Re-learning and remembering in the lesioned brain

Neural mechanisms underlying improved new-word learning with high-density transcranial direct current stimulation

Neurobehavioral studies have provided evidence for the effectiveness of anodal tDCS on language production, by stimulation of the left Inferior Frontal Gyrus (IFG) or of left Temporo-Parietal Junction (TPJ). However, tDCS is currently not used in clinical practice outside of trials, because behavioral effects have been inconsistent and underlying neural effects unclear. Here, we propose to elucidate the neural correlates of verb and noun learning and to determine if they can be modulated with anodal high-definition (HD) tDCS stimulation. Thirty-six neurotypical participants were randomly allocated to anodal HD-tDCS over either the left IFG, the left TPJ, or sham stimulation. On day one, participants performed a naming task (pre-test). On day two, participants underwent a new-word learning task with rare nouns and verbs concurrently to HD-tDCS for 20 min. The third day consisted of a post-test of naming performance. EEG was recorded at rest and during naming on each day. Verb learning was significantly facilitated by left IFG stimulation. HD-tDCS over the left IFG enhanced functional connectivity between the left IFG and TPJ and this correlated with improved learning. HD-tDCS over the left TPJ enabled stronger local activation of the stimulated area (as indexed by greater alpha and beta-band power decrease) during naming, but this did not translate into better learning. Thus, tDCS can induce local activation or modulation of network interactions. Only the enhancement of network interactions, but not the increase in local activation, leads to robust improvement of word learning. This emphasizes the need to develop new neuromodulation methods influencing network interactions. Our study suggests that this may be achieved through behavioral activation of one area and concomitant activation of another area with HD-tDCS.

An examination of retrieval practice and production training in the treatment of lexical-semantic comprehension deficits in aphasia

OBJECTIVE

Little research has addressed the treatment of lexical-semantic comprehension deficits (i.e., difficulty retrieving the meanings of words) in people with aphasia (PWA). Research suggests that practice retrieving names for depicted objects from long-term memory (production-based retrieval practice) more strongly benefits word retrieval for production in PWA compared to errorless learning (i.e., word repetition), which eschews retrieval practice. This study assessed whether production-based and comprehension-based retrieval practice enhance performance on errorful word-comprehension items in PWA measured relative to nonretrieval forms of training and untrained control items.

METHOD

In a within-participant group study of PWA, errorful comprehension items were assigned to (a) a production-based training module (retrieval practice vs. errorless learning); (b) a comprehension-based training module (a receptive form of retrieval practice vs. restudy). Each module comprised one training session and a 1-day and 1-week comprehension posttest on the module's trained items and an untrained item set.

RESULTS

The comprehension module conditions produced similar and superior posttest performance relative to untrained items. Both production module conditions improved posttest performance relative to untrained items, with retrieval practice conferring more durable learning and generalization indicative of refinement of semantic representations compared to errorless learning.

CONCLUSIONS

Results suggest comprehension- and production-based forms of training are both beneficial for improving lexical-semantic deficits in aphasia, with production-based retrieval practice conferring additional benefits to the targeted deficit compared to errorless learning. Future studies should examine these learning factors in schedules of training more commensurate with clinical practice and in other neurological populations (e.g., semantic dementia). (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Multimodal Neural and Behavioral Data Predict Response to Rehabilitation in Chronic Poststroke Aphasia

BACKGROUND

Poststroke recovery depends on multiple factors and varies greatly across individuals. Using machine learning models, this study investigated the independent and complementary prognostic role of different patient-related factors in predicting response to language rehabilitation after a stroke.

METHODS

Fifty-five individuals with chronic poststroke aphasia underwent a battery of standardized assessments and structural and functional magnetic resonance imaging scans, and received 12 weeks of language treatment. Support vector machine and random forest models were constructed to predict responsiveness to treatment using pretreatment behavioral, demographic, and structural and functional neuroimaging data.

RESULTS

The best prediction performance was achieved by a support vector machine model trained on aphasia severity, demographics, measures of anatomic integrity and resting-state functional connectivity (F1=0.94). This model resulted in a significantly superior prediction performance compared with support vector machine models trained on all feature sets (F1=0.82, P<0.001) or a single feature set (F1 range=0.68-0.84, P<0.001). Across random forest models, training on resting-state functional magnetic resonance imaging connectivity data yielded the best F1 score (F1=0.87).

CONCLUSIONS

While behavioral, multimodal neuroimaging data and demographic information carry complementary information in predicting response to rehabilitation in chronic poststroke aphasia, functional connectivity of the brain at rest after stroke is a particularly important predictor of responsiveness to treatment, both alone and combined with other patient-related factors.

Investigating the network consequences of focal brain lesions through comparisons of real and simulated lesions

Given the increased interest in the functional human connectome, a number of computer simulation studies have sought to develop a better quantitative understanding of the effects of focal lesions on the brain’s functional network organization. However, there has been little work evaluating the predictions of this simulation work vis a vis real lesioned connectomes. One of the few relevant studies reported findings from real chronic focal lesions that only partially confirmed simulation predictions. We hypothesize that these discrepancies arose because although the effects of focal lesions likely consist of two components: short-term node subtraction and long-term network re-organization, previous simulation studies have primarily modeled only the short-term consequences of the subtraction of lesioned nodes and their connections. To evaluate this hypothesis, we compared network properties (modularity, participation coefficient, within-module degree) between real functional connectomes obtained from chronic stroke participants and “pseudo-lesioned” functional connectomes generated by subtracting the same sets of lesioned nodes/connections from healthy control connectomes. We found that, as we hypothesized, the network properties of real-lesioned connectomes in chronic stroke differed from those of the pseudo-lesioned connectomes which instantiated only the short-term consequences of node subtraction. Reflecting the long-term consequences of focal lesions, we found re-organization of the neurotopography of global and local hubs in the real but not the pseudo-lesioned connectomes. We conclude that the long-term network re-organization that occurs in response to focal lesions involves changes in functional connectivity within the remaining intact neural tissue that go well beyond the short-term consequences of node subtraction.

Pseudoword spelling ability predicts response to word spelling treatment in acquired dysgraphia

Although rehabilitation of acquired dysgraphia can be quite effective, identifying predictors of responsiveness to treatment is useful for prognosis and individualization of treatment protocols. This study examined whether various features of treatment response were predicted by the integrity of one or more of the central cognitive components of spelling: orthographic long-term memory, orthographic working memory, and phoneme-grapheme conversion. Twenty dysgraphic individuals received 12 weeks of bi-weekly, individualized, lexically-based spelling rehabilitation using a spell-study-spell paradigm. Linear multiple regression modelling examined whether the type and severity of the dysgraphic deficit, assessed before rehabilitation, predicted the magnitude and rate of improvement, generalization to untrained items and maintenance of treatment gains. The results revealed that pseudoword spelling accuracy - indexing the integrity of the phoneme-grapheme conversion system - was the only factor examined that significantly predicted the rate of accuracy gains for trained words as well as the extent of generalization to untrained words. Pre-treatment pseudoword spelling accuracy also predicted retention of gains for trained and untrained words at 3-month follow-up. These findings reveal that the integrity of the phoneme-grapheme conversion system prior to dysgraphia rehabilitation may play a key role in rehabilitation-driven recovery, even when the treatment approach targets lexical rather than pseudoword spelling processes.

Re-learning to be different: Increased neural differentiation supports post-stroke language recovery

Identifying the neural changes that support recovery of cognitive functions after a brain lesion is important to advance our understanding of human neuroplasticity, which, in turn, forms the basis for the development of effective treatments. To date, the preponderance of neuroimaging studies has focused on localizing changes in average brain activity associated with functional recovery. Here, we took a novel approach by evaluating whether cognitive recovery in chronic stroke is related to increases in the differentiation of local neural response patterns. This approach is supported by research indicating that, in the intact brain, local neural representations become more differentiated (dissimilar) with learning (Glezer et al., 2015). We acquired fMRI data before and after 21 individuals received approximately 12 weeks of behavioral treatment for written language impairment due to a left-hemisphere stroke. We used Local-Heterogeneity Regression Analysis (Purcell and Rapp, 2018) to measure local neural response differentiation associated with written language processing, assuming that greater heterogeneity in the pattern of activity across adjacent neural areas indicates more well-differentiated neural representations. First, we observed pre to post-treatment increases in local neural differentiation (Local-Hreg) in the ventral occipital-temporal cortex of the left hemisphere. Second, we found that, in this region, higher local neural response differentiation prior to treatment was associated with less severe written language impairment, and that it also predicted greater future responsiveness to treatment. Third, we observed that changes in neural differentiation were systematically related to performance changes for trained and untrained items. Fourth, we did not observe these brain-behavior relationships for mean BOLD responses, only for Local-Hreg. Thus, this is the first investigation to quantify changes in local neural differentiation in the recovery of a cognitive function and the first to demonstrate the clear behavioral relevance of these changes. We conclude that the findings provide strong support for the novel hypothesis that the local re-differentiation of neural representations can play a significant role in functional recovery after brain lesion.