SENSORIMOTOR WORKING MEMORY CAPACITY LIMITS

Two novel studies examining the capacity and characteristics of working memory for object weights, experienced through lifting, were completed. Both studies employed visually identical objects of varying weight and focused on memories linking object locations and weights. Whereas numerous studies have examined the capacity of visual working memory, the capacity of sensorimotor memory involved in motor control and object manipulation has not yet been explored. In addition to assessing working memory for object weights using an explicit perceptual test, we also assessed memory for weight using an implicit measure based on motor performance. The vertical lifting or LF and the horizontal GF applied during lifts, measured from force sensors embedded in the object handles, were used to assess participants’ ability to predict object weights. In Experiment 1, participants were presented with sets of 3, 4, 5, 7 or 9 objects. They lifted each object in the set and then repeated this procedure 10 times with the objects lifted either in a fixed or random order. Sensorimotor memory was examined by assessing, as a function of object set size, how lifting forces changed across successive lifts of a given object. The results indicated that force scaling for weight improved across the repetitions of lifts, and was better for smaller set sizes when compared to the larger set sizes, with the latter effect being clearest when objects were lifting in a random order. However, in general the observed force scaling was poorly scaled. In Experiment 2, working memory was examined in two ways: by determining participants’ ability to detect a change in the weight of one of 3 to 6 objects lifted twice, and by simultaneously measuring the fingertip forces applied when lifting the objects. The results showed that, even when presented with 6 objects, participants were extremely accurate in explicitly detecting which object changed weight. In addition, force scaling for object weight, which was generally quite weak, was similar across set sizes. Thus, a capacity limit less than 6 was not found for either the explicit or implicit measures collected.

LEARNING IMPULSE CONTROL IN A NOVEL ANIMAL MODEL: SYNAPTIC, CELLULAR, AND PHARMACOLOGICAL SUBSTRATES

Impulse control, an executive process that restrains inappropriate actions, is impaired in numerous psychiatric conditions. This thesis reports three experiments that utilized a novel animal model of impulse control, the response inhibition (RI) task, to examine the substrates that underlie learning this task. In the first experiment, rats were trained to withhold responding on the RI task, and then euthanized for electrophysiological testing. Training in the RI task increased the AMPA/NMDA ratio at the synapses of pyramidal neurons in the prelimbic, but not infralimbic, region of the medial prefrontal cortex. This enhancement paralleled performance as subjects underwent acquisition and extinction of the inhibitory response. AMPA/NMDA was elevated only in neurons that project to the ventral striatum. Thus, this experiment identified a synaptic correlate of impulse control. In the second experiment, a separate group of rats were trained in the RI task prior to electrophysiological testing. Training in the RI task produced a decrease in membrane excitability in prelimbic, but not infralimbic, neurons as measured by maximal spiking evoked in response to increasing current injection. Importantly, this decrease was strongly correlated with successful inhibition in the task. Fortuitously, subjects trained in an operant control condition showed elevated infralimbic, but not prelimbic, excitability, which was produced by learning an anticipatory signal that predicted imminent reward availability. These experiments revealed two cellular correlates of performance, corresponding to learning two different associations under distinct task conditions. In the final experiment, rats were trained on the RI task under three conditions: Short (4-s), long (60-s), or unpredictable (1-s to 60-s) premature phases. These conditions produced distinct errors on the RI task. Interestingly, amphetamine increased premature responding in the short and long conditions, but decreased premature responding in the unpredictable condition. This dissociation may arise from interactions between amphetamine and underlying cognitive processes, such as attention, timing, and conditioned avoidance. In summary, this thesis showed that learning to inhibit a response produces distinct synaptic, cellular, and pharmacological changes. It is hoped that these advances will provide a starting point for future therapeutic interventions of disorders of impulse control.

Higher brain neurons succumb to acute stroke-like insult while lower brain neurons strongly resist

Pyramidal neurons (PyNs) in ‘higher’ brain are highly susceptible to acute stroke injury yet ‘lower’ brain regions better survive global ischemia, presumably because of better residual blood flow. Here we show that projection neurons in ‘lower’ brain regions of hypothalamus and brainstem intrinsically resist acute stroke-like injury independent of blood flow in the brain slice. In contrast `higher` projection neurons in neocortex, hippocampus, striatum and thalamus are highly susceptible. In live brain slices from rat deprived of oxygen and glucose (OGD), we imaged anoxic depolarization (AD) as it propagates through these regions. AD, the initial electrophysiological event of stroke, is a depolarizing front that drains residual energy in compromised gray matter. The extent of AD reliably determines ensuing damage in higher brain, but using whole-cell recordings we found that all CNS neurons do not generate a robust AD. Higher neurons generate strong AD and show no functional recovery in contrast to neurons in hypothalamus and brainstem that generate a weak and gradual AD. Most dramatically, lower neurons recover their membrane potential, input resistance and spike amplitude when oxygen and glucose is restored, while higher neurons do not. Following OGD, new recordings could be acquired in all lower (but not higher) brain regions, with some neurons even withstanding multiple OGD exposure. Two-photon laser scanning microscopy confirmed neuroprotection in lower, but not higher gray matter. Specifically pyramidal neurons swell and lose their dendritic spines post-OGD, whereas neurons in hypothalamus and brainstem display no such injury. Exposure to the Na+/K+ ATPase inhibitor ouabain (100 μM), induces depolarization similar to OGD in all cell types tested. Moreover, elevated [K+]o evokes spreading depression (SD), a milder version of AD, in higher brain but not hypothalamus or brainstem so weak AD correlates with the inability to generate SD. In summary, overriding the Na+/K+ pump using OGD, ouabain or elevated [K+]o evokes steep and robust depolarization of higher gray matter. We show that this important regional difference can be largely accounted for by the intrinsic properties of the resident neurons and that Na+/K+ ATPase pump efficiency is a major determining factor generating strong or weak spreading depolarizations.

Robotic Outcome Assessment in a Non-Human Primate Model of Middle Cerebral Artery Stroke

Stroke is a prevalent disorder with immense socioeconomic impact. A variety of chronic neurological deficits result from stroke. In particular, sensorimotor deficits are a significant barrier to achieving post-stroke independence. Unfortunately, the majority of pre-clinical studies that show improved outcomes in animal stroke models have failed in clinical trials. Pre-clinical studies using non-human primate (NHP) stroke models prior to initiating human trials are a potential step to improving translation from animal studies to clinical trials. Robotic assessment tools represent a quantitative, reliable, and reproducible means to assess reaching behaviour following stroke in both humans and NHPs. We investigated the use of robotic technology to assess sensorimotor impairments in NHPs following middle cerebral artery occlusion (MCAO). Two cynomolgus macaques underwent transient MCAO for 90 minutes. Approximately 1.5 years following the procedure these NHPs and two non-stroke control monkeys were trained in a reaching task with both arms in the KINARM exoskeleton. This robot permits elbow and shoulder movements in the horizontal plane. The task required NHPs to make reaching movements from a centrally positioned start target to 1 of 8 peripheral targets uniformly distributed around the first target. We analyzed four movement parameters: reaction time, movement time (MT), initial direction error (IDE), and number of speed maxima to characterize sensorimotor deficiencies. We hypothesized reduced performance in these attributes during a neurobehavioural task with the paretic limb of NHPs following MCAO compared to controls. Reaching movements in the non-affected limbs of control and experimental NHPs showed bell-shaped velocity profiles. In contrast, the reaching movements with the affected limbs were highly variable. We found distinctive patterns in MT, IDE, and number of speed peaks between control and experimental monkeys and between limbs of NHPs with MCAO. NHPs with MCAO demonstrated more speed peaks, longer MTs, and greater IDE in their paretic limb compared to controls. These initial results qualitatively match human stroke subjects’ performance, suggesting that robotic neurobehavioural assessment in NHPs with stroke is feasible and could have translational relevance in subsequent human studies. Further studies will be necessary to replicate and expand on these preliminary findings.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.