523 results on '"Vince, A."'
Search Results
2. Group Information Guided Smooth Independent Component Analysis Method for Brain Functional Network Analysis
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Du, Yuhui, Huang, Chen, Guo, Yating, He, Xingyu, Calhoun, Vince D., Magjarević, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Wang, Guangzhi, editor, Yao, Dezhong, editor, Gu, Zhongze, editor, Peng, Yi, editor, Tong, Shanbao, editor, and Liu, Chengyu, editor
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- 2024
- Full Text
- View/download PDF
3. Multimodel Order Independent Component Analysis: A Data-Driven Method for Evaluating Brain Functional Network Connectivity Within and Between Multiple Spatial Scales.
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Meng, Xing, Iraji, Armin, Fu, Zening, Kochunov, Peter, Belger, Aysenil, Mueller, Bryon, Pearlson, Godfrey, Turner, Jessica, Erp, Theo, Sui, Jing, Calhoun, Vince, McEwen, Sara, Mathalon, Daniel, Ford, Judith, Potkin, Steven, and Preda, Adrian
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functional network connectivity ,independent component analysis ,intrinsic connectivity networks ,machine learning ,multiple spatial scales ,resting fMRI ,Brain ,Brain Mapping ,Humans ,Magnetic Resonance Imaging ,Rest ,Schizophrenia - Abstract
Background: While functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown. Methods: We proposed an independent component analysis (ICA)-based approach to capture information at multiple-model orders (component numbers), and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting-state functional magnetic resonance imaging (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine-based classification. Results: In addition to consistent predictive patterns at both multiple-model orders and single-model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model orders 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared with other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ. Conclusions: In sum, multimodel order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results, which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches. Impact statement Multimodel order independent component analysis (ICA) provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single-model order analysis. This work expands upon and adds to the relatively new literature on resting functional magnetic resonance imaging-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.
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- 2022
4. A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia
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David Sutherland Blair, Robyn L. Miller, and Vince D. Calhoun
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functional network connectivity ,dynamic functional connectivity ,Shannon entropy ,NeuroMark ,independent component analysis ,principal component analysis ,Science ,Astrophysics ,QB460-466 ,Physics ,QC1-999 - Abstract
Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated meaningful transition alterations in disorders and over the course of development. Recently, researchers have begun to analyze these data from the perspective of dynamic systems and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Little attention has been paid to the effects of psychiatric disease on these measures, however. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, the results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients’ brain function.
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- 2024
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- View/download PDF
5. Covarying structural alterations in laterality of the temporal lobe in schizophrenia: A case for source-based laterality.
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DeRamus, Thomas, Silva, Rogers, Iraji, Armin, Damaraju, Eswar, Belger, Aysenil, McEwen, Sarah, Mueller, Bryon, Pearlson, Godfrey, Calhoun, Vince, Turner, Jessica, Vaidya, Jatin, Mathalon, Daniel, Ford, Judith, Potkin, Steven, Van Erp, Theodorus, and Preda, Adrian
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brain laterality ,independent component analysis ,schizophrenia ,voxel-based morphometry ,Adolescent ,Adult ,Brain Mapping ,Computer Simulation ,Female ,Functional Laterality ,Humans ,Linear Models ,Male ,Middle Aged ,Nerve Net ,Schizophrenia ,Statistics ,Nonparametric ,Temporal Lobe ,Young Adult - Abstract
The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation. To address this limitation, source-based laterality (SBL) leverages an independent component analysis for the identification of laterality-specific alterations, identifying covarying components between hemispheres across subjects. SBL is successfully implemented with simulated data with inherent differences in laterality. SBL is then compared with a voxel-wise analysis utilizing structural data from a sample of patients with schizophrenia and controls without schizophrenia. SBL group comparisons identified three distinct temporal regions and one cerebellar region with significantly altered laterality in patients with schizophrenia relative to controls. Previous work highlights reductions in laterality (ie, reduced left gray matter volume) in patients with schizophrenia compared with controls without schizophrenia. Results from this pilot SBL project are the first, to our knowledge, to identify covarying laterality differences within discrete temporal brain regions. The authors argue SBL provides a unique focus to detect covarying laterality differences in patients with schizophrenia, facilitating the discovery of laterality aspects undetected in previous work.
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- 2020
6. N-BiC: A Method for Multi-Component and Symptom Biclustering of Structural MRI Data: Application to Schizophrenia.
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Rahaman, Md Abdur, Turner, Jessica A, Gupta, Cota Navin, Rachakonda, Srinivas, Chen, Jiayu, Liu, Jingyu, van Erp, Theo GM, Potkin, Steven, Ford, Judith, Mathalon, Daniel, Lee, Hyo Jong, Jiang, Wenhao, Mueller, Bryon A, Andreassen, Ole, Agartz, Ingrid, Sponheim, Scott R, Mayer, Andrew R, Stephen, Julia, Jung, Rex E, Canive, Jose, Bustillo, Juan, and Calhoun, Vince D
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Brain ,Humans ,Magnetic Resonance Imaging ,Schizophrenia ,Algorithms ,Image Processing ,Computer-Assisted ,Adolescent ,Adult ,Middle Aged ,Female ,Male ,Young Adult ,Data Mining ,Loading ,Neuroimaging ,Magnetic resonance imaging ,Grey matter ,Independent component analysis ,Image segmentation ,Multi-component and symptom biclustering ,schizophrenia ,structural MRI ,N-BiC ,N-way biclustering ,SYMBiCs ,Symptom biclusters ,subtypes ,independent component analysis ,Image Processing ,Computer-Assisted ,Biomedical Engineering ,Artificial Intelligence and Image Processing ,Electrical and Electronic Engineering - Abstract
ObjectiveWe propose and develop a novel biclustering (N-BiC) approach for performing N-way biclustering of neuroimaging data. Our approach is applicable to an arbitrary number of features from both imaging and behavioral data (e.g., symptoms). We applied it to structural MRI data from patients with schizophrenia.MethodsIt uses a source-based morphometry approach [i.e., independent component analysis of gray matter segmentation maps] to decompose the data into a set of spatial maps, each of which includes regions that covary among individuals. Then, the loading parameters for components of interest are entered to an exhaustive search, which incorporates a modified depth-first search technique to carry out the biclustering, with the goal of obtaining submatrices where the selected rows (individuals) show homogeneity in their expressions of selected columns (components) and vice versa.ResultsFindings demonstrate that multiple biclusters have an evident association with distinct brain networks for the different types of symptoms in schizophrenia. The study identifies two components: inferior temporal gyrus (16) and brainstem (7), which are related to positive (distortion/excess of normal function) and negative (diminution/loss of normal function) symptoms in schizophrenia, respectively.ConclusionN-BiC is a data-driven method of biclustering MRI data that can exhaustively explore relationships/substructures from a dataset without any prior information with a higher degree of robustness than earlier biclustering applications.SignificanceThe use of such approaches is important to investigate the underlying biological substrates of mental illness by grouping patients into homogeneous subjects, as the schizophrenia diagnosis is known to be relatively nonspecific and heterogeneous.
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- 2020
7. Transient Patterns of Functional Dysconnectivity in Clinical High Risk and Early Illness Schizophrenia Individuals Compared with Healthy Controls
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Mennigen, Eva, Fryer, Susanna L, Rashid, Barnaly, Damaraju, Eswar, Du, Yuhui, Loewy, Rachel L, Stuart, Barbara K, Calhoun, Vince D, and Mathalon, Daniel H
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Biological Psychology ,Biomedical and Clinical Sciences ,Psychology ,Brain Disorders ,Mental Health ,Prevention ,Schizophrenia ,Clinical Research ,Pediatric ,Serious Mental Illness ,Aetiology ,2.1 Biological and endogenous factors ,Mental health ,Adult ,Brain ,Connectome ,Female ,Humans ,Image Processing ,Computer-Assisted ,Magnetic Resonance Imaging ,Male ,Neural Pathways ,Psychotic Disorders ,Risk Factors ,Young Adult ,clinical high risk ,dysconnectivity ,independent component analysis ,schizophrenia ,Neurosciences ,Biological psychology - Abstract
Schizophrenia shows abnormal dynamic functional network connectivity (dFNC), but it is unclear whether these abnormalities are present early in the illness course or precede illness onset in individuals at clinical high risk (CHR) for psychosis. We examined dFNC from resting-state functional magnetic resonance imaging data in CHR (n = 53), early illness schizophrenia (ESZ; n = 58), and healthy control (HC; n = 70) individuals. We applied a sliding temporal window approach capturing five distinct dFNC states. In ESZ patients, the likelihood of transitioning from state 4, a state that exhibited greater cortical-subcortical hyperconnectivity and also lacked typically observed anticorrelation between the default mode network and other functional networks, to a hypoconnected state was increased compared with HC and CHR groups. Furthermore, we investigated the interaction of group and state on dFNC. Overall, HC individuals showed significant changes of connectivity between states that were absent or altered in ESZ patients and CHR individuals. Connectivity differences between groups were identified primarily in two out of the five states, in particular, between HC and ESZ groups. In summary, it appears that the interaction effect was mostly driven by (1) dynamic connectivity changes in HC that were abnormal in CHR and ESZ individuals and (2) the fact that dysconnectivity between groups was only present in some states. These findings underscore the likelihood that abnormalities are present not only in static FNC but also in dFNC, in individuals at CHR for schizophrenia.
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- 2019
8. A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia.
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Blair, David Sutherland, Miller, Robyn L., and Calhoun, Vince D.
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FUNCTIONAL connectivity ,INFORMATION theory ,SYSTEMS theory ,SPACE trajectories ,KOLMOGOROV complexity ,SCHIZOPHRENIA - Abstract
Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated meaningful transition alterations in disorders and over the course of development. Recently, researchers have begun to analyze these data from the perspective of dynamic systems and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Little attention has been paid to the effects of psychiatric disease on these measures, however. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, the results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients' brain function. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
- View/download PDF
9. High and Low Levels of an NTRK2-Driven Genetic Profile Affect Motor- and Cognition-Associated Frontal Gray Matter in Prodromal Huntington's Disease.
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Ciarochi, Jennifer A, Liu, Jingyu, Calhoun, Vince, Johnson, Hans, Misiura, Maria, Bockholt, H Jeremy, Espinoza, Flor A, Caprihan, Arvind, Plis, Sergey, Turner, Jessica A, Paulsen, Jane S, and PREDICT-HD Investigators and Coordinators of the Huntington Study Group
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PREDICT-HD Investigators and Coordinators of the Huntington Study Group ,Huntington’s disease ,brain-derived neurotrophic factor ,independent component analysis ,supplementary motor ,tropomyosin receptor kinase B ,Huntington's Disease ,Brain Disorders ,Neurodegenerative ,Neurosciences ,Genetics ,Rare Diseases ,Neurological ,Psychology ,Cognitive Sciences - Abstract
This study assessed how BDNF (brain-derived neurotrophic factor) and other genes involved in its signaling influence brain structure and clinical functioning in pre-diagnosis Huntington's disease (HD). Parallel independent component analysis (pICA), a multivariate method for identifying correlated patterns in multimodal datasets, was applied to gray matter concentration (GMC) and genomic data from a sizeable PREDICT-HD prodromal cohort (N = 715). pICA identified a genetic component highlighting NTRK2, which encodes BDNF's TrkB receptor, that correlated with a GMC component including supplementary motor, precentral/premotor cortex, and other frontal areas (p < 0.001); this association appeared to be driven by participants with high or low levels of the genetic profile. The frontal GMC profile correlated with cognitive and motor variables (Trail Making Test A (p = 0.03); Stroop Color (p = 0.017); Stroop Interference (p = 0.04); Symbol Digit Modalities Test (p = 0.031); Total Motor Score (p = 0.01)). A top-weighted NTRK2 variant (rs2277193) was protectively associated with Trail Making Test B (p = 0.007); greater minor allele numbers were linked to a better performance. These results support the idea of a protective role of NTRK2 in prodromal HD, particularly in individuals with certain genotypes, and suggest that this gene may influence the preservation of frontal gray matter that is important for clinical functioning.
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- 2018
10. Identifying functional network changing patterns in individuals at clinical high-risk for psychosis and patients with early illness schizophrenia: A group ICA study
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Du, Yuhui, Fryer, Susanna L, Lin, Dongdong, Sui, Jing, Yu, Qingbao, Chen, Jiayu, Stuart, Barbara, Loewy, Rachel L, Calhoun, Vince D, and Mathalon, Daniel H
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Biological Psychology ,Pharmacology and Pharmaceutical Sciences ,Biomedical and Clinical Sciences ,Psychology ,Schizophrenia ,Neurosciences ,Mental Health ,Brain Disorders ,Serious Mental Illness ,Clinical Research ,2.1 Biological and endogenous factors ,Aetiology ,Mental health ,Adult ,Antipsychotic Agents ,Brain ,Brain Mapping ,Disease Progression ,Female ,Humans ,Magnetic Resonance Imaging ,Male ,Neural Pathways ,Psychotic Disorders ,Risk Factors ,Young Adult ,Psychosis-risk syndrome ,Resting-state ,Functional magnetic resonance imaging ,Brain intrinsic functional networks ,Independent component analysis ,Biological psychology ,Clinical and health psychology - Abstract
Although individuals at clinical high risk (CHR) for psychosis exhibit a psychosis-risk syndrome involving attenuated forms of the positive symptoms typical of schizophrenia (SZ), it remains unclear whether their resting-state brain intrinsic functional networks (INs) show attenuated or qualitatively distinct patterns of functional dysconnectivity relative to SZ patients. Based on resting-state functional magnetic imaging data from 70 healthy controls (HCs), 53 CHR individuals (among which 41 subjects were antipsychotic medication-naive), and 58 early illness SZ (ESZ) patients (among which 53 patients took antipsychotic medication) within five years of illness onset, we estimated subject-specific INs using a novel group information guided independent component analysis (GIG-ICA) and investigated group differences in INs. We found that when compared to HCs, both CHR and ESZ groups showed significant differences, primarily in default mode, salience, auditory-related, visuospatial, sensory-motor, and parietal INs. Our findings suggest that widespread INs were diversely impacted. More than 25% of voxels in the identified significant discriminative regions (obtained using all 19 possible changing patterns excepting the no-difference pattern) from six of the 15 interrogated INs exhibited monotonically decreasing Z-scores (in INs) from the HC to CHR to ESZ, and the related regions included the left lingual gyrus of two vision-related networks, the right postcentral cortex of the visuospatial network, the left thalamus region of the salience network, the left calcarine region of the fronto-occipital network and fronto-parieto-occipital network. Compared to HCs and CHR individuals, ESZ patients showed both increasing and decreasing connectivity, mainly hypo-connectivity involving 15% of the altered voxels from four INs. The left supplementary motor area from the sensory-motor network and the right inferior occipital gyrus in the vision-related network showed a common abnormality in CHR and ESZ groups. Some brain regions also showed a CHR-unique alteration (primarily the CHR-increasing connectivity). In summary, CHR individuals generally showed intermediate connectivity between HCs and ESZ patients across multiple INs, suggesting that some dysconnectivity patterns evident in ESZ predate psychosis in attenuated form during the psychosis risk stage. Hence, these connectivity measures may serve as possible biomarkers to predict schizophrenia progression.
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- 2018
11. Biclustered Independent Component Analysis for Complex Biomarker and Subtype Identification from Structural Magnetic Resonance Images in Schizophrenia.
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Gupta, Cota Navin, Castro, Eduardo, Rachkonda, Srinivas, van Erp, Theo GM, Potkin, Steven, Ford, Judith M, Mathalon, Daniel, Lee, Hyo Jong, Mueller, Bryon A, Greve, Douglas N, Andreassen, Ole A, Agartz, Ingrid, Mayer, Andrew R, Stephen, Julia, Jung, Rex E, Bustillo, Juan, Calhoun, Vince D, and Turner, Jessica A
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biclustering ,gray matter concentration ,group information-guided independent component analysis ,independent component analysis ,positive and negative syndrome scale symptoms ,subtypes ,Neurosciences ,Mental Health ,Clinical Research ,Brain Disorders ,Biomedical Imaging ,Schizophrenia ,Good Health and Well Being ,Clinical Sciences ,Public Health and Health Services ,Psychology - Abstract
Clinical and cognitive symptoms domain-based subtyping in schizophrenia (Sz) has been critiqued due to the lack of neurobiological correlates and heterogeneity in symptom scores. We, therefore, present a novel data-driven framework using biclustered independent component analysis to detect subtypes from the reliable and stable gray matter concentration (GMC) of patients with Sz. The developed methodology consists of the following steps: source-based morphometry (SBM) decomposition, selection and sorting of two component loadings, subtype component reconstruction using group information-guided ICA (GIG-ICA). This framework was applied to the top two group discriminative components namely the insula/superior temporal gyrus/inferior frontal gyrus (I-STG-IFG component) and the superior frontal gyrus/middle frontal gyrus/medial frontal gyrus (SFG-MiFG-MFG component) from our previous SBM study, which showed diagnostic group difference and had the highest effect sizes. The aggregated multisite dataset consisted of 382 patients with Sz regressed of age, gender, and site voxelwise. We observed two subtypes (i.e., two different subsets of subjects) each heavily weighted on these two components, respectively. These subsets of subjects were characterized by significant differences in positive and negative syndrome scale (PANSS) positive clinical symptoms (p = 0.005). We also observed an overlapping subtype weighing heavily on both of these components. The PANSS general clinical symptom of this subtype was trend level correlated with the loading coefficients of the SFG-MiFG-MFG component (r = 0.25; p = 0.07). The reconstructed subtype-specific component using GIG-ICA showed variations in voxel regions, when compared to the group component. We observed deviations from mean GMC along with conjunction of features from two components characterizing each deciphered subtype. These inherent variations in GMC among patients with Sz could possibly indicate the need for personalized treatment and targeted drug development.
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- 2017
12. The risk of cannabis use disorder is mediated by altered brain connectivity: A chronnectome study.
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Fazio, Giovanni, Olivo, Daniele, Wolf, Nadine D., Hirjak, Dusan, Schmitgen, Mike M., Werler, Florian, Witteman, Miriam, Kubera, Katharina M., Calhoun, Vince D., Reith, Wolfgang, Wolf, Robert Christian, and Sambataro, Fabio
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MARIJUANA abuse ,FUNCTIONAL magnetic resonance imaging ,INDEPENDENT component analysis ,LARGE-scale brain networks ,FUNCTIONAL connectivity - Abstract
The brain mechanisms underlying the risk of cannabis use disorder (CUD) are poorly understood. Several studies have reported changes in functional connectivity (FC) in CUD, although none have focused on the study of time‐varying patterns of FC. To fill this important gap of knowledge, 39 individuals at risk for CUD and 55 controls, stratified by their score on a self‐screening questionnaire for cannabis‐related problems (CUDIT‐R), underwent resting‐state functional magnetic resonance imaging. Dynamic functional connectivity (dFNC) was estimated using independent component analysis, sliding‐time window correlations, cluster states and meta‐state indices of global dynamics and were compared among groups. At‐risk individuals stayed longer in a cluster state with higher within and reduced between network dFNC for the subcortical, sensory‐motor, visual, cognitive‐control and default‐mode networks, relative to controls. More globally, at‐risk individuals had a greater number of meta‐states and transitions between them and a longer state span and total distance between meta‐states in the state space. Our findings suggest that the risk of CUD is associated with an increased dynamic fluidity and dynamic range of FC. This may result in altered stability and engagement of the brain networks, which can ultimately translate into altered cortical and subcortical function conveying CUD risk. Identifying these changes in brain function can pave the way for early pharmacological and neurostimulation treatment of CUD, as much as they could facilitate the stratification of high‐risk individuals. [ABSTRACT FROM AUTHOR]
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- 2024
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13. Functional MRI Evaluation of Multiple Neural Networks Underlying Auditory Verbal Hallucinations in Schizophrenia Spectrum Disorders
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Thoma, Robert J, Chaze, Charlotte, Lewine, Jeffrey David, Calhoun, Vince D, Clark, Vincent P, Bustillo, Juan, Houck, Jon, Ford, Judith, Bigelow, Rose, Wilhelmi, Corbin, Stephen, Julia M, and Turner, Jessica A
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Biomedical and Clinical Sciences ,Clinical Sciences ,Clinical Research ,Schizophrenia ,Brain Disorders ,Neurosciences ,Mental Health ,Underpinning research ,1.1 Normal biological development and functioning ,Mental health ,auditory verbal hallucinations ,schizophrenia ,functional magnetic resonance imaging ,independent component analysis ,general linear model ,Public Health and Health Services ,Psychology ,Clinical sciences - Abstract
Functional MRI studies have identified a distributed set of brain activations to be associated with auditory verbal hallucinations (AVH). However, very little is known about how activated brain regions may be linked together into AVH-generating networks. Fifteen volunteers with schizophrenia or schizoaffective disorder pressed buttons to indicate onset and offset of AVH during fMRI scanning. When a general linear model was used to compare blood oxygenation level dependence signals during periods in which subjects indicated that they were versus were not experiencing AVH ("AVH-on" versus "AVH-off"), it revealed AVH-related activity in bilateral inferior frontal and superior temporal regions; the right middle temporal gyrus; and the left insula, supramarginal gyrus, inferior parietal lobule, and extranuclear white matter. In an effort to identify AVH-related networks, the raw data were also processed using independent component analyses (ICAs). Four ICA components were spatially consistent with an a priori network framework based upon published meta-analyses of imaging correlates of AVH. Of these four components, only a network involving bilateral auditory cortices and posterior receptive language areas was significantly and positively correlated to the pattern of AVH-on versus AVH-off. The ICA also identified two additional networks (occipital-temporal and medial prefrontal), not fully matching the meta-analysis framework, but nevertheless containing nodes reported as active in some studies of AVH. Both networks showed significant AVH-related profiles, but both were most active during AVH-off periods. Overall, the data suggest that AVH generation requires specific and selective activation of auditory cortical and posterior language regions, perhaps coupled to a release of indirect influence by occipital and medial frontal structures.
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- 2016
14. Gradient Theories of Brain Activation: a Novel Application to Studying the Parental Brain
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Rutherford, Helena J. V., Xu, Jiansong, Worhunsky, Patrick D., Zhang, Rubin, Yip, Sarah W., Morie, Kristen P., Calhoun, Vince D., Kim, Sohye, Strathearn, Lane, Mayes, Linda C., and Potenza, Marc N.
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- 2019
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15. Impact of autocorrelation on functional connectivity
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Arbabshirani, Mohammad R, Damaraju, Eswar, Phlypo, Ronald, Plis, Sergey, Allen, Elena, Ma, Sai, Mathalon, Daniel, Preda, Adrian, Vaidya, Jatin G, Adali, Tülay, and Calhoun, Vince D
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Biomedical and Clinical Sciences ,Health Sciences ,Clinical Research ,Brain Disorders ,Mental Health ,Schizophrenia ,Neurosciences ,Brain ,Brain Mapping ,Computer Simulation ,Humans ,Magnetic Resonance Imaging ,Nerve Net ,Regression Analysis ,Spatio-Temporal Analysis ,Autocorrelation ,Functional connectivity ,Independent component analysis ,Autoregressive process ,Resting-state fMRI ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Neurology & Neurosurgery ,Biomedical and clinical sciences ,Health sciences - Abstract
Although the impact of serial correlation (autocorrelation) in residuals of general linear models for fMRI time-series has been studied extensively, the effect of autocorrelation on functional connectivity studies has been largely neglected until recently. Some recent studies based on results from economics have questioned the conventional estimation of functional connectivity and argue that not correcting for autocorrelation in fMRI time-series results in "spurious" correlation coefficients. In this paper, first we assess the effect of autocorrelation on Pearson correlation coefficient through theoretical approximation and simulation. Then we present this effect on real fMRI data. To our knowledge this is the first work comprehensively investigating the effect of autocorrelation on functional connectivity estimates. Our results show that although FC values are altered, even following correction for autocorrelation, results of hypothesis testing on FC values remain very similar to those before correction. In real data we show this is true for main effects and also for group difference testing between healthy controls and schizophrenia patients. We further discuss model order selection in the context of autoregressive processes, effects of frequency filtering and propose a preprocessing pipeline for connectivity studies.
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- 2014
16. Multimodel Order Independent Component Analysis: A Data-Driven Method for Evaluating Brain Functional Network Connectivity Within and Between Multiple Spatial Scales
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Theo G.M. van Erp, Jessica A. Turner, Bryon A. Mueller, Armin Iraji, Sara McEwen, Xing Meng, Vince D. Calhoun, Godfrey D. Pearlson, Peter Kochunov, Zening Fu, Steven G. Potkin, Jing Sui, Judith M. Ford, Adrian Preda, Aysenil Belger, and Daniel H. Mathalon
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Brain Mapping ,Computer science ,General Neuroscience ,Functional connectivity ,Resting fmri ,Rest ,Work (physics) ,Brain ,computer.software_genre ,Independent component analysis ,Magnetic Resonance Imaging ,Data-driven ,Functional networks ,Order (biology) ,Schizophrenia ,Humans ,Data mining ,computer - Abstract
Background: While functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connec...
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- 2023
17. Group independent components underpin responses to items from a depression scale.
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Stoyanov, Drozdstoy, Khorev, Vladimir, Paunova, Rossitsa, Kandilarova, Sevdalina, Kurkin, Semen, and Calhoun, Vince D.
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PREFRONTAL cortex ,FUNCTIONAL magnetic resonance imaging ,INDEPENDENT component analysis ,LARGE-scale brain networks ,CINGULATE cortex - Abstract
Objective: The aim of the present study is to investigate the brain circuits or networks that underpin diagnostically specific tasks by means of group independent component analysis for FMRI toolbox (GIFT). We hypothesised that there will be neural network patterns of activation and deactivation, which correspond to real-time performance on clinical self-evaluation scales. Methods: In total, 20 healthy controls (HC) and 22 patients with major depressive episode have been included. All subjects were scanned with functional magnetic resonance imaging (fMRI) with paradigm composed of diagnostic clinical self-assessment depression scale contrasted to neutral scale. The data were processed with group independent component analysis for functional MRI toolbox and statistical parametric mapping. Results: The results have demonstrated that there exist positively or negatively modulated brain networks during processing of diagnostic specific task questions for depressive disorder. There have also been confirmed differences in the networks processing diagnostic versus off blocks between patients and controls in anterior cingulate cortex and middle frontal gyrus. Diagnostic conditions (depression scale) when contrasted to neutral conditions demonstrate differential activity of right superior frontal gyrus and right middle cingulate cortex in the comparison of patients with HC. Conclusion: Potential neuroimaging of state-dependent biomarkers has been directly linked with clinical assessment self-evaluation scale, administered as stimuli simultaneously with the fMRI acquisition. It may be regarded as further evidence in support of the convergent capacity of both methods to distinguish groups by means of incremental translational cross-validation. [ABSTRACT FROM AUTHOR]
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- 2024
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18. Intra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer's Disease and Cognitive Impairment.
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Kolla, Sahithi, Falakshahi, Haleh, Abrol, Anees, Fu, Zening, and Calhoun, Vince D.
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ALZHEIMER'S disease ,COGNITION disorders ,INDEPENDENT component analysis ,FUNCTIONAL magnetic resonance imaging ,MILD cognitive impairment ,LARGE-scale brain networks - Abstract
Network neuroscience, a multidisciplinary field merging insights from neuroscience and network theory, offers a profound understanding of neural network intricacies. However, the impact of varying node sizes on computed graph metrics in neuroimaging data remains underexplored. This study addresses this gap by adopting a data-driven methodology to delineate functional nodes and assess their influence on graph metrics. Using the Neuromark framework, automated independent component analysis is applied to resting state fMRI data, capturing functional network connectivity (FNC) matrices. Global and local graph metrics reveal intricate connectivity patterns, emphasizing the need for nuanced analysis. Notably, node sizes, computed based on voxel counts, contribute to a novel metric termed 'node-metric coupling' (NMC). Correlations between graph metrics and node dimensions are consistently observed. The study extends its analysis to a dataset comprising Alzheimer's disease, mild cognitive impairment, and control subjects, showcasing the potential of NMC as a biomarker for brain disorders. The two key outcomes underscore the interplay between node sizes and resultant graph metrics within a given atlas, shedding light on an often-overlooked source of variability. Additionally, the study highlights the utility of NMC as a valuable biomarker, emphasizing the necessity of accounting for node sizes in future neuroimaging investigations. This work contributes to refining comparative studies employing diverse atlases and advocates for thoughtful consideration of intra-atlas node size in shaping graph metrics, paving the way for more robust neuroimaging research. [ABSTRACT FROM AUTHOR]
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- 2024
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19. Dynamic phase-locking states and personality in sub-acute mild traumatic brain injury: An exploratory study.
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van der Horn, Harm J., de Koning, Myrthe E., Visser, Koen, Kok, Marius G. J., Spikman, Jacoba M., Scheenen, Myrthe E., Renken, Remco J., Calhoun, Vince D., Vergara, Victor M., Cabral, Joana, Mayer, Andrew R., and van der Naalt, Joukje
- Subjects
BRAIN injuries ,FUNCTIONAL magnetic resonance imaging ,DEFAULT mode network ,INDEPENDENT component analysis ,WHITE matter (Nerve tissue) ,DIFFUSION magnetic resonance imaging ,PERSONALITY - Abstract
Research has shown that maladaptive personality characteristics, such as Neuroticism, are associated with poor outcome after mild traumatic brain injury (mTBI). The current exploratory study investigated the neural underpinnings of this process using dynamic functional network connectivity (dFNC) analyses of resting-state (rs) fMRI, and diffusion MRI (dMRI). Twenty-seven mTBI patients and 21 healthy controls (HC) were included. After measuring the Big Five personality dimensions, principal component analysis (PCA) was used to obtain a superordinate factor representing emotional instability, consisting of high Neuroticism, moderate Openness, and low Extraversion, Agreeableness, and Conscientiousness. Persistent symptoms were measured using the head injury symptom checklist at six months post-injury; symptom severity (i.e., sum of all items) was used for further analyses. For patients, brain MRI was performed in the sub-acute phase (~1 month) post-injury. Following parcellation of rs-fMRI using independent component analysis, leading eigenvector dynamic analysis (LEiDA) was performed to compute dynamic phase-locking brain states. Main patterns of brain diffusion were computed using tract-based spatial statistics followed by PCA. No differences in phase-locking state measures were found between patients and HC. Regarding dMRI, a trend significant decrease in fractional anisotropy was found in patients relative to HC, particularly in the fornix, genu of the corpus callosum, anterior and posterior corona radiata. Visiting one specific phase-locking state was associated with lower symptom severity after mTBI. This state was characterized by two clearly delineated communities (each community consisting of areas with synchronized phases): one representing an executive/saliency system, with a strong contribution of the insulae and basal ganglia; the other representing the canonical default mode network. In patients who scored high on emotional instability, this relationship was even more pronounced. Dynamic phase-locking states were not related to findings on dMRI. Altogether, our results provide preliminary evidence for the coupling between personality and dFNC in the development of long-term symptoms after mTBI. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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20. dcSBM: A federated constrained source‐based morphometry approach for multivariate brain structure mapping.
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Saha, Debbrata K., Silva, Rogers F., Baker, Bradley T., Saha, Rekha, and Calhoun, Vince D.
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BRAIN anatomy ,BRAIN mapping ,MORPHOMETRICS ,INDEPENDENT component analysis ,MAGNETIC resonance imaging - Abstract
The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among the many existing methods and tools for the analysis of brain anatomy based on structural magnetic resonance imaging data, data‐driven source‐based morphometry (SBM) focuses on the exploratory detection of such patterns. Here, we implement a semi‐blind extension of SBM, called constrained source‐based morphometry (constrained SBM), which enables the extraction of maximally independent reference‐alike sources using the constrained independent component analysis (ICA) approach. To do this, we combine SBM with a set of reference components covering the full brain, derived from a large independent data set (UKBiobank), to provide a fully automated SBM framework. This also allows us to implement a federated version of constrained SBM (cSBM) to allow analysis of data that is not locally accessible. In our proposed decentralized constrained source‐based morphometry (dcSBM), the original data never leaves the local site. Each site operates constrained ICA on its private local data using a common distributed computation platform. Next, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to estimate the significance of the sources. Finally, we utilize two additional multisite patient data sets to validate our model by comparing the resulting group difference estimates from both cSBM and dcSBM. [ABSTRACT FROM AUTHOR]
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- 2023
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21. Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude‐only fMRI data.
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Zhang, Chao‐Ying, Lin, Qiu‐Hua, Niu, Yan‐Wei, Li, Wei‐Xing, Gong, Xiao‐Feng, Cong, Fengyu, Wang, Yu‐Ping, and Calhoun, Vince D.
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INDEPENDENT component analysis ,LARGE-scale brain networks ,FUNCTIONAL magnetic resonance imaging ,MATHEMATICAL mappings - Abstract
Brain networks extracted by independent component analysis (ICA) from magnitude‐only fMRI data are usually denoised using various amplitude‐based thresholds. By contrast, spatial source phase (SSP) or the phase information of ICA brain networks extracted from complex‐valued fMRI data, has provided a simple yet effective way to perform the denoising using a fixed phase change. In this work, we extend the approach to magnitude‐only fMRI data to avoid testing various amplitude thresholds for denoising magnitude maps extracted by ICA, as most studies do not save the complex‐valued data. The main idea is to generate a mathematical SSP map for a magnitude map using a mapping framework, and the mapping framework is built using complex‐valued fMRI data with a known SSP map. Here we leverage the fact that the phase map derived from phase fMRI data has similar phase information to the SSP map. After verifying the use of the magnitude data of complex‐valued fMRI, this framework is generalized to work with magnitude‐only data, allowing use of our approach even without the availability of the corresponding phase fMRI datasets. We test the proposed method using both simulated and experimental fMRI data including complex‐valued data from University of New Mexico and magnitude‐only data from Human Connectome Project. The results provide evidence that the mathematical SSP denoising with a fixed phase change is effective for denoising spatial maps from magnitude‐only fMRI data in terms of retaining more BOLD‐related activity and fewer unwanted voxels, compared with amplitude‐based thresholding. The proposed method provides a unified and efficient SSP approach to denoise ICA brain networks in fMRI data. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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22. Frequency-Aware Summarization of Resting-State fMRI Data
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Maziar Yaesoubi, Rogers F. Silva, Armin Iraji, and Vince D. Calhoun
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resting-state fMRI ,time-frequency analysis ,dimension reduction ,canonical correlation analysis ,independent component analysis ,functional connectivity ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Many brain imaging modalities reveal interpretable patterns after the data dimensionality is reduced and summarized via data-driven approaches. In functional magnetic resonance imaging (fMRI) studies, such summarization is often achieved through independent component analysis (ICA). ICA transforms the original data into a relatively small number of interpretable bases in voxel space (referred to as ICA spatial components, or spatial maps) and corresponding bases in the time domain (referred to as time-courses of corresponding spatial maps) In this work, we use the word “basis” to broadly refer to either of the two factors resulting from the transformation. A precise summarization for fMRI requires accurately detecting co-activation of voxels by measuring temporal dependence. Accurate measurement of dependence requires a proper understanding of the underlying temporal characteristics of the data. One way to understand such characteristics is to study the frequency spectrum of fMRI data. Researchers have argued that information regarding the underlying neuronal activity might be spread over a range of frequencies as a result of the heterogeneous temporal nature of the neuronal activity, which is reflected in its frequency spectrum. Many studies have accounted for heterogeneous characteristics of the frequency of the signal by either directly inspecting the contents of frequency domain-transformed data or augmenting their analyses with such information. For example, studies on fMRI data have investigated brain functional connectivity by leveraging frequency-adjusted measures of dependence (e.g., when a correlation is measured as a function of frequency, as with “coherence”). Although these studies measure dependence as a function of frequency, the formulation does not capture all characteristics of the frequency-based dependence. Incorporating frequency information into a summarization approach would enable the retention of important frequency-related information that exists in the original space but might be lost after performing a frequency-independent summarization. We propose a novel data-driven approach built upon ICA, which is based on measuring dependence as a generalized function of frequency. Applying this approach to fMRI data provides evidence of existing cross-frequency functional connectivity between different areas of the brain.
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- 2020
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23. NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders
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Yuhui Du, Zening Fu, Jing Sui, Shuang Gao, Ying Xing, Dongdong Lin, Mustafa Salman, Anees Abrol, Md Abdur Rahaman, Jiayu Chen, L. Elliot Hong, Peter Kochunov, Elizabeth A. Osuch, and Vince D. Calhoun
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fMRI ,Independent component analysis ,Brain disorders ,Reproducible and comparable biomarkers ,NeuroMark ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. However, it is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed. Here, we propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer’s disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers). Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder.
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- 2020
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24. Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis
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Yuhui Du, Hui Hao, Shuhua Wang, Godfrey D Pearlson, and Vince D. Calhoun
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Functional magnetic resonance imaging ,Dynamic functional connectivity ,Independent component analysis ,Schizophrenia ,Bipolar disorder ,Schizoaffective disorder ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
It is difficult to distinguish schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar disorder with psychosis (BPP) as their clinical diagnoses rely on symptoms that overlap. In this paper, we investigate if there is biological evidence to support the symptom-based clinical categories by looking across the three disorders using dynamic connectivity measures, and provide meaningful characteristics on which brain functional connectivity measures are commonly or uniquely impaired. Large-sample functional magnetic resonance image (fMRI) datasets from 623 subjects including 238 healthy controls (HCs), 113 SZ patients, 132 SAD patients, and 140 BPP patients were analyzed. First, we computed whole-brain dynamic functional connectivity (DFC) using a sliding-window technique, and then extracted the individual connectivity states by applying our previously proposed decomposition-based DFC analysis method. Next, with the features from the dominant connectivity state, we assessed the clinical categories by performing both four-group (SZ, SAD, BPP and healthy control groups) and pair-wise classification using a support vector machine within cross-validation. Furthermore, we comprehensively summarized the shared and unique connectivity alterations among the disorders. In terms of the classification performance, our method achieved 69% in the four-group classification and >80% in the between-group classifications for the mean overall accuracy; and yielded 66% in the four-group classification and >80% in the between-group classifications for the mean balanced accuracy. Through summarizing the features that were automatically selected in the classifications, we found that among the three symptom-related disorders, their disorder-common impairments primarily included the decreased connectivity strength between thalamus and cerebellum and the increased strength between postcentral gyrus and thalamus. The disorder-unique changes included more various brain regions, mainly in the temporal and frontal gyrus. Our work demonstrates that dynamic functional connectivity provides biological evidence that both common and unique impairments exist in psychosis sub-groups.
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- 2020
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25. Consecutive Independence and Correlation Transform for Multimodal Data Fusion: Discovery of One-to-Many Associations in Structural and Functional Imaging Data
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Chunying Jia, Mohammad Abu Baker Siddique Akhonda, Yuri Levin-Schwartz, Qunfang Long, Vince D. Calhoun, and Tülay Adali
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independent component analysis ,independent vector analysis ,multimodal data fusion ,brain imaging ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
Brain signals can be measured using multiple imaging modalities, such as magnetic resonance imaging (MRI)-based techniques. Different modalities convey distinct yet complementary information; thus, their joint analyses can provide valuable insight into how the brain functions in both healthy and diseased conditions. Data-driven approaches have proven most useful for multimodal fusion as they minimize assumptions imposed on the data, and there are a number of methods that have been developed to uncover relationships across modalities. However, none of these methods, to the best of our knowledge, can discover “one-to-many associations”, meaning one component from one modality is linked with more than one component from another modality. However, such “one-to-many associations” are likely to exist, since the same brain region can be involved in multiple neurological processes. Additionally, most existing data fusion methods require the signal subspace order to be identical for all modalities—a severe restriction for real-world data of different modalities. Here, we propose a new fusion technique—the consecutive independence and correlation transform (C-ICT) model—which successively performs independent component analysis and independent vector analysis and is uniquely flexible in terms of the number of datasets, signal subspace order, and the opportunity to find “one-to-many associations”. We apply C-ICT to fuse diffusion MRI, structural MRI, and functional MRI datasets collected from healthy controls (HCs) and patients with schizophrenia (SZs). We identify six interpretable triplets of components, each of which consists of three associated components from the three modalities. Besides, components from these triplets that show significant group differences between the HCs and SZs are identified, which could be seen as putative biomarkers in schizophrenia.
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- 2021
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26. Multivariate Fusion of EEG and Functional MRI Data Using ICA: Algorithm Choice and Performance Analysis
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Levin-Schwartz, Yuri, Calhoun, Vince D., Adalı, Tülay, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Vincent, Emmanuel, editor, Yeredor, Arie, editor, Koldovský, Zbyněk, editor, and Tichavský, Petr, editor
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- 2015
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27. Changes in patterns of age-related network connectivity are associated with risk for schizophrenia.
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Passiatore, Roberta, Antonucci, Linda A., DeRamus, Thomas P., Fazio, Leonardo, Stolfa, Giuseppe, Sportelli, Leonardo, Kikidis, Gianluca C., Blasi, Giuseppe, Qiang Chen, Dukart, Juergen, Goldman, Aaron L., Mattay, Venkata S., Popolizio, Teresa, Rampino, Antonio, Sambataro, Fabio, Selvaggi, Pierluigi, Ulrich, William, Weinberger, Daniel R., Bertolino, Alessandro, and Calhoun, Vince D.
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PEOPLE with schizophrenia ,MONOGENIC & polygenic inheritance (Genetics) ,INDEPENDENT component analysis ,SCHIZOPHRENIA ,DISEASE risk factors ,NEURODIVERSITY - Abstract
Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single-session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlates with genetic risk for SCZ and is detectable with MRI in young participants. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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28. A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis.
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Sun, Mingyu, Gabrielson, Ben, Akhonda, Mohammad Abu Baker Siddique, Yang, Hanlu, Laport, Francisco, Calhoun, Vince, and Adali, Tülay
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FUNCTIONAL magnetic resonance imaging ,VECTOR analysis ,BLIND source separation ,INDEPENDENT component analysis - Abstract
Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data's true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the "shared" subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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29. Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder
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Dongmei Zhi, Vince D. Calhoun, Luxian Lv, Xiaohong Ma, Qing Ke, Zening Fu, Yuhui Du, Yongfeng Yang, Xiao Yang, Miao Pan, Shile Qi, Rongtao Jiang, Qingbao Yu, and Jing Sui
- Subjects
major depressive disorder ,independent component analysis ,dynamic functional network connectivity ,graph theory ,resting-state functional magnetic resonance imaging ,Psychiatry ,RC435-571 - Abstract
Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder.
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- 2018
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30. Biclustered Independent Component Analysis for Complex Biomarker and Subtype Identification from Structural Magnetic Resonance Images in Schizophrenia
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Cota Navin Gupta, Eduardo Castro, Srinivas Rachkonda, Theo G. M. van Erp, Steven Potkin, Judith M. Ford, Daniel Mathalon, Hyo Jong Lee, Bryon A. Mueller, Douglas N. Greve, Ole A. Andreassen, Ingrid Agartz, Andrew R. Mayer, Julia Stephen, Rex E. Jung, Juan Bustillo, Vince D. Calhoun, and Jessica A. Turner
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gray matter concentration ,biclustering ,independent component analysis ,subtypes ,positive and negative syndrome scale symptoms ,group information-guided independent component analysis ,Psychiatry ,RC435-571 - Abstract
Clinical and cognitive symptoms domain-based subtyping in schizophrenia (Sz) has been critiqued due to the lack of neurobiological correlates and heterogeneity in symptom scores. We, therefore, present a novel data-driven framework using biclustered independent component analysis to detect subtypes from the reliable and stable gray matter concentration (GMC) of patients with Sz. The developed methodology consists of the following steps: source-based morphometry (SBM) decomposition, selection and sorting of two component loadings, subtype component reconstruction using group information-guided ICA (GIG-ICA). This framework was applied to the top two group discriminative components namely the insula/superior temporal gyrus/inferior frontal gyrus (I-STG-IFG component) and the superior frontal gyrus/middle frontal gyrus/medial frontal gyrus (SFG-MiFG-MFG component) from our previous SBM study, which showed diagnostic group difference and had the highest effect sizes. The aggregated multisite dataset consisted of 382 patients with Sz regressed of age, gender, and site voxelwise. We observed two subtypes (i.e., two different subsets of subjects) each heavily weighted on these two components, respectively. These subsets of subjects were characterized by significant differences in positive and negative syndrome scale (PANSS) positive clinical symptoms (p = 0.005). We also observed an overlapping subtype weighing heavily on both of these components. The PANSS general clinical symptom of this subtype was trend level correlated with the loading coefficients of the SFG-MiFG-MFG component (r = 0.25; p = 0.07). The reconstructed subtype-specific component using GIG-ICA showed variations in voxel regions, when compared to the group component. We observed deviations from mean GMC along with conjunction of features from two components characterizing each deciphered subtype. These inherent variations in GMC among patients with Sz could possibly indicate the need for personalized treatment and targeted drug development.
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- 2017
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31. A Correlated Noise-assisted Decentralized Differentially Private Estimation Protocol, and its application to fMRI Source Separation
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Jafar Mohammadi, Sergey M. Plis, Anand D. Sarwate, Bradley T. Baker, Hafiz Imtiaz, Calhoun D. Vince, and Rogers F. Silva
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Signal processing ,Computer science ,Noise (signal processing) ,computer.software_genre ,Independent component analysis ,Blind signal separation ,Article ,Feature (computer vision) ,Sample size determination ,Signal Processing ,Source separation ,Leverage (statistics) ,Data mining ,Electrical and Electronic Engineering ,computer - Abstract
Blind source separation algorithms such as independent component analysis (ICA) are widely used in the analysis of neuroimaging data. To leverage larger sample sizes, different data holders/sites may wish to collaboratively learn feature representations. However, such datasets are often privacy-sensitive, precluding centralized analyses that pool the data at one site. In this work, we propose a differentially private algorithm for performing ICA in a decentralized data setting. Due to the high dimension and small sample size, conventional approaches to decentralized differentially private algorithms suffer in terms of utility. When centralizing the data is not possible, we investigate the benefit of enabling limited collaboration in the form of generating jointly distributed random noise. We show that such (anti) correlated noise improves the privacy-utility trade-off, and can reach the same level of utility as the corresponding non-private algorithm for certain parameter choices. We validate this benefit using synthetic and real neuroimaging datasets. We conclude that it is possible to achieve meaningful utility while preserving privacy, even in complex signal processing systems.
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- 2022
32. ICA for Fusion of Brain Imaging Data
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Calhoun, Vince D., Adali, Tülay, Mandic, Danilo, editor, Golz, Martin, editor, Kuh, Anthony, editor, Obradovic, Dragan, editor, and Tanaka, Toshihisa, editor
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- 2008
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33. A method for capturing dynamic spectral coupling in resting fMRI reveals domain-specific patterns in schizophrenia.
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Alaçam, Deniz, Miller, Robyn, Agcaoglu, Oktay, Preda, Adrian, Ford, Judith, and Calhoun, Vince
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GENDER differences (Psychology) ,FUNCTIONAL magnetic resonance imaging ,LARGE-scale brain networks ,PEOPLE with schizophrenia ,INDEPENDENT component analysis - Abstract
Introduction: Resting-state functional magnetic resonance imaging (rs-fMRI) is a powerful tool for assessing functional brain connectivity. Recent studies have focused on shorter-term connectivity and dynamics in the resting state. However, most of the prior work evaluates changes in time-series correlations. In this study, we propose a framework that focuses on time-resolved spectral coupling (assessed via the correlation between power spectra of the windowed time courses) among different brain circuits determined via independent component analysis (ICA). Methods: Motivated by earlier work suggesting significant spectral differences in people with schizophrenia, we developed an approach to evaluate time-resolved spectral coupling (trSC). To do this, we first calculated the correlation between the power spectra of windowed time-courses pairs of brain components. Then, we subgrouped each correlation map into four subgroups based on the connectivity strength utilizing quartiles and clustering techniques. Lastly, we examined clinical group differences by regression analysis for each averaged count and average cluster size matrices in each quartile. We evaluated the method by applying it to resting-state data collected from 151 (114 males, 37 females) people with schizophrenia (SZ) and 163 (117 males, 46 females) healthy controls (HC). Results: Our proposed approach enables us to observe the change of connectivity strength within each quartile for different subgroups. People with schizophrenia showed highly modularized and significant differences in multiple network domains, whereas males and females showed less modular differences. Both cell count and average cluster size analysis for subgroups indicate a higher connectivity rate in the fourth quartile for the visual network in the control group. This indicates increased trSC in visual networks in the controls. In other words, this shows that the visual networks in people with schizophrenia have less mutually consistent spectra. It is also the case that the visual networks are less spectrally correlated on short timescales with networks of all other functional domains. Conclusions: The results of this study reveal significant differences in the degree to which spectral power profiles are coupled over time. Importantly, there are significant but distinct differences both between males and females and between people with schizophrenia and controls. We observed a more significant coupling rate in the visual network for the healthy controls and males in the upper quartile. Fluctuations over time are complex, and focusing on only time-resolved coupling among time-courses is likely to miss important information. Also, people with schizophrenia are known to have impairments in visual processing but the underlying reasons for the impairment are still unknown. Therefore, the trSC approach can be a useful tool to explore the reasons for the impairments. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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34. Symmetric data-driven fusion of diffusion tensor MRI: Age differences in white matter.
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Mendez Colmenares, Andrea, Hefner, Michelle B., Calhoun, Vince D., Salerno, Elizabeth A., Fanning, Jason, Gothe, Neha P., McAuley, Edward, Kramer, Arthur F., and Burzynska, Agnieszka Z.
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AGE differences ,DIFFUSION magnetic resonance imaging ,WHITE matter (Nerve tissue) ,DIFFUSION tensor imaging ,INDEPENDENT component analysis ,WATER sampling - Abstract
In the past 20 years, white matter (WM) microstructure has been studied predominantly using diffusion tensor imaging (DTI). Decreases in fractional anisotropy (FA) and increases in mean (MD) and radial diffusivity (RD) have been consistently reported in healthy aging and neurodegenerative diseases. To date, DTI parameters have been studied individually (e.g., only FA) and separately (i.e., without using the joint information across them). This approach gives limited insights into WM pathology, increases the number of multiple comparisons, and yields inconsistent correlations with cognition. To take full advantage of the information in a DTI dataset, we present the first application of symmetric fusion to study healthy aging WM. This data-driven approach allows simultaneous examination of age differences in all four DTI parameters. We used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) in cognitively healthy adults (age 20-33, n = 51 and age 60-79, n = 170). Four-way mCCA + jICA yielded one high-stability modality-shared component with co-variant patterns of age differences in RD and AD in the corpus callosum, internal capsule, and prefrontal WM. The mixing coefficients (or loading parameters) showed correlations with processing speed and fluid abilities that were not detected by unimodal analyses. In sum, mCCA + jICA allows data-driven identification of cognitively relevant multimodal components within the WM. The presented method should be further extended to clinical samples and other MR techniques (e.g., myelin water imaging) to test the potential of mCCA+jICA to discriminate between different WM disease etiologies and improve the diagnostic classification of WM diseases. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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35. Reliability and clinical utility of spatially constrained estimates of intrinsic functional networks from very short fMRI scans.
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Duda, Marlena, Iraji, Armin, Ford, Judith M., Lim, Kelvin O., Mathalon, Daniel H., Mueller, Bryon A., Potkin, Steven G., Preda, Adrian, Van Erp, Theo G. M., and Calhoun, Vince D.
- Subjects
INDEPENDENT component analysis ,FUNCTIONAL magnetic resonance imaging ,TIME series analysis ,AFFECTIVE disorders ,PEOPLE with schizophrenia - Abstract
Resting‐state functional network connectivity (rsFNC) has shown utility for identifying characteristic functional brain patterns in individuals with psychiatric and mood disorders, providing a promising avenue for biomarker development. However, several factors have precluded widespread clinical adoption of rsFNC diagnostics, namely a lack of standardized approaches for capturing comparable and reproducible imaging markers across individuals, as well as the disagreement on the amount of data required to robustly detect intrinsic connectivity networks (ICNs) and diagnostically relevant patterns of rsFNC at the individual subject level. Recently, spatially constrained independent component analysis (scICA) has been proposed as an automated method for extracting ICNs standardized to a chosen network template while still preserving individual variation. Leveraging the scICA methodology, which solves the former challenge of standardized neuroimaging markers, we investigate the latter challenge of identifying a minimally sufficient data length for clinical applications of resting‐state fMRI (rsfMRI). Using a dataset containing rsfMRI scans of individuals with schizophrenia and controls (M = 310) as well as simulated rsfMRI, we evaluated the robustness of ICN and rsFNC estimates at both the subject‐ and group‐level, as well as the performance of diagnostic classification, with respect to the length of the rsfMRI time course. We found individual estimates of ICNs and rsFNC from the full‐length (5 min) reference time course were sufficiently approximated with just 3–3.5 min of data (r = 0.85, 0.88, respectively), and significant differences in group‐average rsFNC could be sufficiently approximated with even less data, just 2 min (r = 0.86). These results from the shorter clinical data were largely consistent with the results from validation experiments using longer time series from both simulated (30 min) and real‐world (14 min) datasets, in which estimates of subject‐level FNC were reliably estimated with 3–5 min of data. Moreover, in the real‐world data we found rsFNC and ICN estimates generated across the full range of data lengths (0.5–14 min) more reliably matched those generated from the first 5 min of scan time than those generated from the last 5 min, suggesting increased influence of "late scan" noise factors such as fatigue or drowsiness may limit the reliability of FNC from data collected after 10+ min of scan time, further supporting the notion of shorter scans. Lastly, a diagnostic classification model trained on just 2 min of data retained 97%–98% classification accuracy relative to that of the full‐length reference model. Our results suggest that, when decomposed with scICA, rsfMRI scans of just 2–5 min show good clinical utility without significant loss of individual FNC information of longer scan lengths. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
36. Identification of Homogeneous Subgroups from Resting-State fMRI Data.
- Author
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Yang, Hanlu, Vu, Trung, Long, Qunfang, Calhoun, Vince, and Adali, Tülay
- Subjects
INDEPENDENT component analysis ,FUNCTIONAL magnetic resonance imaging ,HUMAN fingerprints ,PEOPLE with mental illness ,NEUROPSYCHOLOGY ,CINGULATE cortex ,PREFRONTAL cortex - Abstract
The identification of homogeneous subgroups of patients with psychiatric disorders can play an important role in achieving personalized medicine and is essential to provide insights for understanding neuropsychological mechanisms of various mental disorders. The functional connectivity profiles obtained from functional magnetic resonance imaging (fMRI) data have been shown to be unique to each individual, similar to fingerprints; however, their use in characterizing psychiatric disorders in a clinically useful way is still being studied. In this work, we propose a framework that makes use of functional activity maps for subgroup identification using the Gershgorin disc theorem. The proposed pipeline is designed to analyze a large-scale multi-subject fMRI dataset with a fully data-driven method, a new constrained independent component analysis algorithm based on entropy bound minimization (c-EBM), followed by an eigenspectrum analysis approach. A set of resting-state network (RSN) templates is generated from an independent dataset and used as constraints for c-EBM. The constraints present a foundation for subgroup identification by establishing a connection across the subjects and aligning subject-wise separate ICA analyses. The proposed pipeline was applied to a dataset comprising 464 psychiatric patients and discovered meaningful subgroups. Subjects within the identified subgroups share similar activation patterns in certain brain areas. The identified subgroups show significant group differences in multiple meaningful brain areas including dorsolateral prefrontal cortex and anterior cingulate cortex. Three sets of cognitive test scores were used to verify the identified subgroups, and most of them showed significant differences across subgroups, which provides further confirmation of the identified subgroups. In summary, this work represents an important step forward in using neuroimaging data to characterize mental disorders. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
37. Joint connectivity matrix independent component analysis: Auto‐linking of structural and functional connectivities.
- Author
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Wu, Lei and Calhoun, Vince
- Subjects
- *
INDEPENDENT component analysis , *FUNCTIONAL connectivity , *FUNCTIONAL magnetic resonance imaging , *MULTISENSOR data fusion - Abstract
The study of human brain connectivity, including structural connectivity (SC) and functional connectivity (FC), provides insights into the neurophysiological mechanism of brain function and its relationship to human behavior and cognition. Both types of connectivity measurements provide crucial yet complementary information. However, integrating these two modalities into a single framework remains a challenge, because of the differences in their quantitative interdependencies as well as their anatomical representations due to distinctive imaging mechanisms. In this study, we introduced a new method, joint connectivity matrix independent component analysis (cmICA), which provides a data‐driven parcellation and automated‐linking of SC and FC information simultaneously using a joint analysis of functional magnetic resonance imaging (MRI) and diffusion‐weighted MRI data. We showed that these two connectivity modalities produce common cortical segregation, though with various degrees of (dis)similarity. Moreover, we show conjoint FC networks and structural white matter tracts that directly link these cortical parcellations/sources, within one analysis. Overall, data‐driven joint cmICA provides a new approach for integrating or fusing structural connectivity and FC systematically and conveniently, and provides an effective tool for connectivity‐based multimodal data fusion in brain. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
38. Heritability of multivariate gray matter measures in schizophrenia
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Turner, Jessica A, Calhoun, Vince D, Michael, Andrew, van Erp, Theo GM, Ehrlich, Stefan, Segall, Judith M, Gollub, Randy L, Csernansky, John, Potkin, Steven G, Ho, Beng-Choon, Bustillo, Juan, Schulz, SCharles, and Wang, Lei
- Published
- 2012
39. Functional MRI Evaluation of Multiple Neural Networks Underlying Auditory Verbal Hallucinations in Schizophrenia Spectrum Disorders
- Author
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Robert J Thoma, Charlotte eChaze, Jeffrey David Lewine, Vince D Calhoun, Vincent P Clark, Juan eBustillo, Jon eHouck, Judith M Ford, Rose eBigelow, Corbin eWilhelmi, Julia M Stephen, and Jessica A Turner
- Subjects
Schizophrenia ,functional magnetic resonance imaging ,Auditory verbal hallucinations ,Independent Component Analysis ,general linear model ,Psychiatry ,RC435-571 - Abstract
Functional MRI studies have identified a distributed set of brain activations to be associated with auditory verbal hallucinations (AVH). However, very little is known about how activated brain regions may be linked together into AVH-generating networks. Fifteen volunteers with schizophrenia or schizoaffective disorder pressed buttons to indicate on-set and off-set of AVH during fMRI scanning. When a general linear model (GLM) was used to compare BOLD signals during periods in which subjects indicated that they were versus were not experiencing AVH (‘AVH-on’ versus ‘AVH-off’), it revealed AVH-related activity in bilateral inferior frontal and superior temporal regions; the right middle temporal gyrus; and the left insula, supramarginal gyrus, inferior parietal lobule and extra-nuclear white matter. In an effort to identify AVH-related networks, the raw data were also processed using independent component analyses (ICA). Four ICA components were spatially consistent with an a priori network framework based upon published meta-analyses of imaging correlates of AVH. Of these four components, only a network involving bilateral auditory cortices and posterior receptive language areas was significantly and positively correlated with the pattern of AVH-on versus AVH-off. The ICA also identified two additional networks (occipital-temporal and medial pre-frontal), not fully matching the meta-analysis framework, but nevertheless containing nodes reported as active in some studies of AVH. Both networks showed significant AVH-related profiles, but both were most active during AVH-off periods. Overall, the data suggest that AVH generation requires specific and selective activation of auditory cortical and posterior language regions, perhaps coupled to a release of indirect influence by occipital and medial frontal structures.
- Published
- 2016
- Full Text
- View/download PDF
40. A Neural Signature of Parkinsonism in Patients With Schizophrenia Spectrum Disorders: A Multimodal MRI Study Using Parallel ICA
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Robert Christian Wolf, Katharina M. Kubera, Georg Northoff, Lena S. Geiger, Vince D. Calhoun, Stefan Fritze, Dusan Hirjak, Heike Tost, Fabio Sambataro, and Mahmoud Rashidi
- Subjects
Adult ,Male ,False discovery rate ,medicine.medical_specialty ,motor abnormalities ,Neuroimaging ,Comorbidity ,Audiology ,Correlation ,03 medical and health sciences ,0302 clinical medicine ,Parkinsonian Disorders ,Thalamus ,Cerebellum ,Connectome ,medicine ,Humans ,In patient ,Gray Matter ,parkinsonism ,Cerebral Cortex ,Principal Component Analysis ,medicine.diagnostic_test ,Positive and Negative Syndrome Scale ,business.industry ,MRI ,schizophrenia ,Parkinsonism ,Magnetic resonance imaging ,Middle Aged ,medicine.disease ,Magnetic Resonance Imaging ,Independent component analysis ,030227 psychiatry ,Psychiatry and Mental health ,Psychotic Disorders ,Schizophrenia ,Female ,Nerve Net ,business ,030217 neurology & neurosurgery ,Regular Articles ,Schizophrenia spectrum - Abstract
Motor abnormalities in schizophrenia spectrum disorders (SSD) have increasingly attracted scientific interest in the past years. However, the neural mechanisms underlying parkinsonism in SSD are unclear. The present multimodal magnetic resonance imaging (MRI) study examined SSD patients with and without parkinsonism, as defined by a Simpson and Angus Scale (SAS) total score of ≥4 (SAS group, n = 22) or
- Published
- 2020
41. Age‐related structural and functional variations in 5,967 individuals across the adult lifespan
- Author
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Jing Sui, Jessica A. Turner, Vince D. Calhoun, Victor M. Vergara, Anees Abrol, Yuhui Du, Na Luo, Jiayu Chen, Yong Xu, Zening Fu, Eswar Damaraju, and Dongdong Lin
- Subjects
Adult ,Male ,Aging ,multivariate linear regression model ,Adolescent ,structural network correlation ,Precuneus ,Biology ,050105 experimental psychology ,Correlation ,03 medical and health sciences ,Young Adult ,0302 clinical medicine ,Neuroimaging ,Age related ,Bayesian multivariate linear regression ,medicine ,Image Processing, Computer-Assisted ,Humans ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,Gray Matter ,Default mode network ,Research Articles ,Aged ,age‐related variations ,Brain Mapping ,Principal Component Analysis ,Radiological and Ultrasound Technology ,05 social sciences ,Brain ,Default Mode Network ,Cognition ,functional network connectivity ,Middle Aged ,Magnetic Resonance Imaging ,medicine.anatomical_structure ,Neurology ,independent component analysis ,Linear Models ,Female ,Neurology (clinical) ,Anatomy ,Nerve Net ,Insula ,Neuroscience ,030217 neurology & neurosurgery ,adult lifespan ,Research Article - Abstract
Exploring brain changes across the human lifespan is becoming an important topic in neuroscience. Though there are multiple studies which investigated the relationship between age and brain imaging, the results are heterogeneous due to small sample sizes and relatively narrow age ranges. Here, based on year‐wise estimation of 5,967 subjects from 13 to 72 years old, we aimed to provide a more precise description of adult lifespan variation trajectories of gray matter volume (GMV), structural network correlation (SNC), and functional network connectivity (FNC) using independent component analysis and multivariate linear regression model. Our results revealed the following relationships: (a) GMV linearly declined with age in most regions, while parahippocampus showed an inverted U‐shape quadratic relationship with age; SNC presented a U‐shape quadratic relationship with age within cerebellum, and inverted U‐shape relationship primarily in the default mode network (DMN) and frontoparietal (FP) related correlation. (b) FNC tended to linearly decrease within resting‐state networks (RSNs), especially in the visual network and DMN. Early increase was revealed between RSNs, primarily in FP and DMN, which experienced a decrease at older ages. U‐shape relationship was also revealed to compensate for the cognition deficit in attention and subcortical related connectivity at late years. (c) The link between middle occipital gyrus and insula, as well as precuneus and cerebellum, exhibited similar changing trends between SNC and FNC across the adult lifespan. Collectively, these results highlight the benefit of lifespan study and provide a precise description of age‐related regional variation and SNC/FNC changes based on a large dataset.
- Published
- 2019
42. Consecutive Independence and Correlation Transform for Multimodal Data Fusion: Discovery of One-to-Many Associations in Structural and Functional Imaging Data
- Author
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Qunfang Long, Vince D. Calhoun, Tulay Adali, Chunying Jia, Yuri Levin-Schwartz, and M. A. B. S. Akhonda
- Subjects
Technology ,genetic structures ,QH301-705.5 ,Computer science ,QC1-999 ,brain imaging ,Neuroimaging ,otorhinolaryngologic diseases ,General Materials Science ,Biology (General) ,QD1-999 ,Instrumentation ,Fluid Flow and Transfer Processes ,independent vector analysis ,Modalities ,Modality (human–computer interaction) ,business.industry ,Physics ,Process Chemistry and Technology ,General Engineering ,food and beverages ,Pattern recognition ,Engineering (General). Civil engineering (General) ,Sensor fusion ,Independent component analysis ,Computer Science Applications ,Functional imaging ,Chemistry ,multimodal data fusion ,independent component analysis ,Artificial intelligence ,TA1-2040 ,business ,psychological phenomena and processes ,Signal subspace ,Diffusion MRI - Abstract
Brain signals can be measured using multiple imaging modalities, such as magnetic resonance imaging (MRI)-based techniques. Different modalities convey distinct yet complementary information, thus, their joint analyses can provide valuable insight into how the brain functions in both healthy and diseased conditions. Data-driven approaches have proven most useful for multimodal fusion as they minimize assumptions imposed on the data, and there are a number of methods that have been developed to uncover relationships across modalities. However, none of these methods, to the best of our knowledge, can discover “one-to-many associations”, meaning one component from one modality is linked with more than one component from another modality. However, such “one-to-many associations” are likely to exist, since the same brain region can be involved in multiple neurological processes. Additionally, most existing data fusion methods require the signal subspace order to be identical for all modalities—a severe restriction for real-world data of different modalities. Here, we propose a new fusion technique—the consecutive independence and correlation transform (C-ICT) model—which successively performs independent component analysis and independent vector analysis and is uniquely flexible in terms of the number of datasets, signal subspace order, and the opportunity to find “one-to-many associations”. We apply C-ICT to fuse diffusion MRI, structural MRI, and functional MRI datasets collected from healthy controls (HCs) and patients with schizophrenia (SZs). We identify six interpretable triplets of components, each of which consists of three associated components from the three modalities. Besides, components from these triplets that show significant group differences between the HCs and SZs are identified, which could be seen as putative biomarkers in schizophrenia.
- Published
- 2021
- Full Text
- View/download PDF
43. High and Low Levels of an NTRK2-Driven Genetic Profile Affect Motor- and Cognition-Associated Frontal Gray Matter in Prodromal Huntington’s Disease
- Author
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Jennifer A. Ciarochi, Jingyu Liu, Vince Calhoun, Hans Johnson, Maria Misiura, H. Jeremy Bockholt, Flor A. Espinoza, Arvind Caprihan, Sergey Plis, Jessica A. Turner, Jane S. Paulsen, and the PREDICT-HD Investigators and Coordinators of the Huntington Study Group
- Subjects
Huntington’s disease ,brain-derived neurotrophic factor ,tropomyosin receptor kinase B ,supplementary motor ,independent component analysis ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
This study assessed how BDNF (brain-derived neurotrophic factor) and other genes involved in its signaling influence brain structure and clinical functioning in pre-diagnosis Huntington’s disease (HD). Parallel independent component analysis (pICA), a multivariate method for identifying correlated patterns in multimodal datasets, was applied to gray matter concentration (GMC) and genomic data from a sizeable PREDICT-HD prodromal cohort (N = 715). pICA identified a genetic component highlighting NTRK2, which encodes BDNF’s TrkB receptor, that correlated with a GMC component including supplementary motor, precentral/premotor cortex, and other frontal areas (p < 0.001); this association appeared to be driven by participants with high or low levels of the genetic profile. The frontal GMC profile correlated with cognitive and motor variables (Trail Making Test A (p = 0.03); Stroop Color (p = 0.017); Stroop Interference (p = 0.04); Symbol Digit Modalities Test (p = 0.031); Total Motor Score (p = 0.01)). A top-weighted NTRK2 variant (rs2277193) was protectively associated with Trail Making Test B (p = 0.007); greater minor allele numbers were linked to a better performance. These results support the idea of a protective role of NTRK2 in prodromal HD, particularly in individuals with certain genotypes, and suggest that this gene may influence the preservation of frontal gray matter that is important for clinical functioning.
- Published
- 2018
- Full Text
- View/download PDF
44. Fusion of Multiple Spatial Networks Derived from Complex-Valued fMRI Data via CNN Classification
- Author
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Jing Sui, Yue Qiu, Qiu-Hua Lin, Vince D. Calhoun, Chao-Ying Zhang, and Yan-Wei Niu
- Subjects
Brain network ,Fusion ,Computer science ,business.industry ,Component (UML) ,Complex valued ,Pattern recognition ,Artificial intelligence ,business ,Convolutional neural network ,Independent component analysis - Abstract
Convolutional neural network (CNN) can achieve better classification by using independent component analysis (ICA) components derived from complex-valued fMRI data than from magnitude-only fMRI data due to incorporating additional phase information. However, thus far magnitude slices of only a single brain network (i.e. spatial component) has been used in the classification. This study aims to take advantages of multiple ICA components in providing rich information and to provide a conclusion for efficient multiple-component fusion. More precisely, we present three fusion approaches: 1) averaging multiple ICA components as inputs of a single-component CNN, 2) concatenating features of multiple single-component CNN, and 3) averaging predictive probabilities of multiple single-component CNN. We evaluate the proposed methods using resting-state fMRI data collected from 42 schizophrenia patients and 40 healthy controls. Experimental results show that all three fusion approaches can improve classification accuracy compared to the single-component CNN, and the first approach performs the best. No matter which fusion method is used, we reach the same conclusion that four-component fusion is sufficient to obtain satisfying performance, and two-component fusion yields higher improvement and better performance than the single-component classification, especially when using components having good accuracy for single-component CNN classification.
- Published
- 2021
45. Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach.
- Author
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Rootes-Murdy, Kelly, Edmond, Jesse T., Wenhao Jiang, Rahaman, Md A., Jiayu Chen, Perrone-Bizzozero, Nora I., Calhoun, Vince D., van Erp, Theo G. M., Ehrlich, Stefan, Agartz, Ingrid, Jönsson, Erik G., Andreassen, Ole A., Westlye, Lars T., Lei Wang, Pearlson, Godfrey D., Glahn, David C., Hong, Elliot, Buchanan, Robert W., Kochunov, Peter, and Voineskos, Aristotle
- Subjects
BIPOLAR disorder ,INDEPENDENT component analysis ,GRAY matter (Nerve tissue) ,CINGULATE cortex ,HIERARCHICAL clustering (Cluster analysis) - Abstract
Background: Structural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles. Materials and methods: We analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N - 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP. Results: Component A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences. Conclusion: These multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
46. Nonlinear functional network connectivity in resting functional magnetic resonance imaging data.
- Author
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Motlaghian, Sara M., Belger, Aysenil, Bustillo, Juan R., Ford, Judith M., Iraji, Armin, Lim, Kelvin, Mathalon, Daniel H., Mueller, Bryon A., O'Leary, Daniel, Pearlson, Godfrey, Potkin, Steven G., Preda, Adrian, van Erp, Theo G. M., and Calhoun, Vince D.
- Subjects
FUNCTIONAL magnetic resonance imaging ,FUNCTIONAL connectivity ,INDEPENDENT component analysis ,VISUAL cortex - Abstract
In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (NMI) that calculates the nonlinear relationship between different brain regions. We demonstrate our proposed approach using simulated data and then apply it to a dataset previously studied by Damaraju et al. This resting‐state fMRI data included 151 schizophrenia patients and 163 age‐ and gender‐matched healthy controls. We first decomposed these data using group independent component analysis (ICA) and yielded 47 functionally relevant intrinsic connectivity networks. Our analysis showed a modularized nonlinear relationship among brain functional networks that was particularly noticeable in the sensory and visual cortex. Interestingly, the modularity appears both meaningful and distinct from that revealed by the linear approach. Group analysis identified significant differences in explicitly nonlinear functional network connectivity (FNC) between schizophrenia patients and healthy controls, particularly in the visual cortex, with controls showing more nonlinearity (i.e., higher normalized mutual information between time courses with linear relationships removed) in most cases. Certain domains, including subcortical and auditory, showed relatively less nonlinear FNC (i.e., lower normalized mutual information), whereas links between the visual and other domains showed evidence of substantial nonlinear and modular properties. Overall, these results suggest that quantifying nonlinear dependencies of functional connectivity may provide a complementary and potentially important tool for studying brain function by exposing relevant variation that is typically ignored. Beyond this, we propose a method that captures both linear and nonlinear effects in a "boosted" approach. This method increases the sensitivity to group differences compared to the standard linear approach, at the cost of being unable to separate linear and nonlinear effects. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
47. Dynamic Connectivity States Estimated from Resting fMRI Identify Differences among Schizophrenia, Bipolar Disorder, and Healthy Control Subjects
- Author
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Barnaly eRashid, Eswar eDamaraju, Godfrey D. Pearlson, and Vince D Calhoun
- Subjects
Schizophrenia ,Independent Component Analysis ,Intrinsic connectivity networks ,Dynamic Functional Connectivity ,bipolar disorder. ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Schizophrenia and bipolar disorder share significant overlap in clinical symptoms, brain characteristics, and risk genes, and both are associated with dysconnectivity among large-scale brain networks. Resting state functional magnetic resonance imaging (rsfMRI) data facilitates studying macroscopic connectivity among distant brain regions. Standard approaches to identifying such connectivity include seed-based correlation and data-driven clustering methods such as independent component analysis (ICA) but typically focus on average connectivity. In this study, we utilize ICA on rsfMRI data to obtain intrinsic connectivity networks (ICNs) in cohorts of healthy controls (HC) and age matched schizophrenia and bipolar disorder patients. Subsequently, we investigated difference in functional network connectivity (FNC), defined as pairwise correlations among the timecourses of ICNs, between healthy controls and patients. We quantified differences in both static (average) and dynamic (windowed) connectivity during the entire scan duration. Disease-specific differences were identified in connectivity within different dynamic states. Schizophrenia patients showed more differences from healthy subjects than did bipolars, including both hyper and hypo connectivity in one common connectivity state (dynamic state 3). Also group differences between schizophrenia and bipolar patients were identified in patterns (states) of connectivity involving the frontal (dynamic state 1) and frontal-parietal regions (dynamic state 3). Our results provide new information about these illnesses and strongly suggest that state-based analyses are critical to avoid averaging together important factors that can help distinguish these clinical groups.
- Published
- 2014
- Full Text
- View/download PDF
48. 3-way Parallel Fusion of Spatial (sMRI/dMRI) and Spatio-temporal (fMRI) Data with Application to Schizophrenia
- Author
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Victor M. Vergara, Rogers F. Silva, Jing Sui, Vince D. Calhoun, Shile Qi, Robyn L. Miller, Dongmei Zhi, Rongtao Jiang, and Sergey M. Plis
- Subjects
Linkage (software) ,Modalities ,medicine.diagnostic_test ,business.industry ,Computer science ,Schizophrenia (object-oriented programming) ,05 social sciences ,Stability (learning theory) ,Pattern recognition ,Independent component analysis ,050105 experimental psychology ,03 medical and health sciences ,0302 clinical medicine ,Neuroimaging ,medicine ,Leverage (statistics) ,0501 psychology and cognitive sciences ,Artificial intelligence ,business ,Functional magnetic resonance imaging ,030217 neurology & neurosurgery - Abstract
Advances in brain imaging acquisition techniques allow data from multiple modalities to be collected from each subject offering different but limited views of the structural, functional or temporal properties of human brain. Multimodal fusion can provide a way to leverage different perspectives from multiple complementary modalities. However, most current fMRI-related multimodal fusion approaches are restricted to the second-level 3D features, rather than the original 4D fMRI data. Here we are motivated to propose a novel 3-way fusion approach that can incorporate temporal information from fMRI by parallelizing “group ICA” and “parallel ICA” under a global optimization. Simulations show that GICA+pICA provides more accurate inter-modality linkage detection under both strong and weak correlations. In real data application, one linked fMRI-sMRI-dMRI component was identified showing differences between schizophrenia and controls in all modalities, demonstrating the stability of GICA+pICA to identify inter-modality linkage among three modalities.
- Published
- 2021
49. Extraction of Time-Varying Spatiotemporal Networks Using Parameter-Tuned Constrained IVA
- Author
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Suchita Bhinge, Vince D. Calhoun, Rami Mowakeaa, and Tulay Adali
- Subjects
Adult ,Databases, Factual ,Computer science ,Feature extraction ,Blind signal separation ,Article ,030218 nuclear medicine & medical imaging ,Data modeling ,03 medical and health sciences ,0302 clinical medicine ,Component analysis ,Image Interpretation, Computer-Assisted ,medicine ,Humans ,Electrical and Electronic Engineering ,Dynamic functional connectivity ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,business.industry ,Dimensionality reduction ,fMRI analysis ,Brain ,Pattern recognition ,Middle Aged ,Magnetic Resonance Imaging ,Independent component analysis ,Computer Science Applications ,Constraint (information theory) ,Schizophrenia ,Blind source separation ,Artificial intelligence ,Connectivity analysis ,business ,Functional magnetic resonance imaging ,Algorithms ,Software - Abstract
Dynamic functional connectivity (dFC) analysis is an effective way to capture the networks that are functionally associated and continuously changing over the scanning period. However, these methods mostly analyze the dynamic associations across the activation patterns of the spatial networks while assuming that the spatial networks are stationary. Hence, a model that allows for the variability in both domains and reduces the assumptions imposed on the data provides an effective way for extracting spatio-temporal networks. Independent vector analysis is a joint blind source separation technique that allows for estimation of spatial and temporal features while successfully preserving variability. However, its performance is affected for higher number of datasets. Hence, we develop an effective two-stage method to extract time-varying spatial and temporal features using IVA, mitigating the problems with higher number of datasets while preserving the variability across subjects and time. The first stage is used to extract reference signals using group independent component analysis (GICA) that are used in a parameter-tuned constrained IVA (pt-cIVA) framework to estimate time-varying representations of these signals by preserving the variability through tuning the constraint parameter. This approach effectively captures variability across time from a large-scale resting-state fMRI data acquired from healthy controls and patients with schizophrenia and identifies more functionally relevant connections that are significantly different among healthy controls and patients with schizophrenia, compared with the widely used GICA method alone.
- Published
- 2019
50. Parallel group ICA+ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia
- Author
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Steven G. Potkin, Jingyu Liu, Adrian Preda, Jessica A. Turner, Vince D. Calhoun, Jiayu Chen, Dongdong Lin, Daniel H. Mathalon, James T. Voyvodic, Sarah McEwen, Armin Iraji, Mustafa Salman, Dongmei Zhi, Rogers F. Silva, Zening Fu, Tulay Adali, Bryon A. Mueller, Jing Sui, Aysenil Belger, Shile Qi, Juan R. Bustillo, Rongtao Jiang, Eswar Damaraju, and Judith M. Ford
- Subjects
Male ,Computer science ,Image Processing ,multimodal fusion ,Diagnostic Radiology ,Computer-Assisted ,0302 clinical medicine ,Theoretical ,Models ,Image Processing, Computer-Assisted ,Research Articles ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,group independent component analysis ,05 social sciences ,Experimental Psychology ,Cognition ,parallel independent component analysis ,Middle Aged ,Magnetic Resonance Imaging ,Phase III as Topic ,Mental Health ,Neurology ,Biomedical Imaging ,Female ,Cognitive Sciences ,Anatomy ,Adult ,Schizophrenia (object-oriented programming) ,050105 experimental psychology ,temporal information ,Young Adult ,03 medical and health sciences ,Neuroimaging ,Clinical Research ,Component (UML) ,medicine ,Humans ,Clinical Trials ,Computer Simulation ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,Linkage (software) ,subjects' variability ,Modality (human–computer interaction) ,business.industry ,Functional Neuroimaging ,Neurosciences ,Pattern recognition ,Models, Theoretical ,Independent component analysis ,Brain Disorders ,schizophrenia ,Clinical Trials, Phase III as Topic ,Schizophrenia ,Neurology (clinical) ,Artificial intelligence ,Nerve Net ,business ,Functional magnetic resonance imaging ,030217 neurology & neurosurgery - Abstract
There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called "parallel group ICA+ICA" that incorporates temporal fMRI information from group independent component analysis (GICA) into a parallel independent component analysis (ICA) framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g., structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate intermodality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities, and this linkage can be replicated in an independent cohort. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.
- Published
- 2019
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