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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

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT05119023
Enrollment
18
Registered
2021-11-12
Start date
2022-06-06
Completion date
2023-09-01
Last updated
2025-03-10

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Aphasia

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.

Detailed description

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.

Interventions

BEHAVIORALSRT Observational Learning

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.

BEHAVIORALAGL Observational Learning

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.

BEHAVIORALAGL Rule-based Learning

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.

BEHAVIORALStandardized cognitive-linguistic assessment

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.

OTHERBrain imaging

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.

Sponsors

National Institute on Deafness and Other Communication Disorders (NIDCD)
CollaboratorNIH
MGH Institute of Health Professions
Lead SponsorOTHER

Study design

Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No

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

Design outcomes

Primary

MeasureTime frameDescription
SRT Observational Learning AbilityStudy visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalancedFor 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.
AGL Observational Learning AbilityStudy visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalancedFor the AGL Observational learning task, a percent accuracy score is computed for the test phase. Higher scores indicate better outcome.
AGL Rule-based Learning AbilityStudy visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalancedFor the rule-based AGL task, a percent accuracy score is computed for the test phase. Higher scores indicate better outcome.

Secondary

MeasureTime frameDescription
Standardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Executive FunctionStudy visit 1 or 2Composite scores based on standardized assessments of attention, working memory, and executive function were computed. Each score is computed and reported as an percent score. Minimum score is 0, maximum is 100. Higher scores indicate better cognitive ability.
Standardized Assessment of Cognitive Linguistic Ability - Language SeverityStudy visit 1 or 2Standardized measure of severity of expressive and receptive language deficits (Western Aphasia Battery \[WAB\] score range 0 - 100 with high scores indicating lower severity)
Percent Spared Tissue Per ROIStudy visit 3, between one-month and five months from behavioral testing of learningLesion maps, in which the lesioned voxels are assigned a binary value (1 or 0), are normalized from native to the Montreal Neurological Institute (MNI) template (a standard brain template utilized in imaging studies). Individualized lesion maps are subtracted from each brain region of interest (ROI) to yield the volume of spared tissue per ROI. The percentage of spared tissue in each region is calculated by dividing the volume of spared tissue by the total volume of the MNI template atlas ROI. Two ROIs have been selected based on prior research showing differential activation in observational versus rule-based learning: the prefrontal cortex and the striatum.
Standardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : AttentionStudy visit 1 or 2Composite scores based on standardized assessments of attention, working memory, and executive function were computed. Each score is computed and reported as an percent score. Minimum score is 0, maximum is 100. Higher scores indicate better cognitive ability.
Standardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Working MemoryStudy visit 1 or 2Composite scores based on standardized assessments of attention, working memory, and executive function were computed. Each score is computed and reported as an percent score. Minimum score is 0, maximum is 100. Higher scores indicate better cognitive ability.

Countries

United States

Participant flow

Recruitment details

Individuals with aphasia were recruited by referral from physicians, speech-language pathologists, neuropsychologists. Individuals with aphasia were also recruited from the MGH-Institute of Health Professions Aphasia Center. Participants were recruited via word of mouth, flyers/presentations and through Rally at Mass General Brigham, an online portal advertising research studies being carried out within the Mass General Brigham Network. The recruitment period lasted from 5/15/22 - 7/15/23

Participants by arm

ArmCount
Characterization of Learning
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). Observational Learning: 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. Rule-based Learning: 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.
18
Total18

Withdrawals & dropouts

PeriodReasonFG000
Characterization of Learning-visits 1&2Change in health status unrelated to study rendered unable to participate2
Characterization of Learning-visits 1&2Lost to Follow-up1
Characterization of Learning-visits 1&2Withdrawal by Subject1

Baseline characteristics

CharacteristicCharacterization of Learning
Age, Continuous61.89 Years
STANDARD_DEVIATION 10.22
Ethnicity (NIH/OMB)
Hispanic or Latino
0 Participants
Ethnicity (NIH/OMB)
Not Hispanic or Latino
17 Participants
Ethnicity (NIH/OMB)
Unknown or Not Reported
1 Participants
Months post stroke106 Months
STANDARD_DEVIATION 96.2
Race (NIH/OMB)
American Indian or Alaska Native
0 Participants
Race (NIH/OMB)
Asian
0 Participants
Race (NIH/OMB)
Black or African American
3 Participants
Race (NIH/OMB)
More than one race
0 Participants
Race (NIH/OMB)
Native Hawaiian or Other Pacific Islander
0 Participants
Race (NIH/OMB)
Unknown or Not Reported
1 Participants
Race (NIH/OMB)
White
14 Participants
Region of Enrollment
United States
18 participants
Sex: Female, Male
Female
6 Participants
Sex: Female, Male
Male
12 Participants

Adverse events

Event typeEG000
affected / at risk
deaths
Total, all-cause mortality
0 / 18
other
Total, other adverse events
0 / 18
serious
Total, serious adverse events
0 / 18

Outcome results

Primary

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.

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

ArmMeasureValue (MEAN)Dispersion
Characterization of LearningAGL Observational Learning Ability50.08 Percent accuracyStandard Deviation 12.19
Primary

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.

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

ArmMeasureValue (MEAN)Dispersion
Characterization of LearningAGL Rule-based Learning Ability63.1 Percent accuracyStandard Deviation 21.6
Primary

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.

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

ArmMeasureValue (MEAN)Dispersion
Characterization of LearningSRT Observational Learning Ability-0.03 Cohen's dStandard Deviation 0.34
Secondary

Percent Spared Tissue Per ROI

Lesion maps, in which the lesioned voxels are assigned a binary value (1 or 0), are normalized from native to the Montreal Neurological Institute (MNI) template (a standard brain template utilized in imaging studies). Individualized lesion maps are subtracted from each brain region of interest (ROI) to yield the volume of spared tissue per ROI. The percentage of spared tissue in each region is calculated by dividing the volume of spared tissue by the total volume of the MNI template atlas ROI. Two ROIs have been selected based on prior research showing differential activation in observational versus rule-based learning: the prefrontal cortex and the striatum.

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

ArmMeasureGroupValue (MEAN)Dispersion
Characterization of LearningPercent Spared Tissue Per ROIPrefrontal cortex ROI88.8 Percent Spared Tissue within the ROIStandard Deviation 11.8
Characterization of LearningPercent Spared Tissue Per ROIStriatum ROI79.37 Percent Spared Tissue within the ROIStandard Deviation 18.84
Secondary

Standardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Attention

Composite scores based on standardized assessments of attention, working memory, and executive function were computed. Each score is computed and reported as an percent score. Minimum score is 0, maximum is 100. Higher scores indicate better cognitive ability.

Time frame: Study visit 1 or 2

ArmMeasureValue (MEAN)Dispersion
Characterization of LearningStandardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Attention88.77 percent scoreStandard Deviation 21.87
Comparison: Examination of Attention and Characterization of learning: SRT Observational Learning Scoresp-value: 0.36Pearson's correlation
Comparison: Examination of Attention and Characterization of learning: AGL Observational Learning Scoresp-value: 0.09Pearson's correlation
Comparison: Examination of Attention and Characterization of learning: AGL Rule Based Learningp-value: 0.32Pearson's correlation
Secondary

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

Composite scores based on standardized assessments of attention, working memory, and executive function were computed. Each score is computed and reported as an percent score. Minimum score is 0, maximum is 100. Higher scores indicate better cognitive ability.

Time frame: Study visit 1 or 2

ArmMeasureValue (MEAN)Dispersion
Characterization of LearningStandardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Executive Function75.24 percent scoreStandard Deviation 17.11
Comparison: Examination of Executive Function and Characterization of learning: SRT Observational Learning Scoresp-value: 0.9Pearson's correlation
Comparison: Examination of Executive Function and Characterization of learning: AGL Observational Learning Scoresp-value: 0.09Pearson's correlation
Comparison: Examination of Executive Function and Characterization of learning: AGL Rule-based Learning Scoresp-value: 0.24Pearson's correlation
Secondary

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

Composite scores based on standardized assessments of attention, working memory, and executive function were computed. Each score is computed and reported as an percent score. Minimum score is 0, maximum is 100. Higher scores indicate better cognitive ability.

Time frame: Study visit 1 or 2

ArmMeasureValue (MEAN)Dispersion
Characterization of LearningStandardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Working Memory51.06 percent scoreStandard Deviation 20.67
Comparison: Examination of Working Memory and Characterization of learning: SRT Observational Learning Scoresp-value: 0.64Pearson's correlation
Comparison: Examination of Working Memory and Characterization of learning: AGL Observational Learning Scoresp-value: 0.01Pearson's correlation
Comparison: Examination of Working Memory and Characterization of learning: AGL Rule-based Learning Scoresp-value: 0.79Pearson's correlation
Secondary

Standardized Assessment of Cognitive Linguistic Ability - Language Severity

Standardized measure of severity of expressive and receptive language deficits (Western Aphasia Battery \[WAB\] score range 0 - 100 with high scores indicating lower severity)

Time frame: Study visit 1 or 2

ArmMeasureValue (MEAN)Dispersion
Characterization of LearningStandardized Assessment of Cognitive Linguistic Ability - Language Severity80.5 units on a scale of 100Standard Deviation 13.8
Comparison: Examination of Language Severity and Characterization of learning: SRT Observational Learning Scoresp-value: 0.39Pearson's correlation
Comparison: Examination of Language severity and Characterization of learning: AGL Observational Learning Scoresp-value: <0.01Pearson's correlation
Comparison: Examination of Language severity and Characterization of learning: AGL Rule-based Learning Scoresp-value: 0.95Pearson's correlation

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026