April 29, 2024
Guanfacine is a non-stimulant medication for ADHD. It is an Alpha-2 agonist that targets and excites receptors in the prefrontal cortex of the brain, the region that governs executive functions such as judgment, decision making, planning, and response suppression. These functions tend to be sub-optimal in ADHD.
Most treatment guidelines recommend stimulants as the preferred treatment for ADHD, because they respond faster, and studies show they have higher efficacy in reducing symptoms. But for individuals for whom treatment with stimulants is subpar, guidelines recommend non-stimulants as second-line treatment.
Previous meta-analyses have focused on efficacy among children and adolescents with ADHD. This meta-analysis, by a Chinese study team, expanded its reach to not only update the former, but also include studies of adults.
The team’s systematic search of the medical literature for double-blind randomized controlled trials (RCTs) identified eleven that could be combined for meta-analysis. With only a single study of efficacy for adults, however, no meta-analysis could be performed specific to persons 18 and older.
Meta-analysis of all eleven studies with a combined total of 2,623 participants found guanfacine to be roughly 75% more effective than placebo for reducing ADHD symptoms. Variation between studies (heterogeneity) was low. There was no sign of publication bias.
Breaking that down by length of time on guanfacine found no evidence of a dose-response effect, however. In fact, participants with less than ten weeks of treatment (seven RCTs, 1,771 participants) outperformed those with longer periods of treatment (four RCTs, 852 participants) with a narrow overlap in the 95% confidence limits.
The outcomes were also sensitive to the ADHD symptom scale used. Meta-analysis of RCTs using the Clinical Global Impression of Improvement treatment response score (four studies, 850 participants) reported no significant improvement, while RCTs relying on ADHD-Rating-Scale-IV (six studies, 1,128 participants) reported a significant improvement, but without providing a standardized effect size.
Finally, a meta-analysis of ten RCTs with a combined total of 2,273 persons found a 23% increase in treatment-emergent adverse events for guanfacine relative to placebo. The three most common such events in the guanfacine group were somnolence (38.6%), headache (20.5%), and fatigue (15.2%).
Sijie Yu, Sihao Shen, and Ming Tao, “Guanfacine for the Treatment of Attention-Deficit Hyperactivity Disorder: An Updated Systematic Review and Meta-Analysis,” Journal of Child and Adolescent Psychopharmacology (2023), https://doi.org/10.1089/cap.2022.0038.
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.
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.
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.
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