Bilingual Aphasia

8/18/2019 Bilingual Aphasia

http://slidepdf.com/reader/full/bilingual-aphasia 1/28

A S H A 2 0 1 2

T E R E S A G R A Y & S W A T H I K I R A N

D E P A R T M E N T O F S P E E C H A N D L A N G U A G EH E A R I N G S C I E N C E S , B O S T O N U N I V E R S I T Y

Bilingual Aphasia: What is the roleof proficiency and impairment?



Disclaimer

The authors have no relevant financial or nonfinancialrelationships in the products or services described,reviewed, evaluated or compared in this presentation.



Project Goals

1. Determine patterns of lexical and semanticprocessing deficits in bilingual aphasia andconceptualize a theoretical framework that

accounts for language deficits;2. Identify language proficiency measures that predict

post-stroke language deficits.



Bilingual Language Processing: receptive language

Bilingual Interactive

Activation Model +

(Dijkstra & van Heuven,

2002)



Bilingual Language Processing: expressive language

Revised Hierarchical

Model

(Kroll & Stewart,

1994)



Monolingual Language Processing

(Ellis and Young, 1988)

(Annotated version)

Auditory analysis

system

Auditory input

lexicon

Semantic

system

Speech output

lexicon

Phoneme Level

Speech

Heard Word



ExpressionSpanish

ExpressionEnglish

Comprehension Spanish

ComprehensionEnglish

WordRecognitionSp Eng

SemanticsEnglish

Semantics

Spanish

TranslationSp Eng

TranslationEng Sp

WordRecognitionEng Sp

SemanticsNon-

linguistic

Gray & Kiran (under revision)



Methods

19 Spanish-English bilingual aphasic patients (meanage 63.1, SD 17.82); 11 females

Bilingual Aphasia Test (BAT): English and Spanish +Part C

Boston Naming Test (BNT): English and Spanish

Pyramids and Palm Trees (PPT): Picture Version

Language Use Questionnaire (Kiran, Pena, Bedore, &Sheng, 2010)



Language UseQuestionnaire

Variables

Age of Acquisition

Pre- StrokeLanguage

Ability Rating

Post-StrokeCurrent

Exposure

EducationHistory

LifetimeExposure

Post- StrokeLanguage

Ability Rating

Confidence

FamilyProficiency



Theoretical framework that accounts for languagedeficits

Conceptualize a framework for bilingual languageprocessing

Foundation based on psycholinguistic models

Connections validated with patient data



Expression

Spanish

Expression

English




SemanticsEnglish

SemanticsSpanish

TranslationSp Eng

TranslationEng Sp


SemanticsNon-

linguistic

Semantics Non-Linguistics: PPT-

Picture Version

Semantics English/Spanish: average

of 6 BAT subtests: Semantic

Categories, Synonyms, Antonyms I &

II, Semantic Acceptability, and

Semantic Opposites

Comprehension English/Spanish:

average of 3 BAT subtests: Pointing,

Semi-Complex Commands, and

Complex Commands

Word Recognit ion (Spanish into

English and vice versa): BAT Part C

subtest Word Recognition

Expression English/Spanish: BNT

Translation (Spanish into English

and vice versa): average of 2 BAT

Part C subtests, Word and SentenceTranslation




Figure 1: Schematic of Bilingual Language Processing. All p-values < 0.05.

Expression

Spanish

Expression

English




Semantics

English

Semantics

Spanish

TranslationSp Eng

TranslationEng Sp


SemanticsNon-linguistic

.51

.62

.48

.58

.57.68 .59

.80

.67

.57

.65.68

.86

.65.67

.47

.74

.58

.55

.71

.67





Expression

Spanish

Expression

English




Semantics

English

Semantics

Spanish

TranslationSp Eng

TranslationEng Sp



.51

.62

.48

.58

.57.68 .59

.80

.67

.57

.65.68

.86

.65.67

.47

.74

.58

.55

.71

.67




Identify language proficiency measures that predict post-stroke language deficits

1. Determine language metrics

2. Decide LUQ metrics



Organization of Data Variables

Comprehension English/Spanish

Average of 3 BAT subtests: Pointing, Semi-Complex Commands, andComplex Commands

Semantics English/Spanish

Average of 6 BAT subtests: Semantic Categories, Synonyms, Antonyms I& II, Semantic Acceptability, and Semantic Opposites

Expression English/Spanish

BNT

Word Recognition (Spanish into English and vice versa)

BAT Part C subtest Word Recognition



Language UseQuestionnaire

Variables

Age of Acquisition

Pre- StrokeLanguage

Ability Rating

Post-StrokeCurrent

Exposure

EducationHistory

LifetimeExposure

Post- StrokeLanguage

Ability Rating

Confidence

FamilyProficiency

Dependent Measures:BAT Comprehension, BAT Semantics, BNT and BAT-Word Recognition(Categorical Variable: Language)



Identify language proficiency measures that predict post-

stroke language deficits

BAT Comprehension

BAT Semantics

BNT

BAT Word Recognition

Confidence

Current Exposure

Pre-Stroke LAR

( R2= .499, F (3, 22) = 7.33, p ≤ 0.01)

( R2= .33, F (3, 21) = 3.57, p ≤ 0.05)

( R2= .25, F (3, 22) = 2.52, p = 0.08)



LAR

β = 0.55, t = 2.33, p = 0.02

β = 0.65, t = 2.43, p = 0.023

β = 0.47, t = 1.64, p = 0.11

β = 0.58, t = 2.01, p = 0.058

BAT Comprehension

BAT Semantics

BAT Word Recognition

BNT

Pre-Stroke

Identify language proficiency measures that predict post-

stroke language deficits



Take Home for Clinicians

1. The benefit of using a framework to interpretpatient data:

Where is language breaking down?

2. Our framework can be used with languagecombinations other than Spanish-English

Our framework is meant to explain a diverse set of test results.

3. The importance of pre- and post- morbid languageuse data to interpret bilingual aphasic data:

Language impairment vs pre-morbid proficiency




Expression

Spanish

Expression

English




Semantics

English

Semantics

Spanish

TranslationSp Eng

TranslationEng Sp



.51

.62

.48

.58

.57.68 .59

.80

.67

.57

.65.68

.86

.65.67

.47

.74

.58

.55

.71

.67




Future Directions

1. Expand our sample size to include more patients.

2. Evaluate the data of other language combinations

3. Use pre-stroke LAR as a means to identify

impairment patterns within our patients.



A C K N O W L E D G M E N T S : O U R P A T I E N T S A N D

T H E I R F A M I L I E S ; M E M B E R S O F T H E B U A P H A S I A R E S E A R C H L A B O R A T O R Y

C O N T A C T I N F O R M A T I O N :

T E R E S A G R A Y : T G R A Y @ B U . E D U

THANK YOU



References

de Bot, K. (1992). A bilingual processing model: Levelt’s ‘speaking’model adapted. Applied Linguistics, 13, 1-23.

Dijkstra, T., van Heuven, W. J. B., & Grainger, J. (1998). Simulating cross-language competition with the bilingual interactive activation model. Psychologica Belgica, 38, 177-196.

Dijkstra, T., & van Heuven, W. J. B. (2002). The architecture of the bilingual word recognitionsystem: From identification to decision. Bilingualism: Language and Cognition, 5 (03).

Ellis, A. & and Young, A. (1988). Human cognitive neuropsychology. East Sussex, UK:

Lawrence Erlbaum Associates Ltd., Publishers.

Howard, D., Patterson, K. (1992). Pyramids and Palm Trees test. San Antonio: Pearson.

Kiran, S., Peña, E., Bedore, L., & Sheng, L. (2010). Evaluating the relationship betweencategory generation and language use and proficiency. Paper presented at the Donostia

Workshop on Neurobilingualism, San Sebastian, Spain.Kaplan, E., Goodglass, H., and Weinraub, S. (2001). The Boston Naming Test. 2nd edition. Baltimore: Lippincott, Williams and Wilkins.

Kroll, S. & Stewart, E. (1994). Category interference in translation and picture naming:Evidence for asymmetric connections between bilingual memory representations.

Journal of Memory and Language, 33, 149-174.

Muñoz, M., & Marquardt, T. (2003). Picture naming and identification in bilingual speakers ofSpanish and English with and without aphasia. Aphasiology, 17 (12), 1115-1132.

Paradis, M. (1989). The Bilingual Aphasia Test. Hillsdale, NJ: Lawrence Erlbaum Associates(out of print). Online at http://www.mcgill.ca/linguistics/research/bat/.



Results: Are there distinct subgroups into which patients with bilingual aphasia can be categorized?

Determine the relationship between language proficiency and post-stroke language deficits.

Pre-Stroke Language Proficiency:

pre-stroke LARPost-Stroke Comprehension/single word naming:

BAT Comprehension (average of subtests Pointing, Semi-Complex and Complex Commands)

BNT

We established three subgroups of post-stroke languageimpairment presentations among our patients (N=17).



Results: Are there distinct subgroups into which patients with bilingual aphasia can be categorized? (N=17)

Group 1: Parallel (n=5).




Group 2: Parallel (n = 4).




Group 3: Differential (n=8)

Bilingual Aphasia

Documents

Transcript of Bilingual Aphasia