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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Predictors of Transition from Mild Cognitive Impairment to Normal Cognition and Dementia
Jiage Gao1,2, Lin Liu1,2, Zifeng Yang1,2
1Institute of Psychological and Brain Sciences, Liaoning Normal University, Dalian 116029, China.
Abstract:
Mild cognitive impairment (MCI) represents a heterogeneous state between normal aging and dementia, with varied transition pathways. While factors influencing MCI progression are known, their role in cognitive reversal is unclear. This study analyzed 756 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, classified as progressive MCI (pMCI, N = 272, mean age = 75.10 ± 7.34 years), reversible MCI (rMCI, N = 52, mean age = 69.94 ± 7.98 years) and stable MCI (sMCI, N = 432, mean age = 73.34 ± 7.44 years) based on 36-month follow-up. We compared demographic, lifestyle, clinical, cognitive, neuroimaging, and biomarker data across groups and developed a prediction model. Patients in the rMCI group were significantly younger and had a higher level of education compared with those in the pMCI group. Memory, general cognition, daily functional activities, and hippocampal volume effectively distinguished all three groups. In contrast, Aβ, tau, and other brain regions were able to distinguish only between progressive and non-progressive cases. Informant-reported Everyday Cognition (Ecog) scales outperformed self-reported Ecog scales in differentiating subtypes and predicting progression. Multinomial regression revealed that higher education, larger hippocampal volume, and lower daily functional impairment were associated with reversion, whereas APOE ε4, poorer memory, and greater brain atrophy predicted progression (model accuracy: 78%). The results confirm the significant utility of hippocampal volume, education level, and daily functional activities for assessing baseline disparities and predicting reversion. This study highlights the differential contributions of cognitive abilities and brain regions on MCI reversal, advancing understanding of MCI heterogeneity and providing evidence for precise diagnosis and treatment in early MCI.
Insights
Mild cognitive impairment (MCI) can reverse in some individuals. Higher education, larger hippocampal volume, and better daily function predict MCI reversion, while genetic factors and brain atrophy predict progression.
Area of Science:
- Neuroscience
- Gerontology
- Cognitive Science
Background:
- Mild cognitive impairment (MCI) is a transitional state between normal aging and dementia.
- Factors influencing MCI progression are known, but their role in cognitive reversal remains unclear.
- Understanding MCI heterogeneity is crucial for early diagnosis and treatment.
Purpose of the Study:
- To investigate factors associated with cognitive reversion in MCI.
- To differentiate between progressive, reversible, and stable MCI subtypes.
- To develop a prediction model for MCI outcomes.
Main Methods:
- Analysis of 756 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with 36-month follow-up.
- Comparison of demographic, lifestyle, clinical, cognitive, neuroimaging, and biomarker data across MCI subtypes (pMCI, rMCI, sMCI).
- Development of a prediction model using multinomial regression.
Main Results:
- Reversible MCI (rMCI) patients were younger and more educated than progressive MCI (pMCI) patients.
- Memory, general cognition, daily function, and hippocampal volume distinguished all three MCI groups.
- Higher education, larger hippocampal volume, and lower daily functional impairment predicted reversion; *APOE ε4*, poorer memory, and brain atrophy predicted progression (model accuracy: 78%).
Conclusions:
- Hippocampal volume, education level, and daily functional activities are key indicators for assessing MCI disparities and predicting reversion.
- Cognitive abilities and specific brain regions play differential roles in MCI reversal.
- Findings advance the understanding of MCI heterogeneity and support precise diagnosis and treatment strategies.
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