Autonomic Dysfunction and Autism: Subtypes and Clinical Perspectives

[N A J Med Sci. 2016;9(4):172-180. DOI: 10.7156/najms.2016.0904172]

Rui Song, MD; Jun Liu, PhD; Xuejun Kong, MD

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by impaired social communication, restrictive interests and stereotypical repetitive behaviors. It is known that over-activation of the sympathetic branch of the autonomic nerve system (ANS) on a background of deficient parasympathetic tone is related to social, emotional and cognitive behaviors in autistic people. Different autonomic features have been linked to distinct phenotypes of autism. This review demonstrates a novel method to distinguish autism subtypes and comorbidities using autonomic indices combined with neurotransmitters and neuroimaging patterns. The clinical applications and future perspectives of autonomic indices as promising diagnostic and therapeutic biomarkers are also discussed here.

Key Words: autism spectrum disorder, autonomic dysregulation, heart rate variability, electrodermal activity, autistic subtype

INTRODUCTION Autism spectrum disorder (ASD) is considered a complex neurological and developmental disorder characterized by impaired social communication, restrictive interests, as well as stereotypical repetitive behaviors. As a highly prevalent disorder affecting 1 in 68 children in the United States,1 ASD is considered as a major public health concern not only from medical aspect of view, but also from social and economic perspectives.

Growing evidence has shown the association of ASD with the dysfunction of autonomic nervous system (ANS). ANS is responsible for cognitive, affective and behavioral responses and its dysregulation is found in diverse neuro-psychological disorders, such as anxiety,2 panic disorder,3 social phobia,4 post-traumatic stress disorder,5 attention-deficit hyperactivity disorder6 and ASD.7-10 As an important feature of ASD,7,8 autonomic dysfunction caught increasing attention by researchers in order to better understand the abnormal clinical behaviors in autistic children. A wide range of studies with variable samples and methodologies have revealed the overall boosted sympathetic nerve system and decreased vagal tone in autistic children.9 The considerable heterogeneity in ASD linked different clinical symptoms to distinct autonomic features.7,10 This raised the possibility of the different forms that autonomic dysfunction could take in differentiating ASD phenotypes.

This review will focus on the role of ANS in autism. First we summarize the neuroanatomic nature of ANS and autism, and the common autonomic indices for autistic children in current literature. Second, we discuss the characteristics of autonomic dysfunction in autistic patients versus typical developed (TD) children in the context of different subtypes and comorbidities, followed by analyses of their clinical significance.Last, we further explore the future application value of autonomic parameters as tools for diagnosing, therapy and disease prevention.

THE NEUROANATOMIC NATURE OF AUTONOMIC DYSFUNCTION AND AUTISM The Autonomic nervous system (ANS) encompasses two opposing branches, namely the sympathetic and parasympathetic pathways, to maintain body homeostasis in response to physiological changes and environmental stimuli in an involuntary fashion. The sympathetic nervous system (SNS) can be largely characterized as fight or flight defense system in response to danger signals. The arousal response increases the metabolic output and activate internal organ systems leading to responses such as increased heart rate and pupil dilation, muscle vasculature dilation and the inhibition of digestive system.11,12 The parasympathetic nerve system (PNS) is more complex. According to the well-known Polyvagal Theory brought up by Porges,13,14 PNS divides into two distinct vagal branches controllingdifferent behavioral responses to threat: vegetative vagus in dorsal vagal complex and smart vagus in ventral vagal complex. Vegetative vagus is the more primitive branch controlled by unmyelinated vagal nerves, originated in the dorsal motor nucleus, and would elicit freezing or fainting response when threatened to suppress metabolic demands and is potentially lethal to humans. Prolonged activation of this branch could shut off digestion and cause apnea and bradycardia.15 In contrast, the more evolved branch, the smart vagus, which is unique to mammals and originated from nucleus ambiguous, is controlled by myelinated vagal pathways and is linked to cranial nerves controlling facial expression and vocalization for the regulation of social engagement.15 This branch would regulate cardiac output rapidly and control the striated muscles of face and head, resulting in social communication, calming and self-soothing.15,16 Porges further explained the hierarchical organization of ANS. The newest neural structures of PNS, smart vagus, would cope with a stimulus first with social affiliative behaviors, which requires sustained attention and slows down heart rate by inhibiting acceleratory SNS input. If it is ineffective, response strategies shift to fight or flight behaviors, characterized by rage or panic emotions, by withdrawing the vagal brake to release the activities of SNS.17 If the second strategy also fails, immobilization behaviors, mediated by the oldest response system, would be initiated.15,17 Functional deficiency of the smart vagus would increase the risk of emotional dysregulation and lead to psychiatric conditions.18

From a neuroanatomic aspect of view, the Neurovisceral Integration (NVI) model of ANS suggests that a network of brain areas including prefrontal cortex, cingulate cortex, insula, amygdala and hypothalamus regulates not only the autonomic function, but also social-emotional and cognitive processes.2,19,20 Dysfunction of this network would lead to many psychiatric diseases including ASD.9 Studies have shown thatthe abnormalities of amygdala and other parts of temporal lobe have been related to motor stereotypes ofautismsuch as expressionless faces, spinning and somersaulting.21,22 Pathological changes of amygdala also contributes to seizure activities, gaze problems and failure of recognition of social emotions in autistic children.23-25

Recent studies have found defects of neuronal connectivity within the ANS, as well as between the ANS and other parts of brain region from autistic people. For instance, a whole-brain MRI survey for high-functioning ASD children and those with developmental language disorder has demonstrated reduced functional connection between frontal and parietal-occipital regions as well as lower connectivity between anterior and posterior insula, striatal subregions and limbic cortex.26Annika et al27 investigated thesocio-emotive circuits of the autistic children, the amygdala-cortical pathways by fMRI and figured out abnormal connectivity of its sensory input.

The shared anatomical brain regions that are important for both autonomic dysfunction and social-emotional dysregulations make autonomic status a goodbiomarker for ASD and may provide deeper insight into behavioral features in ASD patients.

THE COMMON AUTONOMIC INDICES IN AUTISM Cardiac Autonomic Measures in ASD Studies Cardiac autonomic parameters are the most common metrics for scientific research reflecting ANS functions. Heart rate is frequently considered as a measure for autonomic arousal, though it has been variably influenced by both subsystems of the ANS. Heart period (HP) or inter-beat-interval (IBI) has an inverse relation to heart rate, with greater IBIs reflecting a slower heart rate, which is also governed by both SNS and PNS.Pure PNS or SNS metrics are discussed below respectively.

Heart rate variability (HRV) is beat-to-beat variation in heart rate, which is quantified by measuring R-R intervals. It is an important parameter for the assessment of autonomic function. HRV reflects the autonomic function of sinoatrial node, and a major portion of these heart rate changes occur in synchrony with respiration, which is called respiratory sinus arrhythmia (RSA). RSA can be visualised by R-R interval variations on ECG, which is shortened during inspiration and prolonged when exhaling.28 Pharmacological blockade studies proved that RSA changes significantly by blocking vagal parasympathetic activity,29,30 therefore it serves as a major cardiac PNS index. Though RSA and HRV are not exactly the same, both terms are often used interchangeably as they both reflect changes of the ANS.31 To evaluate HRV, or the estimated RSA, two approaches are generally applied: frequency and time domain measurements. Frequency domain methods adopt spectral analysis to scrutinize autonomic changes.32,34 Fast Fourier Transformation is the most common approach for conducting this analysis.32,35 The HRV is divided into three frequencies: high frequency, low frequency and very low frequency. The high frequency HRV (HF-HRV) is highly correlated with RSA,36-38 reflecting the parasympathetic activity. The time domain metrics contain root mean successive square difference of normal ECG complex R-R intervals (rMSSD), standard deviation of normal to normal R-R intervals (SDNN) and percentage of the number of pairs of adjacent NN (normal RR) intervals differing by more than 50 ms (pNN50). These all reflect cardiac vagal functions39 and are measured during short-term (minutes) recordings.32,35 They are all correlated with RSA39 and are commonly used in recent literatures.

Sympathetic cardiac control is measured by pre-ejection period (PEP), which is the time interval from heart depolarization to the time when blood enters the aorta. Shorter PEP means greater sympathetic activation. As the measurement of PEP needs echocardiography or MRI, it is rarely used in studies. Other indices of sympathetic activity are controversial, such as low-frequency HRV (LF-HRV), the low-frequency/high-frequency HRV ratio (LF/HF ratio), and so forth.32,40

Previously, electrocardiogram (ECG) is the major device for cardiac indices as it could record cardiac signals cleanly and prominently. But as the technology advanced in recent years, various wearable devices focused on peripheral locations have been commercially available. The photoplethysmo-graphy (PPG) sensor could measure the blood volume pulse (BVP) from peripheral microvasculature, which reflects the heart rate, HRV and other cardiac features. It is convenient and noninvasive, and proved to provide accurate data comparing to ECG.41 However, PPG is very sensitive to motion artifact42 and many biosensor companies suggest to wait at least 5 minutes at the beginning of research to calibrate pulse rate variation data with HRV.

Accelerometers (ACM) and gyroscopes are motion detectors that could picking up signals of subtle body motions associated with heart beating, which is known as ballistocardiography. Javier H et al43used Google Glass Oculus Rift and Galaxy Gear Apple Watch to investigate the subtle motions of peoplewho stayedstill, which showed that cardiac motion and person identity/body position were accurately detected if ACM and gyroscopes are combined. The recognition accuracy reaches as high as 94.25% or 93.79% for Glass or Gear, respectively. Besides, ACM is widely used in tracking seizure episodes and other movement disorders.44 For research purpose, several wrist-worn sensors from differentcompaniessuch Empatica E4 band has integrated the PPG, ACM and electrodermal activity (EDA) indices together for more sensitive and convenient measurements on autonomic function in autism and epilepsy research.

Skin Autonomic Measures in ASD Studies The skin conductance response (SCR) is the most popular functionalindexto interpret sympathetic autonomic function. It is produced by eccrine sweat glands as part of sympathetic (fight or flight) branch of the autonomic nervous system via alpha1-adrenergic receptor by norepinephrine.3,45 It was also known as Galvanic skin response (GSR), psychogalvanic reflex, sympathetic skin response, skin conductance response (SCR), electrodermal response (EDR) and EDA from different literatures.46Currently it is standardized to EDA. EDA could be divided into two types: those related to bodily activity (such as hand gripping)and thoseto cognitive activity (such as sight of a significant person).47 The activity of sweating and the change of skin temperature would indicate the psychological or physiological arousal.

The early studies using silver/silver chloride electrodes worn on index and middle fingers tested palm sweating in the 1990s.45 The dramatic technological advancement in recent two decades has made the recording of EDA more accurate and reproducible.Recent wearable devices prefer wrist sensors rather than palm ones to detect the skin conduction because they have better sensitivity and higher signal peaks.48

Other Autonomic Measures in ASD Studies Pupil size, saliva cortisol level and blood catecholamine levels are also recorded in ASD studies reflecting autonomic status. The neurotransmitters and neuroendocrine biomarkers related to ANS include glutamate, Gamma-aminobutyric acid (GABA), serotonin, dopamine, norepinephrine, acetylcholine and others.49,50

Autonomic Dysfunction and Autism: Subtypes and Comorbidities It is generally believed that autistic children have hyperarousal of sympathetic response and hypofunction of parasympathetic branch.51 Nevertheless, studies with variable samples, methods, and measures have revealed inconsistent findings.9

The controversial results on ANS indicate the possibilities of distinct ANS responses among different subtypes that may confound the results. W. Hirstein et al27 proposed that there are generally two subtypes of autonomic status in autistic children measured by SCRs. Type A responders have steadily rising above-normal levels of EDA but could be ceased completely by shutdown task. This hyperarousal phenomenon was also accompanied by larger range of EDA variation comparing to that in normal children. Nevertheless, a flat response with complete absence of changing in SCRs when upset, crying or doing normal activities was seen in 4 out of 37 patients, which they called type B response or hypoarousal. These patients tend to be engaged in self-injurious behavior and other extreme behaviors to activate autonomic activity. Interestingly, the constantly-elevated EDA levels in Type A children showed persistent sympathetic alert to every environmental stimulus, which made them tagged everything as significant and chaotic. Thus their behaviors tend to shut the system off such as immersing their hands in a large bowl of dry beans with sharp decrease in EDA. Whereas Type B children, with low sympathetic activity may see nothing as significant, which could explain why they may be engaged in self-injurious behaviors and other extreme activities to produce the sense of significance and activate ANS.

In fact, the diverse phenotypes observed in ASD are more than simply autonomic hypoarousal and hyperarousal. An extensive amount of literatures has been attempted to categorize autism based on their phenotypes, though no uniform classification system is available so far. For example, Wings subgroups questionnaire divided ASD patients with aloof, passive, and active but odd subtypes.52 Margot et al53 differentiated three clusters of autistic-type individuals by clinical experience: Cluster A-autistic-like, B-Asperger-like, and C-mild Pervasive Developmental Disorder (PDD) or Pervasive Developmental Disorder Not Otherwise Specified (PDD-NOS). These suggested different levels of social and cognitive impairments. Doshi-Velez F et al54 conducted a cluster analysis using electronic health records and summarized in three distinct medical trajectories. The three subgroups include: patients with seizures; patients with seizures and multisystem general health problems such as gastrointestinal distress, cardiac and/or auditory problems; patients with psychiatric disorders such as general anxiety, obsessive-compulsive disorder (OCD), depression etc. Lane et al55 classified ASD children into four distinct sensory subtypes: sensory adaptive, taste smell sensitive, postural inattentive and generalized sensory difference.

Based on the close relationships between neuroanatomy, neurotransmitters and autonomic functions, we divided ASD phenotypes into five distinct psychomotor subgroups according to autonomic indices, neuroimaging patterns and neuro-hormonal changes. We recognize that there may be phenotypic overlaps between these subtypes, such as seizure with catatonia, anxiety with aggression. The subtypes were summarized in Table 1.

Subtype 1. ASD with anxiety features Anxiety is a major feature of ASD and is present in up to 84% of ASD patients. Sakeena Panju et al56 divided autistic children into high-anxiety group and low-anxiety group and studied their performance in anxiety, attention, response inhibition and social cognition tasks. Besides the fact that ASD children had significantly blunted mean EDA and EDR responses to the anxiety tasks, the high-anxiety ASD subgroup also showed significantly decreased mean EDA compared to both the low-anxiety ASD subgroup and normal controls. More severe symptoms of the high-anxiety ASD subgroup were observed comparing to the low-anxiety ASD group regarding anxiety, depression, attention, aggression, rule breaking, OCD and others. Similar results included a lower heart rate observed in high-anxiety ASD group than low-anxiety group by Hollocks MJ et al.57 Nevertheless, Chiu et al58 discovered increased HR in response to anxiogenic stimuli among 80% of autistic participants. The interpretation of decreased EDA is a concern. As EDA arousal and physiological output are thought to follow an inverted U shaped relation, decreased EDA may reflect sympathetic hyper-arousal in this study. And the overall blunted task responses may also be related to compensated down-regulation by chronic exposure to stress.59 Physical activity wise, a study using ACM in patients with anxiety and/or depression has shown large amounts of sedentary time in anxiety groups.60

Besides autonomic patterns, Hollocks MJ et al59 further investigate the change of HPA-axis and co-morbid anxiety in ASD children. The ASD patients in anxiety subgroup were found to have blunted salivary cortisol level and HR response to psychosocial stress. Furthermore, decreased cortisol level and HR responsiveness were significantly correlated with the severity of anxiety.

Cortical dysconnectivity is another theory for the etiology of ASD. Prater et al61 investigated the amygdala-frontal cortex connectivity in generalized social anxiety disorder during fearful tasks and demonstrated that patients with anxiety had less connectivity between amygdala, rostral anterior cingulate cortex (RACC) and dorsolateral prefrontal cortex (DLPFC). Interestingly, cardiac autonomic function is also modulated by prefrontal cortex (PFC)-amygdala pathway. The NVI model9 [9] suggested that the PFC inhibition during threat could over inhibit the vagal mediated response with a depressed HF-HRV and overactive sympathetic tone, which mirrored the EDA results above.

Subtype 2. ASD with aggression features Many comorbidities of autism, such as ADHD, mood disorders and psychosis had significant aggressive components. Aggression may lead to impulsive outbursts and do harm to patients and their family members. Thus it is very important to recognize it early and treat it properly. Studies have shown that ASD children have more reactive aggression than proactive aggression,62 which was significantly associated with increased attention deficits and internalizing behaviors. Reactive aggression is controlled by amygdala, central nucleus and periaqueductal gray matter pathway and is closely related to sympathetic activity. Studies from Bink et al63 exhibited a dampened vagal tone with sympathetic dominance in both ADHD children and autistic patients with co-morbid ADHD. SDNN, rMSSD, pNN50 and HF-HRV were significantly reduced and LF/HF ratio was higher. Methylphenidate (MPH) therapy could improve the vagal tone and has potential cardio-protective effects.64,65 Besides, studies using ACM to record physical activities of ADHD patients also showed hyperactivities and excessive motor activities.66

Several hormones have been shown to play important roles in aggression. Noradrenaline is the key hormone that triggers aggressive behaviors, and it is produced by both SNS and CNS. It targets neurons with postsynaptic alpha2-adrenoceptors to trigger and maintain aggression for prospective fights. In addition, noradrenaline-induced corticosterone secretion may serve as a situation-dependent modulator through neurons with beta-adrenoceptors.67 Aggression also has close connection with sexual arousal status through medial preoptic area. Testosterone and androgens can participate in neural circuits regulating aggression and produce aggressive behavior. Furthermore, serotonin hypofunction and dopamine hyperfunction in the prefrontal cortex predispose individuals to impulsive aggression.50

Subtype 3. ASD with restricted repetitive behaviors and movement disorders Tics and Tourette syndrome (TS) are common movement disorders in children with ASD. In fact, restricted repetitive behaviors (RRBs) are considered core features of ASD. Brain imaging investigations revealed dysfunction of basal ganglia-thalamo-cortical projection68 during motor control in autistic people. Cerebellar neuropathy also plays a major role in the development of motor disturbances in ASD children, such as ataxia, repetitive/restrictive behaviors and other motor/cognitive behaviors.69

Sympathetic over-activity has been reported in TS patients.70 GSR, reflecting sympathetic arousal, has been positively correlated with tic frequency during biofeedback study.71 Suppressing sympathetic tone using Clonidine (alpha-2 adrenoreceptor agonist) could reduce motor tics episodes.72 On the other hand, the PNS status in tics and TS is unclear. Biofeedback studies showed fewer tics during relaxation with decreased skin conductivity. A case report suggested that Vagus Nerve Stimulation (VNS) can modulate autonomic signals to reduce tic frequency and seizure episodes.73 Tanner et al74 showed that the cholinergic parasympathetic arousal by scopolamine injection decreased motor tics but increased vocal tics in TS patients. The dissociated phenomenon between motor and vocal tics prompt further research.

To monitor tics using ACM, Bernabei M. et al75 described a study using 3D-acceleration device on the trunk to automatically detect motor tics with 80% sensitivity, specificity and accuracy. The tic events were detected as signal peaks after noise filter with a time-variant threshold.75

A large variety of genetic polymorphism and mutations causing changes in neurotransmitters may also contribute to repetitive behaviors and movement disorders. The hormone changes include decreased GABA and dopamine levels, and boosted serotonin and glutamate levels.76

Subtype 4. ASD with seizure About 1/3 of ASD children have coexisting seizure disorders. Epilepsy, especially those occurred at temporal lobe, has a similar neuroanatomic origin with autonomic dysfunction. Ansakorpi et al77 used 24-h ECG in patients with temporal lobe epilepsy(TLE) with long term follow up, which revealed that well-controlled TLE has no significant HRV changes in normal people. In contrast, reduced HRV seems to be progressive in patients with chronic refractory TLE with recurrent episodes. These reflected a disrupted vagal tone in patients with recurrent epilepsy. Besides, recent studies using wrist sensors have shown a huge sympathetic nerve system surge around an impending seizure event, which is measured by a significant peak of EDA.78 Poh M.Z. et al79 examined a total of 34 seizures (22 complex partial and 12 tonic-clonic seizures) from 11 participants and monitored their postictal periods. A surge in EDA and heightened heart rate with coincided persistent suppression of HF-HRV were observed. Both the increased EDA response amplitude and the decrease of HF-HRV are correlated with an increase in the duration of EEG suppression. This study further demonstrated autonomic indices as a predictor for prognosis and sudden unexpected death in epilepsy using wrist biosensor. Wrist biosensor80 recording of EDA and ACM achieved a high detection rate for grand tonic-clonic seizure with an acceptable false alarm rate of 0.74/24h on average, demonstrating its potential as an automated seizure detector. It could potentially provide caregivers of ASD patients with an alarm system for convulsive seizures and serve as an objective quantification of seizure frequency.

From a hormonal perspective, seizure has been related to sympathoexcitation by increased glutamate level in rostral ventrolateral medulla (RVLM) and increased excitatory neuropeptide pituitary adenylate cyclase activating polypeptide (PACAP).81

Subtype 5. ASD with catatonia Catatonia is another comorbid syndrome of autism, which occur in 12-17% of adolescents and young adults of ASD.82,83 It is featured by slow motions and strange behaviors such as mutism, stupor, echolalia, stereotypic speech, repetitive behaviors, grimacing, posturing, rigidity, mannerisms and purposeless agitation. It is believed that catatonia could develop after physical or psychological traumatic events, overwhelming stressful experience or extreme fear in children and adolescents with autism.83-85 40% of catatonic patients have autonomic symptoms including abnormalities of temperature, perspiration and cardiopulmonary responses.86,87 45% of pediatric cases show urinary-fecal incontinence,86 which is another feature of autonomic dysfunction. Stronger vagal tone as evinced by bradycardia87,88 and bronchorrhea was found to be associated with catatonia89 in some studies. Others suggest a tonic immobility governed by the vegetative vagus system, as catatonic patients tend to have diminished HRV, which is the last defense strategy when encountering danger.90 Currently, there is no ACM data or studies of sympathetic index on catatonic patients, although delayed sympathetic hyperactivity was found in catatonic patients after electroconvulsive therapy.91 The sympathetic and motor features of catatonia need future research to clarify.

Hormone-wise, studies have shown that catatonia may be caused in part by the dysfunction of GABA type A receptors in the cortico-cortical networks of the frontal lobes, causing hypofunction of the subcortical dopaminergic transmission.92

Table 1. The autism subtypes.

Subtypes Function HR PNS indices SNS indices ACM Brain region Neurotransmitter
Anxiety features High Blunted response to task Depressed HF-HRV Significantly decreased EDA (sympathetic arousal) large amounts of sedentary time Less connectivity between amygdala, RACC and DLPFC Blunted cortisol response to task
Aggression features High   Higher baseline Significantly reduced HF-HRV Higher LF/HF ratio Hyperactivity Amygdala, central nucleus and periaqueductal gray matter pathway; medial preoptic area; PFC Increased noradrenaline, testosterone and androgens
Movement disorder High Tic frequencies were significantly lower during periods of higher HR None GSR increased Peak Basal ganglia-thalamo-cortical projection; cerebellum Decreased GABA and dopamine, increased serotonin and glutamate
Seizure features Both Heightened HR Persistent suppression of HF-HRV Significant peak of EDA before or during attack Sharp peak during episodes Temporal lobe and others Increased glutamate and PACAP
Catatonia features Low Bradycardia Deminished HRV None None Cortico-cortical networks of the frontal lobes Decreased GABA and dopamine

CLINICAL APPLICATIONS AND FUTURE PERSPECTIVES The Diagnostic Value of Autonomic Status in Autism As mentioned above, autonomic indices and its related neurotransmitters could serve as a tool to identify and diagnose different autistic subtypes and comorbidities with unique patterns. The adrenal stress secondary to sympathetic over-arousal and the subsequent adrenal fatigue syndrome is not uncommon in children with ASD. The chronic stress can lead to hypersecretion and depletion of adrenal cortisols, androgens,93 testerosterones, and thyroid hormones. Thus, testing those hormones would help us to identify adrenal fatigue earlier with appropriate treatment.

Besides, ACM together with autonomic indices could monitor seizure episodes and repetitive motions such as tics with special peaks. As the technology matures, the more sensitive and specific ACM measurements may be able to surveillance seizure and tic episodes in advance to prevent these adverse events.

The Therapeutic Value of Autonomic Status in Autism Knowing the general autonomic status with hyperarousal of sympathetic nerve system and hypofunction of parasympathetic nerve system, it may be beneficial to use medicines to titrate the imbalance of the whole autonomic system for patients with autism. For instance, to down-regulate sympathetic tone, medications suppressing norepinephrine and glutamate levels were used with significant effects. Propranolol, a beta-adrenergic antagonist, is used to improve the social symptoms and communication skills by inhibiting neural norepinephrine release.94 The glutamate receptor antagonists, magnesium and other medications are used to inhibit the excitatory effects of glutamate in order to modulate the behaviors of autistic children, though they were off labeled currently (not FDA approved).95 To upregulate vagal tone, medications aiming to increase the acetylcholine level were used in clinical trials, including acetylcholinesterase inhibitors.95 Natural alternative supplements such as phostidyl choline, phosphatidyl serine and lecithin may take a promising role in improving parasympathetic nerve system.

Familiarity with the brain regions and specific neurological pathways and circuits could guide transcranial stimulation therapy and neuroendocrine therapy targeting the ANS. For instance, overactivation of the amygdala regions induces anxiety, fear and other social-emotional behaviors in autistic children. Oxytocin could decease amygdala activities,96 reduce stress-induced heart racing97 and increase vagal outflow.98 A number of recent studies have shown the important role of autonomic indices to monitor the influence of repetitive transcranial magnetic stimulation (rTMS) for autistic patients. Wan Y et al100 found that a course of 12 weekly inhibitory low-frequency rTMS bilaterally applied to the dorsolateral prefrontal cortex (DLPFC) will improve autonomic balance in ASD participants, with increased HF-HRV, decreased LF/HF ratio and decreased SCR indicating increased cardiac vagal tone and reduced sympathetic arousal. These changes could be possibly worked through improved frontal inhibition of the ANS activity. In this study, decreased irritability, hyperactivity, compulsive behavior and stereotype behavior were correlated with these autonomic variables. Similar results were seen in the literature of Casanova et al.101

Selective acronyms

Abbreviation Full term
HRV Heart rate variability
RSA Respiratory sinus arrhythmia
SDNN Standard deviation of normal to normal R-R intervals
rMMSD Root mean successive square difference of normal ECG complex R-R intervals
pNN50 Percentage of count of number of pairs of adjacent NN(normal RR) intervals differing by more than 50 ms
PEP Pre-ejection period
HF-HRV High frequency heart rate variability
LF-HRV Low frequency heart rate variability
LF/HF ratio Low frequency and high frequency heart rate variability ratio
HP Heart period
IBI Inter-beat-interval
EDA Electrodermal activity
GSR Galvanic skin response
SCR Skin conductance response
EDR Electrodermal response
ACM Accelerometer
PPG Photoplethysmography
NVI Neurovisceral integration
RACC Rostral anterior cingulate cortex
DLPFC Dorsolateral prefrontal cortex
PFC Prefrontal cortex
PACAP Pituitary adenylate cyclase activating polypeptide
ASD Autism spectrum disorder
TS Tourette syndrome

In addition, autonomic biomarkers are also applied to monitor therapeutic effects for ASD comorbidities. EDA biofeedback99 and vagal nerve stimulation73 have been used to control epilepsy and tic activity by stabilizing autonomic regulation.

Limitations The current studies on autonomic dysfunction in autistic children have several limitations such as small sample sizes, unmatched age/gender, non-uniform protocols and medication uses that may confound study results. In particular, ANS patterns in different age groups from toddlers to adolescent and adults may be different and require age-specific evaluations. Moreover, future studies should aim to establish standardized reference ranges in order to better quantify the severity of autonomic dysfunction using comprehensive indices. These limitations require further investigations in the future.

CONCLUSION ASD is a complicated neurodevelopmental disorder with specific psychological, social and behavioral patterns. Autonomic dysregulation was proposed to be a core feature for autism, with distinct patterns in different autism subtypes and comorbidities. The possible roles that the autonomic indices may play in diagnosing, subgrouping, monitoring and preventing ASD make them promising noninvasive biomarkers in the future. Our review demonstrated a novel classification system of autistic subtypes based on ANS dysfunctions including emotional behaviors, autonomic features, neuroanatomic involvement and neurotransmitter changes, which may guide future research in autistic phenotypes.

___________________________________________________________________________

Rui Song, MD;1 Jun Liu, PhD;2 Xuejun Kong, MD*3 1 Harvard Medical School, Boston, MA 2 American Chinese Medical Exchange Society Inc, MA

3 Martinos Center, Mass General Hospital, 149 13th Street, Charlestown, MA

*Corresponding Author: Martinos Center, Mass General Hospital, Charlestown, MA (Email: xkong@bidmc.harvard.edu)

CONFLICT OF INTEREST None.

___________________________________________________________________________

REFERENCES

  1. Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investigators, Centers for Disease Control and Prevention (CDC). Prevalence of autism spectrum disorder among children aged 8 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2010. MMWR Surveill Summ. 2014;63:1-21. PMID: 24670961
  2. Friedman BH. An autonomic flexibility-neurovisceral integration model of anxiety and cardiac vagal tone. Biol Psychol. 2007;74:185-199.
  3. Yeragani VK, Pohl R, Berger R, et al. Decreased heart rate variability in panic disorder patients: a study of power-spectral analysis of heart rate. Psychiatry Res. 1993;46:89-103.
  4. Schmitz J, Krmer M, Tuschen-Caffier B, Heinrichs N, Blechert J. Restricted autonomic flexibility in children with social phobia. J Child Psychol Psychiatry. 2011;52:1203-1211.
  5. Sahar T, Shalev AY, Porges SW. Vagal modulation of responses to mental challenge in posttraumatic stress disorder. Biol Psychiatry. 2001;49:637-643.
  6. Rash JA, Aguirre-Camacho A. Attention-deficit hyperactivity disorder and cardiac vagal control: a systematic review. Atten Defic Hyperact Disord. 2012; 4:167-177.
  7. Kushki A, Brian J, Dupuis A, Anagnostou E. Functional autonomic nervous system profile in children with autism spectrum disorder. Mol Autism. 2014; 5:39.
  8. Cheshire WP. Highlights in clinical autonomic neuroscience: new insights into autonomic dysfunction in autism. Auton Neurosci. 2012;171:4-7.
  9. Benevides TW, Lane SJ. A review of cardiac autonomic measures: considerations for examination of physiological response in children with autism spectrum disorder. J Autism Dev Disord. 2015;45:560-575.
  10. Klusek J, Roberts JE, Losh M. Cardiac autonomic regulation in autism and Fragile X syndrome: a review. Psychol Bull. 2015;141:141-175.
  11. Porges SW. Vagal tone: a physiologic marker of stress vulnerability. Pediatrics. 1992;90:498-504.
  12. Thayer JF, Sternberg E. Beyond heart rate variability: vagal regulation of allostatic systems. Ann N Y Acad Sci. 2006;1088:361-372.
  13. Porges SW. Orienting in a defensive world: mammalian modifications of our evolutionary heritage. A Polyvagal Theory. Psychophysiology. 1995;32: 301-318.
  14. Reed SF, Ohel G, David R, Porges SW. A neural explanation of fetal heart rate patterns: A test of the polyvagal theory. Dev Psychobiol. 1999;35:108-118.
  15. Porges SW. The polyvagal theory: phylogenetic substrates of a social nervous system. Int J Psychophysiol. 2001;42:123-146.
  16. Heilman KJ, Bal E, Bazhenova OV, Porges SW. Respiratory sinus arrhythmia and tympanic membrane compliance predict spontaneous eye gaze behaviors in young children: a pilot study. Dev Psychobiol. 2007;49:531-542.
  17. Beauchaine TP, Gatzke-Kopp L, Mead HK. Polyvagal Theory and developmental psychopathology: emotion dysregulation and conduct problems from preschool to adolescence. Biol Psychol. 2007;74:174-184.
  18. Beauchaine T. Vagal tone, development, and Gray's motivational theory: toward an integrated model of autonomic nervous system functioning in psychopathology. Dev Psychopathol. 2001;13:183-214.
  19. Thayer JF, Lane RD. Claude Bernard and the heart-brain connection: further elaboration of a model of neurovisceral integration. Neurosci Biobehav Rev. 2009;33:81-88.
  20. Lang H, Tuovinen T, Valleala P. Amygdaloid Afterdischarge and Galvanic Skin Response. Electroencephalogr Clin Neurophysiol. 1964;16:366-374.
  21. Bachevalier J. Medial temporal lobe structures and autism: a review of clinical and experimental findings. Neuropsychologia. 1994;32:627-648.
  22. Bachevalier J. Brief report: medial temporal lobe and autism: a putative animal model in primates. J Autism Dev Disord. 1996;26:217-220.
  23. Baron-Cohen S, Baldwin DA, Crowson M. Do children with autism use the speaker's direction of gaze strategy to crack the code of language? Child Dev. 1997;68:48-57.
  24. Baron-Cohen S, Ring HA, Bullmore ET, Wheelwright S, Ashwin C, Williams SC. The amygdala theory of autism. Neurosci Biobehav Rev. 2000;24:355-364.
  25. Adolphs R, Baron-Cohen S, Tranel D. Impaired recognition of social emotions following amygdala damage. J Cogn Neurosci. 2002;14:1264-1274.
  26. Herbert MR, Ziegler DA, Deutsch CK et al., Brain asymmetries in autism and developmental language disorder: a nested whole-brain analysis. Brain. 2005;128:213-226.
  27. Rausch A, Zhang W, Haak KV, et al. Altered functional connectivity of the amygdaloid input nuclei in adolescents and young adults with autism spectrum disorder: a resting state fMRI study. Mol Autism. 2016;7:13.
  28. Yasuma F, Hayano J. Respiratory sinus arrhythmia: why does the heartbeat synchronize with respiratory rhythm? Chest. 2004;125:683-690.
  29. Berntson GG, Cacioppo JT, Binkley PF, Uchino BN, Quigley KS, Fieldstone A. Autonomic cardiac control. III. Psychological stress and cardiac response in autonomic space as revealed by pharmacological blockades. Psychophysiology. 1994;31:599-608.
  30. Martinmki K, Rusko H, Kooistra L, Kettunen J, Saalasti S. Intraindividual validation of heart rate variability indexes to measure vagal effects on hearts. Am J Physiol Heart Circ Physiol. 2006;290:H640-H647.
  31. Billman GE. Heart rate variability - a historical perspective. Front Physiol. 2011;2:86.
  32. Berntson GG, Bigger JT Jr, Eckberg DL, et al. Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology. 1997;34:623-648.
  33. Calkins SD. Cardiac vagal tone indices of temperamental reactivity and behavioral regulation in young children. Dev Psychobiol. 1997;31:125-135.
  34. Calkins SD, Keane SP. Cardiac vagal regulation across the preschool period: stability, continuity, and implications for childhood adjustment. Dev Psychobiol. 2004;45:101-112.
  35. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation. 1996;93:1043-1065.
  36. Grossman P, van Beek J, Wientjes C. A comparison of three quantification methods for estimation of respiratory sinus arrhythmia. Psychophysiology. 1990;27:702-714.
  37. Akinci A, Celiker A, Baykal E, Tezi T. Heart rate variability in diabetic children: sensitivity of the time- and frequency-domain methods. Pediatr Cardiol. 1993;14:140-146.
  38. Goto M, Nagashima M, Baba R, et al., Analysis of heart rate variability demonstrates effects of development on vagal modulation of heart rate in healthy children. J Pediatr. 1997;130:725-729.
  39. Paiva VC, Santana KR, Silva BM, et al. Comparison of assessment methods of cardiac vagal modulation. Arq Bras Cardiol. 2011;97:493-501.
  40. Reyes del Paso GA, Langewitz W, Mulder LJ, van Roon A, Duschek S. The utility of low frequency heart rate variability as an index of sympathetic cardiac tone: a review with emphasis on a reanalysis of previous studies. Psychophysiology. 2013;50:477-487.
  41. Lu G, Yang F, Taylor JA, Stein JF. A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects. J Med Eng Technol. 2009;33:634-641.
  42. Alqaraawi A, Alwosheel A, Alasaad A. Heart rate variability estimation in photoplethysmography signals using Bayesian learning approach. Healthc Technol Lett. 2016;3:136-142.
  43. Javier H, Daniel JM, Rosalind WP. BioInsights: Extracting Personal Data from Still Wearable Motion Sensors. Paper presented at: 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks(BSN). 2015 June 9-12; Cambridge, MA.
  44. Velez M, Fisher RS, Bartlett V, Le S. Tracking generalized tonic-clonic seizures with a wrist accelerometer linked to an online database. Seizure. 2016; 39:13-18.
  45. Venables PH. Autonomic activity. Ann N Y Acad Sci. 1991;620:191-207.
  46. Critchley HD. Electrodermal responses: what happens in the brain. Neuroscientist. 2002;8:132-142.
  47. Hirstein W, Iversen P, Ramachandran VS. Autonomic responses of autistic children to people and objects. Proc Biol Sci. 2001;268:1883-1888.
  48. Sano A, Picard RW, Stickgold R. Quantitative analysis of wrist electrodermal activity during sleep. Int J Psychophysiol. 2014;94:382-389.
  49. El-Ansary A, Al-Ayadhi L. GABAergic/glutamatergic imbalance relative to excessive neuroinflammation in autism spectrum disorders. J Neuroinflammation. 2014;11:189.
  50. Seo D, Patrick CJ, Kennealy PJ. Role of Serotonin and Dopamine System Interactions in the Neurobiology of Impulsive Aggression and its Comorbidity with other Clinical Disorders. Aggress Violent Behav. 2008;13:383-395.
  51. Goodwin MS, Groden J, Velicer WF, et al. Cardiovascular Arousal in Individuals With Autism. Focus Autism Other Dev Disabl. 2006;21:100-123.
  52. Castelloe P, Dawson G. Subclassification of children with autism and pervasive developmental disorder: a questionnaire based on Wing's subgrouping scheme. J Autism Dev Disord. 1993;23:229-241.
  53. Prior M, Eisenmajer R, Leekam S, et al. Are there subgroups within the autistic spectrum? A cluster analysis of a group of children with autistic spectrum disorders. J Child Psychol Psychiatry. 1998;39:893-902.
  54. Doshi-Velez F, Ge Y, Kohane I. Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis. Pediatrics. 2014;133:e54-e63.
  55. Lane AE, Molloy CA, Bishop SL. Classification of children with autism spectrum disorder by sensory subtype: a case for sensory-based phenotypes. Autism Res. 2014;7:322-333.
  56. Panju S, Brian J, Dupuis A, Anagnostou E, Kushki A. Atypical sympathetic arousal in children with autism spectrum disorder and its association with anxiety symptomatology. Mol Autism. 2015;6:64.
  57. Hollocks MJ, Jones CR, Pickles A, et al. The association between social cognition and executive functioning and symptoms of anxiety and depression in adolescents with autism spectrum disorders. Autism Res. 2014;7:216-228.
  58. Chiu TA, Anagnostou E, Brian J, Chau T, Kushki A. Specificity of autonomic arousal to anxiety in children with autism spectrum disorder. Autism Res. 2016;9:491-501.
  59. Hollocks MJ, Howlin P, Papadopoulos AS, Khondoker M, Simonoff E. Differences in HPA-axis and heart rate responsiveness to psychosocial stress in children with autism spectrum disorders with and without co-morbid anxiety. Psychoneuroendocrinology. 2014;46:32-45.
  60. Helgadottir B, Forsell Y, Ekblom O. Physical activity patterns of people affected by depressive and anxiety disorders as measured by accelerometers: a cross-sectional study. PLoS One. 2015;10:e0115894.
  61. Prater KE, Hosanagar A, Klumpp H, Angstadt M, Phan KL. Aberrant amygdala-frontal cortex connectivity during perception of fearful faces and at rest in generalized social anxiety disorder. Depress Anxiety. 2013;30:234-241.
  62. Farmer C, Butter E, Mazurek MO, et al. Aggression in children with autism spectrum disorders and a clinic-referred comparison group. Autism. 2015; 19:281-291.
  63. Bink M, Popma A, Bongers IL, van Boxtel GJ, Denissen A, van Nieuwenhuizen Ch. Cardiac reactivity and stimulant use in adolescents with autism spectrum disorders with comorbid ADHD versus ADHD. J Autism Dev Disord. 2015;45:481-494.
  64. Buchhorn R, Conzelmann A, Willaschek C, Strk D, Taurines R, Renner TJ. Heart rate variability and methylphenidate in children with ADHD. Atten Defic Hyperact Disord. 2012;4:85-91.
  65. Rukmani MR, Seshadri SP, Thennarasu K, Raju TR, Sathyaprabha TN. Heart Rate Variability in Children with Attention-Deficit/Hyperactivity Disorder: A Pilot Study. Ann Neurosci. 2016;23:81-88.
  66. Gawrilow C, Khnhausen J, Schimid J, Stadler G. Hyperactivity and Motoric Activity in ADHD: Characterization, Assessment, and Intervention. Front Psychiatry. 2014;5:171.
  67. Haller J, Makara GB, Kruk MR. Catecholaminergic involvement in the control of aggression: hormones, the peripheral sympathetic, and central noradrenergic systems. Neurosci Biobehav Rev. 1998;22:85-97.
  68. Leisman G, Braun-Benjamin O, Melillo R. Cognitive-motor interactions of the basal ganglia in development. Front Syst Neurosci. 2014;8:16.
  69. Jaber M. Le cervelet comme acteur majeur dans les troubles moteurs des syndromes autistiques [The cerebellum as a major player in motor disturbances related to Autistic Syndrome Disorders]. L' Encphale [Encephale]. 2016 Sep 8 (Epub ahead of print) [In French]. Doi: 10.1016/j.encep.2016.03.018
  70. Hawksley J, Cavanna AE, Nagai Y. The role of the autonomic nervous system in Tourette Syndrome. Front Neurosci. 2015;9:117.
  71. Nagai Y, Cavanna A, Critchley HD. Influence of sympathetic autonomic arousal on tics: implications for a therapeutic behavioral intervention for Tourette syndrome. J Psychosom Res. 2009;67:599-605.
  72. Yang C, Hao Z, Zhu C, Guo Q, Mu D, Zhang L. Interventions for tic disorders: An overview of systematic reviews and meta analyses. Neurosci Biobehav Rev. 2016;63:239-255.
  73. Diamond A, Kenney C, Jankovic J. Effect of vagal nerve stimulation in a case of Tourette's syndrome and complex partial epilepsy. Mov Disord. 2006; 21:1273-1275.
  74. Tanner CM, Goetz CG, Klawans HL. Cholinergic mechanisms in Tourette syndrome. Neurology. 1982;32:1315-1317.
  75. Bernabei M, Andreoni G, Mendez Garcia MO, et al. Automatic detection of tic activity in the Tourette Syndrome. Conf Proc IEEE Eng Med Biol Soc. 2010;2010:422-425.
  76. Lewis M, Kim SJ. The pathophysiology of restricted repetitive behavior. J Neurodev Disord. 2009;1:114-132.
  77. Suorsa E, Korpelainen JT, Ansakorpi H, et al. Heart rate dynamics in temporal lobe epilepsy-A long-term follow-up study. Epilepsy Res. 2011;93:80-83.
  78. Poh MZ, Loddenkemper T, Swenson NC, Goyal S, Madsen JR, Picard RW. Continuous Monitoring of Electrodermal Activity During Epileptic Seizures Using a Wearable Sensor. Conf Proc IEEE Eng Med Biol Soc. 2010;2010:4415-4418.
  79. Poh MZ, Loddenkemper T, Reinsberger C, et al. Autonomic changes with seizures correlate with postictal EEG suppression. Neurology. 2012;78:1868-1876.
  80. Poh MZ, Loddenkemper T, Reinsberger C, et al. Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor. Epilepsia. 2012;53:e93-e97.
  81. Bhandare AM, Kapoor K, Pilowsky P, Farnham MM. Seizure-Induced Sympathoexcitation Is Caused by Activation of Glutamatergic Receptors in RVLM That Also Causes Proarrhythmogenic Changes Mediated by PACAP and Microglia in Rats. J Neurosci. 2016;36:506-517.
  82. Billstedt E, Gillberg IC, Gillberg C. Autism after adolescence: population-based 13- to 22-year follow-up study of 120 individuals with autism diagnosed in childhood. J Autism Dev Disord. 2005;35:351-360.
  83. Wing L, Shah A. Catatonia in autistic spectrum disorders. Br J Psychiatry. 2000;76:357-362.
  84. Saito S, Yamaga K, Kobayashi T, Kato S. [A case of Asperger's disorder with catatonia originally suspected of being catatonic schizophrenia]. Seishin Shinkeigaku Zasshi. 2011;113:241-247. [In Japanese]
  85. Shah A, Wing L. Psychological approaches to chronic catatonia-like deterioration in autism spectrum disorders. Int Rev Neurobiol. 2006;72:245-264.
  86. Dhossche DM, Bouman NH. Catatonia in an adolescent with Prader-Willi Syndrome. Ann Clin Psychiatry. 1997;9:247-253.
  87. Fink M, Taylor MA. Catatonia: A Clinician's Guide to Diagnosis and Treatment. Cambridge, UK: Cambridge University Press; 2003.
  88. Wachtel LE, Griffin MM, Dhossche DM, Reti IM. Brief report: Electroconvulsive therapy for malignant catatonia in an autistic adolescent. Autism. 2010;14:349-358.
  89. Hayashi H, Aoshima T, Otani K. Malignant catatonia with severe bronchorrhea and its response to electroconvulsive therapy. Prog Neuropsychopharmacol Biol Psychiatry. 2006;30:310-311.
  90. Volchan E, Souza GG, Franklin CM, et al. Is there tonic immobility in humans? Biological evidence from victims of traumatic stress. Biol Psychol. 2011;88:13-19.
  91. Kuwahara T, Yoshino A, Horikawa N, Nomura S. Delayed sympathetic hyperactivity following electroconvulsive therapy in patients with catatonic schizophrenia. Nihon Shinkei Seishin Yakurigaku Zasshi. 2009;29:79-83.
  92. Komatsu T, Nomura T, Takami H, et al. Catatonic Symptoms Appearing before Autonomic Symptoms Help Distinguish Neuroleptic Malignant Syndrome from Malignant Catatonia. Intern Med. 2016;55:2893-2897.
  93. Majewska MD, Hill M, Urbanowicz E, et al. Marked elevation of adrenal steroids, especially androgens, in saliva of prepubertal autistic children. Eur Child Adolesc Psychiatry. 2014;23:485-498.
  94. Narayanan A, White CA, Saklayen S, et al. Effect of propranolol on functional connectivity in autism spectrum disorder--a pilot study. Brain Imaging Behav. 2010;4:189-197.
  95. Rossignol DA. Novel and emerging treatments for autism spectrum disorders: a systematic review. Ann Clin Psychiatry. 2009;21:213-236.
  96. Sobota R, Mihara T, Forrest A, Featherstone RE, Siegel SJ. Oxytocin reduces amygdala activity, increases social interactions, and reduces anxiety-like behavior irrespective of NMDAR antagonism. Behav Neurosci. 2015;129:389-398.
  97. Grippo AJ, Trahanas DM, Zimmerman RR 2nd, Porges SW, Carter CS. Oxytocin protects against negative behavioral and autonomic consequences of long-term social isolation. Psychoneuroendocrinology. 2009;34:1542-1553.
  98. Higa KT, Mori E, Viana FF, Morris M, Michelini LC. Baroreflex control of heart rate by oxytocin in the solitary-vagal complex. Am J Physiol Regul Integr Comp Physiol. 2002;282:R537-R545.
  99. Nagai Y. Modulation of autonomic activity in ,neurological conditions: Epilepsy and Tourette Syndrome. Front Neurosci. 2015;9:278.
  100. Wang Y, Hensley MK, Tasman A, Sears L, Casanova MF, Sokhadze EM. Heart Rate Variability and Skin Conductance During Repetitive TMS Course in Children with Autism. Appl Psychophysiol Biofeedback. 2016;41:47-60.
  101. Casanova MF, Hensley MK, Sokhadze EM, et al. Effects of weekly low-frequency rTMS on autonomic measures in children with autism spectrum disorder. Front Hum Neurosci. 2014;8:851.

Refbacks

  • There are currently no refbacks.