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Updated: Jan 12, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Integrating imaging and omics for enhanced subtyping of mild cognitive impairment associated with Alzheimer's disease
Sotiroula Afxenti1,2, Margarita Zachariou1, Efi Athieniti1
1Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Iroon Avenue 6, 2371, Nicosia, Cyprus.
Background:
Mild Cognitive Impairment (MCI), considered the prodromal stage of Alzheimer's disease (AD), is a heterogeneous condition characterised by mild but measurable cognitive decline. However, not all individuals with MCI follow the same trajectory-some remain stable, while others progress rapidly to AD. Understanding variation in clinical, molecular, and imaging features is crucial for reducing disease heterogeneity, improving prognosis, and developing targeted interventions. This study aims to increase MCI subtyping resolution by generating enriched individual-level profiles through the integration of imaging and omics data, facilitating precision medicine approaches for AD prevention and treatment.
Methods:
We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including structural MRI, CSF peptidomics/proteomics, and clinical indices. Using a multi-modal integration and clustering framework, we identified distinct MCI subgroups, characterised by clinical and neuropsychological scores, AD biomarkers, biological pathway enrichments, and imaging patterns. We further employed supervised multi-modal integration and correlation analyses to explore the links between imaging, peptidomic/proteomic and clinical features within each subgroup. Additionally, we labelled individuals by future conversion to AD and analysed longitudinal cognitive function (CDRSB and MMSE scores). Finally, we performed in silico drug repurposing to identify candidate drugs targeting each subgroup's molecular profile.
Results:
(1) Multi-modal integration revealed two distinct MCI subgroups. (2) The Resilient Neuronal Hyperplasticity subgroup was characterised by elevated markers of neuronal plasticity, minimal brain atrophy and cortical thinning, better clinical scores, and upregulated peptide/protein markers associated with less severe structural changes. In contrast, the Vulnerable Neurodegenerative subgroup exhibited AD-like disturbances, pronounced atrophy and cortical thinning, primarily affecting executive functions, and downregulation of peptide/protein markers linked to significant structural changes. (3) Future conversion analysis revealed the second subgroup predominantly comprised fast converters, while the first predominantly consisted of stable individuals. (4) Longitudinal cognitive analysis showed a more pronounced decline in the second subgroup compared to the first. (5) Drug repurposing identified both shared and subgroup-specific candidate compounds aligned with the underlying pathologies.
Conclusions:
This study delineates two MCI subgroups, using multi-modal integration, offering insights into disease heterogeneity and laying the foundation for precision medicine and AI-driven strategies in MCI and AD research and clinical care.
Insights
Researchers identified two distinct subgroups of Mild Cognitive Impairment (MCI), a precursor to Alzheimer's disease (AD). One subgroup showed resilience, while the other exhibited vulnerability, paving the way for personalized Alzheimer's disease treatments.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Genomics
Background:
- Mild Cognitive Impairment (MCI) represents a heterogeneous prodromal stage of Alzheimer's disease (AD).
- Understanding the variability in clinical, molecular, and imaging features is critical for improving prognosis and developing targeted interventions.
- Current approaches lack the resolution to fully capture MCI's heterogeneity, hindering precision medicine strategies.
Purpose of the Study:
- To enhance MCI subtyping resolution by integrating imaging and omics data for individualized profiles.
- To facilitate precision medicine approaches for Alzheimer's disease prevention and treatment.
- To identify distinct MCI subgroups with unique clinical, molecular, and imaging characteristics.
Main Methods:
- Utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including structural MRI, CSF peptidomics/proteomics, and clinical indices.
- Employed a multi-modal integration and clustering framework to identify MCI subgroups.
- Performed supervised multi-modal analyses, future conversion labeling, longitudinal cognitive function analysis, and in silico drug repurposing.
Main Results:
- Two distinct MCI subgroups were identified: 'Resilient Neuronal Hyperplasticity' and 'Vulnerable Neurodegenerative'.
- The 'Resilient' subgroup showed markers of plasticity, minimal atrophy, and better clinical scores, while the 'Vulnerable' subgroup exhibited AD-like changes, atrophy, and cognitive decline.
- The 'Vulnerable' subgroup predominantly consisted of fast converters to AD, whereas the 'Resilient' subgroup comprised stable individuals. Drug repurposing identified both shared and subgroup-specific candidate compounds.
Conclusions:
- This study successfully delineated two MCI subgroups through multi-modal data integration.
- The findings provide crucial insights into MCI heterogeneity, supporting the development of precision medicine.
- The research lays the groundwork for AI-driven strategies in MCI and AD research and clinical care.
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