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1. Predicting Biological Gender and Intelligence from fMRI via Dynamic Functional Connectivity

   https://doi.org/10.1109/TBME.2020.3011363  

   Sen, Bhaskar; Parhi, Keshab K. (July 2020, IEEE Transactions on Biomedical Engineering)

   null (Ed.)

   Recent works have explored the neuronal functional differences in biological gender and intelligence using static functional connectivity. Objective: This paper explores the predictive capability of dynamic functional connectivity extracted from functional magnetic resonance imaging (fMRI) of the human brain. Methods: Several state-of-the-art features extracted from static functional connectivity of the brain are employed to predict biological gender and intelligence using publicly available Human Connectome Project (HCP) database. Next, a novel tensor parallel factor (PARAFAC) decomposition model is proposed to decompose sequence of dynamic connectivity matrices into common connectivity components that are orthonormal to each other, common time-courses, and corresponding distinct subject-wise weights. The subject-wise loading of the components are employed to predict biological gender and intelligence using a random forest classifier (respectively,regressor) using5-foldcross-validation. Results:The results demonstrate that dynamic functional connectivity can indeed classify biological gender with a high accuracy (0.94, where male identification accuracy was 0.87 and female identification accuracy was 0.97). It can also predict intelligence with less normalized mean square error (0.139 for fluid intelligence and 0.031 for fluid ability metrics) compared with other functional connectivity measures (the nearest mean square error were 0.147 and 0.037 for fluid intelligence and fluid ability metrics, respectively using static connectivity approaches). Conclusion: Our work is an important milestone for the understanding of non-stationary behavior of hemodynamic blood-oxygen level dependent (BOLD) signal in brain and how they are associated with biological gender and intelligence. Significance: The paper demonstrates that dynamic behavior of brain can contribute substantially towards forming a fingerprint of biological gender and intelligence. 

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2. GPU-DFC: A GPU-Based Parallel Algorithm for Computing Dynamic-Functional Connectivity of Big fMRI Data

   https://doi.org/10.1109/BigDataService.2019.00022  

   Eslami, Taban; Saeed, Fahad (April 2019, Proceedings of IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService))

   Studying dynamic-functional connectivity (DFC) using fMRI data of the brain gives much richer information to neuroscientists than studying the brain as a static entity. Mining of dynamic connectivity graphs from these brain studies can be used to classify diseased versus healthy brains. However, constructing and mining dynamic-functional connectivity graphs of the brain can be time consuming due to size of fMRI data. In this paper, we propose a highly scalable GPU-based parallel algorithm called GPU-DFC for computing dynamic-functional connectivity of fMRI data both at region and voxel level. Our algorithm exploits sparsification of correlation matrix and stores them in CSR format. Further reduction in the correlation matrix is achieved by parallel decomposition techniques. Our GPU-DFC algorithm achieves 2 times speed-up for computing dynamic correlations compared to state-of-the-art GPU-based techniques and more than 40 times compared to a sequential CPU version. In terms of storage, our proposed matrix decomposition technique reduces the size of correlation matrices more than 100 times. Reconstructed values from decomposed matrices show comparable results as compared to the correlations with original data. The implemented code is available as GPL license on GitHub portal of our lab (https://github.com/pcdslab/GPU-DFC). 

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3. A Framework to Control Functional Connectivity in the Human Brain

   https://doi.org/10.1109/CDC40024.2019.9029223  

   Menara, Tommaso; Baggio, Giacomo; Bassett, Danielle S.; Pasqualetti, Fabio (December 2019, IEEE Conference on Decision and Control)

   In this paper, we propose a framework to control brain-wide functional connectivity by selectively acting on the brain's structure and parameters. Functional connectivity, which measures the degree of correlation between neural activities in different brain regions, can be used to distinguish between healthy and certain diseased brain dynamics and, possibly, as a control parameter to restore healthy functions. In this work, we use a collection of interconnected Kuramoto oscillators to model oscillatory neural activity, and show that functional connectivity is essentially regulated by the degree of synchronization between different clusters of oscillators. Then, we propose a minimally invasive method to correct the oscillators' interconnections and frequencies to enforce arbitrary and stable synchronization patterns among the oscillators and, consequently, a desired pattern of functional connectivity. Additionally, we show that our synchronization-based framework is robust to parameter mismatches and numerical inaccuracies, and validate it using a realistic neurovascular model to simulate neural activity and functional connectivity in the human brain. 

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4. A spatially constrained independent component analysis jointly informed by structural and functional network connectivity

   https://doi.org/10.1162/netn_a_00398  

   Fouladivanda, Mahshid; Iraji, Armin; Wu, Lei; van_Erp, Theodorus_G_M; Belger, Aysenil; Hawamdeh, Faris; Pearlson, Godfrey_D; Calhoun, Vince_D (June 2024, Network Neuroscience)

   Abstract There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. Brain connectivity of different modalities provides insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multi-modal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multi-objective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multi-modal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared to the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity. 

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5. Classification of Adolescent Major Depressive Disorder via Static and Dynamic Connectivity

   https://doi.org/10.1109/JBHI.2020.3043427  

   Sen, Bhaskar; Cullen, Kathryn R.; Parhi, Keshab K. (December 2020, IEEE Journal of Biomedical and Health Informatics)

   null (Ed.)

   Prior papers have explored the functional connectivity changes for patients suffering from major depressive disorder (MDD). This paper introduces an approach for classifying adolescents suffering from MDD using resting-state fMRI. Accurate diagnosis of MDD involves interviews with adolescent patients and their parents, symptom rating scales based on Diagnostic and Statistical Manual of Mental Disorders (DSM), behavioral observation as well as the experience of a clinician. Discovering predictive biomarkers for diagnosing MDD patients using functional magnetic resonance imaging (fMRI) scans can assist the clinicians in their diagnostic assessments. This paper investigates various static and dynamic connectivity measures extracted from resting-state fMRI for assisting with MDD diagnosis. First, absolute Pearson correlation matrices from 85 brain regions are computed and they are used to calculate static features for predicting MDD. A predictive sub-network extracted using sub-graph entropy classifies adolescent MDD vs. typical healthy controls with high accuracy, sensitivity and specificity. Next, approaches utilizing dynamic connectivity are employed to extract tensor based, independent component based and principal component based subject specific attributes. Finally, features from static and dynamic approaches are combined to create a feature vector for classification. A leave-one-out cross-validation method is used for the final predictor performance. Out of 49 adolescents with MDD and 33 matched healthy controls, a support vector machine (SVM) classifier using a radial basis function (RBF) kernel using differential sub-graph entropy combined with dynamic connectivity features classifies MDD vs. healthy controls with an accuracy of 0.82 for leave-one-out cross-validation. This classifier has specificity and sensitivity of 0.79 and 0.84, respectively. This performance demonstrates the utility of MRI based diagnosis of psychiatric disorders like MDD using a combination of static and dynamic functional connectivity features of the brain. 

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