Brain Research Unit, Low Temperature Laboratory, Helsinki University of Technology, Espoo, Finland, , 1 Psychology Department, Newcastle University, Newcastle upon Tyne, UK and , 2 Physiology Department, Oxford University, Oxford, UK
![]() |
Abstract |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
![]() |
Introduction |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Reading is assumed to proceed through several stages, the exact nature and relative spatiotemporal cortical organization of which are still unsettled. Visual word recognition begins with feature analysis. Thereafter fluent reading has been suggested to continue via serial or parallel analysis of the letters of a word to activation of the whole visual word form from the mental lexicon. Finally word meaning is accessed from the semantic store (Coltheart, 1978; McClelland, 1987
; Monsell, 1987
; Coltheart et al., 1993
).
Only techniques measuring neural electromagnetic events directly, like electroencephalography (EEG) or magnetoencephalography (MEG), can follow the flow of information in the cortex with millisecond resolution. Unlike electric potentials, magnetic fields penetrate the skull and scalp undistorted. Localization of cortical signals associated even with complex cognitive tasks can thus be accomplished totally non-invasively using MEG. According to MEG and intracranial event-related potential (ERP) recordings, the earliest letter-string-specific responses occur in the inferior occipito-temporal areas within 140200 ms after stimulus onset (Halgren et al., 1994; Nobre et al., 1994
; Tarkiainen et al., 1999
). As the signals in the left occipito-temporal area have been reported to be equally strong to words and pseudowords (Salmelin et al., 1996
), this early activation probably reflects pre-lexical processing. Around 400 ms after word onset, scalp-recorded ERPs are sensitive to the semantic appropriateness of a word in sentence context (Kutas and Hillyard, 1980
). According to MEG measurements, written word and sentence comprehension recruits neuronal populations predominantly in the left superior temporal cortex between 250 and 600 ms after word onset in fluent readers (Helenius et al., 1998
).
Several studies have reported differences between fluent and dyslexic readers in the ERPs around 400 ms after word presentation (Stelmack et al., 1988; Stelmack and Miles, 1990
; Brandeis et al., 1994
). Further, in a recent MEG study the beginning of responses reflecting semantic analysis of inappropriate sentence-final words was delayed in dyslexic individuals, thus indicating problems already in the preceding stages (Helenius et al., 1999
). This finding is in agreement with earlier results of Salmelin et al. (Salmelin et al., 1996
) who discovered that while in fluent readers the left occipito-temporal area was activated around 180 ms after presentation of isolated words and pseudowords, this area remained silent or was activated much later in dyslexic adults. Only one study has looked specifically at the early visual responses evoked during reading in dyslexic subjects. ERPs around 100 ms after word onset were abnormally weak in dyslexic children during reading (Brandeis et al., 1994
), but as localization of the responses was not attempted the full nature of these differences is unknown.
In a recent MEG study of early processes of reading, Tarkiainen et al. (Tarkiainen et al., 1999) succeeded in dissociating visual feature analysis and letter-string-specific processing in fluent adult readers in both space and time. The stimuli consisted of words, syllables and letters as well as equally long symbol-strings which were presented on a grey background or were degraded by variable addition of Gaussian noise. Activation in the postero-medial extrastriate areas around 100 ms after stimulus onset increased with increasing noise levels, i.e. with increasing local luminance contrasts in the image, suggesting involvement in visual feature analysis. A totally different pattern of activation was detected in the left occipito-temporal area, where activation was delayed or decreased with increasing noise level, i.e. with decreasing visibility of the word, around 150 ms after word onset. Further, this lateral occipital area displayed clear letter-string preference since activation elicited by equally long symbol-strings was significantly delayed or was weaker compared with that for words or syllables. In the present study the same words and symbol-strings were used as stimuli to elucidate the functional organization of early stages of visual word recognition in dyslexic subjects.
![]() |
Materials and Methods |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
We studied ten adults with a history of developmental dyslexia (four females, six males; age 2236 years, mean 30 years). These inviduals had been diagnosed during school years by a psychologist, a speech therapist or a specialist teacher. All subjects had finished the minimum of 2 years of formal education following comprehensive school (9 years in Finland), indicating that their reading problems were not due to inferior intellectual capacity. There was a family history of reading problems in six cases (affected parent or sibling). One of the dyslexic subjects was left-handed. Dyslexic subjects were tested with a concise behavioural test battery consisting of verbal working memory, naming and reading tasks known to be sensitive for dyslexia (Denckla and Rudel, 1976; Jorm, 1983
; Scarborough, 1984
; Wolf, 1986
; Wolf and Obregon, 1992
; Korhonen, 1995
).
As a group, the dyslexics performed significantly worse than 20 age-matched control subjects in these behavioural tasks [F(1,27) = 37.1, P < 0.0001]. Planned contrasts revealed statistically significant differences between groups in digit span forwards (5.4 ± 0.4 in dyslexic and 6.4 ± 0.2 in control subjects; mean ± SEM, P < 0.04) and backwards (4.3 ± 0.5 and 5.7 ± 0.4, P < 0.03) (Wechsler, 1955). Also naming speed of both colours (648 ± 29 and 472 ± 28 ms/item, P < 0.0001), and colours, digits and numbers (701 ± 37 and 532 ± 19 ms, P < 0.0001) presented in a matrix was slower in dyslexic compared with control subjects, as well as the oral reading speed of a narrative (671 ± 52 and 391 ± 10 ms/word, P < 0.0001). The dyslexic subjects were also markedly slow in detecting real Finnish words in a computerized lexical decision task compared with control group (1202 ± 192 and 554 ± 22 ms, P < 0.0001). In each dyslexic individual either the oral reading or word recognition speed was at least two standard deviations slower than the mean of the control group.
The MEG data from 12 normal right-handed subjects (four females, eight males; age 21 42 years, mean 29 years), reported independently by Tarkiainen et al. (Tarkiainen et al., 1999), are the basis for the following comparisons between dyslexic and control subjects. Anatomical magnetic resonance (MR) images were available for all control and three dyslexic subjects. Informed consent was obtained from all subjects.
Stimuli in the MEG experiment
The stimuli comprised Gaussian noise patches (noise levels 0, 8, 16, 24), dark grey words presented in variable Gaussian noise (noise levels 0, 8, 16, 24), or symbol-strings (noise level 0) (see Fig. 1). The size of the noise patches was 5° x 2°. In the noise level 0 the patches were evenly grey. When noise was added, the luminance of each pixel was allowed to vary along the black-to-white scale at three probability levels (noise levels 8, 16, 24). Different noise levels in stimulus images were obtained by changing the grey level value of each pixel randomly. The amount of change was picked from a Gaussian distribution with zero mean and standard deviation corresponding to the applied noise level (8, 16, 24). If the new grey level value fell outside the possible range 063, the procedure was repeated for that pixel. Effectively this manipulation increased the local luminance contrast in the noise patches with increasing noise level, although the overall luminance remained essentially constant.
|
Procedure in the MEG experiment
Measurements were conducted in a magnetically shielded room. The stimuli were projected on a screen at a distance of about 1 m from the subject. The stimuli were presented once every 2 s and the display duration was 60 ms. Each subject was studied during a single day in two 2030 min sessions separated by a break. During one session subjects were shown words (noise levels 024) or symbol-strings (noise level 0). In the other session only noise patches (noise levels 024) were presented. The order of the two sessions was alternated between successive subjects. Within sessions the order of the noise levels was randomized. Moreover, we ensured that, in the session including words, the same word was not repeated within 30 s to minimize repetition effects. Subjects were asked to pay attention to the stimuli and in the word session they were instructed to say aloud the word they had just seen when prompted by a question mark (1.5 % probability).
MEG Recordings and Analysis
MEG detects magnetic fields associated with synchronous activation of thousands of nerve cells, non-invasively outside of the head. The main contribution to MEG signals arises from the fissural cortex (Hämäläinen et al., 1993). The MEG measurements in the present study were conducted using the Neuromag-122TM whole head system (Ahonen et al., 1993
). Neuromagnetic signals were averaged on-line from 200 ms before stimulus presentation to 800 ms after. Signals were bandpass filtered to 0.03120 Hz and sampled at 0.4 kHz. Both horizontal and vertical eye movements were recorded on-line (bandpass 0.03100 Hz) and epochs contaminated by eye or lid movements were rejected. For each stimulus category, at least 70 artefact-free responses were gathered.
Individually for each subject, the neuromagnetic signals detected by the 122 channels were reduced into time behaviour of distinct brain areas using equivalent current dipole (ECD) analysis (Hämäläinen et al., 1993). ECD represents the centre and strength of activation in a given brain area and the orientation of current flow therein. The magnetic field patterns were visually inspected to identify local dipolar fields that were not distorted by simultaneous activation in nearby cortical areas. Isolated dipoles were calculated from signals in those channels that covered the field pattern. Thereafter, all individually determined dipoles (on average 10 in both control and dyslexic subjects) were incorporated into a multidipole model. Dipole locations and orientations were fixed, but their strengths were allowed to vary in time in order to achieve optimal fit to the measured signals. To focus on the early reading related activation, multidipole modelling was carried out for the first 300 ms after stimulus onset using only the dipoles predominantly active during this period. Analysis was conducted using a spherical head model emphasizing best agreement with the shape of the skull over the occipital areas. In a separate modelling all localized dipoles were used to explain data for the whole averaging period. In this analysis we used a head model with the best description of the skull curvature over the temporal areas. In those seven dyslexics for whom magnetic resonance images (MRIs) were not available we used average head models derived from female and male brains. The locations of three head position indicator coils attached to the subject's head were determined with respect to the measurement helmet in the beginning of the session. The location of these coils with respect to nasion and reference points anterior to ear canals was further used to align functional MEG and anatomical MRI data.
Detection of Activation Related to Visual Feature and Letter-string Analysis
Based on the criterion used in Tarkiainen et al. (Tarkiainen et al., 1999), a dipole was judged to be involved in visual feature analysis if its strength systematically increased with noise level (noise level 0
noise level 8
noise level 16
noise level 24) in the condition were noise patches were shown without embedded words/symbols. We required that the peak activation was statistically significantly stronger for the patches with highest level (24) of Gaussian noise than for patches with zero level of noise (evenly grey patches), i.e. the difference had to exceed 1.96 times the standard deviation in the pre-stimulus period (200 ms to stimulus onset). In the study by Tarkiainen et al. (Tarkiainen et al., 1999
) only the most robust sources were reported sources that displayed statistically significant differences already between noise levels 0 and 8.
The same criteria as in Tarkiainen et al. (Tarkiainen et al., 1999) were used for determining whether dipole activity reflected preference to letter-string stimuli. Firstly, only those sources were accepted which showed either significantly stronger or earlier response to words than to symbol-strings. This condition was fulfilled if the peak response was either statistically significantly stronger to non-degraded words (noise 0) than to symbol-strings, i.e. the difference exceeded 1.96 times the standard deviation in the pre-stimulus period (200 ms to stimulus onset), or if the peak latency of the response to the symbol-strings was delayed with respect to non-degraded words by at least 5 ms (twice the minimum time resolution defined by the sampling rate). Secondly, peak activation had to be reduced for the most degraded words or systematically delayed as the words became harder to recognize, i.e. peak response was either statistically significantly stronger to non-degraded words (noise 0) than to the most heavily degraded words (noise 24), or the peak latency of the response to the most degraded words was delayed with respect to non-degraded words by at least 5 ms. If the latency criterion was used, the response amplitude to the most degraded words was still not allowed to exceed that to non-degraded words.
![]() |
Results |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Figure 2A displays all the activated brain areas in control and dyslexic subjects that were involved in visual feature analysis up to 130 ms after stimulus presentation. In these source areas the responses to the noisiest patches were significantly stronger than those to the noiseless patches. This activation pattern was detected in nine control subjects (Tarkiainen et al., 1999
) and in seven dyslexic subjects. The overall number of feature analysis sources was 21 in control and 11 in dyslexic subjects. On average dyslexic subjects had 1.1 and control subjects 1.8 sources (MannWhitney U-test, non-significant); one control subject had as many as five sources of this kind. Activation was mainly distributed along the ventral visual stream, bordering on V1 area and extending laterally as far as V4v. Figure 2B
displays the average time behaviour of all these visual sources to easily detectable (noise 0) and the most degraded words (noise 24) and to symbol-strings. Activation peaked around 100 ms after stimulus presentation and the source strength increased with increase of noise level, i.e. with local luminance contrasts.
|
The main effect of subject group on response strengths across all stimulus types also failed to reach statistical significance regardless of whether the analysis was done across the sources that peaked first in each subject (8.3 ± 2.1 nAm for control and 7.5 ± 1.1 nAm for dyslexic subjects in response to non-degraded words) or across the mean source strengths in each subject (8.6 ± 1.0 nAm for control and 6.8 ± 1.0 nAm for dyslexic subjects in response to non-degraded words). Even when all the sources were treated as independent observations, a significant between groups difference failed to emerge (see the histograms of Fig. 2C). It should further be noted that the mean response strengths of the control group were slightly biased by the data of two subjects, who showed more than two standard deviations stronger responses for all stimuli than the rest of the control group; exclusion of these two subjects would have reduced the mean source strength for non-degraded words from 8.4 to 7.0 nAm in Figure 2C
. Noise increased the response strengths both in control [F(3,60) = 18.3, P < 0.0001] and dyslexic subjects [F(3,27) = 3.8, P < 0.04], as revealed by repeated-measures univariate analysis of variance performed across all visual feature analysis sources (within-subjects factor word type; words in noise levels 0, 8, 16 and 24). Thus, we were not able to detect any systematic differences in the amount, latency or strength of cortical sources related to visual feature analysis between fluent and dyslexic readers.
Activation Associated with Letter-string Processing
Figure 3A shows all the activated brain areas specifically associated with letter-string processing up to 180 ms after word presentation. In control subjects the activation was clearly left lateralized, concentrating in the left inferior occipito-temporal area. Ten of the 12 subjects showed activation in this area, modulated by the visibility of the word (Tarkiainen et al., 1999
). One control subject had a letter-string-specific source exclusively in the right occipital cortex and one control did not have any source that would have shown reliable modulation according to visibility or word-likeness of the stimuli. In sharp contrast with fluent adult readers, only two dyslexic subjects had letterstring-specific activation in this area (dashed rectangle) (Fisher's exact probability test, P < 0.008). Two dyslexic subjects also had activation more medially in the left occipital area, but more than two standard deviations posterior to the mean source location of the control subjects. Further, one dyslexic subject had two letterstring-specific sources in the right occipital area.
|
The strength of the letter-string-specific sources could not be compared between subject groups using the mean source strengths of each individual as observations due to the small number of sources detected in dyslexic subjects. When all sources were treated as independent observations, the overall strength of the letter-string specific activation was significantly weaker in dyslexic than in control subjects [repeated-measures analysis of variance with stimulus type as within-subjects factor and subject group as between-subjects factor; F(1,19) = 5.4, P < 0.03] (see Fig. 3C). Planned comparisons revealed that this difference was statistically significant for non-degraded (P < 0.009) and moderately degraded words (noise levels 8 and 16, P < 0.02). Thus, the earliest cortical activation related specifically to letter-string processing was abnormal in dyslexic subjects.
Late Letter-string-specific Responses in the Anterior Brain Regions
Degradation also affected the processing stages involved in reading beyond orthographic (letter) analysis. Figure 4A displays all sources that were statistically significantly earlier or stronger to non-degraded words than to the most heavily degraded words and symbol-strings in the anterior brain regions. In both control and dyslexic subjects these letter-string-specific sources were extremely variable both in time behaviour and location. Most consistently, letter-string-specific sources were found in the left superior temporal cortex, where nine sources were detected in 8/12 control subjects (Tarkiainen et al., 1999
). Similarly, in the dyslexic subjects the highest absolute number of sources was detected in the left superior temporal region (five sources in five dyslexic subjects). In half of the dyslexic subjects temporal letter-string-specific sources were, however, missing.
|
![]() |
Discussion |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Dissociating Between Object-specific and Non-specific Visual Processing
Haemodynamic and intracranial studies of object and face processing have used object degradation to detect functional specialization within the ventral stream. Malach et al. (Malach et al., 1995) decreased the detectability of objects and faces by adding visual noise to pictures. This manipulation, which was similar to the one used in the present study, decreased the strength of activation in the lateral occipital area. The opposite behaviour was observed in the V1 area where the activation increased with increasing noise level. Scrambled objects or texture patterns also produce more activation than nonscrambled objects or faces in the visual areas V1, V2 and V3 (Puce et al., 1996
; Grill-Spector et al., 1998
). Using intracranial recordings in epileptic patients, Allison et al. (Allison et al., 1994
) reported a stronger response around 120 ms to scrambled faces than to clearly identifiable faces in an electrode placed on the surface of the occipital pole. Both noise and scrambling increase the number of local luminance-contrast borders, i.e the amount of visual features. Puce et al. (Puce et al., 1996
) have suggested that the activation along the ventral stream in areas V2, VP and V4 is to be considered an intermediate step between object processing in the primary visual and object-specific visual areas.
The study of Tarkiainen et al. (Tarkiainen et al., 1999) is in accordance with previous imaging studies with respect to location and timing of visual feature analysis. In the present study, we could detect no differences between fluent and dyslexic readers at this non-specific level of visual object analysis. It should be noted, however, that our approach provided only a crude overall view to visual cortical activation related to feature analysis. As knowledge of human visual areas is rapidly increasing, more detailed mapping of the functional properties of visual areas in dyslexia is clearly warranted in the future.
Processes Affected by the Visibility of the Word
As reported by Tarkiainen et al. (Tarkiainen et al., 1999), the visibility and word-likeness of the stimulus affected activation in the left inferior occipito-temporal cortex in fluent adult readers activation in this area was earlier or stronger for both syllables and words than for symbol-strings. Thus, this activation likely reflects pre-lexical, orthographic analysis. The letter-stringspecific responses in the left occipito-temporal area of the fluent readers are in line with previous imaging studies with respect to location (Puce et al., 1996
) and timing (Nobre et al., 1994
). In the dyslexic group, only two individuals showed letter-stringspecific activation in the left inferior occipito-temporal cortex. This finding agrees with earlier MEG (Salmelin et al., 1996
; Helenius et al., 1999
) and haemodynamic (Rumsey et al., 1997
; Shaywitz et al., 1998
) studies of dyslexic adults showing delayed and/or reduced activation of occipito-temporal areas during reading. Thus, in contrast to the essentially identical activation with respect to visual feature analysis in dyslexic and fluent readers, the earliest cortical activation related specifically to letter-string processing was abnormal in dyslexic subjects. Lesion compromising left inferior occipital areas leads to selective impairment of reading in the absence of obvious difficulties with spoken language, i.e. pure alexia (Damasio and Damasio, 1983
; Henderson, 1986
). When patients with pure alexia retain residual word recognition skills, reading proceeds laboriously in letter-by-letter fashion. Thus, studies on both acquired and developmental dyslexics suggests an important role for the left occipito-temporal area in fluent automated reading.
The naming speed of written words decreases when words are visually degraded (Meyer et al., 1975; Tarkiainen et al., 1999
). Thus, not only is orthographic processing affected, but all processes beyond this stage that are involved in the production of oral output. Effectively, stimulus degradation can help to reveal activation directly linked to word recognition, although the exact nature of activation beyond the pre-lexical stage cannot be determined using the present stimuli. Both fluent and dyslexic readers showed activation in the left superior temporal area that was influenced by the visibility of the words. In control subjects, the temporal activation could reflect semantic processing (Helenius et al., 1998
). In dyslexic subjects, however, the degradation-dependent activation was either undetectable (five subjects) or abnormally early (four subjects). The reduced activation of temporal areas is in accordance with two previous MEG (Salmelin et al., 1996
; Helenius et al., 1998
) and haemodynamic (Rumsey et al., 1997
; Shaywitz et al., 1998
) studies. The abnormally early activation latency is not readily interpretable based on the previous studies. However, as the early temporal activation was associated with undetectable letter-string-specific occipito-temporal signals, temporal activation might have a compensatory role in dyslexic readers. Further, it is worth noting that, unlike fluent readers, who seem to have essentially the same pattern of cortical activation across different reading related MEG studies (single words, sentences), in dyslexic readers the pattern of activation can vary substantially across studies (Salmelin et al., 1996
; Helenius et al., 1999
). This could indicate unstable and unautomated processing of written words in dyslexic readers, who may be utilizing a multitude of individual and task-dependent strategies in order to aid visual word recognition.
Causes of the Defective Pre-lexical Word Analysis in the Dyslexic Brain
In the evolution of human species reading has appeared relatively recently and, thus, cortical areas specialized in processing written language are likely to emerge through environmental exposure. Consequently, any processing deficit that hampers the acquisition of fluent reading could result in abnormal reading-related visual activation. For example, poor phonological awareness could prevent the association between sounds and corresponding visual entities both at the behavioural level (Frith, 1985) and in the brain (Salmelin et al., 1996
; Helenius et al., 1999
) and, thus, impede the functional maturation of a word/ letter-string-specific visual area. But visual problems cannot be excluded either. The ventral visual stream is crucial for object and word recognition, and it is reciprocally connected with the dorsal stream involved in processing spatial information and motion (Ungerleider and Mishkin, 1982
; Felleman and Van Essen, 1991
; Ungerleider, 1995
). Magnocellular visual functioning, dominating in parts of the dorsal stream (Maunsell et al., 1990
), has been suggested to have an important role in reading, especially for encoding letter positions within words (Cornelissen et al., 1998
). Accordingly, deficient magnocellular functioning in dyslexic individuals (Lovegrove et al., 1986
; Livingstone et al., 1991
; Cornelissen et al., 1995
; Stein and Walsh, 1997
; Demb et al., 1998
) could be manifested as impaired letter position encoding during reading. This again could impede tuning of letter-string-specific neuronal populations. Similarly, a deficit in attention-related functions [for review, see Stein and Walsh (Stein and Walsh, 1997
)] or poor integration of visual and phonological codes could be reflected in functional tuning of letter-string-specific areas. Unlike the causal relation between phonological problems and dyslexia (Bradley and Bryant, 1983
), the contribution of other factors to poor reading is, however, still mostly hypothetical. In the future, it would be of great importance to be able to accurately characterize cortical dynamics of reading acquisition, and its dependence on phonological or visual processing in individual brains, in order to clarify the mystery of poor reading in developmental dyslexia.
![]() |
Notes |
---|
Address correspondence to Päivi Helenius, Low Temperature Laboratory, Helsinki University of Technology, PO Box 2200, FIN-02015 HUT, Espoo, Finland. Email: paivi{at}neuro.hut.fi.
![]() |
References |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Allison T, McCarthy G, Nobre A, Puce A, Belger A (1994) Human extrastriate visual cortex and the perception of faces, words, numbers, and colors. Cereb Cortex 5:544554.
Bradley L, Bryant PE (1983) Categorizing sounds and learning to read a causal connection. Nature 301:419421.[ISI]
Brandeis D, Vitacco D, Steinhausen H-C (1994) Mapping brain electric micro-states in dyslexic children during reading. Acta Paedopsychiat 56:239247.
Coltheart M (1978) Lexical access in simple reading tasks. In: Strategies of information processing (Underwood G, ed.), pp. 151216. New York: Academic Press.
Coltheart M, Curtis B, Atkins P, Haller M (1993) Models of reading aloud: dual-route and parallel-distributed-processing approaches. Psychol Rev 100:589608.[ISI]
Cornelissen P, Richardson A, Mason A, Fowler S, Stein J (1995) Contrast sensitivity and coherent motion detection measured at photic luminance levels in dyslexics and controls. Vis Res 35:14831494.[ISI][Medline]
Cornelissen PL, Hansen PC, Hutton JL, Evangelinou V, Stein JF (1998) Magnocellular visual function and children's single word reading. Vis Res 38:471482.[ISI][Medline]
Damasio AR, Damasio H (1983) The anatomic basis of pure alexia. Neurology 33:15731583.[Abstract]
Demb JB, Boynton GM, Heeger DJ (1998) Functional magnetic resonance imaging of early visual pathway in dyslexia. J Neurosci 18:69396951.
Denckla MB, Rudel RG (1976) Rapid `automatized' naming (R.A.N.): dyslexia differentiated from other learning disabilities. Neuropsychologia 14:471479.[ISI][Medline]
Felleman DJ, Van Essen DC (1991) Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex 1:147.[Abstract]
Flowers DL (1993) Brain basis for dyslexia: a summary of work in progress. J Learn Disabil 26:575582.[ISI][Medline]
Frith U (1985) Beneath the surface of developmental dyslexia. In: Surface dyslexia: neuropsychological and cognitive studies of phonological reading (Patterson KE, Marshall JC, Coltheart M, eds), pp. 301330. London: Lawrence Erlbaum.
Grill-Spector K, Kushnir T, Hendler T, Edelman S, Itzchak Y, Malach R (1998) A sequence of object-processing stages revealed by fMRI in the human occipital lobe. Hum Brain Map 6:316328.[ISI][Medline]
Halgren E, Baudena P, Heit G, Clarke M, Marinkovic K (1994) Spatiotemporal stages in face and word processing. 1. Depth-recorded potentials in the human occipital, temporal and parietal lobes. J Physiol 88:150.
Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV (1993) Magnetoencephalography theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys 65:413497.[ISI]
Helenius P, Salmelin R, Service E, Connolly JF (1998) Distinct time courses of word and context comprehension in the left temporal cortex. Brain 121:11331142.[Abstract]
Helenius P, Salmelin R, Service E, Connolly JF (1999) Semantic cortical activation in dyslexic readers. J Cogn Neurosci (in press).
Henderson VW (1986) Anatomy of posterior pathways in reading: a reassessment. Brain Lang 29:119133.[ISI][Medline]
Jorm AF (1983) Specific reading retardation and working memory: a review. Br J Psychol 74:311342.[ISI][Medline]
Korhonen TT (1995) The persistence of rapid naming problems in children with reading disabilities: a nine-year follow-up. J Learn Disabil 28:232239.[ISI][Medline]
Kutas M, Hillyard SA (1980) Reading senseless sentences: brain potentials reflect semantic incongruity. Science 207:203205.[ISI][Medline]
Landerl K, Wimmer H, Frith U (1997) The impact of orthographic consistency on dyslexia: a GermanEnglish comparison. Cognition 63:315334.[ISI][Medline]
Livingstone MS, Rosen GD, Drislane FW, Galaburda AM (1991) Physiological and anatomical evidence for a magnocellular defect in developmental dyslexia. Proc Natl Acad Sci USA 88:79437947.[Abstract]
Lovegrove W, Martin F, Slaghuis W (1986) A theoretical and experimental case for a visual deficit in specific reading disability. Cogn Neuropsychol 3:225267.[ISI]
Lundberg I, Olofsson A, Wall S (1980) Reading and spelling skills in the first school years predicted from phonemic awareness skills in kindergarten. Scand J Psychol 21:159173.[ISI]
Malach R, Reppas JB, Benson RR, Kwong KK, Jiang H, Kennedy WA, Ledden PJ, Brady TJ, Rosen BR, Tootell RBH (1995) Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex. Proc Natl Acad Sci USA 92:81358139.[Abstract]
Maunsell JHR, Nealey TA, DePriest DD (1990) Magnocellular and parvocellular contributions to responses in the middle temporal visual area (MT) of the macaque monkey. J Neurosci 10:33233334.[Abstract]
McClelland JL (1987) The case for interactionism in language processing. In: Attention and performance XII: The psychology of reading (Coltheart M, ed.), pp. 336. Hove: Lawrence Erlbaum.
Meyer DE, Schvaneveldt RW, Ruddy MG (1975) Loci of contextual effects on visual word-recognition. In: Attention and performance V (Rabbitt PMA, Dornic S, eds), pp. 98118. London: Academic Press.
Monsell S (1987) Nonvisual orthographic processing and the orthographic input lexicon. In: Attention and performance XII: The psychology of reading (Coltheart M, ed.), pp. 299323. Hove: Lawrence Erlbaum.
Muter V, Hulme C, Snowling M, Taylor S (1997) Segmentation, not rhyming, predicts early progress in learning to read. J Exp Child Psychol 65:370396.[ISI][Medline]
Nobre AC, Allison T, McCarthy G (1994) Word recognition in the human inferior temporal lobe. Nature 372:260263.[ISI][Medline]
Puce A, Allison T, Asgari M, Gore JC, McCarthy G (1996) Differential sensitivity of human visual cortex to faces, letterstrings, and textures: a functional magnetic resonance imaging study. J Neurosci 16:52055215.
Rack JP, Snowling MJ, Olson R (1992) The nonword reading deficit in developmental dyslexia: a review. Reading Res Q 27:2953.
Rumsey JM (1996) Developmental dyslexia: anatomical and functional neuroimaging. Ment Retard Devel Disabil Res Rev 2:2838.
Rumsey JM, Nace K, Donohue B, Wise D, Maisog J, Andreason P (1997) A positron emission tomographic study of impaired word recognition and phonological processing in dyslexic men. Arch Neurol 54: 562573.[Abstract]
Salmelin R, Service E, Kiesilä P, Uutela K, Salonen O (1996) Impaired visual word processing in dyslexia revealed with magnetoencephalography. Ann Neurol 40:157162.[ISI][Medline]
Scarborough HS (1984) Continuity between childhood dyslexia and adult reading. Br J Psychol 75:329348.[ISI][Medline]
Shankweiler D, Crain S, Katz L, Fowler AE, Liberman AM, Brady SA, Thornton R, Lundquist E, Dreyer L, Fletcher JM, Stuebing KK, Shaywitz SE, Shaywitz BA (1995) Cognitive profiles of readingdisabled children: comparison of language skills in phonology, morphology, and syntax. Psychol Sci 6:149156.[ISI]
Shaywitz SE, Shaywitz BA, Pugh KR, Fulbright RK, Constable RT, Mencl WE, Shankweiler DP, Liberman AM, Skudlarski P, Fletcher JM, Katz L, Marchione KE, Lacadie C, Gatenby C, Gore JC (1998) Functional disruption in the organization of the brain for reading in dyslexia. Proc Natl Acad Sci USA 95:26362641.
Stein J, Walsh V (1997) To see but not to read; the magnocellular theory of dyslexia. Trends Neurosci 20:147152.[ISI][Medline]
Stelmack RM, Miles J (1990) The effect of picture priming on eventrelated potentials of normal and disabled readers during a word recognition memory task. J Clin Exp Neuropsychol 12:887903.[ISI][Medline]
Stelmack RM, Saxe BJ, Noldy-Cullum N, Campbell KB, Armitage R (1988) Recognition memory for words and event-related potentials: a comparison of normal and disabled readers. J Clin Exp Neuropsychol 10:185200.[ISI][Medline]
Tarkiainen A, Helenius P, Hansen PC, Cornelissen PL, Salmelin R (1999) Dynamics of letter string perception in the human occipito-temporal cortex (in press).
Ungerleider LG (1995) Functional brain imaging studies of cortical mechanisms for memory. Science 270:769775.[Abstract]
Ungerleider LG, Mishkin M (1982) Two cortical visual systems. In: Analysis of visual behaviour (Ingle DJ, Goodale MD, Mansfield RJW, eds), pp. 549586. Cambridge, MA: MIT Press.
Wagner RK, Torgesen JK (1987) The nature of phonological processing and its causal role in the acquisition of reading skills. Psychol Bull 101:192212.[ISI]
Wechsler D (1955) Wechsler adult intelligence scale. Manual. New York: Psychological Corporation.
Wimmer H (1993) Characteristics of developmental dyslexia in a regular writing system. Appl Psycholing 14:133.[ISI]
Wolf M (1986) Rapid alternating stimulus naming in the developmental dyslexias. Brain Lang 27:360379.[ISI][Medline]
Wolf M, Obregon M (1992) Early naming deficits, developmental dyslexia, and a specific deficit hypothesis. Brain Lang 42:219247.[ISI][Medline]