Machine learning classifiers and associations of cognitive performance with hippocampal subfields in amnestic mild cognitive impairment

Qi Feng; Luoyu Wang; Xue Tang; Xiuhong Ge; Hanjun Hu; Zhengluan Liao; Zhongxiang Ding

doi:10.3389/fnagi.2023.1273658

Machine learning classifiers and associations of cognitive performance with hippocampal subfields in amnestic mild cognitive impairment

Front Aging Neurosci. 2023 Nov 30:15:1273658. doi: 10.3389/fnagi.2023.1273658. eCollection 2023.

Authors

Qi Feng^#¹, Luoyu Wang^#¹, Xue Tang², Xiuhong Ge¹, Hanjun Hu³, Zhengluan Liao⁴, Zhongxiang Ding¹

Affiliations

¹ Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China.
² School of Medical Imaging, Hangzhou Medical College, Hangzhou, China.
³ Zhejiang Chinese Medical University, Hangzhou, China.
⁴ Department of Psychiatry, Zhejiang Provincial People's Hospital/People's Hospital of Hangzhou Medical College, Hangzhou, China.

^# Contributed equally.

Abstract

Background: Neuroimaging studies have demonstrated alterations in hippocampal volume and hippocampal subfields among individuals with amnestic mild cognitive impairment (aMCI). However, research on using hippocampal subfield volume modeling to differentiate aMCI from normal controls (NCs) is limited, and the relationship between hippocampal volume and overall cognitive scores remains unclear.

Methods: We enrolled 50 subjects with aMCI and 44 NCs for this study. Initially, a univariate general linear model was employed to analyze differences in the volumes of hippocampal subfields. Subsequently, two sets of dimensionality reduction methods and four machine learning techniques were applied to distinguish aMCI from NCs based on hippocampal subfield volumes. Finally, we assessed the correlation between the relative volumes of hippocampal subfields and cognitive test variables (Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA)).

Results: Significant volume differences were observed in several hippocampal subfields, notably in the left hippocampus. Specifically, the volumes of the hippocampal tail, subiculum, CA1, presubiculum, molecular layer, GC-ML-DG, CA3, CA4, and fimbria differed significantly between the two groups. The highest area under the curve (AUC) values for left and right hippocampal machine learning classifiers were 0.678 and 0.701, respectively. Moreover, the volumes of the left subiculum, left molecular layer, right subiculum, right CA1, right molecular layer, right GC-ML-DG, and right CA4 exhibited the strongest and most consistent correlations with MoCA scores.

Conclusion: Hippocampal subfield volume may serve as a predictive marker for aMCI. These findings underscore the sensitivity of hippocampal subfield volume to overall cognitive performance.

Keywords: Alzheimer’s disease; amnestic mild cognitive impairment; hippocampal subfields; machine learning; magnetic resonance imaging.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by Natural Science Foundation of Zhejiang Province (Y22H185692) and Zhejiang Provincial Medical and Health Technology Project (2024KY1313).