Biology subjects

Guassi Moreira, J. F.

Publications and source records attributed to Guassi Moreira, J. F..

7 recordsLinked to original sources

bioRxiv2025

Getting to Know You: Neural Representations of Other People Grow More Perceiver-Specific Over Time

Mental representations of others are central to social behavior, yet little is known about how these representations evolve over significant periods of time as people get to know one another. In this longitudinal functional magnetic resonance imaging study, we tracked how neural representations of familiar peers changed over the course of a school year among two cohorts of first-year high school students (N = 150) embedded in newly formed social networks. We assessed the extent to which individuals neural representations of their classmates converged with group-level norms--capturing the balance between information that is idiosyncratic to each perceiver and that which is shared across perceivers--and how this changed over time. We identified brain regions where neural representations of familiar peers were well aligned across perceivers, including several areas implicated in person perception, social cognition, and visual processing. Across both cohorts of participants, neural representations in the hippocampus and lateral prefrontal cortex began as highly aligned across participants, then grew more idiosyncratic to each perceiver over time. Similar effects were observed in the ventral medial prefrontal cortex, but only in one cohort of participants. Regions often linked to social cognition (temporoparietal junction, superior temporal sulcus) evinced strong encoding for group normative information at both timepoints. Taken together, these findings suggest that neural representations of familiar others may initially be predominantly driven by information that is consistent across perceivers (e.g., physical appearance, face-based evaluations, and trait impressions), then become more unique over time as each perceiver gains their own experiences with and impressions of these individuals. Significance StatementUnderstanding how humans form mental representations of others is fundamental to the study of social behavior. Yet we know little about how these representations evolve as people get to know each other over time. Using brain imaging in a real-world social network of first-year high school students, we tracked how participants neural representations of their classmates changed over the course of a year. While some brain regions encoded stable, shared representations of peers, others showed increasingly individualized patterns. These findings shed light on how real-world social experiences shape representations of familiar others and delineate how the brain balances both personal experience and shared social information. This work informs theories of social cognition by showing how mental representations evolve over time.

neuroscience↗

bioRxiv2025

Connectome-Based Predictive Modeling of Concurrent and Prospective Substance Use in Adolescence

Understanding the neural mechanisms of adolescent substance use is a critical public health issue, with direct implications for bolstering prevention and treatment strategies. Yet this effort is challenging because substance use is multi-faceted, commonly used brain network features are not optimized to capture both local and global aspects of intrinsic connectivity, and because substance use facets change over time. In this study, we operationalized adolescent substance use along three dimensions--intent, access, and family-developmental history--and trained predictive models of each facet using resting-state connectivity. Trait impulsivity, a known risk factor, was also examined. Using Baseline and 2 Year Follow-Up data from the ABCD Bids Community Collection (ABCC), we found that prediction was more successful at follow-up than baseline. At baseline, predictive accuracy was modest and intent to use substances was the most accurately predicted facet. Prediction accuracies at follow-up were much higher, with access and family-developmental history being better predicted, signaling a developmental shift in the brain-behavior mapping of substance use vulnerabilities. These findings suggest that the neurobiological correlates of substance use are dynamic across adolescence, possibly reflecting changing phenotype. More broadly, these results underscore the importance of modeling distinct substance use facets and accounting for developmental timing to understand risk trajectories, while contributing to a growing literature that shows early-developing individual differences are predictive of later outcomes.

neuroscience↗

bioRxiv2024

Social Decision Preferences for Close Others are Embedded in Neural and Linguistic Representations

Humans frequently make decisions that impact close others. Prior research has shown that people have stable preferences regarding such decisions and maintain rich, nuanced mental representations of their close social partners. Yet, if and how such mental representations shape social decisions preferences remains to be seen. Using a combination of functional magnetic resonance imaging (fMRI) and natural language processing (NLP), this study investigated how neural and linguistic representations of close others relate to social decision-making. After nominating a parent and friend, male and female participants (N = 63) rated their characteristics and made hypothetical social decisions while undergoing fMRI. Neural representations of parents and friends, relative to the self, were linked to social decision preferences. Specifically, greater neural similarity between self and parent in the temporoparietal junction (TPJ) and nucleus accumbens (NAcc) was associated with a preference for parents, while greater self-friend similarity in the medial prefrontal cortex (mPFC) was associated with friend-preference. Additionally, linguistic analysis of written descriptions of close others from a separate sample of males and females (N = 1,641) revealed that social decision preferences could be reliably predicted from semantic features of the text. High correspondence between neural and linguistic data in the imaging sample further strengthened the association with social decision preferences. These findings help elucidate the neural and linguistic underpinnings of social decision-making, emphasizing the critical role of mental representations in guiding choices involving familiar others. Significance StatementThis study provides novel insights into how mental representations of close others relate to social decision-making. By combining brain imaging and natural language processing, we show that both neural and linguistic representations of familiar individuals (parents and friends), can predict social preferences. We found that neural representations of these close others are linked to the choices people make about these individuals. Additionally, the way people describe their close others in writing are reliably associated with their decision preferences. Our approach, integrating neuroscience and language analysis, significantly advances our understanding of the cognitive mechanisms behind social decision-making, posing implications for fields ranging from psychology to artificial intelligence. These findings highlight the complexity of human relationships and their impact on everyday decisions.

neuroscience↗

bioRxiv2022

Value-Based Neural Representations Predict Social Decision Preferences

Social decision-making is omnipresent in everyday life, carrying the potential for both positive and negative consequences for the decision-maker and those closest to them. While evidence suggests that decision makers use value-based heuristics to guide choice behavior, very little is known about how decision makers representations of other agents influence social choice behavior. We used multivariate pattern expression analyses on fMRI data to understand how value-based processes shape neural representations of those affected by ones social decisions and whether value-based encoding is associated with social decision preferences. We found that stronger value-based encoding of a given close other (e.g., parent) relative to a second close other (e.g., friend) was associated with a greater propensity to favor the former during subsequent social decision-making. These results are the first to our knowledge to explicitly show that value-based processes affect decision behavior via representations of close others.

neuroscience↗

bioRxiv2020

Revisiting the Neural Architecture of Decision-Making: Univariate and Multivariate Evidence for System-Based Models in Adolescence

Understanding adolescent decision-making is significant for informing basic models of neurodevelopment as well as for the domains of public health and criminal justice. System-based theories posit that adolescent decision-making is guided by activity amongst reward and control processes. While successful at explaining behavior, system-based theories have received inconsistent support at the neural level, perhaps because of methodological limitations. Here, we used two complementary approaches to overcome said limitations and rigorously evaluate system-based models. Using decision-level modeling of fMRI data from a risk-taking task in a sample of 2000+ decisions across 51 human adolescents (25 females, mean age = 15.00 years), we find support for system-based theories of decision-making. Neural activity in lateral prefrontal cortex and a multivariate pattern of cognitive control both predicted a reduced likelihood of risk-taking, whereas increased activity in the nucleus accumbens predicted a greater likelihood of risk-taking. Interactions between decision-level brain activity and age were not observed. These results garner support for system-based accounts of adolescent decision-making behavior. Significance StatementAdolescent decision-making behavior is of great import for basic science, and carries equally consequential implications for public health and criminal justice. While dominant psychological theories seeking to explain adolescent decision-making have found empirical support, their neuroscientific implementations have received inconsistent support. This may be partly due to statistical approaches employed by prior neuroimaging studies of system-based theories. We used brain modeling--an approach that predicts behavior from brain activity--of univariate and multivariate neural activity metrics to better understand how neural components of psychological systems guide decision behavior in adolescents. We found broad support for system-based theories such that neural systems involved in cognitive control predicted a reduced likelihood to make risky decisions, whereas value-based systems predicted greater risk-taking propensity.

neuroscience↗

bioRxiv2019

Characterizing the Network Architecture of Emotion Regulation Neurodevelopment

The ability to regulate emotions is key to goal attainment and wellbeing. Although much has been discovered about how the human brain develops to support the acquisition of emotion regulation, very little of this work has leveraged information encoded in whole-brain networks. Here we employed a network neuroscience framework to parse the neural underpinnings of emotion regulation skill acquisition while accounting for age in a sample of youth (N = 70, 34 female). Focusing on three key network metrics--network differentiation, modularity, and community structure differences between active regulation and a passive emotional baseline--we found that the control network, the default mode network, and limbic network were each related to emotion regulation ability while controlling for chronological age. Greater network differentiation in the control and limbic networks was related to better emotion regulation ability. With regards to network community structure, more communities and more crosstalk between modules (i.e., less modularity) in the control network were associated with better regulatory ability. By contrast, less crosstalk (i.e., more modularity) between modules in the default mode network was associated with better regulatory ability. Together, these findings highlight possible whole-brain connectome features that support the acquisition of emotion regulation in youth.

neuroscience↗

bioRxiv2018

Spatial and Temporal Cortical Variability Track with Age and Affective Experience During Emotion Regulation in Youth

Variability is a fundamental feature of human brain activity that is particularly pronounced during development. However, developmental neuroimaging research has only recently begun to move beyond characterizing brain function exclusively in terms of magnitude of neural activation to incorporate estimates of variability. No prior neuroimaging study has done so in the domain of emotion regulation. We investigated how age and affective experiences relate to spatial and temporal variability in neural activity during emotion regulation. In the current study, 70 typically developing youth aged 8-17 years completed a cognitive reappraisal task of emotion regulation while undergoing functional magnetic resonance imaging. Estimates of spatial and temporal variability during regulation were calculated across a network of brain regions, defined a priori, and were then related to age and affective experiences. Results showed that increasing age was associated with reduced spatial and temporal variability in a set of frontoparietal regions (e.g., dorsomedial prefrontal cortex, superior parietal lobule) known to be involved in effortful emotion regulation. In addition, youth who reported less negative affect during regulation had less spatial variability in the ventrolateral prefrontal cortex, which has previously been linked to cognitive reappraisal. We interpret age-related reductions in spatial and temporal variability as implying neural specialization. These results suggest that the development of emotion regulation is undergirded by a process of neural specialization and open up a host of possibilities for incorporating neural variability into the study of emotion regulation development.

neuroscience↗