ORIGINAL PAPER
Autism Spectrum Disorder: Does Neuroimaging Supportthe DSM-5 Proposal for a Symptom Dyad? A Systematic Reviewof Functional Magnetic Resonance Imaging and Diffusion TensorImaging Studies
Laura Pina-Camacho • Sonia Villero • David Fraguas • Leticia Boada •
Joost Janssen • Francisco J. Navas-Sanchez • Maria Mayoral •
Cloe Llorente • Celso Arango • Mara Parellada
Published online: 20 September 2011
� Springer Science+Business Media, LLC 2011
Abstract A systematic review of 208 studies comprising
functional magnetic resonance imaging and diffusion tensor
imaging data in patients with ‘autism spectrum disorder’
(ASD) was conducted, in order to determine whether these
data support the forthcoming DSM-5 proposal of a social
communication and behavioral symptom dyad. Studies
consistently reported abnormal function and structure of
fronto-temporal and limbic networks with social and prag-
matic language deficits, of temporo-parieto-occipital net-
works with syntactic–semantic language deficits, and of
fronto-striato-cerebellar networks with repetitive behaviors
and restricted interests in ASD patients. Therefore, this
review partially supports the DSM-5 proposal for the ASD
dyad.
Keywords Autism spectrum disorder � Autistic disorder �Asperger syndrome � Functional magnetic resonance
imaging � Diffusion tensor imaging
Introduction
The diagnostic criteria for autistic disorders are undergoing
scrutiny in preparation for the forthcoming revision to the
DSM system (see: www.dsm5.org). Based on the literature,
workgroup discussions, and clinical and research findings
(Klin and Volkmar 2003; Levy et al. 2009), and taking up
the concept introduced by Lorna Wing in the 1990s (Wing
1996), the DSM-5 classification aims to integrate autistic
disorder, Asperger syndrome (AS), childhood disintegra-
tive disorder, and pervasive developmental disorder-not
otherwise specified (PDD-NOS) into a single diagnostic
category: ‘autism spectrum disorder’ (ASD). Instead of the
classical ‘social-communication-behavioral’ triad, broad-
ened by DSM-III-R classification in 1987 (American Psy-
chiatric Association 1987) and maintained in DSM-IV
(American Psychiatric Association 1994), DSM-5 proposes
a symptom dyad for ASD, which would consist of (a) the
presence of deficits in social communication and interac-
tions and (b) the presence of repetitive patterns of behavior,
interests, and activities. In addition, ASD diagnosis would
be adapted to the individual’s clinical presentation by
inclusion of clinical specifiers, such as severity or verbal
abilities, and of associated features, such as known genetic
disorders or intellectual disability (ID). The authors of the
DSM-5 justify the change to the dyad by considering
deficits in communication and social behaviors as
Electronic supplementary material The online version of thisarticle (doi:10.1007/s10803-011-1360-4) contains supplementarymaterial, which is available to authorized users.
L. Pina-Camacho (&) � L. Boada � J. Janssen � M. Mayoral �C. Llorente � C. Arango � M. Parellada
Child and Adolescent Psychiatry Department, Hospital General
Universitario Gregorio Maranon, Centro de Investigacion
Biomedica en Red de Salud Mental, CIBERSAM, C/Ibiza, 43,
Madrid 28009, Spain
e-mail: [email protected]
S. Villero
Unit of Child and Adolescent Mental Health, Department of
Psychiatry, Complejo Hospitalario Mancha Centro, Alcazar de
San Juan, Ciudad Real, Spain
D. Fraguas
Mental Health Department, Complejo Hospitalario Universitario
de Albacete, Centro de Investigacion Biomedica en Red de Salud
Mental, CIBERSAM, Albacete, Spain
F. J. Navas-Sanchez
Department of Experimental Medicine, Hospital General
Universitario Gregorio Maranon, Centro de Investigacion
Biomedica en Red de Salud Mental, CIBERSAM, Madrid, Spain
123
J Autism Dev Disord (2012) 42:1326–1341
DOI 10.1007/s10803-011-1360-4
inseparable and more accurately considered as a single set
of symptoms with contextual and environmental specifici-
ties. Thus, for diagnosis, both criteria would have to be
completely fulfilled, which would improve the specificity
of the diagnosis without impairing sensitivity (see:
www.dsm5.org).
Therefore, the classical DSM-IV social interaction
cluster, defined by impairments in the social abilities of
mentalizing and empathizing (Baron-Cohen 2002; Baron-
Cohen et al. 1985), and the communication cluster, which
includes impairments in semantic-syntactic language
comprehension, in the pragmatic use of language, and in
prosody of speech (Rapin and Dunn 2003), would be col-
lapsed by DSM-5 into a single symptom domain: the social
communication and interaction symptom cluster (see
www.dsm5.org), whereas language delay is proposed as a
clinical specifier and not a diagnostic criterion.
Do neuroimaging findings in ASD support this proposal
of a dyadic grouping? From the beginning, research has
highlighted the role of several specific brain regions in the
pathogenesis of ASD (Volkmar and Pauls 2003). Since the
first findings in the late eighties (Courchesne et al. 1987;
Garber et al. 1989), research in ASD used structural
magnetic resonance imaging (sMRI) (Mana et al. 2010) to
identify specific brain regions affected in this disorder and
the relationship between impaired brain structure and
clinical features (Hardan et al. 2009). The most replicated
sMRI findings in brains of patients with ASD compared
with controls are the increase in total brain volume (TBV),
cerebellar hemispheres volume and caudate nucleus vol-
ume, together with a reduction of the corpus callosum
volume (CCV) (Stanfield et al. 2008). Some sMRI studies
have found several correlations between brain structural
abnormalities and ASD symptoms. For instance, increased
volume of the caudate in autistic patients has been corre-
lated with the severity of repetitive behaviors (Hollander
et al. 2005). However, the sMRI data are still too variable
to adequately elucidate brain-behavior relationships
(Palmen and van Engeland 2004). This may be due to
methodological and design limitations of these studies,
including the use of small and heterogeneous samples with
regard to age, sex, intelligence quotient (IQ) cut-off,
diagnostic criteria, etc. (Stanfield et al. 2008), and also to
the fact that ASD deficits are not always related to ana-
tomical, but often to functional abnormalities (Minshew
and Williams 2007).
In the past decade, some childhood and adolescent
neurobehavioral disorders, such as ASD, have been
described as disorders of impaired structural and functional
network connectivity (Frank and Pavlakis 2001; Stevens
2005). Brains of patients with ASD apparently present
inter- and intra-hemispheric functional ‘underconnectivity’
compared with the general population, together with
reduced structural integrity of white matter (WM) tracts
(Hughes 2007; Minshew and Williams 2007; Muller 2007;
Williams and Minshew 2007). In view of these findings,
current research paradigms in ASD are assessing impair-
ment of specific brain networks instead of focusing on
specific brain regions (Muller 2008). From this research,
new concepts have emerged, such as ‘ASD multi-system
brain disconnectivity–dyssynchrony (MBD)’ (Gepner and
Feron 2009) and ‘developmental disconnection disorder’
(Geschwind and Levitt 2007). However, it is not clear
whether ‘aberrant connectivity’ should be seen as part of
the primary pathogenesis of autism, or whether disrupted
connectivity in ASD emerges over time (Wass 2011).
According to the latter, neuroimaging techniques such as
functional magnetic resonance imaging (fMRI) and diffu-
sion tensor imaging (DTI) can be used to study functional
and structural brain connectivity, respectively (Basser
1995; Marti-Climent et al. 2010), both providing useful
information about the neuroanatomical correlates of ASD
deficits (Deb and Thompson 1998; Rumsey and Ernst
2000; Verhoeven et al. 2010).
Functional MRI detects activation signals based on
increased blood flow and blood oxygenation in regions of
enhanced synaptic transmission (Logothetis 2003) (see
Table 1). In ASD, fMRI provides additional information
(Minshew and Keller 2010), as it can detect functional
changes in anatomically intact brain regions, including
local abnormal activation and abnormal functional con-
nectivity between regions when performing a mental task
or even at rest (Marti-Climent et al. 2010). The presence of
these impaired networks has been related to different
impaired social, communication, and behavioral processes
in ASD (Di Martino et al. 2009a). Furthermore, fMRI
studies conducted in patients with ASD, their siblings, and
healthy controls, have provided further information by
studying differences in brain circuitry and compensatory
activity among them (Kaiser et al. 2010).
A further technique that provides information on the
structure of local and diffuse cortical networks is diffusion
tensor imaging (DTI). This technique analyses the struc-
tural integrity of WM tracts by measuring the diffusion of
water molecules along the axons (Basser and Pierpaoli
1996) (see Table 1). DTI studies have described impaired
network structural connectivity and, sometimes, correlated
it to social, communication, or behavioral deficits of
patients with ASD (Ke et al. 2009).
Given the forthcoming diagnostic changes in the
DSM-5, it would be interesting to know whether findings
published to date from fMRI and DTI studies’ in ASD
support this proposal of a symptom dyad. In particular, it
would be helpful to know whether the social and com-
munication deficits that are seen in ASD appear to be
associated with the shared differences in brain networks, so
J Autism Dev Disord (2012) 42:1326–1341 1327
123
they can be collapsed into a single domain, and whether these
networks are different from those that have been associated
with repetitive behaviors and restricted interests. Thus, the
objective of this systematic review is to determine if fMRI
and DTI findings on ASD support a neuroanatomical sub-
strate for the DSM-5 proposal of a symptom dyad.
Methods
According to the PRISMA guidelines (Preferred Reporting
Items for Systematic Reviews and Meta-Analyses) (Liberati
et al. 2009; Moher et al. 2009), a systematic Medline/Pubmed
review of the literature published in English between January
1990 and April 2011 was conducted on functional MRI and
DTI imaging studies in ASD. The following database search
strategy was used: (‘‘Autism spectrum disorders’’[All Fields]
OR ‘‘Asperger syndrome’’[All Fields] OR ‘‘Asperger’s syn-
drome’’[All Fields] OR ‘‘Autistic disorder’’[All Fields]) OR
‘‘CDD’’[All Fields] OR ‘‘Childhood disintegrative disor-
der’’[All Fields]) AND (‘‘Magnetic resonance imaging’’[All
Fields] OR ‘‘Diffusion tensor imaging’’[All Fields]) NOT
pubstatusaheadofprint. In press papers were not included, as
not all of those were available in full text.
After the database search, 612 records were identified
and screened. Out of these, 190 full-text articles were eli-
gible, as they fulfilled all the following inclusion criteria:
(a) being a review or an original article; (b) including
patients with ASD, autistic disorder and/or Asperger syn-
drome; and (c) using a functional MRI and/or a DTI
imaging technique. A total of 422 full-text articles were
excluded because a) they were not a review or an original
article (n = 20); (b) they did not focus on patients with
ASD, Asperger or autism (n = 67); (c) they did not pro-
vide any neuroimaging data (n = 73); or (d) they provided
only sMRI, PET, or SPECT imaging data (n = 262). We
also identified 18 relevant articles that were referenced in
these 190 eligible studies but did not appear in the initial
database search. Thus, a total of 208 studies were finally
included in this review.
We decided not to include Rett syndrome in this review,
as it has disappeared from the DSM-5 proposal on the
grounds that although autistic features of Rett syndrome
are sometimes indistinguishable from autism, they are only
present during a certain phase of the condition, i.e.,
between 1 and 3 years of age, and will alter such that they
no longer meet criteria for ASD diagnosis, and there are
clear gene markers for Rett syndrome—MeCP2 and
CDKL5 (see www.dsm5.org). Regarding CDD, after con-
ducting the search with combination of these terms:
(‘‘CDD’’[All Fields] OR ‘‘Childhood disintegrative disor-
der’’[All Fields] AND (‘‘Magnetic resonance imaging’’[All
Fields] OR ‘‘Diffusion tensor imaging’’[All Fields]), there
were no papers fulfilling our inclusion criteria.
Studies were classified according to the neuroimaging
technique used as follows: (a) fMRI studies using tasks
related to social cognition and interaction, (b) fMRI studies
using language-related tasks, (c) fMRI studies using tasks
related to repetitive behaviors or restricted interests,
(d) studies using ‘fMRI at resting state,’ and (e) studies
using a DTI technique. Within each fMRI study, we
extracted the information about the affected regions in
patients with ASD compared with healthy controls, in
terms of abnormal activity (hypo- or hyper-activation) or
functional connectivity (abnormal activation time series of
this brain region within a brain network). Similarly for DTI
studies, we looked at those affected regions and networks
in patients with ASD compared with controls, in terms of
abnormal structural integrity of WM tracts.
Results
Of the 208 articles included in this review, 120 studies
were original articles using an fMRI and/or DTI technique
and comparing imaging data of patients with ASD and
controls, one of those being a meta-analysis (Di Martino
et al. 2009a). They are all summarized in Figs. 1 and 2. The
‘y’ axis of both figures shows different brain regions and
networks. The ‘x’ axis shows different grayscale shaded
Table 1 Main neuroimaging techniques
Neuroimaging
technique
Assessed issue Basis of image construction
Structural
neuroimaging
(sMRI)
Volumetric measurements in regions of interest
(ROI)
Volumetric data
Functional
neuroimaging
(fMRI)
Regional brain activation
Functional connectivity: correlation between the
activation time series of two brain areas
Blood flow and oxygenation in regions and networks with
enhanced synaptic transmission during a mental task or at rest
Diffusion tensor
imaging (DTI)
Structural connectivity: microstructural integrity of
selected networks
Based on directionality of diffusing water molecules of WM tracts
1328 J Autism Dev Disord (2012) 42:1326–1341
123
bars, each representing a different neuroimaging technique.
The length of each bar represents the number of neuro-
imaging studies describing an abnormal activity and/or
functional or structural connectivity within any given brain
region in patients with ASD compared with controls.
Figure 1 shows data from 59 fMRI studies using social
cognition and interaction-related tasks (represented by
black bars within the figure), five fMRI studies using
semantic-pragmatic-related tasks (represented by light gray
bars), ten studies using syntactic-semantic-related tasks
(represented by gray striped bars), and nine fMRI studies
using tasks related to repetitive behaviors and restricted
interests (represented by dark gray bars). Figure 2 includes
data from nine studies exploring the default mode network
by using ‘fMRI at resting state’ (represented by gray hat-
ched bars), and 28 studies used a DTI technique (three of
them in combination with an fMRI language-related tech-
nique and one of them in combination with an fMRI
repetitive behavior-related technique). These DTI studies
are represented by gray dotted bars. Additional data from
these fMRI and DTI studies have been displayed inde-
pendently in Supplementary Tables 1, 2, 3, and 4.
Symptom Cluster (a) Proposal: Deficits in Social
Communication and Interaction
Social Cognition and Interaction Deficits
Functional MRI studies using a social cognitive-related
task and comparing brain activation and connectivity pat-
terns in brains of patients with ASD and controls are rep-
resented by black bars within Fig. 1. Processing of facial
expressions is, by far, the task most frequently used to
assess social cognition in functional studies of ASD, fol-
lowed by gaze-processing tasks or mentalization-related
tasks (Ashwin et al. 2007; Baron-Cohen et al. 1999, 2006;
Bird et al. 2010; Bolte et al. 2008; Bookheimer et al. 2008;
Corbett et al. 2009; Chiu et al. 2008; Dalton et al. 2005,
Fig. 1 Main fMRI studies on
ASD social, language, and
behavioral deficits. The ‘y’ axis
shows different brain regions
and networks. The ‘x’ axis
shows different grayscaleshaded bars, each representing a
different fMRI task-related
technique. The length of each
bar represents the number of
neuroimaging studies describing
an abnormal activity and/or
functional connectivity within
any given brain region in
patients with ASD compared
with controls. R-LAT right-
lateralization, PFC prefrontal
cortex, IFG inferior frontal
gyrus, MNs mirror neuron
system, F lobe frontal lobe, Tlobe temporal lobe, FG fusiform
gyrus, LIMB limbic system
(including amygdala and
hippocampus), ACC anterior
cingulate cortex, CING middle
or posterior cingulate, INSinsula, P lobe parietal lobe, Olobe occipital lobe, CBcerebellum, STR striatum, EVCearly visual cortex, EXT-STRextra striate visual areas
J Autism Dev Disord (2012) 42:1326–1341 1329
123
2007, 2008; Dapretto et al. 2006; Deeley et al. 2007; Di
Martino et al. 2009a; Dichter and Belger 2007, 2008;
Greimel et al. 2010b; Grezes et al. 2009; Hadjikhani et al.
2004, 2007, 2009; Hall et al. 2010a; Hubl et al. 2003;
Humphreys et al. 2008; Kana et al. 2009; Kleinhans et al.
2008b, 2009, 2011; Knaus et al. 2008; Koshino et al. 2008;
Lombardo et al. 2010; Malisza et al. 2011; Mason et al.
2008; Monk et al. 2010; Nishitani et al. 2004; Noonan et al.
2009; Oktem et al. 2001; Pelphrey et al. 2005, 2007; Pierce
et al. 2001, 2004; Pierce and Redcay 2008; Piggot et al.
2004; Scott-Van Zeeland et al. 2010a; Schmitz et al. 2008;
Schulte-Ruther et al. 2011; Schultz et al. 2000; Shamay-
Tsoory et al. 2010; Shih et al. 2010; Silani et al. 2008;
Spengler et al. 2010; Takeuchi et al. 2004; Uddin et al.
2008; Wang et al. 2004, 2007; Welchew et al. 2005;
Williams et al. 2006).
For the majority of ‘face-processing studies,’ ASD sub-
jects have decreased activation of the fusiform gyrus (FG),
especially of the face-fusiform area (FFA), and an inability to
vary the activity of this area when varying the intensity of the
facial emotional expression. Other replicated findings during
performance of social-related tasks include the abnormal
activation and connectivity of fronto-temporal cortical net-
works, including the mirror neuron system (located in the
inferior frontal gyrus and strongly related to mentalization
abilities) or the anterior cingulate cortex (ACC), and sub-
cortical networks such as the amygdala-hippocampal system
(see Fig. 1, black bars).
The findings of abnormal activation of these brain net-
works associated with impaired social cognitive processing
are corroborated by fMRI studies performed during a passive
‘resting state’ (Assaf et al. 2010; Cherkassky et al. 2006; Di
Martino et al. 2011; Kennedy and Courchesne 2008a, 2008b;
Kennedy et al. 2006; Lai et al. 2010; Monk et al. 2009; Paakki
et al. 2010; Weng et al. 2010). In patients with ASD, the
‘default mode network,’ which includes the posterior
Fig. 2 Main fMRI resting-state and DTI studies on ASD. The ‘y’
axis shows different brain regions and networks. The ‘x’ axis shows
different grayscale shaded bars, each representing a different
neuroimaging technique. The length of each bar represents the
number of neuroimaging studies describing an abnormal activity and/
or functional or structural connectivity within any given brain region
in patients with ASD compared with controls. R-LAT right-laterali-
zation, PFC prefrontal cortex, IFG inferior frontal gyrus, MNs mirror
neuron system, F lobe frontal lobe, T lobe temporal lobe, FG fusiform
gyrus, LIMB limbic system (including amygdala and hippocampus),
ACC anterior cingulate cortex, CING middle or posterior cingulate,
INS insula, P lobe parietal lobe, O lobe occipital lobe, CB cerebellum,
STR striatum, EVC early visual cortex, EXT-STR extra striate visual
areas, CC corpus callosum, IC internal capsule, LF longitudinal
fasciculus, AF arcuate fasciculus
1330 J Autism Dev Disord (2012) 42:1326–1341
123
cingulate cortex, retrosplenial cortex, lateral parietal cortex,
angular gyrus, medial prefrontal cortex, superior frontal
gyrus, temporal lobe, and parahippocampal gyrus, shows
impaired activity and intrinsic connectivity during a passive
resting state (see Fig. 2, gray hatched bars). This abnormal
connectivity underlies the abnormal social processing and
social impairments in these patients with ASD.
Similarly, DTI supports these MRI findings by showing
a reduced integrity of WM tracts that are part of these
‘social’ fronto-temporal cortical and subcortical networks
(Alexander et al. 2007; Barnea-Goraly et al. 2004; Barnea-
Goraly et al. 2010; Ben Bashat et al. 2007; Bloemen et al.
2010; Brito et al. 2009; Brun et al. 2009; Catani et al. 2008;
Conturo et al. 2008; Cheng et al. 2010; Cheung et al. 2009;
Groen et al. 2011; Jou et al. 2011; Ke et al. 2009; Keller
et al. 2007; Knaus et al. 2010; Lange et al. 2010; Lee et al.
2007a; Noriuchi et al. 2010; Pardini et al. 2009; Pugliese
et al. 2009; Sahyoun et al. 2010; Shukla et al. 2010a, 2011;
Sivaswamy et al. 2010; Sundaram et al. 2008; Thakkar
et al. 2008; Weinstein et al. 2011). See Fig. 2, gray dotted
bars.
Language Processing and Communication Deficits
Concerning fMRI findings in this field, studies use tasks
related either to the syntactic–semantic or to the semantic-
pragmatic component of language. When performing a
syntactic-semantic task, patients with ASD show hyperac-
tivation of Wernicke’s area [instead of the typical activa-
tion of the inferior frontal gyrus (IFG) seen in controls],
reduced or reversed leftward activation asymmetry, and a
tendency to use early visual (parieto-occipital) pathways to
support word processing and reasoning (see Fig. 1, gray
striped bars) (Gaffrey et al. 2007; Harris et al. 2006; Just
et al. 2004; Kana et al. 2006; Kleinhans et al. 2008a; Knaus
et al. 2008, 2010; Redcay and Courchesne 2008; Sahyoun
et al. 2010; Scott-Van Zeeland et al. 2010b; Soulieres et al.
2009). Additionally, one study combining an fMRI syn-
tactic/semantic task and DTI found reduced WM integrity
in tracts of the arcuate fasciculus, which connects Broca’s
and Wernicke’s areas (Knaus et al. 2010). Another similar
study found greater engagement of posterior brain regions
in patients with ASD with respect to controls along with
weaker connections to frontal language areas (Sahyoun
et al. 2010). For DTI studies, see Fig. 2, gray dotted bars.
On the other hand, when performing semantic-pragmatic
tasks, the most replicated findings include lower activation
signals in the left IFG (Broca’s area), prefrontal cortex, and
temporo-parietal areas in brains of patients with ASD
compared with controls, exhibiting functional undercon-
nectivity and undersynchronization within these regions
(Anderson et al. 2010; Hesling et al. 2010; Tesink et al.
2009; Wang et al. 2006, 2007) (see Fig. 1, light gray bars).
Moreover, the lower the activity in these networks, the
more severe the degree of language impairment (Wang
et al. 2007).
Symptom Cluster (b) Proposal: Repetitive Patterns
of Behavior, Interests, and Activities
Compared to fMRI and DTI studies on social and language
deficits in ASD, there are far fewer studies assessing this
symptom cluster. Functional MRI studies that use tasks
assessing repetitive and stereotyped behaviors and autistic
traits, such as resistance to change and obsessive traits (see
Fig. 1, dark gray bars) (Agam et al. 2010; Di Martino et al.
2009b; Gomot et al. 2008; Kana et al. 2007; Lee et al.
2009b; Monk et al. 2009; Shafritz et al. 2008; Thakkar
et al. 2008), together with fMRI studies exploring default
network activity (see Fig. 2, gray hatched bars), have
related these symptoms to abnormal functional connectiv-
ity in patients with ASD compared with controls, within
fronto-cerebellar network, fronto-striatal system, anterior
and posterior cingulate, posterior parietal regions, posterior
regions of corpus callosum (CC), cerebellar vermis and
peduncles (Assaf et al. 2010; Cherkassky et al. 2006; Di
Martino et al. 2011; Kennedy and Courchesne 2008a, b;
Kennedy et al. 2006; Lai et al. 2010; Monk et al. 2009;
Paakki et al. 2010; Weng et al. 2010). These findings are
corroborated by DTI studies showing reduced integrity of
WM tracts that are part of these impaired networks (see
Fig. 2, gray dotted bars) (Alexander et al. 2007; Barnea-
Goraly et al. 2004, 2010; Ben Bashat et al. 2007; Bloemen
et al. 2010; Brito et al. 2009; Brun et al. 2009; Catani et al.
2008; Conturo et al. 2008; Cheng et al. 2010; Cheung et al.
2009; Groen et al. 2011; Jou et al. 2011; Kana et al. 2007;
Ke et al. 2009; Keller et al. 2007; Knaus et al. 2010; Lange
et al. 2010; Noriuchi et al. 2010; Pardini et al. 2009;
Pugliese et al. 2009; Sahyoun et al. 2010; Shukla et al.
2010a, 2011; Sivaswamy et al. 2010; Sundaram et al. 2008;
Thakkar et al. 2008; Weinstein et al. 2011). Finally, dis-
rupted local activation and connectivity in the aforesaid
networks (including the cerebellum, fronto-striatal system,
etc.) have been related to executive function impairments
in patients with ASD, suggesting a common neuroana-
tomical substrate for these deficits (Gilbert et al. 2008; Just
et al. 2007; Silk et al. 2006; Solomon et al. 2009).
Discussion
Functional MRI and DTI findings support the notion that
the brains of patients with ASD share a global pattern of
abnormal structural and functional connectivity and syn-
chronization within different brain networks. Considering
the DSM-5 proposal for a symptom dyad in ASD, a review
J Autism Dev Disord (2012) 42:1326–1341 1331
123
of these studies provides support for separate neuroana-
tomical substrates for the social communication and the
behavioral symptom domains. However, the available
neuroimaging data only partially support the collapse of the
classical social and language symptom domains into a
single ‘social communication’ domain.
Globally considering the processing strategies of
patients with ASD, studies point to the presence of
abnormal connectivity between areas involved in high
order and low order perception processes (Castelli et al.
2002), and to their tendency to favor local over global
aspects when processing information (Hubl et al. 2003;
Manjaly et al. 2007; Minshew and Williams 2007; Muller
et al. 2003).
Functional MRI studies during social tasks and DTI
studies find in brains of patients with ASD an abnormal
structural and/or functional connectivity of cortical and
subcortical regions and networks such as the prefrontal
cortex, inferior frontal gyrus, temporal and cingulate cor-
tex, or the amygdala-fusiform system. Some authors term
all these regions the ‘social areas of the brain’ (Ashwin
et al. 2007). These regions typically activate in healthy
subjects when performing a social-related task (Gamer and
Buchel 2009; Greimel et al. 2010a; Hall et al. 2010b),
whereas they show abnormal patterns of activation or
connectivity in patients with ASD, this being consistently
related to ASD social deficits. On the other hand, func-
tional MRI-resting state studies in patients with ASD show
impaired activity and intrinsic connectivity in the ‘default
mode network’ (DMN) during a passive resting state. The
DMN includes some regions that are activated at rest, in
the absence of any task, such as the medial prefrontal
cortex, retrosplenial cortex/posterior cingulate cortex or
precuneus, among other regions. This DMN typically
activates when individuals are engaged in internally
focused tasks including autobiographical memory retrieval,
envisioning the future, and conceiving the perspectives of
others (Broyd et al. 2009; Buckner et al. 2008; Kennedy
and Courchesne 2008b; Weng et al. 2010). The abnormal
activity and intrinsic connectivity of this network may
underlie the abnormal social processing and social
impairments in patients with ASD. Social deficits, such as
impaired face processing, usually coexist with deficits in
processing socially relevant auditory information (Gervais
et al. 2004) or in viewing social animations involving
geometric shapes (Klin 2008), and contrast with normal
visual processing of objects and places (Humphreys et al.
2008). In fact, ASD patients have strategies that suggest
more non-face object perception (Schultz et al. 2000),
maybe because they base this processing on visual infor-
mation and not on the social significance of the stimuli
(Bookheimer et al. 2008). This may be due to the presence
of more pronounced impairment in later developing
cortical systems (e.g., face-processing system), than earlier
maturing systems (e.g., those that process objects and
places) (Humphreys et al. 2008). Finally, several studies
indicate that some clinical characteristics, such as level of
social anxiety, may mediate the neural response to this
emotional and social cognition processing (Kleinhans et al.
2010).
Supporting these neuroimaging findings related to social
deficits in ASD, there is one study conducted in general
population that found a positive correlation between par-
ticipant scores on the Social Responsiveness Scale-Adult
version (SRS-A) and the degree of ACC-insula functional
connectivity in a resting state (Di Martino et al. 2009b).
Moreover, some studies support a brain functional overlap
between the aforesaid ‘social regions’ and other neuro-
cognitive processes, These include autobiographical
memory processing (Cabeza et al. 2004; Markowitsch et al.
2000; Spreng et al. 2009), which is commonly impaired in
patients with ASD (Adler et al. 2010; Crane and Goddard
2008; Lind and Bowler 2010), as well as motor processing
(Allen and Courchesne 2003; Allen et al. 2004; Brieber
et al. 2010; Dinstein et al. 2010; Martineau et al. 2010;
Mizuno et al. 2006; Mostofsky et al. 2009; Muller et al.
2001, 2003, 2004; Villalobos et al. 2005) and attention
processing (Dichter and Belger 2007). These data suggest a
common neuroanatomical substrate for all these ASD
deficits (Mostofsky et al. 2009).
The specific contribution of each region and neural
network to these brain-based social deficits still remains
unclear (Dinstein et al. 2010) and seems to be very com-
plex (Williams 2008).For instance, it seems that sometimes
reduced function in a specific network could be due to the
influence on its activity by other structures, e.g., the
reduced activity of the FG during face processing could be
due to a modulatory influence of the amygdala (Schultz
2005) or the posterior cingulate (Klin 2008).
Regarding the communication cluster, this review sup-
ports an overlapped neuroanatomical substrate for seman-
tic-pragmatic and social cognitive deficits in ASD brains,
which is different from the syntactic-semantic deficit sub-
strate. In fact, semantic-pragmatic deficits are more related
to abnormal activity in the left IFG, (Broca’s area), pre-
frontal cortex and temporo-parietal regions, which are also
much more related to social-cognitive and Theory of Mind
(ToM) deficits (see Fig. 1). On the other hand, syntactic-
semantic deficits in ASD are much more related to
decreased activity of the IFG and increased activity of
Wernicke’s area, to the use of visual pathways to support
language comprehension, and to the presence of reduced or
reversed leftward asymmetry. This ‘visually-mediated’
language processing coexists in these patients with abnor-
mal visuomotor processing, showing a functional overlap
between both cognitive processes, where the processing
1332 J Autism Dev Disord (2012) 42:1326–1341
123
strategy depends to an abnormally large extent on activa-
tion of parieto-occipital pathways (Belmonte et al. 2010;
Belmonte and Yurgelun-Todd 2003; Bolte et al. 2008;
Damarla et al. 2010; Keehn et al. 2008; Ring et al. 1999;
Shukla et al. 2010b). The distinction between the syntactic-
semantic and semantic-pragmatic component of language
has been supported by neuropsychological and neuroim-
aging studies. Neuropsychological studies tend to distin-
guish between patients with a ‘semantic-pragmatic
disorder’, also called ‘pragmatic language impairment’
(PLI), versus those with a ‘syntactic-semantic disorder’ or
‘specific language impairment’ (SLI). PLI subjects have a
profile where language is fluent, complex, and clearly
articulated, but there are abnormalities in the way in which
language is used, in understanding and producing con-
nected discourse. These patients use stereotyped language
with abnormal intonation and prosody and give conversa-
tional responses that are socially inappropriate (Bishop and
Norbury 2002). In contrast, SLI has been explained as a
language impairment specifically affecting processing of
grammar (the syntax and morphology of language) or of
words (Ullman 2004). There is still a debate about whether
PLI is a communicative difficulty only found in verbal
people with autism or not (Bishop and Norbury 2002).
Some authors report that PLI is often seen in high-func-
tioning autistic patients but is not restricted to them alone
(Rapin and Allen 1987), while others think that PLI is a
form of high-functioning autism, with a much closer neu-
ropsychological relationship between PLI and autistic dis-
order than between PLI and typical SLI (Shields et al.
1996). In fact, Bishop et al. (2008) reported that many adult
individuals with autism had been identified with pragmatic
impairments in childhood. Finally, it has even been sug-
gested that PLI is an intermediate disorder between SLI
and core autism (Bishop et al. 2000) or that SLI, PLI and
ASD are related disorders that vary along qualitative
dimensions of language structure, language use, and cir-
cumscribed interests (Whitehouse et al. 2009). Concerning
neuroimaging findings supporting the distinction between
syntactic and semantic-pragmatic deficits, we find fMRI
studies conducted in a healthy population reporting that
syntactic-semantic processing tasks usually involve a
strongly left-dominant activation pattern in the IFG
(Broca’s area) and superior and middle temporal gyri
(Wernicke’s area) (Chou et al. 2006; Holland et al. 2007),
whereas semantic-pragmatic processing underlies more
‘posterior language areas,’ such as the temporo-parietal
junction, and clearly produces a more bilateral distribution
of activation than syntactic-semantic processing (Holland
et al. 2007; Otzenberger et al. 2005). Additionally, it has
been suggested that declarative memory and word pro-
cessing deficits (both components of a SLI), which are
frequently impaired in patients with ASD, share an
abnormal temporo-parietal substrate, as do working mem-
ory and grammar acquisition impairments (another com-
ponent of SLI deficit), sharing a fronto-striatal-cerebellar
overlap (Ullman 2004). In fact, some fMRI studies
exploring working memory in brains of patients with ASD
have described aberrant connectivity of this network,
compared with controls, when performing related tasks
(Koshino et al. 2005; Lee et al. 2007b; Luna et al. 2002;
Manjaly et al. 2007; Ring et al. 1999). In summary, further
studies of these so-called ‘language pathways,’ conducted
in healthy populations and both in patients with specific
language impairments and with ASD would be of great
interest in disentangling which aspects of language pertain
to the ASD domain and which correspond to an indepen-
dent set of disorders.
Finally, regarding the proposed symptom cluster of
‘repetitive patterns of behavior, interests, and activities,’
fMRI and DTI studies have described reduced activation and
connectivity in brains of patients with ASD compared with
controls, mainly within fronto-striatal and posterior brain
structures, such as the posterior parietal lobe, posterior cin-
gulate, posterior corpus callosum and cerebellum, with less
involvement of the ‘social brain areas’ (Courchesne et al.
2004; Cheung et al. 2009; Keller et al. 2007). These networks
also show abnormal function when patients with ASD per-
form an executive function or a working memory task,
suggesting that these deficits may share a common neuro-
anatomical substrate with this behavioral symptom cluster
(Schmitz et al. 2006).
Supporting conclusions of this systematic review, we
find fMRI and DTI studies conducted in other population
groups, such as general population with autistic traits (Di
Martino et al. 2009b), samples of patients with psychosis
and social cognitive deficits (Abdi and Sharma 2004;
Pinkham et al. 2008; Sasson et al. 2007), patients with
cerebellar malformations (Tavano et al. 2007) or injury to
the cerebellar vermis (Limperopoulos et al. 2007), very low
birth weight children with WM injury (Skranes et al. 2007),
and subjects with the fragile X premutation (Hessl et al.
2007) or the fragile X syndrome (Garrett et al. 2004). All
the clinical groups above may demonstrate social, com-
munication, and behavioral impairments that are similar to
those seen in ASD, and have differences in neural con-
nectivity that are demonstrable in similar brain regions and
networks.
However, there are some limitations that could bias the
conclusions of this systematic review. Firstly, fMRI ‘task-
related’ studies do not always characterize cognitive,
emotional, and behavioral responses simultaneously, which
would be a more accurate way to gauge their interaction
(Klin 2008). Secondly, results of task-related studies could
be biased by the confounding effect of abnormal visual
fixation patterns commonly present in patients with ASD
J Autism Dev Disord (2012) 42:1326–1341 1333
123
and of their lower degree of success, motivation, and
interest when performing these tasks (Hadjikhani et al.
2006; Klin 2008; Yerys et al. 2009). Thirdly, studies usu-
ally point to the presence of highly variable individualistic
responses in different patients with ASD (Hasson et al.
2009), and tend not to differentiate between subgroups,
such as patients with autism (low- and high-functioning) or
patients with Asperger syndrome. Only a small number of
studies have been conducted in patients with Asperger
syndrome (Bloemen et al. 2010; Catani et al. 2008;
Nishitani et al. 2004; Oktem et al. 2001; Pugliese et al.
2009; Shamay-Tsoory et al. 2010), and none of them have
directly compared processing abnormalities between
patients with high-functioning autism and patients with
Asperger syndrome, which may be interesting for refining
the neuroanatomical substrate of their language and social
deficits. Fourthly, fMRI and DTI studies have been fraught
with limitations such as small sample sizes, cross-sectional
designs, heterogeneous subject characteristics, and varying
methodologies (Stigler et al. 2011). They should be con-
ducted longitudinally (Brambilla et al. 2004) in larger
samples of ASD children, and preferably with a wider
range of IQ. For instance, we only found a single DTI study
comparing patients with low-functioning autism (LFA) and
age-matched healthy controls, describing lower fractional
anisotropy (FA) values in brains of LFA patients at the
orbitofrontal cortex and a positive relationship between FA
values and IQ in patients with LFA (Pardini et al. 2009).
Finally, generalization of all fMRI and DTI findings in
ASD may be restricted, as these studies focus only on
specific brain areas, showing increased versus decreased
activation signals, the meaning of which is not clear when
there is not a parallel range of tasks to probe different brain
system deficits (Minshew and Keller 2010). Moreover, few
studies have combined both fMRI and DTI techniques
(Knaus et al. 2010; Sahyoun et al. 2010; Thakkar et al.
2008), or those techniques with sMRI (Cody et al. 2002;
Corbett et al. 2009; Ke et al. 2009). This would be desir-
able in order to obtain more accurate data on affected loci
and networks of ASD brains, as well as on the main causes
of this abnormal neural connectivity and synchronization.
A new type of integrated research has been developed in
the last few years, by using new imaging techniques and
applications and different techniques applied to the same
subjects, looking for converging results (Boddaert and
Zilbovicius 2002; Ingalhalikar et al. 2010; Lee et al. 2009a;
Schippers et al. 2010). Similarly, recent neuroimaging
research is starting to integrate brain-imaging data into
clinical, etiological, diagnostic, and therapeutic research on
ASD (Belmonte et al. 2008; Bolte et al. 2006; Dichter et al.
2010; Greene et al. 2008; Narayanan et al. 2010; Roy et al.
2009; Thompson et al. 2010). However, this type of
research needs to be further developed.
In summary, this systematic review on functional and
DTI neuroimaging studies in ASD only partially supports
the DSM-5 proposal for a social communication and
behavioral symptom dyad. Supporting the dyad, this review
finds a different neuroanatomical substrate for the social
communication and the behavioral domains. However, the
available neuroimaging data only partially support the
proposed collapse of the social and communication
symptom domains into the same symptom cluster. As we
have mentioned before, DSM-IV mixed syntactic and
pragmatic language impairments in its second set of criteria
(‘qualitative impairments in communication’). Data from
our review support the idea that syntactic language
impairment and pragmatic language impairment should be
considered separately. Therefore, our data are congruent
with the DSM-5 idea of considering syntactic language
impairment as an independent clinical specifier. However,
neuroimaging data also support that semantic-pragmatic
language impairments should be merged with social com-
munication deficits. Therefore, it could be suggested that
an explicit mention of pragmatic language deficits be
included within the social communication criteria (criterion
A of the DSM-5 proposal).
Further neuroimaging studies conducted in patients with
ASD would be of great interest in disentangling the DSM-5
dyad controversy by providing disease-specific biological
markers. If different deficits have different neurobiological
underpinnings, this could have substantial implications for
treatment development (Hyman 2007). For instance, if
patients with different language impairments may benefit
from different therapeutic approaches, that may not be
evident if a heterogeneous group with multiple pathophy-
siologies is lumped together. Thus, this distinction may
lead to more accurate diagnoses classifications and to new
research on more specific and effective treatments for these
patients.
Acknowledgments Supported by Centro de Investigacion Biome-
dica en Red de Salud Mental, CIBERSAM, Instituto de Salud Carlos
III, Spanish Ministry of Science and Innovation. Laura Pina-Camacho
has received a grant from Instituto de Salud Carlos III, Spanish
Ministry of Science and Innovation.
References
Abdi, Z., & Sharma, T. (2004). Social cognition and its neural
correlates in schizophrenia and autism. CNS Spectrums, 9(5),
335–343.
Adler, N., Nadler, B., Eviatar, Z., & Shamay-Tsoory, S. G. (2010).
The relationship between theory of mind and autobiographical
memory in high-functioning autism and Asperger syndrome.
Psychiatry Research, 178(1), 214–216.
Agam, Y., Joseph, R. M., Barton, J. J., & Manoach, D. S. (2010).
Reduced cognitive control of response inhibition by the anterior
1334 J Autism Dev Disord (2012) 42:1326–1341
123
cingulate cortex in autism spectrum disorders. Neuroimage,52(1), 336–347.
Alexander, A. L., Lee, J. E., Lazar, M., Boudos, R., DuBray, M. B.,
Oakes, T. R., et al. (2007). Diffusion tensor imaging of the
corpus callosum in Autism. Neuroimage, 34(1), 61–73.
Allen, G., & Courchesne, E. (2003). Differential effects of develop-
mental cerebellar abnormality on cognitive and motor functions
in the cerebellum: An fMRI study of autism. American Journalof Psychiatry, 160(2), 262–273.
Allen, G., Muller, R. A., & Courchesne, E. (2004). Cerebellar
function in autism: Functional magnetic resonance image
activation during a simple motor task. Biological Psychiatry,56(4), 269–278.
American Psychiatric Association. (1987). Diagnostic and statisticalmanual of mental disorders (3rd ed.). Washington, DC: American
Psychiatric Association.
American Psychiatric Association. (1994). Diagnostic and statisticalmanual of mental disorders (4th ed.). Washington, DC: American
Psychiatric Association.
Anderson, J. S., Lange, N., Froehlich, A., DuBray, M. B., Druzgal, T.
J., Froimowitz, M. P., et al. (2010). Decreased left posterior
insular activity during auditory language in autism. AJNR.American Journal of Neuroradiology, 31(1), 131–139.
Ashwin, C., Baron-Cohen, S., Wheelwright, S., O’Riordan, M., &
Bullmore, E. T. (2007). Differential activation of the amygdala
and the ‘social brain’ during fearful face-processing in Asperger
Syndrome. Neuropsychologia, 45(1), 2–14.
Assaf, M., Jagannathan, K., Calhoun, V. D., Miller, L., Stevens, M.
C., Sahl, R., et al. (2010). Abnormal functional connectivity of
default mode sub-networks in autism spectrum disorder patients.
Neuroimage, 53(1), 247–256.
Barnea-Goraly, N., Kwon, H., Menon, V., Eliez, S., Lotspeich, L., &
Reiss, A. L. (2004). White matter structure in autism:
Preliminary evidence from diffusion tensor imaging. BiologicalPsychiatry, 55(3), 323–326.
Barnea-Goraly, N., Lotspeich, L. J., & Reiss, A. L. (2010). Similar
white matter aberrations in children with autism and their
unaffected siblings: A diffusion tensor imaging study using tract-
based spatial statistics. Archives of General Psychiatry, 67(10),
1052–1060.
Baron-Cohen, S. (2002). The extreme male brain theory of autism.
Trends in Cognitive Sciences, 6(6), 248–254.
Baron-Cohen, S., Leslie, A. M., & Frith, U. (1985). Does the autistic
child have a ‘‘theory of mind’’? Cognition, 21(1), 37–46.
Baron-Cohen, S., Ring, H., Chitnis, X., Wheelwright, S., Gregory, L.,
Williams, S., et al. (2006). fMRI of parents of children with
Asperger Syndrome: A pilot study. Brain and Cognition, 61(1),
122–130.
Baron-Cohen, S., Ring, H. A., Wheelwright, S., Bullmore, E. T.,
Brammer, M. J., Simmons, A., et al. (1999). Social intelligence
in the normal and autistic brain: An fMRI study. EuropeanJournal of Neuroscience, 11(6), 1891–1898.
Basser, P. J. (1995). Inferring microstructural features and the
physiological state of tissues from diffusion-weighted images.
NMR in Biomedicine, 8(7–8), 333–344.
Basser, P. J., & Pierpaoli, C. (1996). Microstructural and physiolog-
ical features of tissues elucidated by quantitative-diffusion-
tensor MRI. Journal of Magnetic Resonance. Series B, 111(3),
209–219.
Belmonte, M. K., Gomot, M., & Baron-Cohen, S. (2010). Visual
attention in autism families: ‘Unaffected’ sibs share atypical
frontal activation. Journal of Child Psychology and Psychiatry,51(3), 259–276.
Belmonte, M. K., Mazziotta, J. C., Minshew, N. J., Evans, A. C.,
Courchesne, E., Dager, S. R., et al. (2008). Offering to share:
How to put heads together in autism neuroimaging. Journal ofAutism and Developmental Disorders, 38(1), 2–13.
Belmonte, M. K., & Yurgelun-Todd, D. A. (2003). Functional
anatomy of impaired selective attention and compensatory
processing in autism. Brain Research Cognitive Brain Research,17(3), 651–664.
Ben Bashat, D., Kronfeld-Duenias, V., Zachor, D. A., Ekstein, P. M.,
Hendler, T., Tarrasch, R., et al. (2007). Accelerated maturation
of white matter in young children with autism: A high b value
DWI study. Neuroimage, 37(1), 40–47.
Bird, G., Silani, G., Brindley, R., White, S., Frith, U., & Singer, T.
(2010). Empathic brain responses in insula are modulated by
levels of alexithymia but not autism. Brain, 133(Pt 5),
1515–1525.
Bishop, D. V., Chan, J., Adams, C., Hartley, J., & Weir, F. (2000).
Conversational responsiveness in specific language impairment:
Evidence of disproportionate pragmatic difficulties in a subset of
children. Developmental Psychopathology, 12(2), 177–199.
Bishop, D. V., & Norbury, C. F. (2002). Exploring the borderlands of
autistic disorder and specific language impairment: A study
using standardised diagnostic instruments. Journal of ChildPsychology and Psychiatry, 43(7), 917–929.
Bishop, D. V., Whitehouse, A. J., Watt, H. J., & Line, E. A. (2008).
Autism and diagnostic substitution: Evidence from a study of
adults with a history of developmental language disorder.
Developmental Medicine and Child Neurology, 50(5), 341–345.
Bloemen, O. J., Deeley, Q., Sundram, F., Daly, E. M., Barker, G. J.,
Jones, D. K., et al. (2010). White matter integrity in Asperger
syndrome: A preliminary diffusion tensor magnetic resonance
imaging study in adults. Autism Research, 3(5), 203–213.
Boddaert, N., & Zilbovicius, M. (2002). Functional neuroimaging and
childhood autism. Pediatric Radiology, 32(1), 1–7.
Bolte, S., Hubl, D., Dierks, T., Holtmann, M., & Poustka, F. (2008).
An fMRI-study of locally oriented perception in autism: Altered
early visual processing of the block design test. Journal ofNeural Transmission, 115(3), 545–552.
Bolte, S., Hubl, D., Feineis-Matthews, S., Prvulovic, D., Dierks, T., &
Poustka, F. (2006). Facial affect recognition training in autism:
Can we animate the fusiform gyrus? Behavioral Neuroscience,120(1), 211–216.
Bookheimer, S. Y., Wang, A. T., Scott, A., Sigman, M., &
Dapretto, M. (2008). Frontal contributions to face processing
differences in autism: Evidence from fMRI of inverted face
processing. Journal of the International NeuropsychologicalSociety, 14(6), 922–932.
Brambilla, P., Hardan, A. Y., di Nemi, S. U., Caverzasi, E., Soares, J.
C., Perez, J., et al. (2004). The functional neuroanatomy of
autism. Functional Neurology, 19(1), 9–17.
Brieber, S., Herpertz-Dahlmann, B., Fink, G. R., Kamp-Becker, I.,
Remschmidt, H., & Konrad, K. (2010). Coherent motion
processing in autism spectrum disorder (ASD): An fMRI study.
Neuropsychologia, 48(6), 1644–1651.
Brito, A. R., Vasconcelos, M. M., Domingues, R. C., Hygino da Cruz,
L. C., Jr., Rodrigues Lde, S., Gasparetto, E. L., et al. (2009).
Diffusion tensor imaging findings in school-aged autistic chil-
dren. Journal of Neuroimaging, 19(4), 337–343.
Broyd, S. J., Demanuele, C., Debener, S., Helps, S. K., James, C. J., &
Sonuga-Barke, E. J. (2009). Default-mode brain dysfunction in
mental disorders: A systematic review. Neuroscience andBiobehavioral Reviews, 33(3), 279–296.
Brun, C. C., Nicolson, R., Lepore, N., Chou, Y. Y., Vidal, C. N.,
DeVito, T. J., et al. (2009). Mapping brain abnormalities in boys
with autism. Human Brain Mapping, 30(12), 3887–3900.
Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The
brain’s default network: Anatomy, function, and relevance to
J Autism Dev Disord (2012) 42:1326–1341 1335
123
disease. Annals of the New York Academy of Sciences, 1124,
1–38.
Cabeza, R., Prince, S. E., Daselaar, S. M., Greenberg, D. L., Budde,
M., Dolcos, F., et al. (2004). Brain activity during episodic
retrieval of autobiographical and laboratory events: An fMRI
study using a novel photo paradigm. Journal of CognitiveNeuroscience, 16(9), 1583–1594.
Castelli, F., Frith, C., Happe, F., & Frith, U. (2002). Autism,
Asperger syndrome and brain mechanisms for the attribution
of mental states to animated shapes. Brain, 125(Pt 8),
1839–1849.
Catani, M., Jones, D. K., Daly, E., Embiricos, N., Deeley, Q.,
Pugliese, L., et al. (2008). Altered cerebellar feedback projec-
tions in Asperger syndrome. Neuroimage, 41(4), 1184–1191.
Cheng, Y., Chou, K. H., Chen, I. Y., Fan, Y. T., Decety, J., & Lin, C.
P. (2010). Atypical development of white matter microstructure
in adolescents with autism spectrum disorders. Neuroimage,50(3), 873–882.
Cherkassky, V. L., Kana, R. K., Keller, T. A., & Just, M. A. (2006).
Functional connectivity in a baseline resting-state network in
autism. Neuroreport, 17(16), 1687–1690.
Cheung, C., Chua, S. E., Cheung, V., Khong, P. L., Tai, K. S., Wong,
T. K., et al. (2009). White matter fractional anisotrophy
differences and correlates of diagnostic symptoms in autism.
Journal of Child Psychology and Psychiatry, 50(9), 1102–1112.
Chiu, P. H., Kayali, M. A., Kishida, K. T., Tomlin, D., Klinger, L. G.,
Klinger, M. R., et al. (2008). Self responses along cingulate
cortex reveal quantitative neural phenotype for high-functioning
autism. Neuron, 57(3), 463–473.
Chou, T. L., Booth, J. R., Bitan, T., Burman, D. D., Bigio, J. D., Cone,
N. E., et al. (2006). Developmental and skill effects on the neural
correlates of semantic processing to visually presented words.
Human Brain Mapping, 27(11), 915–924.
Cody, H., Pelphrey, K., & Piven, J. (2002). Structural and functional
magnetic resonance imaging of autism. International Journal ofDevelopmental Neuroscience, 20(3–5), 421–438.
Conturo, T. E., Williams, D. L., Smith, C. D., Gultepe, E., Akbudak,
E., & Minshew, N. J. (2008). Neuronal fiber pathway abnor-
malities in autism: An initial MRI diffusion tensor tracking study
of hippocampo-fusiform and amygdalo-fusiform pathways.
Journal of the International Neuropsychological Society, 14(6),
933–946.
Corbett, B. A., Carmean, V., Ravizza, S., Wendelken, C., Henry, M.
L., Carter, C., et al. (2009). A functional and structural study of
emotion and face processing in children with autism. PsychiatryResearch, 173(3), 196–205.
Courchesne, E., Hesselink, J. R., Jernigan, T. L., & Yeung-Courchesne,
R. (1987). Abnormal neuroanatomy in a nonretarded person with
autism. Unusual findings with magnetic resonance imaging.
Archives of Neurology, 44(3), 335–341.
Courchesne, E., Redcay, E., & Kennedy, D. P. (2004). The autistic
brain: Birth through adulthood. Current Opinion in Neurology,17(4), 489–496.
Crane, L., & Goddard, L. (2008). Episodic and semantic autobio-
graphical memory in adults with autism spectrum disorders.
Journal of Autism and Developmental Disorders, 38(3),
498–506.
Dalton, K. M., Holsen, L., Abbeduto, L., & Davidson, R. J. (2008).
Brain function and gaze fixation during facial-emotion process-
ing in fragile X and autism. Autism Research, 1(4), 231–239.
Dalton, K. M., Nacewicz, B. M., Alexander, A. L., & Davidson, R. J.
(2007). Gaze-fixation, brain activation, and amygdala volume in
unaffected siblings of individuals with autism. BiologicalPsychiatry, 61(4), 512–520.
Dalton, K. M., Nacewicz, B. M., Johnstone, T., Schaefer, H. S.,
Gernsbacher, M. A., Goldsmith, H. H., et al. (2005). Gaze
fixation and the neural circuitry of face processing in autism.
Nature Neuroscience, 8(4), 519–526.
Damarla, S. R., Keller, T. A., Kana, R. K., Cherkassky, V. L.,
Williams, D. L., Minshew, N. J., et al. (2010). Cortical
underconnectivity coupled with preserved visuospatial cognition
in autism: Evidence from an fMRI study of an embedded figures
task. Autism Research, 3(5), 273–279.
Dapretto, M., Davies, M. S., Pfeifer, J. H., Scott, A. A., Sigman, M.,
Bookheimer, S. Y., et al. (2006). Understanding emotions in
others: Mirror neuron dysfunction in children with autism
spectrum disorders. Nature Neuroscience, 9(1), 28–30.
Deb, S., & Thompson, B. (1998). Neuroimaging in autism. BritishJournal of Psychiatry, 173, 299–302.
Deeley, Q., Daly, E. M., Surguladze, S., Page, L., Toal, F., Robertson,
D., et al. (2007). An event related functional magnetic resonance
imaging study of facial emotion processing in Asperger
syndrome. Biological Psychiatry, 62(3), 207–217.
Di Martino, A., Kelly, C., Grzadzinski, R., Zuo, X. N., Mennes, M.,
Mairena, M. A., et al. (2011). Aberrant striatal functional
connectivity in children with autism. Biological Psychiatry,69(9), 847–856.
Di Martino, A., Ross, K., Uddin, L. Q., Sklar, A. B., Castellanos, F.
X., & Milham, M. P. (2009a). Functional brain correlates of
social and nonsocial processes in autism spectrum disorders:
An activation likelihood estimation meta-analysis. BiologicalPsychiatry, 65(1), 63–74.
Di Martino, A., Shehzad, Z., Kelly, C., Roy, A. K., Gee, D. G., Uddin,
L. Q., et al. (2009b). Relationship between cingulo-insular
functional connectivity and autistic traits in neurotypical adults.
American Journal of Psychiatry, 166(8), 891–899.
Dichter, G. S., & Belger, A. (2007). Social stimuli interfere with
cognitive control in autism. Neuroimage, 35(3), 1219–1230.
Dichter, G. S., & Belger, A. (2008). Atypical modulation of cognitive
control by arousal in autism. Psychiatry Research, 164(3),
185–197.
Dichter, G. S., Sikich, L., Mahorney, S., Felder, J. N., Lam, K. S.,
Turner-Brown, L., et al. (2010). fMRI tracks reductions in
repetitive behaviors in autism: Two case studies. Neurocase,16(4), 307–316.
Dinstein, I., Thomas, C., Humphreys, K., Minshew, N., Behrmann,
M., & Heeger, D. J. (2010). Normal movement selectivity in
autism. Neuron, 66(3), 461–469.
Frank, Y., & Pavlakis, S. G. (2001). Brain imaging in neurobehavioral
disorders. Pediatric Neurology, 25(4), 278–287.
Gaffrey, M. S., Kleinhans, N. M., Haist, F., Akshoomoff, N.,
Campbell, A., Courchesne, E., et al. (2007). Atypical [corrected]
participation of visual cortex during word processing in autism:
An fMRI study of semantic decision. Neuropsychologia, 45(8),
1672–1684.
Gamer, M., & Buchel, C. (2009). Amygdala activation predicts gaze
toward fearful eyes. Journal of Neuroscience, 29(28),
9123–9126.
Garber, H. J., Ritvo, E. R., Chiu, L. C., Griswold, V. J., Kashanian,
A., Freeman, B. J., et al. (1989). A magnetic resonance imaging
study of autism: Normal fourth ventricle size and absence of
pathology. American Journal of Psychiatry, 146(4), 532–534.
Garrett, A. S., Menon, V., MacKenzie, K., & Reiss, A. L. (2004).
Here’s looking at you, kid: Neural systems underlying face and
gaze processing in fragile X syndrome. Archives of GeneralPsychiatry, 61(3), 281–288.
Gepner, B., & Feron, F. (2009). Autism: A world changing too fast for
a mis-wired brain? Neuroscience and Biobehavioral Reviews,33(8), 1227–1242.
Gervais, H., Belin, P., Boddaert, N., Leboyer, M., Coez, A., Sfaello,
I., et al. (2004). Abnormal cortical voice processing in autism.
Nature Neuroscience, 7(8), 801–802.
1336 J Autism Dev Disord (2012) 42:1326–1341
123
Geschwind, D. H., & Levitt, P. (2007). Autism spectrum disorders:
Developmental disconnection syndromes. Current Opinion inNeurobiology, 17(1), 103–111.
Gilbert, S. J., Bird, G., Brindley, R., Frith, C. D., & Burgess, P. W.
(2008). Atypical recruitment of medial prefrontal cortex in
autism spectrum disorders: An fMRI study of two executive
function tasks. Neuropsychologia, 46(9), 2281–2291.
Gomot, M., Belmonte, M. K., Bullmore, E. T., Bernard, F. A., & Baron-
Cohen, S. (2008). Brain hyper-reactivity to auditory novel targets
in children with high-functioning autism. Brain, 131(Pt 9),
2479–2488.
Greene, C. M., Braet, W., Johnson, K. A., & Bellgrove, M. A. (2008).
Imaging the genetics of executive function. Biological Psychol-ogy, 79(1), 30–42.
Greimel, E., Schulte-Ruther, M., Fink, G. R., Piefke, M., Herpertz-
Dahlmann, B., & Konrad, K. (2010a). Development of neural
correlates of empathy from childhood to early adulthood: An
fMRI study in boys and adult men. Journal of Neural Trans-mission, 117(6), 781–791.
Greimel, E., Schulte-Ruther, M., Kircher, T., Kamp-Becker, I.,
Remschmidt, H., Fink, G. R., et al. (2010b). Neural mechanisms
of empathy in adolescents with autism spectrum disorder and
their fathers. Neuroimage, 49(1), 1055–1065.
Grezes, J., Wicker, B., Berthoz, S., & de Gelder, B. (2009). A failure
to grasp the affective meaning of actions in autism spectrum
disorder subjects. Neuropsychologia, 47(8–9), 1816–1825.
Groen, W. B., Buitelaar, J. K., van der Gaag, R. J., & Zwiers, M. P.
(2011). Pervasive microstructural abnormalities in autism: A
DTI study. Journal of Psychiatry and Neuroscience, 36(1),
32–40.
Hadjikhani, N., Joseph, R. M., Manoach, D. S., Naik, P., Snyder, J.,
Dominick, K., et al. (2009). Body expressions of emotion do not
trigger fear contagion in autism spectrum disorder. SocialCognative Affective Neuroscience, 4(1), 70–78.
Hadjikhani, N., Joseph, R. M., Snyder, J., Chabris, C. F., Clark, J.,
Steele, S., et al. (2004). Activation of the fusiform gyrus
when individuals with autism spectrum disorder view faces.
Neuroimage, 22(3), 1141–1150.
Hadjikhani, N., Joseph, R. M., Snyder, J., & Tager-Flusberg, H.
(2006). Anatomical differences in the mirror neuron system and
social cognition network in autism. Cerebral Cortex, 16(9),
1276–1282.
Hadjikhani, N., Joseph, R. M., Snyder, J., & Tager-Flusberg, H.
(2007). Abnormal activation of the social brain during face
perception in autism. Human Brain Mapping, 28(5), 441–449.
Hall, G. B., Doyle, K. A., Goldberg, J., West, D., & Szatmari, P.
(2010a). Amygdala engagement in response to subthreshold
presentations of anxious face stimuli in adults with autism
spectrum disorders: Preliminary insights. PLoS One, 5(5),
e10804.
Hall, J., Whalley, H. C., McKirdy, J. W., Sprengelmeyer, R., Santos, I.
M., Donaldson, D. I., et al. (2010b). A common neural system
mediating two different forms of social judgement. PsychologicalMedicine, 40(7), 1183–1192.
Hardan, A. Y., Libove, R. A., Keshavan, M. S., Melhem, N. M., &
Minshew, N. J. (2009). A preliminary longitudinal magnetic
resonance imaging study of brain volume and cortical thickness
in autism. Biological Psychiatry, 66(4), 320–326.
Harris, G. J., Chabris, C. F., Clark, J., Urban, T., Aharon, I., Steele, S.,
et al. (2006). Brain activation during semantic processing in
autism spectrum disorders via functional magnetic resonance
imaging. Brain and Cognition, 61(1), 54–68.
Hasson, U., Avidan, G., Gelbard, H., Vallines, I., Harel, M.,
Minshew, N., et al. (2009). Shared and idiosyncratic cortical
activation patterns in autism revealed under continuous real-life
viewing conditions. Autism Research, 2(4), 220–231.
Hesling, I., Dilharreguy, B., Peppe, S., Amirault, M., Bouvard, M., &
Allard, M. (2010). The integration of prosodic speech in high
functioning autism: A preliminary FMRI study. PLoS One, 5(7),
e11571.
Hessl, D., Rivera, S., Koldewyn, K., Cordeiro, L., Adams, J., Tassone,
F., et al. (2007). Amygdala dysfunction in men with the fragile X
premutation. Brain, 130(Pt 2), 404–416.
Holland, S. K., Vannest, J., Mecoli, M., Jacola, L. M., Tillema, J. M.,
Karunanayaka, P. R., et al. (2007). Functional MRI of language
lateralization during development in children. InternationalJournal of Audiology, 46(9), 533–551.
Hollander, E., Anagnostou, E., Chaplin, W., Esposito, K., Haznedar,
M. M., Licalzi, E., et al. (2005). Striatal volume on magnetic
resonance imaging and repetitive behaviors in autism. BiologicalPsychiatry, 58(3), 226–232.
Hubl, D., Bolte, S., Feineis-Matthews, S., Lanfermann, H., Federspiel,
A., Strik, W., et al. (2003). Functional imbalance of visual
pathways indicates alternative face processing strategies in
autism. Neurology, 61(9), 1232–1237.
Hughes, J. R. (2007). Autism: The first firm finding = underconnec-
tivity? Epilepsy & Behavior, 11(1), 20–24.
Humphreys, K., Hasson, U., Avidan, G., Minshew, N., & Behrmann,
M. (2008). Cortical patterns of category-selective activation for
faces, places and objects in adults with autism. Autism Research,1(1), 52–63.
Hyman, S. E. (2007). Can neuroscience be integrated into the
DSM-V? Nature Reviews Neuroscience, 8(9), 725–732.
Ingalhalikar, M., Kanterakis, S., Gur, R., Roberts, T. P., &
Verma, R. (2010). DTI based diagnostic prediction of a
disease via pattern classification. Proceedings of MedicalImage Computing and Computer-Assisted Intervention, 13(Pt 1),
558–565.
Jou, R. J., Jackowski, A. P., Papademetris, X., Rajeevan, N., Staib, L.
H., & Volkmar, F. R. (2011). Diffusion tensor imaging in autism
spectrum disorders: Preliminary evidence of abnormal neural
connectivity. Australian and New Zealand Journal of Psychiatry,45(2), 153–162.
Just, M. A., Cherkassky, V. L., Keller, T. A., Kana, R. K., &
Minshew, N. J. (2007). Functional and anatomical cortical
underconnectivity in autism: Evidence from an FMRI study of
an executive function task and corpus callosum morphometry.
Cerebral Cortex, 17(4), 951–961.
Just, M. A., Cherkassky, V. L., Keller, T. A., & Minshew, N. J.
(2004). Cortical activation and synchronization during sentence
comprehension in high-functioning autism: Evidence of under-
connectivity. Brain, 127(Pt 8), 1811–1821.
Kaiser, M. D., Hudac, C. M., Shultz, S., Lee, S. M., Cheung, C.,
Berken, A. M., et al. (2010). Neural signatures of autism.
Proceedings of the National Academy of Sciences, USA, 107(49),
21223–21228.
Kana, R. K., Keller, T. A., Cherkassky, V. L., Minshew, N. J., & Just, M.
A. (2006). Sentence comprehension in autism: Thinking in pictures
with decreased functional connectivity. Brain, 129(Pt 9),
2484–2493.
Kana, R. K., Keller, T. A., Cherkassky, V. L., Minshew, N. J., & Just,
M. A. (2009). Atypical frontal-posterior synchronization of
theory of mind regions in autism during mental state attribution.
Social Neuroscience, 4(2), 135–152.
Kana, R. K., Keller, T. A., Minshew, N. J., & Just, M. A. (2007).
Inhibitory control in high-functioning autism: Decreased activa-
tion and underconnectivity in inhibition networks. BiologicalPsychiatry, 62(3), 198–206.
Ke, X., Tang, T., Hong, S., Hang, Y., Zou, B., Li, H., et al. (2009).
White matter impairments in autism, evidence from voxel-based
morphometry and diffusion tensor imaging. Brain Research,1265, 171–177.
J Autism Dev Disord (2012) 42:1326–1341 1337
123
Keehn, B., Brenner, L., Palmer, E., Lincoln, A. J., & Muller, R. A.
(2008). Functional brain organization for visual search in ASD.
Journal of the International Neuropsychological Society, 14(6),
990–1003.
Keller, T. A., Kana, R. K., & Just, M. A. (2007). A developmental
study of the structural integrity of white matter in autism.
Neuroreport, 18(1), 23–27.
Kennedy, D. P., & Courchesne, E. (2008a). Functional abnormalities
of the default network during self- and other-reflection in autism.
Social Cognitive and Affective Neuroscience, 3(2), 177–190.
Kennedy, D. P., & Courchesne, E. (2008b). The intrinsic functional
organization of the brain is altered in autism. Neuroimage, 39(4),
1877–1885.
Kennedy, D. P., Redcay, E., & Courchesne, E. (2006). Failing to
deactivate: Resting functional abnormalities in autism. Proceed-ings of the National Academy of Sciences, USA, 103(21),
8275–8280.
Kleinhans, N. M., Johnson, L. C., Richards, T., Mahurin, R.,
Greenson, J., Dawson, G., et al. (2009). Reduced neural
habituation in the amygdala and social impairments in autism
spectrum disorders. American Journal of Psychiatry, 166(4),
467–475.
Kleinhans, N. M., Muller, R. A., Cohen, D. N., & Courchesne, E.
(2008a). Atypical functional lateralization of language in autism
spectrum disorders. Brain Research, 1221, 115–125.
Kleinhans, N. M., Richards, T., Johnson, L. C., Weaver, K. E.,
Greenson, J., Dawson, G., et al. (2011). fMRI evidence of neural
abnormalities in the subcortical face processing system in ASD.
Neuroimage, 54(1), 697–704.
Kleinhans, N. M., Richards, T., Sterling, L., Stegbauer, K. C., Mahurin,
R., Johnson, L. C., et al. (2008b). Abnormal functional connec-
tivity in autism spectrum disorders during face processing. Brain,131(Pt 4), 1000–1012.
Kleinhans, N. M., Richards, T., Weaver, K., Johnson, L. C.,
Greenson, J., Dawson, G., et al. (2010). Association between
amygdala response to emotional faces and social anxiety in
autism spectrum disorders. Neuropsychologia, 48(12),
3665–3670.
Klin, A. (2008). Three things to remember if you are a functional
magnetic resonance imaging researcher of face processing in
autism spectrum disorders. Biological Psychiatry, 64(7),
549–551.
Klin, A., & Volkmar, F. R. (2003). Asperger syndrome: Diagnosis
and external validity. Child and Adolescent Psychiatric Clinicsof North America, 12(1), 1–13, v.
Knaus, T. A., Silver, A. M., Kennedy, M., Lindgren, K. A., Dominick,
K. C., Siegel, J., et al. (2010). Language laterality in autism
spectrum disorder and typical controls: A functional, volumetric,
and diffusion tensor MRI study. Brain and Language, 112(2),
113–120.
Knaus, T. A., Silver, A. M., Lindgren, K. A., Hadjikhani, N., &
Tager-Flusberg, H. (2008). fMRI activation during a language
task in adolescents with ASD. Journal of the InternationalNeuropsychological Society, 14(6), 967–979.
Koshino, H., Carpenter, P. A., Minshew, N. J., Cherkassky, V. L.,
Keller, T. A., & Just, M. A. (2005). Functional connectivity in an
fMRI working memory task in high-functioning autism. Neuro-image, 24(3), 810–821.
Koshino, H., Kana, R. K., Keller, T. A., Cherkassky, V. L., Minshew,
N. J., & Just, M. A. (2008). fMRI investigation of working
memory for faces in autism: Visual coding and underconnectiv-
ity with frontal areas. Cerebral Cortex, 18(2), 289–300.
Lai, M. C., Lombardo, M. V., Chakrabarti, B., Sadek, S. A., Pasco,
G., Wheelwright, S. J., et al. (2010). A shift to randomness of
brain oscillations in people with autism. Biological Psychiatry,68(12), 1092–1099.
Lange, N., Dubray, M. B., Lee, J. E., Froimowitz, M. P., Froehlich,
A., Adluru, N., et al. (2010). Atypical diffusion tensor
hemispheric asymmetry in autism. Autism Research, 3(6),
350–358.
Lee, J. E., Bigler, E. D., Alexander, A. L., Lazar, M., DuBray, M. B.,
Chung, M. K., et al. (2007a). Diffusion tensor imaging of white
matter in the superior temporal gyrus and temporal stem in
autism. Neuroscience Letters, 424(2), 127–132.
Lee, J. E., Chung, M. K., Lazar, M., DuBray, M. B., Kim, J., Bigler,
E. D., et al. (2009a). A study of diffusion tensor imaging by
tissue-specific, smoothing-compensated voxel-based analysis.
Neuroimage, 44(3), 870–883.
Lee, P. S., Foss-Feig, J., Henderson, J. G., Kenworthy, L. E., Gilotty,
L., Gaillard, W. D., et al. (2007b). Atypical neural substrates of
embedded figures task performance in children with autism
spectrum disorder. Neuroimage, 38(1), 184–193.
Lee, P. S., Yerys, B. E., Della Rosa, A., Foss-Feig, J., Barnes, K. A.,
James, J. D., et al. (2009b). Functional connectivity of the
inferior frontal cortex changes with age in children with autism
spectrum disorders: A fcMRI study of response inhibition.
Cerebral Cortex, 19(8), 1787–1794.
Levy, S. E., Mandell, D. S., & Schultz, R. T. (2009). Autism. Lancet,374(9701), 1627–1638.
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gotzsche, P. C.,
Ioannidis, J. P., et al. (2009). The PRISMA statement for
reporting systematic reviews and meta-analyses of studies that
evaluate healthcare interventions: Explanation and elaboration.
BMJ, 339, b2700.
Limperopoulos, C., Bassan, H., Gauvreau, K., Robertson, R. L., Jr.,
Sullivan, N. R., Benson, C. B., et al. (2007). Does cerebellar
injury in premature infants contribute to the high prevalence of
long-term cognitive, learning, and behavioral disability in
survivors? Pediatrics, 120(3), 584–593.
Lind, S. E., & Bowler, D. M. (2010). Episodic memory and episodic
future thinking in adults with autism. Journal of AbnormalPsychology, 119(4), 896–905.
Logothetis, N. K. (2003). The underpinnings of the BOLD functional
magnetic resonance imaging signal. Journal of Neuroscience,23(10), 3963–3971.
Lombardo, M. V., Chakrabarti, B., Bullmore, E. T., Sadek, S. A.,
Pasco, G., Wheelwright, S. J., et al. (2010). Atypical neural self-
representation in autism. Brain, 133(Pt 2), 611–624.
Luna, B., Minshew, N. J., Garver, K. E., Lazar, N. A., Thulborn, K.
R., Eddy, W. F., et al. (2002). Neocortical system abnormalities
in autism: An fMRI study of spatial working memory. Neurol-ogy, 59(6), 834–840.
Malisza, K. L., Clancy, C., Shiloff, D., Foreman, D., Holden, J.,
Jones, C., et al. (2011). Functional evaluation of hidden figures
object analysis in children with autistic disorder. Journal ofAutism and Developmental Disorders, 41(1), 13–22.
Mana, S., Paillere Martinot, M. L., & Martinot, J. L. (2010). Brain
imaging findings in children and adolescents with mental
disorders: A cross-sectional review. European Psychiatry,25(6), 345–354.
Manjaly, Z. M., Bruning, N., Neufang, S., Stephan, K. E., Brieber, S.,
Marshall, J. C., et al. (2007). Neurophysiological correlates of
relatively enhanced local visual search in autistic adolescents.
Neuroimage, 35(1), 283–291.
Markowitsch, H. J., Thiel, A., Reinkemeier, M., Kessler, J.,
Koyuncu, A., & Heiss, W. D. (2000). Right amygdalar and
temporofrontal activation during autobiographic, but not during
fictitious memory retrieval. Behavioral Neurology, 12(4),
181–190.
Marti-Climent, J. M., Prieto, E., Lopez Lafuente, J., & Arbizu, J.
(2010). Neuroimaging: Technical aspects and practice. RevistaEspanola de Medicina Nuclear, 29(4), 189–210.
1338 J Autism Dev Disord (2012) 42:1326–1341
123
Martineau, J., Andersson, F., Barthelemy, C., Cottier, J. P., &
Destrieux, C. (2010). Atypical activation of the mirror neuron
system during perception of hand motion in autism. BrainResearch, 1320, 168–175.
Mason, R. A., Williams, D. L., Kana, R. K., Minshew, N., & Just, M.
A. (2008). Theory of Mind disruption and recruitment of the
right hemisphere during narrative comprehension in autism.
Neuropsychologia, 46(1), 269–280.
Minshew, N. J., & Keller, T. A. (2010). The nature of brain
dysfunction in autism: Functional brain imaging studies. CurrentOpinion in Neurology, 23(2), 124–130.
Minshew, N. J., & Williams, D. L. (2007). The new neurobiology of
autism: Cortex, connectivity, and neuronal organization.
Archives of Neurology, 64(7), 945–950.
Mizuno, A., Villalobos, M. E., Davies, M. M., Dahl, B. C., & Muller,
R. A. (2006). Partially enhanced thalamocortical functional
connectivity in autism. Brain Research, 1104(1), 160–174.
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009).
Preferred reporting items for systematic reviews and meta-
analyses: The PRISMA statement. BMJ, 339, b2535.
Monk, C. S., Peltier, S. J., Wiggins, J. L., Weng, S. J., Carrasco, M.,
Risi, S., et al. (2009). Abnormalities of intrinsic functional
connectivity in autism spectrum disorders. Neuroimage, 47(2),
764–772.
Monk, C. S., Weng, S. J., Wiggins, J. L., Kurapati, N., Louro, H. M.,
Carrasco, M., et al. (2010). Neural circuitry of emotional face
processing in autism spectrum disorders. Journal of Psychiatryand Neuroscience, 35(2), 105–114.
Mostofsky, S. H., Powell, S. K., Simmonds, D. J., Goldberg, M. C.,
Caffo, B., & Pekar, J. J. (2009). Decreased connectivity and
cerebellar activity in autism during motor task performance.
Brain, 132(Pt 9), 2413–2425.
Muller, R. A. (2007). The study of autism as a distributed disorder.
Mental Retardation and Developmental Disabilities ResearchReviews, 13(1), 85–95.
Muller, R. A. (2008). From loci to networks and back again:
Anomalies in the study of autism. Annals of the New YorkAcademy of Sciences, 1145, 300–315.
Muller, R. A., Cauich, C., Rubio, M. A., Mizuno, A., & Courchesne,
E. (2004). Abnormal activity patterns in premotor cortex during
sequence learning in autistic patients. Biological Psychiatry,56(5), 323–332.
Muller, R. A., Kleinhans, N., Kemmotsu, N., Pierce, K., &
Courchesne, E. (2003). Abnormal variability and distribution
of functional maps in autism: An FMRI study of visuomotor
learning. American Journal of Psychiatry, 160(10),
1847–1862.
Muller, R. A., Pierce, K., Ambrose, J. B., Allen, G., & Courchesne, E.
(2001). Atypical patterns of cerebral motor activation in autism:
A functional magnetic resonance study. Biological Psychiatry,49(8), 665–676.
Narayanan, A., White, C. A., Saklayen, S., Scaduto, M. J., Carpenter,
A. L., Abduljalil, A., et al. (2010). Effect of propranolol on
functional connectivity in autism spectrum disorder—a pilot
study. Brain Imaging Behavioral, 4(2), 189–197.
Nishitani, N., Avikainen, S., & Hari, R. (2004). Abnormal imitation-
related cortical activation sequences in Asperger’s syndrome.
Annals of Neurology, 55(4), 558–562.
Noonan, S. K., Haist, F., & Muller, R. A. (2009). Aberrant functional
connectivity in autism: Evidence from low-frequency BOLD
signal fluctuations. Brain Research, 1262, 48–63.
Noriuchi, M., Kikuchi, Y., Yoshiura, T., Kira, R., Shigeto, H., Hara,
T., et al. (2010). Altered white matter fractional anisotropy and
social impairment in children with autism spectrum disorder.
Brain Research, 1362, 141–149.
Oktem, F., Diren, B., Karaagaoglu, E., & Anlar, B. (2001). Functional
magnetic resonance imaging in children with Asperger’s
syndrome. Journal of Child Neurology, 16(4), 253–256.
Otzenberger, H., Gounot, D., Marrer, C., Namer, I. J., & Metz-Lutz,
M. N. (2005). Reliability of individual functional MRI brain
mapping of language. Neuropsychology, 19(4), 484–493.
Paakki, J. J., Rahko, J., Long, X., Moilanen, I., Tervonen, O.,
Nikkinen, J., et al. (2010). Alterations in regional homogeneity
of resting-state brain activity in autism spectrum disorders. BrainResearch, 1321, 169–179.
Palmen, S. J., & van Engeland, H. (2004). Review on structural
neuroimaging findings in autism. Journal of Neural Transmis-sion, 111(7), 903–929.
Pardini, M., Garaci, F. G., Bonzano, L., Roccatagliata, L., Palmieri,
M. G., Pompili, E., et al. (2009). White matter reduced
streamline coherence in young men with autism and mental
retardation. European Journal of Neurology, 16(11), 1185–1190.
Pelphrey, K. A., Morris, J. P., & McCarthy, G. (2005). Neural basis of
eye gaze processing deficits in autism. Brain, 128(Pt 5),
1038–1048.
Pelphrey, K. A., Morris, J. P., McCarthy, G., & Labar, K. S. (2007).
Perception of dynamic changes in facial affect and identity in
autism. Social Cognative Affective Neuroscience, 2(2), 140–149.
Pierce, K., Haist, F., Sedaghat, F., & Courchesne, E. (2004). The brain
response to personally familiar faces in autism: Findings of
fusiform activity and beyond. Brain, 127(Pt 12), 2703–2716.
Pierce, K., Muller, R. A., Ambrose, J., Allen, G., & Courchesne, E.
(2001). Face processing occurs outside the fusiform ‘face area’
in autism: evidence from functional MRI. Brain, 124(Pt 10),
2059–2073.
Pierce, K., & Redcay, E. (2008). Fusiform function in children with
an autism spectrum disorder is a matter of ‘‘who’’. BiologicalPsychiatry, 64(7), 552–560.
Piggot, J., Kwon, H., Mobbs, D., Blasey, C., Lotspeich, L., Menon,
V., et al. (2004). Emotional attribution in high-functioning
individuals with autistic spectrum disorder: A functional imaging
study. Journal of the American Academy of Child and Adoles-cent Psychiatry, 43(4), 473–480.
Pinkham, A. E., Hopfinger, J. B., Pelphrey, K. A., Piven, J., & Penn,
D. L. (2008). Neural bases for impaired social cognition in
schizophrenia and autism spectrum disorders. SchizophreniaResearch, 99(1–3), 164–175.
Pugliese, L., Catani, M., Ameis, S., Dell’Acqua, F., Thiebaut de
Schotten, M., Murphy, C., et al. (2009). The anatomy of
extended limbic pathways in Asperger syndrome: A preliminary
diffusion tensor imaging tractography study. Neuroimage, 47(2),
427–434.
Rapin, I., & Allen, D. (1987). Developmental dysphasia and autism inpre-school children: Characteristics and subtypes The firstinternational symposium on specific speech and languagedisorders in children (pp. 20–35). London: AFASIC.
Rapin, I., & Dunn, M. (2003). Update on the language disorders of
individuals on the autistic spectrum. Brain and Development,25(3), 166–172.
Redcay, E., & Courchesne, E. (2008). Deviant functional magnetic
resonance imaging patterns of brain activity to speech in 2–3-
year-old children with autism spectrum disorder. BiologicalPsychiatry, 64(7), 589–598.
Ring, H. A., Baron-Cohen, S., Wheelwright, S., Williams, S. C.,
Brammer, M., Andrew, C., et al. (1999). Cerebral correlates of
preserved cognitive skills in autism: A functional MRI study of
embedded figures task performance. Brain, 122(Pt 7),
1305–1315.
Roy, M., Dillo, W., Bessling, S., Emrich, H. M., & Ohlmeier, M. D.
(2009). Effective methylphenidate treatment of an adult
J Autism Dev Disord (2012) 42:1326–1341 1339
123
Aspergers Syndrome and a comorbid ADHD: A clinical
investigation with fMRI. Journal of Attention Disorders, 12(4),
381–385.
Rumsey, J. M., & Ernst, M. (2000). Functional neuroimaging of
autistic disorders. Mental Retardation and Developmental Dis-abilities Research Reviews, 6(3), 171–179.
Sahyoun, C. P., Belliveau, J. W., Soulieres, I., Schwartz, S., & Mody,
M. (2010). Neuroimaging of the functional and structural
networks underlying visuospatial vs. linguistic reasoning in
high-functioning autism. Neuropsychologia, 48(1), 86–95.
Sasson, N., Tsuchiya, N., Hurley, R., Couture, S. M., Penn, D. L.,
Adolphs, R., et al. (2007). Orienting to social stimuli differen-
tiates social cognitive impairment in autism and schizophrenia.
Neuropsychologia, 45(11), 2580–2588.
Schippers, M. B., Roebroeck, A., Renken, R., Nanetti, L., & Keysers,
C. (2010). Mapping the information flow from one brain to
another during gestural communication. Proceedings of theNational Academy of Sciences, USA, 107(20), 9388–9393.
Schmitz, N., Rubia, K., Daly, E., Smith, A., Williams, S., & Murphy,
D. G. (2006). Neural correlates of executive function in autistic
spectrum disorders. Biological Psychiatry, 59(1), 7–16.
Schmitz, N., Rubia, K., van Amelsvoort, T., Daly, E., Smith, A., &
Murphy, D. G. (2008). Neural correlates of reward in autism.
British Journal of Psychiatry, 192(1), 19–24.
Schulte-Ruther, M., Greimel, E., Markowitsch, H. J., Kamp-Becker,
I., Remschmidt, H., Fink, G. R., et al. (2011). Dysfunctions in
brain networks supporting empathy: An fMRI study in adults
with autism spectrum disorders. Social Neuroscience, 6(1), 1–21.
Schultz, R. T. (2005). Developmental deficits in social perception in
autism: The role of the amygdala and fusiform face area.
International Journal of Developmental Neuroscience, 23(2–3),
125–141.
Schultz, R. T., Gauthier, I., Klin, A., Fulbright, R. K., Anderson, A.
W., Volkmar, F., et al. (2000). Abnormal ventral temporal
cortical activity during face discrimination among individuals
with autism and Asperger syndrome. Archives of GeneralPsychiatry, 57(4), 331–340.
Scott-Van Zeeland, A. A., Dapretto, M., Ghahremani, D. G.,
Poldrack, R. A., & Bookheimer, S. Y. (2010a). Reward
processing in autism. Autism Research, 3(2), 53–67.
Scott-Van Zeeland, A. A., McNealy, K., Wang, A. T., Sigman, M.,
Bookheimer, S. Y., & Dapretto, M. (2010b). No neural evidence
of statistical learning during exposure to artificial languages in
children with autism spectrum disorders. Biological Psychiatry,68(4), 345–351.
Shafritz, K. M., Dichter, G. S., Baranek, G. T., & Belger, A. (2008).
The neural circuitry mediating shifts in behavioral response and
cognitive set in autism. Biological Psychiatry, 63(10), 974–980.
Shamay-Tsoory, S. G., Gev, E., Aharon-Peretz, J., & Adler, N.
(2010). Brain asymmetry in emotional processing in Asperger
syndrome. Cognitive Behavioral Neurology, 23(2), 74–84.
Shields, J., Varley, R., Broks, P., & Simpson, A. (1996). Social
cognition in developmental language disorders and high-level
autism. Developmental Medicine and Child Neurology, 38(6),
487–495.
Shih, P., Shen, M., Ottl, B., Keehn, B., Gaffrey, M. S., & Muller, R.
A. (2010). Atypical network connectivity for imitation in autism
spectrum disorder. Neuropsychologia, 48(10), 2931–2939.
Shukla, D. K., Keehn, B., Lincoln, A. J., & Muller, R. A. (2010a).
White matter compromise of callosal and subcortical fiber tracts
in children with autism spectrum disorder: A diffusion tensor
imaging study. Journal of American Academy of Child Psychi-atry, 49(12), 1269–1278, 1278 e1261–1262.
Shukla, D. K., Keehn, B., & Muller, R. A. (2010b). Regional
homogeneity of fMRI time series in autism spectrum disorders.
Neuroscience Letters, 476(1), 46–51.
Shukla, D. K., Keehn, B., & Muller, R. A. (2011). Tract-specific
analyses of diffusion tensor imaging show widespread white
matter compromise in autism spectrum disorder. Journal ofChild Psychology and Psychiatry, 52(3), 286–295.
Silani, G., Bird, G., Brindley, R., Singer, T., Frith, C., & Frith, U.
(2008). Levels of emotional awareness and autism: An fMRI
study. Social Neuroscience, 3(2), 97–112.
Silk, T. J., Rinehart, N., Bradshaw, J. L., Tonge, B., Egan, G.,
O’Boyle, M. W., et al. (2006). Visuospatial processing and the
function of prefrontal-parietal networks in autism spectrum
disorders: A functional MRI study. American Journal ofPsychiatry, 163(8), 1440–1443.
Sivaswamy, L., Kumar, A., Rajan, D., Behen, M., Muzik, O.,
Chugani, D., et al. (2010). A diffusion tensor imaging study of
the cerebellar pathways in children with autism spectrum
disorder. Journal of Child Neurology, 25(10), 1223–1231.
Skranes, J., Vangberg, T. R., Kulseng, S., Indredavik, M. S., Evensen,
K. A., Martinussen, M., et al. (2007). Clinical findings and white
matter abnormalities seen on diffusion tensor imaging in adoles-
cents with very low birth weight. Brain, 130(Pt 3), 654–666.
Solomon, M., Ozonoff, S. J., Ursu, S., Ravizza, S., Cummings, N.,
Ly, S., et al. (2009). The neural substrates of cognitive control
deficits in autism spectrum disorders. Neuropsychologia, 47(12),
2515–2526.
Soulieres, I., Dawson, M., Samson, F., Barbeau, E. B., Sahyoun, C. P.,
Strangman, G. E., et al. (2009). Enhanced visual processing
contributes to matrix reasoning in autism. Human BrainMapping, 30(12), 4082–4107.
Spengler, S., Bird, G., & Brass, M. (2010). Hyperimitation of actions
is related to reduced understanding of others’ minds in autism
spectrum conditions. Biological Psychiatry, 68(12), 1148–1155.
Spreng, R. N., Mar, R. A., & Kim, A. S. (2009). The common neural
basis of autobiographical memory, prospection, navigation,
theory of mind, and the default mode: A quantitative meta-
analysis. Journal of Cognitive Neuroscience, 21(3), 489–510.
Stanfield, A. C., McIntosh, A. M., Spencer, M. D., Philip, R., Gaur,
S., & Lawrie, S. M. (2008). Towards a neuroanatomy of autism:
A systematic review and meta-analysis of structural magnetic
resonance imaging studies. European Psychiatry, 23(4),
289–299.
Stevens, M. C. (2005). Functional neuroimaging in child and
adolescent psychiatry. Connecticut Medicine, 69(9), 561–570.
Stigler, K. A., McDonald, B. C., Anand, A., Saykin, A. J., &
McDougle, C. J. (2011). Structural and functional magnetic
resonance imaging of autism spectrum disorders. BrainResearch, 1380, 146–161.
Sundaram, S. K., Kumar, A., Makki, M. I., Behen, M. E., Chugani, H.
T., & Chugani, D. C. (2008). Diffusion tensor imaging of frontal
lobe in autism spectrum disorder. Cerebral Cortex, 18(11),
2659–2665.
Takeuchi, M., Harada, M., Matsuzaki, K., Nishitani, H., & Mori, K.
(2004). Difference of signal change by a language task on
autistic patients using functional MRI. Journal of MedicalInvestigation, 51(1–2), 59–62.
Tavano, A., Grasso, R., Gagliardi, C., Triulzi, F., Bresolin, N.,
Fabbro, F., et al. (2007). Disorders of cognitive and affective
development in cerebellar malformations. Brain, 130(Pt 10),
2646–2660.
Tesink, C. M., Buitelaar, J. K., Petersson, K. M., van der Gaag, R. J.,
Kan, C. C., Tendolkar, I., et al. (2009). Neural correlates of
pragmatic language comprehension in autism spectrum disor-
ders. Brain, 132(Pt 7), 1941–1952.
Thakkar, K. N., Polli, F. E., Joseph, R. M., Tuch, D. S., Hadjikhani,
N., Barton, J. J., et al. (2008). Response monitoring, repetitive
behaviour and anterior cingulate abnormalities in autism spec-
trum disorders (ASD). Brain, 131(Pt 9), 2464–2478.
1340 J Autism Dev Disord (2012) 42:1326–1341
123
Thompson, L., Thompson, M., & Reid, A. (2010). Functional
neuroanatomy and the rationale for using EEG biofeedback for
clients with Asperger’s syndrome. Applied Psychophysiologyand Biofeedback, 35(1), 39–61.
Uddin, L. Q., Davies, M. S., Scott, A. A., Zaidel, E., Bookheimer, S.
Y., Iacoboni, M., et al. (2008). Neural basis of self and other
representation in autism: An FMRI study of self-face recogni-
tion. PLoS One, 3(10), e3526.
Ullman, M. T. (2004). Contributions of memory circuits to language:
The declarative/procedural model. Cognition, 92(1–2), 231–270.
Verhoeven, J. S., De Cock, P., Lagae, L., & Sunaert, S. (2010).
Neuroimaging of autism. Neuroradiology, 52(1), 3–14.
Villalobos, M. E., Mizuno, A., Dahl, B. C., Kemmotsu, N., & Muller,
R. A. (2005). Reduced functional connectivity between V1 and
inferior frontal cortex associated with visuomotor performance
in autism. Neuroimage, 25(3), 916–925.
Volkmar, F. R., & Pauls, D. (2003). Autism. Lancet, 362(9390),
1133–1141.
Wang, A. T., Dapretto, M., Hariri, A. R., Sigman, M., & Bookheimer,
S. Y. (2004). Neural correlates of facial affect processing in
children and adolescents with autism spectrum disorder. Journalof the American Academy of Child and Adolescent Psychiatry,43(4), 481–490.
Wang, A. T., Lee, S. S., Sigman, M., & Dapretto, M. (2006). Neural
basis of irony comprehension in children with autism: The role
of prosody and context. Brain, 129(Pt 4), 932–943.
Wang, A. T., Lee, S. S., Sigman, M., & Dapretto, M. (2007). Reading
affect in the face and voice: Neural correlates of interpreting
communicative intent in children and adolescents with autism
spectrum disorders. Archives of General Psychiatry, 64(6),
698–708.
Wass, S. (2011). Distortions and disconnections: Disrupted brain
connectivity in autism. Brain and Cognition, 75(1), 18–28.
Weinstein, M., Ben-Sira, L., Levy, Y., Zachor, D. A., Ben Itzhak, E.,
Artzi, M., et al. (2011). Abnormal white matter integrity in young
children with autism. Human Brain Mapping, 32(4), 534–543.
Welchew, D. E., Ashwin, C., Berkouk, K., Salvador, R., Suckling, J.,
Baron-Cohen, S., et al. (2005). Functional disconnectivity of the
medial temporal lobe in Asperger’s syndrome. BiologicalPsychiatry, 57(9), 991–998.
Weng, S. J., Wiggins, J. L., Peltier, S. J., Carrasco, M., Risi, S., Lord,
C., et al. (2010). Alterations of resting state functional connec-
tivity in the default network in adolescents with autism spectrum
disorders. Brain Research, 1313, 202–214.
Whitehouse, A. J., Watt, H. J., Line, E. A., & Bishop, D. V. (2009).
Adult psychosocial outcomes of children with specific language
impairment, pragmatic language impairment and autism. Inter-national Journal of Language Communication Disorders, 44(4),
511–528.
Williams, J. H. (2008). Self-other relations in social development and
autism: Multiple roles for mirror neurons and other brain bases.
Autism Research, 1(2), 73–90.
Williams, D. L., & Minshew, N. J. (2007). Understanding autism and
related disorders: What has imaging taught us? NeuroimagingClinics of North America, 17(4), 495–509, ix.
Williams, J. H., Waiter, G. D., Gilchrist, A., Perrett, D. I., Murray, A.
D., & Whiten, A. (2006). Neural mechanisms of imitation and
‘mirror neuron’ functioning in autistic spectrum disorder.
Neuropsychologia, 44(4), 610–621.
Wing, L. (1996). Autistic spectrum disorders. BMJ, 312(7027),
327–328.
Yerys, B. E., Jankowski, K. F., Shook, D., Rosenberger, L. R.,
Barnes, K. A., Berl, M. M., et al. (2009). The fMRI success rate
of children and adolescents: Typical development, epilepsy,
attention deficit/hyperactivity disorder, and autism spectrum
disorders. Human Brain Mapping, 30(10), 3426–3435.
J Autism Dev Disord (2012) 42:1326–1341 1341
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