Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020; 23rd International C Anne L. Martel,Purang Abolmaesumi,Leo Joskow

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Parkinson’s Disease Detection from fMRI-Derived Brainstem Regional Functional Connectivity Networksvolvement in Parkinson’s disease, is largely unexplored in the domain of functional medical imaging. Here we propose a data-driven, connectivity-pattern based framework to extract functional sub-regions within the brainstem and devise a machine learning based tool that can discriminate Parkinson’s d

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Persistent Feature Analysis of Multimodal Brain Networks Using Generalized Fused Lasso for EMCI Iden By jointly analyzing cross-information among different neuroimaging data, an increased interest recently emerges in multimodal fusion to better understand clinical measurements with respect to both structural and functional connectivity. In this paper, we propose a novel multimodal brain network mo

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Recovering Brain Structural Connectivity from Functional Connectivity via Multi-GCN Based Generativeis critical for revealing organizational principles of human brain. However, brain’s many-to-one function-structure mode, i.e., diverse functional patterns may be associated with the same SC, and the complex direct/indirect interactions in both structural and functional connectivity make it challeng

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Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regressione common ancestors and the specific ones that might be related to individualized evolution strategies. Both the shared or specific patterns could help promote the understanding of mechanisms of brain structural and functional architectures and brain dynamics. Many previous studies can be found to re

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Self-weighted Multi-task Learning for Subjective Cognitive Decline Diagnosis’s disease (AD). Early diagnosis of MCI is important because early identification and intervention can delay or even reverse the progression of this disease. This paper proposes an automatic diagnostic framework for SCD and MCI. Specifically, we design a new multi-task learning model to integrate ne