The currently available interventions only partially restore language abilities in patients with post-stroke aphasia; preventing successful reintegration into society. This study will increase our knowledge of how we can use assistive technology interventions to help people with aphasia restore language function. Further, this project will help us identify regions of the brain responsible for these changes.

In aphasia rehabilitation, usual care is focused on helping people recuperate as much of their pre-stroke language capacity as possible.Typically, usual care is a non-standardized therapy that is tailored to the specific needs of the person with aphasia.Once a person reaches a plateau in language recovery, AAC is implemented with a focus on circumventing, or compensating for the communication challenges associated with aphasia.

The ability of people with aphasia to (1) recover language function well-into the chronic phase of stroke recovery and (2) self-cue to promote word retrieval during anomic events offer the solution for how AAC could be employed as a dual-purpose tool to augment language recovery and compensate for deficits. This approach, however, this requires a shift in how AAC is implemented. With the goal of language recovery, treatment needs to focus on instructing people with aphasia how to use AAC as a mechanism for self-cueing, rather than as a tool to replace speaking. Based on our pilot data, we hypothesize that this novel method to AAC implementation will promote language recovery by coupling the canonical language and visual processing neural networks.

This work will also contribute to our ability to identify, a priori, who will respond to this particular AAC intervention and who will not, by combining neuroimaging with behavioral and clinical data. This has the potential to reduce the cost of healthcare for stroke recovery by implementing the most effective treatment possible. Importantly, when we identify non-responders, this will allow us to construct a profile and identify features of the AAC treatment that require adjustment to meet their unique needs.

Inclusion Criteria:

• at least 18 years old
• native speaker of american English
• compatible for 3 Tesla MRI
• Ischemic, left middle cerebral artery stroke
• at least 12 months post stroke
• pass hearing screening
• pass vision screening
• diagnosis of aphasia on the Western Aphasia Bedside Screen
• ability to produce 5-10 intelligible words
• no more than a moderate apraxia of speech or dysarthria
• minimal or no AAC/iPad experience
• written consent by self or guardian

Exclusion Criteria:

• fails to meet the above
• Underlying degenerative or metabolic disorder or supervening medical illness
• Severe depression or other psychiatric disorder
• Report of pregnancy by women of childbearing age

Even though the final dataset will be stripped of identifiers prior to release for sharing, we believe that there remains the possibility of deductive disclosure of subjects based on their private health information and language profiles. Further, it is possible to reconstruct faces from MRI data and the patients are easily identified through the testing and treatment videos; thus, we will make the data and associated documentation available to users only under a data-sharing agreement that provides for: (1) a commitment to using the data only for research purposes and not to identify any individual participant; (2) a commitment to securing the data using appropriate computer technology; and (3) a commitment to destroying or returning the data after analyses are completed. Data will be shared at the time of publication, or shortly thereafter.

Upon study completion