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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."
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.
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