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

Juha M. Lahnakoski, Tobias Nolte, Alec Solway, Iris Vilares, Andreas Hula, Janet Feigenbaum, Terry Lohrenz, Brooks King-Casas, Peter Fonagy, P. Read Montague, Leonhard Schilbach

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)345-353
Number of pages9
JournalJournal of Affective Disorders
Volume360
DOIs
StatePublished - Sep 1 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors

Keywords

  • BPD
  • Borderline personality disorder
  • Classification
  • Functional connectivity
  • Multivariate
  • fMRI

PubMed: MeSH publication types

  • Journal Article

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