首页|Max-Planck-Institute for Psychiatry Reports Findings in Machine Learning (A mach ine-learning approach for differentiating borderline personality disorder from c ommunity participants with brainwide functional connectivity)
Max-Planck-Institute for Psychiatry Reports Findings in Machine Learning (A mach ine-learning approach for differentiating borderline personality disorder from c ommunity participants with brainwide functional connectivity)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting from Munich, Germany, by News Rx journalists, research stated, “Functional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personali ty disorder (BPD). However, small sample sizes and lack of within-study replicat ions have led to divergent findings with no clear spatial foci.” The news correspondents obtained a quote from the research from Max-Planck-Insti tute for Psychiatry, “Evaluate discriminative performance and generalizability o f functional connectivity markers for BPD. Whole-brain fMRI resting state functi onal connectivity in matched subsamples of 116 BPD and 72 control individuals de fined by three grouping strategies. We predicted BPD status using classifiers wi th repeated cross-validation based on multiscale functional connectivity within and between regions of interest (ROIs) covering the whole brain-global ROI-based network, seed-based ROI-connectivity, functional consistency,and voxel-to-voxe l connectivity-and evaluated the generalizability of the classification in the l eft-out portion of non-matched data. Full-brain connectivity allowed classificat ion ( 70 %) of BPD patients vs. controls in matched inner cross-val idation. The classification remained significant when applied to unmatched out-o f-sample data ( 61-70 %). Highest seed-based accuracies were in a s imilar range to global accuracies ( 70-75 %), but spatially more sp ecific. The most discriminative seed regions included midline, temporal and soma tomotor regions. Univariate connectivity values were not predictive of BPD after multiple comparison corrections, but weak local effects coincided with the most discriminative seed- ROIs. Highest accuracies were achieved with a full clinical interview while self-report results remained at chance level. The accuracies va ry considerably between random sub-samples of the population, global signal and covariates limiting the practical applicability.”
MunichGermanyEuropeCyborgsEmergi ng TechnologiesMachine Learning
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