{'fraction_non_alphanumeric': 0.03204681621847569, 'fraction_numerical': 0.016545910547582556, 'mean_word_length': 4.892651888341543, 'pattern_counts': {'":': 0, '<': 2, '<?xml version=': 0, '>': 0, 'https://': 1, 'lorem ipsum': 0, 'www.': 4, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Behavioral and neurophysiological aspects of working memory impairment in children with dyslexia

The present study aimed to identify behavioral and neurophysiological correlates of dyslexia which could potentially predict reading difficulty. One hundred and three Chinese children with and without dyslexia (Grade 2 or 3, 7- to 11-year-old) completed both verbal and visual working memory (n-back) tasks with concurrent EEG recording. Data of 74 children with sufficient usable EEG data are reported here. Overall, the typically developing control group (N = 28) responded significantly faster and more accurately than the group with dyslexia (N = 46), in both types of tasks. Group differences were also found in EEG band power in the retention phase of the tasks. Moreover, forward stepwise logistic regression demonstrated that both behavioral and neurophysiological measures predicted reading difficulty uniquely. Dyslexia was associated with higher frontal midline theta activity and reduced upper-alpha power in the posterior region. This finding is discussed in relation to the neural efficiency hypothesis. Whether these behavioral and neurophysiological patterns can longitudinally predict later reading development among preliterate children requires further investigation.


Brain oscillations in relation to WM processes and individual differences.
Despite the close relation between WM and reading development, few studies have investigated the potential neurophysiological substrate of a WM deficit in dyslexia. Nevertheless, the relation between brain oscillations and maintenance of information in WM has been extensively studied in the past few decades. A recent review 18 has shown that theta activity in the frontal midline region is most consistently associated with WM maintenance, especially in verbal WM tasks. The most typical finding is an increase in frontal midline theta during a retention phase of the WM task relative to the baseline 19 . Among 15 EEG studies identified in the review that involved at least two load levels of verbal WM, ten reported stepwise increase of theta with memory load 20 . On the other hand, theta increase has been less consistently observed in visual WM tasks (around half of the reviewed studies).
Due to its commonly observed increase at higher task difficulties, frontal midline theta is considered to reflect the mental effort engaged in the task 21 . Hence, one may expect to see more pronounced theta increase in less apt individuals, since they probably need even more effort to complete a demanding task than more apt individuals do. This prediction is consistent with the neural efficiency hypothesis 22 , which posits that better performing individuals show reduced (more efficient) neural activity in cognitive tasks. In an EEG study by Maurer and colleagues 23 , healthy adults were required to memorize 2 or 4 unfamiliar symbols (low or high load) for 3.5 s and then to decide if a probe was one of the presented symbols (i.e., a Sternberg task 24 ). The participants' behavioral performance decreased from the low load condition to the high load condition, while their frontal midline theta in the retention phase increased. Moreover, the theta increase correlated significantly with the decrease in accuracy at the individual level, indicating that the more difficult a given task seemed to an individual (as indicated by the larger decrease in accuracy), the larger increase in frontal midline theta was observed. Another study by Brzezicka and colleagues 25 recorded intracranial EEG while patients with epilepsy were performing a Sternberg task with 3 load levels (1, 2, or 3 pictures). Theta power in the retention phase increased with memory load in the hippocampus but decreased in the dorsolateral prefrontal cortex (DLPFC). Furthermore, the faster one participant responded, the larger theta power decrease with memory load was found in the DLPFC. Although the load effect on theta power was partly in different directions, both studies found that better performing participants showed a tendency of reduced theta power (smaller increase or larger decrease with memory load) in the retention phase of a WM task. www.nature.com/scientificreports/ In addition to theta power, alpha power has also been found to change with WM load, although the direction of alpha change was inconsistent. According to the review by Pavlov and Kotchoubey 18 , the ratio of studies on verbal WM that found alpha increase versus decrease was 4:1, and this ratio became 3:2 for studies on visual WM. By dividing the alpha band into subbands (lower-and upper-alpha), some researchers found that upper-alpha activity tended to increase with memory load while lower-alpha activity tended to decrease 20,23 . One explanation is that upper-alpha increase reflects inhibition of task-irrelevant regions in the retention phase and that loweralpha decrease reflects release of inhibition in task-relevant regions 20,26 . In terms of individual difference, Grabner and colleagues 27 found that higher intelligence was associated with higher upper-alpha power (i.e., lower brain activation) in WM tasks, consistent with the neural efficiency hypothesis 22 .
The present study. So far, much fewer studies have investigated WM-related brain oscillations in children 28 , and almost none for children with dyslexia 29 . The present study recorded concurrent EEG while the children with and without dyslexia were doing the n-back task, in order to compare not only behavioral performance but also brain oscillations of the two groups in a WM task. In addition to verbal WM, nonverbal stimuli were also included to tap visual WM (considering the logographic nature of the Chinese writing system), which seemed to have received less attention in the reading literature. Based on the previous findings mentioned above, we chose to examine theta power in the frontal midline region 19,21 as well as lower-and upper-alpha power 23 in the posterior region 30,31 . Moreover, logistic regression analysis was conducted to find out whether these behavioral and neurophysiological measures were significant predictors of dyslexia 32,33 . We hypothesized that the children with dyslexia would show poorer behavioral performance in the WM task. The neural efficiency hypothesis predicted that the children with dyslexia would manifest higher theta and lower alpha power (i.e., more brain activation) during the retention phase of the task.

Results
In the current design, Type of task (verbal, visual) and Load level (1-back, 2-back) were two within-participants factors, while Group (control, dyslexic) was a between-participants factor. Since the control group had a significantly higher non-verbal intelligence than the group with dyslexia (p = 0.014), ANCOVA was conducted on each of the dependent variables in the n-back task with Intelligence as a covariate: (1) reaction time (RT, i.e., the time interval between stimulus offset and response) when the target was correctly hit, (2) d prime 17 , which was calculated based on the hit and false alarm rates, (3) log-transformed theta power in the frontal midline region, (4) log-transformed lower-alpha power in the posterior region, and (5) log-transformed upper-alpha power in the posterior region. The restriction to the frontal midline region for theta analysis was based on previous studies that showed a topographically more restricted effect for theta compared to alpha 20,23 , while alpha activity was relatively widespread and most prominent over posterior areas 30,34 . d prime. Calculated based on the hit and false alarm rates, d prime (d') can be considered as an index reflecting how well one can differentiate targets from non-targets (the higher d', the better). Table 1  Frontal midline theta. In the n-back task, the children needed to make same-different judgment for each presented item and to maintain the newly updated items before seeing the next one. The mean RT in each condition ranged from 779 to 1173 ms. We can infer that the last 1-s interval of the 3.5-s fixation period fell into the retention phase of the n-back task, before which the encoding of new information and the same-different judgment were completed. Thus, band power during WM maintenance was obtained from this time window (see Fig. 1 and Fig. S1 for the topographic maps of band power differences between groups and between load levels, respectively).  Table 1. Behavioral performance and EEG band power of the typically developing and dyslexic children in the n-back task (standard errors in parentheses).   Distinguishing children with and without dyslexia. To find out which variables were most strongly and uniquely associated with dyslexia, the following variables from the WM tasks were entered by block 32 into logistic regression models as predictors of dyslexia: (1) Block 1 included general control variables: age, grade, non-verbal intelligence; (2) Block 2 included 8 behavioral variables: reaction time and d prime in each of the four conditions; and (3) Block 3 included 12 neurophysiological variables: log-transformed frontal midline theta, posterior lower-and upper-alpha power in each of the four conditions. The forward Wald method 33 was adopted in each block so as to identify behavioral measures (if any) that uniquely predicted dyslexia beyond control variables as well as unique neurophysiological predictors (if any) beyond both control and behavioral variables. Table 2 shows the three logistic regression models generated in each block. In Block 1, the Wald statistic showed that only non-verbal intelligence was a significant predictor (p = 0.040). Model 1 made significantly better prediction of dyslexia than a null model (χ 2 (1) = 4.99, p = 0.025). In Block 2, two behavioral variables (i.e., RT in the visual 1-back condition, d' in the verbal 2-back condition) were entered (ps ≤ 0.009), and Intelligence became non-significant (p = 0.328). Model 2 significantly improved prediction of dyslexia relative to Model 1 (χ 2 (2) = 24.53, p < 0.001). The classification accuracy increased from 59.5% (dyslexic: 82.6%; control: 21.4%) to 81.1% (dyslexic: 84.8%; control: 75.0%), and Nagelkerke's R 2 improved from 0.089 to 0.448. In Block 3, two neurophysiological variables (i.e., log-transformed frontal midline theta in the verbal 2-back condition, log-transformed posterior upper-alpha in the visual 1-back condition) were further entered (ps ≤ 0.006), while the two behavioral variables remained significant (ps ≤ 0.002). Model 3 significantly improved prediction of dyslexia relative to Model 2 (χ 2 (2) = 16.27, p < 0.001). The classification accuracy further increased from 81.1 to 82.4% (dyslexic: 87.0%; control: 75.0%). Nagelkerke's R 2 of Model 3 was 0.628, suggesting a relatively strong relation between the predictors and dyslexia (see Fig. S2 for the distribution of predicted probabilities in the classification plots). To sum up, significant and unique predictors of dyslexia were found from both behavioral and neurophysiological measures of WM in the present study. Figure 2 displays the scatterplots of the two groups, showing each significant predictor of dyslexia (y axis) as a function of non-verbal intelligence (x axis). To further examine the predictive role of WM measures in dyslexia without any confounding effect of non-verbal intelligence, we conducted additional logistic regression analysis on the control group and a subsample of the group with dyslexia (i.e., excluding those who scored 21 or lower in non-verbal intelligence), whose non-verbal intelligence scores were comparable (p = 0.860). The results showed that non-verbal intelligence was no longer a significant predictor while behavioral and neurophysiological measures of WM (i.e., verbal 2-back RT, visual 1-back d', verbal 2-back theta and upper-alpha) still significantly and uniquely predicted dyslexia. More details of the additional logistic regression analysis can be found in the Supplemental Material.

Discussion
The present study required Chinese children with and without dyslexia to complete verbal and visual WM tasks with concurrent EEG recording. Consistent with the findings of previous studies [10][11][12] , the control group responded significantly faster and more accurately than the group with dyslexia, in both types of tasks. In the retention phase of the tasks, the frontal midline theta power of the control group tended to decrease from the low load to the high load condition, while the group with dyslexia showed an opposite trend. The control group also demonstrated a higher upper-alpha power than the group with dyslexia did across all conditions. Forward stepwise logistic regression identified a few significant predictors of dyslexia, including both behavioral and neurophysiological measures of WM.

WM-related neurophysiological correlates of dyslexia.
In the logistic regression model, frontal midline theta activity in the verbal 2-back task was positively associated with dyslexia, and posterior upperalpha power in the visual 1-back task was negatively associated with dyslexia. This means that children in our sample who spent more mental effort and inhibited brain activation to a lesser extent during WM maintenance tended to have dyslexia, which is consistent with the neural efficiency hypothesis 22 . Importantly, these neurophysiological correlates uniquely predicted reading difficulty in addition to non-verbal intelligence (though non-significant in the final model) and behavioral correlates. While behavioral indicators (speed and accuracy) reflected the children's final achievement in WM tasks, neurophysiological measures (theta and alpha oscillations) reflected the amount of mental effort and neural resources engaged during WM maintenance 21,23 . These variables tapped into different aspects of WM, and all predicted reading difficulty uniquely. www.nature.com/scientificreports/ A few fMRI studies have also compared brain activation of dyslexic and non-dyslexic readers during WM tasks. While some found reduced prefrontal activation in dyslexia 35 , some found both increased and decreased activation in the prefrontal cortex 36 . These mixed results could be partly due to the differences in the tasks. Researchers have found that neural efficiency could be modulated by task difficulty and other factors 37 . There are situations where better performing individuals may display the same amount of or even more brain activation 38 . Besides, the relatively low temporal resolution of the fMRI technique could be another reason. An EEG study by Jaušovec and Jaušovec 39 showed that in a modified n-back task, the theta power of high-intelligence individuals was higher than that of low-intelligence ones in 0-500 ms post-stimulus onset. Then it decreased soon and the group difference was reversed in 1000-2000 ms, suggesting that the high-intelligence individuals were more intensely engaged with the task in the first 500 ms and soon lowered their mental effort in the following retention phase. Hence, the retention phase, the phase selected for analysis in the present study, might be an optimal time interval for reduced neural activation in better performing individuals (i.e., neural efficiency) to occur. Previous fMRI studies might have captured neural activation across different phases, which could be one of the reasons for the mixed findings. By using EEG, the present study was able to examine brain activation in the retention phase more precisely.
In contrast to the prominent Group effect, the Load effect on brain oscillations seemed to be relatively minor in the present study. For frontal midline theta power, only the Load × Group interaction was significant, caused by the opposite directions of theta change in the two groups with increasing load. While the control group had a marginally significant theta decrease, the group with dyslexia demonstrated a non-significant trend of theta increase. For lower-and upper-alpha power, no significant difference was found between load levels. Different patterns of theta and alpha changes with WM load have been reported in previous studies, and the modulating factors remain largely unclear 18 . For example, a few studies found an inverted U-shape influence of WM load on frontal midline theta power, i.e., highest theta power with a moderate load level 40,41 . So, one explanation for the trend of theta decrease in the control group could be that the current 2-back tasks were beyond the moderate load level for early grade children. However, this account would predict a theta decrease in the group with dyslexia as well, since their behavioral performance was even worse than that of the control group. This prediction contradicted the observed trend of theta increase in the group with dyslexia. Note that the Load effect was not significant in either group. Further studies are needed to examine the robustness of the observed theta change when children perform WM tasks with different load levels. WM impairment: cause or effect. The present study identified a few behavioral and neurophysiological correlates (i.e., visual 1-back RT and upper-alpha, verbal 2-back d' and theta) of WM impairment in dyslexia (i.e., more neural resources engaged but poorer behavioral performance). However, we cannot tell whether poor WM is a cause or an effect of dyslexia. In the verbal n-back tasks, the children were able to make use of phonological codes of the Chinese characters to achieve better performance than in the visual tasks, where only visual codes were usable. The group differences in the verbal tasks could result from inefficient processing of Chinese characters in the children with dyslexia, due to their poor orthography-phonology conversion 42 . Hence, poor verbal WM could be a consequence of other deficits in dyslexia.
On the other hand, similar group differences were observed in the visual n-back tasks, where non-verbal stimuli were used without involving orthographic or phonological encoding. The visual 1-back RT and upperalpha were also unique predictors of dyslexia in the logistic regression model. This finding seems to support a deficit in the central executive component of WM 43,44 , whose dysfunction impairs both verbal and visual working memories. Nevertheless, an alternative explanation is that the development of visual processing is influenced by the children's reading skills 45 . In a longitudinal study, Pan and colleagues 42 tracked Chinese children's character reading accuracy and pure visual skill for three years (at ages 6 to 8). They found that reading accuracy predicted subsequent performance in a pure visual task but not vice versa. Hence, poor visual WM could be a consequence of reading difficulty as well.
Despite these alternative possibilities, we believe that the children with dyslexia are highly likely to have central executive dysfunction given the available evidence. Melby-Lervåg and colleagues 7 have shown that verbal short-term memory does not contribute uniquely to word reading performance when metalinguistic skills are controlled. But WM together with other executive functions seems to have a unique contribution to reading achievement 8,9,46 , suggesting the importance of central executive in reading development. In the present study, similar group differences were observed across the two types of WM tasks, consistent with the existence of central executive dysfunction in dyslexia. Note that developmental dyslexia can be classified into different subtypes 47,48 , so not all children with dyslexia have the same deficits or the same cause of the deficits. The scatterplots in Fig. 2 show that not all children in the dyslexic group appeared to have WM impairment and that some control children demonstrated poor WM. Although WM differences can be observed in between-groups comparisons, WM impairment does not always co-occur with dyslexia 49 . In the current logistic regression analysis, Model 3 only correctly classified 87.0% of the children with dyslexia and 75.0% of the control children. Hence, behavioral and neurophysiological measures of WM could potentially predict dyslexia, but these measures alone are insufficient to classify children as having dyslexia or not.
The present study adopted the n-back task and identified both behavioral and neurophysiological correlates of WM impairment in dyslexia, among early grade children. Importantly, EEG band power (theta and alpha oscillations) uniquely predicted reading difficulty in addition to the behavioral measures (RT and d'). One limitation of the present study is that the non-verbal intelligence of the two groups was not matched, although additional logistic regression analysis on the subsample showed a similar pattern of results after matching the non-verbal intelligence scores. Besides, previous studies found that dyslexic children's deficits in certain short-term memory tasks disappeared when they were matched to controls on non-verbal intelligence and oral language abilities 50 www.nature.com/scientificreports/ Although the current n-back tasks required key-pressing responses only and involved the use of oral language minimally (especially the visual tasks), this study could be improved by including measures of oral language abilities. With stricter control on non-verbal intelligence and oral language abilities, future studies may replicate the present study among younger or preliterate children and follow them up 52 to find out whether earlier behavioral and neurophysiological measures of WM longitudinally predict later reading development. Compared to the backward span task and some other WM tasks, the n-back task has at least two advantages. First, the children are able to respond by simply pressing a key without verbal articulation, so that any confounding effects of language abilities are minimized. Second, the procedure of this task allows concurrent EEG recording and frequency analysis in a retention phase, which could potentially improve the prediction success. Longitudinal studies are needed to examine the effectiveness of these potential predictors and to determine whether they can be used in clinical practice.

Conclusions
In the 1-back and 2-back WM tasks, the typically developing children performed better than the children with dyslexia. Frontal midline theta and posterior upper-alpha power in the retention phase of the tasks reflected the amount of mental effort and neural resources being engaged, and they predicted dyslexia uniquely in addition to indices of speed and accuracy. However, it remains unclear whether these behavioral and neurophysiological patterns are merely consequences of reading difficulty or not. Further investigation is needed to examine whether the current measures can be used to predict reading difficulty in pre-readers.

Methods
Participants. Data 53 , whose criteria included adequate IQ (higher than 85), poor literacy (− 1 SD or below), and at least one area of cognitive-linguistic deficit (− 1 SD or below) 54 . Besides, they had no history of significant sensory impairment, birth complications, or brain injury. Based on the parents' responses in a questionnaire (N = 70), 7 children from the dyslexic group (16.7%) and 2 from the control group (7.1%) had a formal diagnosis of language impairment (p = 0.244). Table 3 shows the demographic information of the two groups, who did not differ significantly in gender, age, grade, maternal or paternal education level, or monthly family income (ps ≥ 0.248).
Procedure. Raven's Standard Progressive Matrix (RSPM). RSPM 55 , Sets A to C were used to assess nonverbal intelligence. Each test item required the children to choose an option out of six (Sets A and B) or eight (Set C) to fill the missing part of a design. There were 12 test items in each set and thus 36 items in total.
Verbal n-back task. Sixty Grade-2 level characters with a varying number of strokes (from 4 to 13) were selected from the Hong Kong Corpus of Primary School Chinese 56 and formed a sequence for verbal 1-back and 2-back tasks respectively. In each task, twenty of the characters appeared twice, and those appearing at the second time were targets. The targets appeared immediately after the same characters in the 1-back task and appeared the second after the same characters in the 2-back task (Fig. 3). The same set of characters were used in both tasks, but the target characters were mostly different so that one could not predict whether a character was a target or not based on its status in an earlier task. Each task contained 60 non-targets and 20 targets and was divided into two blocks of 40 characters. The characters were sequenced in a way that there was no obvious semantic relatedness or orthographic similarity between each pair of consecutive characters, except for the targets in the 1-back task. The n-back task was administered with the E-Prime 3.0 software (https:// pstnet. com/ produ cts/e-prime/). Each character appeared for 500 ms, followed by a 3500-ms fixation and then the next character. The children needed to press "1" on the keyboard as accurately and quickly as possible, when they detected a target, and did not need to press any key for non-targets. At the beginning of each task, the task requirement (1-back or 2-back) was explained to the children, and they needed to complete a practice block with 14 characters (4 targets) to make sure that they understood the task requirement. They were able to repeat the practice once or more if needed. Visual n-back task. Thirty 3 × 3 checkerboard patterns were created for visual 1-back and 2-back tasks. Each pattern contained 3 black squares and 6 white ones (see Supplemental Material). We did not use 60 different patterns, as it would yield too many similar patterns making the task too difficult. Similar to the verbal n-back task, both visual 1-back and 2-back tasks consisted of two blocks of 40 items (30 non-targets and 10 targets each). The patterns were sequenced in a way that each pair of consecutive patterns did not look similar, except for the targets in the 1-back task. The procedure was the same as that of the verbal n-back task. Each child completed all the four tasks (2 types × 2 load levels), whose order was counterbalanced across participants for each group. www.nature.com/scientificreports/ EEG recording and preprocessing. EEG data were recorded at 500 Hz (online filter: 0.1-100 Hz; Cz as recording reference) using an EGI (Electrical Geodesics, Inc.) 128-channel system while each child was completing the n-back task in a quiet room. Impedances were controlled below 50 kΩ. The EEG recordings were offline filtered (0.1-70 Hz; notch filter: 50 Hz). Bad channels were excluded, and the remaining data were submitted to independent component analysis for eye movement correction 57 . The excluded channels were then spline interpolated 58 . All data were re-referenced to the average reference 59 and then segmented to include the last 1-s interval of the fixation period (i.e., before the onset of the next item; see Fig. 4). Only segments following nontargets (without motor artefacts from key pressing) and not exceeding ± 100 μV were used in further analysis. Twenty-nine children with poor EEG data quality in any of the four conditions were excluded from the present study, so that all the 74 children reported here had no fewer than 10 usable epochs in each condition. The average number of usable EEG epochs was 29, 28, 31, and 29 in the verbal 1-back, verbal 2-back, visual 1-back, and visual 2-back conditions respectively. Before the n-back task, EEG recordings were also obtained during a 3-min eyes-closed resting state block, with the same setting. The resting EEG data went through similar preprocessing steps and were segmented into 1-s epochs for determination of individual alpha frequency 60 (IAF). The fast Fourier transformation was applied to each of the EEG epochs obtained from the n-back task and the eyes-closed state. For each child, the frequency with peak power density in the alpha band (8)(9)(10)(11)(12)(13) Hz) across all electrodes in the eyes-closed state was identified as IAF. For two children, the alpha peak failed to be detected with power density averaged across electrodes, and power density at the occipital electrode Oz was then used to identify IAF; for another two children with eyes-closed EEG data of poor quality, their IAF was set at 10. The two groups did not differ significantly in IAF (p = 0.438).

Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.