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 recovery1-7. 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 function1-7. 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.
Stroke is the fourth leading cause of death in the United States as well as the leading cause of long-term disability. Each year about 800,000 people suffer a new or recurrent stroke in the United States. 85% of these patients survive and require rehabilitation, making it the leading cause of long-term disability in the U.S. The majority of the stroke costs(e.g. $57 billion in 2003 to $73.7 billion in 2010 and from 2005-2050 is projected to be $2.2 trillion) are needed for long-term care and rehabilitation (>2/3). Although there are already several rehabilitation techniques aimed at stroke recovery including traditional physical-occupational-speech therapy, novel therapies such as constraint-induced therapy, robot-aided therapy, Transcranial Magnetic Stimulation (TMS), real-time biofeedback, and brain-computer interface treatments, few guidelines exist for these interventions. The recommendations from the 2009 workshop sponsored by the NIH blueprint for neuroscience research heralded the translation of neuroplasticity as key to developing guidelines for effective clinical therapies in rehabilitation. Furthermore, the high prevalence of stroke and the high economic costs associated with it make rehabilitation or reduction of stroke-related disability a national healthcare priority. This research proposal is designed to characterize brain plasticity changes using neuroimaging measures and: 1) identify prognostic predictors in terms of neuroimaging and clinical measures so specific rehabilitation treatment plans can be tailored for a particular patient based on these predictors 2) identify a time window using neuroimaging measures which best predicts stroke recovery where stroke interventions may be maximally effective and 3) characterize the brain networks involved in stroke recovery so interventions can facilitate adaptive networks and suppress maladaptive networks which may lead to faster and more optimal rehabilitation. The results of this study could potentially help in the designing of effectve patient specific rehabilitation plans. The ability to plan patient-specific rehabilitation techniqus for optimal recovery will result in longer productive life-spans for the patients and less burden o the care- givers. This research would lay down the groundwork for utilizing neuroimaging as an objective tool for stroke rehabilitation.
|Remsik, Alexander B; Dodd, Keith; Williams Jr, Leroy et al. (2018) Behavioral Outcomes Following Brain-Computer Interface Intervention for Upper Extremity Rehabilitation in Stroke: A Randomized Controlled Trial. Front Neurosci 12:752|
|Mohanty, Rosaleena; Sinha, Anita M; Remsik, Alexander B et al. (2018) Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity. Front Neurosci 12:353|
|Mohanty, Rosaleena; Sinha, Anita M; Remsik, Alexander B et al. (2018) Early Findings on Functional Connectivity Correlates of Behavioral Outcomes of Brain-Computer Interface Stroke Rehabilitation Using Machine Learning. Front Neurosci 12:624|
|Meyer, Erin J; Gaggl, Wolfgang; Gilloon, Benjamin et al. (2017) The Impact of Intracranial Tumor Proximity to White Matter Tracts on Morbidity and Mortality: A Retrospective Diffusion Tensor Imaging Study. Neurosurgery 80:193-200|
|Dodd, Keith C; Nair, Veena A; Prabhakaran, Vivek (2017) Role of the Contralesional vs. Ipsilesional Hemisphere in Stroke Recovery. Front Hum Neurosci 11:469|
|Young, Brittany M; Stamm, Julie M; Song, Jie et al. (2016) Brain-Computer Interface Training after Stroke Affects Patterns of Brain-Behavior Relationships in Corticospinal Motor Fibers. Front Hum Neurosci 10:457|
|Nair, Veena A; Beniwal-Patel, Poonam; Mbah, Ifeanyi et al. (2016) Structural Imaging Changes and Behavioral Correlates in Patients with Crohn's Disease in Remission. Front Hum Neurosci 10:460|
|La, C; Nair, V A; Mossahebi, P et al. (2016) Recovery of slow-5 oscillations in a longitudinal study of ischemic stroke patients. Neuroimage Clin 11:398-407|
|Raut, Ryan V; Nair, Veena A; Sattin, Justin A et al. (2016) Hypercapnic evaluation of vascular reactivity in healthy aging and acute stroke via functional MRI. Neuroimage Clin 12:173-9|
|La, Christian; Mossahebi, Pouria; Nair, Veena A et al. (2016) Differing Patterns of Altered Slow-5 Oscillations in Healthy Aging and Ischemic Stroke. Front Hum Neurosci 10:156|
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