Search Results for author: Andrew J. Saykin

Found 3 papers, 1 papers with code

Multidimensional representations in late-life depression: convergence in neuroimaging, cognition, clinical symptomatology and genetics

1 code implementation • 20 Oct 2021 • Junhao Wen, Cynthia H. Y. Fu, Duygu Tosun, Yogasudha Veturi, Zhijian Yang, Ahmed Abdulkadir, Elizabeth Mamourian, Dhivya Srinivasan, Jingxuan Bao, Guray Erus, Haochang Shou, Mohamad Habes, Jimit Doshi, Erdem Varol, Scott R Mackin, Aristeidis Sotiras, Yong Fan, Andrew J. Saykin, Yvette I. Sheline, Li Shen, Marylyn D. Ritchie, David A. Wolk, Marilyn Albert, Susan M. Resnick, Christos Davatzikos

We sought to delineate, cross-sectionally and longitudinally, disease-related heterogeneity in LLD linked to neuroanatomy, cognitive functioning, clinical symptomatology, and genetic profiles.

Anatomy

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Cognitive Biomarker Prioritization in Alzheimer's Disease using Brain Morphometric Data

no code implementations • 18 Feb 2020 • Bo Peng, Xiaohui Yao, Shannon L. Risacher, Andrew J. Saykin, Li Shen, Xia Ning

This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of the prioritization list.

Learning-To-Rank

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Deep Learning in Alzheimer's disease: Diagnostic Classification and Prognostic Prediction using Neuroimaging Data

no code implementations • 2 May 2019 • Taeho Jo, Kwangsik Nho, Andrew J. Saykin

The combination of traditional machine learning for classification and stacked auto-encoder (SAE) for feature selection produced accuracies of up to 98. 8% for AD classification and 83. 7% for prediction of conversion from mild cognitive impairment (MCI), a prodromal stage of AD, to AD.

Classification feature selection +1

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