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June 17, 2026
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.
Choi H, Hong J, Kang HG, Park MH, Ha S, Lee J, Yoon S, Kim D, Park YR, Cheon KA. Retinal fundus imaging as biomarker for ADHD using machine learning for screening and visual attention stratification. NPJ Digit Med. 2025 Mar 17;8(1):164. doi: 10.1038/s41746-025-01547-9. PMID: 40097590; PMCID: PMC11914053.
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The Neuroeconomic Perspective
Neuroeconomics combines neuroscience, psychology, and economics to understand how people make decisions. Neuroeconomic studies suggest that brain regions responsible for evaluating risk and reward, including the prefrontal cortex and dopamine pathways, function differently in individuals with ADHD. These insights are crucial for developing more tailored interventions. For example, understanding how ADHD affects reward processing might inform strategies that help individuals resist impulsive choices or increase motivation for delayed rewards.
Understanding Decision-Making in ADHD
We know that decision-making is a sophisticated process involving various cognitive procedures. It’s not just about choosing between options but also about how to weigh risks, rewards, and potential future outcomes; Attention, motivation, and cognitive control are core to this process. For individuals with ADHD, however, this neural framework is affected by impairments in attention and impulse control, often resulting in “delay discounting”—the tendency to prefer smaller, immediate rewards over larger, delayed ones.
This propensity for impulsive decisions is more than a personal challenge; it has broader societal and economic implications. Previous studies have shown that these tendencies in ADHD can lead to issues in academics, work, finances, and personal relationships, emphasizing the need for targeted support and interventions.
Implications and Future Directions
This review highlights a need for continued research to bridge the gaps in understanding how ADHD-specific cognitive deficits influence decision-making. Viewing ADHD through a neuroeconomic lens clarifies how cognitive and neural differences affect decision-making, often leading to impulsive choices with economic and social impacts. This perspective opens doors to more effective interventions, improving decision-making for individuals with ADHD. Future policies informed by this approach could enhance support and reduce associated societal costs.
Typically, clinicians rely on both subjective and objective observations, patient interviews and questionnaires, as well as reports from family and (in the case of children) parents and teachers, in order to diagnose ADHD.
A group of researchers are aiming to find a diagnostic test that is purely objective and utilizes recent technological advancements. The method they developed involves analyzing videos of children in outpatient settings, focusing on their movements. The study included 96 children, half of whom had ADHD and half who did not.
This new method could potentially provide a more objective way to diagnose ADHD, reducing the reliance on subjective observations and reports. It can help doctors make more accurate diagnoses, ensuring that those who need help get it and that those who don't aren't misdiagnosed.
Background:
Our understanding of Attention-deficit/hyperactivity disorder (ADHD) has grown and evolved considerably since it first appeared in the DSM-II as “Hyperkinetic Reaction of Childhood.” This study aimed to find the disorder’s placement within the modern psychopathology classification systems like the Hierarchical Taxonomy Of Psychopathology (HiTOP).
The HiTOP model aims to address limitations of traditional classification systems for mental illness, such as the DSM-5 and ICD-10, by organizing psychopathology according to evidence from research on observable patterns of mental health problems.. Is ADHD best categorized under externalizing conditions, neurodevelopmental disorders, or something else entirely? A recent study by Zheyue Peng, Kasey Stanton, Beatriz Dominguez-Alvarez, and Ashley L. Watts takes a closer look at this question using a symptom-focused approach.
The Study:
Traditionally, ADHD has been associated with externalizing behaviors, such as impulsivity and hyperactivity, or with neurodevelopmental traits, like cognitive delays. However, this study challenges the idea of placing ADHD into a single category. Instead, it maps ADHD symptoms across three major psychopathology spectra: externalizing, neurodevelopmental, and internalizing.
The findings reveal that ADHD symptoms don’t fit neatly into one box. For example, symptoms like impulsivity, poor school performance, and low perseverance were strongly associated with externalizing behaviors. On the other hand, cognitive disengagement (e.g., daydreaming, blank staring) and immaturity were closely linked to neurodevelopmental challenges. Interestingly, cognitive disengagement also showed ties to internalizing symptoms, such as anxiety or depression.
This research underscores the complexity of ADHD. Rather than treating ADHD as a single, unitary construct, the study advocates for a symptom-based approach to better understand and treat individuals. By acknowledging that ADHD symptoms relate to multiple psychopathology spectra, clinicians and researchers can move toward more nuanced classification systems and targeted interventions.
Conclusion:
Ultimately, this study highlights the need for modern systems to move beyond rigid categories and adopt a more flexible, symptom-focused framework for understanding ADHD’s place in psychopathology.
Methylphenidate is an effective treatment for ADHD in adults who also have bipolar disorder (BD), but it carries a potential risk of triggering manic episodes. Current guidelines therefore recommend using it only alongside mood-stabilizing medication. A new study using French nationwide claims data sought to test and extend those recommendations with greater statistical power than previous research.
The study built on findings by Viktorin et al. (2017), who observed that adults with BD not taking mood stabilizers had more than a sixfold higher risk of manic events (defined as hospitalization for mania or a new antimanic prescription) within six months of starting methylphenidate. Patients on mood-stabilizing treatment, by contrast, showed nearly half the baseline risk in the first three months. Those findings were limited, however, by small event counts (fewer than 61 manic episodes) and an effect that did not persist beyond the initial three-month window.
To build on this, researchers drew on the French National Health Data System (which is a claims database covering more than 60 million people) spanning 2008 to 2024. The final sample included 6,022 adults with BD (56% women) who had started methylphenidate. Using a self-controlled design, the study compared each patient's rate of manic events in the six months before their first methylphenidate prescription with the rate in the six months after, effectively eliminating stable individual differences as a confound.
Patients were classified as receiving continuous mood-stabilizing treatment if they had been dispensed at least two courses of specific antipsychotics (aripiprazole, olanzapine, or quetiapine) or mood stabilizers (lithium or valproate) in the nine months before starting methylphenidate, including at least one dispensation in the final six months of that window.
The results largely confirmed the earlier findings. Among the 2,745 patients not on mood stabilizers, the rate of inpatient mania diagnosis was 5.1 times higher in the first three months after starting methylphenidate, though this elevation fell to a non-significant level over the subsequent three months. Patients receiving continuous mood-stabilizing treatment showed no statistically significant change in mania risk across the full six-month post-initiation period. A formulation-specific pattern also emerged: patients without mood-stabilizing treatment had a 2.5-fold higher risk associated with extended-release methylphenidate, while no significant risk increase was seen with the immediate-release formulation or in treated patients regardless of formulation.
The authors conclude that methylphenidate at doses below 30 mg does not appear to elevate manic relapse risk when prescribed alongside mood stabilizers. The elevated risk seen in untreated patients, particularly with extended-release formulations, must be interpreted cautiously, given limited statistical power and the likelihood that it partly reflects the natural fluctuation of manic relapse over time. The authors flag this as an inherent limitation of self-controlled survival analyses when studying drug-induced mania, where temporal trends in the underlying condition can be difficult to disentangle from treatment effects.
The FDA has approved a new once-daily pill called centanafadine (trade name SIMTRIYO®). Approved for adults and kids aged 6 and older (weighing at least 44 lbs / 20 kg), centanafadine is a new category of ADHD treatment that aims to give fast results with fewer of the downsides of traditional stimulants.
To understand why centanafadine is unique among medications for ADHD, it helps to look at how ADHD brain chemistry works:
Stimulants like Ritalin and Adderall work mainly in the dopamine system. Nonstimulants like atomoxetine, viloxazine, clonidine and guanfacine work mainly on the norepinephrine system. Centanafadine is the first drug in a new class called NDSRIs (Norepinephrine, Dopamine, and Serotonin Reuptake Inhibitors). We can describe its effects as follows:
What Did Clinical Trials Show?
The FDA approved centanafadine based on studies involving thousands of adults, teens, and children. Here are the key findings:
Centanafadine showed some improvement in ADHD symptoms within the very first week of taking it although a full effect takes about six weeks.
In adult trials, taking 200 mg or 400 mg daily led to significant improvements in real-world skills:
In trials with children (ages 6–12) and teens (ages 13–17), centanafadine significantly reduced core ADHD symptoms like hyperactivity, impulsivity, and lack of focus compared to a placebo.
About 30% to 40% of adults with ADHD also suffer from anxiety. Traditional stimulants can make anxiety worse. In a trial specifically designed for adults dealing with both ADHD and anxiety, centanafadine effectively treated ADHD symptoms without firing up their anxiety, which might be due to its serotonin boost.
Does Centanafadine have Side Effects?
While Centanafadine was well-tolerated by most people in studies, like any prescription medication, it comes with important safety guidelines.
Prescribing Warnings:
Common Side Effects:
Other Warnings:
The Bottom Line
Overall, centanafadine is a new step forward in how we treat ADHD. Because it acts differently in the brain than traditional treatments, patients who struggle with stimulant-related anxiety or side effects may find it useful to explore with their doctor.
A landmark nationwide study from South Korea has uncovered a significant link between prenatal exposure to air pollution — specifically nitrogen dioxide (NO2) — and an increased risk of ADHD in children.
While researchers have long suspected that air pollutants interfere with fetal brain development through inflammation and oxidative stress, this study is one of the largest and most comprehensive of its kind, following nearly 1.5 million births for up to 13 years.
Why South Korea?
South Korea provided a unique countrywide “laboratory” for this research due to two key infrastructure strengths:
Key Findings: The “Smoking Gun” of NO2
While the study looked at several pollutants, nitrogen dioxide — a byproduct of fossil fuel combustion in cars and power plants — emerged as the primary concern.
Pollutant
Association with ADHD Risk
Nitrogen Dioxide (NO2)
Strongest Link: Every 1-ppb (part-per-billion) increase in exposure linked to a 22% rise in risk.
Sulfur Dioxide (SO2)
Minimal Link: Only a slight 4% increase per ppb.
Ozone (O3) carbon monoxide (CO), & particulates
No significant association was found.
The scale of the NO2 risk is particularly striking. Over the typical range of exposure levels found in the study (an interquartile range of 13 ppb), the data suggest a threefold increase in ADHD risk for children in the highest-exposure groups compared to the lowest.
Accounting for Other Factors
To ensure the results weren’t skewed by other variables, the researchers controlled for a wide range of confounders including:
The authors emphasized the strong association between maternal nitrogen dioxide exposure and ADHD, while also noting the small but significant association with sulfur dioxide.
The Take-Away: A New Frontier for Public Health
Historically, air quality laws have been designed to protect our lungs and hearts. However, this study adds to a growing body of evidence suggesting that the brain is likewise vulnerable.
In a commentary on the findings, expert George Ayoub argued that “neurodevelopment should be explicitly considered” when governments perform cost-benefit analyses on air quality regulation. My view is a bit different. The association is intriguing but the study does not establish cause and effect. Many statistically significant environmental risk associations for neurodevelopmental disorders have disappeared after controlling for maternal risk for ADHD. I hope this research team will do those analyses if feasible.
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