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Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain

S. Hofmann, F. Beyer, S. Lapuschkin, O. Goltermann, M. Loeffler, K. M√ºller, A. Villringer, W. Samek, and A. Witte. NeuroImage, (2022)
DOI: https://doi.org/10.1016/j.neuroimage.2022.119504

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Genetic architecture of subcortical brain structures in 38,851 individualsC. Satizabal, H. Adams, D. Hibar, C. White, M. Knol, J. Stein, M. Scholz, M. Sargurupremraj, N. Jahanshad, G. Roshchupkin and 279 other author(s). Nat. Genet., 51 (11): 1624--1636 (November 2019)Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adultsE. Hofer, G. Roshchupkin, H. Adams, M. Knol, H. Lin, S. Li, H. Zare, S. Ahmad, N. Armstrong, C. Satizabal and 90 other author(s). Nat. Commun., 11 (1): 4796 (September 2020)Common genetic variation indicates separate causes for periventricular and deep white matter hyperintensitiesN. Armstrong, K. Mather, M. Sargurupremraj, M. Knol, R. Malik, C. Satizabal, L. Yanek, W. Wen, V. Gudnason, N. Dueker and 76 other author(s). Stroke, 51 (7): 2111--2121 (July 2020)Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive functionG. Davies, M. Lam, S. Harris, J. Trampush, M. Luciano, W. Hill, S. Hagenaars, S. Ritchie, R. Marioni, C. Fawns-Ritchie and 212 other author(s). Nat. Commun., (May 2018)Genome-wide association study of 23,500 individuals identifies 7 loci associated with brain ventricular volumeD. Vojinovic, H. Adams, X. Jian, Q. Yang, A. Smith, J. Bis, A. Teumer, M. Scholz, N. Armstrong, E. Hofer and 81 other author(s). Nat. Commun., 9 (1): 3945 (September 2018)Genome-wide association and transcriptome analysis suggests total serum ghrelin to be linked with GFRALD. Wittekind, M. Scholz, J. Kratzsch, M. Löffler, K. Horn, H. Kirsten, V. Witte, A. Villringer, and M. Kluge. Eur. J. Endocrinol., 184 (6): 847--856 (June 2021)Higher BMI, but not obesity-related genetic polymorphisms, correlates with lower structural connectivity of the reward network in a population-based studyF. Beyer, R. Zhang, M. Scholz, K. Wirkner, M. Loeffler, M. Stumvoll, A. Villringer, and A. Witte. Int. J. Obes. (Lond), 45 (3): 491--501 (March 2021)Global and regional development of the human cerebral cortex: Molecular architecture and occupational aptitudesJ. Shin, S. Ma, E. Hofer, Y. Patel, D. Vosberg, S. Tilley, G. Roshchupkin, A. Sousa, X. Jian, R. Gottesman and 60 other author(s). Cereb. Cortex, 30 (7): 4121--4139 (June 2020)Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imagingL. Lampe, H. Huppertz, S. Anderl-Straub, F. Albrecht, T. Ballarini, S. Bisenius, K. Mueller, S. Niehaus, K. Fassbender, K. Fliessbach and 13 other author(s). NeuroImage Clin., 37 (103320): 103320 (January 2023)Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brainS. Hofmann, F. Beyer, S. Lapuschkin, O. Goltermann, M. Loeffler, K. M√ºller, A. Villringer, W. Samek, and A. Witte. NeuroImage, (2022)

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Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain

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Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain