Aberrant Striatal Functional Connectivity in Children with Autism

Aberrant Striatal Functional Connectivity in Childrenwith AutismAdriana Di Martino, Clare Kelly, Rebecca Grzadzinski, Xi-Nian Zuo, Maarten Mennes,Maria Angeles Mairena, Catherine Lord, F. Xavier Castellanos, and Michael P. Milham

Background: Models of autism spectrum disorders (ASD) as neural disconnection syndromes have been predominantly supported byexaminations of abnormalities in corticocortical networks in adults with autism. A broader body of research implicates subcortical structures,particularly the striatum, in the physiopathology of autism. Resting state functional magnetic resonance imaging has revealed detailedmaps of striatal circuitry in healthy and psychiatric populations and vividly captured maturational changes in striatal circuitry during typicaldevelopment.

Methods: Using resting state functional magnetic resonance imaging, we examined striatal functional connectivity (FC) in 20 children withASD and 20 typically developing children between the ages of 7.6 and 13.5 years. Whole-brain voxelwise statistical maps quantifiedwithin-group striatal FC and between-group differences for three caudate and three putamen seeds for each hemisphere.

Results: Children with ASD mostly exhibited prominent patterns of ectopic striatal FC (i.e., functional connectivity present in ASD but notin typically developing children), with increased functional connectivity between nearly all striatal subregions and heteromodal associativeand limbic cortex previously implicated in the physiopathology of ASD (e.g., insular and right superior temporal gyrus). Additionally, wefound striatal functional hyperconnectivity with the pons, thus expanding the scope of functional alterations implicated in ASD. Secondaryanalyses revealed ASD-related hyperconnectivity between the pons and insula cortex.

Conclusions: Examination of FC of striatal networks in children with ASD revealed abnormalities in circuits involving early developingareas, such as the brainstem and insula, with a pattern of increased FC in ectopic circuits that likely reflects developmental derangement
rather than immaturity of functional circuits.
iccnscbcaoglv

pt(Feca(iotet

rmpva

Key Words: Autism, brainstem, development, functional connec-tivity, insula, striatum

H uman brain development is characterized by progressiveremodeling of the brain’s structural and functional architec-ture. Resting state functional connectivity (FC) approaches

vividly capture these developmental changes. Consistent with his-topathological and structural magnetic resonance imaging (MRI)findings (1,2), diffuse patterns of local FC are gradually replaced bya distributed architecture, dominated by focal long-range connec-tions (3– 6). Concurrently, the strength of FC between cortical andsubcortical regions progressively decreases (6), suggesting de-creased subcortical influence over cortical functioning with age.Disturbances in these maturational processes are increasingly im-plicated in neurodevelopmental conditions such as autism spec-trum disorders (ASD) (7).

Models of ASD as a brain disconnection syndrome (8 –13) haveemerged from analyses of abnormalities in corticocortical connec-tions. Studies of FC in ASD have found evidence of reduced long-range FC in various cortical circuits (14 –24). Because development

From the Phyllis Green and Randolph Co�wen Institute for Pediatric Neuro-science at the Child Study Center (ADM, CK, RG, X-NZ, MM, FXC, MPM),New York University Langone Medical Center, New York, New York;Hospital Sant Joan de Déu (MAM), Barcelona, Spain; University of Mich-igan Autism and Communication Disorders Center (CL), Ann Arbor,Michigan; Nathan Kline Institute for Psychiatric Research (FXC, MPM),Orangeburg, New York; and Division of Cognitive and DevelopmentalPsychology (X-NZ), Institute of Psychology, Chinese Academy of Sci-ences, Beijing, China.

Address correspondence to Michael P. Milham, M.D., Ph.D., NYU LangoneMedical Center, Phyllis Green and Randolph Co�wen Institute for Pediat-ric Neuroscience at the Child Study Center, 215 Lexington Avenue, 14thFloor, New York, NY 10016; E-mail: [email protected].

eReceived Aug 12, 2010; revised Oct 18, 2010; accepted Oct 20, 2010.

0006-3223/$36.00doi:10.1016/j.biopsych.2010.10.029

s characterized by an overall increase in long-range functionalonnections (3–5,25), these studies suggest dysmaturation as aore feature of ASD pathophysiology. Examples of ASD hypocon-ectivity include findings of reduced FC in frontoparietal circuitsupporting working memory (26), in medial-wall circuitry impli-ated in social cognition (27–29), and in a left inferior frontal gyrus-ased network supporting language processes (10). Although spe-ific affected circuits vary depending on the process examined andpproach (15–24,30), the common denominator has been evidencef abnormal corticocortical circuitry. Partly, this may reflect areater focus on higher order cognitive and social processes,

argely subserved by cortical regions found to be abnormal in indi-iduals with ASD (31,32).

Here, we draw attention to subcortical-cortical interactions as aotential locus of dysfunction in ASD. We focus on striatal circuits

hat contribute to maturational changes in subcortical-cortical FC6). Although infrequently examined, abnormal subcortical-corticalC has been detected in ASD. Specifically, a resting-state positronmission tomography study found weaker correlations in glucoseonsumption between frontal regions and both striatum and thal-mus in young adults with ASD compared with typical adults (TA)33). In contrast, in a task-based functional magnetic resonancemaging (fMRI) study, increased striatal FC with frontal, parietal, andccipital lobes was reported in adults with ASD during a sensorimo-

or task, relative to TA (34). These divergences may reflect differ-nces in imaging modalities, design (i.e., presence vs. absence of aask), analyses, or small sample sizes.

While direct examinations of striatal FC in ASD are limited, ASD-elated structural and functional impairments have been docu-

ented, particularly, but not exclusively (35), in the caudate. Mor-hological studies have generally reported increased caudateolume in ASD (36,37). Individuals with ASD showed striatal hypo-ctivation relative to healthy control subjects in studies of facial
xpression imitation (38) and cognitive flexibility (39). On the other
BIOL PSYCHIATRY 2011;69:847–856© 2011 Society of Biological Psychiatry

mailto:[email protected]

M

P

sacwa(rdtttd(Sw1pws

iaISPpuct

em(e

M

848 BIOL PSYCHIATRY 2011;69:847–856 A. Di Martino et al.

hand, caudate hyperactivation was evident in a study of sensorimo-tor processes in ASD (40). Regardless of the direction of the aberrantstriatal activation (hypoactivation or hyperactivation), cortical com-ponents of striatal circuits, such as dorsolateral prefrontal cortex,dorsal anterior cingulate cortex, and inferior parietal sulcus, exhib-ited the same type of aberrant activation (38 – 40). Overall, thisliterature suggests abnormal striatal-cortical circuits in ASD andhighlights the need for their direct investigation in this population.

Using resting state fMRI (R-fMRI), we previously establishedthat seed-based FC analyses delineate detailed maps of striatalcircuitry (41). We employed seeds in three caudate and threeputamen regions, adapted from a meta-analysis of the task-based literature (42). Independent applications of the same ap-proach yielded similar results (43,44). Accordingly, we used R-fMRI to comprehensively examine striatal functional circuitry inschool-age children with ASD compared with typically develop-ing children (TDC).

Consistent with the notion that ASD is characterized by neuro-developmental dysmaturity (7,10,31,45– 48), initial R-fMRI studiesof adults with ASD demonstrated reduced long-range FC in large-scale networks (e.g., [3,5,49]). This resembles TDC functional archi-tecture rather than that of TA (3,5,25), suggesting developmentalimmaturity in ASD. Given 1) our focus on children, 2) evidence ofincreased striatal-cortical FC in TDC relative to TA (6), and 3) initialfindings of increased striatal-cortical FC in ASD (34), we predictedthat children with ASD would exhibit immature FC development.Specifically, we anticipated that caudate and putamen circuits inASD would show stronger and more diffuse FC with paralimbic,associative, and sensory cortex compared with age-matched TDC.Based on the frequent findings of inappropriate patterns of coacti-vation in task-based studies (31,32), we also expected ectopic FC inASD—FC between regions that are not typically functionally con-nected in pediatric or adult samples. Additionally, we conductedsecondary analyses to contrast striatal FC in TDC and TA included inour earlier striatal study (41). In preparation for large-scale longitu-dinal studies, such analyses allow interpreting the findings of aber-rant striatal FC in children with ASD in the context of possible

Table 1. TDC and ASD Clinical Characteristics

ASD(n � 20)

ales, n (%) 17 (85)SES (Class 4 or 5) n (%)a 15 (75)

Mean (SD) Minimum Maximum

Age (years) 10.4 (1.7) 7.6 12.8Full IQ 109 (12) 88 134Verbal IQ 107 (11) 93 139Performance IQ 110 (18) 83 146ADOS Totalb 13 (4) 7 22ADOS SA Total 10 (4) 4 18ADOS RRB Total 3 (1) 1 5

ADOS, Autism Diagnostic Observation Schedule; ANOVA, analysis of varrepetitive behaviors; SA, social affect; SES, socioeconomic status; TDC, typic

aThirty-eight parents provided demographic forms used to compute SESenough information to compute SES class.

bADOS mean scores are based on 17 research-reliable administrations.

deviance from typical development. T

www.sobp.org/journal

ethods and Materials

articipantsChildren with ASD were recruited through the New York Univer-

ity Child Study Center, parent groups, flyers, and web/newspaperdvertisements. We enrolled 26 children with ASD and excluded 6hildren due to excessive movement. For the remaining 20 childrenith ASD, the Autism Diagnostic Interview-Revised (n � 19) (50)

nd the Autism Diagnostic Observation Schedule, Module 3 (ADOS)n � 20) (51) supported clinicians’ DSM-IV-TR diagnoses. Research-eliable scores were obtained for 17 (85%) children using the newiagnostic ADOS algorithm (52). The remaining ADOS and most of

he Autism Diagnostic Interview-Revised scores (n � 16) were ob-ained from clinical administrations; therefore, Table 1 reports onlyhe 17 research-reliable scores. Clinicians’ best-estimate DSM-IV-TRiagnoses were Autistic Disorder (n � 12, 60%), Asperger’s Disorder

n � 7, 35%), and Pervasive Developmental Disorder-Not Otherwisepecified (n � 1, 5%). Ten children were medication-naive, and 7ere not taking psychoactive medications for periods ranging fromweek to � 12 months. Two children were currently treated withsychostimulants (discontinued 24 hours before scanning) and 1ith melatonin. All other psychoactive medications were exclu-

ionary (49).Twenty TDC selected from a larger pool of children participating

n ongoing studies were group-matched for age, sex, estimated IQ,nd handedness. Inclusion as a TDC required absence of any DSM-

V-TR diagnoses and treatment with psychoactive medications. Thechedule of Affective Disorders and Schizophrenia for Children—resent and Lifetime Version (53) was separately administered toarent(s) and their child in 17 cases, and for 2 cases, due to sched-ling constraints, to one parent or the child only. For the remaininghild, the Anxiety Diagnostic Interview Schedule (54) was adminis-ered to one parent.

The Wechsler Abbreviated Scale of Intelligence (55) provided IQstimates. All parents but three (two TDC, one ASD) provided de-ographic information to compute socioeconomic status (SES)

56). Evidence of known neurological or genetic syndromes and/orstimates of full-scale IQ � 80 were exclusionary. The ASD and the

TDC(n � 20)

Chi-Square

�2(1) p

14 (70) 1.3 .2613 (77) .5 .49

Mean (SD) Minimum Maximum

ANOVA

F(1,38) p

10.9 (1.6) 7.9 13.5 1.1 .31116 (16) 80 138 2.1 .15113 (15) 85 135 1.5 .22115 (16) 72 137 1.2 .29

— —— —— —

; ASD, autism spectrum disorders; IQ, intelligence quotient; RRB, restrictedeveloping children.em one parent of a child with ASD and two parents of TDC did not provided

ianceally d, of th

DC groups did not differ significantly with respect to IQ, age, sex,

paUid

A

cessCher

uaAttitqim(tacpmtl2u(pts

F

ivst3

msaasrsoSrt.

R

S

vsgt(bsrpsresby

poAFifmiswlcspr3

rTsfswtstcr

A. Di Martino et al. BIOL PSYCHIATRY 2011;69:847–856 849

or SES. Except for two TDC, all children completed the EdinburghHandedness Inventory (57). All TDC were right-handed (two basedon self-report); 18 of the 20 children with ASD were right-handed(groups did not differ). Per parent-reported ethnicity (collected for19 children per group), Caucasian (11 and 8, in the TDC and ASDgroups, respectively) and African American (4 and 3 for the TDC andthe ASD groups, respectively) predominated (groups did not differ).Thirty-five adults (mean age: 28.4 � 8.5 years) described in our

revious study (41) were included for secondary analyses of striatalge-related changes. Per the New York University and the New Yorkniversity School of Medicine Institutional Review Boards, written

nformed consent (parents and adult participants) and assent (chil-ren) were completed.

cquisition and PreprocessingWe collected a 6-minute, 38-second rest scan comprising 197

ontiguous echo planar imaging functional volumes (3.0 Tesla, rep-tition time � 2000 msec; echo time � 25 msec; flip angle � 90°, 39lices, matrix � 64 � 64; field of view � 192 mm; acquisition voxelize � 3 mm3). Participants were asked to relax with eyes open.omplete cerebellar coverage was not obtained for all children. Aigh-resolution T1-weighted magnetization prepared rapid gradi-nt echo anatomical image was also acquired (Methods and Mate-ials in Supplement 1).

As detailed elsewhere (41,43,44), preprocessing was carried outsing both Analysis of Functional NeuroImages (AFNI) (http://fni.nimh.nih.gov/afni/) and FSL (http://www.fmrib.ox.ac.uk) (bothnalysis Group, FMRIB, Oxford, United Kingdom). It comprised slice

ime correction for interleaved acquisitions, three-dimensional mo-ion correction, despiking, spatial smoothing (full width at half max-mum 6 mm), mean-based intensity normalization of all volumes byhe same factor, temporal filtering (.009 –.1 Hz), and linear anduadratic detrending. Registration of each participant’s anatomical

mage to Montreal Neurological Institute 152 stereotaxic space (2m3) comprised a 12 degrees of freedom affine transformation

FLIRT; Analysis Group, FMRIB) (58) and a nonlinear refinement ofhe initial registration (FNIRT; NIMH, Bethesda, Maryland) (59,60). Toddress concerns about age-related errors in registration (61), weonducted secondary analyses in data registered to a local tem-late (Methods and Materials in Supplement 1). Root mean squareovement in each of the cardinal directions (x, y, and z) and rota-

ional movement about three axes (pitch, yaw, and roll) were calcu-ated for each participant to include only data with displacement �.5 mm/2.5°. Each participant’s four-dimensional preprocessed vol-me was regressed on nine predictors modeling nuisance signals

white matter, cerebrospinal fluid, global signal, and six motionarameters). Resulting four-dimensional residual volumes were

hen spatially normalized to Montreal Neurological Institute 152pace using the previously computed transformation.

C AnalysesWe employed six previously validated (41) striatal regions of

nterest (“seeds”). Caudate seeds included the inferior and superiorentral striatum (VSi and VSs) and the dorsal caudate (DC). Putameneeds included the dorsal-caudal putamen (dcP), dorsal-rostral pu-amen (drP), and ventral rostral putamen (vrP). Each seed covered3 voxels in 2 mm3 space (radius � 4 mm). Comparable seed place-

ment in both groups and seed coordinates are illustrated in FigureS1 and Table S1 in Supplement 1.

For each subject, we derived whole-brain voxelwise correlationsassociated with the mean time series for each of the 12 seeds (6 perhemisphere) using 3dfim� (AFNI). Correlation maps were con-
verted to Z-value maps (Fisher r-to-z transformation). Individual FC g
aps were entered into group-level analyses (ordinary leastquares; FSL FEAT). No significant group differences were noted forge or IQ. However, given their wide ranges (Table 1), we covariedge and full-scale IQ. We did not covary sex, given the few femaleubjects (three ASD, six TDC). These analyses generated maps ofegions exhibiting significant positive and negative FC for eacheed and each group (ASD, TDC). Direct comparisons yielded mapsf voxels exhibiting significant FC group differences for each seed.econdary analyses comparing TDC with TA were conducted cova-ying age and sex. Gaussian random field theory was used for clus-er-level multiple comparisons correction (minimum Z � 2.3; p �05, corrected).

esults

triatal FCConsistent with prior work (41), seed-based FC analyses pro-

ided detailed maps of distinct functional circuits for each of the sixeeds per hemisphere. Patterns of striatal FC obtained for TDC wererossly similar to those previously obtained with TA (41,43,44),

hough with notably more diffuse and stronger local positive FCFigure S2 in Supplement 1). As previously reported in TDC (6),oth child groups showed significant FC between all striataleeds and paralimbic regions such as insula and associativeegions such as superior temporal gyrus (STG) and planum tem-orale. However, in the ASD group, nearly all striatal seedshowed significantly stronger and more diffuse FC with bothegions typically included in striatal networks, as well as withctopic areas, which are not functionally connected with thetriatum in TDC (Figures 1-3). Specific findings are describedelow. The FC analyses in data registered to a local templateielded substantially similar results (Figure S3 in Supplement 1).

Inferior and Superior Ventral Striatum. Both groups dis-layed an FC gradient from ventromedial to dorsolateral divisionsf prefrontal and anterior cingulate cortex going from VSi to VSs.dditionally, both ventral striatal seeds showed significant positiveC with paralimbic and associative areas in both child groups. These

ncluded correlations with the anterior and mid insula as well as STGor both hemispheres. Children with ASD displayed stronger and

ore extended positive relationships between these areas. Specif-cally, in children with ASD, the right VSi showed more diffuse andignificantly stronger FC with the right STG and the right mid insula,hile the left VSs showed increased FC with the left central opercu-

um. In children with ASD, significantly increased FC extended toortical areas that exhibited negative FC with the ventral striataleeds in TDC. Specifically, in children with ASD the left VSi showedositive FC with the left supramarginal gyrus, in contrast, these two

egions were negatively connected in the TDC group (Figures 1 and, Table 2; Figure S4 in Supplement 1).

Dorsal Caudate. In both TDC and ASD, DC showed positiveelationships with regions implicated in cognitive control (62– 64).hese included dorsal anterior cingulate and lateral frontal cortex,uch as right middle and inferior frontal gyri and ventrolateral pre-rontal and lateral orbitofrontal cortex. Both child groups alsohowed positive FC with anterior and posterior insula, as well asith superior and middle temporal gyri bilaterally, areas not

ypically observed in DC circuitry in TA. Direct group compari-ons revealed ASD-related increases in positive FC that extendedo heteromodal sensory processing regions that were negativelyorrelated with DC in TDC. Specifically, in the ASD group, theight DC exhibited positive FC with the left temporal fusiform
yrus, while the left DC showed positive FC with the supramar-

http://afni.nimh.nih.gov/afni/

http://afni.nimh.nih.gov/afni/

http://www.fmrib.ox.ac.uk

I

daesaTppdiit

S

hdAmp

g ch


ginal gyrus bilaterally (Figures 1 and 3, Table 2; Figures S4 and S6in Supplement 1).

Dorsal Caudal and Dorsal Rostral Putamen. Consistent withtheir role in primary motor control, the dorsal putamen seeds ex-hibited significant positive FC with primary and secondary sensori-motor areas for both TDC and ASD. However, relative to TDC, ASDagain showed a more diffuse pattern of FC extending to regionsthat did not exhibit FC with the putamen in TDC. Specifically, bothright dorsal putamen seeds were strongly correlated with a brains-tem region in the pons; for the right dcP, significant FC also ex-tended to the right parahippocampal gyrus and hippocampus. Fur-ther, in ASD, the left dcP was significantly more correlated with theright temporal-occipital fusiform gyrus (Figures 2 and 3; Figures S5and S6 in Supplement 1).

Ventral Rostral Putamen. Positive relationships with poste-rior and dorsal aspects of the insula bilaterally were observed forboth ASD and TDC. Children with ASD showed more extendedand significantly stronger FC between right vrP and right STGand planum temporale (Figure 3). Right vrP was the one region inwhich ASD-related hypoconnectivity was also detected, with acluster extending into the superior division of the lateral occipi-tal cortex in the left hemisphere (Figure 2; Figures S5 and S6 in

Figure 1. Within- and between-group statistical maps for the right hemisphchildren, autism spectrum disorders) analyses obtained for three caudate seeall analyses, Gaussian random field theory was employed to carry out clusterp � .05, corrected). Axial maps and inflated surface maps were generated uFunctional NeuroImages Surface Mapper software (http://afni.nimh.nih.gov/in Supplement 1. For results of the caudate seeds located in the left hemispcaudate; LH, left hemisphere; RH, right hemisphere; TDC, typically developin

Supplement 1). a


terative FC Brainstem AnalysesGiven 1) the finding of increased putamen-brainstem FC in chil-

ren with ASD, 2) the role of the brainstem in basic functions such aslertness, arousal, and sensory and autonomic processes, and 3)arlier models of brainstem pathology in ASD (65), we conducted aecondary FC analysis of the pons cluster that emerged as function-lly hyperconnected with both right drP and dcP in ASD relative toDC. Both groups showed significant relationships between theons and cerebellum, thalamus, putamen, caudate, and parahip-ocampal gyrus. In ASD, not only were these correlations moreiffuse but they also extended to areas unrelated to the brainstem

n TDC. Massive FC was observed between the pons and insula,nvolving the posterior and middle insula divisions bilaterally andhe anterior insula in the right hemisphere only (Figure 4).

triatal Age-Sensitive Areas and ASD AbnormalitiesWe examined patterns of age-related changes in FC for the 12

yperconnected and 1 hypoconnected functional circuits in chil-ren with ASD relative to TDC. With the exception of the right VSi,SD-related abnormalities in FC did not suggest delayed develop-ent (i.e., ASD � TDC � TA, or TA � TDC � ASD). Rather, the

rofiles observed suggest ectopic FC in ASD. For nearly all the

audate seeds. Results of within- and between-group (typically developingright hemisphere are depicted on the left and right panels, respectively. For

correction for multiple comparisons (minimum Z � 2.3; cluster significance:Analysis of Functional NeuroImages and surface mapping with Analysis ofuma). For seed placement and seed coordinates, see Figure S1 and Table S1see Figure S4 in Supplement 1. ASD, autism spectrum disorder; DC, dorsal

ildren; VSi, ventral striatum inferior; VSs, ventral striatum superior.

ere cds for

-levelsingafni/shere,

berrant circuits, significantly positive FC was observed in the ASD

http://afni.nimh.nih.gov/afni/suma

arBi

Trlioiapc

tdillvdfeie


group, but FC was either absent or negative in each of the other twogroups (TDC, TA). Post hoc tests verified differences between ASDand both TDC and TA (Figure 5; Table S2 in Supplement 1).

Correlation with SymptomsSecondary analyses explored potential relationships between

ASD-related abnormalities in FC and symptom severity. In the 17children with research-reliable ADOS scores, we correlated thesescores and FC for each of the 13 circuits that differed significantlybetween ASD and TDC. Consistent with our primary analyses, wecomputed partial correlations, controlling for age and IQ. We ob-served a positive correlation between restrictive repetitive behav-ior scores and right vrP/right STG FC [partial r(13) � .69, p � .005]

nd a negative correlation for the right dcP/right pons FC [partial(13) � �.64, p � .011], although these correlations did not surviveonferroni correction for multiple comparisons (p � .004; Table S3

n Supplement 1).

Discussion

Our seed-based resting state fMRI examination of striatal func-tional architecture in school-age children with ASD revealed a wide-

Figure 2. Within- and between-group statistical maps for the right hemisphchildren, autism spectrum disorders) analyses obtained for the three putrespectively. For all analyses, Gaussian random field theory was employed tcluster significance: p � .05, corrected). Axial maps and inflated surface mapswith Analysis of Functional NeuroImages Surface Mapper software (http://afnS1 and Table S1 in Supplement 1. For results of the putamen seeds locatedisorder; dcP, dorsal caudal putamen; drP, dorsal rostral putamen; LH, left hrostral putamen.

spread pattern of excessive FC in striatal-cortical circuitry, relative to e

DC. Excessive striatal FC was evident between nearly all striatalegions examined and a variety of heteromodal associative andimbic cortex previously implicated in the pathophysiology of ASD,ncluding the right STG and insular cortex. We also found evidencef striatal functional hyperconnectivity with the pons, thus expand-

ng the scope of functional alterations implicated in ASD. Second-ry FC analyses focusing on this brainstem area revealed broadatterns of ASD-related hyperconnectivity with bilateral insularortex.

One of the challenges facing fMRI studies of neurodevelopmen-al disorders is ascertaining the pathophysiological processes un-erlying increased or decreased FC. Functional hyperconnectivity

n clinical populations is increasingly interpreted as reflecting de-ayed or stunted maturational processes. For example, increasedocal and reduced long-range FC— characteristic of immature de-elopment— has been reported to be widespread in Tourette’sisorder (along with two anomalous circuits) (66) and was also

ound in attention-deficit/hyperactivity disorder (67). Here, the onlyvidence suggesting developmental immaturity was ASD-related

ncreases in FC between VSi and right insula/STG. Beyond this singlexample, the remaining ASD-related differences were suggestive of

utamen seeds. Results of within- and between-group (typically developingseeds for right hemispheres are depicted on the left and right panels,

ry out cluster-level correction for multiple comparisons (minimum Z � 2.3;generated using Analysis of Functional NeuroImages and surface mappingh.nih.gov/afni/suma). For seed placement and seed coordinates, see Figuree left hemisphere, see Figure S5 in Supplement 1. ASD, autism spectrum

here; RH, right hemisphere; TDC, typically developing children; vrP, ventral

ere pameno carwerei.nim

d in themisp

ctopic FC—patterns of inappropriate, rather than residual, FC.



wyit

rb

ooeaeagpmepsudrIebr

Ae

AFFts


Most aberrant FC observed in ASD was with regions not correlatedwith the striatal seeds for either TDC or TA. The idea of ectopic FC isconsistent with the task-based literature, which has shown thatparticipants with ASD exhibit inappropriate patterns of task activa-tion in sensory (e.g., occipital cortex), motor (e.g., supplementalmotor cortex), and heteromodal association areas (e.g., ventrome-dial prefrontal cortex) not recruited by the specific tasks examinedin neurotypical populations (11,31). Although intriguing, thismodel remains speculative until validated by cross-sectional andlongitudinal studies beginning in early development. These studieswill elucidate whether increased striatal FC is specific to school-agechildren or extends to younger and older children with ASD. Foryoung children with ASD, no FC studies are available, while for olderchildren, findings of mostly corticocortical decreased FC have beenreported (e.g., [10,24]), albeit with exceptions (28,34,68).

In our data, ASD-related differences in the FC of insular cortexere prominent, being evident in both striatal and brainstem anal-

ses. These findings are timely; a recent meta-analysis of functionalmaging studies of autism highlighted ASD-related insula hypoac-ivation in studies examining social processing (30), prompting calls

for increased attention to insula dysfunction in models of autism(69). In considering the role of the insula in ASD, a logical step hasbeen to focus on the anterior insula (AI), which is implicated inempathy (70), task control (71), and more broadly, in the ability tointegrate visceral, autonomic processes to guide behavior (72–74).

Figure 3. Autism spectrum disorder-related functional hyperconnections inconnectivity in children with autism spectrum disorders relative to typicahemisphere. In each panel, along with the statistical brain maps, Z-transformin the autism spectrum disorder (triangles) and typically developing childrandom field theory was employed to carry out cluster-level correction for m

xial and coronal maps, as well as inflated surface maps, were generated uunctional NeuroImages Surface Mapper software (http://afni.nimh.nih.goigure S6 in Supplement 1. ASD, autism spectrum disorders; DC, dorsal caudemporal gyrus; R, right; RH, right hemisphere; STG, superior temporal gyrtriatum inferior; VSs, ventral striatum superior.

However, recent work suggests that AI function may be more di- d


ectly related to alexithymia, a phenomenon that is associated with,ut distinct from, impaired empathy (75,76).

Our findings underscore the importance of extending the scopef ASD-related insular abnormalities beyond the AI. A meta-analysisf 1768 task-based functional imaging studies highlighted the het-rogeneity of insular cortex and identified four functionally distinctreas (74). These are ventral and dorsal AI areas, associated withmpathy and cognitive functions, respectively; a posterior regionssociated with sensorimotor processes; and an intermediate re-ion involved in olfactory-gustatory functions (74). Considering theotential role of insular circuits in ASD beyond empathy deficitsay provide a more comprehensive model of ASD pathology. For

xample, posterior insular circuitry, implicated in bodily sensoryrocesses (77), may play a role in the ASD-related abnormalities inensory perception and integration (78). Further, it is necessary tonderstand the interactions among the different insular functionalivisions, rather than focusing on any region or circuit in isolation. A

ecent examination of autistic traits in TA illustrates this point (30).n that study, the severity of autistic traits was directly related to thextent to which FC between mid insula and pregenual ACC resem-led that of the ventral AI (low autistic traits) as opposed to poste-

ior insular regions (high autistic traits) (30).Another cortical region hyperconnected with the striatum in

SD was the right STG, particularly its posterior division, whichxhibited hyperconnectivity with both right VSi and vrP. Posterior

riatum. Cortical and subcortical clusters with significantly greater functionalveloping children are illustrated for the striatal seeds located in the right

rrelation coefficients indexing functional connectivity for each participantircles) groups are depicted for the most representative circuits. Gaussianle comparisons (minimum Z � 2.3; cluster significance: p � .05, corrected).

Analysis of Functional NeuroImages and surface mapping with Analysis ofi/suma). For results obtained with the left hemisphere striatal seeds, seecP, dorsal caudal putamen; drP, dorsal rostral putamen; L, left; MTG, middleC, typically developing children; vrP, ventral rostral putamen; VSi, ventral

the stlly de

ed coren (c

ultipsingv/afnate; dus; TD

ivisions of the right STG (pSTG) have been implicated in percep-


mpsttwi

P

cal Inuperi

c


tion of intentional movements such as eye gaze (79), salience ofacoustic cues (80,81), prediction of reward based on other agents’strategies (82), and live face-to-face interactions (83). Consistentwith a role of pSTG in these processes, fMRI studies of ASD havedemonstrated abnormal activation of the pSTG during tasks involv-ing perception of intentional movements, particularly in the righthemisphere (84 – 86). Recent work showed that reductions in white

Table 2. Regions Exhibiting Group Differences (ASD vs. TDC) in Striatal Con

Seed Region with FC Peak

ASD � TDCCaudate R DC Left fusiform cortex

L DC Left supramarginal gyrusRight supramarginal gyrus

L VSs Left frontal operculum/insulaLeft middle frontal gyrus

R Vsi Right superior temporal gyrusRight insula

L Vsi Left precentral gyrusutamen R dcP Right brainstem (pons)

L dcP Right lingual gyrus/cerebellumLeft cuneus/supracalcarine cortex

R drP Right brainstem (pons)L drP Left precentral gyrus

R vrP Right superior/middle temporal gyrusTDC � ASD

R vrP Left lateral occipital cortex/precuneus

Within-cluster peaks of significant FC were identified on the group-levelprogram 3dMaxima, specifying a minimum significance threshold of Z � 2.3in number of voxels and stereotaxic coordinates are reported in MNI space.

AFNI, Analysis of Functional NeuroImages; ASD, autism spectrum disordefunctional connectivity; L, left hemisphere seeds; MNI, Montreal Neurologiventral rostral putamen; VSi, ventral striatum inferior; VSs, ventral striatum s

Figure 4. Brainstem-based aberrant functional connectivity in autism spectrumsummarized as within-group (top row) and between-group (bottom row) statfunctional connectivity in children with autism spectrum disorders relative to tputamen and dorsal caudal putamen) in primary analyses. The pons seed is depsurface maps were generated using Analysis of Functional NeuroImages and s
(http://afni.nimh.nih.gov/afni/suma). Gaussian random field theory was employed to
luster significance: p � .05, corrected). ASD, autism spectrum disorders; TDC, typical

atter volume and hypoactivation of the pSTG during a Stroop taskositively correlated with levels of autistic traits in TA (87). Autismpectrum disorder related abnormalities in temporal gyri gray mat-er have also been noted (88). Increased FC between STG and fron-al cortex in ASD emerged from a study of language processes (89),hile R-fMRI studies of ASD are beginning to reveal STG abnormal-

ties. For example, initial R-fMRI work has revealed ASD-related

vity

Cluster Size (# Voxels) Z-Score x y z

1550 4.43 �28 �42 �20624 3.37 �62 �28 28634 3.90 58 �28 50627 3.75 �40 16 8

3.07 �28 26 42823 4.55 62 �30 6

4.21 38 �6 2627 3.67 �2 �30 58558 3.84 8 �26 �32479 3.96 14 �40 �12492 3.60 �6 �88 12499 4.08 8 �22 �22549 4.68 �48 �14 56

3.07 �64 0 24585 3.73 58 �32 6

656 4.80 �18 �64 40

holded Z-score maps using the peak detection algorithm provided in AFNIminimum distance between peaks equal to 30 mm. Cluster size is reported

dorsal caudate; dcP, dorsal caudal putamen; drP, dorsal rostral putamen; FC,stitute; R, right hemisphere seeds; TDC, typically developing children; vrP,or; Z, Z score of peak of connectivity or local maxima.

er. Results of the secondary analyses of pons-based functional connectivity arel maps. The seed of interest corresponds to the pons region showing greaterlly developing children for both right dorsal putamen seeds (i.e., dorsal rostralin blue in the sagittal map at the bottom of the figure. Axial maps and inflatedmapping with Analysis of Functional NeuroImages Surface Mapper software

necti

thresand a

r; DC,

disordisticaypicaicted

urface
carry out cluster-level correction for multiple comparisons (minimum Z � 2.3;
ly developing children.



Ndwm((iitbfi

tAgdrnsrbarftsp

iabFsfi

(cOrb

sMIAlF

iKv

nStGXfi


increases in FC between STG and posterior cingulate cortex (28) andincreased regional homogeneity of temporal cortices includingSTG (90). Here, we extend the range of STG disconnectivity in ASD,by demonstrating excessive FC with striatal subregions. Given therole of the VSi in reward mechanisms, abnormal FC between theseregions may underlie the hypothesized deficiency of social rewardsin ASD (91).

We detected robust abnormalities in striatum-brainstem andbrainstem-cortical FC, based in the pons. The pons comprises anumber of nuclei that support sensory and motor cranial nervesinnervating the face and the reticular formation—a net of cell bod-ies and bundles of axons that extends from the spinal cord to thethalamus that is essential for heart beat, respiration, alertness,sleep, and a wide array of basic sensory and motor processes (92).

oting the early ontogeny of brainstem development and evi-ence of increased prevalence of putative ASD cases associatedith early exposure to teratogens affecting brainstem develop-ent (93), models of autism have posited brainstem abnormalities

65). Evidence from neuropathology (94) and electrophysiology95) also implicates the brainstem in ASD; morphometric studies ofndividuals with ASD have revealed reduced gray and white mattern the pons and brainstem more broadly (e.g., [88,96]). Despitehese convergent lines of evidence, brainstem dysfunction haseen rarely considered in imaging studies of ASD. We hope ourndings will encourage its inclusion in future studies.

Our findings should be interpreted in light of limitations. Al-hough our sample size is on par with functional imaging studies ofSD, larger samples are needed to account for the marked hetero-eneity characteristic of ASD. Along with sample size, failure toetect robust relationships between FC and symptom severity may

eflect our failure to sample symptoms beyond the cardinal diag-ostic domains. Nevertheless, we tentatively identified relation-hips between right vrP/STG and dcP/brainstem FC and restrictiveepetitive behavior scores. Future work will explore these brain/ehavior relationships in greater depth. Given the role of striatal-nd brainstem-based circuits in the generation of eye movementselated to attention control, reported abnormal in ASD (e.g., [40]),uture studies should record eye movements during R-fMRI. Addi-ionally, we included too few female subjects to examine group byex interactions. Incomplete coverage of the cerebellum in some
articipants prevented full analyses of striatal-cerebellar functional

nteractions. Finally, even though corticostriatal circuits are medi-ted by the thalamus, we did not examine the thalamus directlyecause of current limitations on thalamic functional parcellation.uture work building on recent findings (97,98) should examinetriatal-thalamic-cortical circuitry, taking advantage of ultra-higheld imaging.

In summary, school-age children with ASD exhibited prominent96) ectopic FC affecting striatal interactions with cortical and sub-ortical regions, most notably including the insula and brainstem.ur results support broadening the focus of examination in ASD to

egions involved in early stages of brain development, such as therainstem, insula, and striatum.

This work was supported by Grants from National Alliance for Re-earch on Schizophrenia and Depression and National Institute of

ental Health (K23MH087770) awarded to ADM; from the Nationalnstitute of Child Health and Human Development (R01HD065282),utism Speaks, the Stavros Niarchos Foundation, and the Alicia Kop-

owitz Foundation awarded to FXC; as well as from the Leon Levyoundation awarded to MPM and ADM.

Our strongest gratitude goes to the children and parents who ded-cated their time and effort to this research. We also thank Drs. Amyrain Roy and Christine Cox for helpful editorial suggestions on earlierersions of this manuscript.

Dr. Lord receives royalties from the publication of the Autism Diag-ostic Interview–Revised and the Autism Diagnostic Observationchedule. The royalties received from this research have been donatedo the charity Have Dreams. Adriana Di Martino, Clare Kelly, Rebeccarzadzinski, Xi-Nian Zuo, Maarten Mennes, Maria Angeles Mairena, F.avier Castellanos, and Michael P. Milham reported no biomedicalnancial interests or potential conflicts of interest.

Supplementary material cited in this article is available online.

1. Huttenlocher PR, Dabholkar AS (1997): Regional differences in synapto-genesis in human cerebral cortex. J Comp Neurol 387:167–178.

2. Giedd JN, Blumenthal J, Jeffries NO, Castellanos FX, Liu H, Zijdenbos A, etal. (1999): Brain development during childhood and adolescence: Alongitudinal MRI study. Nat Neurosci 2:861– 863.

3. Fair DA, Dosenbach NU, Church JA, Cohen AL, Brahmbhatt S, Miezin FM,

Figure 5. Striatal functional connectivity (FC) of autismspectrum disorders (ASD), typically developing children(TDC), and typical adults (TA). Group mean functional con-nectivity (Z-transformed correlation coefficients) for chil-dren with ASD (black), TDC (white), and TA (gray) aredepicted for each of the circuits that exhibited signifi-cantly greater FC in ASD relative to TDC in our primaryvoxel-based analyses. Post hoc group comparisonsshowed that only right ventral striatum inferior FC withthe right insula and superior temporal gyrus exhibited apattern of group differences consistent with develop-mental immaturity (i.e., ASD � TDC � TA; with p � .0001for ASD � TDC and p � .05 for TDC � TA). The remainderof the profiles were suggestive of ectopic FC in ASD (i.e.,significantly positive FC in ASD and either absent or neg-ative FC in the other two groups). BL, bilateral; Bstem,brainstem; DC, dorsal caudate; dcP, dorsal caudal puta-men; drP, dorsal rostral putamen; FG, fusiform gyrus; FO,frontal operculum; G, gyrus; ins, insula; L, left; MTG, middletemporal gyrus; PrecCG, precentral gyrus; R, right; STG,superior temporal gyrus; Supram.G, supramarginal gyrus;vrP, ventral rostral putamen; VSi, ventral striatum inferior;VSs, ventral striatum superior.

et al. (2007): Development of distinct control networks through segre-gation and integration. Proc Natl Acad Sci U S A 104:13507–13512.

2

3

3

3

3

3

3

3

3

3

3

4

4

4

4

4

4

4

4

4

4


4. Fair DA, Cohen AL, Dosenbach NU, Church JA, Miezin FM, Barch DM, etal. (2008): The maturing architecture of the brain’s default network. ProcNatl Acad Sci U S A 105:4028 – 4032.

5. Kelly AMC, Di Martino A, Uddin LQ, Shehzad Z, Gee DG, Reiss PT, et al.(2009): Development of anterior cingulate functional connectivity fromlate childhood to early adulthood. Cereb Cortex 19:640 – 657.

6. Supekar K, Musen M, Menon V (2009): Development of large-scale func-tional brain networks in children. PLoS Biol 7:e1000157.

7. Courchesne E, Pierce K, Schumann CM, Redcay E, Buckwalter JA, Ken-nedy DP, Morgan J (2007): Mapping early brain development in autism.Neuron 56:399 – 413.

8. Frith C (2004): Is autism a disconnection disorder? Lancet Neurol 3:577.9. Belmonte MK, Cook EH, Anderson GM, Rubenstein JL, Greenough WT,

Beckel-Mitchener A, et al. (2004): Autism as a disorder of neural informa-tion processing: Directions for research and targets for therapy. MolPsychiatry 9:646 – 663.

10. Just MA, Cherkassky VL, Keller TA, Minshew NJ (2004): Cortical activationand synchronization during sentence comprehension in high-function-ing autism: Evidence of underconnectivity. Brain 127:1811–1821.

11. Minshew NJ, Williams DL (2007): The new neurobiology of autism: Cor-tex, connectivity, and neuronal organization. Arch Neurol 64:945–950.

12. Geschwind DH, Levitt P (2007): Autism spectrum disorders: Develop-mental disconnection syndromes. Curr Opin Neurobiol 17:103–111.

13. Hughes JR (2007): Autism: The first firm finding � underconnectivity?Epilepsy Behav 11:20 –24.

14. Jones TB, Bandettini PA, Kenworthy L, Case LK, Milleville SC, Martin A,Birn RM (2010): Sources of group differences in functional connectivity:An investigation applied to autism spectrum disorder. Neuroimage 49:401– 414.

15. Kennedy DP, Redcay E, Courchesne E (2006): Failing to deactivate: Rest-ing functional abnormalities in autism. Proc Natl Acad Sci U S A 103:8275– 8280.

16. Cherkassky VL, Kana RK, Keller TA, Just MA (2006): Functional connectiv-ity in a baseline resting-state network in autism. Neuroreport 17:1687–1690.

17. Paakki JJ, Rahko J, Long X, Moilanen I, Tervonen O, Nikkinen J, et al.(2010): Alterations in regional homogeneity of resting-state brain activ-ity in autism spectrum disorders. Brain Res 1321:169 –179.

18. Just MA, Cherkassky VL, Keller TA, Kana RK, Minshew NJ (2007): Func-tional and anatomical cortical underconnectivity in autism: Evidencefrom an FMRI study of an executive function task and corpus callosummorphometry. Cereb Cortex 17:951–961.

19. Kana RK, Keller TA, Cherkassky VL, Minshew NJ, Just MA (2006): Sentencecomprehension in autism: Thinking in pictures with decreased func-tional connectivity. Brain 129:2484 –2493.

20. Koshino H, Kana RK, Keller TA, Cherkassky VL, Minshew NJ, Just MA(2008): fMRI investigation of working memory for faces in autism: Visualcoding and underconnectivity with frontal areas. Cereb Cortex 18:289 –300.

21. Castelli F, Frith C, Happe F, Frith U (2002): Autism, Asperger syndromeand brain mechanisms for the attribution of mental states to animatedshapes. Brain 125:1839 –1849.

22. Villalobos ME, Mizuno A, Dahl BC, Kemmotsu N, Muller RA (2005): Re-duced functional connectivity between V1 and inferior frontal cortexassociated with visuomotor performance in autism. Neuroimage 25:916 –925.

23. Welchew DE, Ashwin C, Berkouk K, Salvador R, Suckling J, Baron-CohenS, Bullmore E (2005): Functional disconnectivity of the medial temporallobe in Asperger’s syndrome. Biol Psychiatry 57:991–998.

24. Kleinhans NM, Richards T, Sterling L, Stegbauer KC, Mahurin R, JohnsonLC, et al. (2008): Abnormal functional connectivity in autism spectrumdisorders during face processing. Brain 131:1000 –1012.

25. Uddin LQ, Supekar K, Menon V (2010): Typical and atypical developmentof functional human brain networks: insights from resting-state FMRI.Front Syst Neurosci 4:21.

26. Koshino H, Carpenter PA, Minshew NJ, Cherkassky VL, Keller TA, Just MA(2005): Functional connectivity in an fMRI working memory task inhigh-functioning autism. Neuroimage 24:810 – 821.

27. Kennedy DP, Courchesne E (2008): The intrinsic functional organizationof the brain is altered in autism. Neuroimage 39:1877–1885.

28. Monk CS, Peltier SJ, Wiggins JL, Weng SJ, Carrasco M, Risi S, Lord C
(2009): Abnormalities of intrinsic functional connectivity in autism spec-trum disorders. Neuroimage 47:764 –772.
5

9. Weng SJ, Wiggins JL, Peltier SJ, Carrasco M, Risi S, Lord C, Monk CS(2010): Alterations of resting state functional connectivity in the defaultnetwork in adolescents with autism spectrum disorders. Brain Res 1313:202–214.

0. Di Martino A, Shehzad Z, Kelly C, Roy AK, Gee DG, Uddin LQ, et al. (2009):Relationship between cingulo-insular functional connectivity and autis-tic traits in neurotypical adults. Am J Psychiatry 166:891– 899.

1. Di Martino A, Ross K, Uddin LQ, Sklar AB, Castellanos FX, Milham MP(2009): Functional brain correlates of social and nonsocial processes inautism spectrum disorders: An activation likelihood estimation meta-analysis. Biol Psychiatry 65:63–74.

2. Minshew NJ, Keller TA (2010): The nature of brain dysfunction in autism:Functional brain imaging studies. Curr Opin Neurol 23:124 –130.

3. Horwitz B, Rumsey JM, Grady CL, Rapoport SI (1988): The cerebral met-abolic landscape in autism. Intercorrelations of regional glucose utiliza-tion. Arch Neurol 45:749 –755.

4. Turner KC, Frost L, Linsenbardt D, McIlroy JR, Muller RA (2006): Atypicallydiffuse functional connectivity between caudate nuclei and cerebralcortex in autism. Behav Brain Funct 2:34.

5. Qiu A, Adler M, Crocetti D, Miller MI, Mostofsky SH (2010): Basal gangliashapes predict social, communication, and motor dysfunctions in boyswith autism spectrum disorder. J Am Acad Child Adolesc Psychiatry 49:539 –551.

6. Langen M, Durston S, Staal WG, Palmen SJ, van Engeland H (2007):Caudate nucleus is enlarged in high-functioning medication-naive sub-jects with autism. Biol Psychiatry 62:262–266.

7. Stanfield AC, McIntosh AM, Spencer MD, Philip R, Gaur S, Lawrie SM(2008): Towards a neuroanatomy of autism: A systematic review andmeta-analysis of structural magnetic resonance imaging studies. EurPsychiatry 23:289 –299.

8. Dapretto M, Davies MS, Pfeifer JH, Scott AA, Sigman M, Bookheimer SY,Iacoboni M (2006): Understanding emotions in others: mirror neurondysfunction in children with autism spectrum disorders. Nat Neurosci9:28 –30.

9. Shafritz KM, Dichter GS, Baranek GT, Belger A (2008): The neural circuitrymediating shifts in behavioral response and cognitive set in autism. BiolPsychiatry 63:974 –980.

0. Takarae Y, Minshew NJ, Luna B, Sweeney JA (2007): Atypical involve-ment of frontostriatal systems during sensorimotor control in autism.Psychiatry Res 156:117–127.

1. Di Martino A, Scheres A, Margulies DS, Kelly AM, Uddin LQ, Shehzad Z, etal. (2008): Functional connectivity of human striatum: A resting stateFMRI study. Cereb Cortex 18:2735–2747.

2. Postuma RB, Dagher A (2006): Basal ganglia functional connectivitybased on a meta-analysis of 126 positron emission tomography andfunctional magnetic resonance imaging publications. Cereb Cortex 16:1508 –1521.

3. Harrison BJ, Soriano-Mas C, Pujol J, Ortiz H, Lopez-Sola M, Hernandez-Ribas R, et al. (2009): Altered corticostriatal functional connectivity inobsessive-compulsive disorder. Arch Gen Psychiatry 66:1189 –1200.

4. Kelly C, De Zubicaray G, Di Martino A, Copland DA, Reiss PT, Klein DF, etal. (2009): L-dopa modulates functional connectivity in striatal cognitiveand motor networks: A double-blind placebo-controlled study. J Neuro-sci 29:7364 –7378.

5. Hazlett HC, Poe M, Gerig G, Smith RG, Provenzale J, Ross A, et al. (2005):Magnetic resonance imaging and head circumference study of brainsize in autism: Birth through age 2 years. Arch Gen Psychiatry 62:1366 –1376.

6. Hazlett HC, Poe MD, Gerig G, Smith RG, Piven J (2006): Cortical gray andwhite brain tissue volume in adolescents and adults with autism. BiolPsychiatry 59:1– 6.

7. Courchesne E (2002): Abnormal early brain development in autism. MolPsychiatry 7(suppl 2):S21–S23.

8. Schumann CM, Bloss CS, Barnes CC, Wideman GM, Carper RA, Akshoo-moff N, et al. (2010): Longitudinal magnetic resonance imaging study ofcortical development through early childhood in autism. J Neurosci30:4419 – 4427.

9. Supekar K, Uddin LQ, Prater K, Amin H, Greicius MD, Menon V (2010):Development of functional and structural connectivity within the de-fault mode network in young children. Neuroimage 52:290 –301.

0. Lord C, Rutter M, Le Couteur A (1994): Autism diagnostic interview-revised: A revised version of a diagnostic interview for caregivers of


5

5

5

5

5

5

5

5

6

6

6

7

7

7

7

8

8

8

8

8

8

8

8

8

8

9

9

9

9

9

9

9

9

9


individuals with possible pervasive developmental disorders. J AutismDev Disord 24:659 – 685.

1. Lord C, Rutter M, DiLavore PC, Risi S (1999): Autism Diagnostic Observa-tion Schedule. Los Angeles: Western Psychological Service.

2. Gotham K, Risi S, Pickles A, Lord C (2007): The Autism Diagnostic Obser-vation Schedule: Revised algorithms for improved diagnostic validity. JAutism Dev Disord 37:613– 627.

3. Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P, et al. (1997):Schedule for Affective Disorders and Schizophrenia for School-Age Chil-dren Present and Lifetime version (K-SADS-PL): Initial reliability andvalidity data. J Am Acad Child Adolesc Psychiatry 36:980-988.

54. Silverman WK (2010): The Anxiety Disorders Interview Schedule for Chil-dren for DSM-IV, Child and Parent Versions. San Antonio, TX: Psychologi-cal Corporation.

5. Wechsler D (1999): Wechsler Abbreviated Scale of Intelligence (WASI). SanAntonio, TX: Psychological Corporation.

6. Hollingshead AB (1975): Four Factor Index of Social Status. New Haven,CT: Department of Sociology, Yale University.

7. Oldfield RC (1971): The assessment and analysis of handedness: TheEdinburgh inventory. Neuropsychologia 9:97–113.

8. Andersson JLR, Jenkinson M, Smith SM (2007): Non-linear registration,aka spatial normalisation. FMRIB technical report TR07JA2. Available at:www.fmrib.ox.ac.uk/analysis/techrep.

9. Jenkinson M, Smith S (2001): A global optimisation method for robustaffine registration of brain images. Med Image Anal 5:143–156.

0. Jenkinson M, Bannister P, Brady M, Smith S (2002): Improved optimiza-tion for the robust and accurate linear registration and motion correc-tion of brain images. Neuroimage 17:825– 841.

1. Wilke M, Schmithorst VJ, Holland SK (2003): Normative pediatric braindata for spatial normalization and segmentation differs from standardadult data. Magn Reson Med 50:749 –757.

2. Milham MP, Banich MT (2005): Anterior cingulate cortex: An fMRI anal-ysis of conflict specificity and functional differentiation. Hum BrainMapp 25:328 –335.

63. Ridderinkhof KR, Ullsperger M, Crone EA, Nieuwenhuis S (2004): The roleof the medial frontal cortex in cognitive control. Science 306:443– 447.

64. Barch DM, Braver TS, Akbudak E, Conturo T, Ollinger J, Snyder A (2001):Anterior cingulate cortex and response conflict: Effects of responsemodality and processing domain. Cereb Cortex 11:837– 848.

65. Rodier PM (2002): Converging evidence for brain stem injury in autism.Dev Psychopathol 14:537–557.

66. Church JA, Fair DA, Dosenbach NU, Cohen AL, Miezin FM, Petersen SE,Schlaggar BL (2009): Control networks in paediatric Tourette syndromeshow immature and anomalous patterns of functional connectivity.Brain 132:225–238.

67. Castellanos FX, Margulies DS, Kelly C, Uddin LQ, Ghaffari M, Kirsch A, etal. (2008): Cingulate-precuneus interactions: A new locus of dysfunctionin adult attention-deficit/hyperactivity disorder. Biol Psychiatry 63:332–337.

68. Mizuno A, Villalobos ME, Davies MM, Dahl BC, Muller RA (2006): Partiallyenhanced thalamocortical functional connectivity in autism. Brain Res1104:160 –174.

69. Uddin LQ, Menon V (2009): The anterior insula in autism: Under-con-nected and under-examined. Neurosci Biobehav Rev 33:1198 –1203.

70. Singer T, Seymour B, O’Doherty J, Kaube H, Dolan RJ, Frith CD (2004):Empathy for pain involves the affective but not sensory components ofpain. Science 303:1157–1162.

71. Nelson SM, Dosenbach NU, Cohen AL, Wheeler ME, Schlaggar BL, Pe-tersen SE (2010): Role of the anterior insula in task-level control and focalattention. Brain Struct Funct 214:669 – 680.

72. Craig AD (2009): How do you feel—now? The anterior insula and humanawareness. Nat Rev Neurosci 10:59 –70.

73. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al.(2007): Dissociable intrinsic connectivity networks for salience process-ing and executive control. J Neurosci 27:2349 –2356.

74. Kurth F, Zilles K, Fox PT, Laird AR, Eickhoff SB (2010): A link between thesystems: Functional differentiation and integration within the humaninsula revealed by meta-analysis. Brain Struct Funct 214:519 –534.

75. Bird G, Silani G, Brindley R, White S, Frith U, Singer T (2010): Empathicbrain responses in insula are modulated by levels of alexithymia but not
autism. Brain 133:1515–1525.

6. Silani G, Bird G, Brindley R, Singer T, Frith C, Frith U (2008): Levels ofemotional awareness and autism: An fMRI study. Soc Neurosci 3:97–112.

7. Craig AD (2003): Interoception: The sense of the physiological conditionof the body. Curr Opin Neurobiol 13:500 –505.

8. American Psychiatric Association (2000): Diagnostic and Statistical Man-ual of Mental Disorders, 4th ed., Text Revision. Washington, DC: AmericanPsychiatric Association.

9. Pelphrey KA, Morris JP, Michelich CR, Allison T, McCarthy G (2005):Functional anatomy of biological motion perception in posterior tem-poral cortex: An FMRI study of eye, mouth and hand movements. CerebCortex 15:1866 –1876.

0. Leitman DI, Wolf DH, Ragland JD, Laukka P, Loughead J, Valdez JN, etal. (2010): “It’s Not What You Say, But How You Say It”: A reciprocaltemporo-frontal network for affective prosody. Front Hum Neurosci4:19.

1. Redcay E (2008): The superior temporal sulcus performs a commonfunction for social and speech perception: Implications for the emer-gence of autism. Neurosci Biobehav Rev 32:123–142.

2. Haruno M, Kawato M (2009): Activity in the superior temporal sulcushighlights learning competence in an interaction game. J Neurosci 29:4542– 4547.

3. Redcay E, Dodell-Feder D, Pearrow MJ, Mavros PL, Kleiner M, Gabrieli JD,Saxe R (2010): Live face-to-face interaction during fMRI: A new tool forsocial cognitive neuroscience. Neuroimage 50:1639 –1647.

4. Frith U (2001): Mind blindness and the brain in autism. Neuron 32:969 –979.

5. Pelphrey KA, Carter EJ (2008): Charting the typical and atypical develop-ment of the social brain. Dev Psychopathol 20:1081–1102.

6. Wang AT, Lee SS, Sigman M, Dapretto M (2007): Reading affect in theface and voice: Neural correlates of interpreting communicative intentin children and adolescents with autism spectrum disorders. Arch GenPsychiatry 64:698 –708.

7. von dem Hagen EA, Nummenmaa L, Yu R, Engell AD, Ewbank MP, CalderAJ (2010): Autism spectrum traits in the typical population predict struc-ture and function in the posterior superior temporal sulcus [publishedonline ahead of print May 3]. Cereb Cortex.

8. Toal F, Daly EM, Page L, Deeley Q, Hallahan B, Bloemen O, et al. (2010):Clinical and anatomical heterogeneity in autistic spectrum disorder: Astructural MRI study. Psychol Med 40:1171–1181.

9. Shih P, Shen M, Ottl B, Keehn B, Gaffrey MS, Muller RA (2010): Atypicalnetwork connectivity for imitation in autism spectrum disorder. Neuro-psychologia 48:2931–2939.

0. Shukla DK, Keehn B, Muller RA (2010): Regional homogeneity of fMRItime series in autism spectrum disorders. Neurosci Lett 476:46 –51.

1. Dawson G, Webb SJ, Wijsman E, Schellenberg G, Estes A, Munson J, FajaS (2005): Neurocognitive and electrophysiological evidence of alteredface processing in parents of children with autism: Implications for amodel of abnormal development of social brain circuitry in autism. DevPsychopathol 17:679 – 697.

2. Fernandez-Gil MA, Palacios-Bote R, Leo-Barahona M, Mora-Encinas JP(2010): Anatomy of the brainstem: A gaze into the stem of life. SeminUltrasound CT MRI 31:196 –219.

3. Miller MT, Ventura L, Stromland K (2009): Thalidomide and misopros-tol: Ophthalmologic manifestations and associations both expec-ted and unexpected. Birth Defects Res A Clin Mol Teratol 85:667–676.

4. Bauman M, Kemper TL (1985): Histoanatomic observations of the brainin early infantile autism. Neurology 35:866 – 874.

5. Russo NM, Skoe E, Trommer B, Nicol T, Zecker S, Bradlow A, Kraus N(2008): Deficient brainstem encoding of pitch in children with autismspectrum disorders. Clin Neurophysiol 119:1720 –1731.

6. Jou RJ, Minshew NJ, Melhem NM, Keshavan MS, Hardan AY (2009):Brainstem volumetric alterations in children with autism. Psychol Med39:1347–1354.

7. Fair DA, Bathula D, Mills KL, Dias TG, Blythe MS, Zhang D, et al. (2010):Maturing thalamocortical functional connectivity across development.Front Syst Neurosci 4:10.

8. Zhang D, Snyder AZ, Fox MD, Sansbury MW, Shimony JS, Raichle ME(2008): Intrinsic functional relations between human cerebral cortex
and thalamus. J Neurophysiol 100:1740 –1748.
http://www.fmrib.ox.ac.uk/analysis/techrep

Documents

Aberrant Striatal Functional Connectivity in Children with Autism