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A lesion and connectivity-based hierarchical model of chronic aphasia recovery dissociates patients and healthy controls.

ABSTRACT: Traditional models of left hemisphere stroke recovery propose that reactivation of remaining ipsilesional tissue is optimal for language processing whereas reliance on contralesional right hemisphere homologues is less beneficial or possibly maladaptive in the chronic recovery stage. However, neuroimaging evidence for this proposal is mixed. This study aimed to elucidate patterns of effective connectivity in patients with chronic aphasia in light of healthy control connectivity patterns and in relation to damaged tissue within left hemisphere regions of interest and according to performance on a semantic decision task. Using fMRI and dynamic causal modeling, biologically-plausible models within four model families were created to correspond to potential neural recovery patterns, including Family A: Left-lateralized connectivity (i.e., no/minimal damage), Family B: Bilateral anterior-weighted connectivity (i.e., posterior damage), Family C: Bilateral posterior-weighted connectivity (i.e., anterior damage) and Family D: Right-lateralized connectivity (i.e., extensive damage). Controls exhibited a strong preference for left-lateralized network models (Family A) whereas patients demonstrated a split preference for Families A and C. At the level of connections, controls exhibited stronger left intrahemispheric task-modulated connections than did patients. Within the patient group, damage to left superior frontal structures resulted in greater right intrahemispheric connectivity whereas damage to left ventral structures resulted in heightened modulation of left frontal regions. Lesion metrics best predicted accuracy on the fMRI task and aphasia severity whereas left intrahemispheric connectivity predicted fMRI task reaction times. These results are discussed within the context of the hierarchical recovery model of chronic aphasia.

SUBMITTER: Meier EL

PROVIDER: S-EPMC6702239 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A lesion and connectivity-based hierarchical model of chronic aphasia recovery dissociates patients and healthy controls.

Meier Erin L EL Johnson Jeffrey P JP Pan Yue Y Kiran Swathi S

NeuroImage. Clinical 20190702

Traditional models of left hemisphere stroke recovery propose that reactivation of remaining ipsilesional tissue is optimal for language processing whereas reliance on contralesional right hemisphere homologues is less beneficial or possibly maladaptive in the chronic recovery stage. However, neuroimaging evidence for this proposal is mixed. This study aimed to elucidate patterns of effective connectivity in patients with chronic aphasia in light of healthy control connectivity patterns and in r ...[more]

PMID: 31491828

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Project description:Traditional models of neural reorganization of language skills in patients with chronic stroke-induced aphasia (PWA) propose activation of reperfused or spared left hemisphere tissue results in the most favorable language outcomes. However, these models do not fully explain variable behavioral recovery patterns observed in chronic patients. Instead, investigation of connectivity patterns of critical network nodes may elucidate better-informed recovery models. In the present study, we combined fMRI and dynamic causal modeling (DCM) to examine effective connectivity of a simple three-node left hemisphere network during a semantic feature decision task in 25 PWA and 18 age-matched neurologically intact healthy controls. The DCM model space utilized in Meier, Kapse, & Kiran (2016), which was organized according to exogenous input to one of three regions (i.e., left inferior frontal gyrus, pars triangularis [LIFGtri], left posterior middle temporal gyrus [LpMTG], or left middle frontal gyrus [LMFG]) implicated in various levels of lexical-semantic processing, was interrogated. This model space included all possible combinations of uni- and bidirectional task-modulated connections between LIFGtri, LMFG and LpMTG, resulting in 72 individual models that were partitioned into three separate families (i.e., Family #1: Input to LIFGtri, Family #2: Input to LMFG, Family #3: Input to LpMTG). Family-wise Bayesian model selection revealed Family #2: Input to LMFG best fit both patient and control data at a group level. Both groups relied heavily on LMFG's modulation of the other two model regions. By contrast, between-group differences in task-modulated coupling of LIFGtri and LpMTG were observed. Within the patient group, the strength of activity in LIFGtri and connectivity of LpMTG ? LIFGtri were positively associated with lexical-semantic abilities inside and outside of the scanner, whereas greater recruitment of LpMTG was associated with poorer lexical-semantic skills.

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Project description:OBJECTIVE:To determine if pretreatment nonlinguistic cognition predicted language treatment outcomes and if so, which specific nonlinguistic cognitive subskills predicted naming therapy outcomes. DESIGN:Retrospective. SETTING:Research clinic. PARTICIPANTS: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. INTERVENTIONS:120-minute sessions of sentence comprehension (n=26) or naming treatment (n=41) 2 times per week for up to 10-12 weeks. MAIN OUTCOME MEASURES: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. RESULTS: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. CONCLUSIONS:Pretreatment nonlinguistic cognitive function significantly influenced language treatment outcomes and maintenance of therapy gains.

Dataset Information

A lesion and connectivity-based hierarchical model of chronic aphasia recovery dissociates patients and healthy controls.

Publications

A lesion and connectivity-based hierarchical model of chronic aphasia recovery dissociates patients and healthy controls.

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