August 25, 2021

Are there adverse effects to long-term treatment of ADHD with methylphenidate?

Methylphenidate (MPH) is one of the most widely-prescribed medications for children. Given that ADHD frequently persists over a large part of an individual's lifespan, any side effects of medication initiated during childhood may well be compounded over time. With funding from the European Union, a recently released review of the evidence looked for possible adverse neurological and psychiatric outcomes.

From the outset, the international team recognized a challenge: ADHD severity may be an important potential confounder, as it may be associated with both the need for long-term MPH therapy and high levels of underlying neuropsychiatric comorbidity. Their searches found a highly heterogeneous evidence base, which made meta-analysis inadvisable. For example, only 25 of 39 group studies reported the presence or absence of comorbid psychiatric conditions, and even among those, only one excluded participants with comorbidities. Moreover, in only 24 of 67 studies was the type of MPH used (immediate or extended-release) specified. The team, therefore, focused on laying out an evidence map to help determine priorities for further research.

The team found the following breakdown for specific types of adverse events:

·        Low mood/depression. All three non-comparative studies found MPH safe. Two large cohort studies, one with over 2,300 participants, and the other with 142,000, favored MPH over the non-stimulant atomoxetine. But many other studies, including a randomized controlled trial (RCT), had unclear results. Conclusion: the evidence base regarding mood outcomes from long-term MPH treatment is relatively strong, includes two well-powered comparative studies, and tends to favor MPH.

·        Anxiety. Here again, all three non-comparative studies found MPH safe. But only two of seven comparative studies favored MPH, with the other five having unclear results. Conclusion: while the evidence about anxiety as an outcome of long-term MPH treatment tends to favor MPH, the evidence base is relatively weak.

·        Irritability/emotional reactivity. A large cohort study with over 2,300 participants favored MPH over atomoxetine. Conclusion: the evidence base is limited, although it includes one well-powered study that found in favor of MPH over atomoxetine.

·        Suicidal behavior/ideation. There were no non-comparative studies, but all five comparative studies favored MPH. That included three large cohort studies, with a combined total of over a hundred thousand participants, that favored MPH over atomoxetine. Conclusion: the evidence base is relatively strong, and tends to favor MPH.

·        Bipolar disorder. A very large cohort study, with well over a quarter-million participants, favored MPH over atomoxetine. A much smaller cohort study comparing MPH with atomoxetine, with less than a tenth the number of participants, pointed toward caution. Conclusion: the evidence base is limited and unclear, although it includes two well-powered studies.

·        Psychosis/psychotic-like symptoms. By far the largest study, with over 145,000 participants, compared MPH with no treatment, and pointed toward caution. A cohort study with over 2,300 participants favored MPH over atomoxetine. Conclusion: These findings indicate that more research is needed into the relationship between ADHD and psychosis, and into whether MPH moderates that risk, as well as research into individual risk factors for MPH-related psychosis in young people with ADHD.

·        Substance use disorders. A cohort study with over 20,000 participants favored MPH over anti-depressants, anti-psychotics, and no medication. Other studies looking at dosages and durations of treatment, age at treatment initiation, or comparing with no treatment or alternative treatment, all favored MPH except a single study with unclear results. Conclusion: the evidence base is relatively strong, includes one well-powered study that compared MPH with antipsychotic and antidepressant treatment, and tends to favor MPH.

·        Tics and other dyskinetic. Of four non-comparative studies, three favored MPH, the other, with the smallest sample size, urged caution. In studies comparing with dexamphetamine, pemoline, Adderall, or no active treatment, three had unclear results and two pointed towards caution. Conclusion: more research is needed regarding the safety and management of long-term MPH in those with comorbidities or tic disorder.

·        Seizuresor EEG abnormalities. With one exception, the studies had small sample sizes. The largest, with over 2,300 participants, compared MPH with atomoxetine, with inconclusive results. Two small studies found MPH safe, one had unclear results, and two others pointed towards caution. Conclusion: While the evidence is limited and unclear, the studies do not indicate evidence for seizures as an AE of MPH treatment in children with no prior history more research is needed into the safety of long-term MPH in children and young people at risk of seizures.

·        Sleep Disorders. All three non-comparative studies found MPH safe, but the largest cohort study, with over 2,300 participants, clearly favored atomoxetine. Conclusion: more research is needed into the relationship between ADHD, sleep, and long-term MPH treatment.

·        Other notable psychiatric outcomes. Two noncomparative studies, with 118 and 289participants, found MPH safe. A cohort study with over 700 participants compared with atomoxetine, with inconclusive results. Conclusion: there is limited evidence regarding long-term MPH treatment and another neuropsychiatric outcome, and that further research may be needed into the relationship between long-term MPH treatment and aggression/hostility.

Although this landmark review points to several gaps sins in the evidence base, it mainly supports prior conclusions of the US Food antidrug Administration (FDA) and other regulatory agencies (based on short-term randomized controlled trials) that MPH is safe for the treatment of ADHD in children and adults.  Given that MPH has been used for ADHD for over fifty years and that the FDA monitors the emergence of rare adverse events, patients, parents, and prescribers can feel confident that the medication is safe when used as prescribed.

Helga Krinzinger, Charlotte L Hall, Madeleine JGroom, Mohammed T Ansari, Tobias Banaschewski, Jan K Buitelaar, Sara CarucciDavid Coghill, Marina Danckaerts, Ralf W Dittmann, Bruno Falissard, PeterGaras, Sarah K Inglis, Hanna Kovshoff, Puja Kochhar, Suzanne McCarthy, PeterNagy, Antje Neubert, Samantha Roberts, Kapil Sayal, Edmund Sonuga-Barke , Ian CK Wong , Jun Xia, Alexander Zuddas, Chris Hollis, Kerstin Konrad, Elizabeth Biddle and the ADDUCE Consortium,Neurological and psychiatric adverse effects of long-term methylphenidate treatment in ADHD: A map of the current evidence, Neuroscience and Biobehavioral Reviews(2019)DOI:https://doi.org/10.1016/j.neubiorev.2019.09.023

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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