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@PHDTHESIS{Krll:1047084,
      author       = {Kröll, Jean-Philippe},
      title        = {{I}nterpretability and {R}eliability in {N}euroimaging},
      school       = {HHU Düsseldorf},
      type         = {Dissertation},
      reportid     = {FZJ-2025-04110},
      pages        = {91},
      year         = {2025},
      note         = {Dissertation, HHU Düsseldorf, 2025},
      abstract     = {The development of magnetic resonance imaging (MRI) based
                      biomarkers is a constant endeavorin the field of clinical
                      neuroscience. Although these biomarkers hold great
                      potential, only few havebeen adopted for routine clinical
                      use. Primary challenges for the translation into clinical
                      use areaccuracy, reliability and interpretability of a given
                      biomarker. Consequently, this dissertationpresents a new
                      machine learning (ML) framework that improves accuracy of
                      diagnosis andprognosis of one of the most common
                      neurological diseases, AlzheimerÕ Disease (AD),
                      byconstructing complex representations of base features.
                      Further, by using a context-free grammar(CFG), the
                      constructed representations are forced to remain humanly
                      interpretable, thus enablingthe validation of a relationship
                      between the biomarker and the supposed underlying
                      pathologiccorrelate. Additionally, it is investigated if
                      naturalistic viewing (NV) paradigms are suited toimprove
                      characteristics of MRI measurements that are important for
                      biomarker development, suchas reliability, reduced
                      intra-subject variability and enhanced detection of
                      individual differences, incomparison with resting-state
                      (RS). Therefore, the effect of NV stimuli with varying
                      levels of socialcontent and different lengths is
                      investigated in 14 functional brain networks. It is shown
                      that, basedon network functional connectivity (NFC), NV
                      stimuli improve the detection of individualdifferences in 10
                      out of 14 networks, with the stimuli with the highest level
                      of social contentachieving the most improvement. A
                      subsequent analysis confirms that movie stimuli with
                      higherlevels of social content evoke similar NFC patterns
                      that are distinct from RS and a stimulus lackingsocial
                      interactions. Further, it is demonstrated that NV stimuli
                      can reduce intra-subject variabilityin meta-analytic
                      networks that are essential for perception and processing of
                      action, behavior andemotions. In addition, it is shown that
                      NV stimuli can increase the reliability of graph
                      metricsextracted from NFC, over RS. However, the results
                      also emphasize that NV stimuli do notunconditionally improve
                      metrics of interest across the whole brain. In particular
                      for networks thatare related to intrinsically oriented
                      functions, RS proves to be the more favorable
                      paradigm.Therefore, selecting the appropriate stimulus and
                      functional network is essential for addressing thespecific
                      research question at hand. Finally, this dissertation
                      provides a new publicly available NVdataset to further
                      analyze the effect of NV stimuli.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.34734/FZJ-2025-04110},
      url          = {https://juser.fz-juelich.de/record/1047084},
}