Aiming for improved outcomes viacollaborative approaches.
The overarching goal of our research 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.
Stroke is the leading cause of adult disability in the United States, making it a major public health concern. 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 are under the age of 60. 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. 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 as 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. The overarching goal of our research 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.
To accomplish our research goals, this project relies 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) focus on internal and external factors that may promote improved outcome of aphasia therapy. Both projects yield a vast, unique dataset that includes measures of brain fitness and response to aphasia treatment. Relying on this dataset, Rorden’s project predicts recovery from aphasia using machine learning approaches, whereas Hickok utilizes the same data to better understand aphasic impairment in relation to contemporary models of speech and language processing.
- To test whether contemporary models of speech processing can be used to predict response to aphasia treatment in chronic stroke patients.
- To examine the effects of transcranial direct current stimulation (tDCS) in relation to selective serotonin reuptake inhibitors (SSRIs) on aphasia treatment outcome in acute and sub-acute patients.
- To develop machine learning approaches to predict impairment and response to treatment in aphasic patients.
- To refine and extend current models of speech processing to better understand aphasic impairment and recovery.