TY  - JOUR
AU  - Lahnakoski, Juha M.
AU  - Nolte, Tobias
AU  - Solway, Alec
AU  - Vilares, Iris
AU  - Hula, Andreas
AU  - Feigenbaum, Janet
AU  - Lohrenz, Terry
AU  - King-Casas, Brooks
AU  - Fonagy, Peter
AU  - Montague, P. Read
AU  - Schilbach, Leonhard
TI  - A machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivity
JO  - Journal of affective disorders
VL  - 360
SN  - 0165-0327
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - FZJ-2024-03568
SP  - 345-353
PY  - 2024
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 38806064
UR  - <Go to ISI:>//WOS:001249875400001
DO  - DOI:10.1016/j.jad.2024.05.125
UR  - https://juser.fz-juelich.de/record/1027000
ER  -