Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

Testing Multiple Coordination Constraints with a Novel Bimanual Visuomotor Task

The acquisition of a new bimanual skill depends on several motor coordination constraints. To date, coordination constraints have often been tested relatively independently of one another, particularly with respect to isofrequency and multifrequency rhythms. Here, we used a new paradigm to test the interaction of multiple coordination constraints. Coordination constraints that were tested included temporal complexity, directionality, muscle grouping, and hand dominance. Twenty-two healthy young adults performed a bimanual dial rotation task that required left and right hand coordination to track a moving target on a computer monitor. Two groups were compared, either with or without four days of practice with augmented visual feedback. Four directional patterns were tested such that both hands moved either rightward (clockwise), leftward (counterclockwise), inward or outward relative to each other. Seven frequency ratios (3∶1, 2∶1, 3∶2, 1∶1, 2∶3. 1∶2, 1∶3) between the left and right hand were introduced. As expected, isofrequency patterns (1∶1) were performed more successfully than multifrequency patterns (non 1∶1). In addition, performance was more accurate when participants were required to move faster with the dominant right hand (1∶3, 1∶2 and 2∶3) than with the non-dominant left hand (3∶1, 2∶1, 3∶2). Interestingly, performance deteriorated as the relative angular velocity between the two hands increased, regardless of whether the required frequency ratio was an integer or non-integer. This contrasted with previous finger tapping research where the integer ratios generally led to less error than the non-integer ratios. We suggest that this is due to the different movement topologies that are required of each paradigm. Overall, we found that this visuomotor task was useful for testing the interaction of multiple coordination constraints as well as the release from these constraints with practice in the presence of augmented visual feedback

Concurrent adaptation to opposing visual displacements during an alternating movement.

It has been suggested that, during tasks in which subjects are exposed to a visual rotation of cursor feedback, alternating bimanual adaptation to opposing rotations is as rapid as unimanual adaptation to a single rotation (Bock et al. in Exp Brain Res 162:513–519, 2005). However, that experiment did not test strict alternation of the limbs but short alternate blocks of trials. We have therefore tested adaptation under alternate left/right hand movement with opposing rotations. It was clear that the left and right hand, within the alternating conditions, learnt to adapt to the opposing displacements at a similar rate suggesting that two adaptive states were formed concurrently. We suggest that the separate limbs are used as contextual cues to switch between the relevant adaptive states. However, we found that during online correction the alternating conditions had a significantly slower rate of adaptation in comparison to the unimanual conditions. Control conditions indicate that the results are not directly due the alternation between limbs or to the constant switching of vision between the two eyes. The negative interference may originate from the requirement to dissociate the visual information of these two alternating displacements to allow online control of the two arms

Excitability of the Motor Cortex Ipsilateral to the Moving Body Side Depends on Spatio-Temporal Task Complexity and Hemispheric Specialization

Unilateral movements are mainly controlled by the contralateral hemisphere, even though the primary motor cortex ipsilateral (M1ipsi) to the moving body side can undergo task-related changes of activity as well. Here we used transcranial magnetic stimulation (TMS) to investigate whether representations of the wrist flexor (FCR) and extensor (ECR) in M1ipsi would be modulated when unilateral rhythmical wrist movements were executed in isolation or in the context of a simple or difficult hand-foot coordination pattern, and whether this modulation would differ for the left versus right hemisphere. We found that M1ipsi facilitation of the resting ECR and FCR mirrored the activation of the moving wrist such that facilitation was higher when the homologous muscle was activated during the cyclical movement. We showed that this ipsilateral facilitation increased significantly when the wrist movements were performed in the context of demanding hand-foot coordination tasks whereas foot movements alone influenced the hand representation of M1ipsi only slightly. Our data revealed a clear hemispheric asymmetry such that MEP responses were significantly larger when elicited in the left M1ipsi than in the right. In experiment 2, we tested whether the modulations of M1ipsi facilitation, caused by performing different coordination tasks with the left versus right body sides, could be explained by changes in short intracortical inhibition (SICI). We found that SICI was increasingly reduced for a complex coordination pattern as compared to rest, but only in the right M1ipsi. We argue that our results might reflect the stronger involvement of the left versus right hemisphere in performing demanding motor tasks

Prefrontal stimulation prior to motor sequence learning alters multivoxel patterns in the striatum and the hippocampus

© The Author(s) 2021. Motor sequence learning (MSL) is supported by dynamical interactions between hippocampal and striatal networks that are thought to be orchestrated by the prefrontal cortex. In the present study, we tested whether individually-tailored theta-burst stimulation of the dorsolateral prefrontal cortex (DLPFC) prior to MSL can modulate multivoxel response patterns in the stimulated cortical area, the hippocampus and the striatum. Response patterns were assessed with multivoxel correlation structure analyses of functional magnetic resonance imaging data acquired during task practice and during resting-state scans before and after learning/stimulation. Results revealed that, across stimulation conditions, MSL induced greater modulation of task-related DLPFC multivoxel patterns than random practice. A similar learning-related modulatory effect was observed on sensorimotor putamen patterns under inhibitory stimulation. Furthermore, MSL as well as inhibitory stimulation affected (posterior) hippocampal multivoxel patterns at post-intervention rest. Exploratory analyses showed that MSL-related brain patterns in the posterior hippocampus persisted into post-learning rest preferentially after inhibitory stimulation. These results collectively show that prefrontal stimulation can alter multivoxel brain patterns in deep brain regions that are critical for the MSL process. They also suggest that stimulation influenced early offline consolidation processes as evidenced by a stimulation-induced modulation of the reinstatement of task pattern into post-learning wakeful rest.Belgian Research Foundation Flanders (FWO; G099516N); KU Leuven; FWO (G0D7918N, G0B1419N, 1524218N); Excellence of Science (EOS, 30446199, MEMODYN); European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement (703490); FWO (132635) postdoctoral fellowship; Air Force Office of Scientific Research (AFOSR, Virginia, USA; FA9550-16-1-0191)

Transfer of learning between unimanual and bimanual rhythmic movement coordination: transfer is a function of the task dynamic.

Under certain conditions, learning can transfer from a trained task to an untrained version of that same task. However, it is as yet unclear what those certain conditions are or why learning transfers when it does. Coordinated rhythmic movement is a valuable model system for investigating transfer because we have a model of the underlying task dynamic that includes perceptual coupling between the limbs being coordinated. The model predicts that (1) coordinated rhythmic movements, both bimanual and unimanual, are organised with respect to relative motion information for relative phase in the coupling function, (2) unimanual is less stable than bimanual coordination because the coupling is unidirectional rather than bidirectional, and (3) learning a new coordination is primarily about learning to perceive and use the relevant information which, with equal perceptual improvement due to training, yields equal transfer of learning from bimanual to unimanual coordination and vice versa [but, given prediction (2), the resulting performance is also conditioned by the intrinsic stability of each task]. In the present study, two groups were trained to produce 90° either unimanually or bimanually, respectively, and tested in respect to learning (namely improved performance in the trained 90° coordination task and improved visual discrimination of 90°) and transfer of learning (to the other, untrained 90° coordination task). Both groups improved in the task condition in which they were trained and in their ability to visually discriminate 90°, and this learning transferred to the untrained condition. When scaled by the relative intrinsic stability of each task, transfer levels were found to be equal. The results are discussed in the context of the perception–action approach to learning and performance

Error correction in bimanual coordination benefits from bilateral muscle activity: evidence from kinesthetic tracking

Although previous studies indicated that the stability properties of interlimb coordination largely result from the integrated timing of efferent signals to both limbs, they also depend on afference-based interactions. In the present study, we examined contributions of afference-based error corrections to rhythmic bimanual coordination using a kinesthetic tracking task. Furthermore, since we found in previous research that subjects activated their muscles in the tracked (motor-driven) arm, we examined the functional significance of this activation to gain more insight into the processes underlying this phenomenon. To these aims, twelve subjects coordinated active movements of the right hand with motor-driven oscillatory movements of the left hand in two coordinative patterns: in-phase (relative phase 0°) and antiphase (relative phase 180°). They were either instructed to activate the muscles in the motor-driven arm as if moving along with the motor (active condition), or to keep these muscles as relaxed as possible (relaxed condition). We found that error corrections were more effective in in-phase than in antiphase coordination, resulting in more adequate adjustments of cycle durations to compensate for timing errors detected at the start of each cycle. In addition, error corrections were generally more pronounced in the active than in the relaxed condition. This activity-related difference was attributed to the associated bilateral neural control signals (as estimated using electromyography), which provided an additional reference (in terms of expected sensory consequences) for afference-based error corrections. An intimate relation was revealed between the (integrated) motor commands to both limbs and the processing of afferent feedback

Spatial and nonspatial implicit motor learning in Korsakoff’s amnesia: evidence for selective deficits

Patients with amnesia have deficits in declarative memory but intact memory for motor and perceptual skills, which suggests that explicit memory and implicit memory are distinct. However, the evidence that implicit motor learning is intact in amnesic patients is contradictory. This study investigated implicit sequence learning in amnesic patients with Korsakoff’s syndrome (N = 20) and matched controls (N = 14), using the classical Serial Reaction Time Task and a newly developed Pattern Learning Task in which the planning and execution of the responses are more spatially demanding. Results showed that implicit motor learning occurred in both groups of participants; however, on the Pattern Learning Task, the percentage of errors did not increase in the Korsakoff group in the random test phase, which is indicative of less implicit learning. Thus, our findings show that the performance of patients with Korsakoff’s syndrome is compromised on an implicit learning task with a strong spatial response component