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A new study from Japan suggests that infants born with craniosynostosis are significantly more likely to be diagnosed with ADHD later in childhood. Craniosynostosis is a condition in which the bony plates of the skull fuse prematurely, leading to increased intracranial pressure.
The Background:
Craniosynostosis affects roughly one in every 2,000 births. When the skull’s natural seams close prematurely, it can restrict brain growth and increase intracranial pressure, potentially reducing blood flow to the brain. Because the condition is relatively rare, it has been difficult to study at scale until now.
The Study:
To overcome this, researchers tapped into a large Japanese insurance database compiled by JMDC, Inc., which holds records on around 20 million people, or about 15% of Japan’s population. Drawing on two decades of data, the team tracked over 338,000 mother-child pairs. Children with related genetic syndromes or chromosomal conditions such as Down syndrome were excluded to keep the focus on craniosynostosis itself.
Of the children studied, around 1,145 had craniosynostosis, and 7,325 were diagnosed with ADHD. After accounting for factors like sex, birth year, maternal age, mental health history, pregnancy infections, and birth complications, children with craniosynostosis were found to have roughly 2.4 times the risk of a subsequent ADHD diagnosis compared to those without it.
To test whether shared family genetics or home environment might be driving the association rather than the skull condition itself, the researchers conducted a separate analysis among siblings. The elevated risk remained at 2.2 times. The consistency of the finding across both analyses strengthens the case for a genuine biological link.
The Results:
The results point to raised intracranial pressure and restricted cerebral blood flow as plausible mechanisms, though the study’s observational design means causation cannot be confirmed. Ultimately, these findings highlight the need for proactive, long-term care strategies for those born with craniosynostosis. By establishing a solid link between premature skull fusion and a significantly higher risk of ADHD, the research demonstrates that medical care for this condition should not end once the skull's physical structure is addressed.
The Takeaway:
Pediatricians, neurologists, and parents can use this data to implement early, routine behavioral and developmental screening for these children as they grow. This additional support would ensure that those who do develop ADHD can receive timely interventions, educational aids, and therapies, ultimately improving their long-term developmental outcomes.
Takanori Yanai, Satomi Yoshida, and Koji Kawakami, “Association between craniosynostosis and neurodevelopmental disorders: a nationwide claims database,” BMC Pediatrics (2026), https://doi.org/10.1186/s12887-026-06726-5.
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.”
A recent CNN report, http://tinyurl.com/yannlfd6, highlighted a paper published in Pediatrics, which reported that pregnant women who use acetaminophen during pregnancy put their unborn child at two-fold increased risk for attention deficit hyperactivity disorder (ADHD). In that study, acetaminophen use during pregnancy was common; nearly half of women surveyed used the painkiller during pregnancy. Other studies have reported similar associations of acetaminophen, also known as paracetamol with ADHD or with other problems in childhood (e.g., https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5300094/, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4177119/, https://www.ncbi.nlm.nih.gov/pubmed/24566677, https://www.ncbi.nlm.nih.gov/pubmed/24163279). Given these prior findings, it seems unlikely that the new report is a chance finding. But does it make any biological sense? One answer to that question came from an epigenetic study. Such studies figure out if assaults from the environment change the genetic code. One epigenetic study found that prenatal exposure changes the fetal genome via a process called methylation. Such genomic changes could increase the risk for ADHD (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5540511/). Because all of these studies are observational studies, one cannot assert with certainty that there is a causal link between acetaminophen use during pregnancy.
The observed association could be due to some unmeasured third factor. Although the researchers did a respectable job ruling out some third factors, we must acknowledge some uncertainty in the finding. That said, what should pregnant women do if they need acetaminophen. I suggest you bring this information to your physician and ask if there is a suitable alternative.
Many media outlets have reported on a study suggesting that mothers who use acetaminophen during pregnancy may put their unborn child at risk for ADHD. Given that acetaminophen is used in many over-the-counter painkillers, correctly reporting such information is crucial. As usual, rather than relying on one study, looking at the big picture using all available studies is best. Because it is not possible to examine this issue with a randomized trial, we must rely on naturalistic studies.
One registry study (http://www.ncbi.nlm.nih.gov/pubmed/24566677)reported that fetal exposure to acetaminophen predicted an increased risk of ADHD with a risk ratio of 1.37. The risk was dose-dependent, in the sense that it increased with increased maternal use of acetaminophen. Of particular note, the authors made sure that their results were not accounted for by potential confounds (e.g., maternal fever, inflammation, and infection). Similar results were reported by another group (http://www.ncbi.nlm.nih.gov/pubmed/25251831), which also showed that the risk for ADHD was not predicted by maternal use of aspirin, antacids, or antibiotics. But that study only found an increased risk at age 7 (risk ratio = 2.0) not at age 11. In a Spanish study, (http://www.ncbi.nlm.nih.gov/pubmed/27353198), children exposed prenatally to acetaminophen were more likely to show symptoms of hyperactivity and impulsivity later in life. The risk ratio was small (1.1) but it increased with the frequency of prenatal acetaminophen use by their mothers.
We can draw a few conclusions from these studies. There does seem to be aweak, yet real, the association between maternal use of acetaminophen while pregnant and subsequent ADHD or ADHD symptoms in the exposed child. The association is weak in several ways: there are not many studies, they are all naturalistic, and the risk ratios are small. So mothers that have used acetaminophen during pregnancy and have an ADHD child should not conclude that their acetaminophen usecausedtheir child's ADHD. On the other hand, pregnant women who are considering the use of acetaminophen for fever or pain should discuss other options with their physician. As with many medical decisions, one must balance competing for risks to make an informed decision.
Find more evidence-based blogs at www.adhdinaduls.com.
ADHD is a neurodevelopmental condition rooted in delayed or atypical maturation of the prefrontal cortex (the brain region that governs self-regulation). This maturational lag underlies the hallmark difficulties with attention, hyperactivity, and impulsivity, and also impairs what researchers call executive function: the cognitive toolkit we rely on for working memory, impulse control, mental flexibility, emotional regulation, and the ability to tolerate delays in reward.
The Background:
Standard treatments work through two main routes. Stimulant and non-stimulant medications are considered very safe and effective treatments, but are not without risk of side effects and are not appropriate for every ADHD patient. Behavioral and psychosocial interventions can improve self-regulation and social functioning, but they require sustained effort and produce variable results. These limitations have kept the search for better alternatives active.
One candidate that has drawn growing attention is transcranial direct current stimulation (tDCS). The technique is appealingly simple: a weak electrical current is applied to the scalp through small electrodes, modulating the excitability of neurons in the underlying cortex without requiring surgery, anesthesia, or significant discomfort. Its safety profile and ease of use have made it attractive to researchers.
The Study:
A newly published meta-analysis set out to give the technique its most rigorous test yet, pooling results from randomized controlled trials, including crossover designs, that compared active tDCS against sham stimulation in people with ADHD across all age groups.
The Results:
The findings were consistently null. Across seven trials enrolling 303 participants, tDCS produced no significant reduction in overall ADHD symptom severity compared with sham. Breaking symptoms into their components made no difference: neither hyperactivity/impulsivity nor inattention improved. Turning to executive function, 18 studies with 872 participants found no meaningful gain in inhibitory control, and 12 studies with 506 participants found the same for working memory. Smaller bodies of evidence, including three studies on cognitive flexibility (122 participants) and two on hot executive function, the motivational and emotional dimension of self-regulation (86 participants), similarly came up empty. Variation in outcomes across studies was small to moderate, and there was no evidence of publication bias skewing the picture.
The authors’ conclusion was succinct: tDCS was well tolerated but “did not demonstrate significant overall efficacy for core ADHD symptoms or executive functions.”
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Disordered eating (a broad category of persistent, harmful patterns in eating or weight control) affects between 5% and 22% of children and adolescents worldwide, with similar rates seen in the United States. The consequences are far-reaching: these conditions are linked to bone fractures, anemia, malnutrition, dental erosion, obesity, diabetes, hypertension, and elevated cholesterol and triglycerides. They also carry one of the highest mortality rates of any psychiatric illness.
Eating disorders rarely occur in isolation. They frequently arise alongside other psychiatric and neurological conditions. Yet, until now, no large-scale study had examined these co-occurrences in a nationally representative U.S. sample. A new study addresses that gap, focusing on children and adolescents aged 6–17 and the conditions most commonly associated with disordered eating, including ADHD.
The Study:
Researchers drew on data from the 2022–2023 National Survey of Children's Health (NSCH), a nationally representative, cross-sectional survey covering all 50 states and Washington, D.C. Households were selected using stratified, address-based sampling, and parents or guardians completed surveys about one randomly selected child per household. The final sample included 68,000 children and adolescents.
Results:
After accounting for factors including sex, age, race and ethnicity, household income, educational attainment, insurance status, and household language, children and adolescents with ADHD were 2.6 times more likely to have some form of disordered eating compared to their typically developing peers.
The elevated risk appeared across a range of specific behaviors:
A greater tendency toward using diet pills, laxatives, or diuretics was also observed in the ADHD group, though this finding did not reach statistical significance.
The Take-Away:
These findings underscore a need to improve both prevention and treatment strategies for disordered eating, particularly in children and adolescents who have ADHD. Clinicians working with this population are advised to screen for a wide spectrum of disordered eating behaviors.
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.
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