A machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivity.

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.

Informations de copyright

Copyright © 2024. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest All authors report no conflicts of interest.