Deviation from Model of Normal Aging in Alzheimer's Disease: Application of Deep Learning to Structural MRI Data and Cognitive Tests

Tetiana Habuza, Nazar Zaki, Elfadil A. Mohamed, Yauhen Statsenko

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Background. Psychophysiological and cognitive tests as well as other functional studies can detect pre-symptomatic stages of dementia. When assembled with structural data, cognitive tests diagnose NDs more reliably thus becoming a multimodal diagnostic tool. Objective. Our main goal is to improve screening for dementia by studying an association between the brain structure and its function. Hypothetically, the brain structure-function association has features specific for either disease-related cognitive deterioration or normal neurocognitive slowing while aging. Materials and methods. We studied a total number of 287 cognitively normal cases, 646 of mild cognitive impairment, and 369 of Alzheimer's disease. To work out a new marker of neurodegeneration, we created a convolutional neural network-based regression model and predicted the cognitive status of the cognitively preserved examinee from the brain MRI data. This was a model of normal aging. A big deviation from the model suggests a high risk of accelerated cognitive decline. Results. The deviation from the model of normal aging can accurately distinguish cognitively normal subjects from MCI patients (AUC = 0.9957). We also achieved creditable performance in the MCI-versus-AD classification (AUC = 0.9793). We identified a considerable difference in the MMSE test between A-positive and A-negative demented individuals according to ATN-criteria (6.27±1.82 vs 5.32±1.9; p< 0.05). Conclusion. The deviation from the model of normal aging can be potentially used as a marker of dementia and as a tool for differentiating Alzheimer's disease from non-Alzheimer's dementia.

Original languageEnglish
Pages (from-to)53234-53249
Number of pages16
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 2022

Keywords

  • Alzheimer’s disease
  • Error of cognitive score prediction
  • aging
  • biomarker
  • cognitive decline
  • convolutional neural network
  • deep learning
  • dementia
  • neuroimaging

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering

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