Cookie Preferences
By clicking, you agree to store cookies on your device to enhance navigation, analyze usage, and support marketing. More Info
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
October 17, 2025
Executive function impairment is a key feature of ADHD, with its severity linked to the intensity of ADHD symptoms. Executive function involves managing complex cognitive tasks for organized behavior and includes three main areas: inhibitory control (suppressing impulsive actions), working memory (holding information briefly), and cognitive flexibility (switching between different mental tasks). Improving executive functions is a critical objective in the treatment of ADHD.
Amphetamines and methylphenidate are commonly used to treat ADHD, but can cause side effects like reduced appetite, sleep problems, nausea, and headaches. Long-term use may also lead to stunted growth and cardiovascular issues. This encourages the search for non-invasive methods to enhance executive function in children with ADHD.
Neurological techniques like neurofeedback and transcranial stimulation are increasingly used to treat children with neurodevelopmental disorders. Neurofeedback is the most adopted method; it is noninvasive and aims to improve brain function by providing real-time feedback on brainwave activity so participants can self-regulate targeted brain regions.
The systematic search and meta-analysis examined children and adolescents aged 6–18 with ADHD. It included randomized and non-randomized controlled trials, as well as quasi-experimental studies that reported statistical data such as participant numbers, means, and standard deviations. Studies were required to use validated measures of executive function, including neurocognitive tasks or questionnaires. They also had to have control groups.
A meta-analysis of ten studies (539 participants) found a small-to-medium improvement in inhibitory control after neurofeedback training, with no publication bias and minimal study heterogeneity*. Long-term treatment (over 21 hours) showed benefits, while short-term treatment did not. However, publication bias was present in the long-term treatment studies and was not addressed.
A meta-analysis of seven studies with 370 children and adolescents found a small-to-medium improvement in working memory after neurofeedback, with no publication bias overall but high heterogeneity. A dose-response effect was observed: treatments over 21 hours showed benefits, while shorter ones did not. However, publication bias was present in the long-term treatment studies and was not addressed.
The study team also looked at sustained effects six months to a year after conclusion of training. Meta-analysis of two studies totaling 131 participants found a sustained small-to-medium improvement in inhibitory control, with negligible heterogeneity. Meta-analysis of three studies combining 182 participants found a sustained medium improvement in working memory, with moderate heterogeneity and no sign of publication bias.
The team concluded, “NFT is an effective intervention for improving executive function in children with ADHD, specifically inhibitory control and working memory. This approach demonstrates a more pronounced impact on working memory when extended beyond 1000 min [sic], with inhibitory control following closely behind. Furthermore, the evidence suggests that NFT may have sustained effects on both working memory and inhibitory control. Given the relatively small number of studies assessing long-term effects and the potential for publication bias, further research is necessary to confirm these effects.”
Moreover, because 1) RCTs are the gold standard, and the meta-analyses combined RCTs with non-RCTs, and 2) data from neurocognitive tasks was combined with data from more subjective and less accurate questionnaires, these meta-analysis results should be interpreted with further caution.
*Heterogeneity refers to the rate of variation between individual study outcomes. High heterogeneity means that there was substantial variation in the results. When a meta-anaylysis has high heterogeneity, it suggests that the studies differ significantly in their populations, methods, interventions, or outcomes, making the combined result much less reliable.
Xiaoke Zhong, Xiaoxia Yuan, Yuanfu Dai, Xinbi Zhang, and Changhao Jiang, “Neurofeedback training for executive function in ADHD children: a systematic review and meta-analysis,” Scientific Reports (2025), 15: 28148, https://doi.org/10.1038/s41598-025-94242-4.
Neurofeedback, also known as EEG (electroencephalogram)biofeedback, is a treatment that seeks to alleviate symptoms of various neurological and mental health disorders, including ADHD. It does this through immediate feedback from a computer program that tracks a client's brainwave activity, then uses sound or visual signals to retrain these brain signals. This in principle enables patients to learn to regulate and improve their brain function and reduce symptoms.
An Iranian study team recently performed a systematic search of the peer-reviewed medical literature. It identified seventeen randomized-controlled trials (RCTs) of neurofeedback treatment for children and adolescents with ADHD that could be aggregated for meta-analysis.
A meta-analysis of twelve RCTs with a combined total of 740 youths looked at parent ratings of changes in hyperactivity/impulsivity symptoms, and separately of changes in inattention symptoms. In both instances, the net pooled effect centered on zero.
A meta-analysis of nine RCTs with a combined total of 787 youths examined teacher ratings. Once again, the pooled change hyperactivity/impulsivity symptoms centered on zero. For inattention symptoms, the teacher ratings centered on a tiny improvement, but it did not approach statistical significance. The 95% confidence interval stretched well into negative territory.
There was no sign of publication bias. Between-study heterogeneity, on the other hand, was high, with some small sample size RCTs pointing to reduced symptoms, and other small sample size RCTs pointing to increased symptoms. However, the RCTs with the larger sample sizes clustered close around zero effect size.
The authors concluded,"The results provide preliminary evidence that neurofeedback treatment is not an efficacious clinical method for ADHD."
Noting that “The efficacy of surface electroencephalographic neurofeedback (EEG‐NF) for improving attentional performance assessed by laboratory measures in patients with attention‐deficit/hyperactivity disorder (ADHD) remains unclear,” a Taiwanese study team systematically searched seven databases, including the U.S. clinical trials database, for randomized controlled trials (RCTs) through January of 2022.
They identified fourteen RCTs with a combined 718 participants that met criteria for inclusion in meta-analysis. The net outcome was a small-to-medium effect size improvement in attentional performance for participants receiving EEG neurofeedback by contrast with “comparators.”
The comparators varied widely: waitlist, treatment as usual, physical exercise, behavioral therapy, attention skills training, computer-aided attention training, medications, electromyographic biofeedback, sham EEG neurofeedback. This alone brings into question the meta-analysis outcome.
But there were additional methodological shortcomings. There was strong evidence of publication bias. And though the authors promised, “On encountering funnel plot asymmetry, potentially missing studies were imputed by using the Duval and Tweedie’s trim and fill method,” they never shared the outcome.
Another shortcoming was that only two of the fourteen RCTs blinded the participants, meaning that in twelve RCTs the participants were likely to be aware they were in the EEG neurofeedback group rather than the control group. And that made all the difference. The twelve unblinded RCTs were responsible for all the small-to-medium effect size improvement. There was no sign of improvement in the two blinded RCTs.
The authors tried to give a positive spin to these results, stating “our results supported the use of surface EEG-NF for improving attentional performance through the modulation of basic neurocognitive functioning in patients with ADHD,” while conceding, “However, given the small number of trials and the poor methodological qualities regarding blinding, our findings need to be judiciously interpreted and warrant further investigations for validation.”
A more candid assessment of this meta-analysis would be the one they began with: “The efficacy of surface electroencephalographic neurofeedback (EEG‐NF) for improving attentional performance assessed by laboratory measures in patients with attention‐deficit/hyperactivity disorder (ADHD) remains unclear.”
Attention Deficit Hyperactivity Disorder (ADHD) is a common condition affecting children and adolescents worldwide, characterized by symptoms such as hyperactivity, impulsivity, and inattention. While traditional treatments like medication and behavioral therapy are often used, some individuals are turning to complementary and alternative therapies (CAM) for help. One such option gaining attention is acupuncture. But does it really work for ADHD?
A recent comprehensive study aimed to evaluate the effectiveness of acupuncture in treating ADHD symptoms. Here’s a breakdown of the findings, with a focus on the age groups included in the research and what these findings could mean for ADHD treatment options.
The study in question conducted a systematic review and meta-analysis (SR/MA) of acupuncture trials for ADHD, comparing its effects to traditional treatments such as pharmacotherapy and behavioral therapy. The researchers focused on acupuncture’s impact on core ADHD symptoms like hyperactivity, impulsivity, inattention, and conduct problems, while also exploring how acupuncture might help with other issues, such as learning difficulties and psychosomatic symptoms.
One key feature of this study was the inclusion of a broad age range of participants, specifically children and adolescents. These two groups are the most commonly diagnosed with ADHD, and their responses to treatments can vary significantly. Understanding how acupuncture works for these age groups is critical for evaluating its effectiveness as an ADHD treatment.
Here’s what the study found across the different age groups:
Despite these promising results, the study also highlighted several limitations:
In short, and as is so often the way of evidence-based medicine, we still can’t say with absolute certainty one way or the other. These studies may show promise in improving hyperactivity, impulsivity, inattention, and conduct problems– in both children and adolescents. However, the evidence is not yet strong enough to recommend it as a primary treatment. While it may serve as a helpful complement to standard therapies, especially for those struggling with medication side effects or access to behavioral therapy, more research is needed to establish its effectiveness.
ADHD is one of the most common neurodevelopmental disorders in children, yet anyone familiar with this disorder, from clinicians and researchers to parents and patients, knows how differently it can manifest from one individual to the next. One person diagnosed with ADHD may primarily struggle with focus and staying on-task; another may find it nearly impossible to regulate their impulses or even start tasks; a third may frequently find themselves frozen with overwhelm and subject to emotional reactivity…
These are not just variations in severity; they may reflect genuinely different patterns of brain organization.
Our current diagnostic system groups all of these presentations under a single label (ADHD), with three behavioral subtypes (Hyperactive, Inattentive, and Combined) defined by symptom checklists. This framework has real clinical value of course, but it was built from behavioral observation rather than neurobiology, and may leave room for substantial heterogeneity to remain unexplained. In a new study, published in JAMA Psychiatry, researchers asked whether it’s possible to identify distinct neurobiologically subgroups within ADHD by analyzing patterns of brain structure, and whether those subgroups would map onto meaningful clinical differences.
How the Brain Was Analyzed
Researchers analyzed structural MRI scans from 446 children with ADHD and 708 typically-developing children across multiple research sites. From each scan, they constructed a morphometric similarity network; that is, a map of how different brain regions resemble one another in their structural properties. These networks reflect underlying biological organization, including shared patterns of cellular architecture and gene expression across brain regions.
From each individual's network, the research team calculated three properties that capture how each brain region functions within the broader network: how many connections it has, how efficiently it communicates with other regions, and how well it bridges different functional communities in the brain. Regions that score highly on these measures are sometimes called "hubs" and they play particularly influential roles in how information is integrated across the brain.
Rather than comparing the ADHD group to controls as a whole and looking for average differences, they used a normative modeling approach. This works similarly to a growth chart in pediatric medicine: instead of asking whether a child is above or below the group average, it asks how much a given child deviates from the expected range for their age and sex. This allows for individual variation across the ADHD group rather than flattening it into a single average profile.
The team then applied a data-driven clustering algorithm to these individual deviation profiles, allowing the data to reveal whether subgroups of children with ADHD shared similar patterns of brain network atypicality, without using any clinical symptom information to guide the clustering.
The Results:
Three stable, reproducible subtypes emerged from this analysis.
The first subtype was characterized by the most widespread differences from the normative range, particularly in regions connecting the medial prefrontal cortex to the pallidum (a deep brain structure involved in motivation and emotional regulation). Children in this group had the highest levels of both inattention and hyperactivity/impulsivity, and over a four-year follow-up period showed more persistent difficulties with emotional self-regulation than the other groups. They also had a higher rate of mood disorder comorbidity during follow-up, though this difference did not reach statistical significance given the sample size. The brain deviation patterns of this subtype showed correspondence with the spatial distributions of several neurotransmitter systems, including serotonin, dopamine, and acetylcholine, all of which have been previously implicated in ADHD pathophysiology.
The second subtype showed alterations concentrated in the anterior cingulate cortex and pallidum, a circuit involved in action control and response selection. This subtype had a predominantly hyperactive/impulsive profile, and its brain deviation patterns were associated with glutamate and cannabinoid receptor distributions.
The third subtype showed more focal differences in the superior frontal gyrus, a region involved in sustained attention. This subtype had a predominantly inattentive profile, with brain patterns linked to a specific serotonin receptor subtype.
A particularly important observation was that these brain-derived groupings aligned with clinically meaningful symptom differences, even though no symptom information was used in the clustering process. The fact that an analysis of brain structure alone arrived at groupings that correspond to recognizable clinical patterns is meaningful evidence that these subtypes reflect genuine neurobiological differences rather than statistical noise.
Replication in an Independent Sample
Scientific findings are only as trustworthy as their ability to replicate. The research team tested this clustering model in an entirely independent cohort of 554 children with ADHD from the Healthy Brain Network, a large, publicly available dataset collected under different conditions. The three subtypes were successfully identified in this new sample, with strong correlations between the brain deviation patterns observed in the original and validation cohorts. Differences in hyperactivity/impulsivity across subtypes were consistent with the discovery cohort, providing meaningful external validation of the approach.
What This Does and Doesn't Mean
It is important to be clear about what these findings do and do not imply. This study does not establish that these three subtypes are categorically distinct biological entities with sharp boundaries. They probably represent distinguishable regions along an underlying continuum of neurobiological variation. The neurochemical associations reported are exploratory and spatial in nature; they describe correspondences between brain deviation maps and neurotransmitter receptor density maps derived from separate imaging studies, and do not directly establish that any particular neurotransmitter system is altered in each subtype, nor do they currently inform treatment decisions.
The samples were not entirely medication-naive, and the strict comorbidity exclusion criteria may limit how well these findings generalize to typical clinical populations where comorbidities are the rule rather than the exception. All data came from research sites in the United States and China, and broader generalizability remains to be established.
What the study does demonstrate is that structured neurobiological heterogeneity exists within the ADHD diagnosis, that it can be reliably detected using brain imaging and data-driven methods, and that it aligns with meaningful clinical differences. The subtype defined by the most extensive brain network differences and the most severe, persistent clinical profile may be of particular importance, representing a group that could benefit most from early identification and targeted support.
The longer-term goal of this line of research is to move toward a more biologically grounded understanding of ADHD that complements existing diagnostic approaches and that may ultimately help guide more individualized treatment decisions. That goal, for now, remains a research ambition rather than a clinical reality, but this study takes a meaningful step in that direction.
Managing high blood pressure requires more than just getting a prescription; it means taking medication consistently, day after day, often for years. For people with ADHD, that kind of routine can be genuinely difficult. In our new study, published in BMC Medicine, we set out to understand just how much ADHD affects whether people stick with their blood pressure medication, and whether ADHD treatment itself might make a difference.
Why This Question Matters
Hypertension affects nearly a third of adults worldwide and is one of the leading drivers of heart disease and stroke. At the same time, ADHD, long thought of as a childhood disorder, affects around 2.5% of adults and is increasingly recognized as a risk factor for cardiovascular problems, including high blood pressure. Yet no large-scale study had ever examined whether having ADHD affects how well people follow through with their blood pressure treatment. We wanted to fill that gap.
What We Did
We analyzed health records from over 12 million adults across seven countries, Australia, Denmark, the Netherlands, Norway, Sweden, the UK, and the US, who had started antihypertensive (blood pressure-lowering) medication between 2010 and 2020. About 320,000 of them had ADHD. We tracked two things: whether they stopped their blood pressure medication entirely within five years, and whether they were taking it consistently enough (covering at least 80% of days) over one, two, and five years of follow-up.
What We Found
Across nearly all countries, adults with ADHD were more likely to stop their blood pressure medication and less likely to take it consistently. Overall, those with ADHD had about a 14% higher rate of discontinuing treatment within five years, and were 45% more likely to have poor adherence in the first year, a gap that widened to 64% by the five-year mark. These patterns were most pronounced in middle-aged and older adults.
Interestingly, young adults with ADHD were actually slightly less likely to discontinue treatment than their peers without ADHD, a finding we think may reflect the fact that younger people with ADHD are often more actively engaged with healthcare systems, especially given the cardiovascular monitoring that comes with ADHD medication use.
Perhaps the most encouraging finding was this: among people with ADHD who were also taking ADHD medication, adherence to blood pressure treatment was substantially better. Those on ADHD medication were about 38% less likely to have poor adherence at one year, and nearly 50% less likely at five years. While we can't establish causation from this type of study, one plausible explanation is that treating ADHD, reducing inattention and impulsivity, makes it easier to maintain the routines that consistent medication use requires. It's also possible that people on ADHD medication simply have more regular contact with healthcare providers, which keeps other health problems better monitored and managed.
What This Means in Practice
The core ADHD symptoms of inattention and poor organization are precisely the traits that make long-term medication adherence difficult. Add in the complexity of managing multiple disorders and medications, and it's easy to see why people with ADHD face extra challenges. Our findings suggest that clinicians treating adults with ADHD for cardiovascular disorders should be aware of these challenges and consider tailored support strategies, things like regular follow-up appointments, patient education, and tools that help with routine and organization.
There's also a broader message here about the potential ripple effects of treating ADHD well. Supporting someone in managing their ADHD may not just improve their attention and daily functioning; it may also help them take better care of their physical health, including disorders as serious as hypertension.
Future research should explore which specific support strategies are most effective, and whether these findings hold in lower- and middle-income countries where the data don't yet exist.
If you or someone you know has ADHD, you may be familiar with the challenge of staying on medication. Stimulants like methylphenidate (Ritalin) are the most common and effective treatment for ADHD, but a surprisingly large number of people stop taking them within the first year. In our new study, published in Translational Psychiatry, we sought to determine whether a person's genetic makeup plays a role in the development of the disorder.
What We Did
We analyzed data from over 18,000 people with ADHD in Denmark, all of whom had started stimulant medication. We tracked whether they stopped treatment within the first year, defined as going more than six months without filling a prescription. Nearly 4 in 10 (39%) had discontinued by that point. We then looked at their genetic data to see whether DNA differences could help explain who was more likely to stop.
What We Found
The short answer is: genetics does play a role, but it's modest. No single gene had a dramatic effect. Instead, we found that a collection of small genetic influences—distributed across the genome—contributed to the likelihood of stopping treatment early.
One of the most consistent findings was that people with a higher genetic predisposition for psychiatric disorders like schizophrenia, depression, or general mental health difficulties were more likely to discontinue their medication. This was true across all age groups. Interestingly, having a higher genetic risk for ADHD itself was not associated with stopping treatment, suggesting that the genetics of having ADHD and the genetics of staying on medication are quite different things.
We also found that the genetic picture looks different depending on age. In children under 16, body weight genetics (BMI) played a surprising role, children with a genetic tendency toward higher weight were actually less likely to stop, possibly because stimulant-related appetite suppression is less of a problem for them. In older adolescents and adults, higher genetic potential for educational attainment and IQ was linked to staying on treatment, possibly reflecting better access to information and healthcare support.
On the rare variant side, we found a tentative signal that people who stopped treatment had fewer disruptive variants in genes involved in dopamine, the brain chemical that stimulants work on. This might mean that those who continue on medication genuinely have more disruption in their dopamine system and benefit more from stimulant treatment.
What This Means
Our findings suggest that stopping ADHD medication early isn't simply a matter of willpower or forgetting to take a pill. Biology matters. A person's broader genetic vulnerabilities, particularly for other psychiatric disorders, may make it harder to stay on treatment, perhaps because of side effects, poor response, or the complexity of managing multiple mental health challenges at once.
We're still far from being able to use genetics to predict who will stop their medication, the effects we found are real but small, and much of the variation in treatment persistence remains unexplained. But this work is a step toward understanding the biological foundations of treatment challenges in ADHD, and hopefully toward more personalized approaches to care in the future.
Larger studies and research that can distinguish why people stop (side effects versus poor response versus practical barriers), will be the next steps.
_logo%201.svg)
