Functional imbalance of visual pathways indicates alternative face processing strategies in autism D. Hubl, MD; S. Bölte, PhD; S. Feineis–Matthews; H. Lanfermann, MD; A. Federspiel, PhD; W. Strik, MD; F. Poustka, MD; and T. Dierks, MD
Abstract—Objective: To investigate whether autistic subjects show a different pattern of neural activity than healthy individuals during processing of faces and complex patterns. Methods: Blood oxygen level– dependent (BOLD) signal changes accompanying visual processing of faces and complex patterns were analyzed in an autistic group (n ⫽ 7; 25.3 [6.9] years) and a control group (n ⫽ 7; 27.7 [7.8] years). Results: Compared with unaffected subjects, autistic subjects demonstrated lower BOLD signals in the fusiform gyrus, most prominently during face processing, and higher signals in the more object-related medial occipital gyrus. Further signal increases in autistic subjects vs controls were found in regions highly important for visual search: the superior parietal lobule and the medial frontal gyrus, where the frontal eye fields are located. Conclusions: The cortical activation pattern during face processing indicates deficits in the face-specific regions, with higher activations in regions involved in visual search. These findings reflect different strategies for visual processing, supporting models that propose a predisposition to local rather than global modes of information processing in autism. NEUROLOGY 2003;61:1232–1237
Behavioral-level disturbances in face processing in subjects with autism have been reported,1-3 giving rise to discussion of whether such disturbances indicate a general perceptual deficit. However, observations such as superior performance in visual search tasks4,5 or in face processing when faces were presented upside down1 suggest the use of generally different strategies during visual processing in autism. Whereas the fusiform face area (FFA)6-8 in the inferior temporal lobe is selectively involved in the perception of faces, the parietal lobe, on the other hand, is important for visuospatial feature analysis9,10 and general visual attention.11 Both mechanisms are essential in tasks that require visual search strategies. Imaging studies of face perception imply holistic rather than feature-based processing of faces,12,13 whereas feature-based mechanisms are essential in processing of nonface objects.14,15 Recent imaging studies within the ventral visual processing stream in subjects with autism have demonstrated divergent patterns of neuronal activation during face Additional material related to this article can be found on the Neurology Web site. Go to www.neurology.org and scroll down the Table of Contents for the November 11 issue to find the title link for this article.
processing, including lower activation in the FFA and higher activation in more object-related areas14 as well as activation in regions outside the FFA proper.16 These results suggest that when solving cognitive tasks, subjects with autism evoke strategies different from those of control groups, resulting in a preservation of comparable performance. In the current fMRI study, we extend those investigations by combining a face-processing and a visual search task targeting both the ventral and the dorsal visual streams. We hypothesize a differential activation in regions beyond the face-specific cortical region (FFA) and that these differences might constitute differing processing strategies in autism. Methods and materials. Subjects. We investigated 10 adolescent and adult autistic individuals (all men) (International Classification of Diseases 10, F84.0)17 with IQ above the retarded range (IQ ⬎ 70) and 10 age- and sex-matched healthy control subjects. The clinical diagnosis of autistic disorder was corroborated using the German versions of the Autism Diagnostic Interview–Revised (ADI-R)18,19 and the Autism Diagnostic Observation Schedule (ADOS).20,21 On the ADI-R algorithm, the mean scores for qualitative impairments in social interaction, in communication, and in stereotyped, repetitive behavior were 21.4 (SD ⫽ 4.4), 16.4 (SD ⫽ 3.8), and 6.2 (SD ⫽ 2.3). On the social communication scale of the ADOS algorithm, the mean value sample was 13.2 (SD ⫽ 2.2). Nonverbal IQ was estimated by application of the standard progressive matrices by Raven in both groups.22 To exclude clini-
From the Department of Psychiatric Neurophysiology (Drs. Hubl, Federspiel, Strik, and Dierks), University Hospital of Clinical Psychiatry, Bern, Switzerland; and Department of Child and Adolescent Psychiatry (Drs. Bölte and Pousstka, S. Feineis–Matthews) and Institute of Neuroradiology (Dr. Lanfermann), Department of Radiology, University of Frankfurt, Germany. Supported in part by grant Po 255/17 from the German Research Foundation (Deutsche Forschungsgemeinschaft) (F.P.) and the Alzheimer Forschung Initiative eV (T.D.). Received February 14, 2002. Accepted in final form July 22, 2003. Address correspondence and reprint requests to Dr. D. Hubl, Department of Psychiatric Neurophysiology, University Hospital of Clinical Psychiatry, Bolligenstrasse 111, CH-3000, Bern, Switzerland 60; e-mail:
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Copyright © 2003 by AAN Enterprises, Inc.
cally relevant psychopathology, the Youth Self-Report23 or Young Adult Self-Report,24 as appropriate, was completed by each of the healthy control subjects. None of the subjects showed any relevant medical disorder (except autism in the patient group) or received any psychotropic medication during the study. Owing to noncorrectable movement artifacts during fMRI acquisition, some subjects had to be excluded from the analysis. Finally, comprehensive data were available for seven subjects in each group, between which the parameters for translatory and rotatory motion correction did not differ. The average age was 27.7 (SD ⫽ 7.8) years in the autistic group and 25.3 (SD ⫽ 6.9) years in the control group. Age did not differ (Mann–Whitney U, p ⬎ 0.05). The nonverbal IQ of 112 (SD ⫽ 9) for the control subjects and 98 (SD ⫽ 17) for the patients did not differ either (Mann–Whitney U, p ⬎ 0.05). The investigation was conducted in accordance with the Declaration of Helsinki (1964) and approved by the local ethics committee of the University of Frankfurt. All of the patients, their parents or caregivers, and the healthy subjects gave their written informed consent after full explanation of the planned study procedure. Experimental design and stimulus material. Two fMRI trials were conducted, each of which followed a classic block design.25 Each trial consisted of 16 blocks; 8 stimulation blocks were alternated with 8 resting blocks that served as a passive baseline. Each block included eight scanning volumes and lasted 32 seconds for a total trial duration of 8 minutes 32 seconds (figure 1). First trial. Standardized faces of women and men expressing different emotions (happy, sad, angry, and neutral)26 and scrambled faces (produced from the aforementioned stimuli to ensure that the same physical information was present) formed the stimulus material. In each of the eight stimulation blocks, 12 visual stimuli were presented for 0.8 second with an interstimulus interval of 1.88 seconds. Three tasks were differentiated, each consistently presented within an entire block: 1) scrambled faces (two blocks); 2) real faces, with the task of pressing a button with the right index finger when a happy face occurred (three blocks); and 3) real faces, with instructions to respond when a face of a woman occurred (three blocks). The stimulus material was identical for tasks 2 and 3, with only the instructions switched between the tasks. Each task was indicated using a one-word cue at the beginning of each block. To control for attention differences, one of the task 1 blocks had the same instruction as task 2 and the other task 1 block had the same instruction as task 3. Face stimuli are known to activate cortical regions located mainly on the ventral stream. Second trial. To test the specificity of the regions found in trial 1 (face detection), a second set of tasks was presented consisting of a visual search paradigm. Visual search tasks are mainly processed via the dorsal visual stream.27 Activations evoked in the cortical areas by a visual search task consisting of complex patterns were analyzed for these regions, demonstrating differences between the groups during the processing of faces in the first trial. The stimuli were presented following the block design described above. Two tasks were applied: first, a geometric–mathematical mosaic task derived from the block design test in the Wechsler Intelligence Scale,28 alternating with a color-counting task. In the mosaic task, subjects had to count and name the number of black triangles; in the color-counting task, they had to count and name the number of different colors shown in the stimulus object. In both trials, the tasks were presented in pseudorandom order (see figure 1). Behavioral data: analysis and statistics. In addition to monitoring task performance during scanning, we assessed task performance outside the scanner as presented on the screen of a laptop computer. The face tasks consisted of 44 face stimuli for each category, and in the pattern task, 25 mosaic or color pattern stimuli were presented. Data gathered here were compared between the groups using Mann–Whitney U tests, with an ␣ level of 5%. MRI. A 1.5 T whole-body MRI system (Magnetom Vision; Siemens Medical Systems, Erlangen, Germany) was used for the investigation. Fifteen axial slices, covering the widest parts of the whole brain, were acquired for functional imaging.10 Each functional time series consisted of 128 volumes. Moreover, a highresolution three-dimensional whole-brain anatomic template10 was collected for each subject.
FMRI data: analysis and statistics. The objective of the fMRI analysis was to identify brain regions that differed in activation level between healthy control subjects and autistic subjects during face processing. No assumptions with regard to critical regions were made. Regions differing between groups were determined functionally for each subject, and stimulus activation levels from these regions were statistically compared. For data analysis, registration, and visualization, the fMRI software package BrainVoyager 2000 (BrainInnovation, Maastricht, the Netherlands) was used. After preprocessing,29 the twodimensional statistical maps were superimposed on the original functional scans and incorporated into the three-dimensional anatomic data sets through interpolation of the functional voxels to the same resolution as the anatomic voxels (1 ⫻ 1 ⫻ 1 mm). The statistical analysis of blood oxygenation level– dependent (BOLD) signal data was performed in the following steps: In a first step, cerebral areas differing between healthy control subjects and autistic subjects during the processing of faces were determined through multiple regression maps (the general linear model [GLM] in BrainVoyager 2000) using a random effect model.30 The areas were identified by the contrast between control and autistic subjects during face processing (trial 1). To avoid the problem of multiple testing, the minimum cluster size should be set large enough to make it unlikely that a cluster of that size would occur by chance, assuming that falsely activated voxels should be randomly dispersed. This relies further on the assumption that areas of true neural activity tend to stimulate signal changes over contiguous voxels.31 To identify the most involved regions in a physiologically reasonable volume, clusters containing ⱖ200 neighboring voxels (0.2 cm3) and p ⬍ 0.05 (R ⬎ 0.23) were identified. The activation patterns gained by the GLM did not reveal whether the differences found were specific to face processing (trial 1) or were also valid for other visual pattern stimuli (trial 2). Therefore, and to account for individual differences in activated areas, in a second step, in the areas determined in step 1, a second individual GLM was computed to calculate the contrast between real faces (tasks 2 and 3 in trial 1) and scrambled faces (task 1 in trial 1) in each subject. For these areas, the mean signal time course over all voxels in the area was calculated for the face and the mosaic tasks, and a mean value of the percentage of signal change was computed. Because we did not aim to identify only the FFA but all regions differing during face processing in autism in comparison with controls, the contrast between real faces and scrambled faces was computed. This contrast revealed brain regions involved in face processing but was not appropriate to determine the exact location of the face-specific area in the fusiform gyrus (FG), namely, the FFA. To test the influence of a certain task in the areas differing during face processing between the groups, in a third step, these mean values for each condition were used in a two-way analysis of variance (ANOVA) with the factors “diagnose” (control group, autistic group) and “task” (happy faces, faces of women, scrambled faces, mosaic and color counting). General influences were investigated by a four-way ANOVA with the factors “diagnose,” “task,” “region” (FG/Brodmann area [BA] 37, medial occipital gyrus [GOm]/BA 19, superior parietal lobule [SPL]/BA 19, precentral gyrus [GPreC]/BA 6, and insula [INS]), and “hemisphere” (left, right). Finally, in a fourth step, significant main effects and interactions in the two-way ANOVA in each of the GLM-identified regions were tested post hoc with the Mann–Whitney U test (p ⬍ 0.05).
Results. Behavioral data. The analysis of the data collected during scanning showed 1) that the subjects did perform the tasks and 2) that the results were comparable with results from data measured outside the scanner, with no difference in accuracy but with longer latencies for the face task in the autistic group. Owing to the limited statistical power of the data collected within the scanner, only data from the more extensive assessment outside the scanner are presented. Reaction times were significantly longer in the autistic November (1 of 2) 2003
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Figure 1. The stimulation protocol and materials. Two functional scans (1, face task; 2, pattern task) were performed. For detailed task descriptions, see the text. The color frame of the blocks corresponds to the stimulus protocol. (A) Scrambled faces; (B) happy faces; (C) faces of women; (D) mosaic; (E) colorcounting task; (F) rest.
subjects, and this was especially pronounced in the face recognition task (table). Accuracy did not differ between the two groups in either the face or the pattern-counting task. An exploratory questionnaire revealed that the healthy individuals experienced the object-counting task as more difficult than the face detection task, whereas the autistic subjects reported the opposite (suggesting that the subjective experience did not reflect the objective behavioral data). FMRI. The statistical GLM contrast analysis for the processing of faces (the tasks of detecting faces of women and happy faces) between autistic subjects and control subjects in step 1 revealed significant BOLD signal differences bilaterally in the FG, the GOm, SPL, GPreC, and INS, whereas BA 9 and BA 40 differed only in the right hemisphere (see table E-1 in the supplementary material on the Neurology Web site; go to www.neurology.org; figure 2). Four-way ANOVA with the factors “diagnose,” “task,” “region,” and “hemisphere” showed no influence of the factor “diagnose” (p ⫽ 0.23) and “hemisphere” (p ⫽ 0.31) but differences for the factor “task” and “region” (p ⱕ 0.01). Further, the interactions “task” ⫻ “diagnose,” “task” ⫻ “region,” and “task” ⫻ “region” ⫻ “diagnose” differed (p ⱕ 0.01). The other interactions did not show significance. There was no general difference in visual processing between the groups in the investigated areas, as only interactions with the factor “task” were significant. The factor “hemisphere” was neither as main factor nor as factor in an interaction significant for the bilateral regions. FG. The contrast between real faces and scrambled faces reveals brain regions involved in face processing, but it is not appropriate to determine the face-specific FFA.
However, the Talairach coordinates indicating activated regions in the FG were in accordance with coordinates previously described for the FFA6,32,33 (for Talairach coordinates of the anatomic regions, see table E-1 in the supplementary material on the Neurology Web site). In the two-way ANOVA calculated for each of the regions, the factor “task” was significant for both hemispheres, with the highest BOLD signals being found for the face detection tasks, followed by the pattern tasks. The factor “diagnose” was significant for the right hemisphere;
Table Behavioral data for each task category Reaction time, s
Accuracy, %
Autistic group
Control group
Autistic group
Control group
Happy face
2.17 (1.80)
0.98 (0.25)*
86 (19)
96 (3)
Face of women
1.55 (0.67)
0.95 (0.85)*
95 (7)
99 (1)
Mosaic
9.73 (3.28)
5.92 (1.75)†
74 (11)
86 (14)
Color
8.21 (3.34)
4.53 (1.91)†
85 (10)
90 (8)
Data
Mean reaction time (SD) and averaged accuracy (SD) for the tasks were calculated. *p ⬍ 0.01. †p ⬍ 0.05. 1234
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Figure 2. Statistical (general linear model) contrast maps for the mean blood oxygenation level– dependent signal difference between autistic subjects and healthy control subjects during face processing are shown in coronal slices; y values indicate corresponding Talairach coordinates. (A) Fusiform gyrus (y ⫽ ⫺45); (B) medial frontal gyrus and insular region (y ⫽ 19); (C) medial occipital gyrus and superior parietal lobule (y ⫽ ⫺62); (D) precentral gyrus (y ⫽ ⫺2).
Figure 3. Mean blood oxygenation level– dependent signal changes for each task category. (A) Scrambled faces; (B) happy faces; (C) faces of women; (D) mosaic; (E) colorcounting task in the regions differentiating between autistic subjects (n ⫽ 7) and control subjects (n ⫽ 7). *Post hoc results with p ⬍ 0.05. a) fusiform gyrus, b) medial occipital gyrus, c) superior parietal lobule, d) precentral gyrus.
the control group showed significantly higher BOLD signal changes during the face detection task (detection of faces of women) and the pattern task (mosaic counting) than the autistic group. The interaction “diagnose” ⫻ “task” nearly reached significance in the left hemisphere, probably because of a higher signal change in the happy face detection task than in the gender detection task in control subjects. This difference was not apparent in the autistic group (see table E-2 in the supplementary material on the Neurology Web site; figure 3). GOm. The region in the occipital cortex revealed by computation of the contrast during face processing in control vs autistic subjects is in accordance with the region called the lateral occipital complex.32,34,35 The lateral occip-
ital complex has been described as an important region in object and face recognition. In the region of the lateral occipital complex in the GOm, the factor “task” revealed significant differences in both hemispheres. Both groups demonstrated higher signals for face than for object processing. No significant effect was attained for the factor “diagnose”; however, for all tasks except the scrambled face task, the autistic subjects demonstrated higher signal changes than the control subjects. These changes were significant at a descriptive level for the happy face detection task in the right hemisphere (see table E-2 in the supplementary material on the Neurology Web site; also figure 3). SPL. The ANOVA factor “task” was significant for both hemispheres; BOLD signals were highest during the object-counting tasks. In the left hemisphere, the interaction “diagnose” ⫻ “task” was significant; during object processing, the control group demonstrated a higher signal change than the autistic group, whereas during face and scrambled face processing, the reverse pattern was observed, with higher signal changes in the autistic group than in the control group (see table E-2 in the supplementary material on the Neurology Web site; also figure 3). GPreC. The area in the GPreC corresponds to the functionally defined frontal eye fields.36 Comparisons of the Talairach coordinates of all areas demonstrating differences between the autistic and healthy subjects revealed significant values for only 5 of the 36 coordinates, of which 3 defined the frontal eye fields. According to the literature, variability of the location of the frontal eye fields is very high.36 In the left hemisphere, the factors “task” (for which BOLD signals were highest during the object processing in controls) and “diagnose” (which in general had higher signals in the healthy group than in the autistic one) were significant. The interaction between the two factors was significant for both hemispheres. During object processing, the control group revealed higher signal changes than the autistic group (significantly higher signals in both hemispheres for the mosaic and color-counting tasks). During face processing, the activation pattern was reversed, with higher signal changes in the autistic group than in the controls (significant in the right hemisphere for the autistic group in the gender detection task) (see table E-2 in the supplementary material on the Neurology Web site; also figure 3). INS. ANOVA revealed significant effects for the factor “task” in the right hemisphere. The highest BOLD signal increases were observed during face processing. Descriptively in the left hemisphere, the control subjects showed slightly higher BOLD signals for each task category than the autistic group. Post hoc analysis revealed no significant differences (see table E-2 in the supplementary material on the Neurology Web site). BA 9/medial frontal gyrus. ANOVA (right hemisphere) was significant for the factor “task” in BA 9. The BOLD signal increase during face processing was higher than during the object-processing task. Post hoc analysis revealed no significant differences (see table E-2 in the supplementary material on the Neurology Web site). BA 40/inferior parietal lobule. ANOVA (right hemisphere) was significant for the factor “task” with a higher BOLD signal change during the face tasks in BA 40. On a descriptive level, post hoc analysis of the mosaic counting November (1 of 2) 2003
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task showed a significant difference between the groups, with higher signals in the control group than in the autistic group (see table E-2 in the supplementary material on the Neurology Web site).
Discussion. In this imaging study, we investigated the hypothesis that clinically apparent deficits in face processing in autism are related to different cerebral activation patterns that might constitute differing processing strategies. The relatively high IQ of the studied autistic population make this population not fully representative of common autistic subjects. However, it was not possible to investigate a general autistic population using such a relative complex study task as the one presented here. Studies on visual search led to a proposed model of autism with a predisposition toward local rather than global modes of information processing.37 These results have been extended in face-processing investigations in which lower or missing activations in face-specific regions were found16 in combination with higher activations in regions typical for nonface processing.14 These studies concentrated on the temporal lobe and investigated either holistic14,16 or feature-based37 strategies. In the current study, we extend those results by combining the investigation of a face-processing task with a task utilizing visual search strategies in a methodologic approach that targets the ventral and dorsal visual streams. Our visual search task focuses on those mechanisms that are discussed to provide alternative strategies in autism for face processing. The behavioral and imaging results obtained here—alterations in the temporal and the parietal-to-frontal visual pathways—support the model of a preference for detailed processing rather than holistic processing in autism. There were no significant differences in accuracy between the two groups in any of the tasks. We did not find a deficit in the accuracy of emotion detection, perhaps because the identification of happiness, as employed in the current study, is supposed to be less susceptible to impairment than the identification of more complex emotions.38 Performance of visual search tasks by autistic subjects that is comparable with or superior to that of healthy subjects has been well described.4,5 The significantly longer reaction times in persons with autism may indicate that they utilize networks other than the typical and specialized ones and that it therefore takes them longer to perform accurately. An alternative explanation could be that autistic subjects exert greater effort to perform comparably with healthy controls. The highest signals in the FG were found during face detection in both groups but were lower in the autistic subjects than in the control group, which is consistent with previous studies.14,16,39 The FFA, located in the FG, is known to be specifically involved in face processing in healthy subjects.13,40 Thus, current evidence points to a general deficit in the FFA in autism, which might be primary or developmen1236
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tal. In the FG, we found the highest BOLD signals on the left side. Most studies suggest that the right FFA is more specialized in face processing.12 However, because we did not look for specific FFA activation but rather for differences between the two groups, the higher activation in the left hemisphere in our data might be not comparable. In the lateral occipital complex of the GOm, signal changes during the face- and pattern-processing tasks were generally higher in the patient group than in controls. Because the lateral occipital complex is typically more involved in object processing than the FFA,32,34 our results indicate face-processing strategies in autism that utilize an unusually large extent of visual systems for object feature analysis, similar to previous reports.14,37 The high level of activation observed in the SPL during the pattern tasks reflects the visuospatial component of those tasks.27,41 In normal subjects, facial processing relies more on holistic strategies, and visuospatial processing is involved to a lesser degree; this was reflected by significantly lower signal changes in the SPL during face processing than during object processing for control groups. However, in autistic subjects, a reversed activation pattern was found. Because increases in BOLD signal correlate with task difficulty42 and visual attention,11 our results in the SPL suggest that autistic subjects need more visuospatial effort for the face task and less for the pattern task compared with healthy subjects in control groups. The frontal eye field, a region highly involved in overt and covert eye movements and thus important for visual search tasks, is located bilaterally in the GPreC.27,41 In control groups, we found high signals in the GPreC during the pattern processing, with lower signals detected only during the faceprocessing task. In subjects with autism, this finding was reversed in a manner analogous to our observations in the SPL, confirming prior results.37 These findings fit the hypothesis that visual search typically follows feature-based strategies that require scanning eye movements and that face processing occurs in a more holistic way. In autism, these mechanisms seem to be disturbed; visual search is stronger and is used instead of holistic processing during face perception. In INS, the BA 9 and BA 40 BOLD signals were relatively low compared with those in the aforementioned areas and did not exhibit significant interactions. The INS has been reported to be activated when subjects make judgments about any facial emotion,43 with greater involvement in implicit emotional processing than in explicit emotional processing.44 In the current study, we found higher signals in control subjects than in persons with autism. These results are in line with the well-known deficit in autistic persons’ recognition of emotional expression in human faces. Signal increases in BA 40 can be explained through the region’s involvement in the encoding of faces.45,46
Deficits in social interaction in autism may prohibit normal cortical development. The global cortical activation pattern we found indicates deficits in the face-specific regions in ventral visual pathways coexistent with higher activations in dorsally located regions involved in visual search. These findings reflect different strategies for visual processing and thus extend existing knowledge about models that propose a predisposition toward local rather than global modes of information processing. The deficits in the FG/FFA may be discussed in the context of a compensatory recruitment of regions involved in object processing and utilization of other strategies (including visual search). On the other hand, the results may reflect different strategies for feature processing or give evidence of a hardwiring issue; or they may just reflect an epiphenomenon of a more pervasive abnormality. Acknowledgment The authors thank David Prvulovic for assistance with the fMRI measurements.
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