FMRI complexity is associated with tau‐PET and cognitive decline in Alzheimer's disease: Development of new models and analysis methods: Novel imaging analysis. (7th December 2020)

• Record Type: Journal Article
• Authors: Jann, Kay; Kim, Hosung; Albrecht, Daniel; Wang, Danny J.J.
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 2
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.045411
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Amyloid‐PET is considered an early marker for the preclinical stage of AD, while the neurofibrillary tangle pathology detected with tau‐PET imaging correlates more closely with neuronal injury and cognitive decline. However, PET scans are expensive and involve radiation exposure. Recently, entropy measures have been explored as indices of the complexity of rs‐fMRI time‐series. Reduced entropy values were associated with aging, APOE ɛ4 genotype and cognitive decline in AD. Here we use complexity of BOLD signals as an index of the information processing capacity of regional neuron populations, and test the hypothesis that it is sensitive to tau‐related neuronal injury and cognitive decline in the AD processes. Method: Data used in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our cohort consisted of a sample of 50 subjects form ADNI‐3 (age = 72.4±8.2, 19M/31F, 25CN, 19MCI, 6AD) with a T1 structural, a tau‐PET (tracer: 18F‐AV1451) and two fMRI scans. FMRI data underwent standard preprocessing before computing Multi‐Scale Entropy (MSE). Parametric SUVR images were calculated with a cerebellar reference region. Correlations between SUVR tau‐PET and MSE were calculated for 195 ROIs (Craddock atlas parcellation) including age, gender and regional gray matter volume as covariates. To evaluate if MSE and PET can be used to classify AD from CN we trained and tested a random‐forest classifier ( via 10‐fold cross‐validation). Result: We found significant negative correlations between low frequency MSE and tau‐PET measures in hippocampus, parahippocampal gyrus and posterior cingulate cortex (Figure 2). The classification model revealed that MSE and PET have similar prediction accuracy and sensitivity (Table Figure 3A). The most informative areas for the prediction revealed a large degree of overlap between MSE and tau‐PET (Figure 3B). Conclusion: Correlation and classification results for MSE in relation with tau‐PET revealed areas in PCC, temporal and parietal lobes. These areas are implicated in tau pathology and cognitive decline. Hence, these overlapping findings support our hypothesis that the complexity of rs‐fMRI is associated with regional tau protein accumulation measured by PET and could provide a marker for information processing capacity of regional neuron populations and prediction of cognitive decline in the AD processes.