Neural Correlates of Executive Function in Autistic Spectrum Disorders

Neural Correlates of Executive Function in Autistic Spectrum Disorders Nicole Schmitz, Katya Rubia, Eileen Daly, Anna Smith, Steve Williams, and Declan G.M. Murphy Background: Some clinical characteristics of high-functioning individuals with autistic spectrum disorder (ASD) such as repetitive stereotyped behaviors, perseveration, and obsessionality have been related to executive function (EF) deficits, more specifically to deficits in inhibitory control and set shifting and mediating frontostriatal neural pathways. However, to date, no functional imaging study on ASD has investigated inhibition and cognitive flexibility and no one has related EF brain activation to brain structure. Methods: We compared brain activation (using functional magnetic resonance imaging) in 10 normal intelligence adults with ASD and 12 healthy control subjects during three different EF tasks: 1) motor-inhibition (GO/NO-GO); 2) cognitive interference-inhibition (spatial STROOP); and 3) set shifting (SWITCH). Using voxel-based morphometry, we investigated if cortical areas which were functionally different in people with ASD were also anatomically abnormal. Results: Compared with control subjects, ASD individuals showed significantly increased brain activation in 1) left inferior and orbital frontal gyrus (motor-inhibition); 2) left insula (interference-inhibition); and 3) parietal lobes (set shifting). Moreover, in individuals with ASD, increased frontal gray matter density and increased functional activation shared the same anatomical location. Conclusions: Our findings suggest an association between successful completion of EF tasks and increased brain activation in people with ASD, which partially may be explained by differences in brain anatomy.

Key Words: Autistic spectrum disorder, executive function, frontal cortex, functional magnetic resonance imaging, fMRI, voxel based morphometry, VBM

A

utistic spectrum disorder (ASD), comprising autism and Asperger Syndrome (AS), is a strongly genetic neurodevelopmental condition (Bailey et al 1995), affecting many more people than previously recognized (approximately 60 per 10,000 children under the age of 8 years) (Chakrabarti and Fombonne 2001). Autistic spectrum disorder is characterized by pervasive abnormalities in socioemotional communication and stereotyped and obsessional behaviors (Wing 1997; Gillberg 1993). There is, however, clinical heterogeneity within ASD (Rutter 1978). The clinical symptoms of ASD have a profound impact on daily life and social and economic outcome (e.g., the societal cost of ASD in the United Kingdom exceeds £1 billion) (Jarbrink and Knapp 2001). However, the neurobiological determinants of ASD are poorly understood. Widespread abnormalities in brain anatomy of people with ASD have been reported (Carper and Courchesne 2005; Bauman and Kemper 2005; McAlonan et al 2005; Piven et al 1990); in particular, frontal, limbic, basal ganglia, parietal, and cerebellar regions are increasingly implicated in the disorder (for review see Bauman and Kemper 2005). Functional imaging studies in ASD encompass social communication, visual-spatial processing, visual search and attention, motor function, language, and (most recently) executive cognitive function in spatial working memory. Autistic spectrum disorder individuals compared with control subjects are reported to have functional abnormalities in 1) frontostriatal and cingulate regions during socioemotional tasks (Gallagher et al 2000; Critch-

From the Department of Psychological Medicine, Section of Brain Maturation (NS, ED, DM), Department of Child and Adolescent Psychiatry (KR, AS), and NeuroImaging Research Unit (SW), Institute of Psychiatry, King’s College London, London, United Kingdom. Address reprint requests to Dr N. Schmitz, Department of Radiology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands; E-mail: [email protected]. Received November 30, 2004; revised May 24, 2005; accepted June 3, 2005.

0006-3223/06/$32.00 doi:10.1016/j.biopsych.2005.06.007

ley et al 2000; Baron-Cohen et al 1999; Happe et al 1996); 2) striate and ventral occipital cortex during visual-attention (Belmonte and Yurgelun-Todd 2003) and visual-motor processing (Mueller et al 2003); 3) occipitotemporal areas during visual search (Ring et al 1999); 4) superior temporal and inferior frontal cortex during language processing (Just et al 2004); 5) the cerebellum during visual-attention tasks (Allen and Courchesne 2003); and 6) dorsolateral prefrontal and anterior cingulate cortex during spatial working memory (Luna et al 2002). There is an increasing understanding of brain function in people with ASD. There are, however, relatively few studies on the putative anatomicofunctional basis of repetitive, stereotyped behaviors in ASD. It has been proposed that these symptoms may be related to executive function (EF) deficits. Executive function may be fundamentally impaired in ASD, particularly inhibition of response (or inhibitory control) encompassing motor-response inhibition and inhibition of interference (Bishop and Norbury 2005; Ozonoff and Jensen 1999). Further, it has been proposed that these impairments underpin motor and cognitive inflexibility typically observed in ASD (Baron-Cohen 2005; Gillberg 1993). However, more general deficits in EF have also been studied in individuals with ASD (Hughes et al 1994). The authors reported that ASD individuals have difficulty changing their response during cognitive-flexibility tasks, leading to stimulus overselectivity and a repetitive response style (Hughes et al 1994). Neuropsychological studies of EF in individuals with ASD are, however, inconclusive, with some reporting deficits in specific EF tasks, such as set shifting and response inhibition, (Ozonoff and Jensen 1999; Hughes et al 1994) but not others (Griffith et al 1999; Ozonoff et al 1991). There are no studies on brain function during EF tasks of inhibitory control and set shifting in high-functioning people with ASD, and no one has investigated if brain regions that are functionally different are also anatomically abnormal. One of the most prominent brain regions implicated in EF is the frontal cortex and its connections to striatal and parietal brain regions (Schroeter et al 2004). Frontostriatal pathways have been reported to be anatomically (Carper and Courchesne 2005; McAlonan et al 2002; Abell et al 1999) and metabolically (Murphy et al 2002) abnormal in adults with ASD compared with control subjects. Further, within the general population, EF deficits are BIOL PSYCHIATRY 2006;59:7–16 © 2005 Society of Biological Psychiatry

8 BIOL PSYCHIATRY 2006;59:7–16

N. Schmitz et al

associated with frontal lobe pathology, and it is has been proposed that they are the underlying cause of clinical symptoms of autism such as perseveration, rule-bound behaviors, and obsessionality (Russell et al 1999). Thus, we hypothesized that high-functioning adults with ASD would show differences in frontal brain function while performing EF tasks of inhibitory control and cognitive flexibility and that brain areas that are functionally different in individuals with ASD would be also anatomically abnormal compared with control subjects. To test these hypotheses, we compared the functional neuroanatomy of people with ASD to that of control subjects in three tasks of motor and cognitive inhibitory control: 1) GO/ NO-GO motor response-inhibition task; 2) motor version of a spatial STROOP cognitive-interference inhibition task; and 3) set-shifting (SWITCH) task. These three tasks have previously been shown to activate specific frontostriatal pathways: motor response-inhibition in the GO/NO-GO task activated the right prefrontal cortex and the caudate nucleus (Rubia et al 2005, Rubia et al, in press); interference inhibition in the spatial STROOP task activated a left-hemisphere network of dorsolateral prefrontal cortex, anterior cingulate gyrus, parietal lobes, and putamen (Rubia et al 2005; Liu et al 2004; Peterson et al 2002); and the set-shifting (SWITCH) task activated the right inferior prefrontal and parietal cortices and the putamen (Rubia et al 2005; Smith et al 2004). We also compared cortical brain anatomy of people with ASD with that of control subjects using voxel-based morphometry (VBM) analysis (Ashburner and Friston 2000). Furthermore, to determine if functional differences may be partially accounted for by anatomical variation, cortical gray matter differences and functional activation maps of individuals with ASD compared with control subjects were assessed in a preliminary post hoc analysis.

Methods and Materials Subjects We included 10 right-handed adult male individuals of normal intelligence with ASD (8 with AS and 2 with high-functioning autism [HFA]) and 12 right-handed male control subjects who did not differ significantly in age or intelligence quotient (IQ) (see Table 1). People with ASD were recruited through the Maudsley Hospital/Institute of Psychiatry, whereas control subjects were recruited locally by advertisement. Asperger Syndrome and HFA were diagnosed by a consultant psychiatrist (D.M.), using ICD-10 criteria (World Health Organization). In addition, where parental informants were available, the Autistic Diagnostic Interview (ADI, Lord et al 1994) was carried out (this was possible in 7 out of 10 ASD individuals; Table 1). All participants of the study gave written informed consent as approved by the local research ethics committee (Institute of Psychiatry, South London and Maudsley Trust) and were between 18 and 52 years of age at time of scanning. Every subject underwent a structured clinical examination (including eyesight and routine blood tests) to exclude comorbid medical and psychiatric disorders and biochemical, hematologic, or chromosomal abnormalities (including fragile X syndrome) possibly affecting brain function. None of the participants had a history of major medical illnesses or psychiatric disorders other than ASD. Intelligence quotient was measured using the Wechsler Adult Intelligence Scale-Revised (WAIS-R) short form (Crawford et al 1996). None of the subjects were taking medication. Neuroimaging All participants were scanned at the Neuroimaging Unit of the Institute of Psychiatry (IOP), London, United Kingdom using a 1.5 Tesla GE Signa System (General Electric, Milwaukee, Wisconsin).

Table 1. Subject Characteristics Autism Diagnostic Interview (ADI)c

Patient

Age at Scanning in Years

IQa(full)

Verbal

Performance

Diagnosisb

18 27 29 34 35 39 47 48 50 52

108 96 102 119 104 127 109 92 111 123

97 93 100 108 104 112 99 90 100 87

96 90 103 121 103 139 119 96 121 97

AS AS AS AS HF AS HF AS AS AS

38 9

105 14

99 8

108 17

39 6

106 13

104 9

108 4

1 2 3 4 5 6 7 8 9 10 All Patients Mean SD All Control Subjects Mean SD

IQ, intelligence quotient; AS, Asperger syndrome; HF, high functioning. a Wechsler intelligence scale. b ICD-10 diagnosis. c Social/nonverbal cut-off for autism: 10/8. d Cut-off for autism: 3. e Based on neurological examination.

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— —

Social

Nonverbal Communication

Restricted Interests and Repetitive Behaviourd

16 20 15 10 21

12 14 11 14 10

5 11 3 4 3

16

13

5

12

12

10

16 8 — — —

13 4 — — —

5 7 — — —

Handednesse right right right right right right right right right right right

right — —

N. Schmitz et al All anatomical and functional images were acquired in the same session. Visual images were projected into the bore of the magnet using an active matrix video projector (enhanced graphics adapter [EGA]-mode, 70 Hz refresh rate) (Eiki, Japan) and presented on a screen viewed via an integrated periscope assembly. For response, a keypad with four response buttons connected to an Intel 3 personal computer (PC, Dell, Round Rock, Texas) running VISUAL-BASIC software (Microsoft Inc., Redmond, Washington) for stimulus presentation was used by the subjects. Two hundred eight event-related functional images with a jittered repetition time (TR) of 1.8 seconds were acquired for the GO/NO-GO and spatial STROOP tasks (152 images, 2.4 seconds TR for the SWITCH task), using a T2* weighted gradient echo, echo planar imaging (EPI) sequence, sensitive to blood oxygen level dependent (BOLD) contrast (TR: 1.8 [2.4]; echo time [TE]: 40 milliseconds; flip angle 90°; matrix: 64 x 64; field of view [FOV]: 240 mm; 12 slices, slice thickness 9 mm (.9-mm gap); 3.75 mm inplane resolution). To allow equilibrium to reach steadystate, four EPI volumes corresponding to 8 seconds were introduced before each sequence and discarded from the analysis. Axial Spoiled Gradient Recalled Acquisition in Steady State (spoiled GRASS [SPGR]) volumetric images were acquired in the same session, with full head coverage, using 124 contiguous slices (1.5-mm thick with .89 ⫻ .89 mm inplane resolution), a 256 ⫻ 256 ⫻ 124 matrix, and a TR/TE of 24/5 milliseconds (flip angle 45°, FOV 24 cm). Functional Magnetic Resonance Imaging (fMRI) Paradigms Pretesting. Prior to the neuroimaging investigation, all participants were made familiar with the scanner and the scanning procedures and pretested with a field version of the three EF tasks (taken from the Maudsley Attention and Response Suppression battery [MARS-II]) (Rubia et al 2005). All participants were able to successfully solve the tasks and there was no significant between-group difference in task performance (measured using error rates and reaction time). Experimental Conditions. Three tasks from the MARS battery (for details of the tasks, see Rubia et al 2005, Rubia et al, in press; Smith et al 2004) were chosen to investigate inhibition and cognitive flexibility during EF tasks. All tasks were presented with standardized instructions and in random order to account for systematic errors and fatigue. Throughout image acquisition, the subject’s responses were monitored via button press and recorded on a PC. GO/NO-GO Task In the GO/NO-GO task, a motor response had to be selectively inhibited or executed depending on whether a GO signal or a NO-GO signal was displayed on the screen. The basic task requires motor response inhibition and selective attention. Arrows (of 500 milliseconds each) pointing to either the left or right side appear on the screen with a mean interstimulus interval (ISI) of 1.8 seconds. The subject has to press the left or right response button on a diamond-shaped keypad as response. Infrequently (in 12% of trials), arrows pointing to the top (NO-GO signals) appear. Subjects have to inhibit their motor response to these stimuli. In 12% of trials, slightly slanted (by 25°) arrows pointing left or right appear and subjects have to treat them as GO signals. These “odd/slanted” GO signals control for the low-frequency effect of the NO-GO stimuli. The task comprises 24 NO-GO stimuli, 24 odd/slanted

BIOL PSYCHIATRY 2006;59:7–16 9 (control) GO stimuli, and 160 high-frequency GO stimuli (Rubia et al 2005, Rubia et al, in press). STROOP Task The fMRI adaptation of the spatial motor STROOP task (Rubia et al 2005) involves a STROOP-like stimulus-response incompatibility effect (Schulz and Liebing 1991) called the “spatial incompatibility” or the “Simon-effect” (Simon and Berbaum 1990). In contrast to the conventional Color-Word Stroop task (Stroop 1935), it involves the suppression of spatial (but not lexical) information in favor of iconic information. However, neural networks involved in interference inhibition of the motor STROOP task are similar to those involved in the Color-Word Stroop task (Liu et al 2004; Peterson et al 2002). During the spatial motor STROOP task, subjects have to press a left or right button depending on whether an arrow (displayed for 500 milliseconds) indicating left appears on the left side of the screen or an arrow pointing right appears on the right side of the screen (184 trials, ISI of 1.8 seconds.). The STROOP (incongruent) condition consists of 24 (low-frequency) incongruent trials, where an arrow pointing right appears on the left side of the screen or an arrow pointing left appears on the right side of the screen. Here, subjects have to press a button according to the direction the arrow points to, ignoring the conflict of the interfering information of the wrong screen side. In 12% of trials, slightly slanted (by 25°) congruent arrows pointing left or right appear on the left or right side, respectively, and subjects have to respond to them as to the congruent signals. These odd/slanted congruent signals control for the low-frequency effect of the STROOP (incongruent) stimuli. The task comprises 24 incongruent stimuli, 24 odd/ slanted (control) congruent stimuli, and 160 high-frequency congruent stimuli. SWITCH Task The modified version of the Meiran SWITCH task (Smith et al 2004; Meiran et al 2002) assesses cognitive set shifting by measuring the 1) inhibition of previously learned stimulus response associations to engage in new association patterns and 2) ability to switch responses between two association patterns. We presented subjects with a grid divided into four squares, in the center of which was a double-headed arrow pointing either

Figure 1. SWITCH task: example of stimulus presentation and response display. (A) Association trail, press bottom button. (B) Association trail, press top button. (C) SWITCH trail, press left button.


10 BIOL PSYCHIATRY 2006;59:7–16 horizontally or vertically (Figure 1). A red dot would appear in any of the four squares (Figure 1A). Subjects were asked to shift their attention and response between two spatial dimensions, the horizontal and the vertical dimensions, and use all four buttons of a diamond-shaped keypad to make responses. With the double-headed arrow pointing vertically (1600 milliseconds), the red dot would appear (after 200 ms), for example, in the lower right square; here, the subject would have to press the lower button on the keypad to indicate the selection for vertically and lower panel (Figure 1A). The same answer would be true for the red dot appearing in the lower left square. Subjects could make their response anytime after presentation of the red dot. After 1600 milliseconds of stimulus presentation, a blank screen was presented for 800 milliseconds. This trial (with a total ISI of 2.4 seconds) was repeated for a minimum of four but not more than six times, with the red dot appearing randomly in different squares (Figure 1B). The repeat trials were then followed by a SWITCH trial. Here, the double-headed arrow changed to a horizontal position (Figure 1C). The subject now had to indicate whether the dot was either in one of the two left or two right squares by pressing either the left or the right button on the keypad (e.g., horizontal and left panel; Figure 1C). This answer would also be true had the dot appeared in the lower left square. Subjects thus had to switch their attention and response between vertical and horizontal dimensions. The SWITCH trials were separated from each other by a minimum of 9.6 seconds (four TR intervals with ISI ⫽ 2.4 seconds) to allow optimal separation of the hemodynamic response. SWITCH trials appeared pseudorandomly either after four, five, or six repeat trials (i.e., every 9.6 seconds, 12 seconds, or 14.4 seconds) to avoid predictability. The SWITCH task consisted of 152 high-frequency repeat (association) trials (79%) interspersed with 32 low-frequency SWITCH trials (21%). Hence, on average, one in five trials was a SWITCH trial.

N. Schmitz et al thresholded at p ⬍ .0001 (with zt ⫽ 4.7) and corrected for multiple comparisons at p ⬍ .05; furthermore, only those voxels were accepted as significant that belonged to a cluster of at least 10 significantly activated neighboring voxels (minimum cluster size: 10 voxels, extended height threshold of p ⬍ .0001, surviving correction for multiple comparisons at p ⬍ .05). Voxels and clusters were localized using the Montreal Neurological Institute (MNI) coordinates and transformed into Talairach and Tournoux (T&T) (Talairach and Tournoux 1988) coordinates (Brett et al 2002). Morphometric Analysis. We used VBM to identify regional differences in cortical gray matter concentration (density) of individuals with ASD compared with control subjects. Standard VBM techniques (implemented in SPM99) including spatial normalization, tissue segmentation, and smoothing (Ashburner and Friston 2000) were employed. All SPGR images were spatially normalized to a 2 ⫻ 2 ⫻ 2 mm MNI template, interpolated (using 9 ⫻ 9 ⫻ 9 sinc interpolation) and segmented into compartments of gray and white matter and cerebral spinal fluid (CSF). Thereafter, only the gray matter segments were used for further analysis and smoothed with a 10-mm FWHM isotropic Gaussian kernel. Regionally, specific gray matter differences between people with ASD and control subjects were assessed using t statistics. Student t tests were carried out, investigating group differences on a voxel-by-voxel basis for the gray matter segments of individuals with ASD compared with control subjects (n ⫽ 22). The t tests for group comparisons were thresholded at p ⬍ .001, with a minimal cluster size (cluster extend threshold at p ⬍ .001) of 10 voxels. In a post hoc region of interest approach, functional group comparison maps were used as masks to be overlaid onto anatomical group images of gray matter differences to identify whether areas that were functionally different between groups were at the same time also structurally abnormal.

Results Image Processing Functional Analysis. All fMRI data were processed using SPM99 (Wellcome-Department of Cognitive Neurology, London, United Kingdom) modified for event-related designs. The functional scans were corrected for subject head motion by realignment and co-registration (mean intrasubject head motion was below 3 mm translation and 2° rotation), then normalized using the same transformation matrix as the anatomical images and smoothed with a 10-mm full-width at half-maximum (FWHM) Gaussian kernel. Statistical parametric maps (SPM) were calculated for all data using a general linear model, with separate hemodynamic response functions modeling the events of the functional tasks; estimated models include: 1) GO/NO-GO task: NO-GO events were contrasted with oddball events, controlling for low-frequency effects; 2) STROOP task: incongruent events contrasted with low-frequency congruent events; and 3) SWITCH task: SWITCH trials contrasted with repeat trials. These estimated models resulted in SPM(f) maps per subject and task. Significantly activated brain regions were hence obtained per subject, reflecting the three different models. To test for regionally specific task effects, group activation maps (for the group of ASD individuals and the control group separately) were created for all three EF tasks (using a threshold of p ⬍ .001, uncorrected, SPM[t]). To test for group differences per task, group per task interactions were calculated, using one-sample t tests against the null hypothesis of zero event-related activation differences between groups. The set of t voxel values for each group comparison was www.sobp.org/journal

Behavioral Findings All subjects were task compliant during the entire study. There were no significant differences in task performance between groups (measured using mean number of errors [GO/ NO-GO task: mean number of errors/standard deviation: 1.4/5.8 for control subjects and 1.2/4.3 for ASD, p ⬍ .96; STROOP task: 3.0/5.5 for control subjects, 3.2/3.9 for ASD, p ⬍ .98; SWITCH task: 1.3/3.1 for control subjects, 3.4/6.4 for ASD, p ⬍ .89], and percentage of correct answers (inhibitions), percentage of errors, and mean reaction time [Table 2]). fMRI Findings Main Task Effects Per Group. Control subjects activated significantly (p ⬍ .001, uncorrected) the 1) inferior parietal lobes, bilaterally; left cuneus and right middle frontal and posterior cingulate gyrus during correct inhibition of NO-GO trials (Table 3a); 2) right inferior parietal lobes, left caudate nucleus, and right globus pallidus during correct responses to STROOP trials (Table 3b); and 3) left middle frontal gyrus, inferior parietal lobe, insula, caudate nucleus and putamen and right fusiform, inferior temporal and cingulate gyri during correct responses to SWITCH trials (Table 3c). Individuals with ASD activated significantly (p ⬍ .001, uncorrected) the 1) inferior frontal and anterior cingulate gyrus, bilaterally, and the left superior temporal gyrus during correct inhibition of NO-GO trials (Table 4a); 2) superior temporal and parietal lobes, bilaterally; the left insula and middle frontal gyrus;

BIOL PSYCHIATRY 2006;59:7–16 11

N. Schmitz et al Table 2. Task Performance Measures Task/Condition

ASD Individuals Mean (SD)

Control Individuals Mean (SD)

p-Value (t)

489 (73) 98 (2)

468 (51) 99 (5)

.86 .99

481 (87) 131 (58) 9 (5) 1 (3)

434 (79) 125 (68) 5 (7) 1 (1)

.84 .78 .57 .89

111 (69) 835 (113) 8 (27) 7 (31)

105 (56) 703 (58) 7 (13) 4 (18)

.82 .67 .98 .57

GO/NO-GO Mean Reaction Time (MRT) GO (milliseconds) Correct Inhibitions (NO-GO) in % STROOP MRT Congruent Stimuli (milliseconds) STROOP Effect (milliseconds) STROOP Errors in % Congruent Stimuli Errors in % SWITCH SWITCH Effect (milliseconds) MRT Repeat Trials (milliseconds) SWITCH Errors in % Repeat (Congruent) Errors in %

STROOP effect: MRT to STROOP trials – MRT to congruent trials; SWITCH effect: MRT switch trials– MRT repeat trials. p-value: significance measure of t test at p ⬍ .01. ASD, autistic spectrum disorder; MRT, mean reaction time.

and the right inferior frontal and parietal lobe and anterior cingulate gyrus during correct responses to STROOP trials (Table 4b); and 3) the right inferior parietal lobe and insula and the left parietal lobe (postcentral gyrus) and anterior cingulate gyrus during correct answers to SWITCH trials (Table 4c). Group by Task Interaction. Compared with control subjects, individuals with ASD had significantly (p ⬍ .05, corrected)

greater activation of the 1) left middle/inferior (Brodmann area [BA] 10/46) and orbitofrontal gyrus (BA 47/11) (cluster extending from T&T coordinates ⫺28, 48, 4 to ⫺30, 42, ⫺8; Figure 2A, Table 5) during correct inhibition of NO-GO trials; 2) the left insula during correct responses to STROOP trials (Figure 2B, Table 5); and 3) right inferior and left mesial parietal cortex during correct responses to SWITCH trials (Figure 2C, Table 5).

Table 3. Control Subjects (n ⫽ 12), Group Activation per Task (p ⬍ .001, Uncorrected) Brain Area

Brodman Area (BA)

p ⬍ .001 Uncorrected

Z Valuea/Voxels per Clusterb

.001

⬎3.84/15

.000 .000

⬎3.43/13 ⬎2.98/10

.001

⬎3.03/10

.000

⬎3.08/11

40 38 2 2 ⫺4

.000 .001 .000

⬎4.45/17 ⬎4.00/16 ⬎3.83/15

16 16 0 0 2 4 34 48 46

.001

⬎4.21/16

.000 .000 .001 .000 .001

⬎3.89/15 ⬎3.73/12 ⬎4.42/17 ⬎3.00/10 ⬎2.93/10

Talairach and Tournoux (T&T) Coordinates

a. GO/NO-GO Task: Areas Activated During Correct NO-GO Inhibitions Cuneus BA 17/18 L ⫺8 ⫺88 L ⫺12 ⫺92 L ⫺18 ⫺92 Cingulate Gyrus BA 31 R 4 ⫺40 Inferior Parietal Lobe BA 39/40 L ⫺42 ⫺56 L ⫺48 ⫺50 BA 39/40 R 50 ⫺44 R 54 ⫺50 R 48 ⫺54 Middle Frontal Gyrus BA 6 R 38 8 BA 9 R 40 16 b. STROOP Task: Areas Activated During Correct STROOP Responses Inferior Parietal Lobe BA 40 R 36 ⫺46 R 48 ⫺32 Globus Pallidus R 8 0 Caudate Nucleus L ⫺2 4 Caudate Nucleus L ⫺6 0 c. SWITCH Task: Areas Activated During Correct SWITCH Answers Insula L ⫺26 ⫺14 Caudate Nucleus L ⫺18 ⫺4 Putamen L ⫺26 2 Putamen L ⫺24 ⫺6 Inferior Temporal Gyrus BA 37 R 52 ⫺62 Fusiform Gyrus BA 37/19 R 44 ⫺68 Inferior Parietal Lobe BA 40 L ⫺48 ⫺26 Middle Frontal Gyrus BA 32 L ⫺4 6 Cingulate Gyrus BA 24 R 2 0

8 ⫺4 4 32 40 38 40 32 32 46 28

BA, Brodmann area; T & T, Talairach and Tournoux; L, left; R, right. a Threshold for cluster significance at p ⬍ .001. b Number of significantly (p ⬍ .001) activated neighboring connected voxels per cluster.



N. Schmitz et al

Table 4. ASD Individuals (n ⫽ 10), Group Activation per Task (p ⬍ .001, Uncorrected) Brain Area

Brodman Area (BA)

p ⬍ .001 Uncorrected

Z Valuea/Voxels per Clusterb

4 18 28 8 14 28

.000 .001 .000

⬎4.02/16 ⬎3.92/15 ⬎3.38/11

.000

⬎3.82/14

42 26 ⫺2 ⫺6 34 48 ⫺2 ⫺8 42

.000 .001 .000 .001 .000 .001 .001 .000 .000

⬎4.45/18 ⬎4.33/17 ⬎2.94/10 ⬎3.93/15 ⬎3.30/11 ⬎2.05/10 ⬎3.29/11 ⬎3.32/11 ⬎2.83/10

44 46 52 2 44 44 30 24 32

.000

⬎3.88/14

.001 .000

⬎3.45/12 ⬎3.38/12

.001

⬎3.80/13

Talairach and Tournoux (T&T) Coordinates

a. GO/NO-GO Task: Areas Activated During Correct NO-GO Inhibitions Inferior Frontal Gyrus BA 10/46 L ⫺28 44 BA 46 R 34 38 Anterior Cingulate Gyrus BA 24/32 L ⫺4 ⫺10 BA 24/32 R 2 38 Superior Temporal Gyrus BA 39/42 L ⫺52 ⫺52 L ⫺48 ⫺58 b. STROOP Task: Areas Activated During Correct STROOP Responses Middle Frontal Gyrus BA 32 L ⫺2 10 Anterior Cingulate Gyrus BA 24 R 2 22 Superior Temporal Gyrus BA 22 L ⫺58 6 BA 38 R 54 12 Parietal Lobe (Precuneus) BA 7 L ⫺26 ⫺68 BA 7 R 18 ⫺63 Insula BA 13 L ⫺36 18 Inferior Frontal Gyrus BA 47 R 50 36 Inferior Parietal Lobe BA 40 R 50 ⫺30 c. SWITCH Task: Areas Activated During Correct SWITCH Answers Inferior Parietal Lobe BA 40 R 32 ⫺36 R 40 ⫺24 R 32 ⫺38 Insula R 32 4 Parietal Lobe (Postcentral) BA 40 L ⫺38 ⫺18 L ⫺40 ⫺30 Anterior Cingulate Gyrus BA 24 L ⫺4 ⫺10 BA 24 L ⫺6 6 BA 24 L ⫺4 0

ASD, autistic spectrum disorder; BA, Brodmann area; T&T, Talairach and Tournoux; L, left; R, right. Threshold for cluster significance at p ⬍ .001. b Number of significantly (p ⬍ .001) activated neighboring connected voxels per cluster. a

There were no significant areas of decreased activation in the individuals with ASD compared with control subjects. Compared with individuals with ASD, control subjects had no significantly (p ⬍ .05, corrected) increased areas of activation.

GO/NO-GO task at the anatomical location of the left inferior frontal gyrus (cluster extending from T&T coordinates ⫺30, 42, ⫺8 to ⫺32, 38, ⫺8) (Figures 3C and 4).

Discussion Structural Brain Analysis There were no significant between-group differences in brain anatomy that survived correction for multiple comparisons (at p ⬍ .05). However, uncorrected exploratory analysis (p ⬍ .001, uncorrected) revealed that people with ASD, compared with control subjects, had significantly (p ⬍ .001, uncorrected) increased gray matter density in the left inferior frontal gyrus (BA 10/46; T&T: ⫺30, 40, 6; z-value: 3.25), the anterior cingulate gyrus (BA 24; T&T: ⫺4, 2, 30; z-value: 3.79), the right superior frontal gyrus (BA 10; T&T: 22, 45, 25; z-value: 2.93), and middle frontal gyrus bilaterally (BA 8; T&T: ⫺21, 35, 40; z-value: 3.11 and T&T: 19, 32, 39; z-value: 2.89) (Figure 3A). A preliminary post hoc region of interest approach, guided by activation maps of functional group differences (per EF task, Figure 3B), revealed previously identified areas of increased gray matter density to match with locations of increased functional activation (Figure 3C). Significantly (p ⬍ .001, uncorrected) increased frontal gray matter density (cluster extending from T&T coordinates ⫺29, 42, 6 to ⫺30, 40, 6) of individuals with ASD compared with control subjects corresponded with significantly (p ⬍ .05, corrected) increased functional brain activation of individuals with ASD compared with control subjects during the www.sobp.org/journal

Correct performance on EF tasks elicited task-relevant brain activation in both groups of subjects, mainly in the frontal cortex, anterior cingulate gyrus, insula, and the parietal lobes. In a direct comparison, individuals with ASD compared with control subjects showed increased activation of frontal, insula, and parietal cortices during the GO/NO-GO, STROOP, and SWITCH tasks, respectively, despite equal task performance of both groups in all three EF tasks. Individuals with ASD had significantly increased activation in the left inferior and orbitofrontal cortex (GO/ NO-GO task), the left insula (STROOP task), and left inferior and right mesial parietal cortex (SWITCH task). Moreover, our preliminary analysis of differences in brain anatomy found that people with ASD compared with control subjects had increased gray matter density in the inferior frontal cortex, indicating that anatomical and functional abnormalities coexist in frontal brain regions in ASD (Figure 4). Increased inferior and orbitofrontal cortex activation in individuals with ASD during response inhibition was detected in the left hemisphere only. In contrast, predominantly right prefrontal brain regions have been shown to mediate inhibitory control in normal healthy adults (Rubia et al 2001, 2005, Rubia et al, in press). Right hemispheric inferior, dorsolateral, and orbitofrontal

BIOL PSYCHIATRY 2006;59:7–16 13

N. Schmitz et al

insula. The insula plays an important role in central processes such as pain or motivation, emotion, and higher cognitive functions such as control and inhibition. The insula is closely connected to prefrontal cortex and anterior cingulate gyrus and forms part of a frontostriatal attentional network. It has been suggested that a particular role of the insula is to respond to violations of expectations, monitoring sudden changes in events and violations of probabilities of these events occurring with a certain frequency (Sohn and Anderson 2003; Dove et al 2000). The increase of insula activation in individuals with ASD, in a direct comparison of the two groups, could stem from the violation of expectations regarding the STROOP trials being perceived stronger by individuals with ASD compared with control subjects. Individuals with ASD might be more prone to expect a rigid task environment and be less inclined to deal with rapid changes, which could explain the stronger insula activation. During the SWITCH task, individuals with ASD showed greater activation compared with control subjects in the right inferior and left mesial parietal lobes. The parietal lobes have been shown to play a fundamental role for pure task switching in this task (Rubia et al 2005; Smith et al 2004) and in other switching paradigms (Sohn and Anderson 2003; Dove et al 2000; Russell et al 1999). The increased activation in ASD during a predominantly parietal lobe mediated task suggests that brain abnormalities in people with ASD during cognitive challenge are task-specific, rather than confined to specific frontal brain regions. Autistic spectrum disorder subjects showed increased activation in inferior and orbitofrontal regions during the frontal lobe mediated GO/NO-GO task but increased parietal activation during a predominantly parietal lobe mediated SWITCH task. It is possible that the increased parietal lobe activation in ASD is related to reported structural and metabolic abnormalities in this brain area (McAlonan et al 2005; Fatemi et al 2002; Rumsey et al 1985). Anatomically affected areas may thus have to be recruited with more reinforcement than nonaffected areas. Across all three EF tasks, patients with ASD showed increased activation in predominantly left hemispheric regions of frontal, insular, and inferior parietal cortices. This left hemisphere laterality effect of increased activation may point toward an abnormal left lateralization in patients with ASD of functions of inhibitory control, which are normally mediated by right hemisphere. For example, we reported that people with AS had white matter deficits mainly in the left hemisphere. The left hemisphere normally develops later than the right and frontotemporal and frontoparietal pathways reach maturation later than those linking lower order regions (Paus et al 1999). Thus, neurodevelopmental

Figure 2. Group by task interaction. Areas of increased functional activation for individuals with ASD compared with control subjects (highlighted by arrows*). (A) GO/NO-GO task. (B) STROOP task. (C) SWITCH task. *cluster size is determined by the number of connected neighboring voxels, significantly active at p ⬍ .05, corrected for multiple comparisons. ASD, autistic spectrum disorder.

brain regions are key areas responsible for motor response inhibition (Rubia et al 2001, 2003, 2005, Rubia et al, in press; Casey et al 2000). It is possible that the increased activation in ASD in the homologue left hemispheric regions of inferior and orbitofrontal cortex during NO-GO performance reflects the use of an alternative left hemispheric strategy to achieve correct inhibitory performance in this task. During the STROOP task, individuals with ASD compared with control subjects showed increased activation of the left

Table 5. Group by Task Interaction, Significantly (p ⬎ .05, corrected) Increased Areas of Functional Activation of Individuals with ASD Compared with Control Subjects Brain Area GO/NO-GO Task Middle/Inferior Frontal Gyrus Orbitofrontal Gyrus STROOP Task Insula SWITCH Task Inferior Parietal Lobe Mesial Parietal Lobe

Brodmann Area (BA)

BA 10/46 BA 47/11

BA 7 BA 7

T&T Coordinates

Z Value

Cluster Size

Corrected at p ⬍ .05

L L

⫺28 ⫺30

48 42

4 ⫺8

6.36 6.34

11 11

.007 .028

L

⫺40

⫺8

⫺4

4.90

10

.031

R L

34 ⫺4

⫺24 ⫺34

44 48

6.33 6.38

11 11

.018 .032

ASD, autistic spectrum disorder; BA, Brodmann area; T&T, Talairach and Tournoux coordinates; L, left hemisphere; R, right hemisphere.



N. Schmitz et al

Figure 3. Anatomical and functional abnormalities of individuals with ASD compared with control subjects. (A) Gray matter: areas of significantly increased gray matter density in individuals with ASD versus control subjects. (B) Areas of significantly increased functional activation of ASD individuals (from left to right: 1) GO/NO-GO; 20 STROOP; 3) SWITCH task). (C) Anatomicofunctional overlap between (A) and (B). ASD, autistic spectrum disorder.

delay in autism may particularly impact on the left hemisphere and consequently explain some of the developmental EF anomalies found in the disorder. The underlying mechanism(s) for the increased brain activation in task-relevant brain regions in frontal, insular, and parietal cortices in people with ASD during EF tasks are unknown. An alternative explanation to the anatomical deficit hypothesis is that the differences in blood flow may simply reflect the use of different cognitive strategies by individuals with ASD to successfully solve EF tasks (Luna et al 2002; Siegel et al 1995; Zilbovicius et al 1995). A further potential explanation suggested by our work is that the abnormal functioning of some (e.g., frontal and parietal) brain regions is related to an underlying abnormality in neuroanatomical development. This could be caused by differences in programmed cell death, lack of functional specialization of the frontal cortex during synaptogenesis, deviant myelination during

Figure 4. Coexisting areas of anatomical (i) and functional (ii) abnormalities in the frontal cortex of individuals with ASD compared with control subjects at T&T: -30, 42, -8 (BA 10/L) to - 32, 38, -8 (BA 10/L). ASD, autistic spectrum disorder; T&T, Talairach and Tournoux; BA, Brodman area; L, left hemisphere.


brain development, or a disturbance in the serotonergic dentatothalamocortical pathway (Carper and Courchesne 2005; Chugani et al 1997). Our preliminary finding of increased gray matter in the inferior frontal cortex suggests that the anatomicofunctional abnormalities are related to abnormal neurodevelopment. We have previously reported that adults with ASD have greater frontal neuronal density (as measured using magnetic resonance spectroscopy), which was also related to the severity of repetitive behaviors in that population (Murphy et al 2002). Others reported that people with ASD have abnormalities in prefrontal and anterior cingulate cortex metabolism (Carper and Courchesne 2005; Siegel et al 1995; Horwitz et al 1988) and white matter growth curves, possibly reflecting abnormal connectivity to parietal and neocortical and subcortical regions (Courchesne et al 2001). Our prior work (McAlonan et al 2002; Murphy et al 2002) and that of others (Aylward et al 1999; Hashimoto et al 1997) suggest that functional abnormalities in people with ASD may be associated with differences in postnatal brain maturation. We therefore propose that differences in the function of frontal regions in people with ASD most likely stem from abnormalities in brain maturation with resultant differences in the number and connectivity of metabolically active neurons, reduced “computational efficiency,” and the need to use more frontal capacity to successfully process information of EF tasks. While the anatomicofunctional brain abnormalities in frontal brain areas, which we hypothesized, are consistent with existing evidence for brain abnormalities in these regions (from structural and biochemical studies), the abnormal function of the parietal lobes was not hypothesized a priori, and we also did not find anatomical differences in these brain regions. Healthy control subjects are reported to activate the parietal cortex during the SWITCH task, which could be partly explained by the spatial attention demands of the task (Rubia et al 2005; Smith et al 2004). Morris et al (1999) reported individuals with AS to be impaired in identifying specific spatial representations of working memory tasks; however, they were unimpaired in formation of spatial strategies, suggestive of abnormalities of dorsolateral prefrontal and parietal lobe function. Functional neuroimaging studies on spatial attention in autism have also reported abnormal activation patterns of the parietal cortex (Belmonte and Yurgelun-Todd 2003). Increased activation of the parietal cortex in ASD could therefore be effort related. Increased parietal lobe activation could also reflect a compensatory mechanism for dysfunctional frontal brain regions and abnormal frontoparietal connectivity. Some structural studies have reported differences in parietal lobe anatomy in ASD (McAlonan et al 2005; Abell et al 1999).

N. Schmitz et al However, we and others found no difference in parietal lobe neuronal integrity (Murphy et al 2002; Aylward et al 1999) or anatomy (Bauman and Kemper 2005; McAlonan et al 2002). We therefore suggest that currently the most parsimonious explanation of our results is that people with ASD have widespread differences in neurodevelopment and that in adulthood this is detectable as differences in frontal function and connectivity to other brain regions implicated in EF such as insula and parietal lobes. There are limitations to our study that need to be considered when interpreting the current findings. We only studied highfunctioning adults with ASD. We do not know if our findings will generalize across the spectrum of people with ASD to, for example, children or adults with “typical” autism (i.e., those with learning disability and developmental language delay). Group differences in brain anatomy did not survive correction for multiple comparisons and should therefore be regarded as preliminary. The relationship between increased brain function during successful completion of EF tasks and clinical symptoms and the biological basis of this hyperfunction need to be clarified in future studies using larger sample sizes and more extensive clinical and neuroimaging data basis. We conclude that inhibitory control at motor and higher cognitive levels as a component of EF appears to be a preserved ability in people with high-functioning ASD. It is, however, associated with increased brain activation in task-relevant frontal, insular, and parietal brain regions, some of which may also be anatomically abnormal compared with control subjects. The cause of the increased brain activation might be due to inefficient neuronal network recruitment consequent to abnormal brain development or the use of alternative cognitive strategies. Further studies are required to investigate the subcomponents of EF in individuals with ASD, their anatomical substrates, and their relationship to clinical symptoms.

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