Stroke Plasticity

行程可塑性

• 批准号: 9242083
• 负责人: Vivek Prabhakaran
• 金额: $ 18.47万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-15 至 2017-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9242083
• 关键词: Acute; Adult; Adverse effects; Aging; American; Applications Grants; Area; Behavior; Behavioral; Biofeedback; Brain; Brain Injuries; Brain region; Caregivers; Cause of Death; Chronic; Clinical; Contralateral; Decision Making; Development; Disease; Educational workshop; Elderly; Evaluation; Functional Magnetic Resonance Imaging; Future; Guidelines; Healthcare; High Prevalence; Intervention; Language; Lead; Life; Link; Long-Term Care; Longevity; Machine Learning; Measures; Methods; Motor; Neuronal Plasticity; Neurosciences Research; Occupational; Outcome; Patients; Pattern; Performance; Phase; Process; Recommendation; Recovery; Recovery of Function; Recruitment Activity; Recurrence; Rehabilitation therapy; Research; Research Proposals; Rest; Robot; Scanning; Severities; Signal Transduction; Speech Therapy; Stroke; Techniques; Time; Transcranial magnetic stimulation; Translations; United States; United States National Institutes of Health; Vascular blood supply; aging population; base; behavior test; brain computer interface; clinical application; constraint induced therapy; cost; design; disability; economic cost; functional disability; improved; indexing; individualized medicine; neuroimaging; novel; novel therapeutics; outcome prediction; post stroke; prognostic; public health relevance; rehabilitation strategy; stroke intervention; stroke recovery; stroke rehabilitation; therapy development; tool; treatment planning

DESCRIPTION (provided by applicant): Stroke occurs when blood supply to some part of the brain is compromised and can lead to focal motor, language, and general functional deficits important for activities of daily life. Recovery after deficits in stroke patients is linked to bran plasticity changes occurring over time. There is evidence that these plasticity changes can be adaptive as well as maladaptive towards functional recovery and rehabilitation aimed at facilitating adaptive networks and suppressing maladaptive networks may hasten stroke recovery. One way to characterize plasticity changes over time is by utilizing fMRI methods in adults who have suffered an insult (e.g., stroke) resulting in damage to an area typically associated with a specific language or motor function. Studies have shown that these patients show recovery through brain reorganization changes over time where a network of areas are recruited while performing the language or motor function. There are a number of novel stroke rehabilitation treatments aimed at improving recovery. Yet no set guidelines exist on how to utilize these treatments. There is an important need to develop a set of prognostic predictors to make decisions about which patients are appropriate for which treatment, identify a time window that best predicts stroke recovery and therefore ideal for intervention, as well as characterize adaptive and maladaptive brain plasticity changes in order to facilitate faster and more effective rehabilitation. This proposal has three aims: 1) Identify prognostic predictors of stroke recovery, 2) Identify a time window which best predicts stroke recovery, and 3) Identify adaptive and maladaptive brain plasticity changes involved in stroke recovery. Stroke patients will undergo through neuroimaging and behavioral testing at the acute, subacute and chronic stages. Neuroimaging measures (e.g., fMRI activation) along with clinical measures will be utilized to predict behavior. It is hypothesized that neuroimaging along with clinical measures will predict motor, language, and general functional stroke recovery more accurately than either measure. It is also hypothesized a subacute time window would best predict stroke recovery, given that the most robust plasticity changes occur at this time window. It is hypothesized that that brain plasticity changes assessed by brain measures over time will predict behavioral performance changes over time, characterizing adaptive and maladaptive plasticity networks essential for motor, language, and general functional stroke recovery. Overall this would lead to better prognostic prediction of recovery in stroke patients, identify a critical time window for intervention, identify adaptive and maladaptive networks involved in reorganization. Subsequently this would allow us to provide individualized treatments based on the prognostics, allow us to intervene at a particular time window for optimal effect, and expand the potential for a range of rehabilitation strategies aimed at facilitating adaptive and suppressing maladaptive networks hastening and maximizing functional recovery.

描述（由申请人提供）：当大脑某些部分的血液供应受损时发生中风，并可导致对日常生活活动重要的局灶性运动、语言和一般功能缺陷。中风患者的恢复与随着时间的推移发生的麸皮可塑性变化有关。有证据表明，这些可塑性变化可以是适应性的，以及适应不良的功能恢复和康复，旨在促进适应性网络和抑制适应不良的网络可能会加速中风恢复。 表征可塑性随时间变化的一种方法是利用功能磁共振成像方法在遭受侮辱的成年人中进行（例如，中风）导致通常与特定语言或运动功能相关的区域的损伤。研究表明，这些患者随着时间的推移通过大脑重组变化显示出恢复，其中在执行语言或运动功能时招募了一个区域网络。有许多新的中风康复治疗旨在改善恢复。然而，对于如何使用这些治疗方法并没有既定的指导方针。有一个重要的需求，以开发一套预后预测，以作出决定，哪些患者是适当的治疗，确定一个时间窗口，最好的预测中风恢复，因此理想的干预，以及表征适应性和适应不良的大脑可塑性变化，以促进更快，更有效的康复。 该建议有三个目的：1）确定预后预测因子 中风恢复，2）识别最佳预测中风恢复的时间窗，以及3）识别中风恢复中涉及的适应性和不适应性脑可塑性变化。中风患者将在急性、亚急性和慢性阶段接受神经影像学和行为测试。神经成像测量（例如，fMRI激活）沿着临床测量将被用于预测行为。据推测，神经影像学沿着临床措施将预测运动，语言和一般功能性中风恢复更准确地比任何措施。还假设亚急性时间窗将最好地预测中风恢复，因为最稳健的可塑性变化发生在该时间窗。假设通过大脑测量评估的大脑可塑性变化将预测行为表现随时间的变化，表征对于运动、语言和一般功能性中风恢复至关重要的适应性和适应不良可塑性网络。 总的来说，这将导致更好的预后预测中风患者的恢复，确定一个关键的时间窗口， 干预，识别参与重组的适应性和适应不良网络。随后，这将使我们能够提供个性化的治疗的基础上的行为，使我们能够在特定的时间窗口进行干预，以获得最佳效果，并扩大了一系列康复策略的潜力，旨在促进适应和抑制适应不良的网络，加速和最大限度地恢复功能。

项目成果

Recovery of slow-5 oscillations in a longitudinal study of ischemic stroke patients.

• DOI: 10.1016/j.nicl.2016.03.008
• 发表时间: 2016
• 期刊: NeuroImage. Clinical
• 作者: La C;Nair VA;Mossahebi P;Stamm J;Birn R;Meyerand ME;Prabhakaran V
• 通讯作者: Prabhakaran V

The effect of resting condition on resting-state fMRI reliability and consistency: a comparison between resting with eyes open, closed, and fixated.

• DOI: 10.1016/j.neuroimage.2013.04.013
• 发表时间: 2013-09
• 期刊: NEUROIMAGE
• 影响因子: 5.7
• 作者: Patriat, Remi;Molloy, Erin K.;Meier, Timothy B.;Kirk, Gregory R.;Nair, Veena A.;Meyerand, Mary E.;Prabhakaran, Vivek;Birn, Rasmus M.
• 通讯作者: Birn, Rasmus M.