Biology subjects

de Brouwer, A. J.

Publications and source records attributed to de Brouwer, A. J..

7 recordsLinked to original sources

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

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↗

bioRxiv2021

Eye-hand coordination during online reach corrections is task-dependent

To produce accurate movements, the human motor system needs to deal with errors that can occur due to inherent noise, changes in the body, or disturbances in the environment. Here, we investigated the temporal coordination of rapid corrections of the eye and hand in response to a change in visual target location during the movement. In addition to a classic double-step task in which the target stepped to a new position, participants performed a set of modified double-step tasks in which the change in movement goal was indicated by the appearance of an additional target, or by a spatial or symbolic cue. We found that both the absolute correction latencies of the eye and hand and the relative eye-hand correction latencies were dependent on the visual characteristics of the target change, with increasingly longer latencies in tasks that required more visual and cognitive processing. Typically, the hand started correcting slightly earlier than the eye, especially when the target change was indicated by a symbolic cue, and in conditions where visual feedback of the hand position was provided during the reach. Our results indicate that the oculomotor and limb-motor system can be differentially influenced by processing requirements of the task and emphasize that temporal eye-hand coordination is flexible rather than rigid. New & NoteworthyEye movements support hand movements in many situations. Here we used variations of a double-step task to investigate temporal coupling of corrective hand and eye movements in response to target displacements. Correction latency coupling depended on the visual and cognitive processing demands of the task. The hand started correcting before the eye, especially when the task required decoding a symbolic cue. These findings highlight the flexibility and task-dependency of eye-hand coordination.

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bioRxiv2020

Gaze Behaviour Reveals Flexible Encoding of Competing Reach Goals Under Conditions of Target Uncertainty

In daily tasks, we are often confronted with competing potential targets and must select one to act on. It has been suggested that, prior to target selection, the human brain encodes the motor goals of multiple, potential targets. However, this view remains controversial and it has been argued that only a single motor goal is encoded, or that motor goals are only specified after target selection. To investigate this issue, we measured participants gaze behaviour while viewing two potential reach targets, one of which was cued after a preview period. We applied visuomotor rotations to dissociate each visual target location from its corresponding motor goal location; i.e., the location participants needed to aim their hand toward to bring the rotated cursor to the target. During the preview period, participants most often fixated both motor goals but also frequently fixated one, or neither, motor goal location. Further gaze analysis revealed that on trials in which both motor goals were fixated, both locations were held in memory simultaneously. These findings show that, at the level of single trials, the brain most often encodes multiple motor goals prior to target selection, but may also encode either one or no motor goals. This result may help reconcile a key debate concerning the specification of motor goals in cases of target uncertainty.

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bioRxiv2020

Variation in error-based and reward-based human motor learning is related and associated with entorhinal volume

Error-based and reward-based processes are critical for motor learning, and are thought to be mediated via distinct neural pathways. However, recent behavioral work in humans suggests that both learning processes are supported by cognitive strategies and that these contribute to individual differences in motor learning ability. While it has been speculated that medial temporal lobe regions may support this strategic component to learning, direct evidence is lacking. Here we first show that faster and more complete learning during error-based visuomotor adaptation is associated with better learning during reward-based shaping of reaching movements. This result suggests that strategic processes, linked to faster and better learning, drive individual differences in both error-based and reward-based motor learning. We then show that right entorhinal cortex volume was larger in good learning individuals--classified across both motor learning tasks--compared to their poorer learning counterparts. This suggests that strategic processes underlying both error- and reward-based learning are linked to neuroanatomical differences in entorhinal cortex. Significance StatementWhile it is widely appreciated that humans vary greatly in their motor learning abilities, little is known about the processes and neuroanatomical bases that underlie these differences. Here, using a data-driven approach, we show that individual variability in error-based and reward-based motor learning is tightly linked, and related to the use of cognitive strategies. We further show that structural differences in entorhinal cortex predict this intersubject variability in motor learning, with larger entorhinal volumes being associated with better overall error-based and reward-based learning. Together, these findings provide support for the notion that the ability to recruit strategic processes underlies intersubject variability in both error-based and reward-based learning, which itself may be linked to structural differences in medial temporal regions.

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bioRxiv2017

Gaze Behaviour During Sensorimotor Adaptation Parcellates the Explicit and Implicit Contributions to Learning

Successful motor performance relies on our ability to adapt to changes in the environment by learning novel mappings between motor commands and sensory outcomes. Such adaptation is thought to involve two distinct mechanisms: An implicit, error-based component linked to slow learning and an explicit, strategic component linked to fast learning and savings (i.e., faster relearning). Because behaviour, at any given moment, is the resultant combination of these two processes, it has remained a challenge to parcellate their relative contributions to performance. The explicit component to visuomotor rotation (VMR) learning has recently been measured by having participants verbally report their aiming strategy used to counteract the rotation. However, this procedure has been shown to magnify the explicit component. Here we tested whether task-specific eye movements, a natural component of reach planning--but poorly studied in motor learning tasks--can provide a direct read-out of the state of the explicit component during VMR learning. We show, by placing targets on a visible ring and including a delay between target presentation and reach onset, that individual differences in gaze patterns during sensorimotor adaptation are linked to participants rates of learning and can predict the expression of savings. Specifically, we find that participants who, during reach planning, naturally fixate an aimpoint, rotated away from the target location, show faster initial adaptation and readaptation 24 hrs. later. Our results demonstrate that gaze behaviour can not only uniquely identify individuals who implement cognitive strategies during learning, but also how their implementation is linked to differences in learning.

neuroscience↗