June 2, 2021

The State of the Art on Identifying and Treating Persons with Comorbid ADHD and Substance Use Disorders

An international group of twelve experts recently published a consensus report examining the state of the evidence and offering recommendations to guide the screening, diagnosis, and treatment of individuals with ADHD-SUD comorbidity.[1]

In a clear sign that we are still in the early stages of understanding this relationship, five of the thirteen recommendations received the lowest recommendation grade (D), eight received the next-lowest (C), and none received the highest (A and B). The lower grades reflected the absence of the highest level of evidence, obtained from meta-analyses or systematic reviews of relevant randomized controlled trials (RCTs).

Nevertheless, with these limitations in mind, the experts agreed on the following points:

Diagnosis

  •  The strongest recommendation, the only one based on a 2+ level of evidence (well-conducted case-control or cohort studies with allowing risk of confounding or bias and a moderate probability that the relationship is causal) is that the "Short Version of the Adult ADHD Self-Report Scale (ASRS-SV) screener is currently the most widely used and investigated screening tool in individuals with ADHD and comorbid SUD, with good sensitivity and specificity across studies."
  • Two other recommendations were graded C: The diagnostic process should include current and past substance abuse, and seek to involve partners and relatives in evaluating symptoms and functional impairments.
  • Four recommendations got the lowest grade, D. The experts suggested starting the diagnostic process as soon as possible and focusing on drug- and alcohol-free periods in the patient's life during history taking. They also recommended that physicians and clinical psychologists should only make diagnoses if they have extensive training in diagnosing ADHD, as well as experience with adults with ADHD and with addiction care, and that they should consider treating adults with sufficiently severe ADHD symptoms.

Treatmen

  •  In general, evidence was stronger in this area, and only one of the six recommendations was graded D. The other five recommendations were graded C, with the highest level of evidence being 2(cohort or case and control studies with undetermined risk of bias), although in three cases it was level 3 (non-analytical studies, such as case reports and case series).
  • The grade D recommendation was to always consider a combination of psychotherapy and pharmacotherapy.

The grade C recommendations included considering adequate medical treatment of both ADHD and SUD; integrating ADHD treatment with SUD treatment as soon as possible;

Cleo L. Crunelle at al., "InternationalConsensus Statement on Screening, Diagnosis and Treatment of Substance UseDisorder Patients with Comorbid Attention Deficit/Hyperactivity Disorder," European Addiction Research, publishedonline March 6, 2018, DOI: 10.1159/000487767.

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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