Perception of matching and conflicting audiovisual speech in dyslexic and fluent readers: An fMRI study at 3 T

www.elsevier.com/locate/ynimg NeuroImage 29 (2006) 797 – 807

Perception of matching and conflicting audiovisual speech in dyslexic and fluent readers: An fMRI study at 3 T Johanna Pekkola,a,b,c,* Marja Laasonen,d,e Ville Ojanen,a,c Taina Autti,b Iiro P. Ja¨a¨skela¨inen,a,c Teija Kujala,f,g,h and Mikko Samsa,c a

Laboratory of Computational Engineering, Helsinki University of Technology, Espoo, Finland Helsinki Medical Imaging Center, Helsinki University Central Hospital, Helsinki, Finland c Advanced Magnetic Imaging Centre, Helsinki University of Technology, Espoo, Finland d Department of Psychology, University of Helsinki, Helsinki, Finland e Phoniatric Department, Helsinki University Central Hospital, Helsinki, Finland f Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki, Finland g Cognitive Brain Research Unit, Department of Psychology, University of Helsinki, Helsinki, Finland h Helsinki Brain Research Centre, Helsinki, Finland b

Received 6 February 2005; revised 12 June 2005; accepted 7 September 2005 Available online 15 December 2005

We presented phonetically matching and conflicting audiovisual vowels to 10 dyslexic and 10 fluent-reading young adults during ‘‘clustered volume acquisition’’ functional magnetic resonance imaging (fMRI) at 3 T. We further assessed co-variation between the dyslexic readers’ phonological processing abilities, as indexed by neuropsychological test scores, and BOLD signal change within the visual cortex, auditory cortex, and Broca’s area. Both dyslexic and fluent readers showed increased activation during observation of phonetically conflicting compared to matching vowels within the classical motor speech regions (Broca’s area and the left premotor cortex), this activation difference being more extensive and bilateral in the dyslexic group. The betweengroup activation difference in conflicting > matching contrast reached significance in the motor speech regions and in the left inferior parietal lobule, with dyslexic readers exhibiting stronger activation than fluent readers. The dyslexic readers’ BOLD signal change co-varied with their phonological processing abilities within the visual cortex and Broca’s area, and to a lesser extent within the auditory cortex. We suggest these findings as reflecting dyslexic readers’ greater use of motor-articulatory and visual strategies during phonetic processing of audiovisual speech, possibly to compensate for their difficulties in auditory speech perception. D 2005 Elsevier Inc. All rights reserved.

* Corresponding author. Helsinki University Central Hospital, Department of Radiology, PO Box 340, FIN-00029 HUS, Finland. Fax: +358 9 471 74404. E-mail address: [email protected] (J. Pekkola). Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2005.09.069

Introduction Dyslexia, a specific difficulty in learning to read and write despite normal intelligence and schooling, displays a considerable prevalence of 5 to 10% (Shaywitz et al., 1990). The neural basis of dyslexia is under intensive research, and a central cognitive defect across different languages indicates problems in phonological processing—the use of a language’s sound structure to process oral and written information (Snowling, 1981; Paulesu et al., 2001; Frith, 2001). Dyslexic readers show difficulties (Bradley and Bryant, 1978; Kinsbourne et al., 1991) in all three aspects (Torgesen et al., 1994) of phonological processing: phonological awareness, phonological memory, and rapid access to phonological information stored in long-term memory. The underlying cause of these deficits is currently unclear. Developmentally deviant speech perception at the phonetic level that could interfere with processing and manipulating phonological information (Manis et al., 1997; Habib, 2000) is one possibility. A number of studies have shown that dyslexic readers’ speech sound categorizations are less sharp than those of fluent readers’ (Godfrey et al., 1981; Adlard and Hazan, 1998; Brady et al., 1983; Werker and Tees, 1987; Reed, 1989). Dyslexic readers also show nonlinguistic visual and auditory sensory disturbances (Eden et al., 1996; Habib, 2000; Hari and Renvall, 2001; Renvall and Hari, 2002) that, in the auditory modality, are proposed to especially involve processing of rapidly changing acoustic information analogous to stop consonants in natural speech (Tallal, 1980; Hari and Kiesila¨, 1996; see, however, Mody et al., 1997; McAnally et al., 1997; Nittrouer, 1999; Renvall and Hari, 2002). In addition to their auditory speech perception deficits (Godfrey et al., 1981; Adlard and Hazan, 1998; Brady et al.,

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1983; Werker and Tees, 1987; Reed, 1989), dyslexic individuals also show impaired speech-reading skills (De Gelder and Vroomen, 1998; Hayes et al., 2003). Seen articulation gestures (visual speech) and heard speech sounds (auditory speech) transmit information about different aspects of spoken language (Binnie et al., 1974), and both are essential during language development (Ehri, 1992); face-to-face speech perception is a complex, multimodal process that requires rapid and accurate integration of visual and auditory speech information. In dyslexic readers, sensory integration problems across several modalities emerge, exemplified by their impairments in multisensory temporal processing of nonlinguistic material (Laasonen et al., 2000, 2002). The importance of the multisensory aspect in the study of dyslexia is further suggested by the observation of improved reading skills in dyslexic children after nonlinguistic audiovisual training (Kujala et al., 2001). Functional neuroimaging studies on linguistic processing in dyslexia have mostly concentrated on the processing of written material, showing consistently diminished left temporoparietal cortex activation (Rumsey et al., 1997; Shaywitz et al., 1998, 2003; Brunswick et al., 1999; Temple et al., 2000; Simos et al., 2000a,b, 2002; Paulesu et al., 2001; Shaywitz et al., 2002; Aylward et al., 2003), loss of normal hemispheric lateralization of language functions (Simos et al., 2000a,b), and evidence pointing to a connection deficit between anterior and posterior language regions (Paulesu et al., 1996: Klingberg et al., 2000). A disrupted left temporoparietal neural response during phonological processing has been suggested as a fundamental defect in dyslexia (for reviews, see Habib, 2000; Temple, 2002). Results concerning dyslexic readers’ auditory speech processing are, however, less consistent with respect to the temporoparietal activation pattern (Rumsey et al., 1992, 1994; Simos et al., 2000a; Corina et al., 2001; McCrory et al., 2000). Still, abnormalities in dyslexic readers’ early phonetic/phonological processing during pseudoword listening (Helenius et al., 2002a) and spoken-word segmentation (Helenius et al., 2002b) are shown by magnetoencephalographic (MEG) studies that provide precise temporal resolution of the cortical activation flow. To our knowledge, there are no prior functional neuroimaging studies on audiovisual speech perception in dyslexia. We aimed to map activation differences between dyslexic and fluent-reading young adults during perception and processing of audiovisually presented vowels and to investigate the covariance of BOLD signal change with phonological processing abilities in the dyslexic readers. We employed a paradigm (Ojanen et al., 2005) in which presentation of temporally and spatially congruent but phonetically either matching or conflicting audiovisual vowels allowed assessment of regions underlying the processing of phonetic features extracted from auditory and visual speech stimuli. Based on our earlier results suggesting the importance of Broca’s area and premotor cortex in phonological processing of audiovisual speech (Ojanen et al., 2005) and the known abnormalities in dyslexic readers’ phonological processing, we specifically hypothesized that activation of these motor speech areas would differ between the dyslexic and fluent readers during processing of phonetic features extracted from auditory and visual speech stimuli. Additionally, we explored the co-variance between the dyslexic readers’ phonological processing abilities and the activity within Broca’s area and the sensory-specific auditory and visual cortices.

Materials and methods Participants Ten dyslexic (6 males, ages 27 – 31, mean 28.1 years) and 10 fluent-reading right-handed native Finnish speakers (6 males, ages 22 – 34, mean 27.0 years) volunteered for the study. Dyslexic participants were recruited by advertisements posted on university campuses and via student organizations. Fluent-reading participants were recruited similarly, or were members of the laboratory staff. All had either completed a university degree or were university students, ensuring a comparable general intelligence level across groups. Prior to participation, they gave informed consent to the protocol approved by the local ethics committee in accordance with the Helsinki declaration. Neuropsychological examination Each dyslexic participant reported a childhood history of reading difficulties. As part of the study, their dyslexia was confirmed by at least average intelligence combined with poor performance (at least
1 standard deviations according to criteria adjusted to the Finnish-speaking adult population by Leinonen et al., 2001) in at least three reading-related tasks assessing phonological processing, reading speed, reading accuracy, and reading comprehension (as detailed in Laasonen et al., 2002, and Table 1). To facilitate interpretation, we created three composite variables reflecting the aspects of phonological processing (Torgesen et al., 1994): phonological awareness, phonological memory, and rapid naming; the specific tasks contributing to each

Table 1 Dyslexic participants’ performance in the reading-related and intelligence tasks Task Phonological processing Awareness (accuracy)a Phonological Discrimination Phonological Synthesis Memory (span length)a Nonword span Digit span Naming (speed) Rapid alternating stimulus naminga Reading Speed (correct items) Lexical decision reaction time Word-chain segmentation Reading aloud Comprehension (accuracy) Fact Fiction Intelligence quotient Verbal Performance Full

Mean

SD


0.38 0.39
1.16
0.97
1.41
0.53

1.06 1.34 1.65 0.76 0.70 1.28


1.29

1.65


1.51
2.07
0.88
1.57
0.17 0.04
0.37

1.39 2.95 1.07 0.73 0.57 0.56 1.00

112.8 109.5 112.2

8.83 12.87 9.91

Figures indicate participants’ age-corrected performance as compared to the norms (Leinonen et al., 2001; Laasonen et al., 2002). Intelligence quotients have a mean of 100 and a standard deviation of 15. All other variables have been standardized (mean 0, SD 1) and transformed to indicate better performance by a larger value. a Variables used in the co-variance analysis.

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composite variable can be found in Table 1. Fluent-reading participants were selected based on self-report lacking any linguistic, reading, or learning difficulties. Stimuli and experimental design We employed the same stimuli and design as in our earlier experiment on fluent readers (Ojanen et al., 2005). The stimuli were made of video tapes of a female speaker’s face that articulated Finnish vowels /a/, /o/, /i/, and /y/. These audiovisual vowel stimuli were temporally and spatially matching, but phonetically either matching or conflicting. Matching stimuli (/a/, /o/, /i/, and /y/) had matching visual and auditory components (e.g., visual /a/ and auditory /a/). Conflicting stimuli were: acoustic /a/ + visual /y/, acoustic /y/ + visual /a/, acoustic /o/ + visual /i/, acoustic /i/ + visual /o/. These were specifically designed not to cause a McGurk effect (McGurk and MacDonald, 1976); instead, there appeared a perceptual conflict between the acoustic and the visual component. Speech sound onset lagged 95 ms behind the onset of the articulatory lip movement, which is a natural asynchrony in the uttering of single vowels. The baseline stimulus was a still face image with no auditory input. Auditory stimuli were digitized at 44 100 Hz and presented at a comfortable intensity level through MRI-compatible electrostatic headphones (custom-modified Koss-ESP-900 electrostatic headphones, Koss Ltd., Milwaukee, WI, USA). Visual stimuli (height 18-, 25 Hz frame rate) were projected on a mirror attached inside the scanner’s head coil. The durations of the acoustic /a/, /o/, /i/, and /y/ were 439, 445, 440, and 444 ms, and the duration of the visual stimuli was always 780 ms. To avoid masking the stimuli with scanner noise, we used ‘‘clustered volume acquisition’’ (Edmister et al., 1999), in which 3.5-s periods of silence were separated by 2.5 s of noisy image acquisition, during which one brain volume was obtained. To further ensure silence during stimulus presentation, the scanner’s coolant pump was switched off for the duration of the functional runs. Three stimuli of one type (matching, conflicting, or baseline) were presented during each 3.5-s silent period, with inter-stimulus intervals varying from 100 to 400 ms in 50-ms steps. A block consisted of 2 to 5 consecutive periods of the same stimulation type, and each participants’ task was to press a button with the right index finger when a block (and thus stimulation type) changed. There was thus one button press in the beginning of each stimulus block, and the number of responses remained constant across conditions. We randomized block order and duration to avoid predictability, equalizing, however, the total presentation time of each stimulus type. Image acquisition We scanned the participants with a 3.0 T GE Signa system and a quadrature birdcage head coil, collecting a total of 101 volumes of gradient-echo echo-planar images (TE 40 ms, TR 2500 ms, flip angle 90-) depicting blood oxygenated level-dependent (BOLD) contrast (Ogawa et al., 1990) per condition. The imaged area covered the whole brain in 26 axial oblique slices (thickness 4 mm, gap between 1 mm, FOV 22  22 cm, matrix size 96  96). For anatomic co-alignment, we obtained a T1-weighted fast spin echo volume with a slice prescription identical to the functional images but denser (0.85  0.85 mm) in-plane resolution.

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fMRI data analysis Data were analyzed with FEAT (FMRI Expert Analysis Tool) Version 3.1, part of FSL (FMRIB’s Software Library, www.fmrib. ox.ac.uk/fsl). Preprocessing steps included nonbrain tissue extraction, motion correction (Jenkinson et al., 2002), discarding the first two volumes of each run, gaussian spatial smoothing (full-widthhalf-maximum 5 mm), mean-based intensity normalization of all volumes by the same factor, and high-pass temporal filtering. Voxel time series were analyzed using General Linear Model (GLM) with local autocorrelation correction (Woolrich et al., 2001), measuring the extent to which an individual voxel’s signal intensity throughout the time series fits the predicted time course of modeled predictors. In the current experiment, the model was not convolved to a hemodynamic response function, due to the sparseness of data sampling. The first volume of each block was discarded from the time series analysis to ensure that the volumes included in the analysis sampled the hemodynamic response at its plateau. Individual data were co-registered (Jenkinson et al., 2002; Jenkinson and Smith, 2001) to a standard brain to carry out a mixedeffects (often referred to as ‘‘random-effects’’) group analysis (Woolrich et al., 2004). Four contrasts were calculated for each group: matching > baseline, conflicting > baseline, matching > conflicting, and conflicting > matching stimulation. Between-group activation differences were studied by contrasting the time series of dyslexic and fluent readers during matching and conflicting stimulation, and by investigating the composite contrast: ½conflicting vs: matchingdyslexic readers
½conflicting vs: matchingfluent readers In all the above a-priori contrasts, the voxel-wise thresholding was set at Z > 1.8 with cluster-wise correction threshold (Forman et al., 1995; Worsley et al., 1992) set at P < 0.05 (corrected for multiple comparisons across the acquisition volume), adhering to Friston et al., 1994. The neuropsychological composite variables reflecting phonological awareness, memory, and rapid naming (Table 1) served as co-variates in the dyslexic group’s analysis to detect their covariance with BOLD signal change during matching and conflicting stimulation within areas relevant to audiovisual speech processing. Based on our earlier study on brain areas that participate in audiovisual phonetic processing (Ojanen et al., 2005), the search was limited to temporal-lobe auditory and heteromodal cortex (BA 41/42/22/21), occipital visual cortex (BA 17/18/19), and the left-hemisphere frontal region that includes Broca’s area (BA 44/45). The exact anatomical locations for significant co-variance clusters were determined, since Broca’s area covers the trigonal and opercular parts of the inferior frontal gyrus, but not necessarily all of the left BA 44/45. The significance threshold in the covariance analysis was set at P < 0.01 (uncorrected), with clusters >20 voxels in extent included.

Results Neuropsychological examination Table 1 presents the dyslexic participants’ neuropsychological test index scores (group mean and standard deviation). Each had at

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least average verbal performance and full intelligence quotients but was impaired in at least one aspect of phonological processing. Of the individual tasks, at least one assessing phonological awareness, memory, or rapid naming posed difficulties for a respective 4, 9, or 5 participants. Performance in at least one task assessing reading speed or comprehension was poor in 9 and 3 participants, respectively. fMRI results Dyslexic readers correctly identified 90% (SEM T 4%) and fluent readers 93% (SEM T 4%) of the stimulus-type changes during the experimental task. The identification accuracy difference between the groups was nonsignificant. When matching and conflicting stimulation was contrasted with baseline, dyslexic and fluent readers activated essentially the same extensive network during both conditions, including bilateral auditory, visual, premotor, supplementary motor and inferior frontal cortices, and bilateral parietal lobules (Fig. 1). The between-group activation differences during matching and conflicting stimulation are demonstrated in Fig. 2 and Table 2; for peak Z values for the between-group differences, see Table 2. Dyslexic readers exhibited stronger activation than did fluent readers during matching stimulation in the bilateral anterior to middle auditory association cortex (superior and middle temporal gyri, BA 22/21), insulae (BA 48), basal ganglia, and the right inferior frontal and orbitofrontal cortex (BA45/47/48). More extensive activation differences emerged during conflicting stimulation, when dyslexic readers exhibited stronger activation than did fluent readers additionally in the bilateral ventral visual cortex (BA 18/19/37), supplementary motor areas (BA 6/8/32), anterior cingulate cortex (BA 32), and cerebellar vermis. No activation differences in the reverse direction (fluent > dyslexic readers) emerged during either condition. Fig. 3 and Table 3 show the activation differences when matching and conflicting stimulation were contrasted directly with each other; the peak Z values for between-conditions contrasts are presented in Table 3. Both fluent and dyslexic readers showed greater activity during conflicting than during matching stimulation. In fluent readers, this greater activity emerged within Broca’s area (left BA 44/45), the left premotor cortex (BA6), and bilateral

Fig. 2. Activation differences (Z > 1.8, P < 0.05) between dyslexic and fluent readers during matching and conflicting stimulation, with dyslexic readers showing stronger activation during both conditions. Dyslexic readers’ stronger activation within basal ganglia, cerebellar vermis, and ventral visual cortex is situated in deeper brain structures and thus not shown in these surface reconstructions; they are presented in Table 2. SMA = supplementary motor area, AC = anterior cingulated cortex.

supplementary motor areas (BA 6/8/32). In dyslexic readers, the between-condition activation difference was more widespread, the stronger activation during conflicting stimulation extending additionally to the corresponding right-hemisphere frontal regions (right BA 6/44/45), bilateral inferior parietal lobules (BA 7/40), the left posterior temporal cortex including the posterior superior temporal sulcus (BA 42/22/21), the left ventral visual cortex (BA 19/37), and cerebellar vermis. Dyslexic readers also exhibited activation in the reverse direction (matching > conflicting stimulation) within the prefrontal and cingulate cortex and precuneus (see Table 3), but no such activation emerged in fluent readers. The composite contrast revealed stronger betweencondition activation difference (direction: conflicting > matching) in dyslexic than in fluent readers within the bilateral inferior frontal cortex, covering Broca’s area and its right-hemisphere homologue and extending to the left premotor cortex (BA 6/44/45), bilateral supplementary motor areas (BA 6/8/32), and the left inferior parietal lobule (BA 7/40).

Fig. 1. The cortical network activated in dyslexic and fluent readers during perception of matching and conflicting audiovisual phonemes (Z > 1.8, P < 0.05).

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801

Table 2 Areas more strongly activated in dyslexic than in fluent readers: cluster sizes, maximum Z values, peak voxel co-ordinates, and clusters’ extents Stimulation type

Voxels

P

Max Z

Xa

Ya

Za

BA

Left/right

Anatomical area

Matching vowels

1570

<0.001 ** <0.001 ** <0.0001 ** ** <0.001 <0.001

3.85 3.44 3.79 3.64 4.31 4.25 4.14 3.89 3.77


64
22 36 28
34 54
18 2
2


18 4 32 4 20 18 22 32
52

0
4 0 2
4
2 8 36
16

22/21/48 NA 22/45/47/48 NA 22/47/48 45/47/48 NA 6/8/32 19/18/37, NA

Left Left Right Right Left Right Bilateral Bilateral Bilateral

Insula, STG, MTG Putamen, pallidum Insula, IFG, OFC, STG, MTG Putamen, pallidum Insula, STG Insula, IFC, OFC Putamen, pallidus, nucleus caudatus SMA, anterior cingulate cortex Lingual and fusiform gyri, cerebellar vermis

1317 Conflicting vowels

5716

1436 1421

**Local maxima, BA = Brodmann area. STG = superior temporal gyrus, MTG = middle temporal gyrus, IFG = inferior frontal gyrus, OFC = orbitofrontal cortex, SMA = supplementary motor area, NA = non-applicable. a MNI coordinates.

Co-variance between neuropsychological index scores and BOLD signal change All three neuropsychological composite variables reflecting phonological awareness, phonological memory, and rapid naming in the dyslexic group co-varied with BOLD signal strength within the visual cortex during both stimulation conditions (Table 4),

showing a stronger visual-cortex BOLD signal with better neuropsychological scores. Within Broca’s area, BOLD signal strength co-varied with scores reflecting phonological awareness and phonological memory during both stimulation conditions (Table 4). Co-variance within the auditory cortex occurred for two variables: phonological awareness during conflicting stimulation and rapid naming during both stimulation conditions.

Fig. 3. Areas more strongly (Z > 1.8, P < 0.05) activated by conflicting compared to matching stimulation in the fluent and dyslexic readers, and significant between-group activation differences in the contrast conflicting > matching stimulation. IPL = inferior parietal lobule, SMA = supplementary motor area.

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Table 3 Between-condition activation differences in dyslexic and fluent readers: cluster sizes, maximum Z values, peak voxel co-ordinates, and clusters’ extents Contrast

Group

Voxels

P

Max Z

Xa

Ya

Za

BA

Left/right

Anatomical area

Conflicting > matching

Fluent readers

1668 677 10549

1937 1537 725 680

<0.0001 <0.05 <0.0001 ** ** <0.0001 <0.0001 <0.01 <0.05

3.60 3.08 4.26 4.04 3.92 3.89 3.93 3.36 3.04


46 0 48
4
51
32 50
58 4

16 24 16 24 24
44
40
46
56

32 48 28 52 44 42 46 14
14

6/44/45 6/8/32 6/44/45 6/8/32 6/44/45 7/40 7/40 21/22/42 19/37, NA

Left Bilateral Right Bilateral Left Left Right Left Left

1111 807 748 729 727

<0.01 <0.05 <0.05 <0.05 <0.01

3.79 3.38 3.37 3.73 3.42


32
4
44 46 2


44 18 12 18 70

42 54 22 34 0

7/40 6/8/32 6/44/45 44/45 10/11/32

Left Bilateral Left Right Bilateral

665

<0.05

3.25


4


50

34

23

Bilateral

Broca’s area, premotor cortex SMA IFG, MFG, premotor cortex SMA Broca’s area, premotor cortex IPL IPL, supramarginal gyrus STG, MTG, STS Fusiform gyrus, cerebellar vermis IPL SMA Broca’s area, premotor cortex IFG Prefrontal cortex, anterior cingulate Posterior cingulate cortex, precuneus

Dyslexic readers

Dyslexic > fluent readers

Matching > conflicting

Dyslexic readers

**Local maxima, BA = Brodmann area. SMA = supplementary motor area, IFG = inferior frontal gyrus, MFG = middle frontal gyrus, IPL = inferior parietal lobule, STG = superior temporal gyrus, MTG = middle temporal gyrus, STS = superior temporal sulcus.

Table 4 Clusters showing co-variance of phonological processing index scores with BOLD signal change within the dyslexic group: cluster sizes, maximum Z-values, peak voxel co-ordinates, clusters’ extents, and peak voxels’ anatomical locations Region

Matching vowels

Phonological awareness

Occipital

97

2.99

22


52

Frontal Occipital

68 53 33 22 32 22

2.68 3.31 2.55 2.33 2.81 2.28


24 6 34 24
48
18

Temporal Occipital

68 59 155 57 22 92 146

2.65 2.49 3.8 3.05 3.16 3.14 2.94

Frontal Temporal Occipital

96 43 41 33 30 21 51 48 68 38 35 97 71 48 35 91

Frontal Rapid naming

Conflicting vowels

Phonological awareness

Phonological memory

Occipital

Frontal

Rapid naming

Occipital

Frontal Temporal a

MNI coordinates.

Max Z

Ya

Index score

Phonological memory

Voxels

Xa

Stimulation type

Za

BA

Left/right

Anatomical location (Max Z)


4

19/18

Right

Lingual gyrus


86
78
68
74 22
58

26
2 30 24 16 2

19/18 17 19 19 45 18/19

Left Right Right Right Left Left

Superior occipital gyrus Lingual gyrus Middle occipital gyrus Superior occipital gyrus Inferior frontal gyrus, trigonal part Calcarine gyrus


34
43 14
14
1 56
30

6 34
86
80
88
52
72

32 38 32 44 10 4 22

44 44/45 18/19 19 18 21 19/18

Left Left Right Left Left Right Left

Inferior frontal gyrus, opercular part Middle frontal gyrus Cuneus Superior occipital gyrus Calcarine gyrus Middle temporal gyrus Middle occipital gyrus

2.77 3.21 2.99 2.86 2.48 2.68 4.18 2.58

20
10 36 4 26
50 66
14


52
66
64
82
76 24
10
54


6 4 32
2 28 16
10 2

19/18 17/18 19 17 19 45 21/22 18/19

Right Left Right Right Right Left Right Left

Lingual gyrus Lingual gyrus Middle occipital gyrus Calcarine gyrus Superior occipital gyrus Inferior frontal gyrus, trigonal part Middle temporal gyrus Lingual gyrus

2.39 2.48 3.42 4.35 3.29 3.44 3.71 2.90


44
32
36 14
38
6
36 54

32 6 36
88
68
82 12
50

38 30 12 34 32 44 42 4

45 44 45 19/18 19 19 44 21

Left Left Left Right Left Left Left right

Middle frontal gyrus Inferior frontal gyrus, opercular part Inferior frontal gyrus, trigonal part Cuneus Middle occipital gyrus Superior occipital gyrus Precentral gyrus Middle temporal gyrus

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Discussion As novel findings, we observed significant activation differences between dyslexic and fluent-reading participants during perception and phonetic processing of audiovisual vowels within areas relevant to speech processing (Figs. 2 and 3, Tables 2 and 3). In addition, we detected co-variance between BOLD signal strength and phonological processing abilities in the dyslexic group within Broca’s area, visual cortex, and to a lesser extent within auditory cortex (Table 4). We suggest our findings to reflect dyslexic readers’ heightened reliance on motor-articulatory and visual strategies during processing of audiovisual speech. All our stimuli had spatially and temporally matching auditory and visual components, differing only with respect to their phonetic congruency. During matching stimulation, the participants perceived a unified audiovisual percept. Conversely, conflicting stimulation required simultaneous processing of two separate phonetic inputs (auditory and visual, e.g., auditory /i/ and visual /o/). Although visual articulatory gestures do not carry acoustic information, the findings of auditory cortex and Broca’s area activation by visual speech (Calvert et al., 1997; Campbell et al., 2001; Nishitani and Hari, 2002; Paulesu et al., 2003; Calvert and Campbell, 2003, Pekkola et al., 2005) suggest that they are encoded as speech and possibly processed in a way analogous to auditory speech in the higher processing areas. Thus, comparing BOLD responses to matching and conflicting stimuli in the current experiment was hypothesized to reveal areas involved in the processing of phonetic features extracted from auditory and visual speech stimuli. The greater Broca’s area activation during conflicting compared to matching stimulation in the fluent readers (Fig. 3, upper row) agrees with our earlier experiment (Ojanen et al., 2005), suggesting involvement of Broca’s area in the processing of phonetic features of audiovisual speech. Broca’s area is strongly involved in speech production and, together with the left premotor cortex, considered as a motor speech region. There is, however, evidence pointing that the classical motor speech regions also participate in speech perception. Broca’s area is activated during perception of visual articulations (Campbell et al., 2001; Nishitani and Hari, 2002; Paulesu et al., 2003; Calvert and Campbell, 2003) as well as during phonetic analysis of auditory speech (Zatorre et al., 1992; Zatorre et al., 1996; Burton et al., 2000). Further, a recent fMRI study directly shows that the ventral premotor cortex activation during listening to syllables overlaps the activation caused by production of the same syllables (Wilson et al., 2004), and transcranial magnetic stimulation experiments demonstrate that observation of visual (Sundara et al., 2001; Watkins and Paus, 2004) and auditory (Fadiga et al., 2002; Watkins and Paus, 2004) speech enhances the excitability of the orofacial motor system. Additionally, viewing speech potentiates magnetic evoked potential responses to tactile stimulation of the lips (Mo¨tto¨nen et al., 2005). A theoretical framework for such involvement of motorarticulatory regions in speech perception is offered by the motor theory of speech (Liberman et al., 1967; Liberman and Mattingly, 1985), according to which the objects of speech perception are articulatory gestures rather than the acoustic features of speech sounds. Specifically, the theory hypothesizes that the perceived articulatory objects are intended articulatory movements (i.e., neuromotor commands to the lips, tongue, and vocal cords). Taking into account the role of Broca’s area and the left premotor cortex as motor speech areas, they could well contain motor

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representations of articulatory gestures onto which speech inputs could be mapped during perception and phonetic analysis of speech. A possible mediating neural mechanism for such action might be constituted by the visuo-motor mirror neuron system. Primate brain areas analogous to the human Broca’s area, its righthemisphere homologue and the inferior parietal lobules contain visuo-motor neurons, proposed to act as an interface that codes observed object-based actions into knowledge (Di Pellegrino et al., 1992; Rizzolatti et al., 1996a; Gallese et al., 1996). Several functional studies support the existence of an equivalent mirrorneuron system in humans (Rizzolatti et al., 1996b; Grafton et al., 1996; Krams et al., 1998), and its involvement in the perception and silent imitation of visual speech (Nishitani and Hari, 2002; Nishitani et al., 2005). Thus, our findings of these regions’ stronger activation in dyslexic than in fluent readers in the conflicting > matching contrast might suggest dyslexic readers’ greater mirror-neuron network activation when the conflicting auditory component cannot be used to support processing of the visual speech input. That our dyslexic readers also activated the left posterior superior temporal sulcus (STS) more strongly in the same contrast was surprising, since STS is a central audiovisual speech integration area (Calvert et al., 2000; Sekiyama et al., 2003; Wright et al., 2003; Callan et al., 2003) and would thus be expected to show weaker activation in such a comparison. The left STS, however, interacts with the visuo-motor mirror neuron system (Rizzolatti et al., 2001; Nishitani and Hari, 2002; Nishitani et al., 2005) and its greater activation during conflicting stimulation in the current experiment might be due to increased input from the frontal and parietal areas of the mirror neuron system. We thus suggest that the dyslexic readers’ more widespread and stronger activation in the conflicting > matching contrast compared to that of fluent readers (Fig. 3), encompassing the motor speech areas, their right-hemisphere homologues and supplementary motor areas, may reflect their heightened use of sub-vocal motor-articulatory strategies during phonetic processing of audiovisual speech. This could occur to compensate for the dyslexic readers’ visual (De Gelder and Vroomen, 1998; Hayes et al., 2003) and auditory (Godfrey et al., 1981; Adlard and Hazan, 1998; Brady et al., 1983; Werker and Tees, 1987; Reed, 1989) speech perception difficulties. The stronger activation of basal ganglia and the cerebellar vermis in dyslexic than in fluent readers during both matching and conflicting stimulation (Table 2) is consistent with this argument, since these areas are known to participate in learned motor behavior and motor control (Brooks, 1995; Doyon et al., 2002; Taniwaki et al., 2003). The bilaterality of the activation within the dyslexic group in the conflicting > matching contrast, instead of the left-lateralized finding shown by the fluent readers (Fig. 3), might be attributable to the less distinctly lateralized language functions in dyslexic than in fluent readers (Simos et al., 2000a,b; Habib, 2000). Our lack of a nonverbal audiovisual control condition, however, somewhat limits the interpretation of the results since the findings might be attributed to processing of matching vs. conflicting audiovisual information in general; this aspect should be addressed in future investigations. The co-variance between our dyslexic readers’ phonological processing abilities and BOLD signal change during audiovisual speech perception within Broca’s area (Table 4) supports the idea of motor speech area activation as a compensatory mechanism; it is

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possible that those individuals who most efficiently recruit motorarticulatory mechanisms during speech perception have also been able to develop the most advanced phonetic processing abilities. Most earlier studies have shown weaker Broca’s area activation by dyslexic than by fluent readers during different reading and phonological processing tasks (Georgiewa et al., 1999; Aylward et al., 2003; Eden et al., 2004), although other studies have produced opposing results (Rumsey et al., 1997, Shaywitz et al., 1998; Brunswick et al., 1999; Georgiewa et al., 2002). The reason for this discrepancy remains unclear but could in part be related to different experimental tasks and populations. Suggesting a possibility of heightened Broca’s area activity as a compensatory mechanism, activity within Broca’s area has, however, been shown to correlate with age in a population of dyslexic children (Shaywitz et al., 2002), and with decoding labor during speech-reading in fluent readers (Paulesu et al., 2003). Contrary to the findings for dyslexic readers obtained with visually presented (read) words (Salmelin et al., 1996; Helenius et al., 1999) and phonological tasks involving written material (Shaywitz et al., 1998), we detected no diminished occipitotemporal cortex activity in our dyslexic participants. Instead, their ventral visual cortex activity was stronger than in fluent-reading participants during conflicting stimulation (Table 2), and was also greater during conflicting than matching stimulation (Table 3). These findings may reflect the behavioral results suggesting that despite their difficulties in visual speech perception (De Gelder and Vroomen, 1998; Hayes et al., 2003), dyslexic individuals still strongly rely on visual information in speech perception (Hayes et al., 2003). Additionally, all phonological processing scores covaried with visual cortex activity in our dyslexic group (Table 4). We thus consider possible that those dyslexic readers with effective visual analysis strategies, here showing stronger visual cortex activation, have also developed phonetic processing abilities better than those with less effective strategies. The relative lack of covariance between activity and phonological processing scores within the auditory cortex (Table 4) must, instead, be interpreted cautiously, since our stimuli were devoid of the fast formant transitions that have been suggested as a key to the dyslexic readers’ auditory speech processing impairments (Tallal, 1980; Nagarajan et al., 1999; Ruff et al., 2003; Renvall and Hari, 2002). The greater lateral temporal and insular activation in our dyslexic than in normal-reading participants during both matching and conflicting stimulation (Fig. 2) differs from the reports of consistently diminished left temporoparietal and insular activation in dyslexic participants during linguistic processing of written material (Rumsey et al., 1997; Shaywitz et al., 1998, 2003; Brunswick et al., 1999; Temple et al., 2000; Simos et al., 2000a,b, 2002; Paulesu et al., 2001; Shaywitz et al., 2002; Aylward et al., 2003). The differing tasks between the experiments clearly complicate comparing the results to each other. It is, however, quite possible that our task was not sufficiently demanding to disrupt the temporoparietal phonological processing system and thus bring out the frequently observed diminished temporoparietal activity in the dyslexic readers. The lateral temporal regions more strongly activated by the dyslexic than the fluent readers in the current experiment include bilateral superior temporal sulcus (STS) (Fig. 2). This central node in audiovisual speech integration (Calvert et al., 2000; Sekiyama et al., 2003; Wright et al., 2003; Callan et al., 2003) is suggested to mediate the convergence of visual speech input to the auditory cortex observed in several studies (Calvert et al., 1997; Campbell et al., 2001; Paulesu et al.,

2003; Calvert and Campbell, 2003; Pekkola et al., 2005). Thus, its greater activity might here reflect the greater need of dyslexic readers to extract information from both auditory and visual speech components when decoding the message. Another explanation for the observed greater lateral temporal activation in dyslexic readers could be that BOLD response as a function of task difficulty may follow an inverted U-shape function that differs between dyslexic and fluent readers, leading the same task to be more demanding for dyslexic participants and thus leading to greater activation via increased effort. Since masking auditory stimuli with noise increases activation in the anterior to middle auditory association cortex (Scott et al., 2004), these areas’ greater activity in dyslexic readers might be due to such increased task difficulty. Results obtained with various auditory linguistic processing tasks, however, show either diminished (McCrory et al., 2000; Rumsey et al., 1992), equivalent (Rumsey et al., 1994; Simos et al., 2000a), or increased (Corina et al., 2001) temporoparietal activation for dyslexic compared to that for fluent readers, suggesting the possibility that dyslexic readers’ temporoparietal activation pattern during linguistic processing is essentially task-dependent.

Conclusion Our results show stronger activation within the motor speech areas in dyslexic than in fluent readers in a contrast hypothesized to reflect processing of phonetic features extracted from auditory and visual speech. Activity within Broca’s area and the visual cortex co-varied with dyslexic readers’ phonological processing abilities during audiovisual speech perception. We suggest these findings to reflect dyslexic readers’ heightened reliance on motor-articulatory and visual speech processing strategies, possibly as a compensatory mechanism to overcome linguistic perceptual difficulties. This information supports the relevance of speech perception disturbances in dyslexia, and might in future be useful in planning multisensory rehabilitation programs for dyslexic children.

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