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@ARTICLE{Bajaj:999190,
      author       = {Bajaj, Sahil and Blair, Karina S. and Dobbertin, Matthew
                      and Patil, Kaustubh R. and Tyler, Patrick M. and Ringle, Jay
                      L. and Bashford-Largo, Johannah and Mathur, Avantika and
                      Elowsky, Jaimie and Dominguez, Ahria and Schmaal, Lianne and
                      Blair, R. James R.},
      title        = {{M}achine learning based identification of structural brain
                      alterations underlying suicide risk in adolescents},
      journal      = {Discover mental health},
      volume       = {3},
      number       = {1},
      issn         = {2731-4383},
      address      = {[Cham]},
      publisher    = {Springer International Publishing},
      reportid     = {FZJ-2023-01219},
      pages        = {6},
      year         = {2023},
      abstract     = {Suicide is the third leading cause of death for individuals
                      between 15 and 19 years of age. The high suicide mortality
                      rate and limited prior success in identifying neuroimaging
                      biomarkers indicate that it is crucial to improve the
                      accuracy of clinical neural signatures underlying suicide
                      risk. The current study implements machine-learning (ML)
                      algorithms to examine structural brain alterations in
                      adolescents that can discriminate individuals with suicide
                      risk from typically developing (TD) adolescents at the
                      individual level. Structural MRI data were collected from 79
                      adolescents who demonstrated clinical levels of suicide risk
                      and 79 demographically matched TD adolescents.
                      Region-specific cortical/subcortical volume (CV/SCV) was
                      evaluated following whole-brain parcellation into 1000
                      cortical and 12 subcortical regions. CV/SCV parameters were
                      used as inputs for feature selection and three ML algorithms
                      (i.e., support vector machine [SVM], K-nearest neighbors,
                      and ensemble) to classify adolescents at suicide risk from
                      TD adolescents. The highest classification accuracy of
                      $74.79\%$ (with $sensitivity = 75.90\%,$
                      $specificity = 74.07\%,$ and area under the receiver
                      operating characteristic $curve = 87.18\%)$ was obtained
                      for CV/SCV data using the SVM classifier. Identified
                      bilateral regions that contributed to the classification
                      mainly included reduced CV within the frontal and temporal
                      cortices but increased volume within the cuneus/precuneus
                      for adolescents at suicide risk relative to TD adolescents.
                      The current data demonstrate an unbiased region-specific ML
                      framework to effectively assess the structural biomarkers of
                      suicide risk. Future studies with larger sample sizes and
                      the inclusion of clinical controls and independent
                      validation data sets are needed to confirm our findings.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525) / 5254 -
                      Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252 / G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37861863},
      UT           = {WOS:001319058800001},
      doi          = {10.1007/s44192-023-00033-6},
      url          = {https://juser.fz-juelich.de/record/999190},
}