Pure-Tone Frequency Discrimination and Auditory Functional Connectivity in Developmental Dyslexia

Abstract

Background:

In previous studies, children with developmental dyslexia (DD) have been found to exhibit alterations in auditory sampling within the delta/theta and low-frequency gamma bands in auditory cortical areas during the initial processing stages, which affects the development of phonological skills. It has been suggested that auditory frequency discrimination measures sensory processing in language disorders such as DD. However, it is unclear how the pure-tone frequency discrimination task can detect abnormalities in functional connectivity in DD.

Methods:

We investigated local and global topological properties of functional networks in electroencephalographic (EEG) frequency bands from δ to γ2 based on a small-world propensity (SWP) model. This was done in both groups during pure-tone frequency discrimination.

Results:

Auditory α-, β-, and γ1-networks in the DD group were more integrated and less segregated than those of the control group. They were also not as functionally specialized, as indicated by larger deviations in characteristic path lengths and smaller deviations in clustering. The balanced segregation and integration (moderate clustering and path length) observed in the control group’s γ2-network may explain the optimal structure underlying their better performance. In the low-tone auditory θ- and γ2-frequency networks, the DD group, when compared with controls, lacked hubs in the inferior frontal cortex (IFC) and parietal connectivity to sensory areas. In the control group, however, the superior parietal lobes (SPL) mediated sensory connections. In the high-tone auditory network, only the controls had strong hubs in the right sensorimotor/auditory cortex (δ-frequency), bilateral IFC (γ1), and bilateral anterior temporal cortex (aITG, γ2), while the main hubs in the DD group were only in the left hemisphere. In the γ1 (high-tone) and γ2 (both tones) networks, controls showed strong right frontal-parietal-sensory hubs, which were lacking in the DD group during the task discrimination.

Conclusion:

The impairment in low-tone discrimination in the DD group is due to a lack of SPL-prefrontal connectivity within the auditory network. For high-tone discrimination, the DD group showed engagement of only the left-sided auditory network, with bilateral prefrontal recruitment (δ-network). In contrast, the SPL in the control group integrates sensory input for tone prediction, establishing tone-specific sensory/auditory connections with left prefrontal involvement (δ-network). Lower predictability for high tones in the DD group led to more localized processing with prefrontal influence. Overall, reduced frontotemporal connectivity in the DD group may indicate poorer auditory processing. This is likely due to impaired prefrontal-sensory communication and reduced interhemispheric auditory communication, which may underlie perceptual-cognitive biases in tone frequency discrimination.

Graphical abstract

Keywords

developmental dyslexia / auditory processing / functional connectivity

1. Introduction

Developmental dyslexia (DD), a reading and spelling disorder affecting children with normal intelligence, is primarily linked to difficulties in processing speech sounds, known as phonemic awareness [1, 2, 3, 4, 5]. Research also suggests that individuals with dyslexia may struggle with both speech and non-speech sound perception [6, 7, 8].

Auditory skills, which are crucial for language development, involve two distinct but related abilities: auditory recognition and auditory discrimination. Auditory recognition refers to the ability to identify and remember sounds, an early step in vocabulary building and language comprehension that focuses on overall sound processing rather than fine distinctions. In contrast, auditory discrimination involves a more refined level of listening. It is the ability to perceive and differentiate between subtle variations in sounds, such as pitch, tone, frequency, or duration. This skill is particularly critical for developing phonological awareness in young children—the capacity to hear and manipulate the sounds that make up words. Consequently, without strong auditory discrimination, children may struggle to distinguish between similar sounds, which can lead to difficulties in reading, spelling, and articulate speech.

Studies reveal links between auditory processing impairments and differences in the auditory cortex [5, 9, 10]. A prominent hypothesis suggests that dyslexia originates from a deficit in processing rapid auditory changes, which in turn leads to phonological and reading problems. Numerous studies indicate a fundamental auditory processing deficit in dyslexia [8, 11, 12, 13, 14, 15, 16], specifically concerning the processing of rapid temporal changes that differentiate consonants within a language [17, 18, 19]. The phonological problems associated with dyslexia may result from difficulties in processing rapid speech timing, a notion supported by the observed link to musical rhythm processing [20]. However, research has not definitively confirmed the precise nature of this auditory deficit—such as whether it is specific to speech or encompasses more general acoustic processing [11], or if it is limited to rapid temporal processing [8, 12, 13, 14, 15, 16, 17] versus broader spectrotemporal processing [18]. A core deficit in dyslexia is the ability to process and manipulate individual speech sounds (phonemes) [21, 22].

Dyslexia is not solely a language-based disorder; it also has fundamental auditory processing underpinnings, with both temporal and tone processing affecting phonemic awareness [23, 24, 25, 26]. These auditory deficits extend beyond speech, impacting the processing of various non-speech sounds, and their connection with linguistic abilities suggests shared neural pathways and processing mechanisms. Research highlights the significant role of tone processing in understanding dyslexia. Beyond temporal processing, studies also link phonemic awareness to pitch processing. Since speech sounds have distinct frequencies and pitch perception reflects frequency, it is hypothesized that both phoneme and pitch perception may rely on a common mechanism of frequency discrimination. Consequently, pitch sensitivity is relevant to reading because phonemic awareness has been shown to correlate with performance on pitch-related tasks [27]. Furthermore, the fact that specific language impairment (SLI) is associated with deficits in pitch discrimination strengthens the connection between musical pitch ability and phonemic awareness [28, 29, 30, 31].

The hypothesis of a common neural substrate for linguistic sound and musical processing, which could explain both dyslexia and tone-deafness, is supported by evidence that the auditory deficit affects both speech and non-speech perception, as well as by the established link between phonemic awareness and pitch perception [19, 21, 22]. This framework suggests that understanding the relationship between developmental language disorders and tone-deafness is crucial for future research.

Frequency discrimination, a fundamental auditory skill [32], is significantly affected in children with auditory processing challenges, particularly auditory processing disorder (APD). Both APD and SLI impact frequency discrimination and the detection of frequency differences above a certain threshold level (\mathrm{\Delta }f). APD involves difficulties processing auditory information despite normal hearing, while SLI involves language acquisition difficulties without other underlying issues. When tested at various levels of difficulty, with frequency differences (\mathrm{\Delta }f) ranging from small (20 Hz) to large (150 Hz), children with APD, SLI, or both performed better at larger frequency differences [32, 33]. For children with SLI, the impairment primarily affected the discrimination of larger, easier-to-detect frequency differences, which suggests a higher-level cognitive component. In contrast, APD primarily impacted the discrimination of smaller, more-difficult-to-detect frequency differences, which suggests a deficit in a perceptual component. This highlights the importance of examining frequency discrimination above the difference threshold (\mathrm{\Delta }f), as this is more relevant to everyday listening [32, 33].

Research on pure-tone frequency discrimination in typically developing school-aged children [34] reveals significant variability among them. Some children could detect very small frequency differences (around 1% of a 1000 Hz base frequency [34]), while others required much larger differences (around 10%) in a bimanual presentation [33]. Children with dyslexia have demonstrated impaired tone discrimination, with a difference threshold of up to 15% (e.g., distinguishing 0.9 kHz from 1.05 kHz [33]). This impairment is even greater in dichotic presentations, where a 20% impairment has been reported [35].

1.1 Neuroimaging Studies of the Auditory Functional System

Crucial for cortical sound processing [36], the auditory network encompasses several key brain regions: the primary and auditory association cortices, both residing in the superior temporal cortex. This network reaches beyond the temporal lobe to include portions of the parietal lobe, the posterior superior and middle temporal cortices, and the temporal pole. Additionally, subregions of the inferior parietal cortex (IPL) and the temporal-parieto-occipital junction contribute. These auditory regions functionally connect with frontal areas, alongside the primary somatosensory and middle occipital cortices [37]. The parietal and frontal lobes handle the final stages of sound processing. The primary auditory cortex (A1) is the main site for simplified auditory processing, handling simple sounds like unchanging single-frequency tones. However, complex sounds, such as noise bursts or speech, require more extensive processing that spans A1 and other auditory areas. Within this network, neural frequency tuning seems to mirror perceptual qualities, such as tone, rather than just the sound stimulus’s physical traits [38, 39]. Significantly, the right auditory cortex is more sensitive to sound’s spectral details (tonality), while the left auditory cortex shows greater attunement to rapid temporal dynamics, evident in speech [40]. The auditory cortex’s belt region, particularly its caudal fields, forms significant connections with the secondary visual [41] and secondary somatosensory cortices [42, 43]. Moreover, tonality is represented not only within the auditory cortex but also in the rostromedial prefrontal cortex [44].

Individuals with dyslexia often show auditory processing differences, marked by altered neural activity in the auditory cortex. These manifest as prolonged auditory evoked potentials (P1, N1, P2) and atypical mismatch negativity (MMN) responses. The MMN responses are elicited solely by the brain’s ability to distinguish deviant from standard stimuli, regardless of focused attention. The brain’s MMN, a response to changes in repeated sounds, serves as an indicator of central auditory processing, relying on both novel acoustic input and the memory of preceding sounds [45, 46]. The finding of reduced electroencephalographic (EEG) mismatch responses in dyslexia has led to their widespread application in auditory and cognitive research [47, 48, 49, 50]. Both adults and children demonstrate MMN responses even to subtle frequency differences [51]. An insensitivity to repetition could explain the abnormal mismatch response in individuals with dyslexia [25, 52], especially for speech, by disrupting repetition-induced neural habituation [53]. Within a predictive coding framework [54, 55], this phenomenon highlights the brain’s continuous model updating for stimulus consistency, a process fundamental to perception and learning [45]. In children, the P1-N1-P2 acoustic complex shows age dependency and reflects sensitivity to frequency changes, capable of detecting variations as minor as 1% of the base frequency. This complex, recorded at temporal electrodes, holds significant importance for studying auditory cortex activity related to frequency discrimination. Magnetoencephalography (MEG) studies, specifically, have documented delayed brain responses (P1, N1, P2) in individuals with dyslexia, suggesting an underlying auditory and sensory processing deficit [51]. Further, recent research points to structural and functional changes within the auditory cortex in dyslexia [37, 56, 57, 58].

Expanding on connections with other disorders and insights from dyslexia research, the auditory processing hypothesis for dyslexia proposes that the auditory difficulties experienced by individuals with dyslexia aren’t confined to complex sounds [56, 57, 58, 59, 60, 61]. Instead, it suggests they may also encounter challenges discriminating simple tones that typically demand minimal auditory processing. These anticipated difficulties with both pure tones and complex sounds would inherently require broad engagement from brain networks, including both primary and associative auditory cortices [62]. Brain abnormalities are evident in Neuroimaging studies of disorders such as dyslexia (a reading and language impairment), tone-deafness (congenital amusia), and developmental prosopagnosia (face recognition impairment). These issues notably affect the inferior frontal cortex (IFC) and the crucial pathway linking the frontal, parietal, and temporal lobes [63, 64]. A shared underlying pathology, like early neuronal migration disorders or problems with axon outgrowth or guidance during development, could be at play. This common root might result in subtle malformations across multiple brain networks, manifesting as various functional impairments: a more pronounced effect on the left hemisphere for dyslexia, the right for tone-deafness, and more widespread regions for prosopagnosia. Functional magnetic resonance imaging (fMRI) and electrophysiology investigations have identified brain abnormalities within the auditory cortex of children presenting with SLI and APD. However, MMN studies have yielded inconsistent results: some indicate reduced MMN amplitude in SLI children who performed poorly on a gap detection task [65, 66], while others report no significant differences. Notably, children with both SLI and APD displayed the weakest MMN responses [60, 67]. Anomalies in the P1, N1, and P2 components have also been observed, indicative of immature or delayed auditory pathway development.

While control subjects typically show a right hemisphere preference for tone processing, this is frequently absent in individuals with dyslexia [35]. Compensated dyslexia, even with typical speech lateralization, might involve less specialized general auditory lateralization [64]. Furthermore, the medial temporal lobe plays a supportive role, enabling individuals with dyslexia and other procedural memory deficits to sustain performance on serial reaction time tasks [68].

The reliability of neural findings, particularly MMN evidence concerning reduced automatic speech discrimination in dyslexia, remains a subject of ongoing debate [51]. To reconcile these conflicting outcomes, future research should differentiate between speech perception and general regularity detection and, critically, establish a stronger link between neural measures, behavioral performance, and broader language difficulties [67]. Ultimately, dyslexia is best characterized by a reduced capacity to learn and effectively utilize short-term statistical regularities, which are essential for enhancing perception and minimizing processing costs.

It’s been proposed that graph theory [69] be applied to brain networks, offering another perspective for understanding auditory processing in DD [70]. In functional brain networks, unusual graph metrics often point to changes in aspects like path length, clustering coefficients, or how hubs are distributed. Crucially, researchers have observed that DD involves both altered graph metrics and changes in the overall network structure. This includes a disrupted balance between local specialization and global integration within the brain’s networks. By using graph theory to analyze functional connectivity data, we can uncover precisely how different brain regions interact and organize themselves to support cognitive functions. This powerful approach could illuminate the specific ways network development is disrupted in DD, paving the way for deeper understanding and potential interventions. This research considers to studying auditory processing in children with DD to explore its developmental relationship with different frequency networks.

1.2 Hypotheses and Goals of the Study

Examining pure-tone frequency discrimination is essential for understanding auditory processing deficits in children with DD. Research suggests that the sharpness of high-frequency components is crucial for speech encoding, recognition, and retrieval [71]. This is particularly relevant in Bulgarian, where consonants exhibit distinct high-frequency acoustic characteristics depending on whether they precede front or back vowels [72, 73].

This study investigates the impact of simple pure tones on auditory discrimination in the DD group, specifically exploring the relationship between tone frequency and neural processing. We further examine brain connectivity in the DD group using graph theoretical analysis and the small-world propensity (SWP) model.

Given that no previous research has investigated how pure-tone discrimination affects functional brain networks in the DD group, a key question emerges: Can the analysis of brain functional networks help us understand the neurophysiological origins of DD, as well as the connection between the functional auditory network and specific local brain hubs? Our primary goal is to determine the variability in brain networks between the control and DD groups during a tone discrimination task.

We hypothesize that the DD group will exhibit altered SWP and reduced connectivity within various frequency-specific brain networks compared to typically developing children. We further hypothesize that these alterations may be modulated by tone frequency. We expect that if differences are found in the functional auditory networks of the DD group, they will be manifested as changes in specific local hubs within certain frequency networks during the auditory task.

2. Materials and Methods

2.1 Auditory Discrimination of Pure Tones

All participants had hearing thresholds better than 20 dB hearing level (HL) in both ears across standard audiometric frequencies (250 Hz to 8 kHz). With the exception of pure-tone audiometry, all auditory stimuli were digitally created in MATLAB R2018a (MathWorks, Inc., Natick, MA, USA) and transformed into analog signals via a 24-bit sound card at a 44 kHz sampling rate. The stimuli consisted of 50 ms pure tones at 900 Hz (low) and 1050 Hz (high). To ensure smooth onset and offset, 5 ms half-Hanning windows were applied. Tones were presented binaurally through Sound Blaster Z SE speakers powered by a Sound Blaster Z Audio Processor (Cat. No. SB1500, Creative Technology Ltd., Singapore) in blocks. Each block contained 40 low and 40 high tones, presented in a pseudo-random order. The interstimulus interval (ISI) varied between 1.5 and 2.5 seconds during each experimental session (Supplementary Fig. 1). Stimuli were calibrated to 60 dB SPL using a sound level meter (Brüel & Kjaer, mod. 2238; MicroPrecision, Grass Valley, CA, USA) in a sound-attenuated booth. Participants responded by pressing the right button for a low tone and the left button for a high tone. The percentage of correct responses was displayed to the experimenter on a computer monitor at the end of each session.

2.2 Selection of Groups

Eligibility for this quantitative cross-sectional study was determined through a neuropsychological examination [74] administered to all children. The study included a DD group (n = 36; 18 boys/girls; 103 \pm 3 months) and a normolexic control group (n = 36; 20 boys/16 girls; 102 \pm 4 months). Both groups were composed of second-grade students. All participants were right-handed [75], native Bulgarian speakers with normal or corrected-to-normal vision and no known neurological or psychological deficits.

2.3 Instruments

2.3.1 Participant Groups and Initial Assessment

The dyslexic group was formed by selecting children who demonstrated reading difficulties, specifically scoring below the average range (more than one standard deviation) for their age on measures of reading speed and accuracy from both the Dyslexia & Developmental Dysorthography Assessment Battery-Second Edition (DDE-2) battery [76, 77] and the “Reading Abilities” test [78] (Supplementary Table 1). Age-matched children from the same socio-demographic backgrounds, who exhibited typical reading performance within the norms of the DDE-2 and “Reading Abilities” assessments, formed the control group that included no children diagnosed with dyslexia or any co-occurring language disorders.

2.3.2 Diagnostic and Psychometric Measures

A standardized test battery, including the DDE-2 for DD [76, 77], “Reading Abilities” tests [78], and the Girolami-Boulinier’s test for non-verbal perception with “differently oriented signs” [79, 80], was administered to all children. This battery revealed a wide range of reading and writing difficulties in the children with DD (Supplementary Table 1). Additionally, all participants completed Raven’s Progressive Color Matrices [81] to assess nonverbal intelligence.

Psychometric testing included a battery designed for primary school-aged children to assess reading, writing, and phonological awareness [78] (Supplementary Table 1). The “Reading Abilities” test battery evaluated reading fluency through reading a 133-word text aloud, and writing was assessed via dictation of 30 sentences with missing compound words. Phonological awareness was evaluated through tasks involving word identification and the omission of a word’s initial sound (“without the first sound-letter”) or final syllable (“without the last syllable”). Differences between the control and DD groups were observed in both the accuracy and speed of test completion (Supplementary Table 1). Importantly, all children with DD demonstrated normal nonverbal intelligence quotient (IQ) scores on the Raven test, consistent with age-appropriate norms.

2.4 EEG Data Acquisition and Processing

2.4.1 EEG Data Acquisition

EEG signals were recorded using a 40-channel wireless system (https://portal.bpo.bg/bpo-registers/utility-models/view/BG_U_2023_5703) [82] featuring star-shaped dry gold sensors. Data were sampled at 250 Hz. Electrodes were positioned according to both the 10–10 and 10–20 systems (Supplementary Fig. 2), encompassing the following locations: 10/10 (AF3–AF4, F7–F8, FT9–F10, FC3–FC4, FC5–FC6, C1–C2, C5–C6, CP1–CP2, CP3–CP4, TP7–TP8, P7–P8, PO3–PO4, PO7–PO8) and 10/20 (Fz, F3–F4, C3–C4, Cz, T7–T8, P3–P4, Pz, O1–O2, Oz; Supplementary Fig. 2) [83, 84]. Reference electrodes were placed on the processus mastoidei and the ground electrode was positioned on the forehead. Rereferencing to global median reference was performed online to eliminate the volume conduction [85]. Skin impedance was maintained below 5 k\mathrm{\Omega } to ensure good signal quality.

2.4.2 EEG Data Preprocessing

Individual trials were segmented into 800-ms epochs locked to stimulus onset. EEG signals underwent bandpass filtering (1–70 Hz; Chebyshev II) and a 50 Hz notch filter was applied. Before we analyzed the data set was band-pass filtered by using a 4th-order Butterworth filter in forward and backward directions to avoid artificial phase shifts in each frequency band: \delta (1.5–4 Hz), \theta (4–8 Hz), \alpha (8–13 Hz), \beta1 (13–20 Hz), \beta2 (20–30 Hz), \gamma1 (30–48 Hz), and \gamma2 (52–70 Hz). Artifact-contaminated EEG trials (e.g., eye blinks, muscle activity) with amplitudes exceeding \pm200 µV were removed. Each trial also had to meet a signal-to-noise ratio (SNR) criterion [86]. We calculated SNR by dividing the mean signal’s peak-to-peak amplitude by twice the standard deviation of the noise. To get the noise for a sensor, we subtracted the average signal from each trial, and then found the standard deviation of these resulting residuals. Only trials meeting these noise removal criteria and correct behavioral responses were used in further analysis.

2.4.3 Analyzed Data and Frequency Bands

Following preprocessing, the average number of artifact-free data epochs per condition and subject (mean \pm sd) was 34 \pm 5 for controls and 25 \pm 8 for children with DD. To compensate for this imbalance, additional sessions were conducted with children with DD to equalize the number of epochs. The EEG data were further analyzed within the following frequency bands: \delta, \theta, \alpha, \beta1, \beta2, \gamma1, and \gamma2 Hz.

2.5 Functional Connectivity

Functional connectivity between brain regions was assessed by computing the weighted phase lag index (wPLI) [87, 88]. For all possible pairs of EEG sensors, an adjacency matrix was constructed, with separate computations performed for each time series and for each frequency band. The wPLI quantifies the extent of phase synchronization between two brain regions and reduces the bias introduced by volume conduction [87, 88]. The wPLI is calculated as a fraction: the numerator highlights the magnitude of phase lag asymmetry, while the denominator scales the result, ensuring the wPLI value always falls between 0 and 1. A wPLI value near 0 indicates an absence of a consistent phase lag, whereas a value closer to 1 indicates strong and consistent phase synchronization. The wPLI was used to determine the weighted adjacency matrix, which was then applied to calculate the network’s SWP [89].

The efficiency of information transfer within the network was quantified using SWP (\varphi) [89]. SWP is a measure that evaluates a network’s clustering coefficient and characteristic path length. Small-world networks are defined by exhibiting both a high clustering coefficient (signifying dense local connections) and a short characteristic path length (indicating efficient global communication). This balance allows for efficient local information processing alongside fast global communication [89].

Small-world characteristics are quantitatively defined by the deviation of the observed network’s clustering coefficient (\mathrm{\Delta }C) and characteristic path length (\mathrm{\Delta }L) from those of reference random (Crand, Lrand) and regular (Clatt, Llatt) models. In empirical networks, high SWP (\varphi) values are indicative of robust small-world attributes. Networks with \varphi values near 1 show high Cobs and short to moderate Lobs, or moderate Cobs and short Lobs. Conversely, lower \varphi values (close to 0) correspond to larger deviations (\mathrm{\Delta }L, \mathrm{\Delta }C) from null models, indicating a weaker small-world propensity [89].

For each frequency-specific functional network, brain connectivity characteristics, such as SWP (\varphi), \mathrm{\Delta }L, and \mathrm{\Delta }C, were extracted from each subject’s single-trial full wPLI matrix. This encompassed data for specific frequencies and tones (MATLAB functions from the Brain Connectivity Toolbox (http://www.brain-connectivity-toolbox.net); [89]). These global measures were then statistically compared using nonparametric methods.

Local node measures included strength (defined as the sum of weights to all other nodes) and betweenness centrality (BC), which is the proportion of all shortest paths in the network that pass through a given node [90, 91, 92]. Both strength and BC values were calculated for each node using the weights within the adjacency matrix. These values were then normalized for each subject by dividing a node’s score by the average of the local measures across all nodes. Nodes exhibiting high strength (and consequently high BC) are crucial for information processing within the network, as they are involved in a large number of the shortest paths between other nodes [93]. The most crucial nodes in a network are often termed hubs [91]. A key indicator of a network’s integration is a higher maximum BC or strength [90, 92]. Hubs were defined as nodes whose strength or BC, averaged across all participants, was at least one standard deviation greater than the group’s average for that measure. The most important links were then determined by identifying edges whose BC, averaged across all participants, was at least one standard deviation above the mean group edge BC.

The characteristics of individual brain networks, summarized at the participant level for each frequency band and tone, were established by combining anatomical parcellation methods, brain mapping techniques, and connectivity measures using the BrainNet Viewer functional package 1.7 (http://www.nitrc.org/projects/bnv, [93]). This package was then used to map nodes (brain regions) and edges (connections) onto a brain template, with their properties represented by size and color.

2.6 Statistical Analysis

During EEG recording sessions, Kruskal-Wallis nonparametric tests (KW test) were used to compare reaction times and performance accuracy between control and DD groups for each tone condition (low/high).

A multi-way analysis of variance (ANOVA) was conducted on the global measures, with the within-subject factors of tone (low, high) and frequency (\delta, \theta, \alpha, \beta1, \beta2, \gamma1, \gamma2) and the between-group factor of group (control vs. children with DD) [94]. Subsequently, a nonparametric procedure with 1000 permutations was employed for between-group pairwise comparisons of each global SWP measure (\varphi, \mathrm{\Delta }L, \mathrm{\Delta }C) across each frequency band and tone [95, 96]. The Bonferroni correction was applied to control the family-wise error rate (FWER) in multiple hypothesis testing. The adjusted significance level was calculated as p = \alpha/3 = 0.05/3 \approx 0.0167 to account for the three dependent measures. Furthermore, the Benjamini & Hochberg’s procedure [97] was also used to control the false discovery rate (FDR) within a family of hypothesis tests.

A multi-way ANOVA was conducted on the local measures, employing the same factors as described for the global measures. Local measures of individual nodes were assessed using statistical methods based on non-parametric permutation analyses with cluster-mass tests [94, 96]. Hubs were identified as nodes where the maximum strength or BC value was at least one standard deviation greater than their respective group’s average for that measure. Significant clusters were identified by applying a critical value to the maximum cluster statistic, with the false alarm rate controlled through multiple comparison corrections. The nodes within these clusters were indexed according to their corresponding EEG sensors, and the medians of their distributions were used to examine hemispheric differences. Permutation tests with cluster-mass tests were employed to effectively control the FWER and address the multiple comparisons problem for the dependent local measures (adjusted significance level: p = \alpha/2 = 0.025). An additional procedure [97] was used to control the FDR within a family of hypothesis tests. The same selective criteria were applied to the links and edges presented in the figures, highlighting those with specific values of BC and strength (Str).

3. Results

3.1 Behaviour Parameters in Pure Tone Discrimination

Children with DD were significantly faster than controls only for the high-tone condition (mean \pm s.e.: children with DD, 796.82 \pm 10.39 ms; controls, 844.4 \pm 7.78 ms; p = 2.01 \times 10^-6; \chi² = 22.59), but not for the low-tone condition (children with DD, 777.69 \pm 10.13 ms; controls, 812.64 \pm 7.58 ms; p = 0.35, \chi² = 0.84; Fig. 1).

The control group gave better success discrimination compared to the children with DD for low tone (children with DD, 64.54 \pm 2.62%; controls, 88.4 \pm 2.55, p = 3.54 \times 10^-9; \chi² = 34.86), and for high tone (children with DD, 59.92 \pm 2.64%; controls, 84.4 \pm 3.29%; p = 2.65 \times 10^-8; \chi² = 30.9; Fig. 1).

When comparing two tones in a series of trials, performance is influenced by an implicit memory of previously presented tones [98]. Both control children and those with DD form this implicit memory, which they use to aid in subsequent tone discrimination. However, children with DD benefit less from this effect, particularly for low tones. This deficit is reflected in faster reaction times, a finding observed exclusively in the high-tone condition.

3.2 Comparison of Global SWP Measures of Functional Connectivity

Analysis of global measures showed a significant within-group effect for tones (F(1, 1036) = 8.79, p \le 0.003) and frequencies (F(6, 1036) = 78.66, p \le 0.000001). A significant between-group main effect was also observed for group (F(1, 980) = 19.36, p \le 0.00001).

Significant differences in global network measures between children with DD and controls in low- and high-tone conditions showed (Fig. 2; Supplementary Tables 2,3) that the controls exhibited statistically higher \mathrm{\Delta }C and smaller SWP (\varphi), \mathrm{\Delta }L than children with DD in low-tone in: (1) the \alpha-\beta1 frequency networks (\varphi, \mathrm{\Delta }C, \mathrm{\Delta }L: \chi² > 6.115; p 0.013; Supplementary Table 2); (2) the \beta2-\gamma1 frequency networks (\varphi, \mathrm{\Delta }C: \chi² > 10.006; p 0.00156; Supplementary Table 2); (3) the \gamma2 frequency network (\mathrm{\Delta }C, \mathrm{\Delta }L: \chi² > 5.842; p 0.015; Supplementary Table 3).

Significant differences between groups were found in the high tone at: (1) \gamma1-frequency: lower SWP, higher \mathrm{\Delta }C for controls than children with DD (\chi² > 8.73, p 0.003; Fig. 2, Supplementary Table 3); (2) \gamma2-frequencies, \gamma2: lower \mathrm{\Delta }C, higher \mathrm{\Delta }L for controls than children with DD (\chi² > 12.145, p 0.001; Fig. 2, Supplementary Table 3).

A difference was found between the groups in the \alpha, \beta1, \beta2, and \gamma1 frequency networks in global measures in low-tone discrimination. The group of children with DD showed larger values of small-world tendency SWP and larger change in characteristic path length dL and smaller changes in cluster coefficients \mathrm{\Delta }C of frequency functional networks (Fig. 2, Supplementary Table 2). The difference between the functional networks was in \gamma frequencies (Fig. 2, Supplementary Table 3). An EEG study using graph analysis revealed altered global characteristics of functional brain networks in children with DD during a task involving low- and high-tone discrimination.

These global features suggest that these frequency networks in dyslexic children are more integrated than in controls and not as well segregated as in controls. Heterogeneous trajectories of cortical network maturation during childhood underlie variations associated with disruptions in brain network development across the studied groups.

3.3 Comparison of Local Measures of Functional Connectivity

Analysis of EEG graphs revealed changes in the local characteristics (hub strength, hub BC, and connections between them) of functional brain networks in control children and those with DD. Analysis of local measures showed a significant within-group effect for tones (F(1, 1484) = 4.55, p \le 0.033) and frequencies (F(6, 1456) = 3.71 p \le 0.0012). A significant between-group main effect was also observed for group (F(1, 1484) = 9.25, p \le 0.0024).

Different rates of cortical network maturation between groups during childhood contribute to variations in local measures (BC, Str) of certain frequency networks associated with disruptions in brain network development. These local measures indicate that certain frequency networks in children with DD are more integrated compared to controls and are not as well segregated as in controls.

3.3.1 Low Tone Discrimination

In the low-tone condition, the distribution of the strongest hubs in the \theta-network differed significantly between groups (\chi² = 6.188, p = 0.012; Table 1; Fig. 3). In controls, these hubs were located in the medial frontal cortex (MFC: Fz), inferior frontal gyrus (IFG: F7), middle frontal gyrus (MFG: FC3), anterior inferior temporal gyrus (aITG: FT9), precentral gyrus (PreCG: C1), postcentral gyrus (PstCG: C5), middle temporal gyri (MTG: TP8 BA21/22/37/20), superior parietal lobe (SPL: P4), and occipital lobe (OL: Oz). In children with DD, the strongest hubs were found in the middle frontal gyrus (MFG: FC3–4) and postcentral gyrus (PstCG: C3, C5).

In the \gamma2-network, hub strength was greatest in controls at MFG (intermediate frontal: F3), aITG (FT9), PreCG (C2), and SPL (CP2), and in children with DD at MFG (including premotor/pre-SMA, supplementary motor area, SMA: FC3), aITG (FT9), PreCG (C2, Cz), and PstCG (C3). The group difference was statistically significant (\chi² = 44.110, p = 1.116 \times 10^-16; Table 2; Fig. 3). Controls showed the highest hub BC at aITG (FT9–10), IFG (FC6), PreCG (C2), SPL (CP2), superior occipital lobe (SOG: PO4), and OL (Oz), while children with DD showed it at superior frontal cortex (SFC or dorsolateral prefrontal cortex DLPC: AF3), aITG (FT9–10), and PreCG (C2) (\chi² = 15.935, p = 0.00016; Table 2; Fig. 3).

3.3.2 High Tone Discrimination

The distribution of hubs with the greatest strength in the \delta-network for a high tone differed significantly between controls and children with DD (\chi² = 13.360, p = 0.0002; Table 2; Fig. 4). In controls, these hubs were located at SFC (AF3), PstCG (C6), posterior Superior Temporal Gyrus (pSTG: T7), and OL (Oz), while in children with DD, they were at SFC (AF3–4), IFG (F7), aITG (FT9), PstCG (C5), pSTG (T7), SOG (PO4 BA19/18/39), pITG (P8), and SPL (Pz).

In the \gamma1-network, there were statistically significant differences in the distribution of hubs with the greatest BC between groups (\chi² = 5.302, p = 0.021; Table 2; Fig. 4). Controls showed these hubs at IFC (F7–8), aITG (FT9–10), PstCG (C5), middle temporal gyrus (MTG: TP8), SOG (PO4), and middle occipital gyrus (MOG: PO7), while children with DD showed them at IFG (F7), PreCG (C2), PstCG (C5), pSTG (T7–8), and MTG (TP7).

In the \gamma2-network, hub strength and BC differed significantly between controls and children with DD. The greatest hub strength was observed at MFG (F3), PreCG (C2), PstCG (C3, C5), and SPL (CP2) for controls, and at anterior temporal gyrus (ATG: FT9), PreCG (C2, Cz), and PstCG (C3) for children with DD (\chi² = 68.752, p = 1.116 \times 10^-16; Table 2; Fig. 4). The highest BC was found at aITG (FT9–10), PstCG (C5), SPL (CP2), posterior part of inferior temporal gyrus (pITG: P8), and superior occipital lobe (SOG: PO4) for controls, and at SFC (AF3), aITG (FT9–10), and PreCG (C2) for children with DD (\chi² = 14.242, p = 0.00016; Table 2; Fig. 4).

4. Discussion

The multifaceted relationship between DD and auditory processing offers a deeper understanding of this learning disorder. A central debate revolves around whether auditory deficits in dyslexia are specific to speech or reflect more general auditory processing challenges. Dyslexia is linked to alterations in auditory cortex anatomy and function. Specifically, children with DD exhibit alterations in auditory sampling within the delta/theta [99, 100] and low-frequency gamma bands [101, 102] in auditory cortical areas [103] during early stages of processing.

4.1 Global Measures of Network Connectivity During Tone Discrimination

The small-world propensity (\varphi) vary between groups. Lower \varphi values indicate substantial deviations from null model predictions for clustering and path length, suggesting the network is less “small-world”. A low \varphi results from high \mathrm{\Delta }C (lack of local clustering) while maintaining a small path length and increased randomness. Even a small \mathrm{\Delta }L can decrease \varphi, though a higher \mathrm{\Delta }C can also drive this decrease [89]. In response to a low tone, controls showed increased local network connectivity and decreased global integration in auditory \alpha and \beta1 networks. This was characterized by a weak small-world propensity (\varphi), small deviations in \mathrm{\Delta }L, and a large deviation in \mathrm{\Delta }C. Compared to children with DD, controls also exhibited lower \varphi and smaller \mathrm{\Delta }L in the \beta1 (low-tone) and \gamma1 (both tones) networks. A substantial \mathrm{\Delta }C (0.75 for controls in \beta1) indicates strong divergence from a lattice network, while a very low \mathrm{\Delta }L for controls in \beta1 highlights its similarity to a random network. This behavior suggests a rewiring mechanism that can transform the network between these two topologies. The observed “small-world” properties are thus almost entirely driven by short path length. This suggests the network does not genuinely exemplify small-world principles due to deficient local clustering, indicating topological reorganization and a minimal tendency towards a true small-world structure. A genuine small-world structure is characterized by high local clustering and short path lengths, which facilitates information flow. In contrast, children with DD exhibited larger deviations in path lengths (\alpha, \beta1), indicating greater variability in connectivity, and smaller deviations in clustering (\alpha, \beta, \gamma1), suggesting increased global integration of these networks. The more balanced network segregation and integration, characterized by moderate clustering and path length, observed in the controls’ \gamma2 frequency network, might explain the optimal network structure supporting their better performance.

4.2 Local Measures of Tone-Dependent Functional Connectivity

The study revealed that tone differences were reflected in functional networks by variations in the strength and BC of major hubs across frontal, parietal, and primary sensory cortices, alongside the diverse contributions of individual nodes.

In the low-tone \theta-frequency network, controls showed their strongest hubs in the frontal lobes (medial, middle, and inferior), the anterior inferior temporal region, and the primary somatosensory and auditory cortices. These hubs were connected through the superior parietal lobe to primary sensory cortices, including the right middle temporal cortex and the occipital lobe. Conversely, children with DD showed their strongest hubs only in the middle frontal cortex of both hemispheres and the primary somatosensory and auditory cortices. These DD-specific hubs lacked high BC, and no connections through the parietal lobe to primary sensory areas were observed.

In the control group’s low-tone \gamma2 network, stronger, more central hubs were prominent in the frontal lobes, particularly the right inferior frontal lobe. These hubs connected via the superior parietal lobe to other sensory areas, including the superior occipital lobe and occipital cuneus. Conversely, in children with DD, the frontal lobes (prefrontal and middle) showed the highest strength in the \gamma2 network but connected only through primary somatosensory and motor areas to the right middle temporal and superior occipital regions. Notably, these primary sensory regions lacked strong \gamma2 connectivity in the DD group (Fig. 3). Additionally, the pre-supplementary motor areas (pre-SMA) showed increased engagement during low-tone processing.

These findings suggest the middle frontal cortex, particularly the pre-SMA, plays a tone-structure-modulated role in DD children’s \theta- and \gamma2 networks, independent of specific auditory frequency. This points to the pre-SMA’s primary contribution in movement prediction and temporal control, driven more by temporal information than auditory tone.

In the high-tone \delta network, children with DD exhibited widespread bilateral activity across prefrontal, frontal, temporal, parietal, and occipital regions, though the left superior temporal gyrus (STG) and posterior superior parietal lobe had weak primary sensory connections. Conversely, controls’ strongest primary somatosensory and auditory cortices (right hemisphere) and left STG showed high BC with other sensory cortices, including the occipital lobe, with the right auditory cortex appearing to favor high-tone processing. Both groups had robust left posterior superior temporal lobe (pSMG) strength. The prefrontal region (left dorsolateral prefrontal cortex (dLPFC) for controls; bilateral for DD) is implicated in temporal processing and action planning [104, 105]. DD children’s bilateral “ramping activity” suggests cognitive interval tuning, separate from motor responses. Attention and arousal significantly modulate temporal processing: reduced attention shortens perceived duration and alters activity in limbic, medial cortical, and cortico-thalamic-basal ganglia circuits, as well as frontal, temporal, and parietal cortices [105].

In the high-tone \gamma1 network, children with DD showed the highest BC in the left IFG, left primary somatosensory and auditory cortices (connected to both hemispheric pSTGs), and the left MTG. Conversely, controls exhibited their highest BC in both hemispheric IFGs and aITGs, extending through the left somatosensory and auditory cortex to primary sensory regions like the right MTG, right SOG, and left MOG. Unlike controls, children with DD lacked high-BC hubs in the aITG in both hemispheres at the \gamma1 network, mirroring findings in patients with specific lesions [106]. The \gamma2 network in controls featured the highest strength in the middle frontal cortex, primary somatosensory, motor, and auditory cortices, and the SPL. These areas, especially the auditory cortex and SPL, also had high BC with bilateral aITG and primary sensory regions (ITG, SOG). In contrast, children with DD showed less developed parietal lobes with poor connections to the frontal lobe and bilateral aITGs. Instead, their bilateral aITG had the highest BC, but only with the primary somatosensory and premotor cortices. In controls, the IPL consistently connected to main hubs across low- and high-frequency auditory conditions, reinforcing its role in integrating sensory inputs for higher-order timing and transmitting information to prefrontal areas for pitch prediction [107]. This IPL connectivity with primary somatosensory and auditory cortices was tone-dependent only in controls, with connections from the left primary somatosensory/auditory cortices (high-tone) and connections from the right primary motor/premotor/somatosensory cortices (low-tone). In these conditions, sensory processing largely remained in early sensory areas, with weak prefrontal recruitment in the \delta band. However, high tones (\gamma1) potentially involved bilateral Broca’s areas, while low tones (\theta) involved only the left side, suggesting weaker auditory network engagement. This aligns with studies on sensory-motor coupling in rhythm tracking [108, 109]. This \delta-frequency network pattern was only observed for the high-tone in children with DD. In summary, when timing is predictable, the core network is functionally affected by development, indicated by weak IPL–prefrontal interactions. When predictability is low, children with DD exhibited a localized high-tone auditory network strategy, with left somatosensory and auditory regions primarily influenced by the SPL and substantial prefrontal involvement. This tone-dependent connectivity highlights the importance of top-down prefrontal control for temporal predictions and the adaptive, yet compensatory, role of sensory regions when predictability is diminished. A stronger functional connectivity both between hemispheres of the auditory network and between the auditory system and prefrontal/parietal areas in the controls than in the dyslexics tended to be associated with facilitation in the encoding and discrimination of the tone sequence, a relationship less evident in dyslexics [110, 111, 112]. The task-driven bilateral frontotemporal auditory network encoding tones showed frequency-specific activity: in the \theta-frequency range for low tones, in \delta- and \gamma1-frequency ranges for high tones, and \gamma2-frequency ranges for both tone frequencies.

Additional hubs observed in controls for high tone included primary somatosensory, auditory, and supramarginal cortex (right in \delta, left in \gamma1), and right occipitotemporal gyrus (\gamma2), while for low tone—right IFG and occipital lobe (\gamma2). In contrast, children with DD only showed additional hubs for low tone in the left dLPFC, left pre-SMA, SMA, and right MTG (\gamma2). Strong interhemispheric connections between auditory cortices in typically developing children likely support accurate tone perception and retention, crucial for speech decoding and phonetic categorization [113, 114]. The dLPFC is key for working memory, maintaining and manipulating sensory information from posterior sensory areas [115]. Consequently, weakened connectivity between the DLPFC and sensory regions can impair both fine detail and overall auditory feature processing. Reduced frontotemporal connectivity, observed in DD, is linked to difficulties retrieving tonal information [116, 117, 118, 119], potentially indicating poorer auditory processing [119].

A potential neurophysiological mechanism for dyslexia’s deficits may involve impaired functional communication between sensory regions and the prefrontal cortex, alongside excessive communication between homologous auditory areas. The strength of interhemispheric functional connections within relevant sensory networks could underpin specific neurophysiological mechanisms leading to perception-cognitive biases or frequency discrimination difficulties.

5. Conclusion

The study reveals distinct local and global functional network properties in children with DD during auditory processing. It highlights differences in integration, segregation, hub organization, and connectivity, particularly involving the parietal and prefrontal cortices. It is important to interpret scalp-level EEG network results with caution, even though the estimations of functional connectivity are highly reliable [120]. These findings suggest neurophysiological differences that may underlie the auditory processing difficulties associated with DD.

Availability of Data and Materials

The data are not publicly available due to privacy and confidentiality concerns but are available from the corresponding author on reasonable request.

References

[1] Bradley L, Bryant PE. Categorizing sounds and learning to read—a causal connection. Nature. 1983; 301: 419–421. https://doi.org/10.1038/301419a0.

[2] Stanovich KE. Explaining the differences between the dyslexic and the garden-variety poor reader: the phonological-core variable-difference model. Journal of Learning Disabilities. 1988; 21: 590–604. https://doi.org/10.1177/002221948802101003.

[3] Elhassan Z, Crewther SG, Bavin EL. The Contribution of Phonological Awareness to Reading Fluency and Its Individual Sub-skills in Readers Aged 9- to 12-years. Frontiers in psychology. 2017, 8: 533. https://doi.org/10.3389/fpsyg.2017.00533.

[4] Snow CE, Burns MS, Griffin P (eds.) Preventing reading difficulties in young children. National Academy Press: Washington, DC. 1998.

[5] Démonet JF, Taylor MJ, Chaix Y. Developmental dyslexia. Lancet. 2004; 363: 1451–1460. https://doi.org/10.1016/S0140-6736(04)16106-0.

[6] Habib M. The neurological basis of developmental dyslexia: an overview and working hypothesis. Brain. 2000; 123: 2373–2399. https://doi.org/10.1093/brain/123.12.2373.

[7] Vandermosten M, Boets B, Luts H, Poelmans H, Golestani N, Wouters J, et al. Adults with dyslexia are impaired in categorizing speech and nonspeech sounds on the basis of temporal cues. Proceedings of the National Academy of Sciences of the United States of America. 2010; 107: 10389–10394. https://doi.org/10.1073/pnas.0912858107.

[8] Hämäläinen JA, Salminen HK, Leppänen PHT. Basic auditory processing deficits in dyslexia: systematic review of the behavioral and event-related potential/ field evidence. Journal of Learning Disabilities. 2013; 46: 413–427. https://doi.org/10.1177/0022219411436213.

[9] Habib M. The Neurological Basis of Developmental Dyslexia and Related Disorders: A Reappraisal of the Temporal Hypothesis, Twenty Years on. Brain Sciences. 2021; 11: 708. https://doi.org/10.3390/brainsci11060708.

[10] Christiner M, Serrallach BL, Benner J, Bernhofs V, Schneider P, Renner J, et al. Examining Individual Differences in Singing, Musical and Tone Language Ability in Adolescents and Young Adults with Dyslexia. Brain Sciences. 2022; 12: 744. https://doi.org/10.3390/brainsci12060744.

[11] Ramus F, Rosen S, Dakin SC, Day BL, Castellote JM, White S, et al. Theories of developmental dyslexia: insights from a multiple case study of dyslexic adults. Brain. 2003; 126: 841–865. https://doi.org/10.1093/brain/awg076.

[12] Talcott JB, Witton C, McLean MF, Hansen PC, Rees A, Green GG, et al. Dynamic sensory sensitivity and children’s word decoding skills. Proceedings of the National Academy of Sciences of the United States of America. 2000; 97: 2952–2957. https://doi.org/10.1073/pnas.040546597.

[13] Hämäläinen J, Leppänen PHT, Torppa M, Müller K, Lyytinen H. Detection of sound rise time by adults with dyslexia. Brain and Language. 2005; 94: 32–42. https://doi.org/10.1016/j.bandl.2004.11.005.

[14] Goswami U. A temporal sampling framework for developmental dyslexia. Trends in Cognitive Sciences. 2011; 15: 3–10. https://doi.org/10.1016/j.tics.2010.10.001.

[15] Flanagan S, Wilson AM, Gabrielczyk FC, MacFarlane A, Mandke KN, Goswami U. Amplitude rise time sensitivity in children with and without dyslexia: differential task effects and longitudinal relations to phonology and literacy. Frontiers in Psychology. 2024; 15: 1245589. https://doi.org/10.3389/fpsyg.2024.1245589.

[16] Qi T, Mandelli ML, Watson Pereira CL, Wellman E, Bogley R, Licata AE, et al. Anatomical and behavioural correlates of auditory perception in developmental dyslexia. Brain. 2025; 148: 833–844. https://doi.org/10.1093/brain/awae298.

[17] Tallal P. Improving language and literacy is a matter of time. Nature Reviews. Neuroscience. 2004; 5: 721–728. https://doi.org/10.1038/nrn1499.

[18] Tallal P, Gaab N. Dynamic auditory processing, musical experience and language development. Trends in Neurosciences. 2006; 29: 382–390. https://doi.org/10.1016/j.tins.2006.06.003.

[19] Loui P, Kroog K, Zuk J, Winner E, Schlaug G. Relating pitch awareness to phonemic awareness in children: implications for tone-deafness and dyslexia. Frontiers in Psychology. 2011; 2: 111. https://doi.org/10.3389/fpsyg.2011.00111.

[20] Overy K. Dyslexia and music. From timing deficits to musical intervention. Annals of the New York Academy of Sciences. 2003; 999: 497–505. https://doi.org/10.1196/annals.1284.060.

[21] Steinbrink C, Knigge J, Mannhaupt G, Sallat S, Werkle A. Are Temporal and Tonal Musical Skills Related to Phonological Awareness and Literacy Skills? - Evidence From Two Cross-Sectional Studies With Children From Different Age Groups. Frontiers in Psychology. 2019; 10: 805. https://doi.org/10.3389/fpsyg.2019.00805.

[22] Witton C, Swoboda K, Shapiro LR, Talcott JB. Auditory frequency discrimination in developmental dyslexia: A meta-analysis. Dyslexia. 2020; 26: 36–51. https://doi.org/10.1002/dys.1645.

[23] Näätänen R, Alho K. Mismatch negativity–the measure for central sound representation accuracy. Audiology & Neuro-Otology. 1997; 2: 341–353. https://doi.org/10.1159/000259255.

[24] Hari R, Renvall H. Impaired processing of rapid stimulus sequences in dyslexia. Trends in Cognitive Sciences. 2001; 5: 525–532. https://doi.org/10.1016/s1364-6613(00)01801-5.

[25] Schulte-Körne G, Bruder J. Clinical neurophysiology of visual and auditory processing in dyslexia: a review. Clinical Neurophysiology. 2010; 121: 1794–1809. https://doi.org/10.1016/j.clinph.2010.04.028.

[26] Volkmer S, Schulte-Körne G. Cortical responses to tone and phoneme mismatch as a predictor of dyslexia? A systematic review. Schizophrenia Research. 2018; 191: 148–160. https://doi.org/10.1016/j.schres.2017.07.010.

[27] Forgeard M, Winner E, Norton A, Schlaug G. Practicing a musical instrument in childhood is associated with enhanced verbal ability and nonverbal reasoning. PLoS ONE. 2008; 3: e3566. https://doi.org/10.1371/journal.pone.0003566.

[28] Jentschke S, Koelsch S, Friederici AD. Investigating the relationship of music and language in children: influences of musical training and language impairment. Annals of the New York Academy of Sciences. 2005; 1060: 231–242. https://doi.org/10.1196/annals.1360.016.

[29] Mengler ED, Hogben JH, Michie P, Bishop DVM. Poor frequency discrimination is related to oral language disorder in children: a psychoacoustic study. Dyslexia. 2005; 11: 155–173. https://doi.org/10.1002/dys.302.

[30] Jones JL, Zalewski C, Brewer C, Lucker J, Drayna D. Widespread auditory deficits in tune deafness. Ear and Hearing. 2009; 30: 63–72. https://doi.org/10.1097/AUD.0b013e31818ff95e.

[31] Moreno S, Marques C, Santos A, Santos M, Castro SL, Besson M. Musical training influences linguistic abilities in 8-year-old children: more evidence for brain plasticity. Cerebral Cortex. 2009; 19: 712–723. https://doi.org/10.1093/cercor/bhn120.

[32] Miller CA, Wagstaff DA. Behavioral profiles associated with auditory processing disorder and specific language impairment. Journal of Communication Disorders. 2011; 44: 745–763. https://doi.org/10.1016/j.jcomdis.2011.04.001.

[33] Rota-Donahue C, Schwartz RG, Shafer V, Sussman ES. Perception of Small Frequency Differences in Children with Auditory Processing Disorder or Specific Language Impairment. Journal of the American Academy of Audiology. 2016; 27: 489–497. https://doi.org/10.3766/jaaa.15122.

[34] Moore DR, Ferguson MA, Halliday LF, Riley A. Frequency discrimination in children: perception, learning and attention. Hearing Research. 2008; 238: 147–154. https://doi.org/10.1016/j.heares.2007.11.013.

[35] Sebastian C, Yasin I. Speech versus tone processing in compensated dyslexia: discrimination and lateralization with a dichotic mismatch negativity (MMN) paradigm. International Journal of Psychophysiology. 2008; 70: 115–126. https://doi.org/10.1016/j.ijpsycho.2008.08.004.

[36] Alemán-Gómez Y., Griffa A., Houde JC, Najdenovska E, Magon S, Cuadra MB, et al. A multi-scale probabilistic atlas of the human connectome. Scientific data. 2022; 9: 516. https://doi.org/10.1038/s41597-022-01624-8.

[37] Lestang JH, Cai H, Averbeck BB, Cohen YE. Functional network properties of the auditory cortex. Hearing Research. 2023; 433: 108768. https://doi.org/10.1016/j.heares.2023.108768.

[38] Bendor D, Wang X. Cortical representations of pitch in monkeys and humans. Current Opinion in Neurobiology. 2006; 16: 391–399. https://doi.org/10.1016/j.conb.2006.07.001.

[39] Kikuchi Y, Kumar S, Baumann S, Overath T, Gander PE, Sedley W, et al. The distribution and nature of responses to broadband sounds associated with pitch in the macaque auditory cortex. Cortex. 2019; 120: 340–352. https://doi.org/10.1016/j.cortex.2019.07.005.

[40] Lakatos P, Musacchia G, O’Connel MN, Falchier AY, Javitt DC, Schroeder CE. The spectrotemporal filter mechanism of auditory selective attention. Neuron. 2013; 77: 750–761. https://doi.org/10.1016/j.neuron.2012.11.034.

[41] Falchier A, Schroeder CE, Hackett TA, Lakatos P, Nascimento-Silva S, Ulbert I, et al. Projection from visual areas V2 and prostriata to caudal auditory cortex in the monkey. Cerebral Cortex. 2010; 20: 1529–1538. https://doi.org/10.1093/cercor/bhp213.

[42] Cappe C, Barone P. Heteromodal connections supporting multisensory integration at low levels of cortical processing in the monkey. The European Journal of Neuroscience. 2005; 22: 2886–2902. https://doi.org/10.1111/j.1460-9568.2005.04462.x.

[43] Smiley JF, Hackett TA, Ulbert I, Karmas G, Lakatos P, Javitt DC, et al. Multisensory convergence in auditory cortex, I. Cortical connections of the caudal superior temporal plane in macaque monkeys. The Journal of Comparative Neurology. 2007; 502: 894–923. https://doi.org/10.1002/cne.21325.

[44] Janata P, Birk JL, Van Horn JD, Leman M, Tillmann B, Bharucha JJ. The cortical topography of tonal structures underlying Western music. Science. 2002; 298: 2167–2170. https://doi.org/10.1126/science.1076262.

[45] Beach SD, Ozernov-Palchik O, May SC, Centanni TM, Perrachione TK, Pantazis D, et al. The Neural Representation of a Repeated Standard Stimulus in Dyslexia. Frontiers in Human Neuroscience. 2022; 16: 823627. https://doi.org/10.3389/fnhum.2022.823627.

[46] Näätänen R, Jacobsen T, Winkler I. Memory-based or afferent processes in mismatch negativity (MMN): a review of the evidence. Psychophysiology. 2005; 42: 25–32. https://doi.org/10.1111/j.1469-8986.2005.00256.x.

[47] Kujala T. The Role of Early Auditory Discrimination Deficits in Language Disorders. Journal of Psychophysiology. 2007; 21: 239–250. https://doi.org/10.1027/0269-8803.21.34.239.

[48] Bonte ML, Poelmans H, Blomert L. Deviant neurophysiological responses to phonological regularities in speech in dyslexic children. Neuropsychologia. 2007; 45: 1427–1437. https://doi.org/10.1016/j.neuropsychologia.2006.11.009.

[49] Noordenbos MW, Segers E, Serniclaes W, Mitterer H, Verhoeven L. Allophonic mode of speech perception in Dutch children at risk for dyslexia: a longitudinal study. Research in Developmental Disabilities. 2012; 33: 1469–1483. https://doi.org/10.1016/j.ridd.2012.03.021.

[50] Näätänen R, Kujala T, Escera C, Baldeweg T, Kreegipuu K, Carlson S, et al. The mismatch negativity (MMN)–a unique window to disturbed central auditory processing in ageing and different clinical conditions. Clinical Neurophysiology. 2012; 123: 424–458. https://doi.org/10.1016/j.clinph.2011.09.020.

[51] Bishop DVM. Using mismatch negativity to study central auditory processing in developmental language and literacy impairments: where are we, and where should we be going? Psychological Bulletin. 2007; 133: 651–672. https://doi.org/10.1037/0033-2909.133.4.651.

[52] Gu C, Bi HY. Auditory processing deficit in individuals with dyslexia: A meta-analysis of mismatch negativity. Neuroscience and Biobehavioral Reviews. 2020; 116: 396–405. https://doi.org/10.1016/j.neubiorev.2020.06.032.

[53] May PJC, Tiitinen H. Mismatch negativity (MMN), the deviance-elicited auditory deflection, explained. Psychophysiology. 2010; 47: 66–122. https://doi.org/10.1111/j.1469-8986.2009.00856.x.

[54] Rao RP, Ballard DH. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience. 1999; 2: 79–87. https://doi.org/10.1038/4580.

[55] Baldeweg T. Repetition effects to sounds: evidence for predictive coding in the auditory system. Trends in Cognitive Sciences. 2006; 10: 93–94. https://doi.org/10.1016/j.tics.2006.01.010.

[56] Brambati SM, Termine C, Ruffino M, Stella G, Fazio F, Cappa SF, et al. Regional reductions of gray matter volume in familial dyslexia. Neurology. 2004; 63: 742–745. https://doi.org/10.1212/01.wnl.0000134673.95020.ee.

[57] Kuhl U, Neef NE, Kraft I, Schaadt G, Dörr L, Brauer J, et al. The emergence of dyslexia in the developing brain. Neuroimage. 2020; 211: 116633. https://doi.org/10.1016/j.neuroimage.2020.116633.

[58] Skeide MA, Bazin PL, Trampel R, Schäfer A, Männel C, von Kriegstein K, et al. Hypermyelination of the left auditory cortex in developmental dyslexia. Neurology. 2018; 90: e492–e497. https://doi.org/10.1212/WNL.0000000000004931.

[59] Vandermosten M, Boets B, Wouters J, Ghesquière P. A qualitative and quantitative review of diffusion tensor imaging studies in reading and dyslexia. Neuroscience and Biobehavioral Reviews. 2012; 36: 1532–1552. https://doi.org/10.1016/j.neubiorev.2012.04.002.

[60] Vandermosten M, Vanderauwera J, Theys C, De Vos A, Vanvooren S, Sunaert S, et al. A DTI tractography study in pre-readers at risk for dyslexia. Developmental Cognitive Neuroscience. 2015; 14: 8–15. https://doi.org/10.1016/j.dcn.2015.05.006.

[61] Vandermosten M, Correia J, Vanderauwera J, Wouters J, Ghesquière P, Bonte M. Brain activity patterns of phonemic representations are atypical in beginning readers with family risk for dyslexia. Developmental Science. 2020; 23: e12857. https://doi.org/10.1111/desc.12857.

[62] Altarelli I, Leroy F, Monzalvo K, Fluss J, Billard C, Dehaene-Lambertz G, et al. Planum temporale asymmetry in developmental dyslexia: Revisiting an old question. Human Brain Mapping. 2014; 35: 5717–5735. https://doi.org/10.1002/hbm.22579.

[63] Catani M, Thiebaut de Schotten M. A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior. 2008; 44: 1105–1132. https://doi.org/10.1016/j.cortex.2008.05.004.

[64] Ivanova MV, Zhong A, Turken A, Baldo JV, Dronkers NF. Functional Contributions of the Arcuate Fasciculus to Language Processing. Frontiers in Human Neuroscience. 2021; 15: 672665. https://doi.org/10.3389/fnhum.2021.672665.

[65] Ahmmed AU, Clarke EM, Adams C. Mismatch negativity and frequency representational width in children with specific language impairment. Developmental Medicine and Child Neurology. 2008; 50: 938–944. https://doi.org/10.1111/j.1469-8749.2008.03093.x.

[66] Rota-Donahue C. Neurophysiological bases of frequency discrimination in children with Auditory Processing Disorder or Specific Language Impairment. (2014). CUNY Academic Works. https://academicworks.cuny.edu/gc_etds/102.

[67] Tuomainen OT. Auditory short-term memory trace formation for nonspeech and speech in SLI and dyslexia as indexed by the N100 and mismatch negativity electrophysiological responses. Neuroreport. 2015; 26: 374–379. https://doi.org/10.1097/WNR.0000000000000357.

[68] Marinelli CV, Martelli M, Zoccolotti P. Does the procedural deficit hypothesis of dyslexia account for the lack of automatization and the comorbidity among developmental disorders? Cognitive Neuropsychology. 2024; 41: 93–112. https://doi.org/10.1080/02643294.2024.2393447.

[69] Sporns O. Graph theory methods: applications in brain networks. Dialogues in Clinical Neuroscience. 2018; 20: 111–121. https://doi.org/10.31887/DCNS.2018.20.2/osporns.

[70] Gallego-Molina NJ, Ortiz A, Martínez-Murcia FJ, Formoso MA, Giménez A. Complex network modeling of EEG band coupling in dyslexia: an exploratory analysis of auditory processing and diagnosis. Knowledge-Based Systems. 2022; 240: 108098. https://doi.org/10.1016/j.knosys.2021.108098.

[71] Jones SD, Stewart HJ, Westermann G. A maturational frequency discrimination deficit may explain developmental language disorder. Psychological Review. 2024; 131: 695–715. https://doi.org/10.1037/rev0000436.

[72] Marinov V. Acoustic characteristics of the consonants л and л’ in Bulgarian and l and л’ in the Wallachian dialect. In Ilieva M (ed.) Collection in papers AUT INVENIAM VIAM, AUT FACIAM in honor of Corresponding Member Prof. Dr. of Philology Stoyan Burov (pp. 316–324). University Publishing House St. St. Kiril i Metodiy & Faber: Veliko Tarnovo. 2019.

[73] Sabev M, Andreeva B. The acoustics of Contemporary Standard Bulgarian vowels: A corpus study. The Journal of the Acoustical Society of America. 2024; 155: 2128–2138. https://doi.org/10.1121/10.0025293.

[74] Raichev P, Geleva T, Valcheva M, Rasheva M, Raicheva M. Protocol on neurological and neuropsychological studies of children with specific learning disabilities. In Evgenieva E (ed.) Integrated Learning and Resource Teacher (pp. 82–105). Publishing House “Dr. Ivan Bogorov”: Sofia, Bulgaria. 2005. (In Bulgarian)

[75] Annett M. A classification of hand preference by association analysis. British Journal of Psychology. 1970; 61: 303–321. https://doi.org/10.1111/j.2044-8295.1970.tb01248.x.

[76] Matanova V, Todorova E. DDE-2 Test Battery for Evaluation of Dyslexia of Development—Bulgarian Adaptation; OS Bulgaria Ltd.: Sofia, Bulgaria. 2013. Available at: https://www.giuntipsy.bg/testove/dde-2-testova-baterija-za-ocenka-na-disleksija-na-razvitieto (Accessed: 1 October 2013).

[77] Sartori G, Remo J, Tressoldi PE. Updated and revised edition for the evaluation of dyslexia. In DDE-2, Battery for the Developmental Dyslexia and Evolutionary Disorders-2, 1995. Giunti O.S.: Florence, Italy. 2007.

[78] Kalonkina A, Lalova J. Normative indicators for the test battery for a written speech assessment. Logopedical Centre Romel. 2016; 30–38. (In Bulgarian)

[79] Girolami-Boulinier A. Contrôle des Aptitudes à la Lecture et àl’Ecriture (CALE). CALE: Paris, France. 1985. (In French)

[80] Yakimova R. Abnormalities of Written Speech. Rommel Publuling House: Sofia, Bulgaria. 2004. (In Bulgarian)

[81] Raven J, Raven JC, Court JH. Manual for Raven’s Progressive Matrices and Vocabulary Scales. Section 2: The Colored Progressive matrices; Oxford Psychologists Press: Oxford, UK; The Psychological Corporation: San Antonio, TX, USA. 1998.

[82] Totev T, Taskov T, Dushanova J. A wireless EEG system for Neurofeedback training. Applied Sciences. 2022; 13: 96. https://doi.org/10.3390/app13010096.

[83] Koessler L, Maillard L, Benhadid A, Vignal JP, Felblinger J, Vespignani H, et al. Automated cortical projection of EEG sensors: anatomical correlation via the international 10-10 system. NeuroImage. 2009; 46: 64–72. https://doi.org/10.1016/j.neuroimage.2009.02.006.

[84] Giacometti P, Perdue KL, Diamond SG. Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain. Journal of Neuroscience Methods. 2014; 229: 84–96. https://doi.org/10.1016/j.jneumeth.2014.04.020.

[85] Ríos-Herrera WA, Olguín-Rodríguez PV, Arzate-Mena JD, Corsi-Cabrera M, Escalona J, Marín-García A, et al. The Influence of EEG References on the Analysis of Spatio-Temporal Interrelation Patterns. Frontiers in Neuroscience. 2019; 13: 941. https://doi.org/10.3389/fnins.2019.00941.

[86] Dushanova J, Christov M. Auditory event-related brain potentials for an early discrimination between normal and pathological brain aging. Neural Regeneration Research. 2013; 8: 1390–1399. https://doi.org/10.3969/j.issn.1673-5374.2013.15.006.

[87] Stam CJ, Nolte G, Daffertshofer A. Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human Brain Mapping. 2007; 28: 1178–1193. https://doi.org/10.1002/hbm.20346.

[88] Vinck M, Oostenveld R, van Wingerden M, Battaglia F, Pennartz CMA. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. NeuroImage. 2011; 55: 1548–1565. https://doi.org/10.1016/j.neuroimage.2011.01.055.

[89] Muldoon SF, Bridgeford EW, Bassett DS. Small-World Propensity and Weighted Brain Networks. Scientific Reports. 2016; 6: 22057. https://doi.org/10.1038/srep22057.

[90] Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews. Neuroscience. 2009; 10: 186–198. https://doi.org/10.1038/nrn2575.

[91] Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage. 2010; 52: 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003.

[92] Stam CJ, van Straaten ECW. The organization of physiological brain networks. Clinical Neurophysiology. 2012; 123: 1067–1087. https://doi.org/10.1016/j.clinph.2012.01.011.

[93] Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE. 2013; 8: e68910. https://doi.org/10.1371/journal.pone.0068910.

[94] Frossard J, Renaud O. Permutation Tests for Regression, ANOVA, and Comparison of Signals: The permuco Package. Journal of Statistical Software. 2021; 99: 1–32. https://doi.org/10.18637/jss.v099.i15.

[95] Mason DM, Newton MA. A Rank Statistics Approach to the Consistency of a General Bootstrap. The Annals of Statistics. 1992; 20: 1161–1624. https://www.jstor.org/stable/2242030.

[96] Maris E, Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods. 2007; 164: 177–190. https://doi.org/10.1016/j.jneumeth.2007.03.024.

[97] Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B: Statistical Methodology. 1995; 57: 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.

[98] Jaffe-Dax S, Frenkel O, Ahissar M. Dyslexics’ faster decay of implicit memory for sounds and words is manifested in their shorter neural adaptation. eLife. 2017; 6: e20557. https://doi.org/10.7554/eLife.20557.

[99] Giraud AL, Kleinschmidt A, Poeppel D, Lund TE, Frackowiak RSJ, Laufs H. Endogenous cortical rhythms determine cerebral specialization for speech perception and production. Neuron. 2007; 56: 1127–1134. https://doi.org/10.1016/j.neuron.2007.09.038.

[100] Morillon B, Lehongre K, Frackowiak RSJ, Ducorps A, Kleinschmidt A, Poeppel D, et al. Neurophysiological origin of human brain asymmetry for speech and language. Proceedings of the National Academy of Sciences of the United States of America. 2010; 107: 18688–18693. https://doi.org/10.1073/pnas.1007189107.

[101] Lehongre K, Ramus F, Villiermet N, Schwartz D, Giraud AL. Altered low-γ sampling in auditory cortex accounts for the three main facets of dyslexia. Neuron. 2011; 72: 1080–1090. https://doi.org/10.1016/j.neuron.2011.11.002.

[102] Goswami U, Thomson J, Richardson U, Stainthorp R, Hughes D, Rosen S, et al. Amplitude envelope onsets and developmental dyslexia: A new hypothesis. Proceedings of the National Academy of Sciences of the United States of America. 2002; 99: 10911–10916. https://doi.org/10.1073/pnas.122368599.

[103] Dushanova J, Lalova Y, Kalonkina A, Tsokov S. Speech-Brain Frequency Entrainment of Dyslexia with and without Phonological Deficits. Brain Sciences. 2020; 10: 920. https://doi.org/10.3390/brainsci10120920.

[104] Coull J, Nobre A. Dissociating explicit timing from temporal expectation with fMRI. Current Opinion in Neurobiology. 2008; 18: 137–144. https://doi.org/10.1016/j.conb.2008.07.011.

[105] Merchant H, Harrington DL, Meck WH. Neural basis of the perception and estimation of time. Annual Review of Neuroscience. 2013; 36: 313–336. https://doi.org/10.1146/annurev-neuro-062012-170349.

[106] Wieser H, Wittlieb-Verpoort E. Tone Discrimination in Patients with Temporal Lobe Lesions. In Steinberg R (ed.) Music and the Mind Machine (pp. 115–126). Springer: Berlin, Heidelberg. 1995. https://doi.org/10.1007/978-3-642-79327-1_12.

[107] Bueti D, Macaluso E. Auditory temporal expectations modulate activity in visual cortex. NeuroImage. 2010; 51: 1168–1183. https://doi.org/10.1016/j.neuroimage.2010.03.023.

[108] Teki S, Grube M, Kumar S, Griffiths TD. Distinct neural substrates of duration-based and beat-based auditory timing. The Journal of Neuroscience. 2011; 31: 3805–3812. https://doi.org/10.1523/JNEUROSCI.5561-10.2011.

[109] Rimmele JM, Morillon B, Poeppel D, Arnal LH. Proactive Sensing of Periodic and Aperiodic Auditory Patterns. Trends in Cognitive Sciences. 2018; 22: 870–882. https://doi.org/10.1016/j.tics.2018.08.003.

[110] Andoh J, Zatorre RJ. Interhemispheric Connectivity Influences the Degree of Modulation of TMS-Induced Effects during Auditory Processing. Frontiers in Psychology. 2011; 2: 161. https://doi.org/10.3389/fpsyg.2011.00161.

[111] Andoh J, Zatorre RJ. Mapping interhemispheric connectivity using functional MRI after transcranial magnetic stimulation on the human auditory cortex. NeuroImage. 2013; 79: 162–171. https://doi.org/10.1016/j.neuroimage.2013.04.078.

[112] Lumaca M, Kleber B, Brattico E, Vuust P, Baggio G. Functional connectivity in human auditory networks and the origins of variation in the transmission of musical systems. eLife. 2019; 8: e48710. https://doi.org/10.7554/eLife.48710.

[113] Elmer S, Kühnis J. Functional Connectivity in the Left Dorsal Stream Facilitates Simultaneous Language Translation: An EEG Study. Frontiers in Human Neuroscience. 2016; 10: 60. https://doi.org/10.3389/fnhum.2016.00060.

[114] Elmer S, Kühnis J, Rauch P, Abolfazl Valizadeh S, Jäncke L. Functional connectivity in the dorsal stream and between bilateral auditory-related cortical areas differentially contribute to speech decoding depending on spectro-temporal signal integrity and performance. Neuropsychologia. 2017; 106: 398–406. https://doi.org/10.1016/j.neuropsychologia.2017.10.030.

[115] Rypma B, D’Esposito M. The roles of prefrontal brain regions in components of working memory: effects of memory load and individual differences. Proceedings of the National Academy of Sciences of the United States of America. 1999; 96: 6558–6563. https://doi.org/10.1073/pnas.96.11.6558.

[116] Hyde KL, Zatorre RJ, Peretz I. Functional MRI evidence of an abnormal neural network for pitch processing in congenital amusia. Cerebral Cortex. 2011; 21: 292–299. https://doi.org/10.1093/cercor/bhq094.

[117] Albouy P, Mattout J, Bouet R, Maby E, Sanchez G, Aguera PE, et al. Impaired pitch perception and memory in congenital amusia: the deficit starts in the auditory cortex. Brain. 2013; 136: 1639–1661. https://doi.org/10.1093/brain/awt082.

[118] Albouy P, Mattout J, Sanchez G, Tillmann B, Caclin A. Altered retrieval of melodic information in congenital amusia: insights from dynamic causal modeling of MEG data. Frontiers in Human Neuroscience. 2015; 9: 20. https://doi.org/10.3389/fnhum.2015.00020.

[119] Leveque Y, Fauvel B, Groussard M, Caclin A, Albouy P, Platel H, et al. Altered intrinsic connectivity of the auditory cortex in congenital amusia. Journal of Neurophysiology. 2016; 116: 88–97. https://doi.org/10.1152/jn.00663.2015.

[120] Lai M, Demuru M, Hillebrand A, Fraschini M. A comparison between scalp- and source-reconstructed EEG networks. Scientific Reports. 2018; 8: 12269. https://doi.org/10.1038/s41598-018-30869-w.

Funding

Bulgarian Ministry of Education and Science under the National Program ‘Young Scientists and Postdoctoral Students-2’