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@ARTICLE{Kernbach:850304,
      author       = {Kernbach, Julius M. and Satterthwaite, Theodore D. and
                      Bassett, Danielle S. and Smallwood, Jonathan and Margulies,
                      Daniel and Krall, Sarah and Shaw, Philip and Varoquaux,
                      Gaël and Thirion, Bertrand and Konrad, Kerstin and Bzdok,
                      Danilo},
      title        = {{S}hared endo-phenotypes of default mode dsfunction in
                      attention deficit/hyperactivity disorder and autism spectrum
                      disorder},
      journal      = {Translational Psychiatry},
      volume       = {8},
      number       = {1},
      issn         = {2158-3188},
      address      = {London},
      publisher    = {Nature Publishing Group},
      reportid     = {FZJ-2018-04346},
      pages        = {133},
      year         = {2018},
      abstract     = {Categorical diagnoses from the Diagnostic and Statistical
                      Manual of Mental Disorders (DSM) or International
                      Classification of Diseases (ICD) manuals are increasingly
                      found to be incongruent with emerging neuroscientific
                      evidence that points towards shared neurobiological
                      dysfunction underlying attention deficit/hyperactivity
                      disorder and autism spectrum disorder. Using resting-state
                      functional magnetic resonance imaging data, functional
                      connectivity of the default mode network, the dorsal
                      attention and salience network was studied in 1305 typically
                      developing and diagnosed participants. A transdiagnostic
                      hierarchical Bayesian modeling framework combining Indian
                      Buffet Processes and Latent Dirichlet Allocation was
                      proposed to address the urgent need for objective
                      brain-derived measures that can acknowledge shared brain
                      network dysfunction in both disorders. We identified three
                      main variation factors characterized by distinct coupling
                      patterns of the temporoparietal cortices in the default mode
                      network with the dorsal attention and salience network. The
                      brain-derived factors were demonstrated to effectively
                      capture the underlying neural dysfunction shared in both
                      disorders more accurately, and to enable more reliable
                      diagnoses of neurobiological dysfunction. The brain-derived
                      phenotypes alone allowed for a classification accuracy
                      reflecting an underlying neuropathology of $67.33\%$
                      (+/−3.07) in new individuals, which significantly
                      outperformed the $46.73\%$ (+/−3.97) accuracy of
                      categorical diagnoses. Our results provide initial evidence
                      that shared neural dysfunction in ADHD and ASD can be
                      derived from conventional brain recordings in a data-led
                      fashion. Our work is encouraging to pursue a translational
                      endeavor to find and further study brain-derived phenotypes,
                      which could potentially be used to improve clinical
                      decision-making and optimize treatment in the future.},
      cin          = {INM-3 / INM-11},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406 / I:(DE-Juel1)INM-11-20170113},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572)},
      pid          = {G:(DE-HGF)POF3-572},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30018328},
      UT           = {WOS:000439509200001},
      doi          = {10.1038/s41398-018-0179-6},
      url          = {https://juser.fz-juelich.de/record/850304},
}