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@INPROCEEDINGS{Nicolaisen:903647,
      author       = {Nicolaisen, Eliana and Kharabian, Shahrzad and Mihalik,
                      Agoston and Ferreira, Fabio and Maleki Balajoo, Somayeh and
                      Eickhoff, Simon and Mourao-Miranda, Janaina and GENON, Sarah
                      and Thomas Yeo, B. T. and Valk, Sofie},
      title        = {{T}wo latent dimensions linking multi-featured brain
                      structure to behaviour in healthy adults},
      reportid     = {FZJ-2021-05296},
      year         = {2021},
      abstract     = {The development of solid theories of brain-behaviour
                      relationships is weakened by a replication crisis and
                      publication biases1,2⁠. To overcome this, data-driven
                      multivariate approaches that integrate neurobiology and
                      behaviour have been promoted. For instance, Canonical
                      Correlation Analysis (CCA) can identify sets of behavioural
                      variables that correlate with sets of brain variables (i.e.,
                      latent dimensions). With such approach, studies in young
                      healthy adults found latent dimensions associated with mood
                      and cognitive-control/general-intelligence, but brain
                      patterns were relatively inconsistent3–5⁠. More robust
                      results could be found with a regularized version of CCA
                      (RCCA) embedded in a machine learning framework, assessing
                      generalizability (evaluating how the model performs in new
                      data) and stability (if similar variables are selected in
                      different data splits)6⁠. With this framework, we studied
                      robust latent dimensions linking behaviour and
                      multi-featured brain structure in young healthy adults.We
                      used T1 anatomical scans of the Human Connectome Project
                      Young Adult sample (S1200, n: 1047, age: 28.78 ±3.67 (mean
                      ±sd); 560 females). Grey Matter Volume (GMV) was estimated
                      with CAT12, and Cortical Thickness (CT) and Surface Area
                      (SA) with FreeSurfer v5.3. Brain data were averaged by
                      regions (200 cortical, 73 subcortical) using the Schaefer
                      atlas7⁠. Behavioural measures spanned cognition, emotion,
                      mental health and life outcome. RCCA linked the
                      multi-featured structural brain data (concatenating GMV, CT
                      and SA) with behaviour. In the main analysis, age and gender
                      were excluded of the model whilst in a supplementary
                      analysis both were regressed out from brain and behavioural
                      data. A multiple holdout machine learning framework6,8⁠
                      was used for model selection and statistical inference. In
                      particular, generalizability and stability were used to find
                      the optimal regularization parameters. Significance of the
                      latent dimensions was assessed with 1000 permutations
                      respecting the family structure of the data.We found two
                      latent dimensions. The first dimension (r=0.43-0.55,
                      p=0.015) was positively associated with high cognitive
                      abilities (such as fluid intelligence, working memory and
                      executive functions) and self/goal-driven aspects (Fig. 1A).
                      This dimension was positively associated with anterior
                      temporal and medial prefrontal (pole) regions (GMV and SA),
                      amygdala and hippocampus (GMV), and insula (SA and CT) (Fig
                      1B). This dimension remained significant when regressing out
                      gender and age. The second dimension (r=0.27-0.41, p=0.015)
                      was positively associated with cognition (including
                      attention, language and episodic memory) and negatively
                      associated with negative social behaviour (such as rule
                      breaking and aggression) (Fig 2A). Brain associations with
                      this dimension were positive in visual regions (CT) and
                      putamen (GMV), and negative in the cerebellum (GMV),
                      superior frontal regions (CT), as well as Broca’s area and
                      visual, paralimbic and language regions (SA) (Fig 2B).
                      Importantly, this dimension did not remain significant when
                      regressing out gender and age.Two dimensions of structural
                      brain-behaviour interindividual variability were found. The
                      first dimension mainly reflected higher cognitive and
                      self-driven functions. Accordingly, it was associated with
                      brain structural variability in higher-hierarchical regions
                      of the self-centric and motivation network. The second
                      dimension appears to mainly reflect education-related
                      performance aspects and was negatively associated with
                      negative social behaviour. Importantly, this dimension did
                      not remain significant when removing variance related to
                      gender categories and age, suggesting the influence of
                      demographical factors on this brain-behaviour dimension.
                      Hence, future studies should investigate the influence of
                      genetic vs environmental factors on these dimensions, in
                      particular with heritability assessment. Better
                      characterizing these dimensions should help to prevent
                      pathological extremes.},
      month         = {Jun},
      date          = {2021-06-21},
      organization  = {27th Annual Meeting of the
                       Organization for Human Brain Mapping,
                       Virtual (Virtual), 21 Jun 2021 - 25 Jun
                       2021},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/903647},
}