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Updated: Jul 28, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Comparing three neuropsychological subgrouping approaches in subjective and mild cognitive impairment from a
Anna Rennie1, Urban Ekman2, John Wallert3
1Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institute, Stockholm, Sweden; Theme Inflammation and Aging, Karolinska University Hospital, Stockholm, Sweden.
Abstract:
Subjective cognitive impairment (SCI) and mild cognitive impairment (MCI) are two clinical groups with an increased risk to develop dementia, but they are highly heterogeneous. This study compared three different approaches to subgroup SCI and MCI patients and investigated their capacity to disentangle cognitive and biomarker heterogeneity. We included 792 patients from the MemClin-cohort (142 SCI and 650 MCI). Biomarkers included cerebrospinal fluid measures of beta-amyloid-42 and phosphorylated tau, as well as visual ratings of medial temporal lobe atrophy and white matter hyperintensities on magnetic resonance imaging. We found that a more inclusive approach identified individuals with a positive beta-amyloid-42 biomarker; a less inclusive approach captured individuals with higher medial temporal lobe atrophy; and a data-driven approach captured individuals with high white matter hyperintensities burden. The three approaches also captured some neuropsychological differences. We conclude that choice of approach may differ depending on the purpose. This study helps to advance our current understanding of the clinical and biological heterogeneity within SCI and MCI, particularly in the unselected memory clinic setting.
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