Semantic Categorization of Naming Responses Based on Prearticulatory Electrical Brain Activity.

PURPOSE: Object naming requires visual decoding, conceptualization, semantic categorization, and phonological encoding, all within 400 to 600 ms of stimulus presentation and before a word is spoken. In this study, we sought to predict semantic categories of naming responses based on prearticulatory brain activity recorded with scalp EEG in healthy individuals. METHODS: We assessed 19 healthy individuals who completed a naming task while undergoing EEG. The naming task consisted of 120 drawings of animate/inanimate objects or abstract drawings. We applied a one-dimensional, two-layer, neural network to predict the semantic categories of naming responses based on prearticulatory brain activity. RESULTS: Classifications of animate, inanimate, and abstract responses had an average accuracy of 80%, sensitivity of 72%, and specificity of 87% across participants. Across participants, time points with the highest average weights were between 470 and 490 milliseconds after stimulus presentation, and electrodes with the highest weights were located over the left and right frontal brain areas. CONCLUSIONS: Scalp EEG can be successfully used in predicting naming responses through prearticulatory brain activity. Interparticipant variability in feature weights suggests that individualized models are necessary for highest accuracy. Our findings may inform future applications of EEG in reconstructing speech for individuals with and without speech impairments.

Normative Values for Word Syllable Duration With Interpretation in a Large Sample of Stroke Survivors With Aphasia.

PURPOSE: Slow speech rate and abnormal temporal prosody are primary diagnostic criteria for differentiating between people with aphasia who do and do not have apraxia of speech. We sought to identify appropriate cutoff values for abnormal word syllable duration (WSD) in a word repetition task, interpret them relative to a data set of people with chronic aphasia, and evaluate the extent to which manually derived measures could be approximated through an automated process that relied on commercial speech recognition technology. METHOD: Fifty neurotypical participants produced 49 multisyllabic words during a repetition task. Audio recordings were submitted to an automated speech recognition (ASR) service (IBM Watson) to measure word duration and generate an orthographic transcription. The transcribed words were compared to a lexical database, and the number of syllables was identified. Automatic and manual measures were compared for 50% of the sample. Results were interpreted relative to WSD scores from an existing data set of 195 people with mostly chronic aphasia. RESULTS: ASR correctly identified 83% of target words and 98% of target syllable counts. Automated word duration calculations were longer than manual measures due to imprecise cursor placement. Upon applying regression coefficients to the automated measures and examining the frequency distributions for both manual and estimated measures, a WSD of 303-316 ms was found to indicate longer-than-normal performance (corresponding to the 95th percentile). With this cutoff, 40%-45% of participants with aphasia in our comparison sample had an abnormally long WSD. CONCLUSIONS: We recommend using a rounded WSD cutoff score between 303 and 316 ms for manual measures. Future research will focus on customizing automated WSD methods to speech samples from people with aphasia, identifying target words that maximize production and measurement reliability, and developing WSD standard scores based on a large participant sample with and without aphasia.

Neural network bases of thematic semantic processing in language production.

Semantic processing is a central component of language and cognition. The anterior temporal lobe is postulated to be a key hub for semantic processing, but the posterior temporoparietal cortex is also involved in thematic associations during language. It is possible that these regions act in concert and depend on an anteroposterior network linking the temporal pole with posterior structures to support thematic semantic processing during language production. We employed connectome-based lesion-symptom mapping to examine the causal relationship between lesioned white matter pathways and thematic processing language deficits among individuals with post-stroke aphasia. Seventy-nine adults with chronic aphasia completed the Philadelphia Naming Test, and semantic errors were coded as either thematic or taxonomic to control for taxonomic errors. Controlling for nonverbal conceptual-semantic knowledge as measured by the Pyramids and Palm Trees Test, lesion size, and the taxonomic error rate, thematic error rate was associated with loss of white matter connections from the temporal pole traversing in peri-Sylvian regions to the posterior cingulate and the insula. These findings support the existence of a distributed network underlying thematic relationship processing in language as opposed to discrete cortical areas.

Language Recovery after Brain Injury: A Structural Network Control Theory Study.

Aphasia recovery after stroke depends on the condition of the remaining, extralesional brain network. Network control theory (NCT) provides a unique, quantitative approach to assess the interaction between brain networks. In this longitudinal, large-scale, whole-brain connectome study, we evaluated whether controllability measures of language-related regions are associated with treated aphasia recovery. Using probabilistic tractography and controlling for the effects of structural lesions, we reconstructed whole-brain diffusion tensor imaging (DTI) connectomes from 68 individuals (20 female, 48 male) with chronic poststroke aphasia who completed a three-week language therapy. Applying principles of NCT, we computed regional (1) average and (2) modal controllability, which decode the ability of a region to (1) spread control input through the brain network and (2) to facilitate brain state transitions. We tested the relationship between pretreatment controllability measures of 20 language-related left hemisphere regions and improvements in naming six months after language therapy using multiple linear regressions and a parsimonious elastic net regression model with cross-validation. Regional controllability of the inferior frontal gyrus (IFG) pars opercularis, pars orbitalis, and the anterior insula were associated with treatment outcomes independently of baseline aphasia severity, lesion volume, age, education, and network size. Modal controllability of the IFG pars opercularis was the strongest predictor of treated aphasia recovery with cross-validation and outperformed traditional graph theory, lesion load, and demographic measures. Regional NCT measures can reflect the status of the residual language network and its interaction with the remaining brain network, being able to predict language recovery after aphasia treatment.SIGNIFICANCE STATEMENT Predicting and understanding language recovery after brain injury remains a challenging, albeit a fundamental aspect of human neurology and neuroscience. In this study, we applied network control theory (NCT) to fully harness the concept of brain networks as dynamic systems and to evaluate their interaction. We studied 68 stroke survivors with aphasia who underwent imaging and longitudinal behavioral assessments coupled with language therapy. We found that the controllability of the inferior frontal regional network significantly predicted recovery in language production six months after treatment. Importantly, controllability outperformed traditional demographic, lesion, and graph-theoretical measures. Our findings shed light on the neurobiological basis of human language and can be translated into personalized rehabilitation approaches.

Cortical microstructural changes associated with treated aphasia recovery.

OBJECTIVES: To investigate the hypothesis that language recovery in post-stroke aphasia is associated with structural brain changes. METHODS: We evaluated whether treatment-induced improvement in naming is associated with reorganization of tissue microstructure within residual cortical regions. To this end, we performed a retrospective longitudinal treatment study using comprehensive language-linguistic assessments and diffusion MRI sequences optimized for the assessment of complex microstructure (diffusional kurtosis imaging) to evaluate the relationship between language treatment response and cortical changes in 26 individuals with chronic stroke-induced aphasia. We employed elastic net statistical models controlling for baseline factors including age, sex, and time since the stroke, as well as lesion volume. RESULTS: We observed that improved naming accuracy (Philadelphia Naming Test) was statistically associated with increased post-treatment microstructural integrity in the left posterior superior temporal gyrus. Moreover, increase in microstructural integrity in the left middle temporal gyrus and left inferior temporal gyrus was specifically associated with a decrease in semantic paraphasias. This longitudinal relationship between brain tissue integrity and language improvement was not observed in other non-language related brain regions. INTERPRETATION: Our findings provide evidence that structural brain changes in the preserved left hemisphere regions are associated with treatment-induced language recovery in aphasia and are part of the mechanisms supporting language and brain injury recovery.