Aberrations in functional connectivity in children with developmental dyslexia have been found in electroencephalographic studies using graph analysis. How training with visual tasks can modify the functional semantic network in developmental dyslexia remains unclear. We investigate local and global topological properties of functional networks in multiple EEG frequency ranges based on a small-world propensity method in controls, pre- and post-training dyslexic children during visual word/pseudoword processing. Results indicated that the EEG network topology in dyslexics before the training was more integrated than controls, and after training - more segregated and similar to that of the controls in the theta (
Developmental dyslexia (DD) is a childhood disorder related to reading, writing, spelling, and learning inability of these skills, despite normal intellectual abilities. Early diagnosis of dyslexia (Stein, 2014) can prevent specific problems of children with developmental dyslexia related to their social and mental development. Therefore, some actions are needed to prevent cognitive disorders complicating the childhood of children with dyslexia. Although developmental dyslexia has been studied extensively on a behavioral level (Benassi et al., 2010; Boets et al., 2011; Cornelissen et al., 1998; Demb et al., 1998; Pammer and Wheatley, 2001; Sperling et al., 2005; Stein, 2014), there is no consensus regarding its causes. According to the ’dual route’ model, the words can be read either by lexical or sub-lexical route (Coltheart et al., 2001). In the lexical route, the words are directly recognized as lexicon members and associated with verbal semantic representations, when they are familiar, automatically identified by their visual form (Coltheart et al., 2001). In the sub-lexical route, based on grapheme to phoneme correspondence rules for unfamiliar words, the word is broken down into its constituent letters and corresponding phonemes (Coltheart et al., 2001). Impairment in either of these routes will result in a characteristic pattern of reading difficulties (Castles and Coltheart, 1993). When the children’s deficits are in phonological skills (so-called phonological dyslexia, Funnell, 1983), they probably use the lexical route to compensate for the sub-lexical route (Siegel, 1993), as well as naming irregular words well, but not pseudo-words (nonsense words). When the problems are in the lexical route, the sub-lexical route is used (Ebrahimi et al., 2019). Then, the pseudo-words could be processed, but not the irregular words (Hodges and Patterson, 2007; Patterson and Hodges, 1992; surface dyslexia).
Different behavioral studies of dyslexics have found various deficits in the sensitivity to a coherence motion perception (Benassi et al., 2010; Boets et al., 2011), velocity discrimination (Demb et al., 1998; Eden et al., 1996), motion direction encoding (Cornelissen et al., 1998; Stein, 2014), contrast sensitivity to stimuli with low-/high-spatial frequency in external noise (Pammer and Wheatley, 2001; Sperling et al., 2005). These deficits are selectively associated with low accuracy or slow performance on reading sub-skills (Wilmer et al., 2004), problems with clearly seeing letters and their order, orienting and focusing on visual-spatial attention (Lalova et al., 2018; Stein, 2014). The deficits in the magnocellular pathway, established by the coherent motion perception, were associated with letter decoding disability (Cornelissen et al., 1998; Facoetti et al., 2003; Sperling et al., 2003). The magnocellular system is the visual input to the dorsal pathway that mediates motion perception and object localization (Goodale and Westwood, 2004) due to the projections to the visual motion-sensitive area and the posterior parietal cortex. For the reading, the dorsal pathway has a major role in directing visual attention and control of eye movements (Stein, 2014). The efficacy of intervention efforts has also been studied (Chouake et al., 2012; Lalova et al., 2019; Lawton, 2011, 2016; Lawton and Shelley-Tremblay, 2017; Qian and Bi, 2015). After visual magnocellular training, children with reading difficulties improved coherent motion detection, saccadic eye movements, as well as reading accuracy and visual errors (Ebrahimi et al., 2019). By detecting progressively faster movements in the coherent motion discrimination, the lexical decision and reading accuracy improved at the magnocellular system’s higher visual levels (Chouake et al., 2012). The phonological errors for dyslexic readers decreased after magnocellular intervention by figure-ground movement discrimination (Lawton and Shelley-Tremblay, 2017; Lawton, 2016). The reduction in phonological errors and visual timing deficits, the improvements in reading fluency, attention, phonological processing, and working memory result from an improvement in the dorsal stream’s functioning levels. The detection of abnormal functioning brain regions and changed communication with other brain regions in dyslexia is directed to create early diagnosis methods (Stein, 2014). Generally, due to anterior and middle temporal lobe deficits, affecting primary ventral, occipitotemporal, lexical route, children with severe phonological disabilities rely more on ventral (Funnell, 1983; occipitotemporal lexical) brain regions, whereas other dyslexic readers with less pronounced phonological deficits use more the dorsal (occipitoparietal sublexical) route. The dorsal stream consists of three pathways with projections to the prefrontal and premotor cortices, with a major projection to the medial temporal lobe with direct and indirect courses through the posterior cingulate and retrosplenial cortices with reciprocal projections to the visual cortex (Kravitz et al., 2011). The ventral stream is a multisynaptic pathway with projections from the striate cortex to the anterior temporal part in the inferior temporal cortex, with a further projection from the rostral inferior temporal to the ventral prefrontal cortex. When the lexical route has deficits, as in dyslexics with less pronounced phonological deficits, partial compensation appears to be possible by over-recruitment of the slower, attention dependent, sublexical one. Therefore, neuropsychological evidence for the dyslexic groups supposes that partially distinct neural substrates as a dorsal and a ventral route, respectively, underlie sublexical and lexical processes (Ebrahimi et al., 2019; Stein, 2014). A lexicosemantic route involves the left basal temporal language area, the posterior part of the middle temporal gyrus, and the inferior frontal gyrus, and a sublexical route involving the left-lateralized superior temporal area, supramarginal gyrus, and the opercular part of the inferior frontal gyrus (Jobard et al., 2003). Nevertheless, controversies still exist about the anatomical substrates involved in lexical and sublexical reading routes (Jobard et al., 2003; Mechelli et al., 2003). Neuroplastic mechanisms and synaptic reorganization that mediate the training effects in functionally implement visual rehabilitation (Magosso et al., 2017) among uni-sensory areas strengthen due to learning mechanisms, which stimulate the maturation in typically developing children (Cuppini et al., 2017) and the multisensory development emerging quite late during development (Dekker et al., 2015). The training stimulates not only compensations in the visual and oculomotor functioning (Magosso et al., 2017). Still, it may further contribute to the multisensory integration in semantic memory and semantic link content with lexical language features (Ursino et al., 2015).
It has been established that human semantic knowledge is supported by large brain networks, encompassing many different brain regions and coordinated by a central hub or hubs. Among the candidates for these semantic hubs are the anterior temporal lobe (ATL), the posterior inferior parietal lobe (particularly the angular gyrus, AG), the middle temporal gyrus (MTG) and the inferior frontal gyrus (IFG) (Binder et al., 2009; Farahibozorg et al., 2019). The dynamics of the heteromodal semantic network and the roles of the hubs have been investigated by neuro-computational modeling (Tomasello et al., 2017) and experimentally using visually presented words (Farahibozorg et al., 2019). The question is whether methods, based on the graph theory, can be used as a screening test for developmental dyslexia, and whether this approach can shed light on the neurophysiological causes for the effect of visual nonverbal training (Ebrahimi et al., 2019; Lalova et al., 2019; Lawton, 2011; Wilmer et al., 2004)? The hypothesis is that the visual training intervention on children with dyslexia can lead to changes in the EEG-based functional networks, making them more similar to controls. We also hypothesize that the training, affecting the dorsal pathway, may influence the semantic network’s functioning related to the word meanings (semantics) or their structure (syntax).
The study’s first goal is to prove that network-level statistics is a useful tool to screen developmental dyslexia, comparing controls and dyslexics by focusing on their connectivity networks using the small-world propensity (SWP) algorithm applied to electroencephalogram (EEG) data. The second goal is to demonstrate that visual nonverbal training can help reduce the brain networks’ abnormality related to developmental dyslexia by network-level statistics between the pre- and post-training groups with dyslexia (cf. Taskov and Dushanova, 2020).
A longitudinal study was conducted in the schools that involved repeated observations of children with developmental dyslexia over a long period. In this observational study with the exposure of visual intervention in non-trial research, the dyslexics are then followed out over time to observe the outcome from the visual training and evaluate the extent to which the visual tasks contribute to the alteration of this childhood disorder.
Reliable electrophysiological data were obtained from forty-three children: 22 children with dyslexia (12 boys and 10 girls) and 21 normal children (11 boys and 10 girls). The age range for both groups was 8-9 years old. All children and their parents gave informed consent for an EEG following the Helsinki Declaration. The Ethics Committee approved the Institute of Neurobiology study, the Institute for Population and Human Studies, BAS, the State Logopedic Center, and the Ministry of Education and Science. All participants in the study spoke Bulgarian as their first language. All children were right-handed. The handedness was assessed by a classification of hand preference (Annett, 1970). All participants had non-verbal intelligence scores of 98 or higher (Raven et al., 1998). All children had normal or corrected-to-normal vision. The controls were paid 100 Bulgarian Lev for participating.
The children underwent a series of tests, including neuropsychological tests (Raichev et al., 2005), a DDE-2 test battery for the evaluation of developmental dyslexia (Matanova and Todorova, 2013; Sartori et al., 2007), psychometric tests for the evaluation of phonological awareness, tests for the evaluation of reading and writing skills (Kalonkina and Lalova, 2016), Girolami-Boulinier’s “Different Oriented Marks” nonverbal perception test (Girolami-Boulinier, 1985; Yakimova, 2004), and Raven’s Progressive Matrices test for nonverbal intelligence (Raven et al., 1998). In the dyslexic group were included children with reading difficulties combined with below-norm performance in either speed or accuracy below one standard deviation from age-matched standardized control data in reading subtests in the DDE-2 battery (word list reading, pseudoword list reading, choosing the correct meaning of a word, search for misspellings of words; writing of word/pseudoword in dictation), as well as in the test battery “Reading abilities” (identifying the first sound in a heard word and omitted it in the word, fragmentation of the word in syllables and missed the last syllable, text reading, dictation of sentences filling in a missing compound word). The control participants included age-matched children with the same socio-demographical background as the dyslexic group. There was no dyslexia or co-occurring language disorders confirmed by within-norm performance in speed and accuracy in reading. The results are shown in Table S1.
The participants were exposed to two types of visual stimuli (Fig. 1), presented on a laptop with a screen resolution of 1920 × 1080 pixels and a refresh rate of 60 Hz at a distance of 57 cm from the observer. The stimuli stayed on the computer screen for 800 ms and consisted of multi-syllabic (2/3-syllables with 5.4
The first and third block-diagrams are tasks for visual discrimination of words/pseudowords during the EEG sessions, recording before and after training. The words/pseudowords were in Bulgarian in the original experiment. The words are shown translated into English. The second block-diagrams present the training nonverbal tasks.
The stimuli were presented in two to four blocks during daily EEG experimental sessions. Each block contained 40 words and 40 pseudowords (Fig. 1). For the first two blocks, stimuli were randomized and presented once. The presentation of all stimuli was repeated in blocks 3 and 4 to increase the number of trials and consequently signal to noise ratio. Participants were asked to blink only during the interstimulus interval (1.5-2.5 s) to prevent artifacts in the EEG records during the words/pseudowords stimuli. The participants were instructed to push a button with the right hand when seeing a word and push a different button with the left hand when the stimulus was a pseudoword. Two behavioral parameters were evaluated for each child: the percentage of correctly identified words/pseudowords and the reaction time. To examine whether visual perceptional training can influence the neural semantic network of the children with developmental dyslexia, we recorded EEG session during visual word/pseudoword task one month later after the three-month training with five visual program interventions. Hence, irrespective of the word/pseudoword task, the experimental group received an intensive procedure with training tasks, presented in an arbitrary order and divided twice a week into individual sessions lasting 45 minutes over three months. This long term period does not enable the children with developmental dyslexia to memorize information about the word/pseudoword task performed before the training period.
The visual perceptual training comprised five visual program interventions that do not include any direct phonological input on the children with developmental dyslexia (Fig. 1). The training program, based on direction discrimination of coherent vertical motion (Benassi et al., 2010; Boets et al., 2011), stimulated the magnocellular function. Coherent vertical motion of white dots in randomly moving elements with a size of 0.1 deg was presented within a circle (diameter 20 deg) on a black screen at a viewing distance of 57 cm for 200 ms. The velocity of the moving dots was 4.4 deg/s. The coherent motion threshold was 50% of the randomly moving dots. The inter-stimulus interval (ISI) was 1.5-2.5 sec. The child was instructed to press a button with the left hand for the upwards dots movement and press a different button with the right hand when the stimulus moves downwards.
The training program, based on velocity discrimination (Joshi and Falkenberg, 2015), induced changes in the MT/V5 brain area. Two pair circular stimuli with radial moving white dots’ elements from center to periphery of optical flow (a diameter of 10 deg) appeared sequentially one after other on a screen. Each stimulus pair’s first item was always with a constant slow speed (4.5 deg/s). The second item in the pair of stimuli had a speed of the flow (5.0 deg/s) close to that of the first stimulus (4.5 deg/s) or with a higher (5.5 deg/s). The first item appeared for 300 ms, and after 500 ms, the second item in the stimulus pair appeared for 300 ms at a viewing distance of 57 cm. The ISI was 1.5-3.5 sec. The instruction was to press a key with the right hand when the pair’s speed was slow or another key with a left hand when the second stimulus in the pair had a higher speed than the first stimulus’s constant speed.
Low-contrast discrimination of low-spatial frequency sinusoidal gratings (2 cycles per degree of visual angle, (cpd)) and high-temporal frequencies (counter-phase flicker at 15 reversals/s), vertically flicking in external noise region (11
The visual-spatial attentional task with high peripheral processing demands was to search and track either color change or color preservation of a square in a cue (Ross-Sheehy et al., 2011). The cue was a black frame for 300 ms in either left or right visual field on a white screen, before the square color array (each with size 3
The EEG was recorded with an in-house developed 40-channel Wi-Fi EEG system using dry EEG sensors (each sensor is a matrix with 16 golden pins in a star-shaped configuration, Brain Rhythm Inc., Taiwan; Liao et al., 2011). Reference sensors were placed to both processus mastoidei and a ground sensor - on the forehead. The sensors were positioned on the head according to the international 10-20 system: F3-4, C3-4, T7-8, P3-4, O1-2; Fz, Cz, Pz, Oz, and additional positions according to the 10-10 system: AF3-4, F7-8, FT9-10, FC3-4, FC5-6, C1-2, C5-6, CP1-2, CP3-4, TP7-8, P7-8, PO3-04, PO7-08. The skin impedance was controlled to be less than 5 k
The functional connectivity for all possible pairs of electrodes was determined using the Phase Lag Index (PLI) (Stam et al., 2007; Vinck et al., 2011). This was done separately for each frequency band between the two-time series. The PLI gives information about the phase synchronization of two signals, i.e., if one signal lags behind the other, by measuring the asymmetry of the distribution of their instantaneous phase differences. The instantaneous phases can be calculated from the analytical signal based on the Hilbert transform. The PLI can have values between 0 and 1. A value of 0 indicates that the two signals are not phase-locked (or that their phase difference is centered on 0 mod
Often brain networks are studied in terms of segregation and integration. Segregation reflects the brain’s ability to process specific information locally, i.e., within a brain region or an interconnected group of adjacent regions. In contrast, integration can combine information from different brain regions (Cohen and D’Esposito, 2016). Graph measures can characterize brain network integration and segregation. Graph theory describes networks as a set of nodes and their connections (links, edges) based on statistical calculations over connectivity strengths and node-node neighboring. The small world is the ratio of these measures, such as the clustering coefficient, the global efficiency and the characteristic path length (Rubinov and Sporns, 2010). Their sensitivity to many parameters, such as the number of connections in the network or their density, the number of nodes, the distribution of weights, makes it difficult to compare them between different EEG-based networks. New approaches have been proposed to solve these problems, one of which is the small-world propensity (SWP) method. The SWP is a metric from the Graph theoretical analysis that provides an objective assessment of small-world structure in EEG-based networks with different densities, taking into account variations in network density (Muldoon et al., 2016). SWP can quantify the degree of small-world structure between different networks and compare the topological network structure between specific groups using network density.
The functional connectivity in the EEG-based networks, calculated using the PLI between each channel, can construct an adjacency matrix. Each row and each column represent a sensor, i.e., a graph that connects all its nodes (channels). Some authors have proposed the use of the method of the small-world propensity (Muldoon et al., 2016) to assess unbiased small-world structure in real-word networks with varying densities to avoid the problem with methodological limitations in the comparison of different functional networks, observed in other network-based methods of the graph theory (Bassett and Bullmore, 2017). Disadvantages of these statistics are dependent on network density and neglect critical variables such as the strengths of connections between nodes, limiting their ability to diagnose and compare small-world structure in different EEG functional networks at different development times. Different age-related maturation processes are a class of weighted networks (Medaglia et al., 2015; Tsujimoto, 2008), where the strong and weak connections differentially contribute to overall network function (Bassett et al., 2012). The weak connections, identified recently as potential biomarkers in pathologies (Bassett et al., 2012), have been ignored because of commonly applied thresholding techniques. The network statistic (SWP) is sensitive to the density and strengths of functional connections between nodes.
The small-word propensity (SWP) is denoted by
The characteristic path length L is determined by the average minimum number of edges between all pairs of nodes, while the clustering coefficient C is determined by the number of triangles around a node relative to the number of all node’s neighbors (Onnela et al., 2005; Boccaletti et al., 2006), which is related to brain segregation (Bullmore and Sporns, 2009; Rubinov and Sporns, 2010).
To calculate
The EEG-based networks with high characteristics of the small world (low
Here, the 40 by 40-weighted adjacency matrices were calculated separately in the frequency bands from
The strengths and betweenness centrality (Bullmore and Sporns, 2009; Rubinov and Sporns, 2010; Stam and van Straaten, 2012) are local nodes’ measures in the network topology. The betweenness centrality of the nodes is determined by converting the weights of the adjacency matrix into distances, i.e., larger correlations lead to smaller distances. The betweenness centrality of a node is the fraction of all shortest paths in the network that pass through a given node. The betweenness centrality of the edges is the fraction of all shortest paths in the network that contain a given edge. Edges with high values of betweenness centrality participate in a large number of shortest paths. The strength is the sum of all weights of connections for a given node. The values of the strength or BC of the nodes are normalized towards the average strengths or BCs across all network nodes. The nodes with high strength or BC participate in many shortest paths and play an important role in processing information in the graph (Boccaletti et al., 2006). The most important nodes of the network are the hubs. A graph with higher maximum strength or BC is assumed to be more integrated (Bullmore and Sporns, 2009; Stam and van Straaten, 2012). The hubs are defined as nodes with strength/or BC, averaged for each participant, at least one standard deviation above the mean group strength/or BC. The most important links are the maximal BC edges, averaged for each participant, representing at least one standard deviation above the mean group edge BC. The measures (e.g., strength, node BC, edge BC) were obtained via Matlab’s brain connectivity toolbox (Rubinov and Sporns, 2010). The figures were generated using BrainNet Viewer version 1.63 (Xia et al., 2013).
In EEG recording sessions, the reaction times and performance accuracy of pre- and post-training dyslexic groups were compared for each condition (words/pseudowords) and dyslexics and neurotypical readers by Kruskal-Wallis nonparametric test (KW test).
For each frequency band and condition (word or pseudoword), a between-group pair comparison of each global SWP measure was performed by a nonparametric bootstrap procedure with 1000 random permutations (Maris and Oostenveld, 2007; Mason and Newton, 1990). Three graph indices (
The results of the between-group comparisons (K-W test) of the behavioral measures (percentage of correct answers and reaction time) are shown in Table 1. The dyslexic group (before training and after training) showed a lower success rate and slower reaction times compared to the controls in both conditions (words and pseudowords: P
Controls | Pre-training Dys | Post-training Dys | Con/Pre-training Dys | Con/Post-training Dys | Pre-training/Post-training Dys | ||||
P |
|
P |
|
P |
|
||||
1. word | |||||||||
Success | 94.66 |
69.83 |
78.6 |
9.51e-08 | 28.5 | 8.07e-06 | 19.2 | 0.03 | 4.51 |
RT | 1149.1 |
1333.8 |
1369.3 |
3.75e-15 | 61.8 | 1.25e-21 | 91.2 | 0.08 | 2.87 |
2. pseudo-word | |||||||||
Success | 91.7 |
55.58 |
69.5 |
1.26e-08 | 32.4 | 0.0003 | 13.05 | 0.02 | 5.29 |
RT | 1313.4 |
1543.3 |
1537.8 |
9.31e-18 | 73.7 | 3.69e-21 | 89.1 | 0.7 | 0.07 |
|
Significant differences in all global SWP measures were found between the controls and the pre-training dyslexic group, as well as between pre- and post-training dyslexic groups in the
The pre-training dyslexics, compared to other groups, had statistically higher
Control | Pre-training Dys | Post-training Dys | Con vs. Pre-training Dys | Con vs. Post-training Dys | Pre-training vs. Post-training Dys | ||||
P |
|
P |
|
P |
|
||||
|
|||||||||
|
0.631 |
0.643 |
0.645 |
0.382 | 0.76 | 0.207 | 1.59 | 0.65 | 0.2 |
|
0.429 |
0.389 |
0.339 |
0.189 | 1.72 | 0.003 | 8.72 | 0.09 | 2.8 |
|
0.234 |
0.232 |
0.282 |
0.421 | 0.65 | 0.030 | 4.69 | 0.005 | 7.8 |
|
|||||||||
|
0.528 |
0.559 |
0.534 |
0.007 | 7.25 | 0.419 | 0.65 | 0.07 | 3.2 |
|
0.636 |
0.566 |
0.623 |
0.0003 | 13.4 | 0.394 | 0.73 | 0.006 | 7.4 |
|
0.141 |
0.186 |
0.146 |
4.11e-05 | 16.8 | 0.637 | 0.22 | 0.0004 | 12.6 |
|
|||||||||
|
0.503 |
0.548 |
0.495 |
2.64e-06 | 22.1 | 0.494 | 0.47 | 2.9e-07 | 26.4 |
|
0.684 |
0.600 |
0.691 |
6.65e-07 | 24.7 | 0.582 | 0.30 | 1.8e-07 | 27.2 |
|
0.119 |
0.147 |
0.119 |
0.001 | 10.8 | 0.614 | 0.26 | 0.0006 | 11.6 |
|
|||||||||
|
0.475 |
0.501 |
0.456 |
0.001 | 10.9 | 0.152 | 2.05 | 5.2e-06 | 20.8 |
|
0.729 |
0.689 |
0.753 |
0.001 | 12.1 | 0.137 | 2.22 | 1.6e-06 | 23 |
|
0.086 |
0.101 |
0.076 |
0.001 | 9.70 | 0.059 | 3.56 | 2.9e-06 | 21.9 |
|
|||||||||
|
0.459 |
0.502 |
0.465 |
5.91e-08 | 29.4 | 0.447 | 0.58 | 5.0e-06 | 20.8 |
|
0.756 |
0.688 |
0.746 |
1.92e-08 | 31.6 | 0.452 | 0.56 | 1.8e-06 | 22.8 |
|
0.073 |
0.094 |
0.075 |
0.001 | 11.2 | 0.833 | 0.04 | 0.0025 | 9.1 |
|
|||||||||
|
0.499 |
0.535 |
0.510 |
1.91e-05 | 18.3 | 0.102 | 2.68 | 0.007 | 7.2 |
|
0.693 |
0.619 |
0.673 |
3.58e-07 | 25.9 | 0.065 | 3.39 | 0.001 | 10.5 |
|
0.101 |
0.141 |
0.117 |
6.07e-08 | 29.3 | 0.005 | 7.75 | 0.007 | 7.3 |
|
|||||||||
|
0.502 |
0.515 |
0.516 |
0.547 | 0.36 | 0.273 | 1.20 | 0.7 | 0.12 |
|
0.379 |
0.471 |
0.406 |
8.59e-06 | 19.8 | 0.213 | 1.55 | 0.005 | 8.0 |
|
0.463 |
0.356 |
0.403 |
5.41e-07 | 25.1 | 0.005 | 7.99 | 0.08 | 3.0 |
Nonparametric statistical comparisons of the global metrics (small-world propensity |
Control | Pre-training Dys | Post-training Dys | Con vs. Pre-training Dys | Con vs. Post-training Dys | Pre-training vs. Post-training Dys | |||||
P |
|
P |
|
P |
|
|||||
|
||||||||||
|
0.662 |
0.636 |
0.627 |
0.047 | 3.95 | 0.073 | 3.22 | 0.79 | 0.07 | |
|
0.338 |
0.380 |
0.395 |
0.073 | 3.21 | 0.128 | 2.31 | 0.87 | 0.03 | |
|
0.268 |
0.263 |
0.270 |
0.610 | 0.26 | 0.563 | 0.33 | 0.87 | 0.03 | |
|
||||||||||
|
0.520 |
0.521 |
0.519 |
0.997 | 1.30e-05 | 0.889 | 0.02 | 0.89 | 0.02 | |
|
0.638 |
0.631 |
0.646 |
0.847 | 0.04 | 0.951 | 0.003 | 0.79 | 0.06 | |
|
0.156 |
0.162 |
0.149 |
0.959 | 0.003 | 0.509 | 0.44 | 0.49 | 0.45 | |
|
||||||||||
|
0.498 |
0.518 |
0.501 |
0.035 | 4.46 | 0.655 | 0.2 | 0.11 | 2.5 | |
|
0.692 |
0.657 |
0.691 |
0.026 | 4.98 | 0.693 | 0.16 | 0.07 | 3.3 | |
|
0.115 |
0.124 |
0.108 |
0.495 | 0.47 | 0.313 | 1.02 | 0.09 | 2.7 | |
|
||||||||||
|
0.466 |
0.498 |
0.466 |
0.001 | 12.09 | 0.781 | 0.08 | 0.0017 | 9.8 | |
|
0.741 |
0.697 |
0.745 |
0.001 | 11.57 | 0.906 | 0.01 | 0.0012 | 10.5 | |
|
0.083 |
0.092 |
0.074 |
0.012 | 6.39 | 0.219 | 1.51 | 0.0004 | 12.4 | |
|
||||||||||
|
0.459 |
0.498 |
0.458 |
3.1e-06 | 21.75 | 0.946 | 0.005 | 1.4e-05 | 18.9 | |
|
0.757 |
0.699 |
0.757 |
2.4e-06 | 22.22 | 0.933 | 0.007 | 1.5e-05 | 18.7 | |
|
0.069 |
0.086 |
0.074 |
0.001 | 10.59 | 0.379 | 0.77 | 0.02 | 5.2 | |
|
||||||||||
|
0.488 |
0.535 |
0.505 |
3.4e-07 | 25.99 | 0.014 | 6.01 | 0.0035 | 8.5 | |
|
0.713 |
0.619 |
0.684 |
2.4e-09 | 35.62 | 0.013 | 6.16 | 0.00029 | 13.1 | |
|
0.095 |
0.140 |
0.103 |
1.1e-05 | 19.26 | 0.358 | 0.84 | 0.0014 | 10.2 | |
|
||||||||||
|
0.500 |
0.504 |
0.502 |
0.982 | 0.001 | 0.969 | 0.001 | 0.97 | 0.001 | |
|
0.418 |
0.487 |
0.419 |
0.002 | 10.06 | 0.964 | 0.002 | 0.0048 | 7.95 | |
|
0.436 |
0.352 |
0.416 |
7.3e-05 | 15.72 | 0.368 | 0.81 | 0.0087 | 6.87 | |
Nonparametric statistical comparisons of the global metrics of the brain networks of controls, pre-training and post-training dyslexic groups during pseudoword discrimination. |
The groups’ graphs exhibit small-world properties. A relatively high small-world propensity
For discrimination of words, the between-group comparisons in the
The hubs on a group level for selected frequency bands for the word condition. Each node corresponds to an EEG sensor. The hubs, presented in red color, obtained from the group averaged strength/BC values. The links represent the most important links with edge BC: (A, 1 graph) Hubs (strength) in the
In the
The between-group comparisons of the hub distributions (BC) in the
In the
For the pseudoword condition in the
In the
The hubs on a group level for selected frequency bands for the pseudoword condition. (A, 1 graph) Hubs (strength) in the
In the
In the
The groups show a small-world network structure in the functional connectivity, but with different characteristics. The networks in the groups exhibit properties that gradually change from higher small-world propensity, less local clustering and shorter path length (more integrated networks) at low frequencies (
In contrast, working memory load in pre-training dyslexics would be associated with decreased local network connectivity and increased global integration. The increased long-range connections integrating across distinct networks were observed in task-related
This finding is consistent with previous functional connectivity studies suggesting impaired network structure and mixed patterns of connectivity abnormalities in dyslexia (Frye et al., 2012; Koyama et al., 2013). The typical direction of the network development in children revealed an age-related decrease in brain specialization with a gradual strengthening of long-distance connections (Hagmann et al., 2010). The children’s brain maturation has been associated with a global decrease of slow-wave activity, including theta oscillations, correlated with language abilities at all ages, and increased higher frequencies (Gaudet et al., 2020). The efficient network architecture optimally balances between local processing and global integration (Bullmore and Sporns, 2012) in the post-training group, most pronounced in
Lower theta frequencies mediate the integration of long distances between processes involving several cortical regions (von Stein and Sarnthein, 2000). The
Increasing topological segregation is associated with increased functional specialization of most brain areas that become less central in the network. The global change between the group comparisons increases in the frequency ranges:
The analysis of the BC and strength of the nodes show theta associated regional abnormality in developmental dyslexia, which may suggest that the attention deficit in developmental dyslexia is linked not only to the whole brain’s functional connectivity but with those in specific nodes. During the word condition in controls and post-training dyslexic groups, bihemispheric hubs were observed in the medial frontal cortex (
The processes of the font-perception in the right hemisphere (
The hubs in the right orbitofrontal cortex, the Broca’s area, and the right middle temporal cortex are more likely to participate in earlier stages of learning in the post-training dyslexics in
The frontal cortex and anterior temporal lobes (ATLs), the supramarginal and angular gyri (SMG, AG) as well as the right occipital lobe as incoming information area are assumed to involve in the lexico-semantic processing as nodes of the functional connectivity in the EEG network during the word/pseudoword visual discrimination. Besides these nodes, the modality-specific semantic access in the sensorimotor cortex was observed in the controls and the post-training dyslexics in
In children with dyslexia, before training, the nodes in the right ATL are observed together with nodes in the right somatosensory cortex in
For the controls and post-training dyslexics, the appearance of nodes simultaneously in the left-hemispheric medial frontal, occipitotemporal, anterior and middle temporal gyri at
The “reading network”, activating during reading, includes the “visual word form area” in the extrastriate cortex, the somatosensory association cortex, the superior and inferior parietal cortices, premotor and motor cortices (Taskov and Dushanova, 2020). These regions are a part of other networks, particularly those of the dorsal attention system. Reading is an important process, spending a lot of time exercising. The regions, incorporated with the reading task, are also utilized for the lexico-semantic network. The specific reading links may not represent routine functional relationships of many of the involved regions in dyslexia. Behavioral flexibility may depend on the ability to usefully configure the network of regions for specific tasks. These configurations are not necessarily representative of the basic way in which these regions are “normally” conjoined. Reading in dyslexia involves disrupting the baseline network’s coherence to create task-specific new networks bound by new sets of dynamic relationships. These relationships render brain networks capable of flexible, adaptive response when the cognitive network maintains recognizable topology across individuals and retains additional freedom for context, stimulus, and task-dependent reconfiguration. Different networks have different contributions. Some may be more engaged in specific processes (visual, motor, reading). Others may be more important for integrating multimodal information or for task switching and control. Together with changes in the properties of the semantic network and the distribution of nodes, the results reveal that the analysis of functional networks can be a promising tool for reflecting the neuropathological mechanism of developmental dyslexia. This network task-specific view provides a basis for understanding the underlying of the reading network in dyslexia.
JD and ST conceived and designed the experiments; JD performed the experiments; JD and ST analyzed the data; JD wrote the paper; ST revised the paper.
The Institute of Neurobiology, BAS, approved the research. Informed consent was obtained.
The study was supported by SNF DN05/14-2016. The authors would like to express their gratitude to the psychologist Dr. Y. Lalova and the logopedist A. Kalonkina for administering and scoring the psychological tests. A grant from the National Science Fund of the Ministry of Education and Science (project DN05/14-2016).
The authors declare no conflict of interest.