September 25, 2023

A Norwegian nationwide cohort study finds link between prescribed drugs widely used as sleeping aids and subsequent ADHD

There have been indications that infants who have difficulty sleeping are more likely to later develop ADHD in childhood. Would this hold up in a large nationwide cohort study?

Noting that "Norway has several national health registries with compulsory and automatically collected information," and "registries can be linked on an individual level, making it possible to conduct large cohort studies," a Norwegian team of researchers studied the association between sleep-inducing medications prescribed to infants under three years old and diagnoses of ADHD between the ages of five and eleven.

Norway has a national health insurance system that covers all residents, and pays in full for youths under 16 years old. Norwegian pharmacies must register all dispensed prescriptions into a national register as a prerequisite for reimbursement.

The study included all children born in Norway from 2004 through 2010, minus those who died or emigrated, leaving a total of 410,555 children.

In addition to traditional hypnotic and sedative drugs and melatonin, the study looked at antihistamines, which though intended for respiratory use, are frequently used for gentle sedation.

The two most frequently prescribed drugs were found to be dexchlorpheniramine (girls 7%, boys 8%) and trimeprazine(girls 3%, boys 4%), both of which are antihistamines.

After adjusting for parental education as an indicator of family socioeconomic status, and parental ADHD as indicated by prescription of ADHD medications, girls who had been prescribed sleeping medications on at least two occasions were twice as likely to subsequently develop ADHD, and boys about 60 percent more likely. For, dexchlorpheniramine equivalent associations were not statistically significant for either boys or girls. But girls prescribed trimeprazine on at least two occasions were almost three times as likely to subsequently develop ADHD, and boys were well over twice as likely.

A limitation of the study was that there was no direct data for sleep diagnosis. The authors noted, "The Norwegian prescription database does not contain diagnosis unless medications are reimbursed and hypnotics are not reimbursed for insomnia or sleep disturbances in general. Sleep diagnoses were also not available from the Norwegian Patient Registry, as there seems to be a clinical tradition for not using the ICD- 10G47 Sleep Disorders diagnosis for children."

The authors concluded, "It has previously been shown that infant regulation problems, including sleep problems, are associated with ADHD diagnosis. We replicate this finding in a large cohort, where continuous data collection ensures no recall bias and no loss to follow-up. We find that the use of hypnotic drugs before 3 years of age, signifying substantial sleeping problems, increases the risk of a later ADHD diagnosis. This was especially true for the antihistaminic drug, trimeprazine."

Ingvild Holdo¸, Jorgen G. Bramness, Marte Handal, Berit Hjelde Hansen, Vidar Hjellvik, Svetlana Skurtveit, "Association Between Prescribed Hypnotics in Infants and Toddlers and Later ADHD: A Large Cohort Study from Norway," Child Psychiatry & Human Development (2020),

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Using Video Analysis and Machine Learning in ADHD Diagnosis

NEWS TUESDAY: Machine Learning and The Possible Future of Diagnosing ADHD

Typically, clinicians rely on both subjective and objective observations, patient interviews and questionnaires, as well as reports from family and (in the case of children) parents and teachers, in order to diagnose ADHD. 

A group of researchers are aiming to find a diagnostic test that is purely objective and utilizes recent technological advancements. The method they developed involves analyzing videos of children in outpatient settings, focusing on their movements. The study included 96 children, half of whom had ADHD and half who did not.

How It Works

  1. Video Recording: Children were recorded during their outpatient visits.
  2. Skeleton Detection: Using a tool called OpenPose, the researchers detected and tracked the children's skeletons (essentially a map of their body's movements) in the videos.
  3. Movement Analysis: The researchers analyzed these movements, looking at 11 different movement features. They specifically focused on the angles of different body parts and how much they moved.
  4. Machine Learning: Six different machine learning models were used to see which movement features could best distinguish between children with ADHD and those without.

Key Findings

  • Movement Differences: Children with ADHD showed significantly more movement in all the features analyzed compared to children without ADHD.
  • Thigh Angle: The angle of the thigh was the most telling feature. On average, children with ADHD had a thigh angle of about 157.89 degrees, while those without ADHD had an angle of 15.37 degrees.
  • High Accuracy: Using thigh angle alone, the model could diagnose ADHD with 91.03% accuracy. It was very sensitive (90.25%) and specific (91.86%), meaning it correctly identified most children with ADHD and correctly recognized most children without it.

This new method could potentially provide a more objective way to diagnose ADHD, reducing the reliance on subjective observations and reports. It can help doctors make more accurate diagnoses, ensuring that those who need help get it and that those who don't aren't misdiagnosed.

May 28, 2024

Understanding Attention to Social Images in Children with ADHD and Autism

NEWS TUESDAY: Understanding Attention to Social Images in Children with ADHD and Autism

In the field of mental health, professionals often use a variety of tools to diagnose and understand neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD). One such tool is the Autism Diagnostic Observation Schedule (ADOS), which is specifically designed to help diagnose autism. However, the ADOS wasn't originally intended for children who have both autism and ADHD, though this comorbidity is not uncommon.

A recent study aimed to explore how children with ADHD, autism, or both, pay attention to social images, such as faces. The study focused on using eye-tracking technology to measure where children direct their gaze when viewing pictures, and how long they look at certain parts of the image. This is important because differences in visual attention can provide insights into the nature of these disorders.

The researchers included 84 children in their study, categorized into four groups: those with ASD, those with ADHD, those with both ASD and ADHD, and neurotypical (NT) children without these conditions. During the study, children were shown social scenes from the ADOS, and their eye movements were recorded. The ADOS assessment was administered afterward. To ensure that the results were not influenced by medications, children who were on stimulant medications for ADHD were asked to pause their medication temporarily.

The results of the study showed that children with ASD, whether they also had ADHD or not, tended to spend less time looking at faces compared to children with just ADHD or NT children. The severity of autism symptoms, measured by the Social Communication Questionnaire (SCQ), was associated with reduced attention to faces. Interestingly, ADHD symptom severity, measured by Conners' Rating Scales (CRS-3), did not correlate with how children looked at faces.

These findings suggest that measuring visual attention might be a valuable addition to the assessment process for ASD, especially in cases where ADHD is also present. The study indicates that if a child with ADHD shows reduced attention to faces, it might point to additional challenges related to autism. The researchers noted that more studies with larger groups of children are needed to confirm these findings, but the results are promising. They hope that such measures could eventually enhance diagnostic processes and help in managing the complexities of cases involving comorbidity of ADHD and ASD.

This research opens up the possibility of using eye-tracking as a supplementary diagnostic tool in the assessment of autism, providing a more nuanced understanding of how attentional differences in social settings are linked to ASD and ADHD.

May 14, 2024

NEW STUDY: RASopathies Influences on Neuroanatomical Variation in Children

NEW STUDY: RASopathies Influences on Neuroanatomical Variation in Children

This study investigates how certain genetic disorders, called RASopathies, affect the structure of the brain in children. RASopathies are conditions caused by mutations in a specific signaling pathway in the body. Two common RASopathies are Noonan syndrome (NS) and neurofibromatosis type 1 (NF1), both of which are linked to a higher risk of autism spectrum disorder (ASD) and attention deficit and hyperactivity disorder (ADHD).

The researchers analyzed brain scans of children with RASopathies (91 participants) and compared them to typically developing children (74 participants). They focused on three aspects of brain structure: surface area (SA), cortical thickness (CT), and subcortical volumes.

The results showed that children with RASopathies had both similarities and differences in their brain structure compared to typically developing children. They had increased SA in certain areas of the brain, like the precentral gyrus, but decreased SA in other regions, such as the occipital regions. Additionally, they had thinner CT in the precentral gyrus. However, the effects on subcortical volumes varied between the two RASopathies: children with NS had decreased volumes in certain structures like the striatum and thalamus, while children with NF1 had increased volumes in areas like the hippocampus, amygdala, and thalamus.

Overall, this study highlights how RASopathies can impact the development of the brain in children. The shared effects on SA and CT suggest a common influence of RASopathies on brain development, which could be important for developing targeted treatments in the future.

In summary, understanding how these genetic disorders affect the brain's structure can help researchers and healthcare professionals develop better treatments for affected children.

April 30, 2024