Event-related EEG oscillations to semantically unrelated words in normal and learning disabled children

Brain and Cognition 80 (2012) 74–82

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Event-related EEG oscillations to semantically unrelated words in normal and learning disabled children Thalía Fernández a,⇑, Thalía Harmony a, Omar Mendoza b, Paula López-Alanís a, José Luis Marroquín b, Gloria Otero c, Josefina Ricardo-Garcell a a b c

Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, UNAM Campus Juriquilla, Querétaro 76230, Mexico Center for Research in Mathematics (CIMAT), Apartado Postal 402, Guanajuato, Gto., 36000, Mexico Facultad de Medicina, Universidad Autónoma del Estado de México, Mexico

a r t i c l e

i n f o

Article history: Accepted 23 April 2012 Available online 25 May 2012 Keywords: Time-frequency analysis Learning disabled children EEG oscillations EEG theta Event related EEG EEG delta

a b s t r a c t Learning disabilities (LD) are one of the most frequent problems for elementary school-aged children. In this paper, event-related EEG oscillations to semantically related and unrelated pairs of words were studied in a group of 18 children with LD not otherwise specified (LD-NOS) and in 16 children with normal academic achievement. We propose that EEG oscillations may be different in LD NOS children versus normal control children that may explain some of the deficits observed in the LD-NOS group. The EEGs were recorded using the 10/20 system. EEG segments were edited by visual inspection 1000 ms before and after the stimulus, and only correct responses were considered in the analysis. Time–frequency (1–50 Hz) topographic maps were obtained for the increases and decreases of power after the event with respect to the pre-stimulus average values. Significant differences between groups were observed in the behavioral responses. LD-NOS children show less number of correct responses and more omissions and false alarms than the control group. The event-induced EEG responses showed significant differences between groups. The control group showed greater power increases in the frequencies 1–6 Hz than the LD-NOS group from 300 to 700 ms. These differences were mainly observed in frontal regions, both to related and non-related words. This was interpreted as a deficit in attention, both to internal and external events, deficits in activation of working memory and deficits in encoding and memory retrieval in the LD-NOS children. Differences between groups were also observed in the suppression of alpha and beta rhythms in the occipital regions to related words in frequencies between 8 and 17 Hz from 450 to 750 ms. LD-NOS children showed shorter durations of the decreases in power than the control group. These results suggest also deficits in attention and memory retrieval. It may be concluded that LD-NOS children showed physiological differences from normal children that may explain their cognitive deficiencies. Ó 2012 Elsevier Inc. All rights reserved.

1. Introduction Learning disabilities (LD) are one of the most frequent problems that afflict children in elementary school (DSM-IV). LDs are diagnosed when an individual’s achievement on individually administered, standardized tests in reading, mathematics, or written expression is substantially below that expected for the individual’s age, schooling, and level of intelligence. LD children are classified as ‘‘specific’’ (reading disorder, mathematics disorder, or disorder of written expression) or ‘‘learning disorder not otherwise specified,’’ which might include problems in all three areas ⇑ Corresponding author. Address: Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico. Fax: +52 442 238 1046. E-mail address: [email protected] (T. Fernández). 0278-2626/$ - see front matter Ó 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.bandc.2012.04.008

(DSM-IV). The children included in this study belonged to the latter group. Although LD children often have deficits in attention processes, the children in our study did not meet the criteria for Attention Deficit Disorder (ADD) and were not hyperactive. Developmental dyslexia, which may be considered a specific learning disorder, has received considerable attention, and its functional deficits have been explored by neuropsychological, electrophysiological and neuroimaging procedures (Backes et al., 2002; Temple, 2011; Van der Mark et al., 2011). However, LD not otherwise specified (LD NOS) children have not received as much attention, although this type of LD is more common. The use of different labels to characterize specific subjects has complicated the comparison between studies (for a review, see Harmony, 2009); for example ‘‘poor readers’’ or less skilled readers have been defined as children who are reading between 1 and 2 years below their expected levels and have been differentiated from children with dyslexia or severe

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reading impairments that tend to persist into adolescence and adulthood (Rayner & Pollatsek, 1989). At present, many authors use the classification listed in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV, APA American Psychiatric Association, 1994). The DSM-IV facilitates comparisons from different reports, and for this reason, we have adopted this classification. Electrophysiological studies of LD NOS children have shown that they frequently present more theta and less alpha power than what is normal for their age during relaxed rest. For this reason, the maturational delay hypothesis has been suggested as an explanation for their deficits (Chabot, di Michele, Prichep, & John, 2001; Fernández et al., 2002; Gasser, Rousson, & Schreiter-Gasser, 2003; Harmony et al., 1990; John et al., 1983). Using source analysis, Fernández et al. (2002) reported that LD children had more theta activity (3.5–7.02 Hz) in their frontal lobes and that control children had more alpha activity (9.75–12.87 Hz) in occipital areas. Because LD NOS children display these EEG characteristics, we wanted to determine if their EEG responses have different characteristics than the normal children. Although the vast majority of authors emphasize the relevance of verbal deficits in LD, others have suggested that attention problems (Greenham, Stelmack, & Van Der Vlugt, 2003; Silva-Pereyra et al., 2010) and deficits in working and short-term memory may be involved (Silva-Pereyra et al., 2003; for a review see Harmony, 2009; Rayner & Pollatsek, 1989). The functions of synchronous oscillations in language processing have been emphasized in recent years (Weiss & Mueller, 2003). There is growing evidence suggesting that synchronization changes in the oscillatory neuronal dynamics in the EEG or the MEG reflect the transient coupling and uncoupling of functional networks related to different aspects of language comprehension (Bastiaansen, Magyari, & Hagoort, 2010) However, these studies were performed in adult subjects and focused on lexical processing (Bastiaansen, Oostenveld, Jensen, & Hagoort, 2008; Bastiaansen, van der Linden, Ter Keurs, Dijkstra, & Hagoort, 2005; Khader & Rösler, 2004) and semantic (Hagoort, Hald, Bastiaansen, & Petersson, 2004; Hald, Bastiaansen, & Hagoort, 2006; Röhm, Klimesch, Haider, & Doppelmayr, 2001; Willems, Oostenveld, & Hagoort, 2008) or syntactic processes (Bastiaansen, Posthuma, Groot, & de Geus, 2002; Davidson & Indefrey, 2007) during sentence comprehension. To our knowledge, EEG oscillations during cognitive tasks in LD children have not been reported. It is therefore of interest to know if their EEGs show normal responses to different events and, in particular, to language-related stimuli. In the present paper, pairs of words that were semantically related or unrelated were presented to both LD NOS children and control children with good scholastic achievement. During this presentation, we recorded event-related EEG responses (EREEG). This task was selected because it requires several language processes and other associated processes, such as attention and working memory. This paradigm has been extensively studied using event-related potentials (Barber, Domínguez, & de Vega, 2002; Khateb, Pegna, Landis, Mouthon, & Annoni, 2010) and has been considered as a way to study semantic violations (Friederici, 2004; Kutas & Hillyard, 1980; Polich, 1985), and to detect these violations, children must comprehend the meaning of what they are reading. In this study we propose that EEG oscillations may be different in LD NOS children versus normal control children in the presence of semantically related and unrelated pairs of words.

which also complied with the Ethical Principles for Medical Research Involving Human Subjects established by the Declaration of Helsinki. All of the children were volunteers, and informed consents from the parents were obtained for all study participants. 2.1. Procedure Before the experiment, all of the children were evaluated using different procedures: a neurological and psychiatric evaluation conducted by an experienced neuropediatrician; the application of WISC-R; the variables of attention test (TOVA); Conner’s questionnaires for parents and teachers; standardized tests in reading, mathematics, and written expression (Evaluación Neuropsicológica Infantil ENI Matute, Roselli, Ardila, & Ostrosky, 2007); and a clinical EEG. 2.2. Participants Two different groups of children were studied: the LD NOS children and a control group of normal children with good academic achievement. Inclusion criteria for the control group included a normal neurological and psychiatric evaluation; a normal EEG; WISC-R values higher than 75; normal TOVA values; normal values in the Conner’s questionnaire for parents and teachers; and normal values in the standardized tests for the evaluation of reading, mathematics and written expression. The LD-NOS children were selected according to the following characteristics: no neurological or psychiatric disorders except for the presence of LD-NOS; IQ scores of at least 75; and scores for reading, mathematics and written expression below the 11th percentile. All children did not have severe sociocultural disadvantages. A total of 16 control children (8 male) and 18 LD-NOS children (14 male) participated in the experiment. All children were righthanded. Table 1 shows the age, gender and WISC-R values for each group. Comparisons of these values between the groups showed significant differences for verbal, performance and total IQ, with higher values in the control group than in the LD-NOS group. The most significant differences were in verbal IQ which was directly related to the learning deficits observed in this group, and because the mean values of the group were in the normal range, the results of the experiment were not considered to be biased by these differences. 2.3. Stimuli The stimuli were presented in white over a black background on a PC monitor, and the subjects were seated 60 cm away from the stimuli. A Mind Tracer system (Neuronic, Inc.) synchronized with a Trackwalker data system (Neuronic, Inc.) presented the task. The stimuli were presented in 4 blocks of 30 pairs of words that lasted 4 min each, and the children rested after each block. There were a

Table 1 Characteristics of the groups. Group

Control Mean s.d. LD-NOS Mean s.d. t-Value p

2. Methods The Ethics Committee of the Instituto de Neurobiología of the Universidad Nacional Autónoma de México approved this study,

a

Age

9.42 1.28 9.40 1.07 NSa

WISC-R Verbal

Performance

Total

108.73 10.35

107.13 14.70

108.93 11.58

89.39 17.79 3.7144 0.0008

98.06 17.46 NSa

NS: no significant differences between groups.

93.00 17.68 2.9920 0.0053

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total of 120 pairs of words, including 60 related and 60 unrelated pairs. The second words did not begin or end with the same phoneme of the previous word. All of the words were Spanish nouns with no more than three syllables. Additionally, each word had a unique meaning according to the Spanish Dictionary of the Royal Academy of Spanish Language and was selected from infantile lectures (Ahumada R. & A., 2007). A pilot study with 12 adults and 8 children was performed to ensure that the words were comprehensible. During the experiment, the children were asked to respond by pressing the right or left mouse button, depending on whether the second word was related or unrelated. The related and unrelated pairs of words were counterbalanced between subjects. The presentation time course is shown in Fig. 1. A warning stimulus () was presented for 300 ms, and after 500 ms, the first word was presented for 2200 ms. Two hundred and fifty milliseconds after the presentation of the first word, the second word was presented for 2200 ms. Five hundred milliseconds after the presentation of the second word, a question sign (?) was presented to indicate that the subject should respond. The period between the word pairs was 2000 ms. 2.4. EEG recordings The subjects were seated in a comfortable chair in a dimly lit room with acoustic isolation. An EEG was recorded from 19 leads using the 10/20 International System using linked ear lobes as a reference. The amplifier bandwidth was set between 0.5 and 100 Hz. The EEG was sampled every 5 ms using a MEDICID IV System and edited offline. Using visual editing, an expert electroencephalographer selected artifact-free EEG segments of 2000 ms, including the pre-stimulus time of 1000 ms. Only the trials with correct answers were analyzed to ensure that the subject was engaged in the task. 3. Data analysis 3.1. Time–frequency decomposition of EEG signals Event Induced Power (EIP) analyses were conducted on the time interval of 0–2000 ms (1000 ms before and after the presentation of the stimulus) for each subject in each experimental condition. The EIP quantifications are based on the time–frequency decomposition of the EEG signals. In this decomposition the goal is to obtain, for each electrode, an estimate of the local signal energy for a set of frequency bands. To facilitate interpretation, one would like to have these estimates, not only for the commonly used broad bands (delta, theta, alpha, beta), but also for narrower bands. The time– frequency decomposition of the input data is accomplished by means of a set of bandpass quadrature filters that are applied to the EEG signal as a means to measure the power at each frequency of interest. In particular, signals are processed by means of a set of sinusoidal quadrature filters tuned to frequencies ranging from 1 to 50 Hz, and having a bandwidth of approximately 1.76 Hz at 3 db gain (Guerrero, Marroquin, Rivera, & Quiroga, 2005). Since the frequency response of the quadrature filters is zero for negative frequencies, the corresponding time domain outputs have both a real and an imaginary part, and the instant power for each time sample may be obtained as the corresponding squared magnitude of this complex response.

More specifically, the space spanned by the latency was considered with respect to the stimulus onset t, the frequency f and the head electrode location e. This space is referred to as the time–frequency– topography (TFT) space (Marroquín, Harmony, Rodríguez, & Valdés, 2004), and each point (t, f, e) belonging to the TFT space is denoted as a voxel. Each voxel is defined for every time sample (e.g., every 5 ms), for each frequency (from 1 through 50 Hz) and for each of the 19 electrodes from which the recordings were performed. 3.2. Computation of significant EIP activations and deactivations from the TFT decomposition For each stimulus type, we subsequently determined the voxels in the TFT space for which the recorded EEG power was significantly different (at a significance level a = 0.05) from the average power in the pre-stimulus segment. The normalized Z-values for each voxel were computed for each trial of each subject, by subtracting from the log-power for each frequency and for each time sample, the corresponding average taken over the pre-stimulus segment and dividing by the corresponding pre-stimulus standard deviation, for the same trial and subject. Positive Z-values indicate an increase of power, and negative values indicate a decrease of power in relation to the pre-stimulus values. In this manner were obtained significance maps without the correction for multiple comparisons. The observation of these uncorrected significance maps revealed for these data large clusters of significant elements in time–frequency space, although the point-wise significance of each element was not too high, so that they would be eliminated by a point-wise method for correcting for multiple tests, such as the false discovery rate (FDR, Benjamini & Hochberg, 1995; Genovese, Lazar, & Nichols, 2002) or the Family-Wise Error Rate (FWER, Nichols & Hayasaka, 2003). For this reason, we decided to use the method described by Friston, Worsley, Frackowiak, Mazziotta, and Evans (1994), which is more sensitive to the spatial extent of these moderately significant clusters. This method is based on the computation of a statistic that measures, for each derivation, the size of connected clusters of elements in time–frequency space that were declared significant using the uncorrected tests described above (with a significance level a = 0.05). The null distribution for this statistic is obtained as the distribution of the maximum cluster size (i.e., the Family-Wise Error Rate) for a large number (we used 2000) sample time–frequency fields that are generated under the null hypothesis: for the case of significant increases or decreases in power with respect to the pre-stimulus segment, these sample fields were obtained using bootstrap sampling of the available pre-stimulus time–frequency maps. For the case of significant differences between the control and experimental groups, the sample fields were obtained by permutation of the group membership labels of the subjects to obtain the corresponding time–frequency difference fields. 4. Results 4.1. Behavioral responses Table 2 shows the behavioral responses. There were no significant differences between the responses to related and non-related words in any group. Only significant differences between groups were found, the LD-NOS group with more omissions and false

Fig. 1. Time course of stimuli presentation.

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alarms and less correct responses than the control group. However there were no differences in the Reaction Times. 4.2. Electrophysiological results All significant differences (p < 0.05) mentioned below have been obtained making corrections for multiple comparisons. 4.2.1. Responses to related words Significant differences (p < 0.05) were observed between the pre-stimulus and the post-stimulus EEG segments in both groups (Table 3). Control children with longer durations of the power increases in frequencies from 1 to 4 and with simultaneous decreases of power in alpha and beta frequencies. Fig. 2 shows the topography of the number of post-stimulus segments that showed increases and decreases in power in the different EEG bands (delta 1–3 Hz, theta 4–7 Hz, alpha 8–13 Hz, beta 14–30 Hz, gamma 31–50 Hz) in both groups. It is possible to observe that the control group showed more increases in power in the delta and theta bands than the LD-NOS group and more decreases in the alpha and beta bands. The increases in power in the slow bands and the decreases in power in the alpha and beta bands were observed in almost all leads in both groups, except for the alpha responses in the LD-NOS group. 4.2.2. Responses to non-related words In both groups significant differences between the pre and the post-stimulus EEG segments were found (Table 4). The control group with more prolonged increases in power and more number of segments with increases in power in the slow frequencies than the LD-NOS group (Fig. 3). 4.2.3. Significant differences between groups Significant differences between groups in the increase of power to related words are shown in Fig. 4. Control group showed greater increases in power than the LD-NOS group in frequencies between 1 and 6 Hz from 300 to 700 ms; these differences appear mainly in frontal (Fp1, Fp2, Fz, F4, F8) and posterior regions (P3, T5, O1, O2), as shown by the number of segments on which the power was higher in the control group than in the LD-NOS group. The LDNOS group exhibited more increases in power than the control group in frequencies 8–17 Hz from 450 to 750 ms; these differences were observed only in the right occipital lead. There was also a tendency (p < 0.07) of the LD-NOS group to show more power than the control group in the frequency range 8–12 Hz from 450 to 800 ms in occipital regions. Significant differences between the two groups were also observed to non-related words (Fig. 5). Children from the control group showed greater increases in power than the LD-NOS group in the slow frequencies (1–6 Hz) from 400 to 800 ms; these differences were observed in the frontal regions.

The LD-NOS group showed a tendency (p < 0.07) to have higher increases in power than the control group to the non-related words in frequencies from 17 to 21 Hz from 50 to 400 ms in the left occipital region. 4.2.4. Comparisons of the responses to related and non-related words When the comparisons were made without a correction for multiple comparisons, significant differences between related and non-related words were observed in both groups. The related words showed higher increases in power in the frequencies 1–6 Hz, in the control group from 100 to 700 ms and in the LD-NOS group during a shorter interval, from 150 to 400 ms. Related words also showed greater increases in power than non-related words at frequencies 11–20 Hz and from 100 to 300 ms in the LD-NOS children that was not observed in the control group. Non-related words had more power than related words in frequencies 10–19 Hz from 300 to 800 ms in the control group and from 400 to 600 ms in the LD-NOS group. However using the correction for multiple comparisons no significant differences were found between related and non-related words in any group. 5. Discussion 5.1. Behavioral results The performance during the recording was better in the control group than in the LD-NOS group, as was expected. No differences between groups in the reaction times were observed. A surprising result was that there were no differences in the behavioral responses to related or non-related words in any group. Thus it seems that the effort realized in one or the other condition was the same. 5.2. Electrophysiological results 5.2.1. Significant differences between the pre and the post-stimulus EEG segments and between groups In both groups related and non-related words produced significant changes respect to the EEG segment previous to the presentation of the words. In the control group increases in power were observed mainly in the slow frequencies (1–4 Hz) and with very prolonged durations (200–800 ms) simultaneous with decreases in frequencies 10 up to 16–18 Hz. In the LD-NOS group increases in power were also observed in the slow frequencies but with shorter durations (200–400 ms). These different durations produced a significant difference between groups in frequencies between 1 and 6 Hz from 400 to 800 ms; the differences to related words were in fronto-central and occipital regions, while the differences to non-related words were observed only in the frontal

Table 2 Behavioral results. Group

a

Correct responses

Omissions

Related

Unrelated

Control Mean s.d.

88.98 5.49

91.58 6.51

LD-NOS Mean s.d. t-Value p

76.39 13.20 3.36 0.0020

73.98 13.09 4.87 0.00002

NS: no significant differences between groups.

Related

False alarms Unrelated

Related

Reaction times Unrelated

Related

Unrelated

4.47 2.93

3.98 4.56

6.55 5.95

4.44 4.10

516.69 206.24

554.12 215.43

10.23 9.23 2.39 0.0230

14.06 11.86 3.19 0.0032

13.24 7.87 2.77 0.0093

11.99 7.19 3.70 0.0008

579.83 167.40 NSa

653.56 178.94 NSa

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Table 3 Related words. Significant differences (p < 0.05) between the post-stimulus and the pre-stimulus segments (post–pre). Group

Power increases

Power decreases

Frequency range (Hz)

Time interval (ms)

Frequency range (Hz)

Time interval (ms)

Control

1–4 5–7 26–30

200–800 200–500 100–200

10–16 22–26 43–48

200–800 400–800 50–250

LD-NOS

1–7 30–33 36–38

150–400 200–400 300–800

10–17 39–44

400–600 50–200

Fig. 2. EEG responses to related words. Topography of the number of post-stimulus segments that showed significant increases (in red) and decreases (in green) in power respect to the pre-stimulus in the different EEG bands in both groups. The circles correspond with the scalp surface (for a subject facing upwards, the nose up) and colors represent the topographic distribution of the number of segments that showed increases or decreases in power. The scales showed the maximum number of segments observed. The control group showed more increases in power in the delta and theta bands than the LD-NOS group and more decreases in the alpha and beta bands. The increases in power in the slow bands and the decreases in power in the alpha and beta bands were observed in almost all leads in both groups except the response of the alpha band in the LD-NPS group. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 4 Non-related words. Significant differences (p < 0.05) between the post-stimulus and the pre-stimulus segments (post–pre). Group

Power increases

Power decreases

Frequency range (Hz)

Time interval (ms)

Frequency range (Hz)

Time interval (ms)

Control

1–6 7–10 30–32 41–43 41–43

200–800 150–300 200–400 50–150 400–550

10–18 28–30 32–35

300–800 400–600 400–600

LD-NOS

1 1 2–7 8–13 18–20

150–300 600–800 200–400 50–200 50–200

8–17 31–34 42–48

400–600 600–800 300–400

regions. Power increases in the delta band (1–3 Hz) in frontal regions have been related to the activation of attention to internal processes by inhibition of non-relevant stimuli (Harmony et al., 1996) and also to signal matching and decision making (Basar-Eroglu, Basar, Demiralp, & Schurmann, 1992); thus the differences in this frequency range may indicate that the control children sustained attention for a more prolonged time than the LD-NOS group, and take more time to make the decision.

Our findings showed also increases in power of the post-stimulus segments in the theta frequencies (4–6 Hz) to both types of words in almost all leads in both groups. However, duration of these increases was longer in the control than in the LD-NOS group and the number of segments showing these increases were greater in the control than in the LD-NOS group. Theta power increase has been reported in several situations: during encoding and memory retrieval (Burgess & Gruzelier,

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Fig. 3. EEG responses to non-related words, same description as in Fig. 2. The control group with more increases in the slow bands that the LD-NOS group, however the two groups showed the same number of decreases in power although with a different distribution, more generalized in the control group. In the LD-NOS group the decreases were focalized to the occipital regions.

Fig. 4. Related words. Multitoposcopic display (TFT) of the significant (p < 0.05) differences between groups of the increases in power. Time is plotted in the X-axis and frequency in the Y-axis. Scalps are represented as circles each Hz and 50 ms. At the right the plots of the heads indicate when the control group had more power than the LDNOS group (red color) and vice versa (green color). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

1997; Klimesch, 1999), activation of working memory (Deiber et al., 2007; Gevins, Smith, McEvoy, & Yu, 1997; Krause et al., 2000), semantic violations (Hald et al., 2006) and allocation of

attention related to target stimuli (Missonier et al., 2006). According to Bastiaansen and Hagoort (2006) event-induced theta responses play a functional role in cell assembly formation, a

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Fig. 5. Non-related words. Same description as in Fig. 4.

process that may constitute the neural basis of memory formation and retrieval of lexical semantic information (Bastiaansen et al., 2005, 2008). In the current experiment these processes were necessary for word discrimination. As the increase in theta power was observed during a large time window and in all regions, it is difficult to distinguish which one of these processes was the most related to the observed theta increase. Significant differences between groups were also observed in the theta frequencies with the same topographic distribution than the significant delta differences. These differences were in a large time window from 400 to 800 ms and they suggest that mainly attention and working memory are still working in the children of the control group while these processes are interrupted earlier in the LD-NOS children. Greater increases in power to related words in the control group than in the LS-NOS group were observed also in occipital regions with shorter latencies, that maybe indicate the activation of the visual areas during reading. Deficits in visual processing in dyslexia have been reported (Cohen, 1980; Neville, Coffey, Holcomb, & Tallal, 1993) as well as in poor readers (Silva-Pereyra et al., 2001, 2003), thus it is also possible that LD-NOS present these deficiencies. Decreases of power in the post-stimulus segments in the alpha and beta bands were more frequently observed in control than in LD-NOS children. The comparison between groups showed that LD-NOS children showed greater increases in power in these frequencies than the control children. The significant difference was observed in the right occipital area. This finding suggests that control children have the expect desynchonization effect of the alpha rhythm after the visual stimulus and that this effect was less notorious in the LD-NOS children. It is known that the suppression of these rhythms correlates with the level of attention and memory load (Bastiaansen et al., 2005; Van Winsum, Sergeant, & Geuze, 1984), thus these results may suggest that LD-NOS children have deficits in these processes.

In both groups the post-stimulus segments showed power decreases of the frequencies within the gamma band more frequently than power increases. No significant differences between the groups were found. Power decreases in the gamma frequencies have been observed during visual analysis of figures being semantically incongruous (Willems et al., 2008), considering that it is related with early context-based detection of the congruity with respect to the preceding language context. 5.2.2. Comparison of the responses to related and non-related words Pairs of related words have been used mainly in the recordings of ERPs to analyze the N400 component in normal subjects (Frishkoff, 2007; Khateb et al., 2010) and dyslexic adults (Rüsseler, Becker, Johannes, & Münte, 2007). Therefore, the characteristics of the stimulation are well known and were expected to have a clear effect on the EEG responses, however our results did not show any significant difference between related and non-related words in any group when corrections for multiple comparisons were made. This was surprising, and as significant differences were observed without this correction we may suppose that the very strict criteria of this type of corrections would mask the differences.

6. Conclusions Our hypothesis that we will observe significant differences between the groups was confirmed, since the main observations were: I. Significant differences between groups were observed in the behavioral responses. LD-NOS children show less number of correct responses and more omissions and false alarms than the control group. II. There were significant differences in the time–frequency– topography maps between the EEG segments prior and after the presentation of the words in both groups.

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III. The event-induced EEG responses showed significant differences between groups. The control group showed greater power increases in the frequencies 1–6 Hz than the LD-NOS group. These differences were mainly observed in frontal regions, both to related and non-related words. This was interpreted as a deficit in attention, both to internal and external events, deficits in activation of working memory and deficits in encoding and memory retrieval in the LD-NOS children. IV. Differences between groups were also observed in the suppression of alpha and beta bands in the occipital regions to related words. LD-NOS children showed shorter durations of the decreases in power than the control group. These results suggest also deficits in attention and memory retrieval.

Acknowledgments The authors want to acknowledge the assistance of Teresa Soledad Medina Malagon, Teresa Alvarez Vázquez, Ma de Lourdes Lara, Leonor Casanova, Nelson Pumariega and Héctor Belmont. This research was partially supported by Grants PAPIIT IN220110 and CONACYT 69145. References APA American Psychiatric Association (1994). Diagnostic and Statistical manual of mental disorders 4th edition (DSM-IV). Washington, DC: British Library Cataloguing in Publication Data. Ahumada R., & Montenegro, A. (2007). Juguemos a leer. Desarrollo de competencias del lenguaje. Libro de lectura. Editorial Trillas Infantil. Backes, W., Vuurman, E., Wennekes, R., Spronk, P., Wuisman, M., van Engelshoven, J., et al. (2002). Atypical brain activation of reading processes in children with developmental dyslexia. Journal of Child Neurology, 17, 867–871. Barber, H., Domínguez, A., & de Vega, M. (2002). Human brain potentials indicate morphological decomposition in visual word recognition. Neuroscience Letters, 318, 149–152. Basar-Eroglu, C., Basar, E., Demiralp, T., & Schurmann, M. (1992). P-300 response: Possible psychophysiological correlates in delta and theta frequency channels. A review. International journal of Psychophysiology, 13, 161–179. Bastiaansen, M. C., Posthuma, D., Groot, P. F., & de Geus, E. J. (2002). Event-related alpha and theta responses in a visuo-spatial working memory task. Clinical Neurophysiology, 113, 1882–1893. Bastiaansen, M. C., van der Linden, M., Ter Keurs, M., Dijkstra, T., & Hagoort, P. (2005). Theta responses are involved in lexical-semantic retrieval during language processing. Journal of Cognitive Neuroscience, 17, 530–541. Bastiaansen, M., & Hagoort, P. (2006). Oscillatory neuronal dynamics during language comprehension. Progress in Brain Research, 159, 179–196. Review. Bastiaansen, M. C., Oostenveld, R., Jensen, O., & Hagoort, P. (2008). I see what you mean: Theta power increases are involved in the retrieval of lexical semantic information. Brain and Language, 106, 15–28. Bastiaansen, M., Magyari, L., & Hagoort, P. (2010). Syntactic unification operations are reflected in oscillatory dynamics during on-line sentence comprehension. Journal of Cognitive Neuroscience, 22, 1333–1347. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society B, 57, 289–300. Burgess, A. P., & Gruzelier, J. H. (1997). Short duration synchronization of human theta rhythm during recognition memory. NeuroReport, 8, 1039–1042. Chabot, R. J., di Michele, F., Prichep, L., & John, E. R. (2001). The clinical role of computerized EEG in the evaluation and treatment of learning and attention disorders in children and adolescents. Journal of Neuropsychiatry and Clinical Neurosciences, 13, 171–186 (Review). Cohen, J. (1980). Cerebral evoked potentials in dyslexic children. In D. A. Otto (Ed.), Multidisciplinary perspectives in event-related brain potential research. Washington, DC. Deiber, M. P., Missonier, P., Bertrand, O., Gold, G., Fazio-Costa, L., Ibanez, V., et al. (2007). Distinction between perceptual and attentional processing in working memory tasks: A study of phase-locked and induced oscillatory brain dynamics. Journal of Cognitive Neuroscience, 19, 158–172. Davidson, D. J., & Indefrey, P. (2007). An inverse relation between event-related and time-frequency violation responses in sentence processing. Brain Research, 1158, 81–92. Fernández, T., Harmony, T., Fernández-Bouzas, A., Silva, J., Herrera, W., SantiagoRodríguez, E., et al. (2002). Sources of EEG activity in learning disabled children. Clinical Electroencephalography, 33, 160–164. Friederici, A. D. (2004). Event-related brain potential studies in language. Current Neurology and Neuroscience Reports, 4, 466–470 (Review).

81

Frishkoff, G. A. (2007). Hemispheric differences in strong versus weak semantic priming: Evidence from event-related brain potentials. Brain and Language, 100, 23–43. Friston, K. J., Worsley, K. J., Frackowiak, R. S. J., Mazziotta, J. C., & Evans, A. C. (1994). Assessing the significance of focal activations using their spatial extent. Human Brain Mapping, 1, 214–220. Gasser, T., Rousson, V., & Schreiter-Gasser, U. (2003). EEG power and coherence in children with educational problems. Journal of Clinical Neurophysiology, 20, 273–282. Genovese, C. R., Lazar, N., & Nichols, T. E. (2002). Thresholding of statisticalmaps in functional neuroimaging using the false discovery rate. Neuroimage, 15(4), 870–878. Gevins, A., Smith, M., McEvoy, L., & Yu, D. (1997). High-resolution EEG mapping of cortical activation related to working memory: Effects of task difficulty, type of processing, and practice. Cerebral Cortex, 7, 374–385. Greenham, S. L., Stelmack, R. M., & Van Der Vlugt, H. (2003). Learning disabilities subtypes and the role of attention during the naming of pictures and words: An event-related potential analysis. Developmental Neuropsychology, 23, 339–358. Guerrero, J. A., Marroquin, J. L., Rivera, M., & Quiroga, J. A. (2005). Adaptive monogenic filtering and normalization of ESPI fringe patterns. Optics Letters, 22, 3018–3020. Hagoort, P., Hald, L., Bastiaansen, M., & Petersson, K. M. (2004). Integration of word meaning and world knowledge in language comprehension. Science, 304, 438–441. Hald, L. A., Bastiaansen, M. H. C., & Hagoort, P. (2006). EEG theta and gamma responses to semantic violations in online sentence processing. Brain and Language, 96, 90–105. Harmony, T. (2009). Psychophysiological assessment of children’s neuropsychological disorders. In C. R. Reynolds & E. Fletchner-Jamzen (Eds.), Handbook of clinical child neuropsychology (3rd ed., pp. 383–400). Springer. Harmony, T., Hinojosa, G., Marosi, E., Becquer, J., Rodríguez, M., Reyes, A., et al. (1990). Correlation between EEG spectral parameters and an educational evaluation. International Journal of Neuroscience, 54, 147–155. Harmony, T., Fernández, T., Silva, J., Bernal, J., Díaz-Comas, L., Reyes, A., et al. (1996). EEG delta activity: An indicator of attention to internal processing during the performance of mental tasks. International journal of Psychophysiology, 24, 161–171. John, E. R., Prichep, L., Ahn, H., Easton, P., Fridman, J., & Kaye, H. (1983). Neurometric evaluation of cognitive dysfunctions and neurological disorders in children. Progress in Neurobiology, 21, 239–290. Khader, P., & Rösler, F. (2004). EEG power and coherence analysis of visually presented nouns and verbs reveals left frontal processing differences. Neuroscience Letters, 354, 111–114. Khateb, A., Pegna, A. J., Landis, T., Mouthon, M. S., & Annoni, J. M. (2010). On the origin of the N400 effects: An ERP waveform and source localization analysis in three matching tasks. Brain Topography, 23, 311–320. Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research Review, 29, 169–195. Krause, C. M., Sillanmaki, L., Koivisto, M., Saarela, C., Haggovist, A., Laine, M., et al. (2000). The effects of memory load on event-related EEG desynchronization and synchronization. Clinical Neurophysiology, 111, 2071–2078. Kutas, M., & Hillyard, S. A. (1980). Reading senseless sentences: Brain potentials reflect semantic incongruity. Science, 207, 203–205. Marroquín, J. L., Harmony, T., Rodríguez, V., & Valdés, P. (2004). Exploratory EEG data analysis for psychophysiological experiments. Neuroimage, 21, 991–999. Matute, E., Roselli, M., Ardila, A., & Ostrosky, F. (2007). Evaluación neuropsicológica infantil. Editorial Manual Moderno. Missonier, P., Deiber, M. P., Gold, G., Millet, P., Gex-Fabry Pun, M., Fazio-Costa, L., et al. (2006). Frontal theta event-related synchronization: Comparison of directed attention and working memory load effects. Journal of Neural Transmission, 113, 1477–1486. Neville, H. J., Coffey, S. A., Holcomb, P. J., & Tallal, P. (1993). The neurobiology of sensory and language processing in language-impaired children. Journal of Cognitive Neuroscience, 5, 235–253. Nichols, T. E., & Hayasaka, S. (2003). Controlling the familywise error rate in functional neuroimaging: A comparative review. Statistical Methods in Medical Research, 12, 419–446. Polich, J. (1985). Semantic categorization and event-related potentials. Brain and Language, 26, 304–321. Rayner, K., & Pollatsek, A. (1989). The psychology of reading. Englewood Cliffs, NJ: Prentice-Hall. Röhm, D., Klimesch, W., Haider, H., & Doppelmayr, M. (2001). The role of theta and alpha oscillations for language comprehension in the human electroencephalogram. Neuroscience Letters, 310, 137–140. Rüsseler, J., Becker, P., Johannes, S., & Münte, T. F. (2007). Semantic, syntactic, and phonological processing of written words in adult developmental dyslexic readers: An event-related brain potential study. BMC Neuroscience, 17(8), 52. Silva-Pereyra, J., Fernández, T., Harmony, T., Bernal, J., Galán, L., Díaz-Comas, L., et al. (2001). Delayed P300 during Sternberg and Color discrimination tasks in poor readers. International Journal of Psychophysiology, 40, 17–32. Silva-Pereyra, J., Rivera-Gaxiola, M., Fernández, T., Díaz-Comas, L., Harmony, T., Fernández-Bouzas, A., et al. (2003). Are poor readers semantically challenged? An event-related brain potential assessment. International Journal of Psychophysiology, 49, 187–199.

82

T. Fernández et al. / Brain and Cognition 80 (2012) 74–82

Silva-Pereyra, J., Bernal, J., Rodríguez-Camacho, M., Yáñez, G., Prieto-Corona, B., Luviano, L., et al. (2010). Poor reading skills may involve a failure to focus attention. NeuroReport, 21, 34–38. Temple, E. (2011). Brain mechanisms in normal and dyslexic readers. Neuroimage, 54, 2426–2436. van der Mark, S., Klaver, P., Bucher, K., Maurer, U., Schulz, E., Brem, S., et al. (2011). The left occipitotemporal system in reading: Disruption of focal fMRI connectivity to left inferior frontal and inferior parietal language areas in children with dyslexia. Neuroimage, 54, 2426–2436.

Van Winsum, W., Sergeant, J., & Geuze, R. (1984). The functional significance of event-related desynchronization of alpha rhythm in attentional and activating tasks. Electroencephalography and Clinical Neurophysiology, 58, 519–524. Weiss, S., & Mueller, H. M. (2003). The contribution of EEG coherence to the investigation of language. Brain and Language, 85, 325–343. Willems, R. M., Oostenveld, R., & Hagoort, P. (2008). Early decreases in alpha and gamma band power distinguish linguistic from visual information during spoken sentence comprehension. Brain Research, 1219, 78–90.