Pattern Similarity Analyses of FrontoParietal Task Coding: Individual Variation and Genetic Influences

Abstract

Pattern similarity analyses are increasingly used to characterize coding properties of brain regions, but relatively few have focused on cognitive control processes in FrontoParietal regions. Here, we use the Human Connectome Project (HCP) N-back task functional magnetic resonance imaging (fMRI) dataset to examine individual differences and genetic influences on the coding of working memory load (0-back, 2-back) and perceptual category (Face, Place). Participants were grouped into 105 monozygotic twin, 78 dizygotic twin, 99 nontwin sibling, and 100 unrelated pairs. Activation pattern similarity was used to test the hypothesis that FrontoParietal regions would have higher similarity for same load conditions, while Visual regions would have higher similarity in same perceptual category conditions. Results confirmed this highly robust regional double dissociation in neural coding, which also predicted individual differences in behavioral performance. In pair-based analyses, anatomically selective genetic relatedness effects were observed: relatedness predicted greater activation pattern similarity in FrontoParietal only for load coding and in Visual only for perceptual coding. Further, in related pairs, the similarity of load coding in FrontoParietal regions was uniquely associated with behavioral performance. Together, these results highlight the power of task fMRI pattern similarity analyses for detecting key coding and heritability features of brain regions.

Frontoparietal pattern similarity analyses of cognitive control in monozygotic twins

“…Current theoretical and empirical work has suggested a domain-general cognitive control capacity ( Assem et al, 2020 ; Kan et al, 2013 ; Miyake and Friedman, 2012 ), which enables flexible adaptation of thoughts and actions to fulfill a variety of task goals and demands ( Cole et al, 2013 ; Braver, 2012 ). Yet this hypothesis could not be tested in prior studies using twin-based neuroimaging designs, since they relied on evaluating heritability of neural activation in single cognitive control tasks ( Etzel et al, 2020 ; Koten et al, 2009 ; Matthews et al, 2007 ). Our work significantly extends these prior findings by demonstrating that twin similarity effects generalize across multiple cognitive control tasks; tasks which tap into different processes within this domain (e.g., selective attention, task-switching, working memory).…”

“…Individual differences in higher cognitive processes and general fluid intelligence have been closely linked to variation in the neural activations and functional connectivity within the fronto-parietal regions ( Cole et al, 2012 ; Duncan et al, 2020 ). In our own prior work ( Etzel et al, 2020 ), we identified clear patterns of fronto-parietal genetic variation related to cognitive control, using a twin-based design and multivariate pattern similarity approach with the N-back task. Nevertheless, the current study departs from our prior one and from other neuroimaging work, in that those prior studies were able to directly estimate genetic heritability effects by incorporating dizygotic (fraternal) as well as monozygotic twins.…”

“…In particular, reliable twin activation similarity effects have been observed in functionally selective brain regions during visual tasks, such as the fusiform face area ( Polk et al, 2007 ; Pinel et al, 2015 ). Utilizing the same approach, we recently demon-strated robust patterns of twin similarity in FPN activation patterns during N-back task performance in Human Connectome Project data ( Etzel et al, 2020 ).…”

Face Processing in Developmental Prosopagnosia: Altered Neural Representations in the Fusiform Face Area

“…Obviously, our paradigm did not tap into the contribution of individual perceptional differences to the current results. As a matter of fact, interindividual variability in perception may be genetically determined ( Etzel et al, 2020 ).…”

NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data

“…Comparing whole dissimilarities among all task conditions in RDM, RSA becomes an effective approach to track the multidimensional representation among task conditions. On the one hand, researchers can construct hypothesis-based RDM for different conditions, then compare these theoretical models with RDMs from real neural activities to calculate how similar they are (Alfred et al, 2018 ; Feng et al, 2018 ; Hall-McMaster et al, 2019 ; Yokoi and Diedrichsen, 2019 ; Etzel et al, 2020 ). As a result, they can infer the information is coded in the brain.…”