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October 5, 2023
It sounds like science fiction, but scientists have been testing computerized methods to train the brains of ADHD people to reduce both ADHD symptoms and cognitive deficits such as difficulties with memory or attention.
Two main approaches have been used: cognitive training and neurofeedback. Cognitive training methods ask patients to practice tasks aimed at teaching specific skills, such as retaining information in memory or inhibiting impulsive responses.
Currently, results from ADHD brain studies suggest that the ADHD brain is not very different from the non-ADHD brain, but that ADHD leads to small differences in the structure, organization, and functioning of the brain. The idea behind cognitive training is that the brain can be reorganized to accomplish tasks through a structured learning process. Cognitive retraining helps people who have suffered brain damage, so it was logical to think it might help the types of brain differences seen in ADHD people. Several software packages have been created to deliver cognitive training sessions to ADHD people.
Neurofeedback was applied to ADHD after it had been observed, in many studies, that people with ADHD have unusual brain waves as measured by the electroencephalogram (EEG). We believe that these unusual brain waves are caused by the different ways that the ADHD brain processes information. Because these differences lead to problems with memory, attention, inhibiting responses, and other areas of cognition and behavior, it was believed that normalizing the brain waves might reduce ADHD symptoms.
In a neurofeedback session, patients sit with a computer that reads their brain waves via wires connected to their heads. The patient is asked to do a task on the computer that is known to produce a specific type of brain wave. The computer gives feedback via sound or a visual on the computer screen that tells the patient how 'normal' their brainwaves are. By modifying their behavior, patients learn to change their brain waves. The method is called neurofeedback because it gives patients direct feedback about how their brains are processing information.
Both cognitive training and neurofeedback have been extensively studied. If you've been reading my blogs about ADHD, you know that I play by the rules of evidence-based medicine. My view is that the only way to be sure that a treatment works is to see what researchers have published in scientific journals. The highest level of evidence is a meta-analysis of randomized controlled clinical trials. This ensures that many rigorous studies have been conducted and summarized with a sophisticated mathematical method.
Although both cognitive training and neurofeedback are rational methods based on good science, meta-analyses suggest that they do not help reduce ADHD symptoms. They may be helpful for specific problems, such as problems with memory, but more work is needed to be certain if that is true. The future may bring better news about these methods if they are modified and become more effective. You can learn more about non-pharmacologic treatment for ADHD from a book I recently edited: Faraone, S. V. &Antshel, K. M. (2014). ADHD: Non-Pharmacologic Interventions. Child Adolesc Psychiatr Clin N Am 23, xiii-xiv.
Background:
While ADHD is generally linked to negative childhood outcomes, individual variability exists. Researchers have found that factors like cognition, emotion, parenting, and social interactions can help some adversity-exposed children develop better than expected. This variability has driven extensive resilience research, which now views resilience not as a single trait, but as a combination of biological, psychological, social, and ecological processes supporting adaptation.
The Study:
This meta-analysis sought to address several key research gaps. First, while many potential resilience factors have been identified, no previous meta-analysis has quantitatively synthesized evidence focused specifically on children with ADHD. Second, relatively little research has clarified how particular resilience factors relate to specific developmental outcomes. Third, there is currently no integrated conceptual model of resilience processes tailored to children and adolescents with ADHD.
To keep the analysis focused and clinically relevant, the authors examined psychosocial and ecological resilience factors only. Biological factors (such as genetics or cardiovascular health) and non-modifiable demographic characteristics (such as age and sex) were excluded, as they do not readily inform interventions. The analysis also focused strictly on outcomes for children and adolescents with ADHD, excluding adult outcomes and those reported for parents or teachers. Only studies based on clinical ADHD diagnoses were included.
In total, 28 studies involving more than 11,600 participants met the inclusion criteria. Fifteen studies were rated as high quality and 13 as fair quality; none were rated low quality. However, the evidence base was relatively thin for many analyses. Of the 50 components examined, only one included five studies, six included four studies, ten included three studies, and most (33) were based on just two studies. While some components involved large samples, most did not, meaning the findings should be viewed as suggestive rather than definitive.
Results:
Unsurprisingly, academic skills and cognitive functioning – specifically including working memory and intelligence – were strongly associated with better educational outcomes for children and adolescents with ADHD. In contrast, social skills and proactive attitudes or behaviors showed no significant link to educational attainment.
Well-being outcomes showed a different pattern. Proactive attitudes and behaviors, cognitive functioning, and parental resources were associated with small-to-moderate improvements in well-being. Emotional regulation and positive parenting or attachment, however, were not significantly related to well-being in this analysis.
For relationship outcomes, peer relationships – especially close friendships – stood out as particularly important, showing strong associations with better relational functioning. Social skills and positive parenting or attachment were linked to moderate improvements, although positive parenting alone had no significant effect. This suggests that the observed benefit likely stemmed from parental warmth and secure parent–child attachment rather than parenting practices in isolation. Parental resources (such as parental social support) and school-based support (including student–teacher relationships) showed no significant association with relationship outcomes.
The study also examined behavioral symptoms. Externalizing symptoms refer to outward-directed behaviors that affect others or the environment, such as aggression, defiance, impulsivity, hyperactivity, and rule-breaking. Peer relationships were linked to a modest reduction in these behaviors, while positive relationships with adults were associated with a strong reduction. In contrast, disciplinary parenting – particularly harsh punishment – was strongly associated with increased externalizing symptoms.
Internalizing symptoms involve inward-directed distress, such as anxiety, depression, withdrawal, excessive worry, and unexplained physical complaints. Here again, positive relationships with adults were important, showing a moderate association with fewer internalizing symptoms. Emotional regulation was also linked to small-to-moderate improvements.
Conclusion:
Overall, the findings highlight that resilience factors tend to be closely tied to specific outcomes rather than broadly protective across domains. For example, emotional regulation was associated with lower levels of both internalizing and externalizing symptoms but showed no significant link to well-being, educational achievement, or relationship quality. This suggests that emotional regulation may play a particularly important role in protecting mental health in children with ADHD, rather than driving broader developmental gains – consistent with evidence that emotional dysregulation is a core difficulty in ADHD.
Similarly, academic skills, social competence, and prosocial behaviors were linked mainly to their most closely related outcomes. Cognitive functioning was associated with both educational and well-being outcomes, but its impact was much stronger in education and more modest for well-being. Together, these context-specific patterns underscore the importance of designing interventions that target particular resilience factors with strategies tailored to specific developmental goals, rather than assuming that any single factor will promote resilience across all areas of life.
Key takeaway: resilience is individual and resilience isn’t one trait; different types of support help different individuals, in different areas.
Background:
Traditional measures of obesity, like body mass index (BMI) and waist circumference, have been linked to ADHD risk — but they aren’t great at capturing where fat is actually stored in the body. A newer index called relative fat mass (RFM), which combines height and waist circumference, does a better job of estimating overall body fat and predicting metabolic risks like heart disease and metabolic syndrome. Because those conditions share some underlying biological mechanisms with ADHD, researchers wondered whether RFM might also help explain the relationship between obesity and ADHD — particularly in children.
That question is complicated by the fact that ADHD doesn't look the same in boys and girls. Boys tend to display more hyperactive and impulsive behavior, making their ADHD easier to spot. Girls more often show inattention, which is quieter and frequently goes undiagnosed.
The Study:
A new study set out to test whether RFM is associated with ADHD in children, and whether that association differs between sexes. Using data from the National Health and Nutrition Examination Survey (NHANES) collected between 1999 and 2004, the researchers narrowed a large initial pool of over 31,000 participants down to 5,089 children and adolescents aged 6 to 14 who had complete data on height, waist circumference, ADHD screening, and other relevant variables.
After adjusting for age, race/ethnicity, Poverty-Income Ratio, maternal age at delivery, maternal smoking during pregnancy, health insurance coverage, and birth weight, the results revealed a striking split along sex lines.
In boys, higher RFM was associated with lower odds of ADHD. Compared to boys in the lowest fat-mass quartile, those in the second quartile had about 10% lower odds of ADHD, rising to over 30% lower in the third quartile and nearly 40% lower in the highest. In girls, the pattern reversed entirely. While girls in the second quartile showed similar odds to those with the lowest RFM, girls in the third and fourth quartiles had 60% to 70% greater odds of ADHD.
Conclusion & Why This Matters:
In recent years, the relationship between obesity and ADHD has become an increasingly important focus in pediatric neurodevelopmental research. Studies have reported higher rates of ADHD symptoms among children and adolescents with obesity compared with their non-obese peers, and difficulties with peer relationships have also been linked to increased obesity risk (Sönmez et al., 2019). From a neurobiological standpoint, both conditions may involve shared underlying mechanisms, particularly dysfunction in dopaminergic pathways.
The authors concluded that higher body fat levels appear to lower ADHD risk in boys while raising it in girls. This finding highlights why sex-specific analysis matters in ADHD research. The underlying biological reasons for this divergence, however, remain an open question and open the door for future research.
While ADHD is a developmental disorder, shaped by biology and genetics, growing evidence shows that it is also influenced by the social and environmental conditions in which children grow up. Research on the social determinants of health emphasizes that development is shaped not only by biology but also by factors such as family income, access to healthcare, neighborhood safety, and material stability. These factors can affect both how developmental challenges appear and whether they are recognized and diagnosed.
Children facing socioeconomic disadvantage consistently show higher risks of developmental and behavioral difficulties. Chronic stress linked to poverty – including financial strain, food insecurity, and limited access to resources – has been associated with problems in attention, emotional regulation, and daily functioning. Children from lower-income families also tend to experience more severe ADHD symptoms and face greater barriers to ongoing care.
Neighborhood conditions matter as well. Unsafe environments can limit opportunities for play and social interaction while increasing caregiver stress, all of which may influence children’s behavior and development. Material hardships, such as food insecurity, can further undermine stability at home.
The Study:
The study analyzed six years of data from the National Survey of Children’s Health (2018–2023), covering more than 205,000 U.S. children aged 3 to 17. After accounting for age, sex, race and ethnicity, region, family structure, survey year, and other social factors, the researchers found a strong income gradient in ADHD prevalence. Compared with children in households earning at least four times the federal poverty level, those in households earning two to four times that level had 28 percent higher odds of ADHD. Odds rose to 70 percent higher in households earning one to two times the poverty level, and more than doubled among children living below the poverty line.
Parental education showed a similar pattern. Compared with children whose parents had completed college, ADHD odds were 20 percent higher among those whose parents had some college education, 40 percent higher among those whose parents had only a high school education, and 80 percent higher among those whose parents had not finished high school.
Children living in unsafe neighborhoods had nearly twice the odds of ADHD compared with those in safe neighborhoods, and food insecurity was also linked to almost double the odds.
By contrast, race and ethnicity alone were associated with much smaller differences. Compared with non-Hispanic White children, children in non-Hispanic Black households had an 18 percent higher likelihood of ADHD, while children in Hispanic households had a 25 percent lower likelihood. No substantial differences were observed for children from other or multiracial households.
Conclusion and Takeaway:
The study team concluded, “Children living in lower-income households, experiencing food insecurity, and residing in unsafe neighborhoods consistently showed higher prevalence and higher adjusted odds of both conditions. … Overall, these findings reinforce the need to view neurodevelopmental disorders within a broader social and structural framework.”
It should be noted that this study is not aiming to name social factors as direct causes of ADHD. Rather, it points to socioeconomic disparities as contributing to the way ADHD develops and how it is treated. This type of research, as well as acknowledging barriers to care, is crucial for clinicians, counselors, teachers, etc., to consider when working with youth with ADHD.
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