Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellation
Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellation
Abstract Understanding the topographic heterogeneity of cortical organisation is an essential step towards precision modelling of neuropsychiatric disorders. While many cortical parcellation schemes have been proposed, few attempt to model inter-subject variability. For those that do, most have been proposed for high-resolution research quality data, without exploration of how well they generalise to clinical quality scans. In this paper, we benchmark and ensemble four different geometric deep learning models on the task of learning the Human Connectome Project (HCP) multimodal cortical parcellation. We employ Monte Carlo dropout to investigate model uncertainty with a view to propagate these labels to new datasets. Models achieved an overall Dice overlap ratio of >0.85 ± 0.02. Regions with the highest mean and lowest variance included V1 and areas within the parietal lobe, and regions with the lowest mean and highest variance included areas within the medial frontal lobe, lateral occipital pole and insula. Qualitatively, our results suggest that more work is needed before geometric deep learning methods are capable of fully capturing atypical cortical topographies such as those seen in area 55b. However, information about topographic variability between participants was encoded in vertex-wise uncertainty maps, suggesting a potential avenue for projection of this multimodal parcellation to new datasets with limited functional MRI, such as the UK Biobank.
Williams Logan Z. J.、Glasser Matthew F.、David Edwards A.、Robinson Emma C.、Fawaz Abdulah
Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King?ˉs College London||Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science,King?ˉs College LondonDepartments of Radiology and Neuroscience, Washington University Medical SchoolCentre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King?ˉs College London||Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King?ˉs College London||MRC Centre for Neurodevelopmental Disorders, King?ˉs College LondonCentre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King?ˉs College London||Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science,King?ˉs College LondonDepartment of Biomedical Engineering, School of Biomedical Engineering and Imaging Science,King?ˉs College London
神经病学、精神病学基础医学生物科学研究方法、生物科学研究技术
Human Connectome ProjectGeometric Deep LearningCortical Parcellation
Williams Logan Z. J.,Glasser Matthew F.,David Edwards A.,Robinson Emma C.,Fawaz Abdulah.Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellation[EB/OL].(2025-03-28)[2025-05-05].https://www.biorxiv.org/content/10.1101/2021.08.18.456790.点此复制
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