Abstract
Resting-state functional connectivity (rs-FC) is a promising neuromarker for cognitive decline in aging population, based on its ability to reveal functional differences associated with cognitive impairment across individuals, and because rs-fMRI may be less taxing for participants than task-based fMRI or neuropsychological tests. Here, we employ an approach that uses rs-FC to predict the Alzheimer's Disease Assessment Scale (11 items; ADAS11) scores, which measure overall cognitive functioning, in novel individuals. We applied this technique, connectome-based predictive modeling, to a heterogeneous sample of 59 subjects from the Alzheimer's Disease Neuroimaging Initiative, including normal aging, mild cognitive impairment, and AD subjects. First, we built linear regression models to predict ADAS11 scores from rs-FC measured with Pearson's r correlation. The positive network model tested with leave-one-out cross validation (LOOCV) significantly predicted individual differences in cognitive function from rs-FC. In a second analysis, we considered other functional connectivity features, accordance and discordance, which disentangle the correlation and anticorrelation components of activity timecourses between brain areas. Using partial least square regression and LOOCV, we again built models to successfully predict ADAS11 scores in novel individuals. Our study provides promising evidence that rs-FC can reveal cognitive impairment in an aging population, although more development is needed for clinical application.
Highlights
Cognitive decline occurs in both normal aging and neurodegenerative disorders (Hedden and Gabrieli, 2004; Jagust, 2013) and has a profound impact on individuals’ quality of life as well as life satisfaction (St John and Montgomery, 2010; Abrahamson et al, 2012)
The brain aging process accompanying such cognitive decline in normal aging is characterized by a tremendous level of heterogeneity, with various extents of dysfunction in multiple brain systems, most notably the default-mode network (DMN), which is critical for memory and the frontoparietal network, which is critical for executive functioning (Ferreira and Busatto, 2013; Jagust, 2013)
Alzheimer’s Disease Neuroimaging Initiative (ADNI) was launched in 2003 as a public-private partnership with the primary goal of testing whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s Disease (AD)
Summary
Cognitive decline occurs in both normal aging and neurodegenerative disorders (Hedden and Gabrieli, 2004; Jagust, 2013) and has a profound impact on individuals’ quality of life as well as life satisfaction (St John and Montgomery, 2010; Abrahamson et al, 2012). The brain aging process accompanying such cognitive decline in normal aging is characterized by a tremendous level of heterogeneity, with various extents of dysfunction in multiple brain systems, most notably the default-mode network (DMN), which is critical for memory and the frontoparietal network, which is critical for executive functioning (Ferreira and Busatto, 2013; Jagust, 2013) Such brain systems are subject to influences by neurodegenerative disorders, such as Alzheimer’s Disease (AD) and Functional Connectivity Predicts Cognitive Impairment mild cognitive impairment (MCI) (Buckner, 2004; Badhwar et al, 2016). Rs-fMRI does not involve the presentation of stimuli and is easier to standardize and share across different study sites
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