Key result
Resting state EEG measures using a linear discriminant analysis achieved a correct classification rate of 86% between learning-disabled and healthy control children.
Why the study?
Can resting state EEG measures accurately discriminate children with learning disabilities from healthy controls?
Case-Control
Can resting state EEG measures accurately discriminate children with learning disabilities from healthy controls?
Resting state EEG measures can classify children with learning disabilities with 86% accuracy, potentially supporting diagnosis and providing insight into neural underpinnings.
Answers are grounded in this paper and cite their sources.
May support EEG research into learning disabilities; leaves open clinical diagnostic use pending prospective validation.
This study examines the usefulness of easy to obtain EEG measures to discriminate learning-disabled children (LD) from healthy control children. Here the spectral power in the delta, theta, alpha, and beta EEG bands and various power ratios (theta/alpha, theta/beta, beta/alpha, beta/theta, beta/[alpha+theta], [delta+theta]/alpha, alpha/delta, and [theta+alpha]/beta) are applied. These measures were subjected to a factor analysis with varimax rotation revealing four factors explaining 90 % of the entire variance. Factor 1 represents the power of the slow EEG frequency bands delta and theta, factor 2 the relationship between fast and slow frequency bands, factor 3 the slow to fast ratios, and factor 4 the absolute power of nearly all frequency bands. Group differences were found for three factor scores (1, 3, and 4). The linear discriminant analysis with the four factor scores as dependent and the group allocation as independent variables revealed a correct classification of 86 %. Although this classification is far from being perfect it is nevertheless reasonable high and statistically significant. Thus, EEG measures like the one used in this study might support the diagnosis of this difficult to diagnose disability. In addition, the EEG measures identified provide a deeper insight into the neural underpinnings of this disability. Based on this knowledge it might be possible to design new therapeutic strategies to treat LD.
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Nsreen Alahmadi (2015) conducted a case-control in Learning disabilities. Resting state EEG measures vs. Healthy control children was evaluated on Correct classification of group allocation. Resting state EEG measures using a linear discriminant analysis achieved a correct classification rate of 86% between learning-disabled and healthy control children.
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