Print Сite this

Biological Bases of Autism: General Analysis

Introduction

Autism is a wide range of conditions that are marked by difficulties in social abilities, cyclic behavior, dialogue, and non-verbal communication. This disorder is sometimes referred to as autism spectrum disorder (ASD) due to the existence of many subtypes. Consequently, each person with autism has a unique group of strengths and difficulties. Aspects such as learning, thought process, and problem-solving can vary from highly skilled to severely compromised in affected individuals. Therefore, certain people with this condition may require assistance to carry out their day-to-day activities, whereas others may function autonomously.

We will write a
custom essay
specifically for you

for only $16.05 $11/page
308 certified writers online
Learn More

The symptoms of autism are usually apparent by the time a child reaches 2 years. The Centers for Disease Control and Prevention [CDC] reports that the incidence of autism in the US is 1 in 59 children and that boys are more likely to have it than girls (2018).

The exact cause of autism is unknown. However, recent investigations suggest that a genetic component is involved in autism due to observed genetic differences between healthy people and those with ASD (Bourgeron, 2016). Nevertheless, more than 100 genetic loci have been shown to play a role in the development of autism. Other studies suggest a complex interaction between genetics and the environment.

For example, the National Institute of Environmental Health Science [NIEHS] reports that conceiving later in life, prenatal exposure to toxic chemicals (pesticides), premature births, childbirth complications that lead to oxygen deficiency in the baby’s brain may cause autism (2019). Mothers with ailments such as diabetes, obesity, and immunological complications have also been reported to have children with autism (Cheng, Eskenazi, Widjaja, Cordero, & Hendren, 2019).

Investigations on ASD show that its symptoms are usually accompanied by sensory sensitivities, which indicate the involvement of the brain in the disease. Certain medical problems such as gastrointestinal disturbances, sleep problems, mental health complications, and seizures may be present. Depression, anxiety, and poor attention are some of the most common mental health issues that are observed. The purpose of this paper is to explain the biological bases of the disease while considering the specific areas of the brain that are affected together with their associated symptoms. The terms autism and ASD are used interchangeably in the paper.

Brain Areas Implicated When Identifying the Symptoms of Autism

Studies to elucidate the neurobiology of autism have used mouse models to identify specific regions of the brain that are affected. They include the frontal lobe, parieto-temporal lobe, hypothalamus, cerebellar cortex, and the striatum (Ellegood et al., 2015). Sub-regional investigations revealed further changes in the deep cerebellar nuclei, hippocampal CA1, and dorsal raphe nuclei. Subsequent studies involving human subjects have confirmed these findings. Preliminary investigations focused first on understanding developmental anomalies of the brain in normal and ASD subjects.

Ecker, Bookheimer, and Murphy (2015) noted that children with autism have a larger brain than normal ones between the ages of 2 and 4 years. The brain volume then shrinks at 6 to 8 years. During this time, the growth curves of normal and diseased children intersect. After this point, there are no further changes. These observations suggest an aberrant brain development trajectory in children with autism. Furthermore, the modified neurodevelopmental course differs in various parts of the brain with the frontal and temporal lobes being disturbed more than the parietal and occipital lobes. This observation implies that autism disturbs the time-based and local order of conventional early brain development.

Get your
100% original paper
on any topic

done in as little as
3 hours
Learn More

Nonetheless, other studies have observed general expansions all through the cerebral cortices of children with autism between 18 and 35 months (Hazlett et al., 2017). These enlargements not only alter the configuration of separate areas of the brain but also contribute to discrepancies in brain morphology and interconnections between systems. The constituents of neural systems that cause autism include the amygdala-hippocampal complex, frontotemporal and frontoparietal regions, basal ganglia, cerebellum, as well as anterior and posterior cingulate areas. These central regions are thought to result in precise clinical symptoms. The amygdala is substantially bigger in children with autism than in normally developing children.

Conversely, the total brain volume follows an age-related reduction after early puberty. The cortical thickness and surface area also obey a similar pattern. Several cross-sectional magnetic resonance imaging (MRI) studies involving people between the ages of 3 and 19 have confirmed the characteristic cortical thinning in different areas of the brain. These observations underscore the importance of considering the different neurodevelopmental phases when contrasting normal individuals with those with ASD given that the rate of cortical development does not follow a linear pattern in humans (Khundrakpam, Lewis, Kostopoulos, Carbonell, & Evans, 2017).

One consequence of cortex expansion is folding to accommodate the enlarging surface of the brain in the limited cavity of the skull. Therefore, the brain exhibits increased cortical folding in ASD, which results in other events such as exertion of a pulling force on the superimposing neocortex by the fibers of the white matter, thereby regulating cortical folding externally through mechanical tension (Yang, Beam, Pelphrey, Abdullahi, & Jou, 2016).

Another way through which cortical folding can be controlled is through developmental alterations in the inner part of the cortical sheet, particularly in the cortical grey matter. For example, the development of cortical gyri is associated with a hastened enlargement of the exterior cortical layers compared to the inner ones in addition to the microstructural intricacy of related grey matter that is brought about by processes such as synaptogenesis, dendritic arborization, and the placement of neurons in space.

Several MRI investigations have revealed uncharacteristic cortical gyrification in the brains of people with autism. For instance, people with autism have more cortical deformities than healthy subjects, including large gyri (microgyria), many small folds (polymicrogyria), and crevices covering the cortical grey matter (schizencephaly). It has also been shown that sulci develop further along the key axes of the brain in children with autism than normal ones. Additionally, the frontal lobes of children and teenagers with ASD have a marked increase in gyrification.

Functional connectivity MRI (fcMRI) investigations focusing on neural networks in autism have enhanced the categorization of autism as a dispersed neural system condition. Brain anomalies in ASD can be classified as cortical underconnectivity or local overconnectivity. However, mixed outcomes are sometimes observed in affected patients, thereby indicating that interrupted brain connectivity is a neural indicator of autism (Maximo, Cadena, & Kana, 2014).

The first scientific account of autism showed that affected individuals exhibited behaviors that resembled those of people with frontal lobe damage (Fellowes, 2015). The dentato-thalamo-cortical path, which is central to speech and advanced cognitive functions, was also compromised in autism. Later studies utilizing positron emission tomography (PET), indicated that adults with autism had an overall upsurge in resting glucose metabolism, which backed the supposition that autism was connected to aberrant brain activity. These preliminary studies formed the basis that changes in brain responses were important in the pathobiology of autism.

We will write a custom
essays
specifically
for you!
Get your first paper with
15% OFF
Learn More

The introduction of contemporary neuroimaging techniques such as magnetoencephalography (MEG), electroencephalography (EEG), proton magnetic resonance spectroscopy, and diffusion tensor imaging (DTI) further improved the quality of outcomes of studies looking into the brain in autism. More studies into the area were prompted by the increasing diagnoses of autism in children (Cheng, Rolls, Gu, Zhang, & Feng, 2015).

Overall, cognitive progressions are computationally taxing and need efficient allotment of the brain’s resources to produce optimal functioning of various areas of the brain. Two principles guide the organization of the brain: functional specialization and functional integration (Ma et al., 2018). The tenet of functional specialization suggests that various sections of the brain are dedicated to diverse cognitive functions, whereas functional integration entails the synchronization of different areas of the brain to complete a chore. Integration means that a given cortical region is specific for certain facets of perceptual or motor handling.

This adaptation is anatomically separated in the cortex. Consequently, early brain development entails striking a subtle balance between the functional adaptation of precise regions and the establishment of networks across these areas via integration. Since autism is characterized by changes in brain developmental course, it interferes with functional specialization and integration. Current neurobiological discoveries of behavioral working in autism indicate that changes in brain connectivity are the main hallmarks of its pathophysiology.

Underconnectivity

Cheng et al. (2015) observed diminished functional connectivity across various parts of the brain in adults with ASD. Enervated functional connectivity, which is also known as cortical underconnectivity, was observed following several fMRI studies that were done using various cognitive and social tasks. Some of the tasks used included language, visual imagery, problem-solving, working memory, response inhibition, social and emotional chores, general processing, cognitive control, and biological motion. The weaker connectivity reported in most of these studies was primarily between the prefrontal cortex and relatively posterior brain areas.

Reduced synchronization of the prefrontal and posterior areas hampers higher-level processing and could be responsible for problems like impaired social, cognitive, and language processing that is observed in autism. For social processing to occur, there should be harmonized working of the “medial prefrontal cortex (MPFC) and the temporoparietal junction (TPJ, associated with ToM), the superior temporal sulcus (STS, associated with biological motion), and the fusiform gyrus (FG, associated with face processing)” (Maximo et al., 2014, p. 4).

Underconnectivity has also been shown in areas beyond the frontal-posterior system like in the middle of the amygdala and temporal and frontal areas and amid the anterior cingulate and frontal eye turfs. Other areas with underconnectivity are between insula and areas of the brain that play a role in the processing of emotional and sensory signals, inside motor networks, anterior cerebellum, and the thalamus. Moreover, areas flanking the prefrontal cortex and premotor and somatosensory cortices, as well as between the amygdala and fusiform gyrus, the cuneus and posterior cingulate may experience underconnectivity.

Systems involving cortical and subcortical areas such as those between the thalamus and cerebellum and the visual cortex, in addition to areas in the middle of the caudate nucleus and superior frontal gyrus have also been documented to have functional underconnectivity in autism. Even though all these investigations observed functional underconnectivity in the non-frontal posterior network, different outcomes were noted across various sets of areas in diverse chores. As a result, it is not possible to pinpoint a precise outline of the disorder (Cheng et al., 2015).

Overconnectivity

Overconnectivity in autism has been reported in areas such as the amygdala, the extrastriate cortex, parahippocampal gyri, frontal and temporal areas. In the same way, higher functional connectivity has been observed amidst temporal lobe, posterior cingulate cortex, and parahippocampal gyrus (Solso et al., 2016). The suggestion of overconnectivity in cortical-subcortical systems was ascertained by elevated functional connectivity in temporo-thalamic zones in people with autism. Increased connectivity in thalamocortical protuberances that comprise the cerebello-thalamo-cortical path can be linked to an untimely decrease in the population and compactness of Purkinje cells in autism. Given the inhibitory functions of Purkinje cells, this disorder disturbs the inhibition-excitation equilibrium, thereby causing overconnectivity.

Need a
100% original paper
written from scratch

by professional
specifically for you?
308 certified writers online
Learn More

Overall, overconnectivity is not an indication of effective connectivity but is deciphered to indicate hyperspecialized systems. Therefore, ASD may present itself in the form of excessive connectivity between unnecessary regions, which permits low-level cross-talk that raises noise signals in the system. Overconnectivity is also attributed to the previously observed brain overgrowth in the early developmental stages of children with autism. Synaptic trimming is a vital process needed in brain orderliness and specialization of networks in characteristic development. However, when this function is impaired in ASD, it may cause overconnectivity in the brain.

When considering outward cytoarchitecture aberrances, overconnectivity has been characteristically noted in the superior temporal gyrus, inside the frontal lobes, and in the adjacent occipital complex (Conti et al., 2017). A local system with overconnectivity can be likened to a headland detached from other parts of the brain and with restricted entry to the remainder of the brain, which forms long-distance underconnectivity. Thus, the brain makes up for anomalous connectivity by integrating most regions that it can access easily, including surrounding areas.

As a result, ASD can be hypothesized as a disturbance in cortical and subcortical underconnectivity at long distances with compensatory malformed shorter circuit overconnectivity. These factors cause the prevalent heightened perception of modest stimuli among people with autism with contemporaneous damage in the successful sensory incorporation of the stimuli into a multiplex gestalt perceptual depiction (Sharma, Gonda, & Tarazi, 2018). It is worth noting that mixed patterns of connectivity have also been reported in addition to under and overconnectivity.

The Function of Brain Areas in Social Interaction

Difficulties in social interactions, which is one of the three distinguishing symptoms of ASD is attributed to underconnectivity in brain areas that mediate language, social cognition, and executive functions. It has been shown that poor connectivity in the area flanking the posterior cingulate cortex and the superior frontal gyrus was responsible for impaired social interaction. These findings were obtained through correlation studies and have contributed to elucidating brain-behavior associations in ASD.

Core Deficits of Autism and Associated Brain Regions

Communication

Cortical underconnectivity impedes the optimization of network links needed to execute tasks such as enhancing communication between task-pertinent areas of the brain. Poor coordination among key areas of a network implies that the ensuing output would be substandard. Poor coherence may be attributed to numerous factors, including impaired brain regions, application of a different cortical pathway, or structural irregularities in specific areas.

Mild morphological incongruities like the abnormal growth of white matter may occur in the early stages of life, uncharacteristic maturation of white matter in newborns and toddlers, changes in its volume and intactness in children have been documented in ASD. Such axonal aberrations can constrict the conveyance of information across various parts of the brain because cognitive operation entails the simultaneous instigation of a network of cortical regions with harmonized activity. This coordination is founded on communication between regions with the aid of pieces of white matter that give the structural connectivity (Cheng et al., 2015).

For example, it has been noted that teenagers with autism who exhibited elevated functional connectivity in the default mode network had diminished abilities in verbal and non-verbal communication. The default mode network consists of the retrosplenial cortex, posterior cingulate cortex, superior frontal gyrus, lateral parietal cortex/angular gyrus, medial prefrontal cortex, and parahippocampal gyrus. Additionally, Ecker et al. (2015) observed that oddities in Broca’s and Wernicke’s area were linked to linguistic and social communication insufficiencies.

Social Behavior

Cheng et al. (2015) evaluated the resting state functional connectivity in 418 individuals with ASD and 509 healthy subjects. They noted that decreased cortical functional connectivity in the mid temporal gyrus and superior temporal sulcus areas. The connectivity increased around the medial thalamus. These areas are thought to be involved in the processing of facial expressions, which are important in social behavior. A decreased functional connectivity of this area with the ventromedial prefrontal cortex affects the emotional aspects of social communication. Ecker et al. (2015) also noted that the amygdala and the frontotemporal areas were associated with defects in socio-emotional processing.

Associated Repetitive Behaviors

Relentlessly repetitive behaviors in ASD are associated with elevated functional connectivity between the parahippocampal gyrus (PHG) and posterior cingulate cortex (PCC). Conversely, overconnectivity in the frontal eye areas and anterior cingulate has a positive correlation with limiting, repetitive activities. The increased connectivity between PHG and PCC can occur as causes or upshots of repetitive and restricting activities in autism. The frontostriatal system, which comprises the orbitofrontal cortex and the caudate nucleus might also facilitate recurrent and typecast behaviors (Ecker et al., 2015). Moreover, neuroimaging substantiation implies that the caudate nucleus is distended in autism. This distension is linked to the gravity of cyclic and stereotyped activities.

Biological Implication of Anxiety

Children with general anxiety disorders often have increased total amygdala volume. Thus, aberrant amygdala anatomy in ASD may be associated with indications of anxiety (Herrington et al., 2017). The role of the amygdala in the sensing, deciphering, and recovery of emotional information has led to its wide implication in autism. However, studies on disparities in amygdala volume in autism have yielded mixed outcomes. Nonetheless, the most convincing evidence regarding the morphology of the amygdala shows that its volume changes according to a developmental course where it increases tremendously in the early stages of growth followed by a rapid deceleration by puberty.

Emotions epitomize changes in psychological and bodily states and are linked to sharp motivational reorganization. A milieu of cortical and subcortical constructions support emotions in the human brain, for example, the anterior cingulate, ventral prefrontal, amygdala, dorsal brainstem, insular cortices, and ventral striatum. Activity in most of these areas corresponds to alterations in physiology such as temperature, blood pressure, and heart rate.

Emotional encounter is regulated by the capacity to sense and detect oscillations in the inner physiological state and the performance of instinctual organs in a process referred to as interoception (Garfinkel et al., 2016). In the same way, people with higher interoceptive precision on heartbeat recognition chores have intense emotional experiences. Besides, personal divergences in interoception have an effect on susceptibility to physical and psychological indications.

These findings corroborate the supposition that identifying physical sensations can influence emotional and affective encounters. The emotion processing challenges noted in autism are related hypothetically to compromised ability to recognize and differentiate emotions in self and others. Grownups with autism present with distinct patterns of neural connectivity and brain activity while dealing with emotional information. These patterns differ from the norm and are evident even if their behavioral insufficiencies are not significant. Additionally, these observations correspond to poor activation or connectivity of the insula, which charts physiological and psychological processes in a manner reachable to consciousness. Therefore, the insula is deemed crucial to the depiction of physiological signals in a way that directs emotional outlooks and actions.

Garfinkel et al. (2016) ascertained that people with autism have decreased interoceptive exactitude and inflated interoceptive sensibility, which reflects poor ability to perceive physical signals accurately together with an increased skewed discernment of bodily sensations. The discrepancy between these two interoceptive alignments is usually calculated as a trait prediction error, which corresponds to insufficiencies in emotional sensitivity and incidence of anxiety signs.

These outcomes have therapeutic implications because they point towards a possible route that can be used to assuage distressing symptoms in autism by exercising enhanced interoceptive precision and better prognostic regulation of inner physical signals. Additional methods that are linked to improved body awareness such as meditation are reported to exhibit anxiolytic upshots. Therefore, the findings reported by Garfinkel et al. (2016) propose that interoceptive training can be a valuable way of managing anxiety and subjective distress in ASD.

Biological Implication of OCD

The connection between autism and obsessive-compulsive disorder (OCD) is based on spotted resemblances in repetitive behaviors that express in both diseases. Repetitive behaviors are part of a wide group of behaviors that have substantial similarities to other ailments that contribute to the high rates of comorbidity in people with autism. Consequently, studies have been conducted to uncover the type of relationship between ASD and related disorders. Ruzzano, Borsboom, and Geurts (2015) tested the interactions between the repetitive activities specific to autism and OCD by using the network approach to psychopathologies that theorizes ailments as simple tags for networks of symptoms that are connected by similar causes.

The authors noted that autism and OCD were two separate clusters of symptoms. Obsessions and compulsions shared a few direct links with the symptoms of ASD. Using the Perceived Causal Clinician Network, it was evident that obsession nodes were directly related to autism nodes for compulsions or rites (Ruzzano et al., 2015). This observation meant that obsessions do not cause or influence any autism nodes. There was a modest to weak link between compulsion and autism indications.

These findings supported the previous assumption that repetitive behaviors were the major linkage between autism and OCD. Nevertheless, these syndromes constantly appeared as distinct groups of symptoms because of robust and more common connection of symptoms within a disease, which meant that distinct interactions among symptoms and processes differentiate the two disorders.

These results inform the conceptualization of autism in relation to OCD, which is usually based on the assumption that they are unique but highly comorbid disorders. Another supposition is the symptoms of the two disorders merely overlap. Ruzzano et al. (2015) indicate that ritualistic and compulsive actions contribute to the autism spectrum and are the main link between the two disorders. Furthermore, these symptoms have a high likelihood of influencing other repetitive mannerisms as well as determining what signs manifest and the extent to which they appear.

The observed similarities between autism and ASD imply that the two diseases share inadequacies in inhibitory control of repetitive and compulsive behaviors. However, it is uncertain whether similar or different neural profiles mediated these symptoms. Carlisi et al. (2017) evaluated the common and disease-specific anatomy and function of neurons that mediate inhibitory functions in the two diseases.

There was decreased function and structure in the dorsomedial prefrontal cortex. OCD had a unique increase in the structure and function of left basal ganglia or insula compared to autism and controls. Conversely, fMRI findings showed that autism patients had a distinct low activation of the left dorsolateral-prefrontal area. Furthermore, individuals with OCD devoid of comorbid ASD have been shown to have enlarged basal ganglia (Guersel, Avram, Sorg, Brandl, & Koch, 2018). These outcomes show that the two diseases have unique and shared abnormalities.

Pharmacological Interventions in Autism

Individuals with autism usually receive treatment to manage emotional and behavioral problems linked to the core symptoms of the disorder. Two categories of treatment are possible: pharmacological and nonpharmacological. Psychotropic drugs are commonly used to assuage emotional and behavioral indications. Different classes of drugs have been used to treat ASD based on the presenting symptoms. This section will discuss the major classes of drugs and together with the commonly prescribed drugs in each category for the treatment of autism in pediatric and adult patients. Findings regarding their efficacy, safety, and tolerability as ascertained by different trials are also highlighted.

Psychostimulants

Psychostimulants are an important class of drugs in ASD due to a high level of comorbidity between autism and attention-deficit hyperactivity disorder (ADHD). Therefore, conventional ADHD psychostimulants such as amphetamines and methylphenidate help control ADHD indications in ASD patients. Earlier randomized controlled trials (RCTs) demonstrated the efficacy of methylphenidate in managing hyperactivity in pediatric patients with autism.

However, later studies revealed a rise in adverse effects such as irritability and social withdrawal. A different trial by the Research Units on Pediatric Psychopharmacology (RUPP) Autism Network showed that this drug assuaged hyperactivity and impulsivity in half of ASD patients aged between 5 and 14 years who displayed hyperactivity. Nevertheless, the efficacy of methylphenidate was lower than the observed 70 to 80% efficacy rates in children with ADHD only. This difference could be explained by lower doses of the drug used in ASD patients because they cannot withstand higher doses typically administered in ADHD (Sharma et al., 2018).

Similar findings were recorded in preschool-aged children when methylphenidate was administered at a maximum dose of 10 mg twice a day. Increasing the dosage beyond this point was linked with more side effects, for example, stereotypic behaviors, irritability, gastrointestinal disturbances, and sleep complications. It is worth noting that psychostimulants are only beneficial in ASD if the patient presents comorbid hyperactivity and impulsivity. However, they do not confer any benefits on other indications such as social withdrawal, speech distortion, irritability, and repetitive behaviors.

Atypical Antipsychotic Drugs

This group of antipsychotic drugs affects receptors specific for dopamine, serotonin, and other related neurotransmitters. They have been used to treat psychotic disorders such as schizophrenia. The commonly prescribed atypical antipsychotic drugs for autism include quetiapine, risperidone, ziprasidone, aripiprazole, and olanzapine.

Risperidone

At an average dose of 2.9 mg per day for 12 weeks, risperidone has been reported to minimize symptoms such as depression, aggression, repetitive behavior, and anxiety in adult patients with ASD (Accordino, Kidd, Politte, Henry, & McDougle, 2016). Tolerability to the drug was satisfactory without indications of seizures, cardiac events, or extrapyramidal outcomes. Administering the drug to children and teenagers at a mean dose of 1.8 mg/kg for 8 weeks led to a 57% decline in irritability compared to 14% in the placebo group. Other symptoms that were improved by risperidone included repetitive behaviors, social withdrawal, aggression, and hyperactivity. A separate trial showed that the drug could lessen hyperactivity, irritability and social withdrawal at an average dose of 1.2 mg per day.

However, notable side effects linked to the drug included fatigue, substantial weight gain, dizziness, and sleepiness. Nonetheless, the benefits of the drug outweighed its risks, which led to the FDA approval of risperidone as an atypical antipsychotic agent for autism. Further proof shows that at doses ranging from 0.5 to 1.5 mg per day, risperidone can decrease irritability and aggression in children with ASD between the ages of 2 and 9 years.

Aripiprazole

The administration of 3 doses of aripiprazole (5, 10, and 15 mg per day for 8 weeks) to children and teenagers with autism between the ages of 6 and 17 led to a substantial reduction in irritability (Sharma et al., 2018). The most commonly observed side effects included drooling, sedation, and tremor, which increased the discontinuation rate of the drug. In a separate study, ASD patients of the same age who exhibited aggression received aripiprazole at a mean dose of 8.6 mg per day or placebo. Aripiprazole could reduce irritability within a week of use and sustained its effects all through the duration of the trial. The drug is approved as the second atypical antipsychotic agent in the management of irritability in children and teenagers with autism (Accordino et al., 2016).

Quetiapine

This drug has not produced conclusive outcomes in different trials. Two trials involving a few participants showed that its response rates were low (25%), whereas associated side effects were high. Some of the observed adverse effects included weight gain, aggressive behavior, and sedation (Cosme & Dharmapuri, 2017). Conversely, other studies observed higher response rates ranging from 40 to 60% with extensive side effects (Sharma et al., 2018). These outcomes show that risperidone is more effective than quetiapine in ASD. However, further studies are needed to establish the tolerability, safety, and efficacy of the drug more accurately.

Ziprasidone

Ziprasidone has led to substantial improvements in ASD symptoms such as agitation, aggressiveness, and irritability at an average dose of 59 mg per day (Sharma et al., 2018). Most patients tolerated the drug well. Notable changes included mean weight losses of 5.8 pounds, which were to be expected following a switch from atypical agents associated with weight gain. The weight loss effect of ziprasidone was ascertained by changing adult patients to ziprasidone from other drugs that lead to significant weight gain. Significant weight loss was observed in all patients after 6 months on ziprasidone at a mean dose of 128 mg per day without worsening their managed behavioral symptoms. However, there is a need for further studies to authenticate the therapeutic advantages of ziprasidone in autism.

Olanzapine

Olanzapine has been shown to improve symptoms such as anger, irritability, anxiety, social withdrawal, hyperactivity, and language at a mean dose of 7.8 mg per day in ASD patients aged between 5 and 42 years. The most common adverse effect was a significant increase in body weight over a treatment period of 12 weeks. An average body weight increase of 8.4 kg was recorded (Sharma et al., 2018). When the efficiency of olanzapine was compared to haloperidol in children with ASD, the former produced a higher response rate. However, children treated with olanzapine had a substantial weight gain of 4.1 kg. Even though olanzapine produces satisfactory response rates in the management of autism symptoms, its clinical use is restricted because of associated metabolic effects, including elevated appetite, weight gain and hampered sensitivity to insulin (Yoon, Wink, Pedapati, Horn, & Erickson, 2016).

Antidepressant Drugs

Antidepressant medications, particularly selective serotonin reuptake inhibitors (SSRIs), are commonly administered to patients with autism. Their use over the recent years has increased despite inconclusive evidence to back their use in alleviating the key ASD symptoms, anxiety, or depression. Some of the widely used SSRIs include sertraline, fluoxetine, escitalopram, citalopram, and fluvoxamine.

Fluoxetine

Treatment with liquid fluoxetine for 20 weeks has been shown to minimize repetitive behaviors in children with autism compared to the administration of placebo (Sharma et al., 2018). There were no significant safety and tolerability concerns, even though the drug did not yield any improvements on other ASD symptoms. A separate RCT recorded uneven restitution of anxiety signs and repetitive activities in a small group of adult patients with comorbid ASD and anxiety over a treatment period of 16 weeks. The Study of Fluoxetine in Autism (SOFIA) RCT did not identify any substantial improvements in repetitive behavior in patients treated with fluoxetine compared to placebo (Herscu et al., 2019). These studies show that there is inadequate evidence to support or oppose the use of fluoxetine in the treatment of ASD symptoms.

Sertraline

The treatment of ASD patients with sertraline for 2 to 8 weeks at a dose ranging from 25 to 50 mg led to a substantial improvement in irritability and anxiety symptoms in 88.8% of the treated patients. A larger trial involving higher doses of the drug (50 to 200 mg) led to a reduction in aggression and repetitive behaviors in adults with ASD. Very few adverse effects were reported, thereby indicating that the drug was well tolerated (Sharma et al., 2018). Further studies are necessary to ascertain additional benefits of sertraline in autism.

Citalopram

Citalopram is reported to alleviate anxiety and belligerence in a few young patients with ASD. However, it did not alter the core signs of the disease. A retrospective review of medical charts belonging to patients with ASD showed that citalopram decreased anxiety, irritability, and repetitive behaviors even though it produced mild side effects. However, a later trial done by the Studies to Advance Autism Research and Treatment (STAART) Autism Network failed to verify these outcomes after noting many side effects without significant therapeutic benefits (Sharma et al., 2018). Some of the observed side effects included insomnia, hyperactivity, and diarrhea. Finally, it was concluded that citalopram is not effective in autism.

Escitalopram

Few studies have evaluated the efficacy of escitalopram in ASD. The drug is reported to improve impulsivity and general psychosocial working in young patients with autism compared to placebo. Nonetheless, many patients reported adverse effects such as aggression, hyperactivity, or irritability, which led to the untimely discontinuation of the drug. There is a need for more trials to assess the safety and efficacy of escitalopram in adult patients with ASD in the presence or absence of comorbid depression or anxiety (Sharma et al., 2018).

Fluvoxamine

There are no conclusive clinical data on the use of fluvoxamine in ASD. One study found out that only 17% of patients with coexisting compulsive and anxiety symptoms reported improvements in their symptoms following 10 weeks of treatment. The administered dosage of fluvoxamine was 1.5 mg/kg per day. Adverse drug effects such as agitation, trouble sleeping, akathisia, appetite alterations, and headaches were noted in those patients. Conversely, a separate 12-week RCT reported that fluvoxamine could lessen symptoms of ASD, such as aggression and repetitive behaviors in adult patients with ASD compared to placebo (Sharma et al., 2018).

Alpha-2 Adrenergic Receptor Agonists

Prominent symptoms in ASD such as sleep disorders, aggressive tendencies, and anxiety have been treated successfully using alpha-2 adrenergic receptor agonists. These drugs block the neurotransmission of norepinephrine in the brainstem, which reduces sympathetic discharge and peripheral opposition (Santosh & Singh, 2016). As a result, motor spasms, hyperarousal, and anxiety are alleviated. Administering clonidine, an example of an alpha-2 adrenergic receptor agonist, for 4 weeks has been shown to reduce states of hyperarousal and enhanced social interactions in young patients with autism.

A separate study reported moderate improvements following the administration of clonidine to children with ASD. A retrospective clinical trial revealed that symptoms such as sleep instigation latency and night awakening were improved by giving clonidine to ASD patients (Sharma et al., 2018). The drug also helped with hyperactivity and aggressiveness in patients with a low tolerability profile. However, more RCTs are needed to provide additional insight into the clinical efficiency and safety of clonidine in autism.

Another example of an alpha-2 adrenergic receptor agonist that has been investigated in ASD is guanfacine extended-release. Children who received prescriptions of the drug to be taken for 8 weeks had improved impulsiveness, hyperactivity, and distractibility compared to those who got placebos. Significant side effects associated with the drug included sleepiness, tiredness, and diminished appetite. These observations were corroborated by a different trial, which indicated that clinicians should be cautious when prescribing guanfacine to ASD patients.

Conclusion

Heterogeneity in the presentation of autism is a common challenge to researchers trying to provide scientific explanations of the disorder as well as clinicians targeting the design of apposite interventions for affected patients. These differences also convolute studies meant to understand the neurobiological basis of autism. From this paper, the observed neurobiological heterogeneity of ASD confirms that it is not a single disorder but a blend of many abnormalities due to its myriad manifestations and varying etiologies. Concerning the brain anatomy implicated in ASD, various studies show that frontal lobe aberrances are common in ASD and responsible for the observed impairments.

However, these abnormalities are also present in other mental and neurodevelopmental diseases such as OCD, schizophrenia, and anxiety disorders. These observations propose that phenotypic resemblances in brain structure between autism and other psychiatric disorders are responsible for their corresponding clinical phenotypes and comorbidities. Conversely, the pharmacological treatment of ASD entails the use of different classes of drugs to treat varying groups of presenting symptoms.

References

Accordino, R. E., Kidd, C., Politte, L. C., Henry, C. A., & McDougle, C. J. (2016). Psychopharmacological interventions in autism spectrum disorder. Expert Opinion on Pharmacotherapy, 17(7), 937-952.

Bourgeron, T. (2016). Current knowledge on the genetics of autism and propositions for future research. Comptes Rendus Biologies, 339(7-8), 300-307.

Carlisi, C. O., Norman, L. J., Lukito, S. S., Radua, J., Mataix-Cols, D., & Rubia, K. (2017). Comparative multimodal meta-analysis of structural and functional brain abnormalities in autism spectrum disorder and obsessive-compulsive disorder. Biological Psychiatry, 82(2), 83-102.

CDC. (2018). Data and statistics on autism spectrum disorder. Web.

Cheng, J., Eskenazi, B., Widjaja, F., Cordero, J. F., & Hendren, R. L. (2019). Improving autism perinatal risk factors: A systematic review. Medical Hypotheses, 127, 26-33.

Cheng, W., Rolls, E. T., Gu, H., Zhang, J., & Feng, J. (2015). Autism: Reduced connectivity between cortical areas involved in face expression, theory of mind, and the sense of self. Brain, 138(5), 1382-1393.

Conti, E., Mitra, J., Calderoni, S., Pannek, K., Shen, K. K., Pagnozzi, A.,… Muratori, F. (2017). Network over‐connectivity differentiates autism spectrum disorder from other developmental disorders in toddlers: A diffusion MRI study. Human Brain Mapping, 38(5), 2333-2344.

Cosme, R., & Dharmapuri, S. (2017). Reconceptualizing agitation in autism as primary affective dysregulation: Case report and literature review of use of quetiapine in a patient with Treacher–Collins syndrome and autism. European Psychiatry, 41, S434.

Ecker, C., Bookheimer, S. Y., & Murphy, D. G. (2015). Neuroimaging in autism spectrum disorder: Brain structure and function across the lifespan. The Lancet Neurology, 14(11), 1121-1134.

Ellegood, J., Anagnostou, E., Babineau, B. A., Crawley, J. N., Lin, L., Genestine, M.,… Geschwind, D. H. (2015). Clustering autism: Using neuroanatomical differences in 26 mouse models to gain insight into the heterogeneity. Molecular Psychiatry, 20(1), 118-125.

Fellowes, S. (2015). Did Kanner actually describe the first account of autism? The mystery of 1938. Journal of Autism and Developmental Disorders, 45(7), 2274-2276.

Garfinkel, S. N., Tiley, C., O’Keeffe, S., Harrison, N. A., Seth, A. K., & Critchley, H. D. (2016). Discrepancies between dimensions of interoception in autism: Implications for emotion and anxiety. Biological Psychology, 114, 117-126.

Guersel, D. A., Avram, M., Sorg, C., Brandl, F., & Koch, K. (2018). Frontoparietal areas link impairments of large-scale intrinsic brain networks with aberrant fronto-striatal interactions in OCD: A meta-analysis of resting-state functional connectivity. Neuroscience & Biobehavioral Reviews, 87, 151-160.

Hazlett, H. C., Gu, H., Munsell, B. C., Kim, S. H., Styner, M., Wolff, J. J.,… Collins, D. L. (2017). Early brain development in infants at high risk for autism spectrum disorder. Nature, 542(7641), 348.

Herrington, J. D., Maddox, B. B., Kerns, C. M., Rump, K., Worley, J. A., Bush, J. C.,… Miller, J. S. (2017). Amygdala volume differences in autism spectrum disorder are related to anxiety. Journal of Autism and Developmental Disorders, 47(12), 3682-3691.

Herscu, P., Handen, B. L., Arnold, L. E., Snape, M. F., Bregman, J. D., Ginsberg, L.,… Minshew, N. (2019). The SOFIA study: Negative multi-center study of low dose fluoxetine on repetitive behaviors in children and adolescents with autistic disorder. Journal of Autism and Developmental Disorders, 1-12.

Khundrakpam, B. S., Lewis, J. D., Kostopoulos, P., Carbonell, F., & Evans, A. C. (2017). Cortical thickness abnormalities in autism spectrum disorders through late childhood, adolescence, and adulthood: A large-scale MRI study. Cerebral Cortex, 27(3), 1721-1731.

Ma, Z., Perez, P., Ma, Z., Liu, Y., Hamilton, C., Liang, Z., & Zhang, N. (2018). Functional atlas of the awake rat brain: A neuroimaging study of rat brain specialization and integration. Neuroimage, 170, 95-112.

Maximo, J. O., Cadena, E. J., & Kana, R. K. (2014). The implications of brain connectivity in the neuropsychology of autism. Neuropsychology Review, 24(1), 16-31.

National Institute of Environmental Health Science. NIEHS. (2019). Autism. Web.

Ruzzano, L., Borsboom, D., & Geurts, H. M. (2015). Repetitive behaviors in autism and obsessive–compulsive disorder: New perspectives from a network analysis. Journal of Autism and Developmental Disorders, 45(1), 192-202.

Santosh, P. J., & Singh, J. (2016). Drug treatment of autism spectrum disorder and its comorbidities in children and adolescents. BJPsych Advances, 22(3), 151-161.

Sharma, S. R., Gonda, X., & Tarazi, F. I. (2018). Autism spectrum disorder: Classification, diagnosis and therapy. Pharmacology & Therapeutics, 190, 91-104.

Solso, S., Xu, R., Proudfoot, J., Hagler Jr, D. J., Campbell, K., Venkatraman, V.,… Eyler, L. (2016). Diffusion tensor imaging provides evidence of possible axonal overconnectivity in frontal lobes in autism spectrum disorder toddlers. Biological Psychiatry, 79(8), 676-684.

Yang, D. Y. J., Beam, D., Pelphrey, K. A., Abdullahi, S., & Jou, R. J. (2016). Cortical morphological markers in children with autism: A structural magnetic resonance imaging study of thickness, area, volume, and gyrification. Molecular Autism, 7(1), 1-12.

Yoon, Y., Wink, L. K., Pedapati, E. V., Horn, P. S., & Erickson, C. A. (2016). Weight gain effects of second-generation antipsychotic treatment in autism spectrum disorder. Journal of Child and Adolescent Psychopharmacology, 26(9), 822-827.

Cite this paper

Select style

Reference

StudyCorgi. (2021, October 20). Biological Bases of Autism: General Analysis. Retrieved from https://studycorgi.com/biological-bases-of-autism-general-analysis/

Reference

StudyCorgi. (2021, October 20). Biological Bases of Autism: General Analysis. https://studycorgi.com/biological-bases-of-autism-general-analysis/

Work Cited

"Biological Bases of Autism: General Analysis." StudyCorgi, 20 Oct. 2021, studycorgi.com/biological-bases-of-autism-general-analysis/.

1. StudyCorgi. "Biological Bases of Autism: General Analysis." October 20, 2021. https://studycorgi.com/biological-bases-of-autism-general-analysis/.


Bibliography


StudyCorgi. "Biological Bases of Autism: General Analysis." October 20, 2021. https://studycorgi.com/biological-bases-of-autism-general-analysis/.

References

StudyCorgi. 2021. "Biological Bases of Autism: General Analysis." October 20, 2021. https://studycorgi.com/biological-bases-of-autism-general-analysis/.

References

StudyCorgi. (2021) 'Biological Bases of Autism: General Analysis'. 20 October.

This paper was written and submitted to our database by a student to assist your with your own studies. You are free to use it to write your own assignment, however you must reference it properly.

If you are the original creator of this paper and no longer wish to have it published on StudyCorgi, request the removal.