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@ARTICLE{Lahnakoski:1027000,
      author       = {Lahnakoski, Juha M. and Nolte, Tobias and Solway, Alec and
                      Vilares, Iris and Hula, Andreas and Feigenbaum, Janet and
                      Lohrenz, Terry and King-Casas, Brooks and Fonagy, Peter and
                      Montague, P. Read and Schilbach, Leonhard},
      title        = {{A} machine-learning approach for differentiating
                      borderline personality disorder from community participants
                      with brain-wide functional connectivity},
      journal      = {Journal of affective disorders},
      volume       = {360},
      issn         = {0165-0327},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2024-03568},
      pages        = {345-353},
      year         = {2024},
      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.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {38806064},
      UT           = {WOS:001249875400001},
      doi          = {10.1016/j.jad.2024.05.125},
      url          = {https://juser.fz-juelich.de/record/1027000},
}