Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is associated with functional brain alterations that underlie the expression of behaviour. Males are diagnosed up to four times more than females, and sex differences have been identified in memory, cognitive flexibility, verbal fluency, and social communication. Unfortunately, there exists a lack of information on the sex-dependent mechanisms of ASD, as well as biological markers to distinguish sex-specific symptoms in ASD. This can often result in a standardized diagnosis for individuals across the spectrum, despite significant differences in the various ASD subtypes. Alterations in neuronal connectivity and oscillatory activity, such as is observed in ASD, are highly coupled to behavioural states. Yet, despite the well-identified sexual dimorphisms that exist in ASD, these functional patterns have rarely been analyzed in the context of sex differences or symptomology. This review summarizes alterations in neuronal oscillatory function in ASD, discusses the age, region, symptom and sex-specific differences that are currently observed across the spectrum, and potential targets for regulating neuronal oscillatory activity in ASD. The need to identify sex-specific biomarkers, in order to facilitate specific diagnostic criteria and allow for more targeted therapeutic approaches for ASD will also be discussed.
Similar content being viewed by others
Introduction
Autism spectrum disorders (ASD) are a complex group of neurodevelopmental disorders characterized by a broad range of cognitive and behavioural symptoms of varying severity. With an increasing worldwide prevalence ASD affects approximately 1 in 160 children [1], and although genetic factors contributing to ASD have been identified, the disorder is most often idiopathic with no known cause [2]. ASD is primarily diagnosed during childhood or early adolescence when social communication difficulties are present alongside impaired social interactions, difficulty understanding others’ emotions, excessively repetitive behaviours, restricted interests, insistence on sameness, and in 90% of cases sensory atypicalities are observed [3,4,5]. It is also important to note that the type of sensory atypicalities vary between individuals diagnosed upon the spectrum, as hypo- or hypersensitivities can occur [5, 6]. Unfortunately, due to the wide range of behavioural characteristics, there is frequent misdiagnosis or a delayed diagnosis [7]. Added to the complexity of ASD is the high comorbidity with other neurological conditions including attention deficit hyperactivity disorder, intellectual disability, anxiety, obsessive-compulsive disorder, seizures and cerebral palsy [8, 9]. Although pharmacological interventions are often used to treat the comorbidities, currently there is no cure for ASD, with behavioural therapies most often employed.
There are prominent sex differences in both the prevalence and behavioural characteristics of ASD. Male individuals are up to four times more likely than female individuals to develop ASD and are most often diagnosed in childhood, whereas females are commonly diagnosed later in adolescence, or even in early adulthood [10]. For many years it was assumed that male and female patients exhibited similar symptoms on the spectrum, as reports detailed a “hyper-masculinization” of many of the behavioural traits expressed in individuals with ASD, which consequently resulted in the “Extreme Male Brain” (EMB) theory [11,12,13]. The EMB theory is an extension of the “Empathizing-Systemizing” (E-S) theory of sex differences, as it infers that the cognitive profile of individuals with ASD is similar to an extreme version of that of a neurotypical male [11]. More specifically, the E-S theory aims to explain sex differences in populations, independent of age or an ASD diagnosis, by categorizing all individuals into five groups depending on their expression of empathy and systemizing [14]. Empathizing is the ability to sense others emotions, usually allowing feelings to guide appropriate responses, whereas systemizing is the ability to take in information and create rules based on this information, allowing one to expect an outcome and separate emotions from day-to-day tasks [11]. In general, neurotypical males and females express different cognitive profiles with females appearing to have a greater ability to empathize, and males exhibiting stronger systemizing abilities [11]. The EMB theory of ASD proposes that these individuals express a more extreme phenotype of heightened systemizing over empathizing which relates to the “hyper-masculinization” of some behavioural traits in the disorder [11]. The mechanisms underlying such an extreme male profile are not fully understood, though female individuals with ASD have shown significantly higher testosterone levels compared to neurotypical females, which potentially underlies this theory [15]. Despite females expressing such characteristics as described by the EMB theory, it is important to emphasize that not all female individuals with ASD exhibit all male-specific characteristics [12] and therefore the EMB theory of ASD remains controversial [16].
Sexual dimorphisms in ASD symptoms and symptom severity exist in memory, cognitive flexibility, verbal fluency, and social communication [10, 17,18,19]. For example, male individuals express difficulties with social communication/interaction skills and greater repetitive and stereotyped behaviours [10, 17,18,19], whereas females exhibit reduced verbal control, increased fine motor skills, have more difficulty internalizing and expressing emotions, but are more sociable [10, 17]. As the presentation of behaviours in ASD differs across the spectrum, where sex differences are of critical importance, it is highly probable that the associated underlying pathologies differ. Despite little being known about sex differences in the molecular and cellular mechanisms that contribute to ASD, it has been suggested that more drastic neurological changes in neuronal signalling are required for ASD progression in females [20, 21]. However, the fact that children are most often grouped in clinical research studies, rather than separated into sub-groups based on symptoms along the spectrum and sex, has likely contributed to the lack of current knowledge of sex differences. This knowledge could be used to establish much needed biomarkers and novel therapeutic approaches to improve diagnosis, prevention, and treatment of these disorders.
Sex-specific biomarkers in ASD are sorely needed if more tailored therapies are to be developed, yet the identification of biomarkers in ASD in general has proven to be elusive. Some evidence suggests that neuronal oscillatory signatures, or brain waves, may represent a viable approach as these oscillatory patterns are widely accepted to be tightly coupled to behavioural states [22,23,24,25], and to play a central role in the pathology of many neuropsychiatric and neurodevelopmental disorders including ASD [20, 25,26,27,28,29,30,31]. This review will therefore focus on what is presently known about oscillatory dysfunction in ASD, with an emphasis on sex differences where possible. Potential targets in the regulation of neuronal oscillatory activity in the context of ASD will also be discussed.
Neuronal oscillations in ASD
Macroscopic oscillations, derived from the synchronous activity of neuronal ensembles, generate rhythmic brain electrophysiological patterns that are coupled to behavioural and cognitive states (i.e., concentration, attention) [32, 33]. These oscillations are a key indicator of the communication status between neurons, neuronal connectivity or synchrony, with these connections forming circuits that collectively make up the large networks that are involved in cognitive processes [25]. Measurement of these electrophysiological rhythms, or of functional network activity, can be done non-invasively in humans through magnetic resonance imaging (MRI) [18], functional MRI (fMRI) [34,35,36], electroencephalography (EEG) [37,38,39], and magnetoencephalography (MEG) [40]. The blood-oxygen-level dependent (BOLD) signal detected in fMRI is also a potential measure of neuronal oscillations as it has been shown to be linked to neuronal oscillatory activity in various brain regions including visual and auditory cortices [34, 35].
Cognitive states have been linked with the coordinated activity of oscillatory rhythms at both low and high frequencies [22, 24, 25]. Oscillatory rhythms are conventionally defined frequencies which are broken down into five distinct waveforms [22]. In adults, low-frequency bands, delta (0.5–4 Hz), theta (>4–8 Hz), and alpha (>8–12 Hz), are slow waves and are critical in long-distance or between region communication, whereas the high frequency bands, beta (>12–30 Hz), and gamma (>30 Hz) are fast waves that play a role in short-distance or within region communication [22]. However, it is noteworthy that the margins of corresponding bands appear to be lower in infants and children [41]. The synchronization of oscillations plays an important role in information transmission, with the coordinated synchronous activity of neuronal ensembles, termed coherence, integral for effective communication between them [22, 26, 42]. Coherence, a measure of functional connectivity, is often utilized to analyze neuronal activity and communication alterations in various disorders as when functional network activity and coherence begins to fail due to pathogenic alterations, cognitive disruptions can arise [28, 43].
As a neurodevelopmental disorder, neuronal connectivity alterations are observed throughout the development of children with ASD [44]. Such differences in neuronal oscillatory function and connectivity have been observed in several brain regions implicated in ASD including the frontal [18, 36, 37, 39], temporal [18, 40], and occipital cortices [18, 38]. Studies have focused on the relationship between neuronal oscillatory alterations and behaviours in ASD, although many inconsistencies exist, potentially as a result of both the lack of inclusion of sex differences in the studies, as well as the grouping of individuals into a single “ASD group” despite significant variations in symptom presentation.
Delta
Several studies have examined delta power in ASD with conflicting results. For example, in children aged 7–13 years diagnosed with ASD, clinical EEG studies indicate that there is lower frontal cortical delta power as well as lower delta coherence across a number of regions [45, 46]. Similarly, in a prospective longitudinal EEG study of children with a sibling diagnosed with ASD, and therefore defined as having an elevated familial likelihood of developing ASD, it was shown that delta power was lower in these children at 6 months of age, although this normalized over the subsequent 18 months [47]. With these findings, it could be speculated that reduced delta power may serve as a biomarker for ASD, except it is not consistently observed, and indeed, elevated delta power has also been demonstrated. For example, an EEG study examining children aged 6–15 years diagnosed with ASD (specifically Asperger’s syndrome) showed increased delta power during non-REM sleep in frontal cortices [48], a finding also supported by an MEG study that demonstrated elevated resting-state frontal relative delta power in awake individuals with ASD [40]. In a rodent model used for the study of fragile X syndrome, whereby the fragile X mental retardation (fmr-1) gene was deleted in mice, there was an elevation in delta power reported within the auditory cortex and frontal cortex [49]. However, other studies show no changes in delta power in the frontal and posterior cortex of adults with a clinical diagnosis of ASD in eyes open or closed resting state [50], or in whole brain analysis of adults diagnosed specifically with fragile X syndrome [51]. Together these studies indicate that while age-specific alterations in resting frontal cortical delta power may accompany an ASD diagnosis in children, there are clear discrepancies in the findings as to whether delta power is up- or downregulated. A more clear delineation of the ASD subtypes utilized in the studies, and the sex of the participants, would provide important insights into the precise role of cortical delta power in ASD.
Theta
In general, a role for theta in ASD has been poorly characterized, surprising given its predominant role in other disorders that display cognitive changes, such as schizophrenia [52] and attention deficit hyperactivity disorder [53]. Resting-state [46] and cognitive functioning EEG [54, 55] studies have observed a decrease in theta band coherence throughout the brain, and specifically the occipital cortex, in children with ASD. Similarly, infants with a greater familial likelihood of developing ASD have significantly lower frontal theta power than their control counterparts at 6, 12, 18 and 24 months of age [47]. In the right posterior brain, however, elevated theta power has been reported in both children [45] and adults [50, 56] with ASD. Some evidence also indicates lower theta coherence across the frontal, temporal, and parietal cortices with posterior regions of the brain in children [45, 48, 55, 57] and adults [48]. With respect to preclinical work, there is little information on theta waves using ASD model systems. Although in one study, the impact of partial deletion of the pogo transposable element derived with ZNF domain (Pogz), a gene with recurrent mutations in ASD, was evaluated using heterozygous loss of function mice (Pogz+/−) [58]. In this model, a deficit in hippocampal-prefrontal theta synchrony was demonstrated and was thought to underlie anxiety-related avoidance, a behavioural characteristic of these mice [58].
Alpha
Unlike the delta and theta frequencies, the alpha frequency has been studied to a much greater extent in ASD. Some studies identified lower alpha power in central and posterior brain regions in individuals with ASD during sleep and resting states [48, 56, 59,60,61,62]. This suppression of alpha power was age-independent, occurring in children 4–8 years of age [60, 62], adolescents 12–18 years of age [59, 61], as well as adults 18 years and older [48, 56, 62]. In one longitudinal study, Walsh and colleagues [62] also identified lower frontal alpha power in anesthetized children (mean 5.4 years of age), which continued to decline as they approached early adulthood (mean 22.5 years of age). In temporal, parietal and occipital regions, reports have also shown an elevation in alpha power in children, adolescents, and adults with ASD [40, 50, 60, 63, 64]. Alterations in alpha power for individuals with an elevated familial likelihood of ASD have also been identified [47, 65]. Gabard-Durnam and colleagues [65] identified changes in frontal alpha power over a 6-month period within the first two years of life, with infants with increased ASD likelihood displaying a shift in increased frontal alpha power from left to right hemispheres, while children without this likelihood displaying a progression from right to left. In another study, infants that had an increased chance of developing ASD exhibited lower resting frontal alpha power that increased as the child aged from 6- to 24-months [47]. It was postulated that such early disruptions in alpha power may potentially predict ASD outcome later in life [47]. A reduction in alpha coherence was also shown in adolescents [57] and adults [48, 50, 56] with ASD between frontal, fronto-temporal, parieto-occipital and posterior regions, indicating disruptions in inter-regional communication at this frequency. Further, the finding that an increase in temporal and parietal alpha power was significantly correlated with greater atypicalities in social behaviour, suggests that varying levels of alpha power may be correlated to the various symptoms of ASD [40]. The researchers correlated alpha power in the anterior temporal cortex and parietal cortex to social responsiveness scale (SRS) scores, where greater SRS scores were indicative of greater social difficulties [40]. In line with this, task-related changes in alpha power were also observed in children diagnosed with high-functioning ASD during a motor task [66]. For those diagnosed with ASD, event-related desynchronization was observed in the occipital and parietal cortex when preparing to initiate a task related to moving the object presented but not initiating movement, defined as the ‘prepare’ stage, whereas once told to move into the ‘go’ stage the event-related desynchronization was observed in the parietal and motor cortex [66]. Neural repetition suppression to tactile stimulation (i.e. Tactile suppression index) captured by changes in alpha desynchronization with repeated stimulation, has recently been shown to be reduced in 10-month old infants with an increased familial likelihood of developing ASD relative to infants at typical likelihood of the condition [67]. This measure appeared to be a predictor for higher levels of developing ASD traits at 24 months of age [67].
Unlike that observed in the delta and theta frequencies, the evidence of a role for alterations the alpha band in ASD appears to be more consistent, albeit region-dependent, and may reflect potential for the alpha wave signature as a biomarker across ASD subtypes. The aforementioned reported correlations of alpha activity to sociality scores [40] and movement [66], for example, suggest that other behavioural symptoms on the spectrum also may be correlated to oscillatory activity in a frequency-specific manner.
Beta
Clinical studies have identified that beta oscillatory power is reduced in frontal, temporal, parietal, and occipital brain regions [48, 63, 66, 68], with task-related changes in beta connectivity [63] and beta power [68] correlated with capabilities in number estimation and the naming of simple objects, respectively, in adults with ASD. Specifically, individuals with ASD displayed reduced inter-regional phase locking between the occipital cortex to the frontal, parietal, and temporal cortices [63], and reduced beta power [68] during a visual stimuli task and naming task respectively. Motor task-related beta changes are also observed in children diagnosed with high-functioning ASD [66]. Event-related desynchronization was observed in the beta band in the parietal cortex in the same ‘prepare’ phase, as described in the previous section, whereas during the ‘go’ stage there was event-related desynchronization in the parietal and motor cortex [66]. A reduction in beta power in the frontal cortex was also evident at rest in infants 6-months of age that had an increased familial likelihood for developing ASD [47]. Of note, however, was the finding that beta power increased over time in these children to levels higher than the control children by 24 months of age, and thereby suggesting that alterations in beta power may play a key role in ASD progression [47]. An increase in beta coherence was also observed between the inferior frontal gyrus and left fusiform gyrus [68], as well as the superior temporal gyrus in the occipital lobes of children with ASD [68, 69], and was postualted to be correlated with altered visual perception [69]. To our knowledge, there have been no preclinical studies performed evaluating a role for beta power or coherence in ASD.
Gamma
Along with alpha, gamma oscillations have been more extensively studied in ASD, with clinical studies demonstrating higher gamma power in frontal, parietal and temporal regions in children [70] and adolescents [71]. In adults diagnosed with fragile X syndrome, elevated gamma power was reported in the frontal and occipital regions, differences that were correlated to disturbances in sensory and social processing [51, 72, 73]. It has been postulated that elevated gamma power at the midline, frontal, and parietal brain regions may reflect a possible excitatory/inhibitory imbalance within these regions, which contributes to cognitive and perceptual processes that play an important role in the behaviour expressed [29]. Reduced motor-related gamma power in children with ASD is also suggested to reflect the excitatory/inhibitory imbalance present in ASD and contribute to the behavioural profiles associated with the disorder [74]. Further, minimally verbal children (4–7 years of age) diagnosed with ASD have exhibited a reduction in gamma power within the anterior cingulate cortex while processing visual stimuli [55]. Clinical coherence studies in children showed elevated cortical gamma connectivity between the inferior frontal gyrus and the fusiform gyrus as well as between the superior temporal gyrus and occipital lobe, thought to play a role in visual processing differences in ASD [68]. As well, lower gamma coherence between bilateral posterior superior temporal sulci is thought to disturb visual integration in adults with ASD [75]. Furthermore, a decrease in gamma phase locking, which is a measurement of the consistency between instantaneous phases of oscillations and is important for communication between cortical networks, has frequently been reported in adolescents and adults with fragile X syndrome [72, 73]. This signifies a potential interference with the coordination of information within networks in individuals with fragile X syndrome and, depending on the regions involved, likely impact cognitive and behavioural processes.
Using the valproic acid (VPA) model of idiopathic ASD, one study demonstrated lower gamma connectivity in the superior temporal gyrus between hemispheres that was proposed to be correlated to social and auditory behaviours similar to that in ASD [76]. Consistent with the clinical findings in patients with fragile X syndrome [73], fmr-1 knock out mice exhibited higher gamma power within the auditory and frontal cortex, an effect exacerbated when the animals were in motion [49]. In addition, when the gamma band was further separated into low and high frequency gamma in each region, this motion-induced increase in power was greater in high gamma range [49]. Similar to clinical findings, there also exists a decrease in gamma phase locking specifically involving fast-spiking inhibitory neurons in the somatosensory cortex of fmr-1 knock-out mice and it was suggested to be correlated to the cognitive atypicalities present in fragile X syndrome and other ASD subtypes [77].
The identification of region-specific neurophysiological patterns associated with ASD is of vital importance to identify subtype-specific biomarkers, yet studies to date are for the most part inconsistent. We hypothesize these inconsistencies arise, at least in part, from a lack of clear identification of all of the participant symptoms, and hence identification of the ASD subtype under investigation. As a neurodevelopmental disorder, it is not surprising that age is also an important determinant of both behavioural output and neurophysiological alterations. Indeed, behavioural symptoms in ASD are not static, with manifestations in early development being lost, or even reversing, during aging [78, 79]. Therefore, more longitudinal studies are necessary to provide insights into how neuronal systems activity changes during development and into adulthood, and the relationship of these neurophysiological changes to symptom alterations. Further, many of the findings described above outlined resting-state oscillatory activity, despite the understanding that these oscillations are tightly coupled to behavioural states and that behaviours vary along the spectrum. Additional research evaluating the relationship between task-related alterations in neuronal oscillatory activity in ASD is therefore required if we are to correlate these changes to specific behavioural symtpoms.
Finally, despite an abundance of evidence that sexual dimorphisms exist in ASD symptom manifestation, little research has been performed to determine whether or not sex differences in neuronal oscillatory function contribute to these behavioural differences. Below we describe what is known to date about how males and females differ in oscillatory systems function in ASD.
Sex differences in neuronal oscillatory function in ASD
There is a significant lack of research that identifies sex-specific differences in neurodevelopmental, neurological, and neuropsychiatric disorders, despite established sex differences in prognosis, diagnosis and symptomatology in numerous disorders. A comprehensive review by McCarthy and colleagues [80] outlined the importance of inclusion of sex differences in research and discussed a strategy for its incorporation. It is notable however that while hormones invariably play a key role in sex differences, adult sex differences may arise through developmental exposure to hormones and not necessarily by present-day changes in hormonal fluctuations and actions. It is therefore important that research including sex differences extend beyond hormones to encompass other systems so sex-specific molecular, cellular, and systems determinants of symptoms can be elucidated, and individualized treatment strategies developed.
There is evidence spanning over several decades showing that there are sex differences in neuronal oscillatory function between neurotypical men and women. For example, compared to men, women exhibit greater resting state and restfulness total EEG power [81,82,83] as well as while performing verbal and spatial tasks [84, 85]. In line with this, at rest and during cognitive tasks, women have been reported to have larger interhemispheric alpha coherence, indicative of a greater similarity in EEG activity between hemispheres [82, 84, 86]. Sex differences in neuronal oscillatory activity also exist for varying emotions, as shown following exposure to movie clips that induce specific emotional states, such as, disgust, fear, sadness, happiness or neutral emotional states [81]. In adults, although the expression of happiness or fear elicited similar alterations in EEG activity in both sexes, men showed increased theta, alpha and beta power in the frontal and temporal lobes during states of disgust and sadness compared to women, in addition to increased frontal alpha power during neutral emotional states [81]. These results indicate that distinct patterns of neuronal activity underly emotional expression and cognitive performance in a sex-specific manner, and are supported by preclinical studies showing sex-specific neuronal oscillatory differences in animals that are correlated to behavioural output, such as auditory fear discrimination or depression-like behaviour [87, 88]. In addition, clinical and preclinical findings have identified hormonal involvement in the regulation of neuronal oscillations under neurotypical conditions and in neuropsychiatric disorders such as major depressive disorder [30], a disorder that also demonstrates robust sex differences.
Given the tight coupling of oscillations to behavioural output, and the known sex-specific structural, functional, and behaviour differences present in the disorder [20, 37, 89], it is likely that sex differences in neuronal connectivity also exist in ASD. These neurophysiological differences remain for the most part unexplored, however some research has been performed, with a summary of the available studies that evaluated sex differences in ASD provided in Table 1. It has been reported that male adults and children with ASD predominantly express hypoconnectivity in the default mode network (DMN), a brain network active at resting state, and that such connectivity patterns are similar to that of typical females and neuronal feminization [20, 89, 90]. Conversely, females within the same age range exhibit hyperconnectivity in the DMN, which is comparable to typical male connectivity levels or neuronal masculinization [20]. It has also been shown that high-functioning female individuals with ASD between the ages of 8 and 17 present increased connectivity between the nucleus accumbens and sensorimotor brain regions that are involved in social cognitive processes, whereas their male counterparts tend to show reduced connectivity between these regions [91]. Furthermore, research on infants that have a greater chance of developing ASD from prenatal insults [92] or familial risks [93] has identified potential age-related and sex-specific alterations that could relate to the development of ASD in some instances. For example, in infants that were 3 months of age with elevated ASD likelihood no sex-specific alterations were identified, although this group showed significantly reduced beta and high-alpha total power across the frontal region for both sexes [93] that may relate to the hypoconnectivity exhibited within the DMN in males that have ASD [20, 89, 90]. However, in infants 12–36 months of age, increased EEG power in the right frontal cortex (22–24 Hz) and in the right temporal cortex (13–36 Hz) was observed in male children with an elevated familial chance of developing ASD, but not in female children from the same group [92]. Few studies have looked at regional sex differences at select oscillatory frequencies, although some evidence indicates greater oscillatory atypicalities in female children with ASD due to slow wave, focal, diffuse, and paroxysmal spike patterns observed through EEG [94], and a higher peak posterior/anterior alpha power ratio in male children [60].
There does appear to be an acknowledgement that neuronal oscillatory differences between males and females with ASD exist [20]. Indeed, it is possible that such differences are associated with the idea that greater familial risk factors and etiologic load may be required for symptomatic progression to occur in females, as the female sex appears to have potential protection in the development of behaviours associated with ASD [21]. Although, it must again be mentioned that symptom masking may be of significant relevance. While acknowledging that a handful of studies have explored sex differences in neuronal oscillations in individuals with ASD there remains a significant gap in our knowledge, from the lack of identification of frequency-specific changes, as well as to the mechanisms that may underlie any observed systems function differences. There is a critical need for this research if sex-specific oscillatory signatures are to be developed, which may aid in the prevention of misdiagnosis or delayed diagnosis, which is often seen in female children with ASD.
The regulation of neuronal oscillations in ASD
There is currently no cure or one standard pharmacological treatment for ASD, rather the range of available treatment strategies aim to provide symptom relief. Current pharmacological therapeutics for ASD typically target behavioural aspects of the disorder and/or comorbidities, for example to improve difficulties in social and communication skills, managing irritability and attention deficits, or anxiety and depression [95,96,97,98]. It has been recently suggested that therapeutic targets for ASD should be done based on molecular alterations in the disorder [99]. Yet the identification of therapeutic targets in ASD remains limited, and is confounded by the multitude of disorders present within the spectrum, as well as the lack of incorporation of sex as an experimental variable. One potential option is to develop frequency- and region-specific oscillatory maps, that are also sex- and subtype-specific, and thus allow for more targeted treatments based on these neurophysiological biomarkers. Indeed, this idea has merit given reported links between oscillations and behaviour in ASD, such as the reduced gamma-band power in parieto-occipital regions correlated to difficulties in perceptual organization in adults with the disorder [100, 101]. Further, aberrant oscillatory patterns have been shown to be normalized by therapeutic drugs in various disorders such as schizophrenia [102, 103] and major depressive disorder [28, 104] in both clinical and preclinical studies. Thus therapeutic efficacy could potentially be determined via the degree of oscillatory normalization, and be used as an additional outcome measure alongside behaviour when testing novel therapeutics. In this regard, in the search for these novel therapies, targeting select molecular pathways known to be involved in the regulation of oscillations may be worthwhile.
Neuronal oscillations are highly regulated by various signalling pathways, for example gamma waves are highly regulated by glutamatergic excitatory and GABAergic inhibitory actions [105, 106]. Alterations in glutamatergic neurotransmission are suggested to play an underlying role in ASD as increased glutamate serum levels have been observed in children [107] and in adult males [108] with ASD. Serum glutamate levels also appear to be correlated to social behaviours and imply altered brain glutamatergic neurotransmission in these individuals [108]. Enhanced excitatory activity in ASD is also supported by work that revealed a lower GABA/glutamate metabolite concentration ratio, as GABAergic activity was decreased and glutamatergic activity increased in the occipital region in adolescents with ASD compared to controls [109]. An alteration in glutamate transmission is also evident from preclinical studies. For example, it has been shown that mice lacking the Rab39b gene, which is involved in the insertion of GluA2/GluA3 α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) subunits at synaptic terminals, express abnormal AMPAR expression and synaptic function and is suggested to cause X-linked intellectual disability, and potentially similar cognitive deficits that occur in ASD [110]. Further, normalizing N-methyl-D-aspartate receptor (NMDAR) function in models of ASD, for example stimulating NMDAR function in the Shank2 mouse mutant model [111] or suppressing receptor function in the VPA model of idiopathic ASD [112], has been shown to normalize social behaviours associated with ASD. Although these disparate findings likely reflect differences between the models themselves, it also raises questions as to the potential mechanistic differences that may exist across the ASD spectrum. Alterations in GABAergic neurotransmission have also been observed, with preclinical work in VPA rats showing reduced expression of GABAergic glutamic acid decarboxylase 67 (GAD67), an important enzyme for GABA biosynthesis, in the hippocampus, cerebellum, and temporal cortex and increased expression in the prefrontal cortex, changes suggested to contribute to the excitatory/inhibitory imbalance associated with the disorder [113]. As well, impairments alterations in GABAergic inhibitory transmission in the temporal cortex [114] and deficits in the gabrb3 gene leading to the production of less efficient GABAA receptors [115] have also been suggested to contribute to an imbalance in the excitation/inhibition (E/I) ratio. A dysregulation of the E/I ratio has been suggested to be linked to social difficulties in ASD [116], as well as been demonstrated to impact cortical network activity through the disruption of oscillatory phase locking [101]. Also involved with protein function, including those involved in GABA transmission, is the mechanism of S-nitrosylation (SNO), a process that plays a pivotal role in the progression of numerous disorders [117]. It was recently reported that sex differences exist between protein SNO in mice, with male- and female-specific protein SNO in the cortex, as well as sex-specific differences in the SNO levels of other proteins [117]. This may be of importance in ASD as in the Shank3 mouse model alterations in SNO of proteins involved with neurotransmission, as well as neurodevelopment of the cortex was shown [118].
Another protein that has been linked to both cognitive dysfunction and neuronal oscillatory activity is glycogen synthase kinase-3 (GSK-3) [28]. GSK-3 is a serine/threonine kinase that acts through various intracellular pathways as a key signalling molecule in numerous biological processes [28, 119,120,121]. GSK-3 has key cellular roles in neurogenesis, neuronal development, learning, and memory [122] and has been recently postulated as a homeostatic regulator of neuronal oscillatory activity [28]. In preclinical studies, the overexpression of GSK-3 has been associated with reduced whole brain volume in GSK-3β[S9A] transgenic mice, mice with persistent activation of GSK-3 [123], and disruptions in learning and memory in the VPA model of ASD [124]. Increasing GSK-3 activity in the prefrontal cortex or hippocampus of rats, via adenoassociated viral infection with the GSK-3β[S9A] mutant, was also shown to directly regulate oscillatory function and induced learning and memory deficits [125]. Therefore it is not surprising that altered activation of GSK-3 in various disorders of cognitive dysfunction have been identified, such as increased activity in models of Alzheimer’s disease [126] and both an increase and decrease in activity in models used to study aspects of schizophrenia [127, 128]. A role for GSK-3 in ASD has also been proposed, with evidence coming from both clinical and preclinical studies, although findings are not in agreement as to whether GSK-3 activity is up- or downregulated [124, 129,130,131,132,133,134,135]. Clinically, decreased GSK-3 activity has been found in T cells from children with ASD, potentially related to an elevation in the Akt/mammalian target of rapamycin (mTOR) pathway, an upstream regulator of GSK-3 [133]. At a preclinical level using genetic models of ASD, GSK-3 activity has been shown to be downregulated in the anterior cingulate cortex of shank3b knock-out mice [135], whereas its activity is upregulated in the striatum, cortex, and the hippocampus of fmr-1 knock-out mice [131]. In the idiopathic VPA model of ASD, GSK-3 activity is suppressed in several regions that include the cerebellum, prefrontal cortex, hippocampus, and amygdala, that is also associated with increased levels of β-catenin [124, 134]. Activation of the GSK-3-βcatenin pathway was also shown to underlie macrocephaly in VPA animals [136], a phenomenon also observed in individuals with ASD [137, 138]. In the VPA model, there was also decreased levels of phosphatase and tensin homolog (PTEN) in the hippocampus and cortex, a protein involved in mitochondrial biogenesis through the AKT/GSK-3β/PGC-1α signaling pathway, and which led to increased GSK-3 phosphorylation and reduced activity [139]. Interestingly, a higher level of GSK-3 phosphorylation was also found in the amygdala of VPA rats as a result of attenuated NMDAR-induced synaptic plasticity [124], which could also play a key role in oscillatory activity. Further to these findings, selective and non-selective GSK-3 inhibitors improved cognitive performance in both clinical [129] and preclinical [140,141,142] fragile X syndrome studies, which supports the use of GSK-3 inhibitors, such as lithium, as therapeutics for ASD. Together these findings suggest that GSK-3 may play a central role in ASD pathology, although whether it is up- or downregulated appears to be region- and model-dependent. It is therefore an interesting notion that GSK-3 may have differing involvement in ASD that may be dependent upon the subtype under investigation. Nonetheless, if the hypothesis regarding GSK-3 as a homeostatic regulator of neuronal oscillations [28] holds true, the therapeutic regulation of this kinase may be a valued approach for several of ASD disorders.
Conclusion
Determining biomarkers for the underlying mechanisms contributing to the various disorders across the ASD spectrum could further advance diagnosis as well as identify potential therapeutic targets for this group of disorders. In the case of neuronal oscillations, normalization of aberrant patterns may represent a valued approach to study novel therapeutics, as alpha and gamma oscillations have already been suggested as potential biomarkers of ASD [76, 143]. It is worth noting, that studies to date have grouped the ASD subtypes together, and therefore, frequency-specific changes within specific subtypes of the disorder have not been thoroughly identified. Further to this, adding sex differences as an additional readout of these measures is required, especially given the widely reported differences in prevalence, age of onset, and symptoms between male and female individuals with ASD. It would be of significant value to therefore generate neurophysiological maps that are not only sex-specific, but also, that have incorporated regional changes based on ASD subtype.
This review identifies the urgent need for more clinical and preclinical research combining sex differences in the pathophysiology of ASD with novel strategies to identify biomarkers and therapeutics for all disorders on the spectrum. Given the varying symptoms and profiles of ASD it is unlikely that a one-drug-one-target strategy will be successful, and it is therefore necessary to be diligent in our biological assessments of individuals with ASD if we are to advance future prognosis and an ASD diagnosis in a timely manner. With no present pharmacological treatment options, a more stringent evaluation of the molecular underpinnings through the utilization of these discrete measures may allow for sex- and subtype-specific symptoms of social behaviour, intellectual disability and visual and/or auditory alterations to be alleviated.
References
Chiarotti F, Venerosi A. Epidemiology of autism spectrum disorders: a review of worldwide prevalence estimates since 2014. Brain Sci. 2020;10:274.
Chen H, Uddin LQ, Guo X, Wang J, Wang R, Wang X, et al. Parsing brain structural heterogeneity in males with autism spectrum disorder reveals distinct clinical subtypes. Hum Brain Mapp. 2019;40:628–37.
American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-5. 5th ed. Arlington, VA: American Psychiatric Association; 2013.
Leekam SR, Nieto C, Libby SJ, Wing L, Gould J. Describing the sensory abnormalities of children and adults with autism. J Autism Dev Disord. 2007;37:894–910.
Ben-Sasson A, Gal E, Fluss R, Katz-Zetler N, Cermak SA. Update of a meta-analysis of sensory symptoms in ASD: a new decade of research. J Autism Dev Disord. 2019;49:4974–96.
Marco EJ, Hinkley LBN, Hill SS, Nagarajan SS. Sensory processing in autism: a review of neurophysiologic findings. Pediatr Res. 2011;69:48–54.
Itzchak EBen, Lahat E, Burgin R, Zachor AD. Cognitive, behavior and intervention outcome in young children with autism. Res Dev Disabil. 2008;29:447–58.
Jang J, Matson JL, Williams LW, Tureck K, Goldin RL, Cervantes PE. Rates of comorbid symptoms in children with ASD, ADHD, and comorbid ASD and ADHD. Res Dev Disabil. 2013;34:2369–78.
Matson J, Goldin R. Comorbidity and autism: trends, topics and future directions. Res Autism Spectr Disord. 2013;7:1228–33.
Carter A, Black D, Tewani S, Connolly C, Kadlec M, Tager-Flusberg H. Sex differences in toddlers with autism spectrum disorders. J Autism Dev Disord. 2007;37:86–97.
Baron-Cohen S. The extreme male brain theory of autism. Trends Cogn Sci. 2002;6:248–54.
Greenberg DM, Warrier V, Allison C, Baron-Cohen S. Testing the empathizing-systemizing theory of sex differences and the extreme male brain theory of autism in half a million people. Proc Natl Acad Sci USA. 2018;115:12152–7.
Crespi B, Read S, Ly A, Hurd P. AMBRA1, autophagy, and the extreme male brain theory of autism. Autism Res Treat. 2019;2019:1968580.
Baron-Cohen S. Autism: the empathizing-systemizing (E-S) theory. Ann N Y Acad Sci. 2009;1156:68–80.
Bejerot S, Eriksson JM, Bonde S, Carlström K, Humble MB, Eriksson E. The extreme male brain revisited: gender coherence in adults with autism spectrum disorder. Br J Psychiatry. 2012;201:116–23.
Nadler A, Camerer CF, Zava DT, Ortiz TL, Watson NV, Carré JM, et al. Does testosterone impair men’s cognitive empathy? Evidence from two large-scale randomized controlled trials. Proc Biol Sci. 2019;286:20191062.
Frazier TW, Georgiades S, Bishop SL, Hardan AY. Behavioral and cognitive characteristics of females and males with autism in the simons simplex collection. J Am Acad Child Adolesc Psychiatry. 2014;53:323–9.
Lo Y, Chen Y, Hsu Y, Tseng WI, Gau SS. Reduced tract integrity of the model for social communication is a neural substrate of social communication deficits in autism spectrum disorder. J Child Psychol Psychiatry. 2017;58:576–85.
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;42:1304–13.
Alaerts K, Swinnen SP, Wenderoth N. Sex differences in autism: a resting-state fMRI investigation of functional brain connectivity in males and females. Soc Cogn Affect Neurosci. 2016;11:1002–16.
Robinson EB, Lichtenstein P, Anckarsäter H, Happé F, Ronald A. Examining and interpreting the female protective effect against autistic behavior. Proc Natl Acad Sci USA. 2013;110:5258–62.
Buzsáki G, Draguhn A. Neuronal oscillations in cortical networks. Science. 2004;304:1926–9.
Buzsáki G, Logothetis N, Singer W. Scaling brain size, keeping timing: evolutionary preservation of brain rhythms. Neuron. 2013;80:751–64.
Buzsáki G, Watson BO. Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease. Dialogues Clin Neurosci. 2012;14:345–67.
Zikopoulos B, Barbas H. Altered neural connectivity in excitatory and inhibitory cortical circuits in autism. Front Hum Neurosci. 2013;7:609.
Barardi A, Sancristóbal B, Garcia-Ojalvo J. Phase-coherence transitions and communication in the gamma range between delay-coupled neuronal populations. PLoS Comput Biol. 2014;10:e1003723–e1003723.
Hoptman MJ, D’Angelo D, Catalano D, Mauro CJ, Shehzad ZE, Kelly AMC, et al. Amygdalofrontal functional disconnectivity and aggression in schizophrenia. Schizophr Bull. 2010;36:1020–8.
Manduca JD, Thériault RK, Perreault ML. Glycogen synthase kinase-3: The missing link to aberrant circuit function in disorders of cognitive dysfunction? Pharm Res. 2020;157:104819.
Orekhova EV, Stroganova TA, Nygren G, Tsetlin MM, Posikera IN, Gillberg C, et al. Excess of high frequency electroencephalogram oscillations in boys with autism. Biol Psychiatry. 2007;62:1022–9.
Thériault R-K, Perreault ML. Hormonal regulation of circuit function: sex, systems and depression. Biol Sex Differ. 2019;10:12.
Uhlhaas PJ, Linden DEJ, Singer W, Haenschel C, Lindner M, Maurer K, et al. Dysfunctional long-range coordination of neural activity during gestalt perception in schizophrenia. J Neurosci. 2006;26:8168–75.
Palop JJ, Mucke L. Network abnormalities and interneuron dysfunction in Alzheimer disease. Nat Rev Neurosci. 2016;17:777–92.
Uhlhaas PJ, Roux F, Rodriguez E, Rotarska-Jagiela A, Singer W. Neural synchrony and the development of cortical networks. Trends Cogn Sci. 2010;14:72–80.
Frühholz S, Trost W, Grandjean D, Belin P. Neural oscillations in human auditory cortex revealed by fast fMRI during auditory perception. Neuroimage. 2020;207:116401.
Lewis LD, Setsompop K, Rosen BR, Polimeni JR. Fast fMRI can detect oscillatory neural activity in humans. Proc Natl Acad Sci USA. 2016;113:E6679–E6685.
Supekar K, Uddin LQ, Khouzam A, Phillips J, Gaillard WD, Kenworthy LE, et al. Brain hyperconnectivity in children with autism and its links to social deficits. Cell Rep. 2013;5:738–47.
Kim SH, Buzzell G, Faja S, Choi YB, Thomas HR, Brito NH, et al. Neural dynamics of executive function in cognitively able kindergarteners with autism spectrum disorders as predictors of concurrent academic achievement. Autism. 2020;24:780–94.
Larrain-Valenzuela J, Zamorano F, Soto-Icaza P, Carrasco X, Herrera C, Daiber F, et al. Theta and alpha oscillation impairments in autistic spectrum disorder reflect working memory deficit. Sci Rep. 2017;7:14328.
Wang Y, Sokhadze EM, El-Baz AS, Li X, Sears L, Casanova MF, et al. Relative power of specific EEG bands and their ratios during neurofeedback training in children with autism spectrum disorder. Front Hum Neurosci. 2016;9:723.
Cornew L, Roberts T, Blaskey L, Edgar J. Resting-state oscillatory activity in autism spectrum disorders. J Autism Dev Disord. 2012;42:1884–94.
Saby JN, Marshall PJ. The utility of EEG band power analysis in the study of infancy and early childhood. Dev Neuropsychol. 2013;37:253–73.
Fries P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn Sci. 2005;9:474–80.
Gliga T, Jones EJH, Bedford R, Charman T, Johnson MH. From early markers to neuro-developmental mechanisms of autism. Dev Rev. 2014;34:189–207.
Hahamy A, Behrmann M, Malach R. The idiosyncratic brain: distortion of spontaneous connectivity patterns in autism spectrum disorder. Nat Neurosci. 2015;4:302–9.
Coben R, Clarke AR, Hudspeth W, Barry RJ. EEG power and coherence in autistic spectrum disorder. Clin Neurophysiol. 2008;119:1002–9.
Zeng K, Kang J, Ouyang G, Li J, Han J, Wang Y, et al. Disrupted brain network in children with autism spectrum disorder. Sci Rep. 2017;7:16253.
Tierney AL, Gabard-Durnam L, Vogel-Farley V, Tager-Flusberg H, Nelson CA. Developmental trajectories of resting EEG power: an endophenotype of autism spectrum disorder. PLoS ONE. 2012;7:e39127.
Lázár AS, Lázár ZI, Bíró A, Gyori M, Tárnok Z, Prekop C, et al. Reduced fronto-cortical brain connectivity during NREM sleep in Asperger syndrome: an EEG spectral and phase coherence study. Clin Neurophysiol. 2010;121:1844–54.
Lovelace JW, Ethell IM, Binder DK, Razak KA. Translation-relevant EEG phenotypes in a mouse model of Fragile X Syndrome. Neurobiol Dis. 2018;115:39–48.
Mathewson KJ, Jetha MK, Drmic IE, Bryson SE, Goldberg JO, Schmidt LA. Regional EEG alpha power, coherence, and behavioral symptomatology in autism spectrum disorder. Clin Neurophysiol. 2012;123:1798–809.
Wang J, Ethridge LE, Mosconi MW, White SP, Binder DK, Pedapati EV, et al. A resting EEG study of neocortical hyperexcitability and altered functional connectivity in fragile X syndrome. J Neurodev Disord. 2017;9:11.
Andreou C, Leicht G, Nolte G, Polomac N, Moritz S, Karow A, et al. Resting-state theta-band connectivity and verbal memory in schizophrenia and in the high-risk state. Schizophr Res. 2015;161:299–307.
Hermens DF, Soei EXC, Clarke SD, Kohn MR, Gordon E, Williams LM. Resting EEG theta activity predicts cognitive performance in attention-deficit hyperactivity disorder. Pediatr Neurol. 2005;32:248–56.
Isler JR, Martien KM, Grieve PG, Stark RI, Herbert MR. Reduced functional connectivity in visual evoked potentials in children with autism spectrum disorder. Clin Neurophysiol. 2010;121:2035–43.
Ortiz-Mantilla S, Cantiani C, Shafer VL, Benasich AA. Minimally-verbal children with autism show deficits in theta and gamma oscillations during processing of semantically-related visual information. Sci Rep. 2019;9:5072.
Murias M, Webb SJ, Greenson J, Dawson G. Resting state cortical connectivity reflected in EEG coherence in individuals with autism. Biol Psychiatry. 2007;62:270–3.
Ye AX, Leung RC, Schäfer CB, Taylor MJ, Doesburg SM. Atypical resting synchrony in autism spectrum disorder. Hum Brain Mapp. 2014;35:6049–66.
Cunniff MM, Markenscoff-Papadimitriou E, Ostrowski J, Rubenstein JL, Sohal VS. Altered hippocampal-prefrontal communication during anxiety-related avoidance in mice deficient for the autism-associated gene Pogz. Elife. 2020;9:e54835.
Keehn B, Westerfield M, Müller R-A, Townsend J. Autism, attention, and alpha oscillations: an electrophysiological study of attentional capture. Biol Psychiatry Cogn Neurosci Neuroimaging. 2017;2:528–36.
Matlis S, Boric K, Chu CJ, Kramer MA. Robust disruptions in electroencephalogram cortical oscillations and large-scale functional networks in autism. BMC Neurol. 2015;15:97.
Seymour RA, Rippon G, Gooding-Williams G, Schoffelen JM, Kessler K. Dysregulated oscillatory connectivity in the visual system in autism spectrum disorder. Brain. 2019;142:3294–305.
Walsh EC, Lee JM, Terzakis K, Zhou DW, Burns S, Buie TM, et al. Age-dependent changes in the propofol-induced electroencephalogram in children with autism spectrum disorder. Front Syst Neurosci. 2018;12:23.
Bangel KA, Batty M, Ye AX, Meaux E, Taylor MJ, Doesburg SM. Reduced beta band connectivity during number estimation in autism. NeuroImage Clin. 2014;6:202–13.
Edgar JC, Heiken K, Chen Y-H, Herrington JD, Chow V, Liu S, et al. Resting-state alpha in autism spectrum disorder and alpha associations with thalamic volume. J Autism Dev Disord. 2015;45:795–804.
Gabard-Durnam L, Tierney AL, Vogel-Farley V, Tager-Flusberg H, Nelson CA. Alpha asymmetry in infants at risk for autism spectrum disorders. J Autism Dev Disord. 2015;45:473–80.
Ewen JB, Lakshmanan BM, Pillai AS, McAuliffe D, Nettles C, Hallett M, et al. Decreased modulation of EEG oscillations in high-functioning autism during a motor control task. Front Hum Neurosci. 2016;10:198.
Piccardi ES, Ali JB, Jones EJH, Mason L, Charman T, Johnson MH, et al. Behavioural and neural markers of tactile sensory processing in infants at elevated likelihood of autism spectrum disorder and/or attention deficit hyperactivity disorder. J Neurodev Disord. 2021;13:1–18.
Buard I, Rogers SJ, Hepburn S, Kronberg E, Rojas DC. Altered oscillation patterns and connectivity during picture naming in autism. Front Hum Neurosci. 2013;7:742.
Ronconi L, Vitale A, Federici A, Pini E, Molteni M, Casartelli L. Altered neural oscillations and connectivity in the beta band underlie detail-oriented visual processing in autism. NeuroImage Clin. 2020;28:102484.
van Diessen E, Senders J, Jansen FE, Boersma M, Bruining H. Increased power of resting-state gamma oscillations in autism spectrum disorder detected by routine electroencephalography. Eur Arch Psychiatry Clin Neurosci. 2015;265:537–40.
Brown C, Gruber T, Boucher J, Rippon G, Brock J. Gamma abnormalities during perception of illusory figures in autism. Cortex. 2005;41:364–76.
Ethridge LE, White SP, Mosconi MW, Wang J, Pedapati EV, Erickson CA, et al. Neural synchronization deficits linked to cortical hyper-excitability and auditory hypersensitivity in fragile X syndrome. Mol Autism 2017;8:38.
Ethridge LE, De Stefano LA, Schmitt LM, Woodruff NE, Brown KL, Tran M, et al. Auditory EEG biomarkers in fragile X syndrome: clinical relevance. Front Integr Neurosci. 2019;13:60.
An K-M, Ikeda T, Yoshimura Y, Hasegawa C, Saito DN, Kumazaki H, et al. Altered gamma oscillations during motor control in children with autism spectrum disorder. J Neurosci. 2018;38:7878–86.
Peiker I, David N, Schneider TR, Nolte G, Schöttle D, Engel AK. Perceptual integration deficits in autism spectrum disorders are associated with reduced interhemispheric gamma-band coherence. J Neurosci. 2015;35:16352–61.
Gandal MJ, Edgar JC, Ehrlichman RS, Mehta M, Roberts TPL, Siegel SJ. Validating γ oscillations and delayed auditory responses as translational biomarkers of autism. Biol Psychiatry. 2010;68:1100–6.
Gibson JR, Bartley AF, Hays SA, Huber KM. Imbalance of neocortical excitation and inhibition and altered UP states reflect network hyperexcitability in the mouse model of fragile X syndrome. J Neurophysiol. 2008;100:2615–26.
Johnson MH, Gliga T, Jones E, Charman T. Annual research review: infant development, autism, and ADHD – early pathways to emerging disorders. J Child Psychol Psychiatry. 2015;56:228–47.
Johnson MH. Autism as an adaptive common variant pathway for human brain development. Dev Cogn Neurosci. 2017;25:5–11.
McCarthy MM, Arnold AP, Ball GF, Blaustein JD, De Vries GJ. Sex differences in the brain: the not so inconvenient truth. J Neurosci. 2012;32:2241–7.
Bao L-Q, Qiu J-L, Tang H, Zheng W-L, Lu B-L. Investigating sex differences in classification of five emotions from EEG and eye movement signals. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:6746–9.
Flor-Henry P. Observations, reflections and speculations on the cerebral determinants of mood and on the bilaterally asymmetrical distributions of the major neurotransmitter systems. Acta Neurol Scand Suppl. 1986;109:75–89.
Matsuura M, Yamamoto K, Fukuzawa H, Okubo Y, Uesugi H, Moriiwa M, et al. Age development and sex differences of various EEG elements in healthy children and adults-quantification by a computerized wave form recognition method. Electroencephalogr Clin Neurophysiol. 1985;60:394–406.
Beaumont JG, Mayes AR, Rugg MD. Asymmetry in EEG alpha coherence and power: effects of task and sex. Electroencephalogr Clin Neurophysiol. 1978;45:393–401.
Volf NV, Razumnikova OM. Sex differences in EEG coherence during a verbal memory task in normal adults. Int J Psychophysiol. 1999;34:113–22.
Corsi-Cabrera M, Herrera P, Malvido M. Correlation between EEG and cognitive abilities: sex differences. Int J Neurosci. 1989;45:133–41.
Day HLL, Suwansawang S, Halliday DM, Stevenson CW. Sex differences in auditory fear discrimination are associated with altered medial prefrontal cortex function. Sci Rep. 2020;10:6300.
Thériault R-K, Manduca JD, Perreault ML. Sex differences in innate and adaptive neural oscillatory patterns link resilience and susceptibility to chronic stress in rats. J Psychiatry Neurosci. 2021;46:E258–E270.
Jung M, Mody M, Saito DN, Tomoda A, Okazawa H, Wada Y, et al. Sex differences in the default mode network with regard to autism spectrum traits: a resting state fMRI study. PLoS ONE. 2015;10:e0143126.
Ypma RJF, Moseley RL, Holt RJ, Rughooputh N, Floris DL, Chura LR, et al. Default mode hypoconnectivity underlies a sex-related autism spectrum. Biol Psychiatry Cogn Neurosci neuroimaging. 2016;1:364–71.
Hernandez LM, Lawrence KE, Padgaonkar NT, Inada M, Hoekstra JN, Lowe JK, et al. Imaging-genetics of sex differences in ASD: distinct effects of OXTR variants on brain connectivity. Transl Psychiatry. 2020;10:82.
Brito NH, Elliott AJ, Isler JR, Rodriguez C, Friedrich C, Shuffrey LC, et al. Neonatal EEG linked to individual differences in socioemotional outcomes and autism risk in toddlers. Dev Psychobiol. 2019;61:1110–9.
Levin AR, Varcin KJ, O’Leary HM, Tager-Flusberg H, Nelson CA. EEG power at 3 months in infants at high familial risk for autism. J Neurodev Disord. 2017;9:34.
Tsai L, Stewart M, August G. Implication of sex differences in the familial transmission of infantile autism. J Autism Dev Disord. 1981;11:165–73.
Erickson CA, Posey DJ, Stigler KA, Mullett J, Katschke AR, McDougle CJ. A retrospective study of memantine in children and adolescents with pervasive developmental disorders. Psychopharmacol (Berl). 2007;191:141–7.
Ghanizadeh A, Ayoobzadehshirazi A. A randomized double-blind placebo-controlled clinical trial of adjuvant buspirone for irritability in autism. Pediatr Neurol. 2015;52:77–81.
Patel K, Curtis LT. A comprehensive approach to treating autism and attention-deficit hyperactivity disorder: a prepilot study. J Alter Complement Med. 2007;13:1091–7.
Sizoo BB, Kuiper E. Cognitive behavioural therapy and mindfulness based stress reduction may be equally effective in reducing anxiety and depression in adults with autism spectrum disorders. Res Dev Disabil. 2017;64:47–55.
Basilico B, Morandell J, Novarino G. Molecular mechanisms for targeted ASD treatments. Curr Opin Genet Dev. 2020;65:126–37.
Sun L, Grützner C, Bölte S, Wibral M, Tozman T, Schlitt S, et al. Impaired gamma-band activity during perceptual organization in adults with autism spectrum disorders: evidence for dysfunctional network activity in frontal-posterior cortices. J Neurosci. 2012;32:9563–73.
Uhlhaas PJ, Singer W. Neuronal dynamics and neuropsychiatric disorders: toward a translational paradigm for dysfunctional large-scale networks. Neuron. 2012;75:963–80.
Alegre M, Molero P, Valencia M, Mayner G, Ortuño F, Artieda J. Atypical antipsychotics normalize low-gamma evoked oscillations in patients with schizophrenia. Psychiatry Res. 2017;247:214–21.
Nelong TF, Manduca JD, Zonneveld PM, Perreault ML. Asenapine maleate normalizes low frequency oscillatory deficits in a neurodevelopmental model of schizophrenia. Neurosci Lett. 2019;711:134404.
Alexander ML, Alagapan S, Lugo CE, Mellin JM, Lustenberger C, Rubinow DR, et al. Double-blind, randomized pilot clinical trial targeting alpha oscillations with transcranial alternating current stimulation (tACS) for the treatment of major depressive disorder (MDD). Transl Psychiatry. 2019;9:106.
Atallah BV, Scanziani M. Instantaneous modulation of gamma oscillation frequency by balancing excitation with inhibition. Neuron. 2009;62:566–77.
Zhou S, Yu Y. Synaptic E-I balance underlies efficient neural coding. Front Neurosci. 2018;12:46.
Khalifa D, Shahin O, Salem D, Raafat O. Serum glutamate was elevated in children aged 3-10 years with autism spectrum disorders when they were compared with controls. Acta Paediatr. 2019;108:295–9.
Shinohe A, Hashimoto K, Nakamura K, Tsujii M, Iwata Y, Tsuchiya KJ, et al. Increased serum levels of glutamate in adult patients with autism. Prog Neuropsychopharmacol Biol Psychiatry. 2006;30:1472–7.
Drenthen GS, Barendse EM, Aldenkamp AP, van Veenendaal TM, Puts NAJ, Edden RAE, et al. Altered neurotransmitter metabolism in adolescents with high-functioning autism. Psychiatry Res Neuroimaging. 2016;256:44–49.
Mignogna ML, Musardo S, Ranieri G, Gelmini S, Espinosa P, Marra P, et al. RAB39B-mediated trafficking of the GluA2-AMPAR subunit controls dendritic spine maturation and intellectual disability-related behaviour. Mol Psychiatry. 2021;2021:1–19.
Won H, Lee H-R, Gee HY, Mah W, Kim J-I, Lee J, et al. Autistic-like social behaviour in Shank2-mutant mice improved by restoring NMDA receptor function. Nature. 2012;486:261–5.
Kang J, Kim E. Suppression of NMDA receptor function in mice prenatally exposed to valproic acid improves social deficits and repetitive behaviors. Front Mol Neurosci. 2015;8:17.
Hou Q, Wang Y, Li Y, Chen D, Yang F, Wang S. A developmental study of abnormal behaviors and altered GABAergic signaling in the VPA-treated rat model of autism. Front Behav Neurosci. 2018;12:182.
Banerjee A, García-Oscos F, Roychowdhury S, Galindo LC, Hall S, Kilgard MP, et al. Impairment of cortical GABAergic synaptic transmission in an environmental rat model of autism. Int J Neuropsychopharmacol. 2013;16:1309–18.
DeLorey TM, Sahbaie P, Hashemi E, Homanics GE, Clark JD. Gabrb3 gene deficient mice exhibit impaired social and exploratory behaviors, deficits in non-selective attention and hypoplasia of cerebellar vermal lobules: A potential model of autism spectrum disorder. Behav Brain Res. 2008;187:207–20.
Rubenstein JLR, Merzenich MM. Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes, Brain Behav. 2003;2:255–67.
Khaliulin I, Kartawy M, Amal H. Sex differences in biological processes and nitrergic signaling in mouse brain. Biomedicines. 2020;8:124.
Amal H, Barak B, Bhat V, Gong G, Joughin BA, Wang X, et al. Shank3 mutation in a mouse model of autism leads to changes in the S-nitroso-proteome and affects key proteins involved in vesicle release and synaptic function. Mol Psychiatry. 2018;25:1835–48.
Albeely AM, Ryan SD, Perreault ML. Pathogenic feed-forward mechanisms in alzheimer’s and parkinson’s disease converge on GSK-3. Brain Plast (Amst, Neth). 2018;4:151–67.
Eldar-Finkelman H, Martinez A. GSK-3 inhibitors: preclinical and clinical focus on CNS. Front Mol Neurosci. 2011;4:32.
Portis S, Giunta B, Obregon D, Tan J. The role of glycogen synthase kinase-3 signaling in neurodevelopment and fragile X syndrome. Int J Physiol Pathophysiol Pharm. 2012;4:140–8.
Kaidanovich-Beilin O, Woodgett JR. GSK-3: functional insights from cell biology and animal models. Front Mol Neurosci. 2011;4:40.
Spittaels K, Van den Haute C, Van Dorpe J, Terwel D, Vandezande K, Lasrado R, et al. Neonatal neuronal overexpression of glycogen synthase kinase-3 beta reduces brain size in transgenic mice. Neuroscience. 2002;113:797–808.
Wu H-F, Chen PS, Chen Y-J, Lee C-W, Chen I-T, Lin H-C. Alleviation of N-methyl-d-aspartate receptor-dependent long-term depression via regulation of the glycogen synthase kinase-3β pathway in the amygdala of a valproic acid-induced animal model of autism. Mol Neurobiol. 2017;54:5264–76.
Albeely AM, Williams OOF, Perreault ML. GSK-3β disrupts neuronal oscillatory function to inhibit learning and memory in male rats. Cell Mol Neurobiol. 2021;2021:1–13.
Griebel G, Stemmelin J, Lopez-Grancha M, Boulay D, Boquet G, Slowinski F, et al. The selective GSK3 inhibitor, SAR502250, displays neuroprotective activity and attenuates behavioral impairments in models of neuropsychiatric symptoms of Alzheimer’s disease in rodents. Sci Rep. 2019;9:18045.
Beaulieu J-M, Sotnikova TD, Yao W-D, Kockeritz L, Woodgett JR, Gainetdinov RR, et al. Lithium antagonizes dopamine-dependent behaviors mediated by an AKT/glycogen synthase kinase 3 signaling cascade. Proc Natl Acad Sci USA. 2004;101:5099–104.
Nadri C, Lipska BK, Kozlovsky N, Weinberger DR, Belmaker RH, Agam G. Glycogen synthase kinase (GSK)-3beta levels and activity in a neurodevelopmental rat model of schizophrenia. Brain Res Dev Brain Res. 2003;141:33–37.
Berry-Kravis E, Sumis A, Hervey C, Nelson M, Porges SW, Weng N, et al. Open-label treatment trial of lithium to target the underlying defect in fragile X syndrome. J Dev Behav Pediatr. 2008;29:293–302.
Klein PS, Melton DA. A molecular mechanism for the effect of lithium on development. Proc Natl Acad Sci USA. 1996;93:8455–9.
Min WW, Yuskaitis CJ, Yan Q, Sikorski C, Chen S, Jope RS, et al. Elevated glycogen synthase kinase-3 activity in Fragile X mice: Key metabolic regulator with evidence for treatment potential. Neuropharmacology. 2009;56:463–72.
Mines MA, Yuskaitis CJ, King MK, Beurel E, Jope RS. GSK3 influences social preference and anxiety-related behaviors during social interaction in a mouse model of fragile X syndrome and autism (FX Social Behavior). PLoS ONE. 2010;5:e9706.
Onore C, Yang H, Van de Water J, Ashwood P. Dynamic Akt/mTOR signaling in children with autism spectrum disorder. Front Pediatr. 2017;5:43.
Qin L, Dai X, Yin Y. Valproic acid exposure sequentially activates Wnt and mTOR pathways in rats. Mol Cell Neurosci. 2016;75:27–35.
Wang M, Liu X, Hou Y, Zhang H, Kang J, Wang F, et al. Decrease of GSK-3beta activity in the anterior cingulate cortex of shank3b-/- mice contributes to synaptic and social deficiency. Front Cell Neurosci. 2019;13:447.
Go HS, Kim KC, Choi CS, Jeon SJ, Kwon KJ, Han S-H, et al. Prenatal exposure to valproic acid increases the neural progenitor cell pool and induces macrocephaly in rat brain via a mechanism involving the GSK-3β/β-catenin pathway. Neuropharmacology. 2012;63:1028–41.
Landa R, Garrett-Mayer E. Development in infants with autism spectrum disorders: a prospective study. J Child Psychol Psychiatry. 2006;47:629–38.
Wetherby AM, Woods J, Allen L, Cleary J, Dickinson H, Lord C. Early indicators of autism spectrum disorders in the second year of life. J Autism Dev Disord. 2004;34:473–93.
Feng C, Chen Y, Zhang Y, Yan Y, Yang M, Gui H, et al. PTEN regulates mitochondrial biogenesis via the AKT/GSK-3β/PGC-1α pathway in autism. Neuroscience. 2021;465:85–94.
Franklin AV, King MK, Palomo V, Martinez A, McMahon LL, Jope RS. Glycogen synthase kinase-3 inhibitors reverse deficits in long-term potentiation and cognition in fragile X mice. Biol Psychiatry. 2014;75:198–206.
Guo W, Murthy AC, Zhang L, Johnson EB, Schaller EG, Allan AM, et al. Inhibition of GSK3β improves hippocampus-dependent learning and rescues neurogenesis in a mouse model of fragile X syndrome. Hum Mol Genet. 2012;21:681–91.
Pardo M, Beurel E, Jope RS. Cotinine administration improves impaired cognition in the mouse model of Fragile X syndrome. Eur J Neurosci. 2017;45:490–8.
Dickinson A, DiStefano C, Senturk D, Jeste SS. Peak alpha frequency is a neural marker of cognitive function across the autism spectrum. Eur J Neurosci. 2018;47:643–51.
Acknowledgements
This work was supported by a research grant from the Scottish Rite Charitable Foundation (to MLP). We would like to especially acknowledge the land in Ontario, Canada on which this research was performed, the ancestral lands of the Attawandaron people and the treaty lands and territory of the Mississaugas of the Credit First Nation, and to offer our respect to the Indigenous people that reside here. This work evaluates ASD using knowledge obtained using a western approach to research as, to our knowledge, there is little known regarding spirituality, spiritual damage, and spiritual development in ASD. The incorporation of Indigenous ways of knowing into mental health research will provide important steps in our path towards reconciliation.
Author information
Authors and Affiliations
Contributions
O.O.F.W. and M.C. wrote the paper. M.L.P. edited the paper. All authors approved the final version.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Williams, O.O.F., Coppolino, M. & Perreault, M.L. Sex differences in neuronal systems function and behaviour: beyond a single diagnosis in autism spectrum disorders. Transl Psychiatry 11, 625 (2021). https://doi.org/10.1038/s41398-021-01757-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41398-021-01757-1