The Experts below are selected from a list of 263634 Experts worldwide ranked by ideXlab platform
Vince D. Calhoun - One of the best experts on this subject based on the ideXlab platform.
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altered static and dynamic Functional Network connectivity in alzheimer s disease and subcortical ischemic vascular disease shared and specific brain connectivity abnormalities
Human Brain Mapping, 2019Co-Authors: Arvind Caprihan, Jing Sui, Vince D. Calhoun, Jiayu Chen, John C Adair, Gary A RosenbergAbstract:Subcortical ischemic vascular disease (SIVD) is a major subtype of vascular dementia with features that overlap clinically with Alzheimer's disease (AD), confounding diagnosis. Neuroimaging is a more specific and biologically based approach for detecting brain changes and thus may help to distinguish these diseases. There is still a lack of knowledge regarding the shared and specific Functional brain abnormalities, especially Functional connectivity changes in relation to AD and SIVD. In this study, we investigated both static Functional Network connectivity (sFNC) and dynamic FNC (dFNC) between 54 intrinsic connectivity Networks in 19 AD patients, 19 SIVD patients, and 38 age-matched healthy controls. The results show that both patient groups have increased sFNC between the visual and cerebellar (CB) domains but decreased sFNC between the cognitive-control and CB domains. SIVD has specifically decreased sFNC within the sensorimotor domain while AD has specifically altered sFNC between the default-mode and CB domains. In addition, SIVD has more occurrences and a longer dwell time in the weakly connected dFNC states, but with fewer occurrences and a shorter dwell time in the strongly connected dFNC states. AD has both similar and opposite changes in certain dynamic features. More importantly, the dynamic features are found to be associated with cognitive performance. Our findings highlight similar and distinct Functional connectivity alterations in AD and SIVD from both static and dynamic perspectives and indicate dFNC to be a more important biomarker for dementia since its progressively altered patterns can better track cognitive impairment in AD and SIVD.
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classification of schizophrenia patients based on resting state Functional Network connectivity
Frontiers in Neuroscience, 2013Co-Authors: Kent A. Kiehl, Godfrey D. Pearlson, Vince D. Calhoun, Mohammad R ArbabshiraniAbstract:There is a growing interest in automatic classification of mental disorders based on neuroimaging data. Small training data sets (subjects) and very large amount of high dimensional data make it a challenging task to design robust and accurate classifiers for heterogeneous disorders such as schizophrenia. Most previous studies considered structural MRI, diffusion tensor imaging and task-based fMRI for this purpose. However, resting-state data has been rarely used in discrimination of schizophrenia patients from healthy controls. Resting data are of great interest, since they are relatively easy to collect, and not confounded by behavioral performance on a task. Several linear and non-linear classification methods were trained using a training dataset and evaluate with a separate testing dataset. Results show that classification with high accuracy is achievable using simple non-linear discriminative methods such as k-nearest neighbors which is very promising. We compare and report detailed results of each classifier as well as statistical analysis and evaluation of each single feature. To our knowledge our effects represent the first use of resting-state Functional Network connectivity features to classify schizophrenia.
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differences in resting state Functional magnetic resonance imaging Functional Network connectivity between schizophrenia and psychotic bipolar probands and their unaffected first degree relatives
Biological Psychiatry, 2012Co-Authors: Vince D. Calhoun, Michael C Stevens, Shashwath A Meda, Adrienne Gill, Raymond P Lorenzoni, David C Glahn, John A SweeneyAbstract:Background Schizophrenia and bipolar disorder share overlapping symptoms and genetic etiology. Functional brain dysconnectivity is seen in both disorders. Methods We compared 70 schizophrenia and 64 psychotic bipolar probands, their respective unaffected first-degree relatives ( n = 70, and n = 52), and 118 healthy subjects, all group age-, gender-, and ethnicity-matched. We used Functional Network connectivity analysis to measure differential connectivity among 16 Functional magnetic resonance imaging resting state Networks. First, we examined connectivity differences between probands and control subjects. Next, we probed these dysFunctional connections in relatives for potential endophenotypes. Network connectivity was then correlated with Positive and Negative Syndrome Scale (PANSS) scores to reveal clinical relationships. Results Three different Network pairs were differentially connected in probands (false-discovery rate corrected q Conclusions Schizophrenia and psychotic bipolar probands share several abnormal resting state Network connections, but there are also unique neural Network underpinnings between disorders. We identified specific connections that might also be candidate psychosis endophenotypes.
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Modular Organization of Functional Network Connectivity in Healthy Controls and Patients with Schizophrenia during the Resting State.
Frontiers in systems neuroscience, 2012Co-Authors: Sergey M. Plis, Erik B. Erhardt, Elena A. Allen, Jing Sui, Kent A. Kiehl, Godfrey D. Pearlson, Vince D. CalhounAbstract:Neuroimaging studies have shown that Functional brain Networks composed from select regions of interest have a modular community structure. However, the organization of Functional Network connectivity (FNC), comprising a purely data-driven Network built from spatially independent brain components, is not yet clear. The aim of this study is to explore the modular organization of FNC in both healthy controls (HCs) and patients with schizophrenia (SZs). Resting state Functional magnetic resonance imaging data of HCs and SZs were decomposed into independent components (ICs) by group independent component analysis (ICA). Then weighted brain Networks (in which nodes are brain components) were built based on correlations between ICA time courses. Clustering coefficients and connectivity strength of the Networks were computed. A dynamic branch cutting algorithm was used to identify modules of the FNC in HCs and SZs. Results show stronger connectivity strength and higher clustering coefficient in HCs with more and smaller modules in SZs. In addition, HCs and SZs had some different hubs. Our findings demonstrate altered modular architecture of the FNC in schizophrenia and provide insights into abnormal topological organization of intrinsic brain Networks in this mental illness.
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dynamic granger causality based on kalman filter for evaluation of Functional Network connectivity in fmri data
NeuroImage, 2010Co-Authors: Martin Havlicek, Vince D. Calhoun, Jiri Jan, Milan BrazdilAbstract:Increasing interest in understanding dynamic interactions of brain neural Networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of Functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating Functional Network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the Functional Networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover, this metric provides identification of causal relation in frequency domain, which allows one to distinguish the frequency components related to the experimental paradigm. The procedure of evaluating Granger causality via dynamic MAR was demonstrated on simulated time series as well as on two sets of group fMRI data collected during an auditory sensorimotor (SM) or auditory oddball discrimination (AOD) tasks. Finally, a comparison with the results obtained from a standard time-invariant MAR model was provided.
Kent A. Kiehl - One of the best experts on this subject based on the ideXlab platform.
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classification of schizophrenia patients based on resting state Functional Network connectivity
Frontiers in Neuroscience, 2013Co-Authors: Kent A. Kiehl, Godfrey D. Pearlson, Vince D. Calhoun, Mohammad R ArbabshiraniAbstract:There is a growing interest in automatic classification of mental disorders based on neuroimaging data. Small training data sets (subjects) and very large amount of high dimensional data make it a challenging task to design robust and accurate classifiers for heterogeneous disorders such as schizophrenia. Most previous studies considered structural MRI, diffusion tensor imaging and task-based fMRI for this purpose. However, resting-state data has been rarely used in discrimination of schizophrenia patients from healthy controls. Resting data are of great interest, since they are relatively easy to collect, and not confounded by behavioral performance on a task. Several linear and non-linear classification methods were trained using a training dataset and evaluate with a separate testing dataset. Results show that classification with high accuracy is achievable using simple non-linear discriminative methods such as k-nearest neighbors which is very promising. We compare and report detailed results of each classifier as well as statistical analysis and evaluation of each single feature. To our knowledge our effects represent the first use of resting-state Functional Network connectivity features to classify schizophrenia.
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Modular Organization of Functional Network Connectivity in Healthy Controls and Patients with Schizophrenia during the Resting State.
Frontiers in systems neuroscience, 2012Co-Authors: Sergey M. Plis, Erik B. Erhardt, Elena A. Allen, Jing Sui, Kent A. Kiehl, Godfrey D. Pearlson, Vince D. CalhounAbstract:Neuroimaging studies have shown that Functional brain Networks composed from select regions of interest have a modular community structure. However, the organization of Functional Network connectivity (FNC), comprising a purely data-driven Network built from spatially independent brain components, is not yet clear. The aim of this study is to explore the modular organization of FNC in both healthy controls (HCs) and patients with schizophrenia (SZs). Resting state Functional magnetic resonance imaging data of HCs and SZs were decomposed into independent components (ICs) by group independent component analysis (ICA). Then weighted brain Networks (in which nodes are brain components) were built based on correlations between ICA time courses. Clustering coefficients and connectivity strength of the Networks were computed. A dynamic branch cutting algorithm was used to identify modules of the FNC in HCs and SZs. Results show stronger connectivity strength and higher clustering coefficient in HCs with more and smaller modules in SZs. In addition, HCs and SZs had some different hubs. Our findings demonstrate altered modular architecture of the FNC in schizophrenia and provide insights into abnormal topological organization of intrinsic brain Networks in this mental illness.
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a method for evaluating dynamic Functional Network connectivity and task modulation application to schizophrenia
Magnetic Resonance Materials in Physics Biology and Medicine, 2010Co-Authors: Unal Sakoglu, Kent A. Kiehl, Godfrey D. Pearlson, Michelle Yongmei Wang, Andrew M Michael, Vince D. CalhounAbstract:Objective In this paper, we develop a dynamic Functional Network connectivity (FNC) analysis approach using correlations between windowed time-courses of different brain Networks (components) estimated via spatial independent component analysis (sICA). We apply the developed method to fMRI data to evaluate it and to study task-modulation of Functional connections.
Anna Rotarskajagiela - One of the best experts on this subject based on the ideXlab platform.
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resting state Functional Network correlates of psychotic symptoms in schizophrenia
Schizophrenia Research, 2010Co-Authors: Anna Rotarskajagiela, Viola Oertelknochel, Peter J Uhlhaas, Kai Vogeley, David Edmund Johannes LindenAbstract:Schizophrenia has been associated with aberrant intrinsic Functional organization of the brain but the relationship of such deficits to psychopathology is unclear. In this study, we investigated associations between resting-state Networks and individual psychopathology in sixteen patients with paranoid schizophrenia and sixteen matched healthy control participants. We estimated whole-brain Functional connectivity of multiple Networks using a combination of spatial independent component analysis and multiple regression analysis. Five Networks (default-mode, left and right fronto-parietal, left fronto-temporal and auditory Networks) were selected for analysis based on their involvement in neuropsychological models of psychosis. Between-group comparisons and correlations to psychopathology ratings were performed on both spatial (connectivity distributions) and temporal features (power-spectral densities of temporal frequencies below 0.06 Hz). Schizophrenia patients showed aberrant Functional connectivity in the default-mode Network, which correlated with severity of hallucinations and delusions, and decreased hemispheric separation of fronto-parietal activity, which correlated with disorganization symptoms. Furthermore, the severity of positive symptoms correlated with Functional connectivity of fronto-temporal and auditory Networks. Finally, default-mode and auditory Networks showed increased spectral power of low frequency oscillations, which correlated with positive symptom severity. These results are in line with findings from studies that investigated the neural correlates of positive symptoms and suggest that psychopathology is associated with aberrant intrinsic organization of Functional brain Networks in schizophrenia.
Fabrizio Tagliavini - One of the best experts on this subject based on the ideXlab platform.
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Functional Network resilience to pathology in presymptomatic genetic frontotemporal dementia
Neurobiology of aging, 2019Co-Authors: Timothy Rittman, Robin J Borchert, Simon Jones, John C. Van Swieten, Barbara Borroni, Daniela Galimberti, Mario Masellis, Maria Carmela Tartaglia, Caroline Graff, Fabrizio TagliaviniAbstract:The presymptomatic phase of neurodegenerative diseases are characterized by structural brain changes without significant clinical features. We set out to investigate the contribution of Functional Network resilience to preserved cognition in presymptomatic genetic frontotemporal dementia. We studied 172 people from families carrying genetic abnormalities in C9orf72, MAPT, or PGRN. Networks were extracted from Functional MRI data and assessed using graph theoretical analysis. We found that despite loss of both brain volume and Functional connections, there is maintenance of an efficient topological organization of the brain's Functional Network in the years leading up to the estimated age of frontotemporal dementia symptom onset. After this point, Functional Network efficiency declines markedly. Reduction in connectedness was most marked in highly connected hub regions. Measures of topological efficiency of the brain's Functional Network and organization predicted cognitive dysfunction in domains related to symptomatic frontotemporal dementia and connectivity correlated with brain volume loss in frontotemporal dementia. We propose that maintaining the efficient organization of the brain's Functional Network supports cognitive health even as atrophy and connectivity decline presymptomatically.
Godfrey D. Pearlson - One of the best experts on this subject based on the ideXlab platform.
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classification of schizophrenia patients based on resting state Functional Network connectivity
Frontiers in Neuroscience, 2013Co-Authors: Kent A. Kiehl, Godfrey D. Pearlson, Vince D. Calhoun, Mohammad R ArbabshiraniAbstract:There is a growing interest in automatic classification of mental disorders based on neuroimaging data. Small training data sets (subjects) and very large amount of high dimensional data make it a challenging task to design robust and accurate classifiers for heterogeneous disorders such as schizophrenia. Most previous studies considered structural MRI, diffusion tensor imaging and task-based fMRI for this purpose. However, resting-state data has been rarely used in discrimination of schizophrenia patients from healthy controls. Resting data are of great interest, since they are relatively easy to collect, and not confounded by behavioral performance on a task. Several linear and non-linear classification methods were trained using a training dataset and evaluate with a separate testing dataset. Results show that classification with high accuracy is achievable using simple non-linear discriminative methods such as k-nearest neighbors which is very promising. We compare and report detailed results of each classifier as well as statistical analysis and evaluation of each single feature. To our knowledge our effects represent the first use of resting-state Functional Network connectivity features to classify schizophrenia.
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Modular Organization of Functional Network Connectivity in Healthy Controls and Patients with Schizophrenia during the Resting State.
Frontiers in systems neuroscience, 2012Co-Authors: Sergey M. Plis, Erik B. Erhardt, Elena A. Allen, Jing Sui, Kent A. Kiehl, Godfrey D. Pearlson, Vince D. CalhounAbstract:Neuroimaging studies have shown that Functional brain Networks composed from select regions of interest have a modular community structure. However, the organization of Functional Network connectivity (FNC), comprising a purely data-driven Network built from spatially independent brain components, is not yet clear. The aim of this study is to explore the modular organization of FNC in both healthy controls (HCs) and patients with schizophrenia (SZs). Resting state Functional magnetic resonance imaging data of HCs and SZs were decomposed into independent components (ICs) by group independent component analysis (ICA). Then weighted brain Networks (in which nodes are brain components) were built based on correlations between ICA time courses. Clustering coefficients and connectivity strength of the Networks were computed. A dynamic branch cutting algorithm was used to identify modules of the FNC in HCs and SZs. Results show stronger connectivity strength and higher clustering coefficient in HCs with more and smaller modules in SZs. In addition, HCs and SZs had some different hubs. Our findings demonstrate altered modular architecture of the FNC in schizophrenia and provide insights into abnormal topological organization of intrinsic brain Networks in this mental illness.
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a method for evaluating dynamic Functional Network connectivity and task modulation application to schizophrenia
Magnetic Resonance Materials in Physics Biology and Medicine, 2010Co-Authors: Unal Sakoglu, Kent A. Kiehl, Godfrey D. Pearlson, Michelle Yongmei Wang, Andrew M Michael, Vince D. CalhounAbstract:Objective In this paper, we develop a dynamic Functional Network connectivity (FNC) analysis approach using correlations between windowed time-courses of different brain Networks (components) estimated via spatial independent component analysis (sICA). We apply the developed method to fMRI data to evaluate it and to study task-modulation of Functional connections.
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a method for Functional Network connectivity among spatially independent resting state components in schizophrenia
NeuroImage, 2008Co-Authors: Madiha J Jafri, Godfrey D. Pearlson, Michael C Stevens, Vince D. CalhounAbstract: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.
