New Map May Lead to Drug Development for Complex Brain Disorders
Autism Severity Detected With Brain Activity Test
Cognitive Cross Training Enhances Learning
Psychopaths are Better at Learning to Lie
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Autism Severity Detected With Brain Activity Test
Summary: EEG tests reveal children on the autism spectrum have lower peak alpha frequency than their peers not on the spectrum. Additionally, the lower the peak alpha frequency, the lower a child’s non-verbal IQ was, the researchers discovered. Researchers believe the peak alpha frequency may not only be a useful biomarker for autism diagnosis, but also a marker to test the severity of the disorder.
Source: UCLA.
Potential biomarker correlates brain wave frequency with nonverbal IQ.
UCLA researchers have discovered that children with autism have a tell-tale difference on brain tests compared with other children. Specifically, the researchers found that the lower a child’s peak alpha frequency — a number reflecting the frequency of certain brain waves — the lower their non-verbal IQ was. This is the first study to highlight peak alpha frequency as a promising biomarker to not only differentiate children with autism from typically developing children, but also to detect the variability in cognitive function among children with autism.
BACKGROUND
Autism spectrum disorder affects an estimated one in 68 children in the United States, causing a wide range of symptoms. While some individuals with the disorder have average or above-average reasoning, memory, attention and language skills, others have intellectual disabilities. Researchers have worked to understand the root of these cognitive differences in the brain and why autism spectrum disorder symptoms are so diverse.
An electroencephalogram, or EEG, is a test that detects electrical activity in a person’s brain using small electrodes that are placed on the scalp. It measures different aspects of brain activity including peak alpha frequency, which can be detected using a single electrode in as little as 40 seconds and has previously been linked to cognition in healthy individuals.
METHOD
The researchers performed EEGs on 97 children ages 2 to 11; 59 had diagnoses of autism spectrum disorder and 38 did not have the disorder. The EEGs were taken while the children were awake and relaxed in dark, quiet rooms. Correlations among age, verbal IQ, non-verbal IQ and peak alpha frequency were then studied.
IMPACT
The discovery that peak alpha frequency relates directly to non-verbal IQ in children with the disorder suggests a link between the brain’s functioning and the severity of the condition. Moreover, it means that researchers may be able to use the test as a biomarker in the future, to help study whether an autism treatment is effective in restoring peak alpha frequency to normal levels, for instance.
More work is needed to understand whether peak alpha frequency can be used to predict the development of autism spectrum disorder in young children before symptoms emerge.
ABOUT THIS NEUROSCIENCE RESEARCH ARTICLE
The authors of the study are Shafali Spurling Jeste, UCLA associate professor in psychiatry, neurology and pediatrics and a lead investigator of the UCLA Center for Autism Research and Treatment; Abigail Dickinson and Charlotte DiStefano, postdoctoral fellows at the UCLA Center for Autism Research and Treatment; and Damla Senturk, associate professor of biostatistics at UCLA.
Funding: The study was funded by Autism Speaks (Meixner Postdoctoral Fellowship in Translational Research), the National Institutes of Mental Health (K23MH094517), the National Institute of General Medical Sciences (R01 GM111378-01A1) and the National Institute of Health (ACE 2P50HD055784-06).
Source: Leigh Hopper – UCLA
Image Source: NeuroscienceNews.com image is in the public domain.
Original Research: Abstract for “Peak alpha frequency is a neural marker of cognitive function across the autism spectrum” by Abigail Dickinson, Charlotte DiStefano, Damla Senturk, andShafali Spurling Jeste in European Journal of Neuroscience. Published online July 12 2017 doi:10.1111/ejn.13645
UCLA “Autism Severity Detected With Brain Activity Test.” NeuroscienceNews. NeuroscienceNews, 25 July 2017.
<http://neurosciencenews.com/brain-activity-test-autism-7172/>.
<http://neurosciencenews.com/brain-activity-test-autism-7172/>.
Abstract
Peak alpha frequency is a neural marker of cognitive function across the autism spectrum
Cognitive function varies substantially and serves as a key predictor of outcome and response to intervention in autism spectrum disorder (ASD), yet we know little about the neurobiological mechanisms that underlie cognitive function in children with ASD. The dynamics of neuronal oscillations in the alpha range (6-12 Hz) are associated with cognition in typical development. Peak alpha frequency is also highly sensitive to developmental changes in neural networks which underlie cognitive function, and therefore it holds promise as a developmentally-sensitive neural marker of cognitive function in ASD.
Here, we measured peak alpha band frequency under a task-free condition in a heterogeneous sample of children with ASD (N=59) and age-matched typically developing (TD) children (N=38). At a group level, peak alpha frequency was decreased in ASD compared to TD children. Moreover, within the ASD group, peak alpha frequency correlated strongly with non-verbal cognition. As peak alpha frequency reflects the integrity of neural networks, our results suggest that deviations in network development may underlie cognitive function in individuals with ASD. By shedding light on the neurobiological correlates of cognitive function in ASD, our findings lay the groundwork for considering peak alpha frequency as a useful biomarker of cognitive function within this population which, in turn, will facilitate investigations of early markers of cognitive impairment and predictors of outcome in high risk infants.
“Peak alpha frequency is a neural marker of cognitive function across the autism spectrum” by Abigail Dickinson, Charlotte DiStefano, Damla Senturk, andShafali Spurling Jeste in European Journal of Neuroscience. Published online July 12 2017 doi:10.1111/ejn.13645
New Map May Lead to Drug Development for Complex Brain Disorders
Summary: Researchers have created a new map that highlights protein associations through brain networks. The map is available through a software platform that allows users to visualize disease risk factors.
Source: USC.
A study of protein interactions could be the first step to finding treatments that focus on problematic pathways.
Just as parents are not the root of all their children’s problems, a single gene mutation can’t be blamed for complex brain disorders like autism, according to a Keck School of Medicine of USC neuroscientist.
To help researchers see the big picture, Marcelo P. Coba created the first map that highlights the brain’s network of protein associations. It’s a first step to developing treatment drugs that operate more like rifles than shotguns.
“The drugs we have now are not working for these brain disorders,” said Coba, senior author of a new study and an assistant professor of psychiatry at the Zilkha Neurogenetic Institute at the Keck School of Medicine.
“Scientists have not developed a new drug target for complex brain diseases in nearly 60 years. The protein map software my colleagues and I created can help researchers create new therapies that hone in on problem pathways.”
The study was published in late June in Nature Neuroscience. Coba and his colleagues isolated 2,876 protein interactions and figured out where in the brain the protein networks lived, how they communicated and at what age in development those pathways became activated.
Researchers stuffed all that information into a software platform that enables users to visualize disease risk factors throughout the brain’s protein networks.
Taking off the blinders
Many current studies scan patients’ genetics to identify problem genes they label as “risk factors” for developing a disorder.
“The problem is that there is a collection of risk factors contributing to brain disorders,” Coba said. “A single risk factor might explain a very low percentage of the population — perhaps 2 percent of those who have the disease.”
Coba used an analogy. If all flights at a Texas airport were grounded, flight schedules and airports across the country would be affected. A disruption in one location cannot be sustained in that region because the flights are connected in a network of airports, he said.
Similarly, genes produce proteins that interact in a protein network. If a gene is mutated, the protein’s connections may experience delays or disruptions. The disorganized protein-to-protein connections from point A to B to C might be the bedrock of brain disorders such as autism, bipolar disorder and schizophrenia, Coba said.
ABOUT THIS NEUROSCIENCE RESEARCH ARTICLE
The new software platform is available here.
Funding: The study was supported by $420,000 from the National Institute of Child Health and Human Development (MH104603-01), the National Institutes of Health (MH108728) and the Simons Foundation Autism Research Initiative (248429 and 345034). Seventy percent of research funding originated from the federal government.
Source: Zen Vuong – USC
Image Source: NeuroscienceNews.com image is credited to Steven Park.
Original Research: Abstract for “Spatiotemporal profile of postsynaptic interactomes integrates components of complex brain disorders” by Jing Li, Wangshu Zhang, Hui Yang, Daniel P Howrigan, Brent Wilkinson, Tade Souaiaia, Oleg V Evgrafov, Giulio Genovese, Veronica A Clementel, Jennifer C Tudor, Ted Abel, James A Knowles, Benjamin M Neale, Kai Wang, Fengzhu Sun & Marcelo P Coba in Nature Neuroscience. Published online June 26 2017 doi:10.1038/nn.4594
USC “New Map May Lead to Drug Development for Complex Brain Disorders.” NeuroscienceNews. NeuroscienceNews, 24 July 2017.
<http://neurosciencenews.com/drug-development-map-7167/>.
<http://neurosciencenews.com/drug-development-map-7167/>.
Abstract
Spatiotemporal profile of postsynaptic interactomes integrates components of complex brain disorders
The postsynaptic density (PSD) contains a collection of scaffold proteins used for assembling synaptic signaling complexes. However, it is not known how the core-scaffold machinery associates in protein-interaction networks or how proteins encoded by genes involved in complex brain disorders are distributed through spatiotemporal protein complexes.
Here using immunopurification, proteomics and bioinformatics, we isolated 2,876 proteins across 41 in vivo interactomes and determined their protein domain composition, correlation to gene expression levels and developmental integration to the PSD.
We defined clusters for enrichment of schizophrenia, autism spectrum disorders, developmental delay and intellectual disability risk factors at embryonic day 14 and adult PSD in mice.
Mutations in highly connected nodes alter protein–protein interactions modulating macromolecular complexes enriched in disease risk candidates. These results were integrated into a software platform, Synaptic Protein/Pathways Resource (SyPPRes), enabling the prioritization of disease risk factors and their placement within synaptic protein interaction networks.
“Spatiotemporal profile of postsynaptic interactomes integrates components of complex brain disorders” by Jing Li, Wangshu Zhang, Hui Yang, Daniel P Howrigan, Brent Wilkinson, Tade Souaiaia, Oleg V Evgrafov, Giulio Genovese, Veronica A Clementel, Jennifer C Tudor, Ted Abel, James A Knowles, Benjamin M Neale, Kai Wang, Fengzhu Sun & Marcelo P Coba in Nature Neuroscience. Published online June 26 2017 doi:10.1038/nn.4594
Dragonfly Brains Predict the Path of Their Prey
Summary: A new study in eLife provides new insights into how complex neural processes in the brains of dragonflies allows them to predict, peruse and catch prey. The findings could prove useful in developing new innovative robot vision systems.
Source: University of Adelaide.
New research from Australia and Sweden has shown how a dragonfly’s brain anticipates the movement of its prey, enabling it to hunt successfully. This knowledge could lead to innovations in fields such as robot vision.
An article published today in the journal eLife by researchers at the University of Adelaide and Lund University has offered more insights into the complexity of brain processing in dragonflies than has previously been understood.
“Until now, the international research community has primarily considered the capabilities of mammals, such as humans, for investigating how animals can predict where a moving object will be in the near future,” says project partner Dr Steven Wiederman from the University of Adelaide’s Adelaide Medical School.
“Understandably, mammals in many ways are more complex organisms than insects, but with each discovery we’re finding that dragonflies have keen visual and neural processes that could be ideal for translating into technological advances,” he says.
The Swedish-Australian collaboration resulted in the discovery of brain cells (neurons) in the dragonfly Hemicordulia that enables them to predictively pursue and catch their flying prey. These neurons make it possible to focus on a small object that moves over a complex background, similarly to how humans can track and catch a ball, even when that ball is moving against the backdrop of a cheering crowd.
Professor David O’Carroll, Professor of Biology at Lund University, says: “The dragonfly neurons can make a selection of a single target from the mass of visual information that the brain receives, such as the motion of another insect, and then predict its direction and future location. The dragonfly, like humans, makes this assessment based on the path along which the object moves.
“In other words, the dragonfly does something very similar to what we do when we track a ball in motion. Despite major differences in the complexity of the brain, evolution has led to the insect using its brain for advanced visual processes that are usually only considered in mammals.”
University of Adelaide PhD student Joseph Fabian and other team members were able to record target-detecting neurons in the dragonfly brain. These neurons increased their responses in a small ‘focus’ area just in front of the location of a moving object being tracked. If the object then disappeared from the field of vision, the focus spread forward over time, allowing the brain to predict where the target was most likely to reappear. The neuronal prediction was based on the previous path along which the prey had flown.
“This is an exciting discovery, and it aids our understanding of how single neurons make advanced predictions based on past history,” Dr Wiederman says.
“Our team is convinced that these results will have practical applications, especially in the development of artificial control and vision systems, such as self-steering vehicles and bionic vision.”
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Funding: This project is an international collaboration funded by the Swedish Research Council, the Australian Research Council (ARC) and STINT, the Swedish Foundation for International Cooperation in Research and Higher Education.
Source: Steven Wiederman – University of Adelaide
Image Source: NeuroscienceNews.com image is credited to David O’Carroll, Lund University.
Original Research: Full open access research for “A predictive focus of gain modulation encodes target trajectories in insect vision” by Steven D Wiederman, Joseph M Fabian, James R Dunbier, and David C O’Carroll in eLife. Published online July 25 2017 doi:10.7554/eLife.26478
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University of Adelaide “Dragonfly Brains Predict the Path of Their Prey.” NeuroscienceNews. NeuroscienceNews, 25 July 2017.
<http://neurosciencenews.com/dragonfly-prediction-prey-7169/>.
<http://neurosciencenews.com/dragonfly-prediction-prey-7169/>.
Abstract
A predictive focus of gain modulation encodes target trajectories in insect vision
When a human catches a ball, they estimate future target location based on the current trajectory. How animals, small and large, encode such predictive processes at the single neuron level is unknown. Here we describe small target-selective neurons in predatory dragonflies that exhibit localized enhanced sensitivity for targets displaced to new locations just ahead of the prior path, with suppression elsewhere in the surround. This focused region of gain modulation is driven by predictive mechanisms, with the direction tuning shifting selectively to match the target’s prior path. It involves a large local increase in contrast gain which spreads forward after a delay (e.g. an occlusion) and can even transfer between brain hemispheres, predicting trajectories moved towards the visual midline from the other eye. The tractable nature of dragonflies for physiological experiments makes this a useful model for studying the neuronal mechanisms underlying the brain’s remarkable ability to anticipate moving stimuli.
“A predictive focus of gain modulation encodes target trajectories in insect vision” by Steven D Wiederman, Joseph M Fabian, James R Dunbier, and David C O’Carroll in eLife. Published online July 25 2017 doi:10.7554/eLife.26478
Psychopaths are Better at Learning to Lie
Summary: A new study using university students reveals those with high psychopathic traits showed a significantly reduces response time when being prompted to lie following training than those low levels of the traits. Researchers say their findings provide evidence that those with higher psychopathic traits may be better at learning to lie.
Source: Biomed Central.
Individuals with high levels of psychopathic traits are better at learning to lie than individuals who show few psychopathic traits, according to a study published in the open access journal Translational Psychiatry. The findings indicate that people with high psychopathic traits may not have a ‘natural’ capacity to lie better, but rather are better at learning how to lie, according to the researchers.
Dr. Tatia Lee and Dr. Robin Shao of the State Key Laboratory of Brain and Cognitive Sciences and the Laboratory of Neuropsychology at The University of Hong Kong found that after practicing a task that involved giving a series of truthful or untruthful responses about whether or not they recognized people in a collection of photographs, individuals with high levels of psychopathic traits were able to lie much more quickly than before practice. By contrast, individuals with low levels of psychopathic traits showed no improvement in their lying speed.
Dr Tatia Lee, the corresponding authors said: “The stark contrast between individuals with high and low levels of psychopathic traits in lying performance following two training sessions is remarkable, given that there were no significant differences in lying performance between the two groups prior to training.”
Dr Shao added: “High psychopathy is characterized by untruthfulness and manipulativeness but the evidence so far was not clear on whether high-psychopathic individuals in the general population tend to lie more or better than others. Our findings provide evidence that people with high psychopathic traits might just be better at learning how to lie.”
To find out if individuals with high levels of psychopathic traits were better at learning how to lie than others, the researchers recruited 52 students from The University of Hong Kong – 23 who showed low levels of psychopathic traits and 29 who showed high levels of psychopathic traits based on a questionnaire that can be used to assess psychopathy in a non-clinical setting.
Students in both groups were shown a series of photographs of familiar and unfamiliar faces. They received a cue to give either an honest or a dishonest response when asked whether they knew the person in the photograph or not. The researchers measured the students’ reaction times for each response and observed their brain activity using functional magnetic resonance imaging methodology (fMRI). Participants then completed a two-session training exercise before repeating the task.
The researchers found that following the training exercise, individuals with high levels of psychopathic traits had significantly shorter response times when being prompted to lie than during the initial task. Individuals with low levels of psychopathic traits showed no changes in response time. The difference may be due to how the brains of individuals with high and low levels of psychopathic traits process lies.
Dr Lee said: “During lying, the ‘true’ information needs to be suppressed and reversed. Thus, lying requires a series of processes in the brain including attention, working memory, inhibitory control and conflict resolution which we found to be reduced in individuals with high levels of psychopathic traits. By contrast, in individuals with low levels of psychopathic traits this lie-related brain activity increased. The additional ‘effort’ it took their brains to process untruthful responses may be one of the reasons why they didn’t improve their lying speed.”
The researchers caution that as all participants in this study were university students, further research is needed to be able to generalize the findings to individuals with high levels of psychopathic traits in other populations.
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Source: Matthew Lam – Biomed Central
Image Source: NeuroscienceNews.com image is in the public domain.
Original Research: Full open access research for “Are individuals with higher psychopathic traits better learners at lying? Behavioural and neural evidence” by R Shao and T M C Lee in Translational Psychiatry. Published online July 25 2017 doi:10.1038/tp.2017.147
Biomed Central “Psychopaths are Better at Learning to Lie.” NeuroscienceNews. NeuroscienceNews, 25 July 2017.
<http://neurosciencenews.com/psychopaths-lying-7171/>.
<http://neurosciencenews.com/psychopaths-lying-7171/>.
Abstract
Are individuals with higher psychopathic traits better learners at lying? Behavioural and neural evidence
High psychopathy is characterized by untruthfulness and manipulativeness. However, existing evidence on higher propensity or capacity to lie among non-incarcerated high-psychopathic individuals is equivocal.
Of particular importance, no research has investigated whether greater psychopathic tendency is associated with better ‘trainability’ of lying. An understanding of whether the neurobehavioral processes of lying are modifiable through practice offers significant theoretical and practical implications.
By employing a longitudinal design involving university students with varying degrees of psychopathic traits, we successfully demonstrate that the performance speed of lying about face familiarity significantly improved following two sessions of practice, which occurred only among those with higher, but not lower, levels of psychopathic traits.
Furthermore, this behavioural improvement associated with higher psychopathic tendency was predicted by a reduction in lying-related neural signals and by functional connectivity changes in the frontoparietal and cerebellum networks. Our findings provide novel and pivotal evidence suggesting that psychopathic traits are the key modulating factors of the plasticity of both behavioural and neural processes underpinning lying.
These findings broadly support conceptualization of high-functioning individuals with higher psychopathic traits as having preserved, or arguably superior, functioning in neural networks implicated in cognitive executive processing, but deficiencies in affective neural processes, from a neuroplasticity perspective.
“Are individuals with higher psychopathic traits better learners at lying? Behavioural and neural evidence” by R Shao and T M C Lee in Translational Psychiatry. Published online July 25 2017 doi:10.1038/tp.2017.147
Cognitive Cross Training Enhances Learning
Summary: Using a combination of brain stimulation, physical exercise and computer based cognitive training, researchers report people were better at specific skill learning than when using cognitive training alone.
Source: University of Illinois.
Just as athletes cross-train to improve physical skills, those wanting to enhance cognitive skills can benefit from multiple ways of exercising the brain, according to a comprehensive new study from University of Illinois researchers.
The 18-week study of 318 healthy young adults found that combining physical exercise and mild electric brain stimulation with computer-based cognitive training promoted skill learning significantly more than using cognitive training alone.
The enhanced learning was skill-specific and did not translate to general intelligence. The study, the largest and most comprehensive to date, was published in the journal Scientific Reports.
“Learning provides the foundation for acquiring new skills and updating prior beliefs in light of new knowledge and experience,” said study leader Aron Barbey, a professor of psychology. “Our results establish a method to enhance learning through multimodal intervention. The beneficial effects of cognitive training can be significantly enhanced with the addition of physical fitness training and noninvasive brain stimulation.”
Psychologists have extensively studied and debated the merits of cognitive training, but have mainly focused on computer-based tasks, Barbey said. The few studies that have incorporated other training modalities, such as physical fitness training or noninvasive brain stimulation, have been small in sample size, short in time or narrow in scope, he said.
The Illinois study divided its numerous subjects into five groups: three experimental groups and active and passive control groups. One experimental group received only cognitive training; the second group received cognitive training and exercise; and the third group received cognitive training, exercise and noninvasive brain stimulation delivered by electrodes on the scalp. The active control group completed different computer-based cognitive training tasks than the experimental group, but did the same number of sessions of the same amount of time as the experimental group. In the first week, participants took a pretest. In the following 16 weeks, the experimental and active groups completed 90-minute training sessions three times a week. In the final week of the study, all participants took a post-test.
“Physical activity and aerobic fitness are known to have beneficial effects on the underlying structures and functions of the brain,” said Barbey, a member of the Beckman Institute for Advanced Science and Technology at Illinois. “And research has shown that specific brain stimulation protocols can enhance cognitive performance, prompting us to investigate their effects on cognitive training.”
The study used six different training tasks, designed to measure specific cognitive skills such as memory, attention and task-switching. In the post-test, the groups that received cognitive training and physical fitness training or all three interventions performed significantly better than the group with cognitive training alone. The group that received all three interventions consistently performed the best, and showed substantial gains in two of the tasks over the group that received cognitive and physical fitness training but did not receive brain stimulation.
The researchers then gave the subjects different cognitive tasks to perform, and found that the enhanced skills did not transfer to the untrained tasks. This suggests that multimodal cognitive training enhances learning for specific skills, Barbey said, but not for general intelligence.
“We can apply this knowledge to develop a personalized approach for each participant, tailoring the intervention to adapt to each person’s skills and needs in an effort to optimize the potential benefits of training and to provide powerful new methods to enhance specific cognitive abilities,” Barbey said.
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Funding: The Office of the Director of National Intelligence, Intelligence Advanced Research Projects Activity supported this work.
Source: Liz Ahlberg Touchstone – University of Illinois
Image Source: NeuroscienceNews.com image is credited to Julie McMahon.
Original Research: Full open access research for “Enhanced Learning through Multimodal Training: Evidence from a Comprehensive Cognitive, Physical Fitness, and Neuroscience Intervention” by N. Ward, E. Paul, P. Watson, G. E. Cooke, C. H. Hillman, N. J. Cohen, A. F. Kramer & A. K. Barbey in Scientific Reports. Published online July 19 doi:10.1038/s41598-017-06237-5
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University of Illinois “Cognitive Cross Training Enhances Learning.” NeuroscienceNews. NeuroscienceNews, 25 July 2017.
<http://neurosciencenews.com/learning-cross-training-7170/>.
<http://neurosciencenews.com/learning-cross-training-7170/>.
Abstract
Enhanced Learning through Multimodal Training: Evidence from a Comprehensive Cognitive, Physical Fitness, and Neuroscience Intervention
The potential impact of brain training methods for enhancing human cognition in healthy and clinical populations has motivated increasing public interest and scientific scrutiny. At issue is the merits of intervention modalities, such as computer-based cognitive training, physical exercise training, and non-invasive brain stimulation, and whether such interventions synergistically enhance cognition. To investigate this issue, we conducted a comprehensive 4-month randomized controlled trial in which 318 healthy, young adults were enrolled in one of five interventions: (1) Computer-based cognitive training on six adaptive tests of executive function; (2) Cognitive and physical exercise training; (3) Cognitive training combined with non-invasive brain stimulation and physical exercise training; (4) Active control training in adaptive visual search and change detection tasks; and (5) Passive control. Our findings demonstrate that multimodal training significantly enhanced learning (relative to computer-based cognitive training alone) and provided an effective method to promote skill learning across multiple cognitive domains, spanning executive functions, working memory, and planning and problem solving. These results help to establish the beneficial effects of multimodal intervention and identify key areas for future research in the continued effort to improve human cognition.
“Enhanced Learning through Multimodal Training: Evidence from a Comprehensive Cognitive, Physical Fitness, and Neuroscience Intervention” by N. Ward, E. Paul, P. Watson, G. E. Cooke, C. H. Hillman, N. J. Cohen, A. F. Kramer & A. K. Barbey in Scientific Reports. Published online July 19 doi:10.1038/s41598-017-06237-5
Antidepressant Use in Pregnancy Linked to Slightly Increased Autism Risk
Antidepressant Use in Pregnancy Linked to Slightly Increased Autism Risk
Summary: A new study in the BMJ links antidepressant use in pregnant women to a very small increased risk of autism in their offspring. Researchers discovered 4.1% of children exposed to antidepressants while in the womb were diagnosed with ASD, where as only 2.9% of children whose mothers had a history of mental health problems but did not take take medications were diagnosed with autism.
Source: Drexel University.
A new study found that antidepressant medications taken during pregnancy may be linked to the development of autism in children — although the effect appears to be limited.
In looking at a cohort of children born between 2001 and 2011 in Stockholm, Sweden, Drexel University’s Brian Lee, PhD, and Craig Newschaffer, PhD, and their co-authors (including lead author Dheeraj Rai, PhD, of the University of Bristol) found that children born to mothers who had taken anti-depressants at any point during their pregnancy were 45 percent more likely to be diagnosed with autism. However, the team’s analysis showed that only 2 percent of autism cases would be prevented if antidepressant use was completely cut off in pregnant women.
“Overall, the increase in risk was quite small,” said Lee. “Of children exposed to antidepressants during pregnancy, 4.1 percent had an autism diagnosis. In comparison, children of mothers with a history of a psychiatric disorder but who did not use antidepressants during pregnancy had a 2.9 percent prevalence of autism.”
The study was published in The BMJ (formerly The British Medical Journal). It focused on prenatal antidepressant use because these medications can cross through the placenta into where the fetus develops.
Past studies have found associations between antidepressant use during pregnancy and autism in children, but there has been some concern that those links were the results of other factors. As such, this study sought to use various methods to rule out any “confounders.”
This included looking into the use of antidepressants by the child’s father during pregnancy, comparing the children to their siblings, and comparing children with similar characteristics, among other methods.
None of these seemed to significantly affect the main finding linking diagnoses to antidepressant use.
“The overall effect remained,” Rai said. “We were specifically looking for consistency in the various analyses we did and the results appeared to concur.”
“We conducted several analyses that seemed to support the validity of the findings,” Lee added. “For example, because a parental history of a psychiatric disorder is associated with increased risk of autism, we examined whether the father’s use of antidepressants was associated with autism. Because there was no increased risk with fathers’ use of antidepressants, this suggested that the increase with mothers’ use was not entirely due to the underlying psychiatric disorder.”
The team found that prenatal antidepressant use seemed to only be linked to autism diagnoses in children who didn’t also have intellectual disabilities. This form of autism has a greater chance of inheritability, according to past studies. Genetic traits were not exclusively looked at for the study, although looking at siblings helped mitigate that potential factor. To better examine it in future studies, the study team suggested looking at larger pools of siblings.
And while there was a noticeable increase in autism diagnoses in children whose mothers used the antidepressants, the study team emphasized that more than 95 percent of those women had children who were not diagnosed with autism.
“Our advice for pregnant women and clinicians is very clear. They should not base decisions about the use of antidepressants during pregnancy on any one study, especially when the research evidence is conflicting, as in this case where different studies have reached different conclusions,” Rai said. “There could be severe risks of stopping the use of antidepressants during pregnancy, both to the mother and the fetus. So the benefits of these medications for mothers who need them should not be forgotten.”
The best course of action is to consult a doctor on medication use during pregnancy.
“Balancing benefits and risks of taking medications during pregnancy is a complex and often difficult decision,” he explained. “Our advice would be for women to discuss their concerns with their treating clinicians who will be able to help them weigh the pros and the cons.”
As a next step, larger studies will help develop a consensus on the role that both antidepressants and depression itself plays into the risk of autism.
“This may be aided by more studies that could help account for confounding and more studies focusing on the autism group without intellectual disability, which seems to be the key category for which the increase in risk is observed,” Rai said.
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Source: Frank Otto – Drexel University
Image Source: NeuroscienceNews.com image is in the public domain.
Original Research: Full open access research for “Antidepressants during pregnancy and autism in offspring: population based cohort study” by Dheeraj Rai, Brian K Lee, Christina Dalman, Craig Newschaffer, Glyn Lewis, and Cecilia Magnusson in The BMJ. Published online July 19 2017 doi:10.1136/bmj.j2811
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Drexel University “Antidepressant Use in Pregnancy Linked to Slightly Increased Autism Risk.” NeuroscienceNews. NeuroscienceNews, 24 July 2017.
<http://neurosciencenews.com/antidepressants-autism-pregnancy-7165/>.
<http://neurosciencenews.com/antidepressants-autism-pregnancy-7165/>.
Abstract
Antidepressants during pregnancy and autism in offspring: population based cohort study
Objectives To study the association between maternal use of antidepressants during pregnancy and autism spectrum disorder (ASD) in offspring.
Design Observational prospective cohort study with regression methods, propensity score matching, sibling controls, and negative control comparison.
Setting Stockholm County, Sweden.
Participants 254 610 individuals aged 4-17, including 5378 with autism, living in Stockholm County in 2001-11 who were born to mothers who did not take antidepressants and did not have any psychiatric disorder, mothers who took antidepressants during pregnancy, or mothers with psychiatric disorders who did not take antidepressants during pregnancy. Maternal antidepressant use was recorded during first antenatal interview or determined from prescription records.
Main outcome measure Offspring diagnosis of autism spectrum disorder, with and without intellectual disability.
Results Of the 3342 children exposed to antidepressants during pregnancy, 4.1% (n=136) had a diagnosis of autism compared with a 2.9% prevalence (n=353) in 12 325 children not exposed to antidepressants whose mothers had a history of a psychiatric disorder (adjusted odds ratio 1.45, 95% confidence interval 1.13 to 1.85). Propensity score analysis led to similar results. The results of a sibling control analysis were in the same direction, although with wider confidence intervals. In a negative control comparison, there was no evidence of any increased risk of autism in children whose fathers were prescribed antidepressants during the mothers’ pregnancy (1.13, 0.68 to 1.88). In all analyses, the risk increase concerned only autism without intellectual disability.
Conclusions The association between antidepressant use during pregnancy and autism, particularly autism without intellectual disability, might not solely be a byproduct of confounding. Study of the potential underlying biological mechanisms could help the understanding of modifiable mechanisms in the aetiology of autism. Importantly, the absolute risk of autism was small, and, hypothetically, if no pregnant women took antidepressants, the number of cases that could potentially be prevented would be small.
“Antidepressants during pregnancy and autism in offspring: population based cohort study” by Dheeraj Rai, Brian K Lee, Christina Dalman, Craig Newschaffer, Glyn Lewis, and Cecilia Magnusson in The BMJ. Published online July 19 2017 doi:10.1136/bmj.j2811