Biology subjects

Gianaros, P. J.

Publications and source records attributed to Gianaros, P. J..

9 recordsLinked to original sources

bioRxiv2026

Spatiotemporal Dissociation of Human Amygdala Response to Negative Affect

The amygdala is central to negative affect and a primary target of top-down regulation, yet its temporal dynamics remain poorly characterized. Most fMRI studies model stimulus-evoked amygdala activity with a canonical hemodynamic response function and collapse across subregions, potentially obscuring functional heterogeneity in time and space. We used finite impulse response modeling in two independent datasets (combined N = 358) to characterize blood oxygenation level-dependent responses during negative picture viewing and instructed cognitive reappraisal, complemented by data-driven clustering of voxelwise time courses. Across two probabilistic atlases, six of seven amygdala subregions showed prolonged activation to negative stimuli but differed in temporal profile. The centromedial amygdala showed sustained activation extending into the post-stimulus rating period, whereas laterobasal and superficial subregions peaked earlier during stimulus presentation. Reappraisal did not substantially alter response magnitude or shape, indicating that amygdala downregulation is not a reliable consequence of instructed regulation under these conditions. Data-driven analysis identified four clusters with distinct temporal profiles corresponding to laterobasal, lateral, superficial, and centromedial territories, converging with established anatomical parcellations while crossing some conventional boundaries. FIR modeling thus reveals a temporal architecture of amygdala responses to negative affective stimuli, characterized by early laterobasal and superficial responses followed by sustained centromedial activity.

neuroscience↗

bioRxiv2026

Altered biological aging-related brain profile in adolescents with autism: A neuroimaging study based on DunedinPACNI

Individuals with Autism Spectrum Disorder (ASD) show increased rates of physical health conditions across major organ systems, some of which are commonly linked with aging, suggesting that body-wide biological profiles may be altered compared to neurotypical controls. Since brain structure associates with peripheral physiology and biomarkers of aging, it may inform understanding of broader biological differences and physical health risks in ASD. Here, we assessed this possibility using DunedinPACNI, a neuroimaging-based model that estimates the pace of longitudinal aging in peripheral organ systems from brain structural information, in 329 adolescents (8-18 years) from the Autism Centers of Excellence network, with replication in an independent age-matched sample of comparable size from the Autism Brain Imaging Data Exchange. Across both datasets and sensitivity analyses, autistic individuals showed significantly larger DunedinPACNI values than controls, consistent with brain structural features that align with patterns associated with a faster pace of biological aging in adults. Group differences were mainly driven by the volume of the 3rd ventricle, cortical thickness of the left entorhinal cortex, grey matter volume of the right entorhinal cortex and grey-to-white matter ratio in the left temporal pole. No association between DunedinPACNI and core autistic traits was found. Our results provide novel evidence for a possible altered brain-body profile in ASD, motivating future studies combining neuroimaging with peripheral biomarkers to better understand the neurobiology of physical health conditions in autism.

neuroscience↗

bioRxiv2026

From the scanner to daily life: Neural expression of the Picture-Induced Negative Emotion Signature is associated with real-world daily and momentary negative affect in midlife adults

Functional magnetic resonance imaging (fMRI) is widely used to map brain systems that process affective stimuli and predict concurrent affective experiences in the scanner. However, the extent to which affect-related fMRI activity in the scanner predicts affective experiences in daily life remains unclear. This study examined whether a multivariate brain pattern previously validated to predict negative affect to emotional images during fMRI testing (the Picture-Induced Negative Emotion Signature; PINES) associates with daily negative affect measured over 7-10 days via daily diaries and ecological momentary assessment. Among 287 midlife adults, those exhibiting greater PINES pattern expression showed greater daily (end-of-day) and hourly negative affect. These findings were not explained by demographic factors and trait negative affect. A cross-validated whole-brain pattern of negative affect generalizes beyond the fMRI testing context to predict negative affect severity in daily life.

neuroscience↗

bioRxiv2025

The elusive neural signature of emotion regulation capabilities: evidence from a large-scale consortium

Cognitive reappraisal is a fundamental emotion regulation strategy for mental and physical well-being, but how its neural mechanisms relate to individual differences remains poorly understood. In a consortium effort analyzing 40 fMRI datasets (N=2,175), we examined the relationship between neural activation during reappraisal tasks and three core individual difference indices of reappraisal capabilities: (1) trait questionnaires, (2) task-based affective ratings, and (3) amygdala down-regulation. Strikingly, there was no shared overlap across these three common indices. Only a very weak correlation emerged between amygdala down-regulation and task-based affective ratings. Whole-brain analyses revealed no reliable neural associations with trait questionnaires, and associations with task-based affective ratings fell outside canonical emotion regulation networks (e.g., prefrontal circuitry). Moreover, amygdala down-regulation, often interpreted as a stable individual marker, was confounded by person-specific whole-brain responses -- a limitation extending to fMRI research beyond the emotion regulation domain. These findings challenge the assumption that an individuals prefrontal activity is a valid indicator of their reappraisal capabilities and suggest that common trait, behavioral, and neural measures might capture distinct facets of emotion regulation. More broadly, our results highlight concrete methodological challenges for fMRI research on individual differences, with implications extending beyond emotion regulation to the neuroscience of personality, psychopathology, and general well-being.

neuroscience↗

bioRxiv2025

The functional neurobiology of negative affective traits across regions, networks, signatures, and a machine learning multiverse

Understanding the neural basis of negative affective traits like neuroticism remains a critical challenge across psychology, neuroscience, and psychiatry. Here, we investigate which level of brain organization--regions, networks, or validated whole-brain machine-learning signatures--best explains negative affective traits in a community sample of 458 adults performing the two most widely used affective fMRI tasks, viewing emotional faces and scenes. Neuroticism could not be predicted from brain activity, with Bayesian evidence against all theory-guided neural measures. However, preregistered whole-brain models successfully decoded vulnerability to stress, a lower-level facet of neuroticism, with results replicating in a hold-out sample. The neural stress vulnerability pattern demonstrated good psychometric properties and indicated that negative affective traits are best represented by distributed whole-brain patterns related to domain-general stimulation rather than localized activity. Together with results from a comprehensive multiverse analysis across 14 traits and 1,176 models-- available for exploration in an online app--the findings speak against simplistic neurobiological theories of negative affective traits, highlight a striking gap between predicting individual differences (r<.35) and within-person emotional states (r=.88), and underscore the importance of aligning psychological constructs with neural measures at the appropriate level of granularity.

neuroscience↗

bioRxiv2023

Cortical and subcortical brain networks predict prevailing heart rate

Resting heart rate may confer risk for cardiovascular disease (CVD) and other adverse cardiovascular events. While the brainstems autonomic control over heart rate is well established, less is known about the regulatory role of higher-level cortical and subcortical brain regions, especially in humans. The present study sought to characterize the brain networks that predict variation in prevailing heart rate in otherwise healthy adults. We used machine learning approaches designed for complex, high-dimensional datasets, to predict variation in instantaneous heart period (the inter-heartbeat-interval) from whole brain hemodynamic signals measured by fMRI. Task-based and resting-state fMRI, as well as peripheral physiological recordings, were taken from two datasets that included extensive repeated measurements within individuals. Our models reliably predicted instantaneous heart period from whole brain fMRI data both within and across individuals, with prediction accuracies being highest when measured within-participants. We found that a network of cortical and subcortical brain regions, many linked to psychological stress, were reliable predictors of variation in heart period. This adds to evidence on brain-heart interactions and constitutes an incremental step towards developing clinically-applicable biomarkers of brain contributions to CVD risk. Impact statementUsing whole brain fMRI data, we reliably predicted instantaneous heart period within and across individuals from the activity of a network of cortical and subcortical brain regions, many linked to psychological stress. This adds to existing evidence on brain-heart interactions and constitutes a step towards developing clinically-applicable biomarkers of brain contributions to CVD risk.

neuroscience↗

bioRxiv2023

Deconstructing the brain bases of emotion regulation: A systems-identification approach using Bayes factors

Cognitive reappraisal is fundamental to cognitive therapies and everyday emotion regulation. Analyses using Bayes factors and an axiomatic systems-identification approach identified four reappraisal-related components encompassing distributed neural activity patterns across two independent fMRI studies (n=182 and n=176): (1) An anterior prefrontal system selectively involved in cognitive reappraisal; (2) A fronto-parietal-insular system engaged by both reappraisal and emotion generation, demonstrating a general role in appraisal; (3) A largely subcortical system activated during negative emotion generation but unaffected by reappraisal, including amygdala, hypothalamus, and periaqueductal gray; and (4) a posterior cortical system of negative emotion-related regions down-regulated by reappraisal. These systems covaried with individual differences in reappraisal success and were differentially related to neurotransmitter binding maps, implicating cannabinoid and serotonin systems in reappraisal. These findings challenge limbic-centric models of reappraisal and provide new systems-level targets for assessing and enhancing emotion regulation.

neuroscience↗

bioRxiv2022

D2 dopamine receptor expression, sensitivity to rewards, and reinforcement learning in a complex value-based decision-making task

In the basal ganglia, different dopamine subtypes have opposing dynamics at post-synaptic receptors, with the ratio of D1 to D2 receptors determining the relative sensitivity to gains and losses, respectively, during value-based learning. This effective sensitivity to reward feedback interacts with phasic dopamine levels to determine the effectiveness of learning, particularly in dynamic feedback situations where frequency and magnitude of rewards need to be integrated over time to make optimal decisions. Using both simulations and behavioral data in humans, we evaluated how reduced sensitivity to losses, relative to gains, leads to suboptimal learning in the Iowa Gambling Task (IGT), a complex value-learning task. In the behavioral data, we tested individuals with a variant of the human dopamine receptor D2 (DRD2; -141C Ins/Del and Del/Del) gene that associates with lower levels of D2 receptor expression (N=119) and compared their performance to non-carrier controls (N=319). The magnitude of the reward response was measured by looking at ventral striatal (VS) reactivity to rewards in the Cards task using fMRI. DRD2 variant carriers had generally lower performance in the IGT than non-carriers, consistent with reduced sensitivity to losses. There was also a positive association between VS reactivity and performance in the IGT, however, we found no statistically significant difference in this effect between DRD2 carriers and non-carriers. Thus, while reduced D2 receptor expression was associated with less efficient learning in the IGT, we did not find evidence for the moderation of this effect by the magnitude of the reward response.

neuroscience↗

bioRxiv2021

Similarity in evoked responses does not imply similarity in macroscopic network states across tasks

There is an ongoing debate as to whether cognitive processes arise from a group of functionally specialized brain modules (modularism) or as the result of distributed nonlinear processes (dynamical systems theory). The former predicts that tasks recruiting similar local brain areas should be equally similar in their network profiles. The latter allows for differential connectivity, even when the areas recruited are largely the same. Here we evaluated both views at the macroscopic level by comparing region-wise activation patterns and functional correlation profiles from a large sample of healthy subjects (N=242) that performed two executive control tasks known to recruit nearly identical brain areas, the color-word Stroop task and the Multi-Source Interference Task (MSIT). Using a measure of instantaneous functional correlations, based on edge time series, we estimated the task-related networks that differed between incongruent and congruent conditions. At the group level, the two tasks were much more different in their network profiles than in their evoked activity patterns. This is found even when matching the degrees of freedom of both activation patterns and functional correlation profiles, when considering subject-level differences, after changing brain parcellations, and if employing alternative methods for defining task-related network profiles. Our results are consistent with the perspective of the brain as a dynamical system, suggesting that task representations should be independently evaluated at both node and edge (connectivity) levels. Significant StatementIf the brain is strictly modular at the macroscopic scale, then recruiting the same brain regions should result in the same functional interactions between regions. However, if the brain is a dynamical system, with information represented at both the node and edge levels, then two tasks could have the same pattern of activation, but largely different functional correlation profiles. Here we tested this contrastive prediction using two tasks with overlapping cognitive demands, but different sensory signals. Despite being nearly identical in their activation patterns, we found that the tasks produced largely different functional correlation profiles. These findings reinforce the view of the brain as a dynamical system, with task states represented both within and across regions.

neuroscience↗