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June 3, 2026
The Background:
ADHD and epilepsy are the two most common neurological disorders in children and adolescents. Additionally, they appear as co-diagnoses more often than chance would predict. Roughly a quarter of children with epilepsy also have ADHD, and children with ADHD face a 2.5-times greater risk of developing epilepsy than their peers.
Clinicians have long suspected that carrying both diagnoses compounds cognitive difficulties, but no rigorous quantitative review has mapped out exactly how much, or in what ways. This new meta-analysis now fills that gap.
The Study:
The team pooled data from peer-reviewed studies that included children and adolescents diagnosed with both conditions alongside at least one comparison group: children with neither condition, children with epilepsy alone, or children with ADHD alone. To capture the breadth of thinking skills, they constructed a general intelligence factor drawing on six cognitive domains:
The Results:
Across eleven studies (995 participants), children and adolescents with both conditions scored moderately lower on general intelligence than those with epilepsy alone. The same pattern held across all six cognitive domains. Seven studies (785 participants) comparing the dual-diagnosis group with those who had ADHD alone found an equally consistent moderate deficit, replicated in every domain.
The clearest signal emerged when researchers compared children and adolescents carrying both diagnoses to typically-developing peers. Seven studies covering 427 individuals revealed a substantially larger gap in general intelligence, with the effects of the two conditions appearing to be roughly additive, meaning the combined burden was approximately equal to the sum of each condition's individual impact. This pattern held across five of the six domains.
The Interpretation:
The results come with meaningful caveats. Variability across individual studies was moderate in the first two comparisons and high in the third, reflecting real differences in how studies were designed, which populations they sampled, and how they measured cognition. While there was no sign of publication bias in the first group, it was not assessed in two of the three analyses.
The authors describe “a widespread profile of cognitive dysfunction” in children and adolescents with both epilepsy and ADHD, while underscoring that the substantial variability between studies warrants caution in drawing overly precise conclusions. The findings nonetheless carry practical weight: children managing both conditions may need more intensive cognitive screening and support than current clinical practice routinely provides.
Steven Wickens, Persephone Gummersall, and Trevor Brown, “Cognitive functioning in children with both epilepsy and ADHD: A systematic review and meta-analysis,” Brain and Development 48 (2026) 104533, published online, https://doi.org/10.1016/j.braindev.2026.104533.
The Background:
Down syndrome (DS) is a genetic disorder resulting from an extra copy of chromosome 21. It is associated with intellectual disability.
Three to five thousand children are born with Down syndrome each year. They have higher risks for conditions like hypothyroidism, sleep apnea, epilepsy, sensory issues, infections, and autoimmune diseases. Research on ADHD in patients with Down syndrome has been inconclusive.
The Study:
The National Health Interview Survey (NHIS) is a household survey conducted by the National Center for Health Statistics at the CDC.
Due to the low prevalence of Down syndrome, a Chinese research team used NHIS records from 1997 to 2018 to analyze data from 214,300 children aged 3 to 17, to obtain a sufficiently large and nationally representative sample to investigate any potential association with ADHD.
DS and ADHD were identified by asking, “Has a doctor or health professional ever diagnosed your child with Down syndrome, Attention Deficit Hyperactivity Disorder (ADHD), or Attention Deficit Disorder (ADD)?”
After adjusting for age, sex, and race/ethnicity, plus family highest education level, family income-to-poverty ratio, and geographic region, children and adolescents with Down syndrome had 70% greater odds of also having ADHD than children and adolescents without Down syndrome. There were no significant differences between males and females.
The Take-Away:
The team concluded, “in a nationwide population-based study of U.S. children, we found that a Down syndrome diagnosis was associated with a higher prevalence of ASD and ADHD. Our findings highlight the necessity of conducting early and routine screenings for ASD and ADHD in children with Down syndrome within clinical settings to improve the effectiveness of interventions.”
Noting that the degree of comorbidity (co-occurrence) between epilepsy and ADHD “has never been quantified based on a systematic review with meta-analysis,” a Chinese study team based at Wuhan university has just reported findings based on doing just that.
Their systematic search of the peer-reviewed medical literature yielded 17 studies examining the prevalence of epilepsy among persons with ADHD, and 49 studies measuring the prevalence of ADHD among persons with epilepsy.
According to the Apple dictionary app, epilepsy is “a neurological disorder marked by sudden recurrent episodes of sensory disturbance, loss of consciousness, or convulsions, associated with abnormal electrical activity in the brain.” Its lifetime prevalence in the general population is about 0.76%, or about one in every 130 persons.
Meta-analysis of 17 studies with a combined total of over 900,000 participants spread over twelve countries on five continents yielded an epilepsy prevalence estimate of 3.4% among individuals with ADHD, or well over four times the prevalence in the general population. There was no sign of publication bias, but variability (heterogeneity) among studies was extremely high.
The worldwide prevalence of ADHD in children, on the other hand, is about 7.2%, affecting roughly one in fourteen.
Meta-analysis of 49 studies with a combined total of 172,206 persons from 16 countries on five continents reported an ADHD prevalence of just over 22% among persons with epilepsy. However, heterogeneity among studies was extremely high, and there was very strong evidence of publication bias.
Using the trim-and-fill correction for publication bias yielded a reduced estimate of 16%, which is still over twice the prevalence in the general population.
Furthermore, the authors noted, “Given that the large sample studies in this study are basically population-based studies and the small sample studies are hospital-based studies, there is also the possibility of Berkson’s bias. Specifically, patients with comorbidities are more likely to need help or seek medical advice. This possibility would yield a higher comorbidity rate in hospital-based studies.”
And that is exactly what emerged from subgroup analysis. The prevalence of ADHD in epilepsy among the hospital-based studies was 27.1%, over twice the 13.2% prevalence reported from the 13 population-based studies. The largest population-based study, a U.S. study with over 114,000 participants, yielded a prevalence of only 3.5%.
The authors cautioned that the very high degree of heterogeneity between studies indicates “it is inappropriate to consider the summary effect as representative of the real effect.”
A working group of the International League Against Epilepsy(ILAE), consisting of twenty experts spanning the globe (U.S., U.K., France, Germany, Japan, India, South Africa, Kenya, Brazil), recently published "consensus paper" summarizing and evaluating what is currently known about comorbid epilepsy with ADHD, and best practices.
ADHD is two to five times more prevalent among children with epilepsy. The authors suggest that ADHD is underdiagnosed in children with epilepsy because its symptoms are often attributed either to epilepsy itself or to the effects of antiepileptic drugs (AEDs).
The working group did a systematic search of the English-language research literature. It then reached a consensus on practice recommendations, graded on the strength of the evidence.
Three recommendations were graded A, indicating they are well-established by evidence:
· Children with epilepsy with comorbid intellectual and developmental disabilities are at increased risk of ADHD.
· There is no increased risk of ADHD in boys with epilepsy compared to girls with epilepsy.
· The anticonvulsant valproate can exacerbate attentional issues in children with childhood absence epilepsy (absence seizures look like staring spells during which the child is not aware or responsive). Moreover, a single high-quality population-based study indicates that valproate use during pregnancy is associated with inattentiveness and hyperactivity in offspring.
Four more were graded B, meaning they are probably useful/predictive:
· Poor seizure control is associated with an increased risk of ADHD.
· Data support the ability of the Strengths and difficulties questionnaire (SDQ) to predict ADHD diagnosis in children with epilepsy: "Borderline or abnormal SDQ total scores are highly correlated with the presence of a validated psychiatric diagnosis (93.6%), of which ADHD is the most common (31.7%)." The SDQ can therefore be useful as a screening tool.
· Evidence supports the efficacy of methylphenidate in children with epilepsy and comorbid ADHD.
· Methylphenidate is tolerated in children with epilepsy.
At the C level of being possibly useful, there is limited evidence that supports that atomoxetine is tolerated in children with ADHD and epilepsy and that the combined use of drugs for ADHD and epilepsy (polytherapy) is more likely to be associated with behavioral problems than monotherapy. In the latter instance, "Studies are needed to elucidate whether the polytherapy itself has resulted in the behavioral problems, or the combination of polytherapy and the underlying brain problem reflects difficult-to-control epilepsy, which, in turn, has resulted in the prescription of polytherapy."
All other recommendations were graded U (for Unproven), "Data inadequate or conflicting; treatment, test or predictor unproven." These included three where the evidence is ambiguous or insufficient:
· Evidence is conflicted on the impact of early seizure onset on the development of ADHD in children with epilepsy.
· Tolerability for amphetamine in children with epilepsy is not defined.
· Limited evidence exists for the efficacy of atomoxetine and amphetamines in children with epilepsy and ADHD.
There were also nine U-graded recommendations based solely on expert opinion. Most notable among these:
· Screening of children with epilepsy for ADHD beginning at age 6.
· Reevaluation of attention function after any change in antiepileptic drug.
· Screening should not be done within 48 hours following a seizure.
· ADHD should be distinguished from childhood absence epilepsy based on history and an EEG with hyperventilation.
· Multidisciplinary involvement in transition and adult ADHD clinics is essential as many patients experience challenges with housing, employment, relationships, and psychosocial wellbeing.
For centuries, we’ve called the eyes the "windows to the soul," but for modern neurologists, they are quite literally a window into the brain. The retina and the central nervous system share the same embryonic origins, developing from the same neural tissue in the womb. Because of this deep biological connection, the back of your eye acts as a non-invasive map of your brain's health, displaying a complex web of nerves and blood vessels that can (theoretically!) mirror certain neurodevelopmental conditions.
Recently, a buzz rippled through the mental health community when a study published in partnership with Seoul National University Bundang Hospital claimed a massive breakthrough. Researchers developed an Artificial Intelligence (AI) model that could screen children for Attention-Deficit/Hyperactivity Disorder (ADHD) using nothing more than a simple retinal photograph. The study, which prospectively recruited children from Severance Hospital and Eunpyeong St. Mary’s Hospital, produced results that were staggering: the AI reportedly achieved an accuracy rate of 96.9%!
In the world of medical testing, scientists use a metric called AUROC (Area Under the Receiver Operating Characteristic) to measure how well a test works.
An AUROC of 96.9% is a near-perfect score, suggesting a tool is ready for immediate, real-world deployment. While headlines promised a revolution in mental health screening, a deeper look into this research and the study’s design has exposed that this 96.9% AUROC was more likely evidence of a flawed methodology rather than a biological reality.
To build their screening tool, researchers analyzed over 1,100 retinal images using a digital pipeline called AutoMorph and a machine-learning model known as XGBoost. The AI was trained to hunt for physical signals of the "Dopamine Connection." Dopamine is the primary neurotransmitter involved in ADHD, but it is also essential to the eye. It regulates synaptic formation, retinal blood flow, and vascular endothelial regulation. Because dopamine dysregulation influences how blood vessels grow and remodel, the study hypothesized that an ADHD brain would leave a unique "fingerprint" on the retinal vasculature, resulting in denser, thicker vessel structures.
On paper, the logic was sound: use AI to spot the subtle vascular remodeling caused by dopaminergic shifts. But a closer look at the investigation revealed that the AI wasn't just spotting ADHD; it was over-indexing on technical noise.
The most significant "smoking gun" flagged by critics is a massive temporal mismatch. In other words, there was a severe disparity in the timeframes and conditions under which the retinal images for the two comparison groups were collected. For an AI to learn a biological condition, it must compare groups under identical technical conditions. Instead, this study created a time-traveling dataset:
A scientific study is only as reliable as its control group. The control in any experiment acts as a baseline against which the study group is compared. In this case, the control group should be composed of children without any neurodevelopmental disorders, or of “typically developing” children.
In this study, the control group wasn't composed of healthy children from the community. Instead, they were patients visiting a tertiary ophthalmology clinic. Children visiting a specialist eye hospital are rarely "typical." They are there because they have symptomatic eye issues. This introduced a massive selection bias involving three major confounders:
When training AI, you must never allow the "test questions" to leak into the "study material." The researchers, however, committed a fundamental violation of machine learning hygiene known as Eye-to-Eye Data Leakage. The study split the data by the eye rather than by the participant.
Human eyes are highly correlated; the left eye is a near-mirror of the right. If a child's left eye was used for training and their right eye was used for testing, the AI was effectively "cheating." Instead of learning the general traits of ADHD, the model was potentially memorizing individuals. This error artificially balloons accuracy metrics.
The true test of medical AI is diagnostic specificity, or differential diagnosis. This refers to the ability to tell one condition apart from another. While the model claimed 96.9% accuracy against a flawed control group, its performance collapsed when faced with real-world complexity.
When the researchers asked the AI to differentiate between ADHD and Autism Spectrum Disorder (ASD), the accuracy plummeted to a poor 63% AUROC. In real-world clinical settings, an accuracy of 63% is dangerously close to a 50% coin flip. Since ADHD frequently co-occurs with ASD, anxiety, or intellectual disabilities, an AI that cannot handle these "clinical differentials" is functionally useless in a doctor's office. The failure at this stage proves the model was likely detecting technical quirks of the dataset rather than a unique biological marker for ADHD.
To move from the lab to the clinic, we must establish a foundation built on rigor rather than high-speed data scraping. Moving forward, we must demand these 3 Pillars of Trusted Medical AI :
The dream of a quick eye scan to diagnose ADHD is not dead, but it must be rescued from "fast science" shortcuts and buzzy headlines.
Background:
One of the more persistent concerns among parents of children with ADHD is whether stimulant medications will stunt their child's growth. A large Israeli cohort study now offers some of the most rigorous reassurance to date, and its methodology sets it apart from earlier research.
The question has long been complicated by a more fundamental uncertainty: do growth differences in children with ADHD stem from the condition itself, from stimulant treatment, or from factors present before any medication is ever prescribed? Without a clear answer, clinicians and families have faced a genuine dilemma when weighing the benefits of stimulant therapy against potential long-term physical costs.
Most previous studies compounded this difficulty by comparing group-average heights, which ignores the crucial variable of genetic potential. A child who is short relative to the general population may simply have short parents. Failing to account for this introduces systematic bias and can make medications appear more harmful than they are.
The Study:
The Israeli research team addressed this directly. Using health records from a nationwide provider, they assembled a retrospective cohort of children born between 1995 and 2003, following them through 2023. This amount of time was long enough for all participants to have reached adult stature (defined as 17 or older for females, 19 or older for males). Their sample included 5,671 children with untreated ADHD, 11,846 who received stimulant treatment, and 47,258 non-ADHD controls. Children who took stimulants for only one to two months, or who had chronic medical conditions requiring long-term medication, were excluded to avoid confounding the results.
Crucially, adult height was evaluated not against population norms but against each individual's expected height, calculated from parental heights using the Tanner-Goldstein-Whitehouse method, a standard approach for estimating genetic height potential via mid-parental height.
When the researchers compared adult heights across the three groups using analysis of variance (ANOVA), they did find statistically significant differences. But statistical significance, particularly in studies with tens of thousands of participants, does not automatically translate into clinical significance. The effect sizes were consistently very small, and the absolute differences were under one centimeter, which is a margin considered clinically negligible.
Their conclusion is measured but clear: after accounting for genetic growth potential, neither an ADHD diagnosis nor stimulant treatment was associated with meaningful reductions in adult height. The findings, they argue, support prioritizing behavioral and functional outcomes when making treatment decisions, since the risk of clinically significant height loss appears to be minimal.
The Take-Away:
For families navigating ADHD treatment, the practical implication is significant: concerns about permanent growth suppression, while understandable, should not be the primary driver of whether or how long a child receives stimulant therapy.
A recent meta-analysis examined how well cognitive behavioral therapy (CBT) improves not just symptoms, but everyday functioning and quality of life in adults with ADHD.
The Background:
ADHD in adults affects far more than attention or impulsivity. It often disrupts key areas of life:
These broad impacts highlight a key issue: reducing symptoms does not automatically translate into better day-to-day functioning.
CBT is a structured, skills-based therapy that helps people:
While both medication (especially stimulants) and CBT improve core ADHD symptoms, CBT is particularly aimed at improving real-world functioning.
The Study:
The researchers analyzed studies involving adults diagnosed with ADHD (or showing clinically significant symptoms). They included:
They focused specifically on outcomes beyond symptoms:
The Results:
1. Strongest Effects: Occupational functioning
CBT showed consistently strong improvements in work-related functioning compared to control groups, both immediately after treatment and at follow-up. This was the most robust finding across domains.
2. Moderate Improvement: Global Functional Impairment
CBT led to moderate improvements in overall daily functioning, with some evidence that gains persist over time. In studies tracking individuals over time, improvements were even stronger at follow-up.
3. Modest Gains: Social Relationships
CBT produced small to moderate improvements in social functioning. Benefits were present both after treatment and at follow-up, but were less pronounced than in work-related outcomes.
4. Limited Effects: Academic Functioning
There were moderate short-term gains when CBT was compared to control groups, but these did not persist at follow-up. Within-subject studies showed only small improvements overall.
5. Modest and Inconsistent Effects: Quality of Life
Improvements in quality of life were small when compared to control groups and often did not last. However, studies tracking individuals over time showed moderate improvements, suggesting some benefit that may not always show up clearly in between-group comparisons.
Overall, the findings suggest:
One notable nuance: CBT did not always outperform other active treatments (like medication or other therapies). This suggests that while CBT is effective, its benefits may partly overlap with broader therapeutic or support effects rather than relying on a single, unique mechanism.
The Take-Away:
CBT is a valuable, evidence-based treatment for adults with ADHD, especially for improving work functioning and overall daily life management. However, its impact on relationships, academic outcomes, and quality of life is more limited and less consistent, pointing to the need for more targeted or combined approaches in those areas.
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