Su Bin Yoon, Class of 2021
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder related to impaired social abilities and repetitive behaviors. In earlier editions of Diagnostic and Statistical Manual of Mental Disorders (DSM), ASD was categorized into Autism, Asperger's syndrome and Pervasive Developmental Disorder Not Otherwise Specified (APA, 1994). In the latest edition of DSM, the three different diagnoses are placed under the umbrella of ASD (APA, 2013). In most ASD patients, the symptoms are displayed by the age of two when the developmental changes are noted. At this time, the people around the baby with ASD may notice a lack of eye contact, gestures and facial expressions. A statistic by the Center for Disease Control and Prevention (CDC) shows that about 3.5% of children have been diagnosed with ASD, which could indicate a greater number of children who are not diagnosed (Christensen, 2016).
To many people’s surprise, the cause of ASD is unknown and there is no quantitative diagnostic method. People can visit a physician in order to check for a list of symptoms, but there is no definitive lab test that would say ‘positive’ or ‘negative’ for ASD. Since we do not know the cause, there is no way to cure or prevent the disorder, and the treatment for the disorder widely varies due to the spectrum of issues that individuals with ASD face. Nevertheless, a timely diagnosis can prevent the symptoms from worsening by starting intervention to promote social skills from early stages (Bryson, 2003). The first signs of ASD appear within the first six months after birth, but most parents fail to make the connection until later on in the child’s development when the symptoms become more pronounced. Most children are diagnosed after the age of four, which significantly sets back the treatment plan.
While the research into the cause of ASD is highly active, there is another approach to help with the issue of late diagnosis - the research of biomarkers of ASD. The focus of such research is in finding an indicator that can be quantified to accurately diagnose ASD as early as possible. Intuitively, there are neurological biomarkers directly related to the ASD symptoms and potential causes. On the other hand, there are metabolic biomarkers that use somewhat counterintuitive measurements. These markers - whether neurological or metabolic - give us a way to improve ASD treatment via early detection and possible classification of ASD.
One of the promising biomarkers utilizes functional magnetic resonance imaging (fMRI). fMRI is able to monitor brain activity by measuring the blood flow to different regions of the brain. Various teams have each taken a different approach with them. technology. Marcel Just Ph.D. and his team performed fMRI on 17 people without autism and 17 people with high-functioning autism (Just, 2014). The brain activities of the participants were measured while they were prompted to think about social interactions such as “adore, ''insult” and “encourage.” Their study found that they can identify the autistic brain with 97% accuracy based on the fMRI activation pattern given these social interaction items (Just, 2014). Other studies also show similar conjectures in that fMRI shows hypoactivation in the parts of the brain that are related to social interactions such as medial prefrontal cortex, the inferior frontal gyrus, the anterior insula, etc. (Dichter, 2012) Once all of these studies have enough data to be used in a clinical setting, it will be able to detect autism with high accuracy.
Another prospective biomarker is electroencephalography (EEG), which measures the electrical activity of the brain. While fMRI can distinguish autistic and neurotypical brains for young adults and adults upon giving social interaction items, it may be less applicable to children of young age who have yet to develop the association to show a discernible change in fMRI. A study at the Boston Children’s Hospital has found that they are able to predict whether infants as young as 3 months old will develop autism using nonlinear analysis of EEG signals and pattern classification methods (Bosl, 2018). This diagnosis method proved to be capable of early detection with high accuracy and quantitative computability.
A notable metabolic biomarker is the vitamin D levels in ASD patients, of which the active form is a neurosteroid. Along with other nutritional and metabolic markers such as copper:zinc ratio and red blood cell manganese, vitamin D level in ASD patients were not within the normal range (Esparham, 2015). It is widely known that unlike other vitamins, vitamin D is mostly generated by our own body when exposed to ultraviolet radiation, which is why getting enough sun is important to our health. Interestingly, there have been correlational studies that found a positive correlation between ASD incidence and lower ultraviolet radiation exposure (Esparham, 2015). Maternal vitamin D deficiency is detrimental to neurodevelopment of the fetus, and vice versa. This trend could be applied to the research into the cause of ASD.
The biomarkers may seem secondary to those looking for a cure at a first glance, but these biomarkers are closely related to finding underlying causes of ASD. Each of these biomarkers serves as a distinguishing factor of ASD patients. In turn, the biomarkers are clues to the mechanism of ASD, thus, the cure as well. The biomarker research contributes to the research into cures by finding out more about how the disorder manifests. With more and more researchers taking on the initiative to find the cause and cure for ASD, there will be more treatment options for ASD patients.
References
American Psychiatric Association (APA). Diagnostic and statistical manual of mental disorders (4th ed.). 4th ed. 1994.
American Psychiatric Association (APA). Diagnostic and statistical manual of mental disorders (5th ed.). 5th ed. 2013.
Bosl WJ, Tager-Flusberg H, Nelson CA. EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach. Scientific Reports. 2018;8(1):1–20.
Bryson SE, Rogers SJ, Fombonne E. Autism Spectrum Disorders: Early Detection, Intervention, Education, and Psychopharmacological Management. The Canadian Journal of Psychiatry. 2003;48(8):506–516.
Christensen DL, Bilder DA, Zahorodny W, Pettygrove S, Durkin MS, Fitzgerald RT, Rice C, Kurzius-Spencer M, Baio J, Yeargin-Allsopp M. Prevalence and Characteristics of Autism Spectrum Disorder Among 4-Year-Old Children in the Autism and Developmental Disabilities Monitoring Network. Journal of Developmental & Behavioral Pediatrics. 2016;37(1):1.
Dichter GS. Functional magnetic resonance imaging of autism spectrum disorders. Dialogues in Clinical Neuroscience. 2012;14(3):319–351.
Esparham AE, Smith T, Belmont JM, Haden M, Wagner LE, Evans RG, Drisko JA. Nutritional and Metabolic Biomarkers in Autism Spectrum Disorders: An Exploratory Study. Integrative Medicine: A Clinician’s Journal. 2015;14(2):40–53.
Just MA, Cherkassky VL, Buchweitz A, Keller TA, Mitchell TM. Identifying Autism from Neural Representations of Social Interactions: Neurocognitive Markers of Autism. PLOS ONE. 2014;9(12):e113879.
To many people’s surprise, the cause of ASD is unknown and there is no quantitative diagnostic method. People can visit a physician in order to check for a list of symptoms, but there is no definitive lab test that would say ‘positive’ or ‘negative’ for ASD. Since we do not know the cause, there is no way to cure or prevent the disorder, and the treatment for the disorder widely varies due to the spectrum of issues that individuals with ASD face. Nevertheless, a timely diagnosis can prevent the symptoms from worsening by starting intervention to promote social skills from early stages (Bryson, 2003). The first signs of ASD appear within the first six months after birth, but most parents fail to make the connection until later on in the child’s development when the symptoms become more pronounced. Most children are diagnosed after the age of four, which significantly sets back the treatment plan.
While the research into the cause of ASD is highly active, there is another approach to help with the issue of late diagnosis - the research of biomarkers of ASD. The focus of such research is in finding an indicator that can be quantified to accurately diagnose ASD as early as possible. Intuitively, there are neurological biomarkers directly related to the ASD symptoms and potential causes. On the other hand, there are metabolic biomarkers that use somewhat counterintuitive measurements. These markers - whether neurological or metabolic - give us a way to improve ASD treatment via early detection and possible classification of ASD.
One of the promising biomarkers utilizes functional magnetic resonance imaging (fMRI). fMRI is able to monitor brain activity by measuring the blood flow to different regions of the brain. Various teams have each taken a different approach with them. technology. Marcel Just Ph.D. and his team performed fMRI on 17 people without autism and 17 people with high-functioning autism (Just, 2014). The brain activities of the participants were measured while they were prompted to think about social interactions such as “adore, ''insult” and “encourage.” Their study found that they can identify the autistic brain with 97% accuracy based on the fMRI activation pattern given these social interaction items (Just, 2014). Other studies also show similar conjectures in that fMRI shows hypoactivation in the parts of the brain that are related to social interactions such as medial prefrontal cortex, the inferior frontal gyrus, the anterior insula, etc. (Dichter, 2012) Once all of these studies have enough data to be used in a clinical setting, it will be able to detect autism with high accuracy.
Another prospective biomarker is electroencephalography (EEG), which measures the electrical activity of the brain. While fMRI can distinguish autistic and neurotypical brains for young adults and adults upon giving social interaction items, it may be less applicable to children of young age who have yet to develop the association to show a discernible change in fMRI. A study at the Boston Children’s Hospital has found that they are able to predict whether infants as young as 3 months old will develop autism using nonlinear analysis of EEG signals and pattern classification methods (Bosl, 2018). This diagnosis method proved to be capable of early detection with high accuracy and quantitative computability.
A notable metabolic biomarker is the vitamin D levels in ASD patients, of which the active form is a neurosteroid. Along with other nutritional and metabolic markers such as copper:zinc ratio and red blood cell manganese, vitamin D level in ASD patients were not within the normal range (Esparham, 2015). It is widely known that unlike other vitamins, vitamin D is mostly generated by our own body when exposed to ultraviolet radiation, which is why getting enough sun is important to our health. Interestingly, there have been correlational studies that found a positive correlation between ASD incidence and lower ultraviolet radiation exposure (Esparham, 2015). Maternal vitamin D deficiency is detrimental to neurodevelopment of the fetus, and vice versa. This trend could be applied to the research into the cause of ASD.
The biomarkers may seem secondary to those looking for a cure at a first glance, but these biomarkers are closely related to finding underlying causes of ASD. Each of these biomarkers serves as a distinguishing factor of ASD patients. In turn, the biomarkers are clues to the mechanism of ASD, thus, the cure as well. The biomarker research contributes to the research into cures by finding out more about how the disorder manifests. With more and more researchers taking on the initiative to find the cause and cure for ASD, there will be more treatment options for ASD patients.
References
American Psychiatric Association (APA). Diagnostic and statistical manual of mental disorders (4th ed.). 4th ed. 1994.
American Psychiatric Association (APA). Diagnostic and statistical manual of mental disorders (5th ed.). 5th ed. 2013.
Bosl WJ, Tager-Flusberg H, Nelson CA. EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach. Scientific Reports. 2018;8(1):1–20.
Bryson SE, Rogers SJ, Fombonne E. Autism Spectrum Disorders: Early Detection, Intervention, Education, and Psychopharmacological Management. The Canadian Journal of Psychiatry. 2003;48(8):506–516.
Christensen DL, Bilder DA, Zahorodny W, Pettygrove S, Durkin MS, Fitzgerald RT, Rice C, Kurzius-Spencer M, Baio J, Yeargin-Allsopp M. Prevalence and Characteristics of Autism Spectrum Disorder Among 4-Year-Old Children in the Autism and Developmental Disabilities Monitoring Network. Journal of Developmental & Behavioral Pediatrics. 2016;37(1):1.
Dichter GS. Functional magnetic resonance imaging of autism spectrum disorders. Dialogues in Clinical Neuroscience. 2012;14(3):319–351.
Esparham AE, Smith T, Belmont JM, Haden M, Wagner LE, Evans RG, Drisko JA. Nutritional and Metabolic Biomarkers in Autism Spectrum Disorders: An Exploratory Study. Integrative Medicine: A Clinician’s Journal. 2015;14(2):40–53.
Just MA, Cherkassky VL, Buchweitz A, Keller TA, Mitchell TM. Identifying Autism from Neural Representations of Social Interactions: Neurocognitive Markers of Autism. PLOS ONE. 2014;9(12):e113879.
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