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Structural connectome and machine learning for Alzheimer’s disease detection
Author(s) -
Francis Farah,
Cabez Manuel Blesa,
Smith Keith,
Luz Saturnino
Publication year - 2021
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.057514
Subject(s) - connectomics , connectome , diffusion mri , computer science , artificial intelligence , metrics , neuroscience , clustering coefficient , neuroimaging , cluster analysis , pattern recognition (psychology) , machine learning , psychology , magnetic resonance imaging , medicine , functional connectivity , static routing , computer network , routing protocol , routing (electronic design automation) , radiology
Background Alzheimer's disease (AD) is hypothesised as a disconnection syndrome where degenerating white matter fibre bundles leads to deterioration in the integration and communication between brain regions. Connectomics allows the study of in vivo brain connectivity and elucidates how the disease changes the brain network. Although some studies have shown evidence of alteration of structural connectivity between AD and cognitively normal individuals (CN), a large proportion of research focused on functional connectomics in AD. Emerging connectomics studies explored the use of machine learning (ML) to distinguish brain structural connectivity differences in AD with promising results. This project aims to identify changes in the structural connectome using novel image processing techniques to generate network metrics and utilise ML to classify between AD and CN. Method We examined data from 143 age‐matched subjects (AD mean: 71.1 ± 2.79 and CN mean: 71.09 ± 2.72) from the Alzheimer's Disease Neuroimaging Initiative cohort 2 (ADNI2). We used magnetic resonance images (T1‐weighted and diffusion‐weighted images) combined with the latest state‐of‐the‐art imaging processing tools to generate structural connectomes. Relevant network metrics were used to measure and compare brain connectivity, while ML algorithms were used to distinguish network metrics between AD and CN. Result We found significant connectivity changes in clustering coefficient (p < 0.05), normalised degree variance (p < 0.0001), hierarchical complexity (p < 0.005) and rich club (p < 0.0001) in AD (table 1). We also established and compared classification performances within our ML model. Random forest yielded sensitivity of 53.06% and specificity of 82.98% (table 2) for imbalanced data (AD=49, CN=94). On balanced data (AD=CN=49), the model was 81.63% specific and 69.39% sensitive (table 3) in detecting AD. Conclusion The results show the feasibility of a connectome analysis of structural imaging combining the latest network metrics with ML for AD detection. While previous ML studies achieved promising results with balanced data, we reported both balanced and imbalanced models. As real‐world AD data are more likely to be imbalanced, the lower performance of the ML models on imbalanced data suggests that further improvement is needed for clinical implementation.