Author

Andrea Mechelli

King's College LondonORCID 0000-0002-6770-2934Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:imaging· 2 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:dimensionality reduction· 1 Approach:statistical model· 1

Frequent Co-Authors

Alberto RedolfiiD· 1 shared Bangshan LiuiD· 1 shared Ching‐Po LiniD· 1 shared Chu‐Chung HuangiD· 1 shared Cristina ScarpazzaiD· 1 shared DIDA-Major Depressive Disorder Working Group· 1 shared Dongtao WeiiD· 1 shared Fei WangiD· 1 shared

Activity by Year

2020-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperNeuroscience

Alberto RedolfiORCID

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2 papers

Recent·Most cited

Connectome Gradient Dysfunction in Major Depression and Its Association with Gene Expression Profiles

Mingrui Xia, Jin Liu, Andrea Mechelli, Xiaoyi Sun, Qing Ma, Xiaoqin Wang, Dongtao Wei, Yuan Chen, Bangshan Liu, Chu‐Chung Huang, Yanting Zheng, Yankun Wu, Taolin Chen, Yuqi Cheng, Xiufeng Xu, , DIDA-Major Depressive Disorder Working Group

This study identifies dysfunction in the principal connectome gradient in MDD and links it to gene expression profiles related to transsynaptic signaling and calcium ion binding.

bioRxiv · Oct 24, 2020 · PreprintNeuroscience

Normative modelling using deep autoencoders: a multi-cohort study on mild cognitive impairment and Alzheimer's disease

Walter Hugo Lopez Pinaya, Cristina Scarpazza, Rafael Garcia‐Dias, Sandra Vieira, Lea Baecker, Pedro F. da Costa, Alberto Redolfi, Giovanni Battista Frisoni, Michela Pievani, Vince D. Calhoun, João Ricardo Sato, Andrea Mechelli, the Alzheimer’s Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of ageing

This study applies deep autoencoders to structural neuroimaging data from Alzheimer's and mild cognitive impairment patients to quantify deviations from healthy norms and compare performance with traditional classifiers.

bioRxiv · Feb 10, 2020 · PreprintNeuroscience