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

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dc.contributor.authorLahnakoski, Juha M.en
dc.contributor.authorNolte, Tobiasen
dc.contributor.authorSolway, Alecen
dc.contributor.authorVilares, Irisen
dc.contributor.authorHula, Andreasen
dc.contributor.authorFeigenbaum, Janeten
dc.contributor.authorLohrenz, Terryen
dc.contributor.authorCasas, Brooksen
dc.contributor.authorFonagy, Peteren
dc.contributor.authorMontague, P. Readen
dc.contributor.authorSchilbach, Leonharden
dc.date.accessioned2024-08-13T16:42:17Zen
dc.date.available2024-08-13T16:42:17Zen
dc.date.issued2024-05-26en
dc.description.abstractBackground: 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.en
dc.description.versionPublished versionen
dc.format.extentPages 345-353en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.jad.2024.05.125en
dc.identifier.eissn1573-2517en
dc.identifier.issn0165-0327en
dc.identifier.otherS0165-0327(24)00868-1 (PII)en
dc.identifier.pmid38806064en
dc.identifier.urihttps://hdl.handle.net/10919/120918en
dc.identifier.volume360en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pubmed/38806064en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectBPDen
dc.subjectBorderline personality disorderen
dc.subjectClassificationen
dc.subjectFunctional connectivityen
dc.subjectMultivariateen
dc.subjectfMRIen
dc.subject.meshBrainen
dc.subject.meshHumansen
dc.subject.meshMagnetic Resonance Imagingen
dc.subject.meshBrain Mappingen
dc.subject.meshCase-Control Studiesen
dc.subject.meshBorderline Personality Disorderen
dc.subject.meshAdulten
dc.subject.meshFemaleen
dc.subject.meshMaleen
dc.subject.meshYoung Adulten
dc.subject.meshConnectomeen
dc.subject.meshMachine Learningen
dc.titleA machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivityen
dc.title.serialJournal of Affective Disordersen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
dcterms.dateAccepted2024-05-24en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/VT Carilion School of Medicineen
pubs.organisational-group/Virginia Tech/VT Carilion School of Medicine/Psychiatry and Behavioral Medicineen
pubs.organisational-group/Virginia Tech/VT Carilion School of Medicine/Psychiatry and Behavioral Medicine/Secondary Appointment-Psychiatry and Behavioral Medicineen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Biomedical Research Institute at VTCen

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