Biology subjects

Gale, D. J.

Publications and source records attributed to Gale, D. J..

13 recordsLinked to original sources

bioRxiv2026

A shared functional architecture for error-based and reinforcement-based motor learning in the human brain

The motor system can flexibly learn from fundamentally different teaching signals, from directional errors to rewards. While computational and neurobiological models have long assigned these to distinct error-based learning (EL) and reinforcement-based learning (RL) processes, whether this separation holds at the level of whole-brain network architecture has not been directly tested. Here, we characterized whole-brain functional connectivity manifolds in the same participants as they performed separate EL and RL motor tasks -- differing in both feedback type and motor structure -- during two fMRI sessions. By jointly embedding covariance patterns from both tasks into a common low-dimensional neural space, we isolated task-general from task-specific learning-related network reconfigurations. We show that both forms of motor learning induce converging changes in the manifold structure of higher-order transmodal cortex, with comparatively limited task-specific modulation. Notably, this convergence extended to the cerebellum and basal ganglia, the canonical substrates of error-based and reward-based learning, which reconfigured comparably across both tasks. Against this shared backdrop, we found that the posterior medial cortex exhibited a unique functional geometry, selectively redistributing its connectivity across distinct brain circuits depending on feedback type and learning stage. We further demonstrate that individual differences in domain-general motor learning ability are associated with stage-dependent reconfigurations within limbic, default-mode, and attentional systems. These findings indicate that flexible motor adaptation, irrespective of the nature of the learning signal, is supported less by changes within task-specific sensorimotor circuits than by the dynamic reorganization of higher-order brain networks that coordinate them.

neuroscience↗

bioRxiv2025

Modular dynamics of conscious and unconscious states in marmoset cortex

General anesthetics are routinely used to induce unconsciousness. While much is known about their effects on receptor function and the activity of individual neurons, much less is known about how these local effects are manifest at the level of largescale, distributed brain networks. Using functional magnetic resonance imaging (fMRI) with the common marmoset (Callithrix jacchus) we investigated the effects of the anaesthetic isoflurane on functional brain networks and their temporal dynamics, comparing network measures during wakeful rest and induced unconsciousness. The anaesthetic condition was characterised by weak functional networks that were more similar to anatomical structure and more fragmented than during wakeful rest. Conversely, the awake condition was characterised by coordinated network reconfiguration and more distinct subnetwork composition. Our findings are consistent with the view that consciousness is an emergent property of the dynamics of functional brain networks, and that anaesthetics impoverish these dynamics by reducing the efficacy of synaptic transmission.

neuroscience↗

bioRxiv2024

Brain Charts for the Rhesus Macaque Lifespan

Recent efforts to chart human brain growth across the lifespan using large-scale MRI data have provided reference standards for human brain development. However, similar models for nonhuman primate (NHP) growth are lacking. The rhesus macaque, a widely used NHP in translational neuroscience due to its similarities in brain anatomy, phylogenetics, cognitive, and social behaviors to humans, serves as an ideal NHP model. This study aimed to create normative growth charts for brain structure across the macaque lifespan, enhancing our understanding of neurodevelopment and aging, and facilitating cross-species translational research. Leveraging data from the PRIMatE Data Exchange (PRIME-DE) and other sources, we aggregated 1,522 MRI scans from 1,024 rhesus macaques. We mapped non-linear developmental trajectories for global and regional brain structural changes in volume, cortical thickness, and surface area over the lifespan. Our findings provided normative charts with centile scores for macaque brain structures and revealed key developmental milestones from prenatal stages to aging, highlighting both species-specific and comparable brain maturation patterns between macaques and humans. The charts offer a valuable resource for future NHP studies, particularly those with small sample sizes. Furthermore, the interactive open resource (https://interspeciesmap.childmind.org) supports cross-species comparisons to advance translational neuroscience research.

neuroscience↗

bioRxiv2024

Individual variability in sensorimotor learning reflects trait-like neurobehavioral subject factors

Models of Human motor behaviour often emphasize the computations performed by the motor system during learning. Yet, there is an emerging consensus that the learning of even simple motor actions can be augmented by sophisticated cognitive strategies, which rely upon executive functions implemented throughout the cortex. These executive functions, in turn, have been linked to stable subject differences in intrinsic brain organization function, observable even during rest. Here we show, using behavioural studies in humans, that individual differences in the rate of sensorimotor adaptation are linked to differences in executive function, as assessed using the classic trail-making task. Secondly, using separate train and test functional MRI datasets, we show that specific patterns of resting-state functional connectivity between higher-order cognitive brain networks, which have been previously linked to executive function, subsequently predict more rapid learning during sensorimotor adaptation. Importantly, this relationship was unique to cognitive brain networks, as the functional connectivity between sensorimotor brain networks did not predict subsequent motor learning performance. Together, these findings suggest that individual differences in motor learning reflect, to a substantial degree, trait-like subject differences in cognitive brain network structure. This perspective invites broader consideration as to the origins of motor learning ability, and links motor performance to an expansive literature implicating the coordinated functioning of the default mode, ventral attention, and frontoparietal networks in flexible behavioral control.

neuroscience↗

bioRxiv2023

Whole-brain modular dynamics at rest predict sensorimotor learning performance

Predictive biomarkers of cognitive performance are informative about the neural mechanisms underlying cognitive phenomena, and have tremendous potential for the diagnosis and treatment of neuropathologies with cognitive symptoms. Among such biomarkers, the modularity (subnetwork composition) of whole-brain functional networks is especially promising, due to its longstanding theoretical foundations and recent success in predicting clinical outcomes. We used functional magnetic resonance imaging to identify whole-brain modules at rest, calculating metrics of their spatio-temporal dynamics before and after a sensorimotor learning task on which fast learning is widely believed to be supported by a cognitive strategy. We found that participants learning performance was predicted by the strength of dynamic modularity scores (clarity of subnetwork composition), the degree of coordination of modular reconfiguration, and the strength of recruitment and integration of networks derived during the task itself. Our findings identify these whole-brain metrics as promising biomarkers of cognition, with relevance to basic and clinical neuroscience.

neuroscience↗

bioRxiv2023

Visual statistical learning is associated with changes in cortical manifold structure

Our brains are in a near constant state of generating predictions, extracting regularities from seemingly random sensory inputs to support later cognition and behavior - a process called statistical learning (SL). Yet, the activity patterns across cortex and subcortex that support this form of associative learning remain unresolved. Here we use human fMRI and a visual SL task to investigate changes in neural activity patterns as participants implicitly learn visual associations from a sequence. By projecting functional connectivity patterns onto a low-dimensional manifold, we reveal that learning is selectively supported by changes along a single neural dimension spanning visual-parietal and perirhinal cortex (PRC). During learning, visual cortex expanded along this dimension, segregating from other networks, while dorsal attention network (DAN) regions contracted, integrating with higher-order transmodal cortex. When we later violated the learned associations, PRC and entorhinal cortex, which initially showed no evidence of learning-related effects, now contracted along this dimension, integrating with the default mode and DAN, while decreasing covariance with visual cortex. Whereas previous studies have linked SL to either broad cortical or medial temporal lobe changes, our findings suggest an integrative view, whereby cortical regions reorganize during association formation, while medial temporal lobe regions respond to their violation.

neuroscience↗

bioRxiv2023

Reconfigurations of cortical manifold structure during reward-based motor learning

Adaptive motor behavior depends on the coordinated activity of multiple neural systems distributed across the brain. While the role of sensorimotor cortex in motor learning has been well-established, how higher-order brain systems interact with sensorimotor cortex to guide learning is less well understood. Using functional MRI, we examined human brain activity during a reward-based motor task where subjects learned to shape their hand trajectories through reinforcement feedback. We projected patterns of cortical and striatal functional connectivity onto a low-dimensional manifold space and examined how regions expanded and contracted along the manifold during learning. During early learning, we found that several sensorimotor areas in the Dorsal Attention Network exhibited increased covariance with areas of the salience/ventral attention network and reduced covariance with areas of the default mode network (DMN). During late learning, these effects reversed, with sensorimotor areas now exhibiting increased covariance with DMN areas. However, areas in posteromedial cortex showed the opposite pattern across learning phases, with its connectivity suggesting a role in coordinating activity across different networks over time. Our results establish the neural changes that support reward-based motor learning and identify distinct transitions in the functional coupling of sensorimotor to transmodal cortex when adapting behavior.

neuroscience↗

bioRxiv2023

The structural-functional neural architectures of implicit and explicit motor learning

Sensorimotor learning is supported by multiple competing processes that operate concurrently, making it a challenge to elucidate their neural underpinnings. Here, using human functional MRI, we identify three distinct axes of connectivity between the motor cortex and other brain regions during sensorimotor adaptation. These three axes uniquely correspond to subjects degree of implicit learning, performance errors and explicit strategy use, and involve different brain networks situated at increasing levels of the cortical hierarchy. We test the generalizability of these neural axes to a separate form of motor learning known to rely mainly on explicit processes, and show that it is only the Explicit neural axis, composed of higher-order areas in transmodal cortex, that predicts learning in this task. Together, our study uncovers multiple distinct patterns of functional connectivity with motor cortex during sensorimotor adaptation, the component processes that these patterns support, and how they generalize to other forms of motor learning.

neuroscience↗

bioRxiv2022

Separate and shared low-dimensional neural architectures for error-based and reinforcement motor learning

AO_SCPLOWBSTRACTC_SCPLOWMotor learning is supported by multiple systems adapted to processing different forms of sensory information (e.g., reward versus error feedback), and by higher-order systems supporting strategic processes. Yet, the extent to which these systems recruit shared versus separate neural pathways is poorly understood. To elucidate these pathways, we separately studied error-based (EL) and reinforcement-based (RL) motor learning in two functional MRI experiments in the same human subjects. We find that EL and RL occupy opposite ends of neural axis broadly separating cerebellar and striatal connectivity, respectively, with somatomotor cortex, and that alignment of this axis to each task is related to performance. Further, we identify a separate neural axis that is associated with strategy use during EL, and show that the expression of this same axis during RL predicts better performance. Together, these results offer a macroscale view of the common versus distinct neural architectures supporting different learning systems.

neuroscience↗

bioRxiv2022

Distinct patterns of cortical manifold expansion and contraction underlie human sensorimotor adaptation

Sensorimotor learning is a dynamic, systems-level process that involves the combined action of multiple neural systems distributed across the brain. Although we understand a great deal about the specialized cortical systems that support specific components of action (such as reaching), we know less about how cortical systems function in a coordinated manner to facilitate adaptive behaviour. To address this gap in knowledge, our study measured human brain activity using functional MRI (fMRI) while participants performed a classic sensorimotor adaptation task, and used a manifold learning approach to describe how behavioural changes during adaptation relate to changes in the landscape of cortical activity. During early adaptation, we found that areas in parietal and premotor cortex exhibited significant contraction along the cortical manifold, which was associated with their increased covariance with regions in higher-order association cortex, including both the default mode and fronto-parietal networks. By contrast, during late adaptation, when visuomotor errors had been largely reduced, we observed a significant expansion of visual cortex along the cortical manifold, which was associated with its reduced covariance with association cortex and its increased intraconnectivity. Lastly, we found that individuals who learned more rapidly exhibited greater covariance between regions in the sensorimotor and association cortices during early adaptation. Together, these findings are consistent with a view that sensorimotor adaptation depends on changes in the integration and segregation of neural activity across more specialized regions of unimodal cortex with regions in association cortex implicated in higher-order processes. More generally, they lend support to an emerging line of evidence implicating regions of the default mode network in task-based performance.

neuroscience↗

bioRxiv2021

Neural Excursions from Low-Dimensional Manifold Structure Explain Intersubject Variation in Human Motor Learning

Humans vary greatly in their motor learning abilities, yet little is known about the neural mechanisms that underlie this variability. Recent neuroimaging and electrophysiological studies demonstrate that large-scale neural dynamics inhabit a low-dimensional subspace or manifold, and that learning is constrained by this intrinsic manifold architecture. Here we asked, using functional MRI, whether subject-level differences in neural excursion from manifold structure can explain differences in learning across participants. We had subjects perform a sensorimotor adaptation task in the MRI scanner on two consecutive days, allowing us to assess their learning performance across days, as well as continuously measure brain activity. We find that the overall neural excursion from manifold activity in both cognitive and sensorimotor brain networks is associated with differences in subjects patterns of learning and relearning across days. These findings suggest that off-manifold activity provides an index of the relative engagement of different neural systems during learning, and that intersubject differences in patterns of learning and relearning across days are related to reconfiguration processes in cognitive and sensorimotor networks during learning.

neuroscience↗

bioRxiv2020

Whole-brain dynamics of human sensorimotor adaptation

Humans vary greatly in their motor learning abilities, yet little is known about the neural processes that underlie this variability. We identified distinct profiles of human sensorimotor adaptation that emerged across two days of learning, linking these profiles to the dynamics of whole-brain functional networks early on the first day, when cognitive strategies toward sensorimotor adaptation are believed to be most prominent. During early learning, greater recruitment of a network of higher-order brain regions, involving prefrontal and anterior temporal cortex, was associated with faster learning. At the same time, greater integration of this cognitive network with a sensorimotor network was associated with slower learning, consistent with the notion that cognitive strategies toward adaptation operate in parallel with implicit learning processes of the sensorimotor system. On the second day, greater recruitment of a network that included the hippocampus was associated with faster re-learning, consistent with the notion that savings involves declarative memory systems. Together, these findings provide novel evidence for the role of higher-order brain systems in driving individual differences in adaptation.

neuroscience↗

bioRxiv2019

Motor planning modulates neural activity patterns in early human auditory cortex

It is well established that movement planning recruits motor-related cortical brain areas in preparation for the forthcoming action. Given that an integral component to the control of action is the processing of sensory information throughout movement, we predicted that movement planning might also modulate early sensory cortical areas, readying them for sensory processing during the unfolding action. To test this hypothesis, we performed two human functional MRI studies involving separate delayed movement tasks and focused on pre-movement neural activity in early auditory cortex, given its direct connections to the motor system and evidence that it is modulated by motor cortex during movement in rodents. We show that effector-specific information (i.e., movements of the left vs. right hand in Experiment 1, and movements of the hand vs. eye in Experiment 2) can be decoded, well before movement, from neural activity in early auditory cortex. We find that this motor-related information is represented in a separate subregion of auditory cortex than sensory-related information and is present even when movements are cued visually instead of auditorily. These findings suggest that action planning, in addition to preparing the motor system for movement, involves selectively modulating primary sensory areas based on the intended action.

neuroscience↗