Stroke is the leading cause of adult disability in the United States, making it a major public health concern (1). The Centers for Disease Control (CDC) estimates the annual cost of stroke in the United States to be $36.5 billion (1). Accordingly, it is clear that the negative personal and societal impact of stroke is vry high. Stroke is typically thought to affect older persons; however, many younger individuals also suffer strokes. For example, at least half of all stroke patients in the state of South Carolina ar under the age of 60 (2). Approximately a quarter of all chronic stroke survivors present with aphasia, a language disorder caused by damage to the speech and language areas of the brain (3, 4). The prevalence of chronic aphasia in the United States is estimated to be one million. Aphasia can vary in severity from very profound impairment that renders patients mute and without the ability to understand others' speech, to milder forms where patients have great difficulty retrieving specific words. In the chronic stage of stroke, aphasia has been identified a the strongest predictor of poor quality of life. Aphasia not only influences the ability to communicate with family and friends, but also drastically decreases education and employment opportunities. Although some degree of spontaneous recovery from aphasia is typical in the first weeks and months following stroke, many patients are left with devastating communication problems. Once aphasia has become a chronic condition, the only road to recovery is through aphasia therapy. Several meta-analysis studies suggest that aphasia therapy is effective. In spite of decades of research, very little is known about which patients benefit the most from treatment and what kind of treatment should be administered to patients with different impairment profiles. The overarching goal of the research proposed here is to improve aphasia treatment effectiveness as well as identify patient factors that can be used to improve diagnosis of language impairment, guide aphasia treatment, and predict prognosis. Specifically, the focus of our center (Center for the Study of Aphasia Recovery; C-STAR) is to examine the extent to which factors such as behavioral aphasia treatment, electrical brain stimulation, and residual brain function influence aphasia recovery. To accomplish our research goals, this project will rely on collaboration among four main investigators: Drs. Julius Fridriksson, Argye Hillis, Chris Rorden, and Greg Hickok. Projects led by Fridriksson (chronic patients) and Hillis (acute patients) will focus on factors that may promote improved outcome of aphasia therapy. Both projects will yield a vast, unique dataset including measures of brain status and response to aphasia treatment. Relying on this dataset, Rorden's project will predict recovery from aphasia using machine learning approaches whereas Hickok will utilize the same data to better understand aphasic impairment in relation to contemporary models of speech and language processing.
Stroke is the leading cause of adult disability in the United States; aphasia, an impairment of the ability to process language, is one of the most devastating results of left hemisphere stroke. The purpose of the work proposed here is to improve recovery from aphasia by better understanding how neurophysiology and anatomy as well as theory driven aphasia treatment relate to long-term outcome. Subproject-1 Modeling treated recovery from aphasia Lead Investigator: Julius Fridriksson, Ph.D. DESCRIPTION (Description as provided by applicant): Stroke is the leading cause of serious adult disability in the United States. One of the most devastating impairments resulting from stroke is aphasia, a language impairment caused by left hemisphere damage involving cortical language areas. It is generally accepted that behavioral aphasia treatment is effective. Nevertheless, different patients experience very different degrees of benefit from aphasia treatment. Despite considerable differences in the response to aphasia treatment, the relationship between patient factors and treatment response is poorly understood and very few reliable prognostic indicators have been identified. This is a major problem, as both time and resources are wasted when clinicians do not know what patients are likely to respond to treatment, or which treatment best fits individual patients. The purpose of the current project is to develop a model that includes biographical and cognitive/linguistic factors to predict patients' response to aphasia treatment. Aphasia severity is one of the few factors that have been identified as a reliable predictor of performance in treatment; it is generally accepted that more severe aphasia is associated with poorer treatment outcomes. However, aphasia severity is a multidimensional construct and patients with similar overall severity scores might demonstrate very different language impairment profiles. To better understand how language impairment relates to treatment outcomes, the dual stream model (DS model;1) will be consulted. Specifically, we will test whether measures of proportional damage to the cortical areas that comprise the DS model improve prediction of aphasia treatment response, beyond biographical and cognitive/linguistic factors. Although the DS model is a functional model grounded in neuroanatomy, we expect measures of speech and language that assesses processes supported by the two major components of the DS model - the dorsal and ventral streams - might be redundant with measures of cortical damage. To understand whether our predictive model can be generalized across different kinds of treatment foci, each patient will undergo treatment devoted to phonological stimulation and a separate treatment phase focusing on semantic stimulation. Ultimately, the goal here is to construct a predictive model that will be made available on- line so that clinicians can enter test scores from individual patients to predic how likely a given patient is to respond to treatment, as well as the focus of that treatment. There is a great need for prognostic indicators of aphasia treatment response. At the completion of our research, we will understand why some patients respond better to aphasia treatment than others. We have selected treatment approaches that are routinely used in clinical practice, allowing for immediate translation of the findings directly into patient management. The current project will yield a vast dataset that will be made publicly available allowing others to study further aphasia treatment response in relation to cognitive/linguistic and lesion factors.
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| Long, Charltien; Sebastian, Rajani; Faria, Andreia V et al. (2018) Longitudinal Imaging of Reading and Naming Recovery after Stroke. Aphasiology 32:839-854 |
| Behroozmand, Roozbeh; Phillip, Lorelei; Johari, Karim et al. (2018) Sensorimotor impairment of speech auditory feedback processing in aphasia. Neuroimage 165:102-111 |
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| Hillis, Argye E; Beh, Yuan Ye; Sebastian, Rajani et al. (2018) Predicting recovery in acute poststroke aphasia. Ann Neurol 83:612-622 |
| Ficek, Bronte N; Wang, Zeyi; Zhao, Yi et al. (2018) The effect of tDCS on functional connectivity in primary progressive aphasia. Neuroimage Clin 19:703-715 |
| Henry, Maya L; Hubbard, H Isabel; Grasso, Stephanie M et al. (2018) Retraining speech production and fluency in non-fluent/agrammatic primary progressive aphasia. Brain 141:1799-1814 |
| Tippett, Donna C; Godin, Brittany R; Oishi, Kumiko et al. (2018) Impaired Recognition of Emotional Faces after Stroke Involving Right Amygdala or Insula. Semin Speech Lang 39:87-100 |
| Karnath, Hans-Otto; Sperber, Christoph; Rorden, Christopher (2018) Mapping human brain lesions and their functional consequences. Neuroimage 165:180-189 |
| Rorden, Christopher; Li, Dongyun; Karnath, Hans-Otto (2018) Biased temporal order judgments in chronic neglect influenced by trunk position. Cortex 99:273-280 |
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