Determining Learning Ability in People With Aphasia

NCT05119023 | Completed Results

• 2 other identifiers: 2021p000033R21DC019203
• Study Type: interventional
• Target: 18
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Aphasia

Outcome Measures

Primary Outcomes (3)

• SRT Observational Learning Ability

  For the SRT observational learning task, responses are made via eye gaze into a visual area of interest (AOI). Reaction times (RTs) are recorded as the time between target onset and gaze fixation within the target AOI. A trial is considered incorrect if an eye fixation was made that does not correspond to the target AOI. RTs for correct trials are examined. Outlier RTs three standard deviations above the mean RT of each block are removed. A score of learning is computed by comparing RTs on the last (7th) sequenced block of trials with RTs on the following (8th) pseudorandomized block (Schwarb \& Schumacher, 2012). A Cohen's d effect size (ES) of observational learning is calculated for each individual participant that compares mean RTs on the final sequenced block (S7) and the pseudorandom block (PS8) using pooled standard deviations. Mean Cohen's d is reported. Negative values indicate better learning.

  Study visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalanced

• AGL Observational Learning Ability

  For the AGL Observational learning task, a percent accuracy score is computed for the test phase. Higher scores indicate better outcome.

  Study visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalanced

• AGL Rule-based Learning Ability

  For the rule-based AGL task, a percent accuracy score is computed for the test phase. Higher scores indicate better outcome.

  Study visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalanced

Secondary Outcomes (5)

• Standardized Assessment of Cognitive Linguistic Ability - Language Severity

  Study visit 1 or 2

• Standardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Attention

  Study visit 1 or 2

• Standardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Working Memory

  Study visit 1 or 2

• Standardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Executive Function

  Study visit 1 or 2

• Percent Spared Tissue Per ROI

  Study visit 3, between one-month and five months from behavioral testing of learning

Study Arms (1)

Characterization of learning

EXPERIMENTAL

All participants complete behavioral (computer-based) learning tasks that measure their ability to learn observationally (observational learning ability: SRT Observational learning and AGL observational learning) and via rules (rule-based AGL learning ability, \[RB AGL\]). Participants additionally complete standardized cognitive-linguistic tests. Learning tasks and cognitive linguistic tests are completed over the course of 2 to 3 sessions, each lasting around 2 hours each. The AGL Observational task was always completed before the rule-based AGL task. SRT Observational and AGL Observational task order was counterbalanced. Enrolled participants who were safe to scan via magnetic resonance imaging (MRI) completed a structural MRI scan between one-month and five months from behavioral testing of learning.

Behavioral: SRT Observational Learning; Behavioral: AGL Observational Learning; Behavioral: AGL Rule-based Learning; Behavioral: Standardized cognitive-linguistic assessment; Other: Brain imaging

Interventions

SRT Observational Learning BEHAVIORAL

All participants completed a computer-based serial response time (SRT) task intended to measure observational (implicit) learning ability. The SRT Observational learning task is a classic paradigm, which has been integral to the understanding of implicit learning (see Schwarb \& Schumacher, 2012). The current task is a replication of classic SRT tasks first described by Nissen and Bullemer (1987), adapted for eye-tracking by Kinder et al. (2008). 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.

Characterization of learning

AGL Observational Learning BEHAVIORAL

All participants completed a computer-based observational artificial grammar learning (AGL) task. The AGL Observational learning task is another classic test of implicit learning involving learning of ordered items through exposure (Schuchard \& Thompson, 2017). Artificial grammars contain hierarchal dependencies, similar to the rules that govern word-order and syntax in natural language. In this task, participants look at sequences of geometric shapes on a computer screen. Participants judged if two sequences matched or did not match. After training, participants are shown sequences and must judge if sequences adhere to the pattern or not.

Characterization of learning

AGL Rule-based Learning BEHAVIORAL

All participants completed a computer-based rule-based learning task intended to measure rule-based (explicit) learning ability of an artificial grammar expressed in nonlinguistic form (sequences of shapes). 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.

Characterization of learning

Standardized cognitive-linguistic assessment BEHAVIORAL

Participants completed standardized cognitive-linguistic assessments that evaluate their ability to produce and understand language and evaluate cognitive skills of attention, executive function and working memory important for learning. Tests involve paper and pencil, looking at pictures, listening to words, indicating responses on a keyboard and talking.

Characterization of learning

Brain imaging OTHER

Enrolled participants who were safe to scan via magnetic resonance imaging (MRI) completed a structural MRI scan between one-month and five months from behavioral testing of learning.

Characterization of learning

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• MGH Institute of Health Professions: lead
• National Institute on Deafness and Other Communication Disorders (NIDCD): collaborator

Study Sites (1)

MGH Institute of Health Professions

Boston, Massachusetts, 02129, United States

Location

Related Publications (6)

• 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.

  PMID: 9697427 BACKGROUND

• 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.

  PMID: 15668101 BACKGROUND

• 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.

  PMID: 19460948 BACKGROUND

• 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.

  PMID: 15301595 BACKGROUND

• 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.

  PMID: 8618923 BACKGROUND

• 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.

  PMID: 23127795 BACKGROUND

MeSH Terms

Conditions

Aphasia

Interventions

Neuroimaging

Condition Hierarchy (Ancestors)

Speech Disorders; Language Disorders; Communication Disorders; Neurobehavioral Manifestations; Neurologic Manifestations; Nervous System Diseases; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Diagnostic Imaging; Diagnostic Techniques and Procedures; Diagnosis; Diagnostic Techniques, Neurological; Investigative Techniques

Limitations and Caveats

Due to the COVID pandemic and institutional policies related to data collection from human subjects, particularly vulnerable populations such as those having experienced a stroke, in-person data collection was delayed for this study. As a result, the sample size is smaller than initially proposed. Due to this more limited sample size, carrying our regression analyses was not appropriate, however, data are sufficient to identify the presence of individualized learning profiles.

Results Point of Contact

• Title: Sofia Vallila Rohter, Project Principle Investigator
• Organization: MGH-Institute of Health Professions

svallilarohter@mghihp.edu

617-724-3824

Study Officials

• Sofia Vallila-Rohter, PhD

  MGH Institute of Health Professions

  PRINCIPAL INVESTIGATOR

Publication Agreements

• PI is Sponsor Employee: Yes

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Study Record Dates

• First Submitted: October 19, 2021
• First Posted: November 12, 2021
• Study Start: June 6, 2022
• Primary Completion: September 1, 2023
• Study Completion: September 1, 2023
• Last Updated: March 10, 2025
• Results First Posted: March 10, 2025

Record last verified: 2025-02

Data Sharing

• IPD Sharing: Will share
• Time Frame: Data requests can be submitted starting 9 months after article publication and the data will be made accessible for up to 24 months
• Access Criteria: 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).

Locations

US (1)