Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations

Abstract

Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.

Topological network properties of resting-state functional connectivity patterns are associated with metal mixture exposure in adolescents

“…In contrast, local efficiency (LE) measures the ability of the brain to perform functionally specialized and segregated processing within a network, requiring densely interconnected regions within modules ( Stanley et al, 2015 ; Rakesh et al, 2020 ). Previous results have demonstrated the utility to characterize the topological network organization of the brain by using graph metrics based on rs-fMRI and link them with human behavior ( Xu et al, 2015 ; Liu et al, 2022 ), cognition ( van den Heuvel et al, 2009 ; Uehara et al, 2013 ), and diseases ( Liu et al, 2008 ; Supekar et al, 2008 ). Recent studies have used rs-fMRI to demonstrate intrinsic functional connectivity patterns in a priori selected brain regions associated with early life exposure to individual metals (i.e., lead, manganese) ( de Water et al, 2018 , 2019 ; Thomason et al, 2019 ).…”

Considering dynamic nature of the brain: the clinical importance of connectivity variability in machine learning classification and prediction

“…We speculate that the main reason of discrepancy was that we used both static-FC and FC-variability and then FC-variability features which had redundant information were less noticeable. Nevertheless, we believe that including more independent and informative features is crucial to utilize neuroimages in psychiatric field such as connectome-based predictive modeling (CPM; Liu et al, 2022;Tian & Zalesky, 2021). Another possibility of discrepancy was that we adopted robust correlation method to estimate static-FC and FC-variability.…”

Test–retest reliability and predictive utility of a macroscale principal functional connectivity gradient

“…Given increasing evidence that complex human behavior depends on distributed networks throughout the brain rather than individual brain regions (Liu et al, 2022; van den Heuvel & Hulshoff Pol, 2010), there is a promising opportunity to search for reliable and behaviorally relevant brain measures that capture putative mechanisms of underlying global functional networks. Studies of intrinsic network structure estimated from resting‐state functional connectivity (rsFC) consistently demonstrate the ability of multivariate global connectivity measures to predict various complex behaviors above chance level, including cognition (Finn et al, 2015; Mansour et al, 2021), personality (Dubois et al, 2018; Hsu et al, 2018; Nostro et al, 2018), and clinical symptoms (Fair et al, 2012; Lake et al, 2019; Wang et al, 2020).…”