Auditory spatial localization and attention deficits in autistic adults

Cognitive Brain Research 23 (2005) 221 – 234 www.elsevier.com/locate/cogbrainres

Research report

Auditory spatial localization and attention deficits in autistic adults Wolfgang A. Teder-S7lej7rvia,*, Karen L. Piercea,b, Eric Courchesnea,b, Steven A. Hillyarda a

Department of Neurosciences 0608, School of Medicine, 9500 Gilman Drive, University of California, San Diego, La Jolla, CA 92093-0608, USA b Laboratory for Research on the Neuroscience of Autism, Children’s Hospital Research Center, La Jolla, CA 92037, USA Accepted 21 October 2004 Available online 11 January 2005

Abstract The objective of this study was to compare autistic adults and matched control subjects in their ability to focus attention selectively on a sound source in a noisy environment. Event-related brain potentials (ERPs) were recorded while subjects attended to a fast paced sequence of brief noise bursts presented in free-field at a central or peripheral location. Competing sequences of noise bursts at adjacent locations were to be ignored. Both behavioral measures of target detection and auditory ERP amplitudes indicated that control subjects were able to focus their attention more sharply on the relevant sound source than autistic subjects. These findings point to a fundamental deficit in the spatial focusing of auditory attention in autism, which may be a factor that impedes social interactions and sensory-guided behavior, particularly in noisy environments. D 2004 Elsevier B.V. All rights reserved. Theme: Sensory systems Topic: Auditory systems: central physiology Keywords: Evoked potentials; Autism; Auditory

1. Introduction Sensory abnormalities, particularly in the auditory domain, are one of the characteristic signs of autism. Behavioral responses to auditory stimuli range from complete ignoring of sounds [27,38] to acoustic hypersensitivity [5,9]. The reason for such erratic responding to auditory stimuli in autism remains unknown. One possibility is that these patients find it difficult to attend selectively to sounds in a noisy environment. In a recent book describing her sensory experiences as a person with autism, Temple Grandin wrote: bWhen two people are talking at once, it is difficult for me to screen out one voice and listen to the other. My ears are like microphones picking up all sounds with equal intensity. In a noisy place I can’t understand speech, because I can’t screen out the background noise.Q [30]. Such confusion and difficulty in separating competing sound * Corresponding author. Fax: +1 858 534 5562. E-mail address: [email protected] (W.A. Teder-S7lej7rvi). 0926-6410/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.cogbrainres.2004.10.021

sources suggest the hypothesis that autism may involve impairment in sound source attribution and selection. This hypothesis of abnormal auditory spatial attention in autism has never been directly tested using neurofunctional techniques, despite the possibility that such impairment could have a significant impact on a variety of behavioral functions. Failure to accurately tune spatial attention to a single sound source among many or, alternatively, tuning too broadly could impede orienting, decreases target detection accuracy and causes information to be missed altogether. Such failures in autism have been theorized to play a major role in the developmental failure to engage in joint social attention [21]. The present study used event-related potential (ERP) measures to characterize the tuning of auditory spatial attention in autism as compared to normal controls. Normal subjects can attend selectively to a single sound source in a noisy environment with competing sources of similar sounds [49,64,65], and ERP recordings have shown that attended sounds typically elicit an enhanced negative wave (the N1) in auditory cortex with a latency of 60–100 ms

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[32,34,66]. The amplitude of the N1 increases as a function of attention allocation to a particular sound source in parallel with improved accuracy at detecting target sounds from that source. In normal subjects, the detection of such targets typically elicits a longer latency ERP component, the P3 or P300; a component associated with target recognition the updating of working memory [46,52]. The paradigm used in the present study was developed by Teder-S7lej7rvi and colleagues [67,68] and taps the ability of an individual to selectively attend to a single auditory source among many competing sources located both in front and to the side. In this task, subjects are instructed to listen selectively to sounds coming from one speaker in a freefield array of 8 speakers that are continuously emitting sounds in a rapid, randomized sequence. In this situation, most normal subjects show a sharply tuned bgradientQ of attention around the attended location, with a progressive decline in target detection responses and ERP amplitudes for sound sources increasingly distant from the single source of focused attention. Moreover, the ERP recordings indicate that this spatial tuning of attention is accomplished in two distinct levels of neural processing: an early, broadly tuned filtering in auditory cortex indexed by the N1 ERP component, and a later more finely tuned stage indexed by the P3 ERP component. ERP and behavioral analyses show that tuning of attention is narrowest for sound sources directly in front of the subject and becomes rather broad for peripheral sources. The present study utilized this paradigm to determine whether autistic patients display abnormalities in the selective tuning of attention to one sound source in the presence of multiple competing sources.

2. Methods 2.1. Subjects Two groups of subjects were paid for participation. The autistic group consisted of seven subjects (all male, age range 29–39, mean 33.3 F 3.1 years), and the control group consisted of seven healthy subjects matched for age, sex, and handedness (all male, ages 28–41, mean 33.4 F 4.4 years). The experiments were undertaken in accordance with the guidelines of the Declaration of Helsinki, and all subjects or their legal guardians gave informed written consent. Autistic subjects were recruited from the Center for Autism Research, Children’s Hospital San Diego. An autism diagnosis was based on meeting multiple assessment criteria (Refs. [3,40,43–45]; see Table 1), additionally a Childhood Autism Rating Scale (CARS) [62] score was also determined for each autistic subject. Diagnostic scores and psychometric test results appear in Table 1; this table shows the autistic group had scaled scores above average for Performance IQ subtests thought to reflect relatively more spared areas of intellectual functioning that is commonly observed in autism. All autistic subjects were negative for fragile-X by

Table 1 Diagnostic and psychometric data for autistic subjects Autism diagnostic inventory—revised [27] Reciprocal social interaction Verbal communication Non-verbal communication Stereotyped behavior Autism diagnostic observation scale—generic [28] Social Communication Stereotyped behavior CARS [29] Performance IQ Block design Object assembly Verbal IQ Full scale IQ

24.0 19.7 12.2 7.8

(2.8) (4.4) (2.2) (2.1)

10.0 (2.2) 7.3 (3.3) 2.3 (2.1) 33.9 (6.0) 97.1 (10.3) 12.0 (2.4) 11.3 (1.4) 81.9 (12.9) 86.9 (11.9)

Values are mean (SD).

DNA or chromosomal analysis. Normal control subjects were screened for history of developmental, psychiatric, or neurological disorders. All autistic and normal control subjects were tested for and shown to have normal hearing with identical stimuli to those used in the experiment. 2.2. Stimuli and apparatus The stimuli were 82-ms bursts of pink noise (including a total of 10 ms rise/fall-time) with a bandwidth of either 500– 5000 Hz (frequent bstandardQ stimuli, P = 0.84) or 500– 15,000 Hz (infrequent bdeviant/targetQ stimuli, P = 0.16), respectively [68]. The stimuli were presented through two arrays of four loudspeakers each (see Fig. 1). The speakers were spaced 68 apart within each array (at 08, 68, 128, and 188 for the frontal array, and at 728, 788, 848, and 908 for the right peripheral array) along a horizontal arc with the central speaker S1 placed directly in front of the subject’s head at a distance of 1.20 m. From left to right, the central speaker positions were labeled S1–S4 and the right peripheral array S5–S8. During all experimental runs, noise bursts were presented in random order from all eight speakers at an intensity of 76 dBA (SPL). Stimulus onset asynchronies varied randomly between 90 and 270 ms (180 ms on the average with a rectangular distribution). The subject’s head position was monitored by reflecting a narrow beam of infrared (IR) light from a small head-mounted mirror (see IR-beam, Fig. 1). 2.3. EEG recording The EEG was recorded from 44 electrodes mounted in an elastic cap (for precise locations, see Ref. [17]), off-line referenced to the averaged mastoids. The EEG and horizontal and vertical EOG signals were amplified with a band-pass of 0.1–100 Hz ( 6 dB points), digitized at 250 Hz, and digitally low-pass filtered with a cut-off frequency of 40 Hz. Prior to averaging, a battery of artifact rejection algorithms was applied to eliminate artifacts due to blinking, muscle activity,

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ple comparisons post-tests were used for comparisons between adjacent locations. Planned comparisons to evaluate differences in gradients of DRRs between the Attend Center and Attend Right conditions were made with two-tailed paired t tests, as in our previous reports [67,68]. 2.6. Sensory discrimination testing

Fig. 1. Experimental arrangement of the central and the right peripheral loudspeaker arrays, with the infrared head movement monitor (IR beam) attached to the subject’s head and the visual feedback fixation point (Fp) beneath the central speaker. The spacing between individual loudspeakers was 68 and each array thus encompassed 188. The labels S1 through S8 for the speaker positions will be used throughout the text. S1 and S8 were the attended positions in this study.

and amplifier blocking. After averaging, the data were digitally low-pass-filtered with a Gaussian finite impulse function, yielding a 6dB point (50% down) at about 40 Hz. 2.4. Procedure The experiment was carried out in an electrically and acoustically shielded room. There were two experimental conditions (Attend Center and Attend Right) with ten runs of 1092 stimuli each presented in each condition. The two task conditions, presented in counterbalanced order, were either to attend to the central speaker S1 (Attend Center), or to the rightmost speaker S8 (Attend Right). In both conditions, the subjects were instructed to press a button to the infrequent higher-pitched noise bursts (targets) at the designated location and to ignore stimuli coming from all other locations. 2.5. Behavioral detection responses Subjects’ performance was quantified as the percentage of correctly detected targets in the attended channel (hits) and the percentage of deviant stimuli incorrectly responded to (false alarms) in adjacent channels. A detection response was assigned to a target or deviant stimulus when it followed that stimulus by 200–1000 ms. The percentages of correct hits and false alarm responses across the speaker arrays were quantified in the same way in terms of Detection Response Rates (DRRs) for each speaker. Using this common metric, the distributions of DRRs across the speaker array and the RTs of these responses were subjected to one-way repeated measures ANOVAs (factor: Location), and Bonferroni multi-

In order to assess possible differences among subjects in sensory discrimination ability, frequency and spatial discrimination tasks were carried out in both experimental groups. Frequency discrimination was tested by using pairs of noise bursts of the same frequencies as used in the actual ERP experiment. The pairs were presented from a central and a peripheral location in separate runs with an SOA of (300 ms). The first tone was a noise burst with a 5 kHz bandwidth, the second tone was either identical to the first tone or of increased bandwidth (15 kHz). The subjects’ task was to press a button when the second tone was judged to differ in frequency. Spatial discrimination of central and peripheral stimuli was assessed using three speaker locations with 68 spacing between them. In the central condition, the first tone of the pair of noise bursts was always presented from the speaker directly in front of the subject (i.e., S1, see Fig. 1). The second stimulus came either from the same location or from one of the two adjacent locations (S2, S3). In the peripheral condition, the first tone of the pair always came from the rightmost speaker (S8), and the second from either S8 or S7. The subject responded when the tones were judged to come from different locations. In both the frequency and spatial discrimination tasks, responses to identical stimuli were considered false alarms. 2.7. ERP analyses In order to examine attentional gradients of ERPs, amplitude measures of the auditory N1 wave (100–200 ms, site Cz) to the standard noise bursts and of the N1 and P3 (350–600 ms, site Pz) waves to the target/deviant noise bursts were taken in response to all eight speakers under both experimental conditions. ERP amplitudes were measured with respect to the mean voltage over a 100-ms baseline preceding stimulus onset. The N1 attention effects were quantified in terms of N1 amplitudes elicited by tones at each speaker.1 1

In the analysis reported here, we chose to report the amplitude of the N1 instead of the negative difference wave (attended minus unattended N1 = Nd) measures for two reasons. First, the N1 and Nd measures yielded practically equivalent attentional gradients. Secondly, the N1 could be more directly linked with the subject’s behavioral responses in this design, because the N1 measure was obtained for each speaker during each attention condition and thus could be unambiguously associated with behavioral detection responses and RTs obtained under the same condition. In contrast, the Nd measure is derived by subtracting ERPs taken under two different attention conditions.

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Attentional gradients of ERP components were evaluated by one-way repeated measures ANOVA (factor: Location) including Bonferroni-corrected post-tests. Planned comparisons to evaluate differences in ERP amplitude gradients between the Attend Center and Attend Right conditions were made with two-tailed, paired t tests [67,68]. 2.8. Topographical mapping For illustrating ERP distributions, isopotential maps were computed using a bicubic spline interpolation algorithm [51]. Scalp distributions of N1 attention effects were compared by repeated measures ANOVAs on mean amplitudes in various configurations: midline anterior/ posterior differences were assessed with the factors Group (Patient Group vs. Control Group) and Electrode (Fz, Cz, Pz); tests for lateral and anterior/posterior differences between distributions employed the factors Group (or Condition) and Quadrant, a total of 20 electrode sites divided into four quadrants (Fp1, F7, F3, FC5, FC1 [Quadrant 1]; Fp2, F8, F4, FC2, FC6 [Quadrant 2]; CP1, P3, TO1, PO1, O1 [Quadrant 3], and CP2, P4, PO2, TO2 and O2 [Quadrant 4]. These comparisons included normalization procedures [47] and adjustments of P values where appropriate [35]. 2.9. MRI anatomical measurements Quantitative volumes of various brain structures were available for the autistic but not the control subjects. Such anatomical data were not available for the control subjects. MRI protocols and anatomical measurement procedures used have been described in detail previously for the corpus callosum [28]; for intracranial, whole brain, cerebral, and cerebellar volumesplay a major role in the developmental failure to engage in joint social attention [23,24] and for frontal, temporal, parietal, and occipital gray and white matter volumes [13]. Statistical comparisons were made between the N = 7 autistic subjects who participated in the present study and an N = 41 normal control group from Carper and Courchesne [12]. These seven autistic patients were among the N = 19 autistic patients reported by Carper and Courchesne [12].

3. Results 3.1. Behavior 3.1.1. Sensory discrimination testing Both groups showed similar performance in discriminating stimuli differing in frequency and location from a standard stimulus (see Fig. 2). In the frequency discrimination test, the patient group discriminated 79.3% of the deviant tones correctly, and the control

Fig. 2. Results of the sensory discrimination tests. Top graph: frequency discrimination of wide-bandwidth noise bursts. Center and bottom graph: spatial discrimination tests. Correct responses and false alarms to stimuli coming from two different central or peripheral spatial locations, respectively.

group discriminated 84.3%; this difference was not significant (t[6] = 0.39, ns). Although the false alarm rate was larger in the patient group (7.6%) than in the

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control group (3.8%), the two groups did not differ significantly (t[6] = 0.56, ns). In the spatial discrimination task, with the central array, the percent correct responses for the two groups did not differ significantly for the 68 (t[6] = 0.87, ns) or 128 separations (t[6] = 1.14, ns). Similar results were obtained with the peripheral array for the 68 (t[6] = 0.39, ns) and the 128 (t[6] = 0.80, ns) separations. No differences between groups were found in false alarm rates in any of these conditions. 3.1.2. Detection response rates (DRRs) 3.1.2.1. Control group. In the main experiment, the control group made more correct target detections at central (S1) than at peripheral (S8) attended locations (t[6] = 4.01, P b 0.01) (see Fig. 3). The fall-off of DRRs as a function of distance from the attended central location was highly significant ( F[3/18] = 117.2, P b 0.0001), mainly due to significant differences between the locations S1 and S2 (t[6] = 11.94, P b 0.001), locations S1 and S3 (t[6] = 16.26, P b 0.001), locations S1 and S4 (t[6] = 16.13, P b 0.001),

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and between locations S2 and S3 (t[6] = 4.19, P b 0.01). For the peripheral array, the fall-off was also significant ( F[3/18] = 10.40, P b 0.0003) due to significant differences between the locations S5 and S8 (t[6] = 4.39, P b 0.01), S6 and S8 (t[6] = 4.35, p b 0.01), and between S6 and S7 (t[6] = 3.39, P b 0.05). 3.1.2.2. Patient group. There was no significant difference in the number of correct responses given to target stimuli coming from the central (S1) vs. peripheral (S8) attended locations. The fall-off of DRRs across the central speaker array S1–S4 was highly significant ( F[3/18] = 14.3, P b 0.0001), mainly due to significant differences between locations S1 and S3 (t[6] = 5.36, P b 0.001) and S1 and S4 (t[6] = 5.65, P b 0.001). Across the peripheral array S5–S8, the overall fall-off of DRRs only approached a trend ( F[3/18] = 2.79, P b 0.07). 3.1.2.3. Group comparison. The control group had higher hit rates than the patient group for targets occurring at the attended central (S1, t[12] = 4.53, P b 0.001) and peripheral (S8, t[12] = 4.1, P b 0.01) locations. The fall-off of responses was steeper in the control group between central neighboring locations S1 and S2 (t[12] = 4.35, P b 0.001) and between the locations S1 and S4 (t[12] = 4.99, P b 0.001). For responses to peripheral stimuli, differences between groups in fall-off between attended location S8 and the nearest neighbor S7 were not significant, but the overall fall-off (S8–S5) was steeper for the control group than for the autistic subjects (t[12] = 2.53, P b 0.05). 3.1.3. Reaction times (RTs) 3.1.3.1. Control group. The control group showed faster reaction times to the central attended location S1 than to the peripheral location S8 (t[6] = 4.11, P b 0.01). The fall-off across the central array was highly significant ( F[3/18] = 16.0, P b 0.0001), mostly due to significant differences between location S1 and S2 (t[6] = 3.68, P b 0.05), S1 and S3 (t[6] = 5.38, P b 0.001), and S1 and S4 (t[6] = 6.46, P b 0.001). Peripheral fall-off effects were not significant.

Fig. 3. Top graph: the distribution of detection response rates for target/ deviant stimuli (hits to attended locations, false alarms to neighboring sources) at each speaker position for the Attend Center and Attend Right conditions (error bars show standard deviation; STD). Bottom graph: the reaction times (RTs) of detection responses to targets/deviants at each position in the Attend Center and Attend Right conditions (error bars show STD). RTs are only shown for the attended half of the array because the RTs of the responses to the unattended half of the array were very few in number and therefore not reliable. Arrows mark the attended locations.

3.1.3.2. Patient group. There was no significant difference in RTs to central vs. peripheral locations in the patient group. The fall-off across the central speaker array was significant ( F[3/18] = 7.16, P b 0.01), mostly because of significant differences between S1 and S4 (t[6] = 4.52, P b 0.01) and S2 vs. S4 (t[6] = 3.1, P b 0.05). There were no statistically significant peripheral fall-off effects. 3.1.3.3. Group comparison. The fall-off of RT was steeper for the control group than for the autistic group for neighboring central locations S1 and S2 (t[12] = 3.31, P b 0.01) and across the whole central array (t[12] = 5.56, P b 0.0001). For the peripheral array, group differences between neighboring locations S8 and S7 did not differ significantly

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and between the extreme locations (S8–S5) within the array only showed a trend (t[12] = 1.73, P b 0.11). 3.2. Event-related potentials 3.2.1. ERPs to standard stimuli In both groups, the ERPs elicited by standard stimuli at the attended locations included a prominent early negative deflection (N1) that was larger in amplitude than the ERPs to the same stimuli when unattended (Fig. 4). The scalp distributions of the N1 waves showed maximum amplitudes over right frontal scalp areas for the central stimulus (S1) and over left frontal areas for

the right peripheral stimulus (S8) (Fig. 5). Because of this anterior distribution, substantial auditory ERP activity was evident in the vertical EOG channel. Although the attended N1 was larger and more prolonged in the central group than in the patients (t[12] = 4.20, P b 0.005), no significant differences in N1 topography (100–200 ms) were found between the two groups for either the central or peripheral attended stimuli. 3.2.2. ERPs to deviant stimuli In the control group, ERPs to attended deviant stimuli at the attended location (S1) showed a fronto-central negative enhancement that included an N1 at 100–200 ms, and a widely distributed P3 wave peaking at around 400 ms for central stimuli (Fig. 6). The control group ERPs to stimuli at the attended peripheral location (S8) included a similar fronto-central negative enhancement followed by a smaller late positivity (P3) beginning at 400 ms and lasting until the end of the analysis period. In the patient group, ERPs to deviant stimuli at the attended locations included a small fronto-centrally distributed N1 and a broadly distributed P3 wave peaking between 400 and 500 ms for central stimuli. For peripheral stimuli, the N1 was followed by a small P3 commencing at around 300 ms and lasting for several hundred milliseconds without a clearly identifiable peak. The P3 showed an amplitude maximum over the parietal scalp areas (Fig. 7) and was larger for the control group than for the patients for the central stimuli (t[12] = 2.32, P b 0.05), and its distribution did not differ significantly between groups for either the central or peripheral attended stimuli. 3.3. Gradients of N1 and P3 3.3.1. Control group 3.3.1.1. Standard stimuli. As shown in Figs. 8 and 10, the amplitude of N1 to attended standard stimuli presented from speaker S1 was larger in amplitude than the N1 amplitude to attended stimuli coming from speaker S8 (t[6] = 2.67, P b 0.04). The amplitude fall-off across the central speaker array was significant ( F[3/18] = 9.49, P b 0.001), mainly due to significant amplitude differences between speaker S1 and S2 (t[6] = 3.16, P b 0.05), speaker S1 and S3 (t[6] = 3.82, P b 0.01), and speaker S1 and S4 (t[6] = 5.13, P b 0.001). The same analysis for the peripheral array (S5–S8) did not indicate a significant falloff in amplitude ( F[3/18] = 1.93, ns).

Fig. 4. Grand-average ERPs to the frequent standard sounds at the attended locations in the Attend Center and Attend Right conditions (for simplicity, ERPs from only 22 of the 44 sites, and the horizontal and vertical EOG are shown). In each condition, ERPs elicited by the attended tones were negatively displaced (N1 component) as compared to the ERPs to the same tones when unattended. ERPs recorded from the vertex (Cz) are shown enlarged.

3.3.1.2. Deviant stimuli. The amplitudes of the N1 waves elicited by deviant stimuli at the attended locations S1 and S8 did not differ significantly (t[6] = 1.19, ns; Figs. 9 and 10). The amplitude fall-off across the central array was significant ( F[3/18] = 3.24, P b 0.05), mainly due to a significant difference between speaker locations S1 and S4 (t[6] = 2.98,

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( F[3/18] = 0.36, ns) and peripheral ( F[3/18] = 0.2, ns) arrays did not produce significant results. 3.3.2.3. P3 wave. The amplitude of the P3 did not differ between the attended locations S1 and S8 (t[6] = 0.49, ns). For the central array, the amplitude fall-off was significant ( F[3/18] = 11.0, P b 0.001), mostly due to significant differences between locations S1 and S3 (t[6] = 5.48, P b 0.001) and S1 and S4 (t[6] = 4.25, P b 0.01). The P3 amplitudes across the peripheral array showed a significant fall-off ( F[3/18] = 5.93, P b 0.01) mainly due to a significant difference between locations S7 and S8 (t[6] = 4.03, P b 0.01). 3.3.3. Group comparison 3.3.3.1. N1 to standard stimuli. The control group showed a steeper fall-off than the patient group in N1 amplitude

Fig. 5. Normalized isopotential maps of the scalp distributions of the N1 components (100–200 ms) elicited by standard stimuli at the attended locations S1 and S8. Normalized voltage ranges are from +1 to 1 AV.

P b 0.05). Across the peripheral array, the overall drop of N1 amplitude with increasing distance from the attended location was significant ( F[3/18] = 3.32, P b 0.05). 3.3.1.3. P3 wave. Deviant stimuli at the attended central location S1 elicited a larger P3 amplitude than stimuli coming from the attended peripheral location S8 (t[6] = 2.8, P b 0.03). For the central speaker array, the fall-off in P3 amplitude was highly significant ( F[3/18] = 17.91, P b 0.0001), mostly due to a significant difference between S1 and S2 (t[6] = 5.23, P b 0.001). The fall-off for the peripheral array only approached significance ( F[3/18] = 2.38, P b 0.10). 3.3.2. Patient group 3.3.2.1. Standard stimuli. The amplitudes of the N1 waves to attended standard stimuli presented from speakers S1 and S8 did not differ in amplitude (t[6] = 0.008, ns; Figs. 8 and 10). The amplitude fall-off across the central speaker array was significant ( F[3/18] = 3.99, P b 0.03), mostly due to a significant difference between speakers S2 and S4 (t[6] = 3.26, P b 0.05). As with the control group, a testing of amplitude values for the peripheral array (S5–S8) showed no evidence for a significant fall-off of N1 amplitude ( F[3/18] = 1.01, ns). 3.3.2.2. Deviant stimuli. The amplitudes of the N1 waves elicited by deviant stimuli at the attended locations S1 and S8 did not differ from each other significantly (t[6] = 0.42, ns). Tests for fall-off effects of N1 amplitudes across the central

Fig. 6. Same as Fig. 4 for the infrequent deviant/target sounds. Normalized voltage ranges are from +1 to 1 AV.

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3.3.3.2. N1 to deviant stimuli. In the attend-center condition the S1–S4 fall-off was significantly steeper in the control than in the patient group (t[12] = 2.64, P b 0.03). For the peripheral array the group difference between S8 and S5 also reached significance (t[12] = 3.16, P b 0.01), again with the control group showing steeper fall-off. 3.3.3.3. P3 wave. The control group showed a steeper falloff for S1–S2 differences than the patient group (t[12] = 2.26, P b 0.05). This was also true for S1–S4 differences (t[12] = 3.0, P b 0.02). No significant between-group differences were obtained for the peripheral stimuli.

Fig. 7. Normalized isopotential maps of the scalp distributions of the P3 wave (300–600 ms) elicited by deviant stimuli at the attended locations S1 and S8.

across the nearest neighbors S1 vs. S2 (t[12] = 5.1, P b 0.001) in the attended-center condition; the S1–S4 difference between groups also reached significance (t[12] = 2.47, P b 0.03). No significant differences between groups were found for peripheral between-speaker differences (see Fig. 10).

3.3.4. MRI anatomical abnormalities in the autistic group Table 2 shows volumetric and area results from comparison of the 41 normal controls in the Carper and Courchesne report [12] to the 7 autistic subjects in the present study (note that these 7 were among the N = 19 autistic patients reported in the Carper and Courchesne study [12]). Although intracranial volumes in autistic and normal groups differed by only 1%, the autistic group had statistically significantly less brain volume ( 7.5%), t[1, 8.3] = 2.35, P = 0.046, and more CSF volume (+58.2%), t[1, 8.7] = 4.80, P = 0.001. Frontal gray matter volume and parietal gray and white matter volumes were also significantly reduced in the autistic group for frontal gray, t[1, 9.9] = 2.56, P = 0.029, parietal gray, t[1, 6.1] = 2.89, P = 0.028, and parietal white matter, t[1, 6.8] = 2.50, P = 0.042. The posterior subregion of the corpus callosum (area 4 in Table 2) that is known to contain temporal and parietal interhemispheric axons was significantly thinner than normal (t[1, 9.8] = 2.62, P = 0.026) as was the

Fig. 8. Grand-average ERPs elicited at two midline sites (Fz, Cz) by frequent standard stimuli from all speaker positions in the central and right peripheral array. Asterisks mark the attended locations.

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Fig. 9. Grand-average ERPs elicited at two midline sites (Cz, Pz) by infrequent target/deviant standard stimuli from all speaker positions in the central and right peripheral array. Asterisks mark the attended locations.

anterior subregion (area 2 in Table 2) that contains dorsolateral prefrontal axons (t[1, 6.9] = 2.58, P = 0.037). In the cerebellum, gray matter volume was reduced by 2.7% in the autism group and white matter was increased by 11.7%, resulting in a significantly reduced gray-to-white ratio t[1, 8.0] = 3.24, P = 0.012.

4. Discussion The present behavioral and ERP results indicate that high functioning autistic adults have a significantly diminished, but not absent, capacity to selectively attend to one sound source among many in the environment. On one hand, we found in a spatial discrimination task that autistic patients could accurately tell when two identical sounds came from two sources only 68 apart as compared to when both came from the same source. However, in this simple same/ different source discrimination there were only three sound sources, only two sounds presented per trial, and a relatively long inter-trial interval. In contrast, these same patients performed significantly worse than normal controls in a more challenging selective listening experiment that had close parallels to the task of sound source localization in the natural environment. In this more difficult experiment, eight closely positioned speakers in free-field each emitted a continuous and rapid stream of two complex sounds (wideband noise bursts), and subjects had to detect occurrences of one of the two sounds (the less-frequent targets) emitted from just one of the sources. Because each

source emitted the same sounds in randomized sequences, successful performance required the subject to continuously tune in to one source and tune out the other seven sources. As compared to controls, the autistic patients were slower and less accurate in detecting the target sources at the designated source. Moreover, while control subjects demonstrated sharply focused gradients of spatial attention, autistic patients showed broader gradients indicative of poor sound source localization and more widely dispersed spatial attention. The ERP results confirmed these group differences in auditory spatial performance. Spatial gradients of N1 amplitude were steeper in normal controls than in the autistic patients, particularly when attention was directed to the central sound source. The lack of a sharp spatial tuning of the N1 in autistic patients suggests a deficiency–perhaps in the auditory cortex where N1 is generated [53]–in the initial stage of attention filtering of irrelevant sources of sound. Less effective tuning of attention to central sound sources was also evident in the abnormally shallow gradient of P3 amplitude to the deviant sounds in the autistic group. This P3 abnormality most likely reflects a still later stage of deviant target identification that was less narrowly confined to the attended source in autistic patients. Interestingly, in a different developmental disorder, congenital blindness, superior behavioral performance in this same task was found in association with enhanced spatial tuning of the N1, particularly to peripheral sound sources, as compared to normal controls [59]. It seems, then, that these ERP components may be sensitive indicators of developmentally

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W.A. Teder-Sa¨leja¨rvi et al. / Cognitive Brain Research 23 (2005) 221–234 Table 2 MRI volumetric data for autistic subjects in present study (N = 7) as compared to a large sample (n = 41) of normal controls from an MRI companion paper [38]

Intracranial volume, ICV Total brain volume, TBV CSF—whole brain Cerebrum—parenchyma Frontal gray Temporal gray Parietal gray Occipital gray Frontal white Temporal white Parietal white Occipital white Cerebellum gray Cerebellum white Gray/White Callosal area 1 Callosal area 2 Callosal area 3 Callosal area 4 Callosal area 5

Autistic subjects in present study, N =7

Normal controls in Carper and Courchesne, N = 41

1473.0 (104.4) 1207.0 (101.8) 288.40 (53.2) 948.9 (92.8) 224.1 (71.5) 141.5 (16.6) 122.8 (19.5) 79.2 (10.3) 119.5 (45.1) 63.9 (8.8) 88.4 (11.3) 50.3 (4.8) 105.1 (5.7) 38.3 (4.3) 2.77 (0.3) 129.6 (34.4) 95.8 (20.1) 96.0 (17.2) 83.4 (11.1) 181.4 (25.1)

1487.6 (112.3) 1305.3 (105.3) 182.28 (58.9) 1037.1 (93.1) 250.6 (31.6) 151.8 (14.2) 146.7 (16.7) 88.1 (12.7) 143.0 (16.5) 65.8 (6.8) 100.9 (12.1) 54.6 (7.2) 107.9 (7.5) 34.3 (3.9) 3.17 (0.29) 131.9 (16.7) 118.8 (20.1) 105.3 (15.9) 97.5 (16.9) 185.4 (27.1)

Values are mean (SD).

Fig. 10. Amplitudes of the N1 (at Cz) in the two groups to frequent standard and infrequent deviant stimuli (top and center graphs), and the mean P3 amplitude elicited by deviant stimuli (bottom graph; site Pz). Arrows mark the attended locations.

altered ability to selectively tune attention to a desired sound source, revealing impairment as in autism, or enhancement as in congenital blindness. In the present study, we found abnormal auditory N1 responses during attention to competing sound sources. Abnormally reduced N1 responses have been reported during passive perception [11] as well as in active focused attention tasks [15,16,19,21,41,42]. Such abnormalities have been hypothesized to reflect dysfunction in auditory association cortex [11]. Recent ERP studies in autistic children have also identified abnormality of the mismatch negativity localized to temporal and frontal regions in response to deviant sounds [29]. If mature, high-functioning autistic patients are impaired in selective tuning of attention to a single sound source among many sources, one might expect to find equal or worse deficits in the toddler and young child with autism, particularly the low-functioning child. The implication of the present results, then, is that autistic individuals may experience confusion and disorientation about where sounds are coming from in a bnoisyQ environment, may attribute sounds to the wrong source, and, so, would likely benefit from being in an acoustically simplified setting. Belmonte [7] similarly concluded that autistic individuals are impaired in fine-grained spatial tuning in a stimulus-dense, rapidly changing visual environment. Impairment in adaptively tuning selective attention would be especially disorienting and confusing for the autistic infant who is faced with the acoustical complexity of the natural world, which places high demands on selective attention as a tool to discover the

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separateness and informational continuity of individual sound streams. Lacking the ability to correctly attribute a sudden sound to a specific source, an infant could not orient towards and benefit from that information; sounds might be more likely either to startle, to be attributed to the wrong source, or to go undetected. Indeed, autistic toddlers and children are often observed to be variably hypersensitive or hyposensitive to sounds, and the presence of such maladaptive responses has long been considered to have diagnostic value [62]. Autistic patients are deficient in orienting and strategically shifting attention processes, as had been shown by many behavioral [33,58,69–71,73,74], ERP [14,19,21], and EEG [7] studies. Previous ERP, EEG, and fMRI studies demonstrate impairment in the ability of autistic patients to shift attention rapidly within and between auditory and visual streams of information and have linked this deficit to anatomical abnormalities in parietal, frontal, and cerebellar structures [1,2,7,10,21,22,33,71,75]. The present study suggests that an additional impediment to the accurate tracking of changing sources of acoustical information is a deficit in the ability to finely focus attention upon a specific source of sounds, even when that source is unchanging and the autistic patient has plenty of time and training to perfect that selection. This deficit may be exacerbated by a reduced auditory frequency selectivity and, hence, increased susceptibility to masking sounds that were reported in a group of highly functioning autistic patients [56]. Impaired sound source attribution and selection may be one of several important factors impeding the development of normal social attention in autistic infants. Among the neural systems that mediate sound source localization, several have been reported to be abnormal in autism, including structures found to be abnormal in our present sample of autistic subjects. The superior olive, which is essential for normal sound localization [72], has been reported in one autistic postmortem case to be almost entirely absent [60] whereas other postmortem studies of autism have not found any abnormality [4,39]. In nonautistic persons, agenesis of the corpus callosum is reported to reduce accuracy in sound source localization [57]. In our sample of autistic patients, we found posterior regions of the callosum that contain axons from auditory cortex to be significantly thinner than normal (see Table 2). Such significant callosal abnormality has also been seen in other studies of autism [20,28,55] suggesting that callosal defect is a common anatomical finding in this disorder and may contribute to deficits in sound source localization in affected individuals. Temporal lobe structures including auditory cortex, superior temporal gyrus (BA 22/42), and planum temporale are also essential for accurate sound localization in humans and animals [31,36,76]. Maldevelopment of the temporal lobes (as well as frontal lobes, limbic system and cerebellum) is a definite feature of autism [18,36]. In the present study,

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autistic adults had 7% less temporal gray matter than normal, although this difference in our small sample did not reach significance (Table 2, P=0.01). In adults with autism, reduced volume of the left planum temporale has been reported [61], and a white matter abnormality of the temporal lobes has been suggested on the basis of diffusion tensor imaging [6]. Deficits in localizing sound sources have been reported in patients with visual–spatial neglect. These deficits can co-occur in adults with acquired lesions damaging the planum temporale, superior temporal gyrus, temporoparietal junction, inferior parietal cortex, and dorsolateral frontal cortex [50,76]. One explanation proposed for this cooccurrence of deficits is that these regions contain multimodal representations of space [50]. Each is a region shown to be abnormal in autism, as reviewed above and elsewhere [18,26,63]. Further, in a series of studies by Townsend and colleagues [69,71,73] in autistic adults, neglect-like inattention in visual spatial tasks was described in those with parietal volume loss, and across individual autistic patients it was found that the greater the loss, the more abnormal the behavioral and ERP measures recorded. The autistic patients in our present study, much like those in the Townsend studies, had substantial parietal volume loss: 16% less parietal gray and 12% less parietal white. Future studies of auditory and visual spatial localization and selective attention in autism would benefit from using precise expert-based anatomic methodologies to subdivide parietal, temporal, and frontal lobes into subregions (e.g., planum temporale, inferior parietal lobule) for gray matter volumetric analyses and correlation with behavioral and functional measures. In autism, brain overgrowth–not reduced size–marks the first years of life according to many recent studies [8,12,13,25,54,63]. This early developmental period of premature overgrowth is followed by abnormally slowed growth or arrest of growth in different structures [8,12,13]. A new developmental theory of autism proposes that this early brain overgrowth is a signal of underlying disruption of the development of pyramidal neurons and connectivity patterns [18], which engenders the various structural and functional defects described above in the mature autistic brain. Courchesne and Pierce [18] suggest that underdevelopment of large frontal pyramidal neurons would result in (1) impaired long distance anterior to posterior cortico-cortical connectivity with loss of top-down control and synchrony between anterior and posterior cortical regions and (2) over-connectivity of local and short distance projections resulting in poorly synchronized hyper-responsiveness and reduced spatiotemporal selectivity in local posterior regions. Such deviant neuronal connectivity would likely impede the normal development of the distributed, relational network that is believed to underlie sound localization in the auditory cortex [48]. In addition to being compatible with the present behavioral and ERP results, this developmental theory is also compatible with recent fMRI

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findings showing reduced anterior activation and reduced anterior–posterior coupling but increased activation in posterior cortices during attention and other complex cognitive tasks in autistic patients [8,37]. In conclusion, the present study found ERP and behavioral evidence of impaired ability in autistic patients to selectively tune attention to specific sound sources, especially central ones, in the context of multiple competing sources. The deficit was not due to poor sound frequency or location discrimination per se, but instead appears due to a failure to establish a sharply focused attention filtering of irrelevant sound sources. The substrates for this deficit likely include cortical regions mediating multimodal spatial processing and selective attention. It is possible that impairment in sound source attribution and selection might be one factor underlying several well-known auditory-related abnormalities in autism, including hyper- and hypo-sensitivity to sounds, confusion and aversive reactions to sounds, and abnormal orienting and shifting attention in response to sound signals. Further, the hypothesis is raised that this deficit may have a significant impact on how the autistic infant reacts to and learns from social and non-social sounds. Lastly, the present results suggest that auditory information processing by autistic patients will be adversely challenged in a natural, acoustically complex and noisy environment, but could be significantly assisted in an acoustically simplified setting.

Acknowledgments We thank Ayrielle B. Goins, Carlos Nava, Matthew M. Marlow, and Marissa Westerfield for technical assistance. This work was supported by grants from NIMH (MH-25594 awarded to Steven A. Hillyard and 2-ROI-MH36840 awarded to Eric Courchesne).

References [1] N.A. Akshoomoff, E. Courchesne, A new role for the cerebellum in cognitive operations, Behav. Neurosci. 106 (1992) 731 – 738. [2] N.A. Akshoomoff, E. Courchesne, J. Townsend, Attention coordination and anticipatory control, Int. Rev. Neurobiol. 41 (1997) 575 – 598. [3] American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders, Washington, DC, American Psychiatric Association, 1994. [4] A. Bailey, P. Luthert, A. Dean, B. Harding, I. Janota, M. Montgomery, M. Rutter, P. Lantos, A clinicopathological study of autism, Brain 121 (1998) 889 – 905. [5] G.T. Baranek, L.G. Foster, G. Berkson, Tactile defensiveness and stereotyped behaviors, Am. J. Occup. Ther. 51 (1997) 91 – 95. [6] N. Barnea-Goraly, H. Kwon, V. Menon, S. Eliez, L. Lotspeich, A.L. Reiss, White matter structure in autism: preliminary evidence from diffusion tensor imaging, Biol. Psychiatry 55 (2004) 323 – 326. [7] M. Belmonte, Abnormal attention in autism shown by steady-state visual evoked potentials, Autism 4 (2000) 269 – 285.

[8] M.K. Belmonte, D.A. Yurgelun-Todd, Functional anatomy of impaired selective attention and compensatory processing in autism, Cogn. Brain Res. 17 (2003) 651 – 664. [9] S. Bettison, The long-term effects of auditory training on children with autism, J. Autism Dev. Disord. 26 (1996) 361 – 374. [10] N. Bruneau, M.C. Dourneau, B. Garreau, L. Pourcelot, G. Lelord, Blood flow response to auditory stimulations in normal, mentally retarded, and autistic children: a preliminary transcranial Doppler ultrasonographic study of the middle cerebral arteries, Biol. Psychiatry 32 (1992) 691 – 699. [11] N. Bruneau, S. Roux, J.L. Adrien, C. Barthelemy, Auditory associative cortex dysfunction in children with autism: evidence from late auditory evoked potentials (N1 wave–T complex), Electroen. Clin. Neurol. 110 (1999) 1927 – 1934. [12] R. Carper, E. Courchesne, Localized enlargement of the frontal lobe in autism (submitted for publication). [13] R.A. Carper, P. Moses, Z.D. Tigue, E. Courchesne, Cerebral lobes in autism: early hyperplasia and abnormal age effects, NeuroImage 16 (2002) 1038 – 1051. [14] R. Ceponie´ne, T. Lepisto, A. Shestakova, R. Vanhala, P. Alku, R. N77t7nen, K. Yaguchi, Speech-sound-selective auditory impairment in children with autism: they can perceive but do not attend, Proc. Natl. Acad. Sci. U. S. A. 100 (2003) 5567 – 5572. [15] K.T. Ciesielski, E. Courchesne, R. Elmasian, Effects of focused selective attention tasks on event-related potentials in autistic and normal individuals, (published erratum appears in Electroen. Clin. Neuro. 1990 Mar; 77(3), 566) Electroencephalogr. Clin. Neurophysiol. 75 (1990) 207 – 220. [16] K.T. Ciesielski, J.E. Knight, R.J. Prince, R.J. Harris, S.D. Handmaker, Event-related potentials in cross-modal divided attention in autism, Neuropsychologia 33 (1995) 225 – 246. [17] V.P. Clark, S. Fan, S.A. Hillyard, Identification of early visually evoked potential generators by retinotopic and topographic analyses, Hum. Brain Mapp. 2 (1995) 170 – 187. [18] E. Courchesne, K. Pierce, Early brain overgrowth in autism: A potential signal of disrupted development of frontal pyramidal neurons and cortical connectivity, Int. J. Dev. Neurosci. (in press). [19] E. Courchesne, B.A. Kilman, R. Galambos, A.J. Lincoln, Autism: processing of novel auditory information assessed by event-related brain potentials, Electroencephalogr. Clin. Neurophysiol. 59 (1984) 238 – 248. [20] E. Courchesne, G.A. Press, R. Yeung-Courchesne, Parietal lobe abnormalities detected with MR in patients with infantile autism, Am. J. Roentgenol. 160 (1993) 387 – 393. [21] E. Courchesne, J. Townsend, N.A. Akshoomoff, O. Saitoh, R. YeungCourchesne, A.J. Lincoln, H.E. James, R.H. Haas, L. Schreibman, L. Lau, Impairment in shifting attention in autistic and cerebellar patients, Behav. Neurosci. 108 (1994) 848 – 865. [22] E. Courchesne, N.A. Akshoomoff, J. Townsend, O. Saitoh, A model system for the study of attention and the cerebellum: infantile autism, Electroencephalogr. Clin. Neurophysiol. 44 (1995) 315 – 325 (Supplement). [23] E. Courchesne, H.J. Chisum, J. Townsend, A. Cowles, J. Covington, B. Egaas, Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers, Radiology 216 (2000) 672 – 682. [24] E. Courchesne, C. Karns, H. Davis, Unusual brain growth patterns in early life in patients with autistic disorder, Neurology 57 (2001) 245 – 254. [25] E. Courchesne, R. Carper, N. Akshoomoff, Evidence for brain overgrowth in the first year of life in autism, J. Med. Assoc. 290 (2003) 337 – 344. [26] E. Courchesne, E. Redcay, D.P. Kennedy, The autistic brain: birth through adulthood, Curr. Opin. Neurol. 17 (2004) 489 – 496. [27] G. Dawson, A.N. Meltzoff, J. Osterling, J. Rinaldi, Neuropsychological correlates of early symptoms of autism, Child Dev. 69 (1998) 1276 – 1285.

W.A. Teder-Sa¨leja¨rvi et al. / Cognitive Brain Research 23 (2005) 221–234 [28] B. Egaas, E. Courchesne, O. Saitoh, Reduced size of corpus callosum in autism, Arch. Neurol. 52 (1995) 794 – 801. [29] M. Gomot, M.H. Giard, J.L. Adrien, C. Barthelemy, N. Bruneau, Hypersensitivity to acoustic change in children with autism: electrophysiological evidence of left frontal cortex dysfunctioning, Psychophysiology 39 (2002) 577 – 584. [30] T. Grandin, Thinking in Pictures and other Reports from my Life with Autism, Doubleday, New York, 1995, p. 68. [31] T.C. Greene, The ability to localize sound: a study of binaural hearing in patients with tumor of the brain, Arch. Surg. (Chicago) 18 (1929) 1825 – 1841. [32] J.C. Hansen, S.A. Hillyard, Endogenous brain potentials associated with selective auditory attention, Electroencephalogr. Clin. Neurophysiol. (1980) 277 – 290. [33] N.S. Harris, E. Courchesne, J. Townsend, R.A. Carper, C. Lord, Neuroanatomic contributions to slowed orienting of attention in children with autism, Cogn. Brain Res. 8 (1999) 61 – 71. [34] S.A. Hillyard, G.R. Mangun, M.G. Woldorff, S.J. Luck, Neural systems mediating selective attention in the cognitive neurosciences, in: M.S. Gazzaniga (Ed.), The Cognitive Neurosciences, MIT Press, Cambridge, MA, 1995, pp. 665 – 681. [35] H. Huynh, L.S. Feldt, Conditions under which mean square ratios repeated measurements designs have exact F-distributions, J. Am. Stat. Assoc. 65 (1970) 1582 – 1589. [36] W.M. Jenkins, M.M. Merzenich, Role of cat primary auditory cortex for sound-localization behavior, J. Neurophysiol. 52 (1984) 819 – 847. [37] M. Just, Cortical underconnectivity in high-functioning autism: brain activation and brain synchronization in cognitive tasks, International Meeting for Autism Research, Sacramento, California, 2004. [38] L. Kanner, Autistic disturbances of affective contact, Nerv. Child 2 (1943) 217 – 250. [39] T. Kemper, M. Bauman, Neuropathology of infantile autism, J. Neuropathol. Exp. Neurol. 57 (1998) 645 – 652. [40] A. Le Couteur, M. Rutter, C. Lord, P. Rios, S. Robertson, M. Holdgrafer, J. McLennan, Autism diagnostic interview: a standardized investigator-based instrument, J. Autism Dev. Disord. 19 (1989) 363 – 387. [41] A.J. Lincoln, E. Courchesne, L. Harms, M. Allen, Contextual probability evaluation in autistic, receptive developmental language disorder, and control children: event-related brain potential evidence, J. Autism Dev. Disord. 23 (1993) 37 – 58. [42] A.J. Lincoln, E. Courchesne, L. Harms, M. Allen, Sensory modulation of auditory stimuli in children with autism and receptive developmental language disorder: event-related brain potential evidence, J. Autism Dev. Disord. 25 (1995) 521 – 539. [43] C. Lord, M. Rutter, S. Goode, J. Heemsbergen, H. Jordan, L. Mawhood, E. Schopler, Autism diagnostic observation schedule: a standardized observation of communicative and social behavior, J. Autism Dev. Disord. 19 (1989) 185 – 212. [44] C. Lord, M. Rutter, A. Le Couteur, Autism diagnostic interviewrevised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders, J. Autism Dev. Disord. 24 (1994) 659 – 685. [45] C. Lord, S. Risi, L. Lambrecht, E.H. Cook Jr, B.L. Leventhal, P.C. DiLavore, A. Pickles, M. Rutter, The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism, J. Autism Dev. Disord. 30 (2000) 205 – 223. [46] J. Martineau, B. Garreau, C. Barthelemy, G. Lelord, Evoked potentials and P300 during sensory conditioning in autistic children, in: R. Karrer, J. Cohen, P. Tueting (Eds.), Brain and Information: Event-related Potentials, New York Academy of Sciences, New York, 1984, pp. 362 – 369. [47] G. McCarthy, C.C. Wood, Scalp distributions of event-related potentials: an ambiguity associated with analysis of variance models, Electroencephalogr. Clin. Neurophysiol. 62 (1985) 203 – 208.

233

[48] J.C. Middlebrooks, L. Xu, A. Clock Eddins, D.M. Green, Codes for sound-source location in nontonotopic auditory cortex, J. Neurophysiol. 80 (1998) 863 – 881. [49] R. N77t7nen, Attention and Brain Function. Hillsdale, Lawrence Erlbaum Associates, New Jersey, 1992, pp 236–304. [50] F. Pavani, E. Ladavas, J. Driver, Auditory and multisensory aspects of visuospatial neglect, Trends Cogn. Sci. 7 (2003) 407 – 414. [51] F. Perrin, O. Bertrand, J. Pernier, Scalp current density mapping: Value and estimation from potential data, Institute of Electrical and Electronics Engineers, Transactions on Biomedical Engineering, 1987, pp. 283–288. [52] T.W. Picton, The P300 wave of the human event-related potential, J. Clin. Neurophysiol. 9 (1992) 456 – 479. [53] T.W. Picton, C. Alain, D.L. Woods, M.S. Kohn, M. Scherg, P. Valdes-Sosa, J. Bosch-Bayard, J. Trujillo, Intracerebral sources of human auditory evoked potentials, Audiol. Neuro-Otol. 4 (1999) 64 – 79. [54] J. Piven, Longitudinal MRI study on 18–35 month olds with autism, International Meeting for Autism Research, Sacramento, California, 2004. [55] J. Piven, J. Bailey, B.J. Ranson, S. Arndt, An MRI study of the corpus callosum in autism, Am. J. Psychiatry 1545 (1997) 1051 – 1055. [56] K. Plaisted, L. Saksida, J. Alca´ntara, E. Weisblatt, Towards an understanding of the mechanisms of weak central coherence effects: experiments in visual configural learning and auditory perception, Philos. Trans. R. Soc., B 358 (2003) 375 – 386. [57] P. Poirier, S. Miljours, M. Lassonde, F. Lepore, Sound localization in acallosal human listeners, Brain 116 (1993) 53 – 69. [58] N.J. Rinehart, J.L. Bradshaw, S.A. Moss, A.V. Brereton, B.J. Tonge, A deficit in shifting attention present in high-functioning autism but not Asperger’s disorder, Autism 5 (2001) 67 – 80. [59] B. Rfder, W.A. Teder-S7lej7rvi, A. Sterr, F. Rfsler, S.A. Hillyard, H.J. Neville, Improved auditory spatial tuning in blind humans, Nature 400 (1999) 162 – 166. [60] P.M. Rodier, J.L. Ingram, B. Tisdale, S. Nelson, J. Romano, Embryological origin for autism: developmental anomalies of the cranial nerve motor nuclei, J. Comp. Neurol. 370 (1996) 247 – 261. [61] D.C. Rojas, S.D. Bawn, T.L. Benkers, M.L. Reite, S.J. Rogers, Smaller left hemisphere planum temporale in adults with autistic disorder, Neurosci. Lett. 328 (2002) 237 – 420. [62] E. Schopler, R.J. Reichler, B. Rochen Renner, The Childhood Autism Rating Scale, Western Psychological Services, 1988. [63] B.F. Sparks, S.D. Friedman, D.W. Shaw, E.H. Aylward, D. Echelard, A.A. Artru, K.R. Maravilla, J.N. Giedd, J. Munson, G. Dawson, S.R. Dager, Brain structural abnormalities in young children with autism spectrum disorder, Neurology 59 (2002) 184 – 192. [64] W. Teder, R. N77t7nen, Selection of speech messages in free-field listening, NeuroReport 5 (1993) 307 – 309. [65] W. Teder, R. N77t7nen, Event-related potentials demonstrate a narrow focus of auditory spatial attention, NeuroReport 5 (1994) 709 – 711. [66] W. Teder, K. Alho, K. Reinikainen, R. N77t7nen, Interstimulus interval and the selective attention effect on auditory ERPs: bN1 enhancementQ versus processing negativity, Psychophysiology 30 (1993) 71 – 81. [67] W.A. Teder-S7lej7rvi, S.A. Hillyard, The gradient of spatial auditory attention in free field: an event-related potential study, Percept. Psychophys. 60 (1998) 1228 – 1242. [68] W.A. Teder-S7lej7rvi, S.A. Hillyard, B. Rfder, H.J. Neville, Spatial attention to central and peripheral auditory stimuli as indexed by event-related potentials, Cogn. Brain Res. 8 (1999) 213 – 227. [69] J. Townsend, E. Courchesne, B. Egaas, Slowed orienting of covert visual–spatial attention in autism: specific deficits associated with cerebellar and parietal abnormality, Dev. Psychopathol. 8 (1996) 563 – 584. [70] J. Townsend, N.S. Harris, E. Courchesne, Visual attention abnormalities in autism: delayed orienting to location, J. Int. Neuropsychol. Soc. 2 (1996) 541 – 550.

234

W.A. Teder-Sa¨leja¨rvi et al. / Cognitive Brain Research 23 (2005) 221–234

[71] J. Townsend, E. Courchesne, N. Singer-Harris, J. Covington, M. Westerfield, P. Lyden, T.P. Lowry, G.A. Press, Spatial attention deficits in patients with acquired or developmental cerebellar abnormality, J. Neurosci. 19 (1999) 5632 – 5642. [72] B.A. van Adel, J.B. Kelly, Kainic acid lesions of the superior olivary complex: effects on sound localization by the albino rat, Behav. Neurosci. 112 (1998) 432 – 446. [73] J.A. Wainright, S.A. Bryson, Visual–spatial orienting in autism, J. Autism Dev. Disord. 26 (1996) 423 – 438.

[74] J.A. Wainwright-Sharp, S.E. Bryson, Visual orienting deficits in high-functioning people with autism, J. Autism Dev. Disord. 23 (1993) 1 – 13. [75] M. Zilbovicius, N. Boddaert, P. Belin, J.B. Poline, P. Remy, J.F. Mangin, L. Thivard, C. Barthelemy, Y. Samson, Temporal lobe dysfunction in childhood autism: a PET study. Positron emission tomography, Am. J. Psychiatry 157 (2000) 988 – 993. [76] U. Zimmer, J. Lewald, H.O. Karnath, Disturbed sound lateralization in patients with spatial neglect, J. Cogn. Neurosci. 15 (2003) 694 – 703.