News Tuesday: Fidgeting and ADHD

A recent study delved into the connection between fidgeting and cognitive performance in adults with Attention-Deficit/Hyperactivity Disorder. Recognizing that hyperactivity often manifests as fidgeting, the researchers sought to understand its role in attention and performance during cognitively demanding tasks. They designed a framework to quantify meaningful fidgeting variables using actigraphy devices.

(Note: Actigraphy is a non-invasive method of monitoring human rest/activity cycles. It involves the use of a small, wearable device called an actigraph or actimetry sensor, typically worn on the wrist, similar to a watch. The actigraph records movement data over extended periods, often days to weeks, to track sleep patterns, activity levels, and circadian rhythms. In this study, actigraphy devices were used to measure fidgeting by recording the participants' movements continuously during the cognitive task. This data provided objective, quantitative measures of fidgeting, allowing the researchers to analyze its relationship with attention and task performance.)

The study involved 70 adult participants aged 18-50, all diagnosed with ADHD. Participants underwent a thorough screening process, including clinical interviews and ADHD symptom ratings. The analysis revealed that fidgeting increased during correct trials, particularly in participants with consistent reaction times, suggesting that fidgeting helps sustain attention. Interestingly, fidgeting patterns varied between early and later trials, further highlighting its role in maintaining focus over time.

Additionally, a correlation analysis validated the relevance of the newly defined fidget variables with ADHD symptom severity. This finding suggests that fidgeting may act as a compensatory mechanism for individuals with ADHD, aiding in their ability to maintain attention during tasks requiring cognitive control.

This study provides valuable insights into the role of fidgeting in adults with ADHD, suggesting that it may help sustain attention during challenging cognitive tasks. By introducing and validating new fidget variables, the researchers hope to standardize future quantitative research in this area. Understanding the compensatory role of fidgeting can lead to better management strategies for ADHD, emphasizing the potential benefits of movement for maintaining focus.

July 16, 2024

Identifying Autistic-Like Symptoms in Children with ADHD

NEWS TUESDAY: Identifying Autistic-Like Symptoms in Children with ADHD

A recent study investigated the presence of autistic-like symptoms in children diagnosed with Attention Deficit/Hyperactivity Disorder (ADHD). Given the overlapping social difficulties in both ADHD and Autism Spectrum Disorder (ASD), distinguishing between the two disorders can be challenging. This study aims to pinpoint specific patterns of autistic symptoms in children with ADHD, comparing them to those with ASD using the Autism Diagnostic Observation Schedule, 2nd edition (ADOS-2).

The research involved 43 school-age children divided into two groups:

  • ADHD Group (25 children): Initially referred for ASD symptoms but later diagnosed with ADHD.
  • ASD Group (18 children): Children diagnosed with ASD.

Researchers used ADOS-2 to evaluate differences in communication deficits, social interaction challenges, and repetitive behaviors between the two groups. The study also compared IQ, age, ADOS-2 domain scores, and externalizing/internalizing problems.

Key Findings:

  • Significant differences were found between the ADHD and ASD groups in ADOS-2 domain scores, including Social Affect, Restricted and Repetitive Behavior, and Total Score.
  • On an individual item level, children with ADHD displayed similar atypical behaviors as those with ASD in social-communication areas such as "Pointing" and "Gestures".
  • Both groups showed comparable frequencies in behaviors like "Stereotyped/idiosyncratic words or phrases", "Mannerisms", and "Repetitive interests and behaviors".

The study highlights the importance of identifying transdiagnostic domains that overlap between ADHD and ASD. The transdiagnostic domain refers to a set of symptoms or behaviors that are common across multiple diagnostic categories rather than being specific to just one. Identifying these domains in mental health practice and in psychological research is crucial to understanding, properly diagnosing, and treating conditions with overlapping features. This understanding could pave the way for tailored treatments addressing the specific needs of children with ADHD, particularly those exhibiting autistic-like symptoms.

July 9, 2024

Non-stimulant Medications for Adults with ADHD: An Overview

NEW STUDY: Non-stimulant Medications for Adults with ADHD: An Overview

Attention-Deficit/Hyperactivity Disorder (ADHD) in adults is commonly treated with stimulant medications such as methylphenidate and amphetamines. However, not all patients respond well to these stimulants or tolerate them effectively. For such cases, non-stimulant medications provide an alternative treatment approach.

Recent research by Brancati et al. reviews the efficacy and safety of non-stimulant medications for adult ADHD. Atomoxetine, a well-studied non-stimulant, has shown significant effectiveness in treating ADHD symptoms in adults. The review highlights the importance of considering dosage, treatment duration, safety, and the presence of psychiatric comorbidities when prescribing atomoxetine.

Additionally, certain antidepressants, including tricyclic compounds, bupropion, and viloxazine, which possess noradrenergic or dopaminergic properties, have demonstrated efficacy in managing adult ADHD. Antihypertensive medications, especially guanfacine, have also been found effective. Other medications like memantine, metadoxine, and mood stabilizers show promise, whereas treatments like galantamine, antipsychotics, and cannabinoids have not yielded positive results.

The expert opinion section of the review emphasizes that while clinical guidelines primarily recommend atomoxetine as a second-line treatment, several other non-stimulant options can be utilized to tailor treatments based on individual patient needs and comorbid conditions. Despite these advancements, the authors call for further research to develop and refine more personalized treatment strategies for adults with ADHD.

This review underscores the growing landscape of non-stimulant treatment options, offering hope for more personalized and effective management of ADHD in adults.

June 25, 2024

NEW STUDY: The Cumulative Impact of ADHD, ASD, and Intellectual Disabilities

NEW STUDY: The cumulative impact of attention deficit hyperactivity disorder, autism and intellectual disability for young people

Neurodevelopmental conditions often coexist, creating a complex web of challenges for affected individuals. A recent study by Hollingdale et al. delves into the cumulative effects of attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and intellectual disability (ID) on young people’s behavioral and socio-emotional well-being, as well as their overall functioning as rated by clinicians.

The researchers conducted a cross-sectional analysis of 2768 young individuals aged 3-17 years, with a mean age of approximately 11.5 years. Diagnostic information along with caregiver-rated behavioral and socio-emotional data, and clinician-rated functioning scores, were collected from electronic patient records at the point of initial diagnosis.

The study aimed to understand whether the number of neurodevelopmental conditions—ranging from one to three—correlates with more pronounced behavioral and socio-emotional issues, and lower levels of clinician-rated functioning. The behavioral and socio-emotional aspects were assessed using the Strengths and Difficulties Questionnaire, while the Children's Global Assessment Scale was used to evaluate clinician-rated functioning.

The findings revealed that young people with multiple neurodevelopmental conditions tend to exhibit higher levels of inattention and hyperactivity, greater peer-related problems, reduced prosocial behaviors, and poorer overall functioning. Interestingly, this cumulative impact was more evident in males compared to females, with females only showing significant cumulative effects in clinician-rated functioning.

This research underscores the importance of recognizing the compounded difficulties faced by young people with multiple neurodevelopmental conditions. It highlights the need for tailored interventions that address the unique and overlapping challenges presented by ADHD, ASD, and ID. For practitioners, understanding these cumulative effects is crucial for developing effective treatment plans that can better support the holistic development and well-being of these young individuals.

In conclusion, the presence of multiple neurodevelopmental conditions can significantly affect various domains of a young person’s life, with notable differences between males and females. This study provides a critical insight into the intricate nature of these conditions and calls for a more nuanced approach in both research and clinical practice.

June 18, 2024

Using Video Analysis and Machine Learning in ADHD Diagnosis

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

Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos

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

News Tuesday: Integrating Cognition and Eye Movement

Integrating Cognitive Factors and Eye Movement Data in Reading Predictive Models for Children with Dyslexia and ADHD-I

In a recent study, researchers delved into the complex interplay of cognitive processes and eye movements in children with dyslexia and Attention-Deficit/Hyperactivity Disorder. Their findings shed light on predictive models for reading outcomes in these children compared to typical readers.

The study involved 59 children: 19 typical readers, 21 with ADHD, and 19 with developmental dyslexia (DD), all in the 4th grade and around 9 years old on average. Each group underwent thorough neuropsychological and linguistic assessments to understand their psycholinguistic profiles.

During the study, participants engaged in a silent reading task where the text underwent lexical manipulation. Researchers then analyzed eye movement data alongside cognitive factors like memory, attention, and visual processes.

Using multinomial logistic regression, the researchers evaluated predictive models based on three key measures: a linguistic model focusing on phonological awareness, rapid naming, and reading fluency; a cognitive neuropsychological model incorporating memory, attention, and visual processes; and an additive model combining lexical word properties with eye-tracking data, specifically examining word frequency and length effects.

By integrating eye movement data with cognitive factors, the researchers enhanced their ability to predict the development of dyslexia or ADHD, in comparison to typically developing readers. This approach significantly improved the accuracy of predicting reading outcomes in children with learning disabilities.

These findings have profound implications for understanding and addressing reading challenges in children. By considering both cognitive processes and eye movement patterns, educators and clinicians can develop more effective interventions tailored to the specific needs of children with dyslexia and ADHD.

April 30, 2024