EEG correlates of developmental dyslexia: a systematic review

Elisa Cainelli; Luca Vedovelli; Barbara Carretti; Patrizia Bisiacchi

doi:10.1007/s11881-022-00273-1

EEG correlates of developmental dyslexia: a systematic review

Cainelli, Elisa; Vedovelli, Luca; Carretti, Barbara; Bisiacchi, Patrizia 2023-07-01 00:00:00 Dyslexia is one of the most studied learning disorders. Despite this, its biological basis and main causes are still not fully understood. Electroencephalography (EEG) could be a powerful tool in identifying the underlying mechanisms, but knowledge of the EEG corre- lates of developmental dyslexia (DD) remains elusive. We aimed to systematically review the evidence on EEG correlates of DD and establish their quality. In July 2021, we carried out an online search of the PubMed and Scopus databases to identify published articles on EEG correlates in children with dyslexia aged 6 to 12 years without comorbidities. We follow the PRISMA guidelines and assess the quality using the Appraisal Tool question- naire. Our final analysis included 49 studies (14% high quality, 63% medium, 20% low, and 2% very low). Studies differed greatly in methodology, making a summary of their results challenging. However, some points came to light. Even at rest, children with dyslexia and children in the control group exhibited differences in several EEG measures, particularly in theta and alpha frequencies; these frequencies appear to be associated with learning perfor- mance. During reading-related tasks, the differences between dyslexic and control children seem more localized in the left temporoparietal sites. The EEG activity of children with dyslexia and children in the control group differed in many aspects, both at rest and during reading-related tasks. Our data are compatible with neuroimaging studies in the same diag- nostic group and expand the literature by offering new insights into functional significance. Keywords Neurophysiology · Spectra · Oscillations · Connectivity · Reading · Learning * Elisa Cainelli elisa.cainelli@unipd.it Department of General Psychology, University of Padova, Via Venezia, 8, 35133 Padua, Italy Unit of Biostatistics, Epidemiology, and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Padua, Italy Padova Neuroscience Centre, PNC, Padua, Italy 1 3 Vol.:(0123456789) E. Cainelli et al. Definition and cognitive mechanisms underlying developmental dyslexia DD manifests as an unexpected difficulty in acquiring reading skills despite adequate edu- cation, intelligence, and sociocultural opportunities and without obvious sensory deficits. Depending on the characteristics of the language, accuracy and/or fluency can be affected (Diamanti et al., 2018). According to the Diagnostic and Statistical Manual for Mental Disorders (5th ed.; DSM-5; American Psychiatric Association 2013), the incidence of spe- cific learning disorders, including DD, ranges from 5 to 12%. Following DSM-5, the main four criteria for diagnosing DD include the presence of difficulties in learning to read that have persisted for at least 6 months despite additional help or targeted instruction being provided. These difficulties interfere with everyday activities (such as academic achieve- ment) and are well below the age-expected level (defined as performance below 1.5 aver - age SD). Reading problems manifest upon admission into the school system and are not explained by other impairments such as intellectual disabilities, sensory, or neurological problems. By involving reading acquisition, a central skill in most school systems, DD is associated with many negative school outcomes, including reduced educational attainment and academic self-efficacy (Elgendi et al., 2021). Due to its developmental nature, DD per - sists until adulthood, with consequences also in the work context (Nalavany et al., 2018). As largely demonstrated, learning to read involves multiple processes ranging from cognitive and linguistic abilities to visual and attentional processes. Although, in the past, the effort was to identify the single causal mechanism of dyslexia, more recently, it has been recognized that variable patterns of weakness can contribute to reading difficulty in children (O’Brien & Yeatman, 2021). Research on developmental dyslexia has indeed documented deficits in vision (e.g., Stein & Walsh, 1997), attention (e.g., Vidyasagar & Pammer, 2010), auditory and temporal processes (e.g., Vandermosten et al., 2010), and phonology and language (e.g., Hulme et al., 2015). In addition, weaknesses in executive functions, particularly in working memory, have been reported (e.g., Lonergan et al., 2019). Using neuroimaging techniques such as functional magnetic resonance imaging (fMRI), researchers have identified brain circuits crucially involved in typical and dyslexic reading. A coarse neuroanatomical model of reading and DD has proposed abnormal brain activa- tion occurs in dyslexic readers in the left posterior temporoparietal cortex (middle temporal gyrus, superior temporal gyrus, supramarginal gyrus, and angular gyrus), the left occipi- totemporal cortex (inferior temporal gyrus and fusiform gyrus), and the left frontal cortex (inferior frontal gyrus and precentral gyrusHancock et al., 2017; Martin et al., 2016; Rich- lan, 2020; Richlan et al., 2009). However, although there are great improvements in comprehending the involved neu- roanatomical circuits, little evidence exists to show that fundamental brain processes are affected and how the brain compensates for those disruptions. Although the spatial resolution is lower compared to fMRI, electrical signals allow for exploring networks with temporal dynamics that functionally do not completely overlap with their fMRI counterparts. Many electrophysiological studies have provided evidence for basic perceptual deficits in DD. Abnormal event-related potentials (ERPs) for auditory and visual processing of speech and non-speech stimuli were found in both children and adults with dyslexia (for example, Bishop, 2007; Hämäläinen et al., 2013; Heim & Keil, 2004; Schulte-Körne & Bruder, 2010). ERPs are measures of electrical activity driven by changes in cognitive processing that are usually time locked to stim- uli and could be defined as a measure of the flow of sensory-related and action-related 1 3 EEG correlates of developmental dyslexia: a systematic review information in neuronal networks of the brain (even if some evidence suggests that some ERP components might be generated by stimulus-induced changes in ongoing brain dynamics (Penny et al., 2002). ERPs are extrapolated from the electroencephalogram (EEG), which, as a whole, provides insight into functional brain organization through the patterns of different brain oscillations. EEG shows overlapping electrical oscillation rhythms representing spontaneous activities in resting states with eyes open and closed. In response to stimuli, EEG rhythms react by synchronizing and desynchronizing, which does not represent signal processing per se, but rather a modulation of the information flow in the brain following stimulation. Although EEG rhythms have been discarded and ignored for years, considered a noisy background activity, the appearance of new methods in recent years has allowed the latter to face its renascence. The spectral power in the different frequency bands is the first and simpler source of information we can obtain from quantitative analysis of EEG, despite the different analysis techniques. It is determined by the synchronous activity of oscillating networks of neurons, and it reflects crucial aspects of processing information in the brain (Buzsáki & Draguhn, 2004). Phase synchronization of brain oscillations across spatially distinct brain regions has been suggested to be an important neuronal communication mechanism by dynamically linking neurons into functional networks (Womelsdorf et al., 2007). Under stimulation, endogenous oscillations phase reset their activity to the rhythmic information in the input, synchronizing cell activ- ity so that peaks in excitation co-occur with stimulus delivery, thereby enhancing neu- ral processing (Canolty et al., 2006; Lakatos et al., 2005). The different frequencies at which the networks oscillate have been divided into five groups—delta (0.5–4 Hz), theta (4–7 Hz), alpha (8–12 Hz), beta (13–30 Hz), and gamma (> 30 Hz)—with different functional meanings and involvement in a variety of perceptual, sensorimotor, and cog- nitive operations. Alpha-band oscillations are the dominant oscillations in the human brain with an active role in information processing and a possible inhibitory function (Klimesch, 2012). Abundant during sleep, in the awake state delta is associated with functional cortical deafferentation or inhibition of the sensory input that interferes with internal concentration (Harmony, 2013). The existence of several beta rhythms with dif- ferent frequencies, topographies, and different functional properties presumes no single neuronal mechanism for their generation (Kropotov, 2009). Finally, it has been shown that gamma band activity plays a crucial role in several cognitive tasks; moreover, it seems to interact with the activity in other frequency bands: in speech tasks, gamma interacts with theta, which accounts for syllabic perception, becoming crucial in pro- cessing linguistic stimuli (Giraud & Poeppel, 2012). The current hypothesis is that alter- ations in the oscillatory patterns of EEG play a critical role in the maintenance of brain functions and, consequently, may offer crucial information about brain functions. This work aims to systematically review the literature on the EEG correlates of DD. We will exclude the broad category of ERPs, given the different functional meanings and also considering the presence in the literature of many reviews about them (for example, Bishop, 2007; Hämäläinen et al., 2013; Heim & Keil, 2004; Schulte-Körne & Bruder, 2010). We focused on children who received the first diagnosis of dyslexia to analyze this problem. Several reports in the literature suggest that the first diagnosis of learning disabilities is more frequent during primary school (e.g., Arrhenius et al., 2021). For this reason, we focused on the age range of 6–12 years. We intended to iden- tify and retrieve international evidence, establish the quality of that evidence, address any uncertainty, and evaluate and synthesize the results. We hope that conflicting evi- dence could lead to further research. 1 3 E. Cainelli et al. Methods Protocol and registration We performed a systematic review of published journal articles on the correlates of EEG in DD, following the PRISMA guidelines (Page et al., 2021). The study protocol has been registered and is publicly available at https:// osf. io/ 4yz7j, where the resources obtained from this study are also available. Eligibility criteria Types of studies Case series and case–control studies investigating the correlates of EEG in DD were included. Participants in each study had a diagnosis of dyslexia according to current diagnostic manuals (e.g., ICD, DSM) and/or national guidelines. No publication date or publication status restrictions were imposed. Only English studies were included. Types of participants Participants aged 6 to 12 years with DD (i.e., not acquired) were included. To limit the exclusion of works, we included those works with broader age ranges but in which the results differentiated for age. That means that works that include older children but allow for extrapolating specific results on 6–12 age ranges have been included. Comorbidities were considered exclusion criteria; in studies in which patients with comor- bidities were also involved, but patients without were also present, only the results for the latter were considered. Types of outcome measures Except for ERPs, all EEG methodologies were included (in the supplementary materials, a description of EEG measures is reported). Information sources We conducted our search in July 2021 using PubMed and SCOPUS (Elsevier API) bibliographic databases, which include most of the EMBASE database (https:// www. elsev ier. com/ solut ions/ embase- biome dical- resea rch). The search was conducted using the following string: dyslexia AND (children OR developmental OR pediatric OR paediatric) AND EEG. This string returned 261 results in Scopus and 458 in PubMed. The final search results were exported to store and remove duplicates in the Mendeley bibliographic software package. There was only an internal duplicate within the PubMed database. Internal and external duplicates between the databases were removed from the list. The electronic database search was supplemented by screening the reference lists of each retrieved paper and scanning relevant reviews, obtaining two additional works. In total, 560 results were selected. Study selection and data collection process The eligibility assessment was performed independently and standardized in an unblinded manner by two reviewers (E. C. and L. V.). A third reviewer resolved disa- greements between reviewers (P. B. or B. C.). We developed a data extraction sheet that 1 3 EEG correlates of developmental dyslexia: a systematic review captured relevant information on key study characteristics and all EEG techniques used to investigate DD. Studies have been double coded. Data items The following information was collected from the records: year of publication, groups (e.g., patients with DD, healthy controls, or controls with other clinical characteristics), sample sizes, age at testing, criteria for defining the diagnosis of DD, EEG methodology, experimen- tal conditions, supplementary neuropsychological/cognitive/achievements measures, EEG results, and correlation between EEG findings and supplementary measures. Risk of bias in individual studies The risk of bias at the study level was assessed by two reviewers (E. C. and L. V.) using the Appraisal Tool for Cross‐Sectional Studies (AXIS; Downes et al., 2016). This 20‐item tool was developed in response to the increase in cross‐sectional studies that inform evidence- based medicine and the consequent importance of ensuring that these studies are of high qual- ity and low bias. AXIS assesses the quality of cross‐sectional studies based on the following criteria: clarity of objectives/objectives and target population; appropriate study design and sampling framework; justification for sample size; measures taken to address non-respond- ents and the potential for response bias; risk factors and outcome variables measured in the study; clarity of methods and statistical approach; appropriate presentation of results, includ- ing internal consistency; justified discussion points and conclusion; discussion of limitations; and identification of ethical approval and conflicts of interest. The scoring system conforms to a “yes,” “no,” or “do not know/comment” design. We classified the studies into four quality categories based on the number of “yes” answers for each of the 20 questions included in the AXIS tool as follows (Bull et al., 2019): “high” (more than 15 positive answers), “medium” (between 10 and 15), “low” (between 5 and 9), and “very low” (equal or less than 4). The overall quality categories of the studies are reported in Table 1. Results Study selection Figure 1 summarizes the following workflow (Haddaway & McGuinness, 2020). The 560 results were screened based on the title of the articles, and 133 were excluded for not being Table 1 The cumulative quality Quality N = 49 score of all studies obtained from the AXIS questionnaire High 7 (14%) Medium 31 (63%) Low 10 (20%) Very low 1 (2.0%) n (%) 1 3 E. Cainelli et al. Fig. 1 Study workflow neurophysiological studies investigating DD, for not being original research (reviews, meta-analyses, abstracts, or proceedings), or for not being in English. The full texts of the remaining 427 articles were screened, and further exclusion criteria excluded 378 addi- tional articles. Articles were excluded based on not being original quantitative research or case reports (n = 14), involving participants outside the age range selected without differen- tiation between ages (n = 85), diagnosing dyslexia in a way not defined according to inclu- sion criteria (n = 1), involving participants with comorbidities (n = 2), not focusing on EEG in DD (n = 265), and being irretrievable (n = 11). The final analysis included 49 studies. Quality of studies Individual scores for each included study are reported in Fig. 2. The cumulative quality score of all studies relative to the AXIS questionnaire is reported in Table 1. Only 14% were classified as high-quality level (> 15 positive answers), while the majority (63%) fell into the medium level. Finally, the quality of 20% was consid- ered low and, for 1 study, very low. The most common vulnerabilities are the sample size estimate, ethical approval information, and missing data management. The percentage of responses for each question is shown in Fig. 3. Some elements of the AXIS on the study design are positive in all studies due to part of the inclusion criteria. 1 3 EEG correlates of developmental dyslexia: a systematic review Fig. 2 “Yes,” “no,” and “not sure” responses to the 20 items of the AXIS questionnaire for each included study Typology of the studies Although EEG is a well-known methodology, we lack normative data to interpret quantitative findings, particularly for relatively new methodologies such as connec- tivity. Therefore, all studies are case–control comparisons of children with dyslexia and control children without the disorder. Only three studies did not compare children with dyslexia with controls. Still, children with dyslexia with nonspecific reading delay (Bosch-Bayard, 2018 2020) and children with dyslexia who have poor read- ing ability were compared to children with dyslexia who have capable reading ability (Mahmoodin 2016). The studies could be at rest (resting state) or during cognitive stimulation recorded by EEG: 12 out of the 49 studies used both approaches. Eighteen of the 49 stud- ies were published before 2000. Table 2 reports all the included studies and their main characteristics (authors, year of publication, sample size, age of the children at the evaluation, language used, methodology, and study quality following the AXIS questionnaire). 1 3 E. Cainelli et al. Fig. 3 Percentage of “yes,” “no,” and “not sure” responses obtained in the sample of studies for each ques- tion of the AXIS questionnaire Resting‑state EEG We found 24 studies investigating resting-state EEG (RS-EEG): 11 out of 24 studies focused solely on RS-EEG, while the other 13 out of 24 studies performed both an RS- EEG and an EEG during a task (in this section, only the results of the RS-EEG will be reported, whereas the results during a task will be described in the next section). Spectral analysis of the RS‑EEG The methodology used most frequently is spectral analy- sis, which shows the spectral content in the different frequency bands (delta, 1.5–4 Hz; theta, 4–7 Hz; alpha, 8–12 Hz; beta, 13–40 Hz; gamma, > 40 Hz). The methodology has been used alone (Arns et al., 2007; Bruni et al., 2009; Colon et al., 1979; Fein et al., 1983, 1986; Galin et al., 1988, 1992; Harmony et al., 1990; Mahmoodin et al., 2016, 2019; Papa- giannopoulou & Lagopoulos, 2016; Remschmidt & Warnke, 1992; Rippon and Brun- swick 2000) or combined with other methods (Babiloni et al., 2012; Bosch-Bayard et al., 2018; Flynn & Deering, 1989a; Flynn et al., 1992; Fraga González et al., 2016; Leisman, 2002; Reda et al., 2021; Xue et al., 2020). Figure 4 shows the results obtained by spectral analysis of RS-EEG. We also reported the non-significant results to render the data more readable. In general, it seems that DD is 1 3 EEG correlates of developmental dyslexia: a systematic review 1 3 Table 2 Authors, year of publication, sample size, age of the children at the evaluation, language used, methodology (type of EEG analysis, at rest/during a task condition), and study quality following the AXIS questionnaire of all studies included in the revision Authors, year Sample size Age Language Methods Quality Arns et al. (2007) 19 DD DD: 10.33 (8.0–15.98) Dutch Spectral power Medium 19 C C: 10.34 (8.0–16.03) At rest Ayers and Torres (1967) 129 DD DD: 104.1 (95–122) English Visual inspection Low 47 C C: 107.2 (96–114) At rest 31 R R: 106.9 (100–134) Babiloni et al. (2012) 26 DD DD: 11 years ± 0.5 Italian Spectral power High 11 C C: 11 years ± 0.7 Loreta At rest Bosch-Bayard et al. (2018) 169 DD DD: 9.1 (7–15) Italian Spectral power High 36 NSRD NSRD: 9.8 (7–15) Vareta At rest Bosch-Bayard et al. (2020) 184 DD DD: 9.1 + 1.9 Italian Coherence High 43 NSRD NSRD: 9.7 + 2.2 At rest Bruni et al. (2009a) 16 DD DD: 10.8 (8–16) Italian Sleep architecture parameters Medium 11 C C: 10.1 (7–16) Nocturnal sleep Bruni et al. (2009b) 16 DD DD: 10.8 (8–16) Italian Spectral power Medium 11 C C: 10.1 (7–16) Nocturnal sleep Colling et al. (2017) 13 DD DD: 119.3 (11.3) English Spectral power Medium 10 C C: 120.9 (7.9) During task Colon et al. (1979) 44 DD 7 and 11.0 Dutch Spectral power Low 49 C At rest Di Liberto et al. (2018) 25 DD 8.6 + 1.5 Australian English Spectral power Medium 45 C During task Duffy et al. (1980a) 11 DD / English Topographic maps Medium 13 C At rest During task Duffy et al. (1980b) 8 DD 9.0 and 10.7 English Topographic maps Medium 10 C At rest During task E. Cainelli et al. 1 3 Table 2 (continued) Authors, year Sample size Age Language Methods Quality Dushanova and Tsokov (2020) 22 DD 8–9 Bulgarian Connectivity Medium 21 C During task Dushanova et al. (2020) 22 DD 8–9 Bulgarian Coherence Medium 21 C During task Dushanova and Tsokov (2021) 22 DD 8–9 Bulgarian Connectivity Medium 21 C During task Eroglu et al. (2022) 16 DD 8.56 + 1.36 English Connectivity Medium 20 C At rest Farrag and El-Behary (1990) 21 DD 2nd and 3rd grades of Arabic Visual inspection Low 16 C elementary school At rest 23 R Fein et al. (1983) 31 DD 10–12 English Spectral power Medium 32 C At rest Fein et al. (1986) 34 DD 10–12 English Spectral power Low 35 C 9–13 At rest 22 DD 22 C Flynn and Deering (1989a) 21 DD 7.4–10.8 English Spectral power Very low 6 C During task Flynn and Deering (1989b) 12 DD disfonetic DD1:104 (93–130) English Spectral power Medium 4 DD disdeitic DD2:96 (89–110) Topographic mapping 5 DD mixed DD3:101 (90–120) At rest 6 C C: 113 (94–128) During task Flynn et al. (1992) 27 DD disfonetic 8.0–9.11 English Spectral power Medium 6 DD disedeitic At rest 6 C During task Fraga Gonzales et al. (2016) 26 DD DD 8.4 + 0.40 Dutch Spectral power High 15 C C 8.75 ± 0.31 Connectivity At rest EEG correlates of developmental dyslexia: a systematic review 1 3 Table 2 (continued) Authors, year Sample size Age Language Methods Quality Galin et al. (1988) 34 DD 1.10–12 English Spectral power Medium 35 C 2. 9–13 At rest 22 DD During task 22 C Galin et al. (1992) 34 DD 1.10–12 English Spectral power Medium 35 C 2. 9–13 At rest 22 DD During task 22 C Harmony et al. (1990) Good: 33 Regular: 23 6–12 English Spectral power Low Poor: 17 At rest Very poor: 8 Haynes et al. (1989) 12 DD 8–12 English Spectral power Low 12 C During task Jakovljevi´c et al. (2021) 18 DD 8–12 Serbian Spectral power Medium 18 C During task Klimesch et al. (2001) 8 DD DD: 11.6 + 0.5 German Spectral power Medium 8 C C: 11.36 + 0.33 During task Leisman (2002) 20 DD DD: 7.6 (7–10.9) English Spectral power Medium 20 C C: 8.2 (7–11.11) Coherence At rest During task Mahmoodin et al. (2016) 9 DD 7–11 Malay Spectral power Low 4 poor DD At rest 5 capable DD During task Mahmoodin et al. (2019) 11 poor DD P DD: 8 (7–12) Malay Spectral power Low 11 capable DD C DD: 8 (7–12) At rest 11 C C: 10.5 (7–12) During task Martinez-Murcia et al. (2020) 16 DD DD: 95.6 + 2.9 Spanish Spectral power Medium 32 C C: 94.1 + 3.3 Connectivity During task E. Cainelli et al. 1 3 Table 2 (continued) Authors, year Sample size Age Language Methods Quality Mattson et al. (1992) 8 DD DD: 11.3 (9–15) English Spectral power Medium 8 arithmetic dis Arit.: 12 (9–15) During task 10 C C: 12.4 (9–15) Ortiz et al. (1992) 14 DD DD:10.31 (9–11.7) Spanish Spectral power Medium 15 C C: 10.38 (9–12) At rest During task Papagiannopoulou and Lagopoulos (2016) 21 DD DD: 8 + 1.40 English Spectral power High 19 C C: 8 + 1.64 At rest Penolazzi et al. (2008) 14 DD DD: 10.12 + 2.23 Italian Spectral power Medium 28 C C: 10.01 + 0.18 During task Reda et al. (2021) 11 DD DD: 11.04 (9–14) Italian Sleep architecture parameters High 18 C C: 11.72 (9–14) Spectral power Nocturnal sleep Low Remschmidt and Warnke (1992) 30 DD DD:10.53 (9–12.11) German Spectral power 30 C C: 10.49 (9–12.11) At rest During task Rippon and Brunswick (2000) 19 DD DD: 10.66 + 1.46 Spectral power Medium 22 C C: 9.96 + 1.69 At rest During task Seri and Cerquiglini (1993) 10 DD 11–12.11 Italian Spectral power Medium 10 C Topographic mapping During task Shiota et al. (2000) 7 DD 7–14 Japanese Coherence Low 7 C At rest Spironelli et al. (2006) 10 DD DD: 9.25 ± 1.34 Italian Spectral power Medium 13 C C: 9.70 ± 1.17 During task Spironelli et al., (2008) 14 DD DD: 10.12 + 2.23 Italian Spectral power Medium 28 C C: 10.01 + 0.18 During task EEG correlates of developmental dyslexia: a systematic review 1 3 Table 2 (continued) Authors, year Sample size Age Language Methods Quality Taskov and Dushanova (2020) 22 DD 8–9 Bulgarian Connectivity Medium 21 C During task Taskov and Dushanova (2021) 25 DD 8–9 Bulgarian Spectral power Medium 21 C Connectivity During task Xue et al. (2020) 27 DD DD: 9.22 + 0.58 Chinese Spectral power High 40 C C: 9.38 + 0.49 Connectivity At rest Zainuddin et al. (2018) 17 DD 7–12 Malay Spectral power Medium 8 capable DD During task 8 C Zaric et al. (2017) 18 moderate DD mDD: 9.02 ± 0.45 Dutch Connectivity Medium 16 severe DD sDD: 8.92 ± 0.41 During task 20 C C: 8.80 ± 0.38 Legend: C, controls; DD, developmental dyslexia; NSRD, non-specific reading delay; R, remedial E. Cainelli et al. Fig. 4 The figure shows the results obtained in the studies using the spectral analysis of RS-EEG. In red the increases and blue the decreases obtained in children with dyslexia compared to controls; in black, if no dif- ferences were reported between the groups characterized by an increase in the delta frequency and theta and a reduction in alpha and beta. Other methodologies Results obtained from other methodologies are less homogeneous, and a summary is not possible; Table 3 reports the results of these studies (Arns et al., 2007; Ayers & Torres, 1967; Babiloni et al., 2012; Bosch-Bayard et al., 2020; Bruni et al., 2009; Duffy et al. 1980a; Eroğlu et al., 2022; Farrag & El-Behary, 1990; Fraga González et al., 2016; Gerald Leisman, 2002; Reda et al., 2021; Shiota et al., 2000; Xue et al., 2020). Correlation between resting EEG and reading performance Some rest studies correlate EEG activity with specific tests performed before or after recordings (Table 4). EEG during a task Thirty-two studies explored EEG brain activity during a task. Figure 5 reports the type of stimulation task and the methodology applied to the EEG. Most studies compared the EEG and the performance of DD and control children in linguistic, reading, or cog- nitive (Go-noGo, attention, reasoning, etc.) tasks (Dushanova & Tsokov, 2020, 2021; Dushanova et al., 2020; Flynn & Deering, 1989a, 1989b; Flynn et al., 1992; Galin et al., 1988, 1992; Jakovljević et al., 2021; Klimesch et al., 2001; Leisman, 2002; Mahmoodin et al., 2016; Ortiz et al., 1992; Penolazzi et al., 2008; Remschmidt & Warnke, 1992; Rippon & Brunswick, 2000; Seri & Cerquiglini, 1993; Spironelli et al., 2006, 2008; Taskov & Dushanova, 2020, 2021; Žarić et al., 2017). A smaller number of other stud- ies evaluate several other tasks: writing, speech, spelling, music, during the vision of an audio story, listening, and tapping (Colling et al., 2017; Di Liberto et al., 2018; 1 3 EEG correlates of developmental dyslexia: a systematic review 1 3 Table 3 The table below reports authors and year of publication, differences found in DD children from the comparison with controls, and the localization of the difference reported Author Measure differences Localization Arns et al. (2007) 1. Increased delta coherence 1. Bilateral fronto-central 2. Increased alpha and beta coherence 2. Right fronto-central Ayers and Torres (1967) Higher than expected incidence of abnormal electroencephalograms / Babiloni et al. (2012) Lower amplitude in low- and high-frequency alpha rhythms Parietal, occipital, and temporal cortical sources Bosch-Bayard et al. (2020) 1. More active hub: the calcarine sulcus is sending information to the right postcentral 1. Left calcarine sulcus gyrus, the left paracentral gyrus, the right angular gyrus, and the right supplemen- 2. Left rolandic operculum tary motor area in almost all frequency bands, including delta and theta band 2. Less active hub Bruni et al. (2009a) Increased spindle density during N2 sleep stage / Bruni et al. (2009b) 1. Lower number of sleep stage shifts per hour of sleep, percentage of N3, and number 2. Frontal areas of R periods 2. Overactivation of the ancillary frontal areas Duffy et al. (1980a) Increased alpha activity Bifrontal areas, left temporal and left posterior regions Farrag and El-Behary (1990) Immature EEG tracing by visual inspection Occipital area Fraga Gonzales et al. (2016) Reduced network integration and communication between network nodes in the theta / band Leisman (2002) Greater coherence within the hemisphere Left parieto-occipital Reda et al. (2021) Reduced slow spindles Occipito-parietal and left fronto-central areas Shiota et al. (2000) 1. Higher interhemispheric coherence values for alpha and beta 1. Temporal 2. Higher interhemispheric coherence values for beta 2. Frontal 3. Higher intrahemispheric coherence in alpha 3. Central, occipital and parietal Xue et al. (2020) Global network deficiencies in the beta band and the network topology was more / path-like E. Cainelli et al. 1 3 Table 4 The table reports the results obtained in DD children compared to controls in studies that correlate the EEG activity with specific tests performed before or after the recordings Author, year Task Results Arns et al. (2007) Rapid naming of letters (rnl), articulation (ART), phoneme deletion + delta coherence with all tests (PD), and spelling (SP) + theta coherence with ART and RNL + alpha with PD and RNL + beta coherence with RNL, PD, and SP Babiloni et al. (2012) Two lists of words and pseudowords and reading accuracy - alpha with a reading time of pseudo-words Bruni et al. (2009a) Memory and learning transfer reading test, word and non-word read- + sigma band in N2 with the word reading and MT reading tests ing test, word, non-word and sentences writing test, WISC-3 + spindle density with the word reading test Bruni et al. (2009b) Memory and learning transfer reading test, word and non-word read- + A1 index in sleep stage N3 with Verbal IQ, full-scale IQ, and ing test, word, non-word and sentences writing test; WISC-3; Child memory and learning transfer reading test Behaviour Checklist (CBCL) + cyclic alternating pattern rate in N3 with verbal IQ Fraga Gonzales et al. (2016) 3DM reading No correlation between connectivity (minimum spanning tree) and reading performance Harmony et al. (1990) Reading (reading comprehension and oral reading) and writing (copy- + theta in almost all leads in children with minor difficulties, no ante- ing, dictation, and functional writing) cedents, and good socioeconomic status + delta in left frontal and temporal areas (F3, F7, and T3) in children with a poor or very poor evaluation EEG correlates of developmental dyslexia: a systematic review Fig. 5 The stimulation task and the methodology applied to the EEG. The figure represents all conditions, so works using multiple tasks and analysis types could be overrepresented Duffy et al., 1980a, 1980b; Flynn et al., 1992; Galin et al., 1992; Haynes et al., 1989; Mahmoodin et al., 2019; Martinez-Murcia et al., 2020; Mattson et al., 1992). The results are not comparable, given the different tasks and EEG analysis method- ologies, but the differences between DD and control children appear mainly localized in the left temporoparietal sites. Table 5 reports the task used, the differences found in children with dyslexia com- pared to controls, and the localization findings. Comparing at‑rest and during‑task conditions Twelve works were performed both at rest and during the task conditions. Some authors found differences in both conditions, but more pronounced in the task condition (Duffy et al. 1992) or the rest condition (Leisman, 2002), whereas others found differences only during the task condition (Flynn et al., 1992; Galin et al., 1988; Ortiz et al., 1992). Finally, some studies did not clearly report what happened during the at-rest condition (Duffy et al. 1980b; Flynn & Deering, 1989b; Galin et al., 1992; Mahmoodin et al., 2016, 2019; Zainuddin et al., 2018). Other kinds of studies Some studies of EEG in children with dyslexia are not possible to include in the previ- ous paragraphs because of the different natures of the works. We will briefly describe them as follows. Bosch-Bayard et al. (2018) wanted to find a classification equation that discriminates the two groups with high accuracy. They obtained a discrimination equation that did not participate in the Boder classification algorithm, with a specificity and sensitivity of 0.94 to discriminate DD from the nonspecific reading delay. Using a statistically based technique, Duffy et al., (1980a, 1980b) searched for rules for the classification of children with dyslexia. They developed classification rules that suc- cessfully diagnosed 80 to 90% of the subjects. Eroglu et al. (2022) investigated possible disturbances in the complexity of EEG signals (connectivity measures) on multiple time scales in people with dyslexia and the potential 1 3 E. Cainelli et al. 1 3 Table 5 Authors and year, a brief description of the task used, the differences in EEG brain activity found in dyslexic children compared to controls, and the findings’ locali- zation Author Stimulation Measure differences Localization Colling et al. (2017) - Tapping to every second beat of a metro- Preferred phase at 2.4 Hz Frontal nome pulse - To listening passively to the beat Di Liberto et al. (2018) Audio-story while watching the correspond- Delta and theta reduction in DD Right hemisphere ing cartoon Duffy et al. (1980a) -Speech: listen and answer questions Theta and alpha increase in DD Left temporal, left posterior quadrant regions, -Music: listen and in the bifrontal area -Kimura figures instruction Dushanova and Tsokov (2020) To discriminate visually presented words Theta, alpha, beta1, and gamma1 strength Left anterior temporal and parietal regions and pseudowords putting a button. Before and betweenness reduction e post-treatment Dushanova et al. (2020) To discriminate auditory presented words 1. Stronger delta-entrainment for C and 1. In the left auditory cortex, anterior tempo- and pseudowords putting a button visual DD ral lobe, frontal, and motor cortices 2. Delta-entrainment deficit for DD 2. the left anterior temporal lobe, frontal, and 3. Higher delta-entrainment for phonologi- the right temporal cal DD 3. posterior temporal Dushanova and Tsokov (2021) To discriminate words and pseudowords put- Theta, alpha, beta degree and betweenness Left anterior temporal and parietal regions ting a button—before and after training centrality reduction Flynn and Deering (1989b) - Reading 1. Increase in theta for Dyseidetic DD 1. Left temporal-parietal - Spelling recognition 2. Increase in theta power dyseidetic DD 2. Left mid to posterior temporal and left - Drawing a clock during reading temporal-parietal Flynn et al. (1992) - Listening to a story Beta reduction during the reading task Right occipital-parietal and left temporal- - Silent reading of text parietal - Oral reading of text - Spelling recognition - Auditory analysis of orally presented words - Drawing a clock Galin et al. (1988) - Kohs block design Stronger alpha asymmetry Temporal leads - Narrative speech EEG correlates of developmental dyslexia: a systematic review 1 3 Table 5 (continued) Author Stimulation Measure differences Localization Galin et al. (1992) - Oral and silent reading of easy and hard Smaller change in theta and low beta power No differences between leads texts between tasks - Listening to a story - Narrative speech Haynes et al. (1989) 1. Vigilance condition; 2. listening to a story Decreased alpha amplitudes in both groups No differences between leads without an ending that had to be retold; 3. of subjects rehearse the story mentally and construct an appropriate ending Jakovljevi´c et al. (2021) Read a story, the text on each slide in differ - 1. Higher values of beta and the broadband Not reported ent colors EEG (0.5–40 Hz) power while reading in purple 2. Increasing theta range power while read- ing with the purple overlay Klimesch et al. (2001) - Reading numbers Large group differences in tonic and phasic Occipital sites - Reading words lower theta for pseudoword processing - Reading pseudowords Leisman (2002) - Rest-eyes closed 1. Greater theta and beta, decreased alpha Left parieto-occipital - Continuous performance tests 2. Lower coherence between hemispheres - Confrontation naming from the Stanford– but greater coherence within the same Binet hemisphere during all tasks - Spache tests (1966) Diagnostic reading tests Mahmoodin et al. (2016) - Rest eyes closed Higher beta in capable DD Frontal (FC6) and parietal (P4) right hemi- - Reading a non-word and writing it down sphere based on an auditory cue Mahmoodin et al. (2019) Listening to amplitude-modulated noise with Higher theta-beta ratio All leads slow-rhythmic prosodic, syllabic, or the phoneme rates E. Cainelli et al. 1 3 Table 5 (continued) Author Stimulation Measure differences Localization Martinez-Murcia et al. (2020) Listening to amplitude-modulated noise with 1. Reduced bilateral connection between 1. Temporal lobe slow-rhythmic prosodic, syllabic, or the electrodes 2. F7 electrode phoneme rates 2. Increased connectivity Mattson et al. (1992) Listening sentences preceded by a warning 1. Reducted 40-Hz activity during verbal 1. Left hemisphere task 2. Right hemisphere 2. Reducted activity in arithmetic disabled compared to DD and C during the nonver- bal task Ortiz et al. (1992) - Resting-state with eyes closed 1. Alpha responsiveness during the task 1. Left hemisphere - Resting condition with eyes open 2. High beta decrease during the task 2. Left posterior quadrant - Auditory phonemic discrimination task Penolazzi et al. (2008) To compare the word pairs based on 1. Greater overall delta amplitude 1. Anterior sites 1. orthographic 2. In the phonological task, larger delta ante- 2. Left anterior and left posterior 2. phonological rior and smaller posterior delta amplitude 3. semantic criteria Remschmidt and Warnke (1992) To mark discriminate letters into letter (3) Faster attenuation of relative alpha power No differences between leads strings increasing cognitive activation and reading (4) DD did not reveal characteristic focal EEG features Rippon and Brunswick (2000) - Phonological processing task 1. Lack of task-related reduction from rest- 1. Parieto-occipital - Visual search task (WISC picture comple- ing levels in the amplitude of alpha 2. Parieto-occipital R > L tion) 2. Marked asymmetry in beta activity 3. Frontal 3. In the phonological task, a theta increase Seri and Cerquiglini (1993) Word length recognition test requiring a Lack of desynchronization 1. Right frontal and temporal, left parietal gentle right finger-lift response Spironelli et al. (2006) - Phonological task (to decide whether the Delayed theta peak activity and was shifted Right instead of left word pairs rhymed) to the right hemisphere - Semantic task (to decide whether the target word was semantically related to the first) EEG correlates of developmental dyslexia: a systematic review 1 3 Table 5 (continued) Author Stimulation Measure differences Localization Spironelli et al. (2008) - Phonological task (to decide whether the 1. Significantly greater beta and theta over 1. Right frontal word pairs rhymed) the right hemisphere during phonological 2. Left posterior - Semantic task (to decide whether the target task word was semantically related to the first) 2. Greater beta and theta over the left hemi- - Orthographic task (to decide whether word sphere during phonological and ortho- pairs were written in the same case). Press- graphic tasks ing a button 3. Delay in behavioral responses, paralleled by sustained theta Taskov and Dushanova (2020) Reading aloud words—before and after 1. Higher leaf fraction, tree hierarchy, kappa, 1. Globally treatment and smaller diameter (theta–gamma fre- 2. Superior, middle, and inferior frontal areas quency) (less segregated neural network) in both brain hemispheres 2. Reduced degree and betweenness central- ity of hubs Taskov and Dushanova (2021) Reading aloud words—before and after Absent functional connectivity nodes Dorsal medial temporal area, left middle treatment derived from theta frequency network for occipitotemporal, parietal both conditions Zaric et al. (2017) Discriminating word from false font pressing 1. Weaker connectivity for words 1. Occipital to inferior-temporal a button 2. Stronger connectivity for words and false 2. From left central to right inferior-temporal fonts in severe DD and occipital sites E. Cainelli et al. positive effects of special neurofeedback and multisensory learning treatment. After treat- ment, the lower complexity of the experimental group increased to the typically developing group on lower and medium temporal scales in all channels. Fein et al. (1983) assessed the test–retest reliability of both absolute and relative spectra. They found excellent absolute and relative power reliability under properly controlled conditions. Finally, Zainuddin et al. (2018) used a support vector machine algorithm to classify EEG signals from typical, poor, and capable children with dyslexia while writing words and nonwords. Beta and theta-to-beta ratios formed the input features for the classifier. It was found that the best performance of the support vector machine was obtained with 91% overall accuracy when both kernel scale and box constraint were set to 1. Differences between dyslexia subtypes Only a few studies evaluated the differences between dyslexia subtypes. In their works, Bosch-Bayard et al. (2018) and Bosch-Bayard et al. (2020) focused on dyslexia with pho- nological deficits (dysphonetic) compared with children with nonspecific reading delays. By analyzing the power spectra, in 2018, they found that the DD group had significantly higher activity in the delta and theta bands than the nonspecific reading delays group in the frontal, central, and parietal areas bilaterally. Two years later, using measures of EEG connectivity, they found that the left calcarine sulcus was more active in the DD group, while the left rolandic operculum was more active in the nonspecific reading delays group. Instead, Flynn and Deering (1989a) and Flynn et al. (1992) compared two types of dys- lexia: dysphonetic and dyseidetic (with orthographic deficits). They found left temporal differences in children with dyseidetic dyslexia and right parietal-occipital differences for those with dysphonetic dyslexia, supporting predictions derived from a compensation- from-strength model of dyslexia. Discussion We performed a systematic review of the evidence using EEG in DD. Finally, we selected 49 works, both EEG studies at rest and during a task. The articles differed greatly in meth- odology, which makes a summary of the results challenging. However, some points have come to light. Even at rest, children with dyslexia and children in the control group exhib- ited differences in several EEG measures, particularly an increase in delta and theta and a reduction in alpha frequencies, without a clear localization. The same frequencies recorded at rest appear to be associated with learning performances. During reading-related tasks, differences between children with dyslexia and children in the control group appear more localized at the left temporoparietal sites, and the spectral frequencies appear differently involved. Theta range remained the frequency band that hosts the main number of differ - ences between children with dyslexia and children in the control group, but some work also found the involvement of the beta and gamma bands. Current research on electrophysiological correlates of language acquisition could help interpret our data. For example, many studies have been done on speech processing, a cog- nitive ability strictly associated with reading. Delta, theta, and gamma oscillations have been shown to be specifically engaged by the quasirhythmic properties of speech (Giraud & Poeppel, 2012). Different frequencies account for different properties of the language: 1 3 EEG correlates of developmental dyslexia: a systematic review the transformation of the auditory signal input into lexical and phrasal units occurs at a very low modulation rate, roughly 1–2 Hz. Frequencies in a slightly higher range (1.5–4; i.e., delta) account for prosodic perception (Ghitza & Greenberg, 2009) and (4–7; i.e., theta) for syllabic perception (Luo & Poeppel, 2007; Poeppel et al., 2008). Higher frequen- cies (30–40 Hz, the high beta/low gamma band) process stimulus information concurrently with the theta band, lying in a nesting relation such that the phase of theta shapes the prop- erties of gamma (Giraud & Poeppel, 2012). Frequency bands could have similar functions in the reading process. In fact, studies in children, adolescents, and adults with dyslexia converge to identify an atypical auditory neural synchronization of oscillations, suggesting deviant neural processing of both syllabic and phonemic rate information (De Vos et al., 2017; Di Liberto et al., 2018; Lehongre et al. 2011; Lizarazu et al., 2015; Molinaro et al., 2016). It has been proposed that if people with dyslexia parse speech at a frequency slightly higher or lower than the usual frequency rate, their phonemic representations could be abnormal (Ziegler et al., 2009). This anomaly would selectively complicate the grapheme- to-phoneme matching, leaving speech perception and production unaffected. These studies are compatible with the results from ERPs, which revealed altered processing of certain acoustic information relevant to speech perception in individuals with dyslexia, such as frequency changes and temporal patterns (Schulte-Körne & Bruder, 2010). Competing neurobiological hypotheses alternatively assign a crucial role to higher ver- sus lower frequency bands. It has been suggested that dyslexic people may be less respon- sive to modulations at specific frequencies that are optimal for phonemic analysis (30 Hz) (Lehongre et al., 2011) or that they may fail to reset gamma activity (Schroeder et al., 2010). Other authors, more in line with the results of our review, emphasized the role of lower frequencies and, in particular, theta oscillations. A deficit in theta is thought to alter low temporal modulation tracking syllable coding and even multisensory processing, with consequences for attention and auditory-visual integration (Goswami, 2011; Ziegler et al., 2009). De Vos and colleagues (De Vos et al., 2017) found in adolescents with DD atypical alpha (reduced) and beta (increased) synchronization. They advocated that the alpha reduc- tion could be related to phonological processing problems. At the same time, the over- synchronization of beta range oscillations could be a compensatory mechanism to improve the processing of phonemic rate information. Although different methodologies and age ranges, we also found numerous abnormalities in alpha and beta frequencies (Duffy et al., 1980a; Dushanova & Tsokov, 2021; Flynn et al., 1992; Galin et al., 1988, 1992; Haynes et al., 1989; Zulkifli Mahmoodin et al., 2019; Ortiz et al., 1992; Rippon & Brunswick, 2000; Spironelli et al., 2008; Taskov & Dushanova, 2021). Furthermore, alpha appears globally reduced in at rest conditions, whereas beta offers more contradictory results. If the theory of the compensatory beta effect is correct, it is possible that our younger samples do not exhibit compensatory effects yet. The relationship between the deficits in different band frequencies and the stages of learning to read could also explain the scarcity of results in the gamma band of our review: It may be that the 12-year filter has determined a specific trend in the type of deficit. Interestingly, in our review, the alterations in the frequency bands appear to be associated with learning performance, supporting the neurobiological meaning of these components. It is noteworthy that when considering EEG frequency bands, it is impor- tant to consider that the bands may not be a perfect match with those of the adult or older child. Particularly, a shift in frequency peaks with age has been shown (Campus et al., 2021; Clarke et al., 2001; Orekhova et al., 2006). The most heterogeneity in our review is in studies using stimulation. Most compared the EEG and the performance of DD and control children’s performance in linguistic, reading, 1 3 E. Cainelli et al. or cognitive tasks. A smaller number of other studies evaluate several other tasks: writing, speech, spelling, music, listening to an audio story or someone reading, and tapping. The majority of tasks explore functions directly involved in dyslexia or strictly connected. How- ever, there are also studies exploring different cognitive functions in DD children. These are interesting because they explore new hypotheses on dyslexia and its association with not obvious cognitive functions, like vigilance and visuospatial abilities. Unfortunately, the results are not comparable, given the different tasks and EEG analysis methodologies, but the differences between DD and control children appear mainly localized in the left tempo- roparietal sites. Still, caution should thus be taken in interpreting power differences between groups in the context of neural tracking differences, as they rely not only on distinct analytical approaches but also on different experimental paradigms. Probably for that reason, the data coming from our review does not capture the complex picture that emerges from the most recent research. For example, intriguing insights came from studies on the hemispheric spe- cialization of specific frequencies in DD. In summary, the left hemisphere appears to specialize in local high-frequency verbal com- putations, while the right hemisphere codes low frequencies of the speech envelope and inter- hemispheric cognitive control (Giraud & Poeppel, 2012). Impairment of the right hemisphere circuitry of frontoparietal attention networks has been hypothesized to be the primary cause of dyslexia (Goswami, 2011; Lehongre et al., 2011; Lizarazu et al., 2015; Molinaro et al., 2016; Power et al., 2016). Such a dysfunction would have a cascading negative effect on pho- nemic processing in the dorsal reading network (Kershner, 2019, 2020). Our evaluation of the quality of the studies highlights an overall weakness of the reported studies. Many studies are old, and the methodological sections do not follow current guide- lines of transparency and reliability of methods. The greatest weakness appears to be the small sample sizes of most studies; furthermore, almost none reported the method for select- ing the sample size. Methodological concerns, the intrinsic high interindividual variability of electrophysiological techniques and the developmental phase, the tendency to publish only positive findings, and the use of different methodologies render the possibility of synthesiz- ing and drawing conclusions very challenging. We could have included the grey literature to overcome these limitations, but we initially decided to limit the search to peer-reviewed published works. All these concerns hinder the possibility of establishing clear markers in EEG correlates of DD. However, a trend emerges despite differences in experimental condi- tions and analysis methodology (at rest, differences and involvement of the theta during read- ing-related tasks). The trend may reflect processing vulnerability in children with dyslexia or compensatory processing strategies that inappropriately activate areas of the reading network in this specific age range. Finally, only a few studies evaluated the differences between dyslexic subtypes. A wide range of literature highlights the presence of different subtypes of DD. The few existing studies support differences at rest and during reading tasks. These conditions have to be better addressed because of the possible different cores (endophenotype) involved and the consequent additional variability in the results if not considered. Conclusion This review seems to highlight some interesting insights: (a) there are abnormalities in spontaneous cerebral activity (“at rest”) of both temporal sites and more widerspread scalp placements in children with dyslexia, and (b) reading-related tasks elicited differences in 1 3 EEG correlates of developmental dyslexia: a systematic review frequencies considered crucial for speech processing, and the differences are localized in the temporoparietal sites. Although EEG localizations do not necessarily correspond to the underlying neuroanatomical regions, the finding of a left temporoparietal involvement is compatible with neuroimaging abnormalities, especially in the left and posterior regions. It should be noted that the current research on EEG correlates in DD is more advanced than is apparent from our review, which comprised a reduced number of works and some very old. This incongruency could denote a trend in research to select older participants, probably due to the greater simplicity of conducting studies with older and more collaborative chil- dren. Adolescents and adults are also suitable for more complex tasks. Furthermore, older age allows more certainty in diagnosis over time. However, we think that the range 6–12 is crucial because it represents the first appearance and diagnosis of the disorder and could offer impor - tant insights into the first phases of consolidation of both abilities and dysfunction. Therefore, we hope that future research addresses the functional role of atypical activation and involve- ment of specific frequencies in 6–12 years of DD to understand how fundamental brain pro- cesses are affected and how the brain compensates for those disruptions. Evaluation of the emergence and characterization of spectral EEG components and their deviation from the expected typical trajectory may be important to understanding early abnormalities of brain development, also in very early phases, as shown in the DD literature (Ozernov-Palchik & Gaab, 2016) and other research fields (Cainelli et al., 2021). This has the potential to lead to more effectiveness and could change the outcome trajectories for those with reading deficits. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s11881- 022- 00273-1. Acknowledgements The present work was carried out within the scope of the research program Diparti- menti di Eccellenza (art.1, commi 314-337 legge 232/2016), which was supported by a grant from Ministero dell’Istruzione, dell’Università e della Ricerca (MIUR) to the Department of General Psychology, Univer- sity of Padua. Funding Open access funding provided by Università degli Studi di Padova within the CRUI-CARE Agreement. Declarations Competing interests The authors declare no competing interests. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com- mons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. 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Publisher: Springer Journals
Copyright: Copyright © The Author(s) 2022
ISSN: 0736-9387
eISSN: 1934-7243
DOI: 10.1007/s11881-022-00273-1
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Abstract

Dyslexia is one of the most studied learning disorders. Despite this, its biological basis and main causes are still not fully understood. Electroencephalography (EEG) could be a powerful tool in identifying the underlying mechanisms, but knowledge of the EEG corre- lates of developmental dyslexia (DD) remains elusive. We aimed to systematically review the evidence on EEG correlates of DD and establish their quality. In July 2021, we carried out an online search of the PubMed and Scopus databases to identify published articles on EEG correlates in children with dyslexia aged 6 to 12 years without comorbidities. We follow the PRISMA guidelines and assess the quality using the Appraisal Tool question- naire. Our final analysis included 49 studies (14% high quality, 63% medium, 20% low, and 2% very low). Studies differed greatly in methodology, making a summary of their results challenging. However, some points came to light. Even at rest, children with dyslexia and children in the control group exhibited differences in several EEG measures, particularly in theta and alpha frequencies; these frequencies appear to be associated with learning perfor- mance. During reading-related tasks, the differences between dyslexic and control children seem more localized in the left temporoparietal sites. The EEG activity of children with dyslexia and children in the control group differed in many aspects, both at rest and during reading-related tasks. Our data are compatible with neuroimaging studies in the same diag- nostic group and expand the literature by offering new insights into functional significance. Keywords Neurophysiology · Spectra · Oscillations · Connectivity · Reading · Learning * Elisa Cainelli elisa.cainelli@unipd.it Department of General Psychology, University of Padova, Via Venezia, 8, 35133 Padua, Italy Unit of Biostatistics, Epidemiology, and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Padua, Italy Padova Neuroscience Centre, PNC, Padua, Italy 1 3 Vol.:(0123456789) E. Cainelli et al. Definition and cognitive mechanisms underlying developmental dyslexia DD manifests as an unexpected difficulty in acquiring reading skills despite adequate edu- cation, intelligence, and sociocultural opportunities and without obvious sensory deficits. Depending on the characteristics of the language, accuracy and/or fluency can be affected (Diamanti et al., 2018). According to the Diagnostic and Statistical Manual for Mental Disorders (5th ed.; DSM-5; American Psychiatric Association 2013), the incidence of spe- cific learning disorders, including DD, ranges from 5 to 12%. Following DSM-5, the main four criteria for diagnosing DD include the presence of difficulties in learning to read that have persisted for at least 6 months despite additional help or targeted instruction being provided. These difficulties interfere with everyday activities (such as academic achieve- ment) and are well below the age-expected level (defined as performance below 1.5 aver - age SD). Reading problems manifest upon admission into the school system and are not explained by other impairments such as intellectual disabilities, sensory, or neurological problems. By involving reading acquisition, a central skill in most school systems, DD is associated with many negative school outcomes, including reduced educational attainment and academic self-efficacy (Elgendi et al., 2021). Due to its developmental nature, DD per - sists until adulthood, with consequences also in the work context (Nalavany et al., 2018). As largely demonstrated, learning to read involves multiple processes ranging from cognitive and linguistic abilities to visual and attentional processes. Although, in the past, the effort was to identify the single causal mechanism of dyslexia, more recently, it has been recognized that variable patterns of weakness can contribute to reading difficulty in children (O’Brien & Yeatman, 2021). Research on developmental dyslexia has indeed documented deficits in vision (e.g., Stein & Walsh, 1997), attention (e.g., Vidyasagar & Pammer, 2010), auditory and temporal processes (e.g., Vandermosten et al., 2010), and phonology and language (e.g., Hulme et al., 2015). In addition, weaknesses in executive functions, particularly in working memory, have been reported (e.g., Lonergan et al., 2019). Using neuroimaging techniques such as functional magnetic resonance imaging (fMRI), researchers have identified brain circuits crucially involved in typical and dyslexic reading. A coarse neuroanatomical model of reading and DD has proposed abnormal brain activa- tion occurs in dyslexic readers in the left posterior temporoparietal cortex (middle temporal gyrus, superior temporal gyrus, supramarginal gyrus, and angular gyrus), the left occipi- totemporal cortex (inferior temporal gyrus and fusiform gyrus), and the left frontal cortex (inferior frontal gyrus and precentral gyrusHancock et al., 2017; Martin et al., 2016; Rich- lan, 2020; Richlan et al., 2009). However, although there are great improvements in comprehending the involved neu- roanatomical circuits, little evidence exists to show that fundamental brain processes are affected and how the brain compensates for those disruptions. Although the spatial resolution is lower compared to fMRI, electrical signals allow for exploring networks with temporal dynamics that functionally do not completely overlap with their fMRI counterparts. Many electrophysiological studies have provided evidence for basic perceptual deficits in DD. Abnormal event-related potentials (ERPs) for auditory and visual processing of speech and non-speech stimuli were found in both children and adults with dyslexia (for example, Bishop, 2007; Hämäläinen et al., 2013; Heim & Keil, 2004; Schulte-Körne & Bruder, 2010). ERPs are measures of electrical activity driven by changes in cognitive processing that are usually time locked to stim- uli and could be defined as a measure of the flow of sensory-related and action-related 1 3 EEG correlates of developmental dyslexia: a systematic review information in neuronal networks of the brain (even if some evidence suggests that some ERP components might be generated by stimulus-induced changes in ongoing brain dynamics (Penny et al., 2002). ERPs are extrapolated from the electroencephalogram (EEG), which, as a whole, provides insight into functional brain organization through the patterns of different brain oscillations. EEG shows overlapping electrical oscillation rhythms representing spontaneous activities in resting states with eyes open and closed. In response to stimuli, EEG rhythms react by synchronizing and desynchronizing, which does not represent signal processing per se, but rather a modulation of the information flow in the brain following stimulation. Although EEG rhythms have been discarded and ignored for years, considered a noisy background activity, the appearance of new methods in recent years has allowed the latter to face its renascence. The spectral power in the different frequency bands is the first and simpler source of information we can obtain from quantitative analysis of EEG, despite the different analysis techniques. It is determined by the synchronous activity of oscillating networks of neurons, and it reflects crucial aspects of processing information in the brain (Buzsáki & Draguhn, 2004). Phase synchronization of brain oscillations across spatially distinct brain regions has been suggested to be an important neuronal communication mechanism by dynamically linking neurons into functional networks (Womelsdorf et al., 2007). Under stimulation, endogenous oscillations phase reset their activity to the rhythmic information in the input, synchronizing cell activ- ity so that peaks in excitation co-occur with stimulus delivery, thereby enhancing neu- ral processing (Canolty et al., 2006; Lakatos et al., 2005). The different frequencies at which the networks oscillate have been divided into five groups—delta (0.5–4 Hz), theta (4–7 Hz), alpha (8–12 Hz), beta (13–30 Hz), and gamma (> 30 Hz)—with different functional meanings and involvement in a variety of perceptual, sensorimotor, and cog- nitive operations. Alpha-band oscillations are the dominant oscillations in the human brain with an active role in information processing and a possible inhibitory function (Klimesch, 2012). Abundant during sleep, in the awake state delta is associated with functional cortical deafferentation or inhibition of the sensory input that interferes with internal concentration (Harmony, 2013). The existence of several beta rhythms with dif- ferent frequencies, topographies, and different functional properties presumes no single neuronal mechanism for their generation (Kropotov, 2009). Finally, it has been shown that gamma band activity plays a crucial role in several cognitive tasks; moreover, it seems to interact with the activity in other frequency bands: in speech tasks, gamma interacts with theta, which accounts for syllabic perception, becoming crucial in pro- cessing linguistic stimuli (Giraud & Poeppel, 2012). The current hypothesis is that alter- ations in the oscillatory patterns of EEG play a critical role in the maintenance of brain functions and, consequently, may offer crucial information about brain functions. This work aims to systematically review the literature on the EEG correlates of DD. We will exclude the broad category of ERPs, given the different functional meanings and also considering the presence in the literature of many reviews about them (for example, Bishop, 2007; Hämäläinen et al., 2013; Heim & Keil, 2004; Schulte-Körne & Bruder, 2010). We focused on children who received the first diagnosis of dyslexia to analyze this problem. Several reports in the literature suggest that the first diagnosis of learning disabilities is more frequent during primary school (e.g., Arrhenius et al., 2021). For this reason, we focused on the age range of 6–12 years. We intended to iden- tify and retrieve international evidence, establish the quality of that evidence, address any uncertainty, and evaluate and synthesize the results. We hope that conflicting evi- dence could lead to further research. 1 3 E. Cainelli et al. Methods Protocol and registration We performed a systematic review of published journal articles on the correlates of EEG in DD, following the PRISMA guidelines (Page et al., 2021). The study protocol has been registered and is publicly available at https:// osf. io/ 4yz7j, where the resources obtained from this study are also available. Eligibility criteria Types of studies Case series and case–control studies investigating the correlates of EEG in DD were included. Participants in each study had a diagnosis of dyslexia according to current diagnostic manuals (e.g., ICD, DSM) and/or national guidelines. No publication date or publication status restrictions were imposed. Only English studies were included. Types of participants Participants aged 6 to 12 years with DD (i.e., not acquired) were included. To limit the exclusion of works, we included those works with broader age ranges but in which the results differentiated for age. That means that works that include older children but allow for extrapolating specific results on 6–12 age ranges have been included. Comorbidities were considered exclusion criteria; in studies in which patients with comor- bidities were also involved, but patients without were also present, only the results for the latter were considered. Types of outcome measures Except for ERPs, all EEG methodologies were included (in the supplementary materials, a description of EEG measures is reported). Information sources We conducted our search in July 2021 using PubMed and SCOPUS (Elsevier API) bibliographic databases, which include most of the EMBASE database (https:// www. elsev ier. com/ solut ions/ embase- biome dical- resea rch). The search was conducted using the following string: dyslexia AND (children OR developmental OR pediatric OR paediatric) AND EEG. This string returned 261 results in Scopus and 458 in PubMed. The final search results were exported to store and remove duplicates in the Mendeley bibliographic software package. There was only an internal duplicate within the PubMed database. Internal and external duplicates between the databases were removed from the list. The electronic database search was supplemented by screening the reference lists of each retrieved paper and scanning relevant reviews, obtaining two additional works. In total, 560 results were selected. Study selection and data collection process The eligibility assessment was performed independently and standardized in an unblinded manner by two reviewers (E. C. and L. V.). A third reviewer resolved disa- greements between reviewers (P. B. or B. C.). We developed a data extraction sheet that 1 3 EEG correlates of developmental dyslexia: a systematic review captured relevant information on key study characteristics and all EEG techniques used to investigate DD. Studies have been double coded. Data items The following information was collected from the records: year of publication, groups (e.g., patients with DD, healthy controls, or controls with other clinical characteristics), sample sizes, age at testing, criteria for defining the diagnosis of DD, EEG methodology, experimen- tal conditions, supplementary neuropsychological/cognitive/achievements measures, EEG results, and correlation between EEG findings and supplementary measures. Risk of bias in individual studies The risk of bias at the study level was assessed by two reviewers (E. C. and L. V.) using the Appraisal Tool for Cross‐Sectional Studies (AXIS; Downes et al., 2016). This 20‐item tool was developed in response to the increase in cross‐sectional studies that inform evidence- based medicine and the consequent importance of ensuring that these studies are of high qual- ity and low bias. AXIS assesses the quality of cross‐sectional studies based on the following criteria: clarity of objectives/objectives and target population; appropriate study design and sampling framework; justification for sample size; measures taken to address non-respond- ents and the potential for response bias; risk factors and outcome variables measured in the study; clarity of methods and statistical approach; appropriate presentation of results, includ- ing internal consistency; justified discussion points and conclusion; discussion of limitations; and identification of ethical approval and conflicts of interest. The scoring system conforms to a “yes,” “no,” or “do not know/comment” design. We classified the studies into four quality categories based on the number of “yes” answers for each of the 20 questions included in the AXIS tool as follows (Bull et al., 2019): “high” (more than 15 positive answers), “medium” (between 10 and 15), “low” (between 5 and 9), and “very low” (equal or less than 4). The overall quality categories of the studies are reported in Table 1. Results Study selection Figure 1 summarizes the following workflow (Haddaway & McGuinness, 2020). The 560 results were screened based on the title of the articles, and 133 were excluded for not being Table 1 The cumulative quality Quality N = 49 score of all studies obtained from the AXIS questionnaire High 7 (14%) Medium 31 (63%) Low 10 (20%) Very low 1 (2.0%) n (%) 1 3 E. Cainelli et al. Fig. 1 Study workflow neurophysiological studies investigating DD, for not being original research (reviews, meta-analyses, abstracts, or proceedings), or for not being in English. The full texts of the remaining 427 articles were screened, and further exclusion criteria excluded 378 addi- tional articles. Articles were excluded based on not being original quantitative research or case reports (n = 14), involving participants outside the age range selected without differen- tiation between ages (n = 85), diagnosing dyslexia in a way not defined according to inclu- sion criteria (n = 1), involving participants with comorbidities (n = 2), not focusing on EEG in DD (n = 265), and being irretrievable (n = 11). The final analysis included 49 studies. Quality of studies Individual scores for each included study are reported in Fig. 2. The cumulative quality score of all studies relative to the AXIS questionnaire is reported in Table 1. Only 14% were classified as high-quality level (> 15 positive answers), while the majority (63%) fell into the medium level. Finally, the quality of 20% was consid- ered low and, for 1 study, very low. The most common vulnerabilities are the sample size estimate, ethical approval information, and missing data management. The percentage of responses for each question is shown in Fig. 3. Some elements of the AXIS on the study design are positive in all studies due to part of the inclusion criteria. 1 3 EEG correlates of developmental dyslexia: a systematic review Fig. 2 “Yes,” “no,” and “not sure” responses to the 20 items of the AXIS questionnaire for each included study Typology of the studies Although EEG is a well-known methodology, we lack normative data to interpret quantitative findings, particularly for relatively new methodologies such as connec- tivity. Therefore, all studies are case–control comparisons of children with dyslexia and control children without the disorder. Only three studies did not compare children with dyslexia with controls. Still, children with dyslexia with nonspecific reading delay (Bosch-Bayard, 2018 2020) and children with dyslexia who have poor read- ing ability were compared to children with dyslexia who have capable reading ability (Mahmoodin 2016). The studies could be at rest (resting state) or during cognitive stimulation recorded by EEG: 12 out of the 49 studies used both approaches. Eighteen of the 49 stud- ies were published before 2000. Table 2 reports all the included studies and their main characteristics (authors, year of publication, sample size, age of the children at the evaluation, language used, methodology, and study quality following the AXIS questionnaire). 1 3 E. Cainelli et al. Fig. 3 Percentage of “yes,” “no,” and “not sure” responses obtained in the sample of studies for each ques- tion of the AXIS questionnaire Resting‑state EEG We found 24 studies investigating resting-state EEG (RS-EEG): 11 out of 24 studies focused solely on RS-EEG, while the other 13 out of 24 studies performed both an RS- EEG and an EEG during a task (in this section, only the results of the RS-EEG will be reported, whereas the results during a task will be described in the next section). Spectral analysis of the RS‑EEG The methodology used most frequently is spectral analy- sis, which shows the spectral content in the different frequency bands (delta, 1.5–4 Hz; theta, 4–7 Hz; alpha, 8–12 Hz; beta, 13–40 Hz; gamma, > 40 Hz). The methodology has been used alone (Arns et al., 2007; Bruni et al., 2009; Colon et al., 1979; Fein et al., 1983, 1986; Galin et al., 1988, 1992; Harmony et al., 1990; Mahmoodin et al., 2016, 2019; Papa- giannopoulou & Lagopoulos, 2016; Remschmidt & Warnke, 1992; Rippon and Brun- swick 2000) or combined with other methods (Babiloni et al., 2012; Bosch-Bayard et al., 2018; Flynn & Deering, 1989a; Flynn et al., 1992; Fraga González et al., 2016; Leisman, 2002; Reda et al., 2021; Xue et al., 2020). Figure 4 shows the results obtained by spectral analysis of RS-EEG. We also reported the non-significant results to render the data more readable. In general, it seems that DD is 1 3 EEG correlates of developmental dyslexia: a systematic review 1 3 Table 2 Authors, year of publication, sample size, age of the children at the evaluation, language used, methodology (type of EEG analysis, at rest/during a task condition), and study quality following the AXIS questionnaire of all studies included in the revision Authors, year Sample size Age Language Methods Quality Arns et al. (2007) 19 DD DD: 10.33 (8.0–15.98) Dutch Spectral power Medium 19 C C: 10.34 (8.0–16.03) At rest Ayers and Torres (1967) 129 DD DD: 104.1 (95–122) English Visual inspection Low 47 C C: 107.2 (96–114) At rest 31 R R: 106.9 (100–134) Babiloni et al. (2012) 26 DD DD: 11 years ± 0.5 Italian Spectral power High 11 C C: 11 years ± 0.7 Loreta At rest Bosch-Bayard et al. (2018) 169 DD DD: 9.1 (7–15) Italian Spectral power High 36 NSRD NSRD: 9.8 (7–15) Vareta At rest Bosch-Bayard et al. (2020) 184 DD DD: 9.1 + 1.9 Italian Coherence High 43 NSRD NSRD: 9.7 + 2.2 At rest Bruni et al. (2009a) 16 DD DD: 10.8 (8–16) Italian Sleep architecture parameters Medium 11 C C: 10.1 (7–16) Nocturnal sleep Bruni et al. (2009b) 16 DD DD: 10.8 (8–16) Italian Spectral power Medium 11 C C: 10.1 (7–16) Nocturnal sleep Colling et al. (2017) 13 DD DD: 119.3 (11.3) English Spectral power Medium 10 C C: 120.9 (7.9) During task Colon et al. (1979) 44 DD 7 and 11.0 Dutch Spectral power Low 49 C At rest Di Liberto et al. (2018) 25 DD 8.6 + 1.5 Australian English Spectral power Medium 45 C During task Duffy et al. (1980a) 11 DD / English Topographic maps Medium 13 C At rest During task Duffy et al. (1980b) 8 DD 9.0 and 10.7 English Topographic maps Medium 10 C At rest During task E. Cainelli et al. 1 3 Table 2 (continued) Authors, year Sample size Age Language Methods Quality Dushanova and Tsokov (2020) 22 DD 8–9 Bulgarian Connectivity Medium 21 C During task Dushanova et al. (2020) 22 DD 8–9 Bulgarian Coherence Medium 21 C During task Dushanova and Tsokov (2021) 22 DD 8–9 Bulgarian Connectivity Medium 21 C During task Eroglu et al. (2022) 16 DD 8.56 + 1.36 English Connectivity Medium 20 C At rest Farrag and El-Behary (1990) 21 DD 2nd and 3rd grades of Arabic Visual inspection Low 16 C elementary school At rest 23 R Fein et al. (1983) 31 DD 10–12 English Spectral power Medium 32 C At rest Fein et al. (1986) 34 DD 10–12 English Spectral power Low 35 C 9–13 At rest 22 DD 22 C Flynn and Deering (1989a) 21 DD 7.4–10.8 English Spectral power Very low 6 C During task Flynn and Deering (1989b) 12 DD disfonetic DD1:104 (93–130) English Spectral power Medium 4 DD disdeitic DD2:96 (89–110) Topographic mapping 5 DD mixed DD3:101 (90–120) At rest 6 C C: 113 (94–128) During task Flynn et al. (1992) 27 DD disfonetic 8.0–9.11 English Spectral power Medium 6 DD disedeitic At rest 6 C During task Fraga Gonzales et al. (2016) 26 DD DD 8.4 + 0.40 Dutch Spectral power High 15 C C 8.75 ± 0.31 Connectivity At rest EEG correlates of developmental dyslexia: a systematic review 1 3 Table 2 (continued) Authors, year Sample size Age Language Methods Quality Galin et al. (1988) 34 DD 1.10–12 English Spectral power Medium 35 C 2. 9–13 At rest 22 DD During task 22 C Galin et al. (1992) 34 DD 1.10–12 English Spectral power Medium 35 C 2. 9–13 At rest 22 DD During task 22 C Harmony et al. (1990) Good: 33 Regular: 23 6–12 English Spectral power Low Poor: 17 At rest Very poor: 8 Haynes et al. (1989) 12 DD 8–12 English Spectral power Low 12 C During task Jakovljevi´c et al. (2021) 18 DD 8–12 Serbian Spectral power Medium 18 C During task Klimesch et al. (2001) 8 DD DD: 11.6 + 0.5 German Spectral power Medium 8 C C: 11.36 + 0.33 During task Leisman (2002) 20 DD DD: 7.6 (7–10.9) English Spectral power Medium 20 C C: 8.2 (7–11.11) Coherence At rest During task Mahmoodin et al. (2016) 9 DD 7–11 Malay Spectral power Low 4 poor DD At rest 5 capable DD During task Mahmoodin et al. (2019) 11 poor DD P DD: 8 (7–12) Malay Spectral power Low 11 capable DD C DD: 8 (7–12) At rest 11 C C: 10.5 (7–12) During task Martinez-Murcia et al. (2020) 16 DD DD: 95.6 + 2.9 Spanish Spectral power Medium 32 C C: 94.1 + 3.3 Connectivity During task E. Cainelli et al. 1 3 Table 2 (continued) Authors, year Sample size Age Language Methods Quality Mattson et al. (1992) 8 DD DD: 11.3 (9–15) English Spectral power Medium 8 arithmetic dis Arit.: 12 (9–15) During task 10 C C: 12.4 (9–15) Ortiz et al. (1992) 14 DD DD:10.31 (9–11.7) Spanish Spectral power Medium 15 C C: 10.38 (9–12) At rest During task Papagiannopoulou and Lagopoulos (2016) 21 DD DD: 8 + 1.40 English Spectral power High 19 C C: 8 + 1.64 At rest Penolazzi et al. (2008) 14 DD DD: 10.12 + 2.23 Italian Spectral power Medium 28 C C: 10.01 + 0.18 During task Reda et al. (2021) 11 DD DD: 11.04 (9–14) Italian Sleep architecture parameters High 18 C C: 11.72 (9–14) Spectral power Nocturnal sleep Low Remschmidt and Warnke (1992) 30 DD DD:10.53 (9–12.11) German Spectral power 30 C C: 10.49 (9–12.11) At rest During task Rippon and Brunswick (2000) 19 DD DD: 10.66 + 1.46 Spectral power Medium 22 C C: 9.96 + 1.69 At rest During task Seri and Cerquiglini (1993) 10 DD 11–12.11 Italian Spectral power Medium 10 C Topographic mapping During task Shiota et al. (2000) 7 DD 7–14 Japanese Coherence Low 7 C At rest Spironelli et al. (2006) 10 DD DD: 9.25 ± 1.34 Italian Spectral power Medium 13 C C: 9.70 ± 1.17 During task Spironelli et al., (2008) 14 DD DD: 10.12 + 2.23 Italian Spectral power Medium 28 C C: 10.01 + 0.18 During task EEG correlates of developmental dyslexia: a systematic review 1 3 Table 2 (continued) Authors, year Sample size Age Language Methods Quality Taskov and Dushanova (2020) 22 DD 8–9 Bulgarian Connectivity Medium 21 C During task Taskov and Dushanova (2021) 25 DD 8–9 Bulgarian Spectral power Medium 21 C Connectivity During task Xue et al. (2020) 27 DD DD: 9.22 + 0.58 Chinese Spectral power High 40 C C: 9.38 + 0.49 Connectivity At rest Zainuddin et al. (2018) 17 DD 7–12 Malay Spectral power Medium 8 capable DD During task 8 C Zaric et al. (2017) 18 moderate DD mDD: 9.02 ± 0.45 Dutch Connectivity Medium 16 severe DD sDD: 8.92 ± 0.41 During task 20 C C: 8.80 ± 0.38 Legend: C, controls; DD, developmental dyslexia; NSRD, non-specific reading delay; R, remedial E. Cainelli et al. Fig. 4 The figure shows the results obtained in the studies using the spectral analysis of RS-EEG. In red the increases and blue the decreases obtained in children with dyslexia compared to controls; in black, if no dif- ferences were reported between the groups characterized by an increase in the delta frequency and theta and a reduction in alpha and beta. Other methodologies Results obtained from other methodologies are less homogeneous, and a summary is not possible; Table 3 reports the results of these studies (Arns et al., 2007; Ayers & Torres, 1967; Babiloni et al., 2012; Bosch-Bayard et al., 2020; Bruni et al., 2009; Duffy et al. 1980a; Eroğlu et al., 2022; Farrag & El-Behary, 1990; Fraga González et al., 2016; Gerald Leisman, 2002; Reda et al., 2021; Shiota et al., 2000; Xue et al., 2020). Correlation between resting EEG and reading performance Some rest studies correlate EEG activity with specific tests performed before or after recordings (Table 4). EEG during a task Thirty-two studies explored EEG brain activity during a task. Figure 5 reports the type of stimulation task and the methodology applied to the EEG. Most studies compared the EEG and the performance of DD and control children in linguistic, reading, or cog- nitive (Go-noGo, attention, reasoning, etc.) tasks (Dushanova & Tsokov, 2020, 2021; Dushanova et al., 2020; Flynn & Deering, 1989a, 1989b; Flynn et al., 1992; Galin et al., 1988, 1992; Jakovljević et al., 2021; Klimesch et al., 2001; Leisman, 2002; Mahmoodin et al., 2016; Ortiz et al., 1992; Penolazzi et al., 2008; Remschmidt & Warnke, 1992; Rippon & Brunswick, 2000; Seri & Cerquiglini, 1993; Spironelli et al., 2006, 2008; Taskov & Dushanova, 2020, 2021; Žarić et al., 2017). A smaller number of other stud- ies evaluate several other tasks: writing, speech, spelling, music, during the vision of an audio story, listening, and tapping (Colling et al., 2017; Di Liberto et al., 2018; 1 3 EEG correlates of developmental dyslexia: a systematic review 1 3 Table 3 The table below reports authors and year of publication, differences found in DD children from the comparison with controls, and the localization of the difference reported Author Measure differences Localization Arns et al. (2007) 1. Increased delta coherence 1. Bilateral fronto-central 2. Increased alpha and beta coherence 2. Right fronto-central Ayers and Torres (1967) Higher than expected incidence of abnormal electroencephalograms / Babiloni et al. (2012) Lower amplitude in low- and high-frequency alpha rhythms Parietal, occipital, and temporal cortical sources Bosch-Bayard et al. (2020) 1. More active hub: the calcarine sulcus is sending information to the right postcentral 1. Left calcarine sulcus gyrus, the left paracentral gyrus, the right angular gyrus, and the right supplemen- 2. Left rolandic operculum tary motor area in almost all frequency bands, including delta and theta band 2. Less active hub Bruni et al. (2009a) Increased spindle density during N2 sleep stage / Bruni et al. (2009b) 1. Lower number of sleep stage shifts per hour of sleep, percentage of N3, and number 2. Frontal areas of R periods 2. Overactivation of the ancillary frontal areas Duffy et al. (1980a) Increased alpha activity Bifrontal areas, left temporal and left posterior regions Farrag and El-Behary (1990) Immature EEG tracing by visual inspection Occipital area Fraga Gonzales et al. (2016) Reduced network integration and communication between network nodes in the theta / band Leisman (2002) Greater coherence within the hemisphere Left parieto-occipital Reda et al. (2021) Reduced slow spindles Occipito-parietal and left fronto-central areas Shiota et al. (2000) 1. Higher interhemispheric coherence values for alpha and beta 1. Temporal 2. Higher interhemispheric coherence values for beta 2. Frontal 3. Higher intrahemispheric coherence in alpha 3. Central, occipital and parietal Xue et al. (2020) Global network deficiencies in the beta band and the network topology was more / path-like E. Cainelli et al. 1 3 Table 4 The table reports the results obtained in DD children compared to controls in studies that correlate the EEG activity with specific tests performed before or after the recordings Author, year Task Results Arns et al. (2007) Rapid naming of letters (rnl), articulation (ART), phoneme deletion + delta coherence with all tests (PD), and spelling (SP) + theta coherence with ART and RNL + alpha with PD and RNL + beta coherence with RNL, PD, and SP Babiloni et al. (2012) Two lists of words and pseudowords and reading accuracy - alpha with a reading time of pseudo-words Bruni et al. (2009a) Memory and learning transfer reading test, word and non-word read- + sigma band in N2 with the word reading and MT reading tests ing test, word, non-word and sentences writing test, WISC-3 + spindle density with the word reading test Bruni et al. (2009b) Memory and learning transfer reading test, word and non-word read- + A1 index in sleep stage N3 with Verbal IQ, full-scale IQ, and ing test, word, non-word and sentences writing test; WISC-3; Child memory and learning transfer reading test Behaviour Checklist (CBCL) + cyclic alternating pattern rate in N3 with verbal IQ Fraga Gonzales et al. (2016) 3DM reading No correlation between connectivity (minimum spanning tree) and reading performance Harmony et al. (1990) Reading (reading comprehension and oral reading) and writing (copy- + theta in almost all leads in children with minor difficulties, no ante- ing, dictation, and functional writing) cedents, and good socioeconomic status + delta in left frontal and temporal areas (F3, F7, and T3) in children with a poor or very poor evaluation EEG correlates of developmental dyslexia: a systematic review Fig. 5 The stimulation task and the methodology applied to the EEG. The figure represents all conditions, so works using multiple tasks and analysis types could be overrepresented Duffy et al., 1980a, 1980b; Flynn et al., 1992; Galin et al., 1992; Haynes et al., 1989; Mahmoodin et al., 2019; Martinez-Murcia et al., 2020; Mattson et al., 1992). The results are not comparable, given the different tasks and EEG analysis method- ologies, but the differences between DD and control children appear mainly localized in the left temporoparietal sites. Table 5 reports the task used, the differences found in children with dyslexia com- pared to controls, and the localization findings. Comparing at‑rest and during‑task conditions Twelve works were performed both at rest and during the task conditions. Some authors found differences in both conditions, but more pronounced in the task condition (Duffy et al. 1992) or the rest condition (Leisman, 2002), whereas others found differences only during the task condition (Flynn et al., 1992; Galin et al., 1988; Ortiz et al., 1992). Finally, some studies did not clearly report what happened during the at-rest condition (Duffy et al. 1980b; Flynn & Deering, 1989b; Galin et al., 1992; Mahmoodin et al., 2016, 2019; Zainuddin et al., 2018). Other kinds of studies Some studies of EEG in children with dyslexia are not possible to include in the previ- ous paragraphs because of the different natures of the works. We will briefly describe them as follows. Bosch-Bayard et al. (2018) wanted to find a classification equation that discriminates the two groups with high accuracy. They obtained a discrimination equation that did not participate in the Boder classification algorithm, with a specificity and sensitivity of 0.94 to discriminate DD from the nonspecific reading delay. Using a statistically based technique, Duffy et al., (1980a, 1980b) searched for rules for the classification of children with dyslexia. They developed classification rules that suc- cessfully diagnosed 80 to 90% of the subjects. Eroglu et al. (2022) investigated possible disturbances in the complexity of EEG signals (connectivity measures) on multiple time scales in people with dyslexia and the potential 1 3 E. Cainelli et al. 1 3 Table 5 Authors and year, a brief description of the task used, the differences in EEG brain activity found in dyslexic children compared to controls, and the findings’ locali- zation Author Stimulation Measure differences Localization Colling et al. (2017) - Tapping to every second beat of a metro- Preferred phase at 2.4 Hz Frontal nome pulse - To listening passively to the beat Di Liberto et al. (2018) Audio-story while watching the correspond- Delta and theta reduction in DD Right hemisphere ing cartoon Duffy et al. (1980a) -Speech: listen and answer questions Theta and alpha increase in DD Left temporal, left posterior quadrant regions, -Music: listen and in the bifrontal area -Kimura figures instruction Dushanova and Tsokov (2020) To discriminate visually presented words Theta, alpha, beta1, and gamma1 strength Left anterior temporal and parietal regions and pseudowords putting a button. Before and betweenness reduction e post-treatment Dushanova et al. (2020) To discriminate auditory presented words 1. Stronger delta-entrainment for C and 1. In the left auditory cortex, anterior tempo- and pseudowords putting a button visual DD ral lobe, frontal, and motor cortices 2. Delta-entrainment deficit for DD 2. the left anterior temporal lobe, frontal, and 3. Higher delta-entrainment for phonologi- the right temporal cal DD 3. posterior temporal Dushanova and Tsokov (2021) To discriminate words and pseudowords put- Theta, alpha, beta degree and betweenness Left anterior temporal and parietal regions ting a button—before and after training centrality reduction Flynn and Deering (1989b) - Reading 1. Increase in theta for Dyseidetic DD 1. Left temporal-parietal - Spelling recognition 2. Increase in theta power dyseidetic DD 2. Left mid to posterior temporal and left - Drawing a clock during reading temporal-parietal Flynn et al. (1992) - Listening to a story Beta reduction during the reading task Right occipital-parietal and left temporal- - Silent reading of text parietal - Oral reading of text - Spelling recognition - Auditory analysis of orally presented words - Drawing a clock Galin et al. (1988) - Kohs block design Stronger alpha asymmetry Temporal leads - Narrative speech EEG correlates of developmental dyslexia: a systematic review 1 3 Table 5 (continued) Author Stimulation Measure differences Localization Galin et al. (1992) - Oral and silent reading of easy and hard Smaller change in theta and low beta power No differences between leads texts between tasks - Listening to a story - Narrative speech Haynes et al. (1989) 1. Vigilance condition; 2. listening to a story Decreased alpha amplitudes in both groups No differences between leads without an ending that had to be retold; 3. of subjects rehearse the story mentally and construct an appropriate ending Jakovljevi´c et al. (2021) Read a story, the text on each slide in differ - 1. Higher values of beta and the broadband Not reported ent colors EEG (0.5–40 Hz) power while reading in purple 2. Increasing theta range power while read- ing with the purple overlay Klimesch et al. (2001) - Reading numbers Large group differences in tonic and phasic Occipital sites - Reading words lower theta for pseudoword processing - Reading pseudowords Leisman (2002) - Rest-eyes closed 1. Greater theta and beta, decreased alpha Left parieto-occipital - Continuous performance tests 2. Lower coherence between hemispheres - Confrontation naming from the Stanford– but greater coherence within the same Binet hemisphere during all tasks - Spache tests (1966) Diagnostic reading tests Mahmoodin et al. (2016) - Rest eyes closed Higher beta in capable DD Frontal (FC6) and parietal (P4) right hemi- - Reading a non-word and writing it down sphere based on an auditory cue Mahmoodin et al. (2019) Listening to amplitude-modulated noise with Higher theta-beta ratio All leads slow-rhythmic prosodic, syllabic, or the phoneme rates E. Cainelli et al. 1 3 Table 5 (continued) Author Stimulation Measure differences Localization Martinez-Murcia et al. (2020) Listening to amplitude-modulated noise with 1. Reduced bilateral connection between 1. Temporal lobe slow-rhythmic prosodic, syllabic, or the electrodes 2. F7 electrode phoneme rates 2. Increased connectivity Mattson et al. (1992) Listening sentences preceded by a warning 1. Reducted 40-Hz activity during verbal 1. Left hemisphere task 2. Right hemisphere 2. Reducted activity in arithmetic disabled compared to DD and C during the nonver- bal task Ortiz et al. (1992) - Resting-state with eyes closed 1. Alpha responsiveness during the task 1. Left hemisphere - Resting condition with eyes open 2. High beta decrease during the task 2. Left posterior quadrant - Auditory phonemic discrimination task Penolazzi et al. (2008) To compare the word pairs based on 1. Greater overall delta amplitude 1. Anterior sites 1. orthographic 2. In the phonological task, larger delta ante- 2. Left anterior and left posterior 2. phonological rior and smaller posterior delta amplitude 3. semantic criteria Remschmidt and Warnke (1992) To mark discriminate letters into letter (3) Faster attenuation of relative alpha power No differences between leads strings increasing cognitive activation and reading (4) DD did not reveal characteristic focal EEG features Rippon and Brunswick (2000) - Phonological processing task 1. Lack of task-related reduction from rest- 1. Parieto-occipital - Visual search task (WISC picture comple- ing levels in the amplitude of alpha 2. Parieto-occipital R > L tion) 2. Marked asymmetry in beta activity 3. Frontal 3. In the phonological task, a theta increase Seri and Cerquiglini (1993) Word length recognition test requiring a Lack of desynchronization 1. Right frontal and temporal, left parietal gentle right finger-lift response Spironelli et al. (2006) - Phonological task (to decide whether the Delayed theta peak activity and was shifted Right instead of left word pairs rhymed) to the right hemisphere - Semantic task (to decide whether the target word was semantically related to the first) EEG correlates of developmental dyslexia: a systematic review 1 3 Table 5 (continued) Author Stimulation Measure differences Localization Spironelli et al. (2008) - Phonological task (to decide whether the 1. Significantly greater beta and theta over 1. Right frontal word pairs rhymed) the right hemisphere during phonological 2. Left posterior - Semantic task (to decide whether the target task word was semantically related to the first) 2. Greater beta and theta over the left hemi- - Orthographic task (to decide whether word sphere during phonological and ortho- pairs were written in the same case). Press- graphic tasks ing a button 3. Delay in behavioral responses, paralleled by sustained theta Taskov and Dushanova (2020) Reading aloud words—before and after 1. Higher leaf fraction, tree hierarchy, kappa, 1. Globally treatment and smaller diameter (theta–gamma fre- 2. Superior, middle, and inferior frontal areas quency) (less segregated neural network) in both brain hemispheres 2. Reduced degree and betweenness central- ity of hubs Taskov and Dushanova (2021) Reading aloud words—before and after Absent functional connectivity nodes Dorsal medial temporal area, left middle treatment derived from theta frequency network for occipitotemporal, parietal both conditions Zaric et al. (2017) Discriminating word from false font pressing 1. Weaker connectivity for words 1. Occipital to inferior-temporal a button 2. Stronger connectivity for words and false 2. From left central to right inferior-temporal fonts in severe DD and occipital sites E. Cainelli et al. positive effects of special neurofeedback and multisensory learning treatment. After treat- ment, the lower complexity of the experimental group increased to the typically developing group on lower and medium temporal scales in all channels. Fein et al. (1983) assessed the test–retest reliability of both absolute and relative spectra. They found excellent absolute and relative power reliability under properly controlled conditions. Finally, Zainuddin et al. (2018) used a support vector machine algorithm to classify EEG signals from typical, poor, and capable children with dyslexia while writing words and nonwords. Beta and theta-to-beta ratios formed the input features for the classifier. It was found that the best performance of the support vector machine was obtained with 91% overall accuracy when both kernel scale and box constraint were set to 1. Differences between dyslexia subtypes Only a few studies evaluated the differences between dyslexia subtypes. In their works, Bosch-Bayard et al. (2018) and Bosch-Bayard et al. (2020) focused on dyslexia with pho- nological deficits (dysphonetic) compared with children with nonspecific reading delays. By analyzing the power spectra, in 2018, they found that the DD group had significantly higher activity in the delta and theta bands than the nonspecific reading delays group in the frontal, central, and parietal areas bilaterally. Two years later, using measures of EEG connectivity, they found that the left calcarine sulcus was more active in the DD group, while the left rolandic operculum was more active in the nonspecific reading delays group. Instead, Flynn and Deering (1989a) and Flynn et al. (1992) compared two types of dys- lexia: dysphonetic and dyseidetic (with orthographic deficits). They found left temporal differences in children with dyseidetic dyslexia and right parietal-occipital differences for those with dysphonetic dyslexia, supporting predictions derived from a compensation- from-strength model of dyslexia. Discussion We performed a systematic review of the evidence using EEG in DD. Finally, we selected 49 works, both EEG studies at rest and during a task. The articles differed greatly in meth- odology, which makes a summary of the results challenging. However, some points have come to light. Even at rest, children with dyslexia and children in the control group exhib- ited differences in several EEG measures, particularly an increase in delta and theta and a reduction in alpha frequencies, without a clear localization. The same frequencies recorded at rest appear to be associated with learning performances. During reading-related tasks, differences between children with dyslexia and children in the control group appear more localized at the left temporoparietal sites, and the spectral frequencies appear differently involved. Theta range remained the frequency band that hosts the main number of differ - ences between children with dyslexia and children in the control group, but some work also found the involvement of the beta and gamma bands. Current research on electrophysiological correlates of language acquisition could help interpret our data. For example, many studies have been done on speech processing, a cog- nitive ability strictly associated with reading. Delta, theta, and gamma oscillations have been shown to be specifically engaged by the quasirhythmic properties of speech (Giraud & Poeppel, 2012). Different frequencies account for different properties of the language: 1 3 EEG correlates of developmental dyslexia: a systematic review the transformation of the auditory signal input into lexical and phrasal units occurs at a very low modulation rate, roughly 1–2 Hz. Frequencies in a slightly higher range (1.5–4; i.e., delta) account for prosodic perception (Ghitza & Greenberg, 2009) and (4–7; i.e., theta) for syllabic perception (Luo & Poeppel, 2007; Poeppel et al., 2008). Higher frequen- cies (30–40 Hz, the high beta/low gamma band) process stimulus information concurrently with the theta band, lying in a nesting relation such that the phase of theta shapes the prop- erties of gamma (Giraud & Poeppel, 2012). Frequency bands could have similar functions in the reading process. In fact, studies in children, adolescents, and adults with dyslexia converge to identify an atypical auditory neural synchronization of oscillations, suggesting deviant neural processing of both syllabic and phonemic rate information (De Vos et al., 2017; Di Liberto et al., 2018; Lehongre et al. 2011; Lizarazu et al., 2015; Molinaro et al., 2016). It has been proposed that if people with dyslexia parse speech at a frequency slightly higher or lower than the usual frequency rate, their phonemic representations could be abnormal (Ziegler et al., 2009). This anomaly would selectively complicate the grapheme- to-phoneme matching, leaving speech perception and production unaffected. These studies are compatible with the results from ERPs, which revealed altered processing of certain acoustic information relevant to speech perception in individuals with dyslexia, such as frequency changes and temporal patterns (Schulte-Körne & Bruder, 2010). Competing neurobiological hypotheses alternatively assign a crucial role to higher ver- sus lower frequency bands. It has been suggested that dyslexic people may be less respon- sive to modulations at specific frequencies that are optimal for phonemic analysis (30 Hz) (Lehongre et al., 2011) or that they may fail to reset gamma activity (Schroeder et al., 2010). Other authors, more in line with the results of our review, emphasized the role of lower frequencies and, in particular, theta oscillations. A deficit in theta is thought to alter low temporal modulation tracking syllable coding and even multisensory processing, with consequences for attention and auditory-visual integration (Goswami, 2011; Ziegler et al., 2009). De Vos and colleagues (De Vos et al., 2017) found in adolescents with DD atypical alpha (reduced) and beta (increased) synchronization. They advocated that the alpha reduc- tion could be related to phonological processing problems. At the same time, the over- synchronization of beta range oscillations could be a compensatory mechanism to improve the processing of phonemic rate information. Although different methodologies and age ranges, we also found numerous abnormalities in alpha and beta frequencies (Duffy et al., 1980a; Dushanova & Tsokov, 2021; Flynn et al., 1992; Galin et al., 1988, 1992; Haynes et al., 1989; Zulkifli Mahmoodin et al., 2019; Ortiz et al., 1992; Rippon & Brunswick, 2000; Spironelli et al., 2008; Taskov & Dushanova, 2021). Furthermore, alpha appears globally reduced in at rest conditions, whereas beta offers more contradictory results. If the theory of the compensatory beta effect is correct, it is possible that our younger samples do not exhibit compensatory effects yet. The relationship between the deficits in different band frequencies and the stages of learning to read could also explain the scarcity of results in the gamma band of our review: It may be that the 12-year filter has determined a specific trend in the type of deficit. Interestingly, in our review, the alterations in the frequency bands appear to be associated with learning performance, supporting the neurobiological meaning of these components. It is noteworthy that when considering EEG frequency bands, it is impor- tant to consider that the bands may not be a perfect match with those of the adult or older child. Particularly, a shift in frequency peaks with age has been shown (Campus et al., 2021; Clarke et al., 2001; Orekhova et al., 2006). The most heterogeneity in our review is in studies using stimulation. Most compared the EEG and the performance of DD and control children’s performance in linguistic, reading, 1 3 E. Cainelli et al. or cognitive tasks. A smaller number of other studies evaluate several other tasks: writing, speech, spelling, music, listening to an audio story or someone reading, and tapping. The majority of tasks explore functions directly involved in dyslexia or strictly connected. How- ever, there are also studies exploring different cognitive functions in DD children. These are interesting because they explore new hypotheses on dyslexia and its association with not obvious cognitive functions, like vigilance and visuospatial abilities. Unfortunately, the results are not comparable, given the different tasks and EEG analysis methodologies, but the differences between DD and control children appear mainly localized in the left tempo- roparietal sites. Still, caution should thus be taken in interpreting power differences between groups in the context of neural tracking differences, as they rely not only on distinct analytical approaches but also on different experimental paradigms. Probably for that reason, the data coming from our review does not capture the complex picture that emerges from the most recent research. For example, intriguing insights came from studies on the hemispheric spe- cialization of specific frequencies in DD. In summary, the left hemisphere appears to specialize in local high-frequency verbal com- putations, while the right hemisphere codes low frequencies of the speech envelope and inter- hemispheric cognitive control (Giraud & Poeppel, 2012). Impairment of the right hemisphere circuitry of frontoparietal attention networks has been hypothesized to be the primary cause of dyslexia (Goswami, 2011; Lehongre et al., 2011; Lizarazu et al., 2015; Molinaro et al., 2016; Power et al., 2016). Such a dysfunction would have a cascading negative effect on pho- nemic processing in the dorsal reading network (Kershner, 2019, 2020). Our evaluation of the quality of the studies highlights an overall weakness of the reported studies. Many studies are old, and the methodological sections do not follow current guide- lines of transparency and reliability of methods. The greatest weakness appears to be the small sample sizes of most studies; furthermore, almost none reported the method for select- ing the sample size. Methodological concerns, the intrinsic high interindividual variability of electrophysiological techniques and the developmental phase, the tendency to publish only positive findings, and the use of different methodologies render the possibility of synthesiz- ing and drawing conclusions very challenging. We could have included the grey literature to overcome these limitations, but we initially decided to limit the search to peer-reviewed published works. All these concerns hinder the possibility of establishing clear markers in EEG correlates of DD. However, a trend emerges despite differences in experimental condi- tions and analysis methodology (at rest, differences and involvement of the theta during read- ing-related tasks). The trend may reflect processing vulnerability in children with dyslexia or compensatory processing strategies that inappropriately activate areas of the reading network in this specific age range. Finally, only a few studies evaluated the differences between dyslexic subtypes. A wide range of literature highlights the presence of different subtypes of DD. The few existing studies support differences at rest and during reading tasks. These conditions have to be better addressed because of the possible different cores (endophenotype) involved and the consequent additional variability in the results if not considered. Conclusion This review seems to highlight some interesting insights: (a) there are abnormalities in spontaneous cerebral activity (“at rest”) of both temporal sites and more widerspread scalp placements in children with dyslexia, and (b) reading-related tasks elicited differences in 1 3 EEG correlates of developmental dyslexia: a systematic review frequencies considered crucial for speech processing, and the differences are localized in the temporoparietal sites. Although EEG localizations do not necessarily correspond to the underlying neuroanatomical regions, the finding of a left temporoparietal involvement is compatible with neuroimaging abnormalities, especially in the left and posterior regions. It should be noted that the current research on EEG correlates in DD is more advanced than is apparent from our review, which comprised a reduced number of works and some very old. This incongruency could denote a trend in research to select older participants, probably due to the greater simplicity of conducting studies with older and more collaborative chil- dren. Adolescents and adults are also suitable for more complex tasks. Furthermore, older age allows more certainty in diagnosis over time. However, we think that the range 6–12 is crucial because it represents the first appearance and diagnosis of the disorder and could offer impor - tant insights into the first phases of consolidation of both abilities and dysfunction. Therefore, we hope that future research addresses the functional role of atypical activation and involve- ment of specific frequencies in 6–12 years of DD to understand how fundamental brain pro- cesses are affected and how the brain compensates for those disruptions. Evaluation of the emergence and characterization of spectral EEG components and their deviation from the expected typical trajectory may be important to understanding early abnormalities of brain development, also in very early phases, as shown in the DD literature (Ozernov-Palchik & Gaab, 2016) and other research fields (Cainelli et al., 2021). This has the potential to lead to more effectiveness and could change the outcome trajectories for those with reading deficits. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s11881- 022- 00273-1. Acknowledgements The present work was carried out within the scope of the research program Diparti- menti di Eccellenza (art.1, commi 314-337 legge 232/2016), which was supported by a grant from Ministero dell’Istruzione, dell’Università e della Ricerca (MIUR) to the Department of General Psychology, Univer- sity of Padua. Funding Open access funding provided by Università degli Studi di Padova within the CRUI-CARE Agreement. Declarations Competing interests The authors declare no competing interests. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com- mons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. 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Journal

Annals of Dyslexia – Springer Journals

Published: Jul 1, 2023

Keywords: Neurophysiology; Spectra; Oscillations; Connectivity; Reading; Learning

References

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Resting-state EEG reveals global network deficiency in dyslexic children

Xue, H; Wang, Z; Tan, Y; Yang, H; Fu, W; Xue, L; Zhao, J
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EEG correlates of developmental dyslexia: a systematic review

EEG correlates of developmental dyslexia: a systematic review

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EEG correlates of developmental dyslexia: a systematic review

EEG correlates of developmental dyslexia: a systematic review

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