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@PHDTHESIS{Wiersch:1038525,
      author       = {Wiersch, Lisa},
      title        = {{M}ultivariate {S}tatistical {A}pproaches to investigate
                      {S}ex {D}ifferences in {B}rain and {C}ognition},
      school       = {HHU Düsseldorf},
      type         = {Dissertation},
      reportid     = {FZJ-2025-01510},
      pages        = {92},
      year         = {2024},
      note         = {Dissertation, HHU Düsseldorf, 2024},
      abstract     = {Decoding individual variability in cognition and brain
                      organization is essential to enhanceour understanding of
                      heterogeneity in the brain and behavior. Individual
                      variability is oftenrelated to specific demographic
                      phenotypes, with sex being a prominent phenotypecontributing
                      to individual variability. Examining how differences between
                      males andfemales are reflected in cognitive and neuroimaging
                      data advances the understanding of sexdifferences in
                      cognitive processing, brain organization, and the
                      heterogeneity ofneuropsychological and mental diseases. To
                      characterize common sources of variability suchas sex, the
                      present work aims to present multivariate statistical
                      methods as powerful tools toidentify patterns in complex
                      datasets such as neuroimaging or cognitive data
                      (commentary).By using multivariate statistical approaches,
                      the present work examines sex differences
                      inneuropsychological (study 1) and brain imaging data (study
                      2 $\&$ study 3). Specifically, study1 examined sex-specific
                      cognitive profiles derived from a battery of
                      neuropsychological testsusing structural equation modeling.
                      Studies 2 and 3 supplement this investigation byexamining
                      sex-related variability in the functional (study 2) and
                      structural (study 3) brainorganization using machine
                      learning (ML) approaches. Additionally,
                      methodologicalconsiderations in ML were taken into account
                      such as the influence of training samples onthe
                      generalization performance of ML models (study 2) and the
                      influence of confoundingvariables (study 3).The commentary
                      highlighted the importance of new methodological approaches
                      such asmultivariate statistical learning to enhance our
                      understanding of the complex nature of sexdifferences in
                      rich data. Study 1 identified sex-specific cognitive
                      profiles pertaining to sexdifferences in component solutions
                      in cognitive processing strategies. Results of study
                      2revealed sex differences in the functional brain
                      organization for some, but not all brainregions, with the
                      highest generalization performance when sex classification
                      models weretrained on a large and heterogeneous sample
                      comprising the data of multiple datasets. Study3
                      demonstrated sex differences in the structural brain
                      organization by accurately classifyingsex with ML models
                      that were debiased for the confounding influence of brain
                      size bymatching males and females for brain size. In sum,
                      the present studies demonstrated thatmultivariate
                      statistical approaches can effectively decode sex-related
                      variability in cognitiveas well as structural and functional
                      brain imaging data while incorporating
                      importantmethodological considerations.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5252 - Brain Dysfunction and Plasticity
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.34734/FZJ-2025-01510},
      url          = {https://juser.fz-juelich.de/record/1038525},
}