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1. 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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2. Identifying dynamic functional connectivity biomarkers using GIG‐ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder

   https://doi.org/10.1002/hbm.23553  

   Du, Yuhui ; Pearlson, Godfrey D. ; Lin, Dongdong ; Sui, Jing ; Chen, Jiayu ; Salman, Mustafa ; Tamminga, Carol A. ; Ivleva, Elena I. ; Sweeney, John A. ; Keshavan, Matcheri S. ; et al ( March 2017 , Human Brain Mapping)

   Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here, we aim to explore DFC across a spectrum of symptomatically‐related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD), and schizophrenia (SZ). We introduce a group information guided independent component analysis procedure to estimate both group‐level and subject‐specific connectivity states from DFC. Using resting‐state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD, and 113 SZ patients, we identified measures differentiating groups from the whole‐brain DFC and traditional static functional connectivity (SFC), separately. Results show that DFC provided more informative measures than SFC. Diagnosis‐related connectivity states were evident using DFC analysis. For the dominant state consistent across groups, we found 22 instances of hypoconnectivity (with decreasing trends from HC to BPP to SAD to SZ) mainly involving post‐central, frontal, and cerebellar cortices as well as 34 examples of hyperconnectivity (with increasing trends HC through SZ) primarily involving thalamus and temporal cortices. Hypoconnectivities/hyperconnectivities also showed negative/positive correlations, respectively, with clinical symptom scores. Specifically, hypoconnectivities linking postcentral and frontal gyri were significantly negatively correlated with the PANSS positive/negative scores. For frontal connectivities, BPP resembled HC while SAD and SZ were more similar. Three connectivities involving the left cerebellar crus differentiated SZ from other groups and one connection linking frontal and fusiform cortices showed a SAD‐unique change. In summary, our method is promising for assessing DFC and may yield imaging biomarkers for quantifying the dimension of psychosis.Hum Brain Mapp 38:2683–2708, 2017. ©2017 Wiley Periodicals, Inc.

    

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3. A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes

   https://doi.org/10.1007/978-3-030-87234-2_59  

   D'Souza, Niharika Shimona ; Nebel, Mary Beth ; Crocetti, Deana ; Robinson, Joshua ; Mostofsky, Stewart ; Venkataraman, Archana ( September 2021 , Medical Image Computing and Computer Assisted Intervention)

   We propose a novel matrix autoencoder to map functional connectomes from resting state fMRI (rs-fMRI) to structural connectomes from Diffusion Tensor Imaging (DTI), as guided by subject-level phenotypic measures. Our specialized autoencoder infers a low dimensional manifold embedding for the rs-fMRI correlation matrices that mimics a canonical outer-product decomposition. The embedding is simultaneously used to reconstruct DTI tractography matrices via a second manifold alignment decoder and to predict inter-subject phenotypic variability via an artificial neural network. We validate our framework on a dataset of 275 healthy individuals from the Human Connectome Project database and on a second clinical dataset consisting of 57 subjects with Autism Spectrum Disorder. We demonstrate that the model reliably recovers structural connectivity patterns across individuals, while robustly extracting predictive and interpretable brain biomarkers in a cross-validated setting. Finally, our framework outperforms several baselines at predicting behavioral phenotypes in both real-world datasets. 

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4. Predicting Affective Cognitions in the Resting Adult Brain

   Bush, Keith A. ; Privratsky, Anthony A. ; Kilts, Clinton D. ( January 2018 , Proceedings of the Conference on Cognitive Computational Neuroscience)

   Patterns of estimated neural activity derived from resting state functional magnetic resonance imaging (rs-fMRI) have been shown to predict a wide range of cognitive and behavioral outcomes in both normative and clinical populations. Yet, without links to established cognitive processes, the functional brain states associated with the resting brain will remain unexplained, and potentially confounded, markers of individual differences. In this work we demonstrate the application of multivoxel pattern classifiers (MVPCs) to predict the valence and arousal properties of spontaneous affect processing in the task-non-engaged resting state. rs-fMRI data were acquired from subjects that were held out from a subject set that underwent image-based affect induction concurrent with fMRI to train the MVPCs. We also validated these affective predictions against a well-established, independent measure of autonomic arousal, skin conductance response. These findings suggest a new neuroimaging methodology for resting state analysis in which models, trained on cognition-specific task-based fMRI acquired from well-matched cohorts, capably predict hidden cognitive processes operating within the resting brain. 

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5. Reliability and clinical utility of spatially constrained estimates of intrinsic functional networks from very short fMRI scans

   https://doi.org/10.1002/hbm.26234  

   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. ; Calhoun, Vince D. ( February 2023 , Human Brain Mapping)

   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.

    

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