Multiple Time Scales of Human Sequence Learning

人类序列学习的多个时间尺度

• 批准号: 8133082
• 负责人: SCOTT Thomas GRAFTON
• 金额: $ 25.99万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA BARBARA
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-06-15 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8133082
• 关键词: Aging; Area; Basal Ganglia; Behavior; Behavioral; Behavioral Model; Brain; Brain Injuries; Decision Theory; Face; Functional Imaging; Functional Magnetic Resonance Imaging; Goals; Health; Hour; Human; Instruction; Learning; Life; Maps; Measures; Metabolic Activation; Modeling; Monkeys; Motor; Motor Cortex; Motor Skills; Movement; Nerve Degeneration; Physiological; Principal Investigator; Specificity; Stroke; System; Task Performances; Techniques; Testing; Time; Training; Transcranial magnetic stimulation; Work; arm; base; human data; imaging modality; long term memory; neural circuit; novel; programs; relating to nervous system; sequence learning; skills; skills training; therapy design

Project 2 Multiple Time Scales of Human Sequence Learning A hallmark of human behavior is the capacity to acquire and maintain motor skills throughout life, both in health and in the face of brain injury or degeneration. This project will focus on mapping changes in motor systems of the human brain as healthy subjects practice and solidify skills into long-term memory. The primary goal of this work is to determine if there exist multiple time scales of learning and if these are based on behavioral features supported by distinct underlying neural circuits. The work is motivated by the need for a comprehensive model that explains when different motor circuits are engaged as a function of time over the course of training, particularly for skills requiring extensive practice. This question is important clinically because current therapies for neurodegeneration or stroke, including forced use paradigms, demand extensive training whereas most learning models consider changes over minutes to hours. The current work will identify longitudinal and cross sectional changes in the brain over extended training periods during sequence learning. Functional magnetic resonance imaging (fMRI) will be used as a physiologic probe of neural circuit recruitment and to model interactions between cortical and subcortical networks that may drive cortical plasticity. To generate causal inferences, disruptive transcranial magnetic stimulation (TMS) will be used to selectively inactivate different areas that contribute to task performance. Specific Aim 1. Distinguish neural systems associated with multiple time scales of learning. The conceptual framework underlying this aim is that dissociable cortical-subcortical networks are engaged over the course of skill training, with these networks demonstrating distinct time scales of recruitment. Specific Aim 2. Use functional imaging to predict long-term skill retention. This will be tested by comparing brain activity in different subjects at the end of training and for sequences trained at different intensities. Specific Aim 3. Determine the time course and substrates associated with sequence generalization. It is hypothesized that generalization of a skill to the other arm is supported is acquired early and on short time scales. In contrast, effector specificity emerges with long-term practice and is associated with changes in neural systems occurring on a long time scale. This has implications for defining when intermanual transfer is effective. RELEVANCE (See instructions): The proposed work is central to the problem of understanding the mechansims where practice leads to to reorganization of the human motor system in the face of aging, neurodeneration, stroke or brain injury. Understanding these mechansims has an impact on the design of therapies directed at preserving function, developing compensator movements and ultimately, developing novel motor capacity.

项目2人类序列学习的多时间尺度