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@ARTICLE{Choi:910729,
      author       = {Choi, Hyoungshin and Byeon, Kyoungseob and Park, Bo-yong
                      and Lee, Jong-eun and Valk, Sofie and Bernhardt, Boris and
                      Martino, Adriana Di and Milham, Michael and Hong, Seok-Jun
                      and Park, Hyunjin},
      title        = {{D}iagnosis-informed connectivity subtyping discovers
                      subgroups of autism with reproducible symptom profiles},
      journal      = {NeuroImage},
      volume       = {256},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2022-04099},
      pages        = {119212 -},
      year         = {2022},
      abstract     = {Clinical heterogeneity has been one of the main barriers to
                      develop effective biomarkers and therapeutic strategies in
                      autism spectrum disorder (ASD). Recognizing this challenge,
                      much effort has been made in recent neuroimaging studies to
                      find biologically more homogeneous subgroups (called
                      ‘neurosubtypes’) in autism. However, most approaches
                      have rarely evaluated how much the employed features in
                      subtyping represent the core anomalies of ASD, obscuring its
                      utility in actual clinical diagnosis. To address this, we
                      combined two data-driven methods, ‘connectome-based
                      gradient’ and ‘functional random forest’, collectively
                      allowing to discover reproducible neurosubtypes based on
                      resting-state functional connectivity profiles that are
                      specific to ASD. Indeed, the former technique provides the
                      features (as input for subtyping) that effectively summarize
                      whole-brain connectome variations in both normal and ASD
                      conditions, while the latter leverages a supervised random
                      forest algorithm to inform diagnostic labels to clustering,
                      which makes neurosubtyping driven by the features of ASD
                      core anomalies. Applying this framework to the open-sharing
                      Autism Brain Imaging Data Exchange repository data
                      (discovery, n = 103/108 for ASD/typically developing [TD];
                      replication, n = 44/42 for ASD/TD), we found three dominant
                      subtypes of functional gradients in ASD and three subtypes
                      in TD. The subtypes in ASD revealed distinct connectome
                      profiles in multiple brain areas, which are associated with
                      different Neurosynth-derived cognitive functions previously
                      implicated in autism studies. Moreover, these subtypes
                      showed different symptom severity, which degree co-varies
                      with the extent of functional gradient changes observed
                      across the groups. The subtypes in the discovery and
                      replication datasets showed similar symptom profiles in
                      social interaction and communication domains, confirming a
                      largely reproducible brain-behavior relationship. Finally,
                      the connectome gradients in ASD subtypes present both common
                      and distinct patterns compared to those in TD, reflecting
                      their potential overlap and divergence in terms of
                      developmental mechanisms involved in the manifestation of
                      large-scale functional networks. Our study demonstrated a
                      potential of the diagnosis-informed subtyping approach in
                      developing a clinically useful brain-based classification
                      system for future ASD research.},
      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       = {35430361},
      UT           = {WOS:000830364700005},
      doi          = {10.1016/j.neuroimage.2022.119212},
      url          = {https://juser.fz-juelich.de/record/910729},
}