Neuron Number and Size in Prefontal Cortex of Children With Autism

Article (PDF Available)inJAMA The Journal of the American Medical Association 306(18):2001-10 · November 2011with42 Reads
DOI: 10.1001/jama.2011.1638 · Source: PubMed
Abstract
Autism often involves early brain overgrowth, including the prefrontal cortex (PFC). Although prefrontal abnormality has been theorized to underlie some autistic symptoms, the cellular defects that cause abnormal overgrowth remain unknown. To investigate whether early brain overgrowth in children with autism involves excess neuron numbers in the PFC. DESIGN, SETTING, AND CASES: Postmortem prefrontal tissue from 7 autistic and 6 control male children aged 2 to 16 years was examined by expert anatomists who were blinded to diagnostic status. Number and size of neurons were quantified using stereological methods within the dorsolateral (DL-PFC) and mesial (M-PFC) subdivisions of the PFC. Cases were from the eastern and southeastern United States and died between 2000 and 2006. Mean neuron number and size in the DL-PFC and M-PFC were compared between autistic and control postmortem cases. Correlations of neuron number with deviation in brain weight from normative values for age were also performed. Children with autism had 67% more neurons in the PFC (mean, 1.94 billion; 95% CI, 1.57-2.31) compared with control children (1.16 billion; 95% CI, 0.90-1.42; P = .002), including 79% more in DL-PFC (1.57 billion; 95% CI, 1.20-1.94 in autism cases vs 0.88 billion; 95% CI, 0.66-1.10 in controls; P = .003) and 29% more in M-PFC (0.36 billion; 95% CI, 0.33-0.40 in autism cases vs 0.28 billion; 95% CI, 0.23-0.34 in controls; P = .009). Brain weight in the autistic cases differed from normative mean weight for age by a mean of 17.6% (95% CI, 10.2%-25.0%; P = .001), while brains in controls differed by a mean of 0.2% (95% CI, -8.7% to 9.1%; P = .96). Plots of counts by weight showed autistic children had both greater total prefrontal neuron counts and brain weight for age than control children. In this small preliminary study, brain overgrowth in males with autism involved an abnormal excess number of neurons in the PFC.
PRELIMINARY
COMMUNICATION
Neuron Number and Size in Prefrontal
Cortex of Children With Autism
Eric Courchesne, PhD
Peter R. Mouton, PhD
Michael E. Calhoun, PhD
Katerina Semendeferi, PhD
Clelia Ahrens-Barbeau, BS
Melodie J. Hallet, MS
Cynthia Carter Barnes, PhD
Karen Pierce, PhD
C
LINICAL SIGNS OF AUTISM ARE
often preceded by or emerge
concurrently with a period of
abnormal brain and head
overgrowth.
1-12
This early neurobio-
logical signal of abnormal develop-
ment has been reported to begin at 9
to 18 months of age.
2,9-11
Overgrowth
13
and neural dysfunc-
tion
14,15
are evident at young ages in
multiple brain regions, including the
prefrontal cortex (PFC),
3,6,7,11,12
that are
involved in higher-order social, emo-
tional, communication, and cognitive
development. Therefore, knowledge of
the neural basis of overgrowth could
point to early causal mechanisms in au-
tism and elucidate the neural func-
tional defects that engender autistic
symptoms. In the first magnetic reso-
nance imaging (MRI) report of early
brain overgrowth in autism a decade
ago, it was theorized that excess num-
bers of neurons could be an underly-
ing cause, perhaps due to prenatal dys-
regulation of proliferation, apoptosis,
or both.
1
However, the neural basis of early
overgrowth remains unknown and can
only be known from direct quantita-
tive studies of the young postmortem
autistic brain. In one study, 4 postmor-
tem cases of 4- to 11-year-olds with au-
tism had approximately 53% more Von
Economo neurons in the frontoinsu-
lar cortex than 3 controls.
16
In an-
other study, the brain of a 3-year-old
with autism had 58% more Von
Economo neurons than that of a 2-year
old control.
17
Since the total number of
Von Economo neurons in the brain is
small, an excess of these specific cell
types cannot account for early brain
overgrowth.
We stereologically quantified total
neuron counts in 2 of the 3 major di-
visions of the PFC, which comprises
For editorial comment see p 2031.
Author Audio I nterview available at
www.jama.com.
Author Affiliations: Department of Neuroscience, NIH-
UCSD Autism Center of Excellence, School of Medi-
cine, University of California San Diego, La Jolla (Drs
Courchesne, Barnes, and Pierce and Mss Ahrens-
Barbeau and Hallet) and Department of Anthropol-
ogy (Dr Semendeferi), University of California San Di-
ego, La Jolla; Department of Pathology & Cell Biology,
University of South Florida School of Medicine, Alz-
heimer’s Institute and Research Center, Tampa (Dr
Mouton); and Sinq Systems Inc, Silver Spring, Mary-
land (Dr Calhoun).
Corresponding Author: Eric Courchesne, PhD, De-
partment of Neuroscience, NIH-UCSD Autism Cen-
ter of Excellence, School of Medicine, University of Cali-
fornia San Diego, La Jolla, CA 92093 (ecourchesne
@ucsd.edu).
Context Autism often involves early brain overgrowth, including the prefrontal cor-
tex (PFC). Although prefrontal abnormality has been theorized to underlie some autistic
symptoms, the cellular defects that cause abnormal overgrowth remain unknown.
Objective To investigate whether early brain overgrowth in children with autism in-
volves excess neuron numbers in the PFC.
Design, Setting, and Cases Postmortem prefrontal tissue from 7 autistic and 6
control male children aged 2 to 16 years was examined by expert anatomists who were
blinded to diagnostic status. Number and size of neurons were quantified using ste-
reological methods within the dorsolateral (DL-PFC) and mesial (M-PFC) subdivisions
of the PFC. Cases were from the eastern and southeastern United States and died be-
tween 2000 and 2006.
Main Outcome Measures Mean neuron number and size in the DL-PFC and M-PFC
were compared between autistic and control postmortem cases. Correlations of neu-
ron number with deviation in brain weight from normative values for age were also
performed.
Results Children with autism had 67% more neurons in the PFC (mean, 1.94 bil-
lion; 95% CI, 1.57-2.31) compared with control children (1.16 billion; 95% CI, 0.90-
1.42; P=.002), including 79% more in DL-PFC (1.57 billion; 95% CI, 1.20-1.94 in
autism cases vs 0.88 billion; 95% CI, 0.66-1.10 in controls; P=.003 ) and 29% more
in M-PFC ( 0.36 billion; 95% CI, 0.33-0.40 in autism cases vs 0.28 billion; 95% CI,
0.23-0.34 in controls; P=.009). Brain weight in the autistic cases differed from nor-
mative mean weight for age by a mean of 17.6% (95% CI, 10.2%-25.0%; P=.001),
while brains in controls differed by a mean of 0.2% (95% CI, −8.7% to 9.1%; P=.96).
Plots of counts by weight showed autistic children had both greater total prefrontal
neuron counts and brain weight for age than control children.
Conclusion In this small preliminary study, brain overgrowth in males with autism
involved an abnormal excess number of neurons in the PFC.
JAMA. 2011;306(18):2001-2010 www.jama.com
©2011 American Medical Association. All rights reserved. JAMA, November 9, 2011—Vol 306, No. 18 2001
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about one-third of all of the cortex, in
autistic children compared with con-
trols. Additionally, to determine
whether excess prefrontal neuron
counts in autism co-occur with abnor-
mally enlarged brains, we compared
brain weight in the autistic children
with age-based normative weights and
with brain weight in the controls.
METHODS
Brains were obtained from the Na-
tional Institute of Child Health and Hu-
man Development (NICHD), Univer-
sity of Maryland Brain and Tissue Bank,
the Autism Tissue Program at the Har-
vard Brain Tissue Resource Center, and
the New York State Institute for Basic
Research in Developmental Disabili-
ties. Young postmortem cases are scarce
and especially so with regard to tissue
suitable for modern stereological study
of the entire dorsolateral (DL-PFC) and
mesial (M-PFC) subdivisions of the
PFC. Such unbiased cell counting pro-
cedures are necessary to ensure valid
cell counts, which cannot be obtained
via density estimates from small blocks
of cortical tissue.
18,19
Brains were ob-
tained from 7 autistic and 6 control
male children aged 2 to 16 years, rep-
resenting all young control male cases
available at the time of the study and
nearly all known young autism cases
that had had the whole PFC uni-
formly sectioned. Cases were not se-
lected for any reason such as autopsy
brain weight, postmortem interval
(PMI), or cause of death, except that the
PFC met requirements for performing
valid stereological procedures.
Cases were from the eastern and
southeastern United States and dates of
death ranged from 2000 to 2006. Peri-
natal and postnatal medical condi-
tions were obtained by the tissue banks
from next of kin. Cause of death, PMI,
and neuropathology were obtained
from coroner’s reports. Race of each
postmortem case was determined by the
tissue banks from information gath-
ered from next of kin, and race and eth-
nicity categories were based on Na-
tional Institutes of Health (NIH)
requirements. Research procedures
were approved by the institutional re-
view board of the University of Cali-
fornia, San Diego. Informed consent or
waiver of consent was not required be-
cause all cases were deceased and de-
identified and anonymized by the tis-
sue banks.
All autism diagnostic classifications
(T
ABLE 1) were based on the results of
postmortem administration ofthe Autism
Diagnostic Interview-Revised(ADI-R) to
a parentor legal guardianof the deceased
by a psychologist, which is the standard
method for autism postmortem research.
The ADI-R is a standardized parent in-
terview used for determining develop-
mental history and behavior for the pur-
poses of diagnosing autism. Questions are
designed to elicit relevant information
through queries closely associated with
diagnostic criteria set forth in the Diag-
nostic and Statistical Manual of Mental Dis-
orders (Fourth Edition; DSM-IV). The
administration, scoring, and diagnostic
determination are the same as when it is
administered to the parent or legal guard-
ian of a living individual. The psycholo-
gists who determined the intellectual abil-
ity level of each autistic child was blinded
to knowledge of the neuropathology and
neuron counts. Nonintellectual disabil-
ity was defined as IQ of 71 or greater on
standardized IQ tests or evidence from
the ADI-R narrative of understanding of
most words and sentences, communica-
tive use of words and language, and some
initiation of appropriate activities such
as looking at books, using computers,
showing some interest in mother, or play-
ing games. Intellectual disability was de-
fined as IQ equal to 70 or lower or little
to no understanding or use of words, lack
of appropriate activities, presence of self-
injuriousbehavior, and/or nonresponsive-
ness to others.
All control cases were from the
NICHD Brain and Tissue Bank. This tis-
sue bank determined control cases to be
free of mental illness, intellectual dis-
ability, and neurological disorder, in-
cluding autism, based on information
gathered in a detailed questionnaire at
the time of death from next of kin. Cases
with a history of chemotherapy or ra-
diation treatment or being resuscitated
following ischemic hypoxia were ex-
cluded as controls by the tissue banks.
Age-Based Normative
Mean Brain Weights
Brain weight in normal male individu-
als increases with age.
5
To test whether
brain weight in the autistic children in
the present study exceeded normal mean
weight for age, the brain weight of each
case was compared with the normative
mean weight for age and expressed as a
percent difference from that normative
mean. To determine whether control
children had brain weights expected for
typical individuals, each weight was like-
wise compared to the normative mean
Table 1. Diagnostic Characteristics of Study Cases With Autism
a
Case Age, y ADI-Social
b
ADI-
Communication
c
ADI-Restrictive
and Repetitive
d
Intellectual
Disability
1 2 14 9 6 No
e
2 3 20 8 3 No
f
3 3 22 14 8 Yes
e
4 4 14 10 3 Yes
f
5 7 29 14 3 Yes
f
6 8 19 7 4 No
f
7 16 29 14 7 Yes
e
Abbreviation: ADI-R, Autism Diagnostic Interview-Revised.
a
All cases were male. All cases met or exceeded cutoffs for a diagnostic classification of autism using the ADI-R in-
strument.
b
Qualitative abnormalities in reciprocal social interaction (cutoff, 10; maximum score, 30).
c
Qualitative abnormalities in communication (cutoff, 7; maximum nonverbal communication score, 14).
d
Restricted, repetitive, and stereotyped patterns of behavior (cutoff, 3; maximum score, 12).
e
Intellectual disability status was determined using available standardized IQ scores (which have an intellectual thresh-
old for intellectual disability on a standardized measure of intelligence of 70 or lower; a mean score of 100).
f
In the absence of a standardized IQ score, determination of intellectual disability was made based on review of spe-
cific questions and responses about verbal communication, expression abilities, and adaptive behavior skills on the
ADI-R Narrative.
PREFRONTAL CORTEX NEURON NUMBER AND SIZE IN AUTISM
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weight for age and expressed as a per-
cent difference. The resulting age-
based percent differences in brain weight
within and between study groups were
then compared. The age-based norma-
tive brain weights for males (eTable 2,
available at http://www.jama.com) are
based on approximately 11 000 cases re-
ported in 10 normative brain weight
studies.
5
Anatomic Delineations
of Prefrontal Subdivisions
We analyzed the DL-PFC and the M-
PFC, 2 of the 3 major prefrontal subdi-
visions (F
IGURE 1; eAppendix); orbital
PFC was not measured. Anatomists
identified all anatomical boundaries of
DL-PFC and M-PFC, blinded to diag-
nostic membership, age of case, and the
purpose, literature, and theories asso-
ciated with this study to ensure unbi-
ased anatomic decisions. Anatomic de-
lineation of the DL-PFC and M-PFC
regions throughout their rostrocaudal
extent was based on previous defini-
tions
6,20
and on overall gross anatomy,
including tracking of sulci. Reliable
boundaries were further refined based
on cytoarchitectonic criteria,
21,22
in-
cluding layer 4 granularity and the pres-
ence of Betz cells; the overall cortical
width and layer 6/white-matter transi-
tion; and density and clarity of corti-
cal columns were also used in some
cases (eAppendix).
Stereology Procedures
Brains were serially sectioned and pre-
pared for stereologic analysis (eAppen-
dix; eTable 1). Quantifications of neu-
ron number and mean cell volume
within the DL-PFC and M-PFC were
carried out blind to diagnostic mem-
bership, age of case, and the purpose,
literature, and theories behind this
study to ensure unbiased anatomic mea-
surement. The sum of these 2 subdivi-
sions gives the combined prefrontal
neuron counts. Microglia and satellite
oligodendrocytes (small nonmyelinat-
Figure 1. Schematic of Dorsolateral Prefrontal Cortex and Mesial Prefrontal Cortex
Central
sulcus
Precentral
sulcus
Middle frontal
sulcus
Lateral
sulcus
Frontomarginal
sulcus
Lateral
orbital sulcus
Lateral
sulcus
Middle frontal
sulcus
12345
Dorsolateral prefrontal cortex
Mesial prefrontal cortex
Orbital prefrontal cortex
Precentral cortex
Cingulate cortex
Cingulate sulcus
Cingulate
sulcus
Middle frontal
sulcus
Central sulcusPrecentral sulcus
Superior frontal
sulcus
Inferior frontal
sulcus
Lateral orbital
sulcus
Lateral
sulcus
Frontomarginal
sulcus
1
2 3 4 5
VIEW
L
L
L
L
L
Schematic of 2 prefrontal subregions, dorsolateral prefrontal cortex (green) and mesial prefrontal cortex (pink). Within each region, neuron counts, neuron size, and
glia counts were performed using blinded stereological methods.
PREFRONTAL CORTEX NEURON NUMBER AND SIZE IN AUTISM
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ing oligodendrocytes found in close as-
sociation with large cortical neurons)
were selected as nonneuronal contrast
cell types and counted. Because in nor-
mal brain development glial cell pro-
liferation continues well beyond the
cessation of neural proliferation,
23
glia counts provided information on
whether excess numbers of nonneuro-
nal as well as neuronal cell types might
occur in autism. The limitation of this
procedure is that the Nissl stained sec-
tions do not enable microglia and sat-
ellite oligodendrocytes to be sepa-
rately counted; glia counts, therefore,
represent both glia cell types.
Counting
An optical fractionator method,
24
which
is independent of volumetric shrink-
age of tissue, was used to estimate the
total number of neurons in each of the
entire DL-PFC and M-PFC volumes
using thin focal plane optical scan-
ning (40-100 oil immersion), accord-
ing to methods detailed previ-
ously.
25,26
Briefly, 100 or more locations
were sampled in the x- and y-axes on
8 to 12 sections per reference space.
Neurons were distinguished from glia
based on prominent nucleolus, clear
nuclear membrane, and high cyto-
plasm-to-nucleus ratio. Neurons were
counted according to Gundersen un-
biased counting rules, with optical dis-
ector height and guard zone of 10 µm
and more than 8 µm, respectively. Total
neuron number was calculated with the
optical fractionator method and sam-
pling continued to a coefficient of er-
ror of 10% or less (CE 0.10). Neuro-
nal density for each reference space was
calculated as the total neuron number
divided by the product of total disec-
tor number and the disector volume.
Volume Measurement
After neurons were sampled and
counted using the unbiased disector
method,
27
the mean cell volume (MCV)
of neurons was estimated using the ro-
tator method.
28
The estimate of MCV
for each reference space was based on
the length of line crossing each cell
using randomly orientated lines. Since
MCV estimates were done on tissue sec-
tioned in the coronal plane, rather than
random planes, a small orientation bias
could be present to the same degree for
both autism and control cases.
Distinguishing Neurons
From Microglia
and Satellite Oligodendrocytes
Detailed morphological examination
and characterization in 3D was per-
formed in this study to distinguish
neurons from microglia and satellite
oligodendrocytes on Nissl-stained sec-
tions. Cell size alone was not suffi-
cient because neurons possess much
more cytoplasm and have distinct char-
acteristics in their nucleus and pro-
cesses. We estimate that, when ran-
domly sampled, difficulty in judging
whether a cell is a neuron, microglia,
or satellite oligodendrocyte occurred in
approximately 1 of 250 cells (which
would contribute an error of 0.4% in
neuron counts).
Statistical Analyses
Analyses were made using SPSS ver-
sion 18.0.0 to assess within-case, be-
tween-case, and main and interaction
effects of region (prefrontal, DL-PFC,
M-PFC) and diagnosis (autism, con-
trol). An analysis of covariance
(ANCOVA) was conducted using di-
agnosis as the independent variable, age
and PMI as covariates, and brain data
(ie, brain weight deviance expressed as
percent difference from brain weight of
age norms, neuron counts, microglia
counts, neuron volume) as the depen-
dent variables. With the neuron count
models, diagnosis remained in the
model as a significant factor; but age,
PMI, and the interaction term were not
significant. A second full model with
PMI, diagnosis, and their interaction
was evaluated. Diagnosis remained in
the model as a significant factor, PMI
and the interaction term were not sig-
nificant. Pearson correlations were used
to examine the relationships between
neuron counts for DL-PFC and M-
PFC, prefrontal neuron counts and
brain weight deviance, and each of these
variables and age. Using a best fit line
of the relationship between neuron
counts and brain weight deviance in
control children, neuron counts in each
autistic child were used to predict the
brain weight deviation from age-
based norms. Independent samples t
test with equal group variances were
performed to test for differences in
group means. Group variances were
tested initially, and none were found to
be significantly different. All tests of sta-
tistical significance were 2-sided. A P
value of less than .05 was considered
significant.
RESULTS
Diagnostic Characteristics
All autistic cases met criteria for autis-
tic disorder on the 3 subscales of the
Autism Diagnostic Interview-Revised
(ADI-R) diagnostic assessment
(Table 1). Scores on social and com-
munication ADI-R scales ranged from
less severe to more severe impair-
ment. Intellectual ability ranged from
having normal language and/or daily
functional abilities to having little or no
language comprehension and produc-
tion and very impaired functional abili-
ties (Table 1). No autism case had a di-
agnosis of Asperger syndrome or
pervasive development disorder-not
otherwise specified. One of the autis-
tic children had received an autism di-
agnosis via the Autism Diagnostic Ob-
servation Schedule that had been
administered when the child was still
living (case 7, Table 1).
Clinical Characteristics
Except for 1 child in the control group,
children in the autism and control
groups were born full term and perinatal
courses were unremarkable (T
ABLE 2).
One 7-year-old in the autism group had
a history of seizures and was being
treated with medication. He had been
diagnosed with a heart murmur at
birth and had a fever 3 days after birth
that required hospitalization. One
7-year-old in the control group re-
ceived medication for hyperactivity. An
8-year-old in the autism group had rhab-
domysarcoma, received treatment in-
cluding chemotherapy, and died of the
PREFRONTAL CORTEX NEURON NUMBER AND SIZE IN AUTISM
2004 JAMA, November 9, 2011—Vol 306, No. 18 ©2011 American Medical Association. All rights reserved.
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condition. Nonbrain fetal developmen-
tal defects were reported for 3 in the au-
tistic group and 1 in the control group
(Table 2). Most of the children died of
acute global ischemic hypoxia (drown-
ing, hanging, electrocution), 1 died in
an automobile crash, 1 died of rhabdo-
myosarcoma, and 1 died suddenly of
possible cardiac arrest (T
ABLE 3). Re-
suscitation was not in the medical his-
tory of any case. The prenatal, perina-
tal, medication, and medical histories
and the causes of death among these 13
cases are not known to be associated
with increases in neuronal numbers or
brain size.
Neuropathological Characteristics
Gross examination of the brain showed
no abnormalities in most autistic and
control cases, according to medical ex-
aminer or neuropathology reports
(eTable 3). In frontal lobes, neuropa-
thology reports stated the presence of
a single focal dysplasia associated with
cortical thickening in 1 autistic case and
a single ectopia in white matter and dis-
tortion of the normal radial orienta-
tion of neurons in superior-posterior
cortex in another (Table 3). In the cer-
ebellum, flocculonodular lobe dyspla-
sia was reported in 4 of the 7 autistic
cases (eTable 3). Pathologies of the ce-
rebrum consistent with acute hypoxic
ischemia were reported for 2 autistic
cases and 2 control cases.
Brain Weight
The mean brain weight of the autistic
children (1484 g; 95% CI, 1324-1644
g) was 2.4% greater than the mean brain
weight reported for autistic 2- to 16-
year-olds in the literature (N=18; 1449
g; 95% CI, 1182-1716 g) (eTable 4).
This difference was not significant
(t
1,23
=-0.5, P=.62).
Brain weight in the autistic sample
deviated from normative mean weight
for age by 17.6% (95% CI, 10.2%-
25.0%; t
6
=5.807; P=.001), while con-
trol brains (1299 g; 95% CI, 1155-
1442 g) deviated from age-based norms
by 0.2% (95% CI, −8.7 to 9.1; t
5
=0.051;
P=.96) (F
IGURE 2; TABLE 4; for age-
based norms, Table 3). This autistic vs
control group difference was signifi-
cant (group comparison, P =.003;
Table 4).
Prefrontal Neuron Counts
Statistically significant differences in
neuron counts in the PFC were found
in the autistic children compared with
controls (Table 4); counts for each au-
tistic and control case in each region are
shown in eTable 5. There were 79%
more neurons in DL-PFC in the autis-
tic cases compared with the control
cases (F
IGURE 3A) and 29% more in M-
PFC (Figure 3B). The mean DL-PFC
count in the autistic children was 1.57
billion neurons (95% CI, 1.20-1.94)
compared with a mean of 0.88 billion
neurons (95% CI, 0.66-1.10) in con-
trol children (P=.003). The mean M-
PFC count in the autistic group was
0.36 billion neurons (95% CI, 0.33-
Table 2. Clinical Characteristics of Autistic and Control Children in the Study
Case Race/Ethnicity
Perinatal
Condition
History of
Medication Seizures
Other
Conditions
Autism
1 White,
Japanese,
Native
American,
Hispanic
42 Weeks
gestation;
cesarean
delivery;
slight
jaundice at
birth
Perimortem
dopamine
No No
2 African
American
Cesarean
delivery
No No No
3 African
American
No No No No
4 White No Unspecified
asthma
medication
No No
5 White Heart murmur at
birth; fever 3
d following
birth that
required
hospitalization
Phenylbarbital,
carbamazepine,
albuterol
Yes No
6 White No Chlorhexidine,
nystatin,
G-CSF,
promethazine,
dexamethasone,
morphine,
chemotherapy,
divalproex
No Syndactyly of
the fingers
and toes
7 White Cesarean
delivery;
mother
under
“emotional
distress” at
time of birth
Pimozide (Orap) No Tourette
syndrome,
heart
murmur at 8
mo of age
Control
8 White No No No No
9 White Hispanic No Tacrolimus,
immuno-
suppressants,
unspecified
antibiotics
and antivirals
No Gastroschisis;
short bowel
syndrome,
renal failure,
multivisceral
transplantation,
viral
pneumonia
10 White Hispanic 25 Weeks
gestation
Methylphenidate,
clonidine
No Hyperactive
disorder
11 White No No No No
12 White No No No No
13 White No No No No
Abbreviation: G-CSF, granulocyte-colony stimulating factor.
PREFRONTAL CORTEX NEURON NUMBER AND SIZE IN AUTISM
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0.40) compared with a mean of 0.28 bil-
lion neurons (95% CI, 0.23-0.34) in
controls (P=.009). Together, these 2
subdivisions gave a total combined pre-
frontal neuron count that was 67%
greater in the autistic children (mean,
1.94 billion; 95% CI, 1.57-2.31) com-
pared with controls (mean, 1.16 bil-
lion; 95% CI, 0.90-1.42; P =.002;
Figure 3C). Significant group differ-
ences remained after controlling for
PMI and age; ANCOVA model results
are given in T
ABLE 5. Neither age nor
PMI was a significant covariate in the
models; however, diagnosis was sig-
Table 3. Neuropathology Characteristics and Postmortem Information on Autistic and Control Cases
Case Age, y
Cause of
Death
Postmortem
Interval, h Hemisphere
Brain
Weight, g
Normative
Mean Brain
Weight for
Age, g
a
% Difference
From
Normative
Mean Brain
Weight for Age
Reported
Frontal Cortex
Neuropathology
Autism
1 2 Drowning 4 Right 1328 1069 24 Single focal site in
middle frontal
gyrus with
dysplasia,
cortical
thickening, loss
of molecular
layer, and
thickening of
layer 2; focal
necrosis with
gliosis and
neurovascularization
of layer 3 in
frontal,
temporal,
parietal, and
occipital
cortices
b
2 3 Drowning 12.5 Left 1389
c
1196 16 No report
d
3 3 Drowning 15 Left 1330 1196 11 None
e
4 4 Drowning No records Right 1280 1196 7 None
b
5 7 Drowning 25 Right 1610 1361 18 Single 3 3mm
ectopia in
periventricular
frontal white
matter lateral to
anterior corpus
callosum;
distortion of
radial
cytoarchitecture
in superior and
posterior frontal
cortices
e
6 8 Rhabdomyo-
sarcoma
22.2 Right 1570 1361 15 None
e
7 16 Undetermined 47.9 Right 1880
f
1434 31 None
b
Control
8 2 Drowning 24 Left 1240 1069 16 None
g
9 2 Respiratory
insufficiency
14 Left 997 1069 −7 None
g
10 7 Drowning 12 Right 1240 1361 −9 None
g
11 13 Asphyxia by
hanging
5 Right 1420 1434 −1 None
g
12 14 Electrocution 20 Right 1464 1434 2 None
g
13 16 Multiple
injuries
16 Right 1440 1434 1 None
g
a
Adapted from Redcay and Courchesne.
5
b
Determined by coroner report and subsequent neuropathology reports.
c
Interpolated from brain volume from magnetic resonance imaging; differs from the 1130 g reported by the Autism Tissue Program.
d
Coroner and/or neuropathology reports not available through the Autism Tissue Program.
e
Determined by neuropathology report only.
f
Fresh brain weight at autopsy; differs from weight of 1990 g reported by the Autism Tissue Program. Parenchyma weight was estimated to be 1751 g, interpolated from in vivo
magnetic resonance imaging brain volume at age 13 years.
g
Determined by coroner report only.
PREFRONTAL CORTEX NEURON NUMBER AND SIZE IN AUTISM
2006 JAMA, November 9, 2011—Vol 306, No. 18 ©2011 American Medical Association. All rights reserved.
at University of California - San Francisco on November 8, 2011jama.ama-assn.orgDownloaded from
nificant for DL-PFC and M-PFC re-
gions and the total combined prefron-
tal regions.
eFigure 1 shows that these global in-
creases in prefrontal neuron numbers
were not apparent either at low or high
magnification, and thus undetectable by
neuropathology visual inspection and
neuron density measurements with-
out formal quantitative stereological
procedures.
Prefrontal Neuron Counts
and Brain Weight
FIGURE 4 plots the total prefrontal neu-
ron counts as a function of percent dif-
ference of brain weight from age-
based norms. The control group had a
strong, significant positive linear cor-
relation between counts and weight de-
viations (r =0.949; P=.004). Six of the
7 cases in the autistic group had neu-
ron counts that met or exceeded the re-
gression line of those in the control
group, indicating that they had as many
or more neurons than would be pre-
dicted from their large brain weights.
The exception was a 7-year-old in the
autistic group (Figure 4) who had a his-
tory of severe seizures. For the 6 cases
in the autistic group without a con-
founding seizure disorder, the mean
brain weight deviation predicted from
their actual total prefrontal neuron
counts was 29.4% beyond age-based
norms.
Neuron Volume and Glia Counts
There were no significant differences in
DL-PFC or M-PFC neuron sizes be-
tween groups. There were also no sig-
nificant differences in glia counts for
DL-PFC or M-PFC regions between
groups (Table 4).
COMMENT
In this small, preliminary study, male
children with autism had a mean 67%
more prefrontal neurons than those in
the control group. The excess was
greater within DL-PFC than in M-
PFC, a difference that parallels MRI
volumetric data showing greater devi-
ance in DL-PFC than M-PFC in living
autistic toddlers.
6
MRI studies show that
enlargement is not restricted to DL-
PFC and M-PFC; whether increased
neuron counts in autism extend be-
yond these 2 major prefrontal subdi-
visions to include other cortical areas
remains to be determined.
The autistic group also had larger
than average brain weight. In 6 of the
7 cases, neuron numbers equaled or ex-
ceeded predictions based on brain
weight compared with controls. These
data indicate that a pathological in-
crease in neuron numbers may be a key
contributor to brain overgrowth in au-
tism. However, our data also illus-
trated a strong positive correlation be-
tween total neuron numbers and brain
weight in the control cases that was not
found in the autistic cases. Thus, the
autistic brains exhibited a substantial
disturbance in the normal linear rela-
tionship between neuron quantity and
overall brain weight. Neuron counts in
the autistic children should have been
accompanied by brain weights consid-
erably larger than was observed, reach-
ing 29.4% enlargement rather than the
observed 17.6% enlargement. Thus, the
size of the autistic brain, overlarge
though it is, might actually underesti-
mate the pathology of excess neuron
numbers.
Because cortical neurons are not gen-
erated in postnatal life, this pathologi-
cal increase in neuron numbers in au-
tistic children indicates prenatal causes,
including unchecked proliferation, re-
duced apoptosis, or both.
23,29-33
Prolif-
eration of cortical neurons is exponen-
tial between 10 and 20 weeks gestation
and normally results in a net overabun-
dance of neurons by as much as 100%.
32
In animal models, dysregulation of ge-
netic mechanisms are known that cause
an even greater neuron overabundance
and lead to increased head, brain, and
cortical size,
34,35
as is found in young chil-
Figure 2. Difference in Brain Weight From
Age-Based Norms in Autism vs Control Group
35.0
30.0
10.0
15.0
25.0
20.0
5.0
0.0
–5.0
–10.0
% Difference of Brain Weight
ControlAutism
Cadaveric Donors
Autistic cases
With intellectual disability
Without intellectual disability
Control cases
Group mean
Brain weight in the autistic group deviated by 17.6%
from the normative mean weight for age, while brain
weight in controls was 0.2% greater than the norma-
tive mean for age. Error bars indicate 95% CIs. P= .003
for between-group comparison.
Table 4. Group Analyses of Brain Weight, Neuron Count and Size, and Glia Count
Mean (SD)
t Value
(df = 11)
P
Value
Control
(n=6)
Autism
(n=7)
Postmortem brain weight, g 1299 (179) 1484 (216) 1.66 .12
Brain weight % difference from
normative mean for age
0.2 (8.5) 17.6 (8.0) 3.81 .003
DL-PFC neuron count, billions 0.88 (0.21) 1.57 (0.40) 3.81 .003
M-PFC neuron count, billions 0.28 (0.05) 0.36 (0.04) 3.20 .009
Total PFC neuron count, billions
a
1.16 (0.24) 1.94 (0.40) 4.12 .002
DL-PFC neuron size, µ
3
1337.12 (483.94) 1169.90 (244.81) −0.81 .44
M-PFC neuron size, µ
3
1256.84 (352.76) 1127.81 (364.66) −0.65 .53
DL-PFC glia count, billions
b
0.36 (0.38)
b
0.26 (0.28) −0.51 .62
M-PFC glia count, billions 0.14 (0.13) 0.12 (0.20) −0.28 .78
Abbreviations: DL-PFC, dorsolateral prefrontal cortex; M-PFC, mesial prefrontal cortex; PFC, prefrontal cortex.
a
Total combined prefrontal neuron count equals DL-PFC count plus M-PFC count.
b
Glia counts in the DL-PFC for 1 case in the control group exceeded 3 standard deviations of the mean and was removed
as an outlier. This child underwent repeated surgeries during early childhood, which may have altered glia numbers;
however, his counts for M-PFC were near the group average.
PREFRONTAL CORTEX NEURON NUMBER AND SIZE IN AUTISM
©2011 American Medical Association. All rights reserved. JAMA, November 9, 2011—Vol 306, No. 18 2007
at University of California - San Francisco on November 8, 2011jama.ama-assn.orgDownloaded from
dren with autism.
1-12
Functional analy-
ses of genes located within copy num-
ber variation regions in autism also raise
the possibility of dysregulation of pro-
liferation during development.
36
Apoptotic mechanisms during the
third trimester and early postnatal life
normally remove subplate neurons,
which comprise about half the neu-
rons produced in the second trimes-
ter.
37
A failure of that key early devel-
opmental process could also create a
pathological excess of cortical neu-
rons. A failure of subplate apoptosis
might additionally indicate abnormal
development of the subplate itself. The
subplate plays a critical role in the matu-
ration of layer 4 inhibitory function-
ing as well as in the early stages of
thalamocortical and corticocortical con-
nectivity development.
37,38
Reduced
inhibitory functioning and defects of
functional and structural connectivity
are characteristic of autism, but the
causes have remained elusive. The
possibility of abnormal development
of the subplate in autism merits inves-
tigation.
Future studies of neuron numbers
and underlying molecular and genetic
mechanisms in autism face many limi-
tations, as encountered in the present
study. For example, the sample of post-
mortem tissue from children with au-
tism—all that were available at the time
of the study—was small. Despite the
small sample size, evidence of excess
neuron numbers in our autistic cases
was statistically robust and occurred in
cases with varying characteristics, such
Table 5. Analysis of Covariance Tests
Controlling for Age
Group
Age, y
t Value P Value t Value P Value
DL-PFC neuron count 3.39 .007 −0.75 .47
M-PFC neuron count 2.954 .01 0.066 .95
Total PFC neuron count
a
2.954 .01 0.066 .95
DL-PFC neuron size −0.58 .57 0.64 .54
M-PFC neuron size −0.43 .68 0.663 .52
DL-PFC glia count
b
−0.22 .84 0.606 .56
M-PFC glia count −0.32 .76 −0.207 .84
Group
PMI
Controlling for PMI t Value P Value t Value P Value
DL-PFC neuron count 3.19 .01 −0.29 .78
M-PFC neuron count 2.69 .03 1.83 .1
Total PFC neuron count
a
3.39 .008 −0.06 .95
DL-PFC neuron size −0.83 .43 0.5 .63
M-PFC neuron size −0.75 .47 0.318 .76
DL-PFC glia count
b
−0.13 .9 −0.859 .42
M-PFC glia count 0.41 .69 −2.114 .06
Abbreviations: DL-PFC, dorsolateral prefrontal cortex; M-PFC, mesial prefrontal cortex; PMI, postmortem interval.
a
Total combined prefrontal neuron count= DL-PFC count plus M-PFC count.
b
Glia counts for DL-PFC for 1 case in the control group exceeded 3 standard deviations of the mean and was re-
moved as an outlier. This child had had repeated surgeries during early childhood, which may have altered glia num-
bers; however, his counts for M-PFC were near the group average
Figure 3. Dorsolateral (DL-PFC) and Mesial Prefrontal Cortex (M-PFC) Neuron Counts in Autism vs Control Group Cases
2.50
2.00
1.50
1.00
0.50
0.00
Total DL-PFC Neuron Count (in Billions)
ControlAutism
Cadaveric Donors
0.50
0.40
0.30
0.20
0.10
0.00
Total M-PFC Neuron Count (in Billions)
ControlAutism
Cadaveric Donors
2.50
2.00
1.50
1.00
0.50
Total Prefrontal Neuron Count (in Billions)
ControlAutism
Cadaveric Donors
Dorsolateral prefrontal cortex neuron count
A
Mesial prefrontal cortex neuron count
B
Total combined prefrontal neuron count
C
Autistic cases
With intellectual disability
Without intellectual disability
Control cases
Group mean
Error bars indicate 95% CIs. For between-group comparisons, statistical tests were as follows: P=.003 for panel A, P= .009 for panel B, and P= .002 for panel C. Autistic
case with lowest neuron count value in panels A and C had a seizure disorder, adverse perinatal medical conditions, and intellectual disability.
PREFRONTAL CORTEX NEURON NUMBER AND SIZE IN AUTISM
2008 JAMA, November 9, 2011—Vol 306, No. 18 ©2011 American Medical Association. All rights reserved.
at University of California - San Francisco on November 8, 2011jama.ama-assn.orgDownloaded from
as with less severe and more severe au-
tistic symptoms, and with and with-
out intellectual disability. None of the
causes of death for autistic cases in this
study produce an increase in postmor-
tem brain weight
39
or neuron num-
bers. Most of the autistic and control
children died of acute global ischemic
hypoxia. Nearly every autistic and con-
trol case came from a full-term preg-
nancy. A history of medication and ad-
verse medical conditions was not
present in most of the cases, particu-
larly for the 4 youngest autistic cases,
each of whom had substantial excess
neuron counts. Conversely, the low-
est prefrontal neuron number in the au-
tism group was found in a 7-year-old
boy with a seizure disorder, which may
explain why he had fewer counts than
other autistic children. The potential ef-
fect of seizures on cellular and molecu-
lar measures in autism is important to
investigate further.
Our sample of autistic children was
not large enough to statistically exam-
ine brain-behavior relationships. Fu-
ture studies with many more cases of
autistic children might reveal impor-
tant relationships between neuron
counts and symptom severity or intel-
lectual ability. Also, our sample of au-
tistic cases had brain weights typical of
brain weights in larger postmortem
samples of autistic children. The 1 au-
tistic child with a brain size within the
95% CI of controls had among the
greatest prefrontal neuron counts in the
study, which raises the question of
whether excess prefrontal neuron
counts may be present in other autis-
tic children who have near normal or
smaller brain sizes.
The small sample of young control
children cannot be viewed as represen-
tative of all healthy young children, but
control cases were not chosen for any
reason other than age, sex, availability
of the required PFC sections, absence
of neurological or mental illness, and
absence of treatment for cancer. Brain
size in our control sample was typical
for age, deviating by only 0.2% from ex-
pected mean weight for age. Also, pre-
frontal neuron counts in controls did
not vary with age, which is concor-
dant with literature that cortical neu-
rons are generated prenatally, not post-
natally.
23,29-33
It would be invaluable to
study larger samples of autistic and con-
trol cases at a younger and narrower age
range to confirm excess counts in au-
tism at the youngest ages, as well as to
study larger samples across a wider age
range to identify patterns of age-
related change in autism. It will be im-
portant to include female cases in fu-
ture studies, as etiological mechanisms
may be discordant between sexes.
Whether female autistic patients also
have excess prefrontal neuron num-
bers at young ages remains to be tested,
but very few cases exist that have com-
plete prefrontal sections.
To our knowledge, this study is the
first direct quantitative test and confir-
mation of the theory that a pathologi-
cal overabundance of neurons in criti-
cal brain regions is present at a young
age in autism. Because cortical neu-
rons are generated in prenatal, not post-
natal life, pathological overabundance
of neurons indicates early developmen-
tal disturbances in molecular and ge-
netic mechanisms that govern prolif-
eration, cell cycle regulation, and
apoptosis. Therefore, the finding has
significance for understanding the etio-
logical and neural development and
functional origins of autism.
Author Contributions: Dr Courchesne had full access
to all of the data in the study and takes responsibility
for the integrity of the data and the accuracy of the
data analysis.
Study concept and design: Courchesne, Mouton,
Semendeferi, Ahrens-Barbeau.
Acquisition of data: Courchesne, Mouton, Calhoun,
Semendeferi, Ahrens-Barbeau, Barnes.
Analysis and interpretation of data: Courchesne,
Mouton, Calhoun, Semendeferi, Ahrens-Barbeau,
Hallet, Barnes, Pierce.
Drafting of the manuscript: Courchesne, Mouton,
Barnes.
Critical revision of the manuscript for important in-
tellectual content: Courchesne, Mouton, Calhoun,
Semendeferi, Ahrens-Barbeau, Hallet, Pierce.
Statistical analysis: Mouton, Calhoun, Hallet, Pierce.
Obtained funding: Courchesne, Semendeferi.
Administrative, technical, or material support:
Courchesne, Calhoun, Ahrens-Barbeau.
Study supervision: Courchesne, Mouton, Calhoun,
Semendeferi, Ahrens-Barbeau.
Conflict of Interest Disclosures: All authors have com-
pleted and submitted the ICMJE Form for Disclosure
of Potential Conflicts of Interest. Dr Calhoun is em-
ployed by and principal owner of Sinq Systems, a con-
tract research organization that performed data col-
lection and analysis for this study, and he is an applicant
on a pending patent on file with the United States Pat-
ent and Trademark Office related to analysis of mi-
croscopic structure.
Funding/Support: This study was supported by
Autism Speaks, Cure Autism Now, The Peter Emch
Family Foundation, the Simons Foundation, The
Thursday Club Juniors, and the University of Califor-
Figure 4. Prefrontal Neuron Counts as a Function of Percent Difference of Brain Weight
From Age-Based Norms
35.0
30.0
10.0
15.0
25.0
20.0
5.0
–5.0
0.0
–10.00
–15.00
% Difference of Brain Weight
0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50
Total Prefrontal Neuron Count (in Billions)
Autistic cases
With intellectual disability
Without intellectual disability
Control cases
Plot of total prefrontal neuron counts as a function of percent difference of brain weight from age-based norms
for each study case. In control cases, the correlation between counts and % difference was r=0.949 (P=.004);
the best-fit line for this is shown. Six of the 7 autistic children had neuron counts that met or exceeded the control
line, indicating that they had equal to or more neurons than predicted from their large brain weight. An autistic
boy with a seizure disorder was an exception in that he had fewer neurons than would be predicted for his brain
weight (closed circle within circle). Three autistic cases without intellectual disability shown with extra open circle;
each also met or exceeded the control regression line. The largest control brain from among approximately 11 000
brains (see Redcay and Courchesne
5
) had a percent difference from age-based norms of 22% where the solid
best-fit line ends. The dashed line is an extrapolation because typical brains do not commonly achieve such size.
PREFRONTAL CORTEX NEURON NUMBER AND SIZE IN AUTISM
©2011 American Medical Association. All rights reserved. JAMA, November 9, 2011—Vol 306, No. 18 2009
at University of California - San Francisco on November 8, 2011jama.ama-assn.orgDownloaded from
nia, San Diego-National Institutes of Health
(UCSD-NIH) Autism Center of Excellence (P50-
MH081755) awarded to Dr Courchesne and by the
National Institute of Mental Health (NIMH)
(MH076541-04) awarded to Dr Mouton. Tissue was
provided by the National Institute of Child Health
and Development (NICHD) Brain and Tissue Bank
for Developmental Disorders under contracts N01-
HD-4-3368 and N01-HD-4-3383, the Brain and
Tissue Bank for Developmental Disorders, Autism
Tissue Program, and direct donations to the
Courchesne laboratory.
Role of the Sponsor: The funding organizations had
no role in the design and conduct of the study; in the
collection, management, analysis, and interpretation
of the data; or in the preparation, review, or ap-
proval of the manuscript.
Online-Only Material: The eAppendix, eTables 1-5,
eFigure, and Author Audio Interview are available at
http://www.jama.com.
Additional Contributions: We thank Chet Sher-
wood, PhD, George Washington University, for
insight and review of prefrontal cytoarchitectonic
features and regional borders; Muhammad Spocter,
PhD, George Washington University, for insight
and review of prefrontal cytoarchitectonic features
and regional borders; Ronald Zielke, PhD, the
Eunice Kennedy Shriver National Institute of Child
Health & Human Development (NICHD) Brain and
Tissue Bank for Developmental Disorders for facili-
tation of case acquisition; Jane Pickett, PhD, the
Autism Tissue Program, for facilitation of case
acquisition; Daniel Lightfoot, PhD, the Autism Tis-
sue Program for facilitation of case acquisition; Pat-
rick Hof, MD, at Mount Sinai School of Medicine,
for facilitation of case acquisition; and Jerzy Wegiel,
VMD-PhD, Institute for Basic Research, for facilita-
tion of case acquisition. None of these individuals
received compensation for their contributions to the
study. We appreciate all parents who made the dif-
ficult choice to support brain research through the
donation of brain tissue from their loved ones. This
autism research is dedicated to the memory of Gail
Erickson Courchesne, passionate and gifted musi-
cian and loving mother.
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PREFRONTAL CORTEX NEURON NUMBER AND SIZE IN AUTISM
2010 JAMA, November 9, 2011—Vol 306, No. 18 ©2011 American Medical Association. All rights reserved.
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    • "The reduction in body weight resulting from CPF treatment at PND 21 in BTBR mice was not associated with a corresponding reduction in brain weight, an observation that might suggest a transient increase in brain size with respect to body weight during the developmental phase. Interestingly, a transient brain overgrowth has been reported in some studies on ASD children ([58] and references therein), in which overgrowth and neural dysfunctions have been suggested to underlie some of the autistic symptoms. Altogether, we show here that a widely diffused insecticide reportedly implicated in increased risk of neuromotor and neuropsychological impairments in children induces a transient up-regulation of an oxidative stress biomarker and a permanent alteration in PGE 2 pathway in a validated mouse model of ASD. "
    [Show abstract] [Hide abstract] ABSTRACT: Background Autism spectrum disorders (ASD) are emerging as polygenic and multifactorial disorders in which complex interactions between defective genes and early exposure to environmental stressors impact on the correct neurodevelopment and brain processes. Organophosphate insecticides, among which chlorpyrifos (CPF), are widely diffused environmental toxicants associated with neurobehavioral deficits and increased risk of ASD occurrence in children. Oxidative stress and dysregulated immune responses are implicated in both organophosphate neurodevelopmental effects and ASD etiopathogenesis. BTBR T+tf/J mice, a well-studied model of idiopathic autism, show several behavioral and immunological alterations found in ASD children, and we recently showed that CPF gestational exposure strengthened some of these autistic-like traits. In the present study, we aimed at investigating whether the behavioral effects of gestational CPF administration are associated with brain increased oxidative stress and altered lipid mediator profile. Methods Brain levels of F2-isoprostanes (15-F2t-IsoP), as index of in vivo oxidative stress, and prostaglandin E2 (PGE2), a major arachidonic acid metabolite released by immune cells and by specific glutamatergic neuron populations mainly in cortex and hippocampus, were assessed by specific enzyme-immuno assays in brain homogenates from BTBR T+tf/J and C57Bl6/J mice, exposed during gestation to either vehicle or CPF. Measures were performed in mice of both sexes, at different postnatal stages (PNDs 1, 21, and 70). Results At birth, BTBR T+tf/J mice exhibited higher baseline 15-F2t-IsoP levels as compared to C57Bl6/J mice, suggestive of greater oxidative stress processes. Gestational treatment with CPF-enhanced 15-F2t-IsoP and PGE2 levels in strain- and age-dependent manner, with 15-F2t-IsoP increased in BTBR T+tf/J mice at PNDs 1 and 21, and PGE2 elevated in BTBR T+tf/J mice at PNDs 21 and 70. At PND 21, CPF effects were sex-dependent being the increase of the two metabolites mainly associated with male mice. CPF treatment also induced a reduction of somatic growth, which reached statistical significance at PND 21. Conclusions These findings indicate that the autistic-like BTBR T+tf/J strain is highly vulnerable to environmental stressors during gestational period. The results further support the hypothesis that oxidative stress might be the link between environmental neurotoxicants such as CPF and ASD. The increased levels of oxidative stress during early postnatal life could result in delayed and long-lasting alterations in specific pathways relevant to ASD, of which PGE2 signaling represents an important one.
    Article · Dec 2016
    • "Microglial cell density was increased in the grey matter with non-significant trends in somal volume [69]. A recent study by Paolicelli and Gross [78] suggests a central role for microglia in synaptic pruning, a process that has been suggested to be aberrant in the developing brain in ASC [22]. Magnetic resonance spectroscopy (MRS) has provided significant insight into the ultrastructural morphology in ASC. "
    [Show abstract] [Hide abstract] ABSTRACT: Growing evidence points toward a critical role for early (prenatal) atypical neurodevelopmental processes in the aetiology of autism spectrum condition (ASC). One such process that could impact early neural development is inflammation. We review the evidence for atypical expression of molecular markers in the amniotic fluid, serum, cerebrospinal fluid (CSF), and the brain parenchyma that suggest a role for inflammation in the emergence of ASC. This is complemented with a number of neuroimaging and neuropathological studies describing microglial activation. Implications for treatment are discussed.
    Full-text · Article · Dec 2016
    • "This large behavioural heterogeneity appears to be paralleled by a wide array of neu-roanatomical abnormalities, which also evolve over development (Zielinski et al., 2014; Wolff et al., 2014 ). Almost every brain region has been implicated in autism, including subcortical (Jacobson et al., 1988; Cerliani et al., 2015) and cerebellar regions (Bauman, 1991; Fatemi et al., 2002), gray-matter and white-matter (Barnea-Goraly et al., 2004; Rojas et al., 2006 ), and regions of all lobes of the cerebrum (Zilbovicius et al., 2000; Courchesne et al., 2011; Lewis et al., 2013 Lewis et al., , 2014). Indeed, the neuroanatomical heterogeneity is so great that replication of results across studies is rare. "
    [Show abstract] [Hide abstract] ABSTRACT: Machine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require large samples and suffer from the challenges associated with multi-site, multi-protocol data. We propose a novel approach to address these challenges, and demonstrate its usefulness with the Autism Brain Imaging Data Exchange (ABIDE) database. We predict symptom severity based on cortical thickness measurements from 156 individuals with autism spectrum disorder (ASD) from four different sites. The proposed approach consists of two main stages: a domain adaptation stage using partial least squares regression to maximize the consistency of imaging data across sites; and a learning stage combining support vector regression for regional prediction of severity with elastic-net penalized linear regression for integrating regional predictions into a whole-brain severity prediction. The proposed method performed markedly better than simpler alternatives, better with multi-site than single-site data, and resulted in a considerably higher cross-validated correlation score than has previously been reported in the literature for multi-site data. This demonstration of the utility of the proposed approach for detecting structural brain abnormalities in ASD from the multi-site, multi-protocol ABIDE dataset indicates the potential of designing machine learning methods to meet the challenges of agglomerative data.
    Full-text · Article · Sep 2016
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