visual processing regions and the dorsal visual stream when viewing low spatial frequency faces, linked to the N170 component, as well as in early secondary visual processing regions when viewing low spatial frequency houses, linked to the P100 component. Additionally, the BDD group exhibited hyperactivity in fusiform cortex when viewing high spatial frequency houses, linked to the N170 component. Greater activity in this component was associated with lower attractiveness ratings of faces. Results provide preliminary evidence of similar abnormal spatiotemporal activation in AN and BDD for configural/holistic information for appearance- and non-appearance-related stimuli. This suggests a common phenotype of abnormal early visual system functioning, which may contribute to perceptual distortions.

Comparing brain graphs in which nodes are regions of interest or independent components: A simulation study.

PubMed

Yu, Qingbao; Du, Yuhui; Chen, Jiayu; He, Hao; Sui, Jing; Pearlson, Godfrey; Calhoun, Vince D

2017-11-01

A key challenge in building a brain graph using fMRI data is how to define the nodes. Spatial brain components estimated by independent components analysis (ICA) and regions of interest (ROIs) determined by brain atlas are two popular methods to define nodes in brain graphs. It is difficult to evaluate which method is better in real fMRI data. Here we perform a simulation study and evaluate the accuracies of a few graph metrics in graphs with nodes of ICA components, ROIs, or modified ROIs in four simulation scenarios. Graph measures with ICA nodes are more accurate than graphs with ROI nodes in all cases. Graph measures with modified ROI nodes are modulated by artifacts. The correlations of graph metrics across subjects between graphs with ICA nodes and ground truth are higher than the correlations between graphs with ROI nodes and ground truth in scenarios with large overlapped spatial sources. Moreover, moving the location of ROIs would largely decrease the correlations in all scenarios. Evaluating graphs with different nodes is promising in simulated data rather than real data because different scenarios can be simulated and measures of different graphs can be compared with a known ground truth. Since ROIs defined using brain atlas may not correspond well to real functional boundaries, overall findings of this work suggest that it is more appropriate to define nodes using data-driven ICA than ROI approaches in real fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers

PubMed Central

Salimi-Khorshidi, Gholamreza; Douaud, Gwenaëlle; Beckmann, Christian F; Glasser, Matthew F; Griffanti, Ludovica; Smith, Stephen M

2014-01-01

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject “at rest”). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing “signal” (brain activity) can be distinguished form the “noise” components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX (“FMRIB’s ICA-based X-noiseifier”), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different Classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL. PMID:24389422

Evaluating the efficacy of fully automated approaches for the selection of eye blink ICA components

PubMed Central

Pontifex, Matthew B.; Miskovic, Vladimir; Laszlo, Sarah

2017-01-01

Independent component analysis (ICA) offers a powerful approach for the isolation and removal of eye blink artifacts from EEG signals. Manual identification of the eye blink ICA component by inspection of scalp map projections, however, is prone to error, particularly when non-artifactual components exhibit topographic distributions similar to the blink. The aim of the present investigation was to determine the extent to which automated approaches for selecting eye blink related ICA components could be utilized to replace manual selection. We evaluated popular blink selection methods relying on spatial features [EyeCatch()], combined stereotypical spatial and temporal features [ADJUST()], and a novel method relying on time-series features alone [icablinkmetrics()] using both simulated and real EEG data. The results of this investigation suggest that all three methods of automatic component selection are able to accurately identify eye blink related ICA components at or above the level of trained human observers. However, icablinkmetrics(), in particular, appears to provide an effective means of automating ICA artifact rejection while at the same time eliminating human errors inevitable during manual component selection and false positive component identifications common in other automated approaches. Based upon these findings, best practices for 1) identifying artifactual components via automated means and 2) reducing the accidental removal of signal-related ICA components are discussed. PMID:28191627

Structural white-matter connections mediating distinct behavioral components of spatial neglect in right brain-damaged patients.

PubMed

Vaessen, Maarten J; Saj, Arnaud; Lovblad, Karl-Olof; Gschwind, Markus; Vuilleumier, Patrik

2016-04-01

Spatial neglect is a neuropsychological syndrome in which patients fail to perceive and orient to stimuli located in the space contralateral to the lesioned hemisphere. It is characterized by a wide heterogeneity in clinical symptoms which can be grouped into distinct behavioral components correlating with different lesion sites. Moreover, damage to white-matter (WM) fiber tracts has been suggested to disconnect brain networks that mediate different functions associated with spatial cognition and attention. However, it remains unclear what WM pathways are associated with functionally dissociable neglect components. In this study we examined nine patients with a focal right hemisphere stroke using a series of neuropsychological tests and diffusion tensor imaging (DTI) in order to disentangle the role of specific WM pathways in neglect symptoms. First, following previous work, the behavioral test scores of patients were factorized into three independent components reflecting perceptual, exploratory, and object-centered deficits in spatial awareness. We then examined the structural neural substrates of these components by correlating indices of WM integrity (fractional anisotropy) with the severity of deficits along each profile. Several locations in the right parietal and frontal WM correlated with neuropsychological scores. Fiber tracts projecting from these locations indicated that posterior parts of the superior longitudinal fasciculus (SLF), as well as nearby callosal fibers connecting ipsilateral and contralateral parietal areas, were associated with perceptual spatial deficits, whereas more anterior parts of SLF and inferior fronto-occipital fasciculus (IFOF) were predominantly associated with object-centered deficits. In addition, connections between frontal areas and superior colliculus were found to be associated with the exploratory deficits. Our results provide novel support to the view that neglect may result from disconnection lesions in distributed brain networks, but also extend these notions by highlighting the role of dissociable circuits in different functional components of the neglect syndrome. However these preliminary findings require replication with larger samples of patients. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mental Models of Invisible Logical Networks

NASA Technical Reports Server (NTRS)

Sanderson, P.

1984-01-01

Subjects were required to discover the structure of a logical network whose links were invisible. Network structure had to be inferred from the behavior of the components after a failure. It was hypothesized that since such failure diagnosis tasks often draw on spatial processes, a good deal of spatial complexity in the network should affect network discovery. Results show that the ability to discover the linkages in the network is directly related to the spatial complexity of the pathway described by the linkages. This effect was generally independent of the amount of evidence available to subjects about the existence of the link. These results raise the question of whether inferences about spatially complex pathways were simply not made, or whether they were made but not retained because of a high load on memory resources.

An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Astrophysics Data System (ADS)

Akbari, D.

2017-11-01

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

Improving Global Models of Remotely Sensed Ocean Chlorophyll Content Using Partial Least Squares and Geographically Weighted Regression

NASA Astrophysics Data System (ADS)

Gholizadeh, H.; Robeson, S. M.

2015-12-01

Empirical models have been widely used to estimate global chlorophyll content from remotely sensed data. Here, we focus on the standard NASA empirical models that use blue-green band ratios. These band ratio ocean color (OC) algorithms are in the form of fourth-order polynomials and the parameters of these polynomials (i.e. coefficients) are estimated from the NASA bio-Optical Marine Algorithm Data set (NOMAD). Most of the points in this data set have been sampled from tropical and temperate regions. However, polynomial coefficients obtained from this data set are used to estimate chlorophyll content in all ocean regions with different properties such as sea-surface temperature, salinity, and downwelling/upwelling patterns. Further, the polynomial terms in these models are highly correlated. In sum, the limitations of these empirical models are as follows: 1) the independent variables within the empirical models, in their current form, are correlated (multicollinear), and 2) current algorithms are global approaches and are based on the spatial stationarity assumption, so they are independent of location. Multicollinearity problem is resolved by using partial least squares (PLS). PLS, which transforms the data into a set of independent components, can be considered as a combined form of principal component regression (PCR) and multiple regression. Geographically weighted regression (GWR) is also used to investigate the validity of spatial stationarity assumption. GWR solves a regression model over each sample point by using the observations within its neighbourhood. PLS results show that the empirical method underestimates chlorophyll content in high latitudes, including the Southern Ocean region, when compared to PLS (see Figure 1). Cluster analysis of GWR coefficients also shows that the spatial stationarity assumption in empirical models is not likely a valid assumption.

Failure and recovery in dynamical networks.

PubMed

Böttcher, L; Luković, M; Nagler, J; Havlin, S; Herrmann, H J

2017-02-03

Failure, damage spread and recovery crucially underlie many spatially embedded networked systems ranging from transportation structures to the human body. Here we study the interplay between spontaneous damage, induced failure and recovery in both embedded and non-embedded networks. In our model the network's components follow three realistic processes that capture these features: (i) spontaneous failure of a component independent of the neighborhood (internal failure), (ii) failure induced by failed neighboring nodes (external failure) and (iii) spontaneous recovery of a component. We identify a metastable domain in the global network phase diagram spanned by the model's control parameters where dramatic hysteresis effects and random switching between two coexisting states are observed. This dynamics depends on the characteristic link length of the embedded system. For the Euclidean lattice in particular, hysteresis and switching only occur in an extremely narrow region of the parameter space compared to random networks. We develop a unifying theory which links the dynamics of our model to contact processes. Our unifying framework may help to better understand controllability in spatially embedded and random networks where spontaneous recovery of components can mitigate spontaneous failure and damage spread in dynamical networks.

Combined ICA-LORETA analysis of mismatch negativity.

PubMed

Marco-Pallarés, J; Grau, C; Ruffini, G

2005-04-01

A major challenge for neuroscience is to map accurately the spatiotemporal patterns of activity of the large neuronal populations that are believed to underlie computing in the human brain. To study a specific example, we selected the mismatch negativity (MMN) brain wave (an event-related potential, ERP) because it gives an electrophysiological index of a "primitive intelligence" capable of detecting changes, even abstract ones, in a regular auditory pattern. ERPs have a temporal resolution of milliseconds but appear to result from mixed neuronal contributions whose spatial location is not fully understood. Thus, it is important to separate these sources in space and time. To tackle this problem, a two-step approach was designed combining the independent component analysis (ICA) and low-resolution tomography (LORETA) algorithms. Here we implement this approach to analyze the subsecond spatiotemporal dynamics of MMN cerebral sources using trial-by-trial experimental data. We show evidence that a cerebral computation mechanism underlies MMN. This mechanism is mediated by the orchestrated activity of several spatially distributed brain sources located in the temporal, frontal, and parietal areas, which activate at distinct time intervals and are grouped in six main statistically independent components.

Single trial decoding of belief decision making from EEG and fMRI data using independent components features

PubMed Central

Douglas, Pamela K.; Lau, Edward; Anderson, Ariana; Head, Austin; Kerr, Wesley; Wollner, Margalit; Moyer, Daniel; Li, Wei; Durnhofer, Mike; Bramen, Jennifer; Cohen, Mark S.

2013-01-01

The complex task of assessing the veracity of a statement is thought to activate uniquely distributed brain regions based on whether a subject believes or disbelieves a given assertion. In the current work, we present parallel machine learning methods for predicting a subject's decision response to a given propositional statement based on independent component (IC) features derived from EEG and fMRI data. Our results demonstrate that IC features outperformed features derived from event related spectral perturbations derived from any single spectral band, yet were similar to accuracy across all spectral bands combined. We compared our diagnostic IC spatial maps with our conventional general linear model (GLM) results, and found that informative ICs had significant spatial overlap with our GLM results, yet also revealed unique regions like amygdala that were not statistically significant in GLM analyses. Overall, these results suggest that ICs may yield a parsimonious feature set that can be used along with a decision tree structure for interpretation of features used in classifying complex cognitive processes such as belief and disbelief across both fMRI and EEG neuroimaging modalities. PMID:23914164

Preserving subject variability in group fMRI analysis: performance evaluation of GICA vs. IVA

PubMed Central

Michael, Andrew M.; Anderson, Mathew; Miller, Robyn L.; Adalı, Tülay; Calhoun, Vince D.

2014-01-01

Independent component analysis (ICA) is a widely applied technique to derive functionally connected brain networks from fMRI data. Group ICA (GICA) and Independent Vector Analysis (IVA) are extensions of ICA that enable users to perform group fMRI analyses; however a full comparison of the performance limits of GICA and IVA has not been investigated. Recent interest in resting state fMRI data with potentially higher degree of subject variability makes the evaluation of the above techniques important. In this paper we compare component estimation accuracies of GICA and an improved version of IVA using simulated fMRI datasets. We systematically change the degree of inter-subject spatial variability of components and evaluate estimation accuracy over all spatial maps (SMs) and time courses (TCs) of the decomposition. Our results indicate the following: (1) at low levels of SM variability or when just one SM is varied, both GICA and IVA perform well, (2) at higher levels of SM variability or when more than one SMs are varied, IVA continues to perform well but GICA yields SM estimates that are composites of other SMs with errors in TCs, (3) both GICA and IVA remove spatial correlations of overlapping SMs and introduce artificial correlations in their TCs, (4) if number of SMs is over estimated, IVA continues to perform well but GICA introduces artifacts in the varying and extra SMs with artificial correlations in the TCs of extra components, and (5) in the absence or presence of SMs unique to one subject, GICA produces errors in TCs and IVA estimates are accurate. In summary, our simulation experiments (both simplistic and realistic) and our holistic analyses approach indicate that IVA produces results that are closer to ground truth and thereby better preserves subject variability. The improved version of IVA is now packaged into the GIFT toolbox (http://mialab.mrn.org/software/gift). PMID:25018704

Approaches to Capture Variance Differences in Rest fMRI Networks in the Spatial Geometric Features: Application to Schizophrenia.

PubMed

Gopal, Shruti; Miller, Robyn L; Baum, Stefi A; Calhoun, Vince D

2016-01-01

Identification of functionally connected regions while at rest has been at the forefront of research focusing on understanding interactions between different brain regions. Studies have utilized a variety of approaches including seed based as well as data-driven approaches to identifying such networks. Most such techniques involve differentiating groups based on group mean measures. There has been little work focused on differences in spatial characteristics of resting fMRI data. We present a method to identify between group differences in the variability in the cluster characteristics of network regions within components estimated via independent vector analysis (IVA). IVA is a blind source separation approach shown to perform well in capturing individual subject variability within a group model. We evaluate performance of the approach using simulations and then apply to a relatively large schizophrenia data set (82 schizophrenia patients and 89 healthy controls). We postulate, that group differences in the intra-network distributional characteristics of resting state network voxel intensities might indirectly capture important distinctions between the brain function of healthy and clinical populations. Results demonstrate that specific areas of the brain, superior, and middle temporal gyrus that are involved in language and recognition of emotions, show greater component level variance in amplitude weights for schizophrenia patients than healthy controls. Statistically significant correlation between component level spatial variance and component volume was observed in 19 of the 27 non-artifactual components implying an evident relationship between the two parameters. Additionally, the greater spread in the distance of the cluster peak of a component from the centroid in schizophrenia patients compared to healthy controls was observed for seven components. These results indicate that there is hidden potential in exploring variance and possibly higher-order measures in resting state networks to better understand diseases such as schizophrenia. It furthers comprehension of how spatial characteristics can highlight previously unexplored differences between populations such as schizophrenia patients and healthy controls.

Neurophysiological correlates of abnormal somatosensory temporal discrimination in dystonia.

PubMed

Antelmi, Elena; Erro, Roberto; Rocchi, Lorenzo; Liguori, Rocco; Tinazzi, Michele; Di Stasio, Flavio; Berardelli, Alfredo; Rothwell, John C; Bhatia, Kailash P

2017-01-01

Somatosensory temporal discrimination threshold is often prolonged in patients with dystonia. Previous evidence suggested that this might be caused by impaired somatosensory processing in the time domain. Here, we tested if other markers of reduced inhibition in the somatosensory system might also contribute to abnormal somatosensory temporal discrimination in dystonia. Somatosensory temporal discrimination threshold was measured in 19 patients with isolated cervical dystonia and 19 age-matched healthy controls. We evaluated temporal somatosensory inhibition using paired-pulse somatosensory evoked potentials, spatial somatosensory inhibition by measuring the somatosensory evoked potentials interaction between simultaneous stimulation of the digital nerves in thumb and index finger, and Gamma-aminobutyric acid-ergic (GABAergic) sensory inhibition using the early and late components of high-frequency oscillations in digital nerves somatosensory evoked potentials. When compared with healthy controls, dystonic patients had longer somatosensory temporal discrimination thresholds, reduced suppression of cortical and subcortical paired-pulse somatosensory evoked potentials, less spatial inhibition of simultaneous somatosensory evoked potentials, and a smaller area of the early component of the high-frequency oscillations. A logistic regression analysis found that paired pulse suppression of the N20 component at an interstimulus interval of 5 milliseconds and the late component of the high-frequency oscillations were independently related to somatosensory temporal discrimination thresholds. "Dystonia group" was also a predictor of enhanced somatosensory temporal discrimination threshold, indicating a dystonia-specific effect that independently influences this threshold. Increased somatosensory temporal discrimination threshold in dystonia is related to reduced activity of inhibitory circuits within the primary somatosensory cortex. © 2016 International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.

Strategies for reducing large fMRI data sets for independent component analysis.

PubMed

Wang, Ze; Wang, Jiongjiong; Calhoun, Vince; Rao, Hengyi; Detre, John A; Childress, Anna R

2006-06-01

In independent component analysis (ICA), principal component analysis (PCA) is generally used to reduce the raw data to a few principal components (PCs) through eigenvector decomposition (EVD) on the data covariance matrix. Although this works for spatial ICA (sICA) on moderately sized fMRI data, it is intractable for temporal ICA (tICA), since typical fMRI data have a high spatial dimension, resulting in an unmanageable data covariance matrix. To solve this problem, two practical data reduction methods are presented in this paper. The first solution is to calculate the PCs of tICA from the PCs of sICA. This approach works well for moderately sized fMRI data; however, it is highly computationally intensive, even intractable, when the number of scans increases. The second solution proposed is to perform PCA decomposition via a cascade recursive least squared (CRLS) network, which provides a uniform data reduction solution for both sICA and tICA. Without the need to calculate the covariance matrix, CRLS extracts PCs directly from the raw data, and the PC extraction can be terminated after computing an arbitrary number of PCs without the need to estimate the whole set of PCs. Moreover, when the whole data set becomes too large to be loaded into the machine memory, CRLS-PCA can save data retrieval time by reading the data once, while the conventional PCA requires numerous data retrieval steps for both covariance matrix calculation and PC extractions. Real fMRI data were used to evaluate the PC extraction precision, computational expense, and memory usage of the presented methods.

Independent representation of parts and the relations between them: evidence from integrative agnosia.

PubMed

Behrmann, Marlene; Peterson, Mary A; Moscovitch, Morris; Suzuki, Satoru

2006-10-01

Whether objects are represented as a collection of parts whose relations are coded independently remains a topic of ongoing discussion among theorists in the domain of shape perception. S. M., an individual with integrative agnosia, and neurologically intact ("normal") individuals learned initially to identify 4 target objects constructed of 2 simple volumetric parts. At test, the targets were mixed with distractors, some of which could be discriminated from the targets on the basis of a mismatching part, whereas the rest could be discriminated only on the basis of the altered spatial arrangements of parts. S. M. learned to identify the target objects, although at a rate slower than that of the normal participants. At test, he correctly rejected distractors on the basis of mismatching parts but was profoundly impaired at rejecting distractors made of the same local components but with mismatching spatial arrangements. These results suggest that encoding the spatial arrangements of parts of an object requires a mechanism that is different from that required for encoding the shape of individual parts, with the former selectively compromised in integrative agnosia. Copyright 2006 APA.

A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification.

PubMed

LeVan, P; Urrestarazu, E; Gotman, J

2006-04-01

To devise an automated system to remove artifacts from ictal scalp EEG, using independent component analysis (ICA). A Bayesian classifier was used to determine the probability that 2s epochs of seizure segments decomposed by ICA represented EEG activity, as opposed to artifact. The classifier was trained using numerous statistical, spectral, and spatial features. The system's performance was then assessed using separate validation data. The classifier identified epochs representing EEG activity in the validation dataset with a sensitivity of 82.4% and a specificity of 83.3%. An ICA component was considered to represent EEG activity if the sum of the probabilities that its epochs represented EEG exceeded a threshold predetermined using the training data. Otherwise, the component represented artifact. Using this threshold on the validation set, the identification of EEG components was performed with a sensitivity of 87.6% and a specificity of 70.2%. Most misclassified components were a mixture of EEG and artifactual activity. The automated system successfully rejected a good proportion of artifactual components extracted by ICA, while preserving almost all EEG components. The misclassification rate was comparable to the variability observed in human classification. Current ICA methods of artifact removal require a tedious visual classification of the components. The proposed system automates this process and removes simultaneously multiple types of artifacts.

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.

Relative Contribution of Matrix Structure, Patch Resources and Management to the Local Densities of Two Large Blue Butterfly Species

PubMed Central

Skórka, Piotr; Nowicki, Piotr; Bonk, Maciej; Król, Wiesław; Szpiłyk, Damian; Woyciechowski, Michal

2016-01-01

The type of matrix, the landscape surrounding habitat patches, may determine the distribution and function of local populations. However, the matrix is often heterogeneous, and its various components may differentially contribute to metapopulation processes at different spatial scales, a phenomenon that has rarely been investigated. The aim of this study was to estimate the relative importance of matrix composition and spatial scale, habitat quality, and management intensity on the occurrence and density of local populations of two endangered large blue butterflies: Phengaris teleius and P. nausithous. Presence and abundance data were assessed over two years, 2011–12, in 100 local patches within two heterogeneous regions (near Kraków and Tarnów, southern Poland). The matrix composition was analyzed at eight spatial scales. We observed high occupancy rates in both species, regions and years. With the exception of area and isolation, almost all of the matrix components contributed to Phengaris sp. densities. The different matrix components acted at different spatial scales (grassland cover within 4 and 3 km, field cover within 0.4 and 0.3 km and water cover within 4 km radii for P. teleius and P. nausithous, respectively) and provided the highest independent contribution to the butterfly densities. Additionally, the effects of a 0.4 km radius of forest cover and a food plant cover on P. teleius, and a 1 km radius of settlement cover and management intensity on P. nausithous densities were observed. Contrary to former studies we conclude that the matrix heterogeneity and spatial scale rather than general matrix type are of relevance for densities of butterflies. Conservation strategies for these umbrella species should concentrate on maintaining habitat quality and managing matrix composition at the most appropriate spatial scales. PMID:28005942

Relative Contribution of Matrix Structure, Patch Resources and Management to the Local Densities of Two Large Blue Butterfly Species.

PubMed

Kajzer-Bonk, Joanna; Skórka, Piotr; Nowicki, Piotr; Bonk, Maciej; Król, Wiesław; Szpiłyk, Damian; Woyciechowski, Michal

2016-01-01

The type of matrix, the landscape surrounding habitat patches, may determine the distribution and function of local populations. However, the matrix is often heterogeneous, and its various components may differentially contribute to metapopulation processes at different spatial scales, a phenomenon that has rarely been investigated. The aim of this study was to estimate the relative importance of matrix composition and spatial scale, habitat quality, and management intensity on the occurrence and density of local populations of two endangered large blue butterflies: Phengaris teleius and P. nausithous. Presence and abundance data were assessed over two years, 2011-12, in 100 local patches within two heterogeneous regions (near Kraków and Tarnów, southern Poland). The matrix composition was analyzed at eight spatial scales. We observed high occupancy rates in both species, regions and years. With the exception of area and isolation, almost all of the matrix components contributed to Phengaris sp. densities. The different matrix components acted at different spatial scales (grassland cover within 4 and 3 km, field cover within 0.4 and 0.3 km and water cover within 4 km radii for P. teleius and P. nausithous, respectively) and provided the highest independent contribution to the butterfly densities. Additionally, the effects of a 0.4 km radius of forest cover and a food plant cover on P. teleius, and a 1 km radius of settlement cover and management intensity on P. nausithous densities were observed. Contrary to former studies we conclude that the matrix heterogeneity and spatial scale rather than general matrix type are of relevance for densities of butterflies. Conservation strategies for these umbrella species should concentrate on maintaining habitat quality and managing matrix composition at the most appropriate spatial scales.

Analysis of the Los Angeles Basin ground subsidence with InSAR data by independent component analysis approach

NASA Astrophysics Data System (ADS)

Xu, B.

2017-12-01

Interferometric Synthetic Aperture Radar (InSAR) has the advantages of high spatial resolution which enable measure line of sight (LOS) surface displacements with nearly complete spatial continuity and a satellite's perspective that permits large areas view of Earth's surface quickly and efficiently. However, using InSAR to observe long wavelength and small magnitude deformation signals is still significantly limited by various unmodeled errors sources i.e. atmospheric delays, orbit induced errors, Digital Elevation Model (DEM) errors. Independent component analysis (ICA) is a probabilistic method for separating linear mixed signals generated by different underlying physical processes.The signal sources which form the interferograms are statistically independent both in space and in time, thus, they can be separated by ICA approach.The seismic behavior in the Los Angeles Basin is active and the basin has experienced numerous moderate to large earthquakes since the early Pliocene. Hence, understanding the seismotectonic deformation in the Los Angeles Basin is important for analyzing seismic behavior. Compare with the tectonic deformations, nontectonic deformations due to groundwater and oil extraction may be mainly responsible for the surface deformation in the Los Angeles basin. Using the small baseline subset (SBAS) InSAR method, we extracted the surface deformation time series in the Los Angeles basin with a time span of 7 years (September 27, 2003-September 25,2010). Then, we successfully separate the atmospheric noise from InSAR time series and detect different processes caused by different mechanisms.

Performance of some biotic indices in the real variable world: a case study at different spatial scales in North-Western Mediterranean Sea.

PubMed

Tataranni, Mariella; Lardicci, Claudio

2010-01-01

The aim of this study was to analyse the variability of four different benthic biotic indices (AMBI, BENTIX, H', M-AMBI) in two marine coastal areas of the North-Western Mediterranean Sea. In each coastal area, 36 replicates were randomly selected according to a hierarchical sampling design, which allowed estimating the variance components of the indices associated with four different spatial scales (ranging from metres to kilometres). All the analyses were performed at two different sampling periods in order to evaluate if the observed trends were consistent over the time. The variance components of the four indices revealed complex trends and different patterns in the two sampling periods. These results highlighted that independently from the employed index, a rigorous and appropriate sampling design taking into account different scales should always be used in order to avoid erroneous classifications and to develop effective monitoring programs.

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

Quantification method for the appearance of melanin pigmentation using independent component analysis

NASA Astrophysics Data System (ADS)

Ojima, Nobutoshi; Okiyama, Natsuko; Okaguchi, Saya; Tsumura, Norimichi; Nakaguchi, Toshiya; Hori, Kimihiko; Miyake, Yoichi

2005-04-01

In the cosmetics industry, skin color is very important because skin color gives a direct impression of the face. In particular, many people suffer from melanin pigmentation such as liver spots and freckles. However, it is very difficult to evaluate melanin pigmentation using conventional colorimetric values because these values contain information on various skin chromophores simultaneously. Therefore, it is necessary to extract information of the chromophore of individual skins independently as density information. The isolation of the melanin component image based on independent component analysis (ICA) from a single skin image was reported in 2003. However, this technique has not developed a quantification method for melanin pigmentation. This paper introduces a quantification method based on the ICA of a skin color image to isolate melanin pigmentation. The image acquisition system we used consists of commercially available equipment such as digital cameras and lighting sources with polarized light. The images taken were analyzed using ICA to extract the melanin component images, and Laplacian of Gaussian (LOG) filter was applied to extract the pigmented area. As a result, for skin images including those showing melanin pigmentation and acne, the method worked well. Finally, the total amount of extracted area had a strong correspondence to the subjective rating values for the appearance of pigmentation. Further analysis is needed to recognize the appearance of pigmentation concerning the size of the pigmented area and its spatial gradation.

Spatial autocorrelation among automated geocoding errors and its effects on testing for disease clustering

PubMed Central

Li, Jie; Fang, Xiangming

2010-01-01

Automated geocoding of patient addresses is an important data assimilation component of many spatial epidemiologic studies. Inevitably, the geocoding process results in positional errors. Positional errors incurred by automated geocoding tend to reduce the power of tests for disease clustering and otherwise affect spatial analytic methods. However, there are reasons to believe that the errors may often be positively spatially correlated and that this may mitigate their deleterious effects on spatial analyses. In this article, we demonstrate explicitly that the positional errors associated with automated geocoding of a dataset of more than 6000 addresses in Carroll County, Iowa are spatially autocorrelated. Furthermore, through two simulation studies of disease processes, including one in which the disease process is overlain upon the Carroll County addresses, we show that spatial autocorrelation among geocoding errors maintains the power of two tests for disease clustering at a level higher than that which would occur if the errors were independent. Implications of these results for cluster detection, privacy protection, and measurement-error modeling of geographic health data are discussed. PMID:20087879

Nonparametric Bayesian Context Learning for Buried Threat Detection

DTIC Science & Technology

2012-01-01

scans of field-collected GPR data collected on dirt (top) and gravel (bottom) lanes at an Eastern US test site. . . . . . . . . . 47 2.11 Plot of ...partition the data into M components, they differ in the information used to partition the data. First, approaches that as- sume independence of observations...and the advantages and disadvantages of using each are discussed. Furthermore, the merits of incorporating spatial information are also highlighted

Functional Connectivity Parcellation of the Human Thalamus by Independent Component Analysis.

PubMed

Zhang, Sheng; Li, Chiang-Shan R

2017-11-01

As a key structure to relay and integrate information, the thalamus supports multiple cognitive and affective functions through the connectivity between its subnuclei and cortical and subcortical regions. Although extant studies have largely described thalamic regional functions in anatomical terms, evidence accumulates to suggest a more complex picture of subareal activities and connectivities of the thalamus. In this study, we aimed to parcellate the thalamus and examine whole-brain connectivity of its functional clusters. With resting state functional magnetic resonance imaging data from 96 adults, we used independent component analysis (ICA) to parcellate the thalamus into 10 components. On the basis of the independence assumption, ICA helps to identify how subclusters overlap spatially. Whole brain functional connectivity of each subdivision was computed for independent component's time course (ICtc), which is a unique time series to represent an IC. For comparison, we computed seed-region-based functional connectivity using the averaged time course across all voxels within a thalamic subdivision. The results showed that, at p < 10 -6 , corrected, 49% of voxels on average overlapped among subdivisions. Compared with seed-region analysis, ICtc analysis revealed patterns of connectivity that were more distinguished between thalamic clusters. ICtc analysis demonstrated thalamic connectivity to the primary motor cortex, which has eluded the analysis as well as previous studies based on averaged time series, and clarified thalamic connectivity to the hippocampus, caudate nucleus, and precuneus. The new findings elucidate functional organization of the thalamus and suggest that ICA clustering in combination with ICtc rather than seed-region analysis better distinguishes whole-brain connectivities among functional clusters of a brain region.

Model-based error diffusion for high fidelity lenticular screening.

PubMed

Lau, Daniel; Smith, Trebor

2006-04-17

Digital halftoning is the process of converting a continuous-tone image into an arrangement of black and white dots for binary display devices such as digital ink-jet and electrophotographic printers. As printers are achieving print resolutions exceeding 1,200 dots per inch, it is becoming increasingly important for halftoning algorithms to consider the variations and interactions in the size and shape of printed dots between neighboring pixels. In the case of lenticular screening where statistically independent images are spatially multiplexed together, ignoring these variations and interactions, such as dot overlap, will result in poor lenticular image quality. To this end, we describe our use of model-based error-diffusion for the lenticular screening problem where statistical independence between component images is achieved by restricting the diffusion of error to only those pixels of the same component image where, in order to avoid instabilities, the proposed approach involves a novel error-clipping procedure.

Excitation-resolved multispectral method for imaging pharmacokinetic parameters in dynamic fluorescent molecular tomography

NASA Astrophysics Data System (ADS)

Chen, Maomao; Zhou, Yuan; Su, Han; Zhang, Dong; Luo, Jianwen

2017-04-01

Imaging of the pharmacokinetic parameters in dynamic fluorescence molecular tomography (DFMT) can provide three-dimensional metabolic information for biological studies and drug development. However, owing to the ill-posed nature of the FMT inverse problem, the relatively low quality of the parametric images makes it difficult to investigate the different metabolic processes of the fluorescent targets with small distances. An excitation-resolved multispectral DFMT method is proposed; it is based on the fact that the fluorescent targets with different concentrations show different variations in the excitation spectral domain and can be considered independent signal sources. With an independent component analysis method, the spatial locations of different fluorescent targets can be decomposed, and the fluorescent yields of the targets at different time points can be recovered. Therefore, the metabolic process of each component can be independently investigated. Simulations and phantom experiments are carried out to evaluate the performance of the proposed method. The results demonstrated that the proposed excitation-resolved multispectral method can effectively improve the reconstruction accuracy of the parametric images in DFMT.

Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia

PubMed Central

Castro, Eduardo; Hjelm, R. Devon; Plis, Sergey M.; Dinh, Laurent; Turner, Jessica A.; Calhoun, Vince D.

2016-01-01

Linear independent component analysis (ICA) is a standard signal processing technique that has been extensively used on neuroimaging data to detect brain networks with coherent brain activity (functional MRI) or covarying structural patterns (structural MRI). However, its formulation assumes that the measured brain signals are generated by a linear mixture of the underlying brain networks and this assumption limits its ability to detect the inherent nonlinear nature of brain interactions. In this paper, we introduce nonlinear independent component estimation (NICE) to structural MRI data to detect abnormal patterns of gray matter concentration in schizophrenia patients. For this biomedical application, we further addressed the issue of model regularization of nonlinear ICA by performing dimensionality reduction prior to NICE, together with an appropriate control of the complexity of the model and the usage of a proper approximation of the probability distribution functions of the estimated components. We show that our results are consistent with previous findings in the literature, but we also demonstrate that the incorporation of nonlinear associations in the data enables the detection of spatial patterns that are not identified by linear ICA. Specifically, we show networks including basal ganglia, cerebellum and thalamus that show significant differences in patients versus controls, some of which show distinct nonlinear patterns. PMID:26891483

A natural basis for efficient brain-actuated control

NASA Technical Reports Server (NTRS)

Makeig, S.; Enghoff, S.; Jung, T. P.; Sejnowski, T. J.

2000-01-01

The prospect of noninvasive brain-actuated control of computerized screen displays or locomotive devices is of interest to many and of crucial importance to a few 'locked-in' subjects who experience near total motor paralysis while retaining sensory and mental faculties. Currently several groups are attempting to achieve brain-actuated control of screen displays using operant conditioning of particular features of the spontaneous scalp electroencephalogram (EEG) including central mu-rhythms (9-12 Hz). A new EEG decomposition technique, independent component analysis (ICA), appears to be a foundation for new research in the design of systems for detection and operant control of endogenous EEG rhythms to achieve flexible EEG-based communication. ICA separates multichannel EEG data into spatially static and temporally independent components including separate components accounting for posterior alpha rhythms and central mu activities. We demonstrate using data from a visual selective attention task that ICA-derived mu-components can show much stronger spectral reactivity to motor events than activity measures for single scalp channels. ICA decompositions of spontaneous EEG would thus appear to form a natural basis for operant conditioning to achieve efficient and multidimensional brain-actuated control in motor-limited and locked-in subjects.

Dynamic Responses in Brain Networks to Social Feedback: A Dual EEG Acquisition Study in Adolescent Couples

PubMed Central

Kuo, Ching-Chang; Ha, Thao; Ebbert, Ashley M.; Tucker, Don M.; Dishion, Thomas J.

2017-01-01

Adolescence is a sensitive period for the development of romantic relationships. During this period the maturation of frontolimbic networks is particularly important for the capacity to regulate emotional experiences. In previous research, both functional magnetic resonance imaging (fMRI) and dense array electroencephalography (dEEG) measures have suggested that responses in limbic regions are enhanced in adolescents experiencing social rejection. In the present research, we examined social acceptance and rejection from romantic partners as they engaged in a Chatroom Interact Task. Dual 128-channel dEEG systems were used to record neural responses to acceptance and rejection from both adolescent romantic partners and unfamiliar peers (N = 75). We employed a two-step temporal principal component analysis (PCA) and spatial independent component analysis (ICA) approach to statistically identify the neural components related to social feedback. Results revealed that the early (288 ms) discrimination between acceptance and rejection reflected by the P3a component was significant for the romantic partner but not the unfamiliar peer. In contrast, the later (364 ms) P3b component discriminated between acceptance and rejection for both partners and peers. The two-step approach (PCA then ICA) was better able than either PCA or ICA alone in separating these components of the brain's electrical activity that reflected both temporal and spatial phases of the brain's processing of social feedback. PMID:28620292

Dissociable brain biomarkers of fluid intelligence.

PubMed

Paul, Erick J; Larsen, Ryan J; Nikolaidis, Aki; Ward, Nathan; Hillman, Charles H; Cohen, Neal J; Kramer, Arthur F; Barbey, Aron K

2016-08-15

Cognitive neuroscience has long sought to understand the biological foundations of human intelligence. Decades of research have revealed that general intelligence is correlated with two brain-based biomarkers: the concentration of the brain biochemical N-acetyl aspartate (NAA) measured by proton magnetic resonance spectroscopy (MRS) and total brain volume measured using structural MR imaging (MRI). However, the relative contribution of these biomarkers in predicting performance on core facets of human intelligence remains to be well characterized. In the present study, we sought to elucidate the role of NAA and brain volume in predicting fluid intelligence (Gf). Three canonical tests of Gf (BOMAT, Number Series, and Letter Sets) and three working memory tasks (Reading, Rotation, and Symmetry span tasks) were administered to a large sample of healthy adults (n=211). We conducted exploratory factor analysis to investigate the factor structure underlying Gf independent from working memory and observed two Gf components (verbal/spatial and quantitative reasoning) and one working memory component. Our findings revealed a dissociation between two brain biomarkers of Gf (controlling for age and sex): NAA concentration correlated with verbal/spatial reasoning, whereas brain volume correlated with quantitative reasoning and working memory. A follow-up analysis revealed that this pattern of findings is observed for males and females when analyzed separately. Our results provide novel evidence that distinct brain biomarkers are associated with specific facets of human intelligence, demonstrating that NAA and brain volume are independent predictors of verbal/spatial and quantitative facets of Gf. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Differential effects of exogenous and endogenous attention on second-order texture contrast sensitivity

PubMed Central

Barbot, Antoine; Landy, Michael S.; Carrasco, Marisa

2012-01-01

The visual system can use a rich variety of contours to segment visual scenes into distinct perceptually coherent regions. However, successfully segmenting an image is a computationally expensive process. Previously we have shown that exogenous attention—the more automatic, stimulus-driven component of spatial attention—helps extract contours by enhancing contrast sensitivity for second-order, texture-defined patterns at the attended location, while reducing sensitivity at unattended locations, relative to a neutral condition. Interestingly, the effects of exogenous attention depended on the second-order spatial frequency of the stimulus. At parafoveal locations, attention enhanced second-order contrast sensitivity to relatively high, but not to low second-order spatial frequencies. In the present study we investigated whether endogenous attention—the more voluntary, conceptually-driven component of spatial attention—affects second-order contrast sensitivity, and if so, whether its effects are similar to those of exogenous attention. To that end, we compared the effects of exogenous and endogenous attention on the sensitivity to second-order, orientation-defined, texture patterns of either high or low second-order spatial frequencies. The results show that, like exogenous attention, endogenous attention enhances second-order contrast sensitivity at the attended location and reduces it at unattended locations. However, whereas the effects of exogenous attention are a function of the second-order spatial frequency content, endogenous attention affected second-order contrast sensitivity independent of the second-order spatial frequency content. This finding supports the notion that both exogenous and endogenous attention can affect second-order contrast sensitivity, but that endogenous attention is more flexible, benefitting performance under different conditions. PMID:22895879

Sleep effects on slow-brain-potential reflections of associative learning.

PubMed

Verleger, Rolf; Ludwig, Janna; Kolev, Vasil; Yordanova, Juliana; Wagner, Ullrich

2011-03-01

Previous research has indicated that information acquired before sleep gets consolidated during sleep. This process of consolidation might be reflected after sleep in changed extent and topography of cortical activation during retrieval of information. Here, we designed an experiment to measure those changes by means of slow event-related EEG potentials (SPs). Retrieval of newly learnt verbal or spatial associations was tested both immediately after learning and two days later. In the night directly following immediate recall, participants either slept or stayed awake. In line with previous studies, SPs measured during retrieval from memory had parietal or left-frontal foci depending on whether the retrieved associations were spatial or verbal. However, contrary to our expectations, sleep-related consolidation did not further accentuate these content-specific topographic profiles. Rather, sleep modified SPs independently of the spatial or verbal type of learned association: SPs were reduced more after sleep than after waking specifically for those stimulus configurations that had been presented in the same combination at retrieval before sleep. The association-independent stimulus-specific effect might generally form a major component of sleep-related effects on memory. Copyright © 2010 Elsevier B.V. All rights reserved.

Excitation of Earth Rotation Variations "Observed" by Time-Variable Gravity

NASA Technical Reports Server (NTRS)

Chao, Ben F.; Cox, C. M.

2005-01-01

Time variable gravity measurements have been made over the past two decades using the space geodetic technique of satellite laser ranging, and more recently by the GRACE satellite mission with improved spatial resolutions. The degree-2 harmonic components of the time-variable gravity contain important information about the Earth s length-of-day and polar motion excitation functions, in a way independent to the traditional "direct" Earth rotation measurements made by, for example, the very-long-baseline interferometry and GPS. In particular, the (degree=2, order= 1) components give the mass term of the polar motion excitation; the (2,O) component, under certain mass conservation conditions, gives the mass term of the length-of-day excitation. Combining these with yet another independent source of angular momentum estimation calculated from global geophysical fluid models (for example the atmospheric angular momentum, in both mass and motion terms), in principle can lead to new insights into the dynamics, particularly the role or the lack thereof of the cores, in the excitation processes of the Earth rotation variations.

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.

Transverse spin and transverse momentum in scattering of plane waves.

PubMed

Saha, Sudipta; Singh, Ankit K; Ray, Subir K; Banerjee, Ayan; Gupta, Subhasish Dutta; Ghosh, Nirmalya

2016-10-01

We study the near field to the far field evolution of spin angular momentum (SAM) density and the Poynting vector of the scattered waves from spherical scatterers. The results show that at the near field, the SAM density and the Poynting vector are dominated by their transverse components. While the former (transverse SAM) is independent of the helicity of the incident circular polarization state, the latter (transverse Poynting vector) depends upon the polarization state. It is further demonstrated that the interference of the transverse electric and transverse magnetic scattering modes enhances both the magnitudes and the spatial extent of the transverse SAM and the transverse momentum components.

Hydrological deformation signals in karst systems: new evidence from the European Alps

NASA Astrophysics Data System (ADS)

Serpelloni, E.; Pintori, F.; Gualandi, A.; Scoccimarro, E.; Cavaliere, A.; Anderlini, L.; Belardinelli, M. E.; Todesco, M.

2017-12-01

The influence of rainfall on crustal deformation has been described at local scales, using tilt and strain meters, in several tectonic settings. However, the literature on the spatial extent of rainfall-induced deformation is still scarce. We analyzed 10 years of displacement time-series from 150 continuous GPS stations operating across the broad zone of deformation accommodating the N-S Adria-Eurasia convergence and the E-ward escape of the Eastern Alps toward the Pannonian basin. We applied a blind-source-separation algorithm based on a variational Bayesian Independent Component Analysis method to the de-trended time-series, being able to characterize the temporal and spatial features of several deformation signals. The most important ones are a common mode annual signal, with spatially uniform response in the vertical and horizontal components and a time-variable, non-cyclic, signal characterized by a spatially variable response in the horizontal components, with stations moving (up to 8 mm) in the opposite directions, reversing the sense of movement in time. This implies a succession of extensional/compressional strains, with variable amplitudes through time, oriented normal to rock fractures in karst areas. While seasonal displacements in the vertical component (with an average amplitude of 4 mm over the study area) are satisfactorily reproduced by surface hydrological loading, estimated from global assimilation models, the non seasonal signal is associated with groundwater flow in karst systems, and is mainly influencing the horizontal component. The temporal evolution of this deformation signal is correlated with cumulated precipitation values over periods of 200-300 days. This horizontal deformation can be explained by pressure changes associated with variable water levels within vertical fractures in the vadose zones of karst systems, and the water level changes required to open or close these fractures are consistent with the fluctuations of precipitation and with the dynamics of karst systems.

Principal and independent component analysis of concomitant functional near infrared spectroscopy and magnetic resonance imaging data

NASA Astrophysics Data System (ADS)

Schelkanova, Irina; Toronov, Vladislav

2011-07-01

Although near infrared spectroscopy (NIRS) is now widely used both in emerging clinical techniques and in cognitive neuroscience, the development of the apparatuses and signal processing methods for these applications is still a hot research topic. The main unresolved problem in functional NIRS is the separation of functional signals from the contaminations by systemic and local physiological fluctuations. This problem was approached by using various signal processing methods, including blind signal separation techniques. In particular, principal component analysis (PCA) and independent component analysis (ICA) were applied to the data acquired at the same wavelength and at multiple sites on the human or animal heads during functional activation. These signal processing procedures resulted in a number of principal or independent components that could be attributed to functional activity but their physiological meaning remained unknown. On the other hand, the best physiological specificity is provided by broadband NIRS. Also, a comparison with functional magnetic resonance imaging (fMRI) allows determining the spatial origin of fNIRS signals. In this study we applied PCA and ICA to broadband NIRS data to distill the components correlating with the breath hold activation paradigm and compared them with the simultaneously acquired fMRI signals. Breath holding was used because it generates blood carbon dioxide (CO2) which increases the blood-oxygen-level-dependent (BOLD) signal as CO2 acts as a cerebral vasodilator. Vasodilation causes increased cerebral blood flow which washes deoxyhaemoglobin out of the cerebral capillary bed thus increasing both the cerebral blood volume and oxygenation. Although the original signals were quite diverse, we found very few different components which corresponded to fMRI signals at different locations in the brain and to different physiological chromophores.

Linked independent component analysis for multimodal data fusion.

PubMed

Groves, Adrian R; Beckmann, Christian F; Smith, Steve M; Woolrich, Mark W

2011-02-01

In recent years, neuroimaging studies have increasingly been acquiring multiple modalities of data and searching for task- or disease-related changes in each modality separately. A major challenge in analysis is to find systematic approaches for fusing these differing data types together to automatically find patterns of related changes across multiple modalities, when they exist. Independent Component Analysis (ICA) is a popular unsupervised learning method that can be used to find the modes of variation in neuroimaging data across a group of subjects. When multimodal data is acquired for the subjects, ICA is typically performed separately on each modality, leading to incompatible decompositions across modalities. Using a modular Bayesian framework, we develop a novel "Linked ICA" model for simultaneously modelling and discovering common features across multiple modalities, which can potentially have completely different units, signal- and contrast-to-noise ratios, voxel counts, spatial smoothnesses and intensity distributions. Furthermore, this general model can be configured to allow tensor ICA or spatially-concatenated ICA decompositions, or a combination of both at the same time. Linked ICA automatically determines the optimal weighting of each modality, and also can detect single-modality structured components when present. This is a fully probabilistic approach, implemented using Variational Bayes. We evaluate the method on simulated multimodal data sets, as well as on a real data set of Alzheimer's patients and age-matched controls that combines two very different types of structural MRI data: morphological data (grey matter density) and diffusion data (fractional anisotropy, mean diffusivity, and tensor mode). Copyright © 2010 Elsevier Inc. All rights reserved.

Gaussian process based independent analysis for temporal source separation in fMRI.

PubMed

Hald, Ditte Høvenhoff; Henao, Ricardo; Winther, Ole

2017-05-15

Functional Magnetic Resonance Imaging (fMRI) gives us a unique insight into the processes of the brain, and opens up for analyzing the functional activation patterns of the underlying sources. Task-inferred supervised learning with restrictive assumptions in the regression set-up, restricts the exploratory nature of the analysis. Fully unsupervised independent component analysis (ICA) algorithms, on the other hand, can struggle to detect clear classifiable components on single-subject data. We attribute this shortcoming to inadequate modeling of the fMRI source signals by failing to incorporate its temporal nature. fMRI source signals, biological stimuli and non-stimuli-related artifacts are all smooth over a time-scale compatible with the sampling time (TR). We therefore propose Gaussian process ICA (GPICA), which facilitates temporal dependency by the use of Gaussian process source priors. On two fMRI data sets with different sampling frequency, we show that the GPICA-inferred temporal components and associated spatial maps allow for a more definite interpretation than standard temporal ICA methods. The temporal structures of the sources are controlled by the covariance of the Gaussian process, specified by a kernel function with an interpretable and controllable temporal length scale parameter. We propose a hierarchical model specification, considering both instantaneous and convolutive mixing, and we infer source spatial maps, temporal patterns and temporal length scale parameters by Markov Chain Monte Carlo. A companion implementation made as a plug-in for SPM can be downloaded from https://github.com/dittehald/GPICA. Copyright © 2017 Elsevier Inc. All rights reserved.

The PAH Emission Characteristics of the Reflection Nebula NGC 2023

NASA Astrophysics Data System (ADS)

Peeters, Els; Bauschlicher, Charles W., Jr.; Allamandola, Louis J.; Tielens, Alexander G. G. M.; Ricca, Alessandra; Wolfire, Mark G.

2017-02-01

We present 5-20 μm spectral maps of the reflection nebula NGC 2023 obtained with the Infrared Spectrograph SL and SH modes on board the Spitzer Space Telescope, which reveal emission from polycyclic aromatic hydrocarbons (PAHs), C60, and H2 superposed on a dust continuum. We show that several PAH emission bands correlate with each other and exhibit distinct spatial distributions that reveal a spatial sequence with distance from the illuminating star. We explore the distinct morphology of the 6.2, 7.7, and 8.6 μm PAH bands and find that at least two spatially distinct components contribute to the 7-9 μm PAH emission in NGC 2023. We report that the PAH features behave independently of the underlying plateaus. We present spectra of compact, oval PAHs ranging in size from C66 to C210, determined computationally using density functional theory, and we investigate trends in the band positions and relative intensities as a function of PAH size, charge, and geometry. Based on the NASA Ames PAH database, we discuss the 7-9 μm components in terms of band assignments and relative intensities. We assign the plateau emission to very small grains with possible contributions from PAH clusters and identify components in the 7-9 μm emission that likely originate in these structures. Based on the assignments and the observed spatial sequence, we discuss the photochemical evolution of the interstellar PAH family as the PAHs are more and more exposed to the radiation field of the central star in the evaporative flows associated with the Photo-Dissociation Regions in NGC 2023.

Method for multimodal analysis of independent source differences in schizophrenia: combining gray matter structural and auditory oddball functional data.

PubMed

Calhoun, V D; Adali, T; Giuliani, N R; Pekar, J J; Kiehl, K A; Pearlson, G D

2006-01-01

The acquisition of both structural MRI (sMRI) and functional MRI (fMRI) data for a given study is a very common practice. However, these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform independent component analysis across image modalities, specifically, gray matter images and fMRI activation images as well as a joint histogram visualization technique. Joint independent component analysis (jICA) is used to decompose a matrix with a given row consisting of an fMRI activation image resulting from auditory oddball target stimuli and an sMRI gray matter segmentation image, collected from the same individual. We analyzed data collected on a group of schizophrenia patients and healthy controls using the jICA approach. Spatially independent joint-components are estimated and resulting components were further analyzed only if they showed a significant difference between patients and controls. The main finding was that group differences in bilateral parietal and frontal as well as posterior temporal regions in gray matter were associated with bilateral temporal regions activated by the auditory oddball target stimuli. A finding of less patient gray matter and less hemodynamic activity for target detection in these bilateral anterior temporal lobe regions was consistent with previous work. An unexpected corollary to this finding was that, in the regions showing the largest group differences, gray matter concentrations were larger in patients vs. controls, suggesting that more gray matter may be related to less functional connectivity in the auditory oddball fMRI task. Hum Brain Mapp, 2005. (c) 2005 Wiley-Liss, Inc.

Artifact removal in the context of group ICA: a comparison of single-subject and group approaches

PubMed Central

Du, Yuhui; Allen, Elena A.; He, Hao; Sui, Jing; Wu, Lei; Calhoun, Vince D.

2018-01-01

Independent component analysis (ICA) has been widely applied to identify intrinsic brain networks from fMRI data. Group ICA computes group-level components from all data and subsequently estimates individual-level components to recapture inter-subject variability. However, the best approach to handle artifacts, which may vary widely among subjects, is not yet clear. In this work, we study and compare two ICA approaches for artifacts removal. One approach, recommended in recent work by the Human Connectome Project, first performs ICA on individual subject data to remove artifacts, and then applies a group ICA on the cleaned data from all subjects. We refer to this approach as Individual ICA based artifacts Removal Plus Group ICA (IRPG). A second proposed approach, called Group Information Guided ICA (GIG-ICA), performs ICA on group data, then removes the group-level artifact components, and finally performs subject-specific ICAs using the group-level non-artifact components as spatial references. We used simulations to evaluate the two approaches with respect to the effects of data quality, data quantity, variable number of sources among subjects, and spatially unique artifacts. Resting-state test-retest datasets were also employed to investigate the reliability of functional networks. Results from simulations demonstrate GIG-ICA has greater performance compared to IRPG, even in the case when single-subject artifacts removal is perfect and when individual subjects have spatially unique artifacts. Experiments using test-retest data suggest that GIG-ICA provides more reliable functional networks. Based on high estimation accuracy, ease of implementation, and high reliability of functional networks, we find GIG-ICA to be a promising approach. PMID:26859308

Does bimodal stimulus presentation increase ERP components usable in BCIs?

NASA Astrophysics Data System (ADS)

Thurlings, Marieke E.; Brouwer, Anne-Marie; Van Erp, Jan B. F.; Blankertz, Benjamin; Werkhoven, Peter J.

2012-08-01

Event-related potential (ERP)-based brain-computer interfaces (BCIs) employ differences in brain responses to attended and ignored stimuli. Typically, visual stimuli are used. Tactile stimuli have recently been suggested as a gaze-independent alternative. Bimodal stimuli could evoke additional brain activity due to multisensory integration which may be of use in BCIs. We investigated the effect of visual-tactile stimulus presentation on the chain of ERP components, BCI performance (classification accuracies and bitrates) and participants’ task performance (counting of targets). Ten participants were instructed to navigate a visual display by attending (spatially) to targets in sequences of either visual, tactile or visual-tactile stimuli. We observe that attending to visual-tactile (compared to either visual or tactile) stimuli results in an enhanced early ERP component (N1). This bimodal N1 may enhance BCI performance, as suggested by a nonsignificant positive trend in offline classification accuracies. A late ERP component (P300) is reduced when attending to visual-tactile compared to visual stimuli, which is consistent with the nonsignificant negative trend of participants’ task performance. We discuss these findings in the light of affected spatial attention at high-level compared to low-level stimulus processing. Furthermore, we evaluate bimodal BCIs from a practical perspective and for future applications.

Measuring spatial patterns in floodplains: A step towards understanding the complexity of floodplain ecosystems: Chapter 6

USGS Publications Warehouse

Scown, Murray W.; Thoms, Martin C.; DeJager, Nathan R.; Gilvear, David J.; Greenwood, Malcolm T.; Thoms, Martin C.; Wood, Paul J.

2016-01-01

Floodplains can be viewed as complex adaptive systems (Levin, 1998) because they are comprised of many different biophysical components, such as morphological features, soil groups and vegetation communities as well as being sites of key biogeochemical processing (Stanford et al., 2005). Interactions and feedbacks among the biophysical components often result in additional phenomena occuring over a range of scales, often in the absence of any controlling factors (sensu Hallet, 1990). This emergence of new biophysical features and rates of processing can lead to alternative stable states which feed back into floodplain adaptive cycles (cf. Hughes, 1997; Stanford et al., 2005). Interactions between different biophysical components, feedbacks, self emergence and scale are all key properties of complex adaptive systems (Levin, 1998; Phillips, 2003; Murray et al., 2014) and therefore will influence the manner in which we study and view spatial patterns. Measuring the spatial patterns of floodplain biophysical components is a prerequisite to examining and understanding these ecosystems as complex adaptive systems. Elucidating relationships between pattern and process, which are intrinsically linked within floodplains (Ward et al., 2002), is dependent upon an understanding of spatial pattern. This knowledge can help river scientists determine the major drivers, controllers and responses of floodplain structure and function, as well as the consequences of altering those drivers and controllers (Hughes and Cass, 1997; Whited et al., 2007). Interactions and feedbacks between physical, chemical and biological components of floodplain ecosystems create and maintain a structurally diverse and dynamic template (Stanford et al., 2005). This template influences subsequent interactions between components that consequently affect system trajectories within floodplains (sensu Bak et al., 1988). Constructing and evaluating models used to predict floodplain ecosystem responses to natural and anthropogenic disturbances therefore require quantification of spatial pattern (Asselman and Middelkoop, 1995; Walling and He, 1998). Quantifying these patterns also provides insights into the spatial and temporal domains of structuring processes as well as enabling the detection of self-emergent phenomena, environmental constraints or anthropogenic interference (Turner et al., 1990; Holling, 1992; De Jager and Rohweder, 2012). Thus, quantifying spatial pattern is an important building block on which to examine floodplains as complex adaptive systems (Levin, 1998). Approaches to measuring spatial pattern in floodplains must be cognisant of scale, self-emergent phenomena, spatial organisation, and location. Fundamental problems may arise when patterns observed at a site or transect scale are scaled-up to infer processes and patterns over entire floodplain surfaces (Wiens, 2002; Thorp et al., 2008). Likewise, patterns observed over the entire spatial extent of a landscape can mask important variation and detail at finer scales (Riitters et al., 2002). Indeed, different patterns often emerge at different scales (Turner et al., 1990) because of hierarchical structuring processes (O'Neill et al., 1991). Categorising data into discrete, homogeneous and predefined spatial units at a particular scale (e.g. polygons) creates issues and errors associated with scale and subjective classification (McGarigal et al., 2009; Cushman et al., 2010). These include, loss of information within classified ‘patches’, as well as the ability to detect the emergence of new features that do not fit the original classification scheme. Many of these issues arise because floodplains are highly heterogeneous and have complex spatial organizations (Carbonneau et al., 2012; Legleiter, 2013). As a result, the scale and location at which measurements are made can influence the observed spatial patterns; and patterns may not be scale independent or applicable in different geomorp

Presurgical localization and spatial shift of resting state networks in patients with brain metastases.

PubMed

Ding, Ju-Rong; Zhu, Fangmei; Hua, Bo; Xiong, Xingzhong; Wen, Yuqiao; Ding, Zhongxiang; Thompson, Paul M

2018-04-02

Brain metastases are the most prevalent cerebral tumors. Resting state networks (RSNs) are involved in multiple perceptual and cognitive functions. Therefore, precisely localizing multiple RSNs may be extremely valuable before surgical resection of metastases, to minimize neurocognitive impairments. Here we aimed to investigate the reliability of independent component analysis (ICA) for localizing multiple RSNs from resting-state functional MRI (rs-fMRI) data in individual patients, and further evaluate lesion-related spatial shifts of the RSNs. Twelve patients with brain metastases and 14 healthy controls were recruited. Using an improved automatic component identification method, we successfully identified seven common RSNs, including: the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN), language network (LN), sensorimotor network (SMN), auditory network (AN) and visual network (VN), in both individual patients and controls. Moreover, the RSNs in the patients showed a visible spatial shift compared to those in the controls, and the spatial shift of some regions was related to the tumor location, which may reflect a complicated functional mechanism - functional disruptions and reorganizations - caused by metastases. Besides, higher cognitive networks (DMN, ECN, DAN and LN) showed significantly larger spatial shifts than perceptual networks (SMN, AN and VN), supporting a functional dichotomy between the two network groups even in pathologic alterations associated with metastases. Overall, our findings provide evidence that ICA is a promising approach for presurgical localization of multiple RSNs from rs-fMRI data in individual patients. More attention should be paid to the spatial shifts of the RSNs before surgical resection.

Spatial profile reconstruction of individual componentsof the nonlinear susceptibility tensors {chi}-circumflex {sup (3)}(z, {omega}', {omega}' -{omega}, {omega}) and {chi}-circumflex {sup (3)}(z, 2{omega}{+-}{omega}', {+-}{omega}', {omega}, {omega}) of a one-dimensionally inhomogeneous medium

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

Golubkov, A A; Makarov, Vladimir A

We have proved for the first time and proposed an algorithm of unique spatial profile reconstruction of the components {chi}-circumflex {sup (3)}{sub yyyy} of complex tensors {chi}-circumflex {sup (3)}(z, {omega}', {omega}', -{omega}, {omega}) and {chi}-circumflex {sup (3)}(z, 2{omega}{+-}{omega}', {+-}{omega}', {omega}, {omega}), describing four-photon interaction of light waves in a one-dimensionally inhomogeneous plate, whose medium has a symmetry plane m{sub y} that is perpendicular to its surface. For the media with an additional symmetry axis 2{sub z}, 4{sub z}, 6{sub z} or {infinity}{sub z} that is perpendicular to the plate surface, the proposed method can be used to reconstruct about one-fifthmore » of all independent components of the above tensors. (nonlinear optical phenomena)« less

Measure Projection Analysis: A Probabilistic Approach to EEG Source Comparison and Multi-Subject Inference

PubMed Central

Bigdely-Shamlo, Nima; Mullen, Tim; Kreutz-Delgado, Kenneth; Makeig, Scott

2013-01-01

A crucial question for the analysis of multi-subject and/or multi-session electroencephalographic (EEG) data is how to combine information across multiple recordings from different subjects and/or sessions, each associated with its own set of source processes and scalp projections. Here we introduce a novel statistical method for characterizing the spatial consistency of EEG dynamics across a set of data records. Measure Projection Analysis (MPA) first finds voxels in a common template brain space at which a given dynamic measure is consistent across nearby source locations, then computes local-mean EEG measure values for this voxel subspace using a statistical model of source localization error and between-subject anatomical variation. Finally, clustering the mean measure voxel values in this locally consistent brain subspace finds brain spatial domains exhibiting distinguishable measure features and provides 3-D maps plus statistical significance estimates for each EEG measure of interest. Applied to sufficient high-quality data, the scalp projections of many maximally independent component (IC) processes contributing to recorded high-density EEG data closely match the projection of a single equivalent dipole located in or near brain cortex. We demonstrate the application of MPA to a multi-subject EEG study decomposed using independent component analysis (ICA), compare the results to k-means IC clustering in EEGLAB (sccn.ucsd.edu/eeglab), and use surrogate data to test MPA robustness. A Measure Projection Toolbox (MPT) plug-in for EEGLAB is available for download (sccn.ucsd.edu/wiki/MPT). Together, MPA and ICA allow use of EEG as a 3-D cortical imaging modality with near-cm scale spatial resolution. PMID:23370059

Stimulus information contaminates summation tests of independent neural representations of features

NASA Technical Reports Server (NTRS)

Shimozaki, Steven S.; Eckstein, Miguel P.; Abbey, Craig K.

2002-01-01

Many models of visual processing assume that visual information is analyzed into separable and independent neural codes, or features. A common psychophysical test of independent features is known as a summation study, which measures performance in a detection, discrimination, or visual search task as the number of proposed features increases. Improvement in human performance with increasing number of available features is typically attributed to the summation, or combination, of information across independent neural coding of the features. In many instances, however, increasing the number of available features also increases the stimulus information in the task, as assessed by an optimal observer that does not include the independent neural codes. In a visual search task with spatial frequency and orientation as the component features, a particular set of stimuli were chosen so that all searches had equivalent stimulus information, regardless of the number of features. In this case, human performance did not improve with increasing number of features, implying that the improvement observed with additional features may be due to stimulus information and not the combination across independent features.

Localization of spontaneous magnetoencephalographic activity of neonates and fetuses using independent component and Hilbert phase analysis.

PubMed

Vairavan, Srinivasan; Eswaran, Hari; Preissl, Hubert; Wilson, James D; Haddad, Naim; Lowery, Curtis L; Govindan, Rathinaswamy B

2010-01-01

The fetal magnetoencephalogram (fMEG) is measured in the presence of large interference from maternal and fetal magnetocardiograms (mMCG and fMCG). These cardiac interferences can be attenuated by orthogonal projection (OP) technique of the corresponding spatial vectors. However, the OP technique redistributes the fMEG signal among the channels and also leaves some cardiac residuals (partially attenuated mMCG and fMCG) due to loss of stationarity in the signal. In this paper, we propose a novel way to extract and localize the neonatal and fetal spontaneous brain activity by using independent component analysis (ICA) technique. In this approach, we perform ICA on a small subset of sensors for 1-min duration. The independent components obtained are further investigated for the presence of discontinuous patterns as identified by the Hilbert phase analysis and are used as decision criteria for localizing the spontaneous brain activity. In order to locate the region of highest spontaneous brain activity content, this analysis is performed on the sensor subsets, which are traversed across the entire sensor space. The region of the spontaneous brain activity as identified by the proposed approach correlated well with the neonatal and fetal head location. In addition, the burst duration and the inter-burst interval computed for the identified discontinuous brain patterns are in agreement with the reported values.

Development of instrumentation for measurements of two components of velocity with a single sensing element

NASA Astrophysics Data System (ADS)

Byers, C. P.; Fu, M. K.; Fan, Y.; Hultmark, M.

2018-02-01

A novel method of obtaining two orthogonal velocity components with high spatial and temporal resolution is investigated. Both components are obtained utilizing a single sensing nanoribbon by combining the two independent operating modes of classic hot wire anemometry and the newly discovered elastic filament velocimetry (EFV). In contrast to hot wire anemometry, EFV measures fluid velocity through correlating the fluid forcing with the internal strain of the wire. In order to utilize both modes of operation, a system that switches between the two operating modes is built and characterized, and the theoretically predicted sensing response time in water is compared to experimental results. The sensing system is capable of switching between the two modes of operation at a frequency of 100 kHz with minimal attenuation with an uncompensated repetition rate up to 3 kHz or up to 10 kHz utilizing modest signal compensation. While further characterization of the sensor performance in air is needed, this methodology enables a technique for obtaining well-resolved yet cost-efficient directional measurements of flow velocities which, for example, can be used for distributed measurements of velocity or measurements of turbulent stresses with excellent spatial resolution.

Model-free fMRI group analysis using FENICA.

PubMed

Schöpf, V; Windischberger, C; Robinson, S; Kasess, C H; Fischmeister, F PhS; Lanzenberger, R; Albrecht, J; Kleemann, A M; Kopietz, R; Wiesmann, M; Moser, E

2011-03-01

Exploratory analysis of functional MRI data allows activation to be detected even if the time course differs from that which is expected. Independent Component Analysis (ICA) has emerged as a powerful approach, but current extensions to the analysis of group studies suffer from a number of drawbacks: they can be computationally demanding, results are dominated by technical and motion artefacts, and some methods require that time courses be the same for all subjects or that templates be defined to identify common components. We have developed a group ICA (gICA) method which is based on single-subject ICA decompositions and the assumption that the spatial distribution of signal changes in components which reflect activation is similar between subjects. This approach, which we have called Fully Exploratory Network Independent Component Analysis (FENICA), identifies group activation in two stages. ICA is performed on the single-subject level, then consistent components are identified via spatial correlation. Group activation maps are generated in a second-level GLM analysis. FENICA is applied to data from three studies employing a wide range of stimulus and presentation designs. These are an event-related motor task, a block-design cognition task and an event-related chemosensory experiment. In all cases, the group maps identified by FENICA as being the most consistent over subjects correspond to task activation. There is good agreement between FENICA results and regions identified in prior GLM-based studies. In the chemosensory task, additional regions are identified by FENICA and temporal concatenation ICA that we show is related to the stimulus, but exhibit a delayed response. FENICA is a fully exploratory method that allows activation to be identified without assumptions about temporal evolution, and isolates activation from other sources of signal fluctuation in fMRI. It has the advantage over other gICA methods that it is computationally undemanding, spotlights components relating to activation rather than artefacts, allows the use of familiar statistical thresholding through deployment of a higher level GLM analysis and can be applied to studies where the paradigm is different for all subjects. Copyright © 2010 Elsevier Inc. All rights reserved.

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.

Noninvasive diagnostics of skin microphysical parameters based on spatially resolved diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Lisenko, S. A.; Kugeiko, M. M.

2013-01-01

The ability to determine noninvasively microphysical parameters (MPPs) of skin characteristic of malignant melanoma was demonstrated. The MPPs were the melanin content in dermis, saturation of tissue with blood vessels, and concentration and effective size of tissue scatterers. The proposed method was based on spatially resolved spectral measurements of skin diffuse reflectance and multiple regressions between linearly independent measurement components and skin MPPs. The regressions were established by modeling radiation transfer in skin with a wide variation of its MPPs. Errors in the determination of skin MPPs were estimated using fiber-optic measurements of its diffuse reflectance at wavelengths of commercially available semiconductor diode lasers (578, 625, 660, 760, and 806 nm) at source-detector separations of 0.23-1.38 mm.

The PAH Emission Characteristics of the Reflection Nebula NGC 2023

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

Peeters, Els; Bauschlicher, Charles W. Jr.; Allamandola, Louis J.

We present 5–20 μ m spectral maps of the reflection nebula NGC 2023 obtained with the Infrared Spectrograph SL and SH modes on board the Spitzer Space Telescope, which reveal emission from polycyclic aromatic hydrocarbons (PAHs), C{sub 60}, and H{sub 2} superposed on a dust continuum. We show that several PAH emission bands correlate with each other and exhibit distinct spatial distributions that reveal a spatial sequence with distance from the illuminating star. We explore the distinct morphology of the 6.2, 7.7, and 8.6 μ m PAH bands and find that at least two spatially distinct components contribute to themore » 7–9 μ m PAH emission in NGC 2023. We report that the PAH features behave independently of the underlying plateaus. We present spectra of compact, oval PAHs ranging in size from C{sub 66} to C{sub 210}, determined computationally using density functional theory, and we investigate trends in the band positions and relative intensities as a function of PAH size, charge, and geometry. Based on the NASA Ames PAH database, we discuss the 7–9 μ m components in terms of band assignments and relative intensities. We assign the plateau emission to very small grains with possible contributions from PAH clusters and identify components in the 7–9 μ m emission that likely originate in these structures. Based on the assignments and the observed spatial sequence, we discuss the photochemical evolution of the interstellar PAH family as the PAHs are more and more exposed to the radiation field of the central star in the evaporative flows associated with the Photo-Dissociation Regions in NGC 2023.« less

Effects of VR system fidelity on analyzing isosurface visualization of volume datasets.

PubMed

Laha, Bireswar; Bowman, Doug A; Socha, John J

2014-04-01

Volume visualization is an important technique for analyzing datasets from a variety of different scientific domains. Volume data analysis is inherently difficult because volumes are three-dimensional, dense, and unfamiliar, requiring scientists to precisely control the viewpoint and to make precise spatial judgments. Researchers have proposed that more immersive (higher fidelity) VR systems might improve task performance with volume datasets, and significant results tied to different components of display fidelity have been reported. However, more information is needed to generalize these results to different task types, domains, and rendering styles. We visualized isosurfaces extracted from synchrotron microscopic computed tomography (SR-μCT) scans of beetles, in a CAVE-like display. We ran a controlled experiment evaluating the effects of three components of system fidelity (field of regard, stereoscopy, and head tracking) on a variety of abstract task categories that are applicable to various scientific domains, and also compared our results with those from our prior experiment using 3D texture-based rendering. We report many significant findings. For example, for search and spatial judgment tasks with isosurface visualization, a stereoscopic display provides better performance, but for tasks with 3D texture-based rendering, displays with higher field of regard were more effective, independent of the levels of the other display components. We also found that systems with high field of regard and head tracking improve performance in spatial judgment tasks. Our results extend existing knowledge and produce new guidelines for designing VR systems to improve the effectiveness of volume data analysis.

Effects of dynamic range compression on spatial selective auditory attention in normal-hearing listeners.

PubMed

Schwartz, Andrew H; Shinn-Cunningham, Barbara G

2013-04-01

Many hearing aids introduce compressive gain to accommodate the reduced dynamic range that often accompanies hearing loss. However, natural sounds produce complicated temporal dynamics in hearing aid compression, as gain is driven by whichever source dominates at a given moment. Moreover, independent compression at the two ears can introduce fluctuations in interaural level differences (ILDs) important for spatial perception. While independent compression can interfere with spatial perception of sound, it does not always interfere with localization accuracy or speech identification. Here, normal-hearing listeners reported a target message played simultaneously with two spatially separated masker messages. We measured the amount of spatial separation required between the target and maskers for subjects to perform at threshold in this task. Fast, syllabic compression that was independent at the two ears increased the required spatial separation, but linking the compressors to provide identical gain to both ears (preserving ILDs) restored much of the deficit caused by fast, independent compression. Effects were less clear for slower compression. Percent-correct performance was lower with independent compression, but only for small spatial separations. These results may help explain differences in previous reports of the effect of compression on spatial perception of sound.

Network-based diffusion analysis reveals cultural transmission of lobtail feeding in humpback whales.

PubMed

Allen, Jenny; Weinrich, Mason; Hoppitt, Will; Rendell, Luke

2013-04-26

We used network-based diffusion analysis to reveal the cultural spread of a naturally occurring foraging innovation, lobtail feeding, through a population of humpback whales (Megaptera novaeangliae) over a period of 27 years. Support for models with a social transmission component was 6 to 23 orders of magnitude greater than for models without. The spatial and temporal distribution of sand lance, a prey species, was also important in predicting the rate of acquisition. Our results, coupled with existing knowledge about song traditions, show that this species can maintain multiple independently evolving traditions in its populations. These insights strengthen the case that cetaceans represent a peak in the evolution of nonhuman culture, independent of the primate lineage.

A method to estimate the effect of deformable image registration uncertainties on daily dose mapping

PubMed Central

Murphy, Martin J.; Salguero, Francisco J.; Siebers, Jeffrey V.; Staub, David; Vaman, Constantin

2012-01-01

Purpose: To develop a statistical sampling procedure for spatially-correlated uncertainties in deformable image registration and then use it to demonstrate their effect on daily dose mapping. Methods: Sequential daily CT studies are acquired to map anatomical variations prior to fractionated external beam radiotherapy. The CTs are deformably registered to the planning CT to obtain displacement vector fields (DVFs). The DVFs are used to accumulate the dose delivered each day onto the planning CT. Each DVF has spatially-correlated uncertainties associated with it. Principal components analysis (PCA) is applied to measured DVF error maps to produce decorrelated principal component modes of the errors. The modes are sampled independently and reconstructed to produce synthetic registration error maps. The synthetic error maps are convolved with dose mapped via deformable registration to model the resulting uncertainty in the dose mapping. The results are compared to the dose mapping uncertainty that would result from uncorrelated DVF errors that vary randomly from voxel to voxel. Results: The error sampling method is shown to produce synthetic DVF error maps that are statistically indistinguishable from the observed error maps. Spatially-correlated DVF uncertainties modeled by our procedure produce patterns of dose mapping error that are different from that due to randomly distributed uncertainties. Conclusions: Deformable image registration uncertainties have complex spatial distributions. The authors have developed and tested a method to decorrelate the spatial uncertainties and make statistical samples of highly correlated error maps. The sample error maps can be used to investigate the effect of DVF uncertainties on daily dose mapping via deformable image registration. An initial demonstration of this methodology shows that dose mapping uncertainties can be sensitive to spatial patterns in the DVF uncertainties. PMID:22320766

Visually induced adaptation in three-dimensional organization of primate vestibuloocular reflex

NASA Technical Reports Server (NTRS)

Angelaki, D. E.; Hess, B. J.

1998-01-01

The adaptive plasticity of the spatial organization of the vestibuloocular reflex (VOR) has been investigated in intact and canal-plugged primates using 2-h exposure to conflicting visual (optokinetic, OKN) and vestibular rotational stimuli about mutually orthogonal axes (generating torsional VOR + vertical OKN, torsional VOR + horizontal OKN, vertical VOR + horizontal OKN, and horizontal VOR + vertical OKN). Adaptation protocols with 0.5-Hz (+/-18 degrees ) head movements about either an earth-vertical or an earth-horizontal axis induced orthogonal response components as high as 40-70% of those required for ideal adaptation. Orthogonal response gains were highest at the adapting frequency with phase leads present at lower and phase lags present at higher frequencies. Furthermore, the time course of adaptation, as well as orthogonal response dynamics were similar and relatively independent of the particular visual/vestibular stimulus combination. Low-frequency (0. 05 Hz, vestibular stimulus: +/-60 degrees ; optokinetic stimulus: +/-180 degrees ) adaptation protocols with head movements about an earth-vertical axis induced smaller orthogonal response components that did not exceed 20-40% of the head velocity stimulus (i.e., approximately 10% of that required for ideal adaptation). At the same frequency, adaptation with head movements about an earth-horizontal axis generated large orthogonal responses that reached values as high as 100-120% of head velocity after 2 h of adaptation (i.e., approximately 40% of ideal adaptation gains). The particular spatial and temporal response characteristics after low-frequency, earth-horizontal axis adaptation in both intact and canal-plugged animals strongly suggests that the orienting (and perhaps translational) but not inertial (velocity storage) components of the primate otolith-ocular system exhibit spatial adaptability. Due to the particular nested arrangement of the visual and vestibular stimuli, the optic flow pattern exhibited a significant component about the third spatial axis (i.e., orthogonal to the axes of rotation of the head and visual surround) at twice the oscillation frequency. Accordingly, the adapted VOR was characterized consistently by a third response component (orthogonal to both the axes of head and optokinetic drum rotation) at twice the oscillation frequency after earth-horizontal but not after earth-vertical axis 0.05-Hz adaptation. This suggests that the otolith-ocular (but not the semicircular canal-ocular) system can adaptively change its spatial organization at frequencies different from those of the head movement.

Modeling fluid injection induced microseismicity in shales

NASA Astrophysics Data System (ADS)

Carcione, José M.; Currenti, Gilda; Johann, Lisa; Shapiro, Serge

2018-02-01

Hydraulic fracturing in shales generates a cloud of seismic—tensile and shear—events that can be used to evaluate the extent of the fracturing (event clouds) and obtain the hydraulic properties of the medium, such as the degree of anisotropy and the permeability. Firstly, we investigate the suitability of novel semi-analytical reference solutions for pore pressure evolution around a well after fluid injection in anisotropic media. To do so, we use cylindrical coordinates in the presence of a formation (a layer) and spherical coordinates for a homogeneous and unbounded medium. The involved differential equations are transformed to an isotropic diffusion equation by means of pseudo-spatial coordinates obtained from the spatial variables re-scaled by the permeability components. We consider pressure-dependent permeability components, which are independent of the spatial direction. The analytical solutions are compared to numerical solutions to verify their applicability. The comparison shows that the solutions are suitable for a limited permeability range and moderate to minor pressure dependences of the permeability. Once the pressure evolution around the well has been established, we can model the microseismic events. Induced seismicity by failure due to fluid injection in a porous rock depends on the properties of the hydraulic and elastic medium and in situ stress conditions. Here, we define a tensile threshold pressure above which there is tensile emission, while the shear threshold is obtained by using the octahedral stress criterion and the in situ rock properties and conditions. Subsequently, we generate event clouds for both cases and study the spatio-temporal features. The model considers anisotropic permeability and the results are spatially re-scaled to obtain an effective isotropic medium representation. For a 3D diffusion in spherical coordinates and exponential pressure dependence of the permeability, the results differ from those of the classical diffusion equation. Use of the classical front to fit cloud events spatially, provides good results but with a re-scaled value of these components. Modeling is required to evaluate the scaling constant in real cases.

Kinematic principles of primate rotational vestibulo-ocular reflex. I. Spatial organization of fast phase velocity axes

NASA Technical Reports Server (NTRS)

Hess, B. J.; Angelaki, D. E.

1997-01-01

The spatial organization of fast phase velocity vectors of the vestibulo-ocular reflex (VOR) was studied in rhesus monkeys during yaw rotations about an earth-horizontal axis that changed continuously the orientation of the head relative to gravity ("barbecue spit" rotation). In addition to a velocity component parallel to the rotation axis, fast phases also exhibited a velocity component that invariably was oriented along the momentary direction of gravity. As the head rotated through supine and prone positions, torsional components of fast phase velocity axes became prominent. Similarly, as the head rotated through left and right ear-down positions, fast phase velocity axes exhibited prominent vertical components. The larger the speed of head rotation the greater the magnitude of this fast phase component, which was collinear with gravity. The main sequence properties of VOR fast phases were independent of head position. However, peak amplitude as well as peak velocity of fast phases were both modulated as a function of head orientation, exhibiting a minimum in prone position. The results suggest that the fast phases of vestibulo-ocular reflexes not only redirect gaze and reposition the eye in the direction of head motion but also reorient the eye with respect to earth-vertical when the head moves relative to gravity. As further elaborated in the companion paper, the underlying mechanism could be described as a dynamic, gravity-dependent modulation of the coordinates of ocular rotations relative to the head.

Denoising the Speaking Brain: Toward a Robust Technique for Correcting Artifact-Contaminated fMRI Data under Severe Motion

PubMed Central

Xu, Yisheng; Tong, Yunxia; Liu, Siyuan; Chow, Ho Ming; AbdulSabur, Nuria Y.; Mattay, Govind S.; Braun, Allen R.

2014-01-01

A comprehensive set of methods based on spatial independent component analysis (sICA) is presented as a robust technique for artifact removal, applicable to a broad range of functional magnetic resonance imaging (fMRI) experiments that have been plagued by motion-related artifacts. Although the applications of sICA for fMRI denoising have been studied previously, three fundamental elements of this approach have not been established as follows: 1) a mechanistically-based ground truth for component classification; 2) a general framework for evaluating the performance and generalizability of automated classifiers; 3) a reliable method for validating the effectiveness of denoising. Here we perform a thorough investigation of these issues and demonstrate the power of our technique by resolving the problem of severe imaging artifacts associated with continuous overt speech production. As a key methodological feature, a dual-mask sICA method is proposed to isolate a variety of imaging artifacts by directly revealing their extracerebral spatial origins. It also plays an important role for understanding the mechanistic properties of noise components in conjunction with temporal measures of physical or physiological motion. The potentials of a spatially-based machine learning classifier and the general criteria for feature selection have both been examined, in order to maximize the performance and generalizability of automated component classification. The effectiveness of denoising is quantitatively validated by comparing the activation maps of fMRI with those of positron emission tomography acquired under the same task conditions. The general applicability of this technique is further demonstrated by the successful reduction of distance-dependent effect of head motion on resting-state functional connectivity. PMID:25225001

Denoising the speaking brain: toward a robust technique for correcting artifact-contaminated fMRI data under severe motion.

PubMed

Xu, Yisheng; Tong, Yunxia; Liu, Siyuan; Chow, Ho Ming; AbdulSabur, Nuria Y; Mattay, Govind S; Braun, Allen R

2014-12-01

A comprehensive set of methods based on spatial independent component analysis (sICA) is presented as a robust technique for artifact removal, applicable to a broad range of functional magnetic resonance imaging (fMRI) experiments that have been plagued by motion-related artifacts. Although the applications of sICA for fMRI denoising have been studied previously, three fundamental elements of this approach have not been established as follows: 1) a mechanistically-based ground truth for component classification; 2) a general framework for evaluating the performance and generalizability of automated classifiers; and 3) a reliable method for validating the effectiveness of denoising. Here we perform a thorough investigation of these issues and demonstrate the power of our technique by resolving the problem of severe imaging artifacts associated with continuous overt speech production. As a key methodological feature, a dual-mask sICA method is proposed to isolate a variety of imaging artifacts by directly revealing their extracerebral spatial origins. It also plays an important role for understanding the mechanistic properties of noise components in conjunction with temporal measures of physical or physiological motion. The potentials of a spatially-based machine learning classifier and the general criteria for feature selection have both been examined, in order to maximize the performance and generalizability of automated component classification. The effectiveness of denoising is quantitatively validated by comparing the activation maps of fMRI with those of positron emission tomography acquired under the same task conditions. The general applicability of this technique is further demonstrated by the successful reduction of distance-dependent effect of head motion on resting-state functional connectivity. Copyright © 2014 Elsevier Inc. All rights reserved.

How microsaccades relate to lateralized ERP components of spatial attention: A co-registration study.

PubMed

Meyberg, Susann; Sommer, Werner; Dimigen, Olaf

2017-05-01

Covert shifts of attention that follow the presentation of a cue are associated with lateralized components in the event-related potential (ERP): the "early directing attention negativity" (EDAN) and the "anterior directing attention negativity" (ADAN). Traditionally, these shifts are thought to take place while gaze is fixated and, thus, in the absence of saccades. However, microsaccades of small amplitude (<1°) occur frequently and involuntarily also during fixation and are closely correlated with spatial attention. To investigate potential links between microsaccades and lateralized ERP components, we simultaneously recorded eye movements and ERPs in a spatial cueing task. As a first major result, we show that both the posterior EDAN and the orientation of microsaccades align more strongly with the location of the task-relevant part of the cue stimulus than with the direction of the attention shift indicated by that cue. A coupling between microsaccades and EDAN was also present on the single-trial level: The EDAN was largest when microsaccades were oriented toward the relevant cue, but absent when microsaccades were oriented away from it, suggesting that EDAN and microsaccades are generated by the same neural network, which selects relevant stimuli and orients behavior toward them. As a second major result, we show that small corneoretinal artifacts from microsaccades, which fall below conventional EOG rejection thresholds, contaminate the measurement of the ADAN. After correcting the EEG for microsaccade-related artifacts with an optimized variant of independent component analysis, ADAN was abolished at frontal sites, but a genuine ADAN was still present at central sites. Thus, the combined measurement of microsaccades and lateralized ERPs sheds new light onto cue-elicited shifts of covert attention. Copyright © 2017 Elsevier Ltd. All rights reserved.

Within-subject joint independent component analysis of simultaneous fMRI/ERP in an auditory oddball paradigm

PubMed Central

MANGALATHU-ARUMANA, J.; BEARDSLEY, S. A.; LIEBENTHAL, E.

2012-01-01

The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. This research aimed to determine the sensitivity and limitations of applying joint independent component analysis (jICA) within-subjects, for ERP and fMRI data collected simultaneously in a parametric auditory frequency oddball paradigm. In a group of 20 subjects, an increase in ERP peak amplitude ranging 1–8 μV in the time window of the P300 (350–700ms), and a correlated increase in fMRI signal in a network of regions including the right superior temporal and supramarginal gyri, was observed with the increase in deviant frequency difference. JICA of the same ERP and fMRI group data revealed activity in a similar network, albeit with stronger amplitude and larger extent. In addition, activity in the left pre- and post- central gyri, likely associated with right hand somato-motor response, was observed only with the jICA approach. Within-subject, the jICA approach revealed significantly stronger and more extensive activity in the brain regions associated with the auditory P300 than the P300 linear regression analysis. The results suggest that with the incorporation of spatial and temporal information from both imaging modalities, jICA may be a more sensitive method for extracting common sources of activity between ERP and fMRI. PMID:22377443

Tensorial extensions of independent component analysis for multisubject FMRI analysis.

PubMed

Beckmann, C F; Smith, S M

2005-03-01

We discuss model-free analysis of multisubject or multisession FMRI data by extending the single-session probabilistic independent component analysis model (PICA; Beckmann and Smith, 2004. IEEE Trans. on Medical Imaging, 23 (2) 137-152) to higher dimensions. This results in a three-way decomposition that represents the different signals and artefacts present in the data in terms of their temporal, spatial, and subject-dependent variations. The technique is derived from and compared with parallel factor analysis (PARAFAC; Harshman and Lundy, 1984. In Research methods for multimode data analysis, chapter 5, pages 122-215. Praeger, New York). Using simulated data as well as data from multisession and multisubject FMRI studies we demonstrate that the tensor PICA approach is able to efficiently and accurately extract signals of interest in the spatial, temporal, and subject/session domain. The final decompositions improve upon PARAFAC results in terms of greater accuracy, reduced interference between the different estimated sources (reduced cross-talk), robustness (against deviations of the data from modeling assumptions and against overfitting), and computational speed. On real FMRI 'activation' data, the tensor PICA approach is able to extract plausible activation maps, time courses, and session/subject modes as well as provide a rich description of additional processes of interest such as image artefacts or secondary activation patterns. The resulting data decomposition gives simple and useful representations of multisubject/multisession FMRI data that can aid the interpretation and optimization of group FMRI studies beyond what can be achieved using model-based analysis techniques.

Coupling of Action-Perception Brain Networks during Musical Pulse Processing: Evidence from Region-of-Interest-Based Independent Component Analysis.

PubMed

Burunat, Iballa; Tsatsishvili, Valeri; Brattico, Elvira; Toiviainen, Petri

2017-01-01

Our sense of rhythm relies on orchestrated activity of several cerebral and cerebellar structures. Although functional connectivity studies have advanced our understanding of rhythm perception, this phenomenon has not been sufficiently studied as a function of musical training and beyond the General Linear Model (GLM) approach. Here, we studied pulse clarity processing during naturalistic music listening using a data-driven approach (independent component analysis; ICA). Participants' (18 musicians and 18 controls) functional magnetic resonance imaging (fMRI) responses were acquired while listening to music. A targeted region of interest (ROI) related to pulse clarity processing was defined, comprising auditory, somatomotor, basal ganglia, and cerebellar areas. The ICA decomposition was performed under different model orders, i.e., under a varying number of assumed independent sources, to avoid relying on prior model order assumptions. The components best predicted by a measure of the pulse clarity of the music, extracted computationally from the musical stimulus, were identified. Their corresponding spatial maps uncovered a network of auditory (perception) and motor (action) areas in an excitatory-inhibitory relationship at lower model orders, while mainly constrained to the auditory areas at higher model orders. Results revealed (a) a strengthened functional integration of action-perception networks associated with pulse clarity perception hidden from GLM analyses, and (b) group differences between musicians and non-musicians in pulse clarity processing, suggesting lifelong musical training as an important factor that may influence beat processing.

Spatial-time-state fusion algorithm for defect detection through eddy current pulsed thermography

NASA Astrophysics Data System (ADS)

Xiao, Xiang; Gao, Bin; Woo, Wai Lok; Tian, Gui Yun; Xiao, Xiao Ting

2018-05-01

Eddy Current Pulsed Thermography (ECPT) has received extensive attention due to its high sensitive of detectability on surface and subsurface cracks. However, it remains as a difficult challenge in unsupervised detection as to identify defects without knowing any prior knowledge. This paper presents a spatial-time-state features fusion algorithm to obtain fully profile of the defects by directional scanning. The proposed method is intended to conduct features extraction by using independent component analysis (ICA) and automatic features selection embedding genetic algorithm. Finally, the optimal feature of each step is fused to obtain defects reconstruction by applying common orthogonal basis extraction (COBE) method. Experiments have been conducted to validate the study and verify the efficacy of the proposed method on blind defect detection.

Improved sensitivity and specificity for resting state and task fMRI with multiband multi-echo EPI compared to multi-echo EPI at 7 T.

PubMed

Boyacioğlu, Rasim; Schulz, Jenni; Koopmans, Peter J; Barth, Markus; Norris, David G

2015-10-01

A multiband multi-echo (MBME) sequence is implemented and compared to a matched standard multi-echo (ME) protocol to investigate the potential improvement in sensitivity and spatial specificity at 7 T for resting state and task fMRI. ME acquisition is attractive because BOLD sensitivity is less affected by variation in T2*, and because of the potential for separating BOLD and non-BOLD signal components. MBME further reduces TR thus increasing the potential reduction in physiological noise. In this study we used FSL-FIX to clean ME and MBME resting state and task fMRI data (both 3.5mm isotropic). After noise correction, the detection of resting state networks improves with more non-artifactual independent components being observed. Additional activation clusters for task data are discovered for MBME data (increased sensitivity) whereas existing clusters become more localized for resting state (improved spatial specificity). The results obtained indicate that MBME is superior to ME at high field strengths. Copyright © 2015 Elsevier Inc. All rights reserved.

Localized attacks on spatially embedded networks with dependencies.

PubMed

Berezin, Yehiel; Bashan, Amir; Danziger, Michael M; Li, Daqing; Havlin, Shlomo

2015-03-11

Many real world complex systems such as critical infrastructure networks are embedded in space and their components may depend on one another to function. They are also susceptible to geographically localized damage caused by malicious attacks or natural disasters. Here, we study a general model of spatially embedded networks with dependencies under localized attacks. We develop a theoretical and numerical approach to describe and predict the effects of localized attacks on spatially embedded systems with dependencies. Surprisingly, we find that a localized attack can cause substantially more damage than an equivalent random attack. Furthermore, we find that for a broad range of parameters, systems which appear stable are in fact metastable. Though robust to random failures-even of finite fraction-if subjected to a localized attack larger than a critical size which is independent of the system size (i.e., a zero fraction), a cascading failure emerges which leads to complete system collapse. Our results demonstrate the potential high risk of localized attacks on spatially embedded network systems with dependencies and may be useful for designing more resilient systems.

Precoded spatial multiplexing MIMO system with spatial component interleaver.

PubMed

Gao, Xiang; Wu, Zhanji

In this paper, the performance of precoded bit-interleaved coded modulation (BICM) spatial multiplexing multiple-input multiple-output (MIMO) system with spatial component interleaver is investigated. For the ideal precoded spatial multiplexing MIMO system with spatial component interleaver based on singular value decomposition (SVD) of the MIMO channel, the average pairwise error probability (PEP) of coded bits is derived. Based on the PEP analysis, the optimum spatial Q-component interleaver design criterion is provided to achieve the minimum error probability. For the limited feedback precoded proposed scheme with linear zero forcing (ZF) receiver, in order to minimize a bound on the average probability of a symbol vector error, a novel effective signal-to-noise ratio (SNR)-based precoding matrix selection criterion and a simplified criterion are proposed. Based on the average mutual information (AMI)-maximization criterion, the optimal constellation rotation angles are investigated. Simulation results indicate that the optimized spatial multiplexing MIMO system with spatial component interleaver can achieve significant performance advantages compared to the conventional spatial multiplexing MIMO system.

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.

A nonlinear circuit architecture for magnetoencephalographic signal analysis.

PubMed

Bucolo, M; Fortuna, L; Frasca, M; La Rosa, M; Virzì, M C; Shannahoff-Khalsa, D

2004-01-01

The objective of this paper was to face the complex spatio-temporal dynamics shown by Magnetoencephalography (MEG) data by applying a nonlinear distributed approach for the Blind Sources Separation. The effort was to characterize and differ-entiate the phases of a yogic respiratory exercise used in the treatment of obsessive compulsive disorders. The patient performed a precise respiratory protocol, at one breath per minute for 31 minutes, with 10 minutes resting phase before and after. The two steps of classical Independent Component Approach have been performed by using a Cellular Neural Network with two sets of templates. The choice of the couple of suitable templates has been carried out using genetic algorithm optimization techniques. Performing BSS with a nonlinear distributed approach, the outputs of the CNN have been compared to the ICA ones. In all the protocol phases, the main components founded with CNN have similar trends compared with that ones obtained with ICA. Moreover, using this distributed approach, a spatial location has been associated to each component. To underline the spatio-temporal and the nonlinearly of the neural process a distributed nonlinear architecture has been proposed. This strategy has been designed in order to overcome the hypothesis of linear combination among the sources signals, that is characteristic of the ICA approach, taking advantage of the spatial information.

Identifying the dominant thermal controls in a small salmonid-bearing creek with DTS and LDCA

NASA Astrophysics Data System (ADS)

Hatch, C. E.; Boughton, D. A.; Mora, E.

2012-12-01

Temperature has long been used as an indicator of ecosystem health and suitability for aquatic species, particularly in sensitive areas crucial to the persistence of declining fish populations. In addition, the EPA may soon implement TMDLs for stream temperatures throughout the U.S. Typically, stream temperature surveys have long duration but only at point locations, limiting the precision of efforts to predict stream temperatures or understand broader climate linkages. Specifically, single temperature loggers give no insight into the spatial heterogeneity of thermal conditions often exploited by biota. Distributed Temperature Sensing (DTS) provides temperature data at high spatial and temporal resolution up to 5-km in length, allowing for detailed assessment of a creek's heat budget. Rather than calculating a detailed energy balance from a single site or using a statistical approach, here we describe a hybrid method that uses Least Dependent Component Analysis (LDCA) capable of taking advantage of DTS data density in time and space. The method identifies distinct thermal components in the stream's heat budget, using only temperature data and an algorithm based on mutual information that "unmixes" signals in the temperature data. These signals can be interpreted as sets of heat-flux elements sharing coordinated (non-independent) dynamics, both simplifying the number of heat budget components as well as the number thermally forcing stream temperatures. Comparing these components to meteorological data and fluvial system structure allowed us to relate the groups back to causal heating and cooling mechanisms, which can be tested directly with targeted heat-budget studies. We applied this method to a small, arid-land creek, and found that a minimum of three distinct components were necessary to describe the thermal heterogeneity of a 1-km reach. We could also estimate a spatial response profile of each component, yielding insight into possible links between stream geomorphology and function. This method shows promise to aid with siting and defining detailed heat-budget studies, determining the dimensionality of heat budgets in natural streams, and more broadly for associating thermal components to fluvial structure and processes.

Automatic classification of artifactual ICA-components for artifact removal in EEG signals.

PubMed

Winkler, Irene; Haufe, Stefan; Tangermann, Michael

2011-08-02

Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies.

Hand classification of fMRI ICA noise components.

PubMed

Griffanti, Ludovica; Douaud, Gwenaëlle; Bijsterbosch, Janine; Evangelisti, Stefania; Alfaro-Almagro, Fidel; Glasser, Matthew F; Duff, Eugene P; Fitzgibbon, Sean; Westphal, Robert; Carone, Davide; Beckmann, Christian F; Smith, Stephen M

2017-07-01

We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Application of Independent Component Analysis for the Data Mining of Simultaneous EEG-fMRI: Preliminary Experience on Sleep Onset

PubMed Central

Lee, Jong-Hwan; Oh, Sungsuk; Jolesz, Ferenc A.; Park, Hyunwook; Yoo, Seung-Schik

2010-01-01

The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with the ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta- and alpha-rhythms that are sleep onset related EEG signatures along with the subsequent neural circuitries from a sleep deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEG-fMRI acquisitions, especially when a reference paradigm is unavailable. PMID:19922343

Application of independent component analysis for the data mining of simultaneous Eeg-fMRI: preliminary experience on sleep onset.

PubMed

Lee, Jong-Hwan; Oh, Sungsuk; Jolesz, Ferenc A; Park, Hyunwook; Yoo, Seung-Schik

2009-01-01

The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta and alpha rhythms that are sleep onset-related EEG signatures along with the subsequent neural circuitries from a sleep-deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEG-fMRI acquisitions, especially when a reference paradigm is unavailable.

Time-Optimized High-Resolution Readout-Segmented Diffusion Tensor Imaging

PubMed Central

Reishofer, Gernot; Koschutnig, Karl; Langkammer, Christian; Porter, David; Jehna, Margit; Enzinger, Christian; Keeling, Stephen; Ebner, Franz

2013-01-01

Readout-segmented echo planar imaging with 2D navigator-based reacquisition is an uprising technique enabling the sampling of high-resolution diffusion images with reduced susceptibility artifacts. However, low signal from the small voxels and long scan times hamper the clinical applicability. Therefore, we introduce a regularization algorithm based on total variation that is applied directly on the entire diffusion tensor. The spatially varying regularization parameter is determined automatically dependent on spatial variations in signal-to-noise ratio thus, avoiding over- or under-regularization. Information about the noise distribution in the diffusion tensor is extracted from the diffusion weighted images by means of complex independent component analysis. Moreover, the combination of those features enables processing of the diffusion data absolutely user independent. Tractography from in vivo data and from a software phantom demonstrate the advantage of the spatially varying regularization compared to un-regularized data with respect to parameters relevant for fiber-tracking such as Mean Fiber Length, Track Count, Volume and Voxel Count. Specifically, for in vivo data findings suggest that tractography results from the regularized diffusion tensor based on one measurement (16 min) generates results comparable to the un-regularized data with three averages (48 min). This significant reduction in scan time renders high resolution (1×1×2.5 mm3) diffusion tensor imaging of the entire brain applicable in a clinical context. PMID:24019951

A method for operative quantitative interpretation of multispectral images of biological tissues

NASA Astrophysics Data System (ADS)

Lisenko, S. A.; Kugeiko, M. M.

2013-10-01

A method for operative retrieval of spatial distributions of biophysical parameters of a biological tissue by using a multispectral image of it has been developed. The method is based on multiple regressions between linearly independent components of the diffuse reflection spectrum of the tissue and unknown parameters. Possibilities of the method are illustrated by an example of determining biophysical parameters of the skin (concentrations of melanin, hemoglobin and bilirubin, blood oxygenation, and scattering coefficient of the tissue). Examples of quantitative interpretation of the experimental data are presented.

Skew chicane based betatron eigenmode exchange module

DOEpatents

Douglas, David

2010-12-28

A skewed chicane eigenmode exchange module (SCEEM) that combines in a single beamline segment the separate functionalities of a skew quad eigenmode exchange module and a magnetic chicane. This module allows the exchange of independent betatron eigenmodes, alters electron beam orbit geometry, and provides longitudinal parameter control with dispersion management in a single beamline segment with stable betatron behavior. It thus reduces the spatial requirements for multiple beam dynamic functions, reduces required component counts and thus reduces costs, and allows the use of more compact accelerator configurations than prior art design methods.

Estimating spatial and temporal components of variation in count data using negative binomial mixed models

USGS Publications Warehouse

Irwin, Brian J.; Wagner, Tyler; Bence, James R.; Kepler, Megan V.; Liu, Weihai; Hayes, Daniel B.

2013-01-01

Partitioning total variability into its component temporal and spatial sources is a powerful way to better understand time series and elucidate trends. The data available for such analyses of fish and other populations are usually nonnegative integer counts of the number of organisms, often dominated by many low values with few observations of relatively high abundance. These characteristics are not well approximated by the Gaussian distribution. We present a detailed description of a negative binomial mixed-model framework that can be used to model count data and quantify temporal and spatial variability. We applied these models to data from four fishery-independent surveys of Walleyes Sander vitreus across the Great Lakes basin. Specifically, we fitted models to gill-net catches from Wisconsin waters of Lake Superior; Oneida Lake, New York; Saginaw Bay in Lake Huron, Michigan; and Ohio waters of Lake Erie. These long-term monitoring surveys varied in overall sampling intensity, the total catch of Walleyes, and the proportion of zero catches. Parameter estimation included the negative binomial scaling parameter, and we quantified the random effects as the variations among gill-net sampling sites, the variations among sampled years, and site × year interactions. This framework (i.e., the application of a mixed model appropriate for count data in a variance-partitioning context) represents a flexible approach that has implications for monitoring programs (e.g., trend detection) and for examining the potential of individual variance components to serve as response metrics to large-scale anthropogenic perturbations or ecological changes.

Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform

PubMed Central

Yu, Yeyang; Jin, Jin; Liu, Feng; Crozier, Stuart

2014-01-01

Compressed Sensing (CS) has been applied in dynamic Magnetic Resonance Imaging (MRI) to accelerate the data acquisition without noticeably degrading the spatial-temporal resolution. A suitable sparsity basis is one of the key components to successful CS applications. Conventionally, a multidimensional dataset in dynamic MRI is treated as a series of two-dimensional matrices, and then various matrix/vector transforms are used to explore the image sparsity. Traditional methods typically sparsify the spatial and temporal information independently. In this work, we propose a novel concept of tensor sparsity for the application of CS in dynamic MRI, and present the Higher-order Singular Value Decomposition (HOSVD) as a practical example. Applications presented in the three- and four-dimensional MRI data demonstrate that HOSVD simultaneously exploited the correlations within spatial and temporal dimensions. Validations based on cardiac datasets indicate that the proposed method achieved comparable reconstruction accuracy with the low-rank matrix recovery methods and, outperformed the conventional sparse recovery methods. PMID:24901331

Spatial patterns of brain amyloid-beta burden and atrophy rate associations in mild cognitive impairment.

PubMed

Tosun, Duygu; Schuff, Norbert; Mathis, Chester A; Jagust, William; Weiner, Michael W

2011-04-01

Amyloid-β accumulation in the brain is thought to be one of the earliest events in Alzheimer's disease, possibly leading to synaptic dysfunction, neurodegeneration and cognitive/functional decline. The earliest detectable changes seen with neuroimaging appear to be amyloid-β accumulation detected by (11)C-labelled Pittsburgh compound B positron emission tomography imaging. However, some individuals tolerate high brain amyloid-β loads without developing symptoms, while others progressively decline, suggesting that events in the brain downstream from amyloid-β deposition, such as regional brain atrophy rates, play an important role. The main purpose of this study was to understand the relationship between the regional distributions of increased amyloid-β and the regional distribution of increased brain atrophy rates in patients with mild cognitive impairment. To simultaneously capture the spatial distributions of amyloid-β and brain atrophy rates, we employed the statistical concept of parallel independent component analysis, an effective method for joint analysis of multimodal imaging data. Parallel independent component analysis identified significant relationships between two patterns of amyloid-β deposition and atrophy rates: (i) increased amyloid-β burden in the left precuneus/cuneus and medial-temporal regions was associated with increased brain atrophy rates in the left medial-temporal and parietal regions; and (ii) in contrast, increased amyloid-β burden in bilateral precuneus/cuneus and parietal regions was associated with increased brain atrophy rates in the right medial temporal regions. The spatial distribution of increased amyloid-β and the associated spatial distribution of increased brain atrophy rates embrace a characteristic pattern of brain structures known for a high vulnerability to Alzheimer's disease pathology, encouraging for the use of (11)C-labelled Pittsburgh compound B positron emission tomography measures as early indicators of Alzheimer's disease. These results may begin to shed light on the mechanisms by which amyloid-β deposition leads to neurodegeneration and cognitive decline and the development of a more specific Alzheimer's disease-specific imaging signature for diagnosis and use of this knowledge in the development of new anti-therapies for Alzheimer's disease.

Characterization of Disease-Related Covariance Topographies with SSMPCA Toolbox: Effects of Spatial Normalization and PET Scanners

PubMed Central

Peng, Shichun; Ma, Yilong; Spetsieris, Phoebe G; Mattis, Paul; Feigin, Andrew; Dhawan, Vijay; Eidelberg, David

2013-01-01

In order to generate imaging biomarkers from disease-specific brain networks, we have implemented a general toolbox to rapidly perform scaled subprofile modeling (SSM) based on principal component analysis (PCA) on brain images of patients and normals. This SSMPCA toolbox can define spatial covariance patterns whose expression in individual subjects can discriminate patients from controls or predict behavioral measures. The technique may depend on differences in spatial normalization algorithms and brain imaging systems. We have evaluated the reproducibility of characteristic metabolic patterns generated by SSMPCA in patients with Parkinson's disease (PD). We used [18F]fluorodeoxyglucose PET scans from PD patients and normal controls. Motor-related (PDRP) and cognition-related (PDCP) metabolic patterns were derived from images spatially normalized using four versions of SPM software (spm99, spm2, spm5 and spm8). Differences between these patterns and subject scores were compared across multiple independent groups of patients and control subjects. These patterns and subject scores were highly reproducible with different normalization programs in terms of disease discrimination and cognitive correlation. Subject scores were also comparable in PD patients imaged across multiple PET scanners. Our findings confirm a very high degree of consistency among brain networks and their clinical correlates in PD using images normalized in four different SPM platforms. SSMPCA toolbox can be used reliably for generating disease-specific imaging biomarkers despite the continued evolution of image preprocessing software in the neuroimaging community. Network expressions can be quantified in individual patients independent of different physical characteristics of PET cameras. PMID:23671030

Characterization of disease-related covariance topographies with SSMPCA toolbox: effects of spatial normalization and PET scanners.

PubMed

Peng, Shichun; Ma, Yilong; Spetsieris, Phoebe G; Mattis, Paul; Feigin, Andrew; Dhawan, Vijay; Eidelberg, David

2014-05-01

To generate imaging biomarkers from disease-specific brain networks, we have implemented a general toolbox to rapidly perform scaled subprofile modeling (SSM) based on principal component analysis (PCA) on brain images of patients and normals. This SSMPCA toolbox can define spatial covariance patterns whose expression in individual subjects can discriminate patients from controls or predict behavioral measures. The technique may depend on differences in spatial normalization algorithms and brain imaging systems. We have evaluated the reproducibility of characteristic metabolic patterns generated by SSMPCA in patients with Parkinson's disease (PD). We used [(18) F]fluorodeoxyglucose PET scans from patients with PD and normal controls. Motor-related (PDRP) and cognition-related (PDCP) metabolic patterns were derived from images spatially normalized using four versions of SPM software (spm99, spm2, spm5, and spm8). Differences between these patterns and subject scores were compared across multiple independent groups of patients and control subjects. These patterns and subject scores were highly reproducible with different normalization programs in terms of disease discrimination and cognitive correlation. Subject scores were also comparable in patients with PD imaged across multiple PET scanners. Our findings confirm a very high degree of consistency among brain networks and their clinical correlates in PD using images normalized in four different SPM platforms. SSMPCA toolbox can be used reliably for generating disease-specific imaging biomarkers despite the continued evolution of image preprocessing software in the neuroimaging community. Network expressions can be quantified in individual patients independent of different physical characteristics of PET cameras. Copyright © 2013 Wiley Periodicals, Inc.

Neural correlates of the spatial and expectancy components of endogenous and stimulus-driven orienting of attention in the Posner task.

PubMed

Doricchi, Fabrizio; Macci, Enrica; Silvetti, Massimo; Macaluso, Emiliano

2010-07-01

Voluntary orienting of visual attention is conventionally measured in tasks with predictive central cues followed by frequent valid targets at the cued location and by infrequent invalid targets at the uncued location. This implies that invalid targets entail both spatial reorienting of attention and breaching of the expected spatial congruency between cues and targets. Here, we used event-related functional magnetic resonance imaging (fMRI) to separate the neural correlates of the spatial and expectancy components of both endogenous orienting and stimulus-driven reorienting of attention. We found that during endogenous orienting with predictive cues, there was a significant deactivation of the right Temporal-Parietal Junction (TPJ). We also discovered that the lack of an equivalent deactivation with nonpredictive cues was matched to drop in attentional costs and preservation of attentional benefits. The right TPJ showed equivalent responses to invalid targets following predictive and nonpredictive cues. On the contrary, infrequent-unexpected invalid targets following predictive cues specifically activated the right Middle and Inferior Frontal Gyrus (MFG-IFG). Additional comparisons with spatially neutral trials demonstrated that, independently of cue predictiveness, valid targets activate the left TPJ, whereas invalid targets activate both the left and right TPJs. These findings show that the selective right TPJ activation that is found in the comparison between invalid and valid trials results from the reciprocal cancelling of the different activations that in the left TPJ are related to the processing of valid and invalid targets. We propose that left and right TPJs provide "matching and mismatching to attentional template" signals. These signals enable reorienting of attention and play a crucial role in the updating of the statistical contingency between cues and targets.

Source-space ICA for MEG source imaging.

PubMed

Jonmohamadi, Yaqub; Jones, Richard D

2016-02-01

One of the most widely used approaches in electroencephalography/magnetoencephalography (MEG) source imaging is application of an inverse technique (such as dipole modelling or sLORETA) on the component extracted by independent component analysis (ICA) (sensor-space ICA + inverse technique). The advantage of this approach over an inverse technique alone is that it can identify and localize multiple concurrent sources. Among inverse techniques, the minimum-variance beamformers offer a high spatial resolution. However, in order to have both high spatial resolution of beamformer and be able to take on multiple concurrent sources, sensor-space ICA + beamformer is not an ideal combination. We propose source-space ICA for MEG as a powerful alternative approach which can provide the high spatial resolution of the beamformer and handle multiple concurrent sources. The concept of source-space ICA for MEG is to apply the beamformer first and then singular value decomposition + ICA. In this paper we have compared source-space ICA with sensor-space ICA both in simulation and real MEG. The simulations included two challenging scenarios of correlated/concurrent cluster sources. Source-space ICA provided superior performance in spatial reconstruction of source maps, even though both techniques performed equally from a temporal perspective. Real MEG from two healthy subjects with visual stimuli were also used to compare performance of sensor-space ICA and source-space ICA. We have also proposed a new variant of minimum-variance beamformer called weight-normalized linearly-constrained minimum-variance with orthonormal lead-field. As sensor-space ICA-based source reconstruction is popular in EEG and MEG imaging, and given that source-space ICA has superior spatial performance, it is expected that source-space ICA will supersede its predecessor in many applications.

Emerging feed-forward inhibition allows the robust formation of direction selectivity in the developing ferret visual cortex

PubMed Central

Escobar, Gina M.; Maffei, Arianna; Miller, Paul

2014-01-01

The computation of direction selectivity requires that a cell respond to joint spatial and temporal characteristics of the stimulus that cannot be separated into independent components. Direction selectivity in ferret visual cortex is not present at the time of eye opening but instead develops in the days and weeks following eye opening in a process that requires visual experience with moving stimuli. Classic Hebbian or spike timing-dependent modification of excitatory feed-forward synaptic inputs is unable to produce direction-selective cells from unselective or weakly directionally biased initial conditions because inputs eventually grow so strong that they can independently drive cortical neurons, violating the joint spatial-temporal activation requirement. Furthermore, without some form of synaptic competition, cells cannot develop direction selectivity in response to training with bidirectional stimulation, as cells in ferret visual cortex do. We show that imposing a maximum lateral geniculate nucleus (LGN)-to-cortex synaptic weight allows neurons to develop direction-selective responses that maintain the requirement for joint spatial and temporal activation. We demonstrate that a novel form of inhibitory plasticity, postsynaptic activity-dependent long-term potentiation of inhibition (POSD-LTPi), which operates in the developing cortex at the time of eye opening, can provide synaptic competition and enables robust development of direction-selective receptive fields with unidirectional or bidirectional stimulation. We propose a general model of the development of spatiotemporal receptive fields that consists of two phases: an experience-independent establishment of initial biases, followed by an experience-dependent amplification or modification of these biases via correlation-based plasticity of excitatory inputs that compete against gradually increasing feed-forward inhibition. PMID:24598528

Spatial and seasonal patterns in water quality in an embayment-mainstem reach of the tidal freshwater Potomac River, USA: a multiyear study.

PubMed

Jones, R Christian; Kelso, Donald P; Schaeffer, Elaine

2008-12-01

Spatial and temporal patterns in water quality were studied for seven years within an embayment-river mainstem area of the tidal freshwater Potomac River. The purpose of this paper is to determine the important components of spatial and temporal variation in water quality in this study area to facilitate an understanding of management impacts and allow the most effective use of future monitoring resources. The study area received treated sewage effluent and freshwater inflow from direct tributary inputs into the shallow embayment as well as upriver sources in the mainstem. Depth variations were determined to be detectable, but minimal due mainly to the influence of tidal mixing. Results of principal component analysis of two independent water quality datasets revealed clear spatial and seasonal patterns. Interannual variation was generally minimal despite substantial variations in tributary and mainstem discharge among years. Since both spatial and seasonal components were important, data were segmented by season to best determine the spatial pattern. A clear difference was found between a set of stations located within one embayment (Gunston Cove) and a second set in the nearby Potomac mainstem. Parameters most highly correlated with differences were those typically associated with higher densities of phytoplankton: chlorophyll a, photosynthetic rate, pH, dissolved oxygen, BOD, total phosphorus and Secchi depth. These differences and their consistency indicated two distinct water masses: one in the cove harboring higher algal density and activity and a second in the river with lower phytoplankton activity. A second embayment not receiving sewage effluent generally had an intermediate position. While this was the most consistent spatial pattern, there were two others of a secondary nature. Stations closer to the effluent inputs in the embayment sometimes grouped separately due to elevated ammonia and chloride. Stations closer to tributary inflows into the embayment sometimes grouped separately due to dilution with freshwater runoff. Segmenting the datasets by spatial region resulted in a clarification of seasonal patterns with similar factors relating to algal activity being the major correlates of the seasonal pattern. A basic seasonal pattern of lower scores in the spring increasing steadily to a peak in July and August followed by a steady decline through the fall was observed in the cove. In the river, the pattern of increases tended to be delayed slightly in the spring. Results indicate that the study area can be effectively monitored with fewer study sites provided that at least one is located in each of the spatial regions.

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.

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Lacey, Simon; Sathian, K

2018-02-01

In a recent study Eklund et al. have shown that cluster-wise family-wise error (FWE) rate-corrected inferences made in parametric statistical method-based functional magnetic resonance imaging (fMRI) studies over the past couple of decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; principally because the spatial autocorrelation functions (sACFs) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggest otherwise. Hence, the residuals from general linear model (GLM)-based fMRI activation estimates in these studies may not have possessed a homogenously Gaussian sACF. Here we propose a method based on the assumption that heterogeneity and non-Gaussianity of the sACF of the first-level GLM analysis residuals, as well as temporal autocorrelations in the first-level voxel residual time-series, are caused by unmodeled MRI signal from neuronal and physiological processes as well as motion and other artifacts, which can be approximated by appropriate decompositions of the first-level residuals with principal component analysis (PCA), and removed. We show that application of this method yields GLM residuals with significantly reduced spatial correlation, nearly Gaussian sACF and uniform spatial smoothness across the brain, thereby allowing valid cluster-based FWE-corrected inferences based on assumption of Gaussian spatial noise. We further show that application of this method renders the voxel time-series of first-level GLM residuals independent, and identically distributed across time (which is a necessary condition for appropriate voxel-level GLM inference), without having to fit ad hoc stochastic colored noise models. Furthermore, the detection power of individual subject brain activation analysis is enhanced. This method will be especially useful for case studies, which rely on first-level GLM analysis inferences.

Temporal changes of diffusion patterns in mild traumatic brain injury via group-based semi-blind source separation.

PubMed

Jing, Min; McGinnity, T Martin; Coleman, Sonya; Fuchs, Armin; Kelso, J A Scott

2015-07-01

Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioral impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and, thus, are less suitable for individual case studies. In this paper, we introduce an approach based on spatial group independent component analysis (GICA), in which the diffusion scalar maps from an individual mTBI subject and the average of a group of controls are arranged according to their data collection time points. In addition, we propose a constrained GICA (CGICA) model by introducing the prior information into the GICA decomposition process, thus taking available knowledge of mTBI into account. The proposed method is evaluated based on DTI data collected from American football players including eight controls and three mTBI subjects (at three time points post injury). The results show that common spatial patterns within the diffusion maps were extracted as spatially independent components (ICs) by GICA. The temporal change of diffusion patterns during recovery is revealed by the time course of the selected IC. The results also demonstrate that the temporal change can be further influenced by incorporating the prior knowledge of mTBI (if available) based on the proposed CGICA model. Although a small sample of mTBI subjects is studied, as a proof of concept, the preliminary results provide promising insight for applications of DTI to study recovery from mTBI and may have potential for individual case studies in practice.

The Hawaii Imaging Fabry-Perot Interferometer (HIFI)

NASA Technical Reports Server (NTRS)

Bland, Jonathan; Cecil, Gerald; Tully, Brent

1990-01-01

At Mauna Kea Observatory, researchers conducted optical, imaging spectrophotometric studies of selected active galaxies using both the Canada-France-Hawaii 3.6m and University of Hawaii 2.2m telecopes (Tully, Bland and Cecil 1988). To maximize spatial resolution, researchers select galaxies independent of luminosity but known to possess interesting morphologies or high-velocity, extranuclear ionized gas (Walker 1968; Rubin and Ford 1968). They study both the large-scale patterns produced in IR-luminous, starburst systems (e.g., M82, NGC 253, NGC 6240) and those with compact, but spatially extended, circumnuclear, narrow line regions (e.g., M51, NGC 1068, NGC 4151). Current studies are restricted to the optical (SII), (NII) and (OIII) lines and the brightest Balmer recombination lines. These lines are, in principle, sufficient to constrain the dynamical structure and dominant excitation mechanism of the ionized component.

Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief.

PubMed

Douglas, P K; Harris, Sam; Yuille, Alan; Cohen, Mark S

2011-05-15

Machine learning (ML) has become a popular tool for mining functional neuroimaging data, and there are now hopes of performing such analyses efficiently in real-time. Towards this goal, we compared accuracy of six different ML algorithms applied to neuroimaging data of persons engaged in a bivariate task, asserting their belief or disbelief of a variety of propositional statements. We performed unsupervised dimension reduction and automated feature extraction using independent component (IC) analysis and extracted IC time courses. Optimization of classification hyperparameters across each classifier occurred prior to assessment. Maximum accuracy was achieved at 92% for Random Forest, followed by 91% for AdaBoost, 89% for Naïve Bayes, 87% for a J48 decision tree, 86% for K*, and 84% for support vector machine. For real-time decoding applications, finding a parsimonious subset of diagnostic ICs might be useful. We used a forward search technique to sequentially add ranked ICs to the feature subspace. For the current data set, we determined that approximately six ICs represented a meaningful basis set for classification. We then projected these six IC spatial maps forward onto a later scanning session within subject. We then applied the optimized ML algorithms to these new data instances, and found that classification accuracy results were reproducible. Additionally, we compared our classification method to our previously published general linear model results on this same data set. The highest ranked IC spatial maps show similarity to brain regions associated with contrasts for belief > disbelief, and disbelief < belief. Copyright © 2010 Elsevier Inc. All rights reserved.

Working Memory Systems in the Rat.

PubMed

Bratch, Alexander; Kann, Spencer; Cain, Joshua A; Wu, Jie-En; Rivera-Reyes, Nilda; Dalecki, Stefan; Arman, Diana; Dunn, Austin; Cooper, Shiloh; Corbin, Hannah E; Doyle, Amanda R; Pizzo, Matthew J; Smith, Alexandra E; Crystal, Jonathon D

2016-02-08

A fundamental feature of memory in humans is the ability to simultaneously work with multiple types of information using independent memory systems. Working memory is conceptualized as two independent memory systems under executive control [1, 2]. Although there is a long history of using the term "working memory" to describe short-term memory in animals, it is not known whether multiple, independent memory systems exist in nonhumans. Here, we used two established short-term memory approaches to test the hypothesis that spatial and olfactory memory operate as independent working memory resources in the rat. In the olfactory memory task, rats chose a novel odor from a gradually incrementing set of old odors [3]. In the spatial memory task, rats searched for a depleting food source at multiple locations [4]. We presented rats with information to hold in memory in one domain (e.g., olfactory) while adding a memory load in the other domain (e.g., spatial). Control conditions equated the retention interval delay without adding a second memory load. In a further experiment, we used proactive interference [5-7] in the spatial domain to compromise spatial memory and evaluated the impact of adding an olfactory memory load. Olfactory and spatial memory are resistant to interference from the addition of a memory load in the other domain. Our data suggest that olfactory and spatial memory draw on independent working memory systems in the rat. Copyright © 2016 Elsevier Ltd. All rights reserved.

Unilateral spatial neglect in the acute phase of ischemic stroke can predict long-term disability and functional capacity.

PubMed

Luvizutto, Gustavo José; Moliga, Augusta Fabiana; Rizzatti, Gabriela Rizzo Soares; Fogaroli, Marcelo Ortolani; Moura Neto, Eduardo de; Nunes, Hélio Rubens de Carvalho; Resende, Luiz Antônio de Lima; Bazan, Rodrigo

2018-05-21

The aim of this study was to assess the relationship between the degree of unilateral spatial neglect during the acute phase of stroke and long-term functional independence. This was a prospective study of right ischemic stroke patients in which the independent variable was the degree of spatial neglect and the outcome that was measured was functional independence. The potential confounding factors included sex, age, stroke severity, topography of the lesion, risk factors, glycemia and the treatment received. Unilateral spatial neglect was measured using the line cancellation test, the star cancellation test and the line bisection test within 48 hours of the onset of symptoms. Functional independence was measured using the modified Rankin and Barthel scales at 90 days after discharge. The relationship between unilateral spatial neglect and functional independence was analyzed using multiple logistic regression that was corrected for confounding factors. We studied 60 patients with a median age of 68 (34-89) years, 52% of whom were male and 74% of whom were Caucasian. The risk for moderate to severe disability increased with increasing star cancellation test scores (OR=1.14 [1.03-1.26], p=0.01) corrected for the stroke severity, which was a confounding factor that had a statistically positive association with disability (OR=1.63 [1.13-2.65], p=0.01). The best chance of functional independence decreased with increasing star cancellation test scores (OR=0.86 [0.78-0.96], p=0.006) corrected for the stroke severity, which was a confounding factor that had a statistically negative association with independence (OR=0.66 [0.48-0.92], p=0.017). The severity of unilateral spatial neglect in acute stroke worsens the degree of long-term disability and functional independence.

Time course based artifact identification for independent components of resting-state FMRI.

PubMed

Rummel, Christian; Verma, Rajeev Kumar; Schöpf, Veronika; Abela, Eugenio; Hauf, Martinus; Berruecos, José Fernando Zapata; Wiest, Roland

2013-01-01

In functional magnetic resonance imaging (fMRI) coherent oscillations of the blood oxygen level-dependent (BOLD) signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting-state networks (RSN). Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA) and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for resting-state fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82, and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group resting-state studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting-state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.

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.

Multiscale sagebrush rangeland habitat modeling in southwest Wyoming

USGS Publications Warehouse

Homer, Collin G.; Aldridge, Cameron L.; Meyer, Debra K.; Coan, Michael J.; Bowen, Zachary H.

2009-01-01

Sagebrush-steppe ecosystems in North America have experienced dramatic elimination and degradation since European settlement. As a result, sagebrush-steppe dependent species have experienced drastic range contractions and population declines. Coordinated ecosystem-wide research, integrated with monitoring and management activities, would improve the ability to maintain existing sagebrush habitats. However, current data only identify resource availability locally, with rigorous spatial tools and models that accurately model and map sagebrush habitats over large areas still unavailable. Here we report on an effort to produce a rigorous large-area sagebrush-habitat classification and inventory with statistically validated products and estimates of precision in the State of Wyoming. This research employs a combination of significant new tools, including (1) modeling sagebrush rangeland as a series of independent continuous field components that can be combined and customized by any user at multiple spatial scales; (2) collecting ground-measured plot data on 2.4-meter imagery in the same season the satellite imagery is acquired; (3) effective modeling of ground-measured data on 2.4-meter imagery to maximize subsequent extrapolation; (4) acquiring multiple seasons (spring, summer, and fall) of an additional two spatial scales of imagery (30 meter and 56 meter) for optimal large-area modeling; (5) using regression tree classification technology that optimizes data mining of multiple image dates, ratios, and bands with ancillary data to extrapolate ground training data to coarser resolution sensors; and (6) employing rigorous accuracy assessment of model predictions to enable users to understand the inherent uncertainties. First-phase results modeled eight rangeland components (four primary targets and four secondary targets) as continuous field predictions. The primary targets included percent bare ground, percent herbaceousness, percent shrub, and percent litter. The four secondary targets included percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata wyomingensis), and sagebrush height (centimeters). Results were validated by an independent accuracy assessment with root mean square error (RMSE) values ranging from 6.38 percent for bare ground to 2.99 percent for sagebrush at the QuickBird scale and RMSE values ranging from 12.07 percent for bare ground to 6.34 percent for sagebrush at the full Landsat scale. Subsequent project phases are now in progress, with plans to deliver products that improve accuracies of existing components, model new components, complete models over larger areas, track changes over time (from 1988 to 2007), and ultimately model wildlife population trends against these changes. We believe these results offer significant improvement in sagebrush rangeland quantification at multiple scales and offer users products that have been rigorously validated.

Advances in nonmarket valuation econometrics: Spatial heterogeneity in hedonic pricing models and preference heterogeneity in stated preference models

NASA Astrophysics Data System (ADS)

Yoo, Jin Woo

In my 1st essay, the study explores Pennsylvania residents. willingness to pay for development of renewable energy technologies such as solar power, wind power, biomass electricity, and other renewable energy using a choice experiment method. Principle component analysis identified 3 independent attitude components that affect the variation of preference, a desire for renewable energy and environmental quality and concern over cost. The results show that urban residents have a higher desire for environmental quality and concern less about cost than rural residents and consequently have a higher willingness to pay to increase renewable energy production. The results of sub-sample analysis show that a representative respondent in rural (urban) Pennsylvania is willing to pay 3.8(5.9) and 4.1(5.7)/month for increasing the share of Pennsylvania electricity generated from wind power and other renewable energy by 1 percent point, respectively. Mean WTP for solar and biomass electricity was not significantly different from zero. In my second essay, heterogeneity of individual WTP for various renewable energy technologies is investigated using several different variants of the multinomial logit model: a simple MNL with interaction terms, a latent class choice model, a random parameter mixed logit choice model, and a random parameter-latent class choice model. The results of all models consistently show that respondents. preference for individual renewable technology is heterogeneous, but the degree of heterogeneity differs for different renewable technologies. In general, the random parameter logit model with interactions and a hybrid random parameter logit-latent class model fit better than other models and better capture respondents. heterogeneity of preference for renewable energy. The impact of the land under agricultural conservation easement (ACE) contract on the values of nearby residential properties is investigated using housing sales data in two Pennsylvania Counties. The spatial-lag (SLM), the spatial error (SEM) and the spatial error component (SEC) models were compared. A geographically weighted regression (GWR) model is estimated to study the spatial heterogeneity of the marginal implicit prices of ACE impact within each county. New hybrid spatial hedonic models, the GWR-SEC and a modified GWR-SEM, are estimated such that both spatial autocorrelation and heterogeneity are accounted. The results show that the coefficient of land under easement contract varies spatially within one county, but not within the other county studied. Also, ACE's are found to have both positive and negative impacts on the values of nearby residential properties. Among global spatial models, the SEM fit better than the SLM and the SEC. Statistical goodness of fit measures showed that the GWR-SEC model fit better than the GWR or the GWR-SEC model. Finally, the GWR-SEC showed spatial autocorrelation is stronger in one county than in the other county.

Bootstrap percolation on spatial networks

NASA Astrophysics Data System (ADS)

Gao, Jian; Zhou, Tao; Hu, Yanqing

2015-10-01

Bootstrap percolation is a general representation of some networked activation process, which has found applications in explaining many important social phenomena, such as the propagation of information. Inspired by some recent findings on spatial structure of online social networks, here we study bootstrap percolation on undirected spatial networks, with the probability density function of long-range links’ lengths being a power law with tunable exponent. Setting the size of the giant active component as the order parameter, we find a parameter-dependent critical value for the power-law exponent, above which there is a double phase transition, mixed of a second-order phase transition and a hybrid phase transition with two varying critical points, otherwise there is only a second-order phase transition. We further find a parameter-independent critical value around -1, about which the two critical points for the double phase transition are almost constant. To our surprise, this critical value -1 is just equal or very close to the values of many real online social networks, including LiveJournal, HP Labs email network, Belgian mobile phone network, etc. This work helps us in better understanding the self-organization of spatial structure of online social networks, in terms of the effective function for information spreading.

Identification of prominent spatio-temporal signals in GRACE derived terrestrial water storage for India

NASA Astrophysics Data System (ADS)

Banerjee, C.; Nagesh Kumar, D.

2014-11-01

Fresh water is a necessity of the human civilization. But with the increasing global population, the quantity and quality of available fresh water is getting compromised. To mitigate this subliminal problem, it is essential to enhance our level of understanding about the dynamics of global and regional fresh water resources which include surface and ground water reserves. With development in remote sensing technology, traditional and much localized in-situ observations are augmented with satellite data to get a holistic picture of the terrestrial water resources. For this reason, Gravity Recovery And Climate Experiment (GRACE) satellite mission was jointly implemented by NASA and German Aerospace Research Agency - DLR to map the variation of gravitational potential, which after removing atmospheric and oceanic effects is majorly caused by changes in Terrestrial Water Storage (TWS). India also faces the challenge of rejuvenating the fast deteriorating and exhausting water resources due to the rapid urbanization. In the present study we try to identify physically meaningful major spatial and temporal patterns or signals of changes in TWS for India. TWS data set over India for a period of 90 months, from June 2003 to December 2010 is use to isolate spatial and temporal signals using Principal Component Analysis (PCA), an extensively used method in meteorological studies. To achieve better disintegration of the data into more physically meaningful components we use a blind signal separation technique, Independent Component Analysis (ICA).

Toward the influence of temporal attention on the selection of targets in a visual search task: An ERP study.

PubMed

Rolke, Bettina; Festl, Freya; Seibold, Verena C

2016-11-01

We used ERPs to investigate whether temporal attention interacts with spatial attention and feature-based attention to enhance visual processing. We presented a visual search display containing one singleton stimulus among a set of homogenous distractors. Participants were asked to respond only to target singletons of a particular color and shape that were presented in an attended spatial position. We manipulated temporal attention by presenting a warning signal before each search display and varying the foreperiod (FP) between the warning signal and the search display in a blocked manner. We observed distinctive ERP effects of both spatial and temporal attention. The amplitudes for the N2pc, SPCN, and P3 were enhanced by spatial attention indicating a processing benefit of relevant stimulus features at the attended side. Temporal attention accelerated stimulus processing; this was indexed by an earlier onset of the N2pc component and a reduction in reaction times to targets. Most importantly, temporal attention did not interact with spatial attention or stimulus features to influence visual processing. Taken together, the results suggest that temporal attention fosters visual perceptual processing in a visual search task independently from spatial attention and feature-based attention; this provides support for the nonspecific enhancement hypothesis of temporal attention. © 2016 Society for Psychophysiological Research.

Full magnetic gradient tensor from triaxial aeromagnetic gradient measurements: Calculation and application

NASA Astrophysics Data System (ADS)

Luo, Yao; Wu, Mei-Ping; Wang, Ping; Duan, Shu-Ling; Liu, Hao-Jun; Wang, Jin-Long; An, Zhan-Feng

2015-09-01

The full magnetic gradient tensor (MGT) refers to the spatial change rate of the three field components of the geomagnetic field vector along three mutually orthogonal axes. The tensor is of use to geological mapping, resources exploration, magnetic navigation, and others. However, it is very difficult to measure the full magnetic tensor gradient using existing engineering technology. We present a method to use triaxial aeromagnetic gradient measurements for deriving the full MGT. The method uses the triaxial gradient data and makes full use of the variation of the magnetic anomaly modulus in three dimensions to obtain a self-consistent magnetic tensor gradient. Numerical simulations show that the full MGT data obtained with the proposed method are of high precision and satisfy the requirements of data processing. We selected triaxial aeromagnetic gradient data from the Hebei Province for calculating the full MGT. Data processing shows that using triaxial tensor gradient data allows to take advantage of the spatial rate of change of the total field in three dimensions and suppresses part of the independent noise in the aeromagnetic gradient. The calculated tensor components have improved resolution, and the transformed full tensor gradient satisfies the requirement of geological mapping and interpretation.

Sea level reconstructions from altimetry and tide gauges using independent component analysis

NASA Astrophysics Data System (ADS)

Brunnabend, Sandra-Esther; Kusche, Jürgen; Forootan, Ehsan

2017-04-01

Many reconstructions of global and regional sea level rise derived from tide gauges and satellite altimetry used the method of empirical orthogonal functions (EOF) to reduce noise, improving the spatial resolution of the reconstructed outputs and investigate the different signals in climate time series. However, the second order EOF method has some limitations, e.g. in the separation of individual physical signals into different modes of sea level variations and in the capability to physically interpret the different modes as they are assumed to be orthogonal. Therefore, we investigate the use of the more advanced statistical signal decomposition technique called independent component analysis (ICA) to reconstruct global and regional sea level change from satellite altimetry and tide gauge records. Our results indicate that the used method has almost no influence on the reconstruction of global mean sea level change (1.6 mm/yr from 1960-2010 and 2.9 mm/yr from 1993-2013). Only different numbers of modes are needed for the reconstruction. Using the ICA method is advantageous for separating independent climate variability signals from regional sea level variations as the mixing problem of the EOF method is strongly reduced. As an example, the modes most dominated by the El Niño-Southern Oscillation (ENSO) signal are compared. Regional sea level changes near Tianjin, China, Los Angeles, USA, and Majuro, Marshall Islands are reconstructed and the contributions from ENSO are identified.

Radiation Products based on a constellation of Geostationary Satellites

NASA Astrophysics Data System (ADS)

Trigo, I. F.; Freitas, S. C.; Barroso, C.; Macedo, J.; Perdigão, R.; Silva, R.; Viterbo, P.

2012-04-01

The various components of the surface radiation budget present high variability in time and space, particularly over land surfaces where spatial heterogeneity of the upward fluxes is high. Geostationary satellites are well-suited to describe the daily cycle of downward and upward radiation fluxes and present spatial resolutions of the order of 3-to-5 km at sub-satellite point, acceptable for many applications. The work presented here is being carried out within the framework of Geoland-2 project, and aims the use of data from geostationary platforms to generate, archive and distribute in near real time four component of the surface radiation budget: land surface albedo, land surface temperature (LST) and downward short- and long-wave fluxes at the surface. All four components are retrieved from the following satellites - GOES-W covering North and South America, Meteosat Second Generation (MSG) covering essentially Europe and Africa, and MTSAT covering part of Asia and Australia. The variables are retrieved independently from each satellite and then merged into a single field, with a 5 km spatial resolution. Data are generated hourly in the case of the downward fluxes and LST, and 10-daily in the case of albedo. In regions covered by both GOES and MSG disks, the interpolated field makes use of both retrievals, giving more weight to those with lower uncertainty. The four components of the surface radiation budget described above are assessed through comparisons with similar parameters retrieved from other sensors (e.g., MODIS, CERES) or from models (e.g., ECMWF forecasts), as well as with in situ observations when available. The presentation will be focused on a brief description of algorithms and auxiliary data used in product estimation. The results of inter-comparisons with other data sources, along with the identification of the retrieval conditions that allow optimal / sub-optimal estimation of these surface radiation parameters will also be analysed. The radiation products generated within the Geoland-2 project are freely available to the user community.

Large-scale changes in network interactions as a physiological signature of spatial neglect

PubMed Central

Baldassarre, Antonello; Ramsey, Lenny; Hacker, Carl L.; Callejas, Alicia; Astafiev, Serguei V.; Metcalf, Nicholas V.; Zinn, Kristi; Rengachary, Jennifer; Snyder, Abraham Z.; Carter, Alex R.; Shulman, Gordon L.

2014-01-01

The relationship between spontaneous brain activity and behaviour following focal injury is not well understood. Here, we report a large-scale study of resting state functional connectivity MRI and spatial neglect following stroke in a large (n = 84) heterogeneous sample of first-ever stroke patients (within 1–2 weeks). Spatial neglect, which is typically more severe after right than left hemisphere injury, includes deficits of spatial attention and motor actions contralateral to the lesion, and low general attention due to impaired vigilance/arousal. Patients underwent structural and resting state functional MRI scans, and spatial neglect was measured using the Posner spatial cueing task, and Mesulam and Behavioural Inattention Test cancellation tests. A principal component analysis of the behavioural tests revealed a main factor accounting for 34% of variance that captured three correlated behavioural deficits: visual neglect of the contralesional visual field, visuomotor neglect of the contralesional field, and low overall performance. In an independent sample (21 healthy subjects), we defined 10 resting state networks consisting of 169 brain regions: visual-fovea and visual-periphery, sensory-motor, auditory, dorsal attention, ventral attention, language, fronto-parietal control, cingulo-opercular control, and default mode. We correlated the neglect factor score with the strength of resting state functional connectivity within and across the 10 resting state networks. All damaged brain voxels were removed from the functional connectivity:behaviour correlational analysis. We found that the correlated behavioural deficits summarized by the factor score were associated with correlated multi-network patterns of abnormal functional connectivity involving large swaths of cortex. Specifically, dorsal attention and sensory-motor networks showed: (i) reduced interhemispheric functional connectivity; (ii) reduced anti-correlation with fronto-parietal and default mode networks in the right hemisphere; and (iii) increased intrahemispheric connectivity with the basal ganglia. These patterns of functional connectivity:behaviour correlations were stronger in patients with right- as compared to left-hemisphere damage and were independent of lesion volume. Our findings identify large-scale changes in resting state network interactions that are a physiological signature of spatial neglect and may relate to its right hemisphere lateralization. PMID:25367028

Expading fluvial remote sensing to the riverscape: Mapping depth and grain size on the Merced River, California

NASA Astrophysics Data System (ADS)

Richardson, Ryan T.

This study builds upon recent research in the field of fluvial remote sensing by applying techniques for mapping physical attributes of rivers. Depth, velocity, and grain size are primary controls on the types of habitat present in fluvial ecosystems. This thesis focuses on expanding fluvial remote sensing to larger spatial extents and sub-meter resolutions, which will increase our ability to capture the spatial heterogeneity of habitat at a resolution relevant to individual salmonids and an extent relevant to species. This thesis consists of two chapters, one focusing on expanding the spatial extent over which depth can be mapped using Optimal Band Ratio Analysis (OBRA) and the other developing general relations for mapping grain size from three-dimensional topographic point clouds. The two chapters are independent but connected by the overarching goal of providing scientists and managers more useful tools for quantifying the amount and quality of salmonid habitat via remote sensing. The OBRA chapter highlights the true power of remote sensing to map depths from hyperspectral images as a central component of watershed scale analysis, while also acknowledging the great challenges involved with increasing spatial extent. The grain size mapping chapter establishes the first general relations for mapping grain size from roughness using point clouds. These relations will significantly reduce the time needed in the field by eliminating the need for independent measurements of grain size for calibrating the roughness-grain size relationship and thus making grain size mapping with SFM more cost effective for river restoration and monitoring. More data from future studies are needed to refine these relations and establish their validity and generality. In conclusion, this study adds to the rapidly growing field of fluvial remote sensing and could facilitate river research and restoration.

Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals

PubMed Central

2011-01-01

Background Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. Methods We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Results Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. Conclusions We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies. PMID:21810266

Implementation of Complex Biological Logic Circuits Using Spatially Distributed Multicellular Consortia

PubMed Central

Urrios, Arturo; de Nadal, Eulàlia; Solé, Ricard; Posas, Francesc

2016-01-01

Engineered synthetic biological devices have been designed to perform a variety of functions from sensing molecules and bioremediation to energy production and biomedicine. Notwithstanding, a major limitation of in vivo circuit implementation is the constraint associated to the use of standard methodologies for circuit design. Thus, future success of these devices depends on obtaining circuits with scalable complexity and reusable parts. Here we show how to build complex computational devices using multicellular consortia and space as key computational elements. This spatial modular design grants scalability since its general architecture is independent of the circuit’s complexity, minimizes wiring requirements and allows component reusability with minimal genetic engineering. The potential use of this approach is demonstrated by implementation of complex logical functions with up to six inputs, thus demonstrating the scalability and flexibility of this method. The potential implications of our results are outlined. PMID:26829588

The complex roles of space and environment in structuring functional, taxonomic and phylogenetic beta diversity of frogs in the Atlantic Forest

PubMed Central

Luiz, Amom Mendes; Sawaya, Ricardo J.

2018-01-01

Ecological communities are complex entities that can be maintained and structured by niche-based processes such as environmental conditions, and spatial processes such as dispersal. Thus, diversity patterns may be shaped simultaneously at different spatial scales by very distinct processes. Herein we assess whether and how functional, taxonomic, and phylogenetic beta diversities of frog tadpoles are explained by environmental and/or spatial predictors. We implemented a distance–based redundancy analysis to explore variation in components of beta diversity explained by pure environmental and pure spatial predictors, as well as their interactions, at both fine and broad spatial scales. Our results indicated important but complex roles of spatial and environmental predictors in structuring phylogenetic, taxonomic and functional beta diversities. The pure fine-scales spatial fraction was more important in structuring all beta diversity components, especially to functional and taxonomical spatial turnover. Environmental variables such as canopy cover and vegetation structure were important predictors of all components, but especially to functional and taxonomic beta diversity. We emphasize that distinct factors related to environment and space are affecting distinct components of beta diversity in different ways. Although weaker, phylogenetic beta diversity, which is structured more on biogeographical scales, and thus can be represented by spatially structured processes, was more related to broad spatial processes than other components. However, selected fine-scale spatial predictors denoted negative autocorrelation, which may be revealing the existence of differences in unmeasured habitat variables among samples. Although overall important, local environmental-based processes explained better functional and taxonomic beta diversity, as these diversity components carry an important ecological value. We highlight the importance of assessing different components of diversity patterns at different scales by spatially explicit models in order to improve our understanding of community structure and help to unravel the complex nature of biodiversity. PMID:29672575

Modality-independent coding of spatial layout in the human brain

PubMed Central

Wolbers, Thomas; Klatzky, Roberta L.; Loomis, Jack M.; Wutte, Magdalena G.; Giudice, Nicholas A.

2011-01-01

Summary In many non-human species, neural computations of navigational information such as position and orientation are not tied to a specific sensory modality [1, 2]. Rather, spatial signals are integrated from multiple input sources, likely leading to abstract representations of space. In contrast, the potential for abstract spatial representations in humans is not known, as most neuroscientific experiments on human navigation have focused exclusively on visual cues. Here, we tested the modality independence hypothesis with two fMRI experiments that characterized computations in regions implicated in processing spatial layout [3]. According to the hypothesis, such regions should be recruited for spatial computation of 3-D geometric configuration, independent of a specific sensory modality. In support of this view, sighted participants showed strong activation of the parahippocampal place area (PPA) and the retrosplenial cortex (RSC) for visual and haptic exploration of information-matched scenes but not objects. Functional connectivity analyses suggested that these effects were not related to visual recoding, which was further supported by a similar preference for haptic scenes found with blind participants. Taken together, these findings establish the PPA/RSC network as critical in modality-independent spatial computations and provide important evidence for a theory of high-level abstract spatial information processing in the human brain. PMID:21620708

Aerosol in the Pacific troposphere

NASA Technical Reports Server (NTRS)

Clarke, Antony D.

1989-01-01

The use of near real-time optical techniques is emphasized for the measurement of mid-tropospheric aerosol over the Central Pacific. The primary focus is on measurement of the aerosol size distribution over the range of particle diameters from 0.15 to 5.0 microns that are essential for modeling CO2 backscatter values in support of the laser atmospheric wind sounder (LAWS) program. The measurement system employs a LAS-X (Laser Aerosol Spectrometer-PMS, Boulder, CO) with a custom 256 channel pulse height analyzer and software for detailed measurement and analysis of aerosol size distributions. A thermal preheater system (Thermo Optic Aerosol Descriminator (TOAD) conditions the aerosol in a manner that allows the discrimination of the size distribution of individual aerosol components such as sulfuric acid, sulfates and refractory species. This allows assessment of the relative contribution of each component to the BCO2 signal. This is necessary since the different components have different sources, exhibit independent variability and provide different BCO2 signals for a given mass and particle size. Field activities involve experiments designed to examine both temporal and spatial variability of these aerosol components from ground based and aircraft platforms.

Parallel ICA of FDG-PET and PiB-PET in three conditions with underlying Alzheimer's pathology

PubMed Central

Laforce, Robert; Tosun, Duygu; Ghosh, Pia; Lehmann, Manja; Madison, Cindee M.; Weiner, Michael W.; Miller, Bruce L.; Jagust, William J.; Rabinovici, Gil D.

2014-01-01

The relationships between clinical phenotype, β-amyloid (Aβ) deposition and neurodegeneration in Alzheimer's disease (AD) are incompletely understood yet have important ramifications for future therapy. The goal of this study was to utilize multimodality positron emission tomography (PET) data from a clinically heterogeneous population of patients with probable AD in order to: (1) identify spatial patterns of Aβ deposition measured by (11C)-labeled Pittsburgh Compound B (PiB-PET) and glucose metabolism measured by FDG-PET that correlate with specific clinical presentation and (2) explore associations between spatial patterns of Aβ deposition and glucose metabolism across the AD population. We included all patients meeting the criteria for probable AD (NIA–AA) who had undergone MRI, PiB and FDG-PET at our center (N = 46, mean age 63.0 ± 7.7, Mini-Mental State Examination 22.0 ± 4.8). Patients were subclassified based on their cognitive profiles into an amnestic/dysexecutive group (AD-memory; n = 27), a language-predominant group (AD-language; n = 10) and a visuospatial-predominant group (AD-visuospatial; n = 9). All patients were required to have evidence of amyloid deposition on PiB-PET. To capture the spatial distribution of Aβ deposition and glucose metabolism, we employed parallel independent component analysis (pICA), a method that enables joint analyses of multimodal imaging data. The relationships between PET components and clinical group were examined using a Receiver Operator Characteristic approach, including age, gender, education and apolipoprotein E ε4 allele carrier status as covariates. Results of the first set of analyses independently examining the relationship between components from each modality and clinical group showed three significant components for FDG: a left inferior frontal and temporoparietal component associated with AD-language (area under the curve [AUC] 0.82, p = 0.011), and two components associated with AD-visuospatial (bilateral occipito-parieto-temporal [AUC 0.85, p = 0.009] and right posterior cingulate cortex [PCC]/precuneus and right lateral parietal [AUC 0.69, p = 0.045]). The AD-memory associated component included predominantly bilateral inferior frontal, cuneus and inferior temporal, and right inferior parietal hypometabolism but did not reach significance (AUC 0.65, p = 0.062). None of the PiB components correlated with clinical group. Joint analysis of PiB and FDG with pICA revealed a correlated component pair, in which increased frontal and decreased PCC/precuneus PiB correlated with decreased FDG in the frontal, occipital and temporal regions (partial r = 0.75, p < 0.0001). Using multivariate data analysis, this study reinforced the notion that clinical phenotype in AD is tightly linked to patterns of glucose hypometabolism but not amyloid deposition. These findings are strikingly similar to those of univariate paradigms and provide additional support in favor of specific involvement of the language network, higher-order visual network, and default mode network in clinical variants of AD. The inverse relationship between Aβ deposition and glucose metabolism in partially overlapping brain regions suggests that Aβ may exert both local and remote effects on brain metabolism. Applying multivariate approaches such as pICA to multimodal imaging data is a promising approach for unraveling the complex relationships between different elements of AD pathophysiology. PMID:24818077

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

On the possibility to use semiconductive hybrid pixel detectors for study of radiation belt of the Earth.

NASA Astrophysics Data System (ADS)

Guskov, A.; Shelkov, G.; Smolyanskiy, P.; Zhemchugov, A.

2016-02-01

The scientific apparatus GAMMA-400 designed for study of electromagnetic and hadron components of cosmic rays will be launched to an elliptic orbit with the apogee of about 300 000 km and the perigee of about 500 km. Such a configuration of the orbit allows it to cross periodically the radiation belt and the outer part of magnetosphere. We discuss the possibility to use hybrid pixel detecters based on the Timepix chip and semiconductive sensors on board the GAMMA-400 apparatus. Due to high granularity of the sensor (pixel size is 55 mum) and possibility to measure independently an energy deposition in each pixel, such compact and lightweight detector could be a unique instrument for study of spatial, energy and time structure of electron and proton components of the radiation belt.

Atmospheric pressure MALDI for the noninvasive characterization of carbonaceous ink from Renaissance documents.

PubMed

Grasso, Giuseppe; Calcagno, Marzia; Rapisarda, Alessandro; D'Agata, Roberta; Spoto, Giuseppe

2017-06-01

The analytical methods that are usually applied to determine the compositions of inks from ancient manuscripts usually focus on inorganic components, as in the case of iron gall ink. In this work, we describe the use of atmospheric pressure/matrix-assisted laser desorption ionization-mass spectrometry (AP/MALDI-MS) as a spatially resolved analytical technique for the study of the organic carbonaceous components of inks used in handwritten parts of ancient books for the first time. Large polycyclic aromatic hydrocarbons (L-PAH) were identified in situ in the ink of XVII century handwritten documents. We prove that it is possible to apply MALDI-MS as a suitable microdestructive diagnostic tool for analyzing samples in air at atmospheric pressure, thus simplifying investigations of the organic components of artistic and archaeological objects. The interpretation of the experimental MS results was supported by independent Raman spectroscopic investigations. Graphical abstract Atmospheric pressure/MALDI mass spectrometry detects in situ polycyclic aromatic hydrocarbons in the carbonaceous ink of XVII century manuscripts.

Comparison of environmental and socio-economic domains of vulnerability to flood hazards

NASA Astrophysics Data System (ADS)

Leidel, M.; Kienberger, S.; Lang, S.; Zeil, P.

2009-04-01

Socio-economic and environmental based vulnerability models have been developed within the research context of the FP6 project BRAHMATWINN. The conceptualisation of vulnerability has been defined in the project and is characterised as a function of sensitivity and adaptive capacity, where sensitivity is used to refer to systems that are susceptible to the impacts of environmental stress. Adaptive capacity is used to refer to systems or resources available to communities that could help them adapt or cope with the adverse consequences of environmental stresses in the recovery phase. In a wider context the approach reflects the wider objective and conceptualizations of the IPCC (Intergovernmental Panel on Climate Change) framework, where vulnerability is characterized as a component of overall risk. A methodology has been developed which delineates spatial units of vulnerability (VULNUS). These units share a specific common characteristic and allow the independent spatial modelling of a complex phenomena independent from administrative units and raster based approaches. An increasing detail of spatial data and complex decision problems require flexible means for scaled spatial representations, for mapping the dynamics and constant changes, and delivering the crucial information. Automated techniques of object-based image analysis (OBIA, Lang & Blaschke, 2006), capable of integrating a virtually unlimited set of spatial data sets, try to match the information extraction with our world view. To account for that, a flexible concept of manageable units is required. The term geon was proposed by Lang (2008) to describe generic spatial objects that are homogenous in terms of a varying spatial phenomena under the influence of, and partly controlled by, policy actions. The geon concept acts as a framework for the regionalization of continuous spatial information according to defined parameters of homogeneity. It is flexible in terms of a certain perception of a problem (specific policy realm, specific hazard domain, etc.). In this study, vulnerability units have been derived as a specific instance of a geon set within an area exposed to flood risk. Using geons, we are capable of transforming singular domains of information on specific systemic components to policy-relevant, conditioned information (Kienberger et al., 2008; Tiede & Lang, 2007). According to the work programme socio-economic vulnerabilities have been modelled for the Salzach catchment. A specific set of indicators has been developed with a strong stakeholder orientation. Next to that, and to allow an easier integration within the aimed development of Water Resource Response Units (WRRUs) the environmental domain of vulnerability has additionally been modelled. We present the results of the socio-economic and environmental based approach to model vulnerability. The research methodology utilises census as well as land use/land cover data to derive and assess vulnerability. As a result, spatial units have been identified which represent common characteristics of socio-economic environmental vulnerability. The results show the spatially explicit vulnerability and its underlying components sensitivity and adaptive capacity for socio-economic and environmental domains and discuss differences. Within the test area, the Salzach River catchment in Austria, primarily urban areas adjacent to water courses are highly vulnerable. It can be stated that the delineation of vulnerability units that integrates all dimensions of sustainability are a prerequisite for a holistic and thus adaptive integrated water management approach. Indeed, such units constitute the basis for future dynamic vulnerability assessments, and thus for the assessment of uncertainties due to climate change. Kienberger, S., S. Lang & D. Tiede (2008): Socio-economic vulnerability units - modelling meaningful spatial units. In: Proceedings of the GIS Research UK 16th Annual conference GISRUK 2008, Manchester. Lang, S. (2008): Object-based image analysis for remote sensing applications: modeling reality - dealing with complexity. In: Blaschke, T., S. Lang & G. Hay (eds.): Object-Based Image Analysis - Spatial concepts for knowledge-driven remote sensing applications. New York: Springer, 3-28. Lang, S. & T. Blaschke (2006) Bridging remote sensing and GIS - what are the most supportive pillars? In: S: Lang & T. Blaschke (eds.): International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences vol. XXXVI-4/C42. CD-ROM and online at www.isprs.org. Tiede D. & S .Lang (2007): Analytical 3D views and virtual globes - putting analytical results into spatial context. ISPRS, ICA, DGfK - Joint Workshop: Visualization and Exploration of Geospatial Data, Stuttgart

Connected Component Model for Multi-Object Tracking.

PubMed

He, Zhenyu; Li, Xin; You, Xinge; Tao, Dacheng; Tang, Yuan Yan

2016-08-01

In multi-object tracking, it is critical to explore the data associations by exploiting the temporal information from a sequence of frames rather than the information from the adjacent two frames. Since straightforwardly obtaining data associations from multi-frames is an NP-hard multi-dimensional assignment (MDA) problem, most existing methods solve this MDA problem by either developing complicated approximate algorithms, or simplifying MDA as a 2D assignment problem based upon the information extracted only from adjacent frames. In this paper, we show that the relation between associations of two observations is the equivalence relation in the data association problem, based on the spatial-temporal constraint that the trajectories of different objects must be disjoint. Therefore, the MDA problem can be equivalently divided into independent subproblems by equivalence partitioning. In contrast to existing works for solving the MDA problem, we develop a connected component model (CCM) by exploiting the constraints of the data association and the equivalence relation on the constraints. Based upon CCM, we can efficiently obtain the global solution of the MDA problem for multi-object tracking by optimizing a sequence of independent data association subproblems. Experiments on challenging public data sets demonstrate that our algorithm outperforms the state-of-the-art approaches.

Applying different independent component analysis algorithms and support vector regression for IT chain store sales forecasting.

PubMed

Dai, Wensheng; Wu, Jui-Yu; Lu, Chi-Jie

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.

Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting

PubMed Central

Dai, Wensheng

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting. PMID:25165740

The association of lesion eccentricity with plaque morphology and components in the superficial femoral artery: a high-spatial-resolution, multi-contrast weighted CMR study.

PubMed

Li, Feiyu; McDermott, Mary McGrae; Li, Debiao; Carroll, Timothy J; Hippe, Daniel S; Kramer, Christopher M; Fan, Zhaoyang; Zhao, Xihai; Hatsukami, Thomas S; Chu, Baocheng; Wang, Jinnan; Yuan, Chun

2010-07-01

Atherosclerotic plaque morphology and components are predictors of subsequent cardiovascular events. However, associations of plaque eccentricity with plaque morphology and plaque composition are unclear. This study investigated associations of plaque eccentricity with plaque components and morphology in the proximal superficial femoral artery using cardiovascular magnetic resonance (CMR). Twenty-eight subjects with an ankle-brachial index less than 1.00 were examined with 1.5 T high-spatial-resolution, multi-contrast weighted CMR. One hundred and eighty diseased locations of the proximal superficial femoral artery (about 40 mm) were analyzed. The eccentric lesion was defined as [(Maximum wall thickness- Minimum wall thickness)/Maximum wall thickness] >or= 0.5. The arterial morphology and plaque components were measured using semi-automatic image analysis software. One hundred and fifteen locations were identified as eccentric lesions and sixty-five as concentric lesions. The eccentric lesions had larger wall but similar lumen areas, larger mean and maximum wall thicknesses, and more calcification and lipid rich necrotic core, compared to concentric lesions. For lesions with the same lumen area, the degree of eccentricity was associated with an increased wall area. Eccentricity (dichotomous as eccentric or concentric) was independently correlated with the prevalence of calcification (odds ratio 3.78, 95% CI 1.47-9.70) after adjustment for atherosclerotic risk factors and wall area. Plaque eccentricity is associated with preserved lumen size and advanced plaque features such as larger plaque burden, more lipid content, and increased calcification in the superficial femoral artery.

Functional Subdivision of Group-ICA Results of fMRI Data Collected during Cinema Viewing

PubMed Central

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. PMID:22860044

Report on Component 2 - Designing New Methods for Visualizing Text in Spatial Contexts

DTIC Science & Technology

2012-10-31

W9132V-11-P-0010 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Alexander Savelyev , Scott Pezanowski, Anthony C. Robinson, and Alan M...e Component 2 – Designing New Methods for Visualizing Text in Spatial Contexts Alexander Savelyev , Scott Pezanowski, Anthony Robinson and Alan...Center, Penn State University Report on Component 2: Component 2 – Designing New Methods for Visualizing Text in Spatial Contexts Alexander

An Investigation of Gender Differences in the Components Influencing the Difficulty of Spatial Ability Items.

ERIC Educational Resources Information Center

Kramer, Gene A.; Smith, Richard M.

2001-01-01

Examined the role that gender differences play in the determination of the components influencing the difficulty of spatial ability items. Results for 2,245 examinees taking a spatial ability test that is part of the Dental School Admission Battery show that component difficulties show little variation across gender. (SLD)

Y-cell receptive field and collicular projection of parasol ganglion cells in macaque monkey retina

PubMed Central

Crook, Joanna D.; Peterson, Beth B.; Packer, Orin S.; Robinson, Farrel R.; Troy, John B.; Dacey, Dennis M.

2009-01-01

The distinctive parasol ganglion cell of the primate retina transmits a transient, spectrally non-opponent signal to the magnocellular layers of the lateral geniculate nucleus (LGN). Parasol cells show well-recognized parallels with the alpha-Y cell of other mammals, yet two key alpha-Y cell properties, a collateral projection to the superior colliculus and nonlinear spatial summation, have not been clearly established for parasol cells. Here we show by retrograde photodynamic staining that parasol cells project to the superior colliculus. Photostained dendritic trees formed characteristic spatial mosaics and afforded unequivocal identification of the parasol cells among diverse collicular-projecting cell types. Loose-patch recordings were used to demonstrate for all parasol cells a distinct Y-cell receptive field ‘signature’ marked by a non-linear mechanism that responded to contrast-reversing gratings at twice the stimulus temporal frequency (second Fourier harmonic, F2) independent of stimulus spatial phase. The F2 component showed high contrast gain and temporal sensitivity and appeared to originate from a region coextensive with that of the linear receptive field center. The F2 spatial frequency response peaked well beyond the resolution limit of the linear receptive field center, showing a Gaussian center radius of ~15 μm. Blocking inner retinal inhibition elevated the F2 response, suggesting that amacrine circuitry does not generate this non-linearity. Our data are consistent with a pooled-subunit model of the parasol-Y cell receptive field in which summation from an array of transient, partially rectifying cone bipolar cells accounts for both linear and non-linear components of the receptive field. PMID:18971470

Indoorgml - a Standard for Indoor Spatial Modeling

NASA Astrophysics Data System (ADS)

Li, Ki-Joune

2016-06-01

With recent progress of mobile devices and indoor positioning technologies, it becomes possible to provide location-based services in indoor space as well as outdoor space. It is in a seamless way between indoor and outdoor spaces or in an independent way only for indoor space. However, we cannot simply apply spatial models developed for outdoor space to indoor space due to their differences. For example, coordinate reference systems are employed to indicate a specific position in outdoor space, while the location in indoor space is rather specified by cell number such as room number. Unlike outdoor space, the distance between two points in indoor space is not determined by the length of the straight line but the constraints given by indoor components such as walls, stairs, and doors. For this reason, we need to establish a new framework for indoor space from fundamental theoretical basis, indoor spatial data models, and information systems to store, manage, and analyse indoor spatial data. In order to provide this framework, an international standard, called IndoorGML has been developed and published by OGC (Open Geospatial Consortium). This standard is based on a cellular notion of space, which considers an indoor space as a set of non-overlapping cells. It consists of two types of modules; core module and extension module. While core module consists of four basic conceptual and implementation modeling components (geometric model for cell, topology between cells, semantic model of cell, and multi-layered space model), extension modules may be defined on the top of the core module to support an application area. As the first version of the standard, we provide an extension for indoor navigation.

EEG Correlates of Preparatory Orienting, Contextual Updating, and Inhibition of Sensory Processing in Left Spatial Neglect.

PubMed

Lasaponara, Stefano; D'Onofrio, Marianna; Pinto, Mario; Dragone, Alessio; Menicagli, Dario; Bueti, Domenica; De Lucia, Marzia; Tomaiuolo, Francesco; Doricchi, Fabrizio

2018-04-11

Studies with event-related potentials have highlighted deficits in the early phases of orienting to left visual targets in right-brain-damaged patients with left spatial neglect (N+). However, brain responses associated with preparatory orienting of attention, with target novelty and with the detection of a match/mismatch between expected and actual targets (contextual updating), have not been explored in N+. Here in a study in healthy humans and brain-damaged patients of both sexes we demonstrate that frontal activity that reflects supramodal mechanisms of attentional orienting (Anterior Directing Attention Negativity, ADAN) is entirely spared in N+. In contrast, posterior responses that mark the early phases of cued orienting (Early Directing Attention Negativity, EDAN) and the setting up of sensory facilitation over the visual cortex (Late Directing Attention Positivity, LDAP) are suppressed in N+. This uncoupling is associated with damage of parietal-frontal white matter. N+ also exhibit exaggerated novelty reaction to targets in the right side of space and reduced novelty reaction for those in the left side (P3a) together with impaired contextual updating (P3b) in the left space. Finally, we highlight a drop in the amplitude and latency of the P1 that over the left hemisphere signals the early blocking of sensory processing in the right space when targets occur in the left one: this identifies a new electrophysiological marker of the rightward attentional bias in N+. The heterogeneous effects and spatial biases produced by localized brain damage on the different phases of attentional processing indicate relevant functional independence among their underlying neural mechanisms and improve the understanding of the spatial neglect syndrome. SIGNIFICANCE STATEMENT Our investigation answers important questions: are the different components of preparatory orienting (EDAN, ADAN, LDAP) functionally independent in the healthy brain? Is preparatory orienting of attention spared in left spatial neglect? Does the sparing of preparatory orienting have an impact on deficits in reflexive orienting and in the assignment of behavioral relevance to the left space? We show that supramodal preparatory orienting in frontal areas is entirely spared in neglect patients though this does not counterbalance deficits in preparatory parietal-occipital activity, reflexive orienting, and contextual updating. This points at relevant functional dissociations among different components of attention and suggests that improving voluntary attention in N+ might be behaviorally ineffective unless associated with stimulations boosting the response of posterior parietal-occipital areas. Copyright © 2018 the authors 0270-6474/18/383792-17$15.00/0.

Iterative approach of dual regression with a sparse prior enhances the performance of independent component analysis for group functional magnetic resonance imaging (fMRI) data.

PubMed

Kim, Yong-Hwan; Kim, Junghoe; Lee, Jong-Hwan

2012-12-01

This study proposes an iterative dual-regression (DR) approach with sparse prior regularization to better estimate an individual's neuronal activation using the results of an independent component analysis (ICA) method applied to a temporally concatenated group of functional magnetic resonance imaging (fMRI) data (i.e., Tc-GICA method). An ordinary DR approach estimates the spatial patterns (SPs) of neuronal activation and corresponding time courses (TCs) specific to each individual's fMRI data with two steps involving least-squares (LS) solutions. Our proposed approach employs iterative LS solutions to refine both the individual SPs and TCs with an additional a priori assumption of sparseness in the SPs (i.e., minimally overlapping SPs) based on L(1)-norm minimization. To quantitatively evaluate the performance of this approach, semi-artificial fMRI data were created from resting-state fMRI data with the following considerations: (1) an artificially designed spatial layout of neuronal activation patterns with varying overlap sizes across subjects and (2) a BOLD time series (TS) with variable parameters such as onset time, duration, and maximum BOLD levels. To systematically control the spatial layout variability of neuronal activation patterns across the "subjects" (n=12), the degree of spatial overlap across all subjects was varied from a minimum of 1 voxel (i.e., 0.5-voxel cubic radius) to a maximum of 81 voxels (i.e., 2.5-voxel radius) across the task-related SPs with a size of 100 voxels for both the block-based and event-related task paradigms. In addition, several levels of maximum percentage BOLD intensity (i.e., 0.5, 1.0, 2.0, and 3.0%) were used for each degree of spatial overlap size. From the results, the estimated individual SPs of neuronal activation obtained from the proposed iterative DR approach with a sparse prior showed an enhanced true positive rate and reduced false positive rate compared to the ordinary DR approach. The estimated TCs of the task-related SPs from our proposed approach showed greater temporal correlation coefficients with a reference hemodynamic response function than those of the ordinary DR approach. Moreover, the efficacy of the proposed DR approach was also successfully demonstrated by the results of real fMRI data acquired from left-/right-hand clenching tasks in both block-based and event-related task paradigms. Copyright © 2012 Elsevier Inc. All rights reserved.

Evidence of stimulus correlated empathy modes--Group ICA of fMRI data.

PubMed

Vemuri, Kavita; Surampudi, Bapi Raju

2015-03-01

The analysis of the cognitive processes in response to a narrative as presented in a movie provides an insight into momentary reaction to a single depicted action like a facial expression and an aggregate processing of the entire sequence of events. In this study we report results from fMRI data analyzed by group independent component analysis (ICA) method from a free viewing experiment using a diverse set of movie clips--an animation, a Hollywood and an Indian Hindi movie. The fMRI data were collected from 15 college students as they viewed 5-8 min clips from three movies. The movie clips were rated for depiction of emotional expressions, the emotion as per the narrative and the viewer's own empathy response. The neural correlates attributed to cognitive, motor and emotional empathy were the focus of the study. The methodology of using long duration stimuli in free viewing mode combined with ICA analysis has the potential to tease out spatially distributed but temporally coherent brain activity as demonstrated in this study. The independent components obtained from group ICA method isolated spatial maps with activations which can be safely ascribed to stimulus processing. We found that the activity in the areas attributable to cognitive and motor empathy was comparable for all the three stimuli while certain critical areas for emotional empathy were not noticed for the animation movie. These findings lead to interesting questions on possible differential emotion response in viewer(s) for computer generated actors compared to actors in live-action movies and the role of narrative and exposure to different genre of movies on racial, ethnic and cultural differences in empathy response. Copyright © 2015 Elsevier Inc. All rights reserved.

A hierarchical model for probabilistic independent component analysis of multi-subject fMRI studies

PubMed Central

Tang, Li

2014-01-01

Summary An important goal in fMRI studies is to decompose the observed series of brain images to identify and characterize underlying brain functional networks. Independent component analysis (ICA) has been shown to be a powerful computational tool for this purpose. Classic ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix. Existing group ICA methods generally concatenate observed fMRI data across subjects on the temporal domain and then decompose multi-subject data in a similar manner to single-subject ICA. The major limitation of existing methods is that they ignore between-subject variability in spatial distributions of brain functional networks in group ICA. In this paper, we propose a new hierarchical probabilistic group ICA method to formally model subject-specific effects in both temporal and spatial domains when decomposing multi-subject fMRI data. The proposed method provides model-based estimation of brain functional networks at both the population and subject level. An important advantage of the hierarchical model is that it provides a formal statistical framework to investigate similarities and differences in brain functional networks across subjects, e.g., subjects with mental disorders or neurodegenerative diseases such as Parkinson’s as compared to normal subjects. We develop an EM algorithm for model estimation where both the E-step and M-step have explicit forms. We compare the performance of the proposed hierarchical model with that of two popular group ICA methods via simulation studies. We illustrate our method with application to an fMRI study of Zen meditation. PMID:24033125

Influence of continuous positive airway pressure on outcomes of rehabilitation in stroke patients with obstructive sleep apnea.

PubMed

Ryan, Clodagh M; Bayley, Mark; Green, Robin; Murray, Brian J; Bradley, T Douglas

2011-04-01

In stroke patients, obstructive sleep apnea (OSA) is associated with poorer functional outcomes than in those without OSA. We hypothesized that treatment of OSA by continuous positive airway pressure (CPAP) in stroke patients would enhance motor, functional, and neurocognitive recovery. This was a randomized, open label, parallel group trial with blind assessment of outcomes performed in stroke patients with OSA in a stroke rehabilitation unit. Patients were assigned to standard rehabilitation alone (control group) or to CPAP (CPAP group). The primary outcomes were the Canadian Neurological scale, the 6-minute walk test distance, sustained attention response test, and the digit or spatial span-backward. Secondary outcomes included Epworth Sleepiness scale, Stanford Sleepiness scale, Functional Independence measure, Chedoke McMaster Stroke assessment, neurocognitive function, and Beck depression inventory. Tests were performed at baseline and 1 month later. Patients assigned to CPAP (n=22) experienced no adverse events. Regarding primary outcomes, compared to the control group (n=22), the CPAP group experienced improvement in stroke-related impairment (Canadian Neurological scale score, P<0.001) but not in 6-minute walk test distance, sustained attention response test, or digit or spatial span-backward. Regarding secondary outcomes, the CPAP group experienced improvements in the Epworth Sleepiness scale (P<0.001), motor component of the Functional Independence measure (P=0.05), Chedoke-McMaster Stroke assessment of upper and lower limb motor recovery test of the leg (P=0.001), and the affective component of depression (P=0.006), but not neurocognitive function. Treatment of OSA by CPAP in stroke patients undergoing rehabilitation improved functional and motor, but not neurocognitive outcomes. URL: http://www.clinicaltrials.gov. Unique identifier: NCT00221065.

Task-evoked brain functional magnetic susceptibility mapping by independent component analysis (χICA).

PubMed

Chen, Zikuan; Calhoun, Vince D

2016-03-01

Conventionally, independent component analysis (ICA) is performed on an fMRI magnitude dataset to analyze brain functional mapping (AICA). By solving the inverse problem of fMRI, we can reconstruct the brain magnetic susceptibility (χ) functional states. Upon the reconstructed χ dataspace, we propose an ICA-based brain functional χ mapping method (χICA) to extract task-evoked brain functional map. A complex division algorithm is applied to a timeseries of fMRI phase images to extract temporal phase changes (relative to an OFF-state snapshot). A computed inverse MRI (CIMRI) model is used to reconstruct a 4D brain χ response dataset. χICA is implemented by applying a spatial InfoMax ICA algorithm to the reconstructed 4D χ dataspace. With finger-tapping experiments on a 7T system, the χICA-extracted χ-depicted functional map is similar to the SPM-inferred functional χ map by a spatial correlation of 0.67 ± 0.05. In comparison, the AICA-extracted magnitude-depicted map is correlated with the SPM magnitude map by 0.81 ± 0.05. The understanding of the inferiority of χICA to AICA for task-evoked functional map is an ongoing research topic. For task-evoked brain functional mapping, we compare the data-driven ICA method with the task-correlated SPM method. In particular, we compare χICA with AICA for extracting task-correlated timecourses and functional maps. χICA can extract a χ-depicted task-evoked brain functional map from a reconstructed χ dataspace without the knowledge about brain hemodynamic responses. The χICA-extracted brain functional χ map reveals a bidirectional BOLD response pattern that is unavailable (or different) from AICA. Copyright © 2016 Elsevier B.V. All rights reserved.

Neural correlates of the natural observation of an emotionally loaded video

PubMed Central

Gonzalez-Santos, Leopoldo

2018-01-01

Studies based on a paradigm of free or natural viewing have revealed characteristics that allow us to know how the brain processes stimuli within a natural environment. This method has been little used to study brain function. With a connectivity approach, we examine the processing of emotions using an exploratory method to analyze functional magnetic resonance imaging (fMRI) data. This research describes our approach to modeling stress paradigms suitable for neuroimaging environments. We showed a short film (4.54 minutes) with high negative emotional valence and high arousal content to 24 healthy male subjects (36.42 years old; SD = 12.14) during fMRI. Independent component analysis (ICA) was used to identify networks based on spatial statistical independence. Through this analysis we identified the sensorimotor system and its influence on the dorsal attention and default-mode networks, which in turn have reciprocal activity and modulate networks described as emotional. PMID:29883494

On the nature of voters’ coalition preferences

PubMed Central

Plescia, Carolina; Aichholzer, Julian

2017-01-01

ABSTRACT An expanding literature indicates that in multiparty systems with coalition governments, citizens consider the post-electoral bargaining process among parties when casting their vote. Yet, we know surprisingly little about the nature of voters’ coalition preferences. This paper uses data from the Austrian National Election Study to examine the determinants as well as the independence of preferences for coalitions as political object. We find that coalition preferences are strongly informed by spatial considerations; but additional non-ideological factors, such as party and leader preferences, also play a fundamental role. We also find that coalitions enjoy a certain degree of independence from other objects of vote choice and they do not always represent a simple average score on the feeling thermometer of the constituent parties. There are, however, substantial differences among voters, with party identifiers and those with extreme ideology being less likely to consider coalitions as separate entities from their component parties. PMID:28824702

On the nature of voters' coalition preferences.

PubMed

Plescia, Carolina; Aichholzer, Julian

2017-07-03

An expanding literature indicates that in multiparty systems with coalition governments, citizens consider the post-electoral bargaining process among parties when casting their vote. Yet, we know surprisingly little about the nature of voters' coalition preferences. This paper uses data from the Austrian National Election Study to examine the determinants as well as the independence of preferences for coalitions as political object. We find that coalition preferences are strongly informed by spatial considerations; but additional non-ideological factors, such as party and leader preferences, also play a fundamental role. We also find that coalitions enjoy a certain degree of independence from other objects of vote choice and they do not always represent a simple average score on the feeling thermometer of the constituent parties. There are, however, substantial differences among voters, with party identifiers and those with extreme ideology being less likely to consider coalitions as separate entities from their component parties.

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA.

PubMed

Labounek, René; Bridwell, David A; Mareček, Radek; Lamoš, Martin; Mikl, Michal; Slavíček, Tomáš; Bednařík, Petr; Baštinec, Jaromír; Hluštík, Petr; Brázdil, Milan; Jan, Jiří

2018-01-01

Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.

Blending of Radial HF Radar Surface Current and Model Using ETKF Scheme For The Sunda Strait

NASA Astrophysics Data System (ADS)

Mujiasih, Subekti; Riyadi, Mochammad; Wandono, Dr; Wayan Suardana, I.; Nyoman Gede Wiryajaya, I.; Nyoman Suarsa, I.; Hartanto, Dwi; Barth, Alexander; Beckers, Jean-Marie

2017-04-01

Preliminary study of data blending of surface current for Sunda Strait-Indonesia has been done using the analysis scheme of the Ensemble Transform Kalman Filter (ETKF). The method is utilized to combine radial velocity from HF Radar and u and v component of velocity from Global Copernicus - Marine environment monitoring service (CMEMS) model. The initial ensemble is based on the time variability of the CMEMS model result. Data tested are from 2 CODAR Seasonde radar sites in Sunda Strait and 2 dates such as 09 September 2013 and 08 February 2016 at 12.00 UTC. The radial HF Radar data has a hourly temporal resolution, 20-60 km of spatial range, 3 km of range resolution, 5 degree of angular resolution and spatial resolution and 11.5-14 MHz of frequency range. The u and v component of the model velocity represents a daily mean with 1/12 degree spatial resolution. The radial data from one HF radar site is analyzed and the result compared to the equivalent radial velocity from CMEMS for the second HF radar site. Error checking is calculated by root mean squared error (RMSE). Calculation of ensemble analysis and ensemble mean is using Sangoma software package. The tested R which represents observation error covariance matrix, is a diagonal matrix with diagonal elements equal 0.05, 0.5 or 1.0 m2/s2. The initial ensemble members comes from a model simulation spanning a month (September 2013 or February 2016), one year (2013) or 4 years (2013-2016). The spatial distribution of the radial current are analyzed and the RMSE values obtained from independent HF radar station are optimized. It was verified that the analysis reproduces well the structure included in the analyzed HF radar data. More importantly, the analysis was also improved relative to the second independent HF radar site. RMSE of the improved analysis is better than first HF Radar site Analysis. The best result of the blending exercise was obtained for observation error variance equal to 0.05 m2/s2. This study is still preliminary step, but it gives promising result for bigger size of data, combining other model and further development. Keyword: HF Radar, Sunda Strait, ETKF, CMEMS

Curvature from Strong Gravitational Lensing: A Spatially Closed Universe or Systematics?

NASA Astrophysics Data System (ADS)

Li, Zhengxiang; Ding, Xuheng; Wang, Guo-Jian; Liao, Kai; Zhu, Zong-Hong

2018-02-01

Model-independent constraints on the spatial curvature are not only closely related to important problems, such as the evolution of the universe and properties of dark energy, but also provide a test of the validity of the fundamental Copernican principle. In this paper, with the distance sum rule in the Friedmann–Lemaître–Robertson–Walker metric, we achieve model-independent measurements of the spatial curvature from the latest type Ia supernovae and strong gravitational lensing (SGL) observations. We find that a spatially closed universe is preferred. Moreover, by considering different kinds of velocity dispersion and subsamples, we study possible factors that might affect model-independent estimations for the spatial curvature from SGL observations. It is suggested that the combination of observational data from different surveys might cause a systematic bias, and the tension between the spatially flat universe and SGL observations is alleviated when the subsample only from the Sloan Lens ACS Survey is used or a more complex treatment for the density profile of lenses is considered.

Probability and the changing shape of response distributions for orientation.

PubMed

Anderson, Britt

2014-11-18

Spatial attention and feature-based attention are regarded as two independent mechanisms for biasing the processing of sensory stimuli. Feature attention is held to be a spatially invariant mechanism that advantages a single feature per sensory dimension. In contrast to the prediction of location independence, I found that participants were able to report the orientation of a briefly presented visual grating better for targets defined by high probability conjunctions of features and locations even when orientations and locations were individually uniform. The advantage for high-probability conjunctions was accompanied by changes in the shape of the response distributions. High-probability conjunctions had error distributions that were not normally distributed but demonstrated increased kurtosis. The increase in kurtosis could be explained as a change in the variances of the component tuning functions that comprise a population mixture. By changing the mixture distribution of orientation-tuned neurons, it is possible to change the shape of the discrimination function. This prompts the suggestion that attention may not "increase" the quality of perceptual processing in an absolute sense but rather prioritizes some stimuli over others. This results in an increased number of highly accurate responses to probable targets and, simultaneously, an increase in the number of very inaccurate responses. © 2014 ARVO.

[Effect of object consistency in a spatial contextual cueing paradigm].

PubMed

Takeda, Yuji

2008-04-01

Previous studies demonstrated that attention can be quickly guided to a target location in a visual search task when the spatial configurations of search items and/or the object identities were repeated in the previous trials. This phenomenon is termed contextual cueing. Recently, it was reported that spatial configuration learning and object identity learning occurred independently, when novel contours were used as search items. The present study examined whether this learning occurred independently even when the search items were meaningful. The results showed that the contextual cueing effect was observed even if the relationships between the spatial locations and object identities were jumbled (Experiment 1). However, it disappeared when the search items were changed into geometric patterns (Experiment 2). These results suggest that the spatial configuration can be learned independent of the object identities; however, the use of the learned configuration is restricted by the learning situations.

Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study

PubMed Central

Yu, Qingbao; Wu, Lei; Bridwell, David A.; Erhardt, Erik B.; Du, Yuhui; He, Hao; Chen, Jiayu; Liu, Peng; Sui, Jing; Pearlson, Godfrey; Calhoun, Vince D.

2016-01-01

The topological architecture of brain connectivity has been well-characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EO) and eyes closed (EC) resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA). EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma). EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics. PMID:27733821

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

USING THE ECLPSS SOFTWARE ENVIRONMENT TO BUILD A SPATIALLY EXPLICIT COMPONENT-BASED MODEL OF OZONE EFFECTS ON FOREST ECOSYSTEMS. (R827958)

EPA Science Inventory

We have developed a modeling framework to support grid-based simulation of ecosystems at multiple spatial scales, the Ecological Component Library for Parallel Spatial Simulation (ECLPSS). ECLPSS helps ecologists to build robust spatially explicit simulations of ...

The Speech, Spatial and Qualities of Hearing Scale (SSQ)

PubMed Central

Gatehouse, Stuart; Noble, William

2017-01-01

The Speech, Spatial and Qualities of Hearing Scale (SSQ) is designed to measure a range of hearing disabilities across several domains. Particular attention is given to hearing speech in a variety of competing contexts, and to the directional, distance and movement components of spatial hearing. In addition, the abilities both to segregate sounds and to attend to simultaneous speech streams are assessed, reflecting the reality of hearing in the everyday world. Qualities of hearing experience include ease of listening, and the naturalness, clarity and identifiability of different speakers, different musical pieces and instruments, and different everyday sounds. Application of the SSQ to 153 new clinic clients prior to hearing aid fitting showed that the greatest difficulty was experienced with simultaneous speech streams, ease of listening, listening in groups and in noise, and judging distance and movement. SSQ ratings were compared with an independent measure of handicap. After differences in hearing level were controlled for, it was found that identification, attention and effort problems, as well as spatial hearing problems, feature prominently in the disability–handicap relationship, along with certain features of speech hearing. The results implicate aspects of temporal and spatial dynamics of hearing disability in the experience of handicap. The SSQ shows promise as an instrument for evaluating interventions of various kinds, particularly (but not exclusively) those that implicate binaural function. PMID:15035561

Optical communications beyond orbital angular momentum

NASA Astrophysics Data System (ADS)

Rosales-Guzmán, Carmelo; Trichili, Abderrahmen; Dudley, Angela; Ndagano, Bienvenu; Ben Salem, Amine; Zghal, Mourad; Forbes, Andrew

2016-09-01

Current optical communication technologies are predicted to face a bandwidth capacity limit in the near future. The nature of the limitation is fundamental rather than technological and is set by nonlinearities in optical fibers. One solution, suggested over 30 years ago, comprises the use of spatial modes of light as information carriers. Along this direction, light beams endowed with orbital angular momentum (OAM) have been demonstrated as potential information carriers in both, free space and fibres. However, recent studies suggest that purely OAM modes does not increase the bandwidth of optical communication systems. In fact, in all work to date, only the azimuthal component of transverse spatial modes has been used. Crucially, all transverse spatial modes require two degrees of freedom to be described; in the context of Laguerre-Gaussian (LGp`) beams these are azimuthal (l) and radial (p), the former responsible for OAM. Here, we demonstrate a technique where both degrees of freedom of LG modes are used as information carrier over free space. We transfer images encoded using 100 spatial modes in three wavelengths as our basis, and employ a spatial demultiplexing scheme that detects all 100 modes simultaneously. Our scheme is a hybrid of MIMO and SMM, and serves as a proof-of-principle demonstration. The cross-talk between the modes is small and independent of whether OAM modes are used or not.

Liana abundance, diversity, and distribution on Barro Colorado Island, Panama.

PubMed

Schnitzer, Stefan A; Mangan, Scott A; Dalling, James W; Baldeck, Claire A; Hubbell, Stephen P; Ledo, Alicia; Muller-Landau, Helene; Tobin, Michael F; Aguilar, Salomon; Brassfield, David; Hernandez, Andres; Lao, Suzanne; Perez, Rolando; Valdes, Oldemar; Yorke, Suzanne Rutishauser

2012-01-01

Lianas are a key component of tropical forests; however, most surveys are too small to accurately quantify liana community composition, diversity, abundance, and spatial distribution - critical components for measuring the contribution of lianas to forest processes. In 2007, we tagged, mapped, measured the diameter, and identified all lianas ≥1 cm rooted in a 50-ha plot on Barro Colorado Island, Panama (BCI). We calculated liana density, basal area, and species richness for both independently rooted lianas and all rooted liana stems (genets plus clones). We compared spatial aggregation patterns of liana and tree species, and among liana species that varied in the amount of clonal reproduction. We also tested whether liana and tree densities have increased on BCI compared to surveys conducted 30-years earlier. This study represents the most comprehensive spatially contiguous sampling of lianas ever conducted and, over the 50 ha area, we found 67,447 rooted liana stems comprising 162 species. Rooted lianas composed nearly 25% of the woody stems (trees and lianas), 35% of woody species richness, and 3% of woody basal area. Lianas were spatially aggregated within the 50-ha plot and the liana species with the highest proportion of clonal stems more spatially aggregated than the least clonal species, possibly indicating clonal stem recruitment following canopy disturbance. Over the past 30 years, liana density increased by 75% for stems ≥1 cm diameter and nearly 140% for stems ≥5 cm diameter, while tree density on BCI decreased 11.5%; a finding consistent with other neotropical forests. Our data confirm that lianas contribute substantially to tropical forest stem density and diversity, they have highly clumped distributions that appear to be driven by clonal stem recruitment into treefall gaps, and they are increasing relative to trees, thus indicating that lianas will play a greater role in the future dynamics of BCI and other neotropical forests.

Ferrotoroidial propertiesof Non-Crystallographic Pointgroups

NASA Astrophysics Data System (ADS)

Sireesha, G.; Devi, S. Uma; Yamini Sankar, CH.

2017-08-01

Primary ferroic crystals are the crystals with domain states that are distinguished by properties like spontaneous magnetization, strain, or polarisation. Secondary ferroic crystals are the crystals with domain states that are distinguished by piezoelectric tensor and they are named as Ferromagnetotoroidic (eV2), Ferromagnetoelastic (aeV [V2]) crystals respectively. Here “e” denotes zero rank tensor that changes under spatial inversion, “a” denotes zero rank tensor that changes under time inversion, and “V” denotes a polar vector. Recent observations (Van Aken et al., 2007) identified the fourth type of primary ferroic crystals, a ferrotoroidic crystal with domains distinguished by a toroidial moment. The number of independent constants of quasi crystals is theoretically derived by Wenge Yang et al., (1995). He also formulated the number of independent components of any physical property tensor of quasi crystals using group representation theory. This paper accounts the effect of symmetry on some ferrotoroidial properties of quasi Crystals with 5-fold, 8-fold, 10-fold and 12-fold symmetries using group theoretical methods. Also the number of independent constants is calculated and tabulated that helps in describing the ferrotoroidial properties.

Extracting alpha band modulation during visual spatial attention without flickering stimuli using common spatial pattern.

PubMed

Fujisawa, Junya; Touyama, Hideaki; Hirose, Michitaka

2008-01-01

In this paper, alpha band modulation during visual spatial attention without visual stimuli was focused. Visual spatial attention has been expected to provide a new channel of non-invasive independent brain computer interface (BCI), but little work has been done on the new interfacing method. The flickering stimuli used in previous work cause a decline of independency and have difficulties in a practical use. Therefore we investigated whether visual spatial attention could be detected without such stimuli. Further, the common spatial patterns (CSP) were for the first time applied to the brain states during visual spatial attention. The performance evaluation was based on three brain states of left, right and center direction attention. The 30-channel scalp electroencephalographic (EEG) signals over occipital cortex were recorded for five subjects. Without CSP, the analyses made 66.44 (range 55.42 to 72.27) % of average classification performance in discriminating left and right attention classes. With CSP, the averaged classification accuracy was 75.39 (range 63.75 to 86.13) %. It is suggested that CSP is useful in the context of visual spatial attention, and the alpha band modulation during visual spatial attention without flickering stimuli has the possibility of a new channel for independent BCI as well as motor imagery.

Separating spatial search and efficiency rates as components of predation risk

PubMed Central

DeCesare, Nicholas J.

2012-01-01

Predation risk is an important driver of ecosystems, and local spatial variation in risk can have population-level consequences by affecting multiple components of the predation process. I use resource selection and proportional hazard time-to-event modelling to assess the spatial drivers of two key components of risk—the search rate (i.e. aggregative response) and predation efficiency rate (i.e. functional response)—imposed by wolves (Canis lupus) in a multi-prey system. In my study area, both components of risk increased according to topographic variation, but anthropogenic features affected only the search rate. Predicted models of the cumulative hazard, or risk of a kill, underlying wolf search paths validated well with broad-scale variation in kill rates, suggesting that spatial hazard models provide a means of scaling up from local heterogeneity in predation risk to population-level dynamics in predator–prey systems. Additionally, I estimated an integrated model of relative spatial predation risk as the product of the search and efficiency rates, combining the distinct contributions of spatial heterogeneity to each component of risk. PMID:22977145

How Many Separable Sources? Model Selection In Independent Components Analysis

PubMed Central

Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen

2015-01-01

Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian. PMID:25811988

A protein-dependent side-chain rotamer library.

PubMed

Bhuyan, Md Shariful Islam; Gao, Xin

2011-12-14

Protein side-chain packing problem has remained one of the key open problems in bioinformatics. The three main components of protein side-chain prediction methods are a rotamer library, an energy function and a search algorithm. Rotamer libraries summarize the existing knowledge of the experimentally determined structures quantitatively. Depending on how much contextual information is encoded, there are backbone-independent rotamer libraries and backbone-dependent rotamer libraries. Backbone-independent libraries only encode sequential information, whereas backbone-dependent libraries encode both sequential and locally structural information. However, side-chain conformations are determined by spatially local information, rather than sequentially local information. Since in the side-chain prediction problem, the backbone structure is given, spatially local information should ideally be encoded into the rotamer libraries. In this paper, we propose a new type of backbone-dependent rotamer library, which encodes structural information of all the spatially neighboring residues. We call it protein-dependent rotamer libraries. Given any rotamer library and a protein backbone structure, we first model the protein structure as a Markov random field. Then the marginal distributions are estimated by the inference algorithms, without doing global optimization or search. The rotamers from the given library are then re-ranked and associated with the updated probabilities. Experimental results demonstrate that the proposed protein-dependent libraries significantly outperform the widely used backbone-dependent libraries in terms of the side-chain prediction accuracy and the rotamer ranking ability. Furthermore, without global optimization/search, the side-chain prediction power of the protein-dependent library is still comparable to the global-search-based side-chain prediction methods.

CHANGES SDSS: the development of a Spatial Decision Support System for analysing changing hydro-meteorological risk

NASA Astrophysics Data System (ADS)

van Westen, Cees; Bakker, Wim; Zhang, Kaixi; Jäger, Stefan; Assmann, Andre; Kass, Steve; Andrejchenko, Vera; Olyazadeh, Roya; Berlin, Julian; Cristal, Irina

2014-05-01

Within the framework of the EU FP7 Marie Curie Project CHANGES (www.changes-itn.eu) and the EU FP7 Copernicus project INCREO (http://www.increo-fp7.eu) a spatial decision support system is under development with the aim to analyse the effect of risk reduction planning alternatives on reducing the risk now and in the future, and support decision makers in selecting the best alternatives. The Spatial Decision Support System will be composed of a number of integrated components. The Risk Assessment component allows to carry out spatial risk analysis, with different degrees of complexity, ranging from simple exposure (overlay of hazard and assets maps) to quantitative analysis (using different hazard types, temporal scenarios and vulnerability curves) resulting into risk curves. The platform does not include a component to calculate hazard maps, and existing hazard maps are used as input data for the risk component. The second component of the SDSS is a risk reduction planning component, which forms the core of the platform. This component includes the definition of risk reduction alternatives (related to disaster response planning, risk reduction measures and spatial planning) and links back to the risk assessment module to calculate the new level of risk if the measure is implemented, and a cost-benefit (or cost-effectiveness/ Spatial Multi Criteria Evaluation) component to compare the alternatives and make decision on the optimal one. The third component of the SDSS is a temporal scenario component, which allows to define future scenarios in terms of climate change, land use change and population change, and the time periods for which these scenarios will be made. The component doesn't generate these scenarios but uses input maps for the effect of the scenarios on the hazard and assets maps. The last component is a communication and visualization component, which can compare scenarios and alternatives, not only in the form of maps, but also in other forms (risk curves, tables, graphs). The envisaged users of the platform are organizations involved in planning of risk reduction measures, and that have staff capable of visualizing and analysing spatial data at a municipal scale.

Continuous-variable quantum computing in optical time-frequency modes using quantum memories.

PubMed

Humphreys, Peter C; Kolthammer, W Steven; Nunn, Joshua; Barbieri, Marco; Datta, Animesh; Walmsley, Ian A

2014-09-26

We develop a scheme for time-frequency encoded continuous-variable cluster-state quantum computing using quantum memories. In particular, we propose a method to produce, manipulate, and measure two-dimensional cluster states in a single spatial mode by exploiting the intrinsic time-frequency selectivity of Raman quantum memories. Time-frequency encoding enables the scheme to be extremely compact, requiring a number of memories that are a linear function of only the number of different frequencies in which the computational state is encoded, independent of its temporal duration. We therefore show that quantum memories can be a powerful component for scalable photonic quantum information processing architectures.

Using joint ICA to link function and structure using MEG and DTI in schizophrenia

PubMed Central

Stephen, JM; Coffman, BA; Jung, RE; Bustillo, JR; Aine, CJ; Calhoun, VD

2013-01-01

In this study we employed joint independent component analysis (jICA) to perform a novel multivariate integration of magnetoencephalography (MEG) and diffusion tensor imaging (DTI) data to investigate the link between function and structure. This model-free approach allows one to identify covariation across modalities with different temporal and spatial scales [temporal variation in MEG and spatial variation in fractional anisotropy (FA) maps]. Healthy controls (HC) and patients with schizophrenia (SP) participated in an auditory/visual multisensory integration paradigm to probe cortical connectivity in schizophrenia. To allow direct comparisons across participants and groups, the MEG data were registered to an average head position and regional waveforms were obtained by calculating the local field power of the planar gradiometers. Diffusion tensor images obtained in the same individuals were preprocessed to provide FA maps for each participant. The MEG/FA data were then integrated using the jICA software (http://mialab.mrn.org/software/fit). We identified MEG/FA components that demonstrated significantly different (p < 0.05) covariation in MEG/FA data between diagnostic groups (SP vs. HC) and three components that captured the predominant sensory responses in the MEG data. Lower FA values in bilateral posterior parietal regions, which include anterior/posterior association tracts, were associated with reduced MEG amplitude (120-170 ms) of the visual response in occipital sensors in SP relative to HC. Additionally, increased FA in a right medial frontal region was linked with larger amplitude late MEG activity (300-400 ms) in bilateral central channels for SP relative to HC. Step-wise linear regression provided evidence that right temporal, occipital and late central components were significant predictors of reaction time and cognitive performance based on the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) cognitive assessment battery. These results point to dysfunction in a posterior visual processing network in schizophrenia, with reduced MEG amplitude, reduced FA and poorer overall performance on the MATRICS. Interestingly, the spatial location of the MEG activity and the associated FA regions are spatially consistent with white matter regions that subserve these brain areas. This novel approach provides evidence for significant pairing between function (electrophysiology) and structure (white matter integrity) and demonstrates the sensitivity of this multivariate, multimodal integration technique to group differences in function and structure. PMID:23777757

The Role of Attention in the Binding of Surface Features to Locations

PubMed Central

Hyun, Joo-seok; Woodman, Geoffrey F.; Luck, Steven J.

2013-01-01

Previous studies have proposed that attention is not necessary for detecting simple features but is necessary for binding them to spatial locations. The present study tested this hypothesis, using the N2pc component of the event-related potential waveform as a measure of the allocation of attention. A simple feature detection condition, in which observers reported whether a target color was present or not, was compared with feature-location binding conditions, in which observers reported the location of the target color. A larger N2pc component was observed in the binding conditions than in the detection condition, indicating that additional attentional resources are needed to bind a feature to a location than to detect the feature independently of its location. This finding supports theories of attention in which attention plays a special role in binding features. PMID:24235876

Neural Processing of Facial Identity and Emotion in Infants at High-Risk for Autism Spectrum Disorders

PubMed Central

Fox, Sharon E.; Wagner, Jennifer B.; Shrock, Christine L.; Tager-Flusberg, Helen; Nelson, Charles A.

2013-01-01

Deficits in face processing and social impairment are core characteristics of autism spectrum disorder. The present work examined 7-month-old infants at high-risk for developing autism and typically developing controls at low-risk, using a face perception task designed to differentiate between the effects of face identity and facial emotions on neural response using functional Near-Infrared Spectroscopy. In addition, we employed independent component analysis, as well as a novel method of condition-related component selection and classification to identify group differences in hemodynamic waveforms and response distributions associated with face and emotion processing. The results indicate similarities of waveforms, but differences in the magnitude, spatial distribution, and timing of responses between groups. These early differences in local cortical regions and the hemodynamic response may, in turn, contribute to differences in patterns of functional connectivity. PMID:23576966

A two-step super-Gaussian independent component analysis approach for fMRI data.

PubMed

Ge, Ruiyang; Yao, Li; Zhang, Hang; Long, Zhiying

2015-09-01

Independent component analysis (ICA) has been widely applied to functional magnetic resonance imaging (fMRI) data analysis. Although ICA assumes that the sources underlying data are statistically independent, it usually ignores sources' additional properties, such as sparsity. In this study, we propose a two-step super-GaussianICA (2SGICA) method that incorporates the sparse prior of the sources into the ICA model. 2SGICA uses the super-Gaussian ICA (SGICA) algorithm that is based on a simplified Lewicki-Sejnowski's model to obtain the initial source estimate in the first step. Using a kernel estimator technique, the source density is acquired and fitted to the Laplacian function based on the initial source estimates. The fitted Laplacian prior is used for each source at the second SGICA step. Moreover, the automatic target generation process for initial value generation is used in 2SGICA to guarantee the stability of the algorithm. An adaptive step size selection criterion is also implemented in the proposed algorithm. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of 2SGICA and made a performance comparison between InfomaxICA, FastICA, mean field ICA (MFICA) with Laplacian prior, sparse online dictionary learning (ODL), SGICA and 2SGICA. Both simulated and real fMRI experiments showed that the 2SGICA was most robust to noises, and had the best spatial detection power and the time course estimation among the six methods. Copyright © 2015. Published by Elsevier Inc.

Micro-Expression Recognition Using Color Spaces.

PubMed

Wang, Su-Jing; Yan, Wen-Jing; Li, Xiaobai; Zhao, Guoying; Zhou, Chun-Guang; Fu, Xiaolan; Yang, Minghao; Tao, Jianhua

2015-12-01

Micro-expressions are brief involuntary facial expressions that reveal genuine emotions and, thus, help detect lies. Because of their many promising applications, they have attracted the attention of researchers from various fields. Recent research reveals that two perceptual color spaces (CIELab and CIELuv) provide useful information for expression recognition. This paper is an extended version of our International Conference on Pattern Recognition paper, in which we propose a novel color space model, tensor independent color space (TICS), to help recognize micro-expressions. In this paper, we further show that CIELab and CIELuv are also helpful in recognizing micro-expressions, and we indicate why these three color spaces achieve better performance. A micro-expression color video clip is treated as a fourth-order tensor, i.e., a four-dimension array. The first two dimensions are the spatial information, the third is the temporal information, and the fourth is the color information. We transform the fourth dimension from RGB into TICS, in which the color components are as independent as possible. The combination of dynamic texture and independent color components achieves a higher accuracy than does that of RGB. In addition, we define a set of regions of interests (ROIs) based on the facial action coding system and calculated the dynamic texture histograms for each ROI. Experiments are conducted on two micro-expression databases, CASME and CASME 2, and the results show that the performances for TICS, CIELab, and CIELuv are better than those for RGB or gray.

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.

Spatial-Simultaneous and Spatial-Sequential Working Memory in Individuals with Down Syndrome: The Effect of Configuration

ERIC Educational Resources Information Center

Carretti, Barbara; Lanfranchi, Silvia; Mammarella, Irene C.

2013-01-01

Earlier research showed that visuospatial working memory (VSWM) is better preserved in Down syndrome (DS) than verbal WM. Some differences emerged, however, when VSWM performance was broken down into its various components, and more recent studies revealed that the spatial-simultaneous component of VSWM is more impaired than the spatial-sequential…

Determining the optimal number of independent components for reproducible transcriptomic data analysis.

PubMed

Kairov, Ulykbek; Cantini, Laura; Greco, Alessandro; Molkenov, Askhat; Czerwinska, Urszula; Barillot, Emmanuel; Zinovyev, Andrei

2017-09-11

Independent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter, related to determining the effective data dimension, remains an open question in the application of blind source separation techniques to transcriptomic data. Here we address the question of optimizing the number of statistically independent components in the analysis of transcriptomic data for reproducibility of the components in multiple runs of ICA (within the same or within varying effective dimensions) and in multiple independent datasets. To this end, we introduce ranking of independent components based on their stability in multiple ICA computation runs and define a distinguished number of components (Most Stable Transcriptome Dimension, MSTD) corresponding to the point of the qualitative change of the stability profile. Based on a large body of data, we demonstrate that a sufficient number of dimensions is required for biological interpretability of the ICA decomposition and that the most stable components with ranks below MSTD have more chances to be reproduced in independent studies compared to the less stable ones. At the same time, we show that a transcriptomics dataset can be reduced to a relatively high number of dimensions without losing the interpretability of ICA, even though higher dimensions give rise to components driven by small gene sets. We suggest a protocol of ICA application to transcriptomics data with a possibility of prioritizing components with respect to their reproducibility that strengthens the biological interpretation. Computing too few components (much less than MSTD) is not optimal for interpretability of the results. The components ranked within MSTD range have more chances to be reproduced in independent studies.

Visual Contrast Sensitivity in Early-Stage Parkinson's Disease.

PubMed

Ming, Wendy; Palidis, Dimitrios J; Spering, Miriam; McKeown, Martin J

2016-10-01

Visual impairments are frequent in Parkinson's disease (PD) and impact normal functioning in daily activities. Visual contrast sensitivity is a powerful nonmotor sign for discriminating PD patients from controls. However, it is usually assessed with static visual stimuli. Here we examined the interaction between perception and eye movements in static and dynamic contrast sensitivity tasks in a cohort of mildly impaired, early-stage PD patients. Patients (n = 13) and healthy age-matched controls (n = 12) viewed stimuli of various spatial frequencies (0-8 cyc/deg) and speeds (0°/s, 10°/s, 30°/s) on a computer monitor. Detection thresholds were determined by asking participants to adjust luminance contrast until they could just barely see the stimulus. Eye position was recorded with a video-based eye tracker. Patients' static contrast sensitivity was impaired in the intermediate spatial-frequency range and this impairment correlated with fixational instability. However, dynamic contrast sensitivity and patients' smooth pursuit were relatively normal. An independent component analysis revealed contrast sensitivity profiles differentiating patients and controls. Our study simultaneously assesses perceptual contrast sensitivity and eye movements in PD, revealing a possible link between fixational instability and perceptual deficits. Spatiotemporal contrast sensitivity profiles may represent an easily measurable metric as a component of a broader combined biometric for nonmotor features observed in PD.

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.

Influence of spherical aberration, stimulus spatial frequency, and pupil apodisation on subjective refractions

PubMed Central

Bradley, Arthur; Xu, Renfeng; Thibos, Larry; Marin, Gildas; Hernandez, Martha

2014-01-01

Purpose To test competing hypotheses (Stiles Crawford pupil apodising or superior imaging of high spatial frequencies by the central pupil) for the pupil size independence of subjective refractions in the presence of primary spherical aberration. Methods Subjective refractions were obtained with a variety of test stimuli (high contrast letters, urban cityscape, high and low spatial frequency gratings) while modulating pupil diameter, levels of primary spherical aberration and pupil apodisation. Subjective refractions were also obtained with low-pass and high-pass stimuli and using “darker” and “sharper” subjective criteria. Results Subjective refractions for stimuli containing high spatial frequencies focus a near paraxial region of the pupil and are affected only slightly by level of Seidel spherical aberration, degree of pupil apodisation and pupil diameter, and generally focused a radius of about 1 to 1.5 mm from the pupil centre. Low spatial frequency refractions focus a marginal region of the pupil, and are significantly affected by level of spherical aberration, amount of pupil apodisation, and pupil size. Clinical refractions that employ the “darker” or “sharper” subjective criteria bias the patient to use lower or higher spatial frequencies respectively. Conclusions In the presence of significant levels of spherical aberration, the pupil size independence of subjective refractions occurs with or without Stiles Crawford apodisation for refractions that optimise high spatial frequency content in the image. If low spatial frequencies are optimised by a subjective refraction, spherical refractive error varies with spherical aberration, pupil size, and level of apodisation. As light levels drop from photopic to scotopic, therefore, we expect a shift from pupil size independent to pupil size dependent subjective refractions. Emphasising a “sharper” criterion during subjective refractions will improve image quality for high spatial frequencies and generate pupil size independent refractions. PMID:24397356

Non-hierarchical Influence of Visual Form, Touch, and Position Cues on Embodiment, Agency, and Presence in Virtual Reality

PubMed Central

Pritchard, Stephen C.; Zopf, Regine; Polito, Vince; Kaplan, David M.; Williams, Mark A.

2016-01-01

The concept of self-representation is commonly decomposed into three component constructs (sense of embodiment, sense of agency, and sense of presence), and each is typically investigated separately across different experimental contexts. For example, embodiment has been explored in bodily illusions; agency has been investigated in hypnosis research; and presence has been primarily studied in the context of Virtual Reality (VR) technology. Given that each component involves the integration of multiple cues within and across sensory modalities, they may rely on similar underlying mechanisms. However, the degree to which this may be true remains unclear when they are independently studied. As a first step toward addressing this issue, we manipulated a range of cues relevant to these components of self-representation within a single experimental context. Using consumer-grade Oculus Rift VR technology, and a new implementation of the Virtual Hand Illusion, we systematically manipulated visual form plausibility, visual–tactile synchrony, and visual–proprioceptive spatial offset to explore their influence on self-representation. Our results show that these cues differentially influence embodiment, agency, and presence. We provide evidence that each type of cue can independently and non-hierarchically influence self-representation yet none of these cues strictly constrains or gates the influence of the others. We discuss theoretical implications for understanding self-representation as well as practical implications for VR experiment design, including the suitability of consumer-based VR technology in research settings. PMID:27826275

Modelling space of spread Dengue Hemorrhagic Fever (DHF) in Central Java use spatial durbin model

NASA Astrophysics Data System (ADS)

Ispriyanti, Dwi; Prahutama, Alan; Taryono, Arkadina PN

2018-05-01

Dengue Hemorrhagic Fever is one of the major public health problems in Indonesia. From year to year, DHF causes Extraordinary Event in most parts of Indonesia, especially Central Java. Central Java consists of 35 districts or cities where each region is close to each other. Spatial regression is an analysis that suspects the influence of independent variables on the dependent variables with the influences of the region inside. In spatial regression modeling, there are spatial autoregressive model (SAR), spatial error model (SEM) and spatial autoregressive moving average (SARMA). Spatial Durbin model is the development of SAR where the dependent and independent variable have spatial influence. In this research dependent variable used is number of DHF sufferers. The independent variables observed are population density, number of hospitals, residents and health centers, and mean years of schooling. From the multiple regression model test, the variables that significantly affect the spread of DHF disease are the population and mean years of schooling. By using queen contiguity and rook contiguity, the best model produced is the SDM model with queen contiguity because it has the smallest AIC value of 494,12. Factors that generally affect the spread of DHF in Central Java Province are the number of population and the average length of school.

CO Component Estimation Based on the Independent Component Analysis

NASA Astrophysics Data System (ADS)

Ichiki, Kiyotomo; Kaji, Ryohei; Yamamoto, Hiroaki; Takeuchi, Tsutomu T.; Fukui, Yasuo

2014-01-01

Fast Independent Component Analysis (FastICA) is a component separation algorithm based on the levels of non-Gaussianity. Here we apply FastICA to the component separation problem of the microwave background, including carbon monoxide (CO) line emissions that are found to contaminate the PLANCK High Frequency Instrument (HFI) data. Specifically, we prepare 100 GHz, 143 GHz, and 217 GHz mock microwave sky maps, which include galactic thermal dust, NANTEN CO line, and the cosmic microwave background (CMB) emissions, and then estimate the independent components based on the kurtosis. We find that FastICA can successfully estimate the CO component as the first independent component in our deflection algorithm because its distribution has the largest degree of non-Gaussianity among the components. Thus, FastICA can be a promising technique to extract CO-like components without prior assumptions about their distributions and frequency dependences.

Large-scale changes in network interactions as a physiological signature of spatial neglect.

PubMed

Baldassarre, Antonello; Ramsey, Lenny; Hacker, Carl L; Callejas, Alicia; Astafiev, Serguei V; Metcalf, Nicholas V; Zinn, Kristi; Rengachary, Jennifer; Snyder, Abraham Z; Carter, Alex R; Shulman, Gordon L; Corbetta, Maurizio

2014-12-01

The relationship between spontaneous brain activity and behaviour following focal injury is not well understood. Here, we report a large-scale study of resting state functional connectivity MRI and spatial neglect following stroke in a large (n=84) heterogeneous sample of first-ever stroke patients (within 1-2 weeks). Spatial neglect, which is typically more severe after right than left hemisphere injury, includes deficits of spatial attention and motor actions contralateral to the lesion, and low general attention due to impaired vigilance/arousal. Patients underwent structural and resting state functional MRI scans, and spatial neglect was measured using the Posner spatial cueing task, and Mesulam and Behavioural Inattention Test cancellation tests. A principal component analysis of the behavioural tests revealed a main factor accounting for 34% of variance that captured three correlated behavioural deficits: visual neglect of the contralesional visual field, visuomotor neglect of the contralesional field, and low overall performance. In an independent sample (21 healthy subjects), we defined 10 resting state networks consisting of 169 brain regions: visual-fovea and visual-periphery, sensory-motor, auditory, dorsal attention, ventral attention, language, fronto-parietal control, cingulo-opercular control, and default mode. We correlated the neglect factor score with the strength of resting state functional connectivity within and across the 10 resting state networks. All damaged brain voxels were removed from the functional connectivity:behaviour correlational analysis. We found that the correlated behavioural deficits summarized by the factor score were associated with correlated multi-network patterns of abnormal functional connectivity involving large swaths of cortex. Specifically, dorsal attention and sensory-motor networks showed: (i) reduced interhemispheric functional connectivity; (ii) reduced anti-correlation with fronto-parietal and default mode networks in the right hemisphere; and (iii) increased intrahemispheric connectivity with the basal ganglia. These patterns of functional connectivity:behaviour correlations were stronger in patients with right- as compared to left-hemisphere damage and were independent of lesion volume. Our findings identify large-scale changes in resting state network interactions that are a physiological signature of spatial neglect and may relate to its right hemisphere lateralization. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Growth in literacy, cognition, and working memory in English language learners.

PubMed

Lee Swanson, H; Orosco, Michael J; Lussier, Catherine M

2015-04-01

This cohort sequential study explored the components of working memory that underlie English reading and language acquisition in elementary school children whose first language is Spanish. To this end, children (N=410) in Grades 1, 2, and 3 at Wave 1 were administered a battery of cognitive (short-term memory [STM], working memory [WM], rapid naming, phonological processing, and random letter and number generation), vocabulary, and reading measures in both Spanish and English. These same measures were administered 1 and 2 years later. The results showed that (a) a three-factor structure (phonological STM, visual-spatial WM, and verbal WM) captured the data within both language systems, (b) growth in both the executive and STM storage components was uniquely related to growth in second language (L2) reading and language acquisition, and (c) the contribution of growth in the executive component of WM to growth in L2 processing was independent of growth in storage, phonological knowledge, inhibition, and rapid naming speed. The results suggested that growth in the phonological storage system does not supersede growth of the executive component of WM as a major contributor to growth in children's L2 reading and language. Copyright © 2015 Elsevier Inc. All rights reserved.

Conformational states and folding pathways of peptides revealed by principal-independent component analyses.

PubMed

Nguyen, Phuong H

2007-05-15

Principal component analysis is a powerful method for projecting multidimensional conformational space of peptides or proteins onto lower dimensional subspaces in which the main conformations are present, making it easier to reveal the structures of molecules from e.g. molecular dynamics simulation trajectories. However, the identification of all conformational states is still difficult if the subspaces consist of more than two dimensions. This is mainly due to the fact that the principal components are not independent with each other, and states in the subspaces cannot be visualized. In this work, we propose a simple and fast scheme that allows one to obtain all conformational states in the subspaces. The basic idea is that instead of directly identifying the states in the subspace spanned by principal components, we first transform this subspace into another subspace formed by components that are independent of one other. These independent components are obtained from the principal components by employing the independent component analysis method. Because of independence between components, all states in this new subspace are defined as all possible combinations of the states obtained from each single independent component. This makes the conformational analysis much simpler. We test the performance of the method by analyzing the conformations of the glycine tripeptide and the alanine hexapeptide. The analyses show that our method is simple and quickly reveal all conformational states in the subspaces. The folding pathways between the identified states of the alanine hexapeptide are analyzed and discussed in some detail. 2007 Wiley-Liss, Inc.

Temporal dissociation between the focal and orientation components of spatial attention in central and peripheral vision.

PubMed

Albonico, Andrea; Malaspina, Manuela; Bricolo, Emanuela; Martelli, Marialuisa; Daini, Roberta

2016-11-01

Selective attention, i.e. the ability to concentrate one's limited processing resources on one aspect of the environment, is a multifaceted concept that includes different processes like spatial attention and its subcomponents of orienting and focusing. Several studies, indeed, have shown that visual tasks performance is positively influenced not only by attracting attention to the target location (orientation component), but also by the adjustment of the size of the attentional window according to task demands (focal component). Nevertheless, the relative weight of the two components in central and peripheral vision has never been studied. We conducted two experiments to explore whether different components of spatial attention have different effects in central and peripheral vision. In order to do so, participants underwent either a detection (Experiment 1) or a discrimination (Experiment 2) task where different types of cues elicited different components of spatial attention: a red dot, a small square and a big square (an optimal stimulus for the orientation component, an optimal and a sub-optimal stimulus for the focal component respectively). Response times and cue-size effects indicated a stronger effect of the small square or of the dot in different conditions, suggesting the existence of a dissociation in terms of mechanisms between the focal and the orientation components of spatial attention. Specifically, we found that the orientation component was stronger in periphery, while the focal component was noticeable only in central vision and characterized by an exogenous nature. Copyright © 2016 Elsevier B.V. All rights reserved.

Independent component processes underlying emotions during natural music listening

PubMed Central

Zollinger, Nina; Elmer, Stefan; Jäncke, Lutz

2016-01-01

The aim of this study was to investigate the brain processes underlying emotions during natural music listening. To address this, we recorded high-density electroencephalography (EEG) from 22 subjects while presenting a set of individually matched whole musical excerpts varying in valence and arousal. Independent component analysis was applied to decompose the EEG data into functionally distinct brain processes. A k-means cluster analysis calculated on the basis of a combination of spatial (scalp topography and dipole location mapped onto the Montreal Neurological Institute brain template) and functional (spectra) characteristics revealed 10 clusters referring to brain areas typically involved in music and emotion processing, namely in the proximity of thalamic-limbic and orbitofrontal regions as well as at frontal, fronto-parietal, parietal, parieto-occipital, temporo-occipital and occipital areas. This analysis revealed that arousal was associated with a suppression of power in the alpha frequency range. On the other hand, valence was associated with an increase in theta frequency power in response to excerpts inducing happiness compared to sadness. These findings are partly compatible with the model proposed by Heller, arguing that the frontal lobe is involved in modulating valenced experiences (the left frontal hemisphere for positive emotions) whereas the right parieto-temporal region contributes to the emotional arousal. PMID:27217116

Modeling of intracerebral interictal epileptic discharges: Evidence for network interactions.

PubMed

Meesters, Stephan; Ossenblok, Pauly; Colon, Albert; Wagner, Louis; Schijns, Olaf; Boon, Paul; Florack, Luc; Fuster, Andrea

2018-06-01

The interictal epileptic discharges (IEDs) occurring in stereotactic EEG (SEEG) recordings are in general abundant compared to ictal discharges, but difficult to interpret due to complex underlying network interactions. A framework is developed to model these network interactions. To identify the synchronized neuronal activity underlying the IEDs, the variation in correlation over time of the SEEG signals is related to the occurrence of IEDs using the general linear model. The interdependency is assessed of the brain areas that reflect highly synchronized neural activity by applying independent component analysis, followed by cluster analysis of the spatial distributions of the independent components. The spatiotemporal interactions of the spike clusters reveal the leading or lagging of brain areas. The analysis framework was evaluated for five successfully operated patients, showing that the spike cluster that was related to the MRI-visible brain lesions coincided with the seizure onset zone. The additional value of the framework was demonstrated for two more patients, who were MRI-negative and for whom surgery was not successful. A network approach is promising in case of complex epilepsies. Analysis of IEDs is considered a valuable addition to routine review of SEEG recordings, with the potential to increase the success rate of epilepsy surgery. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Variability of ICA decomposition may impact EEG signals when used to remove eyeblink artifacts

PubMed Central

PONTIFEX, MATTHEW B.; GWIZDALA, KATHRYN L.; PARKS, ANDREW C.; BILLINGER, MARTIN; BRUNNER, CLEMENS

2017-01-01

Despite the growing use of independent component analysis (ICA) algorithms for isolating and removing eyeblink-related activity from EEG data, we have limited understanding of how variability associated with ICA uncertainty may be influencing the reconstructed EEG signal after removing the eyeblink artifact components. To characterize the magnitude of this ICA uncertainty and to understand the extent to which it may influence findings within ERP and EEG investigations, ICA decompositions of EEG data from 32 college-aged young adults were repeated 30 times for three popular ICA algorithms. Following each decomposition, eyeblink components were identified and removed. The remaining components were back-projected, and the resulting clean EEG data were further used to analyze ERPs. Findings revealed that ICA uncertainty results in variation in P3 amplitude as well as variation across all EEG sampling points, but differs across ICA algorithms as a function of the spatial location of the EEG channel. This investigation highlights the potential of ICA uncertainty to introduce additional sources of variance when the data are back-projected without artifact components. Careful selection of ICA algorithms and parameters can reduce the extent to which ICA uncertainty may introduce an additional source of variance within ERP/EEG studies. PMID:28026876

The X-ray Mirrors for the Astro-E2 Mission

NASA Technical Reports Server (NTRS)

Chan, Kai-Wing; Soong, Yang; Serlemitsos, Peter J.; White, Nicholas E. (Technical Monitor)

2002-01-01

The X-Ray telescopes (XRT) for the US/Japan collaborative mission Astro-E2 will be of the same basic design as those built for the original Astro-E mission which failed to reach orbit in Feb. 2000. The NASA/GSFC X-ray Astrophysics Branch will again provide the five lightweight, broad-band mirrors for the mission. X-ray calibrations of the mirrors delivered for the original Astro-E instrument showed spatial resolutions characterized by Half-Power Diameters (HPD) in the range of 1.8 - 2.2 minutes of arc, essentially independent of photon energy in the soft X-ray band. For the mission Astro-E2, both funding constraints and management decisions drastically limit any design modifications, so reflector fabrication and assembly procedures have remained largely unchanged. Nevertheless, in view of the importance in scientific return of attaining even a modest improvement in the spatial resolution of these mirrors, we have carefully considered the various sources of spatial error and, whenever possible, incorporated promising modifications. In this paper, we discuss our current understanding of the various error components as well as the small changes we have been able to implement.

3D tensor-based blind multispectral image decomposition for tumor demarcation

NASA Astrophysics Data System (ADS)

Kopriva, Ivica; Peršin, Antun

2010-03-01

Blind decomposition of multi-spectral fluorescent image for tumor demarcation is formulated exploiting tensorial structure of the image. First contribution of the paper is identification of the matrix of spectral responses and 3D tensor of spatial distributions of the materials present in the image from Tucker3 or PARAFAC models of 3D image tensor. Second contribution of the paper is clustering based estimation of the number of the materials present in the image as well as matrix of their spectral profiles. 3D tensor of the spatial distributions of the materials is recovered through 3-mode multiplication of the multi-spectral image tensor and inverse of the matrix of spectral profiles. Tensor representation of the multi-spectral image preserves its local spatial structure that is lost, due to vectorization process, when matrix factorization-based decomposition methods (such as non-negative matrix factorization and independent component analysis) are used. Superior performance of the tensor-based image decomposition over matrix factorization-based decompositions is demonstrated on experimental red-green-blue (RGB) image with known ground truth as well as on RGB fluorescent images of the skin tumor (basal cell carcinoma).

Multivariate analysis applied to the study of spatial distributions found in drug-eluting stent coatings by confocal Raman microscopy.

PubMed

Balss, Karin M; Long, Frederick H; Veselov, Vladimir; Orana, Argjenta; Akerman-Revis, Eugena; Papandreou, George; Maryanoff, Cynthia A

2008-07-01

Multivariate data analysis was applied to confocal Raman measurements on stents coated with the polymers and drug used in the CYPHER Sirolimus-eluting Coronary Stents. Partial least-squares (PLS) regression was used to establish three independent calibration curves for the coating constituents: sirolimus, poly(n-butyl methacrylate) [PBMA], and poly(ethylene-co-vinyl acetate) [PEVA]. The PLS calibrations were based on average spectra generated from each spatial location profiled. The PLS models were tested on six unknown stent samples to assess accuracy and precision. The wt % difference between PLS predictions and laboratory assay values for sirolimus was less than 1 wt % for the composite of the six unknowns, while the polymer models were estimated to be less than 0.5 wt % difference for the combined samples. The linearity and specificity of the three PLS models were also demonstrated with the three PLS models. In contrast to earlier univariate models, the PLS models achieved mass balance with better accuracy. This analysis was extended to evaluate the spatial distribution of the three constituents. Quantitative bitmap images of drug-eluting stent coatings are presented for the first time to assess the local distribution of components.

Including foreshocks and aftershocks in time-independent probabilistic seismic hazard analyses

USGS Publications Warehouse

Boyd, Oliver S.

2012-01-01

Time‐independent probabilistic seismic‐hazard analysis treats each source as being temporally and spatially independent; hence foreshocks and aftershocks, which are both spatially and temporally dependent on the mainshock, are removed from earthquake catalogs. Yet, intuitively, these earthquakes should be considered part of the seismic hazard, capable of producing damaging ground motions. In this study, I consider the mainshock and its dependents as a time‐independent cluster, each cluster being temporally and spatially independent from any other. The cluster has a recurrence time of the mainshock; and, by considering the earthquakes in the cluster as a union of events, dependent events have an opportunity to contribute to seismic ground motions and hazard. Based on the methods of the U.S. Geological Survey for a high‐hazard site, the inclusion of dependent events causes ground motions that are exceeded at probability levels of engineering interest to increase by about 10% but could be as high as 20% if variations in aftershock productivity can be accounted for reliably.

Adaptive Numerical Algorithms in Space Weather Modeling

NASA Technical Reports Server (NTRS)

Toth, Gabor; vanderHolst, Bart; Sokolov, Igor V.; DeZeeuw, Darren; Gombosi, Tamas I.; Fang, Fang; Manchester, Ward B.; Meng, Xing; Nakib, Dalal; Powell, Kenneth G.;

The EPIC-MOS Particle-Induced Background Spectra

NASA Technical Reports Server (NTRS)

Kuntz, K. D.; Sowden, S. L.

2007-01-01

In order to analyse diffuse emission that fills the field of view, one must accurately characterize the instrumental backgrounds. For the XMM-Newton EPIC instrument these backgrounds include a temporally variable "quiescent" component. as well as the strongly variable soft proton contamination. We have characterized the spectral and spatial response of the EPIC detectors to these background components and have developed tools to remove these backgrounds from observations. The "quiescent" component was characterized using a combination of the filter-wheel-closed data and a database of unexposed-region data. The soft proton contamination was characterized by differencing images and spectra taken during flared and flare-free intervals. After application of our modeled backgrounds, the differences between independent observations of the same region of "blank sky" are consistent with the statistical uncertainties except when there is clear spectral evidence of solar wind charge exchange emission. Using a large sample of blank sky data, we show that strong magnetospheric SWCX emission requires elevated solar wind fluxes; observations through the densest part of the magnetosheath are not necessarily strongly contaminated with SWCX emission.

Level-1C Product from AIRS: Principal Component Filtering

NASA Technical Reports Server (NTRS)

Manning, Evan M.; Jiang, Yibo; Aumann, Hartmut H.; Elliott, Denis A.; Hannon, Scott

2012-01-01

The Atmospheric Infrared Sounder (AIRS), launched on the EOS Aqua spacecraft on May 4, 2002, is a grating spectrometer with 2378 channels in the range 3.7 to 15.4 microns. In a grating spectrometer each individual radiance measurement is largely independent of all others. Most measurements are extremely accurate and have very low noise levels. However, some channels exhibit high noise levels or other anomalous behavior, complicating applications needing radiances throughout a band, such as cross-calibration with other instruments and regression retrieval algorithms. The AIRS Level-1C product is similar to Level-1B but with instrument artifacts removed. This paper focuses on the "cleaning" portion of Level-1C, which identifies bad radiance values within spectra and produces substitute radiances using redundant information from other channels. The substitution is done in two passes, first with a simple combination of values from neighboring channels, then with principal components. After results of the substitution are shown, differences between principal component reconstructed values and observed radiances are used to investigate detailed noise characteristics and spatial misalignment in other channels.

Age-related differences in early novelty processing: Using PCA to parse the overlapping anterior P2 and N2 components

PubMed Central

Daffner, Kirk R.; Alperin, Brittany R.; Mott, Katherine K.; Tusch, Erich; Holcomb, Phillip J.

2015-01-01

Previous work demonstrated age-associated increases in the anterior P2 and age-related decreases in the anterior N2 in response to novel stimuli. Principal component analysis (PCA) was used to determine if the inverse relationship between these components was due to their temporal and spatial overlap. PCA revealed an early anterior P2, sensitive to task relevance, and a late anterior P2, responsive to novelty, both exhibiting age-related amplitude increases. A PCA factor representing the anterior N2, sensitive to novelty, exhibited age-related amplitude decreases. The late P2 and N2 to novels inversely correlated. Larger late P2 amplitude to novels was associated with better behavioral performance. Age-related differences in the anterior P2 and N2 to novel stimuli likely represent age-associated changes in independent cognitive operations. Enhanced anterior P2 activity (indexing augmentation in motivational salience) may be a compensatory mechanism for diminished anterior N2 activity (indexing reduced ability of older adults to process ambiguous representations). PMID:25596483

Robust demarcation of basal cell carcinoma by dependent component analysis-based segmentation of multi-spectral fluorescence images.

PubMed

Kopriva, Ivica; Persin, Antun; Puizina-Ivić, Neira; Mirić, Lina

2010-07-02

This study was designed to demonstrate robust performance of the novel dependent component analysis (DCA)-based approach to demarcation of the basal cell carcinoma (BCC) through unsupervised decomposition of the red-green-blue (RGB) fluorescent image of the BCC. Robustness to intensity fluctuation is due to the scale invariance property of DCA algorithms, which exploit spectral and spatial diversities between the BCC and the surrounding tissue. Used filtering-based DCA approach represents an extension of the independent component analysis (ICA) and is necessary in order to account for statistical dependence that is induced by spectral similarity between the BCC and surrounding tissue. This generates weak edges what represents a challenge for other segmentation methods as well. By comparative performance analysis with state-of-the-art image segmentation methods such as active contours (level set), K-means clustering, non-negative matrix factorization, ICA and ratio imaging we experimentally demonstrate good performance of DCA-based BCC demarcation in two demanding scenarios where intensity of the fluorescent image has been varied almost two orders of magnitude. Copyright 2010 Elsevier B.V. All rights reserved.

Impact imaging of aircraft composite structure based on a model-independent spatial-wavenumber filter.

PubMed

Qiu, Lei; Liu, Bin; Yuan, Shenfang; Su, Zhongqing

2016-01-01

The spatial-wavenumber filtering technique is an effective approach to distinguish the propagating direction and wave mode of Lamb wave in spatial-wavenumber domain. Therefore, it has been gradually studied for damage evaluation in recent years. But for on-line impact monitoring in practical application, the main problem is how to realize the spatial-wavenumber filtering of impact signal when the wavenumber of high spatial resolution cannot be measured or the accurate wavenumber curve cannot be modeled. In this paper, a new model-independent spatial-wavenumber filter based impact imaging method is proposed. In this method, a 2D cross-shaped array constructed by two linear piezoelectric (PZT) sensor arrays is used to acquire impact signal on-line. The continuous complex Shannon wavelet transform is adopted to extract the frequency narrowband signals from the frequency wideband impact response signals of the PZT sensors. A model-independent spatial-wavenumber filter is designed based on the spatial-wavenumber filtering technique. Based on the designed filter, a wavenumber searching and best match mechanism is proposed to implement the spatial-wavenumber filtering of the frequency narrowband signals without modeling, which can be used to obtain a wavenumber-time image of the impact relative to a linear PZT sensor array. By using the two wavenumber-time images of the 2D cross-shaped array, the impact direction can be estimated without blind angle. The impact distance relative to the 2D cross-shaped array can be calculated by using the difference of time-of-flight between the frequency narrowband signals of two different central frequencies and the corresponding group velocities. The validations performed on a carbon fiber composite laminate plate and an aircraft composite oil tank show a good impact localization accuracy of the model-independent spatial-wavenumber filter based impact imaging method. Copyright © 2015 Elsevier B.V. All rights reserved.

Rapid top-down control over template-guided attention shifts to multiple objects.

PubMed

Grubert, Anna; Fahrenfort, Johannes; Olivers, Christian N L; Eimer, Martin

2017-02-01

Previous research has shown that when observers search for targets defined by a particular colour, attention can be directed rapidly and independently to two target objects that appear in close temporal proximity. We investigated how such rapid attention shifts are modulated by task instructions to selectively attend versus ignore one of these objects. Two search displays that both contained a colour-defined target and a distractor in a different colour were presented in rapid succession, with a stimulus onset asynchrony (SOA) of 100ms. In different blocks, participants were instructed to attend and respond to target-colour objects in the first display and to ignore these objects in the second display, or vice versa. N2pc components were measured to track the allocation of spatial attention to target-colour objects in these two displays. When participants responded to the second display, irrelevant target-colour objects in the first display still triggered N2pc components, demonstrating task-set contingent attentional capture while a feature-specific target template is active. Critically, when participants responded to the first display instead, no N2pc was elicited by target-colour items in the second display, indicating that they no longer rapidly captured attention. However, these items still elicited a longer-latency contralateral negativity (SPCN component), suggesting that attention was oriented towards template-matching objects in working memory. This dissociation between N2pc and SPCN components shows that rapid attentional capture and subsequent attentional selection processes within working memory can be independent. We suggest that early attentional orienting mechanisms can be inhibited when task-set matching objects are no longer task-relevant, and that this type of inhibitory control is a rapid but transient process. Copyright © 2016 Elsevier Inc. All rights reserved.

Violation of a Bell-like inequality in single-neutron interferometry.

PubMed

Hasegawa, Yuji; Loidl, Rudolf; Badurek, Gerald; Baron, Matthias; Rauch, Helmut

2003-09-04

Non-local correlations between spatially separated systems have been extensively discussed in the context of the Einstein, Podolsky and Rosen (EPR) paradox and Bell's inequalities. Many proposals and experiments designed to test hidden variable theories and the violation of Bell's inequalities have been reported; usually, these involve correlated photons, although recently an experiment was performed with (9)Be(+) ions. Nevertheless, it is of considerable interest to show that such correlations (arising from quantum mechanical entanglement) are not simply a peculiarity of photons. Here we measure correlations between two degrees of freedom (comprising spatial and spin components) of single neutrons; this removes the need for a source of entangled neutron pairs, which would present a considerable technical challenge. A Bell-like inequality is introduced to clarify the correlations that can arise between observables of otherwise independent degrees of freedom. We demonstrate the violation of this Bell-like inequality: our measured value is 2.051 +/- 0.019, clearly above the value of 2 predicted by classical hidden variable theories.

DNA concentration modulation on supported lipid bilayers switched by surface acoustic waves.

PubMed

Hennig, Martin; Wolff, Manuel; Neumann, Jürgen; Wixforth, Achim; Schneider, Matthias F; Rädler, Joachim O

2011-12-20

Spatially addressable arrays of molecules embedded in or anchored to supported lipid bilayers are important for on-chip screening and binding assays; however, methods to sort or accumulate components in a fluid membrane on demand are still limited. Here we apply in-plane surface acoustic shear waves (SAWs) to laterally accumulate double-stranded DNA segments electrostatically bound to a cationic supported lipid bilayer. The fluorescently labeled DNA segments are found to segregate into stripe patterns with a spatial frequency corresponding to the periodicity of the standing SAW wave (~10 μm). The DNA molecules are accumulated 10-fold in the regions of SAW antinodes. The superposition of two orthogonal sets of SAW sources creates checkerboard like arrays of DNA demonstrating the potential to generate arrayed fields dynamically. The pattern relaxation time of 0.58 s, which is independent of the segment length, indicates a sorting and relaxation mechanism dominated by lipid diffusion rather than DNA self-diffusion. © 2011 American Chemical Society

Independent component analysis for automatic note extraction from musical trills

NASA Astrophysics Data System (ADS)

Brown, Judith C.; Smaragdis, Paris

2004-05-01

The method of principal component analysis, which is based on second-order statistics (or linear independence), has long been used for redundancy reduction of audio data. The more recent technique of independent component analysis, enforcing much stricter statistical criteria based on higher-order statistical independence, is introduced and shown to be far superior in separating independent musical sources. This theory has been applied to piano trills and a database of trill rates was assembled from experiments with a computer-driven piano, recordings of a professional pianist, and commercially available compact disks. The method of independent component analysis has thus been shown to be an outstanding, effective means of automatically extracting interesting musical information from a sea of redundant data.

Students’ Spatial Performance: Cognitive Style and Sex Differences

NASA Astrophysics Data System (ADS)

Hanifah, U.; Juniati, D.; Siswono, T. Y. E.

2018-01-01

This study aims at describing the students’ spatial abilities based on cognitive styles and sex differences. Spatial abilities in this study include 5 components, namely spatial perception, spatial visualization, mental rotation, spatial relations, and spatial orientation. This research is descriptive research with qualitative approach. The subjects in this research were 4 students of junior high school, there were 1 male FI, 1 male FD, 1 female FI, and 1 female FI. The results showed that there are differences in spatial abilities of the four subjects that are on the components of spatial visualization, mental rotation, and spatial relations. The differences in spatial abilities were found in methods / strategies used by each subject to solve each component problem. The differences in cognitive styles and sex suggested different choice of strategies used to solve problems. The male students imagined the figures but female students needed the media to solve the problem. Besides sex, the cognitive style differences also have an effect on solving a problem. In addition, FI students were not affected by distracting information but FD students could be affected by distracting information. This research was expected to contribute knowledge and insight to the readers, especially for math teachers in terms of the spatial ability of the students so that they can optimize their students’ spatial ability.

Role of spatial inhomogenity in GPCR dimerisation predicted by receptor association-diffusion models

NASA Astrophysics Data System (ADS)

Deshpande, Sneha A.; Pawar, Aiswarya B.; Dighe, Anish; Athale, Chaitanya A.; Sengupta, Durba

2017-06-01

G protein-coupled receptor (GPCR) association is an emerging paradigm with far reaching implications in the regulation of signalling pathways and therapeutic interventions. Recent super resolution microscopy studies have revealed that receptor dimer steady state exhibits sub-second dynamics. In particular the GPCRs, muscarinic acetylcholine receptor M1 (M1MR) and formyl peptide receptor (FPR), have been demonstrated to exhibit a fast association/dissociation kinetics, independent of ligand binding. In this work, we have developed a spatial kinetic Monte Carlo model to investigate receptor homo-dimerisation at a single receptor resolution. Experimentally measured association/dissociation kinetic parameters and diffusion coefficients were used as inputs to the model. To test the effect of membrane spatial heterogeneity on the simulated steady state, simulations were compared to experimental statistics of dimerisation. In the simplest case the receptors are assumed to be diffusing in a spatially homogeneous environment, while spatial heterogeneity is modelled to result from crowding, membrane micro-domains and cytoskeletal compartmentalisation or ‘corrals’. We show that a simple association-diffusion model is sufficient to reproduce M1MR association statistics, but fails to reproduce FPR statistics despite comparable kinetic constants. A parameter sensitivity analysis is required to reproduce the association statistics of FPR. The model reveals the complex interplay between cytoskeletal components and their influence on receptor association kinetics within the features of the membrane landscape. These results constitute an important step towards understanding the factors modulating GPCR organisation.

Perception of global image contrast involves transparent spatial filtering and the integration and suppression of local contrasts (not RMS contrast)

PubMed Central

2017-01-01

When adjusting the contrast setting on a television set, we experience a perceptual change in the global image contrast. But how is that statistic computed? We addressed this using a contrast-matching task for checkerboard configurations of micro-patterns in which the contrasts and spatial spreads of two interdigitated components were controlled independently. When the patterns differed greatly in contrast, the higher contrast determined the perceived global contrast. Crucially, however, low contrast additions of one pattern to intermediate contrasts of the other caused a paradoxical reduction in the perceived global contrast. None of the following metrics/models predicted this: max, linear sum, average, energy, root mean squared (RMS), Legge and Foley. However, a nonlinear gain control model, derived from contrast detection and discrimination experiments, incorporating wide-field summation and suppression, did predict the results with no free parameters, but only when spatial filtering was removed. We conclude that our model describes fundamental processes in human contrast vision (the pattern of results was the same for expert and naive observers), but that above threshold—when contrast pedestals are clearly visible—vision's spatial filtering characteristics become transparent, tending towards those of a delta function prior to spatial summation. The global contrast statistic from our model is as easily derived as the RMS contrast of an image, and since it more closely relates to human perception, we suggest it be used as an image contrast metric in practical applications. PMID:28989735

Spatial synchrony of malaria outbreaks in a highland region of Ethiopia.

PubMed

Wimberly, Michael C; Midekisa, Alemayehu; Semuniguse, Paulos; Teka, Hiwot; Henebry, Geoffrey M; Chuang, Ting-Wu; Senay, Gabriel B

2012-10-01

To understand the drivers and consequences of malaria in epidemic-prone regions, it is important to know whether epidemics emerge independently in different areas as a consequence of local contingencies, or whether they are synchronised across larger regions as a result of climatic fluctuations and other broad-scale drivers. To address this question, we collected historical malaria surveillance data for the Amhara region of Ethiopia and analysed them to assess the consistency of various indicators of malaria risk and determine the dominant spatial and temporal patterns of malaria within the region. We collected data from a total of 49 districts from 1999-2010. Data availability was better for more recent years and more data were available for clinically diagnosed outpatient malaria cases than confirmed malaria cases. Temporal patterns of outpatient malaria case counts were correlated with the proportion of outpatients diagnosed with malaria and confirmed malaria case counts. The proportion of outpatients diagnosed with malaria was spatially clustered, and these cluster locations were generally consistent from year to year. Outpatient malaria cases exhibited spatial synchrony at distances up to 300 km, supporting the hypothesis that regional climatic variability is an important driver of epidemics. Our results suggest that decomposing malaria risk into separate spatial and temporal components may be an effective strategy for modelling and forecasting malaria risk across large areas. They also emphasise both the value and limitations of working with historical surveillance datasets and highlight the importance of enhancing existing surveillance efforts. © 2012 Blackwell Publishing Ltd.

So Close to a Deal: Spatial-Distance Cues Influence Economic Decision-Making in a Social Context.

PubMed

Fatfouta, Ramzi; Schulreich, Stefan; Meshi, Dar; Heekeren, Hauke

2015-01-01

Social distance (i.e., the degree of closeness to another person) affects the way humans perceive and respond to fairness during financial negotiations. Feeling close to someone enhances the acceptance of monetary offers. Here, we explored whether this effect also extends to the spatial domain. Specifically, using an iterated version of the Ultimatum Game in a within-subject design, we investigated whether different visual spatial distance-cues result in different rates of acceptance of otherwise identical monetary offers. Study 1 found that participants accepted significantly more offers when they were cued with spatial closeness than when they were cued with spatial distance. Study 2 replicated this effect using identical procedures but different spatial-distance cues in an independent sample. Importantly, our results could not be explained by feelings of social closeness. Our results demonstrate that mere perceptions of spatial closeness produce analogous-but independent-effects to those of social closeness.

Ki-67 acts as a biological surfactant to disperse mitotic chromosomes

PubMed Central

Cuylen, Sara; Blaukopf, Claudia; Politi, Antonio Z.; Müller-Reichert, Thomas; Neumann, Beate; Poser, Ina; Ellenberg, Jan; Hyman, Anthony A.; Gerlich, Daniel W.

2016-01-01

Summary Eukaryotic genomes are partitioned into chromosomes, which during mitosis form compact and spatially well-separated mechanical bodies1–3.This enables chromosomes to move independently of each other for segregation of precisely one copy of the genome to each of the nascent daughter cells. Despite insights into the spatial organization of mitotic chromosomes4 and the discovery of proteins at the chromosome surface3,5,6, the molecular and biophysical basis of mitotic chromosome individuality have remained unclear. We report that Ki-67, a component of the mitotic chromosome periphery, prevents chromosomes from collapsing into a single chromatin mass after nuclear envelope disassembly, thus enabling independent chromosome motility and efficient interactions with the mitotic spindle. The chromosome separation function of Ki-67 is not confined within a specific protein domain but correlates with size and net charge of truncation mutants that apparently lack secondary structure. This suggests that Ki-67 forms a steric and electrical barrier, similar to surface-active agents (surfactants) that disperse particles or phase-separated liquid droplets in solvents. Fluorescence correlation spectroscopy showed a high surface density of Ki-67 and dual-color labeling of both protein termini revealed an extended molecular conformation, indicating brush-like arrangements that are characteristic for polymeric surfactants. Our study thus elucidates a biomechanical role of the mitotic chromosome periphery and suggests that natural proteins can function as surfactants in intracellular compartmentalization. PMID:27362226

Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data.

PubMed

Glasser, Matthew F; Coalson, Timothy S; Bijsterbosch, Janine D; Harrison, Samuel J; Harms, Michael P; Anticevic, Alan; Van Essen, David C; Smith, Stephen M

2018-06-02

Temporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal "noise" from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, unable to separate spatially widespread "global" structured noise from fMRI data (e.g., blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary-to do or not to do global signal regression-given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task-based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the global positive biases caused by global physiological noise without inducing the network-specific negative biases of global signal regression. We believe that temporal ICA cleanup provides a "best of both worlds" solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data. Copyright © 2018 Elsevier Inc. All rights reserved.

Spatial-simultaneous and spatial-sequential working memory in individuals with Down syndrome: the effect of configuration.

PubMed

Carretti, Barbara; Lanfranchi, Silvia; Mammarella, Irene C

2013-01-01

Earlier research showed that visuospatial working memory (VSWM) is better preserved in Down syndrome (DS) than verbal WM. Some differences emerged, however, when VSWM performance was broken down into its various components, and more recent studies revealed that the spatial-simultaneous component of VSWM is more impaired than the spatial-sequential one. The difficulty of managing more than one item at a time is also evident when the information to be recalled is structured. To further analyze this issue, we investigated the advantage of material being structured in spatial-simultaneous and spatial-sequential tasks by comparing the performance of a group of individuals with DS and a group of typically-developing children matched for mental age. Both groups were presented with VSWM tasks in which both the presentation format (simultaneous vs. sequential) and the type of configuration (pattern vs. random) were manipulated. Findings indicated that individuals with DS took less advantage of the pattern configuration in the spatial-simultaneous task than TD children; in contrast, the two groups' performance did not differ in the pattern configuration of the spatial-sequential task. Taken together, these results confirmed difficulties relating to the spatial-simultaneous component of VSWM in individuals with DS, supporting the importance of distinguishing between different components within this system. The findings are discussed in terms of factors influencing this specific deficit. Copyright © 2012 Elsevier Ltd. All rights reserved.

Using Common Spatial Distributions of Atoms to Relate Functionally Divergent Influenza Virus N10 and N11 Protein Structures to Functionally Characterized Neuraminidase Structures, Toxin Cell Entry Domains, and Non-Influenza Virus Cell Entry Domains

PubMed Central

Weininger, Arthur; Weininger, Susan

2015-01-01

The ability to identify the functional correlates of structural and sequence variation in proteins is a critical capability. We related structures of influenza A N10 and N11 proteins that have no established function to structures of proteins with known function by identifying spatially conserved atoms. We identified atoms with common distributed spatial occupancy in PDB structures of N10 protein, N11 protein, an influenza A neuraminidase, an influenza B neuraminidase, and a bacterial neuraminidase. By superposing these spatially conserved atoms, we aligned the structures and associated molecules. We report spatially and sequence invariant residues in the aligned structures. Spatially invariant residues in the N6 and influenza B neuraminidase active sites were found in previously unidentified spatially equivalent sites in the N10 and N11 proteins. We found the corresponding secondary and tertiary structures of the aligned proteins to be largely identical despite significant sequence divergence. We found structural precedent in known non-neuraminidase structures for residues exhibiting structural and sequence divergence in the aligned structures. In N10 protein, we identified staphylococcal enterotoxin I-like domains. In N11 protein, we identified hepatitis E E2S-like domains, SARS spike protein-like domains, and toxin components shared by alpha-bungarotoxin, staphylococcal enterotoxin I, anthrax lethal factor, clostridium botulinum neurotoxin, and clostridium tetanus toxin. The presence of active site components common to the N6, influenza B, and S. pneumoniae neuraminidases in the N10 and N11 proteins, combined with the absence of apparent neuraminidase function, suggests that the role of neuraminidases in H17N10 and H18N11 emerging influenza A viruses may have changed. The presentation of E2S-like, SARS spike protein-like, or toxin-like domains by the N10 and N11 proteins in these emerging viruses may indicate that H17N10 and H18N11 sialidase-facilitated cell entry has been supplemented or replaced by sialidase-independent receptor binding to an expanded cell population that may include neurons and T-cells. PMID:25706124

Wild boar mapping using population-density statistics: From polygons to high resolution raster maps.

PubMed

Pittiglio, Claudia; Khomenko, Sergei; Beltran-Alcrudo, Daniel

2018-01-01

The wild boar is an important crop raider as well as a reservoir and agent of spread of swine diseases. Due to increasing densities and expanding ranges worldwide, the related economic losses in livestock and agricultural sectors are significant and on the rise. Its management and control would strongly benefit from accurate and detailed spatial information on species distribution and abundance, which are often available only for small areas. Data are commonly available at aggregated administrative units with little or no information about the distribution of the species within the unit. In this paper, a four-step geostatistical downscaling approach is presented and used to disaggregate wild boar population density statistics from administrative units of different shape and size (polygons) to 5 km resolution raster maps by incorporating auxiliary fine scale environmental variables. 1) First a stratification method was used to define homogeneous bioclimatic regions for the analysis; 2) Under a geostatistical framework, the wild boar densities at administrative units, i.e. subnational areas, were decomposed into trend and residual components for each bioclimatic region. Quantitative relationships between wild boar data and environmental variables were estimated through multiple regression and used to derive trend components at 5 km spatial resolution. Next, the residual components (i.e., the differences between the trend components and the original wild boar data at administrative units) were downscaled at 5 km resolution using area-to-point kriging. The trend and residual components obtained at 5 km resolution were finally added to generate fine scale wild boar estimates for each bioclimatic region. 3) These maps were then mosaicked to produce a final output map of predicted wild boar densities across most of Eurasia. 4) Model accuracy was assessed at each different step using input as well as independent data. We discuss advantages and limits of the method and its potential application in animal health.

Wild boar mapping using population-density statistics: From polygons to high resolution raster maps

PubMed Central

Pittiglio, Claudia; Khomenko, Sergei

2018-01-01

The wild boar is an important crop raider as well as a reservoir and agent of spread of swine diseases. Due to increasing densities and expanding ranges worldwide, the related economic losses in livestock and agricultural sectors are significant and on the rise. Its management and control would strongly benefit from accurate and detailed spatial information on species distribution and abundance, which are often available only for small areas. Data are commonly available at aggregated administrative units with little or no information about the distribution of the species within the unit. In this paper, a four-step geostatistical downscaling approach is presented and used to disaggregate wild boar population density statistics from administrative units of different shape and size (polygons) to 5 km resolution raster maps by incorporating auxiliary fine scale environmental variables. 1) First a stratification method was used to define homogeneous bioclimatic regions for the analysis; 2) Under a geostatistical framework, the wild boar densities at administrative units, i.e. subnational areas, were decomposed into trend and residual components for each bioclimatic region. Quantitative relationships between wild boar data and environmental variables were estimated through multiple regression and used to derive trend components at 5 km spatial resolution. Next, the residual components (i.e., the differences between the trend components and the original wild boar data at administrative units) were downscaled at 5 km resolution using area-to-point kriging. The trend and residual components obtained at 5 km resolution were finally added to generate fine scale wild boar estimates for each bioclimatic region. 3) These maps were then mosaicked to produce a final output map of predicted wild boar densities across most of Eurasia. 4) Model accuracy was assessed at each different step using input as well as independent data. We discuss advantages and limits of the method and its potential application in animal health. PMID:29768413

Active and Passive Spatial Learning in Human Navigation: Acquisition of Survey Knowledge

ERIC Educational Resources Information Center

Chrastil, Elizabeth R.; Warren, William H.

2013-01-01

It seems intuitively obvious that active exploration of a new environment would lead to better spatial learning than would passive visual exposure. It is unclear, however, which components of active learning contribute to spatial knowledge, and previous literature is decidedly mixed. This experiment tests the contributions of 4 components to…

Components of Spatial Thinking: Evidence from a Spatial Thinking Ability Test

ERIC Educational Resources Information Center

Lee, Jongwon; Bednarz, Robert

2012-01-01

This article introduces the development and validation of the spatial thinking ability test (STAT). The STAT consists of sixteen multiple-choice questions of eight types. The STAT was validated by administering it to a sample of 532 junior high, high school, and university students. Factor analysis using principal components extraction was applied…

Joint modelling of potentially avoidable hospitalisation for five diseases accounting for spatiotemporal effects: A case study in New South Wales, Australia.

PubMed

Baker, Jannah; White, Nicole; Mengersen, Kerrie; Rolfe, Margaret; Morgan, Geoffrey G

2017-01-01

Three variant formulations of a spatiotemporal shared component model are proposed that allow examination of changes in shared underlying factors over time. Models are evaluated within the context of a case study examining hospitalisation rates for five chronic diseases for residents of a regional area in New South Wales: type II diabetes mellitus (DMII), chronic obstructive pulmonary disease (COPD), coronary arterial disease (CAD), hypertension (HT) and congestive heart failure (CHF) between 2001-2006. These represent ambulatory care sensitive (ACS) conditions, often used as a proxy for avoidable hospitalisations. Using a selected model, the effects of socio-economic status (SES) as a shared component are estimated and temporal patterns in the influence of the residual shared spatial component are examined. Choice of model depends upon the application. In the featured application, a model allowing for changing influence of the shared spatial component over time was found to have the best fit and was selected for further analyses. Hospitalisation rates were found to be increasing for COPD and DMII, decreasing for CHF and stable for CAD and HT. SES was substantively associated with hospitalisation rates, with differing degrees of influence for each disease. In general, most of the spatial variation in hospitalisation rates was explained by disease-specific spatial components, followed by the residual shared spatial component. Appropriate selection of a joint disease model allows for the examination of temporal patterns of disease outcomes and shared underlying spatial factors, and distinction between different shared spatial factors.

The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times

PubMed Central

Bloom, A. Anthony; Exbrayat, Jean-François; van der Velde, Ivar R.; Feng, Liang; Williams, Mathew

2016-01-01

The terrestrial carbon cycle is currently the least constrained component of the global carbon budget. Large uncertainties stem from a poor understanding of plant carbon allocation, stocks, residence times, and carbon use efficiency. Imposing observational constraints on the terrestrial carbon cycle and its processes is, therefore, necessary to better understand its current state and predict its future state. We combine a diagnostic ecosystem carbon model with satellite observations of leaf area and biomass (where and when available) and soil carbon data to retrieve the first global estimates, to our knowledge, of carbon cycle state and process variables at a 1° × 1° resolution; retrieved variables are independent from the plant functional type and steady-state paradigms. Our results reveal global emergent relationships in the spatial distribution of key carbon cycle states and processes. Live biomass and dead organic carbon residence times exhibit contrasting spatial features (r = 0.3). Allocation to structural carbon is highest in the wet tropics (85–88%) in contrast to higher latitudes (73–82%), where allocation shifts toward photosynthetic carbon. Carbon use efficiency is lowest (0.42–0.44) in the wet tropics. We find an emergent global correlation between retrievals of leaf mass per leaf area and leaf lifespan (r = 0.64–0.80) that matches independent trait studies. We show that conventional land cover types cannot adequately describe the spatial variability of key carbon states and processes (multiple correlation median = 0.41). This mismatch has strong implications for the prediction of terrestrial carbon dynamics, which are currently based on globally applied parameters linked to land cover or plant functional types. PMID:26787856

Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment

NASA Astrophysics Data System (ADS)

Widmann, Martin; Bedia, Joaquin; Gutiérrez, Jose Manuel; Maraun, Douglas; Huth, Radan; Fischer, Andreas; Keller, Denise; Hertig, Elke; Vrac, Mathieu; Wibig, Joanna; Pagé, Christian; Cardoso, Rita M.; Soares, Pedro MM; Bosshard, Thomas; Casado, Maria Jesus; Ramos, Petra

2016-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. Within VALUE a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods has been developed. In the first validation experiment the downscaling methods are validated in a setup with perfect predictors taken from the ERA-interim reanalysis for the period 1997 - 2008. This allows to investigate the isolated skill of downscaling methods without further error contributions from the large-scale predictors. One aspect of the validation is the representation of spatial variability. As part of the VALUE validation we have compared various properties of the spatial variability of downscaled daily temperature and precipitation with the corresponding properties in observations. We have used two test validation datasets, one European-wide set of 86 stations, and one higher-density network of 50 stations in Germany. Here we present results based on three approaches, namely the analysis of i.) correlation matrices, ii.) pairwise joint threshold exceedances, and iii.) regions of similar variability. We summarise the information contained in correlation matrices by calculating the dependence of the correlations on distance and deriving decorrelation lengths, as well as by determining the independent degrees of freedom. Probabilities for joint threshold exceedances and (where appropriate) non-exceedances are calculated for various user-relevant thresholds related for instance to extreme precipitation or frost and heat days. The dependence of these probabilities on distance is again characterised by calculating typical length scales that separate dependent from independent exceedances. Regionalisation is based on rotated Principal Component Analysis. The results indicate which downscaling methods are preferable if the dependency of variability at different locations is relevant for the user.

The impact of seasonal signals on spatio-temporal filtering

NASA Astrophysics Data System (ADS)

Gruszczynski, Maciej; Klos, Anna; Bogusz, Janusz

2016-04-01

Existence of Common Mode Errors (CMEs) in permanent GNSS networks contribute to spatial and temporal correlation in residual time series. Time series from permanently observing GNSS stations of distance less than 2 000 km are similarly influenced by such CME sources as: mismodelling (Earth Orientation Parameters - EOP, satellite orbits or antenna phase center variations) during the process of the reference frame realization, large-scale atmospheric and hydrospheric effects as well as small scale crust deformations. Residuals obtained as a result of detrending and deseasonalising of topocentric GNSS time series arranged epoch-by-epoch form an observation matrix independently for each component (North, East, Up). CME is treated as internal structure of the data. Assuming a uniform temporal function across the network it is possible to filter CME out using PCA (Principal Component Analysis) approach. Some of above described CME sources may be reflected as a wide range of frequencies in GPS residual time series. In order to determine an impact of seasonal signals modeling to existence of spatial correlation in network and consequently the results of CME filtration, we chose two ways of modeling. The first approach was commonly presented by previous authors, who modeled with the Least-Squares Estimation (LSE) only annual and semi-annual oscillations. In the second one the set of residuals was a result of modeling of deterministic part that included fortnightly periods plus up to 9th harmonics of Chandlerian, tropical and draconitic oscillations. Correlation coefficients for residuals in parallel with KMO (Kaiser-Meyer-Olkin) statistic and Bartlett's test of sphericity were determined. For this research we used time series expressed in ITRF2008 provided by JPL (Jet Propulsion Laboratory). GPS processing was made using GIPSY-OASIS software in a PPP (Precise Point Positioning) mode. In order to form GPS station network that meet demands of uniform spatial response to the CME we chose 18 stations located in Central Europe. Created network extends up to 1500 kilometers. The KMO statistic indicate whether a component analysis may be useful for a chosen data set. We obtained KMO statistic value of 0.87 and 0.62 for residuals of Up component after first and second approaches were applied, what means that both residuals share common errors. Bartlett's test of sphericity analysis met a requirement that in both cases there are correlations in residuals. Another important results are the eigenvalues expressed as a percentage of the total variance explained by the first few components in PCA. For North, East and Up component we obtain respectively 68%, 75%, 65% and 47%, 54%, 52% after first and second approaches were applied. The results of CME filtration using PCA approach performed on both residual time series influence directly the uncertainty of the velocity of permanent stations. In our case spatial filtering reduces the uncertainty of velocity from 0.5 to 0.8 mm for horizontal components and from 0.6 to 0.9 mm on average for Up component when annual and semi-annual signals were assumed. Nevertheless, while second approach to the deterministic part modelling was used, deterioration of velocity uncertainty was noticed only for Up component, probably due to much higher autocorrelation in the time series when comparing to horizontal components.

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

Increase of posterior connectivity in aging within the Ventral Attention Network: A functional connectivity analysis using independent component analysis.

PubMed

Deslauriers, Johnathan; Ansado, Jennyfer; Marrelec, Guillaume; Provost, Jean-Sébastien; Joanette, Yves

2017-02-15

Multiple studies have found neurofunctional changes in normal aging in a context of selective attention. Furthermore, many articles report intrahemispheric alteration in functional networks. However, little is known about age-related changes within the Ventral Attention Network (VAN), which underlies selective attention. The aim of this study is to examine age-related changes within the VAN, focusing on connectivity between its regions. Here we report our findings on the analysis of 27 participants' (13 younger and 14 older healthy adults) BOLD signals as well as their performance on a letter-matching task. We identified the VAN independently for both groups using spatial independent component analysis. Three main findings emerged: First, younger adults were faster and more accurate on the task. Second, older adults had greater connectivity among posterior regions (right temporoparietal junction, right superior parietal lobule, right middle temporal gyrus and left cerebellum crus I) than younger adults but lower connectivity among anterior regions (right anterior insula, right medial superior frontal gyrus and right middle frontal gyrus). Older adults also had more connectivity between anterior and posterior regions than younger adults. Finally, correlations between connectivity and response time on the task showed a trend toward connectivity in posterior regions for the older group and in anterior regions for the younger group. Thus, this study shows that intrahemispheric neurofunctional changes in aging also affect the VAN. The results suggest that, in contexts of selective attention, posterior regions increased in importance for older adults, while anterior regions had reduced centrality. Copyright © 2016 Elsevier B.V. All rights reserved.

Vanishing point: Scale independence in geomorphological hierarchies

NASA Astrophysics Data System (ADS)

Phillips, Jonathan D.

2016-08-01

Scale linkage problems in geosciences are often associated with a hierarchy of components. Both dynamical systems perspectives and intuition suggest that processes or relationships operating at fundamentally different scales are independent with respect to influences on system dynamics. But how far apart is ;fundamentally different;-that is, what is the ;vanishing point; at which scales are no longer interdependent? And how do we reconcile that with the idea (again, supported by both theory and intuition) that we can work our way along scale hierarchies from microscale to planetary (and vice-versa)? Graph and network theory are employed here to address these questions. Analysis of two archetypal hierarchical networks shows low algebraic connectivity, indicating low levels of inferential synchronization. This explains the apparent paradox between scale independence and hierarchical linkages. Incorporating more hierarchical levels results in an increase in complexity or entropy of the network as a whole, but at a nonlinear rate. Complexity increases as a power α of the number of levels in the hierarchy, with α < 1 and usually ≤ 0.6. However, algebraic connectivity decreases at a more rapid rate. Thus, the ability to infer one part of the hierarchical network from other level decays rapidly as more levels are added. Relatedness among system components decreases with differences in scale or resolution, analogous to distance decay in the spatial domain. These findings suggest a strategy of identifying and focusing on the most important or interesting scale levels, rather than attempting to identify the smallest or largest scale levels and work top-down or bottom-up from there. Examples are given from soil geomorphology and karst flow networks.

A Spatial Poisson Hurdle Model for Exploring Geographic Variation in Emergency Department Visits

PubMed Central

Neelon, Brian; Ghosh, Pulak; Loebs, Patrick F.

2012-01-01

Summary We develop a spatial Poisson hurdle model to explore geographic variation in emergency department (ED) visits while accounting for zero inflation. The model consists of two components: a Bernoulli component that models the probability of any ED use (i.e., at least one ED visit per year), and a truncated Poisson component that models the number of ED visits given use. Together, these components address both the abundance of zeros and the right-skewed nature of the nonzero counts. The model has a hierarchical structure that incorporates patient- and area-level covariates, as well as spatially correlated random effects for each areal unit. Because regions with high rates of ED use are likely to have high expected counts among users, we model the spatial random effects via a bivariate conditionally autoregressive (CAR) prior, which introduces dependence between the components and provides spatial smoothing and sharing of information across neighboring regions. Using a simulation study, we show that modeling the between-component correlation reduces bias in parameter estimates. We adopt a Bayesian estimation approach, and the model can be fit using standard Bayesian software. We apply the model to a study of patient and neighborhood factors influencing emergency department use in Durham County, North Carolina. PMID:23543242

Visual and spatial working memory are not that dissociated after all: a time-based resource-sharing account.

PubMed

Vergauwe, Evie; Barrouillet, Pierre; Camos, Valérie

2009-07-01

Examinations of interference between visual and spatial materials in working memory have suggested domain- and process-based fractionations of visuo-spatial working memory. The present study examined the role of central time-based resource sharing in visuo-spatial working memory and assessed its role in obtained interference patterns. Visual and spatial storage were combined with both visual and spatial on-line processing components in computer-paced working memory span tasks (Experiment 1) and in a selective interference paradigm (Experiment 2). The cognitive load of the processing components was manipulated to investigate its impact on concurrent maintenance for both within-domain and between-domain combinations of processing and storage components. In contrast to both domain- and process-based fractionations of visuo-spatial working memory, the results revealed that recall performance was determined by the cognitive load induced by the processing of items, rather than by the domain to which those items pertained. These findings are interpreted as evidence for a time-based resource-sharing mechanism in visuo-spatial working memory.

Development of a Spatial Decision Support System for Analyzing Changes in Hydro-meteorological Risk

NASA Astrophysics Data System (ADS)

van Westen, Cees

2013-04-01

In the framework of the EU FP7 Marie Curie ITN Network "CHANGES: Changing Hydro-meteorological Risks, as Analyzed by a New Generation of European Scientists (http://www.changes-itn.eu)", a spatial decision support system is under development with the aim to analyze the effect of risk reduction planning alternatives on reducing the risk now and in the future, and support decision makers in selecting the best alternatives. The SDSS is one of the main outputs of the CHANGES network, which will develop an advanced understanding of how global changes, related to environmental and climate change as well as socio-economical change, may affect the temporal and spatial patterns of hydro-meteorological hazards and associated risks in Europe; how these changes can be assessed, modeled, and incorporated in sustainable risk management strategies, focusing on spatial planning, emergency preparedness and risk communication. The CHANGES network consists of 11 full partners and 6 associate partners of which 5 private companies, representing 10 European countries. The CHANGES network has hired 12 Early Stage Researchers (ESRs) and is currently hiring 3-6 researchers more for the implementation of the SDSS. The Spatial Decision Support System will be composed of a number of integrated components. The Risk Assessment component allows to carry out spatial risk analysis, with different degrees of complexity, ranging from simple exposure (overlay of hazard and assets maps) to quantitative analysis (using different hazard types, temporal scenarios and vulnerability curves) resulting into risk curves. The platform does not include a component to calculate hazard maps, and existing hazard maps are used as input data for the risk component. The second component of the SDSS is a risk reduction planning component, which forms the core of the platform. This component includes the definition of risk reduction alternatives (related to disaster response planning, risk reduction measures and spatial planning) and links back to the risk assessment module to calculate the new level of risk if the measure is implemented, and a cost-benefit (or cost-effectiveness/ Spatial Multi Criteria Evaluation) component to compare the alternatives and make decision on the optimal one. The third component of the SDSS is a temporal scenario component, which allows to define future scenarios in terms of climate change, land use change and population change, and the time periods for which these scenarios will be made. The component doesn't generate these scenarios but uses input maps for the effect of the scenarios on the hazard and assets maps. The last component is a communication and visualization component, which can compare scenarios and alternatives, not only in the form of maps, but also in other forms (risk curves, tables, graphs). The envisaged users of the platform are organizations involved in planning of risk reduction measures, and that have staff capable of visualizing and analyzing spatial data at a municipal scale. This paper presents the main components of the SDSS and the overall design and plans for the user interface.

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG.

PubMed

Bai, Ou; Lin, Peter; Vorbach, Sherry; Li, Jiang; Furlani, Steve; Hallett, Mark

2007-12-01

To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG). Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention. Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training. Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.

A smoothed stochastic earthquake rate model considering seismicity and fault moment release for Europe

NASA Astrophysics Data System (ADS)

Hiemer, S.; Woessner, J.; Basili, R.; Danciu, L.; Giardini, D.; Wiemer, S.

2014-08-01

We present a time-independent gridded earthquake rate forecast for the European region including Turkey. The spatial component of our model is based on kernel density estimation techniques, which we applied to both past earthquake locations and fault moment release on mapped crustal faults and subduction zone interfaces with assigned slip rates. Our forecast relies on the assumption that the locations of past seismicity is a good guide to future seismicity, and that future large-magnitude events occur more likely in the vicinity of known faults. We show that the optimal weighted sum of the corresponding two spatial densities depends on the magnitude range considered. The kernel bandwidths and density weighting function are optimized using retrospective likelihood-based forecast experiments. We computed earthquake activity rates (a- and b-value) of the truncated Gutenberg-Richter distribution separately for crustal and subduction seismicity based on a maximum likelihood approach that considers the spatial and temporal completeness history of the catalogue. The final annual rate of our forecast is purely driven by the maximum likelihood fit of activity rates to the catalogue data, whereas its spatial component incorporates contributions from both earthquake and fault moment-rate densities. Our model constitutes one branch of the earthquake source model logic tree of the 2013 European seismic hazard model released by the EU-FP7 project `Seismic HAzard haRmonization in Europe' (SHARE) and contributes to the assessment of epistemic uncertainties in earthquake activity rates. We performed retrospective and pseudo-prospective likelihood consistency tests to underline the reliability of our model and SHARE's area source model (ASM) using the testing algorithms applied in the collaboratory for the study of earthquake predictability (CSEP). We comparatively tested our model's forecasting skill against the ASM and find a statistically significant better performance for testing periods of 10-20 yr. The testing results suggest that our model is a viable candidate model to serve for long-term forecasting on timescales of years to decades for the European region.

Constraining the dynamics of the water budget at high spatial resolution in the world's water towers using models and remote sensing data; Snake River Basin, USA

NASA Astrophysics Data System (ADS)

Watson, K. A.; Masarik, M. T.; Flores, A. N.

2016-12-01

Mountainous, snow-dominated basins are often referred to as the water towers of the world because they store precipitation in seasonal snowpacks, which gradually melt and provide water supplies to downstream communities. Yet significant uncertainties remain in terms of quantifying the stores and fluxes of water in these regions as well as the associated energy exchanges. Constraining these stores and fluxes is crucial for advancing process understanding and managing these water resources in a changing climate. Remote sensing data are particularly important to these efforts due to the remoteness of these landscapes and high spatial variability in water budget components. We have developed a high resolution regional climate dataset extending from 1986 to the present for the Snake River Basin in the northwestern USA. The Snake River Basin is the largest tributary of the Columbia River by volume and a critically important basin for regional economies and communities. The core of the dataset was developed using a regional climate model, forced by reanalysis data. Specifically the Weather Research and Forecasting (WRF) model was used to dynamically downscale the North American Regional Reanalysis (NARR) over the region at 3 km horizontal resolution for the period of interest. A suite of satellite remote sensing products provide independent, albeit uncertain, constraint on a number of components of the water and energy budgets for the region across a range of spatial and temporal scales. For example, GRACE data are used to constrain basinwide terrestrial water storage and MODIS products are used to constrain the spatial and temporal evolution of evapotranspiration and snow cover. The joint use of both models and remote sensing products allows for both better understanding of water cycle dynamics and associated hydrometeorologic processes, and identification of limitations in both the remote sensing products and regional climate simulations.

Rotation of EOFs by the Independent Component Analysis: Towards A Solution of the Mixing Problem in the Decomposition of Geophysical Time Series

NASA Technical Reports Server (NTRS)

Aires, Filipe; Rossow, William B.; Chedin, Alain; Hansen, James E. (Technical Monitor)

2001-01-01

The Independent Component Analysis is a recently developed technique for component extraction. This new method requires the statistical independence of the extracted components, a stronger constraint that uses higher-order statistics, instead of the classical decorrelation, a weaker constraint that uses only second-order statistics. This technique has been used recently for the analysis of geophysical time series with the goal of investigating the causes of variability in observed data (i.e. exploratory approach). We demonstrate with a data simulation experiment that, if initialized with a Principal Component Analysis, the Independent Component Analysis performs a rotation of the classical PCA (or EOF) solution. This rotation uses no localization criterion like other Rotation Techniques (RT), only the global generalization of decorrelation by statistical independence is used. This rotation of the PCA solution seems to be able to solve the tendency of PCA to mix several physical phenomena, even when the signal is just their linear sum.

An intelligent user interface for browsing satellite data catalogs

NASA Technical Reports Server (NTRS)

Cromp, Robert F.; Crook, Sharon

1989-01-01

A large scale domain-independent spatial data management expert system that serves as a front-end to databases containing spatial data is described. This system is unique for two reasons. First, it uses spatial search techniques to generate a list of all the primary keys that fall within a user's spatial constraints prior to invoking the database management system, thus substantially decreasing the amount of time required to answer a user's query. Second, a domain-independent query expert system uses a domain-specific rule base to preprocess the user's English query, effectively mapping a broad class of queries into a smaller subset that can be handled by a commercial natural language processing system. The methods used by the spatial search module and the query expert system are explained, and the system architecture for the spatial data management expert system is described. The system is applied to data from the International Ultraviolet Explorer (IUE) satellite, and results are given.

Independent polarisation control of multiple optical traps

PubMed Central

Preece, Daryl; Keen, Stephen; Botvinick, Elliot; Bowman, Richard; Padgett, Miles; Leach, Jonathan

2009-01-01

We present a system which uses a single spatial light modulator to control the spin angular momentum of multiple optical traps. These traps may be independently controlled both in terms of spatial location and in terms of their spin angular momentum content. The system relies on a spatial light modulator used in a “split-screen” configuration to generate beams of orthogonal polarisation states which are subsequently combined at a polarising beam splitter. Defining the phase difference between the beams with the spatial light modulator enables control of the polarisation state of the light. We demonstrate the functionality of the system by controlling the rotation and orientation of birefringent vaterite crystals within holographic optical tweezers. PMID:18825226

Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis

PubMed Central

Gopal, Shruti; Miller, Robyn L.; Michael, Andrew; Adali, Tulay; Cetin, Mustafa; Rachakonda, Srinivas; Bustillo, Juan R.; Cahill, Nathan; Baum, Stefi A.; Calhoun, Vince D.

2016-01-01

Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and visual cortex. Tests for differences (HC-SZ) in the spatial variability maps suggest, that at rest, SZs exhibit more activity within externally focused sensory and integrative network and less activity in the default mode network thought to be related to internal reflection. Additionally, tests for difference of variance between groups further emphasize that SZs exhibit greater network variability. These results, consistent with our prediction of increased spatial variability within SZs, enhance our understanding of the disease and suggest that it is not just the amplitude of connectivity that is different in schizophrenia, but also the consistency in spatial connectivity patterns across subjects. PMID:26106217

[Spatial heterogeneity of surface soil mineral components in a small catchment in Karst peak-cluster depression area, South China].

PubMed

Gao, Peng; Fu, Tong-Gang; Wang, Ke-Lin; Chen, Hong-Song; Zeng, Fu-Ping

2013-11-01

A total of 163 soil samples (0-20 cm layer) were collected from the grid sampling plots (80 m x 80 m) in Huanjiang Observation and Research Station of Karst Ecosystem in a small catchment in Karst cluster-peak depression area, South China. By using classical statistics and geostatistics, the spatial heterogeneity of mineral components (SiO2, Fe2O3, CaO, MgO, Al2O3, MnO, and TiO2) in the soils were studied. The contents of the seven soil mineral components in the study area differed greatly, being in the order of SiO2 > Al2O3 > CaO > MgO > Fe2O3 > TiO2 > MnO, and the variance coefficients also varied obviously, in the order of CaO > MgO > Fe2O3 > TiO2 > SiO2 > Al2O3 > MnO. The seven mineral components accounted for 69.4% of the total soil mass. The spatial patterns and the fittest models of the seven soil mineral components differed from each other. All the seven soil mineral components had a strong spatial autocorrelation, with shorter variation ranges and stronger spatial dependence. The Kriging contour maps indicated that the distribution patterns of soil SiO2, Fe2O3, Al2O3, MnO, and TiO2 were similar, being higher in south and east, lower in north and west, higher in depression, and lower in slope, while the distribution patterns of soil CaO and MgO were in adverse. Natural conditions (vegetation, bare rock rate, slope degree, and slope aspect, etc. ) and human disturbance were the most important factors affecting the spatial patterns of the soil mineral components.

Implicit Learning of Viewpoint-Independent Spatial Layouts

PubMed Central

Tsuchiai, Taiga; Matsumiya, Kazumichi; Kuriki, Ichiro; Shioiri, Satoshi

2012-01-01

We usually perceive things in our surroundings as unchanged despite viewpoint changes caused by self-motion. The visual system therefore must have a function to process objects independently of viewpoint. In this study, we examined whether viewpoint-independent spatial layout can be obtained implicitly. For this purpose, we used a contextual cueing effect, a learning effect of spatial layout in visual search displays known to be an implicit effect. We investigated the transfer of the contextual cueing effect to images from a different viewpoint by using visual search displays of 3D objects. For images from a different viewpoint, the contextual cueing effect was maintained with self-motion but disappeared when the display changed without self-motion. This indicates that there is an implicit learning effect in environment-centered coordinates and suggests that the spatial representation of object layouts can be obtained and updated implicitly. We also showed that binocular disparity plays an important role in the layout representations. PMID:22740837

Blind source separation in retinal videos

NASA Astrophysics Data System (ADS)

Barriga, Eduardo S.; Truitt, Paul W.; Pattichis, Marios S.; Tüso, Dan; Kwon, Young H.; Kardon, Randy H.; Soliz, Peter

2003-05-01

An optical imaging device of retina function (OID-RF) has been developed to measure changes in blood oxygen saturation due to neural activity resulting from visual stimulation of the photoreceptors in the human retina. The video data that are collected represent a mixture of the functional signal in response to the retinal activation and other signals from undetermined physiological activity. Measured changes in reflectance in response to the visual stimulus are on the order of 0.1% to 1.0% of the total reflected intensity level which makes the functional signal difficult to detect by standard methods since it is masked by the other signals that are present. In this paper, we apply principal component analysis (PCA), blind source separation (BSS), using Extended Spatial Decorrelation (ESD) and independent component analysis (ICA) using the Fast-ICA algorithm to extract the functional signal from the retinal videos. The results revealed that the functional signal in a stimulated retina can be detected through the application of some of these techniques.

Multivariate Associations of Fluid Intelligence and NAA.

PubMed

Nikolaidis, Aki; Baniqued, Pauline L; Kranz, Michael B; Scavuzzo, Claire J; Barbey, Aron K; Kramer, Arthur F; Larsen, Ryan J

2017-04-01

Understanding the neural and metabolic correlates of fluid intelligence not only aids scientists in characterizing cognitive processes involved in intelligence, but it also offers insight into intervention methods to improve fluid intelligence. Here we use magnetic resonance spectroscopic imaging (MRSI) to measure N-acetyl aspartate (NAA), a biochemical marker of neural energy production and efficiency. We use principal components analysis (PCA) to examine how the distribution of NAA in the frontal and parietal lobes relates to fluid intelligence. We find that a left lateralized frontal-parietal component predicts fluid intelligence, and it does so independently of brain size, another significant predictor of fluid intelligence. These results suggest that the left motor regions play a key role in the visualization and planning necessary for spatial cognition and reasoning, and we discuss these findings in the context of the Parieto-Frontal Integration Theory of intelligence. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Is the eccrine gland an integral, functionally important component of the human scalp pilosebaceous unit?

PubMed

Poblet, Enrique; Jiménez-Acosta, Francisco; Hardman, Jonathan A; Escario, Eduardo; Paus, Ralf

2016-02-01

The pilosebaceous unit (PSU) and the eccrine sweat gland (ESG) are classically described as completely independent skin appendages. However, careful inspection of scalp follicular units reveals that the secretory segment of the ESG spatially approximates the hair follicle in a position below the sebaceous gland and the insertion of the arrector pili muscle. Therefore, we propose here that, contrary to conventional wisdom, the PSU and the ESG should not be viewed in isolation, and may form instead, along with the arrector pili muscle and the apocrine gland (where present),one functional unit. For this, we suggest the more inclusive term of 'Hair Cluster' (HC). If confirmed, e.g. by 3D imaging techniques, the novel concept of a functional HC, whose individual components may communicate via secreted molecules and may share selected progenitor cell populations for HC repair/regeneration, has major physiological and pathological implications, which are briefly discussed. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Sex differences in event-related potential components during the solution of complex mental rotation tasks.

PubMed

Jaušovec, Norbert

2012-04-18

The objective of the study was to evaluate the factor of sex in terms of its influence on event-related potential components during the solution of a complex mental rotation task. To evaluate the factor of sex, independent of differences in ability levels and hormonal changes, women and men were equalized with respect to general intelligence and spatial ability. In addition, all women were tested during the low-estrogen phase of the menstrual cycle. The event-related potential analysis indicated that men showed shorter P3 and longer P1 latencies, as well as lower N1 amplitudes. These results suggest that men devoted more time to the analysis of irrelevant information presented in the rotation tasks, which resulted in mental rotation taking place earlier in men than in women. It can be concluded that, even though men and women showed similar performances on complex rotation tasks, they differed in their solution processes.

High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis.

PubMed

Foong, Shaohui; Sun, Zhenglong

2016-08-12

In this paper, a novel magnetic field-based sensing system employing statistically optimized concurrent multiple sensor outputs for precise field-position association and localization is presented. This method capitalizes on the independence between simultaneous spatial field measurements at multiple locations to induce unique correspondences between field and position. This single-source-multi-sensor configuration is able to achieve accurate and precise localization and tracking of translational motion without contact over large travel distances for feedback control. Principal component analysis (PCA) is used as a pseudo-linear filter to optimally reduce the dimensions of the multi-sensor output space for computationally efficient field-position mapping with artificial neural networks (ANNs). Numerical simulations are employed to investigate the effects of geometric parameters and Gaussian noise corruption on PCA assisted ANN mapping performance. Using a 9-sensor network, the sensing accuracy and closed-loop tracking performance of the proposed optimal field-based sensing system is experimentally evaluated on a linear actuator with a significantly more expensive optical encoder as a comparison.

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.

Spatiotemporal source tuning filter bank for multiclass EEG based brain computer interfaces.

PubMed

Acharya, Soumyadipta; Mollazadeh, Moshen; Murari, Kartikeya; Thakor, Nitish

2006-01-01

Non invasive brain-computer interfaces (BCI) allow people to communicate by modulating features of their electroencephalogram (EEG). Spatiotemporal filtering has a vital role in multi-class, EEG based BCI. In this study, we used a novel combination of principle component analysis, independent component analysis and dipole source localization to design a spatiotemporal multiple source tuning (SPAMSORT) filter bank, each channel of which was tuned to the activity of an underlying dipole source. Changes in the event-related spectral perturbation (ERSP) were measured and used to train a linear support vector machine to classify between four classes of motor imagery tasks (left hand, right hand, foot and tongue) for one subject. ERSP values were significantly (p<0.01) different across tasks and better (p<0.01) than conventional spatial filtering methods (large Laplacian and common average reference). Classification resulted in an average accuracy of 82.5%. This approach could lead to promising BCI applications such as control of a prosthesis with multiple degrees of freedom.

CrossWater - Modelling micropollutant loads from different sources in the Rhine basin

NASA Astrophysics Data System (ADS)

Moser, Andreas; Bader, Hans-Peter; Scheidegger, Ruth; Honti, Mark; Stamm, Christian

2017-04-01

The pressure on rivers from micropollutants (MPs) originating from various sources is a growing environmental issue that requires political regulations. The challenges for the water management are numerous, particularly for international water basins. Spatial knowledge of MP sources and the water quality are prerequisites for an effective water quality policy. In this study within the Rhine basin, the spatial patterns of MP sources and concentrations from different use classes of chemicals are investigated with a mass flow analysis and compared to the territorial jurisdictions that shape the spatial arrangement of water management. The source area of MPs depends on the specific use of a compound. Here, we focus on i) herbicides from agricultural land use, ii) biocides from material protection on buildings and iii) human pharmaceuticals from households. The total mass of MPs available for release to the stream network is estimated from statistics of sales and consumption data. Based on GIS data of agricultural land use, vector data of buildings, wastewater treatment plant (WWTP) locations, respectively, the available mass of MPs is spatially distributed to the subcatchments of the Rhine basin. The modelling of concentrations in the rivers consists of two principal components. The first component - the substance transfer module - simulates the actual release of MPs to the stream network. This transfer is affected by many factors rendering spatial distributed modeling a serious challenge. Here we use a parsimonious approach that tries to represent the first order controls of the transfer processes. We use empirical loss rates relating concentration to river discharge for agricultural herbicides and to precipitation for biocides. For the pharmaceuticals the release is coupled to the human metabolism rates and elimination rates in WWTP. The prediction uncertainty was quantified by an error model that takes the seasonality of the herbicide input into account. The second component - the routing module - links contribution of the subcatchments and represents the in-stream transport and fate processes of the substances. The substance transfer module was calibrated using field studies providing simultaneously data on application amounts of substances and on losses to the rivers. However the predictive uncertainty was often large because of some mismatches of high peaks. The model was subsequently validated with independent data from several comprehensive sampling campaigns in Switzerland. Despite acceptable performance in general, some compounds were poorly simulated for some catchments. Data inspection suggests that uncertainty about timing and application amounts are a major limitation. Finally, the calibrated model is used to simulate concentration time series for the Rhine and its main tributaries. The corresponding results will be presented.

Estimating riparian understory vegetation cover with beta regression and copula models

USGS Publications Warehouse

Eskelson, Bianca N.I.; Madsen, Lisa; Hagar, Joan C.; Temesgen, Hailemariam

2011-01-01

Understory vegetation communities are critical components of forest ecosystems. As a result, the importance of modeling understory vegetation characteristics in forested landscapes has become more apparent. Abundance measures such as shrub cover are bounded between 0 and 1, exhibit heteroscedastic error variance, and are often subject to spatial dependence. These distributional features tend to be ignored when shrub cover data are analyzed. The beta distribution has been used successfully to describe the frequency distribution of vegetation cover. Beta regression models ignoring spatial dependence (BR) and accounting for spatial dependence (BRdep) were used to estimate percent shrub cover as a function of topographic conditions and overstory vegetation structure in riparian zones in western Oregon. The BR models showed poor explanatory power (pseudo-R2 ≤ 0.34) but outperformed ordinary least-squares (OLS) and generalized least-squares (GLS) regression models with logit-transformed response in terms of mean square prediction error and absolute bias. We introduce a copula (COP) model that is based on the beta distribution and accounts for spatial dependence. A simulation study was designed to illustrate the effects of incorrectly assuming normality, equal variance, and spatial independence. It showed that BR, BRdep, and COP models provide unbiased parameter estimates, whereas OLS and GLS models result in slightly biased estimates for two of the three parameters. On the basis of the simulation study, 93–97% of the GLS, BRdep, and COP confidence intervals covered the true parameters, whereas OLS and BR only resulted in 84–88% coverage, which demonstrated the superiority of GLS, BRdep, and COP over OLS and BR models in providing standard errors for the parameter estimates in the presence of spatial dependence.

Multiscale combination of climate model simulations and proxy records over the last millennium

NASA Astrophysics Data System (ADS)

Chen, Xin; Xing, Pei; Luo, Yong; Nie, Suping; Zhao, Zongci; Huang, Jianbin; Tian, Qinhua

2018-05-01

To highlight the compatibility of climate model simulation and proxy reconstruction at different timescales, a timescale separation merging method combining proxy records and climate model simulations is presented. Annual mean surface temperature anomalies for the last millennium (851-2005 AD) at various scales over the land of the Northern Hemisphere were reconstructed with 2° × 2° spatial resolution, using an optimal interpolation (OI) algorithm. All target series were decomposed using an ensemble empirical mode decomposition method followed by power spectral analysis. Four typical components were obtained at inter-annual, decadal, multidecadal, and centennial timescales. A total of 323 temperature-sensitive proxy chronologies were incorporated after screening for each component. By scaling the proxy components using variance matching and applying a localized OI algorithm to all four components point by point, we obtained merged surface temperatures. Independent validation indicates that the most significant improvement was for components at the inter-annual scale, but this became less evident with increasing timescales. In mid-latitude land areas, 10-30% of grids were significantly corrected at the inter-annual scale. By assimilating the proxy records, the merged results reduced the gap in response to volcanic forcing between a pure reconstruction and simulation. Difficulty remained in verifying the centennial information and quantifying corresponding uncertainties, so additional effort should be devoted to this aspect in future research.

Independent active and thermodynamic processes govern the nucleolus assembly in vivo

PubMed Central

Falahati, Hanieh; Wieschaus, Eric

2017-01-01

Membraneless organelles play a central role in the organization of protoplasm by concentrating macromolecules, which allows efficient cellular processes. Recent studies have shown that, in vitro, certain components in such organelles can assemble through phase separation. Inside the cell, however, such organelles are multicomponent, with numerous intermolecular interactions that can potentially affect the demixing properties of individual components. In addition, the organelles themselves are inherently active, and it is not clear how the active, energy-consuming processes that occur constantly within such organelles affect the phase separation behavior of the constituent macromolecules. Here, we examine the phase separation model for the formation of membraneless organelles in vivo by assessing the two features that collectively distinguish it from active assembly, namely temperature dependence and reversibility. We use a microfluidic device that allows accurate and rapid manipulation of temperature and examine the quantitative dynamics by which six different nucleolar proteins assemble into the nucleoli of Drosophila melanogaster embryos. Our results indicate that, although phase separation is the main mode of recruitment for four of the studied proteins, the assembly of the other two is irreversible and enhanced at higher temperatures, behaviors indicative of active recruitment to the nucleolus. These two subsets of components differ in their requirements for ribosomal DNA; the two actively assembling components fail to assemble in the absence of ribosomal DNA, whereas the thermodynamically driven components assemble but lose temporal and spatial precision. PMID:28115706

Group Independent Component Analysis (gICA) and Current Source Density (CSD) in the study of EEG in ADHD adults.

PubMed

Ponomarev, Valery A; Mueller, Andreas; Candrian, Gian; Grin-Yatsenko, Vera A; Kropotov, Juri D

2014-01-01

To investigate the performance of the spectral analysis of resting EEG, Current Source Density (CSD) and group independent components (gIC) in diagnosing ADHD adults. Power spectra of resting EEG, CSD and gIC (19 channels, linked ears reference, eyes open/closed) from 96 ADHD and 376 healthy adults were compared between eyes open and eyes closed conditions, and between groups of subjects. Pattern of differences in gIC and CSD spectral power between conditions was approximately similar, whereas it was more widely spatially distributed for EEG. Size effect (Cohen's d) of differences in gIC and CSD spectral power between groups of subjects was considerably greater than in the case of EEG. Significant reduction of gIC and CSD spectral power depending on conditions was found in ADHD patients. Reducing power in a wide frequency range in the fronto-central areas is a common phenomenon regardless of whether the eyes were open or closed. Spectral power of local EEG activity isolated by gICA or CSD in the fronto-central areas may be a suitable marker for discrimination of ADHD and healthy adults. Spectral analysis of gIC and CSD provides better sensitivity to discriminate ADHD and healthy adults. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Independent component processes underlying emotions during natural music listening.

PubMed

Rogenmoser, Lars; Zollinger, Nina; Elmer, Stefan; Jäncke, Lutz

2016-09-01

The aim of this study was to investigate the brain processes underlying emotions during natural music listening. To address this, we recorded high-density electroencephalography (EEG) from 22 subjects while presenting a set of individually matched whole musical excerpts varying in valence and arousal. Independent component analysis was applied to decompose the EEG data into functionally distinct brain processes. A k-means cluster analysis calculated on the basis of a combination of spatial (scalp topography and dipole location mapped onto the Montreal Neurological Institute brain template) and functional (spectra) characteristics revealed 10 clusters referring to brain areas typically involved in music and emotion processing, namely in the proximity of thalamic-limbic and orbitofrontal regions as well as at frontal, fronto-parietal, parietal, parieto-occipital, temporo-occipital and occipital areas. This analysis revealed that arousal was associated with a suppression of power in the alpha frequency range. On the other hand, valence was associated with an increase in theta frequency power in response to excerpts inducing happiness compared to sadness. These findings are partly compatible with the model proposed by Heller, arguing that the frontal lobe is involved in modulating valenced experiences (the left frontal hemisphere for positive emotions) whereas the right parieto-temporal region contributes to the emotional arousal. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

A principal components analysis of dynamic spatial memory biases.

PubMed

Motes, Michael A; Hubbard, Timothy L; Courtney, Jon R; Rypma, Bart

2008-09-01

Research has shown that spatial memory for moving targets is often biased in the direction of implied momentum and implied gravity, suggesting that representations of the subjective experiences of these physical principles contribute to such biases. The present study examined the association between these spatial memory biases. Observers viewed targets that moved horizontally from left to right before disappearing or viewed briefly shown stationary targets. After a target disappeared, observers indicated the vanishing position of the target. Principal components analysis revealed that biases along the horizontal axis of motion loaded on separate components from biases along the vertical axis orthogonal to motion. The findings support the hypothesis that implied momentum and implied gravity biases have unique influences on spatial memory. (c) 2008 APA, all rights reserved.

An inverse approach to determining spatially varying arterial compliance using ultrasound imaging

NASA Astrophysics Data System (ADS)

Mcgarry, Matthew; Li, Ronny; Apostolakis, Iason; Nauleau, Pierre; Konofagou, Elisa E.

2016-08-01

The mechanical properties of arteries are implicated in a wide variety of cardiovascular diseases, many of which are expected to involve a strong spatial variation in properties that can be depicted by diagnostic imaging. A pulse wave inverse problem (PWIP) is presented, which can produce spatially resolved estimates of vessel compliance from ultrasound measurements of the vessel wall displacements. The 1D equations governing pulse wave propagation in a flexible tube are parameterized by the spatially varying properties, discrete cosine transform components of the inlet pressure boundary conditions, viscous loss constant and a resistance outlet boundary condition. Gradient descent optimization is used to fit displacements from the model to the measured data by updating the model parameters. Inversion of simulated data showed that the PWIP can accurately recover the correct compliance distribution and inlet pressure under realistic conditions, even under high simulated measurement noise conditions. Silicone phantoms with known compliance contrast were imaged with a clinical ultrasound system. The PWIP produced spatially and quantitatively accurate maps of the phantom compliance compared to independent static property estimates, and the known locations of stiff inclusions (which were as small as 7 mm). The PWIP is necessary for these phantom experiments as the spatiotemporal resolution, measurement noise and compliance contrast does not allow accurate tracking of the pulse wave velocity using traditional approaches (e.g. 50% upstroke markers). Results from simulations indicate reflections generated from material interfaces may negatively affect wave velocity estimates, whereas these reflections are accounted for in the PWIP and do not cause problems.

Dual Tasking and Working Memory in Alcoholism: Relation to Frontocerebellar Circuitry

PubMed Central

Chanraud, Sandra; Pitel, Anne-Lise; Rohlfing, Torsten; Pfefferbaum, Adolf; Sullivan, Edith V

2010-01-01

Controversy exists regarding the role of cerebellar systems in cognition and whether working memory compromise commonly marking alcoholism can be explained by compromise of nodes of corticocerebellar circuitry. We tested 17 alcoholics and 31 age-matched controls with dual-task, working memory paradigms. Interference tasks competed with verbal and spatial working memory tasks using low (three item) or high (six item) memory loads. Participants also underwent structural MRI to obtain volumes of nodes of the frontocerebellar system. On the verbal working memory task, both groups performed equally. On the spatial working memory with the high-load task, the alcoholic group was disproportionately more affected by the arithmetic distractor than were controls. In alcoholics, volumes of the left thalamus and left cerebellar Crus I volumes were more robust predictors of performance in the spatial working memory task with the arithmetic distractor than the left frontal superior cortex. In controls, volumes of the right middle frontal gyrus and right cerebellar Crus I were independent predictors over the left cerebellar Crus I, left thalamus, right superior parietal cortex, or left middle frontal gyrus of spatial working memory performance with tracking interference. The brain–behavior correlations suggest that alcoholics and controls relied on the integrity of certain nodes of corticocerebellar systems to perform these verbal and spatial working memory tasks, but that the specific pattern of relationships differed by group. The resulting brain structure–function patterns provide correlational support that components of this corticocerebellar system not typically related to normal performance in dual-task conditions may be available to augment otherwise dampened performance by alcoholics. PMID:20410871

Spatial Integration under Contextual Control in a Virtual Environment

ERIC Educational Resources Information Center

Molet, Mikael; Gambet, Boris; Bugallo, Mehdi; Miller, Ralph R.

2012-01-01

The role of context was examined in the selection and integration of independently learned spatial relationships. Using a dynamic 3D virtual environment, participants learned one spatial relationship between landmarks A and B which was established in one virtual context (e.g., A is left of B) and a different spatial relationship which was…

Plant reproductive allocation predicts herbivore dynamics across spatial and temporal scales.

PubMed

Miller, Tom E X; Tyre, Andrew J; Louda, Svata M

2006-11-01

Life-history theory suggests that iteroparous plants should be flexible in their allocation of resources toward growth and reproduction. Such plasticity could have consequences for herbivores that prefer or specialize on vegetative versus reproductive structures. To test this prediction, we studied the response of the cactus bug (Narnia pallidicornis) to meristem allocation by tree cholla cactus (Opuntia imbricata). We evaluated the explanatory power of demographic models that incorporated variation in cactus relative reproductive effort (RRE; the proportion of meristems allocated toward reproduction). Field data provided strong support for a single model that defined herbivore fecundity as a time-varying, increasing function of host RRE. High-RRE plants were predicted to support larger insect populations, and this effect was strongest late in the season. Independent field data provided strong support for these qualitative predictions and suggested that plant allocation effects extend across temporal and spatial scales. Specifically, late-season insect abundance was positively associated with interannual changes in cactus RRE over 3 years. Spatial variation in insect abundance was correlated with variation in RRE among five cactus populations across New Mexico. We conclude that plant allocation can be a critical component of resource quality for insect herbivores and, thus, an important mechanism underlying variation in herbivore abundance across time and space.

Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation

PubMed Central

Młynarski, Wiktor

2014-01-01

To date a number of studies have shown that receptive field shapes of early sensory neurons can be reproduced by optimizing coding efficiency of natural stimulus ensembles. A still unresolved question is whether the efficient coding hypothesis explains formation of neurons which explicitly represent environmental features of different functional importance. This paper proposes that the spatial selectivity of higher auditory neurons emerges as a direct consequence of learning efficient codes for natural binaural sounds. Firstly, it is demonstrated that a linear efficient coding transform—Independent Component Analysis (ICA) trained on spectrograms of naturalistic simulated binaural sounds extracts spatial information present in the signal. A simple hierarchical ICA extension allowing for decoding of sound position is proposed. Furthermore, it is shown that units revealing spatial selectivity can be learned from a binaural recording of a natural auditory scene. In both cases a relatively small subpopulation of learned spectrogram features suffices to perform accurate sound localization. Representation of the auditory space is therefore learned in a purely unsupervised way by maximizing the coding efficiency and without any task-specific constraints. This results imply that efficient coding is a useful strategy for learning structures which allow for making behaviorally vital inferences about the environment. PMID:24639644

CO component estimation based on the independent component analysis

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

Ichiki, Kiyotomo; Kaji, Ryohei; Yamamoto, Hiroaki

2014-01-01

Fast Independent Component Analysis (FastICA) is a component separation algorithm based on the levels of non-Gaussianity. Here we apply FastICA to the component separation problem of the microwave background, including carbon monoxide (CO) line emissions that are found to contaminate the PLANCK High Frequency Instrument (HFI) data. Specifically, we prepare 100 GHz, 143 GHz, and 217 GHz mock microwave sky maps, which include galactic thermal dust, NANTEN CO line, and the cosmic microwave background (CMB) emissions, and then estimate the independent components based on the kurtosis. We find that FastICA can successfully estimate the CO component as the first independentmore » component in our deflection algorithm because its distribution has the largest degree of non-Gaussianity among the components. Thus, FastICA can be a promising technique to extract CO-like components without prior assumptions about their distributions and frequency dependences.« less

Laser system using regenerative amplifier

DOEpatents

Emmett, John L. [Pleasanton, CA

1980-03-04

High energy laser system using a regenerative amplifier, which relaxes all constraints on laser components other than the intrinsic damage level of matter, so as to enable use of available laser system components. This can be accomplished by use of segmented components, spatial filters, at least one amplifier using solid state or gaseous media, and separated reflector members providing a long round trip time through the regenerative cavity, thereby allowing slower switching and adequate time to clear the spatial filters, etc. The laser system simplifies component requirements and reduces component cost while providing high energy output.

Lattice Independent Component Analysis for Mobile Robot Localization

NASA Astrophysics Data System (ADS)

Villaverde, Ivan; Fernandez-Gauna, Borja; Zulueta, Ekaitz

This paper introduces an approach to appearance based mobile robot localization using Lattice Independent Component Analysis (LICA). The Endmember Induction Heuristic Algorithm (EIHA) is used to select a set of Strong Lattice Independent (SLI) vectors, which can be assumed to be Affine Independent, and therefore candidates to be the endmembers of the data. Selected endmembers are used to compute the linear unmixing of the robot's acquired images. The resulting mixing coefficients are used as feature vectors for view recognition through classification. We show on a sample path experiment that our approach can recognise the localization of the robot and we compare the results with the Independent Component Analysis (ICA).

Covariance analyses of satellite-derived mesoscale wind fields

NASA Technical Reports Server (NTRS)

Maddox, R. A.; Vonder Haar, T. H.

1979-01-01

Statistical structure functions have been computed independently for nine satellite-derived mesoscale wind fields that were obtained on two different days. Small cumulus clouds were tracked at 5 min intervals, but since these clouds occurred primarily in the warm sectors of midlatitude cyclones the results cannot be considered representative of the circulations within cyclones in general. The field structure varied considerably with time and was especially affected if mesoscale features were observed. The wind fields on the 2 days studied were highly anisotropic with large gradients in structure occurring approximately normal to the mean flow. Structure function calculations for the combined set of satellite winds were used to estimate random error present in the fields. It is concluded for these data that the random error in vector winds derived from cumulus cloud tracking using high-frequency satellite data is less than 1.75 m/s. Spatial correlation functions were also computed for the nine data sets. Normalized correlation functions were considerably different for u and v components and decreased rapidly as data point separation increased for both components. The correlation functions for transverse and longitudinal components decreased less rapidly as data point separation increased.

GIS-based niche modeling for mapping species' habitats

USGS Publications Warehouse

Rotenberry, J.T.; Preston, K.L.; Knick, S.

2006-01-01

Ecological a??niche modelinga?? using presence-only locality data and large-scale environmental variables provides a powerful tool for identifying and mapping suitable habitat for species over large spatial extents. We describe a niche modeling approach that identifies a minimum (rather than an optimum) set of basic habitat requirements for a species, based on the assumption that constant environmental relationships in a species' distribution (i.e., variables that maintain a consistent value where the species occurs) are most likely to be associated with limiting factors. Environmental variables that take on a wide range of values where a species occurs are less informative because they do not limit a species' distribution, at least over the range of variation sampled. This approach is operationalized by partitioning Mahalanobis D2 (standardized difference between values of a set of environmental variables for any point and mean values for those same variables calculated from all points at which a species was detected) into independent components. The smallest of these components represents the linear combination of variables with minimum variance; increasingly larger components represent larger variances and are increasingly less limiting. We illustrate this approach using the California Gnatcatcher (Polioptila californica Brewster) and provide SAS code to implement it.

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.

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

Statistics of natural binaural sounds.

PubMed

Młynarski, Wiktor; Jost, Jürgen

2014-01-01

Binaural sound localization is usually considered a discrimination task, where interaural phase (IPD) and level (ILD) disparities at narrowly tuned frequency channels are utilized to identify a position of a sound source. In natural conditions however, binaural circuits are exposed to a stimulation by sound waves originating from multiple, often moving and overlapping sources. Therefore statistics of binaural cues depend on acoustic properties and the spatial configuration of the environment. Distribution of cues encountered naturally and their dependence on physical properties of an auditory scene have not been studied before. In the present work we analyzed statistics of naturally encountered binaural sounds. We performed binaural recordings of three auditory scenes with varying spatial configuration and analyzed empirical cue distributions from each scene. We have found that certain properties such as the spread of IPD distributions as well as an overall shape of ILD distributions do not vary strongly between different auditory scenes. Moreover, we found that ILD distributions vary much weaker across frequency channels and IPDs often attain much higher values, than can be predicted from head filtering properties. In order to understand the complexity of the binaural hearing task in the natural environment, sound waveforms were analyzed by performing Independent Component Analysis (ICA). Properties of learned basis functions indicate that in natural conditions soundwaves in each ear are predominantly generated by independent sources. This implies that the real-world sound localization must rely on mechanisms more complex than a mere cue extraction.

Statistics of Natural Binaural Sounds

PubMed Central

Młynarski, Wiktor; Jost, Jürgen

2014-01-01

Binaural sound localization is usually considered a discrimination task, where interaural phase (IPD) and level (ILD) disparities at narrowly tuned frequency channels are utilized to identify a position of a sound source. In natural conditions however, binaural circuits are exposed to a stimulation by sound waves originating from multiple, often moving and overlapping sources. Therefore statistics of binaural cues depend on acoustic properties and the spatial configuration of the environment. Distribution of cues encountered naturally and their dependence on physical properties of an auditory scene have not been studied before. In the present work we analyzed statistics of naturally encountered binaural sounds. We performed binaural recordings of three auditory scenes with varying spatial configuration and analyzed empirical cue distributions from each scene. We have found that certain properties such as the spread of IPD distributions as well as an overall shape of ILD distributions do not vary strongly between different auditory scenes. Moreover, we found that ILD distributions vary much weaker across frequency channels and IPDs often attain much higher values, than can be predicted from head filtering properties. In order to understand the complexity of the binaural hearing task in the natural environment, sound waveforms were analyzed by performing Independent Component Analysis (ICA). Properties of learned basis functions indicate that in natural conditions soundwaves in each ear are predominantly generated by independent sources. This implies that the real-world sound localization must rely on mechanisms more complex than a mere cue extraction. PMID:25285658

Go Pink! The Effect of Secondary Quanta on Detective Quantum Efficiency

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

Watson, Scott

2017-09-05

Photons are never directly observable. Consequently, we often use photoelectric detectors (eg CCDs) to record associated photoelectrons statistically. Nonetheless, it is an implicit goal of radiographic detector designers to achieve the maximum possible detector efficiency1. In part the desire for ever higher efficiency has been due to the fact that detectors are far less expensive than associated accelerator facilities (e.g. DARHT and PHERMEX2). In addition, higher efficiency detectors often have better spatial resolution. Consequently, the optimization of the detector, not the accelerator, is the system component with the highest leverage per dollar. In recent years, imaging scientists have adopted themore » so-called Detective Quantum Efficiency, or DQE as a summary measure of detector performance. Unfortunately, owing to the complex nature of the trade-space associated with detector components, and the natural desire for simplicity and low(er) cost, there has been a recent trend in Los Alamos to focus only on the zerofrequency efficiency, or DQE(0), when designing such systems. This narrow focus leads to system designs that neglect or even ignore the importance of high-spatial-frequency image components. In this paper we demonstrate the significant negative impact of these design choices on the Noise Power Spectrum1 (NPS) and recommend a more holistic approach to detector design. Here we present a statistical argument which indicates that a very large number (>20) of secondary quanta (typically visible light and/or recorded photo-electrons) are needed to take maximum advantage of the primary quanta (typically x-rays or protons) which are available to form an image. Since secondary particles come in bursts, they are not independent. In short, we want to maximize the pink nature of detector noise at DARHT.« less

The Role of Spatial Ability and Strategy Preference for Spatial Problem Solving in Organic Chemistry

ERIC Educational Resources Information Center

Stieff, Mike; Ryu, Minjung; Dixon, Bonnie; Hegarty, Mary

2012-01-01

In organic chemistry, spatial reasoning is critical for reasoning about spatial relationships in three dimensions and representing spatial information in diagrams. Despite its importance, little is known about the underlying cognitive components of spatial reasoning and the strategies that students employ to solve spatial problems in organic…

Anatomical constraints on attention: Hemifield independence is a signature of multifocal spatial selection

PubMed Central

Alvarez, George A; Gill, Jonathan; Cavanagh, Patrick

2012-01-01

Previous studies have shown independent attentional selection of targets in the left and right visual hemifields during attentional tracking (Alvarez & Cavanagh, 2005) but not during a visual search (Luck, Hillyard, Mangun, & Gazzaniga, 1989). Here we tested whether multifocal spatial attention is the critical process that operates independently in the two hemifields. It is explicitly required in tracking (attend to a subset of object locations, suppress the others) but not in the standard visual search task (where all items are potential targets). We used a modified visual search task in which observers searched for a target within a subset of display items, where the subset was selected based on location (Experiments 1 and 3A) or based on a salient feature difference (Experiments 2 and 3B). The results show hemifield independence in this subset visual search task with location-based selection but not with feature-based selection; this effect cannot be explained by general difficulty (Experiment 4). Combined, these findings suggest that hemifield independence is a signature of multifocal spatial attention and highlight the need for cognitive and neural theories of attention to account for anatomical constraints on selection mechanisms. PMID:22637710

The guidance of visual search by shape features and shape configurations.

PubMed

McCants, Cody W; Berggren, Nick; Eimer, Martin

2018-03-01

Representations of target features (attentional templates) guide attentional object selection during visual search. In many search tasks, targets objects are defined not by a single feature but by the spatial configuration of their component shapes. We used electrophysiological markers of attentional selection processes to determine whether the guidance of shape configuration search is entirely part-based or sensitive to the spatial relationship between shape features. Participants searched for targets defined by the spatial arrangement of two shape components (e.g., hourglass above circle). N2pc components were triggered not only by targets but also by partially matching distractors with one target shape (e.g., hourglass above hexagon) and by distractors that contained both target shapes in the reverse arrangement (e.g., circle above hourglass), in line with part-based attentional control. Target N2pc components were delayed when a reverse distractor was present on the opposite side of the same display, suggesting that early shape-specific attentional guidance processes could not distinguish between targets and reverse distractors. The control of attention then became sensitive to spatial configuration, which resulted in a stronger attentional bias for target objects relative to reverse and partially matching distractors. Results demonstrate that search for target objects defined by the spatial arrangement of their component shapes is initially controlled in a feature-based fashion but can later be guided by templates for spatial configurations. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Multi-spectrometer calibration transfer based on independent component analysis.

PubMed

Liu, Yan; Xu, Hao; Xia, Zhenzhen; Gong, Zhiyong

2018-02-26

Calibration transfer is indispensable for practical applications of near infrared (NIR) spectroscopy due to the need for precise and consistent measurements across different spectrometers. In this work, a method for multi-spectrometer calibration transfer is described based on independent component analysis (ICA). A spectral matrix is first obtained by aligning the spectra measured on different spectrometers. Then, by using independent component analysis, the aligned spectral matrix is decomposed into the mixing matrix and the independent components of different spectrometers. These differing measurements between spectrometers can then be standardized by correcting the coefficients within the independent components. Two NIR datasets of corn and edible oil samples measured with three and four spectrometers, respectively, were used to test the reliability of this method. The results of both datasets reveal that spectra measurements across different spectrometers can be transferred simultaneously and that the partial least squares (PLS) models built with the measurements on one spectrometer can predict that the spectra can be transferred correctly on another.

Fixed Pattern Noise pixel-wise linear correction for crime scene imaging CMOS sensor

NASA Astrophysics Data System (ADS)

Yang, Jie; Messinger, David W.; Dube, Roger R.; Ientilucci, Emmett J.

2017-05-01

Filtered multispectral imaging technique might be a potential method for crime scene documentation and evidence detection due to its abundant spectral information as well as non-contact and non-destructive nature. Low-cost and portable multispectral crime scene imaging device would be highly useful and efficient. The second generation crime scene imaging system uses CMOS imaging sensor to capture spatial scene and bandpass Interference Filters (IFs) to capture spectral information. Unfortunately CMOS sensors suffer from severe spatial non-uniformity compared to CCD sensors and the major cause is Fixed Pattern Noise (FPN). IFs suffer from "blue shift" effect and introduce spatial-spectral correlated errors. Therefore, Fixed Pattern Noise (FPN) correction is critical to enhance crime scene image quality and is also helpful for spatial-spectral noise de-correlation. In this paper, a pixel-wise linear radiance to Digital Count (DC) conversion model is constructed for crime scene imaging CMOS sensor. Pixel-wise conversion gain Gi,j and Dark Signal Non-Uniformity (DSNU) Zi,j are calculated. Also, conversion gain is divided into four components: FPN row component, FPN column component, defects component and effective photo response signal component. Conversion gain is then corrected to average FPN column and row components and defects component so that the sensor conversion gain is uniform. Based on corrected conversion gain and estimated image incident radiance from the reverse of pixel-wise linear radiance to DC model, corrected image spatial uniformity can be enhanced to 7 times as raw image, and the bigger the image DC value within its dynamic range, the better the enhancement.

Deriving frequency-dependent spatial patterns in MEG-derived resting state sensorimotor network: A novel multiband ICA technique.

PubMed

Nugent, Allison C; Luber, Bruce; Carver, Frederick W; Robinson, Stephen E; Coppola, Richard; Zarate, Carlos A

2017-02-01

Recently, independent components analysis (ICA) of resting state magnetoencephalography (MEG) recordings has revealed resting state networks (RSNs) that exhibit fluctuations of band-limited power envelopes. Most of the work in this area has concentrated on networks derived from the power envelope of beta bandpass-filtered data. Although research has demonstrated that most networks show maximal correlation in the beta band, little is known about how spatial patterns of correlations may differ across frequencies. This study analyzed MEG data from 18 healthy subjects to determine if the spatial patterns of RSNs differed between delta, theta, alpha, beta, gamma, and high gamma frequency bands. To validate our method, we focused on the sensorimotor network, which is well-characterized and robust in both MEG and functional magnetic resonance imaging (fMRI) resting state data. Synthetic aperture magnetometry (SAM) was used to project signals into anatomical source space separately in each band before a group temporal ICA was performed over all subjects and bands. This method preserved the inherent correlation structure of the data and reflected connectivity derived from single-band ICA, but also allowed identification of spatial spectral modes that are consistent across subjects. The implications of these results on our understanding of sensorimotor function are discussed, as are the potential applications of this technique. Hum Brain Mapp 38:779-791, 2017. © 2016 Wiley Periodicals, Inc. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

The neural basis of form and form-motion integration from static and dynamic translational Glass patterns: A rTMS investigation.

PubMed

Pavan, Andrea; Ghin, Filippo; Donato, Rita; Campana, Gianluca; Mather, George

2017-08-15

A long-held view of the visual system is that form and motion are independently analysed. However, there is physiological and psychophysical evidence of early interaction in the processing of form and motion. In this study, we used a combination of Glass patterns (GPs) and repetitive Transcranial Magnetic Stimulation (rTMS) to investigate in human observers the neural mechanisms underlying form-motion integration. GPs consist of randomly distributed dot pairs (dipoles) that induce the percept of an oriented stimulus. GPs can be either static or dynamic. Dynamic GPs have both a form component (i.e., orientation) and a non-directional motion component along the orientation axis. GPs were presented in two temporal intervals and observers were asked to discriminate the temporal interval containing the most coherent GP. rTMS was delivered over early visual area (V1/V2) and over area V5/MT shortly after the presentation of the GP in each interval. The results showed that rTMS applied over early visual areas affected the perception of static GPs, but the stimulation of area V5/MT did not affect observers' performance. On the other hand, rTMS was delivered over either V1/V2 or V5/MT strongly impaired the perception of dynamic GPs. These results suggest that early visual areas seem to be involved in the processing of the spatial structure of GPs, and interfering with the extraction of the global spatial structure also affects the extraction of the motion component, possibly interfering with early form-motion integration. However, visual area V5/MT is likely to be involved only in the processing of the motion component of dynamic GPs. These results suggest that motion and form cues may interact as early as V1/V2. Copyright © 2017 Elsevier Inc. All rights reserved.

Combined Exact-Repeat and Geodetic Mission Altimetry for High-Resolution Empirical Tide Mapping

NASA Astrophysics Data System (ADS)

Zaron, E. D.

2014-12-01

The configuration of present and historical exact-repeat mission (ERM) altimeter ground tracks determines the maximum resolution of empirical tidal maps obtained with ERM data. Although the mode-1 baroclinic tide is resolvable at mid-latitudes in the open ocean, the ability to detect baroclinic and barotropic tides near islands and complex coastlines is limited, in part, by ERM track density. In order to obtain higher resolution maps, the possibility of combining ERM and geodetic mission (GM) altimetry is considered, using a combination of spatial thin-plate splines and temporal harmonic analysis. Given the present spatial and temporal distribution of GM missions, it is found that GM data can contribute to resolving tidal features smaller than 75 km, provided the signal amplitude is greater than about 1 cm. Uncertainties in the mean sea surface and environmental corrections are significant components of the GM error budget, and methods to optimize data selection and along-track filtering are still being optimized. Application to two regions, Monterey Bay and Luzon Strait, finds evidence for complex tidal fields in agreement with independent observations and modeling studies.

Physical-chemical mechanisms of pattern formation during gastrulation

NASA Astrophysics Data System (ADS)

Bozorgui, Behnaz; Kolomeisky, Anatoly B.; Teimouri, Hamid

2018-03-01

Gastrulation is a fundamental phase during the biological development of most animals when a single layer of identical embryo cells is transformed into a three-layer structure, from which the organs start to develop. Despite a remarkable progress in quantifying the gastrulation processes, molecular mechanisms of these processes remain not well understood. Here we theoretically investigate early spatial patterning in a geometrically confined colony of embryonic stem cells. Using a reaction-diffusion model, a role of Bone-Morphogenetic Protein 4 (BMP4) signaling pathway in gastrulation is specifically analyzed. Our results show that for slow diffusion rates of BMP4 molecules, a new length scale appears, which is independent of the size of the system. This length scale separates the central region of the colony with uniform low concentrations of BMP molecules from the region near the colony edge where the concentration of signaling molecules is elevated. The roles of different components of the signaling pathway are also explained. Theoretical results are consistent with recent in vitro experiments, providing microscopic explanations for some features of early embryonic spatial patterning. Physical-chemical mechanisms of these processes are discussed.

Second-harmonic generation from a thin spherical layer and No-generation conditions

NASA Astrophysics Data System (ADS)

Kapshai, V. N.; Shamyna, A. A.

2017-09-01

In the Rayleigh-Gans-Debye approximation, we solve the problem of second-harmonic generation by an elliptically polarized electromagnetic wave incident on the surface of a spherical particle that is coated by an optically nonlinear layer and is placed in a dielectric. The formulas obtained characterize the spatial distribution of the electric field of the second harmonic in the far-field zone. The most general form of the second-order dielectric susceptibility tensor is considered, which contains four independent components, with three of them being nonchiral and one, chiral. Consistency and inconsistencies between the obtained solution and formulas from works of other authors are found. We analyze the directivity patterns that characterize the spatial distribution of the generated radiation for the nonchiral layer and their dependences on the anisotropy and ellipticity coefficients of the incident wave. It is found that, with increasing radius of the nonlinear layer, the generated radiation becomes more directional. Combinations of parameters for which no radiation is generated are revealed. Based on this, we propose methods for experimental determination of the anisotropy coefficients.

Somatosensory spatial attention modulates amplitudes, latencies, and latency jitter of laser-evoked brain potentials.

PubMed

Franz, Marcel; Nickel, Moritz M; Ritter, Alexander; Miltner, Wolfgang H R; Weiss, Thomas

2015-04-01

Several studies provided evidence that the amplitudes of laser-evoked potentials (LEPs) are modulated by attention. However, previous reports were based on across-trial averaging of LEP responses at the expense of losing information about intertrial variability related to attentional modulation. The aim of this study was to investigate the effects of somatosensory spatial attention on single-trial parameters (i.e., amplitudes, latencies, and latency jitter) of LEP components (N2 and P2). Twelve subjects participated in a sustained spatial attention paradigm while noxious laser stimuli (left hand) and noxious electrical stimuli (right hand) were sequentially delivered to the dorsum of the respective hand with nonnoxious air puffs randomly interspersed within the sequence of noxious stimuli. Participants were instructed to mentally count all stimuli (i.e., noxious and nonnoxious) applied to the attended location. Laser stimuli, presented to the attended hand (ALS), elicited larger single-trial amplitudes of the N2 component compared with unattended laser stimuli (ULS). In contrast, single-trial amplitudes of the P2 component were not significantly affected by spatial attention. Single-trial latencies of the N2 and P2 were significantly smaller for ALS vs. ULS. Additionally, the across-trial latency jitter of the N2 component was reduced for ALS. Conversely, the latency jitter of the P2 component was smaller for ULS compared with ALS. With the use of single-trial analysis, the study provided new insights into brain dynamics of LEPs related to spatial attention. Our results indicate that single-trial parameters of LEP components are differentially modulated by spatial attention. Copyright © 2015 the American Physiological Society.

Contribution of LANDSAT-4 thematic mapper data to geologic exploration

NASA Technical Reports Server (NTRS)

Everett, J. R.; Dykstra, J. D.; Sheffield, C. A.

1983-01-01

The increased number of carefully selected narrow spectral bands and the increased spatial resolution of thematic mapper data over previously available satellite data contribute greatly to geologic exploration, both by providing spectral information that permits lithologic differentiation and recognition of alteration and spatial information that reveals structure. As vegetation and soil cover increase, the value of spectral components of TM data decreases relative to the value of the spatial component of the data. However, even in vegetated areas, the greater spectral breadth and discrimination of TM data permits improved recognition and mapping of spatial elements of the terrain. As our understanding of the spectral manifestations of the responses of soils and vegetation to unusual chemical environments increases, the value of spectral components of TM data to exploration will greatly improve in covered areas.

Anatomical and spatial matching in imitation: Evidence from left and right brain-damaged patients.

PubMed

Mengotti, Paola; Ripamonti, Enrico; Pesavento, Valentina; Rumiati, Raffaella Ida

2015-12-01

Imitation is a sensorimotor process whereby the visual information present in the model's movement has to be coupled with the activation of the motor system in the observer. This also implies that greater the similarity between the seen and the produced movement, the easier it will be to execute the movement, a process also known as ideomotor compatibility. Two components can influence the degree of similarity between two movements: the anatomical and the spatial component. The anatomical component is present when the model and imitator move the same body part (e.g., the right hand) while the spatial component is present when the movement of the model and that of the imitator occur at the same spatial position. Imitation can be achieved by relying on both components, but typically the model's and imitator's movements are matched either anatomically or spatially. The aim of this study was to ascertain the contribution of the left and right hemisphere to the imitation accomplished either with anatomical or spatial matching (or with both). Patients with unilateral left and right brain damage performed an ideomotor task and a gesture imitation task. Lesions in the left and right hemispheres gave rise to different performance deficits. Patients with lesions in the left hemisphere showed impaired imitation when anatomical matching was required, and patients with lesions in the right hemisphere showed impaired imitation when spatial matching was required. Lesion analysis further revealed a differential involvement of left and right hemispheric regions, such as the parietal opercula, in supporting imitation in the ideomotor task. Similarly, gesture imitation seemed to rely on different regions in the left and right hemisphere, such as parietal regions in the left hemisphere and premotor, somatosensory and subcortical regions in the right hemisphere. Copyright © 2015 Elsevier Ltd. All rights reserved.

Tags Extarction from Spatial Documents in Search Engines

NASA Astrophysics Data System (ADS)

Borhaninejad, S.; Hakimpour, F.; Hamzei, E.

2015-12-01

Nowadays the selective access to information on the Web is provided by search engines, but in the cases which the data includes spatial information the search task becomes more complex and search engines require special capabilities. The purpose of this study is to extract the information which lies in spatial documents. To that end, we implement and evaluate information extraction from GML documents and a retrieval method in an integrated approach. Our proposed system consists of three components: crawler, database and user interface. In crawler component, GML documents are discovered and their text is parsed for information extraction; storage. The database component is responsible for indexing of information which is collected by crawlers. Finally the user interface component provides the interaction between system and user. We have implemented this system as a pilot system on an Application Server as a simulation of Web. Our system as a spatial search engine provided searching capability throughout the GML documents and thus an important step to improve the efficiency of search engines has been taken.

Spatially resolved spectroscopy analysis of the XMM-Newton large program on SN1006

NASA Astrophysics Data System (ADS)

Li, Jiang-Tao; Decourchelle, Anne; Miceli, Marco; Vink, Jacco; Bocchino, Fabrizio

2016-04-01

We perform analysis of the XMM-Newton large program on SN1006 based on our newly developed methods of spatially resolved spectroscopy analysis. We extract spectra from low and high resolution meshes. The former (3596 meshes) is used to roughly decompose the thermal and non-thermal components and characterize the spatial distributions of different parameters, such as temperature, abundances of different elements, ionization age, and electron density of the thermal component, as well as photon index and cutoff frequency of the non-thermal component. On the other hand, the low resolution meshes (583 meshes) focus on the interior region dominated by the thermal emission and have enough counts to well characterize the Si lines. We fit the spectra from the low resolution meshes with different models, in order to decompose the multiple plasma components at different thermal and ionization states and compare their spatial distributions. In this poster, we will present the initial results of this project.

The relationships between spatial ability, logical thinking, mathematics performance and kinematics graph interpretation skills of 12th grade physics students

NASA Astrophysics Data System (ADS)

Bektasli, Behzat

Graphs have a broad use in science classrooms, especially in physics. In physics, kinematics is probably the topic for which graphs are most widely used. The participants in this study were from two different grade-12 physics classrooms, advanced placement and calculus-based physics. The main purpose of this study was to search for the relationships between student spatial ability, logical thinking, mathematical achievement, and kinematics graphs interpretation skills. The Purdue Spatial Visualization Test, the Middle Grades Integrated Process Skills Test (MIPT), and the Test of Understanding Graphs in Kinematics (TUG-K) were used for quantitative data collection. Classroom observations were made to acquire ideas about classroom environment and instructional techniques. Factor analysis, simple linear correlation, multiple linear regression, and descriptive statistics were used to analyze the quantitative data. Each instrument has two principal components. The selection and calculation of the slope and of the area were the two principal components of TUG-K. MIPT was composed of a component based upon processing text and a second component based upon processing symbolic information. The Purdue Spatial Visualization Test was composed of a component based upon one-step processing and a second component based upon two-step processing of information. Student ability to determine the slope in a kinematics graph was significantly correlated with spatial ability, logical thinking, and mathematics aptitude and achievement. However, student ability to determine the area in a kinematics graph was only significantly correlated with student pre-calculus semester 2 grades. Male students performed significantly better than female students on the slope items of TUG-K. Also, male students performed significantly better than female students on the PSAT mathematics assessment and spatial ability. This study found that students have different levels of spatial ability, logical thinking, and mathematics aptitude and achievement levels. These different levels were related to student learning of kinematics and they need to be considered when kinematics is being taught. It might be easier for students to understand the kinematics graphs if curriculum developers include more activities related to spatial ability and logical thinking.

Three-mode mode-division-multiplexing passive optical network over 12-km low mode-crosstalk FMF using all-fiber mode MUX/DEMUX

NASA Astrophysics Data System (ADS)

Ren, Fang; Li, Juhao; Wu, Zhongying; Hu, Tao; Yu, Jinyi; Mo, Qi; He, Yongqi; Chen, Zhangyuan; Li, Zhengbin

2017-01-01

We propose three-mode mode-division-multiplexing passive optical network (MDM-PON) based on low mode-crosstalk few-mode fiber (FMF) and all-fiber mode multiplexer/demultiplexer (MUX/DEMUX). The FMF with step-index profile is designed and fabricated for effectively three-independent-spatial-mode transmission and low mode-crosstalk for MDM-PON transmission. The all-fiber mode MUX/DEMUX are composed of cascaded mode selective couplers (MSCs), which simultaneously multiplex or demultiplex multiple modes. Based on the low mode-crosstalk of the FMF and all-fiber mode MUX/DEMUX, each optical network unit (ONU) communicates with the optical line terminal (OLT) independently utilizing a different optical linearly polarized (LP) spatial mode in MDM-PON system. We experimentally demonstrate MDM-PON transmission of three independent-spatial-modes over 12-km FMF with 10-Gb/s optical on-off keying (OOK) signal and direct detection.

Laser system using regenerative amplifier

DOEpatents

Emmett, J.L.

1980-03-04

High energy laser system is disclosed using a regenerative amplifier, which relaxes all constraints on laser components other than the intrinsic damage level of matter, so as to enable use of available laser system components. This can be accomplished by use of segmented components, spatial filters, at least one amplifier using solid state or gaseous media, and separated reflector members providing a long round trip time through the regenerative cavity, thereby allowing slower switching and adequate time to clear the spatial filters, etc. The laser system simplifies component requirements and reduces component cost while providing high energy output. 10 figs.

Vortex Formation During Unsteady Boundary-Layer Separation

NASA Astrophysics Data System (ADS)

Das, Debopam; Arakeri, Jaywant H.

1998-11-01

Unsteady laminar boundary-layer separation is invariably accompanied by the formation of vortices. The aim of the present work is to study the vortex formation mechanism(s). An adverse pressure gradient causing a separation can be decomposed into a spatial component ( spatial variation of the velocity external to the boundary layer ) and a temporal component ( temporal variation of the external velocity ). Experiments were conducted in a piston driven 2-D water channel, where the spatial component could be be contolled by geometry and the temporal component by the piston motion. We present results for three divergent channel geometries. The piston motion consists of three phases: constant acceleration from start, contant velocity, and constant deceleration to stop. Depending on the geometry and piston motion we observe different types of unsteady separation and vortex formation.

Applying metapopulation theory to conservation of migratory birds

USGS Publications Warehouse

Esler, Daniel N.

2000-01-01

Metapopulation theory has proven useful for understanding the population structure and dynamics of many species of conservation concern. The metapopulation concept has been applied almost exclusively to nonmigratory species, however, for which subpopulation demographic independence—a requirement for a classically defined metapopulation - is explicitly related to geographic distribution and dispersal probabilities. Defining the degree of demographic independence among subpopulations of migratory animals, and thus the applicability of metapopulation theory as a conceptual framework for understanding population dynamics, is much more difficult. Unlike nonmigratory species, subpopulations of migratory animals cannot be defined as synonymous with geographic areas. Groups of migratory birds that are geographically separate at one part of the annual cycle may occur together at others, but co-occurrence in time and space does not preclude the demographic independence of subpopulations. I suggest that metapopulation theory can be applied to migratory species but that understanding the degree of subpopulation independence may require information about both spatial distribution throughout the annual cycle and behavioral mechanisms that may lead to subpopulation demographic independence. The key for applying metapopulation theory to migratory animals lies in identifying demographically independent subpopulations, even as they move during the annual cycle and potentially co-occur with other subpopulations. Using examples of migratory bird species, I demonstrate that spatial and temporal modes of subpopulation independence can interact with behavioral mechanisms to create demographically independent subpopulations, including cases in which subpopulations are not spatially distinct in some parts of the annual cycle.

Fostering Spatial vs. Metric Understanding in Geometry

ERIC Educational Resources Information Center

Kinach, Barbara M.

2012-01-01

Learning to reason spatially is increasingly recognized as an essential component of geometry education. Generally taken to be the "ability to represent, generate, transform, communicate, document, and reflect on visual information," "spatial reasoning" uses the spatial relationships between objects to form ideas. Spatial thinking takes a variety…

Direct Simulation of Extinction in a Slab of Spherical Particles

NASA Technical Reports Server (NTRS)

Mackowski, D.W.; Mishchenko, Michael I.

2013-01-01

The exact multiple sphere superposition method is used to calculate the coherent and incoherent contributions to the ensemble-averaged electric field amplitude and Poynting vector in systems of randomly positioned nonabsorbing spherical particles. The target systems consist of cylindrical volumes, with radius several times larger than length, containing spheres with positional configurations generated by a Monte Carlo sampling method. Spatially dependent values for coherent electric field amplitude, coherent energy flux, and diffuse energy flux, are calculated by averaging of exact local field and flux values over multiple configurations and over spatially independent directions for fixed target geometry, sphere properties, and sphere volume fraction. Our results reveal exponential attenuation of the coherent field and the coherent energy flux inside the particulate layer and thereby further corroborate the general methodology of the microphysical radiative transfer theory. An effective medium model based on plane wave transmission and reflection by a plane layer is used to model the dependence of the coherent electric field on particle packing density. The effective attenuation coefficient of the random medium, computed from the direct simulations, is found to agree closely with effective medium theories and with measurements. In addition, the simulation results reveal the presence of a counter-propagating component to the coherent field, which arises due to the internal reflection of the main coherent field component by the target boundary. The characteristics of the diffuse flux are compared to, and found to be consistent with, a model based on the diffusion approximation of the radiative transfer theory.

Attentional Modulation of Change Detection ERP Components by Peripheral Retro-Cueing

PubMed Central

Pazo-Álvarez, Paula; Roca-Fernández, Adriana; Gutiérrez-Domínguez, Francisco-Javier; Amenedo, Elena

2017-01-01

Change detection is essential for visual perception and performance in our environment. However, observers often miss changes that should be easily noticed. A failure in any of the processes involved in conscious detection (encoding the pre-change display, maintenance of that information within working memory, and comparison of the pre and post change displays) can lead to change blindness. Given that unnoticed visual changes in a scene can be easily detected once attention is drawn to them, it has been suggested that attention plays an important role on visual awareness. In the present study, we used behavioral and electrophysiological (ERPs) measures to study whether the manipulation of retrospective spatial attention affects performance and modulates brain activity related to the awareness of a change. To that end, exogenous peripheral cues were presented during the delay period (retro-cues) between the first and the second array using a one-shot change detection task. Awareness of a change was associated with a posterior negative amplitude shift around 228–292 ms (“Visual Awareness Negativity”), which was independent of retrospective spatial attention, as it was elicited to both validly and invalidly cued change trials. Change detection was also associated with a larger positive deflection around 420–580 ms (“Late Positivity”), but only when the peripheral retro-cues correctly predicted the change. Present results confirm that the early and late ERP components related to change detection can be functionally dissociated through manipulations of exogenous retro-cueing using a change blindness paradigm. PMID:28270759

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

Combining remote sensing and water-balance evapotranspiration estimates for the conterminous United States

USGS Publications Warehouse

Reitz, Meredith; Senay, Gabriel; Sanford, Ward E.

2017-01-01

Evapotranspiration (ET) is a key component of the hydrologic cycle, accounting for ~70% of precipitation in the conterminous U.S. (CONUS), but it has been a challenge to predict accurately across different spatio-temporal scales. The increasing availability of remotely sensed data has led to significant advances in the frequency and spatial resolution of ET estimates, derived from energy balance principles with variables such as temperature used to estimate surface latent heat flux. Although remote sensing methods excel at depicting spatial and temporal variability, estimation of ET independently of other water budget components can lead to inconsistency with other budget terms. Methods that rely on ground-based data better constrain long-term ET, but are unable to provide the same temporal resolution. Here we combine long-term ET estimates from a water-balance approach with the SSEBop (operational Simplified Surface Energy Balance) remote sensing-based ET product for 2000–2015. We test the new combined method, the original SSEBop product, and another remote sensing ET product (MOD16) against monthly measurements from 119 flux towers. The new product showed advantages especially in non-irrigated areas where the new method showed a coefficient of determination R2 of 0.44, compared to 0.41 for SSEBop or 0.35 for MOD16. The resulting monthly data set will be a useful, unique contribution to ET estimation, due to its combination of remote sensing-based variability and ground-based long-term water balance constraints.

Spatial and Temporal Reconstruction of Scottish Summer Temperatures for the Last 300 Years

NASA Astrophysics Data System (ADS)

Rydval, Miloš; Cook, Edward R.; Druckenbrod, Daniel; Larsson, Lars-Åke; Wilson, Rob

2015-04-01

It is important to place recent anthropogenic climate change into a longer term context. Despite a good understanding of past climate variation for much of the Scandinavian region, little is known about Scottish climate over recent centuries. In order to fill this current gap in our understanding of northwest European climate dynamics and thus provide the context necessary to assess likely future changes of climate in this climatically important region, the limited spatial and temporal coverage of instrumental data must be extended using proxy data. Tree-rings provide one of the best proxy data sources for such an exercise. Until recently, the development of dendrochronological records in Scotland for climatological purposes has been limited. To help develop insight into the patterns of temperature variability in this region, multiple tree-ring parameters including ring-width (RW), maximum latewood density (MXD) and blue intensity (BI) from a network of 42 living Scots pine (Pinus sylvestris L.) sites distributed throughout the Scottish Highlands were utilized to reconstruct mean summer temperature with a grid resolution of 0.5°. Due to considerable anthropogenic disturbance from past logging events at some locations, RW data were assessed and corrected for disturbance-related growth releases using a Combined Step and Trend Intervention Detection methodology prior to their utilization in reconstruction development. Although the BI parameter offers a cheaper alternative to MXD while providing similar information, some limitations have been noted related to heartwood-sapwood colour differences in some species that may induce low frequency chronology biases. To avoid such BI limitations, in addition to the use of individual parameter site chronologies, corrected RW series were also combined with BI data to develop filtered high-frequency-BI / low-frequency-RW composite band-pass chronologies. Utilizing the TR network, a point-by-point principal component regression nested analysis was used to derive spatially independent reconstructions of (0.5°) gridded summer temperatures. The reconstruction results identified the timing, scale and duration of warmer and colder periods in the recent past, revealing the spatial patterns of temperature variability in this region over the past few centuries. The spatial reconstruction results agree well with a 600-yr composite BI / RW reconstruction from central Scotland using independent Scots pine chronologies extended into the past with samples preserved in Highland lakes.

Parental Socioeconomic Status and the Neural Basis of Arithmetic: Differential Relations to Verbal and Visuo-spatial Representations

PubMed Central

Demir, Özlem Ece; Prado, Jérôme; Booth, James R.

2015-01-01

We examined the relation of parental socioeconomic status (SES) to the neural bases of subtraction in school-age children (9- to 12-year-olds). We independently localized brain regions subserving verbal versus visuo-spatial representations to determine whether the parental SES-related differences in children’s reliance on these neural representations vary as a function of math skill. At higher SES levels, higher skill was associated with greater recruitment of the left temporal cortex, identified by the verbal localizer. At lower SES levels, higher skill was associated with greater recruitment of right parietal cortex, identified by the visuo-spatial localizer. This suggests that depending on parental SES, children engage different neural systems to solve subtraction problems. Furthermore, SES was related to the activation in the left temporal and frontal cortex during the independent verbal localizer task, but it was not related to activation during the independent visuo-spatial localizer task. Differences in activation during the verbal localizer task in turn were related to differences in activation during the subtraction task in right parietal cortex. The relation was stronger at lower SES levels. This result suggests that SES-related differences in the visuo-spatial regions during subtraction might be based in SES-related verbal differences. PMID:25664675

Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities

PubMed Central

Santangelo, Valerio

2018-01-01

Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010) to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets. This design allowed comparing a condition in which participants monitored one stimulus/modality (either visual or auditory) in two spatial locations vs. a condition in which participants monitored two stimuli/modalities (both visual and auditory) in one spatial location. The analysis of the independent components (ICs) revealed that dividing attentional resources across two spatial locations necessitated a brain network involving the left ventro- and dorso-lateral prefrontal cortex plus the posterior parietal cortex, including the intraparietal sulcus (IPS) and the angular gyrus, bilaterally. The analysis of Granger causality highlighted that the activity of lateral prefrontal regions were predictive of the activity of all of the posteriors parietal nodes. By contrast, dividing attention across two sensory modalities necessitated a brain network including nodes belonging to the dorsal frontoparietal network, i.e., the bilateral frontal eye-fields (FEF) and IPS, plus nodes belonging to the salience network, i.e., the anterior cingulated cortex and the left and right anterior insular cortex (aIC). The analysis of Granger causality highlights a tight interdependence between the dorsal frontoparietal and salience nodes in trials requiring divided attention between different sensory modalities. The current findings therefore highlighted a dissociation among brain networks implicated during divided attention across spatial locations and sensory modalities, pointing out the importance of investigating effective connectivity of large-scale brain networks supporting complex behavior. PMID:29535614

Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities.

PubMed

Santangelo, Valerio

2018-01-01

Higher-order cognitive processes were shown to rely on the interplay between large-scale neural networks. However, brain networks involved with the capability to split attentional resource over multiple spatial locations and multiple stimuli or sensory modalities have been largely unexplored to date. Here I re-analyzed data from Santangelo et al. (2010) to explore the causal interactions between large-scale brain networks during divided attention. During fMRI scanning, participants monitored streams of visual and/or auditory stimuli in one or two spatial locations for detection of occasional targets. This design allowed comparing a condition in which participants monitored one stimulus/modality (either visual or auditory) in two spatial locations vs. a condition in which participants monitored two stimuli/modalities (both visual and auditory) in one spatial location. The analysis of the independent components (ICs) revealed that dividing attentional resources across two spatial locations necessitated a brain network involving the left ventro- and dorso-lateral prefrontal cortex plus the posterior parietal cortex, including the intraparietal sulcus (IPS) and the angular gyrus, bilaterally. The analysis of Granger causality highlighted that the activity of lateral prefrontal regions were predictive of the activity of all of the posteriors parietal nodes. By contrast, dividing attention across two sensory modalities necessitated a brain network including nodes belonging to the dorsal frontoparietal network, i.e., the bilateral frontal eye-fields (FEF) and IPS, plus nodes belonging to the salience network, i.e., the anterior cingulated cortex and the left and right anterior insular cortex (aIC). The analysis of Granger causality highlights a tight interdependence between the dorsal frontoparietal and salience nodes in trials requiring divided attention between different sensory modalities. The current findings therefore highlighted a dissociation among brain networks implicated during divided attention across spatial locations and sensory modalities, pointing out the importance of investigating effective connectivity of large-scale brain networks supporting complex behavior.

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.

Towards Solving the Mixing Problem in the Decomposition of Geophysical Time Series by Independent Component Analysis

NASA Technical Reports Server (NTRS)

Aires, Filipe; Rossow, William B.; Chedin, Alain; Hansen, James E. (Technical Monitor)

2000-01-01

The use of the Principal Component Analysis technique for the analysis of geophysical time series has been questioned in particular for its tendency to extract components that mix several physical phenomena even when the signal is just their linear sum. We demonstrate with a data simulation experiment that the Independent Component Analysis, a recently developed technique, is able to solve this problem. This new technique requires the statistical independence of components, a stronger constraint, that uses higher-order statistics, instead of the classical decorrelation a weaker constraint, that uses only second-order statistics. Furthermore, ICA does not require additional a priori information such as the localization constraint used in Rotational Techniques.

Hyperspectral functional imaging of the human brain

NASA Astrophysics Data System (ADS)

Toronov, Vladislav; Schelkanova, Irina

2013-03-01

We performed the independent component analysis of the hyperspectral functional near-infrared data acquired on humans during exercise and rest. We found that the hyperspectral functional data acquired on the human brain requires only two physiologically meaningful components to cover more than 50% o the temporal variance in hundreds of wavelengths. The analysis of the spectra of independent components showed that these components could be interpreted as results of changes in the cerebral blood volume and blood flow. Also, we found significant contributions of water and cytochrome c oxydase into changes associated with the independent components. Another remarkable effect of ICA was its good performance in terms of the filtering of the data noise.

Statistical Compression of Wind Speed Data

NASA Astrophysics Data System (ADS)

Tagle, F.; Castruccio, S.; Crippa, P.; Genton, M.

2017-12-01

In this work we introduce a lossy compression approach that utilizes a stochastic wind generator based on a non-Gaussian distribution to reproduce the internal climate variability of daily wind speed as represented by the CESM Large Ensemble over Saudi Arabia. Stochastic wind generators, and stochastic weather generators more generally, are statistical models that aim to match certain statistical properties of the data on which they are trained. They have been used extensively in applications ranging from agricultural models to climate impact studies. In this novel context, the parameters of the fitted model can be interpreted as encoding the information contained in the original uncompressed data. The statistical model is fit to only 3 of the 30 ensemble members and it adequately captures the variability of the ensemble in terms of seasonal internannual variability of daily wind speed. To deal with such a large spatial domain, it is partitioned into 9 region, and the model is fit independently to each of these. We further discuss a recent refinement of the model, which relaxes this assumption of regional independence, by introducing a large-scale component that interacts with the fine-scale regional effects.

Temporal Lobe and “Default” Hemodynamic Brain Modes Discriminate Between Schizophrenia and Bipolar Disorder

PubMed Central

Calhoun, Vince D.; Maciejewski, Paul K.; Pearlson, Godfrey D.; Kiehl, Kent A.

2009-01-01

Schizophrenia and bipolar disorder are currently diagnosed on the basis of psychiatric symptoms and longitudinal course. The determination of a reliable, biologically-based diagnostic indicator of these diseases (a biomarker) could provide the groundwork for developing more rigorous tools for differential diagnosis and treatment assignment. Recently, methods have been used to identify distinct sets of brain regions or “spatial modes” exhibiting temporally coherent brain activity. Using functional magnetic resonance imaging (fMRI) data and a multivariate analysis method, independent component analysis, we combined the temporal lobe and the default modes to discriminate subjects with bipolar disorder, chronic schizophrenia, and healthy controls. Temporal lobe and default mode networks were reliably identified in all participants. Classification results on an independent set of individuals revealed an average sensitivity and specificity of 90 and 95%, respectively. The use of coherent brain networks such as the temporal lobe and default mode networks may provide a more reliable measure of disease state than task-correlated fMRI activity. A combination of two such hemodynamic brain networks shows promise as a biomarker for schizophrenia and bipolar disorder. PMID:17894392

Temporal lobe and "default" hemodynamic brain modes discriminate between schizophrenia and bipolar disorder.

PubMed

Calhoun, Vince D; Maciejewski, Paul K; Pearlson, Godfrey D; Kiehl, Kent A

2008-11-01

Schizophrenia and bipolar disorder are currently diagnosed on the basis of psychiatric symptoms and longitudinal course. The determination of a reliable, biologically-based diagnostic indicator of these diseases (a biomarker) could provide the groundwork for developing more rigorous tools for differential diagnosis and treatment assignment. Recently, methods have been used to identify distinct sets of brain regions or "spatial modes" exhibiting temporally coherent brain activity. Using functional magnetic resonance imaging (fMRI) data and a multivariate analysis method, independent component analysis, we combined the temporal lobe and the default modes to discriminate subjects with bipolar disorder, chronic schizophrenia, and healthy controls. Temporal lobe and default mode networks were reliably identified in all participants. Classification results on an independent set of individuals revealed an average sensitivity and specificity of 90 and 95%, respectively. The use of coherent brain networks such as the temporal lobe and default mode networks may provide a more reliable measure of disease state than task-correlated fMRI activity. A combination of two such hemodynamic brain networks shows promise as a biomarker for schizophrenia and bipolar disorder.

All-optical video-image encryption with enforced security level using independent component analysis

NASA Astrophysics Data System (ADS)

Alfalou, A.; Mansour, A.

2007-10-01

In the last two decades, wireless communications have been introduced in various applications. However, the transmitted data can be, at any moment, intercepted by non-authorized people. That could explain why data encryption and secure transmission have gained enormous popularity. In order to secure data transmission, we should pay attention to two aspects: transmission rate and encryption security level. In this paper, we address these two aspects by proposing a new video-image transmission scheme. This new system consists in using the advantage of optical high transmission rate and some powerful signal processing tools to secure the transmitted data. The main idea of our approach is to secure transmitted information at two levels: at the classical level by using an adaptation of standard optical techniques and at a second level (spatial diversity) by using independent transmitters. In the second level, a hacker would need to intercept not only one channel but all of them in order to retrieve information. At the receiver, we can easily apply ICA algorithms to decrypt the received signals and retrieve information.

Spatial variability of throughfall in a stand of Scots pine (Pinus sylvestris L.) with deciduous admixture as influenced by canopy cover and stem distance

NASA Astrophysics Data System (ADS)

Kowalska, Anna; Boczoń, Andrzej; Hildebrand, Robert; Polkowska, Żaneta

2016-07-01

Vegetation cover affects the amount of precipitation, its chemical composition and its spatial distribution, and this may have implications for the distribution of water, nutrients and contaminants in the subsurface soil layer. The aim of this study was a detailed diagnosis of the spatio-temporal variability in the amount of throughfall (TF) and its chemical components in a 72-year-old pine stand with an admixture of oak and birch. The spatio-temporal variability in the amount of TF water and the concentrations and deposition of the TF components were studied. The components that are exchanged in canopy (H+, K, Mg, Mn, DOC, NH4+) were more variable than the components whose TF deposition is the sum of wet and dry (including gas) deposition and which undergo little exchange in the canopy (Na, Cl, NO3-, SO42-). The spatial distribution was temporally stable, especially during the leafed period. This study also investigated the effect of the selected pine stand characteristics on the spatial distribution of throughfall and its chemical components; the characteristics included leaf area index (LAI), the proportion of the canopy covered by deciduous species and pine crowns, and the distance from the nearest tree trunk. The LAI measured during the leafed and leafless periods had the greatest effect on the spatial distribution of TF deposition. No relationship was found between the spatial distribution of the amount of TF water and (i) the LAI; (ii) the canopy cover of broadleaf species or pines; or (iii) the distance from the trunks.

Spatial-spectral characterization of focused spatially chirped broadband laser beams.

PubMed

Greco, Michael J; Block, Erica; Meier, Amanda K; Beaman, Alex; Cooper, Samuel; Iliev, Marin; Squier, Jeff A; Durfee, Charles G

2015-11-20

Proper alignment is critical to obtain the desired performance from focused spatially chirped beams, for example in simultaneous spatial and temporal focusing (SSTF). We present a simple technique for inspecting the beam paths and focusing conditions for the spectral components of a broadband beam. We spectrally resolve the light transmitted past a knife edge as it was scanned across the beam at several axial positions. The measurement yields information about spot size, M2, and the propagation paths of different frequency components. We also present calculations to illustrate the effects of defocus aberration on SSTF beams.

Tree species exhibit complex patterns of distribution in bottomland hardwood forests

Treesearch

Luben D Dimov; Jim L Chambers; Brian R. Lockhart

2013-01-01

& Context Understanding tree interactions requires an insight into their spatial distribution. & Aims We looked for presence and extent of tree intraspecific spatial point pattern (random, aggregated, or overdispersed) and interspecific spatial point pattern (independent, aggregated, or segregated). & Methods We established twelve 0.64-ha plots in natural...

Interaction of dissolution, sorption and biodegradation on transport of BTEX in a saturated groundwater system: Numerical modeling and spatial moment analysis

NASA Astrophysics Data System (ADS)

Valsala, Renu; Govindarajan, Suresh Kumar

2018-06-01

Interaction of various physical, chemical and biological transport processes plays an important role in deciding the fate and migration of contaminants in groundwater systems. In this study, a numerical investigation on the interaction of various transport processes of BTEX in a saturated groundwater system is carried out. In addition, the multi-component dissolution from a residual BTEX source under unsteady flow conditions is incorporated in the modeling framework. The model considers Benzene, Toluene, Ethyl Benzene and Xylene dissolving from the residual BTEX source zone to undergo sorption and aerobic biodegradation within the groundwater aquifer. Spatial concentration profiles of dissolved BTEX components under the interaction of various sorption and biodegradation conditions have been studied. Subsequently, a spatial moment analysis is carried out to analyze the effect of interaction of various transport processes on the total dissolved mass and the mobility of dissolved BTEX components. Results from the present numerical study suggest that the interaction of dissolution, sorption and biodegradation significantly influence the spatial distribution of dissolved BTEX components within the saturated groundwater system. Mobility of dissolved BTEX components is also found to be affected by the interaction of these transport processes.

Complexity vs. unity in unilateral spatial neglect.

PubMed

Rode, G; Fourtassi, M; Pagliari, C; Pisella, L; Rossetti, Y

Unilateral spatial neglect constitutes a heterogeneous syndrome characterized by two main entangled components: a contralesional bias of spatial attention orientation; and impaired building and/or exploration of mental representations of space. These two components are present in different subtypes of unilateral spatial neglect (visual, auditory, somatosensory, motor, allocentric, egocentric, personal, representational and productive manifestations). Detailed anatomical and clinical analyses of these conditions and their underlying disorders show the complexity of spatial cognitive deficits and the difficulty of proposing just one explanation. This complexity is in contrast, however, to the widely acknowledged effectiveness of rehabilitation of the various symptoms and subtypes of unilateral spatial neglect, exemplified in the case of prism adaptation. These common effects are reflections of the unity of the physiotherapeutic mechanisms behind the higher brain functions related to multisensory integration and spatial representations, whereas the paradoxical aspects of unilateral spatial neglect emphasize the need for a greater understanding of spatial cognitive disorders. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Using spatial patterns in illegal wildlife uses to reveal connections between subsistence hunting and trade.

PubMed

Sánchez-Mercado, Ada; Asmüssen, Marianne; Rodríguez-Clark, Kathryn M; Rodríguez, Jon Paul; Jedrzejewski, Wlodzimierz

2016-12-01

Although most often considered independently, subsistence hunting, domestic trade, and international trade as components of illegal wildlife use (IWU) may be spatially correlated. Understanding how and where subsistence and commercial uses may co-occur has important implications for the design and implementation of effective conservation actions. We analyzed patterns in the joint geographical distribution of illegal commercial and subsistence use of multiple wildlife species in Venezuela and evaluated whether available data were sufficient to provide accurate estimates of the magnitude, scope, and detectability of IWU. We compiled records of illegal subsistence hunting and trade from several sources and fitted a random-forest classification model to predict the spatial distribution of IWUs. From 1969 to 2014, 404 species and 8,340,921 specimens were involved in IWU, for a mean extraction rate of 185,354 individuals/year. Birds were the most speciose group involved (248 spp.), but reptiles had the highest extraction rates (126,414 individuals/year vs. 3,133 individuals/year for birds). Eighty-eight percent of international trade records spatially overlapped with domestic trade, especially in the north and along the coast but also in western inland areas. The distribution of domestic trade was broadly distributed along roads, suggesting that domestic trade does not depend on large markets in cities. Seventeen percent of domestic trade records overlapped with subsistence hunting, but the spatial distribution of this overlap covered a much larger area than between commercial uses. Domestic trade seems to respond to demand from rural more than urban communities. Our approach will be useful for understanding how IWU works at national scales in other parts of the world. © 2016 Society for Conservation Biology.

Spatial and Climate Literacy: Connecting Urban and Rural Students

NASA Astrophysics Data System (ADS)

Boger, R. A.; Low, R.; Mandryk, C.; Gorokhovich, Y.

2013-12-01

Through a collaboration between the University of Nebraska-Lincoln (UNL), Brooklyn College, and Lehman College, four independent but linked modules were developed and piloted in courses offered at Brooklyn College and UNL simultaneously. Module content includes climate change science and literacy principles, using geospatial technologies (GIS, GPS and remote sensing) as a vehicle to explore issues associated with global, regional, and local climate change in a concrete, quantitative and visual way using Internet resources available through NASA, NOAA, USGS, and a variety of universities and organizations. The materials take an Earth system approach and incorporate sustainability, resilience, water and watersheds, weather and climate, and food security topics throughout the semester. The research component of the project focuses on understanding the role of spatial literacy and authentic inquiry based experiences in climate change understanding and improving confidence in teaching science. In particular, engaging learners in both climate change science and GIS simultaneously provides opportunities to examine questions about the role that data manipulation, mental representation, and spatial literacy plays in students' abilities to understand the consequences and impacts of climate change. Pre and post surveys were designed to discern relationships between spatial cognitive processes and effective acquisition of climate change science concepts in virtual learning environments as well as alignment of teacher's mental models of nature of science and climate system dynamics to scientific models. The courses will again be offered simultaneously in Spring 2014 at Brooklyn College and UNL. Evaluation research will continue to examine the connections between spatial and climate literacy and teacher's mental models (via qualitative textual analysis using MAXQDA text analysis, and UCINET social network analysis programs) as well as how urban-rural learning interactions may influence climate literacy.

Exploration as a mediator of the relation between the attainment of motor milestones and the development of spatial cognition and spatial language.

PubMed

Oudgenoeg-Paz, Ora; Leseman, Paul P M; Volman, M Chiel J M

2015-09-01

The embodied-cognition approach views cognition and language as grounded in daily sensorimotor child-environment interactions. Therefore, the attainment of motor milestones is expected to play a role in cognitive-linguistic development. Early attainment of unsupported sitting and independent walking indeed predict better spatial cognition and language at later ages. However, evidence linking these milestones with the development of spatial language and evidence regarding factors that might mediate this relation are scarce. The current study examined whether exploration of spatial-relational object properties (e.g., the possibility of containing or stacking) and exploration of the space through self-locomotion mediate the effect of, respectively, age of sitting and age of walking on spatial cognition and spatial language. Thus, we hypothesized that an earlier age of sitting and walking predicts, respectively, higher levels of spatial-relational object exploration and exploration through self-locomotion, which in turn, predict better spatial cognition and spatial language at later ages. Fifty-nine Dutch children took part in a longitudinal study. A combination of tests, observations, and parental reports was used to measure motor development, exploratory behavior (age 20 months), spatial memory (age 24 months), spatial processing (age 32 months), and spatial language (age 36 months). Results show that attainment of sitting predicted spatial memory and spatial language, but spatial-relational object exploration did not mediate these effects. Attainment of independent walking predicted spatial processing and spatial language, and exploration through self-locomotion (partially) mediated these relations. These findings extend previous work and provide partial support for the hypotheses about the mediating role of exploration. (c) 2015 APA, all rights reserved).

Spatial Organization In Europe of Decadal and Interdecadal Fluctuations In Annual Rainfall

NASA Astrophysics Data System (ADS)

Lucero, O. A.; Rodriguez, N. C.

In this research the spatial patterns of decadal and bidecadal fluctuations in annual rainfall in Europe are identified. Filtering of time series of anomaly of annual rainfall is carried out using the Morlet wavelet technique. Reconstruction is achieved by sum- ming the contributions from bands of wavelet timescales; the decadal band and the bidecadal band are composed of contributions from the band of (10- to 17-year] and (17- to 27- year] timescales respectively. Results indicate that 1) the spatial organi- zation of decadal and bidecadal components of annual rainfall are standing wave-like organized patterns. Three standing decadal fluctuations zonally aligned formed the spatial pattern from 1900 until 1931; thereafter the pattern changed into a NW-SE orientation. The decadal band shows an average 12-year period. 2) The spatial orga- nization of bidecadal component was composed of three standing fluctuations since 1903 to 1986. After 1987 two standing bidecadal fluctuations were located on Europe. The orientation of bidecadal fluctuations changed during the period under study. Until 1913 the spatial pattern of the bidecadal component was zonally aligned. Since 1913 until 1986 the three bidecadal fluctuations composing the spatial pattern were aligned SW U NE; starting 1987 the spatial pattern is composed of two standing fluctuations zonally aligned. The bidecadal spatial pattern shows an average period of 20- to 22- year length. 3) At decadal and bidecadal timescales, the first principal component of the spatial field of anomaly of annual rainfall and the NAO index are connected. The upper positive third (lower negative third) of values of first principal component are indicative of extensive area with positive (negative) anomaly of annual rainfall. 4) At decadal timescale the relative phase between the first PC and the NAO index changes through the period under study; these changes define three regimes: 1) Dur- ing the regime covering the period 1900 (start of period under study) to about 1945, at the time of peak values of decadal NAO-index it takes place a transition between extremes (a neutral state) of the decadal rainfall spatial pattern (first PC takes small absolute values). Besides, for positive (negative) peak value of NAO index the spatial pattern of annual rainfall is evolving toward an area of predominantly positive (nega- tive) anomaly. 2) The second regime starts about 1946 and reaches up to early 1980s. At the time of negative (positive) peak of decadal NAO there is a prevailing spatial pattern of positive (negative) anomaly of decadal rainfall. 3) The third regime starts 1 about late 1970s and reaches to the end of the period under study (in 1996). There is a change of relative phase within this period in late 1980s. In this regime a spatial pattern of prevailing positive or negative anomaly of decadal rainfall takes place dur- ing values of decadal NAO close to zero. 5) At bidecadal timescale the relative phase between the first PC and the NAO index remains almost constant through the period under study. The first PC of the transformed bidecadal component of annual rainfall anomaly attains its positive (negative) peak about three years before the bidecadal component of NAO reaches its negative (positive) peak. 2

An Integrated High Resolution Hydrometeorological Modeling Testbed using LIS and WRF

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.; Peters-Lidard, Christa D.; Eastman, Joseph L.; Tao, Wei-Kuo

2007-01-01

Scientists have made great strides in modeling physical processes that represent various weather and climate phenomena. Many modeling systems that represent the major earth system components (the atmosphere, land surface, and ocean) have been developed over the years. However, developing advanced Earth system applications that integrates these independently developed modeling systems have remained a daunting task due to limitations in computer hardware and software. Recently, efforts such as the Earth System Modeling Ramework (ESMF) and Assistance for Land Modeling Activities (ALMA) have focused on developing standards, guidelines, and computational support for coupling earth system model components. In this article, the development of a coupled land-atmosphere hydrometeorological modeling system that adopts these community interoperability standards, is described. The land component is represented by the Land Information System (LIS), developed by scientists at the NASA Goddard Space Flight Center. The Weather Research and Forecasting (WRF) model, a mesoscale numerical weather prediction system, is used as the atmospheric component. LIS includes several community land surface models that can be executed at spatial scales as fine as 1km. The data management capabilities in LIS enable the direct use of high resolution satellite and observation data for modeling. Similarly, WRF includes several parameterizations and schemes for modeling radiation, microphysics, PBL and other processes. Thus the integrated LIS-WRF system facilitates several multi-model studies of land-atmosphere coupling that can be used to advance earth system studies.

Component Neural Systems for the Creation of Emotional Memories during Free Viewing of a Complex, Real-World Event

PubMed Central

Botzung, Anne; LaBar, Kevin S.; Kragel, Philip; Miles, Amanda; Rubin, David C.

2010-01-01

To investigate the neural systems that contribute to the formation of complex, self-relevant emotional memories, dedicated fans of rival college basketball teams watched a competitive game while undergoing functional magnetic resonance imaging (fMRI). During a subsequent recognition memory task, participants were shown video clips depicting plays of the game, stemming either from previously-viewed game segments (targets) or from non-viewed portions of the same game (foils). After an old–new judgment, participants provided emotional valence and intensity ratings of the clips. A data driven approach was first used to decompose the fMRI signal acquired during free viewing of the game into spatially independent components. Correlations were then calculated between the identified components and post-scanning emotion ratings for successfully encoded targets. Two components were correlated with intensity ratings, including temporal lobe regions implicated in memory and emotional functions, such as the hippocampus and amygdala, as well as a midline fronto-cingulo-parietal network implicated in social cognition and self-relevant processing. These data were supported by a general linear model analysis, which revealed additional valence effects in fronto-striatal-insular regions when plays were divided into positive and negative events according to the fan's perspective. Overall, these findings contribute to our understanding of how emotional factors impact distributed neural systems to successfully encode dynamic, personally-relevant event sequences. PMID:20508750

Optical network scaling: roles of spectral and spatial aggregation.

PubMed

Arık, Sercan Ö; Ho, Keang-Po; Kahn, Joseph M

2014-12-01

As the bit rates of routed data streams exceed the throughput of single wavelength-division multiplexing channels, spectral and spatial traffic aggregation become essential for optical network scaling. These aggregation techniques reduce network routing complexity by increasing spectral efficiency to decrease the number of fibers, and by increasing switching granularity to decrease the number of switching components. Spectral aggregation yields a modest decrease in the number of fibers but a substantial decrease in the number of switching components. Spatial aggregation yields a substantial decrease in both the number of fibers and the number of switching components. To quantify routing complexity reduction, we analyze the number of multi-cast and wavelength-selective switches required in a colorless, directionless and contentionless reconfigurable optical add-drop multiplexer architecture. Traffic aggregation has two potential drawbacks: reduced routing power and increased switching component size.

RHCV Telescope System Operations Manual

DTIC Science & Technology

2018-01-05

hardware and software components. Several of the components are closely coupled and rely on one-another, while others are largely independent. This...of hardware and software components. Several of the components are closely coupled and rely on one-another, while others are largely independent. This...attendant training The use cases are briefly described in separate sections, and step-by-step instructions are presented. Each section begins on a new

Every day I'm rufflin': Calcium sensing and actin dynamics in the growth factor-independent membrane ruffling of professional phagocytes.

PubMed

Schlam, Daniel; Canton, Johnathan

2017-04-03

Professional phagocytes continuously extend dynamic, actin-driven membrane protrusions. These protrusions, often referred to as membrane ruffles, serve a critical role in the essential phagocyte processes of macropinocytosis and phagocytosis. Small GTPases, such as RAC1/2, spatially and temporally regulate membrane ruffle formation. We have recently shown that extracellular calcium regulates the elaboration of membrane ruffles primarily through the synthesis of phosphatidic acid (PtdOH) at the plasma membrane. RAC1/2 guanine nucleotide exchange factors harbouring polybasic stretches are recruited by PtdOH to sites of ruffle formation. Here we discuss our findings and offer perspectives on how the regulation of dynamic actin structures at the plasma membrane by small GTPases is a critical component of phagocyte function.

Age, gesture span, and dissociations among component subsystems of working memory.

PubMed

Dolman, R; Roy, E A; Dimeck, P T; Hall, C R

2000-01-01

Working memory was examined in old and young adults using a series of span tasks, including the forward versions of the visual-spatial and digit span tasks from the Wechsler Memory Scale-Revised, and comparable hand gesture and visual design span tasks. The observation that the young participants performed significantly better on all the tasks except digit span suggested that aging has an impact on some component subsystems of working memory but not others. Analyses of intercorrelations in span performance supports the dissociation among three component subsystems, one for auditory verbal information (the articulatory loop), one for visual-spatial information (visual-spatial scratch-pad), and one for hand/body postural configuration.

A novel principal component analysis for spatially misaligned multivariate air pollution data.

PubMed

Jandarov, Roman A; Sheppard, Lianne A; Sampson, Paul D; Szpiro, Adam A

2017-01-01

We propose novel methods for predictive (sparse) PCA with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in the observed data as possible, while also ensuring the corresponding principal component scores can be predicted accurately by means of spatial statistics at locations where air pollution measurements are not available. This will make it possible to identify important mixtures of air pollutants and to quantify their health effects in cohort studies, where currently available methods cannot be used. We demonstrate the utility of predictive (sparse) PCA in simulated data and apply the approach to annual averages of particulate matter speciation data from national Environmental Protection Agency (EPA) regulatory monitors.

Lagrange constraint neural network for audio varying BSS

NASA Astrophysics Data System (ADS)

Szu, Harold H.; Hsu, Charles C.

2002-03-01

Lagrange Constraint Neural Network (LCNN) is a statistical-mechanical ab-initio model without assuming the artificial neural network (ANN) model at all but derived it from the first principle of Hamilton and Lagrange Methodology: H(S,A)= f(S)- (lambda) C(s,A(x,t)) that incorporates measurement constraint C(S,A(x,t))= (lambda) ([A]S-X)+((lambda) 0-1)((Sigma) isi -1) using the vector Lagrange multiplier-(lambda) and a- priori Shannon Entropy f(S) = -(Sigma) i si log si as the Contrast function of unknown number of independent sources si. Szu et al. have first solved in 1997 the general Blind Source Separation (BSS) problem for spatial-temporal varying mixing matrix for the real world remote sensing where a large pixel footprint implies the mixing matrix [A(x,t)] necessarily fill with diurnal and seasonal variations. Because the ground truth is difficult to be ascertained in the remote sensing, we have thus illustrated in this paper, each step of the LCNN algorithm for the simulated spatial-temporal varying BSS in speech, music audio mixing. We review and compare LCNN with other popular a-posteriori Maximum Entropy methodologies defined by ANN weight matrix-[W] sigmoid-(sigma) post processing H(Y=(sigma) ([W]X)) by Bell-Sejnowski, Amari and Oja (BSAO) called Independent Component Analysis (ICA). Both are mirror symmetric of the MaxEnt methodologies and work for a constant unknown mixing matrix [A], but the major difference is whether the ensemble average is taken at neighborhood pixel data X's in BASO or at the a priori sources S variables in LCNN that dictates which method works for spatial-temporal varying [A(x,t)] that would not allow the neighborhood pixel average. We expected the success of sharper de-mixing by the LCNN method in terms of a controlled ground truth experiment in the simulation of variant mixture of two music of similar Kurtosis (15 seconds composed of Saint-Saens Swan and Rachmaninov cello concerto).

Responses of primate caudal parabrachial nucleus and Kolliker-fuse nucleus neurons to whole body rotation

NASA Technical Reports Server (NTRS)

Balaban, Carey D.; McGee, David M.; Zhou, Jianxun; Scudder, Charles A.

2002-01-01

The caudal aspect of the parabrachial (PBN) and Kolliker-Fuse (KF) nuclei receive vestibular nuclear and visceral afferent information and are connected reciprocally with the spinal cord, hypothalamus, amygdala, and limbic cortex. Hence, they may be important sites of vestibulo-visceral integration, particularly for the development of affective responses to gravitoinertial challenges. Extracellular recordings were made from caudal PBN cells in three alert, adult female Macaca nemestrina through an implanted chamber. Sinusoidal and position trapezoid angular whole body rotation was delivered in yaw, roll, pitch, and vertical semicircular canal planes. Sites were confirmed histologically. Units that responded during rotation were located in lateral and medial PBN and KF caudal to the trochlear nerve at sites that were confirmed anatomically to receive superior vestibular nucleus afferents. Responses to whole-body angular rotation were modeled as a sum of three signals: angular velocity, a leaky integration of angular velocity, and vertical position. All neurons displayed angular velocity and integrated angular velocity sensitivity, but only 60% of the neurons were position-sensitive. These responses to vertical rotation could display symmetric, asymmetric, or fully rectified cosinusoidal spatial tuning about a best orientation in different cells. The spatial properties of velocity and integrated velocity and position responses were independent for all position-sensitive neurons; the angular velocity and integrated angular velocity signals showed independent spatial tuning in the position-insensitive neurons. Individual units showed one of three different orientations of their excitatory axis of velocity rotation sensitivity: vertical-plane-only responses, positive elevation responses (vertical plane plus ipsilateral yaw), and negative elevation axis responses (vertical plane plus negative yaw). The interactions between the velocity and integrated velocity components also produced variations in the temporal pattern of responses as a function of rotation direction. These findings are consistent with the hypothesis that a vestibulorecipient region of the PBN and KF integrates signals from the vestibular nuclei and relay information about changes in whole-body orientation to pathways that produce homeostatic and affective responses.

Higher-order scene statistics of breast images

NASA Astrophysics Data System (ADS)

Abbey, Craig K.; Sohl-Dickstein, Jascha N.; Olshausen, Bruno A.; Eckstein, Miguel P.; Boone, John M.

2009-02-01

Researchers studying human and computer vision have found description and construction of these systems greatly aided by analysis of the statistical properties of naturally occurring scenes. More specifically, it has been found that receptive fields with directional selectivity and bandwidth properties similar to mammalian visual systems are more closely matched to the statistics of natural scenes. It is argued that this allows for sparse representation of the independent components of natural images [Olshausen and Field, Nature, 1996]. These theories have important implications for medical image perception. For example, will a system that is designed to represent the independent components of natural scenes, where objects occlude one another and illumination is typically reflected, be appropriate for X-ray imaging, where features superimpose on one another and illumination is transmissive? In this research we begin to examine these issues by evaluating higher-order statistical properties of breast images from X-ray projection mammography (PM) and dedicated breast computed tomography (bCT). We evaluate kurtosis in responses of octave bandwidth Gabor filters applied to PM and to coronal slices of bCT scans. We find that kurtosis in PM rises and quickly saturates for filter center frequencies with an average value above 0.95. By contrast, kurtosis in bCT peaks near 0.20 cyc/mm with kurtosis of approximately 2. Our findings suggest that the human visual system may be tuned to represent breast tissue more effectively in bCT over a specific range of spatial frequencies.

Spatial Abilities and Anatomy Knowledge Assessment: A Systematic Review

ERIC Educational Resources Information Center

Langlois, Jean; Bellemare, Christian; Toulouse, Josée; Wells, George A.

2017-01-01

Anatomy knowledge has been found to include both spatial and non-spatial components. However, no systematic evaluation of studies relating spatial abilities and anatomy knowledge has been undertaken. The objective of this study was to conduct a systematic review of the relationship between spatial abilities test and anatomy knowledge assessment. A…

Preliminary design of the spatial filters used in the multipass amplification system of TIL

NASA Astrophysics Data System (ADS)

Zhu, Qihua; Zhang, Xiao Min; Jing, Feng

1998-12-01

The spatial filters are used in Technique Integration Line, which has a multi-pass amplifier, not only to suppress parasitic high spatial frequency modes but also to provide places for inserting a light isolator and injecting the seed beam, and to relay image while the beam passes through the amplifiers several times. To fulfill these functions, the parameters of the spatial filters are optimized by calculations and analyzes with the consideration of avoiding the plasma blow-off effect and components demanding by ghost beam focus. The 'ghost beams' are calculated by ray tracing. A software was developed to evaluate the tolerance of the spatial filters and their components, and to align the whole system on computer simultaneously.

Intelligibility for Binaural Speech with Discarded Low-SNR Speech Components.

PubMed

Schoenmaker, Esther; van de Par, Steven

2016-01-01

Speech intelligibility in multitalker settings improves when the target speaker is spatially separated from the interfering speakers. A factor that may contribute to this improvement is the improved detectability of target-speech components due to binaural interaction in analogy to the Binaural Masking Level Difference (BMLD). This would allow listeners to hear target speech components within specific time-frequency intervals that have a negative SNR, similar to the improvement in the detectability of a tone in noise when these contain disparate interaural difference cues. To investigate whether these negative-SNR target-speech components indeed contribute to speech intelligibility, a stimulus manipulation was performed where all target components were removed when local SNRs were smaller than a certain criterion value. It can be expected that for sufficiently high criterion values target speech components will be removed that do contribute to speech intelligibility. For spatially separated speakers, assuming that a BMLD-like detection advantage contributes to intelligibility, degradation in intelligibility is expected already at criterion values below 0 dB SNR. However, for collocated speakers it is expected that higher criterion values can be applied without impairing speech intelligibility. Results show that degradation of intelligibility for separated speakers is only seen for criterion values of 0 dB and above, indicating a negligible contribution of a BMLD-like detection advantage in multitalker settings. These results show that the spatial benefit is related to a spatial separation of speech components at positive local SNRs rather than to a BMLD-like detection improvement for speech components at negative local SNRs.

Solid perception mechanism by a shading pattern: spatial frequency components in a corrugated wave pattern.

PubMed

Nameda, N

1988-01-01

Illumination allows solid object perception to be obtained and depicted by a shading pattern produced by lighting. The shading cue, as one of solid perception cues (Gibson 1979), was investigated in regard to a white corrugated wave shape, using computer graphic device: Tospix-2. The reason the corrugated wave was chosen, is that an alternately bright and dark pattern, produced by shading, can be conveniently analyzed into contained spatial frequencies. This paper reports spatial frequency properties contained in the shading pattern. The shading patterns, input into the computer graphic device, are analyzed by Fourier Transformation by the same device. After the filtration by various spatial frequency low and high pass filters, Inverse Fourier Transformation is carried out for the residual components. The result of the analysis indicates that the third through higher harmonics components are important in regard to presenting a solid reality feeling in solid perception. Sakata (1983) also reported that an edged pattern, superimposed onto a lower sinusoidal pattern, was important in solid perception. The third through higher harmonics components express the changing position of luminance on the pattern, and a slanted plane relating to the light direction. Detection of a solid shape, constructed with flat planes, is assumed to be on the bottom of the perfect curved solid perception mechanism. Apparent evidence for this assumption, in difficult visual conditions, is that a flat paneled solid is seen before the curved solid. This mechanism is explained by two spatial frequency neural network systems, assumed as having correspondence with higher spatial frequency detection and lower spatial frequency detection.

Spatial and temporal variability of hyperspectral signatures of terrain

NASA Astrophysics Data System (ADS)

Jones, K. F.; Perovich, D. K.; Koenig, G. G.

2008-04-01

Electromagnetic signatures of terrain exhibit significant spatial heterogeneity on a range of scales as well as considerable temporal variability. A statistical characterization of the spatial heterogeneity and spatial scaling algorithms of terrain electromagnetic signatures are required to extrapolate measurements to larger scales. Basic terrain elements including bare soil, grass, deciduous, and coniferous trees were studied in a quasi-laboratory setting using instrumented test sites in Hanover, NH and Yuma, AZ. Observations were made using a visible and near infrared spectroradiometer (350 - 2500 nm) and hyperspectral camera (400 - 1100 nm). Results are reported illustrating: i) several difference scenes; ii) a terrain scene time series sampled over an annual cycle; and iii) the detection of artifacts in scenes. A principal component analysis indicated that the first three principal components typically explained between 90 and 99% of the variance of the 30 to 40-channel hyperspectral images. Higher order principal components of hyperspectral images are useful for detecting artifacts in scenes.

Birthday Cake Activity Structured Arrangement for Helping Children Determining Quantities

ERIC Educational Resources Information Center

Mariana, Neni

2010-01-01

Few researches have been concerned about relation between children's spatial thinking and number sense. Narrowing for this small research, we focused on one component of spatial thinking, that is structuring objects, and one component of number senses, that is cardinality by determining quantities. This study focused on a design research that was…

Spatiotemporal Filtering Using Principal Component Analysis and Karhunen-Loeve Expansion Approaches for Regional GPS Network Analysis

NASA Technical Reports Server (NTRS)

Dong, D.; Fang, P.; Bock, F.; Webb, F.; Prawirondirdjo, L.; Kedar, S.; Jamason, P.

2006-01-01

Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals. The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases. A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis (PCA) and the Karhunen-Loeve expansion (KLE) both decompose network time series into a set of temporally varying modes and their spatial responses. Therefore they provide a mathematical framework to perform spatiotemporal filtering.We apply the combination of PCA and KLE to daily station coordinate time series of the Southern California Integrated GPS Network (SCIGN) for the period 2000 to 2004. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components, which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California. Furthermore, the common mode errors exhibit temporally nonrandom patterns.

Neural networks supporting autobiographical memory retrieval in post-traumatic stress disorder

PubMed Central

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

2013-01-01

Post-traumatic stress disorder (PTSD) affects the functional recruitment and connectivity between neural regions during autobiographical memory (AM) retrieval that overlap with default and control networks. Whether such univariate changes relate to potential differences in the contribution of large-scale neural networks supporting cognition in PTSD is unknown. In the current functional MRI (fMRI) study we employ independent component analysis to examine the influence the engagement of neural networks during the recall of personal memories in PTSD (15 participants) compared to non-trauma exposed, healthy controls (14 participants). We found that the PTSD group recruited similar neural networks when compared to controls during AM recall, including default network subsystems and control networks, but there were group differences in the spatial and temporal characteristics of these networks. First, there were spatial differences in the contribution of the anterior and posterior midline across the networks, and with the amygdala in particular for the medial temporal subsystem of the default network. Second, there were temporal differences in the relationship of the medial prefrontal subsystem of the default network, with less temporal coupling of this network during AM retrieval in PTSD relative to controls. These findings suggest that spatial and temporal characteristics of the default and control networks potentially differ in PTSD versus healthy controls, and contribute to altered recall of personal memory. PMID:23483523

Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project

PubMed Central

Kandala, Sridhar; Nolan, Dan; Laumann, Timothy O.; Power, Jonathan D.; Adeyemo, Babatunde; Harms, Michael P.; Petersen, Steven E.; Barch, Deanna M.

2016-01-01

Abstract Like all resting-state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifacts arising from head motion and physiological processes. Functional connectivity estimates (Pearson's correlation coefficients) were inflated for high-motion time points and for high-motion participants. This inflation occurred across the brain, suggesting the presence of globally distributed artifacts. The degree of inflation was further increased for connections between nearby regions compared with distant regions, suggesting the presence of distance-dependent spatially specific artifacts. We evaluated several denoising methods: censoring high-motion time points, motion regression, the FMRIB independent component analysis-based X-noiseifier (FIX), and mean grayordinate time series regression (MGTR; as a proxy for global signal regression). The results suggest that FIX denoising reduced both types of artifacts, but left substantial global artifacts behind. MGTR significantly reduced global artifacts, but left substantial spatially specific artifacts behind. Censoring high-motion time points resulted in a small reduction of distance-dependent and global artifacts, eliminating neither type. All denoising strategies left differences between high- and low-motion participants, but only MGTR substantially reduced those differences. Ultimately, functional connectivity estimates from HCP data showed spatially specific and globally distributed artifacts, and the most effective approach to address both types of motion-correlated artifacts was a combination of FIX and MGTR. PMID:27571276

Identifying Genetic Hotspots by Mapping Molecular Diversity of Widespread Trees: When Commonness Matters.

PubMed

Souto, Cintia P; Mathiasen, Paula; Acosta, María Cristina; Quiroga, María Paula; Vidal-Russell, Romina; Echeverría, Cristian; Premoli, Andrea C

2015-01-01

Conservation planning requires setting priorities at the same spatial scale at which decision-making processes are undertaken considering all levels of biodiversity, but current methods for identifying biodiversity hotspots ignore its genetic component. We developed a fine-scale approach based on the definition of genetic hotspots, which have high genetic diversity and unique variants that represent their evolutionary potential and evolutionary novelties. Our hypothesis is that wide-ranging taxa with similar ecological tolerances, yet of phylogenetically independent lineages, have been and currently are shaped by ecological and evolutionary forces that result in geographically concordant genetic patterns. We mapped previously published genetic diversity and unique variants of biparentally inherited markers and chloroplast sequences for 9 species from 188 and 275 populations, respectively, of the 4 woody dominant families of the austral temperate forest, an area considered a biodiversity hotspot. Spatial distribution patterns of genetic polymorphisms differed among taxa according to their ecological tolerances. Eight genetic hotspots were detected and we recommend conservation actions for some in the southern Coastal Range in Chile. Existing spatially explicit genetic data from multiple populations and species can help to identify biodiversity hotspots and guide conservation actions to establish science-based protected areas that will preserve the evolutionary potential of key habitats and species. © The American Genetic Association 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Geospatial Characterization of Fluvial Wood Arrangement in a Semi-confined Alluvial River

NASA Astrophysics Data System (ADS)

Martin, D. J.; Harden, C. P.; Pavlowsky, R. T.

2014-12-01

Large woody debris (LWD) has become universally recognized as an integral component of fluvial systems, and as a result, has become increasingly common as a river restoration tool. However, "natural" processes of wood recruitment and the subsequent arrangement of LWD within the river network are poorly understood. This research used a suite of spatial statistics to investigate longitudinal arrangement patterns of LWD in a low-gradient, Midwestern river. First, a large-scale GPS inventory of LWD, performed on the Big River in the eastern Missouri Ozarks, resulted in over 4,000 logged positions of LWD along seven river segments that covered nearly 100 km of the 237 km river system. A global Moran's I analysis indicates that LWD density is spatially autocorrelated and displays a clustering tendency within all seven river segments (P-value range = 0.000 to 0.054). A local Moran's I analysis identified specific locations along the segments where clustering occurs and revealed that, on average, clusters of LWD density (high or low) spanned 400 m. Spectral analyses revealed that, in some segments, LWD density is spatially periodic. Two segments displayed strong periodicity, while the remaining segments displayed varying degrees of noisiness. Periodicity showed a positive association with gravel bar spacing and meander wavelength, although there were insufficient data to statistically confirm the relationship. A wavelet analysis was then performed to investigate periodicity relative to location along the segment. The wavelet analysis identified significant (α = 0.05) periodicity at discrete locations along each of the segments. Those reaches yielding strong periodicity showed stronger relationships between LWD density and the geomorphic/riparian independent variables tested. Analyses consistently identified valley width and sinuosity as being associated with LWD density. The results of these analyses contribute a new perspective on the longitudinal distribution of LWD in a river system, which should help identify physical and/or riparian control mechanisms of LWD arrangement and support the development of models of LWD arrangement. Additionally, the spatial statistical tools presented here have shown to be valuable for identifying longitudinal patterns in river system components.

A classification-based assessment of the optimal spatial and spectral resolution of coastal wetland imagery

NASA Astrophysics Data System (ADS)

Becker, Brian L.

Great Lakes wetlands are increasingly being recognized as vital ecosystem components that provide valuable functions such as sediment retention, wildlife habitat, and nutrient removal. Aerial photography has traditionally provided a cost effective means to inventory and monitor coastal wetlands, but is limited by its broad spectral sensitivity and non-digital format. Airborne sensor advancements have now made the acquisition of digital imagery with high spatial and spectral resolution a reality. In this investigation, we selected two Lake Huron coastal wetlands, each from a distinct eco-region, over which, digital, airborne imagery (AISA or CASI-II) was acquired. The 1-meter images contain approximately twenty, 10-nanometer-wide spectral bands strategically located throughout the visible and near-infrared. The 4-meter hyperspectral imagery contains 48 contiguous bands across the visible and short-wavelength near-infrared. Extensive, in-situ, reflectance spectra (SE-590) and sub-meter GPS locations were acquired for the dominant botanical and substrate classes field-delineated at each location. Normalized in-situ spectral signatures were subjected to Principal Components and 2nd Derivative analyses in order to identify the most botanically explanative image bands. Three image-based investigations were implemented in order to evaluate the ability of three classification algorithms (ISODATA, Spectral Angle Mapper and Maximum-Likelihood) to differentiate botanical regions-of-interest. Two additional investigations were completed in order to assess classification changes associated with the independent manipulation of both spatial and spectral resolution. Of the three algorithms tested, the Maximum-Likelihood classifier best differentiated (89%) the regions-of-interest in both study sites. Covariance-based PCA rotation consistently enhanced the performance of the Maximum-Likelihood classifier. Seven non-overlapping bands (425.4, 514.9, 560.1, 685.5, 731.5, 812.3 and 916.7 nanometers) were identified that represented the best performing bands with respect to classification performance. A spatial resolution of 2 meters or less was determined to be the as being most appropriate in Great Lakes coastal wetland environments. This research represents the first step in evaluating the effectiveness of applying high-resolution, narrow-band imagery to the detailed mapping of coastal wetlands in the Great Lakes region.

Laser tweezer actuated microphotonic array devices for high resolution imaging and analysis in chip-based biosystems

NASA Astrophysics Data System (ADS)

Birkbeck, Aaron L.

A new technology is developed that functionally integrates arrays of lasers and micro-optics into microfluidic systems for the purpose of imaging, analyzing, and manipulating objects and biological cells. In general, the devices and technologies emerging from this area either lack functionality through the reliance on mechanical systems or provide a serial-based, time consuming approach. As compared to the current state of art, our all-optical design methodology has several distinguishing features, such as parallelism, high efficiency, low power, auto-alignment, and high yield fabrication methods, which all contribute to minimizing the cost of the integration process. The potential use of vertical cavity surface emitting lasers (VCSELs) for the creation of two-dimensional arrays of laser optical tweezers that perform independently controlled, parallel capture, and transport of large numbers of individual objects and biological cells is investigated. One of the primary biological applications for which VCSEL array sourced laser optical tweezers are considered is the formation of engineered tissues through the manipulation and spatial arrangement of different types of cells in a co-culture. Creating devices that combine laser optical tweezers with select micro-optical components permits optical imaging and analysis functions to take place inside the microfluidic channel. One such device is a micro-optical spatial filter whose motion and alignment is controlled using a laser optical tweezer. Unlike conventional spatial filter systems, our device utilizes a refractive optical element that is directly incorporated onto the lithographically patterned spatial filter. This allows the micro-optical spatial filter to automatically align itself in three-dimensions to the focal point of the microscope objective, where it then filters out the higher frequency additive noise components present in the laser beam. As a means of performing high resolution imaging in the microfluidic channel, we developed a novel technique that integrates the capacity of a laser tweezer to optically trap and manipulate objects in three-dimensions with the resolution-enhanced imaging capabilities of a solid immersion lens (SIL). In our design, the SIL is a free-floating device whose imaging beam, motion control and alignment is provided by a laser optical tweezer, which allows the microfluidic SIL to image in areas that are inaccessible to traditional solid immersion microscopes.

A Method for Improving Temporal and Spatial Resolution of Carbon Dioxide Emissions

NASA Astrophysics Data System (ADS)

Gregg, J. S.; Andres, R. J.

2003-12-01

Using United States data, a method is developed to estimate the monthly consumption of solid, liquid and gaseous fossil fuels for each state in the union. This technique employs monthly sales data to estimate the relative monthly proportions of the total annual national fossil fuel use. These proportions are then used to estimate the total monthly carbon dioxide emissions for each state. To assess the success of this technique, the results from this method are compared with the data obtained from other independent methods. To determine the temporal success of the method, the resulting national time series is compared to the model produced by Carbon Dioxide Information Analysis Center (CDIAC) and the current model being developed by T. J. Blasing and C. Broniak at the Oak Ridge National Laboratory (ORNL). The University of North Dakota (UND) method fits well temporally with the results of the CDIAC and current ORNL research. To determine the success of the spatial component, the individual state results are compared to the annual state totals calculated by ORNL. Using ordinary least squares regression, the annual state totals of this method are plotted against the ORNL data. This allows a direct comparison of estimates in the form of ordered pairs against a one-to-one ideal correspondence line, and allows for easy detection of outliers in the results obtained by this estimation method. Analyzing the residuals of the linear regression model for each type of fuel permits an improved understanding of the strengths and shortcomings of the spatial component of this estimation technique. Spatially, the model is successful when compared to the current ORNL research. The primary advantages of this method are its ease of implementation and universal applicability. In general, this technique compares favorably to more labor-intensive methods that rely on more detailed data. The more detailed data is generally not available for most countries in the world. The methodology used here will be applied to other nations in the world to better understand their sub-annual cycle and sub-national spatial distribution of carbon dioxide emissions from fossil fuel consumption. Better understanding of the cycle will lead to better models used for predicting and responding to global environmental changes currently observed and anticipated.

Comparisons of Spatial Predictions of Conductivity on a Stream Network in an Appalachian Watershed

EPA Science Inventory

We made spatial predictions of specific conductance based on spatial stream network (SSN) modeling to compare conductivity measurements of components of the network, such as headwaters, tributaries, and mainstem, which have different spatial extents in a study Appalachian watersh...

Modelling the Ecological Comorbidity of Acute Respiratory Infection, Diarrhoea and Stunting among Children Under the Age of 5 Years in Somalia.

PubMed

Kinyoki, Damaris K; Manda, Samuel O; Moloney, Grainne M; Odundo, Elijah O; Berkley, James A; Noor, Abdisalan M; Kandala, Ngianga-Bakwin

2017-04-01

The aim of this study was to assess spatial co-occurrence of acute respiratory infections (ARI), diarrhoea and stunting among children of the age between 6 and 59 months in Somalia. Data were obtained from routine biannual nutrition surveys conducted by the Food and Agriculture Organization 2007-2010. A Bayesian hierarchical geostatistical shared component model was fitted to the residual spatial components of the three health conditions. Risk maps of the common spatial effects at 1×1 km resolution were derived. The empirical correlations of the enumeration area proportion were 0.37, 0.63 and 0.66 for ARI and stunting, diarrhoea and stunting and ARI and diarrhoea, respectively. Spatially, the posterior residual effects ranged 0.03-20.98, 0.16-6.37 and 0.08-9.66 for shared component between ARI and stunting, diarrhoea and stunting and ARI and diarrhoea, respectively. The analysis showed clearly that the spatial shared component between ARI, diarrhoea and stunting was higher in the southern part of the country. Interventions aimed at controlling and mitigating the adverse effects of these three childhood health conditions should focus on their common putative risk factors, particularly in the South in Somalia.

Using independent component analysis for electrical impedance tomography

NASA Astrophysics Data System (ADS)

Yan, Peimin; Mo, Yulong

2004-05-01

Independent component analysis (ICA) is a way to resolve signals into independent components based on the statistical characteristics of the signals. It is a method for factoring probability densities of measured signals into a set of densities that are as statistically independent as possible under the assumptions of a linear model. Electrical impedance tomography (EIT) is used to detect variations of the electric conductivity of the human body. Because there are variations of the conductivity distributions inside the body, EIT presents multi-channel data. In order to get all information contained in different location of tissue it is necessary to image the individual conductivity distribution. In this paper we consider to apply ICA to EIT on the signal subspace (individual conductivity distribution). Using ICA the signal subspace will then be decomposed into statistically independent components. The individual conductivity distribution can be reconstructed by the sensitivity theorem in this paper. Compute simulations show that the full information contained in the multi-conductivity distribution will be obtained by this method.

Design of three-component vaccines against group A streptococcal infections: importance of spatial arrangement of vaccine components.

PubMed

Abdel-Aal, Abu-Baker M; Zaman, Mehfuz; Fujita, Yoshio; Batzloff, Michael R; Good, Michael F; Toth, Istvan

2010-11-25

Immunological assessment of group A streptococcal (GAS) branched lipopeptides demonstrated the impact of spatial arrangement of vaccine components on both the quality and quantity of their immune responses. Each lipopeptide was composed of three components: a GAS B-cell epitope (J14), a universal CD4(+) T-cell helper epitope (P25), and an immunostimulant lipid moiety that differs only in its spatial arrangement. The best systemic immune responses were demonstrated by a lipopeptide featuring the lipid moiety at the lipopeptide C-terminus. However, this candidate did not achieve protection against bacterial challenge. The best protection (100%) was shown by a lipopeptide featuring a C-terminal J14, conjugated through a lysine residue to P25 at the N-terminus, and a lipid moiety on the lysine side chain. The former candidate features α-helical conformation required to produce protective J14-specific antibodies. Our results highlight the importance of epitope orientation and lipid position in the design of three-component synthetic vaccines.

Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively

PubMed Central

Shimamoto, Shoichi; Waldman, Zachary J.; Orosz, Iren; Song, Inkyung; Bragin, Anatol; Fried, Itzhak; Engel, Jerome; Staba, Richard; Sharan, Ashwini; Wu, Chengyuan; Sperling, Michael R.; Weiss, Shennan A.

2018-01-01

Objective To develop and validate a detector that identifies ripple (80–200 Hz) events in intracranial EEG (iEEG) recordings in a referential montage and utilizes independent component analysis (ICA) to eliminate or reduce high-frequency artifact contamination. Also, investigate the correspondence of detected ripples and the seizure onset zone (SOZ). Methods iEEG recordings from 16 patients were first band-pass filtered (80–600 Hz) and Infomax ICA was next applied to derive the first independent component (IC1). IC1 was subsequently pruned, and an artifact index was derived to reduce the identification of high-frequency events introduced by the reference electrode signal. A Hilbert detector identified ripple events in the processed iEEG recordings using amplitude and duration criteria. The identified ripple events were further classified and characterized as true or false ripple on spikes, or ripples on oscillations by utilizing a topographical analysis to their time-frequency plot, and confirmed by visual inspection. Results The signal to noise ratio was improved by pruning IC1. The precision of the detector for ripple events was 91.27 ± 4.3%, and the sensitivity of the detector was 79.4 ± 3.0% (N = 16 patients, 5842 ripple events). The sensitivity and precision of the detector was equivalent in iEEG recordings obtained during sleep or intra-operatively. Across all the patients, true ripple on spike rates and also the rates of false ripple on spikes, that were generated due to filter ringing, classified the seizure onset zone (SOZ) with an area under the receiver operating curve (AUROC) of >76%. The magnitude and spectral content of true ripple on spikes generated in the SOZ was distinct as compared with the ripples generated in the NSOZ (p < .001). Conclusions Utilizing ICA to analyze iEEG recordings in referential montage provides many benefits to the study of high-frequency oscillations. The ripple rates and properties defined using this approach may accurately delineate the seizure onset zone. Significance Strategies to improve the spatial resolution of intracranial EEG and reduce artifact can help improve the clinical utility of HFO biomarkers. PMID:29113719

Pattern masking: the importance of remote spatial frequencies and their phase alignment.

PubMed

Huang, Pi-Chun; Maehara, Goro; May, Keith A; Hess, Robert F

2012-02-16

To assess the effects of spatial frequency and phase alignment of mask components in pattern masking, target threshold vs. mask contrast (TvC) functions for a sine-wave grating (S) target were measured for five types of mask: a sine-wave grating (S), a square-wave grating (Q), a missing fundamental square-wave grating (M), harmonic complexes consisting of phase-scrambled harmonics of a square wave (Qp), and harmonic complexes consisting of phase-scrambled harmonics of a missing fundamental square wave (Mp). Target and masks had the same fundamental frequency (0.46 cpd) and the target was added in phase with the fundamental frequency component of the mask. Under monocular viewing conditions, the strength of masking depends on phase relationships among mask spatial frequencies far removed from that of the target, at least 3 times the target frequency, only when there are common target and mask spatial frequencies. Under dichoptic viewing conditions, S and Q masks produced similar masking to each other and the phase-scrambled masks (Qp and Mp) produced less masking. The results suggest that pattern masking is spatial frequency broadband in nature and sensitive to the phase alignments of spatial components.

The meaning of "independence" for older people in different residential settings.

PubMed

Hillcoat-Nallétamby, Sarah

2014-05-01

Drawing on older people's understandings of "independence" and Collopy's work on autonomy, the article elaborates an interpretive framework of the concept in relation to 3 residential settings-the private dwelling-home, the extra-care, and the residential-care settings. Data include 91 qualitative interviews with frail, older people living in each setting, collected as part of a larger Welsh study. Thematic analysis techniques were employed to identify patterns in meanings of independence across settings and then interpreted using Collopy's conceptualizations of autonomy, as well as notions of space and interdependencies. Independence has multiple meanings for older people, but certain meanings are common to all settings: Accepting help at hand; doing things alone; having family, friends, and money as resources; and preserving physical and mental capacities. Concepts of delegated, executional, authentic, decisional, and consumer autonomy, as well as social interdependencies and spatial and social independence, do provide appropriate higher order interpretive constructs of these meanings across settings. A broader interpretive framework of "independence" should encompass concepts of relative independence, autonomy(ies), as well as spatial and social independence, and can provide more nuanced interpretations of structured dependency and institutionalization theories when applied to different residential settings.

Digital polarization holography advancing geometrical phase optics.

PubMed

De Sio, Luciano; Roberts, David E; Liao, Zhi; Nersisyan, Sarik; Uskova, Olena; Wickboldt, Lloyd; Tabiryan, Nelson; Steeves, Diane M; Kimball, Brian R

2016-08-08

Geometrical phase or the fourth generation (4G) optics enables realization of optical components (lenses, prisms, gratings, spiral phase plates, etc.) by patterning the optical axis orientation in the plane of thin anisotropic films. Such components exhibit near 100% diffraction efficiency over a broadband of wavelengths. The films are obtained by coating liquid crystalline (LC) materials over substrates with patterned alignment conditions. Photo-anisotropic materials are used for producing desired alignment conditions at the substrate surface. We present and discuss here an opportunity of producing the widest variety of "free-form" 4G optical components with arbitrary spatial patterns of the optical anisotropy axis orientation with the aid of a digital spatial light polarization converter (DSLPC). The DSLPC is based on a reflective, high resolution spatial light modulator (SLM) combined with an "ad hoc" optical setup. The most attractive feature of the use of a DSLPC for photoalignment of nanometer thin photo-anisotropic coatings is that the orientation of the alignment layer, and therefore of the fabricated LC or LC polymer (LCP) components can be specified on a pixel-by-pixel basis with high spatial resolution. By varying the optical magnification or de-magnification the spatial resolution of the photoaligned layer can be adjusted to an optimum for each application. With a simple "click" it is possible to record different optical components as well as arbitrary patterns ranging from lenses to invisible labels and other transparent labels that reveal different images depending on the side from which they are viewed.

Advances in audio source seperation and multisource audio content retrieval

NASA Astrophysics Data System (ADS)

Vincent, Emmanuel

2012-06-01

Audio source separation aims to extract the signals of individual sound sources from a given recording. In this paper, we review three recent advances which improve the robustness of source separation in real-world challenging scenarios and enable its use for multisource content retrieval tasks, such as automatic speech recognition (ASR) or acoustic event detection (AED) in noisy environments. We present a Flexible Audio Source Separation Toolkit (FASST) and discuss its advantages compared to earlier approaches such as independent component analysis (ICA) and sparse component analysis (SCA). We explain how cues as diverse as harmonicity, spectral envelope, temporal fine structure or spatial location can be jointly exploited by this toolkit. We subsequently present the uncertainty decoding (UD) framework for the integration of audio source separation and audio content retrieval. We show how the uncertainty about the separated source signals can be accurately estimated and propagated to the features. Finally, we explain how this uncertainty can be efficiently exploited by a classifier, both at the training and the decoding stage. We illustrate the resulting performance improvements in terms of speech separation quality and speaker recognition accuracy.

Investigation of blast-induced traumatic brain injury.

PubMed

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

2014-01-01

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

Investigation of blast-induced traumatic brain injury

PubMed Central

Ludwigsen, John S.; Ford, Corey C.

2014-01-01

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

Quantitative spatial distribution of sirolimus and polymers in drug-eluting stents using confocal Raman microscopy.

PubMed

Balss, K M; Llanos, G; Papandreou, G; Maryanoff, C A

2008-04-01

Raman spectroscopy was used to differentiate each component found in the CYPHER Sirolimus-eluting Coronary Stent. The unique spectral features identified for each component were then used to develop three separate calibration curves to describe the solid phase distribution found on drug-polymer coated stents. The calibration curves were obtained by analyzing confocal Raman spectral depth profiles from a set of 16 unique formulations of drug-polymer coatings sprayed onto stents and planar substrates. The sirolimus model was linear from 0 to 100 wt % of drug. The individual polymer calibration curves for poly(ethylene-co-vinyl acetate) [PEVA] and poly(n-butyl methacrylate) [PBMA] were also linear from 0 to 100 wt %. The calibration curves were tested on three independent drug-polymer coated stents. The sirolimus calibration predicted the drug content within 1 wt % of the laboratory assay value. The polymer calibrations predicted the content within 7 wt % of the formulation solution content. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectra from five formulations confirmed a linear response to changes in sirolimus and polymer content. Copyright 2007 Wiley Periodicals, Inc.

Evaluating visibility of age spot and freckle based on simulated spectral reflectance distribution and facial color image

NASA Astrophysics Data System (ADS)

Hirose, Misa; Toyota, Saori; Tsumura, Norimichi

2018-02-01

In this research, we evaluate the visibility of age spot and freckle with changing the blood volume based on simulated spectral reflectance distribution and the actual facial color images, and compare these results. First, we generate three types of spatial distribution of age spot and freckle in patch-like images based on the simulated spectral reflectance. The spectral reflectance is simulated using Monte Carlo simulation of light transport in multi-layered tissue. Next, we reconstruct the facial color image with changing the blood volume. We acquire the concentration distribution of melanin, hemoglobin and shading components by applying the independent component analysis on a facial color image. We reproduce images using the obtained melanin and shading concentration and the changed hemoglobin concentration. Finally, we evaluate the visibility of pigmentations using simulated spectral reflectance distribution and facial color images. In the result of simulated spectral reflectance distribution, we found that the visibility became lower as the blood volume increases. However, we can see that a specific blood volume reduces the visibility of the actual pigmentations from the result of the facial color images.

Selection of independent components based on cortical mapping of electromagnetic activity

NASA Astrophysics Data System (ADS)

Chan, Hui-Ling; Chen, Yong-Sheng; Chen, Li-Fen

2012-10-01

Independent component analysis (ICA) has been widely used to attenuate interference caused by noise components from the electromagnetic recordings of brain activity. However, the scalp topographies and associated temporal waveforms provided by ICA may be insufficient to distinguish functional components from artifactual ones. In this work, we proposed two component selection methods, both of which first estimate the cortical distribution of the brain activity for each component, and then determine the functional components based on the parcellation of brain activity mapped onto the cortical surface. Among all independent components, the first method can identify the dominant components, which have strong activity in the selected dominant brain regions, whereas the second method can identify those inter-regional associating components, which have similar component spectra between a pair of regions. For a targeted region, its component spectrum enumerates the amplitudes of its parceled brain activity across all components. The selected functional components can be remixed to reconstruct the focused electromagnetic signals for further analysis, such as source estimation. Moreover, the inter-regional associating components can be used to estimate the functional brain network. The accuracy of the cortical activation estimation was evaluated on the data from simulation studies, whereas the usefulness and feasibility of the component selection methods were demonstrated on the magnetoencephalography data recorded from a gender discrimination study.

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

Scaduto, DA; Hu, Y-H; Zhao, W

Purpose: Spatial resolution in digital breast tomosynthesis (DBT) is affected by inherent/binned detector resolution, oblique entry of x-rays, and focal spot size/motion; the limited angular range further limits spatial resolution in the depth-direction. While DBT is being widely adopted clinically, imaging performance metrics and quality control protocols have not been standardized. AAPM Task Group 245 on Tomosynthesis Quality Control has been formed to address this deficiency. Methods: Methods of measuring spatial resolution are evaluated using two prototype quality control phantoms for DBT. Spatial resolution in the detector plane is measured in projection and reconstruction domains using edge-spread function (ESF), point-spreadmore » function (PSF) and modulation transfer function (MTF). Spatial resolution in the depth-direction and effective slice thickness are measured in the reconstruction domain using slice sensitivity profile (SSP) and artifact spread function (ASF). An oversampled PSF in the depth-direction is measured using a 50 µm angulated tungsten wire, from which the MTF is computed. Object-dependent PSF is derived and compared with ASF. Sensitivity of these measurements to phantom positioning, imaging conditions and reconstruction algorithms is evaluated. Results are compared from systems of varying acquisition geometry (9–25 projections over 15–60°). Dependence of measurements on feature size is investigated. Results: Measurements of spatial resolution using PSF and LSF are shown to depend on feature size; depth-direction spatial resolution measurements are shown to similarly depend on feature size for ASF, though deconvolution with an object function removes feature size-dependence. A slanted wire may be used to measure oversampled PSFs, from which MTFs may be computed for both in-plane and depth-direction resolution. Conclusion: Spatial resolution measured using PSF is object-independent with sufficiently small object; MTF is object-independent. Depth-direction spatial resolution may be measured directly using MTF or indirectly using ASF or SSP as surrogate measurements. While MTF is object-independent, it is invalid for nonlinear reconstructions.« less

Collaborative Observation and Research (CORE) Watersheds: new strategies for tracking the regional effects of climate change on complex systems

NASA Astrophysics Data System (ADS)

Murdoch, P. S.

2007-12-01

The past 30 years of environmental research have shown that our world is not made up of discrete components acting independently, but rather of a mosaic of complex relations among air, land, water, living resources, and human activities. Recent warming of the climate is having a significant effect on the functioning of those systems. A national imperative is developing to quickly establish local, regional, and national systems for anticipating environmental degradation from a changing climate and developing cost-effective adaptation or mitigation strategies. In these circumstances, the debate over research versus monitoring becomes moot--there is a clear need for the integrated application of both across a range of temporal and spatial scales. A national framework that effectively addresses the multiple scales and complex multi-disciplinary processes of climate change is being assembled largely from existing programs through collaboration among Federal, State, local, and NGO organizations. The result will be an observation and research network capable of interpreting complex environmental changes at a range of spatial and temporal scales, but at less cost than if the network were funded as an independent initiative. A pilot implementation of the collaborative framework in the Delaware River Basin yielded multi-scale assessments of carbon storage and flux, and the effects of forest fragmentation and soil calcium depletion on ecosystem function. A prototype of a national climate-effects observation and research network linking research watersheds, regional surveys, remote sensing, and ecosystem modeling is being initiated in the Yukon River Basin where carbon flux associated with permafrost thaw could accelerate global warming.

Independent bases on the spatial wavefunction of four-identical-particle systems

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

Xiao, Shuyuan; Deng, Zhixuan; Chen, Hong

2013-12-15

We construct the independent bases on the spatial wavefunction of four-identical-particle systems classified under the rotational group SO(3) and the permutation group S{sub 4} with the usage of transformation coefficients that relate wavefunctions described in one set of internal coordinates with those in another. The basis functions for N⩽ 2 are presented in the explicit expressions based on the harmonic oscillator model. Such independent bases are supposed to play a key role in the construction of the wavefunctions of the five-quark states and the variation calculation of four-body systems. Our prescription avoids the spurious states and can be programmed formore » arbitrary N.« less

Deadlines in space: Selective effects of coordinate spatial processing in multitasking.

PubMed

Todorov, Ivo; Del Missier, Fabio; Konke, Linn Andersson; Mäntylä, Timo

2015-11-01

Many everyday activities require coordination and monitoring of multiple deadlines. One way to handle these temporal demands might be to represent future goals and deadlines as a pattern of spatial relations. We examined the hypothesis that spatial ability, in addition to executive functioning, contributes to individual differences in multitasking. In two studies, participants completed a multitasking session in which they monitored four digital clocks running at different rates. In Study 1, we found that individual differences in spatial ability and executive functions were independent predictors of multiple-task performance. In Study 2, we found that individual differences in specific spatial abilities were selectively related to multiple-task performance, as only coordinate spatial processing, but not categorical, predicted multitasking, even beyond executive functioning and numeracy. In both studies, males outperformed females in spatial ability and multitasking and in Study 2 these sex differences generalized to a simulation of everyday multitasking. Menstrual changes moderated the effects on multitasking, in that sex differences in coordinate spatial processing and multitasking were observed between males and females in the luteal phase of the menstrual cycle, but not between males and females at menses. Overall, these findings suggest that multiple-task performance reflects independent contributions of spatial ability and executive functioning. Furthermore, our results support the distinction of categorical versus coordinate spatial processing, and suggest that these two basic relational processes are selectively affected by female sex hormones and differentially effective in transforming and handling temporal patterns as spatial relations in the context of multitasking.

Spatio-Temporal Structure, Path Characteristics, and Perceptual Grouping in Immediate Serial Spatial Recall

PubMed Central

De Lillo, Carlo; Kirby, Melissa; Poole, Daniel

2016-01-01

Immediate serial spatial recall measures the ability to retain sequences of locations in short-term memory and is considered the spatial equivalent of digit span. It is tested by requiring participants to reproduce sequences of movements performed by an experimenter or displayed on a monitor. Different organizational factors dramatically affect serial spatial recall but they are often confounded or underspecified. Untangling them is crucial for the characterization of working-memory models and for establishing the contribution of structure and memory capacity to spatial span. We report five experiments assessing the relative role and independence of factors that have been reported in the literature. Experiment 1 disentangled the effects of spatial clustering and path-length by manipulating the distance of items displayed on a touchscreen monitor. Long-path sequences segregated by spatial clusters were compared with short-path sequences not segregated by clusters. Recall was more accurate for sequences segregated by clusters independently from path-length. Experiment 2 featured conditions where temporal pauses were introduced between or within cluster boundaries during the presentation of sequences with the same paths. Thus, the temporal structure of the sequences was either consistent or inconsistent with a hierarchical representation based on segmentation by spatial clusters but the effect of structure could not be confounded with effects of path-characteristics. Pauses at cluster boundaries yielded more accurate recall, as predicted by a hierarchical model. In Experiment 3, the systematic manipulation of sequence structure, path-length, and presence of path-crossings of sequences showed that structure explained most of the variance, followed by the presence/absence of path-crossings, and path-length. Experiments 4 and 5 replicated the results of the previous experiments in immersive virtual reality navigation tasks where the viewpoint of the observer changed dynamically during encoding and recall. This suggested that the effects of structure in spatial span are not dependent on perceptual grouping processes induced by the aerial view of the stimulus array typically afforded by spatial recall tasks. These results demonstrate the independence of coding strategies based on structure from effects of path characteristics and perceptual grouping in immediate serial spatial recall. PMID:27891101

Neuronal substrates of Corsi Block span: Lesion symptom mapping analyses in relation to attentional competition and spatial bias.

PubMed

Chechlacz, Magdalena; Rotshtein, Pia; Humphreys, Glyn W

2014-11-01

Spatial working memory problems are frequently reported following brain damage within both left and right hemispheres but with the severity often being grater in individuals with right hemisphere lesions. Clinically, deficits in spatial working memory have also been noted in patients with visuospatial disorders such as unilateral neglect. Here, we examined neural substrates of short-term memory for spatial locations based on the Corsi Block tapping task and the relationship with the visuospatial deficits of neglect and extinction in a group of chronic neuropsychological patients. Principal Component Analysis (PCA) was used to distinguish shared and dissociate functional components. The neural substrates of spatial short-term memory deficits and the components identified by PCA were examined using whole brain voxel-based morphometry and tract-wise lesion deficits analyses. We found that bilateral lesions within occipital cortex (middle occipital gyrus) and right posterior parietal cortex, along with disconnection of the right parieto-temporal segment of arcuate fasciculus, were associated with low spatial memory span. A single component revealed by PCA accounted for over half of the variance and was linked to damage to right posterior brain regions (temporo-parietal junction, the inferior parietal lobule and middle temporal gyrus extending into middle occipital gyrus). We also found link to disconnections within several association pathways including the superior longitudinal fasciculus, arcuate fasciculus, inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. These results indicate that different visuospatial deficits converge into a single component mapped within posterior parietal areas and fronto-parietal white matter pathways. Furthermore, the data presented here fit with the role of posterior parietal cortex/temporo-parietal junction in maintaining a map of salient locations in space, with Corsi Block performance being impaired when the spatial map is damaged. Copyright © 2014 Elsevier Ltd. All rights reserved.

An observational assessment of the influence of mesoscale and submesoscale heterogeneity on ocean biogeochemical reactions

NASA Astrophysics Data System (ADS)

Martin, Adrian P.; Lévy, Marina; van Gennip, Simon; Pardo, Silvia; Srokosz, Meric; Allen, John; Painter, Stuart C.; Pidcock, Roz

2015-09-01

Numerous observations demonstrate that considerable spatial variability exists in components of the marine planktonic ecosystem at the mesoscale and submesoscale (100 km-1 km). The causes and consequences of physical processes at these scales ("eddy advection") influencing biogeochemistry have received much attention. Less studied, the nonlinear nature of most ecological and biogeochemical interactions means that such spatial variability has consequences for regional estimates of processes including primary production and grazing, independent of the physical processes. This effect has been termed "eddy reactions." Models remain our most powerful tools for extrapolating hypotheses for biogeochemistry to global scales and to permit future projections. The spatial resolution of most climate and global biogeochemical models means that processes at the mesoscale and submesoscale are poorly resolved. Modeling work has previously suggested that the neglected eddy reactions may be almost as large as the mean field estimates in some cases. This study seeks to quantify the relative size of eddy and mean reactions observationally, using in situ and satellite data. For primary production, grazing, and zooplankton mortality the eddy reactions are between 7% and 15% of the mean reactions. These should be regarded as preliminary estimates to encourage further observational estimates and not taken as a justification for ignoring eddy reactions. Compared to modeling estimates, there are inconsistencies in the relative magnitude of eddy reactions and in correlations which are a major control on their magnitude. One possibility is that models exhibit much stronger spatial correlations than are found in reality, effectively amplifying the magnitude of eddy reactions.

Spatially Regularized Machine Learning for Task and Resting-state fMRI

PubMed Central

Song, Xiaomu; Panych, Lawrence P.; Chen, Nan-kuei

2015-01-01

Background Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades. New Method A spatially regularized support vector machine (SVM) technique was developed for the reliable brain mapping in task- and resting-state. Unlike most existing SVM-based brain mapping techniques, which implement supervised classifications of specific brain functional states or disorders, the proposed method performs a semi-supervised classification for the general brain function mapping where spatial correlation of fMRI is integrated into the SVM learning. The method can adapt to intra- and inter-subject variations induced by fMRI nonstationarity, and identify a true boundary between active and inactive voxels, or between functionally connected and unconnected voxels in a feature space. Results The method was evaluated using synthetic and experimental data at the individual and group level. Multiple features were evaluated in terms of their contributions to the spatially regularized SVM learning. Reliable mapping results in both task- and resting-state were obtained from individual subjects and at the group level. Comparison with Existing Methods A comparison study was performed with independent component analysis, general linear model, and correlation analysis methods. Experimental results indicate that the proposed method can provide a better or comparable mapping performance at the individual and group level. Conclusions The proposed method can provide accurate and reliable mapping of brain function in task- and resting-state, and is applicable to a variety of quantitative fMRI studies. PMID:26470627

Dealing with ocular artifacts on lateralized ERPs in studies of visual-spatial attention and memory: ICA correction versus epoch rejection.

PubMed

Drisdelle, Brandi Lee; Aubin, Sébrina; Jolicoeur, Pierre

2017-01-01

The objective of the present study was to assess the robustness and reliability of independent component analysis (ICA) as a method for ocular artifact correction in electrophysiological studies of visual-spatial attention and memory. The N2pc and sustained posterior contralateral negativity (SPCN), electrophysiological markers of visual-spatial attention and memory, respectively, are lateralized posterior ERPs typically observed following the presentation of lateral stimuli (targets and distractors) along with instructions to maintain fixation on the center of the visual search for the entire trial. Traditionally, trials in which subjects may have displaced their gaze are rejected based on a cutoff threshold, minimizing electrophysiological contamination by saccades. Given the loss of data resulting from rejection, we examined ocular correction by comparing results using standard fixation instructions against a condition where subjects were instructed to shift their gaze toward possible targets. Both conditions were analyzed using a rejection threshold and ICA correction for saccade activity management. Results demonstrate that ICA conserves data that would have otherwise been removed and leaves the underlying neural activity intact, as demonstrated by experimental manipulations previously shown to modulate the N2pc and the SPCN. Not only does ICA salvage and not distort data, but also large eye movements had only subtle effects. Overall, the findings provide convincing evidence for ICA correction for not only special cases (e.g., subjects did not follow fixation instruction) but also as a candidate for standard ocular artifact management in electrophysiological studies interested in visual-spatial attention and memory. © 2016 Society for Psychophysiological Research.

Spatially resolved spectra of the 'teacup' active galactic nucleus: tracing the history of a dying quasar

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

Gagne, J. P.; Crenshaw, D. M.; Fischer, T. C.

The Sloan Digital Sky Survey (SDSS) Galaxy Zoo project has revealed a number of spectacular galaxies possessing extended emission-line regions (EELRs), the most famous being Hanny's Voorwerp galaxy. We present another EELR object discovered in the SDSS endeavor: the Teacup active galactic nucleus (AGN). Nicknamed for its EELR, which has a 'handle'-like structure protruding 15 kpc into the northeast quadrant of the galaxy. We analyze the physical conditions of this galaxy with long-slit, ground-based spectroscopy from the Lowell, Lick, and KPNO observatories. With the Lowell 1.8 m Perkin's telescope we took multiple observations at different offset positions, allowing us tomore » recover spatially resolved spectra across the galaxy. Line diagnostics indicate the ionized gas is photoionized primarily by the AGN. Additionally we are able to derive the hydrogen density from the [S II] λ6716/λ6731 ratio. We generated two-component photoionization models for each spatially resolved Lowell spectrum. These models allow us to calculate the AGN bolometric luminosity seen by the gas at different radii from the nuclear center of the Teacup. Our results show a drop in bolometric luminosity by more than two orders of magnitude from the EELR to the nucleus, suggesting that the AGN has decreased in luminosity by this amount in a continuous fashion over 46,000 yr, supporting the case for a dying AGN in this galaxy independent of any IR based evidence. We demonstrate that spatially resolved photoionization modeling could be applied to EELRs to investigate long timescale variability.« less

Assessing spatial coupling in complex population dynamics using mutual prediction and continuity statistics

USGS Publications Warehouse

Nichols, J.M.; Moniz, L.; Nichols, J.D.; Pecora, L.M.; Cooch, E.

2005-01-01

A number of important questions in ecology involve the possibility of interactions or ?coupling? among potential components of ecological systems. The basic question of whether two components are coupled (exhibit dynamical interdependence) is relevant to investigations of movement of animals over space, population regulation, food webs and trophic interactions, and is also useful in the design of monitoring programs. For example, in spatially extended systems, coupling among populations in different locations implies the existence of redundant information in the system and the possibility of exploiting this redundancy in the development of spatial sampling designs. One approach to the identification of coupling involves study of the purported mechanisms linking system components. Another approach is based on time series of two potential components of the same system and, in previous ecological work, has relied on linear cross-correlation analysis. Here we present two different attractor-based approaches, continuity and mutual prediction, for determining the degree to which two population time series (e.g., at different spatial locations) are coupled. Both approaches are demonstrated on a one-dimensional predator?prey model system exhibiting complex dynamics. Of particular interest is the spatial asymmetry introduced into the model as linearly declining resource for the prey over the domain of the spatial coordinate. Results from these approaches are then compared to the more standard cross-correlation analysis. In contrast to cross-correlation, both continuity and mutual prediction are clearly able to discern the asymmetry in the flow of information through this system.

Continuous functional magnetic resonance imaging reveals dynamic nonlinearities of "dose-response" curves for finger opposition.

PubMed

Berns, G S; Song, A W; Mao, H

1999-07-15

Linear experimental designs have dominated the field of functional neuroimaging, but although successful at mapping regions of relative brain activation, the technique assumes that both cognition and brain activation are linear processes. To test these assumptions, we performed a continuous functional magnetic resonance imaging (MRI) experiment of finger opposition. Subjects performed a visually paced bimanual finger-tapping task. The frequency of finger tapping was continuously varied between 1 and 5 Hz, without any rest blocks. After continuous acquisition of fMRI images, the task-related brain regions were identified with independent components analysis (ICA). When the time courses of the task-related components were plotted against tapping frequency, nonlinear "dose- response" curves were obtained for most subjects. Nonlinearities appeared in both the static and dynamic sense, with hysteresis being prominent in several subjects. The ICA decomposition also demonstrated the spatial dynamics with different components active at different times. These results suggest that the brain response to tapping frequency does not scale linearly, and that it is history-dependent even after accounting for the hemodynamic response function. This implies that finger tapping, as measured with fMRI, is a nonstationary process. When analyzed with a conventional general linear model, a strong correlation to tapping frequency was identified, but the spatiotemporal dynamics were not apparent.

Spatial Data Transfer Standard (SDTS)

USGS Publications Warehouse

,

1995-01-01

The Spatial Data Transfer Standard (SOTS) is a mechanism for the transfer of spatial data between dissimilar computer systems. The SOTS specifies exchange constructs, addressing formats, structure, and content for spatially referenced vector and raster (including gridded) data. SOTS components are a conceptual model, specifications for a quality report, transfer module specifications, data dictionary specifications, and definitions of spatial features and attributes.

Attenuation and bit error rate for four co-propagating spatially multiplexed optical communication channels of exactly same wavelength in step index multimode fibers

NASA Astrophysics Data System (ADS)

Murshid, Syed H.; Chakravarty, Abhijit

2011-06-01

Spatial domain multiplexing (SDM) utilizes co-propagation of exactly the same wavelength in optical fibers to increase the bandwidth by integer multiples. Input signals from multiple independent single mode pigtail laser sources are launched at different input angles into a single multimode carrier fiber. The SDM channels follow helical paths and traverse through the carrier fiber without interfering with each other. The optical energy from the different sources is spatially distributed and takes the form of concentric circular donut shaped rings, where each ring corresponds to an independent laser source. At the output end of the fiber these donut shaped independent channels can be separated either with the help of bulk optics or integrated concentric optical detectors. This presents the experimental setup and results for a four channel SDM system. The attenuation and bit error rate for individual channels of such a system is also presented.

Hyperspectral Image Denoising Using a Nonlocal Spectral Spatial Principal Component Analysis

NASA Astrophysics Data System (ADS)

Li, D.; Xu, L.; Peng, J.; Ma, J.

2018-04-01

Hyperspectral images (HSIs) denoising is a critical research area in image processing duo to its importance in improving the quality of HSIs, which has a negative impact on object detection and classification and so on. In this paper, we develop a noise reduction method based on principal component analysis (PCA) for hyperspectral imagery, which is dependent on the assumption that the noise can be removed by selecting the leading principal components. The main contribution of paper is to introduce the spectral spatial structure and nonlocal similarity of the HSIs into the PCA denoising model. PCA with spectral spatial structure can exploit spectral correlation and spatial correlation of HSI by using 3D blocks instead of 2D patches. Nonlocal similarity means the similarity between the referenced pixel and other pixels in nonlocal area, where Mahalanobis distance algorithm is used to estimate the spatial spectral similarity by calculating the distance in 3D blocks. The proposed method is tested on both simulated and real hyperspectral images, the results demonstrate that the proposed method is superior to several other popular methods in HSI denoising.

Guided filter and principal component analysis hybrid method for hyperspectral pansharpening

NASA Astrophysics Data System (ADS)

Qu, Jiahui; Li, Yunsong; Dong, Wenqian

2018-01-01

Hyperspectral (HS) pansharpening aims to generate a fused HS image with high spectral and spatial resolution through integrating an HS image with a panchromatic (PAN) image. A guided filter (GF) and principal component analysis (PCA) hybrid HS pansharpening method is proposed. First, the HS image is interpolated and the PCA transformation is performed on the interpolated HS image. The first principal component (PC1) channel concentrates on the spatial information of the HS image. Different from the traditional PCA method, the proposed method sharpens the PAN image and utilizes the GF to obtain the spatial information difference between the HS image and the enhanced PAN image. Then, in order to reduce spectral and spatial distortion, an appropriate tradeoff parameter is defined and the spatial information difference is injected into the PC1 channel through multiplying by this tradeoff parameter. Once the new PC1 channel is obtained, the fused image is finally generated by the inverse PCA transformation. Experiments performed on both synthetic and real datasets show that the proposed method outperforms other several state-of-the-art HS pansharpening methods in both subjective and objective evaluations.

Exploration of walking behavior in Vermont using spatial regression.

DOT National Transportation Integrated Search

2015-06-01

This report focuses on the relationship between walking and its contributing factors by : applying spatial regression methods. Using the Vermont data from the New England : Transportation Survey (NETS), walking variables as well as 170 independent va...

Spatial variability of wildland fuel characteristics in northern Rocky Mountain ecosystems

Treesearch

Robert E. Keane; Kathy Gray; Valentina Bacciu

2012-01-01

We investigated the spatial variability of a number of wildland fuel characteristics for the major fuel components found in six common northern Rocky Mountain ecosystems. Surface fuel characteristics of loading, particle density, bulk density, and mineral content were measured for eight fuel components - four downed dead woody fuel size classes (1, 10, 100, 1000 hr),...

A component-based, integrated spatially distributed hydrologic/water quality model: AgroEcoSystem-Watershed (AgES-W) overview and application

USDA-ARS?s Scientific Manuscript database

AgroEcoSystem-Watershed (AgES-W) is a modular, Java-based spatially distributed model which implements hydrologic/water quality simulation components. The AgES-W model was previously evaluated for streamflow and recently has been enhanced with the addition of nitrogen (N) and sediment modeling compo...

Development of Critical Spatial Thinking through GIS Learning

ERIC Educational Resources Information Center

Kim, Minsung; Bednarz, Robert

2013-01-01

This study developed an interview-based critical spatial thinking oral test and used the test to investigate the effects of Geographic Information System (GIS) learning on three components of critical spatial thinking: evaluating data reliability, exercising spatial reasoning, and assessing problem-solving validity. Thirty-two students at a large…

Exploring component-based approaches in forest landscape modeling

Treesearch

H. S. He; D. R. Larsen; D. J. Mladenoff

2002-01-01

Forest management issues are increasingly required to be addressed in a spatial context, which has led to the development of spatially explicit forest landscape models. The numerous processes, complex spatial interactions, and diverse applications in spatial modeling make the development of forest landscape models difficult for any single research group. New...

An experimental study of the fluid mechanics associated with porous walls

NASA Technical Reports Server (NTRS)

Ramachandran, N.; Heaman, J.; Smith, A.

1992-01-01

The fluid mechanics of air exiting from a porous material is investigated. The experiments are filter rating dependent, as porous walls with filter ratings differing by about three orders of magnitude are studied. The flow behavior is investigated for its spatial and temporal stability. The results from the investigation are related to jet behavior in at least one of the following categories: (1) jet coalescence effects with increasing flow rate; (2) jet field decay with increasing distance from the porous wall; (3) jet field temporal turbulence characteristics; and (4) single jet turbulence characteristics. The measurements show that coalescence effects cause jet development, and this development stage can be traced by measuring the pseudoturbulence (spatial velocity variations) at any flow rate. The pseudoturbulence variation with increasing mass flow reveals an initial increasing trend followed by a leveling trend, both of which are directly proportional to the filter rating. A critical velocity begins this leveling trend and represents the onset of fully developed jetting action in the flow field. A correlation is developed to predict the onset of fully developed jets in the flow emerging from a porous wall. The data further show that the fully developed jet dimensions are independent of the filter rating, thus providing a length scale for this type of flow field (1 mm). Individual jet characteristics provide another unifying trend with similar velocity decay behavior with distance; however, the respective turbulence magnitudes show vast differences between jets from the same sample. Measurements of the flow decay with distance from the porous wall show that the higher spatial frequency components of the jet field dissipate faster than the lower frequency components. Flow turbulence intensity measurements show an out of phase behavior with the velocity field and are generally found to increase as the distance from the wall is increased.

SQuAd - Approach for the Spatial Quantification of the Advection influence on the balance closure of greenhouse gases

NASA Astrophysics Data System (ADS)

Schuetze, C.; Barth, M.; Hehn, M.; Ziemann, A.

2016-12-01

The eddy-covariance (EC) method can provide information about turbulent fluxes of energy and greenhouse gases (GHG) accurately if all necessary corrections and conversions are applied to the measured raw data and all boundary conditions for the method are satisfied. Nevertheless and even in flat terrain, advection can occur leading to a closing gap of energy and matter balances. Without accounting for advection, annual estimates of CO2 sink strength are overestimated, because advection usually results in underestimation of nocturnal CO2 flux. Advection is produced by low-frequent exchange processes, which can occur due to the surface heterogeneity. To measure advective fluxes there is still and strongly a need for ground-based remote sensing techniques which provide the relevant GHG concentration together with wind components spatially resolved within the same voxel structure. The SQuAd-approach applies an integrated method combination of acoustic tomography and open-path optical remote sensing based on infrared spectroscopy with the aim to obtain spatially and temporally resolved information about wind components and GHG concentration. The monitoring approach focuses on the validation of the joint application of the two independent, non-intrusive methods concerning the ability to close the existent gap in GHG balance. The innovative combination of acoustic travel-time tomography (A-TOM) and open-path Fourier transform infrared spectroscopy (OP-FTIR) together with atmospheric modelling will enable an upscaling and enhancement of EC measurements. OP-FTIR instrumentation has the significant advantage of real-time simultaneous measurements of line-averaged concentrations for CO2 and other GHG with high precision. A-TOM is a scalable method to remotely resolve 3D wind and temperature fields. The presentation will give an overview about the proposed method combination and results of experimental validation tests at an ICOS site (flat grassland) in Eastern Germany.

Response-reinforcer dependency and resistance to change.

PubMed

Cançado, Carlos R X; Abreu-Rodrigues, Josele; Aló, Raquel Moreira; Hauck, Flávia; Doughty, Adam H

2018-01-01

The effects of the response-reinforcer dependency on resistance to change were studied in three experiments with rats. In Experiment 1, lever pressing produced reinforcers at similar rates after variable interreinforcer intervals in each component of a two-component multiple schedule. Across conditions, in the fixed component, all reinforcers were response-dependent; in the alternative component, the percentage of response-dependent reinforcers was 100, 50 (i.e., 50% response-dependent and 50% response-independent) or 10% (i.e., 10% response-dependent and 90% response-independent). Resistance to extinction was greater in the alternative than in the fixed component when the dependency in the former was 10%, but was similar between components when this dependency was 100 or 50%. In Experiment 2, a three-component multiple schedule was used. The dependency was 100% in one component and 10% in the other two. The 10% components differed on how reinforcers were programmed. In one component, as in Experiment 1, a reinforcer had to be collected before the scheduling of other response-dependent or independent reinforcers. In the other component, response-dependent and -independent reinforcers were programmed by superimposing a variable-time schedule on an independent variable-interval schedule. Regardless of the procedure used to program the dependency, resistance to extinction was greater in the 10% components than in the 100% component. These results were replicated in Experiment 3 in which, instead of extinction, VT schedules replaced the baseline schedules in each multiple-schedule component during the test. We argue that the relative change in dependency from Baseline to Test, which is greater when baseline dependencies are high rather than low, could account for the differential resistance to change in the present experiments. The inconsistencies in results across the present and previous experiments suggest that the effects of dependency on resistance to change are not well understood. Additional systematic analyses are important to further understand the effects of the response-reinforcer relation on resistance to change and to the development of a more comprehensive theory of behavioral persistence. © 2017 Society for the Experimental Analysis of Behavior.

Automatic removal of eye-movement and blink artifacts from EEG signals.

PubMed

Gao, Jun Feng; Yang, Yong; Lin, Pan; Wang, Pei; Zheng, Chong Xun

2010-03-01

Frequent occurrence of electrooculography (EOG) artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In this paper, a robust method is presented to automatically eliminate eye-movement and eye-blink artifacts from EEG signals. Independent Component Analysis (ICA) is used to decompose EEG signals into independent components. Moreover, the features of topographies and power spectral densities of those components are extracted to identify eye-movement artifact components, and a support vector machine (SVM) classifier is adopted because it has higher performance than several other classifiers. The classification results show that feature-extraction methods are unsuitable for identifying eye-blink artifact components, and then a novel peak detection algorithm of independent component (PDAIC) is proposed to identify eye-blink artifact components. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove EOG artifacts effectively from EEG signals with little distortion of the underlying brain signals.

Global structure of mantle isotopic heterogeneity and its implications for mantle differentiation and convection

NASA Astrophysics Data System (ADS)

Iwamori, Hikaru; Albaréde, Francis; Nakamura, Hitomi

2010-11-01

In order to further our understanding of the global geochemical structure and mantle dynamics, a global isotopic data set of oceanic basalts was analyzed by Independent Component Analysis (ICA), a relatively new method of multivariate analysis. The data set consists of 2773 mid-ocean ridge basalts (MORB) and 1515 ocean island basalts (OIB) with five isotopic ratios of Pb, Nd and Sr. The data set spatially covers the major oceans and enables us to compare the results with global geophysical observations. Three independent components (ICs) have been found, two of which are essentially identical to those previously found for basalts from the Atlantic and Indian Oceans. The two ICs (IC1 and IC2) span a compositional plane that accounts for 95.7% of the sample variance, while the third IC (IC3) accounts for 3.7%. Based on the geochemical nature of ICs and a forward model concerning trace elemental and isotopic compositions, the origin of the ICs is discussed. IC1 discriminates OIB from MORB, and may be related to elemental fractionation associated with melting and the subsequent radiogenic in growth with an average recycling time of 0.8 to 2.4 Ga. IC2 tracks the regional provenance of both MORB and OIB and may be related to aqueous fluid-rock interaction and the subsequent radiogenic ingrowth with an average recycling time of 0.3 to 0.9 Ga. IC3 fingerprints upper continental crustal material and its high value appears in limited geographical and tectonic settings. Variations in the melt component (IC1) and in the aqueous fluid component (IC2) inherited in the mantle most likely reflect mid-ocean ridge and subduction zone processes, respectively. Long-term accumulation of dense materials rich in the IC1 melt component at the base of the convective mantle accounts for its longer recycling time with respect to that for less dense materials rich in the aqueous fluid component (IC2). IC2 broadly correlates with the seismic velocity structures of the lowermost mantle and electric conductivity around the mantle transition zones. We propose that IC2 reflects hydrogen distribution within the mantle and that several global domains enriched in hydrogen could exist as vertical sectors extending all the way down to the core-mantle boundary.

Spatial-frequency spectrum of patterns changes the visibility of spatial-phase differences

NASA Technical Reports Server (NTRS)

Lawton, T. B.

1985-01-01

It is shown that spatial-frequency components over a 4-octave range affected the visibility of spatial-phase differences. Contrast thresholds were measured for discrimination between two (+45- and -45-deg) spatial phases of a sinusoidal test grating added to a background grating. The background could contain one or several sinusoidal components, all in 0-deg phase. Phase differences between the test and the background were visible at lower contrasts when test and background frequencies were harmonically related than when they were not, when test and background frequencies were within 1 octave than when they were farther apart, when the fundamental frequency of the background was low than when it was high, and for some discriminations more than for others, after practice. The visibility of phase differences was not affected by additional components in the background if the fundamental and difference frequencies of the background remained unchanged. Observers' reports of their strategies gave information about the types of attentive processing that were used to discriminate phase differences. Attentive processing facilitated phase discrimination for multifrequency gratings spanning a much wider range of spatial frequencies than would be possible by using only local preattentive processing. These results were consistent with the visibility of phase differences being processed by some combination of even- and odd-symmetric simple cells tuned to a wide range of different spatial frequencies.

Human parietofrontal networks related to action observation detected at rest.

PubMed

Molinari, Elisa; Baraldi, Patrizia; Campanella, Martina; Duzzi, Davide; Nocetti, Luca; Pagnoni, Giuseppe; Porro, Carlo A

2013-01-01

Recent data show a broad correspondence between human resting-state and task-related brain networks. We performed a functional magnetic resonance imaging (fMRI) study to compare, in the same subjects, the spatial independent component analysis (ICA) maps obtained at rest and during the observation of either reaching/grasping hand actions or matching static pictures. Two parietofrontal networks were identified by ICA from action observation task data. One network, specific to reaching/grasping observation, included portions of the anterior intraparietal cortex and of the dorsal and ventral lateral premotor cortices. A second network included more posterior portions of the parietal lobe, the dorsomedial frontal cortex, and more anterior and ventral parts, respectively, of the dorsal and ventral premotor cortices, extending toward Broca's area; this network was more generally related to the observation of hand action and static pictures. A good spatial correspondence was found between the 2 observation-related ICA maps and 2 ICA maps identified from resting-state data. The anatomical connectivity among the identified clusters was tested in the same volunteers, using persistent angular structure-MRI and deterministic tractography. These findings extend available knowledge of human parietofrontal circuits and further support the hypothesis of a persistent coherence within functionally relevant networks during rest.

The flexible focus: whether spatial attention is unitary or divided depends on observer goals.

PubMed

Jefferies, Lisa N; Enns, James T; Di Lollo, Vincent

2014-04-01

The distribution of visual attention has been the topic of much investigation, and various theories have posited that attention is allocated either as a single unitary focus or as multiple independent foci. In the present experiment, we demonstrate that attention can be flexibly deployed as either a unitary or a divided focus in the same experimental task, depending on the observer's goals. To assess the distribution of attention, we used a dual-stream Attentional Blink (AB) paradigm and 2 target pairs. One component of the AB, Lag-1 sparing, occurs only if the second target pair appears within the focus of attention. By varying whether the first-target-pair could be expected in a predictable location (always in-stream) or not (unpredictably in-stream or between-streams), observers were encouraged to deploy a divided or a unitary focus, respectively. When the second-target-pair appeared between the streams, Lag-1 sparing occurred for the Unpredictable group (consistent with a unitary focus) but not for the Predictable group (consistent with a divided focus). Thus, diametrically different outcomes occurred for physically identical displays, depending on the expectations of the observer about where spatial attention would be required.

Shock-Strength Determination With Seeded and Seedless Laser Methods

NASA Technical Reports Server (NTRS)

Herring, G. C.; Meyers, James F.

2008-01-01

Two nonintrusive laser diagnostics were independently used to demonstrate the measurement of time-averaged and spatially-resolved pressure change across a twodimensional (2-D) shock wave. The first method is Doppler global velocimetry (DGV) which uses water seeding and generates 2-D maps of 3-orthogonal components of velocity. A DGV-measured change in flow direction behind an oblique shock provides an indirect determination of pressure jump across the shock, when used with the known incoming Mach number and ideal shock relations (or Prandtl-Meyer flow equations for an expansion fan). This approach was demonstrated at Mach 2 on 2-D shocks and expansions generated from a flat plate at angles-of-attack approx. equals -2.4deg and +0.6deg, respectively. This technique also works for temperature jump (as well as pressure) and for normal shocks (as well as oblique). The second method, laser-induced thermal acoustics (LITA), is a seedless approach that was used to generate 1-D spatial profiles of streamwise Mach number, sound speed, pressure, and temperature across the same shock waves. Excellent agreement was obtained between the DGV and LITA methods, suggesting that either technique is viable for noninvasive shock-strength measurements.

[Continuity and discontinuity of the geomerida: the bionomic and biotic aspects].

PubMed

Kafanov, A I

2005-01-01

The view of the spatial structure of the geomerida (Earth's life cover) as a continuum that prevails in modern phytocoenology is mostly determined by a physiognomic (landscape-bionomic) discrimination of vegetation components. In this connection, geography of life forms appears as subject of the landscapebionomic biogeography. In zoocoenology there is a tendency of synthesis of alternative concepts based on the assumption that there are no absolute continuum and absolute discontinuum in the organic nature. The problem of continuum and discontinuum of living cover being problem of scale aries from fractal structure of geomerida. This problem arises from fractal nature of the spatial structure of geomerida. The continuum mainly belongs to regularities of topological order. At regional and subregional scale the continuum of biochores is rather rare. The objective evidences of relative discontinuity of the living cover are determined by significant alterations of species diversity at the regional, subregional and even topological scale Alternatively to conventionally discriminated units in physionomically continuous vegetation, the same biotic complexes, represented as operational units of biogeographical and biocenological zoning, are distinguished repeatedly and independently by different researchers. An area occupied by certain flora (fauna, biota) could be considered as elementary unit of biotic diversity (elementary biotic complex).

Space/time explicit Hestia version 2.0 fossil fuel CO2 emissions for the Los Angeles Basin: comparison to atmospheric monitoring, emission drivers, and policy implications

NASA Astrophysics Data System (ADS)

Gurney, K. R.; Liang, J.; Patarasuk, R.; O'Keeffe, D.; Newman, S.; Rao, P.; Hutchins, M.; Huang, J.

2016-12-01

The Los Angeles Basin represents one of the largest metropolitan areas in the United States and is home to the Megacity Carbon Project, a multi-institutional effort led by NASA JPL to understand the total carbon budget of the Los Angeles Basin. A key component of that effort is the Hestia bottom-up fossil fuel CO2 emissions data product, which quantifies FFCO2 every hour to the spatial scale of individual buildings and road segments. This data product has undergone considerable revision in the last year and the version 2.0 data product is now complete covering the 2011-2014 time period. In this presentation, we highlight the advances in the Hestia version 2.0 including the improvements to onroad, building and industrial emissions. We make comparisons to the independently reported GHG reporting program of the EPA and to in-situ atmospheric measurement of CO2 at two monotiring locations in Pasadena and Palos Verdes. We provide an analysis of the socioeconomic drivers of emissions in the building and onroad transportation sectors across the domain highlighting hotspots of emissions and spatially-specific opportunities for reductions.

Ion and lipid signaling in apical growth-a dynamic machinery responding to extracellular cues.

PubMed

Malhó, Rui; Serrazina, Susana; Saavedra, Laura; Dias, Fernando V; Ul-Rehman, Reiaz

2015-01-01

Apical cell growth seems to have independently evolved throughout the major lineages of life. To a certain extent, so does our body of knowledge on the mechanisms regulating this morphogenetic process. Studies on pollen tubes, root hairs, rhizoids, fungal hyphae, even nerve cells, have highlighted tissue and cell specificities but also common regulatory characteristics (e.g., ions, proteins, phospholipids) that our focused research sometimes failed to grasp. The working hypothesis to test how apical cell growth is established and maintained have thus been shaped by the model organism under study and the type of methods used to study them. The current picture is one of a dynamic and adaptative process, based on a spatial segregation of components that network to achieve growth and respond to environmental (extracellular) cues. Here, we explore some examples of our live imaging research, namely on cyclic nucleotide gated ion channels, lipid kinases and syntaxins involved in exocytosis. We discuss how their spatial distribution, activity and concentration suggest that the players regulating apical cell growth may display more mobility than previously thought. Furthermore, we speculate on the implications of such perspective in our understanding of the mechanisms regulating apical cell growth and their responses to extracellular cues.

Periodic component analysis as a spatial filter for SSVEP-based brain-computer interface.

PubMed

Kiran Kumar, G R; Reddy, M Ramasubba

2018-06-08

Traditional Spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost. In this paper, Periodic component analysis (πCA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise. The πCA can separate out components corresponding to a given frequency of interest from the background electroencephalogram (EEG) by capturing the temporal information and does not generalize SSVEP based on rigid templates. Data from ten test subjects were used to evaluate the proposed method and the results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction. Statistical tests were performed to validate the results. The experimental results show that πCA provides significant improvement in accuracy compared to standard CCA and MEC in low SNR conditions. The results demonstrate that πCA provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost. Hence πCA is a reliable and efficient alternative detection algorithm for SSVEP based brain-computer interface (BCI). Copyright © 2018. Published by Elsevier B.V.

Sparse modeling of spatial environmental variables associated with asthma

PubMed Central

Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.

2014-01-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s home address was geocoded to one of 3,456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin’s geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. PMID:25533437

Sparse modeling of spatial environmental variables associated with asthma.

PubMed

Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

A first application of independent component analysis to extracting structure from stock returns.

PubMed

Back, A D; Weigend, A S

1997-08-01

This paper explores the application of a signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories, (i) infrequent large shocks (responsible for the major changes in the stock prices), and (ii) frequent smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price is less similar to the original one. ICA is shown to be a potentially powerful method of analyzing and understanding driving mechanisms in financial time series. The application to portfolio optimization is described in Chin and Weigend (1998).

Independent rate and temporal coding in hippocampal pyramidal cells.

PubMed

Huxter, John; Burgess, Neil; O'Keefe, John

2003-10-23

In the brain, hippocampal pyramidal cells use temporal as well as rate coding to signal spatial aspects of the animal's environment or behaviour. The temporal code takes the form of a phase relationship to the concurrent cycle of the hippocampal electroencephalogram theta rhythm. These two codes could each represent a different variable. However, this requires the rate and phase to vary independently, in contrast to recent suggestions that they are tightly coupled, both reflecting the amplitude of the cell's input. Here we show that the time of firing and firing rate are dissociable, and can represent two independent variables: respectively the animal's location within the place field, and its speed of movement through the field. Independent encoding of location together with actions and stimuli occurring there may help to explain the dual roles of the hippocampus in spatial and episodic memory, or may indicate a more general role of the hippocampus in relational/declarative memory.

Spatio-temporal Trends of Climate Variability in North Carolina

NASA Astrophysics Data System (ADS)

Sayemuzzaman, Mohammad

Climatic trends in spatial and temporal variability of maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean) and precipitation were evaluated for 249 ground-based stations in North Carolina for 1950-2009. The Mann-Kendall (MK), the Theil-Sen Approach (TSA) and the Sequential Mann-Kendall (SQMK) tests were applied to quantify the significance of trend, magnitude of trend and the trend shift, respectively. The lag-1 serial correlation and double mass curve techniques were used to address the data independency and homogeneity. The pre-whitening technique was used to eliminate the effect of auto correlation of the data series. The difference between minimum and maximum temperatures, and so the diurnal temperature range (DTR), at some stations was found to be decreasing on both an annual and a seasonal basis, with an overall increasing trend in the mean temperature. For precipitation, a statewide increasing trend in fall (highest in November) and decreasing trend in winter (highest in February) were detected. No pronounced increasing/decreasing trends were detected in annual, spring, and summer precipitation time series. Trend analysis on a spatial scale (for three physiographic regions: mountain, piedmont and coastal) revealed mixed results. Coastal zone exhibited increasing mean temperature (warming) trend as compared to other locations whereas mountain zone showed decreasing trend (cooling). Three main moisture components (precipitation, total cloud cover, and soil moisture) and the two major atmospheric circulation modes (North Atlantic Oscillation and Southern Oscillation) were used for correlative analysis purposes with the temperature (specifically with DTR) and precipitation trends. It appears that the moisture components are associated with DTR more than the circulation modes in North Carolina.

Spectral embedding-based registration (SERg) for multimodal fusion of prostate histology and MRI

NASA Astrophysics Data System (ADS)

Hwuang, Eileen; Rusu, Mirabela; Karthigeyan, Sudha; Agner, Shannon C.; Sparks, Rachel; Shih, Natalie; Tomaszewski, John E.; Rosen, Mark; Feldman, Michael; Madabhushi, Anant

2014-03-01

Multi-modal image registration is needed to align medical images collected from different protocols or imaging sources, thereby allowing the mapping of complementary information between images. One challenge of multimodal image registration is that typical similarity measures rely on statistical correlations between image intensities to determine anatomical alignment. The use of alternate image representations could allow for mapping of intensities into a space or representation such that the multimodal images appear more similar, thus facilitating their co-registration. In this work, we present a spectral embedding based registration (SERg) method that uses non-linearly embedded representations obtained from independent components of statistical texture maps of the original images to facilitate multimodal image registration. Our methodology comprises the following main steps: 1) image-derived textural representation of the original images, 2) dimensionality reduction using independent component analysis (ICA), 3) spectral embedding to generate the alternate representations, and 4) image registration. The rationale behind our approach is that SERg yields embedded representations that can allow for very different looking images to appear more similar, thereby facilitating improved co-registration. Statistical texture features are derived from the image intensities and then reduced to a smaller set by using independent component analysis to remove redundant information. Spectral embedding generates a new representation by eigendecomposition from which only the most important eigenvectors are selected. This helps to accentuate areas of salience based on modality-invariant structural information and therefore better identifies corresponding regions in both the template and target images. The spirit behind SERg is that image registration driven by these areas of salience and correspondence should improve alignment accuracy. In this work, SERg is implemented using Demons to allow the algorithm to more effectively register multimodal images. SERg is also tested within the free-form deformation framework driven by mutual information. Nine pairs of synthetic T1-weighted to T2-weighted brain MRI were registered under the following conditions: five levels of noise (0%, 1%, 3%, 5%, and 7%) and two levels of bias field (20% and 40%) each with and without noise. We demonstrate that across all of these conditions, SERg yields a mean squared error that is 81.51% lower than that of Demons driven by MRI intensity alone. We also spatially align twenty-six ex vivo histology sections and in vivo prostate MRI in order to map the spatial extent of prostate cancer onto corresponding radiologic imaging. SERg performs better than intensity registration by decreasing the root mean squared distance of annotated landmarks in the prostate gland via both Demons algorithm and mutual information-driven free-form deformation. In both synthetic and clinical experiments, the observed improvement in alignment of the template and target images suggest the utility of parametric eigenvector representations and hence SERg for multimodal image registration.

The Extent of Working Memory Deficits Associated with Williams Syndrome: Exploration of Verbal and Spatial Domains and Executively Controlled Processes

ERIC Educational Resources Information Center

Rhodes, Sinead M.; Riby, Deborah M.; Fraser, Emma; Campbell, Lorna Elise

2011-01-01

The present study investigated verbal and spatial working memory (WM) functioning in individuals with the neuro-developmental disorder Williams syndrome (WS) using WM component tasks. While there is strong evidence of WM impairments in WS, previous research has focused on short-term memory and has neglected assessment of executive components of…

Normative shifts of cortical mechanisms of encoding contribute to adult age differences in visual-spatial working memory.

PubMed

Störmer, Viola S; Li, Shu-Chen; Heekeren, Hauke R; Lindenberger, Ulman

2013-06-01

The capacity of visual-spatial working memory (WM) declines from early to late adulthood. Recent attempts at identifying neural correlates of WM capacity decline have focused on the maintenance phase of WM. Here, we investigate neural mechanisms during the encoding phase as another potential mechanism contributing to adult age differences in WM capacity. We used electroencephalography to track neural activity during encoding and maintenance on a millisecond timescale in 35 younger and 35 older adults performing a visual-spatial WM task. As predicted, we observed pronounced age differences in ERP indicators of WM encoding: Younger adults showed attentional selection during item encoding (N2pc component), but this selection mechanism was greatly attenuated in older adults. Conversely, older adults showed more pronounced signs of early perceptual stimulus processing (N1 component) than younger adults. The amplitude modulation of the N1 component predicted WM capacity in older adults, whereas the attentional amplitude modulation of the N2pc component predicted WM capacity in younger adults. Our findings suggest that adult age differences in mechanisms of WM encoding contribute to adult age differences in limits of visual-spatial WM capacity. Copyright © 2013 Elsevier Inc. All rights reserved.

Lack of Awareness for Spatial and Verbal Constructive Apraxia

ERIC Educational Resources Information Center

Rinaldi, Maria Cristina; Piras, Federica; Pizzamiglio, Luigi

2010-01-01

It is still a matter of debate whether constructive apraxia (CA) should be considered a form of apraxia or, rather, the motor expression of a more pervasive impairment in visuo-spatial processing. Constructive disorders were linked to visuo-spatial disorders and to deficits in appreciating spatial relations among component sub-parts or problems in…

An Evaluation of University World Geography Textbook Questions for Components of Spatial Thinking

ERIC Educational Resources Information Center

Scholz, Michael A.; Huynh, Niem Tu; Brysch, Carmen P.; Scholz, Ruojing Wang

2014-01-01

Geography textbooks contain chapter or review questions that may engage students in spatial thinking. This research used Jo and Bednarz's (2009) "Taxonomy of Spatial Thinking" to evaluate the percentage of spatial thinking questions in four university-level world geography course textbooks. The results from this study were then…

The spatial distribution of cropland carbon transfer in Jilin province during 2014

NASA Astrophysics Data System (ADS)

Cai, Xintong; Meng, Jian; Li, Qiuhui; Gao, Shuang; Zhu, Xianjin

2018-01-01

Cropland carbon transfer (CCT, gC yr-1) is an important component in the carbon budget of terrestrial ecosystems. Analyzing the value of CCT and its spatial variation would provide a data basis for assessing the regional carbon balance. Based on the data from Jilin statistical yearbook 2015, we investigated the spatial variation of CCT in Jilin province during 2014. Results suggest that the CCT of Jilin province was 30.83 TgC, which exhibited a decreasing trend from the centre to the border but the west side was higher than the east. The magnitude of carbon transfer per area (MCT), which showed a similar spatial distribution with CCT, was the dominating component of CCT spatial distribution. The spatial distribution of MCT was jointly affected by that of the ratio of planting area to regional area (RPR) and carbon transfer per planting area (CTP), where RPR and CTP contributed 65.55% and 34.5% of MCT spatial distribution, respectively. Therefore, CCT in Jilin province spatially varied, which made it highly needed to consider the difference in CCT among regions when we assessing the regional carbon budget.

Spatially Resolved Spectroscopy of the PMS Quadruple GG Tau: Evidence for a Substellar Companion

NASA Astrophysics Data System (ADS)

White, R. J.; Ghez, A. M.; Schultz, G.; Reid, I. N.

1998-05-01

We present spatially resolved optical spectra from HST (FOS) and the Keck Telescope (HIRES & LRIS) of the components of the quadruple PMS system GG Tau. According to the latest PMS evolutionary models, the coldest component of this system, GG Tau/c B, appears to be substellar with a preliminary mass of only 50 M_J. This putative brown dwarf is especially intriguing as it shows clear signatures of accretion. The components of this quadruple, which span a wide range in mass, are used to test theoretical low mass PMS evolutionary models under the assumption that the components should be coeval.

Radiometric Calibration Assessment of Commercial High Spatial Resolution Multispectral Image Products

NASA Technical Reports Server (NTRS)

Holekamp, Kara; Aaron, David; Thome, Kurtis

2006-01-01

Radiometric calibration of commercial imaging satellite products is required to ensure that science and application communities can better understand their properties. Inaccurate radiometric calibrations can lead to erroneous decisions and invalid conclusions and can limit intercomparisons with other systems. To address this calibration need, satellite at-sensor radiance values were compared to those estimated by each independent team member to determine the sensor's radiometric accuracy. The combined results of this evaluation provide the user community with an independent assessment of these commercially available high spatial resolution sensors' absolute calibration values.

Exploration as a Mediator of the Relation between the Attainment of Motor Milestones and the Development of Spatial Cognition and Spatial Language

ERIC Educational Resources Information Center

Oudgenoeg-Paz, Ora; Leseman, Paul P. M.; Volman, M. J. M.

2015-01-01

The embodied-cognition approach views cognition and language as grounded in daily sensorimotor child-environment interactions. Therefore, the attainment of motor milestones is expected to play a role in cognitive-linguistic development. Early attainment of unsupported sitting and independent walking indeed predict better spatial cognition and…

Low spatial coherence electrically pumped semiconductor laser for speckle-free full-field imaging

PubMed Central

Redding, Brandon; Cerjan, Alexander; Huang, Xue; Lee, Minjoo Larry; Stone, A. Douglas; Choma, Michael A.; Cao, Hui

2015-01-01

The spatial coherence of laser sources has limited their application to parallel imaging and projection due to coherent artifacts, such as speckle. In contrast, traditional incoherent light sources, such as thermal sources or light emitting diodes (LEDs), provide relatively low power per independent spatial mode. Here, we present a chip-scale, electrically pumped semiconductor laser based on a novel design, demonstrating high power per mode with much lower spatial coherence than conventional laser sources. The laser resonator was fabricated with a chaotic, D-shaped cavity optimized to achieve highly multimode lasing. Lasing occurs simultaneously and independently in ∼1,000 modes, and hence the total emission exhibits very low spatial coherence. Speckle-free full-field imaging is demonstrated using the chaotic cavity laser as the illumination source. The power per mode of the sample illumination is several orders of magnitude higher than that of a LED or thermal light source. Such a compact, low-cost source, which combines the low spatial coherence of a LED with the high spectral radiance of a laser, could enable a wide range of high-speed, full-field imaging and projection applications. PMID:25605946

Low spatial coherence electrically pumped semiconductor laser for speckle-free full-field imaging.

PubMed

Redding, Brandon; Cerjan, Alexander; Huang, Xue; Lee, Minjoo Larry; Stone, A Douglas; Choma, Michael A; Cao, Hui

2015-02-03

The spatial coherence of laser sources has limited their application to parallel imaging and projection due to coherent artifacts, such as speckle. In contrast, traditional incoherent light sources, such as thermal sources or light emitting diodes (LEDs), provide relatively low power per independent spatial mode. Here, we present a chip-scale, electrically pumped semiconductor laser based on a novel design, demonstrating high power per mode with much lower spatial coherence than conventional laser sources. The laser resonator was fabricated with a chaotic, D-shaped cavity optimized to achieve highly multimode lasing. Lasing occurs simultaneously and independently in ∼1,000 modes, and hence the total emission exhibits very low spatial coherence. Speckle-free full-field imaging is demonstrated using the chaotic cavity laser as the illumination source. The power per mode of the sample illumination is several orders of magnitude higher than that of a LED or thermal light source. Such a compact, low-cost source, which combines the low spatial coherence of a LED with the high spectral radiance of a laser, could enable a wide range of high-speed, full-field imaging and projection applications.

RiskChanges Spatial Decision Support system for the analysis of changing multi-hazard risk

NASA Astrophysics Data System (ADS)

van Westen, Cees; Zhang, Kaixi; Bakker, Wim; Andrejchenko, Vera; Berlin, Julian; Olyazadeh, Roya; Cristal, Irina

2015-04-01

Within the framework of the EU FP7 Marie Curie Project CHANGES and the EU FP7 Copernicus project INCREO a spatial decision support system was developed with the aim to analyse the effect of risk reduction planning alternatives on reducing the risk now and in the future, and support decision makers in selecting the best alternatives. Central to the SDSS are the stakeholders. The envisaged users of the system are organizations involved in planning of risk reduction measures, and that have staff capable of visualizing and analyzing spatial data at a municipal scale. The SDSS should be able to function in different countries with different legal frameworks and with organizations with different mandates. These could be subdivided into Civil protection organization with the mandate to design disaster response plans, Expert organizations with the mandate to design structural risk reduction measures (e.g. dams, dikes, check-dams etc), and planning organizations with the mandate to make land development plans. The SDSS can be used in different ways: analyzing the current level of risk, analyzing the best alternatives for risk reduction, the evaluation of the consequences of possible future scenarios to the risk levels, and the evaluation how different risk reduction alternatives will lead to risk reduction under different future scenarios. The SDSS is developed based on open source software and following open standards, for code as well as for data formats and service interfaces. Code development was based upon open source software as well. The architecture of the system is modular. The various parts of the system are loosely coupled, extensible, using standards for interoperability, flexible and web-based. The Spatial Decision Support System is composed of a number of integrated components. The Risk Assessment component allows to carry out spatial risk analysis, with different degrees of complexity, ranging from simple exposure (overlay of hazard and assets maps) to quantitative analysis (using different hazard types, temporal scenarios and vulnerability curves) resulting into risk curves. The platform does not include a component to calculate hazard maps, and existing hazard maps are used as input data for the risk component. The second component of the SDSS is a risk reduction planning component, which forms the core of the platform. This component includes the definition of risk reduction alternatives (related to disaster response planning, risk reduction measures and spatial planning) and links back to the risk assessment module to calculate the new level of risk if the measure is implemented, and a cost-benefit (or cost-effectiveness/ Spatial Multi Criteria Evaluation) component to compare the alternatives and make decision on the optimal one. The third component of the SDSS is a temporal scenario component, which allows to define future scenarios in terms of climate change, land use change and population change, and the time periods for which these scenarios will be made. The component doesn't generate these scenarios but uses input maps for the effect of the scenarios on the hazard and assets maps. The last component is a communication and visualization component, which can compare scenarios and alternatives, not only in the form of maps, but also in other forms (risk curves, tables, graphs)

Online and offline awareness deficits: Anosognosia for spatial neglect.

PubMed

Chen, Peii; Toglia, Joan

2018-04-12

Anosognosia for spatial neglect (ASN) can be offline or online. Offline ASN is general unawareness of having experienced spatial deficits. Online ASN is an awareness deficit of underestimating spatial difficulties that likely to occur in an upcoming task (anticipatory ASN) or have just occurred during the task (emergent ASN). We explored the relationships among spatial neglect, offline ASN, anticipatory ASN, and emergent ASN. Research Method/Design: Forty-four survivors of stroke answered questionnaires assessing offline and online self-awareness of spatial problems. The online questionnaire was asked immediately before and after each of 4 tests for spatial neglect, including shape cancellation, address and sentence copying, telephone dialing, and indented paragraph reading. Participants were certain they had difficulties in daily spatial tasks (offline awareness), in the task they were about to perform (anticipatory awareness) and had just performed (emergent awareness). Nonetheless, they consistently overestimated their spatial abilities, indicating ASN. Offline and online ASN appeared independent. Online ASN improved after task execution. Neglect severity was not positively correlated with offline ASN. Greater neglect severity correlated with both greater anticipatory and emergent ASN. Regardless of neglect severity, we found task-specific differences in emergent ASN but not in anticipatory ASN. Individuals with spatial neglect acknowledge their spatial difficulty (certainty of error occurrence) but may not necessarily recognize the extent of their difficulty (accuracy of error estimation). Our findings suggest that offline and online ASN are independent. A potential implication from the study is that familiar and challenging tasks may facilitate emergence of self-awareness. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Acoustic Determination of Near-Surface Soil Properties

DTIC Science & Technology

2008-12-01

requiring geostatistical analysis, while nearby others are spatially independent. In studies involving many different soil properties and chemistry ...Am 116(6), p. 3354-3369. Kravchenko, N., C.W. Boast, D.G. Bullock, 1991. Fractal analysis of soil spatial variability. Agronomy Journal 91

Selective Listening Point Audio Based on Blind Signal Separation and Stereophonic Technology

NASA Astrophysics Data System (ADS)

Niwa, Kenta; Nishino, Takanori; Takeda, Kazuya

A sound field reproduction method is proposed that uses blind source separation and a head-related transfer function. In the proposed system, multichannel acoustic signals captured at distant microphones are decomposed to a set of location/signal pairs of virtual sound sources based on frequency-domain independent component analysis. After estimating the locations and the signals of the virtual sources by convolving the controlled acoustic transfer functions with each signal, the spatial sound is constructed at the selected point. In experiments, a sound field made by six sound sources is captured using 48 distant microphones and decomposed into sets of virtual sound sources. Since subjective evaluation shows no significant difference between natural and reconstructed sound when six virtual sources and are used, the effectiveness of the decomposing algorithm as well as the virtual source representation are confirmed.

Design of an intelligent flight instrumentation unit using embedded RTOS

NASA Astrophysics Data System (ADS)

Estrada-Marmolejo, R.; García-Torales, G.; Torres-Ortega, H. H.; Flores, J. L.

2011-09-01

Micro Unmanned Aerial Vehicles (MUAV) must calculate its spatial position to control the flight dynamics, which is done by Inertial Measurement Units (IMUs). MEMS Inertial sensors have made possible to reduce the size and power consumption of such units. Commonly the flight instrumentation operates independently of the main processor. This work presents an instrumentation block design, which reduces size and power consumption of the complete system of a MUAV. This is done by coupling the inertial sensors to the main processor without considering any intermediate level of processing aside. Using Real Time Operating Systems (RTOS) reduces the number of intermediate components, increasing MUAV reliability. One advantage is the possibility to control several different sensors with a single communication bus. This feature of the MEMS sensors makes a smaller and less complex MUAV design possible.

Organic Analysis in Miller Range 090657 and Buckley Island 10933 CR2 Chondrites: Part 1 In-Situ Observation of Carbonaceous Material

NASA Technical Reports Server (NTRS)

Cao, T.; Nakamura-Messenger, K.; Berger, E. L.; Burton, A. S.; Messenger, S.; Clemett, S. J.

2016-01-01

Primitive carbonaceous chondrites contain a wide variety of organic material, ranging from soluble discrete molecules to insoluble unstructured kerogen-like component as well as structured nano-globules of macromolecular carbon. The relationship between the soluble organic molecules, macromolecular organic material, and host minerals are poorly understood. Due to the differences in extractability of soluble and insoluble organic materials, the analysis methods for each differ and are often performed independently. The combination of soluble and insoluble analyses, when performed concurrently, can provide a wider understanding on spatial distribution, and elemental, structural and isotopic composition of organic material in primitive meteorites. Furthermore, they can provide broader perspective on how extraterrestrial organic ma-terials potentially contributed to the synthesis of life's essential compounds such as amino acids, sugar acids, activated phosphates and nucleobases.

Vector solitons in coupled nonlinear Schrödinger equations with spatial stimulated scattering and inhomogeneous dispersion

NASA Astrophysics Data System (ADS)

Gromov, E. M.; Malomed, B. A.; Tyutin, V. V.

2018-01-01

The dynamics of two-component solitons is studied, analytically and numerically, in the framework of a system of coupled extended nonlinear Schrödinger equations, which incorporate the cross-phase modulation, pseudo-stimulated-Raman-scattering (pseudo-SRS), cross-pseudo-SRS, and spatially inhomogeneous second-order dispersion (SOD). The system models co-propagation of electromagnetic waves with orthogonal polarizations in plasmas. It is shown that the soliton's wavenumber downshift, caused by pseudo-SRS, may be compensated by an upshift, induced by the inhomogeneous SOD, to produce stable stationary two-component solitons. The corresponding approximate analytical solutions for stable solitons are found. Analytical results are well confirmed by their numerical counterparts. Further, the evolution of inputs composed of spatially even and odd components is investigated by means of systematic simulations, which reveal three different outcomes: formation of a breather which keeps opposite parities of the components; splitting into a pair of separating vector solitons; and spreading of the weak odd component into a small-amplitude pedestal with an embedded dark soliton.

Acceleration characteristics of human ocular accommodation.

PubMed

Bharadwaj, Shrikant R; Schor, Clifton M

2005-01-01

Position and velocity of accommodation are known to increase with stimulus magnitude, however, little is known about acceleration properties. We investigated three acceleration properties: peak acceleration, time-to-peak acceleration and total duration of acceleration to step changes in defocus. Peak velocity and total duration of acceleration increased with response magnitude. Peak acceleration and time-to-peak acceleration remained independent of response magnitude. Independent first-order and second-order dynamic components of accommodation demonstrate that neural control of accommodation has an initial open-loop component that is independent of response magnitude and a closed-loop component that increases with response magnitude.

Separation of the global and local components in functional near-infrared spectroscopy signals using principal component spatial filtering

PubMed Central

Zhang, Xian; Noah, Jack Adam; Hirsch, Joy

2016-01-01

Abstract. Global systemic effects not specific to a task can be prominent in functional near-infrared spectroscopy (fNIRS) signals and the separation of task-specific fNIRS signals and global nonspecific effects is challenging due to waveform correlations. We describe a principal component spatial filter algorithm for separation of the global and local effects. The effectiveness of the approach is demonstrated using fNIRS signals acquired during a right finger-thumb tapping task where the response patterns are well established. Both the temporal waveforms and the spatial pattern consistencies between oxyhemoglobin and deoxyhemoglobin signals are significantly improved, consistent with the basic physiological basis of fNIRS signals and the expected pattern of activity associated with the task. PMID:26866047

Long-term consistency in spatial patterns of primate seed dispersal.

PubMed

Heymann, Eckhard W; Culot, Laurence; Knogge, Christoph; Noriega Piña, Tony Enrique; Tirado Herrera, Emérita R; Klapproth, Matthias; Zinner, Dietmar

2017-03-01

Seed dispersal is a key ecological process in tropical forests, with effects on various levels ranging from plant reproductive success to the carbon storage potential of tropical rainforests. On a local and landscape scale, spatial patterns of seed dispersal create the template for the recruitment process and thus influence the population dynamics of plant species. The strength of this influence will depend on the long-term consistency of spatial patterns of seed dispersal. We examined the long-term consistency of spatial patterns of seed dispersal with spatially explicit data on seed dispersal by two neotropical primate species, Leontocebus nigrifrons and Saguinus mystax (Callitrichidae), collected during four independent studies between 1994 and 2013. Using distributions of dispersal probability over distances independent of plant species, cumulative dispersal distances, and kernel density estimates, we show that spatial patterns of seed dispersal are highly consistent over time. For a specific plant species, the legume Parkia panurensis , the convergence of cumulative distributions at a distance of 300 m, and the high probability of dispersal within 100 m from source trees coincide with the dimension of the spatial-genetic structure on the embryo/juvenile (300 m) and adult stage (100 m), respectively, of this plant species. Our results are the first demonstration of long-term consistency of spatial patterns of seed dispersal created by tropical frugivores. Such consistency may translate into idiosyncratic patterns of regeneration.

Capacity of MIMO free space optical communications using multiple partially coherent beams propagation through non-Kolmogorov strong turbulence.

PubMed

Deng, Peng; Kavehrad, Mohsen; Liu, Zhiwen; Zhou, Zhou; Yuan, Xiuhua

2013-07-01

We study the average capacity performance for multiple-input multiple-output (MIMO) free-space optical (FSO) communication systems using multiple partially coherent beams propagating through non-Kolmogorov strong turbulence, assuming equal gain combining diversity configuration and the sum of multiple gamma-gamma random variables for multiple independent partially coherent beams. The closed-form expressions of scintillation and average capacity are derived and then used to analyze the dependence on the number of independent diversity branches, power law α, refractive-index structure parameter, propagation distance and spatial coherence length of source beams. Obtained results show that, the average capacity increases more significantly with the increase in the rank of MIMO channel matrix compared with the diversity order. The effect of the diversity order on the average capacity is independent of the power law, turbulence strength parameter and spatial coherence length, whereas these effects on average capacity are gradually mitigated as the diversity order increases. The average capacity increases and saturates with the decreasing spatial coherence length, at rates depending on the diversity order, power law and turbulence strength. There exist optimal values of the spatial coherence length and diversity configuration for maximizing the average capacity of MIMO FSO links over a variety of atmospheric turbulence conditions.

Judicious use of custom development in an open source component architecture

NASA Astrophysics Data System (ADS)

Bristol, S.; Latysh, N.; Long, D.; Tekell, S.; Allen, J.

2014-12-01

Modern software engineering is not as much programming from scratch as innovative assembly of existing components. Seamlessly integrating disparate components into scalable, performant architecture requires sound engineering craftsmanship and can often result in increased cost efficiency and accelerated capabilities if software teams focus their creativity on the edges of the problem space. ScienceBase is part of the U.S. Geological Survey scientific cyberinfrastructure, providing data and information management, distribution services, and analysis capabilities in a way that strives to follow this pattern. ScienceBase leverages open source NoSQL and relational databases, search indexing technology, spatial service engines, numerous libraries, and one proprietary but necessary software component in its architecture. The primary engineering focus is cohesive component interaction, including construction of a seamless Application Programming Interface (API) across all elements. The API allows researchers and software developers alike to leverage the infrastructure in unique, creative ways. Scaling the ScienceBase architecture and core API with increasing data volume (more databases) and complexity (integrated science problems) is a primary challenge addressed by judicious use of custom development in the component architecture. Other data management and informatics activities in the earth sciences have independently resolved to a similar design of reusing and building upon established technology and are working through similar issues for managing and developing information (e.g., U.S. Geoscience Information Network; NASA's Earth Observing System Clearing House; GSToRE at the University of New Mexico). Recent discussions facilitated through the Earth Science Information Partners are exploring potential avenues to exploit the implicit relationships between similar projects for explicit gains in our ability to more rapidly advance global scientific cyberinfrastructure.

Interactions between space-based and feature-based attention.

PubMed

Leonard, Carly J; Balestreri, Angela; Luck, Steven J

2015-02-01

Although early research suggested that attention to nonspatial features (i.e., red) was confined to stimuli appearing at an attended spatial location, more recent research has emphasized the global nature of feature-based attention. For example, a distractor sharing a target feature may capture attention even if it occurs at a task-irrelevant location. Such findings have been used to argue that feature-based attention operates independently of spatial attention. However, feature-based attention may nonetheless interact with spatial attention, yielding larger feature-based effects at attended locations than at unattended locations. The present study tested this possibility. In 2 experiments, participants viewed a rapid serial visual presentation (RSVP) stream and identified a target letter defined by its color. Target-colored distractors were presented at various task-irrelevant locations during the RSVP stream. We found that feature-driven attentional capture effects were largest when the target-colored distractor was closer to the attended location. These results demonstrate that spatial attention modulates the strength of feature-based attention capture, calling into question the prior evidence that feature-based attention operates in a global manner that is independent of spatial attention.

Flexible hydrological modeling - Disaggregation from lumped catchment scale to higher spatial resolutions

NASA Astrophysics Data System (ADS)

Tran, Quoc Quan; Willems, Patrick; Pannemans, Bart; Blanckaert, Joris; Pereira, Fernando; Nossent, Jiri; Cauwenberghs, Kris; Vansteenkiste, Thomas

2015-04-01

Based on an international literature review on model structures of existing rainfall-runoff and hydrological models, a generalized model structure is proposed. It consists of different types of meteorological components, storage components, splitting components and routing components. They can be spatially organized in a lumped way, or on a grid, spatially interlinked by source-to-sink or grid-to-grid (cell-to-cell) routing. The grid size of the model can be chosen depending on the application. The user can select/change the spatial resolution depending on the needs and/or the evaluation of the accuracy of the model results, or use different spatial resolutions in parallel for different applications. Major research questions addressed during the study are: How can we assure consistent results of the model at any spatial detail? How can we avoid strong or sudden changes in model parameters and corresponding simulation results, when one moves from one level of spatial detail to another? How can we limit the problem of overparameterization/equifinality when we move from the lumped model to the spatially distributed model? The proposed approach is a step-wise one, where first the lumped conceptual model is calibrated using a systematic, data-based approach, followed by a disaggregation step where the lumped parameters are disaggregated based on spatial catchment characteristics (topography, land use, soil characteristics). In this way, disaggregation can be done down to any spatial scale, and consistently among scales. Only few additional calibration parameters are introduced to scale the absolute spatial differences in model parameters, but keeping the relative differences as obtained from the spatial catchment characteristics. After calibration of the spatial model, the accuracies of the lumped and spatial models were compared for peak, low and cumulative runoff total and sub-flows (at downstream and internal gauging stations). For the distributed models, additional validation on spatial results was done for the groundwater head values at observation wells. To ensure that the lumped model can produce results as accurate as the spatially distributed models or close regardless to the number of parameters and implemented physical processes, it was checked whether the structure of the lumped models had to be adjusted. The concept has been implemented in a PCRaster - Python platform and tested for two Belgian case studies (catchments of the rivers Dijle and Grote Nete). So far, use is made of existing model structures (NAM, PDM, VHM and HBV). Acknowledgement: These results were obtained within the scope of research activities for the Flemish Environment Agency (VMM) - division Operational Water Management on "Next Generation hydrological modeling", in cooperation with IMDC consultants, and for Flanders Hydraulics Research (Waterbouwkundig Laboratorium) on "Effect of climate change on the hydrological regime of navigable watercourses in Belgium".

A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings.

PubMed

Tamburro, Gabriella; Fiedler, Patrique; Stone, David; Haueisen, Jens; Comani, Silvia

2018-01-01

EEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts. Our fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity ( p ). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR. The best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs). Apart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings.

A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings

PubMed Central

Tamburro, Gabriella; Fiedler, Patrique; Stone, David; Haueisen, Jens

2018-01-01

Background EEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts. Methods Our fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity (p). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR. Results The best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs). Discussion Apart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings. PMID:29492336

Chromosomal and carcinogenic effects of sequential HZE and low-LET irradiations

NASA Astrophysics Data System (ADS)

Simonson, Dustin Mark

All persons are exposed to a natural background of ionizing radiations with different spatial patterns of energy deposition resulting in differential biologic response. Astronauts, aircrew and radioactive contamination clean-up personnel are exposed to particularly complex radiation spectra. The current method for calculating radiation-induced exposure limits in mixed radiation environments is based on the linear summation of non-threshold risks, a methodology grounded in the premise that each component of the radiation field acts independently of the presence of other components. The assumption of effect independence of in-vitro exposed samples was tested by evaluating the frequency of chromosome aberrations induced by sequential irradiation of immortalized human mammary epithelial cells with 1 GeV/nucleon 56Fe ions and 137Cs gamma-rays. Experimental response was found to be significantly less than calculated on the basis of effect independence, but only when 56Fe ions preceded the photon exposure. That there was order dependence is interpreted as evidence that response may not simply be a result of interactions between similar sublesions but rather may involve qualitatively different time-ordered parameters. The presence of this sub-additive response is phenomenologically similar to adaptive response, which had not been previously reported as a consequence to high-energy heavy ion irradiation. Calculations based on effect independence predict a significantly greater average number and lifetime cumulative incidence of breast cancers in female Sprague-Dawley rats irradiated with both 56Fe ions and 250 MeV protons than was experimentally observed. This finding supports the hypothesis that the presence of non-additive response is not exclusively an in vitro phenomenon. Results from an evaluation of mammary epithelial cell response induced in a rat cancer model are marginally consistent with the use of in vivo induced chromosome aberrations as a biomarker of breast cancer risk. There is also an apparent association between the two endpoints relating to dependence on radiation quality. In conclusion, these data demonstrate that sequential exposures to both HZE and low-LET radiation may result in chromosomal and carcinogenic response that is inconsistent with effect independence. These results provide evidence that the linear summation of health risks from mixed HZE and low-LET radiation fields may not accurately reflect true risk.

The transport along membrane nanotubes driven by the spontaneous curvature of membrane components.

PubMed

Kabaso, Doron; Bobrovska, Nataliya; Góźdź, Wojciech; Gongadze, Ekaterina; Kralj-Iglič, Veronika; Zorec, Robert; Iglič, Aleš

2012-10-01

Intercellular membrane nanotubes (ICNs) serve as a very specific transport system between neighboring cells. The underlying mechanisms responsible for the transport of membrane components and vesicular dilations along the ICNs are not clearly understood. The present study investigated the spatial distribution of anisotropic membrane components of tubular shapes and isotropic membrane components of spherical shapes. Experimental results revealed the preferential distribution of CTB (cholera toxin B)-GM1 complexes mainly on the spherical cell membrane, and cholesterol-sphingomyelin at the membrane leading edge and ICNs. In agreement with previous studies, we here propose that the spatial distribution of CTB-GM1 complexes and cholesterol-sphingomyelin rafts were due to their isotropic and anisotropic shapes, respectively. To elucidate the relationship between a membrane component shape and its spatial distribution, a two-component computational model was constructed. The minimization of the membrane bending (free) energy revealed the enrichment of the anisotropic component along the ICN and the isotropic component in the parent cell membrane, which was due to the curvature mismatch between the ICN curvature and the spontaneous curvature of the isotropic component. The equations of motion, derived from the differentiation of the membrane free energy, revealed a curvature-dependent flux of the isotropic component and a curvature-dependent force exerted on a vesicular dilation along the ICN. Finally, the effects of possible changes in the orientational ordering of the anisotropic component attendant to the transport of the vesicular dilation were discussed with connection to the propagation of electrical and chemical signals. Copyright © 2012 Elsevier B.V. All rights reserved.

The Not-So-Global Blood Oxygen Level-Dependent Signal.

PubMed

Billings, Jacob; Keilholz, Shella

2018-04-01

Global signal regression is a controversial processing step for resting-state functional magnetic resonance imaging, partly because the source of the global blood oxygen level-dependent (BOLD) signal remains unclear. On the one hand, nuisance factors such as motion can readily introduce coherent BOLD changes across the whole brain. On the other hand, the global signal has been linked to neural activity and vigilance levels, suggesting that it contains important neurophysiological information and should not be discarded. Any widespread pattern of coordinated activity is likely to contribute appreciably to the global signal. Such patterns may include large-scale quasiperiodic spatiotemporal patterns, known also to be tied to performance on vigilance tasks. This uncertainty surrounding the separability of the global BOLD signal from concurrent neurological processes motivated an examination of the global BOLD signal's spatial distribution. The results clarify that although the global signal collects information from all tissue classes, a diverse subset of the BOLD signal's independent components contribute the most to the global signal. Further, the timing of each network's contribution to the global signal is not consistent across volunteers, confirming the independence of a constituent process that comprises the global signal.

Study of Stark broadening of Li I 460 and 497 nm spectral lines with independent plasma diagnostics by Thomson scattering

NASA Astrophysics Data System (ADS)

Dzierżȩga, Krzysztof; Piȩta, Tomasz; Zawadzki, Witold; Stambulchik, Evgeny; Gavrilović-Božović, Marijana; Jovićević, Sonja; Pokrzywka, Bartłomiej

2018-02-01

We present results of experimental and theoretical studies of the Stark broadening of the Li I 460 nm spectral line with forbidden components and of the isolated 497 nm line. Plasma was induced by Nd:YAG laser radiation at 1064 nm with pulse duration ˜4.5 ns. Laser-induced plasma was generated in front of the alumina pellet, with some content of Li2CO3, placed in a vacuum chamber filled with argon under reduced pressure. Plasma diagnostics was performed using the laser Thomson scattering technique, free from assumptions about the plasma equilibrium state and its composition and so independently of plasma emission spectra. Spatially resolved spectra with Li lines were obtained from the measured, laterally integrated ones applying the inverse Abel transform. The Stark profiles were calculated by computer simulation method assuming a plasma in the local thermodynamic equilibrium. Calculations were performed for experimentally-inferred electron densities and temperatures, from 1.422 × 1023 to 3.55 × 1022 m-3 and from 1.96 eV to 1.04 eV, respectively. Our studies show very good agreement between experimental Stark profiles and those computer simulated.

The Spatial Behaviour of Animals and Men

ERIC Educational Resources Information Center

Brindley, T. S.

1973-01-01

Describes some common patterns of animal spatial behavior, and discusses spatial relationships that can be observed as an important component of human social behavior. Reports the results of a study relating to the interpersonal distances of people in bus queues in Britain. (JR)

The Mechanisms of Space-Time Association: Comparing Motor and Perceptual Contributions in Time Reproduction

ERIC Educational Resources Information Center

Fabbri, Marco; Cellini, Nicola; Martoni, Monica; Tonetti, Lorenzo; Natale, Vincenzo

2013-01-01

The spatial-temporal association indicates that time is represented spatially along a left-to-right line. It is unclear whether the spatial-temporal association is mainly related to a perceptual or a motor component. In addition, the spatial-temporal association is not consistently found using a time reproduction task. Our rationale for this…

Increased Intra-Participant Variability in Children with Autistic Spectrum Disorders: Evidence from Single-Trial Analysis of Evoked EEG

PubMed Central

Milne, Elizabeth

2011-01-01

Intra-participant variability in clinical conditions such as autistic spectrum disorder (ASD) is an important indicator of pathophysiological processing. The data reported here illustrate that trial-by-trial variability can be reliably measured from EEG, and that intra-participant EEG variability is significantly greater in those with ASD than in neuro-typical matched controls. EEG recorded at the scalp is a linear mixture of activity arising from muscle artifacts and numerous concurrent brain processes. To minimize these additional sources of variability, EEG data were subjected to two different methods of spatial filtering. (i) The data were decomposed using infomax independent component analysis, a method of blind source separation which un-mixes the EEG signal into components with maximally independent time-courses, and (ii) a surface Laplacian transform was performed (current source density interpolation) in order to reduce the effects of volume conduction. Data are presented from 13 high functioning adolescents with ASD without co-morbid ADHD, and 12 neuro-typical age-, IQ-, and gender-matched controls. Comparison of variability between the ASD and neuro-typical groups indicated that intra-participant variability of P1 latency and P1 amplitude was greater in the participants with ASD, and inter-trial α-band phase coherence was lower in the participants with ASD. These data support the suggestion that individuals with ASD are less able to synchronize the activity of stimulus-related cell assemblies than neuro-typical individuals, and provide empirical evidence in support of theories of increased neural noise in ASD. PMID:21716921

Studies for Io's extended atmosphere and neutral clouds and their impact on the local satellite atmosphere and on the planetary magnetosphere

NASA Technical Reports Server (NTRS)

Smyth, William H.

1993-01-01

The research performed in this project is divided in two main investigations: (1) the synthesis and analysis of a collection of independent observations for Io's sodium corona, its sodium extended atmosphere, and the sodium cloud, and (2) the analysis of a (System III longitude correlated) space-time 'bite-out' near western elongation in the 1981 sodium cloud images from the JPL Table Mountain Sodium Cloud Data Set. For the first investigation, modeling analysis of the collective observed spatial profiles has shown that they are reproduced by adopting at Io's exobase a modified sputtering flux speed distribution function which is peaked near 0.5 km/s and has a small high-speed (15-20 km/s) nonisotropic component. The nonisotropic high-speed component is consistent with earlier modeling of the trailing directional feature. For the second investigation, modeling analysis of the 'bite-out' observed near western elongation (but not eastern elongation) has shown that it is reproduced in model calculation by adopting a plasma torus description for the sodium lifetime that is inherently asymmetric in System III longitudes of the active sector and that also has an east-west asymmetry. The east-west and System III longitude asymmetries were determined from independent observations for the plasma torus in 1981. The presence of the 'bite-out' feature only near western elongation may be understood in terms of the relative value for sodium of its lifetime and its transport time through the System III enhanced plasma torus region.

Effects of methylphenidate on working memory components: influence of measurement.

PubMed

Bedard, Anne-Claude; Jain, Umesh; Johnson, Sheilah Hogg; Tannock, Rosemary

2007-09-01

To investigate the effects of methylphenidate (MPH) on components of working memory (WM) in attention-deficit hyperactivity disorder (ADHD) and determine the responsiveness of WM measures to MPH. Participants were a clinical sample of 50 children and adolescents with ADHD, aged 6 to 16 years old, who participated in an acute randomized, double-blind, placebo-controlled, crossover trial with single challenges of three MPH doses. Four components of WM were investigated, which varied in processing demands (storage versus manipulation of information) and modality (auditory-verbal; visual-spatial), each of which was indexed by a minimum of two separate measures. MPH improved the ability to store visual-spatial information irrespective of instrument used, but had no effects on the storage of auditory-verbal information. By contrast, MPH enhanced the ability to manipulate both auditory-verbal and visual-spatial information, although effects were instrument specific in both cases. MPH effects on WM are selective: they vary as a function of WM component and measurement.

Electroencephalographic dynamics of musical emotion perception revealed by independent spectral components.

PubMed

Lin, Yuan-Pin; Duann, Jeng-Ren; Chen, Jyh-Horng; Jung, Tzyy-Ping

2010-04-21

This study explores the electroencephalographic (EEG) correlates of emotional experience during music listening. Independent component analysis and analysis of variance were used to separate statistically independent spectral changes of the EEG in response to music-induced emotional processes. An independent brain process with equivalent dipole located in the fronto-central region exhibited distinct δ-band and θ-band power changes associated with self-reported emotional states. Specifically, the emotional valence was associated with δ-power decreases and θ-power increases in the frontal-central area, whereas the emotional arousal was accompanied by increases in both δ and θ powers. The resultant emotion-related component activations that were less interfered by the activities from other brain processes complement previous EEG studies of emotion perception to music.