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000903647 005__ 20211217142227.0
000903647 037__ $$aFZJ-2021-05296
000903647 1001_ $$0P:(DE-Juel1)180537$$aNicolaisen, Eliana$$b0$$eFirst author
000903647 1112_ $$a27th Annual Meeting of the Organization for Human Brain Mapping$$cVirtual$$d2021-06-21 - 2021-06-25$$gOHBM$$wVirtual
000903647 245__ $$aTwo latent dimensions linking multi-featured brain structure to behaviour in healthy adults
000903647 260__ $$c2021
000903647 3367_ $$033$$2EndNote$$aConference Paper
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000903647 520__ $$aThe 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.
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000903647 7001_ $$0P:(DE-Juel1)171719$$aKharabian, Shahrzad$$b1
000903647 7001_ $$0P:(DE-HGF)0$$aMihalik, Agoston$$b2
000903647 7001_ $$0P:(DE-HGF)0$$aFerreira, Fabio$$b3
000903647 7001_ $$0P:(DE-Juel1)178767$$aMaleki Balajoo, Somayeh$$b4
000903647 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b5
000903647 7001_ $$0P:(DE-HGF)0$$aMourao-Miranda, Janaina$$b6
000903647 7001_ $$0P:(DE-Juel1)161225$$aGENON, Sarah$$b7$$eCorresponding author
000903647 7001_ $$0P:(DE-HGF)0$$aThomas Yeo, B. T.$$b8
000903647 7001_ $$0P:(DE-Juel1)173843$$aValk, Sofie$$b9
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000903647 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom$$b2
000903647 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom$$b2
000903647 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom$$b3
000903647 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom$$b3
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000903647 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom$$b6
000903647 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom$$b6
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000903647 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore, Singapore$$b8
000903647 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore, Singapore$$b8
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000903647 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)173843$$a Otto Hahn Research Group “Cognitive Neurogenetics”, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany$$b9
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000903647 9141_ $$y2021
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000903647 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
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