The contribution of theta and delta to feedback processing in children with developmental language disorder

Declarative Learning Mechanisms Support Declarative but Not Probabilistic Feedback-Based Learning in Children with Developmental Language Disorder (DLD)

Declarative and probabilistic feedback-based learning was evaluated in 8-12-year-old school-age children with developmental language disorder (DLD; n = 14) and age-matched children with typical development (TD; n = 15). Children performed a visual two-choice word-learning task and a visual probabilistic classification task while their electroencephalogram (EEG) was recorded non-invasively from the scalp. Behavioral measures of accuracy and response to feedback, and electrophysiological responses to feedback were collected and compared between the two groups. While behavioral data indicated poorer performance by children with DLD in both learning paradigms, and similar response patterns to positive and negative feedback, electrophysiological data highlighted processing patterns in the DLD group that differed by task. More specifically, in this group, feedback processing in the context of declarative learning, which is known to be dominated by the medial temporal lobe (MTL), was associated with enhanced N170, an event-related brain potential (ERP) associated with MTL activation. The N170 amplitude was found to be correlated with declarative task performance in the DLD group. During probabilistic learning, known to be governed by the striatal-based learning system, the feedback-related negativity (FRN) ERP, which is the product of the cortico-striatal circuit dominated feedback processing. Within the context of probabilistic learning, enhanced N170 was associated with poor learning in the TD group, suggesting that MTL activation during probabilistic learning disrupts learning. These results are interpreted within the context of a proposed feedback parity hypothesis suggesting that in children with DLD, the system that dominates learning (i.e., MTL during declarative learning and the striatum during probabilistic learning) dominates and supports feedback processing.

With or without Feedback?-How the Presence of Feedback Affects Processing in Children with Developmental Language Disorder

Language acquisition depends on the ability to process and learn probabilistic information, often through the integration of performance feedback. Children with developmental language disorder (DLD) have demonstrated weaknesses in both probabilistic learning and feedback processing, but the individual effects of each skill are poorly understood in this population. This study examined school-aged children with DLD (n = 29) and age- and gender-matched children with typical development (TD; n = 44) on a visual probabilistic classification learning task presented with and without feedback. In the feedback-based version of the task, children received performance feedback on a trial-by-trial basis during the training phase of the task. In the feedback-free version, children responded after seeing the correct choice marked with a green border and were not presented with feedback. Children with TD achieved higher accuracy than children with DLD following feedback-based training, while the two groups achieved similar levels of accuracy following feedback-free training. Analyses of event-related potentials (ERPs) provided insight into stimulus encoding processes. The feedback-free task was dominated by a frontal slow wave (FSW) and a late parietal component (LPC) which were not different between the two groups. The feedback-based task was dominated by a parietal slow wave (PSW) and an LPC, both of which were found to be larger in the TD than in the DLD group. In combination, results suggest that engagement with feedback boosts learning in children with TD, but not in children with DLD. When the need to process feedback is eliminated, children with DLD demonstrate behavioral and neurophysiological responses similar to their peers with TD.

Dynamic networks differentiate the language ability of children with cochlear implants

Background

Cochlear implantation (CI) in prelingually deafened children has been shown to be an effective intervention for developing language and reading skill. However, there is a substantial proportion of the children receiving CI who struggle with language and reading. The current study-one of the first to implement electrical source imaging in CI population was designed to identify the neural underpinnings in two groups of CI children with good and poor language and reading skill.

Methods

Data using high density electroencephalography (EEG) under a resting state condition was obtained from 75 children, 50 with CIs having good (HL) or poor language skills (LL) and 25 normal hearing (NH) children. We identified coherent sources using dynamic imaging of coherent sources (DICS) and their effective connectivity computing time-frequency causality estimation based on temporal partial directed coherence (TPDC) in the two CI groups compared to a cohort of age and gender matched NH children.

Findings

Sources with higher coherence amplitude were observed in three frequency bands (alpha, beta and gamma) for the CI groups when compared to normal hearing children. The two groups of CI children with good (HL) and poor (LL) language ability exhibited not only different cortical and subcortical source profiles but also distinct effective connectivity between them. Additionally, a support vector machine (SVM) algorithm using these sources and their connectivity patterns for each CI group across the three frequency bands was able to predict the language and reading scores with high accuracy.

Interpretation

Increased coherence in the CI groups suggest overall that the oscillatory activity in some brain areas become more strongly coupled compared to the NH group. Moreover, the different sources and their connectivity patterns and their association to language and reading skill in both groups, suggest a compensatory adaptation that either facilitated or impeded language and reading development. The neural differences in the two groups of CI children may reflect potential biomarkers for predicting outcome success in CI children.