734 results on '"Vince, A."'
Search Results
2. A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder
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Du, Yuhui, He, Xingyu, Kochunov, Peter, Pearlson, Godfrey, Hong, L Elliot, Erp, Theo GM, Belger, Aysenil, and Calhoun, Vince D
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Pediatric ,Autism ,Clinical Research ,Serious Mental Illness ,Neurosciences ,Intellectual and Developmental Disabilities (IDD) ,Mental Health ,Brain Disorders ,Schizophrenia ,Aetiology ,2.1 Biological and endogenous factors ,Mental health ,Autism Spectrum Disorder ,Brain ,Brain Mapping ,Humans ,Magnetic Resonance Imaging ,Multimodal Imaging ,autism spectrum disorder ,classification ,functional magnetic resonance imaging ,fusion ,schizophrenia ,structural magnetic resonance imaging ,Cognitive Sciences ,Experimental Psychology - Abstract
Schizophrenia (SZ) and autism spectrum disorder (ASD) sharing overlapping symptoms have a long history of diagnostic confusion. It is unclear what their differences at a brain level are. Here, we propose a multimodality fusion classification approach to investigate their divergence in brain function and structure. Using brain functional network connectivity (FNC) calculated from resting-state fMRI data and gray matter volume (GMV) estimated from sMRI data, we classify the two disorders using the main data (335 SZ and 380 ASD patients) via an unbiased 10-fold cross-validation pipeline, and also validate the classification generalization ability on an independent cohort (120 SZ and 349 ASD patients). The classification accuracy reached up to 83.08% for the testing data and 72.10% for the independent data, significantly better than the results from using the single-modality features. The discriminative FNCs that were automatically selected primarily involved the sub-cortical, default mode, and visual domains. Interestingly, all discriminative FNCs relating to the default mode network showed an intermediate strength in healthy controls (HCs) between SZ and ASD patients. Their GMV differences were mainly driven by the frontal gyrus, temporal gyrus, and insula. Regarding these regions, the mean GMV of HC fell intermediate between that of SZ and ASD, and ASD showed the highest GMV. The middle frontal gyrus was associated with both functional and structural differences. In summary, our work reveals the unique neuroimaging characteristics of SZ and ASD that can achieve high and generalizable classification accuracy, supporting their potential as disorder-specific neural substrates of the two entwined disorders.
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- 2022
3. Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics.
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Faghiri, Ashkan, Damaraju, Eswar, Belger, Aysenil, Ford, Judith M, Mathalon, Daniel, McEwen, Sarah, Mueller, Bryon, Pearlson, Godfrey, Preda, Adrian, Turner, Jessica A, Vaidya, Jatin G, Van Erp, Theodorus, and Calhoun, Vince D
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brain dynamics ,density clustering ,functional magnetic resonance imaging ,independent component analyses ,resting state– fMRI ,resting state– ,fMRI ,Neurosciences ,Psychology ,Cognitive Sciences - Abstract
BackgroundA number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time.MethodsWe introduce a new approach that analyzes time series trajectories to identify high traffic nodes in a high dimensional space. First, functional magnetic resonance imaging (fMRI) data are decomposed using spatial ICA to a set of maps and their associated time series. Next, density is calculated for each time point and high-density points are clustered to identify a small set of high traffic nodes. We validated our method using simulations and then implemented it on a real data set.ResultsWe present a novel approach that captures dynamics within a high dimensional space and also does not use any windowing in contrast to many existing approaches. The approach enables one to characterize and study the time series in a potentially high dimensional space, rather than looking at each component pair separately. Our results show that schizophrenia patients have a lower dynamism compared to healthy controls. In addition, we find patients spend more time in nodes associated with the default mode network and less time in components strongly correlated with auditory and sensorimotor regions. Interestingly, we also found that subjects oscillate between state pairs that show opposite spatial maps, suggesting an oscillatory pattern.ConclusionOur proposed method provides a novel approach to analyze the data in its native high dimensional space and can possibly provide new information that is undetectable using other methods.
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- 2021
4. Meta-Modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia.
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Falakshahi, Haleh, Vergara, Victor M, Liu, Jingyu, Mathalon, Daniel H, Ford, Judith M, Voyvodic, James, Mueller, Bryon A, Belger, Aysenil, McEwen, Sarah, Potkin, Steven G, Preda, Adrian, Rokham, Hooman, Sui, Jing, Turner, Jessica A, Plis, Sergey, and Calhoun, Vince D
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Brain ,Humans ,Magnetic Resonance Imaging ,Diffusion Magnetic Resonance Imaging ,Schizophrenia ,Anisotropy ,Computer Simulation ,Functional magnetic resonance imaging ,Diseases ,Graphical models ,Psychiatry ,Correlation ,Translational research ,Connectivity ,covariance matrix ,data fusion ,default mode network ,dMRI ,fMRI ,GGM ,graphical model ,joint estimation ,partial correlation ,precision matrix ,sMRI ,Brain Disorders ,Biomedical Imaging ,Mental Health ,Serious Mental Illness ,cs.LG ,eess.IV ,stat.ML ,Artificial Intelligence and Image Processing ,Biomedical Engineering ,Electrical and Electronic Engineering - Abstract
ObjectiveMultimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on a single modality may lead to incorrect inferences, which is especially important when a studied phenomenon is a disease. In this paper, we introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ).MethodsWe start with estimating and visualizing links within and among extracted multimodal data features using a Gaussian graphical model (GGM). We then propose a modularity-based method that can be applied to the GGM to identify links that are associated with mental illness across a multimodal data set. Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality. We use functional MRI (fMRI), diffusion MRI (dMRI), and structural MRI (sMRI) to compute the fractional amplitude of low frequency fluctuations (fALFF), fractional anisotropy (FA), and gray matter (GM) concentration maps. These three modalities are analyzed using our modularity method.ResultsOur results show missing links that are only captured by the cross-modal information that may play an important role in disconnectivity between the components.ConclusionWe identified multimodal (fALFF, FA and GM) disconnectivity in the default mode network area in patients with SZ, which would not have been detectable in a single modality.SignificanceThe proposed approach provides an important new tool for capturing information that is distributed among multiple imaging modalities.
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- 2020
5. Weighted average of shared trajectory: A new estimator for dynamic functional connectivity efficiently estimates both rapid and slow changes over time.
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Faghiri, Ashkan, Iraji, Armin, Damaraju, Eswar, Belger, Aysenil, Ford, Judy, Mathalon, Daniel, Mcewen, Sarah, Mueller, Bryon, Pearlson, Godfrey, Preda, Adrian, Turner, Jessica, Vaidya, Jatin G, Van Erp, Theo GM, and Calhoun, Vince D
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Brain dynamics ,Dynamic functional network connectivity ,Functional magnetic resonance imaging ,ICA ,Phase ,Resting state ,Shared trajectory ,fMRI ,Neurosciences ,Mental Health ,Schizophrenia ,Brain Disorders ,Mental health ,Psychology ,Cognitive Sciences ,Neurology & Neurosurgery - Abstract
BackgroundDynamic functional network connectivity (dFNC) of the brain has attracted considerable attention recently. Many approaches have been suggested to study dFNC with sliding window Pearson correlation (SWPC) being the most well-known. SWPC needs a relatively large sample size to reach a robust estimation but using large window sizes prevents us to detect rapid changes in dFNC.New methodHere we first calculate the gradients of each time series pair and use the magnitude of these gradients to calculate weighted average of shared trajectory (WAST) as a new estimator for dFNC.ResultsUsing WAST to compare healthy control and schizophrenia patients using a large dataset, we show disconnectivity between different regions associated with schizophrenia. In addition, WAST results reveals patients with schizophrenia stay longer in a connectivity state with negative connectivity between motor and sensory regions than do healthy controls.Comparison with existing methodsWe compare WAST with SWPC and multiplication of temporal derivatives (MTD) using different simulation scenarios. We show that WAST enables us to detect very rapid changes in dFNC (undetected by SWPC) while MTD performance is generally lower.ConclusionsAs large window sizes are unable to detect short states, using shorter window size is desirable if the estimator is robust enough. We provide evidence that WAST requires fewer samples (compared to SWPC) to reach a robust estimation. As a result, we were able to identify rapidly varying dFNC patterns undetected by SWPC while still being able to robustly estimate slower dFNC patterns.
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- 2020
6. Comparison of test–retest reliability of BOLD and pCASL fMRI in a two-center study
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Ibinson, James W., Gillman, Andrea G., Schmidthorst, Vince, Li, Conrad, Napadow, Vitaly, Loggia, Marco L., and Wasan, Ajay D.
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- 2022
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7. Comparison of test–retest reliability of BOLD and pCASL fMRI in a two-center study
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James W. Ibinson, Andrea G. Gillman, Vince Schmidthorst, Conrad Li, Vitaly Napadow, Marco L. Loggia, and Ajay D. Wasan
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Test–retest reliability ,Pseudo-continuous arterial spin labeling ,Blood oxygen level dependent ,Resting state functional connectivity ,Finger tapping ,Functional magnetic resonance imaging ,Medical technology ,R855-855.5 - Abstract
Abstract Background The establishment of test–retest reliability and reproducibility (TRR) is an important part of validating any research tool, including functional magnetic resonance imaging (fMRI). The primary objective of this study is to investigate the reliability of pseudo-Continuous Arterial Spin Labeling (pCASL) and Blood Oxygen Level Dependent (BOLD) fMRI data acquired across two different scanners in a sample of healthy adults. While single site/single scanner studies have shown acceptable repeatability, TRR of both in a practical multisite study occurring in two facilities spread out across the country with weeks to months between scans is critically needed. Methods Ten subjects were imaged with similar 3 T MRI scanners at the University of Pittsburgh and Massachusetts General Hospital. Finger-tapping and Resting-state data were acquired for both techniques. Analysis of the resting state data for functional connectivity was performed with the Functional Connectivity Toolbox, while analysis of the finger tapping data was accomplished with FSL. pCASL Blood flow data was generated using AST Toolbox. Activated areas and networks were identified via pre-defined atlases and dual-regression techniques. Analysis for TRR was conducted by comparing pCASL and BOLD images in terms of Intraclass correlation coefficients, Dice Similarity Coefficients, and repeated measures ANOVA. Results Both BOLD and pCASL scans showed strong activation and correlation between the two locations for the finger tapping tasks. Functional connectivity analyses identified elements of the default mode network in all resting scans at both locations. Multivariate repeated measures ANOVA showed significant variability between subjects, but no significant variability for location. Global CBF was very similar between the two scanning locations, and repeated measures ANOVA showed no significant differences between the two scanning locations. Conclusions The results of this study show that when similar scanner hardware and software is coupled with identical data analysis protocols, consistent and reproducible functional brain images can be acquired across sites. The variability seen in the activation maps is greater for pCASL versus BOLD images, as expected, however groups maps are remarkably similar despite the low number of subjects. This demonstrates that multi-site fMRI studies of task-based and resting state brain activity is feasible.
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- 2022
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8. 4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia.
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Pusuluri, Krishna, Fu, Zening, Miller, Robyn, Pearlson, Godfrey, Kochunov, Peter, Van Erp, Theo G. M., Iraji, Armin, and Calhoun, Vince D.
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FUNCTIONAL magnetic resonance imaging ,LARGE-scale brain networks ,COGNITIVE ability ,PEOPLE with schizophrenia ,COGNITION disorders - Abstract
Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan, that we refer to as dynamic spatial network connectivity (dSNC). We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxel‐wise changes within and between brain networks. [ABSTRACT FROM AUTHOR]
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- 2024
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9. A new transfer entropy method for measuring directed connectivity from complex-valued fMRI data.
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Wei-Xing Li, Qiu-Hua Lin, Chao-Ying Zhang, Yue Han, and Calhoun, Vince D.
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FUNCTIONAL magnetic resonance imaging ,ENTROPY - Abstract
Background: Inferring directional connectivity of brain regions from functional magnetic resonance imaging (fMRI) data has been shown to provide additional insights into predicting mental disorders such as schizophrenia. However, existing research has focused on the magnitude data from complex-valued fMRI data without considering the informative phase data, thus ignoring potentially important information. Methods: We propose a new complex-valued transfer entropy (CTE) method to measure causal links among brain regions in complex-valued fMRI data. We use the transfer entropy to model a general non-linear magnitude-magnitude and phase-phase directed connectivity and utilize partial transfer entropy to measure the complementary phase and magnitude effects on magnitude-phase and phase-magnitude causality. We also define the significance of the causality based on a statistical test and the shuffling strategy of the two complex-valued signals. Results: Simulated results verified higher accuracy of CTE than four causal analysis methods, including a simplified complex-valued approach and three real-valued approaches. Using experimental fMRI data from schizophrenia and controls, CTE yields results consistent with previous findings but with more significant group differences. The proposed method detects new directed connectivity related to the right frontal parietal regions and achieves 10.2-20.9% higher SVM classification accuracy when inferring directed connectivity using anatomical automatic labeling (AAL) regions as features. Conclusion: The proposed CTE provides a new general method for fully detecting highly predictive directed connectivity from complex-valued fMRI data, with magnitude-only fMRI data as a specific case. [ABSTRACT FROM AUTHOR]
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- 2024
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10. Multi-modal deep learning from imaging genomic data for schizophrenia classification.
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Kanyal, Ayush, Mazumder, Badhan, Calhoun, Vince D., Preda, Adrian, Turner, Jessica, Ford, Judith, and Dong Hye Ye
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DEEP learning ,CONVOLUTIONAL neural networks ,FUNCTIONAL magnetic resonance imaging ,MAGNETIC resonance imaging ,SINGLE nucleotide polymorphisms ,CONCEPT learning ,SIGNAL convolution ,SCHIZOAFFECTIVE disorders - Abstract
Background: Schizophrenia (SZ) is a psychiatric condition that adversely affects an individual's cognitive, emotional, and behavioral aspects. The etiology of SZ, although extensively studied, remains unclear, as multiple factors come together to contribute toward its development. There is a consistent body of evidence documenting the presence of structural and functional deviations in the brains of individuals with SZ. Moreover, the hereditary aspect of SZ is supported by the significant involvement of genomics markers. Therefore, the need to investigate SZ from a multi-modal perspective and develop approaches for improved detection arises. Methods: Our proposed method employed a deep learning framework combining features from structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and genetic markers such as single nucleotide polymorphism (SNP). For sMRI, we used a pre-trained DenseNet to extract the morphological features. To identify the most relevant functional connections in fMRI and SNPs linked to SZ, we applied a 1-dimensional convolutional neural network (CNN) followed by layerwise relevance propagation (LRP). Finally, we concatenated these obtained features across modalities and fed them to the extreme gradient boosting (XGBoost) treebased classifier to classify SZ from healthy control (HC). Results: Experimental evaluation on clinical dataset demonstrated that, compared to the outcomes obtained from each modality individually, our proposed multi-modal approach performed classification of SZ individuals from HC with an improved accuracy of 79.01%. Conclusion: We proposed a deep learning based framework that selects multimodal (sMRI, fMRI and genetic) features efficiently and fuse them to obtain improved classification scores. Additionally, by using Explainable AI (XAI), we were able to pinpoint and validate significant functional network connections and SNPs that contributed the most toward SZ classification, providing necessary interpretation behind our findings. [ABSTRACT FROM AUTHOR]
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- 2024
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11. A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis
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Mingyu Sun, Ben Gabrielson, Mohammad Abu Baker Siddique Akhonda, Hanlu Yang, Francisco Laport, Vince Calhoun, and Tülay Adali
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independent vector analysis ,MCCA ,JBSS ,subspace analysis ,multi-subject medical imaging data ,functional magnetic resonance imaging ,Chemical technology ,TP1-1185 - 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.
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- 2023
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12. 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
13. A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer's disease using resting-state functional network connectivity.
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Hassanzadeh, Reihaneh, Abrol, Anees, Pearlson, Godfrey, Turner, Jessica A., and Calhoun, Vince D.
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ALZHEIMER'S disease ,DEEP learning ,K-nearest neighbor classification ,FUNCTIONAL magnetic resonance imaging ,MACHINE learning ,MILD cognitive impairment ,SUPPORT vector machines - Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimer's disease (AD) and schizophrenia (SZ). While most rs-fMRI studies being conducted in AD and SZ compare patients to healthy controls, it is also of interest to directly compare AD and SZ patients with each other to identify potential biomarkers shared between the disorders. However, comparing patient groups collected in different studies can be challenging due to potential confounds, such as differences in the patient's age, scan protocols, etc. In this study, we compared and contrasted resting-state functional network connectivity (rs-FNC) of 162 patients with AD and late mild cognitive impairment (LMCI), 181 schizophrenia patients, and 315 cognitively normal (CN) subjects. We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). Our statistical analysis revealed that FNC between the following network pairs is stronger in AD compared to SZ: subcortical-cerebellum, subcortical-cognitive control, cognitive control-cerebellum, and visual-sensory motor networks. On the other hand, FNC is stronger in SZ than AD for the following network pairs: subcortical-visual, subcortical-auditory, subcortical-sensory motor, cerebellum-visual, sensory motor-cognitive control, and within the cerebellum networks. Furthermore, we observed that while AD and SZ disorders each have unique FNC abnormalities, they also share some common functional abnormalities that can be due to similar neurobiological mechanisms or genetic factors contributing to these disorders' development. Moreover, we achieved an accuracy of 85% in classifying subjects into AD and SZ where default mode, visual, and subcortical networks contributed the most to the classification and accuracy of 68% in classifying subjects into AD, SZ, and CN with the subcortical domain appearing as the most contributing features to the three-way classification. Finally, our findings indicated that for all classification tasks, except AD vs. SZ, males are more predictable than females. [ABSTRACT FROM AUTHOR]
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- 2024
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14. Explainable Multimodal Graph Isomorphism Network for Interpreting Sex Differences in Adolescent Neurodevelopment.
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Patel, Binish, Orlichenko, Anton, Patel, Adnan, Qu, Gang, Wilson, Tony W., Stephen, Julia M., Calhoun, Vince D., and Wang, Yu-Ping
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GRAPH neural networks ,FUNCTIONAL magnetic resonance imaging ,DEFAULT mode network ,DEEP brain stimulation ,LARGE-scale brain networks ,NEURAL development ,MENTAL arithmetic - Abstract
Background: A fundamental grasp of the variability observed in healthy individuals holds paramount importance in the investigation of neuropsychiatric conditions characterized by sex-related phenotypic distinctions. Functional magnetic resonance imaging (fMRI) serves as a meaningful tool for discerning these differences. Among deep learning models, graph neural networks (GNNs) are particularly well-suited for analyzing brain networks derived from fMRI blood oxygen level-dependent (BOLD) signals, enabling the effective exploration of sex differences during adolescence. Method: In the present study, we introduce a multi-modal graph isomorphism network (MGIN) designed to elucidate sex-based disparities using fMRI task-related data. Our approach amalgamates brain networks obtained from multiple scans of the same individual, thereby enhancing predictive capabilities and feature identification. The MGIN model adeptly pinpoints crucial subnetworks both within and between multi-task fMRI datasets. Moreover, it offers interpretability through the utilization of GNNExplainer, which identifies pivotal sub-network graph structures contributing significantly to sex group classification. Results: Our findings indicate that the MGIN model outperforms competing models in terms of classification accuracy, underscoring the benefits of combining two fMRI paradigms. Additionally, our model discerns the most significant sex-related functional networks, encompassing the default mode network (DMN), visual (VIS) network, cognitive (CNG) network, frontal (FRNT) network, salience (SAL) network, subcortical (SUB) network, and sensorimotor (SM) network associated with hand and mouth movements. Remarkably, the MGIN model achieves superior sex classification accuracy when juxtaposed with other state-of-the-art algorithms, yielding a noteworthy 81.67% improvement in classification accuracy. Conclusion: Our model's superiority emanates from its capacity to consolidate data from multiple scans of subjects within a proven interpretable framework. Beyond its classification prowess, our model guides our comprehension of neurodevelopment during adolescence by identifying critical subnetworks of functional connectivity. [ABSTRACT FROM AUTHOR]
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- 2024
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15. Topological state-space estimation of functional human brain networks.
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Chung, Moo K., Huang, Shih-Gu, Carroll, Ian C., Calhoun, Vince D., and Goldsmith, H. Hill
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LARGE-scale brain networks ,FUNCTIONAL magnetic resonance imaging ,K-means clustering - Abstract
We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the data without the need for explicit model specification. We further investigate the genetic underpinnings of these topological features using a twin study design, examining the heritability of such state changes. Our findings suggest that the topology of brain networks, particularly in their dynamic state changes, may hold significant hidden genetic information. Author summary: The paper introduces a new data-driven topological data analysis (TDA) method for studying dynamically changing human functional brain networks obtained from the resting-state functional magnetic resonance imaging (rs-fMRI). Leveraging persistent homology, a multiscale topological approach, we present a framework that incorporates the temporal dimension of brain network data. This allows for a more robust estimation of the topological features of dynamic brain networks. The method employs the Wasserstein distance to measure the topological differences between networks and demonstrates greater efficiency and performance than the commonly used k-means clustering in defining the state spaces of dynamic brain networks. Our method maintains robust performance across different scales and is especially suited for dynamic brain networks. In addition to the methodological advancement, the paper applies the proposed technique to analyze the heritability of overall brain network topology using a twin study design. The study investigates whether the dynamic pattern of brain networks is a genetically influenced trait, an area previously underexplored. By examining the state change patterns in twin brain networks, we make significant strides in understanding the genetic factors underlying dynamic brain network features. Furthermore, the paper makes its method accessible by providing MATLAB codes, contributing to reproducibility and broader application. [ABSTRACT FROM AUTHOR]
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- 2024
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16. 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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17. Cross‐cohort replicable resting‐state functional connectivity in predicting symptoms and cognition of schizophrenia.
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Zhao, Chunzhi, Jiang, Rongtao, Bustillo, Juan, Kochunov, Peter, Turner, Jessica A., Liang, Chuang, Fu, Zening, Zhang, Daoqiang, Qi, Shile, and Calhoun, Vince D.
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FUNCTIONAL connectivity ,FUNCTIONAL magnetic resonance imaging ,LARGE-scale brain networks ,COGNITION ,COGNITION disorders - Abstract
Schizophrenia (SZ) is a debilitating mental illness characterized by adolescence or early adulthood onset of psychosis, positive and negative symptoms, as well as cognitive impairments. Despite a plethora of studies leveraging functional connectivity (FC) from functional magnetic resonance imaging (fMRI) to predict symptoms and cognitive impairments of SZ, the findings have exhibited great heterogeneity. We aimed to identify congruous and replicable connectivity patterns capable of predicting positive and negative symptoms as well as cognitive impairments in SZ. Predictable functional connections (FCs) were identified by employing an individualized prediction model, whose replicability was further evaluated across three independent cohorts (BSNIP, SZ = 174; COBRE, SZ = 100; FBIRN, SZ = 161). Across cohorts, we observed that altered FCs in frontal‐temporal‐cingulate‐thalamic network were replicable in prediction of positive symptoms, while sensorimotor network was predictive of negative symptoms. Temporal‐parahippocampal network was consistently identified to be associated with reduced cognitive function. These replicable 23 FCs effectively distinguished SZ from healthy controls (HC) across three cohorts (82.7%, 90.2%, and 86.1%). Furthermore, models built using these replicable FCs showed comparable accuracies to those built using the whole‐brain features in predicting symptoms/cognition of SZ across the three cohorts (r =.17–.33, p <.05). Overall, our findings provide new insights into the neural underpinnings of SZ symptoms/cognition and offer potential targets for further research and possible clinical interventions. [ABSTRACT FROM AUTHOR]
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- 2024
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18. Striatum- and Cerebellum-Modulated Epileptic Networks Varying Across States with and without Interictal Epileptic Discharges.
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Jiang, Sisi, Pei, Haonan, Chen, Junxia, Li, Hechun, Liu, Zetao, Wang, Yuehan, Gong, Jinnan, Wang, Sheng, Li, Qifu, Duan, Mingjun, Calhoun, Vince D., Yao, Dezhong, and Luo, Cheng
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THALAMOCORTICAL system ,FUNCTIONAL magnetic resonance imaging ,DEFAULT mode network ,PEOPLE with epilepsy ,SALIENCE network ,SENSORIMOTOR cortex ,THALAMIC nuclei - Abstract
Idiopathic generalized epilepsy (IGE) is characterized by cryptogenic etiology and the striatum and cerebellum are recognized as modulators of epileptic network. We collected simultaneous electroencephalogram and functional magnetic resonance imaging data from 145 patients with IGE, 34 of whom recorded interictal epileptic discharges (IEDs) during scanning. In states without IEDs, hierarchical connectivity was performed to search core cortical regions which might be potentially modulated by striatum and cerebellum. Node–node and edge–edge moderation models were constructed to depict direct and indirect moderation effects in states with and without IEDs. Patients showed increased hierarchical connectivity with sensorimotor cortices (SMC) and decreased connectivity with regions in the default mode network (DMN). In the state without IEDs, striatum, cerebellum, and thalamus were linked to weaken the interactions of regions in the salience network (SN) with DMN and SMC. In periods with IEDs, overall increased moderation effects on the interaction between regions in SN and DMN, and between regions in DMN and SMC were observed. The thalamus and striatum were implicated in weakening interactions between regions in SN and SMC. The striatum and cerebellum moderated the cortical interaction among DMN, SN, and SMC in alliance with the thalamus, contributing to the dysfunction in states with and without IEDs in IGE. The current work revealed state-specific modulation effects of striatum and cerebellum on thalamocortical circuits and uncovered the potential core cortical targets which might contribute to develop new clinical neuromodulation techniques. [ABSTRACT FROM AUTHOR]
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- 2024
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19. Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia.
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Ellis, Charles A., Miller, Robyn L., and Calhoun, Vince D.
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DEFAULT mode network ,FUNCTIONAL magnetic resonance imaging ,K-means clustering ,CINGULATE cortex ,SCHIZOPHRENIA - Abstract
Introduction: Dynamic functional network connectivity (dFNC) analysis of resting state functional magnetic resonance imaging data has yielded insights into many neurological and neuropsychiatric disorders. A common dFNC analysis approach uses hard clustering methods like k-means clustering to assign samples to states that summarize network dynamics. However, hard clustering methods obscure network dynamics by assuming (1) that all samples within a cluster are equally like their assigned centroids and (2) that samples closer to one another in the data space than to their centroids are wellrepresented by their centroids. In addition, it can be hard to compare subjects, as in some cases an individual may not manifest a state strongly enough to enter a hard cluster. Approaches that allow a dimensional approach to connectivity patterns (e.g., fuzzy clustering) can mitigate these issues. In this study, we present an explainable fuzzy clustering framework by combining fuzzy c-means clustering with several explainability metrics and novel summary features. Methods: We apply our framework for schizophrenia (SZ) default mode network analysis. Namely, we extract dFNC from individuals with SZ and controls, identify 5 dFNC states, and characterize the dFNC features most crucial to those states with a new perturbation-based clustering explainability approach. We then extract several features typically used in hard clustering and further present a variety of unique features specially designed for use with fuzzy clustering to quantify state dynamics. We examine differences in those features between individuals with SZ and controls and further search for relationships between those features and SZ symptom severity. Results: Importantly, we find that individuals with SZ spend more time in states of moderate anticorrelation between the anterior and posterior cingulate cortices and strong anticorrelation between the precuneus and anterior cingulate cortex. We further find that individuals with SZ tend to transition more rapidly than controls between low-magnitude and high-magnitude dFNC states. Conclusion: We present a novel dFNC analysis framework and use it to identify effects of SZ upon network dynamics. Given the ease of implementing our framework and its enhanced insight into network dynamics, it has great potential for use in future dFNC studies. [ABSTRACT FROM AUTHOR]
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- 2024
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20. 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
21. Neural Correlates of Schizophrenia Negative Symptoms: Distinct Subtypes Impact Dissociable Brain Circuits.
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Shaffer, Joseph J, Peterson, Michael J, McMahon, Mary Agnes, Bizzell, Joshua, Calhoun, Vince, van Erp, Theo GM, Ford, Judith M, Lauriello, John, Lim, Kelvin O, Manoach, Dara S, McEwen, Sarah C, Mathalon, Daniel H, O'Leary, Daniel, Potkin, Steven G, Preda, Adrian, Turner, Jessica, Voyvodic, Jim, Wible, Cynthia G, and Belger, Aysenil
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Auditory oddball ,Functional Biomedical Informatics Research Network ,Functional magnetic resonance imaging ,Negative symptoms ,Schizophrenia ,Brain Disorders ,Behavioral and Social Science ,Clinical Research ,Neurosciences ,Mental Health ,Serious Mental Illness ,Underpinning research ,Aetiology ,2.1 Biological and endogenous factors ,1.1 Normal biological development and functioning ,Neurological ,Mental health - Abstract
BackgroundThe negative symptoms of schizophrenia include deficits in emotional expression and motivation. These deficits are stable over the course of illness and respond poorly to current medications. Previous studies have focused on negative symptoms as a single category; however, individual symptoms might be related to separate neurological disturbances. We analyzed data from the Functional Biomedical Informatics Research Network dataset to explore the relationship between individual negative symptoms and functional brain activity during an auditory oddball task.MethodsFunctional magnetic resonance imaging was conducted on 89 schizophrenia patients and 106 healthy controls during a two-tone auditory oddball task. Blood oxygenation level-dependent (BOLD) signal during the target tone was correlated with severity of five negative symptom domains from the Scale for the Assessment of Negative Symptoms.ResultsThe severity of alogia, avolition/apathy and anhedonia/asociality was negatively correlated with BOLD activity in distinct sets of brain regions associated with processing of the target tone, including basal ganglia, thalamus, insular cortex, prefrontal cortex, posterior cingulate and parietal cortex.ConclusionsIndividual symptoms were related to different patterns of functional activation during the oddball task, suggesting that individual symptoms might arise from distinct neural mechanisms. This work has potential to inform interventions that target these symptom-related neural disruptions.
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- 2015
22. Adolescent Psychopathic Traits Negatively Relate to Hemodynamic Activity within the Basal Ganglia during Error-Related Processing
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Maurer, J. Michael, Steele, Vaughn R., Vincent, Gina M., Rao, Vikram, Calhoun, Vince D., and Kiehl, Kent A.
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- 2019
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23. Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics
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Ashkan Faghiri, Eswar Damaraju, Aysenil Belger, Judith M. Ford, Daniel Mathalon, Sarah McEwen, Bryon Mueller, Godfrey Pearlson, Adrian Preda, Jessica A. Turner, Jatin G. Vaidya, Theodorus Van Erp, and Vince D. Calhoun
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functional magnetic resonance imaging ,brain dynamics ,independent component analyses ,resting state– fMRI ,density clustering ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
BackgroundA number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time.MethodsWe introduce a new approach that analyzes time series trajectories to identify high traffic nodes in a high dimensional space. First, functional magnetic resonance imaging (fMRI) data are decomposed using spatial ICA to a set of maps and their associated time series. Next, density is calculated for each time point and high-density points are clustered to identify a small set of high traffic nodes. We validated our method using simulations and then implemented it on a real data set.ResultsWe present a novel approach that captures dynamics within a high dimensional space and also does not use any windowing in contrast to many existing approaches. The approach enables one to characterize and study the time series in a potentially high dimensional space, rather than looking at each component pair separately. Our results show that schizophrenia patients have a lower dynamism compared to healthy controls. In addition, we find patients spend more time in nodes associated with the default mode network and less time in components strongly correlated with auditory and sensorimotor regions. Interestingly, we also found that subjects oscillate between state pairs that show opposite spatial maps, suggesting an oscillatory pattern.ConclusionOur proposed method provides a novel approach to analyze the data in its native high dimensional space and can possibly provide new information that is undetectable using other methods.
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- 2021
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24. 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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25. 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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26. 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
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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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27. Structure/function interrelationships and illness insight in patients with schizophrenia: a multimodal MRI data fusion study.
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Otte, Marie-Luise, Schmitgen, Mike M., Wolf, Nadine D., Kubera, Katharina M., Calhoun, Vince D., Fritze, Stefan, Geiger, Lena S., Tost, Heike, Seidl, Ulrich W., Meyer-Lindenberg, Andreas, Hirjak, Dusan, and Wolf, Robert Christian
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FUNCTIONAL magnetic resonance imaging ,TEMPORAL lobe ,MULTISENSOR data fusion ,PARIETAL lobe ,CINGULATE cortex ,SCHIZOAFFECTIVE disorders - Abstract
Illness insight in schizophrenia (SZ) has an important impact on treatment outcome, integration into society and can vary over the course of the disorder. To deal with and treat reduced or absent illness insight, we need to better understand its functional and structural correlates. Previous studies showed regionally abnormal brain volume in brain areas related to cognitive control and self-reference. However, little is known about associations between illness insight and structural and functional network strength in patients with SZ. This study employed a cross-sectional design to examine structural and functional differences between patients with SZ (n = 74) and healthy controls (n = 47) using structural and resting-state functional magnetic resonance imaging (MRI). Voxel-based morphometry was performed on structural data, and the amplitude of low frequency fluctuations (ALFF) was calculated for functional data. To investigate abnormal structure/function interrelationships and their association with illness insight, we used parallel independent component analysis (pICA). Significant group (SZ vs. HC) differences were detected in distinct structural and functional networks, predominantly comprising frontoparietal, temporal and cerebellar regions. Significant associations were found between illness insight and two distinct structural networks comprising frontoparietal (pre- and postcentral gyrus, inferior parietal lobule, thalamus, and precuneus) and posterior cortical regions (cuneus, precuneus, lingual, posterior cingulate, and middle occipital gyrus). Finally, we found a significant relationship between illness insight and functional network comprising temporal regions (superior temporal gyrus). This study suggests that aberrant structural and functional integrity of neural systems subserving cognitive control, memory and self-reference are tightly coupled to illness insight in SZ. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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28. 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]
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- 2023
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29. A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis
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Adali, Mingyu Sun, Ben Gabrielson, Mohammad Abu Baker Siddique Akhonda, Hanlu Yang, Francisco Laport, Vince Calhoun, and Tülay
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independent vector analysis ,MCCA ,JBSS ,subspace analysis ,multi-subject medical imaging data ,functional magnetic resonance imaging - 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.
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- 2023
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30. Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies
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Glover, Gary H, Mueller, Bryon A, Turner, Jessica A, van Erp, Theo GM, Liu, Thomas T, Greve, Douglas N, Voyvodic, James T, Rasmussen, Jerod, Brown, Gregory G, Keator, David B, Calhoun, Vince D, Lee, Hyo Jong, Ford, Judith M, Mathalon, Daniel H, Diaz, Michele, O'Leary, Daniel S, Gadde, Syam, Preda, Adrian, Lim, Kelvin O, Wible, Cynthia G, Stern, Hal S, Belger, Aysenil, McCarthy, Gregory, Ozyurt, Burak, and Potkin, Steven G
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Biomedical and Clinical Sciences ,Clinical Sciences ,Neurosciences ,Clinical Research ,Biomedical Imaging ,Biomedical Research ,Community Networks ,Databases ,Factual ,Humans ,Information Storage and Retrieval ,Magnetic Resonance Imaging ,Medical Informatics ,Prospective Studies ,Radiology Information Systems ,United States ,functional magnetic resonance imaging ,fMRI ,multicenter ,multisite ,FIRST Biomedical Informatics Research Network ,FBIRN ,Physical Sciences ,Engineering ,Medical and Health Sciences ,Nuclear Medicine & Medical Imaging ,Clinical sciences - Abstract
This report provides practical recommendations for the design and execution of multicenter functional MRI (MC-fMRI) studies based on the collective experience of the Function Biomedical Informatics Research Network (FBIRN). The study was inspired by many requests from the fMRI community to FBIRN group members for advice on how to conduct MC-fMRI studies. The introduction briefly discusses the advantages and complexities of MC-fMRI studies. Prerequisites for MC-fMRI studies are addressed before delving into the practical aspects of carefully and efficiently setting up a MC-fMRI study. Practical multisite aspects include: (i) establishing and verifying scan parameters including scanner types and magnetic fields, (ii) establishing and monitoring of a scanner quality program, (iii) developing task paradigms and scan session documentation, (iv) establishing clinical and scanner training to ensure consistency over time, (v) developing means for uploading, storing, and monitoring of imaging and other data, (vi) the use of a traveling fMRI expert, and (vii) collectively analyzing imaging data and disseminating results. We conclude that when MC-fMRI studies are organized well with careful attention to unification of hardware, software and procedural aspects, the process can be a highly effective means for accessing a desired participant demographics while accelerating scientific discovery.
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- 2012
31. Independent vector analysis for common subspace analysis: Application to multi-subject fMRI data yields meaningful subgroups of schizophrenia
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Qunfang Long, Suchita Bhinge, Vince D. Calhoun, and Tülay Adali
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Independent vector analysis ,Subspace analysis ,Multi-subject medical imaging data ,Functional magnetic resonance imaging ,Heterogeneity of schizophrenia ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
The extraction of common and distinct biomedical signatures among different populations allows for a more detailed study of the group-specific as well as distinct information of different populations. A number of subspace analysis algorithms have been developed and successfully applied to data fusion, however they are limited to joint analysis of only a couple of datasets. Since subspace analysis is very promising for analysis of multi-subject medical imaging data as well, we focus on this problem and propose a new method based on independent vector analysis (IVA) for common subspace extraction (IVA-CS) for multi-subject data analysis. IVA-CS leverages the strength of IVA in identification of a complete subspace structure across multiple datasets along with an efficient solution that uses only second-order statistics. We propose a subset analysis approach within IVA-CS to mitigate issues in estimation in IVA due to high dimensionality, both in terms of components estimated and the number of datasets. We introduce a scheme to determine a desirable size for the subset that is high enough to exploit the dependence across datasets and is not affected by the high dimensionality issue. We demonstrate the success of IVA-CS in extracting complex subset structures and apply the method to analysis of functional magnetic resonance imaging data from 179 subjects and show that it successfully identifies shared and complementary brain patterns from patients with schizophrenia (SZ) and healthy controls group. Two components with linked resting-state networks are identified to be unique to the SZ group providing evidence of functional dysconnectivity. IVA-CS also identifies subgroups of SZs that show significant differences in terms of their brain networks and clinical symptoms.
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- 2020
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32. Static and dynamic functional connectivity analysis of cerebrovascular reactivity: An fMRI study
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Noah Lewis, Hanzhang Lu, Peiying Liu, Xirui Hou, Eswar Damaraju, Armin Iraji, and Vince Calhoun
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cerebrovascular reactivity ,functional magnetic resonance imaging ,functional network connectivity ,neuroimaging ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Abstract Background Cerebrovascular reactivity (CVR) is an important aspect of brain function, and as such it is important to understand relationship between CVR and functional connectivity. Methods This research studied the role of CVR, or the brain's ability to react to vasoactive stimuli on brain functional connectivity by scanning subjects with blood‐oxygenation‐level‐dependent (BOLD) functional magnetic resonance imaging (fMRI) while they periodically inhale room air and a CO 2‐enriched gas mixture. We developed a new metric to measure the effect of CVR on each intrinsic connectivity network (ICN), which contrasts to voxel‐wise CVR. We also studied the changes in whole‐brain connectivity patterns using both static functional network connectivity (sFNC) and dynamic FNC (dFNC). Results We found that network connectivity is generally weaker during vascular dilation, which is supported by previous research. The dFNC analysis revealed that participants did not return to the pre‐CO 2 inhalation state, suggesting that one‐minute periods of room‐air inhalation is not enough for the CO 2 effect to fully dissipate. Conclusions Cerebrovascular reactivity is one tool that the cerebrovascular system uses to ensure the constant, finely‐tuned flow of oxygen to function properly. Understanding the relationship between CVR and brain dynamism can provide unique information about cerebrovascular diseases and general brain function. We observed that CVR has a wide, but consistent relationship to connectivity patterns between functional networks.
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- 2020
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33. 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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34. Multimodal fusion of multiple rest fMRI networks and MRI gray matter via parallel multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease.
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Khalilullah, K. M. Ibrahim, Agcaoglu, Oktay, Sui, Jing, Adali, Tülay, Duda, Marlena, and Calhoun, Vince D.
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GRAY matter (Nerve tissue) ,ALZHEIMER'S disease ,FUNCTIONAL magnetic resonance imaging ,DEFAULT mode network ,MAGNETIC resonance imaging - Abstract
In this article, we focus on estimating the joint relationship between structural magnetic resonance imaging (sMRI) gray matter (GM), and multiple functional MRI (fMRI) intrinsic connectivity networks (ICNs). To achieve this, we propose a multilink joint independent component analysis (ml‐jICA) method using the same core algorithm as jICA. To relax the jICA assumption, we propose another extension called parallel multilink jICA (pml‐jICA) that allows for a more balanced weight distribution over ml‐jICA/jICA. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimer's disease (AD) versus controls. The results of the pml‐jICA yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for AD versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to GM components within a multimodal data fusion model and challenges the typical view that brain structure is more sensitive to AD than fMRI. [ABSTRACT FROM AUTHOR]
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- 2023
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35. An Approach to Automatically Label and Order Brain Activity/Component Maps
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Mustafa Salman, Tor D. Wager, Zening Fu, Eswar Damaraju, Victor M. Vergara, Vince D. Calhoun, and Anees Abrol
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Brain activity and meditation ,Computer science ,Neuroimaging ,050105 experimental psychology ,03 medical and health sciences ,0302 clinical medicine ,Component (UML) ,medicine ,Humans ,0501 psychology and cognitive sciences ,Brain function ,Brain Mapping ,medicine.diagnostic_test ,General Neuroscience ,Functional connectivity ,05 social sciences ,Brain ,Original Articles ,Magnetic Resonance Imaging ,Order (biology) ,Anatomical atlas ,Artifacts ,Functional magnetic resonance imaging ,Neuroscience ,030217 neurology & neurosurgery - Abstract
Background: Functional magnetic resonance imaging (fMRI) is a brain imaging technique that provides detailed insights into brain function and its disruption in various brain disorders. The data-driven analysis of fMRI brain activity maps involves several postprocessing steps, the first of which is identifying whether the estimated brain network maps capture signals of interest, for example, intrinsic connectivity networks (ICNs), or artifacts. This is followed by linking the ICNs to standardized anatomical and functional parcellations. Optionally, as in the study of functional network connectivity (FNC), rearranging the connectivity graph is also necessary to facilitate interpretation. Methods: Here we develop a novel and efficient method (Autolabeler) for implementing and integrating all of these processes in a fully automated manner. The Autolabeler method is pretrained on a cross-validated elastic-net regularized general linear model from the noisecloud toolbox to separate neuroscientifically meaningful ICNs from artifacts. It is capable of automatically labeling activity maps with labels from several well-known anatomical and functional parcellations. Subsequently, this method also maximizes the modularity within functional domains to generate a more systematically structured FNC matrix for post hoc network analyses. Results: Results show that our pretrained model achieves 86% accuracy at classifying ICNs from artifacts in an independent validation data set. The automatic anatomical and functional labels also have a high degree of similarity with manual labels selected by human raters. Discussion: At a time of ever-increasing rates of generating brain imaging data and analyzing brain activity, the proposed Autolabeler method is intended to automate such analyses for faster and more reproducible research. IMPACT STATEMENT: Our proposed method is capable of implementing and integrating some of the crucial tasks in functional magnetic resonance imaging (fMRI) studies. It is the first to incorporate such tasks without the need for expert intervention. We develop an open-source toolbox for the proposed method that can function as stand-alone software and additionally provides seamless integration with the widely used group independent component analysis for fMRI toolbox (GIFT). This integration can aid investigators to conduct fMRI studies in an end-to-end automated manner.
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- 2022
36. 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]
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- 2023
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37. A deep learning based approach identifies regions more relevant than resting‐state networks to the prediction of general intelligence from resting‐state fMRI
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Vince D. Calhoun, Julien Dubois, Bruno Hebling Vieira, and Carlos Ernesto Garrido Salmon
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Computer science ,Feature extraction ,brain‐behavior ,Intelligence ,Machine learning ,computer.software_genre ,Neuroimaging ,medicine ,Connectome ,REDE NERVOSA ,Humans ,Radiology, Nuclear Medicine and imaging ,Set (psychology) ,Research Articles ,Human Connectome Project ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,Resting state fMRI ,business.industry ,Deep learning ,fMRI ,deep learning ,Brain ,Magnetic Resonance Imaging ,Recurrent neural network ,Neurology ,resting‐state ,Neurology (clinical) ,Artificial intelligence ,Anatomy ,Nerve Net ,Functional magnetic resonance imaging ,business ,computer ,Research Article - Abstract
Prediction of cognitive ability latent factors such as general intelligence from neuroimaging has elucidated questions pertaining to their neural origins. However, predicting general intelligence from functional connectivity limit hypotheses to that specific domain, being agnostic to time‐distributed features and dynamics. We used an ensemble of recurrent neural networks to circumvent this limitation, bypassing feature extraction, to predict general intelligence from resting‐state functional magnetic resonance imaging regional signals of a large sample (n = 873) of Human Connectome Project adult subjects. Ablating common resting‐state networks (RSNs) and measuring degradation in performance, we show that model reliance can be mostly explained by network size. Using our approach based on the temporal variance of saliencies, that is, gradients of outputs with regards to inputs, we identify a candidate set of networks that more reliably affect performance in the prediction of general intelligence than similarly sized RSNs. Our approach allows us to further test the effect of local alterations on data and the expected changes in derived metrics such as functional connectivity and instantaneous innovations., We predict general intelligence from RS‐fMRI timeseries using a recurrent neural network ensemble in Human Connectome Project data. We propose the selection of networks based on the variance of saliencies per ROI. Resting‐state networks (RSNs) impact on prediction can be explained by their size while with our strategy we find salient networks whose importance exceed that of RSNs.
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- 2021
38. 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]
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- 2023
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39. 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.
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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]
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- 2023
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40. Developmental and aging resting functional magnetic resonance imaging brain state adaptations in adolescents and adults: A large N (>47K) study.
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Abrol, Anees, Fu, Zening, Du, Yuhui, Wilson, Tony W., Wang, Yu‐Ping, Stephen, Julia M., and Calhoun, Vince D.
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FUNCTIONAL magnetic resonance imaging ,BRAIN imaging ,AGING ,FUNCTIONAL connectivity ,TEENAGERS - Abstract
The brain's functional architecture and organization undergo continual development and modification throughout adolescence. While it is well known that multiple factors govern brain maturation, the constantly evolving patterns of time‐resolved functional connectivity are still unclear and understudied. We systematically evaluated over 47,000 youth and adult brains to bridge this gap, highlighting replicable time‐resolved developmental and aging functional brain patterns. The largest difference between the two life stages was captured in a brain state that indicated coherent strengthening and modularization of functional coupling within the auditory, visual, and motor subdomains, supplemented by anticorrelation with other subdomains in adults. This distinctive pattern, which we replicated in independent data, was consistently less modular or absent in children and presented a negative association with age in adults, thus indicating an overall inverted U‐shaped trajectory. This indicates greater synchrony, strengthening, modularization, and integration of the brain's functional connections beyond adolescence, and gradual decline of this pattern during the healthy aging process. We also found evidence that the developmental changes may also bring along a departure from the canonical static functional connectivity pattern in favor of more efficient and modularized utilization of the vast brain interconnections. State‐based statistical summary measures presented robust and significant group differences that also showed significant age‐related associations. The findings reported in this article support the idea of gradual developmental and aging brain state adaptation processes in different phases of life and warrant future research via lifespan studies to further authenticate the projected time‐resolved brain state trajectories. [ABSTRACT FROM AUTHOR]
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- 2023
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41. Identification of Homogeneous Subgroups from Resting-State fMRI Data.
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Yang, Hanlu, Vu, Trung, Long, Qunfang, Calhoun, Vince, and Adali, Tülay
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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
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42. Joint connectivity matrix independent component analysis: Auto‐linking of structural and functional connectivities.
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Wu, Lei and Calhoun, Vince
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- *
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]
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- 2023
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43. Building Models of Functional Interactions Among Brain Domains that Encode Varying Information Complexity: A Schizophrenia Case Study
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Zening Fu, Vince D. Calhoun, Anees Abrol, Ishaan Batta, Theo G.M. van Erp, and Adrian Preda
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Computer science ,Schizophrenia (object-oriented programming) ,Variation (game tree) ,ENCODE ,Machine learning ,computer.software_genre ,Article ,Functional connectivity ,Neuroimaging ,Clinical Research ,medicine ,Multilayer perceptron ,Humans ,Hyperparameter optimization ,Bayesian optimization ,Brain Mapping ,Neurology & Neurosurgery ,medicine.diagnostic_test ,business.industry ,General Neuroscience ,fMRI ,Neurosciences ,Brain ,Bayes Theorem ,medicine.disease ,Magnetic Resonance Imaging ,Brain Disorders ,Tree (data structure) ,Identification (information) ,Subdomain analysis ,Mental Health ,Schizophrenia ,Neurological ,Artificial intelligence ,Biochemistry and Cell Biology ,Functional magnetic resonance imaging ,business ,computer ,Software ,Information Systems - Abstract
Revealing associations among various structural and functional patterns of the brain can yield highly informative results about the healthy and disordered brain. Studies using neuroimaging data have more recently begun to utilize the information within as well as across various functional and anatomical domains (i.e., groups of brain networks). However, most whole-brain approaches assume similar complexity of interactions throughout the brain. Here we investigate the hypothesis that interactions between brain networks capture varying amounts of complexity, and that we can better capture this information by varying the complexity of the model subspace structure based on available training data. To do this, we employ a Bayesian optimization-based framework known as the Tree Parzen Estimator (TPE) to identify, exploit and analyze patterns of variation in the information encoded by temporal information extracted from functional magnetic resonance imaging (fMRI) subdomains of the brain. Using a repeated cross-validation procedure on a schizophrenia classification task, we demonstrate evidence that interactions between specific functional subdomains are better characterized by more sophisticated model architectures compared to less complicated ones required by the others for optimally contributing towards classification and understanding the brain’s functional interactions. We show that functional subdomains known to be involved in schizophrenia require more complex architectures to optimally unravel discriminatory information about the disorder. Our study points to the need for adaptive, hierarchical learning frameworks that cater differently to the features from different subdomains, not only for a better prediction but also for enabling the identification of features predicting the outcome of interest.
- Published
- 2022
44. Causality-Based Feature Fusion for Brain Neuro-Developmental Analysis
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Gemeng Zhang, Julia M. Stephen, Li Xiao, Yu-Ping Wang, Peyman Hosseinzadeh Kassani, Vince D. Calhoun, and Tony W. Wilson
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FOS: Computer and information sciences ,Computer Science - Artificial Intelligence ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Context (language use) ,Biology ,Brain mapping ,Article ,030218 nuclear medicine & medical imaging ,Correlation ,03 medical and health sciences ,0302 clinical medicine ,Granger causality ,Feature (machine learning) ,medicine ,Humans ,Electrical and Electronic Engineering ,Child ,10. No inequality ,Brain Mapping ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,Brain ,Human brain ,Magnetic Resonance Imaging ,Causality ,Computer Science Applications ,Artificial Intelligence (cs.AI) ,medicine.anatomical_structure ,FOS: Biological sciences ,Quantitative Biology - Neurons and Cognition ,Neurons and Cognition (q-bio.NC) ,Neural Networks, Computer ,Functional magnetic resonance imaging ,Neuroscience ,Algorithms ,Software - Abstract
Human brain development is a complex and dynamic process that is affected by several factors such as genetics, sex hormones, and environmental changes. A number of recent studies on brain development have examined functional connectivity (FC) defined by the temporal correlation between time series of different brain regions. We propose to add the directional flow of information during brain maturation. To do so, we extract effective connectivity (EC) through Granger causality (GC) for two different groups of subjects, i.e., children and young adults. The motivation is that the inclusion of causal interaction may further discriminate brain connections between two age groups and help to discover new connections between brain regions. The contributions of this study are threefold. First, there has been a lack of attention to EC-based feature extraction in the context of brain development. To this end, we propose a new kernel-based GC (KGC) method to learn nonlinearity of complex brain network, where a reduced Sine hyperbolic polynomial (RSP) neural network was used as our proposed learner. Second, we used causality values as the weight for the directional connectivity between brain regions. Our findings indicated that the strength of connections was significantly higher in young adults relative to children. In addition, our new EC-based feature outperformed FC-based analysis from Philadelphia neurocohort (PNC) study with better discrimination of the different age groups. Moreover, the fusion of these two sets of features (FC + EC) improved brain age prediction accuracy by more than 4%, indicating that they should be used together for brain development studies., 10 pages
- Published
- 2020
45. A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD
- Author
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Vince D. Calhoun, Boris Duka, Desmond J. Oathes, Kanhao Zhao, Yu Zhang, and Hua Xie
- Subjects
Adult ,Computer science ,Brain networks ,Cognitive Neuroscience ,Neurosciences. Biological psychiatry. Neuropsychiatry ,Convolutional neural network ,Attention deficit hyperactivity disorder ,Young Adult ,Neuroimaging ,Connectome ,medicine ,Humans ,Resting-state fMRI ,Cerebral Cortex ,Resting state fMRI ,medicine.diagnostic_test ,business.industry ,Deep learning ,Magnetic Resonance Imaging ,Neurology ,Frontal lobe ,Attention Deficit Disorder with Hyperactivity ,Precision diagnosis ,Graph (abstract data type) ,Neural Networks, Computer ,Artificial intelligence ,Nerve Net ,Graph convolutional networks ,business ,Functional magnetic resonance imaging ,Neuroscience ,RC321-571 - Abstract
The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for studying the brain functional connectome. Most existing methods that have either ignored or simply utilized graph structure, do not fully leverage the potentially important topological information which may be useful in characterizing brain disorders. There is a crucial need for designing novel and efficient approaches which can capture such information. To this end, we propose a new dynamic graph convolutional network (dGCN), which is trained with sparse brain regional connections from dynamically calculated graph features. We also develop a novel convolutional readout layer to improve graph representation. Our extensive experimental analysis demonstrates significantly improved performance of dGCN for ADHD diagnosis compared with existing machine learning and deep learning methods. Visualizations of the salient regions of interest (ROIs) and connectivity based on informative features learned by our model show that the identified functional abnormalities mainly involve brain regions in temporal pole, gyrus rectus, and cerebellar gyri from temporal lobe, frontal lobe, and cerebellum, respectively. A positive correlation was further observed between the identified connectomic abnormalities and ADHD symptom severity. The proposed dGCN model shows great promise in providing a functional network-based precision diagnosis of ADHD and is also broadly applicable to brain connectome-based study of mental disorders.
- Published
- 2022
46. Low-Rank Tucker-2 Model for Multi-Subject fMRI Data Decomposition with Spatial Sparsity Constraint
- Author
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Li-Dan Kuang, Qiu-Hua Lin, Vince D. Calhoun, Yu-Ping Wang, Fengyu Cong, Yue Han, and Xiao-Feng Gong
- Subjects
Rank (linear algebra) ,Computer science ,Matrix norm ,low-rank ,matrix decomposition ,symbols.namesake ,toiminnallinen magneettikuvaus ,Orthogonality ,tensors ,Tensor (intrinsic definition) ,Kronecker delta ,Tucker decomposition ,Humans ,Electrical and Electronic Engineering ,core tensor ,sparsity constraint ,Radiological and Ultrasound Technology ,business.industry ,signaalinkäsittely ,feature extraction ,sparse matrices ,Brain ,Pattern recognition ,brain modeling ,Magnetic Resonance Imaging ,functional magnetic resonance imaging ,Computer Science Applications ,Constraint (information theory) ,data models ,symbols ,Noise (video) ,Artificial intelligence ,business ,multi-subject fMRI data ,Software ,Algorithms - Abstract
Tucker decomposition can provide an intuitive summary to understand brain function by decomposing multi-subject fMRI data into a core tensor and multiple factor matrices, and was mostly used to extract functional connectivity patterns across time/subjects using orthogonality constraints. However, these algorithms are unsuitable for extracting common spatial and temporal patterns across subjects due to distinct characteristics such as high-level noise. Motivated by a successful application of Tucker decomposition to image denoising and the intrinsic sparsity of spatial activations in fMRI, we propose a low-rank Tucker-2 model with spatial sparsity constraint to analyze multi-subject fMRI data. More precisely, we propose to impose a sparsity constraint on spatial maps by using an lp norm (0
- Published
- 2022
47. A deep learning model for data-driven discovery of functional connectivity
- Author
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Usman Mahmood, Vince D. Calhoun, Sergey M. Plis, and Zening Fu
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,lcsh:T55.4-60.8 ,Computer science ,Schizophrenia (object-oriented programming) ,050105 experimental psychology ,lcsh:QA75.5-76.95 ,Theoretical Computer Science ,Data-driven ,Machine Learning (cs.LG) ,03 medical and health sciences ,0302 clinical medicine ,medicine ,lcsh:Industrial engineering. Management engineering ,0501 psychology and cognitive sciences ,Structure (mathematical logic) ,Numerical Analysis ,Artificial neural network ,medicine.diagnostic_test ,Quantitative Biology::Neurons and Cognition ,business.industry ,Covariance matrix ,Deep learning ,05 social sciences ,functional connectivity ,deep learning ,Pattern recognition ,Class discrimination ,schizophrenia ,Computational Mathematics ,Computational Theory and Mathematics ,FOS: Biological sciences ,Quantitative Biology - Neurons and Cognition ,Neurons and Cognition (q-bio.NC) ,Artificial intelligence ,lcsh:Electronic computers. Computer science ,business ,Functional magnetic resonance imaging ,030217 neurology & neurosurgery - Abstract
Functional connectivity (FC) studies have demonstrated the overarching value of studying the brain and its disorders through the undirected weighted graph of fMRI correlation matrix. Most of the work with the FC, however, depends on the way the connectivity is computed, and further depends on the manual post-hoc analysis of the FC matrices. In this work we propose a deep learning architecture BrainGNN that learns the connectivity structure as part of learning to classify subjects. It simultaneously applies a graphical neural network to this learned graph and learns to select a sparse subset of brain regions important to the prediction task. We demonstrate the model's state-of-the-art classification performance on a schizophrenia fMRI dataset and demonstrate how introspection leads to disorder relevant findings. The graphs learned by the model exhibit strong class discrimination and the sparse subset of relevant regions are consistent with the schizophrenia literature., Accepted at Algorithms 2021, 14(3), 75
- Published
- 2021
48. An fMRI examination of the neural basis of suicide attempts: The role of mentalizing in the context of mood.
- Author
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Malhi, Gin S., Das, Pritha, Outhred, Tim, Bryant, Richard A., and Calhoun, Vince D.
- Subjects
ATTEMPTED suicide ,EMOTION recognition ,DEFAULT mode network ,LARGE-scale brain networks ,FUNCTIONAL magnetic resonance imaging ,SUICIDAL behavior in youth - Abstract
Objectives: Facial emotion recognition (FER) deficits in depressed mood disorder patients contribute to suicidality. Prior research shows that intrinsic brain activity patterns are altered by attempting suicide. Therefore, we investigated in depressed patients whether differences in FER contribute to their clinical symptoms of suicide. Methods: Neural activity in response to an FER task was compared across three groups: healthy controls (HCs, N = 66), suicide non‐attempter (SNA, N = 50), suicide attempter (SA, N = 25). Modulation of brain networks by the task and functional connectivity (FC) within (using spatial map, spectral power) and between (using functional network connectivity; FNC) were examined. The contribution of these differences to suicidal symptoms in each group was also examined. Results: Patient groups displayed impaired FC both within and between networks but differed in nature and networks involved. They also showed differential modulation of networks by task, such that compared with both HC and SNA, SA displayed impaired FC within the default‐mode network (DMN) and also its task modulation. In the SA group, FC within the DMN and FNC between two lateral prefrontal networks, and its interaction with the basal ganglia network contributed significantly to the clinical symptoms of suicide. Conclusions: This study affirms differences between SA and SNA brain activity patterns and suggests that suicidal activity probably emanates via different mechanisms in these patient groups. Perhaps, over‐attribution of emotion impairs one's self‐referential thought processes and coupled with diminished emotional control this makes depressed individuals vulnerable to suicide. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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49. Functional MRI Evaluation of Multiple Neural Networks Underlying Auditory Verbal Hallucinations in Schizophrenia Spectrum Disorders
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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.
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- 2016
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50. Biomarker Identification Through Integrating fMRI and Epigenetics
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Yu-Ping Wang, Zille Pascal, Yuntong Bai, Vince D. Calhoun, and Wenxing Hu
- Subjects
medicine.diagnostic_test ,Mechanism (biology) ,Imaging genetics ,0206 medical engineering ,Biomedical Engineering ,02 engineering and technology ,Computational biology ,DNA Methylation ,Biology ,Magnetic Resonance Imaging ,020601 biomedical engineering ,Article ,Epigenesis, Genetic ,Neuroimaging ,CpG site ,DNA methylation ,Schizophrenia ,medicine ,Humans ,Epigenetics ,Functional magnetic resonance imaging ,Biomarkers ,Epigenesis - Abstract
Objective: Integration of multiple datasets is a hot topic in many fields. When studying complex mental disorders, great effort has been dedicated to fusing genetic and brain imaging data. However, an increasing number of studies have pointed out the importance of epigenetic factors in the cause of psychiatric diseases. In this study, we endeavor to fill the gap by combining epigenetics (e.g., DNA methylation) with imaging data (e.g., fMRI) to identify biomarkers for schizophrenia (SZ). Methods: We propose to combine linear regression with canonical correlation analysis (CCA) in a relaxed yet coupled manner to extract discriminative features for SZ that are co-expressed in the fMRI and DNA methylation data. Result: After validation through simulations, we applied our method to real imaging epigenetics data of 184 subjects from the Mental Illness and Neuroscience Discovery Clinical Imaging Consortium. After significance test, we identified 14 brain regions and 44 cytosine-phosphate-guanine(CpG) sites. Average classification accuracy is $\text{88.89}\%$ . By linking the CpG sites to genes, we identified pathways Guanosine ribonucleotides de novo biosynthesis and Guanosine nucleotides de novo biosynthesis, and a GO term Perikaryon. Conclusion: This imaging epigenetics study has identified both brain regions and genes that are associated with neuron development and memory processing. These biomarkers contribute to a good understanding of the mechanism underlying SZ but are overlooked by previous imaging genetics studies. Significance: Our study sheds light on the understanding and diagnosis of SZ with a imaging epigenetics approach, which is demonstrated to be effective in extracting novel biomarkers associated with SZ.
- Published
- 2020
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