Determining Learning Ability in People With Aphasia

Determining the Implicit and Rule-based Learning Ability of Individuals With Aphasia to Better Align Learning Ability and Intervention

Aphasia is an impairment in the expression or comprehension of language that results from stroke, traumatic brain injury or progressive neurological disease. Approximately two million people in the United States suffer from aphasia, which has profound impacts on quality of life, the ability to return to work and participation in life activities. Research has shown that speech-language therapy, the treatment for aphasia, can significantly improve people's ability to communicate. However, a major limitation in the field of aphasia rehabilitation is the lack of predictability in patients' response to therapy and the inability to tailor treatment to individuals. Currently, aphasia treatments are selected largely based on patient's language abilities and language deficits with little consideration of learning ability, which this study refers to as learning phenotype. Learning phenotype has been used to inform rehabilitation approaches in other domains but is not currently considered in aphasia. The overarching hypothesis of this work is that poor alignment of learning ability and language therapy limits progress for patients and presents a barrier to individualizing treatment. The objectives of the proposed study are to (1) determine the learning phenotype of individuals with aphasia, and (2) examine how lesion characteristics (size and location of damage to the brain), language ability and cognitive ability relate to learning ability. To accomplish objectives, investigators propose to measure implicit (observational) and explicit (rule-based) learning ability in people with aphasia via computer-based tasks. Regression models will be used to examine brain and behavioral factors that relate to learning ability.

Aphasia is an impairment in the expression or comprehension of language that can limit people's ability to communicate needs, reduce comprehension in complex environments, and prevent a return to work or limit participation in everyday life activities. An approximate 795,000 individuals suffer from strokes each year, with 25% to 40% resulting in aphasia. The process of aphasia rehabilitation engages many mechanisms of learning as patients are guided to relearn, reaccess or regain functional use of language via therapies that involve stimuli, tasks, cues, and feedback. Currently however, clinicians base decisions about the tasks and targets of treatment methods on language deficits, and the strength and weakness of learning systems is rarely, if ever, considered. The understanding of learning in aphasia and the way that learning influences treatment outcomes is incomplete and presents a barrier in the ability for clinicians to individually tailor treatment and reliably predict outcomes. An in-depth characterization of learning in aphasia is important, as research has suggested that multiple learning systems exist. Furthermore, manipulations to stimuli, task, and feedback can lead to differential recruitment of learning systems and unlock learning potential, particularly in clinical populations. Prior work in aphasia supports the hypothesis that individuals with aphasia suffer from impaired learning mechanisms and are sensitive to task manipulations. Such findings demonstrate that that people with aphasia (PWA) are successful learning in some conditions and not others and provides the rationale for the proposed series of studies focused on characterizing learning abilities in individuals with aphasia. The current project proposes to use a single-subject experimental design to determine the behavioral learning phenotype of individuals with aphasia subsequent to stroke. Implicit (observational) and explicit (rule-based) learning is quantified in individuals with aphasia using short computer-based experimental tasks. Investigators additionally explore whether effect size of learning under observational and rule-based conditions is predicted by lesion characteristics (size and extent of brain damage in regions of interest), cognitive abilities (such as attention, working memory, executive function) and language severity. Findings will help establish the behavioral and biological validity of learning phenotypes in aphasia and will provide essential information needed to support future treatment studies that align learning ability and language therapy to promote enhanced outcomes. Overall Study Design The study will be conducted at the Massachusetts General Hospital (MGH) MGH-Institute of Health Professions. Structural scans will be obtained at the MGH Athinoula A. Martinos Biomedical Imaging Center. Participants with aphasia subsequent to stroke, in the chronic stages of their aphasia (at least 6 months post-stroke) will be recruited to participate. All participants will complete standardized assessments of cognitive and language abilities and will complete computer-based tasks evaluating observational and rule-based learning ability. Structural scans will be obtained to quantify the presence brain damage in parts of the brain that are thought to relate to learning. A key novelty of the approach is to introduce an evaluation of learning ability into diagnostic models of aphasia, incorporating subject-specific behavioral and neural metrics.

All participants are assigned to complete behavioral (computer-based) learning tasks that measure their ability to learn observationally (observational learning ability) and via rules (rule-based learning ability).

All participants will complete a computer-based serial response time task intended to measure observational (implicit) learning ability. In this task, participants look at a dot move from one of 4 positions on a computer screen. Unbeknownst to participants, dot movement followed a 12-movement pattern for most experimental blocks. Eye-tracking data is collected and eye fixations within regions of interest trigger trial advancement. Learning ability is evaluated as a comparison of saccadic response times during sequenced trials relative to pseudorandomized trials.

All participants will complete a computer-based rule-based learning task intended to measure rule-based (explicit) learning ability. In this task, participants look at sequences of geometric shapes on a computer screen. Through visuals and verbal instruction, they are taught 5 rules that govern sequences. After learning rules, participants are asked to judge via button press whether novel sequences adhere to rules or not.

Accuracy on testing phase following rule-based instruction of artificial grammar expressed in nonlinguistic form.

Standardized measure of severity of expressive and receptive language deficits (WAB score range 0 - 100 with high scores indicating lower severity)

Composite score based on standardized assessments of attention, working memory, & executive function (attention from TEA, score range 0 - 7 with high scores indicating better attention; working memory from TALSA Synonymy triplet, score range 0 - 40 with high scores indicating better working memory; executive function from CLQT design generation and trails, score range 0 - 23 with high scores indicating better executive function). Composite scores will thus range from 0 to 70 with higher scores indicating higher cognitive ability.

Inclusion criteria Aphasia due to left hemisphere stroke Must be in the chronic stages of aphasia, at least 6 months post onset of stroke Must be between the ages of 18 and 80 years of age Must have near to normal uncorrected or corrected vision per self-report Must be medically and neurologically stable and at least wheelchair ambulatory Exclusion criteria History of significant psychiatric or medical disease Presence of visual field cuts or visual neglect as determined by the Cognitive Linguistic Quick Test (CLQT; Helm-Estabrooks, 2017) symbol cancellation task Implanted medical devices or metal fragments that are not MRI safe

Investigators will share de-identified data sets, statistical analysis codes and experimental set-ups with interested researchers, educators or clinicians. Materials generated under the project will be disseminated in accordance with NIH policies.

Data requests can be submitted starting 9 months after article publication and the data will be made accessible for up to 24 months

Access to trial individual participant data can be requested by qualified researchers engaging in independent scientific research, and will be provided following review and approval of a research proposal and Statistical Analysis Plan (SAP) and execution of a Data Sharing Agreement (DSA).

Ashby FG, Alfonso-Reese LA, Turken AU, Waldron EM. A neuropsychological theory of multiple systems in category learning. Psychol Rev. 1998 Jul;105(3):442-81. doi: 10.1037/0033-295x.105.3.442.

Ashby FG, O'Brien JB. Category learning and multiple memory systems. Trends Cogn Sci. 2005 Feb;9(2):83-9. doi: 10.1016/j.tics.2004.12.003.

Davis T, Love BC, Maddox WT. Two pathways to stimulus encoding in category learning? Mem Cognit. 2009 Jun;37(4):394-413. doi: 10.3758/MC.37.4.394.

Shohamy D, Myers CE, Onlaor S, Gluck MA. Role of the basal ganglia in category learning: how do patients with Parkinson's disease learn? Behav Neurosci. 2004 Aug;118(4):676-86. doi: 10.1037/0735-7044.118.4.676.

Squire LR, Knowlton BJ. Learning about categories in the absence of memory. Proc Natl Acad Sci U S A. 1995 Dec 19;92(26):12470-4. doi: 10.1073/pnas.92.26.12470.

Vallila-Rohter S, Kiran S. Non-linguistic learning and aphasia: evidence from a paired associate and feedback-based task. Neuropsychologia. 2013 Jan;51(1):79-90. doi: 10.1016/j.neuropsychologia.2012.10.024. Epub 2012 Nov 2.