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Different brain activation patterns in dyslexic children: Evidence from EEG power and coherence patterns for the double-deficit theory of dyslexia

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QEEG and neuropsychological tests were used to investigate the underlying neural processes in dyslexia. A group of dyslexic children were compared with a matched control group from the Brain Resource International Database on measures of cognition and brain function (EEG and coherence). The dyslexic group showed increased slow activity (Delta and Theta) in the frontal and right temporal regions of the brain. Beta-1 was specifically increased at F7. EEG coherence was increased in the frontal, central and temporal regions for all frequency bands. There was a symmetric increase in coherence for the lower frequency bands (Delta and Theta) and a specific right-temporocentral increase in coherence for the higher frequency bands (Alpha and Beta). Significant correlations were observed between subtests such as Rapid Naming Letters, Articulation, Spelling and Phoneme Deletion and EEG coherence profiles. The results support the double-deficit theory of dyslexia and demonstrate that the differences between the dyslexia and control group might reflect compensatory mechanisms. INTEGRATIVE SIGNIFICANCE: These findings point to a potential compensatory mechanism of brain function in dyslexia and helps to separate real dysfunction in dyslexia from acquired compensatory mechanisms.
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Journal of Integrative Neuroscience, Vol. 6, No. 1 (2007) 175–190
c
Imperial College Press
Research Report
DIFFERENT BRAIN ACTIVATION PATTERNS IN DYSLEXIC
CHILDREN: EVIDENCE FROM EEG POWER AND
COHERENCE PATTERNS FOR THE DOUBLE-DEFICIT
THEORY OF DYSLEXIA
MARTIJN ARNS
The Brain Resource Company B.V./Brainquiry B.V.
Nijmegen, 6525 EC, The Netherlands
marns@qeeg.nl
www.qeeg.nl
SYLVIA PETERS
Radboud University Nijmegen, Educational Sciences
Nijmegen, 6525 EC, The Netherlands
s.a.f.peters@chello.nl
RIEN BRETELER
Radboud University Nijmegen/EEG Resource Institute
Nijmegen, 6525 EC, The Netherlands
r.breteler@e egresource.nl
LUDO VERHOEVEN
Radboud University Nijme gen/Behavioral Science Institute
Nijmegen, 6525 EC, The Netherlands
L.Verhoeven@pwo.ru.nl
Received 23 October 2006
Revised 20 December 2006
Aims: QEEG and neuropsychological tests were used to investigate the underlying neural
processes in dyslexia.
Methods: A group of dyslexic children were compared with a matched control group from
the Brain Resource International Database on measures of cognition and brain function
(EEG and coherence).
Results: The dyslexic group showed increased slow activity (Delta and Theta) in the frontal
and right temporal regions of the brain. Beta-1 was specifically increased at F7. EEG
coherence was increased in the frontal, central and temporal regions for all frequency bands.
There was a symmetric increase in coherence for the lower frequency bands (Delta and
Theta) and a specific right-temporocentral increase in coherence for the higher frequency
bands (Alpha and Beta). Significant correlations were observed between subtests such as
Corresponding author.
175
176 Arns et al.
Rapid Naming Letters, Articulation, Spelling and Phoneme Deletion and EEG coherence
profiles.
Discussion: The results support the double-deficit theory of dyslexia and demonstrate
that the differences between the dyslexia and control group might reflect compensatory
mechanisms.
Integrative Significance: These findings point to a potential compensatory mechanism of
brain function in dyslexia and helps to separate real dysfunction in dyslexia from acquired
compensatory mechanisms.
Keywords: Dyslexia; EEG; QEEG; coherence; double-deficit theory.
1. Introduction
Developmental dyslexia is characterized by difficulties with accurate and/or fluent
word recognition, by poor spelling and decoding abilities. These difficulties typi-
cally result from a deficit in the phonological component of language that is often
unrelated to other cognitive abilities [20]. Dyslexia is probably the most common
neurobiological disorder affecting children, with prevalence rates ranging from 5 to
10 percent, and is a persistent, chronic condition [33].
Reading problems manifest themselves mainly in the following areas: difficulty
in learning to utilize correspondence regularities between graphemes and phonemes
[12, 37] poor phonological awareness, i.e., awareness of the sound structure of words,
especially phonemic awareness as manifested in the ability to analyze and manipu-
late sounds within a syllable [34] and poor use of orthographic word reading strate-
gies; and consequently inaccurate and non-fluent word identification [22, 26]. As a
result of these difficulties, full alphabetic or phonological reading skills are often
not attained. A large body of research has been conducted on the relation between
phonological awareness and learning to read. Strong support has been provided that
lack of phonological awareness can cause difficulties with the acquisition of reading
and writing [29, 36]. Being able to distinguish and identify the different phonemes
in a word is part of this awareness. Research in the past decades has provided ample
evidence that dyslexic children have problems with phonological awareness and other
aspects of phonological processing. There is a general agreement that this phonolog-
ical processing deficit has to do with problems in phonological encoding [34]. Poor
readers are less precise in phonemic discrimination, they have problems on a variety
of phoneme segmentation and awareness tasks [40], and they are slower in rapid
naming of objects, digits and letters [42, 43], as well as in producing rhyming words
[15]. It can be hypothesized that dyslexia is fundamentally a linguistic problem
which involves a deficit in phonological encoding. Elbro, Borstrøm and Petersen [6]
tested this hypothesis by predicting dyslexia from phonological processing abilities
of kindergarteners. It was shown that three language measures contributed indepen-
dently to predict dyslexia: letter naming, phoneme identification, and distinctness of
phonological representations. The results further indicated that the quality of phono-
logical representations in the child’s mental lexicon may also be a determinant of the
EEG Power and Coherence in Dyslexia: Double-Deficit 177
development of phonemic awareness. Alternatively, there is the claim that reading
problems originate from a more general temporal processing deficit [35].
Dyslexia has been attributed to deficiencies in visual, linguistic, and low level
sensory functions but most studies have been falsified empirically and logically
[38]. Most research emphasizes the phonological deficit in children with dyslexia
[1, 20, 33, 34], that is segmenting spoken words into their underlying phonological
elements and linking each letter to its corresponding sound. A number of studies also
support the double-deficit theory. The double-deficit theory proposes that reading
disabilities can be the result of: 1) poor phonological awareness and/or, 2) auto-
matic naming skills. Poor phonological awareness refers to disabilities in identifying
and manipulating sounds in speech, whereas poor automatic naming implies the dis-
ability to translate visual information into a phonological code. The double-deficit
hypothesis proposes that accordingly, subtypes of dyslexia can be distinguished
showing a deficit in either one or both of these skills [4, 43]. It is also claimed
that a deficit in both skills yields the lowest reading performance. The dyslexic sub-
types could be produced by differential processing deficits in the frontal-cerebellar
phonological system [4]. The unique contribution of each frontal and cerebellar mea-
sure to the classification of dyslexic participants and the prediction of phonological
and naming performance support this view.
Previous research has also linked dyslexia and reading disabilities to neurologi-
cal data. There are anatomical studies [7, 13] which show an absence of the usual
left-right hemisphere asymmetry of the planum temporale in dyslexia or suggest a
possible role of the left inferior frontal gyrus in speech perception and rapid audi-
tory processing, as well as in phonological aspects of reading [13], although no strong
effects have been reported [13]. Eckert et al. [4] found anatomical anomalies under-
lying the double-deficit subtype of dyslexia. Their findings suggest that impairments
in a frontal-cerebellar network may play a role in delayed reading development in
dyslexia.
To study the neural factors of dyslexia, functional neuroimaging has been used.
However, there is not much evidence with respect to developmental dyslexia since
this research has focused on (young) adults [13]. Only Shaywitz and Shaywitz [32, 33]
used children in their neuroimaging studies in order to examine the neural systems
for reading during the acquisition of literacy. These reports show a failure of left
hemisphere posterior brain systems to function properly during reading [32, 33].
The majority of studies show increased activation in the basal surface of the tempo-
ral lobe, the posterior portion of the superior and middle temporal gyri, extending
into temporoparietal areas and the inferior frontal lobe during tasks requiring read-
ing and phonological processing [38]. Shaywitz et al. [32] supports these findings,
however they show evidence of right hemisphere activation in the posterior temporal
parietal regions. This could reflect compensatory processes or could indicate that
other nonlinguistic factors are related to reading disability [32, 33, 38].
A few studies have focused on event related EEG changes in tasks directly related
to the reading difficulties of dyslexic children. Rippon and Brunswick [28] found
178 Arns et al.
that dyslexic children showed increased frontal theta activity in a phonological task,
whereas there were no differences between the dyslexic group and the control group
in a visual task. Furthermore, there was a marked parieto-occipital right greater than
left asymmetry in beta EEG activity in the dyslexic group with respect to the phono-
logical task and the visual task. Klimesch et al. [17] found that dyslexics have a lack
of attentional control during the encoding of words at left occipital sites and a lack of
a selective topographic activation pattern during the semantic encoding of words.
EEG coherence is a measure which displays functional connectivity between brain
areas, and could hence be an interesting measure to demonstrate deviation in func-
tional connectivity. To date, few EEG studies have considered EEG coherence. Sklar
et al. [31] found higher intrahemispheric coherence and lower interhemispheric coher-
ence during text processing in dyslexics compared with normals. This was also sup-
ported by Leisman and Ashkenazi [19]. During rest, Shiota, Koeda, and Takeshita
[30] reported both increased intra- and inter-hemispheric coherence in dyslexic chil-
dren. Furthermore, Marosi et al. [23] found a frequency-dependent effect on EEG
coherence at rest where differences between children with poor reading/writing abil-
ities were compared with children with good reading/writing abilities, with the for-
mer showing higher coherence in the delta, theta and beta bands and lower coherence
in the alpha bands during rest [39].
Weiss and Mueller [39] proposed that EEG coherence in the different frequency
bands played different roles: increased coherence in the theta band correlates with
language-related mnemonic processes and theta coherence was increased if task
demands increased and more efficient information processing was required. Alpha
coherence seemed important for sensory processing and higher alpha coherence for
semantic processing. Beta and gamma coherence has been linked with more complex
linguistic sub-processes such as syntax or semantics [39].
Our aim was to compare brain function of dyslexic children with non-dyslexic
children on different neurophysiological and neuropsychological measures. Our ques-
tion focused on whether different EEG activation patterns can be found in dyslexia,
and to what extent correlations between reading and spelling abilities and specific
tasks for rapid naming and phonological awareness, can be found to address the
double-deficit theory of dyslexia [43]. We also assessed neuropsychological function
in these groups in order to exclude further cognitive differences between the groups
potentially confounding the EEG findings. Our hypothesis was that the groups will
not show differences on neuropsychological measures, but that children with dyslexia
will show increased inter- and intrahemispheric coherence.
2. Materials and Methods
2.1. Subjects
Nineteen children with dyslexia (11 males and 8 females; average age = 10.33; range
8.0–15.98) and nineteen control children (matched on age, gender and education;
EEG Power and Coherence in Dyslexia: Double-Deficit 179
11 males and 8 females; average age 10.34; range 8.01–16.03) were used to inves-
tigate the differences in brain function and neuropsychological performance. All
dyslexic children went to regular schools. They were diagnosed with dyslexia by
their remedial teachers, who worked with a structured protocol for diagnosing chil-
dren with dyslexia on the basis of their reading and spelling development from
grade 1 [44]. The control group was drawn from the Brain Resource International
Database (BRID: www.brainresource.com, for more details also see [9, 10]) and
children were chosen from this database who did not have dyslexia or learning
disorders.
Exclusion criteria included a personal or family history of mental illness, brain
injury, neurological disorder, serious medical condition, drug/alcohol addiction; and
a family history of genetic disorder. All subjects voluntarily gave written informed
consent.
Subjects were seated in a sound and light attenuated room, controlled at an
ambient temperature of 22
C/72
F. Electroencephalographic and neuropsychologi-
cal assessments were completed in order.
2.2. Language tests
The group of children with dyslexia was submitted to a range of tests to inves-
tigate correlations between EEG and neuropsychological findings of dyslexia. The
included tests were measures of tasks related to reading: Rapid Naming of Letters,
Articulation, Phoneme deletion [16] and Spelling [8].
2.3. Electroencephalographic data acquisition
Participants were seated in a sound and light attenuated room, controlled at an
ambient temperature of 22
C. EEG data were acquired from 28 channels: Fp1, Fp2,
F7, F3, Fz, F4, F8, FC3, FCz, FC4, T3, C3, Cz, C4, T4, CP3, CPz, CP4, T5,
P3, Pz, P4, T6, O1, Oz and O2 (Quikcap; NuAmps; 10–20 electrode international
system). Data were referenced to averaged mastoids with a ground at Fpz. Horizontal
eye-movements were recorded with electrodes placed 1.5 cm lateral to the outer
canthus of each eye. Vertical eye movements were recorded with electrodes placed
3 mm above the middle of the left eyebrow and 1.5 cm below the middle of the
left bottom eye-lid. Skin resistance was < 5 K Ohms and above 1 K Ohm for all
electrodes. A continuous acquisition system was employed and EEG data were EOG
corrected offline [11]. The sampling rate of all channels was 500 Hz. A low pass
filter with attenuation of 40 dB per decade above 100 Hz was employed prior to
digitization.
The EEG data were recorded for two minutes with eyes open (EO). Subjects were
asked to sit quietly. Subjects were asked to fix their eyes on a red dot presented on
a computer screen.
180 Arns et al.
2.4. Electroencephalographic variables
Each two minute epoch was divided into adjacent intervals of four seconds. Power
spectral analysis was performed on each four second interval by first applying a
Welch window to the data, and then performing a Fast Fourier Transform (FFT),
next the average power spectra were calculated.
The power was calculated in the following frequency bands delta (1.5–3.5 Hz),
theta (4–7.5 Hz), alpha (8–13 Hz), alpha1 (8–11 Hz), alpha2 (11–13 Hz), SMR
(12–15 Hz), beta (14.5–30 Hz), beta1 (14.5–20 Hz), beta2 (20–25 Hz) and beta3 (25–
30 Hz). The data were then square-root transformed to approximate the normal
distributional assumptions required by parametric statistical methods.
2.5. Neuropsychology
Neuropsychological assessment was done using a touch screen monitor. Besides the
subtests for dyslexia, other neuropsychological tests were included in order to estab-
lish that the children were otherwise completely normal. Measures included: memory
recall and memory recognition (number of correctly reproduced words on trials 1, 5,
6, 7; number or correctly recognized words), verbal interference test equivalent
to the Stroop test (Number correct text and color condition), tapping test (Number
of taps with the dominant and nondominant hand), timing test (proportional bias)
and switching of Attention test part A and B (equivalent to the WAIS Trails A
and B; time to complete the A and B form) (see [9, 10] for details of these tests).
All tests were fully computerized and subjects’ responses were recorded via touch-
screen presses. Reliability and validity data of these tasks are reported elsewhere
[2, 10, 25].
3. Statistical Analysis
3.1. Missing values
If missing values were present for a given statistical test, those cases were excluded
for that analysis. The number of missing values per group are included in the results
sections.
3.2. Statistical analyses
Since we expected quite local effects on some measures due to the localized differ-
ences in brain function for dyslexia, we did not perform the traditional GLM, since
small localized effects could average out in the overall tests. Therefore, we performed
one-way ANOVA’s but used very stringent alpha correction. Significance levels were
set as follows: for the EEG power data, the significance level was set to p<0.05 and
for the coherence data, significance levels were set to p<0.001. For EEG coherence
there were many more data points per frequency band (> 100 coherence values),
hence the lower p value for coherence compared to EEG power.
EEG Power and Coherence in Dyslexia: Double-Deficit 181
The obtained significant differences between the dyslexia and control group were
then submitted to a bivariate correlation analysis together with the severity ques-
tionnaire data, and a correlation matrix was obtained for correlations between vari-
ables within the group of dyslexic children.
4. Results
4.1. EEG power
An overview of Delta and Theta power for all sites is depicted in Fig. 1 and the
significant differences are indicated.
The following differences between the two groups were found:
Delta: increased Delta power for the dyslexia group at Fp1 (F = 6.315, df = 1, 33,
p =0.017), Fp2 (F = 4.861, df = 1, 34, p =0.034), F7 (F = 4.806, df = 1, 34,
p =0.035) and T6 (F = 6.193, df = 1, 35, p =0.018).
Fig. 1. Mean EEG power for Delta and Theta for the dyslexic group (in red) and the control
group (in black). All findings have p<0.05.
182 Arns et al.
Theta: increased Theta at Fp1 (F = 11.072, df = 1, 33, p =0.002), Fp2 (F =
5.074, df = 1, 34, p =0.031) and F7 (F = 8.267, df = 1, 34, p =0.007).
Beta 1: increased beta-1 at F7 (F = 4.450, df = 1, 34, p =0.042).
4.2. EEG coherence data
Figure 2 gives an overview of the significant increases in coherence per frequency
band. All increases have p values of p<0.001. Due to the many significant findings,
no detailed statistics are given. All coherence values were increased for the dyslexia
group red connections are from homologous pairs (both right and left hemisphere)
and purple are uniquely right or left hemispheric increased coherences. Note the
specific patterns which include mainly frontal, central and temporal sites. Also note
Fig. 2. Significant increased coherences for the dyslexic vs. the control group for the different
frequency bands. Red connections are from homologous pairs (equal right and left) and purple
connections are unique right or left hemispheric increased coherences. All p values’s < 0.001.
EEG Power and Coherence in Dyslexia: Double-Deficit 183
Fig. 3. Significant correlations between dyslexia subscales and significant EEG coherences in the
different EEG bands. Red = Delta Coherence; Orange = Theta Coherence; Light Blue = Alpha
Coherence; Dark Blue = Beta Coherence. Thick lines represent significances of p<0.001 and thin-
ner lines of p<0.05. Note the specific independent patterns for some of the patterns especially
Articulation with a centro-temporal pattern but also the continuous involvement of the right tem-
poral region for all measures. Also note the clear differences between the slow (Delta and Theta;
Red and Orange) vs. higher (Alpha and Beta; Light and Dark Blue) EEG frequencies and the
similarities between Delta/Theta and Alpha/Beta.
the bi-laterally increased delta coherence fronto-central and the right fronto-central
increased coherence specifically in the alpha and beta band.
4.3. Neuropsychology
The dyslexia group named fewer words in the word condition of the verbal interfer-
ence test (F = 6.994, df = 36, p =0.012) but there was no difference on the color
condition of the (F = 2.330, df = 36, p =0.136). The dyslexia group recognized
fewer words as compared to the control group (F = 8.914, df = 36, p =0.005) on
the memory recognition task.
184 Arns et al.
Table 1. Significant correlations between coherence values for the
different frequency bands vs. the 4 dyslexia subtests. Note that all
correlations are rather high and positive, indicating that increased
coherence between a given electrode pair is related to better per-
formance on that test.
Location vs. Subtest Correlation and Sign
Delta C4-C3 vs. ART r =0.568; df = 17; p =0.017
T4-FC4 vs. ART r =0.508; df = 17; p =0.037
C4-T4 vs. ART r =0.527; df = 17; p =0.030
T3-FC3 vs. ART r =0.541; df = 17; p =0.025
T3-FC3 vs. PD r =0.520; df = 17; p =0.033
C3-F7 vs. RNL r =0.638; df = 17; p =0.006
C3-Fp1 vs. RNL r =0.662; df = 16, p =0.005
CP4-F8 vs. RNL r =0.527; df = 18; p =0.025
FC3-F7 vs. RNL r =0.576; df = 17; p =0.015
T4-F8 vs. SPL r =0.529; df = 17; p =0.029
CP4-T4 vs. SPL r =0.491; df = 17; p =0.045
Theta C3-F7 vs. RNL r =0.598; df = 17; p =0.011
FC3-Fp1 vs. RNL r =0.772; df = 16; p<0.000
T3-FC3 vs. ART r =0.527; df = 17; p =0.030
C3-T3 vs. ART r =0.532; df = 17; p =0.028
Alpha T4-FC4 vs. RNL r =0.576; df = 17; p =0.015
T4-FC4 vs. PD r =0.653; df = 17; p =0.005
C4-T4 vs. RNL r =0.508, df = 17; p =0.038
C4-T4 vs. PD r =0.565; df = 17; p =0.018
Beta C4-T4 vs. RNL r =0.501; df = 17; p =0.041
C4-T4 vs. SPL r =0.617, df = 17; p =0.008
C4-T4 vs. PD r =0.602; df = 17; p =0.011
CP4-T4 vs. RNL r =0.521; df = 17; p =0.032
CP4-T4 vs. SPL r =0.637; df = 17; p =0.006
ART = Articulation
PD = Phoneme Deletion
RNL = Rapid Naming Letters
SPL = Spelling
4.4. Within group correlations
The within group correlations were performed on 18 dyslexic children only (one
subject was removed from the analysis due to his age). This child was 16 years old
whereas the majority of the group was around 10 years of age, and his inclusion may
have lead to spurious age-related correlations.
Figure 3 and Table 1 shows the significant correlations between the obtained
significant measures reported in the previous section and the sub-tests used to mea-
sure the severity of dyslexia. All significant EEG power and EEG coherence mea-
sures (63 measures: 8 EEG and 55 coherence) were submitted to the correlation
analysis with the four dyslexia sub-tests: Rapid Naming Letters — RNL; Phoneme
Deletion — PD, Articulation — ART and Spelling SPL. The results are depicted in
EEG Power and Coherence in Dyslexia: Double-Deficit 185
four different colors, each depicting a significant correlation between that variable,
between those locations for the given frequency band. The thickness of the line also
depicts the significance level (thin p<0.05; thick p 0.001).
Interestingly, there were no significant correlations between the EEG power data
and the EEG coherence data within frequency bands, hence the increased coherence
for dyslexic children cannot be explained by the increased delta and theta frontally.
There was only one significant correlation, between EEG power and the severity
of dyslexia: the power of Theta at FP1 and spelling (r =0.510; df = 16; p =0.044).
For coherence the significant differences are depicted in Table 1 and are also
visually depicted in Fig. 3.
5. Discussion
This study focused on brain function patterns and neuropsychological findings in
children with developmental dyslexia and aimed to establish a link between EEG
parameters and dyslexia relevant constructs. EEG findings showed an increased (left)
frontal and right temporal slow activity in the Delta and Theta bands and increased
Beta 1 power at F7. Since all EEG data have been EOG corrected using Gratton
et al. [11], it is very unlikely the frontal increased Delta and Theta is due to resid-
ual EOG. EEG Coherence data showed increased coherence in frontal, central and
temporal regions. However, the increased coherences seemed to show a frequency
specific effect, where the slower frequencies (delta and theta) showed a more sym-
metrical increase in right and left frontal, central and temporal networks, whereas
the higher frequencies (alpha and beta) showed a more specific right-hemispheric
effect originating at T4 and F8. Correlational analysis showed that these increased
coherences were an effect in itself, since there were no correlations between the
increased delta and theta power on the one hand and the increased coherence in
the according band on the other hand; hence the increased delta and theta power
were not the cause of the increased coherence findings. High coherence between
two EEG signals suggests high cooperation and synchronization between underlying
brain regions within a certain frequency band [39]. Increased coherence can thus be
interpreted as increased functional connectivity. This could implicate that dyslexic
children have increased activity within frontal, central and temporal networks.
There were also significant differences between the dyslexic and the control group
on the verbal interference tests (similar to the Stroop test) and the memory recog-
nition test. These findings are directly related to the dyslexic problems experienced
by this group, since dyslexic children have decoding problems. The dyslexic children
named fewer words on the verbal interference test than the control children but
had no impairment when required to name the color of the word relative to normal
children. Dyslexic children also recognized fewer words on a memory recognition
test whereas spontaneous memory recall was not affected at all. These findings sug-
gest that interpretation of neuropsychological data derived from these specific tests
should always be treated with caution, and may be that dyslexic status should be
186 Arns et al.
incorporated into the interpretation of neuropsychological data to safeguard false
positive findings on these tests.
Correlational analyses revealed significant correlations between the obtained sig-
nificant EEG findings and the tests: articulation, rapid naming of letters, spelling
and phoneme deletion. The correlated patterns (as depicted in Fig. 3) showed quite
specific patterns for all these 4 sub-tests. Interestingly, all these correlations were
positive and high (explaining > 30% of the variance), suggesting that better perfor-
mance on these tests was associated with increased coherence. Given the fact that all
the coherence findings were increased in comparison to the control group, it might be
concluded that these patterns reflect compensatory mechanisms and do not explain
the deficit per se (where negative correlations would be expected). The EEG in this
study was not recorded during the completion of the dyslexia specific tests, hence
we recorded resting state eyes open EEG which correlated highly with these tests.
This further supports the fact that these patterns can be considered compensatory
patterns since they are also present at rest. Furthermore, this demonstrates that
clear associations can be found between passive brain states and deviant behavior,
demonstrating the utility of integrative approaches.
There seems to be a clear distinction between delta coherence on the one hand
and beta coherence on the other. The increased coherence for dyslexic children
was prominent and symmetric for the delta band; but localized to the left hemi-
sphere for the beta band. The correlations also demonstrate this; a slow (delta and
theta) coherent network over left frontal and central regions, and a faster (alpha and
beta) network originating at T4. Although EEG coherence between different cortical
regions is largely established by cortico-cortical and thalamo-cortical interactions
[24], subcortical brain areas also contribute to both inter- and intra-hemispheric
functional communication [3]. Lower bandwidths such as delta frequency in the EEG
coherence spectrum have particularly been associated with limbic contributions to
cortico-cortical coupling [18], hence these increased low-frequency coherences could
indicate a limbic contribution.
The core dysfunction in dyslexia seems to consist of increased slow activity at
left frontal and right temporal (T6) regions, and bilateral increased coherence in
the slower frequency bands (delta and theta), as opposed to acquired-compensatory
mechanisms consisting of right-hemispheric increased coherences in the higher fre-
quency bands (alpha and beta) and a left frontal increased coherence in slower bands
originating from C3 and FC3. The increases in coherence in the delta band fronto-
central suggest a strong limbic involvement as part of the core deficit in dyslexia,
although this requires further study.
In this study, children showed delays in both rapid naming and phonological
awareness. These delays correlated with the activation in the frontal-cerebellar
phonological system. The EEG findings in our study showed an increased activation
pattern in dyslexic children, mainly in frontal and temporal lobes. Furthermore, the
correlation analyses showed significant correlations with spelling, phonological skills
and rapid naming with quite different topographical representations, suggesting
EEG Power and Coherence in Dyslexia: Double-Deficit 187
involvement of different neural mechanisms. It can tentatively be concluded that
the frontal-cerebellar network may be critical to the precise timing of mechanisms
that underlie the double-deficit theory of dyslexia, suggesting the existence of three
subtypes of reading disability: dyslexics with deficiencies in phonological skills, poor
rapid naming skills or a combination of both types. Thus, the present study supports
the theory of Eckart et al. [4] hypothesizing that impairments in a frontal-cerebellar
network may play a role in delayed reading impairment in dyslexia. These authors
reported that anomalies in a cerebellar-frontal circuit are associated with rapid
automatic naming and phonological processing.
Previous EEG studies have shown different findings. Rippon and Brunswick [28]
found no specific activation patterns with respect to dyslexic children. Weiss and
Mueller [39] have proposed several roles for coherence in the different frequency
bands (also see introduction), however in this study, we did not use a task-related
protocol, making comparison to this study difficult.
This study contributes to the theory that neurobiological causes underlie
dyslexia. The increased activation patterns of dyslexic children seem to be associ-
ated with the double deficit type of dyslexia. In future research, it will be important
to examine the relation between EEG data and the phonological or orthographic
deficits. Outcomes of these studies might further contribute to the diagnosis of sub-
types of dyslexia.
Finally, this study demonstrated that increased EEG power could not explain
the increased coherence findings in dyslexia, suggesting these measures reflect dif-
ferent neural networks. The positive correlations between coherence and the differ-
ent tests demonstrated that these increased coherences might reflect compensatory
mechanisms rather then being part of the real core dysfunction in dyslexia, whereas
the increased slow activity might be part of the core dysfunction in dyslexia. This
should be taken into account in future studies to elucidate dysfunctional networks in
dyslexia. These dysfunctional networks can be dissociated from acquired compen-
satory mechanisms. Also, treatments focused on normalizing brain function (e.g.,
rTMS, EEG Biofeedback or Neurofeedback) will benefit from this given they could
target the deficit rather than target acquired compensatory mechanisms.
Acknowledgments
Data from The Brain Resource International Database were provided by the Brain
Resource Company (BRC: www.brainresource.com). We would also like to thank
local BRC clinics for data collection of the control group. All scientific decisions
are made independent of Brain Resource Company’s commercial decisions, via the
independently operated scientific division BRAINnet, which is overseen by the inde-
pendently funded Brain Dynamics Center and scientist members. We would also
like to acknowledge the contributions from Ine Giepmans and Minnie War for the
data collection and Sabine de Aukje Bootsma and Hanneke Friesen for acquiring
the QEEG and neuropsychological data.
188 Arns et al.
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... Additionally, there is reduced activity in the left parieto-occipital area (P7, O1) (Rippon & Brunswick, 2000). Children with dyslexia may also have increased sluggish activity in the right temporal and parietal regions (P8 and T8) (Arns et al., 2007), and reduced activity in the left temporal region (Thornton & Carmody, 2005). People with both dyslexia and ADHD may also exhibit increased sluggish activity in the frontal region of the brain. ...
... The alpha and beta bands show increased coherence in the right temporal region (T3 and T4), while the delta and theta bands show increased coherence across both hemispheres (Arns et al., 2007). However, there is reduced coherence in the delta, theta, and alpha bands between P7 and O1. ...
Preprint
The most common neurological diversity that children experience is dyslexia and it manifests itself in reduced reading ability. There is a genetic predisposition for dyslexia, and more recent theories explain it as a disruption in left hemispheric lateralization that reduces effective reading and writing. Software for smartphones called Auto Train Brain helps children with dyslexia to improve their reading comprehension and reading speed. Measuring the efficacy of the mobile app training was done manually with psychometric tests beforehand and we use a biomarker detection software to measure the efficacy of the neurofeedback. Machine learning (ML) techniques have recently been used to classify children with dyslexia and typically developing children (TDC). The data consists of 100 sessions of 2-minute resting-state eyes-open 14-channel Quantitative Electroencephalography (QEEG) data from 100 children with dyslexia and of 100 TDC. We used the dyslexia biomarker detection software to assess the efficacy of 14-channel neurofeedback that was applied with Auto Train Brain. The results have shown that 48% of the sessions of children with dyslexia were classified as electrophysiologically normal, and 61% of the children with dyslexia were classified as electrophysiologically normal for at least 1 session after the 20th session of neurofeedback.
... Additionally, there is reduced activity in the left parieto-occipital area (P7, O1) (Rippon & Brunswick, 2000). Children with dyslexia may also have increased sluggish activity in the right temporal and parietal regions (P8 and T8) (Arns et al., 2007), and reduced activity in the left temporal region (Thornton & Carmody, 2005). People with both dyslexia and ADHD may also exhibit increased sluggish activity in the frontal region of the brain. ...
... The alpha and beta bands show increased coherence in the right temporal region (T3 and T4), while the delta and theta bands show increased coherence across both hemispheres (Arns et al., 2007). However, there is reduced coherence in the delta, theta, and alpha bands between P7 and O1. ...
Preprint
The most common neurological diversity that children experience is dyslexia and it manifests itself in reduced reading ability. There is a genetic predisposition for dyslexia, and more recent theories explain it as a disruption in left hemispheric lateralization that reduces effective reading and writing. A software for smartphones called Auto Train Brain helps children with dyslexia to improve their reading comprehension and reading speed. Measuring the efficacy of the mobile app training was done manually with psychometric tests beforehand and we use a biomarker detection software to measure the efficacy of the neurofeedback. Machine learning (ML) techniques have recently been used to classify children with dyslexia and typically developing children (TDC). The data consists of 100 sessions of 2-minute resting-state eyes-open 14-channel Quantitative Electroencephalography (QEEG) data from 100 children with dyslexia and that of 100 TDC. We used the dyslexia biomarker detection software to assess the efficacy of 14-channel neurofeedback that was applied with Auto Train Brain. The results have shown that 30% of the sessions of children with dyslexia were classified as electrophysiologically normal, and 61% of the children with dyslexia were classified as electrophysiologically normal for at least 1 session after 20th sessions of neurofeedback.
... desynchronize beta-1 activity while completing a reading task in areas related to Broca's area (FC5; speech production, articulation) and the Angular gyrus (CP5, P3), understanding semantics and mathematics , as well as the left parieto-occipital area (P7, O1) (Rippon & Brunswick, 2000). In children with dyslexia, the right temporal and parietal (P8 and T8) regions of the brain have increased sluggish activity (Arns et al., 2007). The left temporal area, according to the researchers, is disrupted (Thornton & Carmody (2005). ...
... Additionally, people with dyslexia and ADHD may exhibit a high degree of frontal sluggish activity. The alpha and beta bands show a clear right-temporal central increase in coherence at T3 and T4, while the delta and theta bands show a symmetric rise in coherence (Arns et al., 2007). In the delta and theta bands, there is bi-hemispheric hyper-coherence (between T3 and T4); yet, between P7 and O1, there is hypo-coherence in the delta, theta, and alpha bands. ...
Preprint
The most common neurological diversity that children experience is dyslexia and it manifests itself in reduced reading ability. There is a genetic predisposition for dyslexia, and more recent theories explain it as a delay in left hemispheric lateralization that reduces effective reading and writing. A software for smartphones called Auto Train Brain helps children with dyslexia to improve their reading comprehension and reading speed. Measuring the efficacy of the mobile app training was done manually with psychometric tests beforehand and we use a biomarker detection software to measure the efficacy of the neurofeedback. Machine learning (ML) techniques have recently been used to classify children with dyslexia and typically developing children (TDC). The data consists of 100 sessions of 2-minute resting-state eyes-open 14-channel Quantitative Electroencephalography (QEEG) data from 100 children with dyslexia and 100 TDC. We used the dyslexia biomarker detection software to assess the effectiveness of the 14-channel neurofeedback that was applied with Auto Train Brain. The results have shown that 30% of the sessions of children with dyslexia were classified as electrophysiologically normal, and 61% of the children with dyslexia were classified as electrophysiologically normal for at least 1 session after the 20th session of neurofeedback.
... The methodology used most frequently is spectral analysis, 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., 1983Fein et al., , 1986Galin et al., 1988Galin et al., , 1992Harmony et al., 1990;Mahmoodin et al., 2016Mahmoodin et al., , 2019Papagiannopoulou & Lagopoulos, 2016;Remschmidt & Warnke, 1992;Rippon and Brunswick 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. ...
... Authors, year 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). ...
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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 correlates 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 questionnaire. 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 performance. 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 diagnostic group and expand the literature by offering new insights into functional significance.
... According to numerous studies, children with dyslexia have slow waves at FC5 and F7 and do not desynchronize beta-1 activity while performing a reading task in regions connected to Broca's area (FC5; speech production, articulation) and the Angular gyrus (CP5, P3), understanding semantics and mathematics [32] as well as the left parieto-occipital area (P7, O1) [33] . Right temporal and parietal (P8 and T8) areas of the brain have elevated sluggish activity in children with dyslexia [34] . According to the researchers, there is a disruption in the left temporal region [35] . ...
... The coherence increases symmetrically. At T3 and T4, the delta and theta bands exhibit a symmetric increase in coherence, while the alpha and beta bands exhibit a distinct right-temporal central increase in coherence [34] . Bi-hemispheric hyper-coherence (between T3 and T4) appears in the delta and theta bands, however, between P7 and O1, there is hypo-coherence in the delta, theta, and alpha bands. ...
Preprint
Auto Train Brain is a neurofeedback-based mobile application that increases reading comprehension and reading speed in dyslexia with EMOTIV EPOC-X which has 14 channels. The clinical trials have been completed on dyslexia beforehand. The left hemisphere-related deficits are known in dyslexia. In this research, we have investigated the positive long-term effects of Auto Train Brain to improve the variance of gamma band sample entropy across neurofeedback sessions. The previous research indicates that the increase in the variance of the gamma band entropy across neurofeedback sessions shows increased adaptations in the functional networks. 14-channel neurofeedback with Auto Train Brain increases the variance of gamma band entropy in the left temporal lobe (T7) over the right temporal lobe (T8) which may be translated as the adaptations of the functional networks in the left temporal region are increased after 100 sessions of neurofeedback in terms of electrophysiology.
... According to numerous studies, children with dyslexia have slow waves at FC5 and F7 and do not desynchronize beta-1 activity while performing a reading task in regions connected to Broca's area (FC5; speech production, articulation) and the Angular gyrus (CP5, P3), understanding semantics and mathematics [33] as well as the left parieto-occipital area (P7, O1) [34] . Right temporal and parietal (P8 and T8) areas of the brain have elevated sluggish activity in children with dyslexia [35] . According to the researchers, there is a disruption in the left temporal region [36] . ...
... The coherence increases symmetrically. At T3 and T4, the delta and theta bands exhibit a symmetric increase in coherence, while the alpha and beta bands exhibit a distinct right-temporal central increase in coherence [35] . Bi-hemispheric hyper-coherence (between T3 and T4) appears in the delta and theta bands, however, between P7 and O1, there is hypo-coherence in the delta, theta, and alpha bands. ...
... A number of studies have used various experimental setups and biomedical features to objectify and quantify dyslexic tendencies. Some studies focus on measuring brain activities during reading, relying on functional magnetic resonance imaging [7], [8], diffusion tensor imaging [8], [9] or electroencephalography (EEG) [10]- [12] to quantify the differences between the dyslexic and non-dyslexic tendencies. ...
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... Similarly, patients with major depressive disorder exhibited significantly higher EEG coherence as compared to controls in several frequencies, including delta and theta bands [82]. Such alterations in resting-state EEG connectivity in slow rhythms (delta and theta) has also been reported in childhood developmental disorders, such as autism spectrum disorders [83] and specific learning disorders [84]. On the contrary, healthy aging is marked by decreased slow frequency activity (band power) in the delta and theta bands during the resting state [85] as well as by reduced EEG network connectivity [86]. ...
Preprint
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... The beta activity decreases in posterior and temporal regions with tasks requiring sustained attention. Beta activity (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) has also been related to cognitive activity. Similar results were obtained by Chabot et al, 24 Clarke et al, 25 and Lazzaro et al. 26 However, several studies also indicate that early intervention can help manage EEG characterizations during resting and activity conditions. ...
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Objective: Databases bring together diverse information in neuroimaging and psychiatry. They usually aim for both size and diversity of measures. The present article outlines the potential insights from the first entirely standardized and centralized International Brain Database. Method: The database consists of data from over 1000 normal subjects (age range 6-70 years) and a growing number of age-matched patients with a psychiatric illness, acquired from seven laboratories (New York, Rhode Island, London, Holland, Adelaide, Melbourne and Sydney). It is an ‘integrative’ neuroimaging (electroencephalography (EEG), event-related potentials (ERP), structural and functional magnetic resonance imaging (sMRI, fMRI)), psychometric, demographic and genomic database. Results: The most notable relationships in normal controls thus far include (i) an association between grey matter volume and EEG alpha frequency in frontal regions; (ii) a systematic reduction with age in cortical arousal (EEG power), speed of processing (ERP components) and most aspects of cognitive function, particularly for >50 years; (iii) a greater cortical arousal in female versus male subjects, but slower speed of processing; and (iv) a dissociation between speed (greater in male subjects) and accuracy/verbal processing (greater in female subjects) for psychological tasks. There is potential to explore the specificity of findings in psychiatric disorders in this international standardized database. Conclusions: The size of this database has allowed for statistical tests of greater power than normal. The combination of size and diversity of measure has broader significance in providing a normative framework for evidence-based psychiatric research. It enables control for widespread individual differences, enhancing investigations of the sensitivity and specificity of brain findings, and the efficacy of medication in psychiatric disorders.
Several methodological issues which impact experimental design and physiological interpretations in EEG coherence studies are considered, including reference electrode and volume conduction contributions to erroneous coherence estimates. A new measure, `reduced coherency', is introduced as the difference between measured coherency and the coherency expected from uncorrelated neocortical sources, based on simulations and analytic-statistical studies with a volume conductor model. The concept of reduced coherency is shown to be in semi-quantitative agreement with experimental EEG data. The impact of volume conduction on statistical confidence intervals for coherence estimates is discussed. Conventional reference, average reference, bipolar, Laplacian, and cortical image coherencies are shown to be partly independent measures of neocortical dynamic function at different spatial scales, due to each method's unique spatial filtering of intracranial source activity.
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This article addresses questions about instruction for children with severe reading disabilities in 2 ways. First, outcomes from 3 recent studies are examined within the context of a hierarchy of instructional goals derived from current theory about the processes involved in acquisition of reading skill. This analysis suggests that we still have much to learn about effective instruction for children with the most severe reading disabilities. The second part of the article reports preliminary results from a 2½-year prevention project in which 138 children received instruction by 3 different methods. The primary instructional contrast involved the intensity and degree of explicitness of instruction in phonological awareness and phonetic decoding strategies for word reading. Results showed a clear advantage in phonetic reading ability for 1 group of children at the end of the second grade. However, this group did not show corresponding advantages in word-reading vocabulary or reading comprehension. The article concludes with a discussion of weaknesses in current research that suggest questions for future intervention studies.