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Background: Evidence-based recommendations for a core outcome set (COS; minimum set of outcomes) for aphasia treatment research have been developed (the Research Outcome Measurement in Aphasia--ROMA, COS). Five recommended core outcome constructs: communication, language, quality of life, emotional well-being and patient-reported satisfaction/impact of treatment, were identified through three international consensus studies. Constructs were paired with outcome measurement instruments (OMIs) during an international consensus meeting (ROMA-1). Before the current study (ROMA-2), agreement had not been reached on OMIs for the constructs of communication or patient-reported satisfaction/impact of treatment. Aim: To establish consensus on a communication OMI for inclusion in the ROMA COS. Methods & Procedures: Research methods were based on recommendations from the Core Outcome Measures in Effectiveness Trials (COMET) Initiative. Participants with expertise in design and conduct of aphasia trials, measurement instrument development/testing and/or communication outcome measurement were recruited through an open call. Before the consensus meeting, participants agreed on a definition of communication, identified appropriate OMIs, extracted their measurement properties and established criteria for their quality assessment. During the consensus meeting they short-listed OMIs and participants without conflicts of interest voted on the two most highly ranked instruments. Consensus was defined a priori as agreement by [greater than or equal to] 70% of participants. Outcomes & Results: In total, 40 researchers from nine countries participated in ROMA-2 (including four facilitators and three-panel members who participated in pre-meeting activities only). A total of 20 OMIs were identified and evaluated. Eight short-listed communication measures were further evaluated for their measurement properties and ranked. Participants in the consensus meeting (n = 33) who did not have conflicts of interest (n = 29) voted on the top two ranked OMIs: The Scenario Test (TST) and the Communication Activities of Daily Living--3 (CADL-3). TST received 72% (n = 21) of 'yes' votes and the CADL-3 received 28% (n = 8) of 'yes' votes. Conclusions & Implications: Consensus was achieved that TST was the preferred communication OMI for inclusion in the ROMA COS. It is currently available in the original Dutch version and has been adapted into English, German and Greek. Further consideration must be given to the best way to measure communication in people with mild aphasia. Development of a patient-reported measure for satisfaction with/impact of treatment and multilingual versions of all OMIs of the COS is still required. Implementation of the ROMA COS would improve research outcome measurement and the quality, relevance, transparency, replicability and efficiency of aphasia treatment research.

Purpose: The impact of stimulus-level psycholinguistic variables and personlevel semantic and phonological processing skills on treatment outcomes in individuals with aphasia requires further examination to inform clinical decision making in treatment prescription and stimuli selection. This study investigated the influence of stimulus-level psycholinguistic properties and person-level semantic and phonological processing skills on word production accuracy and treatment response. Method: This retrospective analysis included 35 individuals with chronic, poststroke aphasia, 30 of whom completed typicality-based semantic feature treatment. Mixed-effects logistic regression models were used to predict binary naming accuracy (a) at baseline and (b) over the course of treatment using stimulus-level psycholinguistic word properties and person-level semantic and phonological processing skills as predictors. Results: In baseline naming, words with less complex lexical-semantic and phonological properties showed greater predicted accuracy. There was also an interaction at baseline between stimulus-level lexical-semantic properties and person-level semantic processing skills in predicting baseline naming accuracy. With treatment, words that were more complex from a lexical-semantic standpoint (vs. less complex) and less complex from a phonological standpoint (vs. more complex) improved more. Individuals with greater baseline semantic and phonological processing skills showed a greater treatment response. Conclusions: This study suggests that future clinical research and clinical work should consider semantic and phonological properties of words in selecting stimuli for semantically based treatment. Furthermore, future clinical research should continue to evaluate baseline individual semantic and phonological profiles as predictors of response to semantically based treatment.

Purpose: Despite a tremendous amount of research in this topic, the precise neural mechanisms underlying language recovery remain unclear. Much of the evidence suggests that activation of remaining left-hemisphere tissue, including perilesional areas, is linked to the best treatment outcomes, yet recruitment of the right hemisphere for various language tasks has also been linked to favorable behavioral outcomes. In this review article, we propose a framework of language recovery that incorporates a network-based view of the brain regions involved in recovery. Method: We review evidence from the extant literature and work from our own laboratory to identify findings consistent with our proposed framework and identify gaps in our current knowledge. Results: Expanding on Heiss and Thiel's (2006) hierarchy of language recovery, we identify 4 emerging themes: (a) Several bilateral regions constitute a network engaged in language recovery; (b) spared left-hemisphere regions are important components of the network engaged in language recovery; (c) as damage increases in the left hemisphere, activation expands to the right hemisphere and domain-general regions; and (d) patients with efficient, control-like network topology show greater improvement than patients with abnormal topology. We propose a mechanistic model of language recovery that accounts for individual differences in behavior, network topology, and treatment responsiveness. Conclusion: Continued work in this topic will lead us to a better understanding of the mechanisms underlying language recovery, biomarkers that influence recovery, and, consequently, more personalized treatment options for individual patients.

Background: Verbal fluency tasks are included in a broad range of aphasia assessments. It is well documented that people with aphasia (PWA) produce fewer items in these tasks. Successful performance on verbal fluency relies on the integrity of both linguistic and executive control abilities. It remains unclear if limited output in aphasia is solely due to their lexical retrieval difficulties or has a basis in their executive control abilities. Analysis techniques, such as temporal characteristics of word retrieved, clustering and switching, are better positioned to inform the debate surrounding the lexical and/or executive control contribution for success in verbal fluency. Aims: To investigate the differences in quantitative (i.e., number of correct words) and qualitative (i.e., switching, clustering and word-retrieval times) performances on animal fluency task as a function of time between PWA and healthy control speakers (CS). Methods & Procedures: Animal fluency data for 60 s were collected from 34 PWA and 34 CS, and responses were time stamped. The 60-s period was divided into four equal intervals of 15 s each (i.e., 15, 30, 45 and 60 s). The number of correct words, cluster size, number of switches, within-cluster pause and between-cluster pause were evaluated as a function of four 15-s time intervals between PWA and CS. Outcomes & Results: Compared with CS, PWA produced fewer words, had smaller cluster sizes and switched a fewer number of times. A decrease in the number of switches correlated with an increase in between-cluster pause durations. PWA showed longer within- and between-cluster pauses than CS. The two groups showed specific differences in the temporal pattern of the responses: as time evolved both PWA and CS showed decreased productivity for the number of correct words, but PWA reached the asymptote earlier in the time course than CS, neither group showed a change in cluster size, and the number of switches decreased as a function of time only for CS. Conclusions & Implications: The findings suggest that for PWA the search and retrieval process is less productive and more effortful. This is indicated by smaller cluster size, fewer switches associated with increased between-cluster pause durations, as well as overall slowed retrieval times for the words. This shows that the difficulties with verbal fluency performance in aphasia have a strong basis in their lexical retrieval processes, as well as some difficulties in the executive component of the task.

Purpose: Our purpose was to study strategy use during nonlinguistic category learning in aphasia. Method: Twelve control participants without aphasia and 53 participants with aphasia (PWA) completed a computerized feedback-based category learning task consisting of training and testing phases. Accuracy rates of categorization in testing phases were calculated. To evaluate strategy use, strategy analyses were conducted over training and testing phases. Participant data were compared with model data that simulated complex multi-cue, single feature, and random pattern strategies. Learning success and strategy use were evaluated within the context of standardized cognitive-linguistic assessments. Results: Categorization accuracy was higher among control participants than among PWA. The majority of control participants implemented suboptimal or optimal multi-cue and single-feature strategies by testing phases of the experiment. In contrast, a large subgroup of PWA implemented random patterns, or no strategy, during both training and testing phases of the experiment. Conclusions: Person-to-person variability arises not only in category learning ability but also in the strategies implemented to complete category learning tasks. PWA less frequently developed effective strategies during category learning tasks than control participants. Certain PWA may have impairments of strategy development or feedback processing not captured by language and currently probed cognitive abilities.

Purpose: The purpose of the current study was to explore nonlinguistic learning ability in individuals with aphasia, examining the impact of stimulus typicality and feedback on success with learning. Method: Eighteen individuals with aphasia and 8 nonaphasic controls participated in this study. All participants completed 4 computerized, nonlinguistic category-learning tasks. Learning ability was probed under 2 methods of instruction: feedback-based (FB) and paired-associate (PA). The impact of task complexity on learning ability was also examined, comparing 2 stimulus conditions: typical and atypical. Performance was compared between groups and across conditions. Results: The controls were able to successfully learn categories under all conditions. For the individuals with aphasia, 2 patterns of performance arose: One subgroup of individuals was able to maintain learning across task manipulations and conditions; the other subgroup demonstrated a sensitivity to task complexity, learning successfully only in the typical training conditions. Conclusion: Results support the hypothesis that impairments of general learning are present in individuals with aphasia. Some individuals demonstrated the ability to extract category information under complex training conditions; others learned only under conditions that were simplified and that emphasized salient category features. Overall, the typical training condition facilitated learning for all of the participants. Findings have implications for treatment, which are discussed. (Contains 3 tables and 4 figures.)

Purpose: The goal of this study was to examine if there was a principled way to understand the nature of rehabilitation in bilingual aphasia such that patterns of acquisition and generalization are predictable and logical. Method: Seventeen Spanish-English bilingual individuals with aphasia participated in the experiment. For each participant, three sets of stimuli were developed for each language: (a) English Set 1, (b) English Set 2 (semantically related to each item in English Set 1), (c) English Set 3 (unrelated control items), (d) Spanish Set 1 (translations of English Set 1), (e) Spanish Set 2 (translations of English Set 2; semantically related to each item in Spanish Set 1), and (f) Spanish Set 3 (translations of English Set 3; unrelated control items). A single-subject experimental multiple baseline design across participants was implemented. Treatment was conducted in 1 language, but generalization to within- and between-language untrained items was examined. Results: Treatment for naming on Set 1 items resulted in significant improvement (i.e., effect size greater than 4.0) on the trained items in 14/17 participants. Of the 14 participants who showed improvement, within-language generalization to semantically related items was observed in 10 participants. Between-language generalization to the translations of trained items was observed in 5 participants, and between-language generalization to the translations of the untrained semantically related items was observed in 6 participants. Conclusion: The results of this study demonstrated within- and between-language patterns that were variable across participants. These differences are indicative of the interplay between facilitation (generalization) and inhibition. (Contains 4 tables, 3 figures and 1 footnote.)

Current research on bilingual aphasia highlights the paucity in recommendations for optimal rehabilitation for bilingual aphasic patients (Edmonds & Kiran, 2006; Roberts & Kiran, 2007). In this paper, we have developed a computational model to simulate an English-Spanish bilingual language system in which language representations can vary by age of acquisition (AoA) and relative proficiency in the two languages to model individual participants. This model is subsequently lesioned by varying connection strengths between the semantic and phonological networks and retrained based on individual patient demographic information to evaluate whether or not the model's prediction of rehabilitation matches the actual treatment outcome. In most cases the model comes close to the target performance subsequent to language therapy in the language trained, indicating the validity of this model in simulating rehabilitation of naming impairment in bilingual aphasia. Additionally, the amount of cross-language transfer is limited both in the patient performance and in the model's predictions and is dependent on that specific patient's AoA, language exposure and language impairment. It also suggests how well alternative treatment scenarios would have fared, including some cases where the alternative would have done better. Overall, the study suggests how computational modeling could be used in the future to design customized treatment recipes that result in better recovery than is currently possible.

Purpose: Two new treatments, 1 based on sentence to picture matching (SPM) and the other on object manipulation (OM), that train participants on the thematic roles of sentences using pictures or by manipulating objects were piloted. Method: Using a single-subject multiple-baseline design, sentence comprehension was trained on the affected sentence type in 1 task-related protocol in 15 participants with aphasia. The 2 tasks were SPM and OM; the treatment stimuli were object relatives, object clefts, passives, and unaccusatives, as well as two control structures--object relatives with a complex noun phrase (NP) and active sentences with three NPs. Results: The criteria for efficacious treatment was an increase in the level of performance from the pretreatment probes to the posttreatment probes for the treated structure such that accuracy rose from at or below chance to above chance and either (a) accuracy rose by 33% or (b) the effect size was 2.6. Based on these criteria, the success rate for training the target structure was 2/6 participants in the SPM condition and 4/7 participants in the OM condition. Conclusion: The outcome of this study illustrates the utility of this theoretically motivated and efficacious treatment for sentence comprehension deficits in individuals with aphasia. (Contains 4 tables and 1 figure.)

Background: The typicality effect is present in neurologically intact populations for natural, ad-hoc, and well-defined categories. Although sparse, there is evidence of typicality effects in persons with chronic stroke aphasia for natural and ad-hoc categories. However, it is unknown exactly what influences the typicality effect in this population. Aims: The present study explores the possible contributors to the typicality effect in persons with aphasia by analyzing and comparing data from both normal and language-disordered populations, from persons with aphasia with more semantic impairment versus those with less semantic impairment, and from two types of categories with very different boundary structure (ad-hoc vs. well-defined). Methods and procedures: A total of 40 neurologically healthy adults (20 older, 20 younger) and 35 persons with aphasia (20 LSI (less-semantically impaired) patients, 15 MSI (more-semantically impaired) patients) participated in the study. Participants completed one of two tasks: either category verification for ad-hoc categories or category verification for well-defined categories. Outcomes and results: Neurologically healthy participants showed typicality effects for both ad-hoc and well-defined categories. MSI patients showed a typicality effect for well-defined categories, but not for ad-hoc categories, whereas LSI patients showed a typicality effect for ad-hoc categories, but not for well-defined categories. Conclusions: These results suggest that the degree of semantic impairment mediates the typicality effect in persons with aphasia depending on the structure of the category. Learning outcomes: After reading this article, the reader should be able to: (1) Describe the typicality effect and in which populations it occurs. (2) Explain how the typicality effect might change depending on category structure. (3) Summarize how semantic impairment influences category representation and/or access. (Contains 3 figures and 3 tables.)

Purpose: Kiran and colleagues (Kiran, 2007, 2008; Kiran & Johnson, 2008; Kiran & Thompson, 2003) previously suggested that training atypical examples within a semantic category is a more efficient treatment approach to facilitating generalization within the category than training typical examples. In the present study, the authors extended previous work examining the notion of semantic complexity within goal-derived (ad hoc) categories in individuals with aphasia. Methods: Six individuals with fluent aphasia (age range = 39-84 years) and varying degrees of naming deficits and semantic impairments were involved. Thirty typical and atypical items, each from 2 categories, were selected after an extensive stimulus norming task. Generative naming for the 2 categories was tested during baseline and treatment. Results: As predicted, training atypical examples in the category resulted in generalization to untrained typical examples in 5 of 5 patient-treatment conditions. In contrast, training typical examples (which was examined in 3 conditions) produced mixed results. One patient showed generalization to untrained atypical examples, whereas 2 patients did not show generalization to untrained atypical examples. Conclusion: Results of the present study supplement existing data on the effect of a semantically based treatment for lexical retrieval by manipulating the typicality of category examples.

The goal of this study was to address the relationship between language proficiency, language impairment and rehabilitation in bilingual Russian-English individuals with aphasia. As a first step, we examined two Russian-English patients' pre-stroke language proficiency using a detailed and comprehensive language use and history questionnaire and evaluated their impairment using the Bilingual Aphasia Test. We then attempted to replicate and extend Kiran and Roberts' study in 2010, examining results of a primarily semantic treatment for anomia in one Russian-English bilingual patient. The patient's ability to name the trained and untrained items in both the trained (English) and untrained (Russian) languages significantly improved by achieving 100% accuracy. Finally, we examined whether improvements observed in treatment were captured by a broader language test such as the Bilingual Aphasia Test. Results are discussed with respect to factors contributing to the successful treatment and the implications of rehabilitation on assessment of language skills as a function of treatment. (Contains 2 tables and 1 figure.)

Purpose: The typicality treatment approach on improving naming was investigated within 2 inanimate categories ("furniture" and "clothing") using a single-subject experimental design across participants and behaviors in 5 patients with aphasia. Method: Participants received a semantic feature treatment to improve naming of either typical or atypical items within semantic categories, whereas generalization was tested to untrained items of the category. The order of typicality and category trained was counterbalanced across participants. Results: Results indicated that 2 out of 4 patients trained on naming of atypical examples demonstrated generalization to naming untrained typical examples. One patient showed trends toward generalization but did not achieve criterion. Furthermore, all 4 patients trained on typical examples demonstrated no generalized naming to untrained atypical examples within the category. Also, analysis of errors indicated an evolution of errors as a result of treatment, from those with no apparent relationship to the target to primarily semantic and phonemic paraphasias. Conclusion: These results extend our previous findings (S. Kiran & C. K. Thompson, 2003a) to patients with nonfluent aphasia and to inanimate categories such as "furniture" and "clothing". Additionally, the results provide support for the claim that training atypical examples is a more efficient method of facilitating generalization to untrained items within a category than training typical examples (S. Kiran, 2007).

Purpose: Our previous work on manipulating typicality of category exemplars during treatment of naming deficits has shown that training atypical examples generalizes to untrained typical examples but not vice versa. In contrast to natural categories that consist of fuzzy boundaries, well-defined categories (e.g., "shapes") have rigid category boundaries. Whether these categories illustrate typicality effects similar to natural categories is under debate. The present study addressed this question in the context of treatment for naming deficits in aphasia. Methods: Using a single-subject experiment design, 3 participants with aphasia received a semantic feature treatment to improve naming of either typical or atypical items of shapes, while generalization was tested to untrained items of the category. Results: For 2 of the 3 participants, training naming of atypical examples of shapes resulted in improved naming of untrained typical examples. Training typical examples in 1 participant did not improve naming of atypical examples. All 3 participants, however, showed weak acquisition trends. Conclusions: Results of the present study show equivocal support for manipulating typicality as a treatment variable within well-defined categories. Instead, these results indicate that acquisition and generalization effects within well-defined categories such as shapes are overshadowed by their inherent abstractness. (Contains 3 tables and 3 figures.)

The present study examined lexical representation in early Spanish-English bilinguals using an unmasked semantic and translation priming paradigm. In Experiment 1, participants were divided into two groups based on performance (more-balanced bilinguals, MB and less-balanced bilinguals, LB) on the experimental task. In Experiment 2, four patients with bilingual aphasia (BA) performed the same experiment. Results from both experiments revealed that all groups were more accurate for English targets (S-E direction) than Spanish targets (S-E direction). In Experiment 1, semantic priming was observed from English to Spanish in both the LB and MB groups although the effect was greater for the LB group. Further, only the LB group showed priming from Spanish to English. For both normal groups, there was no difference between translation and semantic priming effects. In Experiment 2, patients with bilingual aphasia demonstrated different patterns of activation with no clear trends. Two participants demonstrated greater priming from Spanish to English whereas two participants demonstrated the opposite effect. (Contains 8 tables and 3 figures.)

Purpose: This article discusses a novel approach for treatment of lexical retrieval deficits in aphasia in which treatment begins with complex, rather than simple, lexical stimuli. This treatment considers the semantic complexity of items within semantic categories, with a focus on their featural detail. Method and Results: Previous work on training items within animate categories (S. Kiran & C. K. Thompson, 2003b) and preliminary work aimed at items within inanimate categories are discussed in this article. Both these studies indicate that training atypical category items that entail features inherent in the category prototype as well as distinctive features that are not characteristic of the category prototype results in generalization to untrained typical examples which entail only features consistent with the category prototype. Conversely, training typical examples does not result in generalization to untrained atypical examples. In this article, it is argued that atypical items are more complex than typical items within a category, and a theoretical framework for this dimension of semantic complexity is discussed. Then, evidence from treatment studies that support this complexity hierarchy is presented. Potential patient- and stimulus-specific factors that may influence the success of this treatment approach are also discussed. Conclusions: The applications of semantic complexity to treatment of additional semantic categories and functional applications of this approach are proposed. (Contains 3 tables and 3 figures.)

Purpose: The effect of semantic naming treatment on crosslinguistic generalization was investigated in 3 participants with English-Spanish bilingual aphasia. Method: A single-subject experimental designed was used. Participants received semantic treatment to improve naming of English or Spanish items, while generalization was tested to untrained semantically related items in the trained language and translations of the trained and untrained items in the untrained language. Results: Results demonstrated a within- and across-languages effect on generalization related to premorbid language proficiencies. Participant 1 (P1; equal premorbid proficiency across languages) showed within-language generalization in the trained language (Spanish) as well as crosslinguistic generalization to the untrained language (English). Participant 2 (P2) and Participant (P3) were more proficient premorbidly in English. With treatment in English, P2 showed within-language generalization to semantically related items, but no crosslinguistic generalization. With treatment in Spanish, both P2 and P3 exhibited no within-language generalization, but crosslinguistic generalization to English (dominant language) occurred. Error analyses indicated an evolution of errors as a consequence of treatment. Conclusions: These results are preliminary because all participants were not treated in both languages. However, the results suggest that training the less dominant language may be more beneficial in facilitating crosslinguistic generalization than training the more proficient language in an unbalanced bilingual individual.

Four patients with fluent aphasia received a semantic feature treatment to improve naming of either typical or atypical items within semantic categories. Patients trained on naming of atypical exemplars demonstrated generalization to naming of intermediate and typical items. Patients trained on typical items demonstrated no generalized naming effect. (Contains references.) (Author/CR)

Four individuals with agrammatic aphasia were trained to comprehend and produce filler-gap sentences with wh-movement. Two participants received treatment first on the least complex structure (who-questions), and 2 received treatment on the most complex form (object-relative constructions). Object-relative training resulted in robust generalization to both object clefts and who-questions. (Contains references.) (Author/CR)

BackgroundAlthough speech-language therapy (SLT) is proven to be beneficial to recovery of post-stroke aphasia, delivering sufficiently high amounts of dosage remains a problem in real-world clinical practice. Self-managed SLT was introduced to solve the problem. Previous research showed in a 10-week period, increased dosage frequency could lead to better performance, however, it is uncertain if dosage still affects performance over a longer period of practice time and whether gains can be seen following practice over several months.ObjectiveThis study aims to evaluate data from a health app (Constant Therapy) to investigate the relationship between dosage amount and improvements following a 30-week treatment period. Two cohorts of users were analyzed. One was comprised of patients with a consistent average weekly dosage amount and the other cohort was comprised of users whose practice had higher variability.MethodsWe conducted two analyses with two cohorts of post-stroke patients who used Constant Therapy. The first cohort contains 537 “consistent” users, while the second cohort contains 2,159. The 30-week practice period was split into three consecutive 10-week practice windows to calculate average dosage amount. In each 10-week practice period, patients were grouped by their average dosage into low (0–15 min/week), medium (15–40 min/week) and moderate dosage (greater than 40 min/week) groups. Linear mixed-effects models were employed to evaluate if dosage amount was a significant factor affecting performance. Pairwise comparison was also applied to evaluate the slope difference between groups.ResultsFor the consistent cohort, medium (β = .002, t17,700 = 7.64, P

Brain reorganization patterns associated with language recovery after stroke have long been debated. Studying mechanisms of spontaneous and treatment-induced language recovery in post-stroke aphasia requires a network-based approach given the potential for recruitment of perilesional left hemisphere language regions, homologous right hemisphere language regions, and/or spared bilateral domain-general regions. Recent hardware, software, and methodological advances in functional near-infrared spectroscopy (fNIRS) make it well-suited to examine this question. fNIRS is cost-effective with minimal contraindications, making it a robust option to monitor treatment-related brain activation changes over time. Establishing clear activation patterns in neurotypical adults during language and domain-general cognitive processes via fNIRS is an important first step. Some fNIRS studies have investigated key language processes in healthy adults, yet findings are challenging to interpret in the context of methodological limitations. This pilot study used fNIRS to capture brain activation during language and domain-general processing in neurotypicals and individuals with aphasia. These findings will serve as a reference when interpreting treatment-related changes in brain activation patterns in post-stroke aphasia in the future. Twenty-four young healthy controls, seventeen older healthy controls, and six individuals with left hemisphere stroke-induced aphasia completed two language tasks (i.e., semantic feature, picture naming) and one domain-general cognitive task (i.e., arithmetic) twice during fNIRS. The probe covered bilateral frontal, parietal, and temporal lobes and included short-separation detectors for scalp signal nuisance regression. Younger and older healthy controls activated core language regions during semantic feature processing (e.g., left inferior frontal gyrus pars opercularis) and lexical retrieval (e.g., left inferior frontal gyrus pars triangularis) and domain-general regions (e.g., bilateral middle frontal gyri) during hard versus easy arithmetic as expected. Consistent with theories of post-stroke language recovery, individuals with aphasia activated areas outside the traditional networks: left superior frontal gyrus and left supramarginal gyrus during semantic feature judgment; left superior frontal gyrus and right precentral gyrus during picture naming; and left inferior frontal gyrus pars opercularis during arithmetic processing. The preliminary findings in the stroke group highlight the utility of using fNIRS to study language and domain-general processing in aphasia.

Functional neuroimaging and lesion-symptom mapping investigations implicate a left frontal–temporal–parietal network for sentence processing. The majority of studies have focused on sentence comprehension, with fewer in the domain of sentence production, which have not fully elucidated overlapping and/or unique brain structures associated with the two domains, particularly for sentences with noncanonical word order. Using voxel-based lesion symptom mapping (VLSM) we examined the relationship between lesions within the left hemisphere language network and both sentence comprehension and production of simple and complex syntactic structures in 76 participants with chronic stroke-induced aphasia. Results revealed shared regions across domains in the anterior and posterior superior temporal gyri (aSTG, pSTG), and the temporal pole (adjusted for verb production/comprehension). Additionally, comprehension was associated with lesions in the anterior and posterior middle temporal gyri (aMTG, pMTG), the MTG temporooccipital regions, SMG/AG, central and parietal operculum, and the insula. Subsequent VLSM analyses (production versus comprehension) revealed critical regions associated with each domain: anterior temporal lesions were associated with production; posterior temporo-parietal lesions were associated with comprehension, implicating important roles for regions within the ventral and dorsal stream processing routes, respectively. Processing of syntactically complex, noncanonical (adjusted for canonical), sentences was associated with damage to the pSTG across domains, with additional damage to the pMTG and IPL associated with impaired sentence comprehension, suggesting that the pSTG is crucial for computing noncanonical sentences across domains and that the pMTG, and IPL are necessary for re-analysis of thematic roles as required for resolution of long-distance dependencies. These findings converge with previous studies and extend our knowledge of the neural mechanisms of sentence comprehension to production, highlighting critical regions associated with both domains, and further address the mechanism engaged for syntactic computation, controlled for the contribution of verb processing.

Researchers have sought to understand how language is processed in the brain, how brain damage affects language abilities, and what can be expected during the recovery period since the early 19th century. In this review, we first discuss mechanisms of damage and plasticity in the post-stroke brain, both in the acute and the chronic phase of recovery. We then review factors that are associated with recovery. First, we review organism intrinsic variables such as age, lesion volume and location and structural integrity that influence language recovery. Next, we review organism extrinsic factors such as treatment that influence language recovery. Here, we discuss recent advances in our understanding of language recovery and highlight recent work that emphasizes a network perspective of language recovery. Finally, we propose our interpretation of the principles of neuroplasticity, originally proposed by Kleim and Jones (1) in the context of extant literature in aphasia recovery and rehabilitation. Ultimately, we encourage researchers to propose sophisticated intervention studies that bring us closer to the goal of providing precision treatment for patients with aphasia and a better understanding of the neural mechanisms that underlie successful neuroplasticity.

Background: Computer-based therapies can provide an affordable and practical alternative by providing frequent intervention for stroke survivors with chronic aphasia by allowing the opportunity for home exercise practice, however more evidence is needed. The goal of this retrospective analysis was to compare the time course of therapy engagement when therapy was targeted in the clinic or at home by post-stroke individuals. We examined if home users of the therapy were compliant in therapy and if this documented practice time was associated with improved outcomes similar to clinic patients who practiced under the guidance of a clinician.Methods: A retrospective analysis of anonymously aggregated data collected for 3,686 patients with post-stroke aphasia over the course of four years (2013–2017) was conducted. Participants either received therapy delivered through Constant Therapy only at home (N = 2,100) or only in the clinic (N = 1,577). Constant Therapy includes over 70 evidence-based therapies for language and cognitive skills. This program was individualized for each patient with targeted tasks that dynamically adapted to each individual's progress.Results: Patients with 90% accuracy. Results showed that both home-therapy and clinic patients reached 90% accuracy on their tasks similarly (Median = 3 sessions), but the frequency of therapy was significantly different with 50% of home users receiving therapy at least every 2 days while 50% of clinic patients only had therapy once every 5 days (p < 0.001). Thus, home-therapy users were able to master tasks in a shorter time (median of 6 days) than clinic patients (median of 12 days) (p < 0.001).Conclusion: Outcomes of treatment are similar for home users and clinic patients indicating the potential usability of a home-based treatment program for rehabilitation for post-stroke aphasia.

To determine whether MRI-based cerebral small vessel disease (cSVD) burden predicts treatment-induced aphasia recovery in chronic stroke patients above and beyond initial aphasia severity and stroke-lesion volume. Retrospective. Four cSVD neuroimaging markers were rated using validated visual scales: white matter hyperintensities, enlarged perivascular spaces, lacunes, and global cortical atrophy. We also calculated a cSVD total score. We employed linear regression models to model treatment response as a function of cSVD burden. We also ran correlation analyses to determine the association among cSVD burden and pre-treatment linguistic and non-linguistic cognition. Research clinic. The study includes data from 30 chronic stroke patients with aphasia who received treatment for word finding difficulties and completed additional pre-treatment neuroimaging and behavioral assessments (N=30). 120-minute sessions of anomia treatment 2 times per week for up to 12 weeks. Change in accuracy on the treatment probes measured as a percentage (ie, change in accuracy percentage score=post-treatment accuracy percentage minus pre-treatment accuracy percentage). Baseline cSVD burden predicted response to anomia treatment independently from demographic and stroke-related factors. Patients with lower cSVD burden exhibited enhanced rehabilitation response compared with those with higher cSVD burden (β=-6.816e-02, P =.019). cSVD burden was highly associated with nonverbal executive function at baseline (r =-0.49, P =.005): patients with lower cSVD burden exhibited higher performance on nonverbal executive function tasks compared with participants with higher cSVD burden. No association was observed among cSVD burden and performance on language tasks at the baseline. cSVD, a marker of brain reserve and a robust risk factor for post-stroke dementia, may be used as a biomarker for distinguishing patients who are more likely to respond to anomia therapy from those who are less likely to do so and for individualizing treatment parameters (eg, targeting both linguistic and nonlinguistic cognition in severe cSVD). [ABSTRACT FROM AUTHOR]

The utilization of technology has allowed for several advances in aphasia rehabilitation for individuals with acquired brain injury. Thirty-one previous studies that provide technology-based language or language and cognitive rehabilitation are examined in terms of the domains addressed, the types of treatments that were provided, details about the methods and the results, including which types of outcomes are reported. From this, we address questions about how different aspects of the delivery of treatment can influence rehabilitation outcomes, such as whether the treatment was standardized or tailored, whether the participants were prescribed homework or not, and whether intensity was varied. Results differed by these aspects of treatment delivery but ultimately the studies demonstrated consistent improvement on various outcome measures. With these aspects of technology-based treatment in mind, the ultimate goal of personalized rehabilitation is discussed.

While several studies of task-based effective connectivity of normal language processing exist, little is known about the functional reorganization of language networks in patients with stroke-induced chronic aphasia. During oral picture naming, activation in neurologically-intact individuals is found in classic language regions involved with retrieval of lexical concepts (e.g., left middle temporal gyrus; LMTG), word form encoding (e.g., left posterior superior temporal gyrus, LpSTG), and controlled retrieval of semantic and phonological information (e.g., left inferior frontal gyrus; LIFG) as well as domain-general regions within the multiple demands network (e.g., left middle frontal gyrus; LMFG). After stroke, lesions to specific parts of the left hemisphere language network force reorganization of this system. While individuals with aphasia have been found to recruit similar regions for language tasks as healthy controls, the relationship between the dynamic functioning of the language network and individual differences in underlying neural structure and behavioral performance is still unknown. Therefore, in the present study, we used dynamic causal modeling (DCM) to investigate differences between individuals with aphasia and healthy controls in terms of task-induced regional interactions between three regions (i.e., LIFG, LMFG and LMTG) vital for picture naming. The DCM model space was organized according to exogenous input to these regions and partitioned into separate families. At the model level, random effects family-wise Bayesian model selection revealed that models with driving input to LIFG best fit the control data whereas models with driving input to LMFG best fit the patient data. At the parameter level, a significant between-group difference in the connection strength from LMTG to LIFG was seen. Within the patient group, several significant relationships between network connectivity parameters, spared cortical tissue, and behavior were observed. Overall, this study provides some preliminary findings regarding how neural networks for language reorganize for individuals with aphasia and how brain connectivity relates to underlying structural integrity and task performance.

In this study, we examined regions in the left and right hemisphere language network that were altered in terms of the underlying neural activation and effective connectivity subsequent to language rehabilitation. Eight persons with chronic post-stroke aphasia and eight normal controls participated in the current study. Patients received a 10 week semantic feature-based rehabilitation program to improve their skills. Therapy was provided on atypical examples of one trained category while two control categories were monitored; the categories were counterbalanced across patients. In each fMRI session, two experimental tasks were conducted: (a) picture naming and (b) semantic feature verification of trained and untrained categories. Analysis of treatment effect sizes revealed that all patients showed greater improvements on the trained category relative to untrained categories. Results from this study show remarkable patterns of consistency despite the inherent variability in lesion size and activation patterns across patients. Across patients, activation that emerged as a function of rehabilitation on the trained category included bilateral IFG, bilateral SFG, LMFG, and LPCG for picture naming; and bilateral IFG, bilateral MFG, left SFG, and bilateral MTG for semantic feature verification. Analysis of effective connectivity using Dynamic Causal Modeling (DCM) indicated that LIFG was the consistently significantly modulated region after rehabilitation across participants. These results indicate that language networks in patients with aphasia resemble normal language control networks and this similarity is accentuated by rehabilitation.

Introduction. While the evidence for efficacy of rehabilitation of language disorders is fairly robust and conclusive (Allen, et al., 2012; Brady, et al., 2012; Kelly, Brady, & Enderby, 2010; Cherney, et al., 2008), a major limitation identified by these reviews is that the sample size of patients in each of the interventions have been modest (5-20 patients). As technology moves our field forward, we can now collect and analyze larger sets of data to validate theories of rehabilitation. As a first step, we report data from a recently completed study examining the effectiveness of software platform (Constant Therapy) to deliver, monitor and analyze treatment for individuals with aphasia (Des Roches et al., 2015). Methods. Fifty one individuals with language and cognitive deficits were administered standardized tests (Western Aphasia Battery, Boston Naming Test, Pyramids and Palm Trees, and Cognitive Linguistic Quick Test) prior to initiation and following completion of therapy. Forty-two experimental patients used the iPad-based therapy once a week with the clinician and up to six days a week for home practice. Nine control patients practiced therapy on the iPad once per week with the clinician only. Thirty-eight therapy tasks were divided into language and cognitive activities that were developed (Des Roches et al., 2015), 28 of these tasks included buttons that revealed a hint to assist the patient answer the item. The assigned therapy tasks were tailored to that individual’s language and cognitive impairment profile based on an initial baseline assessment. Each task was practiced until accuracy on task reached 100% on multiple occasions at which point that task was replaced with the task at the next level of difficulty. The 51 patients each completed a 10 week program leading to total of 3327 therapy sessions across patients. Analysis and Results: Mixed regression models showed that both the experimental and control groups improved but experimental participants showed more significant changes on the therapy tasks and standardized tests than control patients. Additionally, the more severe patients benefited from the simpler language and cognitive tasks whereas the less severe patients improved on tasks that required a combination of language/cognitive processing. Correlations between starting severity scores and the amount of change on standardized tests showed that participants with lower initial scores showed more improvement on the standardized tests than participants with higher initial scores. In addition, because participants were able to self-administer the hints for individual items, the relationship between levels of hint use and participants’ corresponding accuracy showed that hint use often negatively predicted performance. Correlations between frequency of hint use and participant language/cognitive impairment profiles showed significant negative relationships between hint use and performance on standardized tests indicating that the more severe the participants the more often they utilized hints to complete the therapy tasks. Conclusions: Analysis at the patient, therapy task and item level allow an evaluation of the efficacy of rehabilitation and individual responsiveness to rehabilitation. In addition, preliminary results from a larger data set collected anonymously from over 2,000 independent patient users is also presented.

Over the last decade, a number of large-scale networks in the human cortex that support high-level cognition have been identified. Here, we focus on two of these networks: the fronto-temporal language network (e.g., Fedorenko et al., 2010), and the fronto-parietal “multiple demand (MD)” network (e.g., Duncan, 2010). These two networks are clearly distinct from one another: first, their respective regions show distinct functional profiles, with language regions showing selective responses to language stimuli (Fedorenko et al., 2011; Monti et al., 2012) and MD regions showing domain-general responses to cognitive effort across a wide range of tasks (Duncan & Owen, 2001; Fedorenko et al., 2013). Second, during “rest” and cognitive processing, each network shows strong activity synchronization among its constituent regions, whereas regions across the two networks are not synchronized (Blank et al., 2014; Lee et al., 2012; Mantini et al., 2013). In the current study, we examined how these functional characteristics of the two networks were affected following aphasia-inducing strokes. In particular, we asked whether damage to the language network would alter the involvement of the MD network in linguistic processing, and whether such damage would alter the patterns of synchronization across the two networks. Four male individuals with aphasia (age: M=53), having suffered a single left MCA CVA, were scanned in fMRI on two paradigms that enable basic functional characterization of language and MD regions: (i) a language localizer task, where they passively read sentences and sequences of pseudowords (Fedorenko et al., 2010); and (ii) a spatial working memory task, where they had to remember fewer (easy) or more (hard) locations in a grid (Fedorenko et al., 2013). Language and MD regions were defined in each individual using the sentences > pseudowords contrast and the hard > easy contrast, respectively. Subjects were also scanned while listening to naturalistic stories. We found that language regions maintained their selectivity, showing strong responses to sentences (that were reliably stronger than responses to pseudowords) and little or no response during the spatial working memory task. In contrast, MD regions showed strong responses to the spatial working memory task, with a few regions responding more strongly during the hard condition than during the easy condition. These patterns replicate previous findings in healthy individuals (Fedorenko et al., 2011, 2012). However, unlike in healthy controls – where MD regions respond more strongly to pseudowords than to sentences (presumably because processing pseudowords is more demanding) – in individuals with aphasia a few MD regions responded more strongly to sentences than to pseudowords. These regions were located in the bilateral opercular inferior frontal gyrus, precentral gyrus and supplementary motor area. Nonetheless, language and MD regions remained overall dissociated during story comprehension: regions within each network were more synchronized with each other than with regions of the other network. These results suggest that parts of the domain-general MD network, a system that has been linked to problem solving and general fluid intelligence (e.g., Duncan, 2010; Woogar et al., 2010), may alter their involvement in language processing when the language system is compromised.

The delivery of tablet-based rehabilitation for individuals with post-stroke aphasia is relatively new, therefore, this study examined the effectiveness of an iPad-based therapy to demonstrate improvement in specific therapy tasks and how the tasks affect overall language and cognitive skills. Fifty-one individuals with aphasia due to a stroke or traumatic brain injury were recruited to use an iPad-based software platform, Constant Therapy, for a 10 week therapy program. Participants were split into an experimental (N=42) and control (N=9) group. Both experimental and control participants received a one hour clinic session with a clinician once a week, the experimental participants additionally practiced the therapy at home. Participants did not differ in the duration of the therapy and both groups of participants showed improvement over time in the tasks used for the therapy. However, experimental participants used the application more often and showed greater changes in accuracy and latency on the tasks than the control participants; experimental participants’ severity level at baseline as measured by standardized tests of language and cognitive skills were a factor in improvement on the tasks. Subgroups of task co-improvement appear to occur between different language tasks, between different cognitive tasks, and across both domains. Finally, experimental participants showed more significant and positive changes due to therapy in their standardized tests than control participants. These results provide preliminary evidence for the usefulness of a tablet-based platform to deliver tailored language and cognitive therapy to individuals with aphasia.

Abstract In the current study, we examined the nature of crosslinguistic generalization of treatment for Indian bilinguals with aphasia. We recruited three bilingual (Kannada-English) persons with aphasia and used the treatment protocol described by Edmonds and Kiran (2006). Our findings showed a striking similarity with the previous study, thus providing further empirical evidence for crosslinguistic generalization of semantic treatment in aphasia, especially from an unexplored language pair. Introduction The last two decades witnessed several novel treatment approaches to aphasia therapy. Semantic feature-based therapy is one of such treatment approaches that gained considerable research attention (Boyle & Coelho, 1995). More importantly, this treatment approach has been found effective in bilingual persons with aphasia. For instance, Edmonds and Kiran (2006) administered semantic feature based therapy in Spanish-English bilingual persons with aphasia and reported of crosslinguistic generalization of treatment effect to untreated language. This promising research, however, needs to be replicated and extended to novel language pairs. Research on crosslinguistic generalization of treatment effects is of paramount importance to multilingual countries like India. For instance, with several hundreds of languages and dialects spoken across India and with the pervasive use of English as second language, speech language pathologists (SLPs) in the country are often baffled on the selection of language for treatment in bilingual persons with aphasia. Empirical evidence from Indian languages would add confidence to the SLPs while selecting language for treatment in person with aphasia. In this context, the current study aimed to replicate and extend the earlier findings on crosslinguistic generalization of treatment effects in bilingual persons with aphasia to the Indian context. Methods This investigation was carried out in Karnataka, a southern state in India, where the primary spoken language is Kannada. We recruited three bilingual (Kannada-English) persons with aphasia for the current study. Their premorbid proficiency was calculated with a rating scale rated either by the participants or with the help of an intimate family member. While participant 1 (P1) was equally proficient in both languages, P2 and P3 were more proficient in Kannada. Table 1 provides the demographic data as well as language proficiency of the three participants. Prior to as well as following the administration of semantic feature-based treatment, the language skills of the participants were assessed with Western Aphasia Battery (Chengappa & Kumar, 2008), Bilingual Aphasia Test (Paradis & Rangamani, 1989), and Boston Naming Test (Shanthala, 1997) (see Table 2) in Kannada and English. ________________________ Table 1 about here ________________________ Table 2 about here ________________________ We employed the methods of Edmonds and Kiran (2006) for preparing treatment probes as well as administering treatment in our participants. From an initial set of 300 pictures that were administered on each participant, 50 unsuccessfully named pictures were selected for the training purpose. The names of these pictures were neither cognates nor had 50% or more phonetic similarity in both languages. The selected items for each participant varied from that of other participants as the final items (n = 50) were selected based on the individual naming failures. All items belonged to various semantic categories like animals, fruits, household articles etc. For each participant, six sets of stimuli were developed with 10 items in each set, except in control set, which had only 5 stimuli. The stimuli were categorized into: a) English Set 1 (e.g., cat: N = 10); b) Kannada set 1 (translation equivalent of English set 1: e.g., /bekku/: N = 10); c) English set 2 (semantically related to items in set 1: e.g., dog: N = 10); d) Kannada set 2 (translation equivalent of English set 2: e.g., /na:ji/: N = 10); e) English set 3 (control: semantically unrelated: e.g., stone: N = 5); f) Kannada set 3 (control: translation equivalent of English set 3: e.g., /kallu/: N = 5). For each participant, two sessions of therapy, each lasting for 2 hours, were provided on a weekly basis. Unlike in Edmonds and Kiran (2006), the participants were assessed only twice (i.e., before & after therapy) in the current study. Results Participant P1, equally proficient in both languages, was trained using English set 1. Performance improved on both English set 1 (from 0 - 90%; X2 = 9; df = 1; p = 0.0027) as well as on English set 2 (from 0 - 80%; X2 = 8; df = 1; p = 0.0046). Further, crosslinguistic generalization to both translation equivalents (i.e., to Kannada set 1: 0 - 70%; X2 = 7; df = 1; p = 0.0081) and semantically related items (Kannada set 2: 0 - 60%; X2 = 6; df = 1; p = 0.01431) was also noticed. The control sets, however, did not show any significant improvement following therapy. Participant P2, who was more proficient in Kannada, received treatment in the same language using Kannada set 1. Both trained set (from 0 - 80%; X2 = 8; df = 1; p = 0.0046) and semantically related untrained (i.e., Kannada set 2: 10 - 60%; X2 = 6; df = 1; p = 0.014) showed significant improvement. However, neither English set 1 nor English set 2 showed improvement, indicating poor crosslinguistic generalization. Subsequently, the treatment was provided in English using English set 2. Though the trained set showed improvements (from 10 - 90%; X2 = 8; df = 1; p = 0.0046), it failed to generalize to English set 1. However, interestingly, with treatment initiated in English, P2 showed crosslinguistic generalization to both translation equivalents (i.e., Kannada set2: 60 - 80%) as well as to the semantic related items (i.e., Kannada set 1: 80 – 100%). Neither Kannada (0%-20%; p - ns) nor English (0%-0%; p - ns) control sets showed any notable changes following the training. Participant P3, who was more proficient in Kannada like P2, received treatment in English using English set 1 and crosslinguistic generalization was monitored for Kannada set 1 and set 2. Scores on the trained set improved from 0 to 80% (X2 = 8; df = 1; p = 0.0046). Within language generalization was observed on semantically related untrained items (i.e., English set 2) as the scores improved from 0 to 40% (X2 = 4; df = 1; p = 0.045) and there was crosslinguistic generalization to Kannada set1 (from 10 to 70%; X2 = 6; df = 1; p = 0.014) and to Kannada set2 (from 0 to 60%; X2 = 6; df =1; p = 0.014). Both control sets (0 - 0%; p - ns) did not show any change after the provision of training. Discussion The results of this investigation are, in general, on par with earlier similar investigations (e.g., Edmonds & Kiran, 2006). In their study, these authors observed improvements in both languages subsequent to the provision of treatment in L2 in balanced (i.e., equally proficient) bilinguals. However, in dominant bilinguals, where there exists a difference in proficiency level between the languages, provision of treatment in weaker language resulted in crosslinguistic generalization and the results of this study corroborated their findings. This in turn, supports the Revised Hierarchical Model (Kroll & Stewart, 1994) of bilingual language system. Additionally, it may be noted that the pre- and post-therapy evaluations revealed overall improvement in languages skills of all three participants in the current study. Though unexpected, this observation warrants further exploration of the generalized effects of strengthening the semantic system on the overall language skills. Conclusion The current study replicated and extended the previous evidence on crosslinguistic generalization of semantic therapy to the Indian context. The outcomes from the current study provide empirical evidence for selecting language of treatment in bilingual persons with aphasia in the pervasively bi-/multilingual Indian context. Table 1: Demographic and linguistic proficiency data of the participants Participant Gender Age (years) Education Etiology MPO Self-ratings: Premorbid proficiency in English/Kannada (1- least proficient; 7- most proficient) Bilingual proficiency rating P1 Male 60 Graduate CVA 15 Speech:7/7 Comp: 7/7 Reading:7/7 Writing:6/7 1.00 P2 Male 58 Graduate CVA 11 Speech:5/7 Comp:7/7 Reading:6/7 Writing:5/7 1.16 P3 Male 24 Graduate CVA 13 Speech:5/7 Comp:6/7 Reading:6/7 Writing:5/7 1.27 Table 2: Performance of participants on various test of language in Kannada and English Participant 1 Participant 2 Participant 3 Western Aphasia Battery (Scores in %) English Kannada English Kannada English Kannada PRE POST PRE POST PRE POST PRE POST PRE POST PRE POST Spontaneous speech 70 80 65 70 60 75 55 60 30 45 35 55 Auditory comprehension 75 80 75 80 60 70 65 75 36.6 50 40 50 Repetition 68 76 73 81 64 73 60 80 30 47 34 46 Naming 65 75 60 70 45 60 40 55 26.6 45 28.3 48.3 Auditory word recognition 66.7 71.6 61.6 68.3 53.3 61.6 58.3 65 35 46.6 31.6 51.6 Sequential commands 72.5 78.75 55 62.5 55 63.7 55 66.2 31.2 42.5 28.7 48.7 Word fluency 40 50 40 55 35 50 40 55 25 40 30 45 Sentence completion 80 80 60 80 40 60 60 60 40 60 40 60 Responsive speech 80 80 80 80 40 60 40 50 50 60 40 60 Reading 70 80 75 80 37.5 52.5 45 60 30 45 37.5 52.5 Reading commands 60 65 70 75 50 70 55 70 30 45 35 50 Written word stimulus - object choice matching 100 100 83.3 100 66.6 66.6 66.6 83.3 66.6 83.3 66.6 66.6 Written word stimulus-picture choice matching 100 100 83.3 100 66.6 83.3 50 83.3 66.6 83.3 50 83.3 Picture stimulus written word choice matching 100 100 100 100 50 66.6 66.6 66.6 50 83.3 50 66.6 Spoken words written word choice matching 100 100 100 100 50 50 50 75 50 66.6 50 66.6 Spelled word recognition 66.6 66.6 83.3 83.3 83.3 100 83.3 83.3 50 66.6 66.6 83.3 Spelling 50 66

Inconsistent findings have been reported about the impact of structural disconnections on language function in post-stroke aphasia. This study investigated patterns of structural disconnections associated with chronic language impairments using disconnectome maps. Seventy-six individuals with post-stroke aphasia underwent a battery of language assessments and a structural MRI scan. Support-vector regression disconnectome-symptom mapping analyses were performed to examine the correlations between disconnectome maps, representing the probability of disconnection at each white matter voxel and different language scores. To further understand whether significant disconnections were primarily representing focal damage or a more extended network of seemingly preserved but disconnected areas beyond the lesion site, results were qualitatively compared to support-vector regression lesion-symptom mapping analyses. Part of the left white matter perisylvian network was similarly disconnected in 90% of the individuals with aphasia. Surrounding this common left perisylvian disconnectome, specific structural disconnections in the left fronto-temporo-parietal network were significantly associated with aphasia severity and with lower performance in auditory comprehension, syntactic comprehension, syntactic production, repetition and naming tasks. Auditory comprehension, repetition and syntactic processing deficits were related to disconnections in areas that overlapped with and extended beyond lesion sites significant in SVR-LSM analyses. In contrast, overall language abilities as measured by aphasia severity and naming seemed to be mostly explained by focal damage at the level of the insular and central opercular cortices, given the high overlap between SVR-DSM and SVR-LSM results for these scores. While focal damage seems to be sufficient to explain broad measures of language performance, the structural disconnections between language areas provide additional information on the neural basis of specific and persistent language impairments at the chronic stage beyond lesion volume. Leveraging routinely available clinical data, disconnectome mapping furthers our understanding of anatomical connectivity constraints that may limit the recovery of some language abilities in chronic post-stroke aphasia. [ABSTRACT FROM AUTHOR]

A considerable body of research supports the use of behavioral communication treatment as the standard of care for aphasia. In spite of robust progress in clinical aphasiology, many questions regarding optimal care remain unanswered. One of the major challenges to progress in the field is the lack of a common framework to adequately describe individual treatments, which, if available, would allow comparisons across studies as well as improved communication among researchers, clinicians, and other stakeholders. Here, we describe how aphasia treatment approaches can be systematically characterized using the Rehabilitation Treatment Specification System (RTSS). At the core of the RTSS is a tripartite structure that focuses on targets (the behavior that is expected to change as a result of treatment), ingredients (what a clinician does to affect change in the target), and mechanism(s) of action (why a given treatment works by linking the ingredients to the target). Three separate articles in the current issue specifically describe how the RTSS can be used to describe different kinds of aphasia treatment approaches: functional approaches, cognitive-linguistic approaches, and biological approaches. It is our hope that the application of the RTSS in clinical aphasiology will improve communication in published studies, grant proposals, and in the clinical care of persons with aphasia. [ABSTRACT FROM AUTHOR]

The Rehabilitation Treatment Specification System (RTSS) provides a framework to identify specific components of treatments developed within various rehabilitation disciplines (eg, physical, occupational, or speech-language therapy). Furthermore, this framework offers the opportunity to identify the target and active ingredients of a therapy approach as well as the mechanism of action by which it is hypothesized to effect change in abilities or functions. In this article, we apply the RTSS framework to the characterization of a sample of treatments for aphasia that are based on cognitive-linguistic models of language processing. Our discussion of these applications centers on the benefits of this classification system and additional criteria to consider when evaluating cognitive-linguistic treatments for aphasia. [ABSTRACT FROM AUTHOR]

To determine if pretreatment nonlinguistic cognition predicted language treatment outcomes and if so, which specific nonlinguistic cognitive subskills predicted naming therapy outcomes. Retrospective. Research clinic. Study 1 included data from 67 persons with aphasia who underwent language treatment and a pretreatment cognitive-linguistic assessment battery (N=67). Study 2 included data from 27 study 1 participants who completed additional pretreatment nonlinguistic cognitive assessments. 120-minute sessions of sentence comprehension (n=26) or naming treatment (n=41) 2 times per week for up to 10-12 weeks. Proportion of potential maximal gain (PMG) (assessed immediately after treatment [10-12wk]; formula=mean posttreatment score–mean pretreatment score/total number of trained items–mean pretreatment score) and proportion of potential maximal gain maintained (PMGM) (assessed 12wk after posttreatment [22-24wk]; formula=mean maintenance score–mean pretreatment score/total number of trained items–mean pretreatment score) as outcome variables; and pretreatment assessment scores as predictor variables. In study 1, 37% of participants demonstrated nonlinguistic cognitive deficits. Principal component analyses reduced assessment data to 2 components: linguistic and nonlinguistic cognition. Backward elimination regression revealed that higher linguistic and nonlinguistic cognitive function significantly predicted higher PMG after language therapy. In study 2, principal component analysis of only the nonlinguistic cognitive measures identified 3 components: executive function, verbal short-term memory, and visual short-term memory. Controlling for pretreatment apraxia of speech and auditory comprehension deficits, regression analyses revealed that higher executive function and visual short-term memory significantly predicted higher PMG and PMGM after naming therapy. Pretreatment nonlinguistic cognitive function significantly influenced language treatment outcomes and maintenance of therapy gains. [ABSTRACT FROM AUTHOR]

Background: Although the efficacy of high-dose speech-language therapy (SLT) for individuals with poststroke aphasia has been established in the literature, there is a gap in translating these research findings to clinical practice. Therefore, patients continue to receive suboptimal amounts of SLT, with negative consequences for their functional communication recovery. Recent research has identified self-managed digital health technology as one way to close the dosage gap by enabling high-intensity therapy unrestricted by clinician availability or other practical constraints. However, there is limited empirical evidence available to rehabilitation professionals to guide dose prescriptions for self-managed SLT despite their increasing use in the COVID-19 era and likely beyond. Objective: This study aims to leverage real-world mobile health data to investigate the effects of varied dosage frequency on performance outcomes for individuals with poststroke speech, language, and cognitive deficits following a 10-week period of self-managed treatment via a commercially available digital health platform. Methods: Anonymized data from 2249 poststroke survivors who used the Constant Therapy app between late 2016 and 2019 were analyzed. The data included therapy tasks spanning 13 different language and cognitive skill domains. For each patient, the weekly therapy dosage was calculated based on the median number of days per week of app use over the 10-week therapy period, binned into groups of 1, 2, 3, 4, or ≥5 days per week. Linear mixed-effects models were run to examine change in performance over time as a function of dosage group, with post hoc comparisons of slopes to evaluate the performance gain associated with each additional day of practice. Results: Across all skill domains, linear mixed-effects model results showed that performance improvement was significantly greater for patients who practiced 2 (β =.001; t 15,355=2.37; P =.02), 3 (β =.003; t 9738=5.21; P <.001), 4 (β =.005; t 9289=7.82; P <.001), or ≥5 (β =.005; t 6343=8.14; P <.001) days per week compared with those who only practiced for 1 day per week. Post hoc comparisons confirmed an incremental dosage effect accumulating with each day of practice (ie, 1 day vs 2 days, 2 days vs 3 days, and 3 days vs 4 days), apart from 4 days versus ≥5 days of practice per week. The result of greater improvement for higher versus lower dosage frequency groups was true not only across all domains but also within a majority of individual subdomains. Conclusions: The findings from this study demonstrated that increased dosage frequency is associated with greater therapy gains over a 10-week treatment period of self-managed digital therapy. The use of real-world data maximizes the ecological validity of study results and makes the findings more generalizable to clinical settings. This study represents an important step toward the development of optimal dose recommendations for self-managed SLT. J Med Internet Res 2022;24(7):e36135 doi:10.2196/36135 [ABSTRACT FROM AUTHOR]

Background: For stroke, traumatic brain injury (TBI), and other neurologic conditions associated with speech-language disorders, speech and language therapy is the standard of care for promoting recovery. However, barriers such as clinician time constraints and insurance reimbursement can inhibit a patient's ability to receive the support needed to optimize functional gain. Although digital rehabilitation has the potential to increase access to therapy by allowing patients to practice at home, the clinical and demographic characteristics that impact a patient's level of engagement with technology-based therapy are currently unknown.Objective: This study aimed to evaluate whether the level of engagement with digital therapy differs by various patient characteristics, including age, gender, diagnosis, time from disease onset, and geographic location (urban vs rural).Methods: Data for patients with stroke or TBI that initiated the use of Constant Therapy, a remotely delivered, cloud-based rehabilitation program for patients with speech-language disorders, were retrospectively analyzed. Only data from therapeutic sessions completed at home were included. The following three activity metrics were evaluated: (1) the number of active weeks of therapy, (2) the average number of active therapy days per week, and (3) the total number of therapeutic sessions completed during the first 20 weeks of program access. An active day or week was defined as having at least one completed therapeutic session. Separate multiple linear regression models were performed with each activity measure as the dependent variable and all available patient demographics as model covariates.Results: Data for 2850 patients with stroke or TBI were analyzed, with the average patient completing 8.6 weeks of therapy at a frequency of 1.5 days per week. Contrary to known barriers to technological adoption, older patients were more active during their first 20 weeks of program access, with those aged 51 to 70 years completing 5.01 more sessions than patients aged 50 years or younger (P=.04). Similarly, patients living in a rural area, who face greater barriers to clinic access, were more digitally engaged than their urban counterparts, with rural patients completing 11.54 more (P=.001) sessions during their first 20 weeks of access, after controlling for other model covariates.Conclusions: An evaluation of real-world data demonstrated that patients with stroke and TBI use digital therapy frequently for cognitive and language rehabilitation at home. Usage was higher in areas with limited access to clinical services and was unaffected by typical barriers to technological adoption, such as age. These findings will help guide the direction of future research in digital rehabilitation therapy, including the impact of demographics on recovery outcomes and the design of large, randomized controlled trials. [ABSTRACT FROM AUTHOR]

To establish benchmarks of significant change for aphasia rehabilitation outcome measures (ie, Western Aphasia Battery-Aphasia Quotient [WAB-AQ], Communicative Effectiveness Index [CETI], Boston Naming Test [BNT]) and assess if those benchmarks significantly differ across subgroups (ie, time post onset, dose frequency, treatment type). A comprehensive literature search of 12 databases, reference lists of previous reviews, and evidence-based practice materials was conducted. Randomized controlled trials, quasi-experimental studies, single-subject design, and case studies that used a standardized outcome measure to assess change were included. Titles and full-text articles were screened using a dual review process. Seventy-eight studies met criteria for inclusion. Data were extracted independently, and 25% of extractions were checked for reliability. All included studies were assigned quality indicator ratings and an evidence level. Random-effects meta-analyses were conducted separately for each study design group (ie, within-/between-group comparisons). For within-group designs, the summary effect size after aphasia rehabilitation was 5.03 points (95% confidence interval, 3.95-6.10, P <.001) on the WAB-AQ, 10.37 points (6.08-14.66, P <.001) on the CETI, and 3.30 points (2.43-4.18, P <.001) on the BNT. For between-group designs, the summary effect size was 5.05 points (1.64-8.46, P =.004) on the WAB-AQ and 0.55 points (-1.33 to 2.43, P =.564) on the BNT, the latter of which was not significant. Subgroup analyses for the within-group designs showed no significant differences in the summary effect size as a function of dose frequency or treatment type. This study established benchmarks of significant change on 3 standardized outcome measures used in aphasia rehabilitation. [ABSTRACT FROM AUTHOR]