skip to main content


This content will become publicly available on April 1, 2025

Title: High performers demonstrate greater neural synchrony than low performers across behavioral domains
Abstract

Heterogeneity in brain activity can give rise to heterogeneity in behavior, which in turn comprises our distinctive characteristics as individuals. Studying the path from brain to behavior, however, often requires making assumptions about how similarity in behavior scales with similarity in brain activity. Here, we expand upon recent work (Finn et al., 2020) which proposes a theoretical framework for testing the validity of such assumptions. Using intersubject representational similarity analysis in two independent movie-watching functional MRI (fMRI) datasets, we probe how brain-behavior relationships vary as a function of behavioral domain and participant sample. We find evidence that, in some cases, the neural similarity of two individuals is not correlated with behavioral similarity. Rather, individuals with higher behavioral scores are more similar to other high scorers whereas individuals with lower behavioral scores are dissimilar from everyone else. Ultimately, our findings motivate a more extensive investigation of both the structure of brain-behavior relationships and the tacit assumption that people who behave similarly will demonstrate shared patterns of brain activity.

 
more » « less
Award ID(s):
2043740
NSF-PAR ID:
10508006
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
MIT Press
Date Published:
Journal Name:
Imaging Neuroscience
Volume:
2
ISSN:
2837-6056
Page Range / eLocation ID:
1 to 17
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Intelligence describes the general cognitive ability level of a person. It is one of the most fundamental concepts in psychological science and is crucial for the effective adaption of behavior to varying environmental demands. Changing external task demands have been shown to induce reconfiguration of functional brain networks. However, whether neural reconfiguration between different tasks is associated with intelligence has not yet been investigated. We used functional magnetic resonance imaging data from 812 subjects to show that higher scores of general intelligence are related to less brain network reconfiguration between resting state and seven different task states as well as to network reconfiguration between tasks. This association holds for all functional brain networks except the motor system and replicates in two independent samples (n = 138 and n = 184). Our findings suggest that the intrinsic network architecture of individuals with higher intelligence scores is closer to the network architecture as required by various cognitive demands. Multitask brain network reconfiguration may, therefore, represent a neural reflection of the behavioral positive manifold – the essence of the concept of general intelligence. Finally, our results support neural efficiency theories of cognitive ability and reveal insights into human intelligence as an emergent property from a distributed multitask brain network.

     
    more » « less
  2. Abstract

    Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural ‘score’ to complement traditional scores of an individual’s conceptual understanding. Using a novel data-driven multivariate neuroimaging approach—informational network analysis—we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science.

     
    more » « less
  3. Abstract

    To conserve wide‐ranging species in degraded landscapes, it is essential to understand how the behavior of animals changes in relation to the degree and composition of modification. Evidence suggests that large inter‐individual variation exists in the propensity for use of degraded areas and may be driven by both behavioral and landscape factors. The use of cultivated lands by wildlife is of particular interest, given the importance of reducing human‐wildlife conflicts and understanding how such areas can function as biodiversity buffers. African elephant space use can be highly influenced by human activity and the degree to which individuals crop‐raid. We analyzed GPS data from 56 free‐ranging elephants in the Serengeti‐Mara Ecosystem using resource selection functions (RSFs) to assess how crop use may drive patterns of resource selection and space use within a population. We quantified drivers of similarity in resource selection across individuals using proximity analysis of individual RSF coefficients derived from random forest models. We found wide variation in RSF coefficient values between individuals indicating strongly differentiated resource selection strategies. Proximity assessment indicated the degree of crop use in the dry season, individual repeatability, and time spent in unprotected areas drove similarity in resource selection patterns. Crop selection was also spatially structured in relation to agricultural fragmentation. In areas with low fragmentation, elephants spent less time in crops and selected most strongly for crops further from protected area boundaries, but in areas of high fragmentation, elephants spent twice as much time in crops and selected most strongly for crops closer to the protected area boundary. Our results highlight how individual differences and landscape structure can shape use of agricultural landscapes. We discuss our findings in respect to the conservation challenges of human‐elephant conflict and incorporating behavioral variation into human‐wildlife coexistence efforts.

     
    more » « less
  4. Abstract

    Multiple human behaviors improve early in life, peaking in young adulthood, and declining thereafter. Several properties of brain structure and function progress similarly across the lifespan. Cognitive and neuroscience research has approached aging primarily using associations between a few behaviors, brain functions, and structures. Because of this, the multivariate, global factors relating brain and behavior across the lifespan are not well understood. We investigated the global patterns of associations between 334 behavioral and clinical measures and 376 brain structural connections in 594 individuals across the lifespan. A single-axis associated changes in multiple behavioral domains and brain structural connections (r = 0.5808). Individual variability within the single association axis well predicted the age of the subject (r = 0.6275). Representational similarity analysis evidenced global patterns of interactions across multiple brain network systems and behavioral domains. Results show that global processes of human aging can be well captured by a multivariate data fusion approach.

     
    more » « less
  5. Abstract

    Patterns of whole-brain fMRI functional connectivity, or connectomes, are unique to individuals. Previous work has identified subsets of functional connections within these patterns whose strength predicts aspects of attention and cognition. However, overall features of these connectomes, such as how stable they are over time and how similar they are to a group-average (typical) or high-performance (optimal) connectivity pattern, may also reflect cognitive and attentional abilities. Here, we test whether individuals who express more stable, typical, optimal, and distinctive patterns of functional connectivity perform better on cognitive tasks using data from three independent samples. We find that individuals with more stable task-based functional connectivity patterns perform better on attention and working memory tasks, even when controlling for behavioral performance stability. Additionally, we find initial evidence that individuals with more typical and optimal patterns of functional connectivity also perform better on these tasks. These results demonstrate that functional connectome stability within individuals and similarity across individuals predicts individual differences in cognition.

     
    more » « less