Machine Learning Decomposition of the Anatomy of Neuropsychological Deficit in Alzheimer's Disease and Mild Cognitive Impairment

Ningxin Dong; Changyong Fu; Renren Li; Wei Zhang; Meng Liu; Weixin Xiao; Hugh M Taylor; Peter J Nicholas; Onur Tanglay; Isabella M Young; Karol Z Osipowicz; Michael E Sughrue; Stephane P Doyen; Yunxia Li

doi:10.3389/fnagi.2022.854733

Machine Learning Decomposition of the Anatomy of Neuropsychological Deficit in Alzheimer's Disease and Mild Cognitive Impairment

Front Aging Neurosci. 2022 May 3:14:854733. doi: 10.3389/fnagi.2022.854733. eCollection 2022.

Authors

Affiliations

¹ Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
² Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
³ Omniscient Neurotechnology, Sydney, NSW, Australia.
⁴ International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi'an, China.

Abstract

Objective: Alzheimer's Disease (AD) is a progressive condition characterized by cognitive decline. AD is often preceded by mild cognitive impairment (MCI), though the diagnosis of both conditions remains a challenge. Early diagnosis of AD, and prediction of MCI progression require data-driven approaches to improve patient selection for treatment. We used a machine learning tool to predict performance in neuropsychological tests in AD and MCI based on functional connectivity using a whole-brain connectome, in an attempt to identify network substrates of cognitive deficits in AD.

Methods: Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 149 MCI, and 85 AD patients; and 140 cognitively unimpaired geriatric participants. A novel machine learning tool, Hollow Tree Super (HoTS) was utilized to extract feature importance from each machine learning model to identify brain regions that were associated with deficit and absence of deficit for 11 neuropsychological tests.

Results: 11 models attained an area under the receiver operating curve (AUC-ROC) greater than 0.65, while five models had an AUC-ROC ≥ 0.7. 20 parcels of the Human Connectome Project Multimodal Parcelation Atlas matched to poor performance in at least two neuropsychological tests, while 14 parcels were associated with good performance in at least two tests. At a network level, most parcels predictive of both presence and absence of deficit were affiliated with the Central Executive Network, Default Mode Network, and the Sensorimotor Networks. Segregating predictors by the cognitive domain associated with each test revealed areas of coherent overlap between cognitive domains, with the parcels providing possible markers to screen for cognitive impairment.

Conclusion: Approaches such as ours which incorporate whole-brain functional connectivity and harness feature importance in machine learning models may aid in identifying diagnostic and therapeutic targets in AD.

Keywords: MRI; cognitive impairment; machine learning; neural networks; neuroimaging markers; neuropsychological tests.