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000903648 005__ 20211217142227.0
000903648 037__ $$aFZJ-2021-05297
000903648 1001_ $$0P:(DE-Juel1)180537$$aNicolaisen, Eliana$$b0$$eFirst author$$ufzj
000903648 1112_ $$aINM & IBI Retreat$$cVirtual$$d2021-10-05 - 2021-10-06$$wGermany
000903648 245__ $$aHeritable and robust latent dimension linking cognition and multi-featured brain structure in healthy adults
000903648 260__ $$c2021
000903648 3367_ $$033$$2EndNote$$aConference Paper
000903648 3367_ $$2BibTeX$$aINPROCEEDINGS
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000903648 520__ $$aMultivariate methods are promoted to study solid brain-behaviour relationships1,2. Canonical Correlation Analysis (CCA) has been used to analyse latent dimensions of brain-behaviour interindividual variability3–5⁠. However, more robust results can be found with regularised CCA (RCCA) and assessing generalizability (model performance in new data) and stability (if different data splits yield similar results)6,7⁠. In addition, the heritability of these dimensions would help understand their genetic load.We studied robust brain-behaviour latent dimensions (Human Connectome Project; n: 1047, mean age: 28.78; 560 females). Brain structure included region-wise Grey Matter Volume (GMV; CAT12), Cortical Thickness and Surface Area (CT and SA respectively; FreeSurfer v5.3). Behavioural data spanned cognition, emotion and mental health. We used RCCA and a novel machine learning framework to optimise generalisability and stability6,7⁠. We also studied the heritability of the latent dimension.We found one significant latent dimension (r=0.46-0.32, p=0.04) positively associated with cognitive-control/executive-functions. CT loadings were negative in associative areas and positive in sensoriomotor areas. SA and GMV loadings were positive on the temporal pole, inferior temporal gyri, pars orbitalis, anterior cingulate cortex and postcentral gyri. GMV loadings were negative in cerebellum and basal ganglia. Heritability of brain (h2=0.85) and behavioural scores (h2=0.82) and their genetic correlation (rhog=0.66) were significant (p<0.000).The dimension found captured variability from higher to lower cognitive-control/executive-functions. Accordingly, brain loadings ranged from lower to higher regions of the brain cortex and were similar to the pattern of cortical expansion during evolution and human development. The brain and behavioural scores were found to be heritable and to have shared genetic factors. Better characterizing these dimensions should help to prevent pathological extremes.
000903648 536__ $$0G:(DE-HGF)POF4-5253$$a5253 - Neuroimaging (POF4-525)$$cPOF4-525$$fPOF IV$$x0
000903648 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x1
000903648 7001_ $$0P:(DE-Juel1)171719$$aKharabian, Shahrzad$$b1$$ufzj
000903648 7001_ $$0P:(DE-HGF)0$$aMihalik, Agoston$$b2
000903648 7001_ $$0P:(DE-HGF)0$$aFerreira, Fabio$$b3
000903648 7001_ $$0P:(DE-Juel1)178767$$aMaleki Balajoo, Somayeh$$b4$$ufzj
000903648 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b5$$ufzj
000903648 7001_ $$0P:(DE-HGF)0$$aThomas Yeo, B. T.$$b6
000903648 7001_ $$0P:(DE-HGF)0$$aMourao-Miranda, Janaina$$b7
000903648 7001_ $$0P:(DE-Juel1)161225$$aGENON, Sarah$$b8$$eCorresponding author$$ufzj
000903648 7001_ $$0P:(DE-Juel1)173843$$aValk, Sofie$$b9$$ufzj
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000903648 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171719$$aForschungszentrum Jülich$$b1$$kFZJ
000903648 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
000903648 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
000903648 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
000903648 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
000903648 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178767$$aForschungszentrum Jülich$$b4$$kFZJ
000903648 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b5$$kFZJ
000903648 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore$$b6
000903648 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore, Singapore$$b6
000903648 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom$$b7
000903648 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom$$b7
000903648 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)161225$$aForschungszentrum Jülich$$b8$$kFZJ
000903648 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)173843$$aForschungszentrum Jülich$$b9$$kFZJ
000903648 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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000903648 9141_ $$y2021
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000903648 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
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