Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals

Abstract

Individual differences in working memory relate to performance differences in general cognitive ability. The neural bases of such individual differences, however, remain poorly understood. Here, using a data-driven technique known as connectome-based predictive modeling, we built models to predict individual working memory performance from whole-brain functional connectivity patterns. Using n-back or rest data from the Human Connectome Project, connectome-based predictive models significantly predicted novel individuals' 2-back accuracy. Model predictions also correlated with measures of fluid intelligence and, with less strength, sustained attention. Separate fluid intelligence models predicted working memory score, as did sustained attention models, again with less strength. Anatomical feature analysis revealed significant overlap between working memory and fluid intelligence models, particularly in utilization of prefrontal and parietal regions, and less overlap in predictive features between working memory and sustained attention models. Furthermore, showing the generality of these models, the working memory model developed from Human Connectome Project data generalized to predict memory in an independent data set of 157 older adults (mean age = 69 years; 48 healthy, 54 amnestic mild cognitive impairment, 55 Alzheimer disease). The present results demonstrate that distributed functional connectivity patterns predict individual variation in working memory capability across the adult life span, correlating with constructs including fluid intelligence and sustained attention.

Emergence of the Affect from the Variation in the Whole-Brain Flow of Information

“…Additionally, the observed flow of information among distributed brain regions that were common between the Negative, Neutral, and Positive affects was also in accordance with the affective workspace hypothesis that expresses that the differential affects are the brain states that are supported by flexible than consistently specific set of brain regions [25]. From a broader perspective, our results resonated with the findings that emphasize the importance of the functional connectivity between distributed brain regions that include pre/frontal, parietal, premotor and sensory, and occipitotemporal regions [98] and the implication of such large-scale and distributed networks in the brain functions [99][100][101].…”

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).…”

A connectome-based prediction model of long-term memory

“…In addition to the activation difference in IPS, preparatory activities in the prefrontal cortex and basal ganglia were also associated with individual differences in WM capacity (McNab & Klingberg, 2008). More recent work focuses on functional connectivity, a measure of how synchronized different brain regions are, and characterizes whole-brain networks that are predictive of WM capacity within young adult populations and generalizable to older people or psychiatric populations (Avery et al, 2019;Bertolero et al, 2018;Yamashita et al, 2018). The intense interest in individual differences in WM and its neural basis perhaps comes as no surprise given the foundational role of WM in supporting a suite of more complex cognitive abilities (such as reasoning, fluid intelligence and planning; Conway et al, 2003;Süß et al, 2002) and its impairment in a range of psychiatric populations (such as schizophrenia, autism and major depression: Forbes et al, 2008;Lever et al, 2015;Snyder, 2013).…”

“…Recent developments in connectome-based predictive modeling (CPM; Shen et al, 2017) have validated the utility of building predictive models of different cognitive functions and traits such as fluid intelligence, sustained attention, extraversion and working memory from whole-brain functional connectivity (e.g., Avery et al, 2019;Finn et al, 2015;Hsu et al, 2018;Rosenberg et al, 2015). A distinct advantage of the CPM framework lies in its flexibility: based on the same functional connectivity data, we can build different models that are predictive of a range of behaviors.…”