Microsoft Word - ELSR-V1N1-p61 Education, Language and Sociology Research ISSN 2690-3644 (Print) ISSN 2690-3652 (Online) Vol. 1, No. 1, 2020 www.scholink.org/ojs/index.php/elsr 61 Original Paper Psychophysiological Characteristics of Children with Dyslexia Pop-Jordanova N1*, Markovska-Simoska S1, Loleska S2 & Loleski M.3 1Macedonian Academy of Sciences and Arts, Skopje, North Macedonia 2 Public Health, Medical Faculty, University “Ss. Cyril and Methodius”, Skopje, North Macedonia 3 Forensics Department, Skopje, North Macedonia * Pop-Jordanova N, Macedonian Academy of Sciences and Arts, Skopje, North Macedonia Received: April 30, 2020 Accepted: May 7, 2020 Online Published: May 12, 2020 doi:10.22158/elsr.v1n1p61 URL: http://dx.doi.org/10.22158/elsr.v1n1p61 Abstract Dyslexia is a specific learning disorder that involves difficulty reading due to decoding problems for letters and words. Statistics shows that 5-10% of the general population has dyslexia. The aetiology of reading disorder supposes some biological causes and morphological markers useful in the classification and early identification of the problem. The aim of this article is to find appropriate parameters, which will be useful for early diagnosis and finding the right modalities for treatment. Our findings about QEEG characteristics are not conclusive. However, slowing of brain activity in dyslexic children appeared to be confirmed. These findings lead to the possible hypothesis of delay in neurological development of these children. Significant theta/beta ratio suggest possible comorbidity with ADHD. Further research with more children included is proposed. Keywords dyslexia, children, QEEG, Neurogame 1. Introduction Dyslexia is a specific learning disorder that involves difficulty reading due to decoding problems for letters and words. In the fifth revision of DSM (2013) the entity “Learning disorders” was changed in “Specific learning disorder” including Dyslexia, Dyscalculia and Disorder of Written expression. For exact diagnosis of this entity some core criteria must be fulfilled: the difficulty must persist at least 6 months and failed to improve despite intervention made; it must affects academic skills below those which are age-related and expected; the start of the problem must be in school age; other disorder like www.scholink.org/ojs/index.php/elsr Education, Language and Sociology Research Vol. 1, No. 1, 2020 62 Published by SCHOLINK INC. intellectual disabilities, auditory and visual problems, as well as other neurological disorder must be excluded. Statistics shows that 5-10% of the general population has dyslexia, but this number in some region can be as high as 17%. More precisely, the real incidence varies widely by country. For example, Italy registered only a half of the incidence found in the United States, where an estimated 5 to 15 percent of the population may have dyslexia to some degree. Scientists supposed that the difference of the incidence depends on the complexity in the language used. For example, English alphabet consists of 44 different sounds which can be differently written and pronounced. Having trouble differentiating sounds (phonemes) people have problems in orthography. However, this condition was not understood worldwide until the late 20th century even today. For the experiences in developmental neuropsychology, the acquisition of reading involves two systems: the lexical system (sight-reading) which process familial words, and phonological system, which comprises decoding unfamiliar words. Awareness of phonological structure require knowledge of the correspondence letter-sound. Developmental analysis can facilitate understanding of how reading disabled children compensate their problem. Developmental classification of reading and spelling difficulties clarify the stage where these academic skills are not completed. In this context, reading difficulties are manifested in the stage when advancing from early phase of acquisition, where reading is visually based (logographic), to the alphabetic phase, where letter-sound association are used. In the logographic stage, child lacks strategies to decode unknown words other than by visual approximation to known words. During alphabetic stage the child uses phoneme-grapheme to sound out words, and decode them from the left to right depending of the consistency between letters and sounds. The logographic stage involves instant recognition and have difficulty with nonwords, for which reason the spelling tends to be dysphonetic. The third phase established as orthographic, where features are automatic and flexible; in other words, it is needed the instant analysis into orthographic units (morphemes) without initial phonological conversion (Harris, 1998). The aetiology of reading disorder supposes some biological causes and morphological markers useful in the classification and early identification of the problem. Post mortem studies confirmed some abnormalities in perinatal brain anatomy and physiology, as well as some neurocortical deficits that lead to disruption of cognitive processing. In some cases, cell migration abnormalities and number on chromosome 15 has been identified. Transgenerational appearance of dyslexia in some families supports possible genetic basis of transmission, but exact findings are not yet published. However, several candidate genes for dyslexia susceptibility (e.g., ROBO1, DCDC2, DYX1C1, KIAA0319) have been suggested, and all of these to play an important role in the brain development (Galaburda, LoTurco, Ramus, Fitch, & Rosen, 2006; Hannula-Jouppi, Kaminen-Ahola, Taipale, Eklund, Nopola-Hemmi, Kaariainen, & Kere, 2005; Meng, Hager, Held, Page, Olson, Pennington, & Gruen, 2005; Skiba, Landi, Wagner, & Grigorenko, 2011). www.scholink.org/ojs/index.php/elsr Education, Language and Sociology Research Vol. 1, No. 1, 2020 63 Published by SCHOLINK INC. Magnetic Resonance Imaging (MRI) studies confirmed some asymmetry of the brain, using planum temporale as a marker (Rumsey, Dorwart, Vermess, Denckla, Krussi, & Rapaport, 1986). In newest studies, MRI in children and adults with dyslexia commonly demonstrate hypoactivation in left-hemispheric temporo-parietal, occipital-temporal, and inferior frontal networks. Further, reduced functional connectivity among these regions has also been demonstrated. Additionally, PET scan studies confirmed abnormalities in cerebral blood flow in the left temporoparietal region (Rumsey, 1992). An autoimmune aetiology has been proposed by Galaburda et al. (1993): some ischemic injury to the developing cortex produced by autoimmune damage to the wall of the arterial blood vessels supplying involved brain regions might results in scars and malformations and resulting as dyslexia (Galaburda & Livingstone, 1993). Having non-significant markers for dyslexia, EEG recording as a simple, cost-benefit method was largely used worldwide. The slowing of EEG is the most frequent abnormality in children with learning disabilities. In a review paper, Chabot et al. (Chabot, di Michele, Prichep, & John, 2001) showed poor EEG rhythm, low-voltage background rhythms (Hughes, 1978) and increased generalized slowing (Byring & Jarvilehto, 1985). Abnormalities in children with learning disorders included increased high amplitude atypical alpha, abnormal focal paroxysmal activity, excess focal delta, persistent delta asymmetry, and excessive EEG response to hyperventilation. EEG studies indicate that specific developmental disorders are associated with abnormal EEGs in 25% to 43.5% of these children. Neuropsychological testing is also used to establish the underlying characteristics of reading disorder. Having in mind that reading is a complex function of the nervous system, which require integration of visual and auditory processes, both central and peripheral, Boder (Boder & Jarrico, 1982; Boder, 1973) differentiated the following subtypes of dyslexia: dysphonic, dyseidetic, mixed and non-specific reading delay. For exact differentiation of the type, Bindelli and Chiarenza developed computerized Direct Test of Reading and Spelling (DTRS) for Italian language, which is a modification of original Boder test (Chabot, di Michele, Prichep, & John, 2001). Dyslexia is a school problem and it frequently disappears in the adulthood. Given what we know now, many famous people may have had dyslexia, including Leonardo da Vinci, Saint Teresa, Napoleon, Winston Churchill, Carl Jung, Albert Einstein, and Thomas Edison, as well as Steven Spielberg, Muhammad Ali, Keira Knightley, Danny Glover and other in a new time. There are evidence-based treatments which are effective, even for adults with the condition (like logopaedic exercises, biofeedback, etc.). Comorbidity with attention deficit hyperactivity disorder, anxiety and depression, disruptive problems with impulse-control, conduct disorder, and autism spectrum disorders is frequently found with learning disabilities, especially dyslexia (Hendren, Haft, Black, White, & Hoeft, 2018). In Macedonian speaking population dyslexia has not been exactly diagnosed until the two last decades. Macedonian language comprises letter for every sound and it seems not to be so difficult for reading and writing. But, in the last time, school teachers started to differentiate a group of children with difficulty in reading and writing which not corresponded with the chronological age expectances. In this context, psychologists as well as special educators started with the diagnostics and treatment of these children. www.scholink.org/ojs/index.php/elsr Education, Language and Sociology Research Vol. 1, No. 1, 2020 64 Published by SCHOLINK INC. Unfortunately, in our country the special test for dyslexia does not exists. The most used test is Macedonian translation of test developed by Kostic, Vladisavljevic and Popovic (1983) (Kostic, Vladisavljevic, Popovic, & Cudov, 1983). Some experiences in the assessment of children with dyslexia, dysgraphia and dyscalculia in our context, were published by a group of researchers from the Institute for Special Education, UKIM, Skopje in 2018 (Karovska-Ristovska, Kardaleska, Ajdinski, & Shurbanovska, 2018). For this reason, and the scarce of data for dyslexia in our country, the aim of this research is to find appropriate parameters, which will be useful for early diagnosis and finding the right modalities for treatment. Such parameters would be possible specific abnormalities in EEG recordings, as well as the performances of these children tested with own modality we named as “Neurogame” which helps to evaluate concentration, focus attention and reaction time to some tasks. As far as we know, this is the first study that evaluates this issue in our country. 2. Method 2.1 Sample We selected randomly 10 children diagnosed as dyslexic according to ICD-10 and DSM-V criteria, referred by the Institute for child mental health in Skopje. The diagnostic was made from the team consisted of logopaedist, psychologist, child neurologist, paediatrician as well as child psychiatrist. Mean age of boys was 10.2±1.64 years and mean age of girls was 9.2±1.60 years. The written consent was obtained from parents. In the moment of evaluation children were in good health and without any medication 48 hours before recording. 2.2 Evaluation EEG was recorded using a Mitsar 201 (www.mitsar-medical.com), a PC-controlled 19-channel electroencephalographic system with 19 electrodes, placed according to the international 10-20 system, referenced to linked ears (on the International 10-20 system) with 250 Hz sampling rate in 0.5-50 Hz frequency range in the following conditions: Eyes opened (EO)—5 minutes, and Eyes closed (EC)—5 minutes as well as stimuli presentation protocol (Visual Continuous Performance test—VCPT). The obtained data from VCPT, are not aimed for analysis in this paper and this data will be analysed in another paper. The same equipment and procedures were used for children with dyslexia and controls. Subjects were tested in a quiet air-conditioned room with the experimenter and recording equipment present. During fitting of the electrodes, subjects were familiarized with the testing equipment and the procedure. Vertical Electro-Oculogram (VEOG) was recorded with 2 tin electrodes placed 1 cm above and 1 cm below the right eye. Eye-blink artifacts were corrected by zeroing the activation curves of individual ICA component score responding to eye blinks. In addition, epochs of the filtered electroencephalogram with excessive amplitude (>100 μV) and/or excessively fast (>35 μV in 20-35 Hz band) and slow (>50 μV in 0-1 Hz band) frequency activities were automatically marked and www.scholink.org/ojs/index.php/elsr Education, Language and Sociology Research Vol. 1, No. 1, 2020 65 Published by SCHOLINK INC. excluded from further analysis. Finally, EEG was manually inspected to verify artifact removal. Spectral analysis of relative power using fast Fourier transform was carried out for the four frequency bands: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), and beta (12-20 Hz). Relative power is represented by the percentage of the amplitude in a given frequency band compared with the total amplitude across all frequency bands. Also, we calculated the ratio between theta and beta absolute power in order to obtain the theta-beta ratio (TBR) at Cz. In some article it was published that asymmetric feature for QEEG recording was typical for dyslexic children. Asymmetry is defined as a functional difference between the left and right hemispheres measured for relative power which exists between the homologous electrodes located on both hemispheres. It was calculated using the following equation: Power (Left) – Power (Right) / Power (Left) + Power (Right) where Power (Left) corresponds to the relative power of the electrode located on the left hemisphere, and Power (Right) to the relative power on the right hemisphere. These asymmetry data were statistically analysed. Before the QEEG recording, “Neurogame” was applied. Our original developed application on Android operating system, named “Neurogame” is based on an open source platform to enable assessment the focus and concentration, as well as reaction time, with the ability to monitor the progress of the results over a period of time. The testing for all clients was performed in the morning period 8-12 am (Hughes, 1978). The complete evaluation of children takes around 2 hours. 2.3 Data Analysis The Statistica StatSoft software was used to assess group differences. One-way analysis of variance (ANOVA) was carried out on relative EEG power for each band (delta, theta, alpha beta) in eyes-open condition in 5 regions (frontal [F]: (F3, F4, F7, F8); central [C]: (C3, Cz, C4); temporal [T]: (T3, T4, T5, T6), parietal [P]: (P3, Pz, P4) and occipital [O]: (O1,O2) regions. Additionally, we include and electrodes above Broca’s area (F3, F7 and C3) and Wernicke’s area (T3, T5 and P3). Group (dyslexia and control) was the between-subject factor. For some estimations because of the small sample size, the non-parametric Mann-Whitney U test was used to lower variability in the groups. The level of significance was set at p < .05. 3. Results As mentioned before, the evaluated sample is small, consisting of 10 children, where mean age of boys was 10.2±1.64 years and mean age of girls was 9.2±1.60 years. The results are compared with matched control group consisting of 10 children with normo-typical development without any learning problems or neurodevelopmental delay. Mean age of boys in the control group was 10.4±1.84 years and mean www.scholink.org/ojs/index.php/elsr Education, Language and Sociology Research Vol. 1, No. 1, 2020 66 Published by SCHOLINK INC. age of girls 9.8±1.30 years. They are paired with the examined group according to the age and gender without any significant difference (Current effect: F (1, 18) = .19240, p = .66615). Relative EEG power was estimated in eyes open condition, because in eyes closed condition alpha power usually prevails the other frequencies. Results obtained for Delta and Theta waves in frontal, central and temporal position are presented on Figure 1. As can be seen central, temporal, parietal and occipital slow waves are significantly greater in dyslexic children in comparison to control group (see graphs for p values). Group; LS Means Wilks lambda=.68796, F(4, 15)=1.7009, p=.20208 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals F3 Delta F4 Delta F7 Delta F8 Delta Dyslexia Control group Group 0 50 100 150 200 250 300 Group; LS Means Wilks lambda=.45895, F(4, 15)=4.4208, p=.01470 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals F3 Theta F4 Theta F7 Theta F8 Theta Dyslexia Control group Group 15 20 25 30 35 40 45 50 Group; LS Means Wilks lambda=.56551, F(3, 16)=4.0977, p=.02459 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals C3 Delta Cz Delta C4 Delta Dyslexia Control group Group 20 40 60 80 100 120 140 160 180 200 Group; LS Means Wilks lambda=.86437, F(3, 16)=.83686, p=.49323 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals C3 Theta Cz Theta C4 Theta Dyslexia Control group Group 20 25 30 35 40 45 50 55 60 Group; LS Means Wilks lambda=.45356, F(4, 15)=4.5180, p=.01356 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals T3 Delta T4 Delta T5 Delta T6 Delta Dyslexia Control group Group 20 40 60 80 100 120 140 160 180 200 220 Group; LS Means Wilks lambda=.81741, F(4, 15)=.83765, p=.52233 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals T3 Theta T4 Theta T5 Theta T6 Theta Dyslexia Control group Group 15 20 25 30 35 40 45 50 www.scholink.org/ojs/index.php/elsr Education, Language and Sociology Research Vol. 1, No. 1, 2020 67 Published by SCHOLINK INC. Group; LS Means Wilks lambda=.60939, F(3, 16)=3.4186, p=.04290 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals Delta P3 Delta Pz Delta P4 Dyslexia Control group Group 20 40 60 80 100 120 140 160 180 200 220 Group; LS Means Wilks lambda=.86695, F(3, 16)=.81850, p=.50242 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals Theta P3 Theta Pz Theta P4 Dyslexia Control group Group 24 26 28 30 32 34 36 38 40 42 44 46 Group; LS Means Wilks lambda=.65220, F(2, 17)=4.5328, p=.02644 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals Delta O1 Delta O2 Dyslexia Control group Group 60 80 100 120 140 160 180 200 220 240 260 Group; LS Means Wilks lambda=.49892, F(2, 17)=8.5368, p=.00271 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals Theta O1 Theta O2 Dyslexia Control group Group 20 25 30 35 40 45 50 55 Figure 1. Relative Power for Delta and Theta in Eyes Open Condition for Compared Groups in Frontal, Central, Temporal, Parietal and Occipital Positions For alpha (frontal, central and temporal) and beta we obtained almost the same results for both groups without any significance (Figure 2). Group; LS Means Wilks lambda=.65914, F(4, 15)=1.9392, p=.15609 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals F3 Alpha F4 Alpha F7 Alpha F8 Alpha Dyslexia Control group Group 10 12 14 16 18 20 22 24 26 28 30 32 Group; LS Means Wilks lambda=.75501, F(4, 15)=1.2169, p=.34479 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals F3 Beta F4 Beta F7 Beta F8 Beta Dyslexia Control group Group 8 10 12 14 16 18 20 22 24 www.scholink.org/ojs/index.php/elsr Education, Language and Sociology Research Vol. 1, No. 1, 2020 68 Published by SCHOLINK INC. Group; LS Means Wilks lambda=.91925, F(3, 16)=.46852, p=.70836 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals C3 Alpha Cz Alpha C4 Alpha Dyslexia Control group Group 10 15 20 25 30 35 40 45 50 Group; LS Means Wilks lambda=.62919, F(3, 16)=3.1432, p=.05431 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals C3 Beta Cz Beta C4 Beta Dyslexia Control group Group 4 6 8 10 12 14 16 18 20 Group; LS Means Wilks lambda=.89210, F(4, 15)=.45357, p=.76842 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals T3 Alpha T4 Alpha T5 Alpha T6 Alpha Dyslexia Control group Group 5 10 15 20 25 30 35 40 Group; LS Means Wilks lambda=.79533, F(4, 15)=.96503, p=.45508 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals T3 Beta T4 Beta T5 Beta T6 Beta Dyslexia Control group Group 6 8 10 12 14 16 18 20 22 24 26 28 Group; LS Means Wilks lambda=.98935, F(3, 16)=.05741, p=.98128 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals Alpha P3 Alpha Pz Alpha P4 Dyslexia Control group Group 15 20 25 30 35 40 45 50 Group; LS Means Wilks lambda=.85304, F(3, 16)=.91884, p=.45405 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals Beta P3 Beta Pz Beta P4 Dyslexia Control group Group 6 8 10 12 14 16 18 20   Group; LS Means Wilks lambda=.69916, F(2, 17)=3.6574, p=.04774 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals Alpha O1 Alpha O2 Dyslexia Control group Group 10 15 20 25 30 35 40 Group; LS Means Wilks lambda=.60977, F(2, 17)=5.4396, p=.01493 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals Beta O1 Beta O2 Dyslexia Control group Group 6 8 10 12 14 16 18 20 22 Figure 2. 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