Biology subjects

Gallivan, J.

Publications and source records attributed to Gallivan, J..

9 recordsLinked to original sources

bioRxiv2026

Eye versus hand effector-specificity in the parieto-frontal cortex and cerebellum in humans

The degree and distribution of effector-specific representations for saccade and reach movements in the human brain remain unclear, partly because overlapping regional activation and the limited temporal resolution of fMRI complicate the identification of effector-dependent signals. Using a cue-separated event-related fMRI paradigm, we examined cortical and cerebellar activity across three temporally separated phases: visual target encoding, movement planning, and movement execution in 19 adults. We combined conventional univariate analyses with multivariate analyses of spatial activity patterns to characterize overlapping and distinct neural activity associated with each effector. Univariate analyses revealed widespread overlap across parieto-frontal regions, alongside effector-dependent differences in the magnitude and distribution of activation. Reach-related activity was generally more extensive than saccade-related activity, particularly during planning, while execution revealed distinct patterns of effector-preferential activation across cortical and cerebellar regions. Despite overlapping activation, multivariate analyses identified distinguishable spatial activity patterns across cortical and cerebellar regions. These findings confirm that (1) separating sensorimotor phases improves the detection of effector-preferential signals across the brain, and (2) overlapping activation during eye and hand movements does not necessarily imply equivalent neural activity patterns. Instead, multivariate analyses reveal widespread effector-dependent differences, suggesting a greater degree of spatial differentiation between saccade- and reach-related activity across the human cortex and cerebellum than conventual activation analyses alone would indicate.

neuroscience↗

bioRxiv2026

Thoughts and cortical responses are shaped by the type of change at event boundaries

Continuous experience is divided into discrete and meaningful events, but different kinds of event transitions may shape thought and brain activity in different ways. We examined how perceived event boundaries relate to visual, auditory, and semantic features, ongoing thought, and large-scale cortical states during movie-watching. We combined three independent samples who viewed the same eight movie clips, aligning these measures to a shared stimulus timeline: One marked event boundaries, one reported ongoing thought, and one was scanned with fMRI. Whole-brain spatial correlations showed that event boundaries correlated positively with visual change and negatively with auditory change, but not with any thought pattern. Time-resolved analyses showed that boundaries were associated with a reduction in distracted thoughts and brain states positioned toward visual processing. Clustering boundaries by movie feature change profile identified visual, semantic, interruption, and narrative boundary types. All four types showed a shift toward visual processing in the brain, but their other associations differed. Visual boundaries were associated with less distraction, semantic boundaries with thoughts related to past experience and prior knowledge, and narrative boundaries with more detailed and deliberate thoughts. Visual and semantic boundaries also showed distinct shifts in brain state that were not apparent in the other two types. These findings suggest that event boundaries mark a set of state shifts in ongoing experience, with a shared reorientation toward visual processing and changes in thought and brain state that vary with the type of transition.

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

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↗

bioRxiv2022

Differential reach vector computations in mIPS and PMd as revealed through HD-tDCS

Recent neural and behavioural findings provide support that the medial intraparietal sulcus (mIPS) and dorsal premotor (PMd) activity reflect aspects of a kinematic plan for reaching movements. However, it is unclear how these two regions differentially contribute to reach planning. Here, we used high-definition transcranial direct current stimulation (HD-tDCS; 4 x 1 electrode placement; 2 mA for 20 min; 3 cm radius) to investigate the functional roles of mIPS and PMd in the left hemisphere of humans. We examined the changes in endpoint error in reaching task with different initial hand positions and different target locations spanning both visual hemi-fields. Participants completed the task with (stimulation, post-stimulation) and without stimulation (pre-stimulation) of individually fMRI-localized cortical areas mIPS and PMd. We found a significant interaction effect between initial hand position (IHP) and target position on the difference in horizontal endpoint error after cathodal left mIPS stimulation and significant IHP and target position main effects after cathodal left PMd stimulation, suggesting that IHP and target position are not yet integrated into a movement vector at the input of the mIPS, but are integrated in the input of PMd. Hence, these findings reveal a distinction between mIPS and PMd in the stages of movement vector formation for reaching movements and indicate that HD-tDCS is a viable method for perturbing localized cortical activity to elucidate localized cortical function.

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↗

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↗

bioRxiv2019

HD-tDCS over mIPS causally modulates online reach correction

Brain lesion and stimulation studies have suggested posterior parietal cortex and the medial intraparietal sulcus in particular as a crucial hub for online movement error corrections. However, causal evidence for this is still sparse. Indeed, lesion studies are potentially confounded by compensatory reorganization mechanisms while brain stimulation studies have produced heterogeneous results when employing transcranial magnetic stimulation. Here we designed a new complementary paradigm using fMRI-guided high-definition transcranial direct current stimulation (HD-tDCS) of the left medial intraparietal sulcus (mIPS) together with regression-based mediation analysis to re-examine the causal role of mIPS in online reach corrections to jumping targets. We obtained two independent measures of stimulation-induced changes in brain activity by modeling current flow in the brain and through EEG recordings before and after HD-tDCS stimulation. Third, to quantify behavioral effects of HD-tDCS we computed movement curvature as a measure of online correction. We demonstrate that both of our measurements of brain activity were consistent with a polarity-specific modulation of the online correction for targets jumping to the contralateral side of the stimulation. Importantly, using a mediation analysis of the relationship between stimulation current and movement curvature suggests that the induced current modifies brain activity, which then leads to the observed behavioral changes. This unique combination of methods and analysis thus provides complementary evidence for the crucial role of the posterior parietal cortex in online error correction, while at the same time setting a new methodological standard with respect to the causal influence of transcranial direct current stimulation. New & NoteworthyTranscranial direct current stimulation (tDCS) is an interesting and potentially useful tool for asking causal scientific questions and design clinical treatments. With our unique combination of highly accurate fMRI guided stimulation, current forward modeling, EEG recordings before and after the stimulation and behavioral changes we could unravel the causal structure of tDCS. Our approach naturally deals with the variability of tDCS results, increasing its potential usefulness as a tool for research and clinical applications.

neuroscience↗