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Multi-Modal Deep Learning on Imaging Genetics for Schizophrenia Classification

Multi-Modal Deep Learning on Imaging Genetics for Schizophre...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Ayush Kanyal Srinivas Kandula Vince Calhoun Dong Hye Ye Department of Computer Science Georgia State University USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) USA

Schizophrenia (SZ) is a severe, chronic mental condition that impacts one’s capacity to think, act, and interact with others. It has been established that SZ patients have morphological changes in their brains, along with decreased hippocampal and thalamic volume. Also, it is known that patients with SZ have irregular functional brain connectivity. Furthermore, because SZ is a genetic illness, genetic markers such as single nucleotide polymorphisms (SNP) can be useful to characterize SZ patients. We propose an automatic method to detect changes in SZ patients’ brains considering its heterogeneous multi-modal nature. We present a novel deep-learning method to classify SZ subjects with morphological features from structural MRI (sMRI), brain connectivity features from functional MRI (fMRI), and genetic features from SNPs. For sMRI, we used a pre-trained DenseNet to extract convolutional features which encode the morphological changes induced by SZ. For fMRI, we choose the important connections in functional network connection (FNC) matrix by applying layer-wise relevance propagation (LRP). We also detect SZ-linked SNPs using LRP on a pre-trained 1-dimensional convolutional neural network. Combined features from these three modalities are then fed to an extreme gradient boosting (XGBoost) tree classifier for SZ diagnosis. The experiments using the clinical dataset have shown that our multi-modal approach significantly improved SZ classification accuracy compared with uni-modal deep learning methods.

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Psychosis Multiscale Functional Network Connectivity Subspace Encodes Sex Specificity and Identifies Sex Biotypes 22

Psychosis Multiscale Functional Network Connectivity Subspac...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Ballem, Adithya Ram Bajracharya, Prerana Andrés-Camazón, Pablo Chen, Jiayu Calhoun, Vince D. Iraji, Armin Georgia State University Atlanta United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta United States Institute of Psychiatry and Mental Health Hospital General Universitario Gregorio Marañón IiSGM Cibersam Isciii School of Medicine Universidad Complutense Madrid Spain

ISBN: (纸本)9798331520526

Psychosis is a functionally disruptive condition, however little is known about the varying connectivity between different sexes and the extent of its variability. Resting-state fMRI (rsfMRI) data from the BSNIP consortium involving 626 controls (CON) and 1127 psychosis (PSY) participants (873 Females and 719 Males) were analyzed. Multiscale functional network connectivity (msFNC) for each subject was extracted by applying multivariate objective optimization ICA with Neuromark 2.2 data-driven reference template. The principal component space of PSY msFNC was then identified, within which sex specificity was uncovered. Our analysis revealed 4 statistically significant sex biotypes, 2 female dominant and 2 male dominant ones with unique connectivity characteristics. Psychosis dysconnectivity along cerebellar-subcortical regions was more prominent in males than females. These biotype findings highlight the manifestations of psychosis across different sexes with varying connectivity deficits relative to CON. © 2025 IEEE.

关键词： msFNC sex specificity Multiscale Functional Network Connectivity Psychosis connectivity subspace Psychosis sex biotypes

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Joint Structural and Functional Connectivity Learning Based Independent Component Analysis

Joint Structural and Functional Connectivity Learning Based ...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Mahshid Fouladivanda Armin Iraji Lei Wu Vince. D. Calhoun Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State Georgia Tech Emory Atlanta USA

There has been increasing development in multimodal approaches to neuroimaging with the goal of leveraging complementary information across multiple modalities for brain network analysis. One widely used data-driven approach to identifying brain intrinsic connectivity networks (ICNs) is independent components analysis (ICA). In this paper, a multimodal-guided ICA approach was proposed to determine ICNs during an iterative joint learning procedure using both structural and functional connectivity information. We applied the approach to a dataset of over 300 control and schizophrenia subjects. We found that jointly multimodal learned ICNs are more spatially distinct and show more structurally tuned distance between time courses with respect to the traditional ICNs. In addition, the joint approach is more sensitive to group differences. In summary, our proposed method shows that jointly using structural and functional information identifies more modular integrated ICNs, for the healthy and disordered brain.

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Novel Approach Explains Spatio-Spectral Interactions In Raw Electroencephalogram Deep Learning Classifiers

Novel Approach Explains Spatio-Spectral Interactions In Raw ...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Charles A. Ellis Abhinav Sattiraju Robyn L. Miller Vince D. Calhoun Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia United States

The application of deep learning classifiers to resting-state electroencephalography (rs-EEG) data has become increasingly common. However, relative to studies using traditional machine learning methods and extracted features, deep learning methods are less explainable. A growing number of studies have presented explainability approaches for rs-EEG deep learning classifiers. However, to our knowledge, no approaches give insight into spatio-spectral interactions (i.e., how spectral activity in one channel may interact with activity in other channels). In this study, we combine gradient and perturbation-based explainability approaches to give insight into spatio-spectral interactions in rs-EEG deep learning classifiers for the first time. We present the approach within the context of major depressive disorder (MDD) diagnosis identifying effects upon frontal $\delta$ activity, upon frontal interactions, and upon widespread $\gamma_{{\mathrm {high}}}$ interactions. Our approach provides novel insights and represents a significant step forward for the field of explainable EEG classification.

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An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-Based Schizophrenia Diagnosis

An Explainable and Robust Deep Learning Approach for Automat...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Abhinav Sattiraju Charles A. Ellis Robyn L. Miller Vince D. Calhoun Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

Schizophrenia (SZ) is a neuropsychiatric disorder that affects millions globally. Current diagnosis of SZ is symptom-based, which poses difficulty due to the variability of symptoms across patients. To this end, many recent studies have developed deep learning methods for automated diagnosis of SZ, especially using raw EEG, which provides high temporal precision. For such methods to be productionized, they must be both explainable and robust. Explainable models are essential to identify biomarkers of SZ, and robust models are critical to learn generalizable patterns, especially amidst changes in the implementation environment. One common example is channel loss during EEG recording, which could be detrimental to classifier performance. In this study, we developed a novel channel dropout (CD) approach to increase the robustness of explainable deep learning models trained on EEG data for SZ diagnosis to channel loss. We developed a baseline convolutional neural network (CNN) architecture and implement our approach as a CD layer added to the baseline (CNN-CD). We then applied two explainability approaches to both models for insight into learned spatial and spectral features and show that the application of CD decreases model sensitivity to channel loss. The CNN and CNN-CD achieved accuracies of 81.9% and 80.9% on testing data, respectively. Furthermore, our models heavily prioritized the parietal electrodes and the a-band, which is supported by existing literature. It is our hope that this study motivates the further development of explainable and robust models and bridges the transition from research to application in a clinical decision support role.

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Adaptive Constrained ICA with Mixing Matrix Column Constraints: Application to fMRI data

Adaptive Constrained ICA with Mixing Matrix Column Constrain...

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Annual Conference on Information sciences and Systems (CISS)

作者： Chunying Jia Weixin Wang Trung Vu Hanlu Yang Ben Gabrielson Vince D. Calhoun Tülay Adali University of Maryland Baltimore County Baltimore MD Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

Independent Component Analysis (ICA) is a powerful data-driven method that has been widely applied in functional magnetic resonance imaging (fMRI) data analysis to uncover underlying sources. An attractive way to boost ICA performance is via constraints to guide ICA factors to be similar to user-supplied "references", allowing incorporation of prior-knowledge into the factorization. However, most of existing constrained ICA methods typically only impose source constraints and are unable to impose constraints on the mixing matrix. With multi-subject medical imaging datasets, constraining the mixing matrix with subjects’ symptom-related measurements, such as clinical scores or cognitive variables, enhances the algorithm’s ability to identify brain activities associated with these symptoms. This offers a novel perspective for understanding the pathologies underlying various psychiatric disorders. Therefore, to overcome the limitations of existing constrained ICA algorithms, we introduce a new constrained ICA algorithm: adaptive-reverse constrained matrix entropy bound minimization (arc-M-EBM), which imposes constraints on the mixing matrix and uses adaptive-reverse thresholding to avoid overfitting or underfitting. This approach ensures flexibility and leads to more accurate and interpretable source separation. Simulations demonstrate that arc-M-EBM outperforms traditional ICA methods. Application to resting-state fMRI data from 176 subjects from healthy controls and patients reveals significant relationships between constrained components and clinical measures, enhancing our understanding of brain-behavior relationships.

关键词： Adaptation models Thresholding (Imaging) Source separation Correlation Loading Independent component analysis Functional magnetic resonance imaging Brain modeling data models Context modeling

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Investigating the Impact of Habitual Sleep Quality on Episodic Memory Performance: An Eeg-Based Representational Similarity Analysis

Investigating the Impact of Habitual Sleep Quality on Episod...

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IEEE International Symposium on Biomedical Imaging

作者： Masoud Seraji Soroush Mirjalili Audrey Duarte Vince D Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University/Georgia Institute of Technology/Emory University Atlanta GA USA School of Psychology University of Texas at Austin Austin USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Sleep is crucial for episodic memory consolidation, yet the impact of habitual sleep quality on memory performance remains underexplored. This study investigates the relationship between sleep quality and episodic memory retrieval using EEG-based representational similarity analysis (RSA). Thirty-six participants wore wrist accelerometers for one week to capture habitual sleep patterns, including total sleep time and restlessness. Memory performance was assessed through a paired associate learning task, with EEG data recorded during encoding and retrieval phases. RSA was applied to EEG oscillatory power across time-frequency windows to examine the neural similarity between encoding and retrieval. The results showed both positive and negative correlations between sleep metric and memory performance, with sleep restlessness being linked to both increases and decreases in neural similarity across specific clusters. These findings emphasize the important role of sleep quality in shaping the neural processes underlying episodic memory retrieval, indicating a strong connection between sleep patterns and memory function.

关键词： Wrist Accelerometers Time-frequency analysis Correlation Sleep Electroencephalography Encoding Biomedical imaging

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Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations *

Neuropsychiatric Disorder Subtyping Via Clustered Deep Learn...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Charles A. Ellis Robyn L. Miller Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

Identifying subtypes of neuropsychiatric disorders based on characteristics of their brain activity has tremendous potential to contribute to a better understanding of those disorders and to the development of new diagnostic and personalized treatment approaches. Many studies focused on neuropsychiatric disorders examine the interaction of brain networks over time using dynamic functional network connectivity (dFNC) extracted from resting-state functional magnetic resonance imaging data. Some of these studies involve the use of either deep learning classifiers or traditional clustering approaches, but usually not both. In this study, we present a novel approach for subtyping individuals with neuropsychiatric disorders within the context of schizophrenia (SZ). We trained an explainable deep learning classifier to differentiate between dFNC data from individuals with SZ and controls, obtaining a test accuracy of 79%. We next used cross-validation to obtain robust average explanations for SZ training participants across folds, identifying 5 SZ subtypes that each differed from controls in a distinct manner and that had different degrees of symptom severity. These subtypes specifically differed from one another in their interactions between the visual network and the subcortical, sensorimotor, and auditory networks and between the cerebellar network and the cognitive control and subcortical networks. Additionally, we uncovered statistically significant differences in negative symptom scores between the subtypes. It is our hope that the proposed novel subtyping approach will contribute to the improved understanding and characterization of SZ and other neuropsychiatric disorders.

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A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-state EEG Analysis*

A Convolutional Autoencoder-based Explainable Clustering App...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Charles A. Ellis Robyn L. Miller Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

Machine learning methods have frequently been applied to electroencephalography (EEG) data. However, while supervised EEG classification is well-developed, relatively few studies have clustered EEG, which is problematic given the potential for clustering EEG to identify novel subtypes or patterns of dynamics that could improve our understanding of neuropsychiatric disorders. There are established methods for clustering EEG using manually extracted features that reduce the richness of the feature space for clustering, but only a couple studies have sought to use deep learning-based approaches with automated feature learning to cluster EEG. Those studies involve separately training an autoencoder and then performing clustering on the extracted features, and the separation of those steps can lead to poor quality clustering. In this study, we propose an explainable convolutional autoencoder-based approach that combines model training with clustering to yield high quality clusters. We apply the approach within the context of schizophrenia (SZ), identifying 8 EEG states characterized by varying levels of δ activity. We also find that individuals who spend more time outside of the dominant state tend to have increased negative symptom severity. Our approach represents a significant step forward for clustering resting-state EEG data and has the potential to lead to novel findings across a variety of neurological and neuropsychological disorders in future years.

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A Novel Explainable Fuzzy Clustering Approach for fMRI Dynamic Functional Network Connectivity Analysis*

A Novel Explainable Fuzzy Clustering Approach for fMRI Dynam...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) analysis has illuminated brain network interactions across many neuropsychiatric disorders. A common analysis approach involves using hard clustering methods to identify transitory states of brain activity, and in response to this, other methods have been developed to quantify the importance of specific dFNC interactions to identified states. Some of these methods involve perturbing individual features and examining the number of samples that switch states. However, only a minority of samples switch states. As such, these methods actually identify the importance of dFNC features to the clustering of a subset of samples rather than the overall clustering. In this study, we present a novel approach that more capably identifies the importance of each feature to the overall clustering. Our approach uses fuzzy clustering to output probabilities of each sample belonging to states and then measures their Kullback-Leibler divergence after perturbation. We show the viability of our approach in the context of schizophrenia (SZ) default mode network analysis, identifying significant differences in state dynamics between individuals with SZ and healthy controls. We further compare our approach with an existing approach, showing that it captures the effects of perturbation upon most samples. We also find that interactions between the posterior cingulate cortex (PCC) and the anterior cingulate cortex and the PCC and precuneus are important across methods. We expect that our novel explainable clustering approach will enable further progress in rs-fMRI analysis and to other clustering applications.

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