Next Article in Journal
Proximity Labeling to Identify β-Arrestin1 Binding Partners Downstream of Ligand-Activated G Protein-Coupled Receptors
Next Article in Special Issue
MicroRNAs and Gene Regulatory Networks Related to Cleft Lip and Palate
Previous Article in Journal
A DSC Test for the Early Detection of Neoplastic Gastric Lesions in a Medium-Risk Gastric Cancer Area
Previous Article in Special Issue
Dimerisation of the Yeast K+ Translocation Protein Trk1 Depends on the K+ Concentration
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Sex-Related Changes in the Clinical, Genetic, Electrophysiological, Connectivity, and Molecular Presentations of ASD: A Comparison between Human and Animal Models of ASD with Reference to Our Data

1
Adelson School of Medicine, Ariel University, Ariel 40700, Israel
2
Hadassah Medical School, Hebrew University, Jerusalem 9112102, Israel
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(4), 3287; https://doi.org/10.3390/ijms24043287
Submission received: 15 December 2022 / Revised: 28 January 2023 / Accepted: 3 February 2023 / Published: 7 February 2023

Abstract

:
The etiology of autism spectrum disorder (ASD) is genetic, environmental, and epigenetic. In addition to sex differences in the prevalence of ASD, which is 3–4 times more common in males, there are also distinct clinical, molecular, electrophysiological, and pathophysiological differences between sexes. In human, males with ASD have more externalizing problems (i.e., attention-deficit hyperactivity disorder), more severe communication and social problems, as well as repetitive movements. Females with ASD generally exhibit fewer severe communication problems, less repetitive and stereotyped behavior, but more internalizing problems, such as depression and anxiety. Females need a higher load of genetic changes related to ASD compared to males. There are also sex differences in brain structure, connectivity, and electrophysiology. Genetic or non-genetic experimental animal models of ASD-like behavior, when studied for sex differences, showed some neurobehavioral and electrophysiological differences between male and female animals depending on the specific model. We previously carried out studies on behavioral and molecular differences between male and female mice treated with valproic acid, either prenatally or early postnatally, that exhibited ASD-like behavior and found distinct differences between the sexes, the female mice performing better on tests measuring social interaction and undergoing changes in the expression of more genes in the brain compared to males. Interestingly, co-administration of S-adenosylmethionine alleviated the ASD-like behavioral symptoms and the gene-expression changes to the same extent in both sexes. The mechanisms underlying the sex differences are not yet fully understood.

1. Introduction

Sex differences in the prevalences of many diseases are known phenomena. Such differences are more common in neuropsychiatric disorders, some of which have a significantly higher prevalence in males (e.g., ADHD and schizophrenia) and others in females (e.g., depression and bipolar disorder) [1]. These diseases generally have a genetic origin, with significant influence from the environment. Often, there are also differences in clinical presentations and/or in the effectiveness of treatments (e.g., depression) [2].
In the last few years, sex-related alterations in the clinical presentation of autism spectrum disorder (ASD) among preschool-age children have been described by many investigators [3,4]. However, some studies did not find sex-related differences [5] or observed behavioral alterations between boys and girls with ASD that were different from those generally described [6]. The sex differences in the clinical presentations of ASD also point to the possible need to use different tools in the diagnosis of ASD for boys and girls, or at least different diagnostic scores when using the same tools for boys and girls [7].
In rodents, genetic and non-genetic models of autistic-like behavior have been known for almost 40 years. However, generally, most studies were carried out on male mice and rats; thus, possible sex-related differences in ASD-like behavior were not ascertained. Neither were possible pathophysiological or genetic differences between sexes assessed. In the last few years, several studies have been published that have shown possible differences related to sex in animal models of ASD [8,9,10]. Differences were observed in the results of behavioral tests as well as in pathophysiological, electrophysiological, and gene-expression data.
The purpose of this review is to summarize the published data related to sex differences in all aspects of ASD in humans, as well as in rodent models of ASD-like behavior. In addition, we summarize our published studies on mice [8,9,10], in which ASD-like behavior was induced by prenatal and early postnatal valproic acid (VPA). In these and other studies, many findings differed between male and female animals, in ways surprisingly similar to those described in children with ASD.

2. Sex Differences in the Prevalence and Clinical Presentation of ASD in Children

Children with ASD exhibit communication and sociability problems and difficulties in interaction with others, have repetitive motor movements, seek specific sensory stimulation, and show excessive interest in a limited range of issues. They also often seek the accurate organization of items and have difficulties in adapting to changes [11].
Sex differences in the prevalence of ASD, which is 3–4 times higher in males, are well-established [5,12,13]. Sex differences also exist in several comorbid disorders, such as attention-deficit hyperactivity disorder (ADHD) and anxiety [14].
Several clinical symptoms seem to differ between male and female infants with ASD [5,15,16]. For example, communication problems are less common among girls with ASD compared to boys, while boys present with more restricted and repetitive behaviors and externalizing behavioral problems, including oppositional defiant disorder and greater impulsivity [17].
It is important to note that studies often differ in their results when investigating sex differences in the clinical presentations of children with ASD. This is apparently related to the diagnostic tools used, to the severity of ASD, and to the age at diagnosis [6].
There are many diagnostic tools for the assessment of neurobehavioral deviations in children and adults which are generally used in both sexes. An example is the Achenbach Child Behavior Checklist (CBCL). By using this questionnaire, it was observed that externalizing behavioral problems (e.g., impulsivity) are more common among boys and that internalizing problems (e.g., depression) are more common among girls with ASD [15,16]. Beggiato et al. [15] found, using the Autism Diagnostic Interview, Revised (ADI-R) questionnaire for the diagnosis of ASD, that the main differences between boys and girls with ASD are in the use of facial expressions for communication, which is better in boys, while girls engage better in imaginative play compared to boys with ASD. Unusual preoccupations and narrowly circumscribed interests are less evident in girls. The authors did not find lower intellectual abilities in girls. Mandy et al. [18] observed, in 325 children with high-functioning ASD, that there were no differences in verbal IQ but that females exhibited less repetitive movement and stereotyped behavior and that boys had more externalizing and social problems. However, there were similar social and communicative impairments. They also proposed that the lower intellectual abilities often observed in girls with ASD might not be evident in high-functioning ASD children.
Sex differences in clinical presentations point to the possibility that the methods used for the diagnosis of children with ASD may not be appropriate for both boys and girls and that they may be more appropriate for boys [18]. For example, if one uses the ADOS (Autism Diagnostic Observation Schedule), which is the method most commonly used to diagnose ASD, it may have to be adapted to females versus males. Indeed, girls are considered to have ASD with lower scores compared to boys [16]. This is also true for the CARS (Childhood Autism Rating Scale) diagnostic tool [19].
The few studies discussed above, as well as other studies, differ in their findings, making it difficult to delineate exactly all sex differences in the clinical presentation of ASD [20]. However, most studies agree that there are such differences and that the sex variations in the prevalence of ASD might also be related to these differences, making diagnosis in females more difficult. Moreover, these sex-related differences in symptoms lead to reduced prevalence of ASD in females, explaining, at least in part, their lower rate of ASD [21]. As the research on possible sex differences in the clinical presentations of ASD became more common, sex differences in other parameters that delineate ASD were outlined, as will be further discussed.

3. Sex Differences in Genetic Susceptibility to ASD

The heritability of ASD has been calculated as 85–92%, based on twin studies [22,23]. There seem to be distinct differences in genetic susceptibility between sexes. Females need a higher abnormal polygenic genetic load to show the symptoms compared to males [22]. For example, males with microdeletion of SHANK1 have high-functioning ASD, while females with the same deletion are without ASD symptoms [22]. In male siblings of girls with ASD, the ASD is generally more severe than in male siblings of boys with ASD. However, SHANK1-deletion prevalence in males has been disputed, as Wang et al. [24] found one female with ASD symptoms with a de novo splice donor mutation involving the SHANK1 gene. Numerous ASD-susceptible chromosomal loci and genes have been identified, suggesting a highly heterogeneous polygenic genetic architecture [25,26]. It is accepted that about 10–20% of ASD cases are related to defined rare mutations, genetic syndromes with highly penetrant chromosomal abnormalities, and de novo copy-number variants [26,27].
De novo mutations (DNMs) and risk genes for ASD have been identified among these cases and are considered important factors that contribute to the sex-related differences in terms of higher male versus female genetic liability, susceptibility, and development of ASD, in either sporadic or familial patterns [28,29]. The Simons Foundation Autism Research Initiative (SFARI) provides the most up-to-date database of candidate genes involved in autism susceptibility, the contents of which were integrated from multiple genetic studies and consist of more than 2000 DNMs and copy-number variant (CNV) loci (https://gene.sfari.org/autdb/CNVHome.do, accessed on 14 December 2022).
These DNMs correspond to the genes that are mostly associated with biological pathways related to chromatin remodeling, transcriptional regulation, and synaptic functions [30].
A female protective model was introduced to explain the liability for developing ASD and why a higher threshold of CNVs is required for females as compared with males [31,32]. Several studies reported a higher frequency of autosomal de novo CNVs in autistic females than in males with ASD (reviewed in [33,34]). Interestingly, in males, a higher frequency of rare deletions, such as microdeletions of approximately 555 kilobases on chromosome 16p11.2, was observed [35,36]. Thus, DNMs related to ASD etiology may also occur in non-symptomatic, normal individuals, especially females, who later transmit the mutations to male offspring.
The mammalian X chromosome differs from the autosomes due to its unequal representation in males (one copy) and females (two copies). Females have one inactivated copy of the X chromosome in every cell. Numerous disease conditions are associated with the X chromosome when recessive mutations on one of the maternal chromosomes are transmitted to males. Pinto et.al. [37] found that 4% of ASD-affected subjects had CNVs overlapping autosomal-dominant or X-linked genes and loci implicated in ASD and/or intellectual disability (ID) [37]. However, some genes (~10–15%) can escape X-inactivation [38].
Independent genome-wide studies on ASD susceptibility loci have demonstrated multiple ASD-susceptibility gene loci, including Xp22.3 and Xq13 on the X chromosome [32,39,40]. Rare mutations in two X-linked neuroligin genes, neuroligin 3 (NLGN3) and neuroligin 4X (NLGN4X), are linked to ASD and ID and contribute to the female protective model [41,42,43]. Neuroligins are cell adhesion molecules that are abundant in the postsynaptic membranes of glutamatergic synapses, playing an important role in the proper functioning and signal transmission of excitatory synapses [44]. Females with two X chromosomes will have two copies of NLGN4X, mutated and normal, whereas males have one copy of NLGN4X and one copy of NLGN4Y. Therefore, in males, even a single amino acid change in NLGN4X may lead to sex-linked autism.
Mutations in several genes and their association with ASD and distinct clinical phenotypes have been identified (reviewed in [26]). Deletion or point mutation of the X-linked gene encoding Methyl-CpG-binding protein 2 (MECP2) in females was observed in females with Rett syndrome [45]. Duplication of the MECP2 gene in the Xq28 region has been found in both males and females with ASD [46,47]. The disorder predominantly affects males, but females who carry the duplication on one X chromosome may exhibit intellectual disability, sometimes similar to that seen in males. In addition, some genes may escape from X-inactivation [48,49]. Fragile X syndrome (FXS) is the leading monogenic cause of ASD [50], accounting for up to 3% of all cases, as about half of affected males show autistic behaviors [51]. Basu et al. [52] found a significant overlap with 28 fragile X messenger ribonucleoprotein 1 (FMR1) targets, including several well-studied autism candidate genes, such as NLGN3, NRXN1, SHANK3, PTEN, TSC2, and NF1 [52].

4. Sex Differences in Brain Structure and Imaging Data in Humans with ASD

In recent years, more and more morphological and functional changes have been demonstrated in the brains of autistic children [53,54,55,56]. Several areas were shown to be affected in these children. More synapses, increased dendritic arborization, and abnormal concentrations of neurotransmitters, especially higher levels of dopamine, were demonstrated in the prefrontal cortexes of children with ASD. Some investigators have reported reduced brain volume and decreased cortical thickness in the frontal and temporal areas. A decrease in the number of Purkinje cells has been described in the cerebellum, and in the hippocampus, the site of continuous neuronal proliferation, increased cell apoptosis has been demonstrated. Abnormalities in functional connectivity in different areas of the brain have also been described. It is therefore not surprising that the brains of children with ASD or animal models of ASD-like behavior exhibit differences in electrophysiological characteristics when compared with brains of non-autistic individuals. It is also plausible that there could be sex differences in the morphology and electrophysiology of the brain in ASD. Indeed, a growing body of recent research has reported various sex differences between female and male brains at the cellular level, especially in brain circuitry and synaptic transmission, as will be discussed later (see Table 1).
Sex differences in brain images of children with ASD have been demonstrated by several investigators. For example, in twin studies, Cauvet et al. [55] found decreased cortical volumes and small surface areas in the temporal and frontal lobes in regions involved in social communication. These changes were more prominent in females with ASD as compared to males. The same pattern was observed in monozygotic as well as dizygotic twins. Nordalt et al. [56] found that ASD girls with internalizing problems, but not boys, have larger amygdala (see Table 1).
Several neuroimaging studies utilizing fMRI emphasizing the sex differences in brain connectivity in children with ASD have been published in the last few years [57,58]. In the first of the referenced studies [57], the investigators tried to reveal the correlations with social cognition (SoC) by Social Cognition–Multiple Choice (MASC) assessment in relation to changes in the social brain network. They studied 8 female and 10 male discordant twin couples. As a social brain network, the authors chose 20 regions of interest in the brain, bilaterally, including the insula, the middle frontal gyrus, and the lateral anterior fissure and estimated the cortical volumes, thicknesses, and areas of these regions. Initially, the authors compared the MASC scores of female and male individuals with ASD scores and found that there were no significant differences between them. More brain regions were involved in social cognition in males, but significant sex differences were observed only in the thicknesses of the angular and supramarginal gyri, which were larger in males, and only in dizygotic twin pairs [57]. The same group of investigators found in a twin study of 20 males and 12 females with ASD an association between brain neuroanatomy and restricted/repetitive movements. The severity and frequency of restricted movements were evaluated in terms of restricted and repetitive behaviors and interests (RRBIs). The authors focused on the thickness, volume, and surface area of 18 bilateral regions linked to the cortico-striatal loops; on the volume of the cerebellum; and on some subcortical regions, including the thalamus, the amygdala, and the putamen. Despite comparable differences in RRBIs between the twin pairs, the only neuroanatomical differences found were in females, who exhibited a significantly increased thickness of the right intraparietal sulcus. In males, increased volume of the globus pallidum was associated with RRBIs [58]. These findings demonstrate sex-related correlations between restricted movements and alterations in different brain networks: fronto-parietal networks in females and fronto-striatal networks in males. A subsequent study by Supekar and Menon [59], reporting on correlation of RRBIs and gray matter patterns of cortical regions in females and putamens in males, endorsed these results.
The sociability or social cognition paradigm is believed to be associated with the default mode network (DMN) which handles such categories as “me/myself” and “others” and has been implicated in autobiographical memories [63,64]. Various brain regions, such as the posterior cingulate cortex (PCC), the medial prefrontal cortex (mPFC), and the temporo-parietal junctions (TPJ), have been implicated in ASD. Lawrence et al. [54] analyzed resting-state fMRIs (rs-fMRIs) of 13-year-old autistic and typically developing girls and boys (nearly 50 individuals in each cohort). They found that, within the DMN, connectivity was not altered in the autistic children, nor in the typically developing children. The authors also investigated inter-network connectivity, including the bilateral fronto-insular cortex and the anterior cingulate cortex, together with the central executive network (CEN). In typically developing children, sex differences were observed only in the salience network, which is involved in decision making and behavioral coordination. In autistic children, there was substantially stronger connectivity in females as compared to males between the DMN and the CEN, and autistic females surpassed typically developing females in this parameter, whereas autistic males showed hyper-connectivity in the CEN and negative under-connectivity compared to healthy males [54].
Among the studies performed on the connectivity of networks linked to motor functions and thus with potential impacts on repetitive/restricted behaviors (RRBs), Smith et al. [60] compared cerebellar functional connectivity using rs-fMRI in autistic and typically developed (TD) 21-year-old males and females. At first, they applied “global connectedness” analysis to their data, demonstrating interactions between brain regions according to diagnosis-by-sex criteria. Both cerebellar clusters revealed decreased functional connectivity in ASD males compared to typically developing males, but increased patterns of connectivity in females.
It can therefore be concluded that autistic females are characterized by brain over-connectivity while males are characterized by under-connectivity [54,60,65,66]. However, the differences in the ages of the patients studied and differences in the study designs indicate the need for caution in drawing definite conclusions regarding sex differences in brain connectivity in ASD. We should also take into consideration that the sex differences are not static, as biological sex can impact the ASD phenotype differently during the whole lifespan, as demonstrated by several recent studies [67,68].

5. Sex-Related EEG Changes in Children with ASD (Table 1)

Electrophysiological changes associated with whole-brain activity are generally related to data collected by electroencephalography (EEG) in human patients. The classic evaluation of the functional differences revealed by EEG data is rooted in the separation of decomposed fast Fourier transform signals into several frequency bands: alpha, beta, gamma, delta, and theta [69,70,71]. Differences in these signals have been reported in a variety of neuropsychiatric disorders, such as ADHD, fragile X, Parkinson’s disease, and cognitive and memory function and dysfunction [61,72,73,74].
Abnormal oscillatory activity was found in various syndromes, ages, sexes, and experimental conditions. A comprehensive review of EEG power evaluation by Wang in 2013 summarized the main differences in the resting-state EEGs of ASD patients [71]. The authors pointed out several problems in the evaluation of the studies and explained the huge heterogeneity of these studies in terms of differences in patient sample size, presence or absence of intellectual disabilities, and age. However, some changes consistently prevail in the resting EEGs of ASD subjects. At first, the power spectra of EEGs in ASD are shifted [71]. Abnormal excessive powers are observed in delta, theta, beta, and gamma bands in the frontal, parietal, and right temporal areas of the brain [75,76,77,78,79], as well as in the occipital region in the case of gamma oscillations [80]. Alpha spectral power was found to be decreased in various brain regions of ASD patients [75,76,81], and the authors concluded that the spectral powers of the ASD patients as compared with the typically developing controls were U-shaped [71].
Recent findings conducted over the past 5 years corroborate the results accumulated earlier. For example, Paula et al. compared the power spectra of autistic children with those of healthy children (eight individuals in each group; one out of eight was female) during recognition of faces showing neutral, happy, and angry emotions. They found that the absolute powers of beta and gamma oscillatory bands were upregulated in the frontal, central, parietal, and occipital regions. Additionally, in frontal regions they observed an increase in the spectral power of the delta and theta bands in response to angry faces [82]. Likewise, Dickinson et al. evaluated peak alpha frequency (PAF)—a parameter reflecting the frequency at which alpha oscillations have the highest power, which is closely associated with developmental or cognitive functions in ASD—in 59 autistic versus 38 typically developing children exposed to visual stimuli in a dark room [83]. Peak alpha frequencies in ASD individuals were lower than in typically developing children in frontal, central, and occipital regions [83].
In a recent study by Gabard-Durnam et al. [84], the researchers tried to develop potential biomarkers for distinguishing ASD pathologies at an early age by tracking frontal-lobe EEG longitudinal changes during the first three years of age. Resting-state EEGs were acquired for infants aged 3, 6, 9, 12, 24, and 36 months with a high risk of ASD and having one or more older siblings diagnosed with ASD, and from low-risk infants (no older siblings diagnosed with ASD). All were without comorbid genetic syndromes, such as fragile X or tuberous sclerosis. Finally, by the age of 36 months, children were separated into three cohorts: high-risk familiar, ASD-diagnosed; familiar low-risk non-ASD; and high-risk with non-spectrum abnormalities (ADHD, anxiety, etc.). Analyzing their early-age EEG recordings, the authors referred to diminished gamma-band power at the age of 6 months as well as a lower rate of gamma power increase over the first three years as unique biomarkers for later ASD prediction. Moreover, within the same timeframe, summed delta powers showed steeper rates of increase in infants with ASD, distinguishing them from high-risk familiar ASD children later diagnosed without ASD. Notably, the authors mentioned that the sex of the participants was not a significant predictor of ASD in early-childhood EEG recordings [84].
There seem to have been only a few studies on sex differences using EEGs of children with ASD. Neuhaus et al. recorded resting-state EEGs of 142 children with ASD and compared them with those of 138 typically developed 8–18-year-old children, with nearly 50% females in each group [85]. Consistent with previous findings, they observed lower alpha-band power in the ASD group. They then evaluated power in beta, theta, alpha, beta, and gamma bands in relation to sex and found lower powers in ASD females in all the oscillatory frames around the whole scalp. Male children with ASD, but not females, demonstrated lower alpha and theta powers in the context of enhanced social abilities. A higher gamma power in the resting state was linked in males to reduced non-verbal IQ and more repetitive/restricted movements [85]. The authors concluded that all three main symptomatic aspects—socialization, cognition, and repetitive movements—correlated differently with EEG power.
Another clinical study was conducted recently with patients with FXS [62]. Resting-state EEG recordings revealed that males with FXS exhibit low individual peak alpha frequencies (IAPFs) in comparison to typically developing males. Such differences were not found in fragile X females when compared to healthy females [62]. This finding was also corroborated by Wang et al. [86]. Similar findings of PAF differences in typically developing versus autistic males and females were also obtained by magnetoencephalography (MEG) [87]. In the study, the authors did not find any significant differences in ASD females versus males and decided to evaluate PAF in correlation with SCQ results only in the male sample. The data are summarized in Table 1.

6. Sex Differences in Genetic Mouse Models

Numerous models of knock-out/knock-in mice have been generated based on the various recently defined DNMs and potential risk genes for ASD in human patients [52,88,89,90]. In the SFARI gene database, the Mouse Models module provides integrated envelopment of the current findings at the molecular, cellular, and behavioral levels on ASD. The SFARI gene Animal Models module provides a list of more than 100 genetic mice with ASD phenotypes relevant to the clinical presentation of autism in humans.
The mouse genetic models may be classified into several categories according to the types of genetic defects: modeling of autism is associated with defined genetic syndromes due to mutations in a single gene, such as fragile X or Rett syndromes. Similarly, non-syndromic autism associated with pathological mutations in single genes, such as the Neuroligin or SHANK family genes, and CNVs associated with autism, such as 15q11-q13 or 16p11.2, have been described.
Generally, almost all studies on mouse models with known ASD risk genes and mutations have reported that mice with specific mutations or deletions demonstrated autistic-like behavior, from mild phenotypes to severe behavioral impairments [52,88,89,90]. However, only a few studies have reported ASD-like behavioral differences between males and females. Generally, the findings regarding sexual dimorphism in ASD-like behavior are related to male predominance as regards the severity of autistic features. These sex differences were found to be prominent in a number of behavioral paradigms that have been established to assess autism-like behaviors in mice, such as the three-chamber test and the ultrasonic vocalization test for social communication, self-grooming, marble burying, and hole-board nose poking to assess repetitive behaviors. In addition, differences in the batteries of tests used to assess the comorbid neuropsychiatric symptoms, such as anxiety behavioral tests and memory and learning tests for assessment of intellectual disability, have also been reported [90,91,92].

6.1. Sex Differences in Mice with Single-Gene Mutations: MECP2 and FMR1 Genes

6.1.1. X-Linked Methyl CpG Binding Protein 2 (MECP2) Gene

Mutations in the MECP2 gene, which is located at Xq28, and encodes the methyl CPG binding protein 2, were shown to be the genetic cause of Rett [45,93]. The MECP2 gene is expressed in two isoforms of different lengths that are involved in gene transcription in neuronal cells [94]. Mecp2 expression levels are sex- and time-dependent [95,96,97,98]. Thus, females were found to have higher expression levels than males in the amygdala and hypothalamus on postnatal day 1 (PND1) [97]. This difference in expression levels between the sexes disappeared by day 10 (PN10) [97]. In another study, this difference in protein expression in the rat prefrontal cortex (PFC) was observed from E14 to PN7 but disappeared at PN14 [98].
A transcriptome analysis comparing the cortex tissues and microglia of 22–24-week-old Mecp2 knockout (KO) male and female mice revealed 149 differentially expressed genes (DEGs) for male mice and 430 DEGs for female mice [99]. The DEGs shared by both male and female mice were mainly related to transport processes. The main phenotypic dimorphism was related to body weight, with females tending to weigh less than males [100].
Many mouse models displaying the features found in patients with Rett syndrome have been generated. Among these models, the Mecp2tm1.1—Bird mouse line, which completely lacks the MECP2 protein product [101], and the Mecp2tm1.1—Jae line, which expresses small MECP2 protein fragments [102], are the ones that have been most frequently investigated. Hemizygous male Mecp2-null mice are phenotypically normal until 4 weeks of age when they develop a Rett-like phenotype consisting of hind-limb clasping, tremors, breathing irregularities, loss of muscle tone, and hypoactivity. In addition, these mice develop metabolic abnormalities, increased serum cholesterol and triglycerides, and increased oxidative stress (reviewed in [103]). They also display reduced brain weight and body weight, experience a rapid phenotypic regression, and die between 6 and 12 weeks of age. Female mice heterozygous for Mecp2 deletions develop the same features at 4–6 months of age and typically live a normal lifespan.

6.1.2. X-Linked Intellectual Disability FMR1 Gene (FMR1)

FXS represents the most common monogenic form of ASD associated with an unstable expansion of a CGG trinucleotide repeat within the 5′ untranslated region (5′UTR) of the FMR1 gene. This results in the loss of the fragile X messenger ribonucleoprotein 1 (FMR1), an RNA-binding protein that regulates protein-synthesis-dependent synaptic plasticity [104]. FMR1 is present in the brain in proximal dendrites and axons of neuronal cell bodies and is mainly associated with polyribosomes [105,106,107].
Mutant Fmr1 KO mice [108,109] and rats [110,111] display altered social interaction and social play behavior, social anxiety, defects in visual attention and auditory dysfunctions, cognitive deficits, repetitive behaviors, and hyperactivity mimicking fragile X syndrome in humans. Nolan et al. [112] found that Fmr1 gene deletion produces sex-specific behavioral changes. Thus, Fmr1 KO homozygous females displayed increased repetitive behaviors when tested in the nose-poke test and enhanced motor coordination on the accelerating rotarod compared to wild-type females, while Fmr1 KO males showed only hyperactivity in the open field [112]. However, evaluation of autistic-like behaviors in heterozygous Fmr1 KO female mice revealed abnormal sociability at infancy and at the juvenile stage [113,114]. Follow-up observation at adulthood revealed that these abnormal behaviors of Fmr1 KO female mice disappeared, demonstrating the temporal pattern of autistic-like behavior in females [113].

6.2. Non-Syndromic Autism Associated with Pathological Mutations in Single Genes

Male-preponderant behavioral deficits have been reported in many studies, using various models related to pathological mutations in single genes [115,116,117]. Sex dimorphism was reported in mice bearing mutations in the Chd8 gene, which encodes a chromatin remodeling factor and modulates gene expression. Chd8-mutant mice (knock-in) with a human C-terminal protein-truncating mutation (N2373K) demonstrated male-preponderant behavioral deficits, evaluated at birth, juvenile, and adult stages [115]. Moreover, these ASD-like behaviors were accompanied by elevated neuronal activation in the hippocampus and prefrontal cortex under stressful conditions and changes in pups in ultrasonic vocalization [115] as well as altered neuronal, synaptic, and transcriptomic phenotypes [117,118]. In contrast, reduced brain baseline activity was found in females, which normalized upon maternal separation [115]. In neuronal-subset-specific (NS-Pten) knockout male and female pups, the analysis of ultrasonic vocalizations measured on postnatal days 8 and 11 revealed sex dimorphism in call types, vocalization duration, amplitude, and fundamental frequency, but both sexes had equal numbers of emitted calls [119].
Male predominance was also reported in studies using the Btbr T+ tf/J inbred mouse model of ASD, which mimics the core symptoms of ASD [120]. Significantly severe ASD-like behaviors, such as sociability and social communication, ultrasonic vocalizations, marble burying, and self-grooming, were observed in male BTBR mice more frequently than in female mice [121,122]. Sex dimorphism was also reported in studies with heterozygous and homozygous female and male mice [123] harboring a C-terminal deletion of Shank 3 at exon 21 (Shank3tm1.1Pfw/J), a mutation that is also found in humans [123,124].
Investigation of telomerase reverse transcriptase (Tert) overexpression in mice also demonstrated sex-dependent autistic-like effects. Tert-tg male mice demonstrated decreased sociability and social novelty preferences, reduced anxiety behavior, and decreased electro-seizure thresholds compared to females. In the prefrontal cortexes of Tert-tg male mice, increased expressions of postsynaptic NMDA and AMPA receptor subunit proteins were observed, though apparently not in females [125]. The data are summarized in Table 2.

7. Sex-Related Electrophysiological Changes in Rodents with ASD-like Behavior

There have been only a few electro-physiological studies on animal models of ASD demonstrating sex differences at the cellular or single-circuit levels. Recent research performed by Bódi et al. using a prenatal valproic acid (VPA) model of autism found some dissimilarities in hippocampal synaptic transmission between males and females [126]. Pregnant rats were administered intra-peritoneally a single dose of 500 mg/kg VPA on gestational day 12.5. Acute hippocampal slices were prepared in the offspring at 6 weeks and 3 months of age. In whole-cell patch clamp recordings in CA1 pyramidal cells of the hippocampus, they found that in 6-week-old males and females there were no differences in rheobase (i.e., a minimal current, which required depolarization of the cell) and relative cumulative spike number. However, in 3-month-old VPA-treated males, but not females, they observed a higher cumulative spike number and lower rheobase, demonstrating their higher excitability. Then, they evaluated the amplitudes, areas, and frequencies of spontaneous excitatory postsynaptic currents (sEPSCs) to assess spontaneous release. VPA treatment in female offspring did not alter the areas or amplitudes in either age group. However, the frequencies of sEPSCs in 3-month-old females were lower than in the controls. In 6-week-old male offspring they observed a decrease in sEPSC amplitudes, and the thresholds of population spikes and field excitatory postsynaptic potentials (fEPSPs) were significantly lower than in the controls, demonstrating the higher excitability of such neurons. Finally, they studied the early phase of long-term potentiation (eLTP) using theta-burst stimulation (TBS) and found a slight tendency toward eLTP decrease in 3-month-old VPA-exposed females [126].
Mice with Tert overexpression demonstrated ASD-like behavior [127]. In this model, the behavioral abnormalities, such as social impairment, nest-building impairment, and impaired LTP and spatial learning, are more specific for male mice than for females. In addition, overexpression of vesicular glutamate transporter 1 (vGLUT1) was observed in both males and females, with a slight increase in males [125]. Electrophysiological findings demonstrated enhanced glutamatergic transmission in mice with Tert overexpression, as male offspring had higher amplitudes of mEPSCs and AMPA/NMDA ratios [128].

8. Sex-Related Differences in Non-Genetic Animal Models of ASD: Valproic Acid-Induced Autistic-like Behavior in Rodents

Among the non-genetic models of ASD-like behavior in rodents, that produced with prenatal valproic acid (VPA) administration is the one most commonly used. VPA is a known human teratogen and is also teratogenic in rodents, inducing neural tube defects (NTDs), in addition to other malformations. It is important to administer the VPA to the pregnant rodents post active organogenesis, avoiding the possibility that VPA will induce embryonic malformations. Hence, VPA is usually given in the prenatal model not earlier than day 12 of gestation [129,130].
Only a few studies have tried to assess possible sex differences in the presentation of the ASD-like symptoms in these models. Even fewer studies have looked at possible differences in gene expression or brain structure/function between male and female rodents with ASD-like behavior. Some researchers have used only males, to eliminate possible sex differences [131,132,133,134,135,136,137,138] in behaviors, such as anxiety-related behaviors and repetitive behaviors [139,140]. Kataoka et al. [140] found that prenatal VPA in mice induced transient histone hyper-acetylation in the brains of offspring of male and female mice. However, social impairment at 8 weeks of age was observed only in male offspring.
Kazlauskas et al. [141] treated pregnant mice with 600 mg/Kg VPA on day 12.5 of pregnancy and tested in male and female offspring sociability as well as other neurobehavioral tests typical for ASD-like behavior. They found impaired sociability only in males, but the results of several behavioral tests were similar for both sexes. Moreover, the neuroinflammatory markers studied in the cerebellum and dentate gyrus were abnormal in both sexes but more severe in females.
Very recently, Ghahremani et al. [142] reported that female rats prenatally exposed to VPA performed better than VPA-exposed male offspring in Morris water-maze acquisition, which was conducted for spatial learning and memory analysis. Interval motor training (IT) and continuous training (CT) improved the cognitive functioning of VPA-exposed offspring of both sexes, with better results in females.
Morphological changes, such as a decrease in the number of Nissl-positive cells, have been reported in the somatosensory cortex of VPA-exposed male but not female offspring and may be involved in the sex-dependent social interaction deficits in mice [143].
Knopko et al. [144] found that VPA administration to pregnant dams on gestational day 12.5 induced robust and widespread histone lysine acetylation at specific exons of the Bdnf gene in the brains of offspring. They also reported differences in mRNA levels of Bdnf transcripts between males and females, which may be involved in the female protective effect in ASD. Kim et al. [98] reported a male-specific decrease in MeCP2 in the prefrontal cortex, MeCP2 being involved in transcriptional regulation via histone acetylation and chromatin structure. In addition, MeCP2 knockdown resulted in an increase in PSD95, an important postsynaptic protein, in neural progenitor cells derived from males but not females. Gu et al. [145] reported remarkable alterations in the gut microbiota and fecal metabolites of VPA-exposed rat offspring with sex-specific differences. They proposed that future treatment of ASD may differ between the sexes. Scheggi et al. [146] treated prenatally VPA-exposed rats with a fenofibrate (a peroxisome proliferator-activated receptor α agonist)-enriched diet and found effective relief of social impairment and perseverative behavior only in females.

9. Behavioral and Molecular Studies on Early Postnatal and Prenatal VPA Administration in Mice. (These Studies were Published by us Previously [8,9,10])

VPA is a known epigenetic modulator, and this characteristic might be responsible for the VPA-induced ASD-like behavior. We therefore performed neurobehavioral and molecular studies in ICR mice treated either prenatally or early postnatally with VPA [129,130,147]. We hypothesized that by using an antagonistic epigenetic modulator—S adenosyl-methionine (SAMe)—we might reverse many of the ASD-like behavioral changes induced by VPA. We also hypothesized that many of the clinical and/or pathophysiological features induced by VPA, including expected changes in gene expression, would differ between male and female mice. On the basis of behavioral studies that differed between male and female mice, we also thought that there might be changes in gene expression, with differences between sexes.

9.1. Postnatal Administration of VPA—Behavioral Studies

In our postnatal studies [8,9], we treated 4-day-old mouse offspring with 300 mg/Kg body weight VPA by intraperitoneal injection and studied the ASD-like behavioral and molecular changes. In order to examine whether alleviation of the ASD-like symptoms is also sex-dependent, we added, on postnatal days 5, 6, and 7, 30 mg/Kg SAMe by intragastric lavage or normal saline.
We first recorded weight, growth, and neurobehavioral development. During postnatal days 50–59, we carried out a variety of behavioral tests used for the assessment of ASD-like behavior, and on day 60 we sacrificed the animals and performed our biochemical and molecular studies on the prefrontal cortexes. We compared all studied parameters between males and females [8].
VPA induced in males and females higher grooming frequency, which also means reduced sociability. However, sociability was less impaired in females compared to males. Higher anxiety was observed in VPA-treated females compared to treated males. Increased anxiety (less head dipping) in females and males treated with VPA was also observed in the elevated plus-maze test. VPA-induced anxiety was significantly higher in females compared to males. Analysis of memory from the water T-maze test showed that VPA reduced memory only in females. The results of the three-chamber test for sociability testing showed reduced sociability in males but not in females. All these behavioral differences between male and female mice were very similar to the behavioral differences reported between girls and boys with ASD. All the above-described behavioral changes were alleviated in both sexes by the concomitant administration of SAMe.
Increased brain oxidative stress was observed in the brains of children with ASD as well as in animals with ASD-like behavior [7,148]. We found that VPA-treated females showed decreased activity of the antioxidant enzyme superoxide dismutase (SOD), increased activity of Catalase (CAT), and increased Malone-dialdehyde (MDA) as signs of increased oxidative stress. This was corrected by co-administration of SAMe. In males, VPA only increased the activity of SOD, implying that oxidative stress was less prominent compared to females. The data are summarized in Table 3.

Studies of Gene Expression in the Brain

We used the nCounter neuropathology panel which measures the expression of 770 genes in the brain. These are neural inflammatory genes, neurogenesis genes, and genes involved in various biological pathways, such as the vascular endothelial growth factor (VEGF) pathway, among others. VPA induced a variety of changes in gene expression in the brains of both sexes. There were sex differences in the VPA-induced changes in gene expression. In females, more genes were changed by VPA, and half of the genes were upregulated and half of them were downregulated. In males, most genes the expression of which was changed by VPA were upregulated [9]. SAMe reversed the changes in 75% of the genes in males and 52% in females. Changes of over 50% were observed in the expression of neuroinflammation, neurogenesis, and general biological genes. Most of these changes were reversed by oral SAMe. SAMe alone did not change gene expression [9].

9.2. Prenatal Studies

We treated ICR mice on day 12 of gestation with a single dose of 600 mg/Kg of VPA and administered SAMe on days 13–15, at a dose of 30 mg/Kg/day. Treated mice were allowed to deliver, and the offspring were followed up to two months of age.
Behavioral studies were performed during postnatal days 55–60, using methods similar to those used in the early postnatal animals. We found that VPA induced ASD-like behavioral changes similar to those observed in the animals treated during early postnatal life. VPA reduced sociability, increased anxiety in both sexes, and reduced learning abilities, with differences between males and females. SAMe only partially alleviated the ASD-like behavioral changes caused by prenatal VPA, thus being less effective than in the early postnatal treatment model.
We studied gene expression in the anterior parts of the cerebral hemispheres (mainly the prefrontal cortexes) only in day-one offspring, using the Nanostring nCounter neuropathology panel [10].
VPA did not induce any changes in gene expression of the brains of male or female neonates. In contrast, SAMe produced changes in the expression of many genes in the cerebral hemisphere. There were sex differences in the genes with changed expression levels, as more genes were changed in females compared to males. Interestingly, concomitant prenatal administration of VPA prevented most of the gene-expression changes induced by prenatal SAMe.

10. Possible Explanation of the Sex Differences in ASD in Humans

Since the major difference between males and females is the difference in sex chromosomes, along with numerous sex-linked genes, many studies that have tried to explain the differences in the prevalence and severity of various diseases between males and females have looked at X and Y chromosomes.
An important question, therefore, is whether ASD is a sex-linked disorder, with the X chromosome reducing its prevalence. Most studies have demonstrated that it is not, but there is a higher prevalence of ASD in children who have a lower proportion of X chromosomes. For example, in Turner syndrome, with a female phenotype and only one X chromosome, 3% of individuals have ASD—about three times the risk of the general population. In children with Klienfelter syndrome (XXY), 10% have ASD, and in those with XYY chromosomes about 20% have ASD. There is no increased risk in triple X (XXX) individuals. Hence, the Y chromosome is apparently a risk factor for ASD, or, alternatively, the X chromosome is a protecting factor. However, the authors give no explanation of why in Turner syndrome, with only one X chromosome but no Y chromosome, or in the case of XXY, with two X chromosomes, there is an increased risk for ASD.
Several investigators have attempted to explain the male predominance of ASD by sex-specific genetic changes in single-nucleotide polymorphisms (SNPs), single-nucleotide variants (SNVs), copy-number variants (CNVs), and different sex-biased proteins [22,23,149,150,151,152]. Mutations in specific genes, particularly genes linked to X or Y chromosomes, have been implicated in male vulnerability to ASD [44,153,154]. For example, Nguyen et al. [44] suggested that the X-linked cell adhesion molecule NLGN4X, a mutation in which is known to be associated with ASD or intellectual disabilities, may contribute to the male bias in ASD, because the Y chromosome variant, NLGN4Y, cannot compensate for the functional deficits created by the X-linked mutation. Others have hypothesized that male predominance in ASD occurs due to sex-specific steroidal or hormonal effects on pathways involving immune or inflammatory functions during critical periods of brain development [155,156,157]. Interestingly, in contrast to the male predominance reported in ASD prevalence, in children born to mothers treated with VPA during pregnancy there seems to be no sex difference in the rate of ASD [147].
An alternative theory for ASD sex-related changes suggests that ASD is an “extreme expression of the male brain”, as males have a higer tendency to systemize, while females have a higher drive to “empathize”. These behavioral differnces are the result of the effects of androgens (testosterone) on brain development in males, resulting in sex-related behavioral differences. Hence, the higher levels of testosterone during fetal development increase male vulnerability to ASD [158]. Evidence that intrauterine testosterone might play a role was obtained by measurements of amniotic fluid testosterone from amniocenteses. Amniotic fluid testosterone (of fetal origin) levels were inversely associated with eye contact in children at one year of age and inversely correlated with vocabulary development at two years and with the quality of social relationship and empathy at 4 years. In addition, testosterone levels were positively associated with narrow interests at 4 years and with a tendency to systemize at 8 years [150]. These are behavioral features that are sex-related and which often appear in ASD.

11. Conclusions

The prevalence of ASD, similar to several other neuropsychiatric disorders, is significantly higher in males compared to females. Moreover, there are also differences in clinical presentation and comorbidities between males and females. In the last few years, sex differences have also been observed in the electrophysiology, imaging, and connectivity of the brain. Sex-related differences are also reported in genetic and non-genetic models of autistic-like behavior in rodents. Some of these differences (i.e., behavioral and electrophysiological) are similar to those observed in humans. Moreover, such differences are also observed in autistic-like behavior induced by mutations in sex chromosomes in human or in animal models, implying that the differences are related to the different sex chromosomes. Although the exact mechanisms responsible for these differences are as yet unknown, it seems that they are related to hormonal or genetic changes that define the phenotype of the male or female. Additional studies are clearly needed to further understand the possible interaction between the various genetic entities (X or Y chromosomes and their genes) in the etiology of many diseases that are sex-related. Finally, due to the significant differences in the clinical presentation of ASD between girls and boys, there seems to be a need for the adjustment of diagnostic tests to detect or exclude ASD. There is a greater need for this in autism than in most other neuropsychiatric diseases, where the same methods of diagnosis are used for both sexes.

Author Contributions

Conceptualization—A.O.; Review and editing—A.O.; Writing of introduction, clinical-related differences summary, and conclusion—A.O.; Writing on genetic susceptibility and sex differences in genetic animal models—M.B.; Writing on sex differences in brain structure and imaging data, and on electrophysiology in humans with ASD and in animal models—D.G.; Writing on sex differences in the VPA-induced ASD model—A.O. and L.W.-F.; Writing on a possible explanation of the sex differences in ASD in humans—L.W.-F. and A.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

ADHDAttention-deficit hyperactivity disorder
ADI-RAutism Diagnostic Interview, Revised
ADOSAutism Diagnostic Observation Schedule
AMPAα-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid
ASDAutism spectrum disorder
CATCatalase
CBCLAchenbach Child Behavior Checklist
CENCentral executive network
Chd8Chromodomain Helicase DNA Binding Protein 8
CNVCopy-number variants
CTContinuous training
DEGsDifferentially expressed genes
DMNDefault mode network
DNMsDe novo mutations
EEGElectroencephalography
eLTPEarly phase of long-term potentiation
fEPSPField excitatory postsynaptic potential
FMR1Fragile X Messenger Ribonucleoprotein 1
fMRIFunctional magnetic resonance imaging
FMR1Fragile X messenger ribonucleoprotein 1
FXSFragile X syndrome
IAPFIndividual alpha peak frequency
IQIntelligence quotient
ITInterval motor training
KOKnockout
LTPLong-term potentiation
MASCSocial Cognition–Multiple Choice
MDAMalone-dialdehyde
MECP2Methyl-CpG-binding protein 2
MEGMagnetoencephalography
mEPSCsMiniature excitatory postsynaptic current
mPFCMedial prefrontal cortex
NF1Neurofibromatosis type 1
NLGN3Neuroligin 3
NLGN4XNeuroligin 4X
NMDAN-methyl-D-aspartate
NRXN1Neurexin 1
NS-PtenNeuronal-subset specific
NTDNeural tube defects
PAFPeak alpha frequency
PCCPosterior cingulate cortex
PNPostnatal day
PTENPhosphatase and tensin homolog
RRBsRepetitive/restricted behaviors
RRBIsRestricted and repetitive behaviors and interests
rs-fMRIResting-state fMRI
SAMeS adenosyl-methionine
SCQSocial Communication Questionnaire
sEPSCsSpontaneous excitatory postsynaptic currents
SFARISimons Foundation Autism Research Initiative
SHANK1H3 And Multiple Ankyrin Repeat Domains 1
SNPsSingle-nucleotide polymorphisms
SoCSocial cognition
SODSuperoxide dismutase
TBSTheta-burst stimulation
TDTypically developed
TERTTelomerase reverse transcriptase
Tert-tgTelomerase reverse transcriptase -transgenic
TPJTemporo-parietal junctions
vGLUT1Vesicular glutamate transporter 1
VPAValproic Acid
SNVsSingle-nucleotide variants
5′UTR5′ untranslated region

References

  1. Salk, R.H.; Hyde, J.S.; Abramson, L.Y. Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms. Psychol. Bull. 2017, 143, 783–822. [Google Scholar] [CrossRef] [PubMed]
  2. Parker, G.; Fletcher, K.; Paterson, A.; Anderson, J.; Hong, M. Gender differences in depression severity and symptoms across depressive sub-types. J. Affect. Disord. 2014, 167, 351–357. [Google Scholar] [CrossRef]
  3. Craig, F.; Crippa, A.; De Giacomo, A.; Ruggiero, M.; Rizzato, V.; Lorenzo, A.; Fanizza, I.; Margari, L.; Trabacca, A. Differences in Developmental Functioning Profiles Between Male and Female Preschoolers Children With Autism Spectrum Disorder. Autism Res. Off. J. Int. Soc. Autism Res. 2020, 13, 1537–1547. [Google Scholar] [CrossRef]
  4. Tillmann, J.; Ashwood, K.; Absoud, M.; Bölte, S.; Bonnet-Brilhault, F.; Buitelaar, J.K.; Calderoni, S.; Calvo, R.; Canal-Bedia, R.; Canitano, R.; et al. Evaluating Sex and Age Differences in ADI-R and ADOS Scores in a Large European Multi-site Sample of Individuals with Autism Spectrum Disorder. J. Autism Dev. Disord. 2018, 48, 2490–2505. [Google Scholar] [CrossRef] [PubMed]
  5. Prosperi, M.; Turi, M.; Guerrera, S.; Napoli, E.; Tancredi, R.; Igliozzi, R.; Apicella, F.; Valeri, G.; Lattarulo, C.; Gemma, A.; et al. Sex Differences in Autism Spectrum Disorder: An Investigation on Core Symptoms and Psychiatric Comorbidity in Preschoolers. Front. Integr. Neurosci. 2020, 14, 594082. [Google Scholar] [CrossRef] [PubMed]
  6. Ros-Demarize, R.; Bradley, C.; Kanne, S.M.; Warren, Z.; Boan, A.; Lajonchere, C.; Park, J.; Carpenter, L.A. ASD symptoms in toddlers and preschoolers: An examination of sex differences. Autism Res. Off. J. Int. Soc. Autism Res. 2020, 13, 157–166. [Google Scholar] [CrossRef]
  7. Ornoy, A.; Weinstein-Fudim, L.; Ergaz, Z. Prenatal factors associated with autism spectrum disorder (ASD). Reprod. Toxicol. 2015, 56, 155–169. [Google Scholar] [CrossRef]
  8. Ornoy, A.; Weinstein-Fudim, L.; Tfilin, M.; Ergaz, Z.; Yanai, J.; Szyf, M.; Turgeman, G. S-adenosyl methionine prevents ASD like behaviors triggered by early postnatal valproic acid exposure in very young mice. Neurotoxicol. Teratol. 2019, 71, 64–74. [Google Scholar] [CrossRef] [PubMed]
  9. Weinstein-Fudim, L.; Ergaz, Z.; Turgeman, G.; Yanai, J.; Szyf, M.; Ornoy, A. Gender Related Changes in Gene Expression Induced by Valproic Acid in A Mouse Model of Autism and the Correction by S-adenosyl Methionine. Does It Explain the Gender Differences in Autistic Like Behavior? Int. J. Mol. Sci. 2019, 20, 5278. [Google Scholar] [CrossRef]
  10. Weinstein-Fudim, L.; Ergaz, Z.; Szyf, M.; Ornoy, A. Prenatal S-Adenosine Methionine (SAMe) Induces Changes in Gene Expression in the Brain of Newborn Mice That Are Prevented by Co-Administration of Valproic Acid (VPA). Int. J. Mol. Sci. 2020, 21, 2834. [Google Scholar] [CrossRef] [Green Version]
  11. Edition, F. Diagnostic and statistical manual of mental disorders. Am. Psychiatric. Assoc. 2013, 21, 591–643. [Google Scholar]
  12. American Psychiatric Association (APA). Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; American Psychiatric Press: Washington, DC, USA, 2013. [Google Scholar]
  13. Jensen, C.M.; Steinhausen, H.C.; Lauritsen, M.B. Time trends over 16 years in incidence-rates of autism spectrum disorders across the lifespan based on nationwide Danish register data. J. Autism Dev. Disord. 2014, 44, 1808–1818. [Google Scholar] [CrossRef]
  14. Rucklidge, J.J. Gender differences in attention-deficit/hyperactivity disorder. Psychiatr. Clin. N. Am. 2010, 33, 357–373. [Google Scholar] [CrossRef]
  15. Beggiato, A.; Peyre, H.; Maruani, A.; Scheid, I.; Rastam, M.; Amsellem, F.; Gillberg, C.I.; Leboyer, M.; Bourgeron, T.; Gillberg, C.; et al. Gender differences in autism spectrum disorders: Divergence among specific core symptoms. Autism Res. Off. J. Int. Soc. Autism Res. 2017, 10, 680–689. [Google Scholar] [CrossRef]
  16. Rynkiewicz, A.; Łucka, I. Autism spectrum disorder (ASD) in girls. Co-occurring psychopathology. Sex differences in clinical manifestation. Psychiatr. Pol. 2018, 52, 629–639. [Google Scholar] [CrossRef]
  17. Lai, M.C.; Lombardo, M.V.; Auyeung, B.; Chakrabarti, B.; Baron-Cohen, S. Sex/gender differences and autism: Setting the scene for future research. J. Am. Acad. Child Adolesc. Psychiatry 2015, 54, 11–24. [Google Scholar] [CrossRef] [PubMed]
  18. Mandy, W.; Chilvers, R.; Chowdhury, U.; Salter, G.; Seigal, A.; Skuse, D. Sex differences in autism spectrum disorder: Evidence from a large sample of children and adolescents. J. Autism Dev. Disord. 2012, 42, 1304–1313. [Google Scholar] [CrossRef] [PubMed]
  19. Tsirgiotis, J.M.; Young, R.L.; Weber, N. Sex/Gender Differences in CARS2 and GARS-3 Item Scores: Evidence of Phenotypic Differences Between Males and Females with ASD. J. Autism Dev. Disord. 2022, 52, 3958–3976. [Google Scholar] [CrossRef] [PubMed]
  20. Navarro-Pardo, E.; López-Ramón, F.; Alonso-Esteban, Y.; Alcantud-Marín, F. Diagnostic Tools for Autism Spectrum Disorders by Gender: Analysis of Current Status and Future Lines. Children 2021, 8, 262. [Google Scholar] [CrossRef]
  21. Dean, M.; Harwood, R.; Kasari, C. The art of camouflage: Gender differences in the social behaviors of girls and boys with autism spectrum disorder. Autism Int. J. Res. Pract. 2017, 21, 678–689. [Google Scholar] [CrossRef]
  22. Werling, D.M.; Geschwind, D.H. Sex differences in autism spectrum disorders. Curr. Opin. Neurol. 2013, 26, 146–153. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Bailey, A.; Le Couteur, A.; Gottesman, I.; Bolton, P.; Simonoff, E.; Yuzda, E.; Rutter, M. Autism as a strongly genetic disorder: Evidence from a British twin study. Psychol. Med. 1995, 25, 63–77. [Google Scholar] [CrossRef]
  24. Wang, T.; Guo, H.; Xiong, B.; Stessman, H.A.; Wu, H.; Coe, B.P.; Turner, T.N.; Liu, Y.; Zhao, W.; Hoekzema, K.; et al. De novo genic mutations among a Chinese autism spectrum disorder cohort. Nat. Commun. 2016, 7, 13316. [Google Scholar] [CrossRef] [PubMed]
  25. Yin, J.; Schaaf, C.P. Autism genetics—An overview. Prenat. Diagn. 2017, 37, 14–30. [Google Scholar] [CrossRef] [PubMed]
  26. Bourgeron, T. From the genetic architecture to synaptic plasticity in autism spectrum disorder. Nat. Rev. Neurosci. 2015, 16, 551–563. [Google Scholar] [CrossRef]
  27. Abrahams, B.S.; Geschwind, D.H. Advances in autism genetics: On the threshold of a new neurobiology. Nat. Rev. Genet. 2008, 9, 341–355. [Google Scholar] [CrossRef]
  28. Weiner, D.J.; Wigdor, E.M.; Ripke, S.; Walters, R.K.; Kosmicki, J.A.; Grove, J.; Samocha, K.E.; Goldstein, J.I.; Okbay, A.; Bybjerg-Grauholm, J.; et al. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat. Genet. 2017, 49, 978–985. [Google Scholar] [CrossRef]
  29. Zhao, X.; Leotta, A.; Kustanovich, V.; Lajonchere, C.; Geschwind, D.H.; Law, K.; Law, P.; Qiu, S.; Lord, C.; Sebat, J.; et al. A unified genetic theory for sporadic and inherited autism. Proc. Natl. Acad. Sci. USA 2007, 104, 12831–12836. [Google Scholar] [CrossRef]
  30. Parikshak, N.N.; Luo, R.; Zhang, A.; Won, H.; Lowe, J.K.; Chandran, V.; Horvath, S.; Geschwind, D.H. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 2013, 155, 1008–1021. [Google Scholar] [CrossRef] [PubMed]
  31. Robinson, E.B.; Lichtenstein, P.; Anckarsäter, H.; Happé, F.; Ronald, A. Examining and interpreting the female protective effect against autistic behavior. Proc. Natl. Acad. Sci. USA 2013, 110, 5258–5262. [Google Scholar] [CrossRef]
  32. Ferri, S.L.; Abel, T.; Brodkin, E.S. Sex Differences in Autism Spectrum Disorder: A Review. Curr. Psychiatry Rep. 2018, 20, 9. [Google Scholar] [CrossRef] [PubMed]
  33. Zhang, Y.; Li, N.; Li, C.; Zhang, Z.; Teng, H.; Wang, Y.; Zhao, T.; Shi, L.; Zhang, K.; Xia, K.; et al. Genetic evidence of gender difference in autism spectrum disorder supports the female-protective effect. Transl. Psychiatry 2020, 10, 4. [Google Scholar] [CrossRef]
  34. Jacquemont, S.; Coe, B.P.; Hersch, M.; Duyzend, M.H.; Krumm, N.; Bergmann, S.; Beckmann, J.S.; Rosenfeld, J.A.; Eichler, E.E. A higher mutational burden in females supports a “female protective model” in neurodevelopmental disorders. Am. J. Hum. Genet. 2014, 94, 415–425. [Google Scholar] [CrossRef] [PubMed]
  35. Zufferey, F.; Sherr, E.H.; Beckmann, N.D.; Hanson, E.; Maillard, A.M.; Hippolyte, L.; Macé, A.; Ferrari, C.; Kutalik, Z.; Andrieux, J.; et al. A 600 kb deletion syndrome at 16p11.2 leads to energy imbalance and neuropsychiatric disorders. J. Med. Genet. 2012, 49, 660–668. [Google Scholar] [CrossRef]
  36. Jacquemont, S.; Reymond, A.; Zufferey, F.; Harewood, L.; Walters, R.G.; Kutalik, Z.; Martinet, D.; Shen, Y.; Valsesia, A.; Beckmann, N.D.; et al. Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus. Nature 2011, 478, 97–102. [Google Scholar] [CrossRef]
  37. Pinto, D.; Delaby, E.; Merico, D.; Barbosa, M.; Merikangas, A.; Klei, L.; Thiruvahindrapuram, B.; Xu, X.; Ziman, R.; Wang, Z.; et al. Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. Am. J. Hum. Genet. 2014, 94, 677–694. [Google Scholar] [CrossRef]
  38. Zhang, Y.; Castillo-Morales, A.; Jiang, M.; Zhu, Y.; Hu, L.; Urrutia, A.O.; Kong, X.; Hurst, L.D. Genes That Escape X-Inactivation in Humans Have High Intraspecific Variability in Expression, Are Associated with Mental Impairment but Are Not Slow Evolving. Mol. Biol. Evol. 2013, 30, 2588–2601. [Google Scholar] [CrossRef] [PubMed]
  39. Philippe, A.; Martinez, M.; Guilloud-Bataille, M.; Gillberg, C.; Råstam, M.; Sponheim, E.; Coleman, M.; Zappella, M.; Aschauer, H.; Van Maldergem, L.; et al. Genome-wide scan for autism susceptibility genes. Paris Autism Research International Sibpair Study. Hum. Mol. Genet. 1999, 8, 805–812. [Google Scholar] [CrossRef]
  40. Auranen, M.; Vanhala, R.; Varilo, T.; Ayers, K.; Kempas, E.; Ylisaukko-Oja, T.; Sinsheimer, J.S.; Peltonen, L.; Järvelä, I. A genomewide screen for autism-spectrum disorders: Evidence for a major susceptibility locus on chromosome 3q25-27. Am. J. Hum. Genet. 2002, 71, 777–790. [Google Scholar] [CrossRef]
  41. Laumonnier, F.; Bonnet-Brilhault, F.; Gomot, M.; Blanc, R.; David, A.; Moizard, M.P.; Raynaud, M.; Ronce, N.; Lemonnier, E.; Calvas, P.; et al. X-linked mental retardation and autism are associated with a mutation in the NLGN4 gene, a member of the neuroligin family. Am. J. Hum. Genet. 2004, 74, 552–557. [Google Scholar] [CrossRef]
  42. Jamain, S.; Quach, H.; Betancur, C.; Råstam, M.; Colineaux, C.; Gillberg, I.C.; Soderstrom, H.; Giros, B.; Leboyer, M.; Gillberg, C.; et al. Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4 are associated with autism. Nat. Genet. 2003, 34, 27–29. [Google Scholar] [CrossRef] [Green Version]
  43. Thomas, N.S.; Sharp, A.J.; Browne, C.E.; Skuse, D.; Hardie, C.; Dennis, N.R. Xp deletions associated with autism in three females. Hum. Genet. 1999, 104, 43–48. [Google Scholar] [CrossRef]
  44. Nguyen, T.A.; Wu, K.; Pandey, S.; Lehr, A.W.; Li, Y.; Bemben, M.A.; Badger, J.D., 2nd; Lauzon, J.L.; Wang, T.; Zaghloul, K.A.; et al. A Cluster of Autism-Associated Variants on X-Linked NLGN4X Functionally Resemble NLGN4Y. Neuron 2020, 106, 759–768.e757. [Google Scholar] [CrossRef]
  45. Amir, R.E.; Van den Veyver, I.B.; Wan, M.; Tran, C.Q.; Francke, U.; Zoghbi, H.Y. Rett syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG-binding protein 2. Nat. Genet. 1999, 23, 185–188. [Google Scholar] [CrossRef]
  46. Ramocki, M.B.; Peters, S.U.; Tavyev, Y.J.; Zhang, F.; Carvalho, C.M.; Schaaf, C.P.; Richman, R.; Fang, P.; Glaze, D.G.; Lupski, J.R.; et al. Autism and other neuropsychiatric symptoms are prevalent in individuals with MeCP2 duplication syndrome. Ann. Neurol. 2009, 66, 771–782. [Google Scholar] [CrossRef] [PubMed]
  47. Ak, M.; Suter, B.; Akturk, Z.; Harris, H.; Bowyer, K.; Mignon, L.; Pasupuleti, S.; Glaze, D.G.; Pehlivan, D. Exploring the characteristics and most bothersome symptoms in MECP2 duplication syndrome to pave the path toward developing parent-oriented outcome measures. Mol. Genet. Genom. Med. 2022, 10, e1989. [Google Scholar] [CrossRef]
  48. Wainer Katsir, K.; Linial, M. Human genes escaping X-inactivation revealed by single cell expression data. BMC Genom. 2019, 20, 201. [Google Scholar] [CrossRef] [PubMed]
  49. Balaton, B.P.; Cotton, A.M.; Brown, C.J. Derivation of consensus inactivation status for X-linked genes from genome-wide studies. Biol. Sex Differ. 2015, 6, 35. [Google Scholar] [CrossRef] [PubMed]
  50. Verkerk, A.J.; Pieretti, M.; Sutcliffe, J.S.; Fu, Y.H.; Kuhl, D.P.; Pizzuti, A.; Reiner, O.; Richards, S.; Victoria, M.F.; Zhang, F.P.; et al. Identification of a gene (FMR-1) containing a CGG repeat coincident with a breakpoint cluster region exhibiting length variation in fragile X syndrome. Cell 1991, 65, 905–914. [Google Scholar] [CrossRef]
  51. Hernandez, R.N.; Feinberg, R.L.; Vaurio, R.; Passanante, N.M.; Thompson, R.E.; Kaufmann, W.E. Autism spectrum disorder in fragile X syndrome: A longitudinal evaluation. Am. J. Med. Genet. Part A 2009, 149a, 1125–1137. [Google Scholar] [CrossRef]
  52. Basu, S.N.; Kollu, R.; Banerjee-Basu, S. AutDB: A gene reference resource for autism research. Nucleic Acids Res. 2009, 37, D832–D836. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Hernandez, L.M.; Lawrence, K.E.; Padgaonkar, N.T.; Inada, M.; Hoekstra, J.N.; Lowe, J.K.; Eilbott, J.; Jack, A.; Aylward, E.; Gaab, N.; et al. Imaging-genetics of sex differences in ASD: Distinct effects of OXTR variants on brain connectivity. Transl. Psychiatry 2020, 10, 82. [Google Scholar] [CrossRef] [PubMed]
  54. Lawrence, K.E.; Hernandez, L.M.; Bowman, H.C.; Padgaonkar, N.T.; Fuster, E.; Jack, A.; Aylward, E.; Gaab, N.; Van Horn, J.D.; Bernier, R.A.; et al. Sex Differences in Functional Connectivity of the Salience, Default Mode, and Central Executive Networks in Youth with ASD. Cereb. Cortex 2020, 30, 5107–5120. [Google Scholar] [CrossRef]
  55. Cauvet, É.; Van’t Westeinde, A.; Toro, R.; Kuja-Halkola, R.; Neufeld, J.; Mevel, K.; Bölte, S. Sex Differences Along the Autism Continuum: A Twin Study of Brain Structure. Cereb. Cortex 2019, 29, 1342–1350. [Google Scholar] [CrossRef] [PubMed]
  56. Nordahl, C.W.; Iosif, A.M.; Young, G.S.; Hechtman, A.; Heath, B.; Lee, J.K.; Libero, L.; Reinhardt, V.P.; Winder-Patel, B.; Amaral, D.G.; et al. High Psychopathology Subgroup in Young Children With Autism: Associations With Biological Sex and Amygdala Volume. J. Am. Acad. Child Adolesc. Psychiatry 2020, 59, 1353–1363.e1352. [Google Scholar] [CrossRef]
  57. Cauvet, É.; van’t Westeinde, A.; Toro, R.; Kuja-Halkola, R.; Neufeld, J.; Mevel, K.; Bölte, S. The social brain in female autism: A structural imaging study of twins. Soc. Cogn. Affect. Neurosci. 2020, 15, 423–436. [Google Scholar] [CrossRef]
  58. Van’t Westeinde, A.; Cauvet, É.; Toro, R.; Kuja-Halkola, R.; Neufeld, J.; Mevel, K.; Bölte, S. Sex differences in brain structure: A twin study on restricted and repetitive behaviors in twin pairs with and without autism. Mol. Autism 2019, 11, 1. [Google Scholar] [CrossRef] [PubMed]
  59. Supekar, K.; Menon, V. Sex differences in structural organization of motor systems and their dissociable links with repetitive/restricted behaviors in children with autism. Mol. Autism 2015, 6, 50. [Google Scholar] [CrossRef] [PubMed]
  60. Smith, R.E.W.; Avery, J.A.; Wallace, G.L.; Kenworthy, L.; Gotts, S.J.; Martin, A. Sex Differences in Resting-State Functional Connectivity of the Cerebellum in Autism Spectrum Disorder. Front. Hum. Neurosci. 2019, 13, 104. [Google Scholar] [CrossRef]
  61. Slater, J.; Joober, R.; Koborsy, B.L.; Mitchell, S.; Sahlas, E.; Palmer, C. Can electroencephalography (EEG) identify ADHD subtypes? A systematic review. Neurosci. Biobehav. Rev. 2022, 139, 104752. [Google Scholar] [CrossRef] [PubMed]
  62. Smith, E.G.; Pedapati, E.V.; Liu, R.; Schmitt, L.M.; Dominick, K.C.; Shaffer, R.C.; Sweeney, J.A.; Erickson, C.A. Sex differences in resting EEG power in Fragile X Syndrome. J. Psychiatr. Res. 2021, 138, 89–95. [Google Scholar] [CrossRef]
  63. Gusnard, D.A.; Raichle, M.E. Searching for a baseline: Functional imaging and the resting human brain. Nat. Rev. Neurosci. 2001, 2, 685–694. [Google Scholar] [CrossRef]
  64. Andrews-Hanna, J.R.; Reidler, J.S.; Huang, C.; Buckner, R.L. Evidence for the default network’s role in spontaneous cognition. J. Neurophysiol. 2010, 104, 322–335. [Google Scholar] [CrossRef] [PubMed]
  65. Alaerts, K.; Swinnen, S.P.; Wenderoth, N. Sex differences in autism: A resting-state fMRI investigation of functional brain connectivity in males and females. Soc. Cogn. Affect. Neurosci. 2016, 11, 1002–1016. [Google Scholar] [CrossRef] [PubMed]
  66. Tomasi, D.; Volkow, N.D. Reduced Local and Increased Long-Range Functional Connectivity of the Thalamus in Autism Spectrum Disorder. Cereb. Cortex 2019, 29, 573–585. [Google Scholar] [CrossRef]
  67. Walsh, M.J.M.; Wallace, G.L.; Gallegos, S.M.; Braden, B.B. Brain-based sex differences in autism spectrum disorder across the lifespan: A systematic review of structural MRI, fMRI, and DTI findings. NeuroImage Clin. 2021, 31, 102719. [Google Scholar] [CrossRef] [PubMed]
  68. Mo, K.; Sadoway, T.; Bonato, S.; Ameis, S.H.; Anagnostou, E.; Lerch, J.P.; Taylor, M.J.; Lai, M.C. Sex/gender differences in the human autistic brains: A systematic review of 20 years of neuroimaging research. NeuroImage Clin. 2021, 32, 102811. [Google Scholar] [CrossRef]
  69. Berger, H. Über das elektroenkephalogramm des menschen. Arch. Psychiatr. Nervenkrankh. 1929, 87, 527–570. [Google Scholar] [CrossRef]
  70. Jasper, H.H.; Andrews, H.L. Human brain rhythms: I. Recording techniques and preliminary results. J. Gen. Psychol. 1936, 14, 98–126. [Google Scholar] [CrossRef]
  71. Wang, J.; Barstein, J.; Ethridge, L.E.; Mosconi, M.W.; Takarae, Y.; Sweeney, J.A. Resting state EEG abnormalities in autism spectrum disorders. J. Neurodev. Disord. 2013, 5, 24. [Google Scholar] [CrossRef]
  72. Kenny, A.; Wright, D.; Stanfield, A.C. EEG as a translational biomarker and outcome measure in fragile X syndrome. Transl. Psychiatry 2022, 12, 34. [Google Scholar] [CrossRef]
  73. Meghdadi, A.H.; Stevanović Karić, M.; McConnell, M.; Rupp, G.; Richard, C.; Hamilton, J.; Salat, D.; Berka, C. Resting state EEG biomarkers of cognitive decline associated with Alzheimer’s disease and mild cognitive impairment. PLoS ONE 2021, 16, e0244180. [Google Scholar] [CrossRef] [PubMed]
  74. Wang, Q.; Meng, L.; Pang, J.; Zhu, X.; Ming, D. Characterization of EEG Data Revealing Relationships with Cognitive and Motor Symptoms in Parkinson’s Disease: A Systematic Review. Front. Aging Neurosci. 2020, 12, 587396. [Google Scholar] [CrossRef] [PubMed]
  75. Cantor, D.S.; Thatcher, R.W.; Hrybyk, M.; Kaye, H. Computerized EEG analyses of autistic children. J. Autism Dev. Disord. 1986, 16, 169–187. [Google Scholar] [CrossRef]
  76. Chan, A.S.; Sze, S.L.; Cheung, M.-C. Quantitative electroencephalographic profiles for children with autistic spectrum disorder. Neuropsychology 2007, 21, 74. [Google Scholar] [CrossRef]
  77. Stroganova, T.A.; Nygren, G.; Tsetlin, M.M.; Posikera, I.N.; Gillberg, C.; Elam, M.; Orekhova, E.V. Abnormal EEG lateralization in boys with autism. Clin. Neurophysiol. 2007, 118, 1842–1854. [Google Scholar] [CrossRef]
  78. Daoust, A.-M.; Limoges, É.; Bolduc, C.; Mottron, L.; Godbout, R. EEG spectral analysis of wakefulness and REM sleep in high functioning autistic spectrum disorders. Clin. Neurophysiol. 2004, 115, 1368–1373. [Google Scholar] [CrossRef]
  79. Orekhova, E.V.; Stroganova, T.A.; Nygren, G.; Tsetlin, M.M.; Posikera, I.N.; Gillberg, C.; Elam, M. Excess of high frequency electroencephalogram oscillations in boys with autism. Biol. Psychiatry 2007, 62, 1022–1029. [Google Scholar] [CrossRef]
  80. Murias, M.; Webb, S.J.; Greenson, J.; Dawson, G. Resting state cortical connectivity reflected in EEG coherence in individuals with autism. Biol. Psychiatry 2007, 62, 270–273. [Google Scholar] [CrossRef]
  81. Dawson, G.; Klinger, L.G.; Panagiotides, H.; Lewy, A.; Castelloe, P. Subgroups of autistic children based on social behavior display distinct patterns of brain activity. J. Abnorm. Child Psychol. 1995, 23, 569–583. [Google Scholar] [CrossRef]
  82. Paula, C.A.R.; Reategui, C.; de Sousa Costa, B.K.; da Fonseca, C.Q.; da Silva, L.; Morya, E.; Brasil, F.L. High-Frequency EEG Variations in Children with Autism Spectrum Disorder during Human Faces Visualization. BioMed Res. Int. 2017, 2017, 3591914. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Dickinson, A.; DiStefano, C.; Senturk, D.; Jeste, S.S. Peak alpha frequency is a neural marker of cognitive function across the autism spectrum. Eur. J. Neurosci. 2018, 47, 643–651. [Google Scholar] [CrossRef] [PubMed]
  84. Gabard-Durnam, L.J.; Wilkinson, C.; Kapur, K.; Tager-Flusberg, H.; Levin, A.R.; Nelson, C.A. Longitudinal EEG power in the first postnatal year differentiates autism outcomes. Nat. Commun. 2019, 10, 4188. [Google Scholar] [CrossRef]
  85. Neuhaus, E.; Lowry, S.J.; Santhosh, M.; Kresse, A.; Edwards, L.A.; Keller, J.; Libsack, E.J.; Kang, V.Y.; Naples, A.; Jack, A.; et al. Resting state EEG in youth with ASD: Age, sex, and relation to phenotype. J. Neurodev. Disord. 2021, 13, 33. [Google Scholar] [CrossRef]
  86. Wang, J.; Ethridge, L.E.; Mosconi, M.W.; White, S.P.; Binder, D.K.; Pedapati, E.V.; Erickson, C.A.; Byerly, M.J.; Sweeney, J.A. A resting EEG study of neocortical hyperexcitability and altered functional connectivity in fragile X syndrome. J. Neurodev. Disord. 2017, 9, 1–12. [Google Scholar] [CrossRef] [PubMed]
  87. Edgar, J.C.; Dipiero, M.; McBride, E.; Green, H.L.; Berman, J.; Ku, M.; Liu, S.; Blaskey, L.; Kuschner, E.; Airey, M.; et al. Abnormal maturation of the resting-state peak alpha frequency in children with autism spectrum disorder. Hum. Brain Mapp. 2019, 40, 3288–3298. [Google Scholar] [CrossRef]
  88. Möhrle, D.; Fernández, M.; Peñagarikano, O.; Frick, A.; Allman, B.; Schmid, S. What we can learn from a genetic rodent model about autism. Neurosci. Biobehav. Rev. 2020, 109, 29–53. [Google Scholar] [CrossRef]
  89. Pasciuto, E.; Borrie, S.C.; Kanellopoulos, A.K.; Santos, A.R.; Cappuyns, E.; D’Andrea, L.; Pacini, L.; Bagni, C. Autism Spectrum Disorders: Translating human deficits into mouse behavior. Neurobiol. Learn. Mem. 2015, 124, 71–87. [Google Scholar] [CrossRef]
  90. Provenzano, G.; Chelini, G.; Bozzi, Y. Genetic control of social behavior: Lessons from mutant mice. Behav. Brain Res. 2017, 325, 237–250. [Google Scholar] [CrossRef]
  91. Silverman, J.L.; Yang, M.; Lord, C.; Crawley, J.N. Behavioural phenotyping assays for mouse models of autism. Nat. Rev. Neuroscience 2010, 11, 490–502. [Google Scholar] [CrossRef]
  92. Kazdoba, T.M.; Leach, P.T.; Yang, M.; Silverman, J.L.; Solomon, M.; Crawley, J.N. Translational mouse models of autism: Advancing toward pharmacological therapeutics. Transl. Neuropsychopharmacol. 2015, 28, 1–52. [Google Scholar]
  93. Cuddapah, V.A.; Pillai, R.B.; Shekar, K.V.; Lane, J.B.; Motil, K.J.; Skinner, S.A.; Tarquinio, D.C.; Glaze, D.G.; McGwin, G.; Kaufmann, W.E.; et al. Methyl-CpG-binding protein 2 (MECP2) mutation type is associated with disease severity in Rett syndrome. J. Med. Genet. 2014, 51, 152–158. [Google Scholar] [CrossRef] [Green Version]
  94. Olson, C.O.; Zachariah, R.M.; Ezeonwuka, C.D.; Liyanage, V.R.B.; Rastegar, M. Brain Region-Specific Expression of MeCP2 Isoforms Correlates with DNA Methylation within Mecp2 Regulatory Elements. PLoS ONE 2014, 9, e90645. [Google Scholar] [CrossRef]
  95. Rhees, R.W.; Shryne, J.E.; Gorski, R.A. Onset of the hormone-sensitive perinatal period for sexual differentiation of the sexually dimorphic nucleus of the preoptic area in female rats. J. Neurobiol. 1990, 21, 781–786. [Google Scholar] [CrossRef] [PubMed]
  96. Arnold, A.P.; Gorski, R.A. Gonadal steroid induction of structural sex differences in the central nervous system. Annu. Rev. Neurosci. 1984, 7, 413–442. [Google Scholar] [CrossRef] [PubMed]
  97. Kurian, J.R.; Forbes-Lorman, R.M.; Auger, A.P. Sex difference in mecp2 expression during a critical period of rat brain development. Epigenetics 2007, 2, 173–178. [Google Scholar] [CrossRef]
  98. Kim, K.C.; Choi, C.S.; Kim, J.W.; Han, S.H.; Cheong, J.H.; Ryu, J.H.; Shin, C.Y. MeCP2 Modulates Sex Differences in the Postsynaptic Development of the Valproate Animal Model of Autism. Mol. Neurobiol. 2016, 53, 40–56. [Google Scholar] [CrossRef] [PubMed]
  99. Jeon, S.J.; Gonzales, E.L.; Mabunga, D.F.N.; Valencia, S.T.; Kim, D.G.; Kim, Y.; Adil, K.J.L.; Shin, D.; Park, D.; Shin, C.Y. Sex-specific Behavioral Features of Rodent Models of Autism Spectrum Disorder. Exp. Neurobiol. 2018, 27, 321–343. [Google Scholar] [CrossRef]
  100. Kerr, B.; Alvarez-Saavedra, M.; Sáez, M.A.; Saona, A.; Young, J.I. Defective body-weight regulation, motor control and abnormal social interactions in Mecp2 hypomorphic mice. Hum. Mol. Genet. 2008, 17, 1707–1717. [Google Scholar] [CrossRef]
  101. Guy, J.; Hendrich, B.; Holmes, M.; Martin, J.E.; Bird, A. A mouse Mecp2-null mutation causes neurological symptoms that mimic Rett syndrome. Nat. Genet. 2001, 27, 322–326. [Google Scholar] [CrossRef]
  102. Chen, R.Z.; Akbarian, S.; Tudor, M.; Jaenisch, R. Deficiency of methyl-CpG binding protein-2 in CNS neurons results in a Rett-like phenotype in mice. Nat. Genet. 2001, 27, 327–331. [Google Scholar] [CrossRef]
  103. Vashi, N.; Justice, M.J. Treating Rett syndrome: From mouse models to human therapies. Mamm Genome 2019, 30, 90–110. [Google Scholar] [CrossRef] [Green Version]
  104. Maurin, T.; Zongaro, S.; Bardoni, B. Fragile X Syndrome: From molecular pathology to therapy. Neurosci. Biobehav. Rev. 2014, 46 Pt 2, 242–255. [Google Scholar] [CrossRef]
  105. Christie, S.B.; Akins, M.R.; Schwob, J.E.; Fallon, J.R. The FXG: A presynaptic fragile X granule expressed in a subset of developing brain circuits. J. Neurosci. 2009, 29, 1514–1524. [Google Scholar] [CrossRef]
  106. Stefani, G.; Fraser, C.E.; Darnell, J.C.; Darnell, R.B. Fragile X mental retardation protein is associated with translating polyribosomes in neuronal cells. J. Neurosci. 2004, 24, 7272–7276. [Google Scholar] [CrossRef]
  107. Darnell, J.C.; Van Driesche, S.J.; Zhang, C.; Hung, K.Y.; Mele, A.; Fraser, C.E.; Stone, E.F.; Chen, C.; Fak, J.J.; Chi, S.W.; et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 2011, 146, 247–261. [Google Scholar] [CrossRef] [PubMed]
  108. Baker, K.; Wray, S.; Ritter, R.; Mason, S.; Lanthorn, T.; Savelieva, K. Male and female Fmr1 knockout mice on C57 albino background exhibit spatial learning and memory impairments. Genes Brain Behav. 2010, 9, 562–574. [Google Scholar] [CrossRef]
  109. Ding, Q.; Sethna, F.; Wang, H. Behavioral analysis of male and female Fmr1 knockout mice on C57BL/6 background. Behav. Brain Res. 2014, 271, 72–78. [Google Scholar] [CrossRef] [PubMed]
  110. Hamilton, S.M.; Green, J.R.; Veeraragavan, S.; Yuva, L.; McCoy, A.; Wu, Y.; Warren, J.; Little, L.; Ji, D.; Cui, X. Fmr1 and Nlgn3 knockout rats: Novel tools for investigating autism spectrum disorders. Behav. Neurosci. 2014, 128, 103. [Google Scholar] [CrossRef] [PubMed]
  111. Tian, Y.; Yang, C.; Shang, S.; Cai, Y.; Deng, X.; Zhang, J. Loss of FMRP Impaired Hippocampal Long-Term Plasticity and Spatial Learning in Rats. Front. Mol. Neurosci. 2017, 10, 269. [Google Scholar] [CrossRef]
  112. Nolan, S.; Reynolds, C.; Smith, G.; Holley, A.; Escobar, B.; Chandler, M.; Volquardsen, M.; Jefferson, T.; Pandian, A.; Smith, T. Deletion of Fmr1 results in sex-specific changes in behavior. Brain Behav. 2017, 7, e00800. [Google Scholar] [CrossRef]
  113. Gauducheau, M.; Lemaire-Mayo, V.; D’Amato, F.R.; Oddi, D.; Crusio, W.E.; Pietropaolo, S. Age-specific autistic-like behaviors in heterozygous Fmr1-KO female mice. Autism Res. 2017, 10, 1067–1078. [Google Scholar] [CrossRef]
  114. Reynolds, C.D.; Nolan, S.O.; Jefferson, T.; Lugo, J.N. Sex-& Genotype-Specific Differences in Vocalization Development in FMR1 Knockout Mice. Neuroreport 2016, 27, 1331. [Google Scholar]
  115. Andreae, L.C.; Basson, M.A. Sex bias in autism: New insights from Chd8 mutant mice? Nat. Neurosci. 2018, 21, 1144–1146. [Google Scholar] [CrossRef]
  116. Tilot, A.K.; Gaugler, M.K.; Yu, Q.; Romigh, T.; Yu, W.; Miller, R.H.; Frazier, T.W.; Eng, C. Germline disruption of Pten localization causes enhanced sex-dependent social motivation and increased glial production. Hum. Mol. Genet. 2014, 23, 3212–3227. [Google Scholar] [CrossRef]
  117. Jung, H.; Park, H.; Choi, Y.; Kang, H.; Lee, E.; Kweon, H.; Roh, J.D.; Ellegood, J.; Choi, W.; Kang, J.; et al. Sexually dimorphic behavior, neuronal activity, and gene expression in Chd8-mutant mice. Nat. Neurosci. 2018, 21, 1218–1228. [Google Scholar] [CrossRef]
  118. Lee, S.Y.; Kweon, H.; Kang, H.; Kim, E. Age-differential sexual dimorphism in CHD8-S62X-mutant mouse behaviors. Front. Mol. Neurosci. 2022, 15, 1022306. [Google Scholar] [CrossRef]
  119. Binder, M.S.; Lugo, J.N. NS-Pten knockout mice show sex-and age-specific differences in ultrasonic vocalizations. Brain Behav. 2017, 7, e00857. [Google Scholar] [CrossRef] [PubMed]
  120. McFarlane, H.G.; Kusek, G.; Yang, M.; Phoenix, J.; Bolivar, V.; Crawley, J. Autism-like behavioral phenotypes in BTBR T+ tf/J mice. Genes Brain Behav. 2008, 7, 152–163. [Google Scholar] [CrossRef] [PubMed]
  121. Schwartzer, J.J.; Careaga, M.; Onore, C.E.; Rushakoff, J.A.; Berman, R.F.; Ashwood, P. Maternal immune activation and strain specific interactions in the development of autism-like behaviors in mice. Transl. Psychiatry 2013, 3, e240. [Google Scholar] [CrossRef] [PubMed]
  122. Amodeo, D.A.; Pahua, A.E.; Zarate, M.; Taylor, J.A.; Peterson, S.; Posadas, R.; Oliver, B.L.; Amodeo, L.R. Differences in the expression of restricted repetitive behaviors in female and male BTBR T + tf/J mice. Behav. Brain Res. 2019, 372, 112028. [Google Scholar] [CrossRef]
  123. Matas, E.; Maisterrena, A.; Thabault, M.; Balado, E.; Francheteau, M.; Balbous, A.; Galvan, L.; Jaber, M. Major motor and gait deficits with sexual dimorphism in a Shank3 mutant mouse model. Mol. Autism 2021, 12, 2. [Google Scholar] [CrossRef] [PubMed]
  124. Kouser, M.; Speed, H.E.; Dewey, C.M.; Reimers, J.M.; Widman, A.J.; Gupta, N.; Liu, S.; Jaramillo, T.C.; Bangash, M.; Xiao, B.; et al. Loss of predominant Shank3 isoforms results in hippocampus-dependent impairments in behavior and synaptic transmission. J. Neurosci. Off. J. Soc. Neurosci. 2013, 33, 18448–18468. [Google Scholar] [CrossRef] [Green Version]
  125. Kim, K.C.; Cho, K.S.; Yang, S.M.; Gonzales, E.L.; Valencia, S.; Eun, P.H.; Choi, C.S.; Mabunga, D.F.; Kim, J.-W.; Noh, J.K.; et al. Sex Differences in Autism-Like Behavioral Phenotypes and Postsynaptic Receptors Expression in the Prefrontal Cortex of TERT Transgenic Mice. Biomol. Ther. 2017, 25, 374–382. [Google Scholar] [CrossRef] [PubMed]
  126. Bódi, V.; Májer, T.; Kelemen, V.; Világi, I.; Szűcs, A.; Varró, P. Alterations of the Hippocampal Networks in Valproic Acid-Induced Rat Autism Model. Front. Neural Circuits 2022, 16. [Google Scholar] [CrossRef] [PubMed]
  127. Kim, K.C.; Rhee, J.; Park, J.-E.; Lee, D.-K.; Choi, C.S.; Kim, J.-W.; Lee, H.-W.; Song, M.-R.; Yoo, H.J.; Chung, C. Overexpression of telomerase reverse transcriptase induces autism-like excitatory phenotypes in mice. Mol. Neurobiol. 2016, 53, 7312–7328. [Google Scholar] [CrossRef]
  128. Jeehae, R.; Kwanghoon, P.; Ki Chan, K.; Chan Young, S.; ChiHye, C. Impaired Hippocampal Synaptic Plasticity and Enhanced Excitatory Transmission in a Novel Animal Model of Autism Spectrum Disorders with Telomerase Reverse Transcriptase Overexpression. Mol. Cells 2018, 41, 486–494. [Google Scholar] [CrossRef]
  129. Rodier, P.M.; Ingram, J.L.; Tisdale, B.; Nelson, S.; Romano, J. Embryological origin for autism: Developmental anomalies of the cranial nerve motor nuclei. J. Comp. Neurol. 1996, 370, 247–261. [Google Scholar] [CrossRef]
  130. Ornoy, A. Valproic acid in pregnancy: How much are we endangering the embryo and fetus? Reprod. Toxicol. 2009, 28, 1–10. [Google Scholar] [CrossRef]
  131. Schneider, T.; Przewlocki, R. Behavioral alterations in rats prenatally exposed to valproic acid: Animal model of autism. Neuropsychopharmacol. Off. Publ. Am. Coll. Neuropsychopharmacol. 2005, 30, 80–89. [Google Scholar] [CrossRef]
  132. Lucchina, L.; Depino, A.M. Altered peripheral and central inflammatory responses in a mouse model of autism. Autism Res. 2014, 7, 273–289. [Google Scholar] [CrossRef]
  133. Cheaha, D.; Bumrungsri, S.; Chatpun, S.; Kumarnsit, E. Characterization of in utero valproic acid mouse model of autism by local field potential in the hippocampus and the olfactory bulb. Neurosci. Res. 2015, 98, 28–34. [Google Scholar] [CrossRef]
  134. Hara, Y.; Ago, Y.; Taruta, A.; Katashiba, K.; Hasebe, S.; Takano, E.; Onaka, Y.; Hashimoto, H.; Matsuda, T.; Takuma, K. Improvement by methylphenidate and atomoxetine of social interaction deficits and recognition memory impairment in a mouse model of valproic acid-induced autism. Autism Res. Off. J. Int. Soc. Autism Res. 2015, 9, 926–939. [Google Scholar] [CrossRef] [PubMed]
  135. Narita, M.; Oyabu, A.; Imura, Y.; Kamada, N.; Yokoyama, T.; Tano, K.; Uchida, A.; Narita, N. Nonexploratory movement and behavioral alterations in a thalidomide or valproic acid-induced autism model rat. Neurosci. Res. 2010, 66, 2–6. [Google Scholar] [CrossRef]
  136. Bringas, M.E.; Carvajal-Flores, F.N.; Lopez-Ramirez, T.A.; Atzori, M.; Flores, G. Rearrangement of the dendritic morphology in limbic regions and altered exploratory behavior in a rat model of autism spectrum disorder. Neuroscience 2013, 241, 170–187. [Google Scholar] [CrossRef] [PubMed]
  137. Gao, J.; Wang, X.; Sun, H.; Cao, Y.; Liang, S.; Wang, H.; Wang, Y.; Yang, F.; Zhang, F.; Wu, L. Neuroprotective effects of docosahexaenoic acid on hippocampal cell death and learning and memory impairments in a valproic acid-induced rat autism model. Int. J. Dev. Neurosci. Off. J. Int. Soc. Dev. Neurosci. 2016, 49, 67–78. [Google Scholar] [CrossRef] [PubMed]
  138. Mohammadkhani, R.; Ghahremani, R.; Salehi, I.; Safari, S.; Karimi, S.A.; Zarei, M. Impairment in social interaction and hippocampal long-term potentiation at perforant pathway-dentate gyrus synapses in a prenatal valproic acid-induced rat model of autism. Brain Commun. 2022, 4, fcac221. [Google Scholar] [CrossRef]
  139. Schneider, T.; Roman, A.; Basta-Kaim, A.; Kubera, M.; Budziszewska, B.; Schneider, K.; Przewlocki, R. Gender-specific behavioral and immunological alterations in an animal model of autism induced by prenatal exposure to valproic acid. Psychoneuroendocrinology 2008, 33, 728–740. [Google Scholar] [CrossRef]
  140. Kataoka, S.; Takuma, K.; Hara, Y.; Maeda, Y.; Ago, Y.; Matsuda, T. Autism-like behaviours with transient histone hyperacetylation in mice treated prenatally with valproic acid. Int. J. Neuropsychopharmacol. 2013, 16, 91–103. [Google Scholar] [CrossRef]
  141. Kazlauskas, N.; Seiffe, A.; Campolongo, M.; Zappala, C.; Depino, A.M. Sex-specific effects of prenatal valproic acid exposure on sociability and neuroinflammation: Relevance for susceptibility and resilience in autism. Psychoneuroendocrinology 2019, 110, 104441. [Google Scholar] [CrossRef]
  142. Ghahremani, R.; Mohammadkhani, R.; Salehi, I.; Karimi, S.A.; Zarei, M. Sex Differences in Spatial Learning and Memory in Valproic Acid Rat Model of Autism: Possible Beneficial Role of Exercise Interventions. Front. Behav. Neurosci. 2022, 16, 869792. [Google Scholar] [CrossRef]
  143. Hara, Y.; Maeda, Y.; Kataoka, S.; Ago, Y.; Takuma, K.; Matsuda, T. Effect of prenatal valproic acid exposure on cortical morphology in female mice. J. Pharmacol. Sci. 2012, 118, 543–546. [Google Scholar] [CrossRef] [PubMed]
  144. Konopko, M.A.; Densmore, A.L.; Krueger, B.K. Sexually Dimorphic Epigenetic Regulation of Brain-Derived Neurotrophic Factor in Fetal Brain in the Valproic Acid Model of Autism Spectrum Disorder. Dev. Neurosci. 2017, 39, 507–518. [Google Scholar] [CrossRef] [PubMed]
  145. Gu, Y.Y.; Han, Y.; Liang, J.J.; Cui, Y.N.; Zhang, B.; Zhang, Y.; Zhang, S.B.; Qin, J. Sex-specific differences in the gut microbiota and fecal metabolites in an adolescent valproic acid-induced rat autism model. Front. Biosci. 2021, 26, 1585–1598. [Google Scholar] [CrossRef] [PubMed]
  146. Scheggi, S.; Guzzi, F.; Braccagni, G.; De Montis, M.G.; Parenti, M.; Gambarana, C. Targeting PPARalpha in the rat valproic acid model of autism: Focus on social motivational impairment and sex-related differences. Mol. Autism 2020, 11, 62. [Google Scholar] [CrossRef]
  147. Christensen, J.; Grønborg, T.K.; Sørensen, M.J.; Schendel, D.; Parner, E.T.; Pedersen, L.H.; Vestergaard, M. Prenatal valproate exposure and risk of autism spectrum disorders and childhood autism. JAMA 2013, 309, 1696–1703. [Google Scholar] [CrossRef]
  148. Ornoy, A.; Weinstein-Fudim, L.; Ergaz, Z. Genetic Syndromes, Maternal Diseases and Antenatal Factors Associated with Autism Spectrum Disorders (ASD). Front. Neurosci. 2016, 10, 316. [Google Scholar] [CrossRef] [PubMed]
  149. Carayol, J.; Schellenberg, G.D.; Dombroski, B.; Genin, E.; Rousseau, F.; Dawson, G. Autism risk assessment in siblings of affected children using sex-specific genetic scores. Mol. Autism 2011, 2, 17. [Google Scholar] [CrossRef]
  150. Tropeano, M.; Ahn, J.W.; Dobson, R.J.; Breen, G.; Rucker, J.; Dixit, A.; Pal, D.K.; McGuffin, P.; Farmer, A.; White, P.S.; et al. Male-biased autosomal effect of 16p13.11 copy number variation in neurodevelopmental disorders. PLoS ONE 2013, 8, e61365. [Google Scholar] [CrossRef]
  151. Sato, D.; Lionel, A.C.; Leblond, C.S.; Prasad, A.; Pinto, D.; Walker, S.; O’Connor, I.; Russell, C.; Drmic, I.E.; Hamdan, F.F.; et al. SHANK1 Deletions in Males with Autism Spectrum Disorder. Am. J. Hum. Genet. 2012, 90, 879–887. [Google Scholar] [CrossRef]
  152. Zhou, Y.; Qiu, L.; Sterpka, A.; Wang, H.; Chu, F.; Chen, X. Comparative Phosphoproteomic Profiling of Type III Adenylyl Cyclase Knockout and Control, Male, and Female Mice. Front. Cell Neurosci. 2019, 13, 34. [Google Scholar] [CrossRef] [PubMed]
  153. Trabzuni, D.; Ramasamy, A.; Imran, S.; Walker, R.; Smith, C.; Weale, M.E.; Hardy, J.; Ryten, M.; North American Brain Expression, C. Widespread sex differences in gene expression and splicing in the adult human brain. Nat. Commun. 2013, 4, 2771. [Google Scholar] [CrossRef] [PubMed]
  154. Hegde, R.; Hegde, S.; Kulkarni, S.S.; Pandurangi, A.; Gai, P.B.; Das, K.K. Genetic analysis of the postsynaptic transmembrane X-linked neuroligin 3 gene in autism. Genom. Inf. 2021, 19, e44. [Google Scholar] [CrossRef] [PubMed]
  155. Arnold, M.L.; Saijo, K. Estrogen Receptor beta as a Candidate Regulator of Sex Differences in the Maternal Immune Activation Model of ASD. Front. Mol. Neurosci. 2021, 14, 717411. [Google Scholar] [CrossRef]
  156. McCarthy, M.M.; Wright, C.L. Convergence of Sex Differences and the Neuroimmune System in Autism Spectrum Disorder. Biol. Psychiatry 2017, 81, 402–410. [Google Scholar] [CrossRef]
  157. Werling, D.M.; Parikshak, N.N.; Geschwind, D.H. Gene expression in human brain implicates sexually dimorphic pathways in autism spectrum disorders. Nat. Commun. 2016, 7, 10717. [Google Scholar] [CrossRef] [PubMed]
  158. Baron-Cohen, S.; Lombardo, M.V.; Auyeung, B.; Ashwin, E.; Chakrabarti, B.; Knickmeyer, R. Why are autism spectrum conditions more prevalent in males? PLoS Biol. 2011, 9, e1001081. [Google Scholar] [CrossRef] [Green Version]
Table 1. Sex differences in brain structures and functions evaluated by fMRI and EEG, respectively.
Table 1. Sex differences in brain structures and functions evaluated by fMRI and EEG, respectively.
AuthorMethod of Study/Brain Region of Interest/Participants of StudySex Differences
Cauvet et al. [57]fMRI in correlation with social cognition tasks. Various brain regions. Twin couples of adolescent age.Angular and supramarginal gyri in autistic females showed smaller thicknesses than the corresponding regions in males.
van’t Westeinde et al. [58]fMRI in correlation with repetitive or restricted behaviors (RBBs) movement evaluation. Various brain regions. Twin couples of adolescent age.Autistic females showed significantly increased thickness of right intraparietal sulcus in association with RBBs. Autistic males exhibited a correlation between RBBs and increased volume of the globus pallidum.
Supekar and Menon [59]Structural MRI with gray matter volume evaluation in association with RBBs and social functions in various brain regions. Typically developed and autistic girls and boys 10 years of age were chosen from the ABIDE dataset.Autistic girls exhibired fewer RBBs than boys. Organization of gray matter was different for autistic males and females in the systems linked with motor regulation: motor cortex, crus I of cerebellum, and supplementary motor area. Regions linked with “social brain” angular and fusiform gyri, amygdala, and insula were also different. Gray matter organization in the right putamen was associated with severity of RBBs in males. Social brain differences were not associated with severity of symptoms of sociability.
Lawrence et al. [54]fMRI with functional connectivity analysis of salience, central executive, and default mode networks. Nearly 50 autistic and typically developed children studied.Overconnectivity in autistic females between DMN and CEN, whereas in males a decrease was observed.
Smith et al. [60]Resting-state fMRI in correlation with RBBs, with analysis of intracerebellar connectivity. Twenty-one-year-old neurotypical and autistic males and females.In autistic males: decreased pattern of connectivity; in females: increased connectivity. Both groups were compared with typically developed individuals.
Neuhaus et al. [61]Resting-state EEG. Whole-scalp recording. Autistic and typically developed children 8–18 years old.Autistic females’ oscillatory frames showed diminished power in comparison with those of males. Males possessed lower alpha and theta powers in correlation with enhanced sociability. Higher gamma power was associated with RBBs in males.
Smith et al. [62]Resting-state EEG. Fragile X and typically developed children.Fragile X male patients had lower peak alpha frequencies than typically developed children, without any differences in females.
Table 2. Sex differences in genetic mouse models.
Table 2. Sex differences in genetic mouse models.
AuthorSpecific GeneSex Differences
Jeon SJ et al. [99]
Kerr, B. et al. [100]
Mecp2 geneThe transcriptome analysis of cortex tissues and microglia of 22–24-week-old Mecp2-knockout (KO) mice revealed 149 differentially expressed genes (DEGs) for male mice and 430 DEGs for female mice. The DEGs shared by both male and female mice were mainly related to transport processes [99].
The main phenotypic dimorphism was related to body weight: females weighed less than males [100].
Nolan et al. [112]
Gauducheau, M [113]
Reynolds, C.D [114]
Fmr1 geneFmr1 KO homozygous females displayed increased repetitive behaviors when tested in the nose-poke test and enhanced motor coordination on the accelerating rotarod. Fmr1 KO males showed only hyperactivity in the open field [112].
Heterozygous Fmr1 KO female mice exhibited abnormal sociability at infancy and at the juvenile stage [113,114].
The abnormal behavior of Fmr1 KO female mice has a temporal pattern of autistic-like behavior [113].
Andreae, L.C. et al. [115]
Lee, S.Y. et al. [118]
Jung, H. et al. [117]
Chd8 geneChd8-mutant mice (knock-in) demonstrated male-preponderant behavioral deficits, evaluated at birth, juvenile, and adult stages [115]. ASD-like behaviors are accompanied by elevated neuronal activation in the hippocampus and prefrontal cortex under stressful conditions and changes in pups in terms of ultrasonic vocalization [115]. In female mice, reduced brain baseline activity was found, which normalized upon maternal separation, though not in males [115].
Altered neuronal, synaptic, and transcriptomic phenotypes were more prominent in male than in female mice [117,118].
Binder, M.S. et al. [119]Pten geneIn neuronal-subset-specific (NS-Pten) knockout male and female pups, the analysis of ultrasonic vocalizations measured on postnatal days 8 and 11 revealed sex dimorphism in vocalization duration, amplitude, and fundamental frequency. Mice of both sexes emitted equal numbers of calls [119].
McFarlane et al. 2 [120]
Schwartzer et al. [121]
Amodeo, D.A et al. [122]
Btbr geneMale predominance was also reported in studies using the Btbr T+ tf/J inbred mouse model of ASD [120]. Impairments were observed in sociability and social communication, ultrasonic vocalizations, marble burying, and self-grooming behaviors, these being observed more frequently in male than in female mice [121,122].
Matas, E et al. [123]Shank 3 geneGait parameters demonstrated more severe disturbances in Shank3 ΔC/ΔC male mice than in females and were accompanied by decrease in the levels of cerebellar mGluR5 protein only in male mice [123].
Kim, K.C. et al. [125]Tert geneTERT-tg male mice demonstrated decreased sociability and social novelty preferences, reduced anxiety behavior, and decreased electro-seizure thresholds compared to females. In the prefrontal cortexes of TERT-tg male mice, increased expressions of postsynaptic NMDA and AMPA receptor subunit proteins were observed only in male mice [125].
Table 3. Sex differences in VPA-induced ASD-like behavioral characteristics.
Table 3. Sex differences in VPA-induced ASD-like behavioral characteristics.
AuthorVPA Administration Sex Differences
Kataoka et al. [140]Prenatal day 12.5, 500 mg/kg, miceSocial interaction deficits (decrease in sniffing behavior) at 8 weeks of age were observed in male but not in female mice.
Kazlauskas et al. [141]Prenatal day 12.5, 600 mg/kg, miceVPA-exposed males but not females exhibited reduced sociability levels and a lack of preference for the social stimulus over a novel object, as well as increased basal corticosterone levels in response to an inflammatory stimulus.
Ghahremani et al. [142]Prenatal day 12.5, 500 mg/kg, ratsVPA-exposed female offspring showed better performance than VPA-exposed male offspring in Morris water-maze acquisition, which represents spatial learning and memory.
Hara et al. [143]Prenatal day 12.5, 500 mg/kg, miceVPA-exposed female but not male offspring exhibited decreased Nissl-positive cell numbers in the prefrontal cortex.
Knopko et al. [144]Prenatal day 12.5, 400 mg/kg, miceVPA-exposed male and female offspring exhibited differences in mRNA levels of Bdnf transcripts, which may be involved in the female protective effect in ASD.
Kim et al. [98]Prenatal day 12 400 mg/kg, ratsVPA-exposed male but not female offspring showed reduced levels of methyl-CpG-binding protein 2 (MeCP2) in the prefrontal cortex.
Gu et al. [145]Prenatal day 12.5, 600 mg/kg, ratsDifferentially abundant fecal metabolites and alterations in the gut microbiota reported in both male and female VPA-exposed rats, with sex-specific differences.
Scheggi et al. [146]Prenatal day 12.5, 500 mg/kg, ratsTreatment of VPA-exposed rats with a fenofibrate-enriched diet, a peroxisome-activated receptor α agonist, reduced social impairment, and persistent behavior in females but not males.
Ornoy et al. [8]Postnatal day 4, 300 mg/Kg, miceBehavioral studies showed reduced sociability in VPA-treated males but not in females, while VPA-treated females exhibited higher anxiety and reduced memory compared to VPA-treated males. Sex differences in oxidative stress markers have also been reported.
Weinstein-Fudim et al. [9]Postnatal day 4, 300 mg/Kg, miceGene expression analysis of VPA-treated pups revealed a wide range of gene expression changes with significant sex differences. Most of these changes were corrected by SAMe treatment.
Weinstein-Fudim et al. [10]SAMe and VPA on prenatal day 12.5, 600 mg/Kg VPA, miceVPA prevented SAMe-induced changes in gene expression in the brain in a sex-related manner.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ornoy, A.; Gorobets, D.; Weinstein-Fudim, L.; Becker, M. Sex-Related Changes in the Clinical, Genetic, Electrophysiological, Connectivity, and Molecular Presentations of ASD: A Comparison between Human and Animal Models of ASD with Reference to Our Data. Int. J. Mol. Sci. 2023, 24, 3287. https://doi.org/10.3390/ijms24043287

AMA Style

Ornoy A, Gorobets D, Weinstein-Fudim L, Becker M. Sex-Related Changes in the Clinical, Genetic, Electrophysiological, Connectivity, and Molecular Presentations of ASD: A Comparison between Human and Animal Models of ASD with Reference to Our Data. International Journal of Molecular Sciences. 2023; 24(4):3287. https://doi.org/10.3390/ijms24043287

Chicago/Turabian Style

Ornoy, Asher, Denis Gorobets, Liza Weinstein-Fudim, and Maria Becker. 2023. "Sex-Related Changes in the Clinical, Genetic, Electrophysiological, Connectivity, and Molecular Presentations of ASD: A Comparison between Human and Animal Models of ASD with Reference to Our Data" International Journal of Molecular Sciences 24, no. 4: 3287. https://doi.org/10.3390/ijms24043287

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop