Alzheimer s & Dementia Diagnosis Assessment & Disease MonitoringNeurosciencePeer-reviewed journal article
Classifying Mild Cognitive Impairment from Normal Cognition: fMRI Complexity Matches Tau PET Performance
Kay JanniD, Gilsoon ParkiD, John Matthew RingmaniD, Hosung KimiD
Abstract
BackgroundTau protein accumulation is closely linked to synaptic and neuronal loss in Alzheimers 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. ObjectivesTo 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. DesignA cross-sectional study employing 3D convolutional neural network (CNN) classification with five-fold cross-validation and leave-one-network-out analysis. SettingData from the Alzheimers Disease Neuroimaging Initiative (ADNI) database. ParticipantsOne hundred forty-seven older adults (age 72.5 {+/-} 7.5 years), including 95 CN, 45 MCI, and 7 AD. MeasurementsWe 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. ResultsfMRI 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. ConclusionsfMRI 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.
- organism: human
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 3
- FWCI
- 2.02
- Field percentile
- top 13%
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