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@ARTICLE{Hirjak:276335,
      author       = {D. Hirjak and G. A. Brandt and R. Peretzke$^*$ and S.
                      Fritze and A. Meyer-Lindenberg and K. Maier-Hein$^*$ and P.
                      Neher$^*$},
      title        = {{M}icrostructural white matter biomarkers of symptom
                      severity and therapy outcome in catatonia: {R}ationale,
                      study design and preliminary clinical data of the white{CAT}
                      study.},
      journal      = {Schizophrenia research},
      volume       = {263},
      issn         = {0920-9964},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2023-01065},
      pages        = {160-168},
      year         = {2024},
      note         = {#LA:E230# / 2024 Jan:263:160-168},
      abstract     = {The number of magnetic resonance imaging (MRI) studies on
                      neuronal correlates of catatonia has dramatically increased
                      in the last 10 years, but conclusive findings on white
                      matter (WM) tracts alterations underlying catatonic symptoms
                      are still lacking. Therefore, we conduct an
                      interdisciplinary longitudinal MRI study (whiteCAT) with two
                      main objectives: First, we aim to enroll 100 psychiatric
                      patients with and 50 psychiatric patients without catatonia
                      according to ICD-11 who will undergo a deep phenotyping
                      approach with an extensive battery of demographic,
                      psychopathological, psychometric, neuropsychological,
                      instrumental and diffusion MRI assessments at baseline and
                      12 weeks follow-up. So far, 28 catatonia patients and 40
                      patients with schizophrenia or other primary psychotic
                      disorders or mood disorders without catatonia have been
                      studied cross-sectionally. 49 out of 68 patients have
                      completed longitudinal assessment, so far. Second, we seek
                      to develop and implement a new method for semi-automatic
                      fiber tract delineation using active learning. By training
                      supportive machine learning algorithms on the fly that are
                      custom tailored to the respective analysis pipeline used to
                      obtain the tractogram as well as the WM tract of interest,
                      we plan to streamline and speed up this tedious and
                      error-prone task while at the same time increasing
                      reproducibility and robustness of the extraction process.
                      The goal is to develop robust neuroimaging biomarkers of
                      symptom severity and therapy outcome based on WM tracts
                      underlying catatonia. If our MRI study is successful, it
                      will be the largest longitudinal study to date that has
                      investigated WM tracts in catatonia patients.},
      keywords     = {Catatonia (Other) / DTI (Other) / Longitudinal (Other) /
                      MRI (Other) / Outcome (Other)},
      cin          = {E230 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)E230-20160331 / I:(DE-He78)HD01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37236889},
      doi          = {10.1016/j.schres.2023.05.011},
      url          = {https://inrepo02.dkfz.de/record/276335},
}