Biology subjects

Salman, M. S.

Publications and source records attributed to Salman, M. S..

3 recordsLinked to original sources

bioRxiv2022

Machine Learning Predicts Treatment Response in Bipolar & Major Depressive Disorders

Diagnosis of bipolar disorder (BD) patients with complex symptoms presents a challenge to clinicians. Patients tend to spend more time in a depressive state than a manic state. In such complex cases, the current Diagnostic and Statistical Manual (DSM), which is not based on pathophysiology, can lead to misdiagnosis as major depressive disorder (MDD) and an imperfect or even harmful medication response. A biologically-based classification algorithm is needed to improve the accuracy of diagnosis. Osuch et al. (2018) presented a kernel support vector machine (SVM) algorithm to predict the medication-class of response from new patient samples whose diagnoses were unclear. Here we also utilize the kernel support vector machine (SVM) algorithm but with a few novel contributions. We applied the robust, fully automated neuromark independent component analysis (ICA) framework to extract comparable features in a multi-dataset setting and learn a kernel function for support vector machine (SVM) on multiple feature subspaces. The neuromark framework successfully replicates the prior result with 95.45% accuracy (sensitivity 90.24%, specificity 92.3%). To further evaluate the generalizability of our approach, we incorporated two additional datasets comprising bipolar disorder (BD) and major depressive disorder (MDD) patients. We validated the trained algorithm on these datasets, resulting in a testing accuracy of up to 89% (sensitivity 0.88, specificity 0.89) without using site or scanner harmonization techniques. We also translated the model to predict improvement scores of major depressive disorder (MDD) with up to 70% accuracy. This approach reveals some salient biological markers of medication-class of response within mood disorders. HighlightsO_LIWe demonstrate a DSM-free approach for predicting treatment response from resting-state functional magnetic resonance imaging (fMRI) data. C_LIO_LIWe identify several replicable biomarkers using the approach. C_LIO_LIOur work has potential for clinical application by replacing trial-and-error in treating complex psychiatric disorders. C_LI

neuroscience↗

bioRxiv2020

An Approach to Automatically Label & Order Brain Activity/Component Maps

Functional magnetic resonance imaging (fMRI) is a brain imaging technique which provides detailed in-sights into brain function and its disruption in various brain disorders. fMRI data can be analyzed using data-driven or region-of-interest based methods. The data-driven analysis of brain activity maps involves several steps, the first of which is identifying whether the maps capture what might be interpreted as intrinsic connectivity networks (ICNs) or artifacts. This is followed by linking the ICNs to known anatomical and/or functional parcellations. Optionally, as in the study of functional network connectivity (FNC), rearranging the connectivity graph is also necessary for systematic interpretation. Here we present a toolbox that automates all these processes under minimal or no supervision with high accuracy. We provide a pretrained cross-validated elastic-net regularized general linear model for the noisecloud toolbox to separate the ICNs from artifacts. We include several well-known anatomical and functional parcellations from which researchers can choose to label the activity maps. Finally, we integrate a method for maximizing the within-domain modularity to generate a more systematically structured FNC matrix. We improve upon and integrate existing techniques and new methods to design this toolbox which can take care of all the above needs. Specifically, we show that our pretrained model achieves 89% accuracy and 100% precision at classifying ICNs from artifacts in a validation dataset. Researchers are generating brain imaging data and analyzing brain activity at an ever-increasing rate. The Autolabeller toolbox can help automate such analyses for faster and reproducible research.

neuroscience↗

bioRxiv2018

Group ICA for Identifying Biomarkers in Schizophrenia: ‘Adaptive’ Networks via Spatially Constrained ICA Show More Sensitivity to Group Differences than Spatio-temporal Regression

Brain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject-specific networks from the group-level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, spatio-temporal regression (STR) and spatially constrained ICA approaches such as group information guided ICA (GIG-ICA) can be used to propagate components (indicating networks) to a new subject that is not included in the original subjects. In this study, based on the same a priori network maps, we reconstructed subject-specific networks using these two methods separately from resting-state fMRI data of 151 schizophrenia patients (SZs) and 163 healthy controls (HCs). We investigated group differences in the estimated functional networks and the functional network connectivity (FNC) obtained by each method. The networks were also used as features in a cross-validated support vector machine (SVM) for classifying SZs and HCs. We selected features using different strategies to provide a comprehensive comparison between the two methods. GIG-ICA generally showed greater sensitivity in statistical analysis and better classification performance (accuracy 76.45{+/-}8.9%, sensitivity 0.74{+/-}0.11, specificity 0.79{+/-}0.11) than STR (accuracy 67.45{+/-}8.13%, sensitivity 0.65{+/-}0.11, specificity 0.71{+/-}0.11). Importantly, results were also consistent when applied to an independent dataset including 82 HCs and 82 SZs. Our work suggests that the functional networks estimated by GIG-ICA are more sensitive to group differences, and GIG-ICA is promising for identifying image-derived biomarkers of brain disease.

neuroscience↗