Evaluation of brain structure abnormalities in children with autism spectrum disorder (ASD) using structural magnetic resonance imaging

Abstract

Background
Autism spectrum disorder (ASD) is a group of developmental disorders of the nervous system. Since the core cause of many of the symptoms of autism spectrum disorder is due to changes in the structure of the brain, the importance of examining the structural abnormalities of the brain in these disorder becomes apparent. The aim of this study is evaluation of brain structure abnormalities in children with autism spectrum disorder (ASD) using structural magnetic resonance imaging (sMRI). sMRI images of 26 autistic and 26 Healthy control subjects in the range of 5–10 years are selected from the ABIDE database. For a better assessment of structural abnormalities, the surface and volume features are extracted together from this images. Then, the extracted features from both groups were compared with the sample t test and the features with significant differences between the two groups were identified.

Results
The results of volume-based features indicate an increase in total brain volume and white matter and a change in white and gray matter volume in brain regions of Hammers atlas in the autism group. In addition, the results of surface-based features indicate an increase in mean and standard deviation of cerebral cortex thickness and changes in cerebral cortex thickness, sulcus depth, surface complexity and gyrification index in the brain regions of the Desikan–Killany cortical atlas.

Conclusions
Identifying structurally abnormal areas of the brain and examining their relationship to the clinical features of Autism Spectrum Disorder can pave the way for the correct and early detection of this disorder using structural magnetic resonance imaging. It is also possible to design treatment for autistic people based on the abnormal areas of the brain, and to see the effectiveness of the treatment using imaging.

Full text

Khadem‑Rezaand Zare
Egypt J Neurol Psychiatry Neurosurg (2022) 58:135
https://doi.org/10.1186/s41983‑022‑00576‑5
RESEARCH
Evaluation ofbrain structure abnormalities
inchildren withautism spectrum disorder (ASD)
using structural magnetic resonance imaging
Zahra Khandan Khadem‑Reza and Hoda Zare*
Abstract
Background: Autism spectrum disorder (ASD) is a group of developmental disorders of the nervous system. Since
the core cause of many of the symptoms of autism spectrum disorder is due to changes in the structure of the brain,
the importance of examining the structural abnormalities of the brain in these disorder becomes apparent. The aim of
this study is evaluation of brain structure abnormalities in children with autism spectrum disorder (ASD) using struc‑
tural magnetic resonance imaging (sMRI). sMRI images of 26 autistic and 26 Healthy control subjects in the range of
5–10 years are selected from the ABIDE database. For a better assessment of structural abnormalities, the surface and
volume features are extracted together from this images. Then, the extracted features from both groups were com‑
pared with the sample t test and the features with significant differences between the two groups were identified.
Results: The results of volume‑based features indicate an increase in total brain volume and white matter and a
change in white and gray matter volume in brain regions of Hammers atlas in the autism group. In addition, the
results of surface‑based features indicate an increase in mean and standard deviation of cerebral cortex thickness and
changes in cerebral cortex thickness, sulcus depth, surface complexity and gyrification index in the brain regions of
the Desikan–Killany cortical atlas.
Conclusions: Identifying structurally abnormal areas of the brain and examining their relationship to the clinical
features of Autism Spectrum Disorder can pave the way for the correct and early detection of this disorder using
structural magnetic resonance imaging. It is also possible to design treatment for autistic people based on the abnor‑
mal areas of the brain, and to see the effectiveness of the treatment using imaging.
Keywords: ASD, sMRI, ABIDE, Children, Brain
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Background
e term autism is made up of two parts: autos, which
means "self," and ism, which means "inclination" [1].
Autism Spectrum Disorder (ASD) is a group of devel-
opmental disorders of the nervous system. Its main
manifestations consist of defects in social interactions,
communication, repetitive behaviors and limited inter-
ests. Information from the US Department of Education
shows that the incidence of autism increases by 10–17%
each year [2]. Due to the rapid and progressive rise of
ASD, a lot of research has been done on it recently. A
major feature of ASD is the heterogeneity of its clini-
cal features. A diversity of symptoms along with many
psychological and physiological comorbidities may be
present. Psychological comorbidities include atten-
tion–deficit hyperactivity disorder (ADHD), obsessive–
compulsive disorder (OCD), anxiety, and intellectual
disability [3]. ere is a notable co-occurrence of ADHD
with ASD, and the two conditions share many neuro-
logical and behavioral similarities. Physiological comor-
bidities of ASD include epilepsy, sleep disorders, and
Open Access
The Egyptian Journal of Neurology,
Psychiatry and Neurosurgery
*Correspondence: ZareH@mums.ac.ir
Department of Medical Physics, Faculty of Medicine, Mashhad University
of Medical Sciences, Mashhad, Iran

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Khadem‑Rezaand Zare Egypt J Neurol Psychiatry Neurosurg (2022) 58:135
gastrointestinal (GI) problems [4]. ASD are caused by
genetic or environmental factors or a combination of
these. ASD is considered a complex genetic disorder
with high heritability. Epidemiological twin studies sup-
port the strong genetic component of ASD. Overall, the
SFARI (Simons Foundation Autism Research Initiative)
gene database, a database of autism candidate genes, lists
about 1000 genes associated with ASD. Genes entered
into the database are scored based on their strength
of association with ASD risk. Despite the genetic het-
erogeneity, a recent review of the literature reveals that
a number of these mutations converge on a common
neurodevelopmental pathway involved in neurogenesis,
axon guidance, and synapse formation. Non-genetic
factors mediating ASD risk could include parental age,
maternal nutritional and metabolic status, infection
during pregnancy, prenatal stress, and exposure to cer-
tain toxins, heavy metals, or drugs [5].For many years,
brain development and function have been the focus of
research in ASD. Experimental and postmortem studies
have identified central nervous system (CNS) pathologies
at gross morphological level and cellular level, for exam-
ple, in neurons and glial cells. From these studies, it can
be concluded that neuropathologies are evident in ASD.
However, research in recent years on immune responses
and gut–brain signaling have revealed that pathologies in
ASD also exist outside the CNS [6].
ASD is diagnosed based on behavioral interests and
repetitive behaviors [7]. ese social impairments may
be related to the interpretation of social signals: evidence
from healthy individuals suggest that potentially threat-
ening situations such as others’ proximity can trigger a
number of physiological responses that help regulate the
distance between themselves and others during social
interaction [8] and showing the critical role of social sig-
nal interpretation in social interaction [9]. Individuals
with ASD have social impairments, potentially due to the
lack of social signal interpretation and, therefore, result-
ing unable to interpret these signals to guide appropriate
behaviors.
In the first step, identifying the clinical biomarkers of
ASD using structural brain imaging can pave the way for
recognizing the neurobiological causes of the disorder
and the brain’s areas affected by the disorder. In magnetic
resonance imaging studies, there are generally two cat-
egories of features: volume-based and surface-based fea-
tures. Different articles use one or a combination of both.
Several studies have been carried out to diagnose volu-
metric brain defects in people with autism. ese stud-
ies reinforced the hypothesis that autistic patients had
larger brain volumes than controls [10–15]. Several stud-
ies observed that the significant increased areas of gray
and white matter volume in the autism group were the
frontal, temporal and parietal lobes [16–19]. A more
comprehensive study was conducted in 2016 by Haar and
colleagues. is study showed an increase in ventricu-
lar volume, a decrease in the corpus collasom, and sev-
eral cortical areas [20]. Several studies used Gyrification
Index (GI) to distinguish between autistic and control
groups. is index was higher in several regions in autis-
tic than in the controls [21–24]. Several studies used the
Sulcal depth parameter to analyze the shape anomalies of
structural images. Abnormalities in the sum region were
noticeable in the autistic group [25, 26]. In 2013, Ecker
and colleagues. found, the thickness of the cerebral cortex
was significantly larger in the frontal lobe of autistic sub-
jects and the surface area in the orbitofrontal cortex and
posterior cingulum in the autism group was lower than
the control [27]. Sum studies examined cortical thickness
changes and observed an increase of cortical thickness in
several regions of the brain [20, 27–36]. e contradic-
tory results reported in different studies have been due to
differences in the imaging methods used, heterogeneity
of the subjects and so on.
Since the core cause of many of the symptoms of autism
spectrum disorder is due to changes in the structure of
the brain, the importance of examining the structural
abnormalities of the brain in these children becomes
apparent. So far, few studies have been performed on
structural abnormalities in the brains of autistic children.
is study intended to investigate the images of struc-
tural magnetic resonance and also to detect structural
abnormalities created in the brain attributable to autism
spectrum disorder in children. In this study, to better
evaluate, volume and surface features were employed
simultaneously.
Methods
e steps performed in the study are shown in Fig.1.
Subjects
IN this study, we used structural Magnetic Resonance
images data from Autism BrainImagingData Exchange
(ABIDE II). Because the aim of this study was to investi-
gate the structural abnormalities of children’s brains, we
chose one data set acquired from NYU Langone Medi-
cal Center: Sample 1 (NYU) site. is data set consisted
of 78 children’s brain images. Due to the importance
of early diagnosis of autism disorder for more effec-
tive treatment, the diagnosis of brain abnormalities at a
younger age is more effective, and therefore, only chil-
dren aged 5–10years were examined in this study. Data
from 26 autistic and 26 control subjects in the age range
of 5–10years were used in the present study. ere was
no significant difference in age and sex between autism

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and healthy control groups. e demographic informa-
tion of the participants is given in Table1.
MRI acquisition
MRI scans were acquired from 3T scanners manufac-
tured by Siemens with the Neuroimaging Informatics
Technology Initiative (NIFTI) format with the following
protocol: repetition time and echo time = 3.25 ms , flip
angle = 7°, plane resolution = 1.3 × 1 mm, 1.3mm slice
thickness with 0.665mm gap, 128 slices, 256× 256 mm
field of view, acquisition time = 8:07min.
Preprocessing andsegmentation
e CAT12 and SPM12 toolboxes in MATLAB software
version R2019a have been used to process structural
images of the brain. We performed the preprocessing
steps using CAT12 toolboxes with the default setting,
respectively. Briefly, all 3D T1-weighted MRI scans are
normalized using an affine followed by non-linear reg-
istration, corrected for bias field inhomogeneities and
then segmented into GM, WM, and CSF components
[37]. For this procedure, we used the Diffeomorphic Ana-
tomic Registration through Exponentiated Lie algebra
algorithm (DARTEL) to normalize the segmented scans
into a standard MNI space [38]. e pre-processing and
segmentation steps are shown in Fig.2. At the end of the
image pre-processing and segmentation phase, there is a
summarized QC index derived from CAT12, which can
be used to represent the quality of the data. Furthermore,
at least a visual inspection needs to be done. e pre-
processing and segmentation steps are shown in Fig.2.
3D brain reconstruction
Surface reconstruction steps are also performed using
CAT12 toolbox in MATLAB R2019a software and
include the following steps:
• Estimation of cerebral cortex thickness and central
surface: we use a fully automated method that allows
for the measurement of cortical thickness and recon-
struction of the central surface in one step. It uses
a tissue segmentation to estimate the white mat-
ter (WM) distance, then projects the local maxima
(which is equal to the cortical thickness) to other gray
matter voxels using a neighbor relationship described
by the WM distance [39].
• Topological correction: topological correction is per-
formed to repair topological defects using a method
containing spherical harmonics that allows direct
correction of defects on the brain surface mesh [40].
e reconstructed surface in the CAT12 toolbox is
shown in Fig.3.
Volume andsurface feature extraction
Feature extraction is the process of extracting some
unique and general data from an image. With the help of
the designed algorithm, the features are extracted from
the images, so that a feature vector is specified for each
image. Extraction of sMRI features is done in two steps
using the CAT12 toolbox:
Fig. 1 Steps performed in the study
Table 1 Demographics for the participants
ASD Autism Spectrum Disorder, HC Healthy Controls, M Male, STD Standard
Deviation, F Female, FIQ Full‑Scale Intelligence Quotient, PIQ Performance
Intelligence Quotient, VIQ Verbal Intelligence Quotient, VABS Vineland Adaptive
Behavior Scales, SRS Social Responsiveness Scale; P < 0.05 was considered
statistically signicant, *Statistically Signicant
ASD (n = 26) HC (n = 26) P value(*)
Mean (STD) Mean (STD)
Age 7.12 (0.98) 7.48(1.39) 0.320
Sex 24M/2F 25M/1F 0.584
FIQ 100.64(26.85) 115.92(15.47) 0.016*
PIQ 100.23(19.77) 112.03(15.20) 0.020*
VIQ 99.96(15.69) 117.03(16.46) < 0.001*
VABS Sum Scores 293.11(56.81) 332.68(50.33) 0.011*
SRS Total 75.07(17.27) 45.12(6.21) < 0.001*

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1) Volume-based features extraction: volume-based
features include brain tissue volume measurements
(white matter, gray matter and cerebrospinal fluid)
and regional volume measurements of white matter
and gray matter in 68 regions of the volume-based
Hammer’s Atlas.
2) Extraction of cortical-based features: cortical-based
features include mean and standard deviation of cere-
bral cortex thickness and calculation of parameters of
cerebral cortex thickness, cortical complexity, sulcus
depth and GI in 68 regions of cortical-based Desi-
kan–Killany Atlas. Cortical thickness in each region
is defined as the Euclidean distance between the
inner and outer layers of the cerebral cortex in that
region. cortical complexity (CC) or fractal dimen-
sion (FD) provides a quantitative description of the
structural complexity in the cerebral cortex. After
extracting three-dimensional information from the
cortex surface, FD is measured using the Box Count-
ing algorithm. e three-dimensional surface is first
covered using cubes of the same size. en, the size
of the cubes is changed and this is repeated. e frac-
tal dimension is defined in three dimensions as loga-
rithmic changes in the number of cubes divided by
logarithmic changes in the size of the cubes. How to
compute the fractal dimension is given in Eq. (1) [41]:
e sulcus is a groove in the cerebral cortex that usu-
ally surrounds a gyrus of the brain on both sides [42].
e human cerebral cortex has a complex morphological
structure and is composed of folded or smooth cortical
surfaces. ese morphological features are referred to as
cortical gyrification and are characterized by the GI. e
GI is the ratio between the complete superficial contour
(“the pial surface”) and the outer contour of the cortical
part of the cortex (“the outer smoothed surface”). How to
compute this index is given in Eq. (2) [43]:
Maps of surface parameters calculated by the CAT12
toolbox are prepared in Fig.4.
e volume and surface features extracted from the
structural magnetic resonance images are given in
Table2.
Statistical analysis
WE used the experimental sample t test and the Leven
test to compare between groups for the continuous var-
iable (age), and the chi-squared test for the qualitative
variable (gender). After calculating the overall volumes
of GM, WM, CSF and their sum (total intracranial
(1)
f3D=−
�
log
(
cube count)
�
log
(
cube size
)
(2)
GI
=
Length(2D)or Surface(3D)of pial surfaces
Length
(
2D
)
or surface
(
3D
)
of Smoothed surfaces
Fig. 2 Pre‑processing steps in CAT12 toolbox. A Original image. B Image after intensity normalization. C Segmented image
Fig. 3 Reconstructed surface by CAT12 toolbox. A Reconstruction
of right hemisphere surface of the brain. B Reconstruction of left
hemisphere surface of the brain

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volume; TIV), WM and GM per ROIs of hammers atlas
and cortical thickness, sulcus depth, cortical complex-
ity and GI per ROIs of DK atlas in two groups (inde-
pendent variables), as estimated by the CAT12 toolbox,
then, normality of these data was assessed using the
Kolmogorov–Smirnov test. en, for those variables
in which the normality assumption was satisfied, inde-
pendent sample t tests and Leven test was used. In
other words, the nonparametric method (Mann–Whit-
ney U test) and Leven test were used for non-normality
values. e P value < 0.05 was considered statistically
significant. e results were corrected with Bonferroni
correction for multiple comparisons to be considered
meaningful. All statistical analyses were performed
using SPSS 26.0 software (SPSS Inc., Chicago, IL, USA).
Results
e findings of structural magnetic resonance image pro-
cessing can be divided into volume-based and surface-
based analysis.
Volume‑based parameters
e statistically significant results of volumetric meas-
urement of brain tissue are given in Table3 and the sta-
tistically significant results of volumetric measurement of
white matter and gray matter in the Hammers Atlas are
also given in Tables4 and 5, respectively.
Surface‑based parameters
e statistical results of the mean and standard devia-
tion of cortical thickness are shown in Table6 and the
Fig. 4 Maps of surface parameters calculated by Toolbox CAT12. A Cortical thickness, B sulcus depth, C cortical complexity, D GI

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statistical results of cortical thickness, sulcus depth, GI
and cortical complexity (fractal dimension) in DK atlas
regions are also given in Tables7, 8, 9 and 10, respectively.
Discussion
Autism spectrum disorder is associated with increased
brain volume in childhood and decreased brain vol-
ume in adulthood [44]. Increased brain volume in autis-
tic people compared to controls confirms the studies
[10–19, 29]. Based on the statistical findings of the study
presented in , the volume of white matter in the L and
R amygdala region of the brain in the autism group
shows a meaningful increase compared to the control.
e amygdala is part of the limbic system of the brain
and is associated with emotional and social behaviors
[45], facial recognition [46], and cognitive function [47].
e increase in the volume of this area of the brain in
the autistic group approves the research [13, 15, 48–51].
e volume of white matter in some areas located in the
frontal and temporal lobes also has a considerable differ-
ence between two groups and is higher in the autism one,
which confirms the studies [10, 17, 29, 52]. e frontal
lobe in the brain is responsible for reasoning, planning,
decision-making, and judgment, and generally controls
social and cognitive behaviors [53]. e Temporal lobe is
also involved in understanding language, and emotions,
and is an area of sound and speech processing. Both
lobes play a role in memory [54]. e volume of L and
R putamen white matter, which is generally engaging in
movement and learning [55], also is higher in the autism
one that approves the studies [56–59]. e volume of
white matter in the L and R thalamus and L precentral
gyrus regions is notably lower in autism group. e thala-
mus is part of the limbic system of the brain that is the
site of information amplification and processing. e
precentral gyrus is known as the primary motor cortex
which is responsible for voluntary movements [60]. e
decrease in the volume of L and R thalamus confirmed
by studies [61–63], however, is in contradiction with
the study [64]. Decreased volume of L precentral gyrus
has not been reported in any research. According to the
statistical findings of the present analysis in Table5, the
volume of gray matter in the temporal lobe of the brain
of autistic individuals compared to controls is meaning-
fully higher. In addition, considerable growth in the vol-
ume of the gray matter of R fusiform gyrus (FFG), L and
R Pallidum and L corpus callosum in the autism group is
observed. e social problems seen in ASD may be due
in part to the dysfunction of FFG [65]. pallidum plays a
role in the regulation of voluntary movements [66]. e
corpus callosum is a group of high-density white matter
fibers in the brain that facilitate communication between
the hemispheres. e increase in gray matter volume of R
fusiform gyrus, L and R pallidum and L corpus callosum
in the autism group compared to controls is confirmed
by researches [11, 14], [67, 68] and [69–73], respectively.
Neuroimaging research shows that human intellectual
ability is related to the brain structure, including corti-
cal thickness. Autism spectrum disorder is character-
ized by impaired cognition and social communication,
and in addition to social disabilities [74]. erefore, it is
expected that the cortical thickness in autistic children
is associated with abnormalities. Based on the statisti-
cal discoveries in Table6, the mean and standard devia-
tion of cortical thickness in the autism group, showing a
notable increase which approves the studies [20, 27, 28,
30–36]. Furthermore, according to the statistical findings
Table 2 Volume and surface features extracted from structural
magnetic resonance images
WMV T White Matter Volume Total, GMV T Gray Matter Volume Total, CSF T
Cerebrospinal uid Volume Total, TIV Total Intracranial Volume, WMV White
Matter Volume, GMV Gray Matter Volume, CT Cortical Thickness, STD Standard
Deviation, CC Cortical Thickness, SD Sulcus Depth, GI Gyrication Index
Feature Description
WMV T White Matter Volume Total
GMV T Gray Matter Volume Total
CSFV T Cerebrospinal fluid Volume Total
TIV Total Intracranial Volume
WMV White Matter Volume per Hammers atlas
GMV Gray Matter Volume per Hammers atlas
Mean CT Mean Cortical Thickness per DK atlas
STD CT Standard Deviation Cortical Thickness per DK atlas
CT Cortical Thickness per DK atlas
CC Cortical Complexity per DK atlas
SD Sulcus Depth per DK atlas
GI Gyrification Index per DK atlas
Table 3 Statistical results of brain tissue volume measurement (significant differences)
ASD Autism Spectrum Disorder, HC Healthy Controls, STD Standard Deviation, WMV T White Matter Volume Total, TIV Total Intracranial Volume
ASD HC Mean dierence P value
Mean STD Mean STD
WMV T 484.26 48.06 440.88 30.56 48.38 < 0.001
TIV 1427.65 76.80 1378.61 75.68 49.03 0.025

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in Table7, the cortical thickness in the L and R cuneus, R
lingual, R paracentral, L parsopercularis and R superior
temporal areas in the autism group has a considerable
increase. cuneus and lingual are parts of the brain located
in the occipital lobe and are engaged in visual processing
[75, 76]. e paracentral controls the sensory and motor
nerves of the lower extremity [77]. Pars operculris is a
part of the inferior frontal gyrus located in the Broca area
Table 4 Statistical results of white matter volumetric measurements in Hammers Atlas regions (significant differences)
ASD Autism Spectrum Disorder, HC Healthy Controls, STD Standard Deviation, L Left, R Right
Brain region ASD HC Mean Dierence P value
Mean STD Mean STD
L amygdala 0.25 0.03 0.08 0.02 0.16 < 0.001
R amygdala 0.22 0.03 0.12 0.02 0.10 < 0.001
L anterior medial temporal lobe 2.59 0.23 1.56 0.26 1.02 < 0.001
R anterior medial temporal lobe 2.68 0.26 1.67 0.31 1.00 < 0.001
R superior temporal gyrus 6.25 0.61 5.38 0.57 0.87 < 0.001
L inferior middle temporal gyrus 6.47 0.68 5.54 0.63 0.93 < 0.001
R inferior middle temporal gyrus 6.72 0.64 5.69 0.64 1.03 < 0.001
L middle frontal gyrus 24.38 2.62 22.58 2.08 1.80 0.008
R middle frontal gyrus 24.86 2.71 22.73 2.06 1.13 0.002
L posterior temporal lobe 18.98 1.75 17.71 1.52 1.27 0.008
R posterior temporal lobe 19.26 1.73 17.98 1.74 1.28 0.010
L putamen 0.93 0.10 0.53 0.05 0.40 < 0.001
R putamen 1.07 0.10 0.63 0.07 0.43 < 0.001
L thalamus 2.47 0.40 2.68 0.36 − 0.21 < 0.001
R thalamus 2.44 0.29 2.68 0.38 − 0.23 < 0.001
L precentral gyrus 15.73 1.43 16.74 1.75 − 1.01 0.027
L orbitofrontal gyrus 6.71 0.61 5.95 0.56 0.6 < 0.001
R orbitofrontal gyrus 7.07 0.61 6.15 0.70 0.91 < 0.001
L inferior frontal gyrus 6.53 0.84 5.41 0.55 1.11 < 0.001
R inferior frontal gyrus 6.44 0.74 5.51 0.59 0.92 < 0.001
Table 5 Statistical results of gray matter in Hammers Atlas (significant differences)
ASD Autism Spectrum Disorder, HC Healthy Controls, STD Standard Deviation, L Left, R Right
Brain region ASD HC Mean dierence P value
Mean SD Mean SD
R superior temporal gyrus 10.48 0.82 9.82 0.77 0.65 0.005
R fusiform gyrus 4.22 0.36 3.58 0.48 0.63 < 0.001
L pallidum 0.71 0.08 0.65 0.11 0.06 0.026
R pallidum 0.76 0.08 0.71 0.11 0.05 0.049
L Corpus callosum 0.83 0.11 0.77 0.08 0.06 0.023
L lateral temporal ventricle 1.83 0.22 1.69 0.14 0.14 0.010
Table 6 Statistical results of mean and standard deviation of cortical thickness (significant differences)
ASD Autism Spectrum Disorder, HC Healthy Controls, STD Standard Deviation, Mean CT Mean Cortical Thickness per DK atlas, STD CT Standard Deviation Cortical
Thickness per DK atlas
Brain region ASD HC Mean dierence P value
Mean STD Mean STD
Mean CT 2.86 0.09 2.78 0.08 0.08 0.002
STD CT 0.95 0.05 0.88 0.05 0.10 < 0.001

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Khadem‑Rezaand Zare Egypt J Neurol Psychiatry Neurosurg (2022) 58:135
of the brain [78]. e Broca area in the brain is related
to speech production and processing [79]. Since speech
disorder is one of the main features of ASD, this area is
one of the main parts that encounter abnormalities in
autism spectrum disorder [80, 81]. e superior tempo-
ral is part of the temporal lobe and contains the auditory
cortex, which is responsible of processing sounds. It also
includes the Wernicke area of the brain, which is the main
part for understanding language. is area also plays a
vital role in social cognition and impairment of this part
is one of the main features of ASD [82, 83]. Increased
cerebral cortical thickness in L and R cuneus, R lingual,
R paracentral areas has not been reported in any study.
Increased cortical thickness of the L parsopercularis and
Table 7 Statistical results of cortical thickness parameter in DK atlas regions (significant differences)
ASD Autism Spectrum Disorder, HC Healthy Controls, STD Standard Deviation, L Left, R Right
Brain region ASD HC Mean dierence P value
Mean STD Mean STD
L cuneus 2.40 0.12 2.19 0.02 0.21 < 0.001
R cuneus 2.40 0.12 2.21 0.02 0.18 < 0.001
R lingual 2.41 0.06 2.35 0.13 0.06 0.049
L parahippocampal 2.35 0.23 2.46 0.08 − 0.11 0.027
R parahippocampal 2.39 0.28 2.55 0.14 − 0.015 0.015
R paracentral 2.88 0.09 2.77 0.02 0.12 < 0.001
L parsopercularis 2.82 0.05 2.75 0.14 0.06 0.043
R superior temporal 3.06 0.06 2.98 0.19 0.08 0.039
Table 8 Statistical results of Sulcus depth parameter in Atlas DK areas (significant differences)
ASD Autism Spectrum Disorder, HC Healthy Controls, STD Standard Deviation, L Left, R Right
Brain region ASD HC Mean dierence P value
Mean STD Mean STD
L bankssts 3.53 0.29 3.38 0.18 0.15 0.032
L parsopercularis 3.87 0.29 3.72 0.10 0.15 0.015
L parstriangularis 3.49 0.36 3.30 0.13 0.18 0.015
R rostral anterior cingulate 2.48 0.11 2.53 0.03 − 0.05 0.041
L superior temporal 2.69 0.36 2.54 0.12 0.15 0.048
L temporal pole 2.89 0.39 2.71 0.12 0.17 0.033
R temporal pole 3.05 0.34 2.88 0.13 0.17 0.023
L insula 5.54 0.32 5.41 0.10 0.13 0.049
Table 9 Statistical results of gyrification index parameter in DK Atlas regions (significant differences)
ASD Autism Spectrum Disorder, HC Healthy Controls, STD Standard Deviation, L Left, R Right
Brain region ASD HC Mean dierence P value
Mean STD Mean STD
L entorhinal 27.40 2.81 29.12 1.56 − 1.71 0.009
R inferior parietal 34.50 2.12 31.47 0.41 3.02 < 0.001
L inferior temporal 27.66 0.84 27.08 0.59 0.5720 0.007
L lateral occipital 33.35 0.58 30.89 0.17 2.46 < 0.001
R lateral occipital 33.42 0.80 30.66 0.14 2.75 < 0.001
R parstriangularis 27.82 1.52 27.19 0.54 0.63 0.049
L posterior cingulate 29.47 0.82 29.05 0.30 0.42 0.018
L precuneus 29.92 0.94 27.92 0.16 2.00 < 0.001
R superior parietal 30.67 1.08 28.88 0.31 1.79 < 0.001
L fronta pole 34.23 1.52 32.14 0.30 2.09 < 0.001

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R superior temporal also confirms studies [84, 85] and
[30], respectively. In addition, the cortical thickness of L
and R parahippocampal areas in the autism group was
notably decreased. e parahippocampal is a part of the
limbic system of the brain that plays an important role
in encoding and retrieving memory [86]. is finding of
the current research confirms the study [87]. Based on
the statistical findings of the present study in Table8, the
Sulcus depth of the cerebellum of L bankssts, L parsoper-
cularis, L parstriangularis, L superior temporal, L and R
temporal pole and L insula in the autism group shows a
substantial increase [25, 26, 88, 89]. In ASD, the presence
of anomalies in bankssts is the root of the impairment
in the social activities [90]. e parstriangularis, like
Table 10 Statistical results of surface complexity parameter (fractal dimension) in DK Atlas regions (significant differences)
ASD Autism Spectrum Disorder, HC Healthy Controls, STD Standard Deviation, L Left, R Right
Brain region ASD HC Mean dierence P value
Mean STD Mean STD
R bankssts 2.31 0.48 2.58 0.14 − 0.27 0.009
R caudate anterior cingulate 1.90 0.44 2.23 0.15 − 0.31 0.001
R caudate middle frontal 2.35 0.45 2.67 0.15 − 0.32 0.001
R cuneus 2.05 0.38 2.26 0.08 − 0.20 0.011
R entorhinal 2.09 0.45 2.37 0.14 − 0.28 0.003
R fusiform 2.03 0.45 2.34 0.16 − 0.30 0.003
R inferior parietal 2.19 0.42 2.47 0.14 − 0.28 0.003
R inferior temporal 1.91 0.40 2.15 0.10 − 0.23 0.006
L isthmus cingulate 2.00 0.14 2.06 0.05 − 0.06 0.044
R isthmus cingulate 1.50 0.48 1.85 0.20 − 0.34 0.002
R lateral occipital 1.88 0.41 2.15 0.13 − 0.27 0.003
R lateral orbitofrontal 1.84 0.42 2.13 0.13 − 0.28 0.002
R lingual 2.09 0.49 2.39 0.15 − 0.29 0.006
L medial orbitofrontal 2.55 0.16 2.64 0.05 − 0.08 0.022
R medial orbitofrontal 2.07 0.48 2.38 0.15 − 0.30 0.003
R middle temporal 2.03 0.12 2.33 0.16 − 0.29 0.003
L parahippocampal 2.59 0.11 2.64 0.03 − 0.04 0.047
R parahippocampal 1.99 0.57 2.30 0.18 − 0.30 0.014
R paracentral 2.05 0.43 2.32 0.14 − 0.27 0.004
R parsopercularis 2.31 0.50 2.62 0.17 − 0.30 0.005
L parsorbitalis 2.96 0.20 3.05 0.10 − 0.09 0.034
R parsorbitalis 2.30 0.50 2.63 0.18 − 0.32 0.004
L parstriangularis 2.66 0.12 2.73 0.06 − 0.07 0.011
R parstriangularis 2.25 0.44 2.50 0.12 − 0.24 0.008
R pericalcarine 1.79 0.42 2.10 0.16 − 0.31 0.001
R postcentral 2.30 0.39 2.60 0.16 − 0.29 0.001
R posterior cingulate 1.94 0.45 2.23 0.15 − 0.28 0.003
R precentral 2.25 0.43 2.54 0.15 − 0.29 0.002
R precuneus 2.05 0.45 2.33 0.14 − 0.27 0.004
R rostral anterior cingulate 1.49 0.36 1.71 0.08 − 0.21 0.006
R rostral middle frontal 1.99 0.44 2.27 0.14 − 0.28 0.014
R superior frontal 1.81 0.40 2.07 0.12 − 0.25 0.003
R superior parietal 2.11 0.40 2.36 0.12 − 0.25 0.004
R superior temporal 2.21 0.43 2.50 0.15 − 0.29 0.002
R supramarginal 2.07 0.40 2.32 0.12 − 0.24 0.004
R frontal pole 2.23 0.46 2.50 0.13 − 0.27 0.006
R temporal pole 2.02 0.40 2.28 0.12 − 0.25 0.003
R transverse temporal 1.62 0.45 1.94 0.17 − 0.31 0.002
R insula 1.81 0.38 2.01 0.08 − 0.02 0.012

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Khadem‑Rezaand Zare Egypt J Neurol Psychiatry Neurosurg (2022) 58:135
the parsopercularis, is part of the inferior frontal gyrus
located in the Broca area of the brain [85]. Insula also
participates in understanding consciousness and social
emotions [86, 87]. e sulcus depth of the rostral ante-
rior cingulate in the autism group showed a significant
decrease compared to the control group, which has not
been reported in any study. According to the statistical
data of Table9, the GI of the number of frontal and pari-
etal lobe brain regions in the autism group has increased
considerably. Furthermore, this index has increased in L
and R lateral occipital, R parstriangularis, L posterior cin-
gulate, L precuneus, in the autism group compared to the
control group. is finding of the present analysis con-
firms the studies [21–24, 91–94]. On the other hand, this
index in the L entorhinal brain area in the autism group
is substantially reduced compared to the control group.
Posterior cingulate is involved in memory and emotion
[95, 96] and researches have revealed that abnormality of
this region is one of the main aspects of ASD [97]. e
precuneus is a region of the brain that participated in a
variety of complex functions, including recall and mem-
ory, the integration of information about the environ-
ment, mental imagery strategies, memory retrieval, and
emotional responses [98]. e entorhinal is located in the
middle of the temporal lobe and acts as an extensive net-
work in memory and time perception [99]. According to
the statistical findings of Table10, the cortical complex-
ity parameter in all areas of the right hemisphere of the
autism group compared to the control group has mean-
ingfully decreased. In addition, there is an important dif-
ference between the two groups in the some regions of
the brain located in the left hemisphere. So far, no study
has been performed to calculate the surface cortical
complexity parameter in the DK atlas regions. However,
according to the clinical characteristics of autism spec-
trum disorder and abnormal areas, the findings of the
present study can be correctly understood.
Regions in the frontal and parietal cortices, are
involved in a number of cognitive operations, includ-
ing planning, working memory, impulse control, inhibi-
tion, and set-shifting. ese cognitive domains are often
referred to under the umbrella term of “executive func-
tions,” which broadly refers to the set of processes that
are employed when an individual is involved in a goal-
directed activity. Damage to the frontal cortex, which is
considered the “seat” of executive functioning, interrupts
the ability of individuals to complete many goal-directed
tasks and has been shown to result in the emergence of
perseverative and repetitive behaviors, insistence for
sameness, and impulsivity, all of which are clinical mani-
festations of autism spectrum disorders [100]. Prefrontal
cortex is one region of the emotion processing network.
e prefrontal cortex islike a control center, helping to
guide our actions, and therefore, this area is also involved
during emotion regulation. In the recent years, growing
attention has paid to the involvement of cerebellar and
striatal structures in ASD. In the recent years, growing
attention has paid to the involvement of cerebellar and
striatal structures in ASD. e cerebellum is involved
in both motor and social impairments reported in ASD.
Clumsiness and deficits in motor coordination and man-
ual dexterity, abnormal balance gait and posture are all
dependent on the cerebellar function and are affected
in ASD. ese deficits can be detected even in the first
months of life, with affected babies exhibiting difficul-
ties positioning their body when carried, hypotonia and
uncoordinated movements. e basal ganglia are a group
of subcortical nuclei involved primarily in motor skills.
e term “Basal Ganglia” refers to the striatum and the
globus pallidus, while the substantia nigra (mesencepha-
lon), the subthalamic nuclei (diencephalon) and the pons
are related nuclei. Basal ganglia network is shown to be
deeply affected in ASD models. In the early 2000s, John
Rubenstein and Michael Merzenich formulated the exci-
tation/inhibition (E/I) imbalance hypothesis of ASD, sug-
gesting that the physiopathology of ASD and their related
comorbidities may reflect a disturbance in such a bal-
ance. Even though their work focused only on the cortical
networks, it may be easily extended to striatal networks,
as the striatum receives major excitatory inputs from
cortices areas and major inhibitory inputs from the local
interneurons network [101].
Autism diagnostic methods are currently based on
clinical observations, but these methods have many
errors. e use of diagnostic methods in parallel with
imaging methods can have a great impact on the design
of treatment processes for these patients. ere are vari-
ous methods for treating autism, including speech ther-
apy, occupational therapy, music therapy, game therapy,
behavioral therapy. A combination of these treatment
methods can reduce the symptoms of this disease and
control it. Since autism is a spectrum disorder and the
severity of symptoms is not the same in all patients, a
fixed treatment method cannot be used for all patients.
In general, the method of structural imaging of the
brain and the examination of structural abnormalities
of the brain of autistic people helps to design personal
treatment for each patient and to choose an effective
treatment method. Using the method of this study, it is
possible to evaluate the effect of therapeutic interven-
tions on the patient’s recovery process, and if the desired
result is not achieved, another treatment method substi-
tuted. With brain structural imaging, the abnormal areas
of the brain are identified, and based on the abnormal
areas of each person’s brain, along with clinical observa-
tions, personalized treatment is designed. In addition,

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Khadem‑Rezaand Zare Egypt J Neurol Psychiatry Neurosurg (2022) 58:135
after a period of speech therapy, neuroimaging can be
done again, and by comparing the results of two imaging
sessions, the treatment process can be evaluated and the
improvement of structural abnormalities in these areas
can be observed, which indicates the progress of the
treatment.
Conclusions
is study aimed to investigate and identify structur-
ally abnormality areas of the brain in autism spectrum
disorder using structural magnetic resonance imaging.
In examining the brain volume using sMRI, simultane-
ous volume changes of gray matter and cerebral white
matter were observed in the autism group. ese vol-
ume changes are in the form of an increase in the total
volume of the brain and white matter of the brain and
changing in the volume of white and gray matter in the
identified areas of the Hammers volume in the autism
group. Examining the brain surface using sMRI also
showed abnormalities in the parameters of cerebral cor-
tex thickness, sulcus depth, surface complexity and GI in
the autism group. ese changes are increasing the mean
and standard deviation of the cerebral cortex thickness
and changing the mentioned parameters in the speci-
fied areas of the DK atlas in the autism group. Changes
in the brain structure due to ASD are often related to the
clinical features of autism spectrum disorder, such as the
Broca and Wernicke areas, which are involved in speech
production and speech comprehension. Identifying
structurally abnormality areas of the brain and examin-
ing their relationship to the clinical features of ASD can
pave the way for the correct and early detection of this
disorder using structural magnetic resonance imaging.
It is also possible to design treatment for autistic people
based on the abnormal areas of the brain, and to see the
effectiveness of the treatment using imaging.
e impossibility of collecting imaging and clinical infor-
mation led to the use of ABIDE data. As well as the lack of
imaging and clinical data of infants in both autism and con-
trol groups, caused the use of subjects in the age range of
5–10years. In addition, ASD is an extremely heterogenous
disorder, were any of the selected patients suffered from
associated low IQ, delayed speech, epilepsy or any other
forms of associated diseases. ose comorbidities can
affect both the volume and surface of brain, which could
affect specifity of the diagnosis. Since there is no informa-
tion about autism associated diseases in selected patients,
this problem is one of the limitations of the present study. It
is suggested that the study be performed for more detailed
research with a higher amount of data. In addition, it
can be organized by information from patients who suf-
fer from autism and the control group with younger age.
Other methods of analyzing structural magnetic resonance
imaging should also be observed. Studies with similar data
should be done using other software. e present analy-
sis has focused on the structure of the brain. In future
researches, in addition to structural images of the brain,
other brain imaging modalities such as fMRI and DTI can
be used. Due to the dependence of brain structure on age,
studies can be performed in different age groups to iden-
tify the effect of age on changes in brain structure due to
autism spectrum disorder.
Abbreviations
ASD: Autism spectrum disorder; SMRI: Structural magnetic resonance imaging;
ABIDE: Autism brain imaging data exchange; NIFT: Neuroimaging informatics
technology initiative; GI: Gyrification index; HC: Healthy controls; M: Male; F:
Female; FIQ: Full‑scale intelligence quotient; PIQ: Performance intelligence
quotient; VIQ: Verbal intelligence quotient; VABS: Vineland adaptive behavior
scales; SRS: Social responsiveness scale; CAT : Computational anatomy toolbox;
SPM: Statistical parametric mapping; DARTEL: Diffeomorphic Anatomic
Registration Through Exponentiated Lie algebra algorithm; MNI: Montreal
Neurological Institute; GM: Gray matter; WM: White matter; CSF: Cerebrospinal
fluid; CC: Cortical complexity; CT: Cortical thickness; STD: Standard deviation;
FD: Fractal dimension; ROI: Region of interest; DK: Desikan–Killiany; L: Left; R:
Right; FFG: Fusiform gyrus; FMRI: Functional magnetic resonance imaging; DTI:
Diffusion tensor imaging.
Acknowledgements
This paper was extracted from a MS.c thesis of Medical Physics. The authors
would like to thank the Research Deputy of MUMS for financial support of this
project, numbered (980858). Ethics code: IR.MUMS.MEDICAL.REC.1398.717)
Author contributions
HZ contributed as a research assistant as well as a technical advisor. ZK was a
major contributor to image analyzing and writing the manuscript. All authors
read and approved the final manuscript.
Funding
The Research Deputy of Mashhad University of Medical Sciences financially
supported this research in Terms of M.Sc. dissertation.
Availability of data and materials
The data sets used and/or analyzed during the current study are available
from the corresponding author on Reasonable request.
Declarations
Ethics approval and consent to participate
In this study, patients do not participate directly in the design and only images
of people with autism and the control group that have already been evalu‑
ated are extracted from the ABIDE database (https://fcon_1000.projects.nitrc.
org/indi/abide/abide_I.html).
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Received: 26 April 2022 Accepted: 26 October 2022
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