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Updated: Feb 4, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Examining differences in neuropsychiatric symptom factor trajectories in empirically derived mild cognitive
Alyssa N De Vito1, Matthew Calamia1, Daniel S Weitzner1
1Department of Psychology, Louisiana State University, Baton Rouge, Louisiana.
Objective:
The aim of this study was to examine neuropsychiatric symptom (NPS) factor severity progression over time in empirically derived (ED) mild cognitive impairment (MCI) subtypes.
Methods:
Participants in the Alzheimer's Disease Neuroimaging Initiative study diagnosed with MCI by Alzheimer's Disease Neuroimaging Initiative protocol using conventional clinical (CC) criteria (n = 788) were reclassified using cluster analysis as amnestic, dysnomic, dysexecutive MCI, or cluster-derived normal (CC-Normal) using empirical criteria. Cognitively normal (CN) participants (n = 207) were also identified. The Neuropsychiatric Inventory-Questionnaire (NPI-Q) was administered from baseline through 48-month follow-up. Exploratory factor analysis was completed to determine the NPI-Q factor structure at 6-month follow-up. Multilevel modeling was used to determine NPI-Q symptom severity factor and apathy symptom progression over time by cognitive subtype.
Results:
The exploratory factor analysis revealed that the NPI-Q consisted of 2 factors: hyperactivity/agitation and mood symptoms. Using clinical and empirical criteria, all MCI groups were identified as having more severe hyperactivity/agitation symptoms than CN participants. However, only the amnestic MCI group identified using empirical criteria showed an increase in symptom severity over time relative to CN participants. Mood factor and apathy symptoms were found to be more severe in dysexecutive and amnestic groups in both models. Similarly, both models identified a significant worsening of mood and apathy symptoms over time for dysexecutive and amnestic groups relative to CN participants.
Conclusions:
This study provides further support that empirical criteria aid in examining the progression of clinical characteristics associated with MCI. Further, it helps to identify which MCI subtypes may be at higher risk for NPS progression.
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