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Updated: Jul 16, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Fine-grained age-matching improves atrophy-based detection of mild cognitive impairment more than amyloid-negative
Nils Richter1, Stefanie Brand2, Nils Nellessen3
1Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, 52425 Jülich, Germany; Department of Neurology, University Hospital Cologne and Faculty of Medicine, University of Cologne, 50937 Cologne, Germany.
Introduction:
In clinical practice, differentiating between age-related gray matter (GM) atrophy and neurodegeneration-related atrophy at early disease stages, such as mild cognitive impairment (MCI), remains challenging. We hypothesized that fined-grained adjustment for age effects and using amyloid-negative reference subjects could increase classification accuracy.
Methods:
T1-weighted magnetic resonance imaging (MRI) data of 131 cognitively normal (CN) individuals and 91 patients with MCI from the Alzheimer's disease neuroimaging initiative (ADNI) characterized concerning amyloid status, as well as 19 CN individuals and 19 MCI patients from an independent validation sample were segmented, spatially normalized and analyzed in the framework of voxel-based morphometry (VBM). For each participant, statistical maps of GM atrophy were computed as the deviation from the GM of CN reference groups at the voxel level. CN reference groups composed with different degrees of age-matching, and mixed and strictly amyloid-negative CN reference groups were examined regarding their effect on the accuracy in distinguishing between CN and MCI. Furthermore, the effects of spatial smoothing and atrophy threshold were assessed.
Results:
Approaches with a specific reference group for each age significantly outperformed all other age-adjustment strategies with a maximum area under the curve of 1.0 in the ADNI sample and 0.985 in the validation sample. Accounting for age in a regression-based approach improved classification accuracy over that of a single CN reference group in the age range of the patient sample. Using strictly amyloid-negative reference groups improved classification accuracy only when age was not considered.
Conclusion:
Our results demonstrate that VBM can differentiate between age-related and MCI-associated atrophy with high accuracy. Crucially, age-specific reference groups significantly increased accuracy, more so than regression-based approaches and using amyloid-negative reference groups.
Insights
Accurate differentiation between age-related gray matter atrophy and mild cognitive impairment (MCI) is crucial. Age-specific reference groups significantly improve classification accuracy in distinguishing MCI from normal aging.
Area of Science:
- Neuroimaging
- Neurology
- Biostatistics
Background:
- Distinguishing age-related gray matter (GM) atrophy from neurodegeneration-related atrophy in early stages like mild cognitive impairment (MCI) is clinically challenging.
- Hypothesized that fine-grained age adjustment and amyloid-negative reference subjects enhance classification accuracy.
Purpose of the Study:
- To evaluate the efficacy of age-specific reference groups in improving the accuracy of differentiating between normal aging and MCI using voxel-based morphometry (VBM).
- To compare the performance of age-adjusted VBM methods with amyloid-negative reference groups.
Main Methods:
- T1-weighted MRI data from Alzheimer's Disease Neuroimaging Initiative (ADNI) and an independent validation sample were analyzed using VBM.
- Gray matter (GM) atrophy maps were computed as voxel-wise deviations from age-matched and/or amyloid-status-defined cognitively normal (CN) reference groups.
- The impact of different age-matching strategies, amyloid status of reference groups, spatial smoothing, and atrophy thresholds on classification accuracy was assessed.
Main Results:
- Age-specific reference groups significantly outperformed other age-adjustment strategies, achieving maximum area under the curve (AUC) values of 1.0 (ADNI) and 0.985 (validation sample).
- Regression-based age adjustment improved accuracy compared to a single CN reference group.
- Using strictly amyloid-negative reference groups enhanced accuracy only when age was not considered.
Conclusions:
- Voxel-based morphometry (VBM) effectively differentiates age-related atrophy from MCI-associated atrophy with high accuracy.
- Age-specific reference groups represent a critical advancement, substantially increasing classification accuracy compared to regression-based methods and amyloid-negative reference groups.
