Author Interviews, Autism, Nature, Pediatrics / 27.11.2018
Gene Expression Differences Detected in Toddlers with Autism
MedicalResearch.com Interview with:
[caption id="attachment_46215" align="alignleft" width="149"]
Dr. Lombardo[/caption]
Michael Lombardo, PhD
Assistant professor of Psychology
the University of Cyprus
MedicalResearch.com: What is the background for this study?
Response: Autism is a diagnostic label we give to children with difficulties in the areas of social-communication and restricted, repetitive stereotyped behaviors and interests. The diagnosis is made based on observations about behavior and is a consensus label, meaning that clinicians can show high degrees of agreement that a given set of behaviors is ‘autism’. But aside from the diagnostic label, there is a fair degree of heterogeneity within patients that have the diagnosis. One way in which patients are heterogeneous is with regard to early language development. Some toddlers with autism are minimally verbal, while at the other end, many toddlers with autism develop language typically. An important question to answer is whether that kind of difference in language development indicates a subtype with different underlying biology.
To examine this question, we first split toddlers with autism into two subtypes defined by their language outcome at 4 years of age. Some toddlers were classified as poor language outcome, because their language performance was 1 standard deviation below typical norms. Other toddlers with autism had relatively good language outcome, as their language performance by 4 years of age was within 1 standard deviation of typical norms.
We also measured the biology behind these two autism subtypes. First we used functional magnetic resonance imaging (fMRI), which is a non-invasive method to look at blood oxygenation response that changes according to a task. Blood oxygenation changes are an indirect measure of neural activity. We used fMRI during natural sleep at around 29 months of age while the toddlers were played language stimuli through headphones to elicit neural responses to speech. Second, we measured molecular aspects of biology, by taking blood samples, isolating leukocyte cells, and then quantifying gene expression for all protein coding genes in the genome, at around the same time as the fMRI scan.
Dr. Lombardo[/caption]
Michael Lombardo, PhD
Assistant professor of Psychology
the University of Cyprus
MedicalResearch.com: What is the background for this study?
Response: Autism is a diagnostic label we give to children with difficulties in the areas of social-communication and restricted, repetitive stereotyped behaviors and interests. The diagnosis is made based on observations about behavior and is a consensus label, meaning that clinicians can show high degrees of agreement that a given set of behaviors is ‘autism’. But aside from the diagnostic label, there is a fair degree of heterogeneity within patients that have the diagnosis. One way in which patients are heterogeneous is with regard to early language development. Some toddlers with autism are minimally verbal, while at the other end, many toddlers with autism develop language typically. An important question to answer is whether that kind of difference in language development indicates a subtype with different underlying biology.
To examine this question, we first split toddlers with autism into two subtypes defined by their language outcome at 4 years of age. Some toddlers were classified as poor language outcome, because their language performance was 1 standard deviation below typical norms. Other toddlers with autism had relatively good language outcome, as their language performance by 4 years of age was within 1 standard deviation of typical norms.
We also measured the biology behind these two autism subtypes. First we used functional magnetic resonance imaging (fMRI), which is a non-invasive method to look at blood oxygenation response that changes according to a task. Blood oxygenation changes are an indirect measure of neural activity. We used fMRI during natural sleep at around 29 months of age while the toddlers were played language stimuli through headphones to elicit neural responses to speech. Second, we measured molecular aspects of biology, by taking blood samples, isolating leukocyte cells, and then quantifying gene expression for all protein coding genes in the genome, at around the same time as the fMRI scan.
Dr. Pedersen[/caption]
Professor Oluf Pedersen
Novo Nordisk Foundation Center for Basic Metabolic Research
University of Copenhagen
MedicalResearch.com: What is the background for this study?
Response: We focused our study on healthy people due to the world-wide bottom-up movement among healthy adults to live gluten-free or on a low-gluten diet.
Therefore, we undertook a randomised, controlled, cross-over trial involving 60 middle-aged healthy Danish adults with two eight week interventions comparing a low-gluten diet (2 g gluten per day) and a high-gluten diet (18 g gluten per day), separated by a washout period of at least six weeks with habitual diet (12 g gluten per day).
The two diets were balanced in number of calories and nutrients including the same total amount of dietary fibres. However, the composition of fibres differed markedly between the two diets.
When the low-gluten trend started years back the trend was without any scientific evidence for health benefits. Now we bring pieces of evidence that a low-gluten diet in healthy people may be related to improved intestinal wellbeing due to changes in the intestinal microbiota which to our surprise is NOT induced by gluten itself but by the concomitant change in the type of dietary fibres linked to a low-gluten intake.
![MedicalResearch.com Interview with: Dr. Theodore Satterthwaite MD Assistant professor in the department of Psychiatry, and Cedric Xia, a MD-PhD candidate Perelman School of Medicine at the University of Pennsylvania MedicalResearch.com: What is the background for this study? What are the main findings? Response: Unlike other branches of modern medicine, psychiatry still solely replies on patient reports and physician observations for clinical decision-making. Without biologically-based tests, the diagnostic categories for mental health do not carve nature at its joint. This is evident in the high levels of co-morbidity across disorders and heterogeneity within disorders. Through this research, we studied a large sample of adolescents who completed MRI-based functional imaging, and used recently-developed machine learning techniques to uncover specific abnormalities that are highly predictive of a wide variety of psychiatric symptoms. Essentially, we tried to find brain patterns that were predictive of different types of psychiatric symptoms. We discovered four such brain-guided dimensions of psychopathology: mood, psychosis, fear, and disruptive behavior. While each of these dimensions exhibits a unique pattern of brain connectivity, a common feature of brain anomaly is shared across the dimensions. Notably, in all linked dimensions, the default mode network and fronto-parietal network, two brain regions that usually become increasingly distinct as the brain matures, were abnormally connected. This loss of normal brain network segregation supports the hypothesis that many psychiatric illnesses may be disorders of brain development. MedicalResearch.com: What should readers take away from your report? Response: This study shows that we can start to use the brain to guide our understanding of psychiatric disorders in a way that’s fundamentally different than grouping symptoms into clinical diagnostic categories. By moving away from clinical labels developed decades ago, we can begin to let the biology speak for itself. Our ultimate hope is that understanding the biology of mental illnesses will allow us to develop better treatments for our patients. MedicalResearch.com: What recommendations do you have for future research as a result of this work? Response: This study demonstrates the importance of incorporating vast amounts of biological data to study mental illness across clinical diagnostic boundaries. Moving forward, we hope to integrate genomic data in order to describe pathways from genes to brain to symptoms, which could ultimately be the basis for novel treatments for mental illness. MedicalResearch.com: Is there anything else you would like to add? Response: Future breakthroughs in brain science to understand mental illness requires large amount of data. While the current study takes advantage of one of the largest samples of youth, the size (n=999) remains dwarfed by the complexity of the brain. The neuroscience community is actively working towards collecting higher quality data in even larger samples, so we can validate and build upon the findings. Citation: Cedric Huchuan Xia, Zongming Ma, Rastko Ciric, Shi Gu, Richard F. Betzel, Antonia N. Kaczkurkin, Monica E. Calkins, Philip A. Cook, Angel García de la Garza, Simon N. Vandekar, Zaixu Cui, Tyler M. Moore, David R. Roalf, Kosha Ruparel, Daniel H. Wolf, Christos Davatzikos, Ruben C. Gur, Raquel E. Gur, Russell T. Shinohara, Danielle S. Bassett, Theodore D. Satterthwaite. Linked dimensions of psychopathology and connectivity in functional brain networks. Nature Communications, 2018; 9 (1) DOI: 10.1038/s41467-018-05317-y [wysija_form id="3"] [last-modified] The information on MedicalResearch.com is provided for educational purposes only, and is in no way intended to diagnose, cure, or treat any medical or other condition. Always seek the advice of your physician or other qualified health and ask your doctor any questions you may have regarding a medical condition. In addition to all other limitations and disclaimers in this agreement, service provider and its third party providers disclaim any liability or loss in connection with the content provided on this website.](https://medicalresearch.com/wp-content/uploads/Cross-clinical-diagnostic-categories.jpg)
![MedicalResearch.com Interview with: Dr. Theodore Satterthwaite MD Assistant professor in the department of Psychiatry, and Cedric Xia, a MD-PhD candidate Perelman School of Medicine at the University of Pennsylvania MedicalResearch.com: What is the background for this study? What are the main findings? Response: Unlike other branches of modern medicine, psychiatry still solely replies on patient reports and physician observations for clinical decision-making. Without biologically-based tests, the diagnostic categories for mental health do not carve nature at its joint. This is evident in the high levels of co-morbidity across disorders and heterogeneity within disorders. Through this research, we studied a large sample of adolescents who completed MRI-based functional imaging, and used recently-developed machine learning techniques to uncover specific abnormalities that are highly predictive of a wide variety of psychiatric symptoms. Essentially, we tried to find brain patterns that were predictive of different types of psychiatric symptoms. We discovered four such brain-guided dimensions of psychopathology: mood, psychosis, fear, and disruptive behavior. While each of these dimensions exhibits a unique pattern of brain connectivity, a common feature of brain anomaly is shared across the dimensions. Notably, in all linked dimensions, the default mode network and fronto-parietal network, two brain regions that usually become increasingly distinct as the brain matures, were abnormally connected. This loss of normal brain network segregation supports the hypothesis that many psychiatric illnesses may be disorders of brain development. MedicalResearch.com: What should readers take away from your report? Response: This study shows that we can start to use the brain to guide our understanding of psychiatric disorders in a way that’s fundamentally different than grouping symptoms into clinical diagnostic categories. By moving away from clinical labels developed decades ago, we can begin to let the biology speak for itself. Our ultimate hope is that understanding the biology of mental illnesses will allow us to develop better treatments for our patients. MedicalResearch.com: What recommendations do you have for future research as a result of this work? Response: This study demonstrates the importance of incorporating vast amounts of biological data to study mental illness across clinical diagnostic boundaries. Moving forward, we hope to integrate genomic data in order to describe pathways from genes to brain to symptoms, which could ultimately be the basis for novel treatments for mental illness. MedicalResearch.com: Is there anything else you would like to add? Response: Future breakthroughs in brain science to understand mental illness requires large amount of data. While the current study takes advantage of one of the largest samples of youth, the size (n=999) remains dwarfed by the complexity of the brain. The neuroscience community is actively working towards collecting higher quality data in even larger samples, so we can validate and build upon the findings. Citation: Cedric Huchuan Xia, Zongming Ma, Rastko Ciric, Shi Gu, Richard F. Betzel, Antonia N. Kaczkurkin, Monica E. Calkins, Philip A. Cook, Angel García de la Garza, Simon N. Vandekar, Zaixu Cui, Tyler M. Moore, David R. Roalf, Kosha Ruparel, Daniel H. Wolf, Christos Davatzikos, Ruben C. Gur, Raquel E. Gur, Russell T. Shinohara, Danielle S. Bassett, Theodore D. Satterthwaite. Linked dimensions of psychopathology and connectivity in functional brain networks. Nature Communications, 2018; 9 (1) DOI: 10.1038/s41467-018-05317-y [wysija_form id="3"] [last-modified] The information on MedicalResearch.com is provided for educational purposes only, and is in no way intended to diagnose, cure, or treat any medical or other condition. Always seek the advice of your physician or other qualified health and ask your doctor any questions you may have regarding a medical condition. In addition to all other limitations and disclaimers in this agreement, service provider and its third party providers disclaim any liability or loss in connection with the content provided on this website.](https://medicalresearch.com/wp-content/uploads/Cross-clinical-diagnostic-categories-200x180.jpg)
