Gast ::
| Hauptseite > Publikationsdatenbank > A machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivity |
| Hauptseite > Publikationsdatenbank > A machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivity |
| Journal Article | FZJ-2024-03568 |
; ; ; ; ; ; ; ; ; ;
2024
Elsevier Science
Amsterdam [u.a.]
This record in other databases: 
Please use a persistent id in citations: doi:10.1016/j.jad.2024.05.125 doi:10.34734/FZJ-2024-03568
Abstract: Background:Functional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findings with no clear spatial foci.Aims:Evaluate discriminative performance and generalizability of functional connectivity markers for BPD.Method:Whole-brain fMRI resting state functional connectivity in matched subsamples of 116 BPD and 72 control individuals defined by three grouping strategies. We predicted BPD status using classifiers with 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-voxel connectivity—and evaluated the generalizability of the classification in the left-out portion of non-matched data.Results:Full-brain connectivity allowed classification (∼70 %) of BPD patients vs. controls in matched inner cross-validation. The classification remained significant when applied to unmatched out-of-sample data (∼61–70 %). Highest seed-based accuracies were in a similar range to global accuracies (∼70–75 %), but spatially more specific. The most discriminative seed regions included midline, temporal and somatomotor 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.Limitations:The accuracies vary considerably between random sub-samples of the population, global signal and covariates limiting the practical applicability.Conclusions:Spatially distributed functional connectivity patterns are moderately predictive of BPD despite heterogeneity of the patient population.
; 
; 
; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Current Contents - Social and Behavioral Sciences ; Ebsco Academic Search ; Essential Science Indicators ; Nationallizenz
; SCOPUS ; Science Citation Index Expanded ; Social Sciences Citation Index ; Web of Science Core Collection
|
The record appears in these collections: |
PDF