Working Memory in Children with Learning Disorders: An EEG Power Spectrum Analysis

Abstract

Learning disorders (LDs) are diagnosed in children whose academic skills of reading, writing or mathematics are impaired and lagging according to their age, schooling and intelligence. Children with LDs experience substantial working memory (WM) deficits, even more pronounced if more than one of the academic skills is affected. We compared the task-related electroencephalogram (EEG) power spectral density of children with LDs (n = 23) with a control group of children with good academic achievement (n = 22), during the performance of a WM task. sLoreta was used to estimate the current distribution at the sources, and 18 brain regions of interest (ROIs) were chosen with an extended version of the eigenvector centrality mapping technique. In this way, we lessened some drawbacks of the traditional EEG at the sensor space by an analysis at the brain-sources level over data-driven selected ROIs. Results: The LD group showed fewer correct responses in the WM task, an overall slower EEG with more delta and theta activity, and less high-frequency gamma activity in posterior areas. We explain these EEG patterns in LD children as indices of an inefficient neural resource management related with a delay in neural maturation.

Effects of Neurofeedback in the Working Memory of Children with Learning Disorders: An EEG Power-Spectrum Analysis

“…Moreover, when the WM-related power spectrum of healthy children was compared with adults [25], the children showed more delta, more theta, and less alpha power; EEG patterns which the authors interpreted as compensatory mechanisms due to neural immaturity. These findings are supported by a work that compared LD children with healthy control children in a task-related power-spectrum analysis of the maintenance phase of a WM task [26]. Children with LD group showed a slower overall activity with more delta and theta power, and less gamma power at posterior brain sites; patterns of activity considered as indices of inefficient neural resource management to achieve proper cognitive performance.…”

“…As to the behavioral results of the WM task, besides the expected Low-Load vs. High-Load within-group differences in both the correct responses and response time measures [26]; our additional statistical comparisons yielded a main difference found just for the NFB group: In the pre-post treatment comparison of each separate group, the NFB group showed a faster response time for the High-Load condition after the NFB treatment, with no statistical differences in the percentage of correct responses. Hence, the NFB treatment appears to improve the speed of WM retrieval in children with LD.…”

“…The reason for this addition was to be able to compare how the homologous sources in both hemispheres participated in the task. Figure 2 shows in red the 18 selected ROIs by the data-driven approach, also published by Martínez-Briones et al [26]. For a better insight into its configuration, the cortex regions nearest to the positions of the sensors at the scalp are also illustrated in blue.…”

Individual Alpha Peak Frequency, an Important Biomarker for Live Z-Score Training Neurofeedback in Adolescents with Learning Disabilities

“…Thus, future research with larger and balanced samples should add the gender variable as a potential NF response moderator in adolescents with li-APF or ni-APF. Additionally, as the efficacies of both visual and audio reinforcers vs. placebo sham are acknowledged in the literature [ 1 , 9 , 80 ], including a control group with sham NF will support the idea that improvements in both brain waves and clinical symptoms are related to the NF training. The results reported in the present work need to be replicated in future gender-balanced, multicentric, randomized clinical control trials with larger cohorts, focusing on both optimal and especially non-optimal responder groups (li-APF), to gather more data about these phenotypes and to optimize the treatment outcomes.…”

“…In addition, an important direction for further exploratory studies is to understand the mechanisms involved in the li-APF subgroup of non-optimal responders for our LZT-NF approach using different advanced research designs; our study paves the way for this. For example, a study proposed by Martínez-Briones et al [ 80 ] could be applied in our li-APF non-optimal responders for the LZT-NF. The authors used source localization methods, such as sLORETA (standardized low-resolution electromagnetic tomography analysis), to employ the power spectral density (PSD) analysis of the estimated primary currents at the source level in a specific cognitive task for LD children.…”