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@ARTICLE{Afshani:1006856,
      author       = {Afshani, Mortaza and Mahmoodi-Aznaveh, Ahmad and Noori,
                      Khadijeh and Rostampour, Masoumeh and Zarei, Mojtaba and
                      Spiegelhalder, Kai and Khazaie, Habibolah and Tahmasian,
                      Masoud},
      title        = {{D}iscriminating {P}aradoxical and {P}sychophysiological
                      {I}nsomnia {B}ased on {S}tructural and {F}unctional {B}rain
                      {I}mages: {A} {P}reliminary {M}achine {L}earning {S}tudy},
      journal      = {Brain Sciences},
      volume       = {13},
      number       = {4},
      issn         = {2076-3425},
      address      = {Basel},
      publisher    = {MDPI AG},
      reportid     = {FZJ-2023-01898},
      pages        = {672 -},
      year         = {2023},
      abstract     = {Insomnia disorder (ID) is a prevalent mental illness.
                      Several behavioral and neuroimaging studies suggested that
                      ID is a heterogenous condition with various subtypes.
                      However, neurobiological alterations in different subtypes
                      of ID are poorly understood. We aimed to assess whether
                      unimodal and multimodal whole-brain neuroimaging
                      measurements can discriminate two commonly described ID
                      subtypes (i.e., paradoxical and psychophysiological
                      insomnia) from each other and healthy subjects. We obtained
                      T1-weighted images and resting-state fMRI from 34 patients
                      with ID and 48 healthy controls. The outcome measures were
                      grey matter volume, cortical thickness, amplitude of
                      low-frequency fluctuation, degree centrality, and regional
                      homogeneity. Subsequently, we applied support vector
                      machines to classify subjects via unimodal and multimodal
                      measures. The results of the multimodal classification were
                      superior to those of unimodal approaches, i.e., we achieved
                      $81\%$ accuracy in separating psychophysiological vs.
                      control, $87\%$ for paradoxical vs. control, and $89\%$ for
                      paradoxical vs. psychophysiological insomnia. This
                      preliminary study provides evidence that structural and
                      functional brain data can help to distinguish two common
                      subtypes of ID from each other and healthy subjects. These
                      initial findings may stimulate further research to identify
                      the underlying mechanism of each subtype and develop
                      personalized treatments for ID in the future.},
      cin          = {INM-7},
      ddc          = {570},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37190637},
      UT           = {WOS:000977704900001},
      doi          = {10.3390/brainsci13040672},
      url          = {https://juser.fz-juelich.de/record/1006856},
}