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ERP evidence for the split fovea theory
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Research Report
ERP evidence for the split fovea theory Clara D. Martin a,b,c,⁎, Guillaume Thierry b,d , Jean-François Démonet c , Mark Roberts b , Tatjana Nazir a a
Laboratoire Langage Cerveau et Cognition, Institut des Sciences Cognitives, CNRS, 67 bd Pinel, 69675 Bron Cedex, France School of Psychology, University of Wales, Bangor, UK c INSERM U825, Hôpital de Purpan, F-31059, Toulouse Cedex, France d ESRC Centre for Research on Bilingualism in Theory and Practice, University of Wales, Bangor, UK b
A R T I C LE I N FO
AB S T R A C T
Article history:
According to the ‘bilateral representation theory’, a complete copy of the words presented
Accepted 12 September 2007
foveally is received simultaneously in the left and right visual cortices. However, a growing
Available online 23 October 2007
body of observations, which has led to the ‘split fovea theory’, proposes a functional split of the foveal area between the two hemispheres. In the present study we tested these two
Keywords:
accounts using an adapted version of the Reicher–Wheeler paradigm. Ten control
Split fovea
participants and ten participants with developmental dyslexia undergoing
Event-related potentials
electroencephalographic recordings were asked to identify one of five letters in a string.
Letter identification
The target letter was systematically presented at fixation but the horizontal positioning of
Developmental dyslexia
the letter string was varied such that the stimulus fluctuated in both the visual hemifields
Visual fields
over the experiment. ERP results showed that letter strings encompassing the foveal field
Visual cortex
were not sent to both cerebral hemispheres simultaneously when fixation coincided with extreme letter positions (i.e., first or last). Indeed, the P1 peak was delayed in this case, which was interpreted as the result of a transfer of visual information from the contralateral hemisphere via the splenium of the corpus callosum. Consistent with the ‘split fovea theory’, this result suggests that a minimal amount of graphic input is necessary to induce a P1 event. The interhemispheric transfer time (IHTT) deducted from peak-to-peak P1 latency delays ranged from 26 to 42 ms. As previously observed, the IHTT was significantly faster for rightto-left than left-to-right transfer in the control group. IHTT was marginally shorter in control participants as compared to participants with developmental dyslexia, and the faster transfer to the left hemisphere seen in the former was not found in the latter. © 2007 Elsevier B.V. All rights reserved.
1.
Introduction
The division of the brain in two halves creates a challenge for explaining how foveally presented words are perceived (Brysbaert, 2004). According to a classic theoretical stand, two complete copies of a foveally presented visual stimulus are sent in parallel to the left and the right hemisphere (Leventhal et al.,
1988; Stone et al., 1973; Trauzettel-Klosinski and Reinhard, 1998). This “bilateral representation” theory assumes that left and right visual fields (LVF and RVF) overlap along the vertical meridian and that a copy of visual information presented foveally is sent to the primary visual cortex of each of the hemispheres. Both hemispheres then process the same information without the need for interhemispheric transfer.
⁎ Corresponding author. L2C2, Institut des Sciences Cognitives, CNRS, 6 bd Pinel 69675 Bron Cedex, France. Fax: +33 4 37 91 12 10. E-mail address: [email protected] (C.D. Martin). 0006-8993/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.brainres.2007.09.049
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Support for this theory comes from the fact that patients with hemianopia sometimes show sparing of central vision (see Trauzettel-Klosinski and Reinhard, 1998), and that horseradish peroxidase injections in the dorsal lateral geniculate nucleus show incomplete crossing of the nasal fibers in the optic chiasm (Leventhal et al., 1988; Stone et al., 1973). However, other authors have questioned this hypothesis because supporting behavioral evidence is frail (e.g., Brysbaert, 1994a, b, 2004; Corballis and Trudel, 1993; Fendrich et al., 1996; Lavidor and Ellis, 2003; Lavidor et al., 2004; Celesia et al., 1993; Chiang et al., 2004; Ellis et al., 2005; Gray et al., 1997; Sugishita et al., 1994; Symonds and Mackenzie, 1957; Tootell et al., 1988). Corballis and Trudel (1993), for instance, failed to find evidence for foveal word recognition in a split-brain patient, although his performance was good for parafoveal word presentation in both LVF and RVF. Fendrich et al. (1996), who also tested split-brain patients, suggested that each hemisphere may have a weak representation of the contralateral hemi-retina, which does not allow fast recognition of small letters. Finally, Lavidor and Walsh (2003) showed that unilateral repetitive transcranial magnetic stimulation (rTMS) significantly impairs lexical decision latencies to centrally presented words. This observation clearly supports the notion that representation of foveally displayed words must be split between the cerebral hemispheres (the so-called split fovea theory) because foveal representation of the non-stimulated hemisphere could otherwise serve recognition (see Shillcock et al., 2000).
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The question on the representation of foveally displayed words in the two hemispheres of the brain leads naturally to the study of interhemispheric transfer of information. Based on studies of epileptic patients who have undergone callosotomy, it is established that visual information is transferred from one visual cortex to its contralateral homologue via the splenium of the corpus callosum (e.g., Censori et al., 1989). The time needed for this transfer of information is referred to as interhemispheric transfer time (IHTT). IHTT in humans was first estimated from reaction times in behavioral studies (Brizzolara et al., 1994; Davidson et al., 1990; Poffenberger, 1912; see Bashore, 1981 for a review). In the last two decades, however, event-related potentials (ERPs) have improved the precision of IHTT evaluation based on latency measures of the P1 component, a positive going ERP wave peaking about 100 ms after visual stimulus onset believed to reflect feature extraction in visual areas (Tarkiainen et al., 2002). The P1 is generally slightly delayed and smaller in amplitude over the hemisphere ipsilateral to the visual field in which the stimulus is presented (Bayard et al., 2004). The difference in peak latencies between the P1s recorded over ipsilateral and contralateral regions of the scalp vis-à-vis the stimulated visual field is believed to reflect callosal transfer time (see Saron and Davidson, 1989 for a review). IHTT has been estimated to be about 10–15 ms (Brown et al., 1998; Saron and Davidson, 1989), with a slower left-to-right than right-to-left transfer in right-handed subjects (Ipata et al., 1997; Saron and Davidson, 1989).
Fig. 1 – Stimulus display and predictions from the two competing theories. (a) Position of the stimulus vis-à-vis fixation in the different experimental conditions. The target letter was always centered at fixation and coincided with the first, second, third, fourth or fifth letter of the letter string (LP = letter position). (b) Predictions from the split fovea theory and the bilateral representation theory regarding the projection of foveally displayed information.
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To test the validity of the bilateral representation and split fovea theories, we reanalyzed ERP data reported by Martin et al. (2006), focusing on the time course of the P1 component. Martin et al. (2006) tested skilled readers using the Reicher– Wheeler paradigm (Reicher, 1969; Wheeler, 1970) to describe the interplay of perceptual and lexical effects during identification of letters within letter strings. In the “Reicher–Wheeler paradigm” participants are requested to identify a target letter presented at variable position within a letter string (either a word or a non-word) using a 2-alternative forced choice (e.g., what was the first letter in the string: a ‘t’ or a ‘c’?). In contrast to the classic Reicher–Wheeler paradigm, however, Martin et al. (2006) presented the letter strings at five different horizontal positions such that the target letter was always presented at fixation but could be in any of five positions within each string (Fig. 1a). Visual information from the entire letter string thus encompassed the foveal region of the horizontal meridian and fluctuated in both hemifields over trials. Since forced choice had to be performed on the target letter and since only correct responses were included in the analysis, this paradigm guaranteed that visual attention was maintained at fixation and that letter identification was explicit. Depending on the positioning of the letter string vis-à-vis central fixation, the bilateral representation theory and the split fovea theory make different predictions regarding the time course of P1 over the two hemispheres. Taking into consideration that the fovea spans approximately 2–3° of visual angle, only a subpart of a letter string was perceived foveally in Martin et al. (2006; see Fig. 1b and Experimental procedure). If the fovea is represented bilaterally, the latency of the P1 peak over each hemisphere should be identical independently of stimulus positioning, given that visual information is always present at fixation. By contrast, the split fovea theory predicts that P1 peak latencies should be indistinguishable over the two hemispheres only when fixation falls within the letter string (because visual information within each half of the fovea would elicit a P1 over the contralateral hemisphere). Indeed, when the target letter is in first or last position (i.e., when all but the fixated letter fall in
the RVF or LVF, respectively) an “immediate” P1 should occur only over the hemisphere contralateral to the stimulated visual field. Furthermore, if callosal transfer then intervenes, a “delayed” P1 should be registered over the hemisphere ipsilateral to the stimulated hemifield at a later time. Here, these predictions are tested in skilled readers (control group) and in participants with developmental dyslexia (matched for age and level of education) so as to determine whether low level visual processing stages indexed by P1 already distinguish between the two groups (Walker et al., 2001). The goal of the present study was thus to compare the predictions of the bilateral representation theory and the split fovea theory with respect to early visual processing during word recognition. Lexical effects that take place in later stages of processing have been reported in Martin et al. (2006) and are not discussed in the present paper.
2.
Results
2.1.
Behavioral results
Mean forced choice error rates for the two groups of participants are given in Fig. 2. A 2 between-subject by 2 by 2 by 5 within-subject repeated measures ANOVA with group (control/dyslexia), lexicality (word/non-word), display duration (50/ 66 ms) and letter position (LP1/2/3/4/5) as factors revealed a significant effect of lexicality (F[1,18] = 14.9, p = 0.001), a significant effect of display duration (F[1,18] = 18.2, p = 0.0005), a significant effect of letter position (F[2.82,50.75] = 9.14, p b 0.0001) but no group effect (F[1,18] = 3.54, p = 0.08). The group × lexicality interaction was significant (F[1,18] = 5.25, p = 0.03) and the post hoc analysis revealed a significant lexicality effect in control participants (p = 0.004) but not in participants with developmental dyslexia (p = 0.75). The letter position × lexicality interaction was significant (F[3.05,54.93] = 9.96, p b 0.0001) but the group × letter position interaction was not (F[2.82,50.75] = 1.02, p = 0.39). We observed a word superiority effect (i.e., better performance for letters embedded in words than in non-words) in
Fig. 2 – Mean forced choice error rates in (a) control participants and (b) participants with developmental dyslexia. Behavioral results in the four main conditions (W50, words displayed for 50 ms; W66, words displayed for 66 ms; NW50, non-words displayed for 50 ms; NW66, non-words displayed for 66 ms), as a function of the five letter positions (LP1, 2, 3, 4 and 5).
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control participants but not participants with developmental dyslexia. Moreover, the typical viewing position effect (i.e., better performance for words fixated near the center as compared to the beginning or the end) was obtained for both the groups (significant letter position × lexicality interaction without group × letter position interaction). The viewing position effect shows that letters of the word have been integrated for lexical access (see Nazir et al., 1992, 1998, 2004 for theoretical considerations about the mechanisms underlying the viewing position effect in words).
2.2.
P1 peak analysis
ERPs over the left and right parietooccipital scalp regions are shown in Fig. 3. Data are given separately for the five letter positions within the string. There was no significant correlation between the latency of the P1 and reaction times or accuracy, suggesting that this early ERP component is unrelated to higher level lexical processes. Given that our analyses focused on early visual processes the behavioral data were not analyzed further. In both groups of participants, preliminary analyses showed that the latency of the P1 was not affected by display duration (50 vs. 66 ms) or stimulus type (word vs. non-words). Data were therefore collapsed over these conditions and analyzed together. The ANOVA revealed a significant effect of group (F[1,18] = 8.71; p = 0.01), which was due to an overall
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delay of the P1 peak latency (∼ 20–25 ms) in participants with developmental dyslexia. There was no effect of electrode (F[1,18] = 0.01; p = 0.92) but a significant effect of letter position (F[4,15] = 26.58; p b 0.001), and a significant interaction between letter position and electrode (F[4,15] = 31.66; p b 0.001). This interaction reflects the fact that the latency of the P1 elicited by letter strings fixated at the first or last letter (i.e., when all but the fixated letter are displayed in one visual hemifield only, see Fig. 1) was significantly delayed over the hemisphere ipsilateral to the visual field in which the stimulus was displayed. In the control group, this delay was most visible over left parietooccipital electrodes, where the P1 peaked at 123 ± 14 ms on average for letter position 5, vs. 98 ± 13 ms, 96 ± 14 ms, 97 ± 14 and 98 ± 13 ms for letter positions 1, 2, 3 and 4, respectively. In participants with developmental dyslexia P1 peak latencies were 124 ± 19 ms, 122 ± 16 ms, 122 ± 19 ms, 125 ± 18 ms and 151 ± 17 ms for letter positions 1, 2, 3, 4 and 5, respectively. A mirror-reversed pattern was found over the right parietooccipital electrodes, where the latency of the P1 was significantly delayed for letter position 1 as compared to other letter positions, in both the participant groups. We used Independent Component Analysis (ICA) to test whether differences in P1 peak latencies could reflect contribution of different underlying components (see Experimental procedure). The ICA conducted on the appended series of grand averages obtained in all experimental conditions in the control group identified a common component (#3) which
Fig. 3 – Event-related potential results in (a) control participants and (b) participants with developmental dyslexia. ERPs measured over left (PO7) and right (PO8) parietooccipital regions as a function of the position of the target letter in the letter string (LP1–LP5). The arrows indicate P1 latencies in LP1 (purple) and LP5 (red) conditions. Note the shorter delay between P1 peaks in control participants over the left parietooccipital region.
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matched the latency of the P1 measured at PO4 and generated a similar topographical representation in all conditions. The time course and topography of the extracted component 3 were compared to the time course of grand-average ERPs and P1 topographies in each of the experimental conditions (Fig. 4). Subtraction of independent component 3 from individual ERPs in 8 of the control participants (Klein and Feige, 2005) followed by a direct comparison of peak mean amplitudes in the time range of the P1 (60–160 ms) revealed no significant differences between fixation positions 1 and 5 in any of the experimental conditions (all ps N 0.1).
2.3.
Interhemispheric transfer time (IHTT)
Based on Saron and Davidson (1989), we estimated left-to-right IHTT by subtracting P1 peak latencies measured over the right and left parietooccipital regions (PO7 and PO8) for letter position 1. Right-to-left IHTT was estimated by subtracting P1 peak latencies over the left and the right parietooccipital regions for letter position 5. Note that lateral occipital sites are thought to provide a better evaluation of IHTT than the medial sites (Saron and Davidson, 1989). The mean estimated right-toleft IHTT was 26 ± 14 ms in control participants and 42 ± 17 ms in participants with developmental dyslexia. The mean left-toright IHTT was 42 ± 15 ms in control participants and 40 ± 10 ms in participants with developmental dyslexia. The difference in estimated IHTT between the two groups was marginally significant (F[2,17] = 4.51; p = 0.06). Measure of effect size (eta square value) revealed that 36% of the variance in the dependent variable could be attributed to the group factor. The IHTT of control participants was significantly faster for right-to-left than left-to-right transfer (t[9] = 2.654; p = 0.026). In participants with developmental dyslexia IHTT did not differ in the two conditions (t[9] = −.284; p = 0.783).
3.
Discussion
The present results characterized an early positive ERP event (P1) over occipitoparietal regions contralateral to the stimulated visual hemifield. Peak latency was approximately 100 ms after stimulus onset in control participants and 123 ms in participants with developmental dyslexia. This P1, which is believed to reflect processes in early visual areas that are involved in feature extraction (Tarkiainen et al., 2002) – irrespective of the linguistic category of the stimulus (Rossion et al., 2003) – was unaffected by display duration (50 or 66 ms) or stimulus type (word or non-word).
3.1.
P1 peak latency and the split fovea hypothesis
When participants fixated a letter situated within the letter string (i.e., letter positions 2, 3, and 4) the P1 peaked at about the same latency over the left and right parietooccipital scalp. However, when participants fixated the first or the last letter in the letter string (letter positions 1 and 5), i.e., when all but one letter fell into one visual hemifield, P1 peak latency was delayed by 26 to 42 ms over the ipsilateral scalp. Critically, the extraction of independent components in the P1 range in the different conditions using ICA lead to the characterization of a single component accounting for the P1 for both fixation positions 1 and 5 (Fig. 4). Therefore, the difference in P1 peak latency genuinely reflects a shift in latency of the P1 generators and cannot be attributed to differential contribution of overlapping components in the P1 range. This delay, which was observed in both the groups, is inconsistent with the view that identical copies of stimuli encompassing the foveal field are sent simultaneously to each of the two cerebral hemispheres. Our result is thus consistent
Fig. 4 – Independent Component Analysis of ERPs in conditions LP1 and LP5. (a) Grand-average ERPs in all experimental conditions for LP1 (purple) and LP5 (red) with activity produced by ICA component 3 superimposed (dotted). (b) Comparison of topographic maps of the P1 and ICA component 3 in the various experimental conditions for letter positions 1 and 5.
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with the split fovea theory (Brysbaert, 1994b, 2004; Ellis et al., 2005; Lavidor et al., 2004) and go against the predictions of the bilateral representation theory (Leventhal et al., 1988; Stone et al., 1973; Trauzettel-Klosinski and Reinhard, 1998). The present data also show that a minimal amount of graphic input is necessary (∼1.5 letters in this case, i.e., the amount of visual input to one hemifield when the letter string is fixated at position 2 or 4; see Fig. 1) to elicit a P1 over the contralateral occipitoparietal scalp. When less than 1.5 letters is presented in each hemifield (i.e., when fixation coincides with the first or last letter in the string), no immediate contralateral P1 is observed. In addition, beyond 1.5 letters, P1 peak latency remains constant independently of the number of additional letters displayed in the contralateral hemifield. Given that, in this experiment, 1.5 letters spanned approximately 2° of visual angle, the contralateral P1 found for positions 2 and 4 is probably triggered by visual information perceived on and in the proximity of the fovea. It is important to stress here again that the P1 component probably reflects processes of visual feature extraction (Tarkiainen et al., 2002), which are not distinguishable for words and non-words. Therefore, the present results only enable us to infer that information about foveally displayed visual stimuli is not processed in parallel and redundantly in each of the two hemispheres. Speculations on how visual information from the two visual fields is subsequently integrated in the process of visual word recognition cannot be inferred directly from our results and await dedicated investigations. In this context it is worth noting that Hunter et al. (2007) recently investigated visual word recognition in participants with typical (left) and atypical (right) language lateralization using the same variable viewing position paradigm as that used in the present study. Their results showed that language dominance has a significant impact on the way letters in the two visual fields are processed, with an advantage for letters that are directly sent to the language dominant hemisphere. This result was found even for foveally fixated words, which supports our interpretation that foveally displayed stimuli are split between the two hemispheres and benefit differently from higher level processes.
3.2.
Interhemispheric transfer
We found that visual information is transferred from the contralateral to the ipsilateral hemisphere with a delay of 26 to 42 ms in control participants and 40 to 42 ms in participants with developmental dyslexia. Note that previous studies have estimated IHTT to about 10–15 ms (Barnett and Corballis, 2005; Brown et al., 1998, 1994; Murray et al., 2001; Saron and Davidson, 1989). The longer delays observed in our experiment may be explained by the fact that our stimuli were letter strings, which are substantially more complex than basic visual stimuli typically used to measure IHTT (e.g., checkerboards or flashes). In addition, the delays measured here are consistent with the argument put forward by Ono et al. (2002) that interhemispheric transfer should theoretically exceed axonal conduction time, which is estimated to take about 20 ms. Several studies in healthy participants have shown that right-to-left interhemispheric transfer is typically faster than left-to-right transfer (Brown and Jeeves, 1993; Brown et al.,
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1994; Ipata et al., 1997; Marzi et al., 1991; Saron and Davidson, 1989). Three main explanations have been proposed to account for this asymmetry: (i) according to Marzi et al. (1991) the difference could relate to callosal connection asymmetries, with a greater number of neurons projecting from the right to the left hemisphere than vice versa (see also Saron and Davidson, 1989). (ii) A second possibility is that IHTT is systematically shorter when information is transferred from the non-dominant to the dominant hemisphere — in the present case, the left hemisphere (Nowicka et al., 1996). However, a meta-analysis of ERP studies by Brown et al. (1994) did not support this claim. (iii) Third, Miller (1996) argued that the right hemisphere has more myelinated axons enabling rapid conduction and processing of gestalt representations. In line with Miller (1996), Barnett and Corballis (2005) and Barnett and Kirk (2005) suggested that the presence of faster conducting cortico-cortical myelinated axons in the right hemisphere increases the likelihood of neural summation in that hemisphere, and therefore the speed of right-toleft relative to left-to-right transfer. While IHTT measures in control participants replicated this asymmetry, our results do not enable us to validate or invalidate any of the three hypothetical explanations above.
3.3. Comparison of control participants and participants with developmental dyslexia Even though the ANOVA on P1 latencies revealed a significant group effect, the group × letter position × electrode interaction was not significant. Therefore, interpretations arising from comparisons between groups must be cautious. The P1 peaked significantly earlier in control participants than in participants with developmental dyslexia, indicating that differences can be found in early visual processing stages. IHTT was also marginally faster in control participants than in participants with developmental dyslexia and this difference was driven by the shorter right-to-left IHTT in control participants. Since our finding is only a marginally significant effect, it is not wholly inconsistent with results reported by other investigators (e.g., Davidson et al., 1990), who showed no overall IHTT group differences. In our study, the IHTT difference between control participants and participants with developmental dyslexia was smaller than 11 ms. Such a small difference might have been undetectable with the sample size of Davidson et al. (1990). The absence of asymmetry between left-to-right and right-to-left IHTT in participants with developmental dyslexia may indicate a reduction of white matter density in the left hemisphere, as hypothesized by several authors (Beaulieu et al., 2005; Deutsch et al., 2005; Silani et al., 2005). It is worth noting that global abnormalities of the corpus callosum – which have been described in participants with developmental dyslexia (Duara et al., 1991) – cannot account for the present findings because the difference between control participants and participants with developmental dyslexia was only observed for transfers to the language dominant hemisphere, and not the non-dominant hemisphere. In line with Barnett and Corballis (2005) and Barnett and Kirk (2005), we hypothesize that abnormalities of the myelinated pathways in the right hemisphere of participants with developmental dyslexia might account for the slower right-to-left transfer time. Recall
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though that neuroimaging studies have identified abnormalities in the left rather than the right hemisphere in developmental dyslexia (Beaulieu et al., 2005; Deutsch et al., 2005; Silani et al., 2005). Even though hemispheric dominance does not necessarily play a role in the asymmetry of IHTT in control participants, the atypical hemispheric dominance for language processing often reported in developmental dyslexia may relate to the lack of IHTT asymmetry reported here (see for instance Eckert et al., 2003, 2005; Giraud et al., 2005; McCrory et al., 2000; Simos et al., 2000, 2002). Together with P1 peak latency results, the present findings suggest that early visual processing stages are affected in developmental dyslexia and may thus contribute to associated reading difficulties.
4.
Fig. 5 – Experimental design of the forced two-choice task (adapted version of the Reicher–Wheeler paradigm).
Experimental procedure
The experimental procedure of this paper is largely based on Martin et al. (2006) where additional details can be found.
consonant strings (e.g., ‘PGSRF’). Both words and non-words were 5 letters long.
4.3. 4.1.
Ten control participants (5 females and 5 males; mean age 24.8 ± 1.6 years) and ten participants with developmental dyslexia (2 females and 8 males; mean age 25.2 ± 6.3) gave informed consent to participate in the experiment that was approved by a local ethics committee. They were all righthanded native speakers of French, with normal global IQ (assessed using the WAIS-R), and normal or corrected-to-normal vision. We recruited volunteers who had been diagnosed as dyslexic and had documented histories of reading and spelling difficulties. Participants were tested with tasks designed to assess reading accuracy and speed, and phonological processing: reading of words and non-words, rapid digit naming and phoneme awareness tasks (phoneme deletion, rhyme judgment and spoonerisms; see Table 1).
4.2.
Task and procedure
Participants
Stimuli
Stimuli were 40 French nouns selected from the Brulex database (Content and Radeau, 1990) and 40 unpronounceable
Table 1 – Participant performance in intelligence, reading and phonological tasks in the two groups of participants Control participants Full IQ Verbal IQ Performance IQ Word reading Non-word reading Rapid digit naming Phoneme deletion Rhyme judgment Spoonerisms
118 119 111 533 660 15 99 1220 96
(11) (12) (13) (74) (140) (2) (19) (256) (32)
Dyslexic participants 109 109 107 740 919 22 241 1826 229
(8) (13) (14) (78) (114) (4) (49) (166) (79)
P n.s. n.s. n.s. ⁎ ⁎ ⁎ ⁎⁎ ⁎⁎ ⁎⁎
Standard deviations are shown in parentheses. Average scores and standard deviations refer to marks from normalized neuropsychological tests. n.s.=Not significant; ⁎Pb 0.05; ⁎⁎Pb 0.01.
At the beginning of each trial, a fixation cross was displayed at the center of a computer screen for 2.5 s. The fixation then disappeared for 50 ms before a second fixation appeared for 200 ms. After a second pause of 50 ms, a stimulus (word or nonword) was displayed for either 50 or 66 ms in “Times New Roman” lower case, font size 28. The stimulus position was varied laterally so that each of the five letters in a string could coincide with fixation. The amount of visual information therefore fluctuated across foveal and parafoveal fields between the experimental conditions (Fig. 1). Stimuli subtended 6.65° of visual angle, at a distance of 60 cm. After 50 or 66 ms, the stimulus was replaced by a string made of 7 upper case Xs masking the entire stimulus string (mask) and two probe letters, one above and one below the mask. Subjects had to indicate which of the two probe letters was the letter previously presented at fixation (target letter), by pressing the top or bottom button of a response pad (Fig. 4). The mask and the two probe letters remained on the screen until the response. To increase statistical power, each of the 40 words and non-words were presented 6 times in the 50-ms condition and 6 times in the 66-ms condition. Overall, the 960 trials were pseudo-randomly distributed in 12 blocks of 80, with each item displayed only once per block. Exposure durations (50 and 66 ms) and stimulus categories (words and non-words) were randomized within each block. Block order and response side were counterbalanced across participants (Fig. 5).
4.4.
ERP acquisition and processing
Participants were comfortably seated in a quiet room and asked to refrain from moving and blinking. Electrophysiological data were recorded from 64 Ag/AgCl electrodes (placed according to the extended International 10–20 system) at a sampling rate of 500 Hz, using SynAmps™ amplifiers (Neuroscan™, El Paso, TX, USA). The electrooculogram was recorded using supraorbital and infraorbital electrodes connected to a bipolar channel. Signals were filtered on-line between 0.1 and 100 Hz. Impedances were kept below 20 kΩ. Continuous recordings were digitally band-pass filtered off-
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line in the interval [1–40] Hz. Eye-blink artefacts were mathematically corrected and remaining artefacts manually dismissed. Epochs ranged from −100 to 1000 ms after the onset of the five letter string (word or non-word). Signal variations exceeding ±75 μm anywhere in the analysis window and on any of the channels except the vertical electrooculogram (VEOG) were automatically dismissed. After baseline correction relative to pre-stimulus activity and rejection of errors, there were at least 48 epochs per condition in all participants. Individual difference waveforms and grand-average waveforms were then derived from individual ERPs.
4.5.
Statistical analysis
P1 peak search was confined to the interval 70–130 ms based on the corresponding period of maximal activity in the Mean Global Field Power over the 64 electrodes (Picton et al., 2000). P1 latencies at the electrodes of maximal sensitivity were analyzed using an analysis of variance (ANOVA) with group (control vs. participants with developmental dyslexia) as intersubject variable and electrode (over left and right occipitotemporal regions) and letter position (LP1 to LP5) as intra-subject variables. An “extended” Independent Component Analysis (ICA), using the infomax algorithm (Makeig et al., 1997), was jointly applied to the appended averages of all experimental conditions in order to separate generators associated with different topographies. Visual inspection of both temporal and topographic representations of the ICA matrices allowed identification of one component best accounting for the P1 (see Results). The activity produced by the component was then subtracted from individual ERPs in all conditions in 8 of the 10 control participants and ERP mean amplitudes were compared in the P1 range between conditions (Klein and Feige, 2005). Two participants who failed to display a clear component following ICA were excluded from this analysis.
Acknowledgments The authors wish to thank Yves Paulignan, Chantal Blanchard and Nicolas Chauveau for the useful discussions and technical assistance.
REFERENCES
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a v a i l a b l e a t w w w. s c i e n c e d i r e c t . c o m
w w w. e l s e v i e r. c o m / l o c a t e / b r a i n r e s
Research Report
ERP evidence for the split fovea theory Clara D. Martin a,b,c,⁎, Guillaume Thierry b,d , Jean-François Démonet c , Mark Roberts b , Tatjana Nazir a a
Laboratoire Langage Cerveau et Cognition, Institut des Sciences Cognitives, CNRS, 67 bd Pinel, 69675 Bron Cedex, France School of Psychology, University of Wales, Bangor, UK c INSERM U825, Hôpital de Purpan, F-31059, Toulouse Cedex, France d ESRC Centre for Research on Bilingualism in Theory and Practice, University of Wales, Bangor, UK b
A R T I C LE I N FO
AB S T R A C T
Article history:
According to the ‘bilateral representation theory’, a complete copy of the words presented
Accepted 12 September 2007
foveally is received simultaneously in the left and right visual cortices. However, a growing
Available online 23 October 2007
body of observations, which has led to the ‘split fovea theory’, proposes a functional split of the foveal area between the two hemispheres. In the present study we tested these two
Keywords:
accounts using an adapted version of the Reicher–Wheeler paradigm. Ten control
Split fovea
participants and ten participants with developmental dyslexia undergoing
Event-related potentials
electroencephalographic recordings were asked to identify one of five letters in a string.
Letter identification
The target letter was systematically presented at fixation but the horizontal positioning of
Developmental dyslexia
the letter string was varied such that the stimulus fluctuated in both the visual hemifields
Visual fields
over the experiment. ERP results showed that letter strings encompassing the foveal field
Visual cortex
were not sent to both cerebral hemispheres simultaneously when fixation coincided with extreme letter positions (i.e., first or last). Indeed, the P1 peak was delayed in this case, which was interpreted as the result of a transfer of visual information from the contralateral hemisphere via the splenium of the corpus callosum. Consistent with the ‘split fovea theory’, this result suggests that a minimal amount of graphic input is necessary to induce a P1 event. The interhemispheric transfer time (IHTT) deducted from peak-to-peak P1 latency delays ranged from 26 to 42 ms. As previously observed, the IHTT was significantly faster for rightto-left than left-to-right transfer in the control group. IHTT was marginally shorter in control participants as compared to participants with developmental dyslexia, and the faster transfer to the left hemisphere seen in the former was not found in the latter. © 2007 Elsevier B.V. All rights reserved.
1.
Introduction
The division of the brain in two halves creates a challenge for explaining how foveally presented words are perceived (Brysbaert, 2004). According to a classic theoretical stand, two complete copies of a foveally presented visual stimulus are sent in parallel to the left and the right hemisphere (Leventhal et al.,
1988; Stone et al., 1973; Trauzettel-Klosinski and Reinhard, 1998). This “bilateral representation” theory assumes that left and right visual fields (LVF and RVF) overlap along the vertical meridian and that a copy of visual information presented foveally is sent to the primary visual cortex of each of the hemispheres. Both hemispheres then process the same information without the need for interhemispheric transfer.
⁎ Corresponding author. L2C2, Institut des Sciences Cognitives, CNRS, 6 bd Pinel 69675 Bron Cedex, France. Fax: +33 4 37 91 12 10. E-mail address: [email protected] (C.D. Martin). 0006-8993/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.brainres.2007.09.049
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Support for this theory comes from the fact that patients with hemianopia sometimes show sparing of central vision (see Trauzettel-Klosinski and Reinhard, 1998), and that horseradish peroxidase injections in the dorsal lateral geniculate nucleus show incomplete crossing of the nasal fibers in the optic chiasm (Leventhal et al., 1988; Stone et al., 1973). However, other authors have questioned this hypothesis because supporting behavioral evidence is frail (e.g., Brysbaert, 1994a, b, 2004; Corballis and Trudel, 1993; Fendrich et al., 1996; Lavidor and Ellis, 2003; Lavidor et al., 2004; Celesia et al., 1993; Chiang et al., 2004; Ellis et al., 2005; Gray et al., 1997; Sugishita et al., 1994; Symonds and Mackenzie, 1957; Tootell et al., 1988). Corballis and Trudel (1993), for instance, failed to find evidence for foveal word recognition in a split-brain patient, although his performance was good for parafoveal word presentation in both LVF and RVF. Fendrich et al. (1996), who also tested split-brain patients, suggested that each hemisphere may have a weak representation of the contralateral hemi-retina, which does not allow fast recognition of small letters. Finally, Lavidor and Walsh (2003) showed that unilateral repetitive transcranial magnetic stimulation (rTMS) significantly impairs lexical decision latencies to centrally presented words. This observation clearly supports the notion that representation of foveally displayed words must be split between the cerebral hemispheres (the so-called split fovea theory) because foveal representation of the non-stimulated hemisphere could otherwise serve recognition (see Shillcock et al., 2000).
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The question on the representation of foveally displayed words in the two hemispheres of the brain leads naturally to the study of interhemispheric transfer of information. Based on studies of epileptic patients who have undergone callosotomy, it is established that visual information is transferred from one visual cortex to its contralateral homologue via the splenium of the corpus callosum (e.g., Censori et al., 1989). The time needed for this transfer of information is referred to as interhemispheric transfer time (IHTT). IHTT in humans was first estimated from reaction times in behavioral studies (Brizzolara et al., 1994; Davidson et al., 1990; Poffenberger, 1912; see Bashore, 1981 for a review). In the last two decades, however, event-related potentials (ERPs) have improved the precision of IHTT evaluation based on latency measures of the P1 component, a positive going ERP wave peaking about 100 ms after visual stimulus onset believed to reflect feature extraction in visual areas (Tarkiainen et al., 2002). The P1 is generally slightly delayed and smaller in amplitude over the hemisphere ipsilateral to the visual field in which the stimulus is presented (Bayard et al., 2004). The difference in peak latencies between the P1s recorded over ipsilateral and contralateral regions of the scalp vis-à-vis the stimulated visual field is believed to reflect callosal transfer time (see Saron and Davidson, 1989 for a review). IHTT has been estimated to be about 10–15 ms (Brown et al., 1998; Saron and Davidson, 1989), with a slower left-to-right than right-to-left transfer in right-handed subjects (Ipata et al., 1997; Saron and Davidson, 1989).
Fig. 1 – Stimulus display and predictions from the two competing theories. (a) Position of the stimulus vis-à-vis fixation in the different experimental conditions. The target letter was always centered at fixation and coincided with the first, second, third, fourth or fifth letter of the letter string (LP = letter position). (b) Predictions from the split fovea theory and the bilateral representation theory regarding the projection of foveally displayed information.
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To test the validity of the bilateral representation and split fovea theories, we reanalyzed ERP data reported by Martin et al. (2006), focusing on the time course of the P1 component. Martin et al. (2006) tested skilled readers using the Reicher– Wheeler paradigm (Reicher, 1969; Wheeler, 1970) to describe the interplay of perceptual and lexical effects during identification of letters within letter strings. In the “Reicher–Wheeler paradigm” participants are requested to identify a target letter presented at variable position within a letter string (either a word or a non-word) using a 2-alternative forced choice (e.g., what was the first letter in the string: a ‘t’ or a ‘c’?). In contrast to the classic Reicher–Wheeler paradigm, however, Martin et al. (2006) presented the letter strings at five different horizontal positions such that the target letter was always presented at fixation but could be in any of five positions within each string (Fig. 1a). Visual information from the entire letter string thus encompassed the foveal region of the horizontal meridian and fluctuated in both hemifields over trials. Since forced choice had to be performed on the target letter and since only correct responses were included in the analysis, this paradigm guaranteed that visual attention was maintained at fixation and that letter identification was explicit. Depending on the positioning of the letter string vis-à-vis central fixation, the bilateral representation theory and the split fovea theory make different predictions regarding the time course of P1 over the two hemispheres. Taking into consideration that the fovea spans approximately 2–3° of visual angle, only a subpart of a letter string was perceived foveally in Martin et al. (2006; see Fig. 1b and Experimental procedure). If the fovea is represented bilaterally, the latency of the P1 peak over each hemisphere should be identical independently of stimulus positioning, given that visual information is always present at fixation. By contrast, the split fovea theory predicts that P1 peak latencies should be indistinguishable over the two hemispheres only when fixation falls within the letter string (because visual information within each half of the fovea would elicit a P1 over the contralateral hemisphere). Indeed, when the target letter is in first or last position (i.e., when all but the fixated letter fall in
the RVF or LVF, respectively) an “immediate” P1 should occur only over the hemisphere contralateral to the stimulated visual field. Furthermore, if callosal transfer then intervenes, a “delayed” P1 should be registered over the hemisphere ipsilateral to the stimulated hemifield at a later time. Here, these predictions are tested in skilled readers (control group) and in participants with developmental dyslexia (matched for age and level of education) so as to determine whether low level visual processing stages indexed by P1 already distinguish between the two groups (Walker et al., 2001). The goal of the present study was thus to compare the predictions of the bilateral representation theory and the split fovea theory with respect to early visual processing during word recognition. Lexical effects that take place in later stages of processing have been reported in Martin et al. (2006) and are not discussed in the present paper.
2.
Results
2.1.
Behavioral results
Mean forced choice error rates for the two groups of participants are given in Fig. 2. A 2 between-subject by 2 by 2 by 5 within-subject repeated measures ANOVA with group (control/dyslexia), lexicality (word/non-word), display duration (50/ 66 ms) and letter position (LP1/2/3/4/5) as factors revealed a significant effect of lexicality (F[1,18] = 14.9, p = 0.001), a significant effect of display duration (F[1,18] = 18.2, p = 0.0005), a significant effect of letter position (F[2.82,50.75] = 9.14, p b 0.0001) but no group effect (F[1,18] = 3.54, p = 0.08). The group × lexicality interaction was significant (F[1,18] = 5.25, p = 0.03) and the post hoc analysis revealed a significant lexicality effect in control participants (p = 0.004) but not in participants with developmental dyslexia (p = 0.75). The letter position × lexicality interaction was significant (F[3.05,54.93] = 9.96, p b 0.0001) but the group × letter position interaction was not (F[2.82,50.75] = 1.02, p = 0.39). We observed a word superiority effect (i.e., better performance for letters embedded in words than in non-words) in
Fig. 2 – Mean forced choice error rates in (a) control participants and (b) participants with developmental dyslexia. Behavioral results in the four main conditions (W50, words displayed for 50 ms; W66, words displayed for 66 ms; NW50, non-words displayed for 50 ms; NW66, non-words displayed for 66 ms), as a function of the five letter positions (LP1, 2, 3, 4 and 5).
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control participants but not participants with developmental dyslexia. Moreover, the typical viewing position effect (i.e., better performance for words fixated near the center as compared to the beginning or the end) was obtained for both the groups (significant letter position × lexicality interaction without group × letter position interaction). The viewing position effect shows that letters of the word have been integrated for lexical access (see Nazir et al., 1992, 1998, 2004 for theoretical considerations about the mechanisms underlying the viewing position effect in words).
2.2.
P1 peak analysis
ERPs over the left and right parietooccipital scalp regions are shown in Fig. 3. Data are given separately for the five letter positions within the string. There was no significant correlation between the latency of the P1 and reaction times or accuracy, suggesting that this early ERP component is unrelated to higher level lexical processes. Given that our analyses focused on early visual processes the behavioral data were not analyzed further. In both groups of participants, preliminary analyses showed that the latency of the P1 was not affected by display duration (50 vs. 66 ms) or stimulus type (word vs. non-words). Data were therefore collapsed over these conditions and analyzed together. The ANOVA revealed a significant effect of group (F[1,18] = 8.71; p = 0.01), which was due to an overall
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delay of the P1 peak latency (∼ 20–25 ms) in participants with developmental dyslexia. There was no effect of electrode (F[1,18] = 0.01; p = 0.92) but a significant effect of letter position (F[4,15] = 26.58; p b 0.001), and a significant interaction between letter position and electrode (F[4,15] = 31.66; p b 0.001). This interaction reflects the fact that the latency of the P1 elicited by letter strings fixated at the first or last letter (i.e., when all but the fixated letter are displayed in one visual hemifield only, see Fig. 1) was significantly delayed over the hemisphere ipsilateral to the visual field in which the stimulus was displayed. In the control group, this delay was most visible over left parietooccipital electrodes, where the P1 peaked at 123 ± 14 ms on average for letter position 5, vs. 98 ± 13 ms, 96 ± 14 ms, 97 ± 14 and 98 ± 13 ms for letter positions 1, 2, 3 and 4, respectively. In participants with developmental dyslexia P1 peak latencies were 124 ± 19 ms, 122 ± 16 ms, 122 ± 19 ms, 125 ± 18 ms and 151 ± 17 ms for letter positions 1, 2, 3, 4 and 5, respectively. A mirror-reversed pattern was found over the right parietooccipital electrodes, where the latency of the P1 was significantly delayed for letter position 1 as compared to other letter positions, in both the participant groups. We used Independent Component Analysis (ICA) to test whether differences in P1 peak latencies could reflect contribution of different underlying components (see Experimental procedure). The ICA conducted on the appended series of grand averages obtained in all experimental conditions in the control group identified a common component (#3) which
Fig. 3 – Event-related potential results in (a) control participants and (b) participants with developmental dyslexia. ERPs measured over left (PO7) and right (PO8) parietooccipital regions as a function of the position of the target letter in the letter string (LP1–LP5). The arrows indicate P1 latencies in LP1 (purple) and LP5 (red) conditions. Note the shorter delay between P1 peaks in control participants over the left parietooccipital region.
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matched the latency of the P1 measured at PO4 and generated a similar topographical representation in all conditions. The time course and topography of the extracted component 3 were compared to the time course of grand-average ERPs and P1 topographies in each of the experimental conditions (Fig. 4). Subtraction of independent component 3 from individual ERPs in 8 of the control participants (Klein and Feige, 2005) followed by a direct comparison of peak mean amplitudes in the time range of the P1 (60–160 ms) revealed no significant differences between fixation positions 1 and 5 in any of the experimental conditions (all ps N 0.1).
2.3.
Interhemispheric transfer time (IHTT)
Based on Saron and Davidson (1989), we estimated left-to-right IHTT by subtracting P1 peak latencies measured over the right and left parietooccipital regions (PO7 and PO8) for letter position 1. Right-to-left IHTT was estimated by subtracting P1 peak latencies over the left and the right parietooccipital regions for letter position 5. Note that lateral occipital sites are thought to provide a better evaluation of IHTT than the medial sites (Saron and Davidson, 1989). The mean estimated right-toleft IHTT was 26 ± 14 ms in control participants and 42 ± 17 ms in participants with developmental dyslexia. The mean left-toright IHTT was 42 ± 15 ms in control participants and 40 ± 10 ms in participants with developmental dyslexia. The difference in estimated IHTT between the two groups was marginally significant (F[2,17] = 4.51; p = 0.06). Measure of effect size (eta square value) revealed that 36% of the variance in the dependent variable could be attributed to the group factor. The IHTT of control participants was significantly faster for right-to-left than left-to-right transfer (t[9] = 2.654; p = 0.026). In participants with developmental dyslexia IHTT did not differ in the two conditions (t[9] = −.284; p = 0.783).
3.
Discussion
The present results characterized an early positive ERP event (P1) over occipitoparietal regions contralateral to the stimulated visual hemifield. Peak latency was approximately 100 ms after stimulus onset in control participants and 123 ms in participants with developmental dyslexia. This P1, which is believed to reflect processes in early visual areas that are involved in feature extraction (Tarkiainen et al., 2002) – irrespective of the linguistic category of the stimulus (Rossion et al., 2003) – was unaffected by display duration (50 or 66 ms) or stimulus type (word or non-word).
3.1.
P1 peak latency and the split fovea hypothesis
When participants fixated a letter situated within the letter string (i.e., letter positions 2, 3, and 4) the P1 peaked at about the same latency over the left and right parietooccipital scalp. However, when participants fixated the first or the last letter in the letter string (letter positions 1 and 5), i.e., when all but one letter fell into one visual hemifield, P1 peak latency was delayed by 26 to 42 ms over the ipsilateral scalp. Critically, the extraction of independent components in the P1 range in the different conditions using ICA lead to the characterization of a single component accounting for the P1 for both fixation positions 1 and 5 (Fig. 4). Therefore, the difference in P1 peak latency genuinely reflects a shift in latency of the P1 generators and cannot be attributed to differential contribution of overlapping components in the P1 range. This delay, which was observed in both the groups, is inconsistent with the view that identical copies of stimuli encompassing the foveal field are sent simultaneously to each of the two cerebral hemispheres. Our result is thus consistent
Fig. 4 – Independent Component Analysis of ERPs in conditions LP1 and LP5. (a) Grand-average ERPs in all experimental conditions for LP1 (purple) and LP5 (red) with activity produced by ICA component 3 superimposed (dotted). (b) Comparison of topographic maps of the P1 and ICA component 3 in the various experimental conditions for letter positions 1 and 5.
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with the split fovea theory (Brysbaert, 1994b, 2004; Ellis et al., 2005; Lavidor et al., 2004) and go against the predictions of the bilateral representation theory (Leventhal et al., 1988; Stone et al., 1973; Trauzettel-Klosinski and Reinhard, 1998). The present data also show that a minimal amount of graphic input is necessary (∼1.5 letters in this case, i.e., the amount of visual input to one hemifield when the letter string is fixated at position 2 or 4; see Fig. 1) to elicit a P1 over the contralateral occipitoparietal scalp. When less than 1.5 letters is presented in each hemifield (i.e., when fixation coincides with the first or last letter in the string), no immediate contralateral P1 is observed. In addition, beyond 1.5 letters, P1 peak latency remains constant independently of the number of additional letters displayed in the contralateral hemifield. Given that, in this experiment, 1.5 letters spanned approximately 2° of visual angle, the contralateral P1 found for positions 2 and 4 is probably triggered by visual information perceived on and in the proximity of the fovea. It is important to stress here again that the P1 component probably reflects processes of visual feature extraction (Tarkiainen et al., 2002), which are not distinguishable for words and non-words. Therefore, the present results only enable us to infer that information about foveally displayed visual stimuli is not processed in parallel and redundantly in each of the two hemispheres. Speculations on how visual information from the two visual fields is subsequently integrated in the process of visual word recognition cannot be inferred directly from our results and await dedicated investigations. In this context it is worth noting that Hunter et al. (2007) recently investigated visual word recognition in participants with typical (left) and atypical (right) language lateralization using the same variable viewing position paradigm as that used in the present study. Their results showed that language dominance has a significant impact on the way letters in the two visual fields are processed, with an advantage for letters that are directly sent to the language dominant hemisphere. This result was found even for foveally fixated words, which supports our interpretation that foveally displayed stimuli are split between the two hemispheres and benefit differently from higher level processes.
3.2.
Interhemispheric transfer
We found that visual information is transferred from the contralateral to the ipsilateral hemisphere with a delay of 26 to 42 ms in control participants and 40 to 42 ms in participants with developmental dyslexia. Note that previous studies have estimated IHTT to about 10–15 ms (Barnett and Corballis, 2005; Brown et al., 1998, 1994; Murray et al., 2001; Saron and Davidson, 1989). The longer delays observed in our experiment may be explained by the fact that our stimuli were letter strings, which are substantially more complex than basic visual stimuli typically used to measure IHTT (e.g., checkerboards or flashes). In addition, the delays measured here are consistent with the argument put forward by Ono et al. (2002) that interhemispheric transfer should theoretically exceed axonal conduction time, which is estimated to take about 20 ms. Several studies in healthy participants have shown that right-to-left interhemispheric transfer is typically faster than left-to-right transfer (Brown and Jeeves, 1993; Brown et al.,
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1994; Ipata et al., 1997; Marzi et al., 1991; Saron and Davidson, 1989). Three main explanations have been proposed to account for this asymmetry: (i) according to Marzi et al. (1991) the difference could relate to callosal connection asymmetries, with a greater number of neurons projecting from the right to the left hemisphere than vice versa (see also Saron and Davidson, 1989). (ii) A second possibility is that IHTT is systematically shorter when information is transferred from the non-dominant to the dominant hemisphere — in the present case, the left hemisphere (Nowicka et al., 1996). However, a meta-analysis of ERP studies by Brown et al. (1994) did not support this claim. (iii) Third, Miller (1996) argued that the right hemisphere has more myelinated axons enabling rapid conduction and processing of gestalt representations. In line with Miller (1996), Barnett and Corballis (2005) and Barnett and Kirk (2005) suggested that the presence of faster conducting cortico-cortical myelinated axons in the right hemisphere increases the likelihood of neural summation in that hemisphere, and therefore the speed of right-toleft relative to left-to-right transfer. While IHTT measures in control participants replicated this asymmetry, our results do not enable us to validate or invalidate any of the three hypothetical explanations above.
3.3. Comparison of control participants and participants with developmental dyslexia Even though the ANOVA on P1 latencies revealed a significant group effect, the group × letter position × electrode interaction was not significant. Therefore, interpretations arising from comparisons between groups must be cautious. The P1 peaked significantly earlier in control participants than in participants with developmental dyslexia, indicating that differences can be found in early visual processing stages. IHTT was also marginally faster in control participants than in participants with developmental dyslexia and this difference was driven by the shorter right-to-left IHTT in control participants. Since our finding is only a marginally significant effect, it is not wholly inconsistent with results reported by other investigators (e.g., Davidson et al., 1990), who showed no overall IHTT group differences. In our study, the IHTT difference between control participants and participants with developmental dyslexia was smaller than 11 ms. Such a small difference might have been undetectable with the sample size of Davidson et al. (1990). The absence of asymmetry between left-to-right and right-to-left IHTT in participants with developmental dyslexia may indicate a reduction of white matter density in the left hemisphere, as hypothesized by several authors (Beaulieu et al., 2005; Deutsch et al., 2005; Silani et al., 2005). It is worth noting that global abnormalities of the corpus callosum – which have been described in participants with developmental dyslexia (Duara et al., 1991) – cannot account for the present findings because the difference between control participants and participants with developmental dyslexia was only observed for transfers to the language dominant hemisphere, and not the non-dominant hemisphere. In line with Barnett and Corballis (2005) and Barnett and Kirk (2005), we hypothesize that abnormalities of the myelinated pathways in the right hemisphere of participants with developmental dyslexia might account for the slower right-to-left transfer time. Recall
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though that neuroimaging studies have identified abnormalities in the left rather than the right hemisphere in developmental dyslexia (Beaulieu et al., 2005; Deutsch et al., 2005; Silani et al., 2005). Even though hemispheric dominance does not necessarily play a role in the asymmetry of IHTT in control participants, the atypical hemispheric dominance for language processing often reported in developmental dyslexia may relate to the lack of IHTT asymmetry reported here (see for instance Eckert et al., 2003, 2005; Giraud et al., 2005; McCrory et al., 2000; Simos et al., 2000, 2002). Together with P1 peak latency results, the present findings suggest that early visual processing stages are affected in developmental dyslexia and may thus contribute to associated reading difficulties.
4.
Fig. 5 – Experimental design of the forced two-choice task (adapted version of the Reicher–Wheeler paradigm).
Experimental procedure
The experimental procedure of this paper is largely based on Martin et al. (2006) where additional details can be found.
consonant strings (e.g., ‘PGSRF’). Both words and non-words were 5 letters long.
4.3. 4.1.
Ten control participants (5 females and 5 males; mean age 24.8 ± 1.6 years) and ten participants with developmental dyslexia (2 females and 8 males; mean age 25.2 ± 6.3) gave informed consent to participate in the experiment that was approved by a local ethics committee. They were all righthanded native speakers of French, with normal global IQ (assessed using the WAIS-R), and normal or corrected-to-normal vision. We recruited volunteers who had been diagnosed as dyslexic and had documented histories of reading and spelling difficulties. Participants were tested with tasks designed to assess reading accuracy and speed, and phonological processing: reading of words and non-words, rapid digit naming and phoneme awareness tasks (phoneme deletion, rhyme judgment and spoonerisms; see Table 1).
4.2.
Task and procedure
Participants
Stimuli
Stimuli were 40 French nouns selected from the Brulex database (Content and Radeau, 1990) and 40 unpronounceable
Table 1 – Participant performance in intelligence, reading and phonological tasks in the two groups of participants Control participants Full IQ Verbal IQ Performance IQ Word reading Non-word reading Rapid digit naming Phoneme deletion Rhyme judgment Spoonerisms
118 119 111 533 660 15 99 1220 96
(11) (12) (13) (74) (140) (2) (19) (256) (32)
Dyslexic participants 109 109 107 740 919 22 241 1826 229
(8) (13) (14) (78) (114) (4) (49) (166) (79)
P n.s. n.s. n.s. ⁎ ⁎ ⁎ ⁎⁎ ⁎⁎ ⁎⁎
Standard deviations are shown in parentheses. Average scores and standard deviations refer to marks from normalized neuropsychological tests. n.s.=Not significant; ⁎Pb 0.05; ⁎⁎Pb 0.01.
At the beginning of each trial, a fixation cross was displayed at the center of a computer screen for 2.5 s. The fixation then disappeared for 50 ms before a second fixation appeared for 200 ms. After a second pause of 50 ms, a stimulus (word or nonword) was displayed for either 50 or 66 ms in “Times New Roman” lower case, font size 28. The stimulus position was varied laterally so that each of the five letters in a string could coincide with fixation. The amount of visual information therefore fluctuated across foveal and parafoveal fields between the experimental conditions (Fig. 1). Stimuli subtended 6.65° of visual angle, at a distance of 60 cm. After 50 or 66 ms, the stimulus was replaced by a string made of 7 upper case Xs masking the entire stimulus string (mask) and two probe letters, one above and one below the mask. Subjects had to indicate which of the two probe letters was the letter previously presented at fixation (target letter), by pressing the top or bottom button of a response pad (Fig. 4). The mask and the two probe letters remained on the screen until the response. To increase statistical power, each of the 40 words and non-words were presented 6 times in the 50-ms condition and 6 times in the 66-ms condition. Overall, the 960 trials were pseudo-randomly distributed in 12 blocks of 80, with each item displayed only once per block. Exposure durations (50 and 66 ms) and stimulus categories (words and non-words) were randomized within each block. Block order and response side were counterbalanced across participants (Fig. 5).
4.4.
ERP acquisition and processing
Participants were comfortably seated in a quiet room and asked to refrain from moving and blinking. Electrophysiological data were recorded from 64 Ag/AgCl electrodes (placed according to the extended International 10–20 system) at a sampling rate of 500 Hz, using SynAmps™ amplifiers (Neuroscan™, El Paso, TX, USA). The electrooculogram was recorded using supraorbital and infraorbital electrodes connected to a bipolar channel. Signals were filtered on-line between 0.1 and 100 Hz. Impedances were kept below 20 kΩ. Continuous recordings were digitally band-pass filtered off-
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line in the interval [1–40] Hz. Eye-blink artefacts were mathematically corrected and remaining artefacts manually dismissed. Epochs ranged from −100 to 1000 ms after the onset of the five letter string (word or non-word). Signal variations exceeding ±75 μm anywhere in the analysis window and on any of the channels except the vertical electrooculogram (VEOG) were automatically dismissed. After baseline correction relative to pre-stimulus activity and rejection of errors, there were at least 48 epochs per condition in all participants. Individual difference waveforms and grand-average waveforms were then derived from individual ERPs.
4.5.
Statistical analysis
P1 peak search was confined to the interval 70–130 ms based on the corresponding period of maximal activity in the Mean Global Field Power over the 64 electrodes (Picton et al., 2000). P1 latencies at the electrodes of maximal sensitivity were analyzed using an analysis of variance (ANOVA) with group (control vs. participants with developmental dyslexia) as intersubject variable and electrode (over left and right occipitotemporal regions) and letter position (LP1 to LP5) as intra-subject variables. An “extended” Independent Component Analysis (ICA), using the infomax algorithm (Makeig et al., 1997), was jointly applied to the appended averages of all experimental conditions in order to separate generators associated with different topographies. Visual inspection of both temporal and topographic representations of the ICA matrices allowed identification of one component best accounting for the P1 (see Results). The activity produced by the component was then subtracted from individual ERPs in all conditions in 8 of the 10 control participants and ERP mean amplitudes were compared in the P1 range between conditions (Klein and Feige, 2005). Two participants who failed to display a clear component following ICA were excluded from this analysis.
Acknowledgments The authors wish to thank Yves Paulignan, Chantal Blanchard and Nicolas Chauveau for the useful discussions and technical assistance.
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