The number of people affected by spinal cord injuries (SCI) ranges from 2.5 million to 4 million worldwide. Individuals with cervical SCI commonly experience difficulty in controlling hand function, largely due to a reduced ability to perform individuated finger movements that they may never fully recover. Specifically, impairment in the ability to accurately coordinate skilled hand actions is a well-established deficit of injury (e.g., precision grip between the thumb and index finger). Previous studies in both human and nonhuman primate models of SCI have demonstrated some recovery of these abilities over time through various compensatory pathways and mechanisms, including through neural regrowth or through physical rehabilitation with repetitive behavioral tasks.Behavioral tasks that require coordinated finger movements are important tools to examine functional recovery and potential effects of treatments after SCI. A tool used to evaluate precision hand gestures in nonhuman primates is the Brinkman board. These boards have surfaces with specially shaped grooves, called wells, that encourage the use of precision grip during a simple reach and grasp task. Performance is scored quantitatively based on gradations of the animal’s success or failure. This behavioral data provide an assessment of real-world ability, which can then be correlated with other metrics like neural regrowth. However, the change in ability after SCI may frustrate an animal who was previously able to complete the Brinkman board task with ease; they may not even attempt the task due to the task now being too difficult. Assessments must be challenging enough to be an accurate measure of ability, but a task that’s too difficult can cause the animal to give up, not just on the given task but on future similar measures or repeated assessments. Failure to participate with the Brinkman board task for any reason results in the same completion time score, conflating any distinction between simply not performing the task and trying but failing to complete it.

A primary objective of this study is to improve the quality of behavioral data collection by modifying existing Brinkman boards to expand the range and resolution of behavioral performance (floor behavior) for post-SCI animals to complete while providing comparable metrics to existing data. These modified Brinkman boards, Remedial Brinkman boards, were designed to be easier to complete and provide additional opportunities for food rewards to the animal. These additional opportunities in the Remedial Brinkman board task manifested in higher participation and performance in the Standard Brinkman board task, specifically by keeping the animal engaged with the task.Low resolution quantitative behavioral data collected through the Brinkman board task can be supplemented with a higher resolution quantitative method, such as motion tracking. These quantitative measures of finger movements could provide, higher resolution data that may allow additional insights into functional recovery. However, detailed temporal information of hand movement kinematics during recovery is lacking; there is little information on the precise timecourse during which this control improves. To begin to address this need, unimpaired animals’ hands were recorded from multiple angles during completion of the Brinkman board task. These recordings were partially annotated by hand, and then used to train a deep learning algorithm to automatically annotate all future recordings, providing a detailed quantitative measure of performance. These quantitative measures included movement velocity, the size of the aperture between the thumb (D1) and index digit (D2), the temporal relationship between velocity and maximum aperture, and finger extension of the index digit. Some of these metrics have been seen in previous human and nonhuman primate studies completing reach and grasp tasks. The second objective of this study was to devise a methodology in which behaviors before and after spinal cord injury can be systematically tracked (using high and low resolution quantitative data) in nonhuman primate models, and that could measure changes in their functional abilities.

Interval timing, a subcategory of time perception, is central to healthy cognitive functioning in everyday life. Findings have emerged highlighting a critical role of the motor system in supporting timing functions. Evidence for its importance stems from both neural and behavioral data; timing tasks are shown to activate motor brain regions such as the supplementary motor area, even when the tasks do not require overt motor responses. On the other hand, behavioral studies have shown that incorporating movement into timing tasks leads to changes in timing precision and accuracy, depending on the manipulation. Here, we examined the role of movement in time perception across three studies. Our first study was driven by prior findings that movements covering longer distances are perceived as lasting longer than movements covering shorter distances. We tested whether restricting arm movement via an external manipulation (i.e., a viscous movement environment) would lead to the same effect. As viscosity increased, perceived time was shortened, supporting our hypothesis. This study also highlighted that movement parameters do not need to be self-modulated to induce movement-related distortions. Our second study was motivated by a gap in knowledge surrounding movementtiming relationships: while existing studies had demonstrated that adding movement to timing tasks exerted changes to performance, the process by which participants judged the duration of their own movements was unknown. We compared accuracy and precision performance between three interval timing conditions: auditory, movement, and combined. The novel findings from this study were that movement intervals were underestimated compared to auditory intervals, and importantly, that timing accuracy was highest when these two signals were combined. Finally, we investigated the role of movement in timing for adults with ADHD. Studies of timing in this population are relatively uncommon and yield heterogeneous results, yet it is a topic of interest due to consistent patient reports of experiencing difficulty with time management. Our research previously found a beneficial effect of movement on timing precision in a sample of healthy adults, and here, we tested whether this would be the case for participants with ADHD. Surprisingly, a movement-related improvement in timing was not found for either the ADHD or control group. We suggest this may be due to the within-subjects manipulation and block design of the study, where participants experienced interleaved blocks of movement and non-movement trials. This contrasts with our prior study in which participants were assigned to a movement or non-movement group, and the observed benefit may have been dependent on the continuous exposure to the movement condition. Additionally, our sample size was relatively small, and potential differences may require more data to uncover. However, a notable finding was that participants with ADHD significantly underestimated time intervals compared to controls, which had not been previously established in the duration range that we tested. In summary, our studies addressed novel research questions about the qualities of movement that affect timing, how movements are timed in isolation and combined with concurrent sensory signals, and how movements interact with timing in adult ADHD. These advancements pave the way for further research on the nuances of movement-timing relationships, and how they influence human cognition on a broader scale.

Motivation: Motor adaptation involves utilizing sensory feedback information to modify motor output in response to movement disturbances. This process can occur along a variety of different behavioral aspects (e.g., timeframe: short or long-term changes) and can involve a single effector (unimanual, e.g., brushing your teeth) or multiple effectors (bimanual, e.g., hammering a nail). Understanding the mechanisms underlying sensorimotor adaptation is crucial in developing solutions to motor disorders (e.g., Huntington’s Disease, pediatric upper limb deformities, assistive robotics). While there is a well-developed scientific literature basis for unimanual motor learning, there is a gap in understanding the interplay of sensory feedback and external factors such as time delay on adaptation. Additionally, little work has centered around bimanual motor adaptation. By investigating these areas, we may contribute to developing therapeutics and procedures to make daily tasks more accessible to people experiencing motor impairments.

Objective: To (1) investigate the spatiotemporal properties of unimanual visuomotor adaptation through examining the effects of different types of visual feedback (either provided throughout the movement or restricted to only the endpoint) on the local and global components of intra-limb generalization and inter-limb transfer; and (2) provide proof of concept for a method toward further investigation of these properties in multi-manual coordination strategies

Methods: Three motor adaptation experiments were conducted using a custom experimental setup that consisted of an LCD monitor mounted horizontally 20 centimeters above a digitized drawing tablet (12 in x 19 in.; Intuos 3, WaCom) and a 3D printed stylus (2.5 cm diameter). Using the stylus, participants made point-to-point reaching movements on the tablet. View of the reaching arm was obstructed by the monitor. All subjects first underwent a baseline reaching block in which they received full visual feedback of a cursor that directly represented the arm position. Subjects in all three experiments were split into two training groups: those receiving full visual feedback (FF) (Decay n = 10; Generalization n = 23; Transfer n = 20) and those receiving visual feedback at the onset of the trial and upon reaching the end point only (EPF) (Decay n = 11; Generalization n = 32; Transfer n = 20). In the training block, subjects experienced a visuomotor rotation such that the cursor path deviated ±30° from the hand path. In the decay experiment (Chapter 2), subjects were probed in the absence of visual feedback after a given time delay that varied from 3 to 120 seconds. In the generalization experiment (Chapter 3), subjects moved into a generalization block where the retention of adaptation was probed at different spatial locations (from 0° to ±135° in 15° increments). In the transfer experiment (Chapter 4), subjects were probed using the untrained (left) limb to targets at the different spatial locations outlined above. These studies culminated in the preliminary bimanual motor learning paradigm outlined in Chapter 5 in which subjects were instructed to place a rectangular cursor (1 cm length, 0.5 cm width) in an oriented rectangular target (1.5 cm length, 0.75 cm width) located 12 cm from the starting position using a KINARM robotic manipulandum.

Results: The temporal decay of the adaptation was largely unaffected by the form of visual feedback; retention after a delay of two minutes for endpoint (73.55 ± 7.60%) and full visual feedback (73.43 ± 7.76%) were not significantly different, as well as the respective time constants (τ_EPF= 25.31, τ_FF= 29.96). Adaptation generalization was best represented by a two-Gaussian model while a single Gaussian model best characterized transfer. Modelling results showed that only the local magnitude of intra-limb generalization was temporally modulated, independent of the feedback condition. In contrast, the spatiotemporal patterns of inter-limb transfer remained near constant across time, again for both types of feedback.

Conclusions: Collectively, these results from Chapters 2-4 suggest that learning mechanisms with different temporal properties underlie the generalization and transfer of visuomotor adaptation. The results of Chapter 5 confirm the preliminary paradigm as a method of future bimanual coordination and control study.

Significance statement