A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity

Abstract

Individual differences in cognitive function have been shown to correlate with brain-wide functional connectivity, suggesting a common foundation relating connectivity to cognitive function across healthy populations. However, it remains unknown whether this relationship is preserved in cognitive deficits seen in a range of psychiatric disorders. Using machine learning methods, we built a prediction model of working memory function from whole-brain functional connectivity among a healthy population (N = 17, age 19-24 years). We applied this normative model to a series of not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/222281 doi: bioRxiv preprint first posted online 2 independently collected resting state functional connectivity datasets (N = 968), involving multiple psychiatric diagnoses, sites, ages (18-65 years), and ethnicities. We found that predicted working memory ability was correlated with actually measured working memory performance in both schizophrenia patients (partial correlation, ρ = 0.25, P = 0.033, N = 58) and a healthy population (partial correlation, ρ = 0.11, P = 0.0072, N = 474). Moreover, the model predicted diagnosis-specific severity of working memory impairments in schizophrenia (N = 58, with 60 controls), major depressive disorder (N = 77, with 63 controls), obsessive-compulsive disorder (N = 46, with 50 controls), and autism spectrum disorder (N = 69, with 71 controls) with effect sizes g = -0.68, -0.29, -0.19, and 0.09, respectively. According to the model, each diagnosis's working memory impairment resulted from the accumulation of distinct functional connectivity differences that characterizes each diagnosis, including both diagnosisspecific and diagnosis-invariant functional connectivity differences. Severe working memory impairment in schizophrenia was related not only with fronto-parietal, but also widespread network changes. Autism spectrum disorder showed greater negative connectivity that related to improved working memory function, suggesting that some non-normative functional connections can be behaviorally advantageous. Our results suggest that the relationship between brain connectivity and working memory function in healthy populations can be generalized across multiple psychiatric diagnoses. This approach may shed new light on behavioral variances in psychiatric disease and suggests that whole-brain functional connectivity can provide an individual quantitative behavioral profile in a range of psychiatric disorders.

MEG, myself, and I: individual identification from neurophysiological brain activity

“…The recent leveraging of large, open fMRI datasets has brought empirical evidence that individuals may be identified within a cohort from their brain imaging functional connectome, inspiring the metaphor of a neural fingerprint. Unlike hand fingerprints, their cerebral counterpart predicts task performance and a variety of traits (14,(21)(22)(23)(24). These intriguing findings require a better understanding of their neurophysiological foundations, which we sought to characterize from direct neural signals captured at a large scale with MEG.…”

“…This epistemological question has become particularly vivid with recent research showing that individuals can be identified from a cohort via their respective neural fingerprints derived from structural magnetic resonance imaging (MRI) (10,11), functional MRI (fMRI) (12)(13)(14)(15)(16), electroencephalography (EEG) (17)(18)(19), or functional near-infrared spectroscopy (fNIRS) (20). Strikingly, neural fingerprints are associated with individual traits such as global intelligence, working memory, and attention abilities (21)(22)(23)(24). Most published work so far is methodologically based on inter-individual similarity measures of functional connectivity-understood as statistical dependencies between ongoing signals across brain regions in task-free awake conditions (25,26)-as defining features of neural fingerprints.…”

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).…”

Default Mode and Dorsal Attention Network functional connectivity associated with alpha and beta peak frequency in individuals

“…There is an increasing need for biomarkers of mental and cognitive health. Recent advances in the field of cognitive neuroscience in general and neuroimaging, in particular, allow us to use non-invasive measures like Electroencephalography (EEG), Magnetoencephalography (MEG), functional MRI (fMRI) and diffusion MRI (dMRI) to make estimates about cognitive health (Woo et al, 2017;Dubois & Adolphs, 2016;Yamashita et al, 2018;Lefebvre et al, 2018;Hong et al, 2020;Murty et al, 2020;Cesnaite et al, 2023).…”