Cookie Preferences
By clicking, you agree to store cookies on your device to enhance navigation, analyze usage, and support marketing. More Info
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
May 17, 2021
There are several very effective drugs for ADHD, and those treatment guidelines from professional organizations view these drugs as the first line of treatment for people with ADHD. The only exception is for preschool children where medication is only the first line of treatment for severe ADHD; the guidelines recommend that other preschoolers with ADHD be treated with non-pharmacologic treatments, when available. Despite these guidelines, some parents and patients have been persuaded by the media or the Internet that ADHD drugs are dangerous and that non-drug alternative are as good or even better. Parents and patients may also be influenced by media reports that doctors overprescribe ADHD drugs or that these drugs have serious side effects. Such reports typically simplify and/or exaggerate results from the scientific literature. Thus, many patients and parents of ADHD children are seeking non-drug treatments for ADHD. What are these non-pharmacologic treatments and do they work? My next series of blogs will discuss each of these treatments in detail. Here I'll give an overview of my evidenced-based taxonomy of non-pharmacologic treatments for ADHD described in more detail in a book I recently edited (Faraone, S. V. &Antshel, K. M. (2014). ADHD: Non-Pharmacologic Interventions. Child Adolesc Psychiatry Clin N Am 23, xiii-xiv.). I use the term "evidence-based" in the strict sense applied by the Oxford Center for Evidenced Based Medicine (OCEBM; http://www.cebm.net/). Most of the non-drug treatments for ADHD fall into three categories: behavioral, dietary, and neurocognitive. Behavioral interventions include training parents to optimize methods of reward and punishment for their ADHD child, teaching ADHD children social skills, and helping teachers apply principles of behavior management in their classrooms. Cognitive behavior therapy is a method that teaches behavioral and cognitive skills to adolescent and adult ADHD patients. Dietary interventions include special diets that exclude food coloring or eliminate foods believed to cause ADHD symptoms. Other dietary interventions provide supplements such as iron, zinc, or omega-3 fatty acids. The neurocognitive interventions typically use a computer-based learning setup to teach ADHD patients cognitive skills that will help reduce ADHD symptoms. There are two metrics to consider when thinking about the evidence base for these methods. The first is the quality of the evidence. For example, a study of 10 patients with no control group would be a low-quality study, but a study of 100 patients randomized to either a treatment or control group would be of high quality and the quality would be even higher if the people's rating patient outcomes did not know who was in each group. The second metric is the magnitude of the treatment effect. Does the treatment dramatically reduce ADHD symptoms, or does it have only a small effect? This metric is only available for high-quality studies that compare people treated with the method and people treated with a 'control' method that is not expected to affect ADHD. I used a statistical metric to quantify the magnitude of the effect. Zero means no effect, and larger numbers indicate better effects on treating ADHD symptoms. For comparison, the effect of stimulant drugs for ADHD is about 0.9, which is derived from a very strong evidence base. The effects of dietary treatments are smaller, about 0.4 to 0.5, but because the quality of the evidence is not strong, these results are not certain and the studies of food color exclusions apply primarily to children who have high intakes of such colorants. In contrast to the dietary studies, the evidence base for behavioral treatments is excellent, but the effects of these treatments on ADHD symptoms are very small, less than 0.1. Supplementation with omega-3 fatty acids also has a strong evidence base, but the magnitude of the effect is also small (0.1 to 0.2). The neurocognitive treatments have modest effects on ADHD symptoms (0.2 to 0.4) but their evidence base is weak. This review of non-drug treatments explains why ADHD drug treatments are usually used first. The evidence base is stronger, and they are more effective in reducing ADHD symptoms. There is, however, a role for some non-drug treatments. I'll be discussing that in subsequent blog posts. See more evidence-based information about ADHD at www.adhdinadults.com
Faraone, S. V.&Antshel, K. M. (2014). ADHD: Non-Pharmacologic Interventions. ChildAdolescPsychiatr Clin N Am 23, xiii-xiv.
Faraone, S. V. &Antshel, K. M. (2014).Towards an evidence-based taxonomy of nonpharmacologic treatments for ADHD.Child AdolescPsychiatr Clin N Am 23, 965-72.
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.”
.png)
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.
_logo%201.svg)
