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Cognitive Function Assessment and Prediction for Subjective Cognitive Decline and Mild Cognitive Impairment

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Abstract

Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative dementia. Recent studies found that subjective cognitive decline (SCD) may be the early clinical precursor that precedes mild cognitive impairment (MCI) for AD. SCD subjects with normal cognition may already have some medial temporal lobe atrophy. Although brain changes by AD have been widely studied in the literature, it is still challenging to investigate the anatomical subtle changes in SCD. This paper proposes a machine learning framework by combination of sparse coding and random forest (RF) to identify the informative imaging biomarkers for assessment and prediction of cognitive functions and their changes in individuals with MCI, SCD and normal control (NC) using magnetic resonance imaging (MRI). First, we compute the volumes from both the regions of interest from whole brain and the subregions of hippocampus and amygdala as the features of structural MRIs. Then, sparse coding is applied to identify the relevant features. Finally, the proximity-based RF is used to combine three sets of volumetric features and establish a regression model for predicting clinical scores. Our method has double feature selections to better explore the relevant features for prediction and is evaluated with the T1-weighted structural MR images from 36 MCI, 112 SCD, 78 NC subjects. The results demonstrate the effectiveness of proposed method. In addition to hippocampus and amygdala, we also found that the fimbria, basal nucleus and cortical nucleus subregions are more important than other regions for prediction of Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores and their changes.

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Funding

This study was supported in part by Natural Science Foundation of Shanghai (20ZR1426300), Shanghai Jiao Tong University Scientific and Technological Innovation Funds (2019QYB02), and by the China Ministry of Science and Technology grant (2009BAI77B03), Clinical Research Center of Shanghai Mental Health Center (CRC2017ZD02, 2018-FX-05), an ECNU-SJTU joint grant from the Basic Research Project of Shanghai Science and Technology Commission (No.19JC1410102) and Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102).

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Authors and Affiliations

  1. Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, Shanghai, China

    Aojie Li & Manhua Liu

  2. Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Ling Yue & Shifu Xiao

  3. Alzheimer’s Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China

    Ling Yue & Shifu Xiao

  4. MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China

    Manhua Liu

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  1. Aojie Li
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  2. Ling Yue
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  3. Shifu Xiao
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  4. Manhua Liu
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Correspondence to Ling Yue, Shifu Xiao or Manhua Liu.

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Li, A., Yue, L., Xiao, S. et al. Cognitive Function Assessment and Prediction for Subjective Cognitive Decline and Mild Cognitive Impairment. Brain Imaging and Behavior 16, 645–658 (2022). https://doi.org/10.1007/s11682-021-00545-1

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