ArticlebioRxiv : the preprint server for biology2025
Classifying Mild Cognitive Impairment from Normal Cognition: fMRI Complexity Matches Tau PET Performance.
Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Tau protein accumulation is closely linked to synaptic and neuronal loss in Alzheimer's disease (AD), resulting in progressive cognitive decline. Although tau-PET imaging is a direct biomarker of tau pathology, it is costly, carries radiation risks, and is not widely accessible. Resting-state functional MRI (rs-fMRI) complexity-an entropy-based measure of BOLD signal variation-has been proposed as a non-invasive surrogate biomarker of early neuronal dysfunction associated with tau pathology. Objectives: To determine whether fMRI-based brain complexity (sample entropy and multiscale entropy) can match or exceed tau-PET in classifying cognitively normal (CN) versus cognitively impaired (MCI/AD) individuals. And to investigate and compare the most influential network regions-of-interest (ROIs) for classification between fMRI complexity and tau-PET, thereby identifying key neuroanatomical correlates of AD-related changes. Design: A cross-sectional study employing 3D convolutional neural network (CNN) classification with five-fold cross-validation and leave-one-network-out analysis. Setting: Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Participants: One hundred forty-seven older adults (age 72.5 ± 7.5 years), including 95 CN, 45 MCI, and 7 AD. Measurements: We created whole-brain complexity maps from rs-fMRI and standardized uptake value ratio (SUVR) maps from tau-PET. Each modality was separately fed into CNN classifiers. Region-based analyses (leave-one-network-out) were performed to identify critical ROIs for classification. Results: fMRI complexity showed classification accuracy comparable to tau-PET yet surpassed it in F1-score (0.64 vs. 0.61) and area under the curve (AUC; 0.73 vs. 0.67). Salience and dorsal attention networks contributed most to fMRI-based classification, and a dorsal attention network contributed most to tau-PET-based classification. Conclusions: fMRI complexity performs similarly to tau-PET in detecting cognitive impairment related to AD and identifies partially distinct critical ROIs, suggesting an alternative, radiation-free imaging biomarker for earlier detection and broader clinical application.
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