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Electroencephalograms (EEGs) of children with reading disorders (RDs) are characterized by a higher theta and a lower alpha than those of typically developing children. Neurofeedback (NFB) may be helpful for treating learning disorders by reinforcing a reduction in the theta/alpha ratio. Several studies have suggested that NFB may lead to EEG power normalization and cognitive improvements.
- EEG
- reading disorder
- electroencephalograms (EEGs)
1. Introduction
Learning to read is a cognitive process that takes place at an early age. It is a process that requires formal training and depends on the development and coordination of multiple brain processes in different regions that participate in its acquisition [1,2,3].
Although most people learn to read easily, 5–17% of school-age children [4,5] have difficulties acquiring reading skills [6]. When these difficulties manifest in combination with low scores on standardized reading tests (1.5 standard deviations below the population mean for the child’s age), they provide diagnostic certainty of a specific learning disorder with impairment in reading (RD), which is considered specific because it is not attributable to intellectual disability, hearing disorders, vision disorders, neurological disorders, or motor disorders [7]. Although the term “dyslexia” has been widely used to refer to people with RD, according to the DSM-5, the diagnosis of dyslexia does not include deficits in reading comprehension.
Reading problems have been reported to be associated with structural and/or functional abnormalities, specifically in the left perisylvian regions [4]. Among individuals with reading disorders, abnormalities in cortical connectivity have been found. These could be due to alterations in neuronal migration [8], as reported by Galaburda et al. [9], who found evidence of cortical anomalies in the brains of people with dyslexia in postmortem studies, including neuronal ectopias and dysplasias located predominantly in the left perisylvian regions.
Using imaging techniques such as magnetic resonance imaging, structural differences between dyslexic patients and individuals with typical development have been found. Dyslexic adult patients have reduced brain volume and abnormal lateralization, and their white matter depth is greater than normal [10]. Among children with dyslexia, decreased cortical thickness has been reported not only in regions associated with reading (occipitotemporal and occipitoparietal areas of both hemispheres) but also in other brain areas, including the right orbitofrontal cortex and left anterior cingulate cortex [11]. Regarding gyrification, Casanova et al. [10] reported lower gyrification in dyslexic adults, while Williams et al. [11] reported higher gyrification in dyslexic children. The increase in gyrification serves to improve communication between neighboring areas; however, it comes at the cost of reducing effective long-range connectivity.
A cortical network in the left hemisphere involves three specialized regions that participate in reading: (1) the left dorsal temporoparietal circuit located around the Wernicke's area, including the extrastriate cortex, fusiform gyrus, and inferior temporal region, which specializes in phonological decoding; (2) the left ventral occipitotemporal circuit that houses the visual area of word form, to which visual and orthographic recognition based on memory is attributed; and (3) the left inferior frontal circuit, classically called Broca’s area, which is involved in the articulation of words [12]. It has been proposed that people with dyslexia exhibit dysfunction in this reading network, consisting of underactivation of the left temporoparietal region, involving the inferior parietal region, temporal lobe, and fusiform gyrus; underactivation of the left inferior frontal gyrus; and overactivation in the left primary motor cortex and in the anterior insula [13]. Among children with dyslexia, unlike adults with dyslexia, underactivation in the temporoparietal region has not been confirmed, and occipitotemporal underactivation is less extended than in adults [14]. Various studies have reported overactivation of the right hemisphere in individuals with dyslexia during reading, and this has been interpreted as a mechanism to compensate for deficits in the left hemisphere network associated with reading [15]. Furthermore, the activation of the right frontal region (homolog of Broca’s area) during reading and the greater integrity of the white matter of the right superior longitudinal fasciculus, which includes fibers that ipsilaterally connect the right ventrolateral prefrontal region with the temporoparietal region (homolog of Wernicke’s area), are able to predict reading improvement after 2.5 years in dyslexic children. A previous study highlighted that this is a specific phenomenon observed in dyslexia [16].
Numerous noninvasive techniques can provide information on brain function. Due to their high temporal resolution, electroencephalogram (EEG) recordings provide a useful tool for the study of brain dynamics [17,18,19,20] and for estimating connectivity [21].
2. EEG Characteristics in Children with RD
The human electroencephalogram (EEG) reflects the ongoing rhythmic electrical activity of the brain. EEG characteristics are different depending on recording conditions.
The most commonly reported resting-state EEG pattern in children with RDs is an excess of slow activity, primarily in the theta frequency range [22,23,24,25,26,27,28,29], and an alpha activity deficit [26,28,29,30,31,32] when compared to children with typical development. However, there is no consensus regarding what brain regions with atypical patterns are implicated. Some authors have found theta excess in almost all leads [23,26], and other studies have found theta excess in bilateral frontal lobes [22,24,30], the left temporal region [22,25], the right temporal region [27] or bilateral parietooccipital areas [25,28]. Regarding alpha power, some authors found alpha deficits in all leads except in the bilateral occipital and right centroparietal areas [26], while other studies have found deficits in the bilateral occipital area [22]. Most studies found alpha deficits in the parietooccipital area [25,28,30,32] with left predominance [25]. However, Harmony et al. [23] detected alpha deficits in right frontal and left frontocentral leads when they controlled for socioeconomic status, and Jäncke and Alahmadi [24] reported alpha deficits in the left frontal region of children with nonspecified learning disorders and the left centro–temporal–parietal region of children with learning disorders with verbal deficits.
The increased theta and decreased alpha activity pattern described in children with RDs correspond to a normal EEG pattern of a younger child; therefore, this finding has been interpreted as these children with RDs exhibiting a delay in electroencephalographic maturation [23,31].
The nervous system is organized into neural networks for information processing. Therefore, the study of brain connectivity is relevant. However, most studies exploring brain activity in children with learning disorders using EEGs have been based on EEG power analysis, and few have focused on functional connectivity measures. Functional connectivity is a way of measuring the strength of the connection between different structures. There is an increasing number of theoretical and empirical studies that approach the study of the function of the human brain from a network perspective. Specifically, it is of interest to examine the connection between the regions that constitute the reading network, namely, the posterior dorsal pathway, the posterior ventral pathway, and the anterior network, located in the left hemisphere, to evaluate processes involved in reading in children with RDs (phonological decoding, fast orthographic word recognition, and phonological and articulatory processes). Examining these connections may explain the behavioral deficits observed in accuracy, comprehension, and reading speed. Beyond the identification of isolated areas that participate in a certain function, the study of brain connectivity allows us to explore the neural networks involved and their interactions [33].
EEG coherence has been widely used to study the functional connectivity between different brain areas [34] because it provides a quantifiable measure of the synchrony between the electrical activity of structures beneath the recording electrodes on the scalp [35,36,37,38]. Currently, there are better measures to assess EEG connectivity [27]; however, the coherence between sensors was examined because it is the measure that is used in the clinical setting. Furthermore, coherence has been previously used to characterize the EEGs of children with RDs, and thus, it is possible to compare the results between studies and validate their reproducibility.
Previous studies have reported that children with learning disorders have a different maturational process for EEG coherence than children with typical development [39]. Children with poor reading performance have shown higher coherence values, specifically in the delta [40], theta [28,40], and beta frequency bands [28,40,41,42]. In the alpha band, the results are not consistent. Marosi et al. [40] observed lower alpha coherence values in 54 children with learning disorders than in 98 controls; in contrast, Arns et al. [42] and Shiota et al. [41] found higher alpha coherence values. However, it must be taken into account that the study by Arns et al. [42] was the only one performed in a resting condition with eyes open; furthermore, in the study by Shiota et al. [41], the sample was very small and heterogeneous, since two of the seven children with dyslexia suffered from epilepsy and three suffered from ADHD. Usually, higher intrahemispheric coherence of the delta, theta, and beta bands occurs between regions of the left hemisphere, while lower intrahemispheric alpha coherence, described by Marosi et al. [40], occurs between leads of the right hemisphere. Regarding interhemispheric and intrahemispheric coherence, children with dyslexia are characterized by lower interhemispheric coherence and higher intrahemispheric coherence in the alpha frequency band compared to classmates of the same age [29].
This entry is adapted from the peer-reviewed paper 10.3390/app13052825
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