Biology subjects

Hearne, L. J.

Publications and source records attributed to Hearne, L. J..

9 recordsLinked to original sources

bioRxiv2026

Complementary frontoparietal and corticothalamic contributions to relational reasoning

Complex reasoning requires frontal, parietal, and thalamic systems to manage increasing relational demands, yet the underlying circuit mechanisms remain unclear. Here, we combined EEG with biologically grounded corticothalamic neural field modelling while participants solved relational problems of graded complexity. Successful reasoning was associated with dissociable frontoparietal dynamics. Frontal regions showed increased theta-band power, whereas parietal regions showed reduced alpha- and beta-band power. Neural field modelling linked these dynamics to complementary complexity-dependent circuit adaptations. Parietal regions showed modulation of intracortical and corticothalamic gains, intrathalamic inhibition, prolonged loop delays, and faster synaptic filtering, whereas frontal regions primarily adjusted intracortical gains in a manner consistent with maintaining local excitatory-inhibitory balance and supporting longer temporal integration windows. Together, these empirical and model-derived insights demonstrate that problem-solving under increasing reasoning demand relies not simply on greater frontoparietal engagement, but on additional, region-specific reconfigurations of cortical and corticothalamic circuits.

neuroscience↗

bioRxiv2025

Aligning transformer circuit mechanisms to neural representations in relational reasoning

Relational reasoning--the capacity to understand how elements relate to one another--is a defining feature of human intelligence, yet its computational basis remains unclear. Here, we combined human neuroimaging (7T fMRI) with artificial neural network modeling to identify circuit-level analogues of human reasoning computations. Using the Latin Square Task, we found that humans and transformers were able to generalize the task reliably, while standard architectures used in cognitive neuroscience could not. Analysing the transformer components revealed distinct computational roles: positional encoding captured the spatial structure of the task and aligned with representations in visual cortex, whereas attention encoded relational structure and mapped onto frontoparietal and default-mode networks. Attention weights tracked the relational complexity of the task, providing a computational analogue of reasoning demands. These results advance knowledge on the core algorithmic computations supporting complex reasoning, highlighting attention-based architectures as powerful models for investigating the neural and computational basis of higher cognition.

neuroscience↗

bioRxiv2025

Brain-heart coupling shapes large scale brain dynamics

Functional brain networks reconfigure to support adaptive behaviour, balancing integration and segregation. These dynamics are influenced by neuromodulatory arousal, reflected in heart rate (HR) fluctuations. We leverage intracranial EEG (iEEG), functional MRI (fMRI), and HR recordings during emotional movie viewing to probe neurocardiac contributions to large-scale network dynamics. Network dynamics using high-frequency iEEG activity (60-140 Hz), an established marker of neuronal firing, reveal that greater network integration tracks elevated HR, while segregation aligns with lower HR. However, we observe an inverted relationship between HR and network dynamics in a separate fMRI dataset utilising the same movie, and replicate this in another independent fMRI dataset. Biophysical modelling of BOLD from iEEG reproduces the iEEG-HR findings, suggesting neurovascular transformation may obscure neural-cardiac links in fMRI. These results link large-scale network dynamics to physiological arousal through HR driven adrenergic-cholinergic neuromodulation and highlight the complex interplay between neural activity, autonomic regulation, and haemodynamics.

neuroscience↗

bioRxiv2024

Temporally delayed representations in alpha and beta rhythms in higher-order cortical networks track increasing relational integration demands

Relational reasoning is the ability to infer and understand the relations between multiple elements. In humans, this ability supports higher cognitive functions and is linked to fluid intelligence. Relational complexity (RC) is a cognitive framework that offers a generalisable method for classifying the complexity of reasoning problems. To date, increased RC has been linked to static patterns of brain activity supported by the frontoparietal system, but limited work has assessed the multivariate spatiotemporal dynamics that code for RC. To address this, we conducted representational similarity analysis in two independent neuroimaging datasets (Dataset 1 fMRI, n=40; Dataset 2 EEG, n=45), where brain activity was recorded while participants completed a visuospatial reasoning task that included different levels of RC (Latin Square Task). Our findings revealed that, spatially, RC representations were widespread, peaking in brain networks associated with higher-order cognition (frontoparietal, dorsal-attention, and cingulo-opercular). Temporally, RC was represented in the 2.5 - 4.1 seconds post-stimuli window and emerged in the alpha and beta frequency range. Finally, multimodal fusion analysis demonstrated that shared variability within EEG-fMRI signals within higher-order cortical networks were better explained by the theorised RC model, relative to a model of cognitive effort (CE). Altogether, the results further our understanding of the neural representations supporting relational processing, highlight the spatially distributed coding of RC and CE across cortical networks, and emphasise the importance of late-stage, frequency-specific neural dynamics in resolving RC.

neuroscience↗

bioRxiv2024

Targeted Time-Varying Functional Connectivity

To elucidate the neurobiological basis of cognition, which is dynamic and evolving, various methods have emerged to characterise time-varying functional connectivity (FC) and track the temporal evolution of functional networks. However, given a selection of regions, many of these methods are based on modelling all possible pairwise connections, diluting a potential focus of interest on individual connections. This is the case with the hidden Markov model (HMM), which relies on region-by-region covariance matrices across all pairs of selected regions, assuming that fluctuations in FC occur across all investigated connections; that is, that all connections are locked to the same temporal pattern. To address this limitation, we introduce Targeted Time-Varying FC (T-TVFC), a variant of the HMM that explicitly models the temporal fluctuations between two sets of regions in a targeted fashion, rather than across the entire connectivity matrix. In this study, we apply T-TVFC to both simulated and real-world data. Specifically, we investigate thalamocortical connectivity, hypothesizing distinct temporal signatures compared to corticocortical networks. Given the thalamuss role as a critical hub, thalamocortical connections might contain unique information about cognitive processing that could be overlooked in a coarser representation. We tested these hypotheses on high-field functional magnetic resonance data from 60 participants engaged in a reasoning task with varying complexity levels. Our findings demonstrate that the time-varying interactions captured by T-TVFC contain task-related information not detected by more traditional decompositions.

neuroscience↗

bioRxiv2020

Activity flow underlying abnormalities in brain activations and cognition in schizophrenia

Cognitive dysfunction is a core feature of many brain disorders such as schizophrenia (SZ), and has been linked to both aberrant brain functional connectivity (FC) and aberrant cognitive brain activations. We propose that aberrant network activity flow over FC pathways leads to altered cognitive activations that produce cognitive dysfunction in SZ. We tested this hypothesis using activity flow mapping - an approach that models the movement of task-related activity between brain regions as a function of FC. Using fMRI data from SZ individuals and healthy controls during a working memory task, we found that activity flow models accurately predict aberrant cognitive activations across multiple brain networks. Within the same framework, we simulated a connectivity-based clinical intervention, predicting specific treatments that normalized brain activations and behavior in independent patients. Our results suggest that dysfunctional task-evoked activity flow is a large-scale network mechanism contributing to the emergence of cognitive dysfunction in SZ.

neuroscience↗

bioRxiv2020

Core and Matrix Thalamic Sub-Populations Relate to Spatio-Temporal Cortical Connectivity Gradients

Recent neuroimaging experiments have defined low-dimensional gradients of functional connectivity in the cerebral cortex that subserve a spectrum of capacities that span from sensation to cognition. Despite well-known anatomical connections to the cortex, the subcortical areas that support cortical functional organization have been relatively overlooked. One such structure is the thalamus, which maintains extensive anatomical and functional connections with the cerebral cortex across the cortical mantle. The thalamus has a heterogeneous cytoarchitecture, with at least two distinct cell classes that send differential projections to the cortex: granular-projecting Core cells and supragranular-projecting Matrix cells. Here we use high-resolution 7T resting-state fMRI data and the relative amount of two calcium-binding proteins, parvalbumin and calbindin, to infer the relative distribution of these two cell-types (Core and Matrix, respectively) in the thalamus. First, we demonstrate that thalamocortical connectivity recapitulates large-scale, low-dimensional connectivity gradients within the cerebral cortex. Next, we show that diffusely-projecting Matrix regions preferentially correlate with cortical regions with longer intrinsic fMRI timescales. We then show that the Core-Matrix architecture of the thalamus is important for understanding network topology in a manner that supports dynamic integration of signals distributed across the brain. Finally, we replicate our main results in a distinct 3T resting-state fMRI dataset. Linking molecular and functional neuroimaging data, our findings highlight the importance of the thalamic organization for understanding low-dimensional gradients of cortical connectivity.

neuroscience↗

bioRxiv2018

Increased cognitive complexity reveals abnormal brain network activity in individuals with corpus callosum dysgenesis

Cognitive reasoning is thought to require functional interactions between whole-brain networks. Such networks rely on both cerebral hemispheres, with the corpus callosum providing cross-hemispheric communication. Here we used high-field functional magnetic resonance imaging (7T fMRI), a well validated cognitive task, and brain network analyses to investigate the functional networks underlying cognitive reasoning in individuals with corpus callosum dysgenesis (CCD), an anatomical abnormality that affects the corpus callosum. Participants with CCD were asked to solve cognitive reasoning problems while their brain activity was measured using fMRI. The complexity of these problems was parametrically varied by changing the complexity of relations that needed to be established between shapes within each problem matrix. Behaviorally, participants showed a typical reduction in task performance as problem complexity increased. Task-evoked neural activity was observed in brain regions known to constitute two key cognitive control systems: the fronto-parietal and cingulo-opercular networks. Under low complexity demands, network topology and the patterns of local neural activity in the CCD group closely resembled those observed in neurotypical controls. By contrast, when asked to solve more complex problems, participants with CCD showed a reduction in neural activity and connectivity within the fronto-parietal network. These complexity-induced, as opposed to resting-state, differences in functional network activity help resolve the apparent paradox between preserved network architecture found at rest in CCD individuals, and the heterogeneous deficits they display in response to cognitive task demands [preprint: https://doi.org/10.1101/312629].

neuroscience↗

bioRxiv2017

Reconfiguration of brain network architectures between resting state and complexity-dependent cognitive reasoning

Our capacity for higher cognitive reasoning has a measureable limit. This limit is thought to arise from the brains capacity to flexibly reconfigure interactions between spatially distributed networks. Recent work, however, has suggested that reconfigurations of task-related networks are modest when compared with intrinsic resting state network architecture. Here we combined resting state and task-driven functional magnetic resonance imaging to examine how flexible, task-specific reconfigurations associated with increasing reasoning demands are integrated within a stable intrinsic brain topology. Human participants (21 males and 28 females) underwent an initial resting state scan, followed by a cognitive reasoning task involving different levels of complexity, followed by a second resting state scan. The reasoning task required participants to deduce the identity of a missing element in a 4 x 4 matrix, and item difficulty was scaled parametrically as determined by relational complexity theory. Analyses revealed that external task engagement was characterized by a significant change in functional brain modules. Specifically, resting state and null-task demand conditions were associated with more segregated brain network topology, whereas increases in reasoning complexity resulted in merging of resting state modules. Further increments in task complexity did not change the established modular architecture, but impacted selective patterns of connectivity between fronto-parietal, subcortical, cingulo-opercular and default-mode networks. Larger increases in network efficiency within the newly established task modules were associated with higher reasoning accuracy. Our results shed light on the network architectures that underlie external task engagement, and highlight selective changes in brain connectivity supporting increases in task complexity.\n\nSignificance StatementHumans have clear limits in their ability to solve complex reasoning problems. It is thought that such limitations arise from flexible, moment-to-moment reconfigurations of functional brain networks. It is less clear how such task-driven adaptive changes in connectivity relate to stable, intrinsic networks of the brain and behavioral performance. We found that increased reasoning demands rely on selective patterns of connectivity within cortical networks that emerged in addition to a more general, task-induced modular architecture. This task-driven architecture reverted to a more segregated resting state architecture both immediately before and after the task. These findings reveal how flexibility in human brain networks is integral to achieving successful reasoning performance across different levels of cognitive demand.

neuroscience↗