Behavioral, computational, and neuroimaging studies of acquired apraxia of speech

A critical examination of speech motor control depends on an in-depth understanding of network connectivity associated with Brodmann areas 44 and 45 and surrounding cortices. Damage to these areas has been associated with two conditions-the speech motor programming disorder apraxia of speech (AOS) and the linguistic/grammatical disorder of Broca's aphasia. Here we focus on AOS, which is most commonly associated with damage to posterior Broca's area (BA) and adjacent cortex. We provide an overview of our own studies into the nature of AOS, including behavioral and neuroimaging methods, to explore components of the speech motor network that are associated with normal and disordered speech motor programming in AOS. Behavioral, neuroimaging, and computational modeling studies are indicating that AOS is associated with impairment in learning feedforward models and/or implementing feedback mechanisms and with the functional contribution of BA6. While functional connectivity methods are not yet routinely applied to the study of AOS, we highlight the need for focusing on the functional impact of localized lesions throughout the speech network, as well as larger scale comparative studies to distinguish the unique behavioral and neurological signature of AOS. By coupling these methods with neural network models, we have a powerful set of tools to improve our understanding of the neural mechanisms that underlie AOS, and speech production generally

Inside the brain of an elite athlete: The neural processes that support high achievement in sports

Events like the World Championships in athletics and the Olympic Games raise the public profile of competitive sports. They may also leave us wondering what sets the competitors in these events apart from those of us who simply watch. Here we attempt to link neural and cognitive processes that have been found to be important for elite performance with computational and physiological theories inspired by much simpler laboratory tasks. In this way we hope to inspire neuroscientists to consider how their basic research might help to explain sporting skill at the highest levels of performance

Maximizing the Effects of Passive Training on Visuomotor Adaptation By Incorporating Other Motor Learning Strategies

Passive training has been shown to be an effective rehabilitation approach for stroke survivors, especially for those who suffer from severe control loss or complete paralysis. However, the effectiveness of the treatments that utilize passive assist training is still low. The goal of this dissertation was to develop a training condition that can maximize the effects of passive training on motor learning by combining its effect with other motor learning strategies. To achieve this goal, two specific aims were pursued: one aim was to determine the effects of passive training on learning a visuomotor adaptation task; and the other aim was to determine the effects of passive training in combination with other strategies on learning a visuomotor adaptation task. Experimental results indicated that passive training has a positive effect on visuomotor learning. Furthermore, it was confirmed that a training condition consisting of action observation and passive training leads to significant performance gains beyond what either intervention alone can do. This suggests that passive training could elicit motor representational changes, inducing instance-reliant learning process (use-dependent plasticity) that encodes motor instances associated with specific effectors and task conditions. The findings from this study show great potential for developing specific rehabilitation protocols that utilize passive training and action observation together for severely impaired stroke patients in the future

Many hats: intra-trial and reward-level dependent bold activity in the striatum and premotor cortex

2012 Spring.Includes bibliographical references.Lesion, drug, single-cell recording, as well as human fMRI studies, suggest dopaminergic projections from VTA/SNc (ventral tagmental area/substantia nigra pars compacta) and cortically driven striatal activity plays a key role in associating sensory events with rewarding actions both by facilitating reward processing and prediction (i.e. reinforcement learning) and biasing and later updating action selection. We, for the first time, isolated BOLD signal changes for stimulus, pre-response, response and feedback delivery at three reward levels. This design allowed us to estimate the degree of involvement of individual striatal regions across these trial components, the reward sensitivity of each component and allowed for a novel comparison of potential (and potentially competing) reinforcement learning computations. Striatal and lateral premotor cortex regions of interest (ROIs) significant activations were universally observed (excepting the ventral striatum) during stimulus presentation, pre-response, response and feedback delivery, confirming these areas importance in all aspects of visuomotor learning. The head of the caudate showed a precipitous drop in activity pre-response, while in the body of the caudate showed no significant changes in activity. The putamen peaked in activity during response. Activation in the lateral premotor cortex was strongest during stimulus presentation, but the drop off was followed by a trend of increasing activity as feedback approached. Both the head and body of the caudate as well as the putamen displayed reward-level sensitivity only during stimulus, while the ventral striatum showed reward sensitivity at both stimulus and feedback. The lack of reward sensitivity surrounding response is inconsistent with theories that the head and ventral striatum encode the value of actions. Which of the three examined reinforcement learning models correlated best with BOLD signal changes varied as a function of trial component and ROI suggesting these regions computations vary depending on task demand

Remembering Forward: Neural Correlates of Memory and Prediction in Human Motor Adaptation

We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions – including prefrontal, parietal and hippocampal cortices – exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancelation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures

Investigation of sequence processing: A cognitive and computational neuroscience perspective

Serial order processing or sequence processing underlies many human activities such as speech, language, skill learning, planning, problem-solving, etc. Investigating the neural bases of sequence processing enables us to understand serial order in cognition and also helps in building intelligent devices. In this article, we review various cognitive issues related to sequence processing with examples. Experimental results that give evidence for the involvement of various brain areas will be described. Finally, a theoretical approach based on statistical models and reinforcement learning paradigm is presented. These theoretical ideas are useful for studying sequence learning in a principled way. This article also suggests a two-way process diagram integrating experimentation (cognitive neuroscience) and theory/ computational modelling (computational neuroscience). This integrated framework is useful not only in the present study of serial order, but also for understanding many cognitive processes

Investigating the Contribution of Instance-Reliant Learning in Visuomotor Adaptation and Its Generalization

Motor adaptation has been of great interest in the past two decades as it reflects how movement skills are acquired and consolidated by the nervous system. In our recent studies, instance-reliant learning is considered as an essential component of visuomotor adaptation, since it plays a unique role in fast and automatized control of movement output. The goal of this dissertation is to investigate the nature of instance-reliant learning on two aspects: to determine the differential contributions of algorithmic learning and instance-reliant learning to visuomotor adaptation; and to determine the nature of movement instance involved in visuomotor adaptation and its generalization across different situations that involve magnitude, workspace, and limb configuration. Experimental results show that both algorithmic and instance-reliant learnings are positively associated with the improvements in the subsequent performance, which is compatible with our expectation. However, compared to algorithmic learning, which has been intensively studied before, instance-reliant learning exhibits different characteristics in terms of both visuomotor adaptation and its generalization. In Experiment 1 and 2, we found that algorithmic and instance-reliant learning led to substantial improvements in movement errors; but the learning rate in the subsequent test was only sensitive to algorithmic learning. In Experiment 3, 4, and 5, the movement instances associated with the reaching performance were magnitude, workspace, and limb configuration specific, although it could still generalize to a certain degree. Thus, the distinct contributions of instance-reliant learning to motor adaptation are elucidated in this dissertation. We expect that findings from this dissertation would prove valuable for developing rehabilitation strategies for patients who suffer from neuromotor impairments

Multiple Motor Learning Processes in Humans: Defining Their Neurophysiological Bases

Learning new motor behaviors or adjusting previously learned actions to account for dynamic changes in our environment requires the operation of multiple distinct motor learning processes, which rely on different neuronal substrates. For instance, humans are capable of acquiring new motor patterns via the formation of internal model representations of the movement dynamics and through positive reinforcement. In this review, we will discuss how changes in human physiological markers, assessed with noninvasive brain stimulation techniques from distinct brain regions, can be utilized to provide insights toward the distinct learning processes underlying motor learning. We will summarize the findings from several behavioral and neurophysiological studies that have made efforts to understand how distinct processes contribute to and interact when learning new motor behaviors. In particular, we will extensively review two types of behavioral processes described in human sensorimotor learning: (1) a recalibration process of a previously learned movement and (2) acquiring an entirely new motor control policy, such as learning to play an instrument. The selected studies will demonstrate in-detail how distinct physiological mechanisms contributions change depending on the time course of learning and the type of behaviors being learned

Reward and punishment: the neural correlates of reinforcement feedback during motor learning

‘By the carrot or the stick’ reward or punishment has been contemplated by instructors to motivate their pupils to learn a new motor skill. The reinforcements of reward and punishment have demonstrated dissociable effects on motor learning with punishment enhancing the learning rate and reward increasing retention of the motor task. However it is still unclear how the brain processes reward and punishment during motor learning. This study sought to investigate the role of reinforcement feedback in cortical neural activity associated with motor learning. A novel visuomotor rotation task was employed with reward punishment or null feedback as the participants adapted their movement to a 30-degree counter-clockwise rotation. We measured movement time and task accuracy throughout the task. Surface electroencephalography was utilized to record cortical neural activity throughout the learning and retention of the motor task. Event-related potentials (ERPs) were calculated to assess how the brain processes the reinforcement feedback and prepares for movement. Repeated measures ANOVAs were utilized to detect differences in the movement parameters and ERP amplitudes. This study found that reward and punishment feedback did not produce different effects on the rate of task learning. However punishment feedback impaired the retention (memory) of the motor task. These behavioral effects were accompanied by changes in the amplitude of ERPs during feedback presentation and movement preparation. These results suggest that punishment feedback alters brain processes involved in memory formation during motor learning