Proton magnetic resonance spectroscopic imaging of the brain in childhood autism

Proton Magnetic Resonance Spectroscopic Imaging of the Brain in Childhood Autism Jennifer G. Levitt, Joseph O’Neill, Rebecca E. Blanton, Susan Smalley, David Fadale, James T. McCracken, Donald Guthrie, Arthur W. Toga, and Jeffrey R. Alger Background: Autism is a developmental disorder of unknown neurologic basis. Based on prior work, we used proton magnetic resonance spectroscopic imaging (1HMRSI) to investigate brain structures, including cingulate and caudate, that we hypothesized would reveal metabolic abnormalities in subjects with autism. Methods: In 22 children with autism, 5 to 16 years old, and 20 age-matched healthy control subjects, 1H-MRSI assessed levels of N-acetyl compounds (NAA), choline compounds (Cho), and creatine plus phosphocreatine (Cr) at 272 msec echo-time and 1.5 T. Results: In subjects with autism compared with control subjects, Cho was 27.2% lower in left inferior anterior cingulate and 19.1% higher in the head of the right caudate nucleus; Cr was 21.1% higher in the head of the right caudate nucleus, but lower in the body of the left caudate nucleus (17.9%) and right occipital cortex (16.6%). Conclusions: Results are consistent with altered membrane metabolism, altered energetic metabolism, or both in the left anterior cingulate gyrus, both caudate nuclei, and right occipital cortex in subjects with autism compared with control subjects. Biol Psychiatry 2003;54: 1355–1366 © 2003 Society of Biological Psychiatry Key Words: Autism, child, magnetic resonance spectroscopy, brain, cingulate gyrus, caudate nucleus

Introduction

A

utism is a severe disorder of development marked by abnormalities in communication, social reciprocity, and repetitive and stereotyped behaviors (American Psychiatric Association 1994). Although a variety of research methodologies provide strong evidence for a neurobiolog-

From the Department of Psychiatry and Biobehavioral Sciences, Neuropsychiatric Institute (JGL, JO, SS, DF, JTM, DG), Laboratory of Neuroimaging (REB, AWT), and Department of Radiological Sciences (JRA), University of California-Los Angeles School of Medicine, Los Angeles, California. Address reprint requests to Jennifer G. Levitt, M.D., University of California-Los Angeles, Neuropsychiatric Institute, 760 Westwood Plaza, Los Angeles CA 90024. Received December 19, 2002; revised June 20, 2003; accepted June 25, 2003.

© 2003 Society of Biological Psychiatry

ical basis of this disorder, there is little consensus regarding the underlying etiology. There are few postmortem studies of this illness, in part because of the rarity of the disorder. The findings from these studies, although inconsistent, implicate limbic forebrain structures (Kemper and Bauman 1993), the cerebellum (Bailey et al 1998; Bauman and Kemper 1985, 1986, 1990; Ritvo et al 1986), and more recently the cerebral cortex, white matter, and brain stem (Bailey et al 1998) in the pathophysiology of this disorder. Structural magnetic resonance imaging (MRI) findings in autism are inconsistent as well. Whereas some studies support the neuropathologic findings in limbic (Abell et al 1999; Aylward et al 1999; Mountz et al 1995) and cerebellar regions (Carper and Courschesne 2000; Courschesne et al 1988, 1994; Kates et al 1998), other studies have not replicated these results (Holttum et al 1992; Kleimen et al 1992; Piven et al 1992, 1998; Saitoh et al 1995). Recently, data from morphometric and functional MRI methods are accumulating that, consistent with the Bailey et al (1998) postmortem study, find cerebral cortex and white matter abnormalities in autism. Qualitative examinations provide evidence for developmental abnormalities (Berthier 1994; Courchesne et al 1993; Piven et al 1990), whereas quantitative analyses demonstrate abnormalities of gray and white matter volumes (Abell et al 1999; Carper et al 2002; McAlonan et al 2002) and threedimensional mapping shows irregularities of sulcal anatomy (Levitt et al 2003) throughout the cerebrum in subjects with autism. Functional imaging techniques such as [18F]fluorodeoxyglucose positron emission tomography (18FDG-PET; Rumsey et al 1985) and single photon emission correlated tomography (SPECT; Mountz et al 1995; Ohnishi et al 2000) provide evidence for abnormal perfusion or metabolism in the frontal lobes (Zilbovicius et al 1995), cingulate gyrus (Haznedar et al 2000), and temporal lobes (Boddaert et al 2001; Critchley et al 2000; Ohnishi et al 2000; Pierce et al 2001; Schultz et al 2000; Zilbovicious et al 2000) of autistic subjects. 0006-3223/03/$30.00 doi:10.1016/S0006-3223(03)00688-7

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Recent studies of subcortical regions in autism have produced evidence of abnormal structure and function, with especially noteworthy findings in the caudate nuclei. Sears et al (1999) found a negative correlation between caudate volume and Autism Diagnostic Interview—Revised (ADI-R) measures of ritualistic-repetitive behaviors in subjects with autism. These results parallel findings of a negative correlation between caudate volume and severity of symptoms in obsessive– compulsive disorder (OCD; Rosenberg and Keshavan 1998; Rosenberg et al 1997). More recently, McAlonan et al (2002) found that caudate volume did not develop normally with age in Asperger’s subjects. Other studies of this region, including computer tomography (CT; Jacobson et al 1988), MRI (Kates et al 1998), 18FDG-PET (Rumsey et al 1985), and 99mTc HMPAO SPECT (Mountz et al 1995), also demonstrate caudate nucleus abnormalities in the pathophysiology of autism. Additional evidence from magnetic resonance spectroscopy (MRS) is beginning to illuminate metabolic aspects of this picture. Both 31P-MRS (Minshew et al 1993), and 1 H-MRS studies have shown abnormalities in frontal (Chugani et al 1999; Minshew et al 1993), lateral temporal (Hisaoka et al 2001) and medial temporal lobes (Otsuka et al 1999) and cerebellum (Chugani et al 1999; Otsuka et al 1999) in children with autism. Preliminary work in our laboratory demonstrated significant N-acetyl compounds/ creatine plus phosphocreatine (NAA/Cr) reductions in the medial temporal lobe with a trend toward low NAA/Cr in the cingulate gyrus of children with autism. Recently, two studies have demonstrated an association between symptom severity and abnormal 1H-MRS metabolite rations in the temporal lobes of autistic subjects (Sokol et al 2002) and in the prefrontal region of Asperger’s subjects (Murphy et al 2002). 1 H-MRS indicates aspects of the cellular composition of brain tissues scanned, and yields inferences into cellularenergetic and membrane metabolism. The 1H-MRS resonance assigned to NAA, for instance, is used as a marker of neurons, that is, to index tissue neuron density (Birken and Oldendorf 1989; Urenjak et al 1993). The peak assigned to choline-containing compounds (Cho) may indicate glial cell density (Brand et al 1993; Gupta et al 2000; Urenjak et al 1993) and also represents key reactants in membrane metabolism (Gill et al 1990; Speck et al 1996). The peak assigned to creatine plus phosphocreatine (Cr) represents two key molecules in cellular energetics (Siesjo¨ 1978) and may also reflect glial or overall (neurons plus glia) cellular density (Brand et al 1993; Urenjak et al 1993). Thus, if regional pathologic changes in neuron density, glial density, cell-membrane processes, or energetic metabolism are present in brains of subjects with autism, 1H-MRS may be sensitive to them.

This study used proton magnetic resonance spectroscopic imaging (1H-MRSI) in children and adolescents with autism and age-matched healthy control subjects to investigate potential between-group differences in metabolite concentrations in cerebral cortex, white matter, and subcortical areas. Based on the research outlined above and prior work, we hypothesized that prefrontal, cingulate, and parietal regions, as well as caudate nucleus, would demonstrate abnormalities of neuronal viability or cellular energetics in subjects with autism. The 1H-MRSI acquisition volume selected was positioned too superior to sample the temporal lobes comprehensively or to sample the cerebellum.

Methods and Materials Subjects Participants in the study included 22 children and adolescents (4 girls, 18 boys) aged 5.4 –15.7 years (mean ⫽ 10.4 years; SD ⫽ 3.4 years) meeting DSM-IV (American Psychiatric Association 1994) criteria for autism or pervasive developmental disorder and 20 (10 girls, 10 boys) healthy control children and adolescents aged 6.8 –16.3 years (mean age ⫽ 11.7 years; SD ⫽ 2.9 years). The diagnosis of autism was determined using the Autism Diagnostic Interview Revised (ADI-R; Lord et al 1994) and the Autism Diagnostic Observation Schedule (ADOS; Lord et al 1999). Exclusionary criteria included the presence of major medical or neurologic illness. Potential control children were recruited from public and private schools in the community and were screened for neurologic, psychiatric, language, or hearing disorders by clinical interview, developmental history, and the Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime Version (Kaufman et al 1997) interviews with the parent. Exclusion criteria for normal children included any lifetime significant medical disorder or Axis I mental disorder. Handedness for all subjects was assessed using the Edinburgh Inventory (Oldfield 1971). Mean IQ scores for most of the autistic children and all of the control subjects were based on the Weschler Intelligence Scale for Children—Third Edition (Weschler 1991). Three of the autistic children were unable to complete the WISC-III and were administered either the Mullen Scales of Early Learning (Mullen 1989) or Raven’s Colored Progressive Matricies (Raven et al 1995). Mean IQ scores for the autism group were 95.0 (SD ⫽ 18.3) for full-scale, 83.0 (SD ⫽ 24.8) for verbal, and 89.2 (SD ⫽ 19.9) for performance; for the control group, scores were 118.4 (SD ⫽ 16.2) for full-scale, 116.8 (SD ⫽ 19.0) for verbal, and 116.7 (SD ⫽ 15.4) for performance. Based on a requirement that the IQ score on the Mullen scales or Raven’s Matricies or on at least one of the WISC-III scales be ⱖ 70, 21 subjects with autism were classified as high-functioning and 1 (a 7-year-old boy) as low-functioning. Results for this boy were not distinguished from those of the high-functioning subjects with autism on any

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Table 1. Demographics, Diagnoses, Current and Past Medication, and Sedation During MRI/1HMRSI Examination for Sample with Autism Age (years)

Gender

IQ (Fu/V/P)

Medication

History

Sedation

M M M M M M M M M M M M M M M M M M F F F F

93/91/98 105/115/93 93/101/86 113/121/104 61/52/75 100R/63PP/– 105/106/103 –/33Mu/59Mu 122R/50Mu/46Mu 82/79/96 73/56/96 89/82/99 74/78/74 112R/–/– 104/95/112 127/124/127 64/71/63 80/67/98 103R/–/– 97/99/95 114R/87PP 85/91/81

Pax, Rit Pro None Clon, Dex, Dxs Zol None None None Ami, Ris, Sti None None Dex, Pax None Rit Rit None Bus None Zan None Clon, Teg, Pro None

Hal, Luv, Pro None None Clon, Dex, Rit None None Ten None Rit None None Ten Clon, Zol Pro Rit None Luv, Pax, Pro, Ris None None None Dep None

No No No No Yes Yes No Yes Yes No Yes Yes No Yes No No No Yes Yes No Yes No

14.3 15.7 13.5a 9.7 11.0 7.7 13.2 7.0b 7.1 14.5 7.9c 9.6 12.9 7.5 9.7 12.5 11.8 7.5 5.8 9.0 5.4c 11.8

M, male; F, female; Fu, full scale; V, verbal; P, performance; R, Ravens; Mu, Mullins; PP, Peabody Picture Vocabulary Test; Ami, Amicar; Bus, Buspar; Clon, Clonidine; Dep, Depakote; Dex, Dexadrine; Dxs, Dexastat; Hal, Haldol; IQ, intelligence quotient; Luv, Luvox; Pax, Paxil; Ris, Risperidone; Rit, Ritalin; Pro, Prozac; Sti, Stimate; Teg, Tegretol; Ten, Tenex; Zan, Zantac; Zol, Zoloft. a Premature birth b Low-functioning autistic c Sibling pair

measure. Data on subject medications and use of sedation during MR scans are summarized in Table 1. Individual demographics for the control subjects are presented in Table 2. Table 2. Demographics for Healthy Control Sample Age (years) 7.2 8.0 10.4 12.0 12.7 12.8 13.1 13.4 14.5 15.9 6.8 8.0 8.9 10.0 10.2 12.2 13.0 13.8 15.4 16.3

The study was conducted under the supervision of the UCLA Human Subjects Review Board. Informed consent was obtained from all parents or legal guardians, and written assent was obtained from all children before participation.

Gender

IQ (Fu/V/P)

MRI and 1H-MRSI Acquisition

M M M M M M M M M M F F F F F F F F F F

131/137/120 133/145/113 137/142/125 103/104/102 107/101/112 123/121/121 137/140/129 110/115/103 110/105/115 121/–/– 124/108/137 131/128/129 137/140/129 123/121/121 119/113/121 83/83/84 121/102/137 104/108/96 131/125/132 84/81/91

As indicated, for some subjects with autism (Table 1), MR procedures were carried out under propofol-induced sedation. The MRI and 1H-MRSI of the brain were acquired in the same session on a 1.5-T Signa Horizon 5.x using a standard head coil. The MR sequences were acquired from each subject in the following order. After an initial sagittal localizer scan, an axial fast spin-echo MRI was acquired (repetition time [TR] ⫽ 3000 msec, echo time [TE] ⫽ 13 msec, slice thickness ⫽ 3 mm contiguous, in-plane resolution ⫽ .94 ⫻ .94 mm2), yielding proton-density-weighted images. These images were used to identify the neuroanatomic structures within which individual 1 H-MRSI voxels were selected during postprocessing. These images further provided proton-density intensity values to which 1 H-MRSI metabolite resonance intensities were normalized for absolute quantitation. Next, a sagittal volumetric acquisition was performed using a spoiled gradient recalled (SPGR) sequence (TR ⫽ 24 msec, TE ⫽ 9 msec, partition thickness ⫽ 1.2 mm contiguous, in-plane resolution ⫽ .94 ⫻ .94 mm2), yielding T1-weighted images used for tissue segmentation. Finally, watersuppressed multislice 1H-MRSI was acquired using an inversion-

M, male; F, female; IQ, intelligence quotient; Fu, full scale; V, verbal; P, performance.

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rejected manually. Manual rejection was performed by one blinded operator and checked by a second blinded operator. Peak intensities were integrated for NAA (2.01 ppm), Cho (3.23 ppm), and Cr (3.03 ppm).

MRI and 1H-MRSI Coprocessing

Figure 1. Sagittal T1-weighted magnetic resonance imaging scan of brain of a 9.7-year-old boy with autism showing positioning of three proton magnetic resonance spectroscopic imaging acquisition slices.

recovery sequence (TR ⫽ 2300 msec, inversion time ⫽ 170 msec, TE ⫽ 272 msec, 1 average, slice thickness ⫽ 12 mm, in-plane resolution ⫽ 10 ⫻ 10 mm2, nominal voxel volume ⫽ 1.2 mL) from three contiguous axial slices (Figure 1). Slice 1 centered on the dorsoventral midplane of the basal ganglia, slice 2 was through the ventricles, and slice 3 was a supraventricular slice. Long echo time 1H-MRSI was chosen over short echo time because the former yields a stable baseline that is relatively insensitive to lipid interference. The MRI and 1H-MRSI postprocessing was conducted with the operators blinded to subject identity. Tissue-segmentation of T1-weighted MRI was calculated as described previously (Blanton et al 2001). The MRI tissue segmentation operators taking part in our study were required to maintain ⬎ 95% intra- and interrater reliability. The resulting gray matter, white matter, and cerebrospinal fluid (CSF) component volumes were then coregistered onto the axial proton-density weighted MRI volume, which was already in register with the 1H-MRSI volume (Woods et al 1993). 1

H-MRSI Postprocessing

After Fourier transformation, each subject’s 1H-MRSI volume underwent sine-bell spatial filtering, 2.0-Hz lorenztian temporal apodization, and polynomial automated baseline fitting using home-written Interactive Data Language software routines. 1HMRSI voxels with lipid signals exceeding the NAA signal (i.e., those having a substantial contribution from nonbrain tissue) with NAA signal-to-noise ratio less than 2.0, with line width greater than 10.0 Hz, or with other detectable artifact (e.g., aliased extracranial lipid signals arising from movement) were

Using the coregistered proton-density-weighted MRI to identify anatomy, a single 1H-MRSI voxel was selected within each of the following brain structures in left and right cerebral hemispheres: frontal cortex, parietal cortex, occipital cortex, inferior anterior cingulate, superior anterior cingulate, frontal white matter, parietal white matter, head of the caudate nucleus, body of the caudate nucleus, putamen, and thalamus. The volume percent gray-matter, white-matter, and CSF in each selected 1 H-MRSI voxel was calculated from the coregistered gray matter, white matter, and CSF MRI component volumes. 1HMRSI voxels selected in cortical gray-matter sites were retained if they contained ⱖ 75% gray matter; voxels in white-matter sites were retained if they contained ⱖ 75% white matter; and voxels in nuclear gray-matter sites were retained if they contained ⱖ 50% gray matter. Agreement between results obtained by two independent blinded operators using the voxel-selection procedure was ⱖ 95%. Across subjects, agreement in the voxel coordinates selected for the various sites ranged from 82.8%– 97.9% within the autism group and 81.6%–97.6% within the control group. Between the two subject groups, agreement in the voxel coordinates selected was greater than 85% for all sites sampled. Regarding tissue composition of voxels selected, the group with autism had 5.9% more gray matter in the right parietal cortex voxel (82.3% ⫾ 7.8% vs. 76.4% ⫾ 7.7%; p ⫽ .038) and 6.6% less white matter in the left occipital cortex voxel (14.5% ⫾ 10.1% vs. 21.1% ⫾ 10.3%; p ⫽ .043) than the healthy control group. No metabolite effects were seen at either of these sites. No significant between-group differences in gray- or white-matter content were obtained at any other site. Metabolite peak areas were corrected for intersubject differences in transmitter and receiver gains, normalized to the intensity of the subject’s proton density-weighted MRI, and divided by the volume fraction tissue (gray matter plus white matter) in the 1H-MRSI voxel. The last operation corrected for potential intervoxel differences in CSF content. These operations yielded as end result absolute metabolite levels, uncorrected for T1- and T2-relaxation, expressed in institutional units (IU).

Data Analysis Neuropathologic and neuroimaging findings provided a priori rationale for metabolic abnormalities in autism in several structures. Nonetheless, as we collected data on three metabolites from two subject groups, in 11 regions in each of two brain hemispheres, were we to compare subjects with autism and control subjects directly with simple t tests in each region, 66 tests would result; the chances of random findings would then be unacceptably elevated. Following standard methods for repeatedmeasures data, we therefore computed an “omnibus” repeatedmeasures analysis of variance (ANOVA) for each metabolite, with group as between-subjects factor and 22 within-subject

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region-hemisphere combinations. A mixed linear model permitting general covariance structure was used to determine whether the metabolite measures varied significantly from one region to another. Hemisphere was included as a factor in the model. Subjects’ gender was included as covariate. Because recent investigations (Bennetto et al 1996; Spiker et al 2002) demonstrate an association between IQ and severity of symptoms of autism; including IQ as a covariate would effectively remove variance associated with the illness. Therefore, IQ was not used as a covariate. Significance was based on F statistics with 21 numerator degrees of freedom. If a group-by-region interaction was found significant (p ⬍ .05), we then compared groups in each region using protected post hoc-t tests to discover the source of differences. “Exploratory” unprotected post hoc t tests were also performed for any metabolite for which no significant interaction resulted. As an added precaution, for each region-metabolite combination for which there was a significant finding, post hoc tests (linear regression or t test) were performed to determine potential effects of age, gender, and full-scale IQ within the healthy control group. Where an effect was found, appropriately matched subgroups of subjects with autism and control subjects were compared to determine if the finding in question was still significant. Several subjects with autism were taking psychiatric medication or underwent propofol sedation at time of scan (Table 1). This was not the case for any control subject. To assess possible effects of medication or of sedation on results, for each significant finding, post hoc t tests were repeated three times: once excluding those subjects with autism currently on medication, once excluding those who had been sedated during MR acquisition, and once excluding subjects with autism who were either medicated or sedated. The medicated subgroup with autism was also compared with the unmedicated subgroup; the same was done for the sedated and unsedated subgroups with autism.

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Figure 2. Axial proton-density-weighted magnetic resonance imaging section of the brain of a healthy 10.3-year-old boy showing location of single proton magnetic resonance spectroscopic imaging (1H-MRSI) voxel sampled in the left inferior anterior cingulate cortex (top left). 1H-MR spectrum obtained in sampled voxel after postprocessing, featuring major peaks for N-acetyl compounds (NAA), choline compounds (Cho), and creatine plus phosphocreatine (Cr; top right). Same for boy with autism from Figure 1 (bottom). Note diminished Cho and Cr intensities relative to NAA.

Neurometabolite Concentrations

Results Subject IQ Scores Although the population with autism consisted primarily of high-functioning subjects, full-scale, verbal, and performance IQ were nonetheless all significantly lower for the group with autism than for the control group (all t ⬎ 4.33, all ps ⬍ .0005, two-tailed). 1

H-MRSI Data Quality

Figure 2 shows a spectrum from a representative 1H-MRSI voxel in the left inferior anterior cingulate gyrus of a 9.7-year-old boy with autism alongside a comparison spectrum from a healthy 10.3-year-old boy. At this long echo time (272 msec), MR spectra acquired from juvenile brains were typically of high quality, featuring prominent peaks for NAA, Cho, and Cr.

Table 3 lists group-mean absolute levels of NAA, Cho, and Cr measures. Figure 3 plots the same for key structure pairs showing local effects of autism. In repeated-measures ANOVA covarying gender, significant group-by-region interactions resulted for Cho [F(21,39) ⫽ 4.60, p ⬍ .0001] and for Cr [F(21,39) ⫽ 2.20, p ⫽ .0165]. No such significant interaction resulted for NAA. Therefore, NAA findings were considered exploratory. In protected post hoc t tests, Cho was 27.2% lower in the left inferior anterior cingulate [t(38) ⫽ ⫺3.06, p ⫽ .0030, two-tailed] and 19.1% higher in the head of the right caudate nucleus [t(38) ⫽ 1.96, p ⫽ .047, two-tailed] in subjects with autism than in controls. Cr was 17.9% lower in the body of the left caudate nucleus [t(38) ⫽ ⫺2.80, p ⫽ .0068, two-tailed], 21.1% higher in the head of the right caudate nucleus [t(36) ⫽ 2.20, p ⫽ .031, twotailed], and 16.6% lower in right occipital cortex [t(39) ⫽ ⫺2.06, p ⫽ .042, two-tailed] in subjects with autism than in control subjects. In exploratory post hoc t tests, there

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Table 3. Absolute Levels of N-Acetyl (NAA) Compounds, Choline-Containing Compounds (Cho), and Creatine Plus Phosphocreatine (Cr) in Multiple Regions of Interest (ROI) in the Brain in Children and Adolescents with Autism and Normal Control Children and Adolescents NAA ROI FC L R PC L R OC L R infAC L R supAC L R Cdhd L R Cdbd L R Put L R Th L R FWM L R PWM L R

Cho

Cr

Autism

Control

Autism

Control

Autism

Control

7.2 ⫾ 1.4 7.4 ⫾ 1.3

7.8 ⫾ 1.6 8.2 ⫾ 1.1

3.0 ⫾ 1.0 3.2 ⫾ .8

2.9 ⫾ .9 3.0 ⫾ .7

2.5 ⫾ .6 2.8 ⫾ .7

2.6 ⫾ .8 3.0 ⫾ .7

8.1 ⫾ 1.3 7.7 ⫾ 1.1

8.2 ⫾ 1.0 7.6 ⫾ 1.4

2.5 ⫾ .8 2.5 ⫾ .6

2.5 ⫾ .7 2.4 ⫾ .6

2.6 ⫾ .8 2.5 ⫾ .7

2.5 ⫾ .4 2.6 ⫾ .8

7.1 ⫾ 1.3 7.3 ⫾ 1.2

7.4 ⫾ 1.0 7.3 ⫾ 1.1

2.2 ⫾ .8 2.3 ⫾ .6

2.3 ⫾ .8 2.4 ⫾ 1.0

2.4 ⫾ .7 2.2 ⴞ .6 p ⫽ .043

2.4 ⫾ .8 2.7 ⴞ .8

5.7 ⫾ 1.1

6.0 ⫾ 1.6

3.5 ⴞ .8

2.4 ⫾ .9

2.7 ⫾ .9

5.3 ⫾ 1.0

5.7 ⫾ 1.7

2.5 ⴞ 1.1 p ⫽ .003 2.7 ⫾ .9

2.9 ⫾ 1.1

2.3 ⫾ .6

2.6 ⫾ .9

5.8 ⫾ 1.4 6.2 ⫾ 1.3

6.1 ⫾ 1.6 6.0 ⫾ 1.5

3.3 ⫾ 1.0 3.5 ⫾ 1.2

3.2 ⫾ 1.1 3.4 ⫾ 1.0

2.7 ⫾ .8 2.6 ⫾ .6

2.7 ⫾ .6 2.8 ⫾ .5

4.4 ⫾ 1.7 4.2 ⫾ 1.4

4.6 ⫾ 1.6 3.8 ⫾ 1.4

3.5 ⫾ 1.3 4.0 ⴞ 1.3 p ⫽ .047

4.0 ⫾ 1.5 3.4 ⴞ .6

2.7 ⫾ 1.0 2.8 ⴞ .8 p ⫽ .031

3.0 ⫾ .8 2.3 ⴞ .5

5.4 ⴞ 1.2 p ⫽ .044a 5.5 ⫾ 1.4

6.1 ⴞ 1.0

2.7 ⫾ .8

3.2 ⫾ 1.0

3.0 ⴞ .7

5.0 ⫾ 1.5

2.6 ⫾ 1.0

2.7 ⫾ 1.2

2.4 ⴞ .5 p ⫽ .007 2.5 ⫾ .6

5.7 ⫾ 1.5 5.5 ⫾ 1.6

5.4 ⫾ 1.8 5.1 ⫾ 1.6

2.8 ⫾ 1.0 2.9 ⫾ .9

2.4 ⫾ .9 2.6 ⫾ .7

2.8 ⫾ .8 2.8 ⫾ .6

2.6 ⫾ .8 2.4 ⫾ .9

6.8 ⫾ 1.6 6.7 ⫾ 1.5

7.0 ⫾ 1.2 7.0 ⫾ 1.0

4.0 ⫾ 1.0 3.8 ⫾ 1.3

4.0 ⫾ .9 3.7 ⫾ 1.1

2.7 ⫾ .7 2.6 ⫾ .8

2.6 ⫾ .6 2.6 ⫾ .4

6.3 ⴞ 1.2 p ⫽ .029a 6.2 ⫾ 1.4

7.3 ⴞ 1.5

4.2 ⫾ 1.4

4.5 ⫾ 1.4

2.1 ⫾ .7

2.4 ⫾ .8

6.7 ⫾ 1.7

4.2 ⫾ 1.0

4.2 ⫾ 1.0

2.1 ⫾ .6

2.5 ⫾ .6

8.6 ⴞ 1.4 p ⫽ .019a 8.8 ⫾ 1.4

9.9 ⴞ 1.9

3.6 ⫾ 1.1

3.7 ⫾ 1.2

2.4 ⫾ .6

2.4 ⫾ .8

8.6 ⫾ 2.4

3.5 ⫾ 1.1

3.5 ⫾ 1.3

2.4 ⫾ .6

2.6 ⫾ .6

2.8 ⫾ .8

Group means ⫾ SD in institutional units. p values versus control in two-tailed post hoc t test. Significant comparisons in boldface. L, left; R, right; FC, frontal cortex; PC, parietal cortex; OC, occipital cortex; infAC, inferior anterior cingulate; supAC, superior anterior cingulate; Cdhd, caudate head; Cdbd, caudate body; Put, putamen; Th, thalamus; FWM, frontal white matter; PWM, parietal white matter. a Exploratory (unprotected).

were three significant effects for NAA in subjects with autism compared with healthy control subjects: NAA was 11.9% lower in the body of the left caudate nucleus [t(36) ⫽ ⫺2.02, p ⫽ .044, two-tailed], 13.3% lower in the left frontal white matter [t(39) ⫽ ⫺2.25, p ⫽ .029, two-tailed], and 13.1% lower in left parietal white matter [t(38) ⫽ ⫺2.49, p ⫽ .019, two-tailed]. No between-group effects were significant for any other region or metabolite.

Within the healthy control group, no significant effect of age was found for any of the metabolite-region combinations for which a significant between-group effect was observed. In the head of the right caudate nucleus in healthy subjects, Cho was 21.4% lower in boys than in girls [t(18) ⫽ ⫺2.14, p ⫽ .047, two-tailed]. When subsequently compared, boys with autism had 25.0% higher Cho than control boys [t(25) ⫽ 1.83, p ⫽ .079, trend,

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⫺2.31, p ⫽ .046, two-tailed] were separately excluded, but not when both were excluded at the same time. The finding of higher Cho in the head of the right caudate nucleus remained significant when medicated subjects were excluded [t(27) ⫽ 2.77, p ⫽ .032, two-tailed], but not when sedated subjects were excluded. When both medicated and sedated subjects with autism were excluded, the NAA findings in left caudate body [t(21) ⫽ ⫺2.27, p ⫽ .011, two-tailed] and in left parietal white matter [t(23) ⫽ ⫺2.06, p ⫽ .022, two-tailed] remained significant. The NAA finding in left frontal white matter was reduced to a trend [t(24) ⫽ ⫺1.51, p ⫽ .085, two-tailed]. In the head of the right caudate, Cr was 24.0% lower in medicated than in unmedicated subjects with autism [t(17) ⫽ ⫺2.16, p ⫽ .036, two-tailed], rendering the Cr content of medicated subjects with autism closer to that of control subjects. Significant differences between medicated and unmedicated subgroups with autism were not found for the other regions tested, nor were significant differences found between the sedated and unsedated subgroups with autism. Thus, Cho findings in the caudate may in part represent influences of sedation. Medication may also have “normalized” Cr levels in the right caudate head. Figure 3. Localized effects of autism on proton magnetic resonance spectroscopic imaging for N-acetyl compounds (NAA), choline compounds (Cho), and creatine plus phosphocreatine (Cr) levels in institutional units (IU). Note absence or reversal of normal asymmetry in subjects with autism at several sites. Cross-hatched bars, children and adolescents with autism; solid bars, age-matched healthy controls. *p ⬍ .05 versus controls (analysis of variance, covarying gender).

two-tailed]. In left frontal white matter, NAA decreased with increasing full-scale IQ in healthy control subjects [r(1,18) ⫽ ⫺.50, p ⫽ .018, linear regression]. When the population with autism and the control population were confined to subjects with full-scale IQ between 83 and 127, left frontal white matter NAA was still lower in the subgroup with autism [t(24) ⫽ ⫺2.50, p ⫽ .018, twotailed].

Effects of Psychiatric Medication and Sedation When t tests were repeated excluding subjects with autism who were either medicated or sedated, Cr findings remained significant for right caudate head [t(23) ⫽ 2.34, p ⫽ .028, two-tailed], left caudate body [t(23) ⫽ ⫺2.41, p ⫽ .0058, two-tailed], and right occipital cortex [t(25) ⫽ ⫺2.38, p ⫽ .013, two-tailed]. The Cho finding in the left inferior anterior cingulate remained significant both when medicated subjects with autism [t(27) ⫽ ⫺2.42, p ⫽ .042, two-tailed] and when sedated subjects with autism [t(28) ⫽

Discussion This study represents an extensive investigation of brain metabolite levels at long echo time in a relatively large group of children and adolescents with autism and agematched control subjects. The results are consistent with altered membrane or energetic metabolism (Cho, Cr) in the left anterior cingulate gyrus, both caudate nuclei, and right occipital cortex in the subjects with autism, and with loss of neuronal viability (decreased NAA) in the left caudate and left parietal white matter. These findings provide further evidence for the involvement of multiple cortical and subcortical regions in the pathophysiology of autism. The presence of multiple findings such as these may implicate a variety of neural circuits in the behavioral manifestations of autism. For instance, the anterior cingulate gyrus is considered an executive region for affect and cognition, essential to emotional processing (Lane et al 1998), modulation of motor responses to emotional cues (Devinsky et al 1995), and attention (Pardo et al 1990). In patients with mood disorders, glial and neuronal deficits localized to subsections of the anterior cingulate and prefrontal cortices have been determined (Bouras et al ¨ ngu¨ r et al 1998; Rajkowska 2001; Cotter et al 2001; O 2000; Rajkowska et al 1999). In concert with the caudate nucleus, the anterior cingulate gyrus has been implicated in the pathophysiology of obsessive-compulsive symptoms (Baxter et al 1996; Insel 1992; Modell et al 1989;

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Rapoport and Wise 1988), which bear similarities to the repetitive-stereotyped behaviors symptom domain seen in autism (Sears et al 1999). Our findings of substantially decreased Cho in the left inferior anterior cingulate gyrus suggest a local disturbance of cellular membrane metabolism (Gill et al 1990; Speck et al 1996), of glial cell density (Brand et al 1993; Gupta et al 2000; Urenjak et al 1993), or both and may represent a 1H-MRS metabolite correlate of the metabolic and volumetric abnormalities seen in previous PET (Haznedar et al 1997, 2000; Rumsey et al 1985; Siegel et al 1992) and MRI (Haznedar et al 1997, 2000; McAlonan et al 2002) studies of individuals with autism. Like previous findings of abnormal volume (Sears et al 1999) and glucose metabolic rate (GMR; Rumsey et al 1985), our measurements of altered NAA, Cho, and Cr in the caudate nucleus point to disturbances in that structure in autism. Cr abnormalities might reasonably be expected to coexist with irregularities in 18FDG-PET GMR as the 18 FDG-PET signal is believed to arise primarily from cytosolic glycolysis (Phelps et al 1979) that supplies ATP stored by the creatine-phosphocreatine reactant pair (Siesjo¨ 1978). Our findings of higher Cho and Cr in the head of the right caudate nucleus and lower NAA and Cr in the body of the left caudate nucleus in subjects with autism compared with control subjects, are noteworthy in light of 18FDG-PET studies demonstrating a differential response to therapeutic intervention of GMR in the right relative to the left caudate in subjects with OCD (Baxter 1992; Baxter et al 1987; Schwartz et al 1996). There are dense glutamatergic projections from the anterior cingulate gyrus to the caudate-putamen region (Wang and Pickel 2000). L-glutamic acid regulates striatal acetylcholine release (Pisani et al 2002; Scatton 1987) and, possibly through its effect on dopamine and striatal cholinergic interneurons, decreases striatal 5-HT release (Becquet et al 1990). In light of the findings outlined here, as well as 1H-MRS findings by Rosenberg et al (2000) of altered glutamate-glutamine (Glx) and Cr concentrations in the caudate nuclei of children with OCD, our observations of metabolite alterations in both the cingulate gyrus and caudate in subjects with autism provide further support for the hypothesis that the repetitive and stereotyped behavioral symptoms seen in autism may reflect brain abnormalities similar to those seen in OCD. Further investigation of these brain structures using short echotime 1H-MRS is indicated; in particular, an examination of the Glx peak as it relates to repetitive and stereotyped symptoms in autism may be especially fruitful. Our observation of diminished Cr in the right occipital cortex is consistent with other evidence of cortical dysfunction in the occiput in autism (Abell et al 1999; Chiron et al 1995; Critchley et al 2000; Jambaque et al 1998;

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McAlonan et al 2002; Mottron et al 1997; Pierce et al 2001; Rumsey et al 1985; Schultz et al 2000; Siegel et al 1992). Rumsey et al (1985) and Siegel et al (1992) found elevated GMR specifically lateralized to the right occipital cortex. Mountz et al (1995) found reduced rCBF in the left occipital cortex. These data suggest that further study of a hypothesized clinical and etiologic relationship between visual agnosia and autism is warranted (Jambaque et al 1998; Mottron et al 1997). These results include an exploratory finding of reduced NAA in left parietal white matter of our subjects with autism. This finding is in accord with a modest body of neuropathologic (Bailey et al 1998) and MRI (Courschesne et al 1993; McAlonan et al 2002) evidence suggesting white-matter abnormalities in autism. Using 1 H-MRS, Hashimoto et al (1997) failed to find changes in NAA/Cr or Cho/Cr in parietal white matter of children with autism. Because their single voxel was acquired from the right parietal lobe, it is possible that they missed effects localized to left parietal white matter. In a study published during review of this article, Friedman et al (2003) demonstrated widespread abnormalities of 1H-MRS in a group of 3- to 4-year-old children with autism spectrum disorder. Although the specific metabolic abnormalities are not consistent, probably due to differences in subject age or techniques used, it is striking that this group found alterations in brain chemistry in similar regions as we found in this study. In particular, they found metabolic alterations in the cingulate gyrus, the caudate nucleus, and occipital gray matter. In addition, although our findings in left frontal white matter did not reach significance, Friedman et al (2003) also found chemical alterations in frontal and parietal white but not gray matter. A common misconception in neuroscience is that NAA is present in much lower quantities in white matter than in gray matter. In fact, levels of NAA detected by 1H-MRS in white matter are typically comparable to those measured in gray matter (Hetherington et al 1994, 1996; Lim and Spielman 1997; Pouwels and Frahm 1998; Soher et al 1996; Wang and Li 1998). Diffusion measurements (Assaf and Cohen 1998a, 1998b) suggest that NAA exists in two compartments in the brain, one neuronal somatal, the other axonal and dendritic. Because NAA is present not only in the cell bodies, but also in the cell processes of neurons, our findings may reflect lower local axon density in parietal regions in subjects with autism, consistent with previous studies demonstrating diminished parietal lobe cortex and white matter in some individuals with autism (Courchesne et al 1993). The parietal lobes are involved in aspects of brain function that, when disrupted, may contribute to the behavioral abnormalities seen in autism. For instance, the parietal lobe is functionally associated with

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aspects of eye gaze (Hoffman and Haxby 2000), as well as spatial perception and memory (Corbetta et al 1993, 1998; Haxby et al 1994; Nobre et al 1997). These brain functions could figure importantly in the disturbances of socially directed eye gaze (American Psychiatric Association, 1994) seen as a cardinal feature of autism. Some of the present Cho and Cr findings suggest that certain regional glial cell populations may be abnormally high or abnormally low in brains of subjects with autism. This suggests a role (as yet unverified from pathology) for glial cells in the pathophysiology of autism. Biochemically, glia differ radically from neurons (Coyle and Schwarcz 2000), and therapeutic strategies might specifically target glial cells (Rajkowska 2000). Sokol et al (2002) have correlated Cho and Cr in the hippocampus with severity of autism and indicate that 1H-MRS Cho levels may respond to cholinotherapy (Satlin et al 1997). This study has several limitations. The sample with autism, commensurate with the population with autism generally, had many more boys than girls, whereas the normal control sample had equal numbers of boys and girls. Although gender was included as a covariate in statistical analyses and potential gender effects within the control population were assessed, it would be preferable to match the two groups for gender. Some of the children with autism were medicated at the time of their MR scan. For ethical reasons, we did not ask these children to interrupt their medication regimens. In an effort to address this potential confound, we analyzed the effect of medication upon the results. The most significant medication effect observed revealed a “normalization” of the Cr content in the caudate nucleus of the subjects with autism. These data appear similar to the findings of Rosenberg et al (2000) that selective serotonin inhibitors reverse abnormalities of the Glx peak in the caudate of subjects with OCD and strongly support the need for further study of this region in subjects with autism. Furthermore, some children with autism required anesthesia (propofol) to complete MRI scanning, whereas none of the healthy control children was sedated. Although no studies have been conducted directly on propofol, 1HMRS investigations of barbiturate anesthetics (Lundbom et al 1999) and isoflurane (Holzmuller et al 1992) concluded that these agents produced no effect on the parameters studied. Post hoc challenges suggest our results were little affected by sedation. Nonetheless, future data collected on larger groups of subjects will allow us to analyze comparison groups without these potential confounds. Finally, although the tissue composition of 1H-MRSI acquisition voxels was determined and controlled for, this tissue-composition determination did not take the pointspread function of 1H-MRSI into account. We performed CSF-corrected absolute quantitation of 1H-MRSI metabo-

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lite levels; however, results were expressed in terms of IUs, rather than mmol concentrations, because no correction was made for T1 and T2 relaxation effects. Whereas Hisaoka et al (2001) found no significant differences between subjects with autism and control subjects for metabolite T1 and T2 values, Friedman et al (2003) found widespread T2 relaxation effects in subjects with Autism Spectrum Disorder (ASD). Thus, pathology-related differences in metabolite relaxation times represent a possible alternative explanation for group differences in apparent metabolite concentrations detected in this study. These limitations notwithstanding, our results suggest that long echo-time 1H-MRSI of the brain can distinguish between populations with autism and age-matched healthy control populations, demonstrating differences in regional brain metabolite levels that may be relevant to specific behavioral symptoms (eye gaze, repetitive stereotypies) of autism.

This research was supported by National Institute of Mental Health Grant No. K08 MH01385 and NICHD research grant 1P01 HD35482-01. We thank Rochelle Noel for expert assistance with paper preparation, and Laura Heinichen, Leah Miner, and Mimi Lee for their assistance with data processing.

References Abell F, Krams M, Ashburner J, Passingham R, Friston K, Frackowiak R, et al (1999): The neuroanatomy of autism: A voxel-based whole brain analysis of structural scans. Neuroreport 10:1647–1651. American Psychiatric Association (1994): Diagnostic and Statistical Manual of Mental Disorders, 4th ed. Washington, DC: American Psychiatric Press. Assaf Y, Cohen Y (1998a): In vivo and in vitro bi-exponential diffusion of N-acetyl aspartate (NAA) in rat brain: A potential structural problem? NMR Biomed 11:67–74. Assaf Y, Cohen Y (1998b): Non-mono-exponential attenuation of water and N-acetyl aspartate signals due to diffusion in brain tissue. J Magn Reson 131:69 –85. Aylward E, Minshew N, Goldstein G, Honeycutt N, Augustine A, Yates K, et al (1999): MRI volumes of amygdale and hippocampus in non-mentally retarded autistic adolescents and adults. Neurology 53:2145–2150. Bailey A, Luthert P, Dean A, Harding B, Janota K, Montgomery M, et al (1998): A clinicopathological study of autism. Brain 121:889 –905. Bauman ML, Kemper TL (1985): Histoanatomic observations of the brain in early infantile autism. Neurology 35:866 –874. Baxter LR Jr (1992): Neuroimaging studies of obsessive compulsive disorder. Psychiatr Clin North Am 15:871–884. Baxter LR Jr, Phelps ME, Mazziotta JC, Guze BH, Schwartz JM, Selin CE (1987): Local cerebral glucose metabolic rates in obsessive-compulsive disorder. Arch Gen Psychiatry 44:211– 218.

1364

BIOL PSYCHIATRY 2003;54:1355–1366

Baxter LR Jr, Saxena S, Brody AL, Ackermann RF, Colgan M, Schwartz JM, et al (1996): Brain mediation of obsessivecompulsive disorder symptoms: Evidence from functional brain imaging studies in the human and nonhuman primate. Semin Clin Neuropsychiatry 1:32–47. Becquet D, Faudon M, Hery F (1990): In vivo evidence for an inhibitory glutamatergic control of serotonin release in the cat caudate nucleus: Involvement of GABA neurons. Brain Research 519:82–88. Bennetto L, Pennington B, Rogers S (1996): Intact and impaired memory functions in autism. Child Dev 67:1816 –1835. Berthier M (1994): Corticocallosal anomalies in Asperger’s syndrome. AJR Am J Roentgenol 162:236 –237. Birken DL, Oldendorf WH (1989): N-acetyl-L-aspartic acid: A literature review of a compound prominent in 1H-NMR spectroscopic studies of brain. Neurosci Biobehav Rev 13:23– 31. Blanton RE, Levitt JG, Thompson PM, Narr KL, CapetilloCunliffe L, Nobel A, et al (2001): Mapping cortical asymmetry and complexity patterns in normal children. Psychiatry Res 107:29 –43. Boddaert N, Zilbovicius M, Chabanne N, Mouren-Simoni M, Barthelemy C, Samson Y (2001): Temporal lobe dysfunction in childhood autism: A PET auditory activation study. Pediatr Radiol 31(suppl 1):S3. Bouras C, Ko¨ vaeri E, Hof PR, Riederer BM, Giannakopoulos P (2001): Anterior cingulate cortex pathology in schizophrenia and bipolar disorder. Acta Neuropathol 102:373–379. Brand A, Richter-Landsberg C, Leibfritz D (1993): Multinuclear NMR studies on the energy metabolism of glial and neuronal cells. Dev Neurosci 15:289 –298. Carper R, Courchesne E (2000): Inverse correlation between frontal lobe and cerebellum sizes in children with autism. Brain 123:836 –844. Carper R, Moses P, Tigue Z, Courchesne E (2002): Cerebral lobes in autism: Early hyperplasia and abnormal age effects. Neuroimage 16:1038. Chiron C, Leboyer M, Leon F, Jambaque I, Nuttin C, Syrota A (1995): SPECT of the brain in childhood autism: Evidence for a lack of normal hemispheric asymmetry. Dev Med Child Neurol 37:849 –860. Chugani DC, Sundram BS, Behen M, Lee ML, Moore GJ (1999): Evidence of altered energy metabolism in autistic children. Prog Neuropsychopharmacol Biol Psychiatry 23:635–641. Corbetta M, Akbudak E, Conturo T, Snyder A, Ollinger J, Drury H, et al (1998): A common network of functional areas for attention and eye movements. Neuron 21:761–773. Corbetta M, Miezin F, Shulman G, Petersen S (1993): A PET study of visuospatial attention. J Neurosci 13:1202–1226. Cotter D, Mackay D, Landau S, Kerwin R, Everall I (2001): Reduced glial cell density and neuronal size in the anterior cingulate cortex in major depressive disorder. Arch Gen Psychiatry 58:545–553. Courchesne E, Press GA, Yeung-Courchesne R (1993): Parietal lobe abnormalities detected with MR in patients with infantile autism. AJR Am J Roentgenol 160:387–393. Courchesne E, Saitoh O, Yeung-Courchesne R, Press G, Lincoln A, Haas R, et al (1994): Abnormality of cerebellar vermian

J.G. Levitt et al

lobules VI and VII in patients with infantile autism: Identification of hypoplastic and hyperplastic subgroups with MR imaging. AJR Am J Roentgenol 162:123–130. Courchesne E, Yeung-Courchesne R, Press G, Hesselink J, Jernigan T (1988): Hypoplasia of cerebellar lobules VI and VII in infantile autism. New Engl J Med 318:1349 –1354. Coyle JT, Schwarcz R (2000): Mind glue: Implications of glial cell biology for psychiatry. Arch Gen Psychiatry 57:90 –99. Critchley H, Daly E, Bullmore E, Williams S, Van Amelsvoort T, Robertson D, et al (2000): The functional neuroanatomy of social behavior: Changes in cerebral blood flow when people with autistic disorder process facial expressions. Brain 123:2203–2212. Devinsky O, Morrell MJ, Vogt BA (1995): Contributions of anterior cingulate cortex to behaviour. Brain 118:279 –306. Friedman SD, Shaw DW, Artru AA, Richards TL, Gardner J, Dawson G, et al (2003): Regional brain chemical alterations in young children with autism spectrum disorder. Neurology 60:100 –107. Gill SS, Thomas DG, Van Bruggen N, Gadian DG, Peden CJ, Bell JD, et al (1990): Proton MR spectroscopy of intracranial tumours: In vivo and in vitro studies. J Comp Assist Tomogr 14:497–504. Gupta RK, Cloughesy TF, Sinha U, Garakian J, Rubino G, Rubino L, et al (2000): Relationships between choline magnetic resonance spectroscopy, apparent diffusion coefficient and quantitative histopathology in human glioma. J Neurooncol 50:215–226. Hashimoto T, Tayama M, Miyazaki M, Yoneda Y, Yoshimoto T, Harada M, et al (1997): Differences in brain metabolites between patients with autism and mental retardation as detected by in vivo localized proton magnetic resonance spectroscopy. J Child Neurol 12:91–96. Haxby JV, Horwitz B, Ungerleider LG, Maisog JM, Pietrini P, Grady CL (1994): The functional organization of human extrastriate cortex: A PET-rCBF study of selective attention to faces and locations. J Neurosci 14:6336 –6353. Haznedar MM, Buchsbaum MS, Metzger M, Solimando A, Spiegel-Cohen J, Hollander E (1997): Anterior cingulate gyrus volume and glucose metabolism in autistic disorder. Am J Psychiatry 154:1047–1050. Haznedar MM, Buchsbaum MS, Wei T-C, Hof PR, Cartwright C, Bienstock CA, et al (2000): Limbic circuitry in patients with autism spectrum disorders studied with positron emission tomography and magnetic resonance imaging. Am J Psychiatry 157:1994 –2001. Hetherington HP, Mason GF, Pan JW, Ponder SL, Vaughan JT, Twieg DB, Pohost GM (1994): Evaluation of cerebral gray and white matter metabolite differences by spectroscopic imaging at 4.1 T. Magn Reson Med 32:565–571. Hetherington HP, Pan JW, Mason GF, Adams D, Vaughan MJ, Twieg DB, Pohost GM (1996): Quantitative 1H spectroscopic imaging of human brain at 4.1 T using image segmentation. Magn Reson Med 36:21–29. Hisaoka S, Harada M, Nishitani H, Mori K (2001): Regional magnetic resonance spectroscopy of the brain in autistic individuals. Neuroradiology 43:496 –498. Hoffman E, Haxby J (2000): Distinct representations of eye gaze and identity in the distributed human neural system for face perception. Nat Neurosci 3:80 –84.

1

H MRSI in Childhood Autism

Holttum JR, Minshew NJ, Sanders RS, Phillips NE (1992): Magnetic resonance imaging of the posterior fossa in autism. Biol Psychiatry 32:1091–1101. Holzmuller P, Moser E, Werba A, Markis EM, Gomiscek G (1992): In vitro NMR investigation of controlled single and repeated isoflurane anesthesia. Magn Reson Imaging 10:393– 400. Insel TR (1992): Toward a neuroanatomy of obsessive compulsive disorder. Psychiatr Clin North Am 15:813–824. Jacobson R, Le Couteur A, Howlin P, Rutter M (1988): Selective subcortical abnormalities in autism. Psychol Med 18:39 –48. Jambaque I, Mottron L, Ponsot G, Chiron C (1998): Autism and visual agnosia in a child with right occipital lobectomy. J Neurol Neurosurg Psychiatry 65:555–560. Kates WR, Mostofsky SH, Zimmerman AW, Mazzocco MM, Landa R, Warsofsky IS, et al (1998): Neuroanatomical and neurocognitive differences in a pair of monozygous twins discordant for strictly defined autism. Ann Neurol 43:782– 791. Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P, et al (1997): Schedule for affective disorders and schizophrenia for school-age children-present and lifetime version (K-SADS-PL): Initial reliability and validity data. J Am Acad Child Adolesc Psychiatry 36:980 –988. Kemper T, Bauman M (1993): The contribution of neuropathologic studies to the understanding of autism. Neurol Clin 11:175–186. Kleiman MD, Neff S, Rosman NP (1992): The brain in infantile autism: Are posterior fossa structures abnormal? Neurology 42:753–760. Lane RD, Reiman EM, Axelrod B, Yun LS, Holmes A, Schwartz GE (1998): Neural correlates of levels of emotional awareness. Evidence of an interaction between emotion and attention in the anterior cingulate cortex. J Cogn Neurosci 10:525– 535. Levitt JG, Blanton R, Smalley S, Thompson PM, Guthrie D, McCracken JT, et al (2003): Cortical sulcal maps in autism. Cereb Cortex 7:728 –735. Lim KO, Spielman DM (1997): Estimating NAA in cortical gray matter with applications for measuring changes due to aging. Magn Reson Med 37:372–377. Lord C, Rutter M, DiLavore P, Risi S (1999): Autism Diagnostic Observation Schedule-WPS (WPS ed). Los Angeles: Western Psychological Services. Lord C, Rutter M, Le Couteur A (1994): Autism Diagnostic Interview—Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord 24:659 –685. Lundbom NM, Manner T, Komu M, Peltola O, Leino KA, Kirvela OA (1999): Barbiturate anesthesia and brain proton spectroscopy. Am J Neuroradiol 20:1543–1546. McAlonan GM, Daly E, Jumari V, Critchley HD, van Amelsvoort T, Suckling J, et al (2002): Brain anatomy and sensorimotor gating in Asperger’s syndrome. Brain 125:1594 – 1606. Minshew NJ, Goldstein G, Dombrowski SM, Panchalingam K, Pettegrew JW (1993): A preliminary 31P MRS study of autism: Evidence for undersynthesis and increased degradation of brain membranes. Biol Psychiatry 33:762–773.

BIOL PSYCHIATRY 2003;54:1355–1366

1365

Modell JG, Mountz JM, Curtis GC, Greden JF (1989): Neurophysiologic dysfunction in basal ganglia/limbic striatal and thalamocortical circuits as a pathogenetic mechanism of obsessive-compulsive disorder. J Neuropsychiatry Clin Neurosci 1:27–36. Mottron L, Mineau S, Decarie JC, Jambaque I, Labrecque R, Pepin JP, et al (1997): Visual agnosia with bilateral temporooccipital brain lesions in a child with autistic disorder: A case study. Dev Med Child Neurol 39:699 –705. Mountz J, Bradley L, Modell J, Alexander R, Triana-Alexander M, Aaron L, et al (1995): Functional deficits in autistic disorder: Characterization by technetium-99-HMPAO and SPECT. J Nucl Med 36:1156 –1162. Mullen E (1989): Mullen Scales of Early Learning. Cranston, RI: T.O.T.A.L. Child. Murphy DG, Critchley HD, Schmitz N, McAlonan G, Van Amelsvoort T, Robertson D, et al (2002): Asperger syndrome: A proton magnetic resonance spectroscopy study of brain. Arch Gen Psychiatry 59:885–891. Nobre A, Sebestyen G, Gitelman D, Mesulam M, Frackowiak R, Frith C (1997): Functional localization of the system for visuospatial attention using positron emission tomography. Brain 120:515–533. Ohnishi T, Matsuda H, Hashimoto T, Kunihiro T, Nishikawa M, Uema T, et al (2000): Abnormal regional blood flow in childhood autism. Brain 123:1838 –1844. Oldfield RC (1971): The assessment and analysis of handedness: The Edinburgh Inventory. Neuropschologia 9:97–113. Onger D, Drevets WC, Price JL (1998): Glial reduction in the subgenual prefrontal cortex in mood disorders. Proc Natl Acad Sci USA 95:13240 –13245. Otsuka H, Harada M, Mori K, Hisaoka S, Nishitani H (1999): Brain metabolites in the hippocampus-amygdala region and cerebellum in autism: An 1H-MR spectroscopy study. Neuroradiology 41:517–519. Pardo JV, Pardo PJ, Janer KW, Raichle ME (1990): The anterior cingulate cortex mediates processing in the Stroop attentional conflict paradigm. Proc Natl Acad Sci U S A 87:256 –259. Phelps ME, Huang SC, Hoffman EJ, Selin C, Sokoloff L, Kuhl DE (1979): Tomographic measurement of local cerebral glucose metabolic rate in humans with (F-18)2-fluoro-2deoxy-D-glucose: Validation of method. Ann Neurol 6:371– 388. Pierce K, Muller R, Ambrose J, Allen G, Courchesne E (2001): Face processing occurs outside the fusiform “face area” in autism: Evidence from functional MRI. Brain 124:2059 – 2073. Pisani A, Bonsi P, Catania MV, Giuffrida R, Morari M, Marti M, et al (2002): Metabotropic glutamate 2 receptors modulate synaptic inputs and calcium signals in striatal cholinergic interneurons. J Neurosci 22:6176 –6185. Piven J, Bailey J, Ranson BJ, Arndt S (1998): No difference in hippocampus volume detected on magnetic resonance imaging in autistic individuals. J Autism Dev Disord 28:105–110. Piven J, Berthier M, Starkstein S, Nehme E, Pearlson G, Folstein S (1990): Magnetic resonance imaging evidence for a defect of cerebral cortical development in autism. Am J Psychiatry 147:734 –739.

1366

BIOL PSYCHIATRY 2003;54:1355–1366

Piven J, Nehme E, Simon J, Barta P, Pearlson G, Folstein SE (1992): Magnetic resonance imaging in autism: Measurement of the cerebellum, pons, and fourth ventricle. Biol Psychiatry 31:491–504. Pouwels PJW, Frahm J (1998): Regional metabolite concentrations in human brain as determined by quantitative localized proton MRS. Magn Reson Med 39:53–60. Rajkowska G (2000): Postmortem studies in mood disorders indicate altered numbers of neurons and glial cells. Biol Psychiatry 48:766 –777. Rajkowska G, Miguel-Hidalgo JJ, Wei J, Dilley G, Pittman SD, Meltzer HY, et al (1999): Morphometric evidence for neuronal and glial prefrontal cell pathology in major depression. Biol Psychiatry 45:1085–1098. Rapoport JL, Wise SP (1988): Obsessive-compulsive disorder: Evidence for basal ganglia dysfunction. Psychopharmacol Bull 24:380 –384. Raven JC, Court JH, Raven J (1995): Raven’s Coloured Progressive Matricies, 1995 ed. Oxford, UK: Oxford Psychologists Press. Ritvo E, Freeman B, Scheibel A, Duong T, Robinson H, Gurthie D, et al (1986): Lower Purkinje cell counts in the cerebellar of four autistic subjects: Initial findings of the UCLA-NSAC Autopsy Research Report. Am J Psychiatry 143:862–866. Rosenberg DR, Keshavan MS (1998): A.E. Bennett Research Award. Toward a neurodevelopmental model of obsessivecompulsive disorder. Biol Psychiatry 43:623–640. Rosenberg DR, Keshavan MS, O’Hearn KM, Dick EL, Bagwell WW, Saymor AB, et al (1997): Fronto-striatal measurements of treatment-naı¨ve pediatric obsessive compulsive disorder. Arch Gen Psychiatry 54:824 –830. Rosenberg DR, MacMaster FP, Keshavan MS, Fitzgerald KD, Stewart CM, Moore GJ (2000): Decrease in caudate glutamatergic concentrations in pediatric obsessive-compulsive disorder patients taking paroxetine. J Am Acad Child Adolesc Psychiatry 39:1096 –1103. Rumsey JM, Duara R, Grady C, Rapoport JL, Margolin RA, Rapoport SI, et al (1985): Brain metabolism in autism: Resting cerebral glucose utilization rates as measured with positron emission tomography. Arch Gen Psychiatry 42:448 – 455. Saitoh O, Courchesne E, Egaas B, Lincoln AJ, Schreibman L (1995): Cross-sectional area of the posterior hippocampus in autistic patients with cerebellar and corpus callosum abnormalities. Neurology 45:317–324. Satlin A, Bodick N, Offen WW, Renshaw PF (1997): Brain proton magnetic resonance spectroscopy in Alzheimer’s disease: Changes after treatment with xanomeline, an M sub 1 selective cholinergic agonist. Am J Psychiatry 154:1459 – 1461. Scatton B (1987): Excitatory amino acid and GABA influence on rat striatal cholinergic transmission. In Sanler M, et al, editors. Neurotransmitter Interactions in the Basal Ganglia. New York: Raven, 121–131.

J.G. Levitt et al

Schultz R, Gauthier I, Klin A, Fulbright R, Anderson A, Volkmar F, et al (2000): Abnormal ventral temporal cortical activity during face discrimination among individuals with autism and Asperger syndrome. Arch Gen Psychiatry 57:331–340. Schwartz JM, Stoessel PW, Baxter LR Jr, Martin KM, Phelps ME (1996): Systematic changes in cerebral glucose metabolic rate after successful behavior modification treatment of obsessive-compulsive disorder. Arch Gen Psychiatry 53:109 – 113. Sears LL, Vest C, Mohamed S, Bailey J, Ranson BJ, Piven J (1999): An MRI study of the basal ganglia in autism. Prog Neuro Psychopharmacol Biol Psychiatry 23:613–624. Siegel BV Jr, Asarnow R, Tanguay P, Call JD, Abel L, Ho A, et al (1992): Regional cerebral glucose metabolism and attention in adults with a history of childhood autism. J Neuropsychiatry Clin Neurosci 4:406 –414. Siesjo¨ BK (1978): Brain Energy Metabolism. Chichester, UK: Wiley. Soher BJ, van Zijl PCM, Duyn JH, Barker PB (1996): Quantitative proton MR spectroscopic imaging of the human brain. Magn Reson Med 35:356 –363. Sokol DK, Dunn DW, Edwards-Brown M, Feinberg J (2002): Hydrogen proton magnetic resonance spectroscopy in autism: Preliminary evidence of elevated choline/creatine ratio. J Child Neurology 17:245–249. Speck O, Thiel T, Hennig J (1996): Grading and therapy monitoring of astrocytomas with 1H-spectroscopy: Preliminary study. Anticancer Res 16:1581–1585. Spiker D, Lotspeich L, Dimiceli S, Myers R, Risch N (2002): Behavioral phenotypic variation in autism multiplex families: Evidence for continuous severity gradient. Am J Med Genet 114:129 –136. Urenjak J, Williams SR, Gadian DG, Noble M (1993): Proton nuclear magnetic resonance spectroscopy unambiguously identifies different neural cell types. J Neurosci 13:981–989. Wang H, Pickel VM (2000): Presence of NMDA-type glutamate receptors in cingulate corticostriatal terminals and their postsynaptic targets. Synapse 35:300 –310. Wang Y, Li S-J (1998): Differentiation of metabolic concentrations between gray matter and white matter of human brain by in vivo 1H magnetic resonance spectroscopy. Magn Reson Med 39:28 –33. Weschler D (1991): Examiner’s Manual: Weschler Intelligence Scale for Children, third ed. New York: Psychological Corporation. Woods RP, Mazziotta JC, Cherry SR (1993): MRI-PET registration with automated algorithm. J Comput Assist Tomogr 17:536 –546. Zilbovicius M, Boddaert N, Belin P, Poline J, Remy P, Mangin J, et al (2000): Temporal lobe dysfunction in childhood autism: A PET study. Am J Psychiatry 157:1988 –1993. Zilbovicius M, Garreau B, Samson Y, Remy P, Barthelemy C, Syrota A, et al (1995): Delayed maturation of the frontal cortex in childhood autism. Am J Psychiatry 152:248 –252.