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March 18, 2026
The first few weeks of life are the time when babies are most vulnerable to seizures (known as neonatal seizures). This is partly because of events that can occur during birth, and partly because the newborn brain is naturally in a more excitable state than a mature brain, making it more prone to seizure activity.
Seizures affect roughly 1 to 3 in every 1,000 full-term babies born, and the rate is considerably higher in premature babies, at around 11 to 14 per 1,000. In most cases, seizures at this age are triggered by a specific event or injury affecting the brain. In full-term newborns, the most common cause is a condition called hypoxic-ischemic encephalopathy (HIE), which occurs when the brain is deprived of adequate oxygen and blood flow around the time of birth. Other causes include genetic or metabolic conditions, stroke, bleeding in the brain, and structural abnormalities in how the brain developed. In very premature babies, bleeding into the fluid-filled spaces of the brain (known as intraventricular hemorrhage) is the leading culprit.
Diagnosing seizures in newborns is tricky because many normal or abnormal movements and behaviors in this age group can look like seizures without actually being them. For this reason, monitoring the baby’s brain activity using an electroencephalogram (EEG) – a test that records electrical signals in the brain – is essential to confirm whether a seizure is truly occurring.
Sweden’s single-payer health system provides universal coverage, with national registers linking healthcare and population data. Researchers tracked infants with EEG/aEEG-confirmed seizures born between 2009 and 2020 and compared them to controls without neonatal seizures.
Altogether, 1062 infants with neonatal seizures were matched with 5310 controls.
The team adjusted for birth, mode of delivery, sex, birth weight, and Apgar scores – quick, standardized assessments used to evaluate newborns’ health minutes after birth.
With these adjustments, infants who had neonatal seizures were twice as likely to subsequently be diagnosed with ADHD and three times as likely to be subsequently diagnosed with autism spectrum disorder.
The authors emphasized that because the study was observational, it cannot demonstrate a direct cause-and-effect relationship between neonatal seizures and outcomes. Factors like seizure frequency, genetics, and socioeconomic status are thought to significantly impact the prognosis of affected children, but these could not be included in this study due to data limitations.
Hanna Westergren, Helena Marell Hesla, Maria Altman, and Ronny Wickström, “Neurological outcomes beyond epilepsy following electroencephalographically verified neonatal seizures: A Swedish nationwide cohort study,” Neuroepidemiology (2026), published online, https://doi.org/10.1159/000551055.
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.
Although there has been much research documenting that ADHD adults are at risk for other psychiatric and substance use disorders, relatively little is known about whether ADHD puts adults at risk specifically for somatic medical disorders.
Given that people with ADHD tend toward being disorganized and inattentive, and that they tend to favor short-term over long-term rewards, it seems logical that they should be at higher risk for adverse medical outcomes. But what does the data say?
In a systematic review of the literature, Instances and colleagues have provided a thorough overview of this issue. Although they found 126 studies, most were small and were of "modest quality". Thus, their results must be considered to be suggestive, not definitive for most of the somatic conditions they studied.
Also, they excluded articles about traumatic injuries because the association between ADHD and such injuries is well established. Using qualitative review methods, they classified associations as being a) well-established; b) tentative, or c) lacking sufficient data.
Only three conditions met their criteria for being a well-established association: asthma, sleep disorders, and obesity.
They found tentative evidence implicating ADHD as a risk factor for three conditions: migraine headaches, celiac disease, and diseases of the circulatory system.
These data are intriguing, but cannot tell us why ADHD people are at increased risk for somatic conditions. One possibility is that suffering from ADHD symptoms can lead to an unhealthy lifestyle, which leads to increased medical risk. Another possibility is that the biological systems that are dysregulated in ADHD are also dysregulated in some medical disorders. For example, we know that there is some overlap between the genes that increase the risk for ADHD and those that increase the risk for obesity. We also know that the dopamine system has been implicated in both disorders.
Instances and colleagues also point out that some medical conditions might lead to symptoms that mimic ADHD. They give sleep-disordered breathing as an example of a condition that can lead to the symptom of inattention.
But this seems to be the exception, not the rule. Other medical conditions co-occurring with ADHD seem to be true comorbidities, rather than the case of one disorder causing the other. Thus, primary care clinicians should be alert to the fact that many of their patients with obesity, asthma, or sleep disorders might also have ADHD.
By screening such patients for ADHD and treating that disorder, you may improve their medical outcomes indirectly via increased compliance with your treatment regime and an improvement in health behaviors. We don't yet have data to confirm these latter ideas, as the relevant studies have not yet been done.
Roughly five of every thousand women (0.5%) have epilepsy, a neurological disorder characterized by sudden recurrent episodes of sensory disturbance, loss of consciousness, or convulsions, associated with abnormal electrical activity in the brain. Primary treatment consists of anti-seizure medications (ASMs).
Yet, research has shown that ASMs cross the human placenta. In rodents, ASMs have been shown to lead to abnormal neuronal development, and some research has pointed to the risk of adverse birth outcomes and neurodevelopmental disorders in humans. But samples have been too small for reliable conclusions, and in most cases confounding factors are not addressed.
For a more comprehensive evaluation of risk from ASMs, an international team of researchers examined a nationwide cohort using Swedish national registers that track health outcomes for virtually the entire population.
Using the Medical Birth Register, the National Patient Register, and the Multi-Generation Register, they were able to identify 14,614 children born from 1996-to 2011 to mothers with epilepsy.
Through the prescribed Drug Register, they also examined the first-trimester use of anti-seizure medications (ASMs) by these mothers. The three most frequently used ASMs "frequent enough to yield useful data“ were valproic acid, lamotrigine, and carbamazepine.
The researchers identified ADHD in offspring in one of two ways: ICD-10 (international classification of Diseases, 10th Revision) diagnoses, or filled prescriptions of ADHD medication.
Finally, they consulted the Integrated Database for Labor Market Research and the Education Register to explore potential confounding variables. These included maternal and paternal age at birth, the highest education, cohabitation status, and country of origin. They also included maternal and paternal disposable income in the year of birth and a measure of neighborhood deprivation.
Using the medical registers, they considered parental psychiatric and behavioral problems diagnosed before pregnancy, including bipolar disorder, suicide attempt, schizophrenia diagnosis, substance use disorder, and criminal convictions. They adjusted for inpatient diagnosis of seizures in the year before pregnancy to capture and adjust for indication severity.
Other covariates explored included year of birth, birth order, child sex, maternal-reported smoking during pregnancy, and use of other psychotropic medications.
After fully adjusting for all these confounders, children of mothers who were taking valproic acid were more than 70% more likely to develop ADHD than those of mothers not taking an anti-seizure medicine during pregnancy. The sample size was 699, and the 95% confidence interval stretched from 28% to 138% more likely to develop ADHD.
By contrast, children of mothers who were taking lamotrigine were at absolutely no greater risk(Hazard Ratio = 1) of developing ADHD than those of mothers not taking an anti-seizure medicine during pregnancy.
Finally, children of mothers who were taking carbamazepine were 18% more likely to develop ADHD than those of mothers not taking an anti-seizure medicine during pregnancy, but this result was not statistically significant (the 95% confidence interval ranged from 9% less likely to 52% more likely).
The authors concluded, "The present study did not find support for a causal association between maternal use of lamotrigine in pregnancy and ASD [Autism Spectrum Disorder] and ADHD in children. We observed an elevated risk of ASD and ADHD related to maternal use of valproic acid, while associations with carbamazepine were weak and not statistically significant. Although we could not rule out all potential confounding factors, our findings add to a growing body of evidence that suggests that certain ASMs (i.e., lamotrigine) may be safer than others in pregnancy."
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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