Pre-articulatory electrical activity associated with correct naming in individuals with aphasia

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.

Speaker-induced Suppression in EEG during a Naturalistic Reading and Listening Task

Speaking elicits a suppressed neural response when compared with listening to others' speech, a phenomenon known as speaker-induced suppression (SIS). Previous research has focused on investigating SIS at constrained levels of linguistic representation, such as the individual phoneme and word level. Here, we present scalp EEG data from a dual speech perception and production task where participants read sentences aloud then listened to playback of themselves reading those sentences. Playback was separated into immediate repetition of the previous trial and randomized repetition of a former trial to investigate if forward modeling of responses during passive listening suppresses the neural response. Concurrent EMG was recorded to control for movement artifact during speech production. In line with previous research, ERP analyses at the sentence level demonstrated suppression of early auditory components of the EEG for production compared with perception. To evaluate whether linguistic abstractions (in the form of phonological feature tuning) are suppressed during speech production alongside lower-level acoustic information, we fit linear encoding models that predicted scalp EEG based on phonological features, EMG activity, and task condition. We found that phonological features were encoded similarly between production and perception. However, this similarity was only observed when controlling for movement by using the EMG response as an additional regressor. Our results suggest that SIS operates at a sensory representational level and is dissociated from higher order cognitive and linguistic processing that takes place during speech perception and production. We also detail some important considerations when analyzing EEG during continuous speech production.

Electrophysiologic evidence of reorganization in poststroke aphasia

Electrophysiologic methods have been used to investigate neural changes in individuals with poststroke aphasia. The major types of electrophysiologic measures include the event-related potential (ERP) and spectral power, and aspects of both (including amplitude, topography, and power) have been shown to differ in people with aphasia. Not only that, these measures are sensitive to spontaneous and treatment-induced language change. The purpose of this chapter is to review evidence of poststroke reorganization in the language network that has been identified in the acute and chronic phases of poststroke aphasia. The chapter will begin with a brief introduction to electrophysiologic methods and then focus on evidence from the most commonly studied ERPs and spectral bands in aphasia.

Structural disconnection of the posterior medial frontal cortex reduces speech error monitoring

Optimal performance in any task relies on the ability to detect and correct errors. The anterior cingulate cortex and the broader posterior medial frontal cortex (pMFC) are active during error processing. However, it is unclear whether damage to the pMFC impairs error monitoring. We hypothesized that successful error monitoring critically relies on connections between the pMFC and broader cortical networks involved in executive functions and the task being monitored. We tested this hypothesis in the context of speech error monitoring in people with post-stroke aphasia. Diffusion weighted images were collected in 51 adults with chronic left-hemisphere stroke and 37 age-matched control participants. Whole-brain connectomes were derived using constrained spherical deconvolution and anatomically-constrained probabilistic tractography. Support vector regressions identified white matter connections in which lost integrity in stroke survivors related to reduced error detection during confrontation naming. Lesioned connections to the bilateral pMFC were related to reduce error monitoring, including many connections to regions associated with speech production and executive function. We conclude that connections to the pMFC support error monitoring. Error monitoring in speech production is supported by the structural connectivity between the pMFC and regions involved in speech production, comprehension, and executive function. Interactions between pMFC and other task-relevant processors may similarly be critical for error monitoring in other task contexts.

The role and use of event-related potentials in aphasia: A scoping review

Event-related potentials (ERPs) can provide important insights into underlying language processes in both unimpaired and neurologically impaired populations and may be particularly useful in aphasia. This scoping review was conducted to provide a comprehensive summary of how ERPs have been used with people with aphasia (PWA), with the goal of exploring the potential clinical application of ERPs in aphasia assessment and treatment. We identified 117 studies that met inclusionary criteria, reflecting six thematic domains of inquiry that relate to understanding both unimpaired and aphasic language processing and the use of ERPs with PWA. In these studies, a wide variety of ERP components were reported. Inconsistencies in reporting of participant characteristics and study protocols limit our ability to generalize beyond the individual studies and understand implications for clinical applicability. We discuss the potential roles of ERPs in aphasia management and make recommendations for further developing ERPs for clinical utility in PWA.

High-Density EEG in Current Clinical Practice and Opportunities for the Future

SUMMARY

High-density EEG (HD-EEG) recordings use a higher spatial sampling of scalp electrodes than a standard 10-20 low-density EEG montage. Although several studies have demonstrated improved localization of the epileptogenic cortex using HD-EEG, widespread implementation is impeded by cost, setup and interpretation time, and lack of specific or sufficient procedural billing codes. Despite these barriers, HD-EEG has been in use at several institutions for years. These centers have noted utility in a variety of clinical scenarios where increased spatial resolution from HD-EEG has been required, justifying the extra time and cost. We share select scenarios from several centers, using different recording techniques and software, where HD-EEG provided information above and beyond the standard low-density EEG. We include seven cases where HD-EEG contributed directly to current clinical care of epilepsy patients and highlight two novel techniques which suggest potential opportunities to improve future clinical care. Cases illustrate how HD-EEG allows clinicians to: case 1-lateralize falsely generalized interictal epileptiform discharges; case 2-improve localization of falsely generalized epileptic spasms; cases 3 and 4-improve localization of interictal epileptiform discharges in anatomic regions below the circumferential limit of standard low-density EEG coverage; case 5-improve noninvasive localization of the seizure onset zone in lesional epilepsy; cases 6 and 7-improve localization of the seizure onset zone to guide invasive investigation near eloquent cortex; case 8-identify epileptic fast oscillations; and case 9-map language cortex. Together, these nine cases illustrate that using both visual analysis and advanced techniques, HD-EEG can play an important role in clinical management.

Predicting naming responses based on pre-articulatory electrical activity in individuals with aphasia

OBJECTIVE

To investigate whether pre-articulatory neural activity could be used to predict correct vs. incorrect naming responses in individuals with post-stroke aphasia.

METHODS

We collected 64-channel high density electroencephalography (hdEEG) data from 5 individuals with chronic post-stroke aphasia (2 female/3 male, median age: 54 years) during naming of 80 concrete images. We applied machine learning on continuous wavelet transformed hdEEG data separately for alpha and beta energy bands (200 ms pre-stimulus to 1500 ms post-stimulus, but before articulation), and determined whether electrode/time-range/energy (ETE) combinations were predictive of correct vs incorrect responses for each participant.

RESULTS

The five participants correctly named between 30% and 70% of the 80 stimuli correctly. We observed that pre-articulatory scalp EEG ETE combinations could predict correct vs incorrect responses with accuracies ranging from 63% to 80%. For all but one participant, the prediction accuracies were statistically better than chance.

CONCLUSIONS

Our findings indicate that pre-articulatory neural activity may be used to predict correct vs incorrect naming responses for some individuals with aphasia.

SIGNIFICANCE

The individualized pre-articulatory neural pattern associated with correct naming responses could be used to both predict naming problems in aphasia and lead to the development of brain stimulation strategies for treatment.