Biology subjects

Albert, S. T.

Publications and source records attributed to Albert, S. T..

12 recordsLinked to original sources

bioRxiv2025

SORDINO for Silent, Sensitive, Specific, and Artifact-Resisting fMRI in awake behaving mice

Blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) has revolutionized our understanding of the brain activity landscape, bridging circuit neuroscience in animal models with noninvasive brain mapping in humans. This immensely utilized technique, however, faces challenges such as acoustic noise, electromagnetic interference, motion artifacts, magnetic-field inhomogeneity, and limitations in sensitivity and specificity. Here, we introduce Steady-state On-the-Ramp Detection of INduction-decay with Oversampling (SORDINO), a transformative fMRI technique that addresses these challenges by maintaining a constant total gradient amplitude while acquiring data during continuously changing gradient direction. When benchmarked against conventional fMRI on a 9.4T system, SORDINO is silent, sensitive, specific, and resistant to motion and susceptibility artifacts. SORDINO offers superior compatibility with multimodal experiments and carries novel contrast mechanisms distinct from BOLD. It also enables brain-wide activity and connectivity mapping in awake, behaving mice, overcoming stress- and motion-related confounds that are among the most challenging barriers in current animal fMRI studies.

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bioRxiv2024

Time affects a time-consuming, flexible component and a fast, inflexible component of sensorimotor adaptation.

Prior learning can impair future learning when the requirements of the two memories conflict, a phenomenon termed anterograde interference. In sensorimotor adaptation, the passage of time between initial and future learning can reduce such interference effects. However, we still do not fully understand how time affects learning, as some studies found no effects of time on interference. One possible explanation for such inconclusive findings is that time affects distinct processes underpinning sensorimotor adaptation differently, and these processes may compensate for each others effects on behaviour. Here, we used task manipulations that (1) dissociate adaptation processes driven by task errors from adaptation processes driven by sensory prediction errors, and (2) separate the task-error driven adaptation processes into a flexible component that could not be acquired under time-pressure, or a less-flexible component that could not be acquired under time-pressure. The time between initial and subsequent learning seemed to alter both the flexible and inflexible components of adaptation driven by task errors. Time also led to a small reduction of interference arising from sensory prediction errors. Thus, we provide evidence that multiple components of sensorimotor adaptation are sensitive to the passage of time.

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bioRxiv2024

Protection of savings by reducing the salience of opposing errors

When humans encounter the same perturbation twice, they typically adapt faster the second time, a phenomenon called savings. Studies have examined savings following adaptation to a gradually introduced perturbation, with mixed results. These inconsistencies might be caused by differences in how behavior returns to its baseline state during the washout phase in between learning periods. To test this, participants controlled a cursor that was subject to a visual rotation in its motion direction. The rotation was applied during two learning periods, separated by a washout period where the rotation was removed abruptly, gradually, or without error feedback. We found that the type of error experienced during washout affected savings: abrupt washout with large errors eliminated savings, whereas gradual or no-feedback washout preserved it. Model-based analyses indicated these effects were driven by changes in error sensitivity suggesting that salient, opposing errors experienced during washout downregulate the response to error, nullifying savings.

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bioRxiv2023

A software tool for at-home measurement of sensorimotor adaptation

Sensorimotor adaptation is traditionally studied in well-controlled laboratory settings with specialized equipment. However, recent public health concerns such as the COVID-19 pandemic, as well as a desire to recruit a more diverse study population, have led the motor control community to consider at-home study designs. At-home motor control experiments are still rare because of the requirement to write software that can be easily used by anyone on any platform. To this end, we developed software that runs locally on a personal computer. The software provides audiovisual instructions and measures the ability of the subject to control the cursor in the context of visuomotor perturbations. We tested the software on a group of at-home participants and asked whether the adaptation principles inferred from in-lab measurements were reproducible in the at-home setting. For example, we manipulated the perturbations to test whether there were changes in adaptation rates (savings and interference), whether adaptation was associated with multiple timescales of memory (spontaneous recovery), and whether we could selectively suppress subconscious learning (delayed feedback, perturbation variability) or explicit strategies (limited reaction time). We found remarkable similarity between in-lab and at-home behaviors across these experimental conditions. Thus, we developed a software tool that can be used by research teams with little or no programming experience to study mechanisms of adaptation in an at-home setting. SignificanceSensorimotor learning is traditionally studied in the laboratory, but recent public health emergencies have caused the community to consider at-home data collection. To accelerate this effort, we implemented a software tool that remotely tracks motor learning. Compared with previous remote data collection strategies, our software (1) generates experiments of arbitrary length that (2) run locally on a participants laptop which (3) can be modified without any programming expertise in the research laboratory. Here we show a close correspondence between behaviors captured by our tool and those observed in laboratory environments including savings, interference, spontaneous recovery, and variations in implicit and explicit learning due to changes in perturbation variance, reaction time constraints, and feedback delay. Our software and its corresponding manuals are available here: https://osf.io/e8b63/.

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bioRxiv2023

Sensory prediction error drives subconscious motor learning outside of the laboratory

Sensorimotor adaptation is supported by at least two parallel learning systems: an intentionally controlled explicit strategy, and an involuntary implicit learning system. To investigate the error sources driving these two systems, past work focused on constrained reaches or finger movements in laboratory environments has shown subconscious learning systems to be driven in part by sensory prediction error (SPE), i.e., the mismatch between the realized and expected outcome of an action. We designed a ball rolling task to explore whether SPEs can drive implicit motor adaptation during complex whole-body movements that impart physical motion on external objects. After applying a visual shift, participants rapidly adapted their rolling angles to reduce the error between the ball and target. We removed all visual feedback and told participants to aim their throw directly toward the primary target, revealing an unintentional 5.06{degrees} implicit adjustment to reach angles that decayed over time. To determine whether this implicit adaptation was driven by SPE, we gave participants a second aiming target that would solve the visual shift, as in Mazzoni and Krakauer (2006). Remarkably, after rapidly reducing ball rolling error to zero (due to enhancements in strategic aiming), the additional aiming target caused rolling angles to deviate beyond the primary target by 3.15{degrees}. This involuntary overcompensation, which worsened task performance, is a hallmark of SPE-driven implicit learning. These results show that SPE-driven implicit processes, previously observed within simplified finger or planar reaching movements, actively contribute to motor adaptation in more complex naturalistic skill-based tasks. New and NoteworthyImplicit and explicit learning systems have been detected using simple, constrained movements inside the laboratory. How these systems impact movements during complex whole-body, skill-based tasks has not been established. Here we demonstrate that sensory prediction errors significantly impact how a person updates their movements, replicating findings from the laboratory in an unconstrained ball-rolling task. This real-world validation is an important step towards explaining how subconscious learning helps humans execute common motor skills in dynamic environments.

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bioRxiv2022

Independence and interaction between the control of moving and holding still in post-stroke arm paresis

Moving and holding-still (holding) have been posited to be under separate control regimes for both eye and arm movements. The paretic arm after stroke exhibits different abnormalities during rest vs. movement, providing an opportunity to ask whether control of these behaviors is independently affected in stroke. Here, we quantified resting postural abnormalities in stroke patients by measuring their biases in force production as they held their hand still in various locations in a planar workspace, and then assessed the influence of these resting force biases on reaching in the same workspace. We found that patients had marked resting force biases at each location, even when the arm was supported. However, these biases did not transfer to arm-supported planar reaching movements: not during initial acceleration, not in response to mid-trajectory perturbations, and not during deceleration to a stop. Rather, the abnormal resting forces only appeared to switch on after a movement had fully stopped. These findings suggest that moving and holding are functionally separable modes of control. At the same time, we found that the resting biases mirrored characteristics of abnormal synergies during movement: they markedly decreased when arm support was provided; they were higher in more distant positions that require breaking out of flexion; and they scaled with the Fugl-Meyer score for the upper extremity (a measure of intrusion of abnormal synergies during active movement). These three shared features suggest a common mechanism for resting biases and abnormal synergies, which appears to be a contradiction given the functional separation of moving and holding observed in the same patients. To resolve this paradox, we propose a conceptual model that predicts a breakdown in the functional separation between reaching and holding when patients move in the absence of weight support. This conceptual model posits that synergies are the behavioral manifestation of a spillover of posture into movement. Mapping these functional systems onto anatomical and physiological details of lesioned substrate after stroke may provide implementation-level insight into how normal arm motor control is assembled.

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bioRxiv2022

Perturbation variance suppresses error sensitivity in the implicit learning system

When variability is added to a sensorimotor perturbation, total adaptation is impaired. In Albert et al.1 we explored this phenomenon, and observed that it is the brains implicit, i.e., subconscious learning system that is most affected by perturbation variance. We observed that perturbation variability impaired implicit learning by downregulating its sensitivity to error. Recently, Wang et al.2 present an alternate viewpoint: implicit error sensitivity does not change with experience, only the errors observed by the implicit system change. Here we evaluated this alternate view by empirically measuring error sensitivity as a function of error size. We found that perturbation variability strongly downregulates implicit error sensitivity when controlling for error size, consistent with our original results, counter to the inflexible model argued by Wang et al. With that said, a pre-existing relationship between error sensitivity and error magnitude noted by Wang et al. can contribute at least in part to implicit behavior. State-space models that start with this pre-existing error sensitivity curve and then update it with training according to a memory of errors most accurately tracked measured behavior.

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bioRxiv2021

A conversion from slow to fast memory in response to passive motion

When the same perturbation is experienced consecutively, learning is accelerated on the second attempt. This savings is a central property of sensorimotor adaptation. Current models suggest that these improvements in learning are due to changes in the brains sensitivity to error. Here, we tested whether these increases in error sensitivity could be facilitated by passive movement experiences. In each experimental group, a robot moved the arm passively in the direction that solved the upcoming rotation, with no visual feedback provided. Following that, participants adapted to a visuomotor rotation. Prior passive movements substantially improved motor learning, increasing total compensation in each group by approximately 30%. Similar to savings, a state-space model suggested that this improvement in learning was due to an increase in error sensitivity, but not memory retention. When we considered the possibility that learning was supported by parallel fast and slow adaptive processes, a striking pattern emerged: whereas initial improvements in learning were driven by a slower adaptive state, increases in error sensitivity gradually transferred to a faster learning system with the passage of time. These findings suggest that passive errors engage motor learning systems, but the resulting behavioral patterns migrate between slow and fast adaptive circuits as the passive memory is consolidated.

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bioRxiv2020

Competition between parallel sensorimotor learning systems

Sensorimotor adaptation benefits from learning in two parallel systems: one that has access to explicit knowledge, and another that relies on implicit, unconscious correction. However, it is unclear how these systems interact: does enhancing one systems contributions, for example through instruction, impair the other, or do they learn independently? Here we illustrate that certain contexts can lead to competition between implicit and explicit learning. In some cases, each system is responsive to a task-related visual error. This shared error appears to create competition between these systems, such that when the explicit system increases its response, errors are siphoned away from the implicit system, thus reducing its learning. This model suggests that explicit strategy can mask changes in implicit error sensitivity related to savings and interference. Other contexts suggest that the implicit system can respond to multiple error sources. When these error sources conflict, a second type of competition occurs. Thus, the data show that during sensorimotor adaptation, behavior is shaped by competition between parallel learning systems.

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bioRxiv2020

Spontaneous recovery and the multiple timescales of human motor memory

In numerous paradigms, from fear conditioning to motor adaptation, memory exhibits a remarkable property: acquisition of a novel behavior followed by its extinction results in spontaneous recovery of the original behavior. A current model suggests that spontaneous recovery occurs because learning is supported by two different adaptive processes: one fast (high error sensitivity, low retention), and the other slow (low error sensitivity, high retention). Here, we searched for signatures of these hypothesized processes in the commands that guided single movements. We examined human saccadic eye movements and observed that following experience of a visual error, there was an adaptive change in the motor commands of the subsequent saccade, partially correcting for the error. However, the error correcting commands were expressed only during the deceleration period. If the errors persisted, the acceleration period commands also changed. Adaptation of acceleration period commands exhibited poor sensitivity to error, but the learning was resistant to forgetting. In contrast, the deceleration period commands adapted with high sensitivity to error, and the learning suffered from poor retention. Thus, within a single saccade, a fast-like process influenced the deceleration period commands, whereas a slow-like process influenced the acceleration period commands. Following extinction training, with passage of time motor memory exhibited spontaneous recovery, as evidenced by return of saccade endpoints toward their initial adapted state. The temporal dynamics of spontaneous recovery suggested that a single saccade is controlled by two different adaptive controllers, one active during acceleration, and the other during deceleration. Significance statementA feature of memory in many paradigms is the phenomenon of spontaneous recovery: learning followed by extinction inevitably leads to reversion toward the originally learned behavior. A theoretical model posits that spontaneous recovery is a feature of memory systems that learn with two independent learning processes, one fast, and the other slow. However, there have been no direct measures of these putative processes. Here, we found potential signatures of the two independent adaptive processes during control of a single saccade. The results suggest that distinct adaptive controllers contribute to the acceleration and deceleration phases of a saccade, and that each controller is supported by a fast and a slow learning process.

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bioRxiv2019

Asymptotic limits of sensorimotor adaptation

After extended practice, motor adaptation reaches a limit in which learning appears to stop, despite the fact that residual errors persist. What prevents the brain from eliminating the residual errors? Here we found that the adaptation limit was causally dependent on the second order statistics of the perturbation; when variance was high, learning was impaired and large residual errors persisted. However, when learning relied solely on explicit strategy, both the adaptation limit and its dependence on perturbation variability disappeared. In contrast, when learning depended entirely, or in part on implicit learning, residual errors developed. Residual errors in implicit performance were caused by variance-dependent modifications to error sensitivity, not forgetting. These observations are consisted with a model of learning in which the implicit system becomes more sensitive to error when errors are consistent, but forgets this memory of errors over time. Thus, residual errors in motor adaptation are a signature of the implicit learning system, caused by an error sensitivity that depends on the history of past errors.

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bioRxiv2019

Holding the arm still through subcortical mathematical integration of cortical commands

A period of holding still follows every movement. It has been assumed that for the arm, moving and holding are functionally independent: movement is via an adaptive, feedback-dependent controller that generates commands to transport the arm, while holding is via setting of reflexes that produce a postural field at movement endpoint. This assumption predicts that commands that move the arm should not affect the postural field at movement termination. Surprisingly, we found that as the reach commands changed, so did the ensuing postural field. The postural commands depended on mathematical integration of the reach commands. Following damage to the corticospinal tract, despite severe reach impairments, the holding system faithfully integrated the imperfect reach commands on a trial-by-trial basis. Together, these findings suggest that holding the arm still is accomplished via a separate, likely subcortical structure that acts as a mathematical integrator of the commands generated by the cortical reach controller.

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