Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease

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.

Behavioral and neural signatures of working memory in childhood

“…Our results are not inconsistent with this conceptualization, but demonstrate that a high versus low A neural signature of working memory based on task activation data complements a growing body of work identifying neuromarkers of individual differences from functional brain connectivity. In particular, patterns of task-based and resting-state functional connectivity, or statistical dependence between two brain regions' activity time courses, have been used to predict individual differences in abilities including attention, fluid intelligence, and aspects of memory (71)(72)(73)(74)(75)(76)(77). Recent work suggests that models based on task connectivity generally outperform those based on resting-state connectivity for predicting behavior, potentially because tasks engage circuits related to a process of interest to magnify individual differences in behaviorally relevant neural phenotypes, thereby improving predictions (55,(78)(79)(80).…”

Predicting dysfunctional age-related task activations from resting-state network alterations

“…Theoretically, activity flow modeling can be applied to any meaningful categorization of unhealthy and healthy subgroups, given that it makes predictions on the basis of the unhealthy subjects' restFC altering a healthy group activation template. Notably, the mechanistic basis of the activity flow framework (Cole et al, 2016) differentiates it from other more data-driven 'fingerprinting' approaches with similar predictive aims (Rosenberg et al, 2016;Saygin et al, 2016;Tavor et al, 2016;Lin et al, 2018). These approaches use abstract coefficients to translate an individual's connectivity profile into predicted task activations (or behavior), as estimated via intensive optimization/cross-validation during model training.…”

Differential Effects of Tai Chi Chuan (Motor-Cognitive Training) and Walking on Brain Networks: A Resting-State fMRI Study in Chinese Women Aged 60

“…Interestingly, there is solid evidence in the literature that reveals that there are considerable changes in resting-state functional connectivity (e.g., in default mode network (DMN)) in neurological diseases such as Alzheimer's disease [10][11][12][13][14][15][16]. These changes in the resting-state functional connectivity are important because they are linked to changes in cognitive performance [13,14,17,18]. Such changes in resting-state functional connectivity seem also to be of high relevance with regard to tasks of daily living (e.g., mobility) because in individuals suffering from mild cognitive impairments (MCI) it has been observed that changes in default mode network and sensory-motor network connectivity are associated with changes in life-space mobility [19].…”