Aphasia, Post-stroke Aphasia
Conditions
Keywords
augmentative and alternative communication, aphasia, fMRI, DTI, neuroimaging
Brief summary
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.
Detailed description
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.
Interventions
BEHAVIORALAAC for Language Recovery (AAC-LaRc)
iPads will be programmed with a personalized communication application (app) and a structured 4-step intervention will be employed to instruct patients how to cue themselves during anomic events via pictures, text, or speak buttons.
Sponsors
Children's Hospital Medical Center, Cincinnati
University of Cincinnati
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
TREATMENT
Masking
NONE
Intervention model description
Participant cohorts: We will use a 2 (groups) X 4 (measurement time points) switching replications design\[18\] to examine the effects of AAC on aphasia severity and spoken language (AIM 1), elucidate the neurobiological mechanism of AAC-induced language recovery (AIM 2), and identify AAC treatment responder subgroups (Exploratory Aim). This design will allow Group 2 to serve as the control group; however, they will still receive AAC treatment; thus, allowing us to increase the total N who receives AAC treatment to 20 people.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Western Aphasia Battery-Revised Aphasia Quotient | 3 years | a diagnostic tool used to determine aphasia type and severity |
| Visual Regions of Interest Activation intensity | 3 years | A functional magnetic resonance imaging (fMRI) measure to indicate change in brain involvement during resting state and language tasks. |
| Connectivity Indices | 3 years | Connectivity indices reflect the temporal correlation between canonical language and visual regions of interest during language and resting state fMRI tasks. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Stroke and Aphasia Quality of Life-39 (SAQOL-39) | 3 years | The SAQOL-39 is a valid and reliable measure of health related quality of life, post-stroke that uses an aphasia friendly 5 point Likert scale format. |
| Communication Effectiveness Index (CETI) | 3 years | The CETI measures functional communication scenarios that caregivers rate the ability of the patient to complete tasks, as compared to before their stroke, via a 10 cm visual analogue scale. |
| Motor-Free Visual Perception Test-4 | 3 years | The Motor-Free Visual Perception Test-4 assesses visual perceptual ability without requiring motor responses. |
| Mean Diffusivity (MD) | 3 years | MD is used to map tracts and serves as a measure of health or disease in white matter. We will determine the MD for the following pathways: (1) superior, (2) inferior fronto-occipital, (3) middle longitudinal, (4) inferior longitudinal fasciculus, (5) arcuate, and (6) uncinate |
| Language Lateralization Indices (LI) | 3 years | LI is a measure that reflects hemispheric dominance for language, while accounting for lesioned tissue. |
| Fractional Anisotropy (FA) | 3 years | FA is a value of diffusion and reflects white matter density and myelination. We will examine the following tracts: (1) superior, (2) inferior fronto-occipital, (3) middle longitudinal, (4) inferior longitudinal fasciculus, (5) arcuate, and (6) uncinate |
| Discourse Analyses | 3years | We will calculate percentage of counted words, mazes, correct information units, t-units, during personal story retells with and without the AAC support. |
| Communication analyses | 3 years | We will calculate percentage of communication conveyed via pictures, text boxes, and speak button during personal story retells with and without the iPad AAC support. |
Countries
United States
Contacts
Primary ContactAimee Dietz, PhD
Backup ContactJennifer Vannest, PhD
Outcome results
None listed