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May 22, 2025
Background
A meta-analysis examined whether noninvasive brain stimulation (NIBS) techniques could help reduce core symptoms of ADHD and improve cognitive function. NIBS refers to techniques that stimulate brain activity using low electrical or magnetic currents applied from outside the head. They studied transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS), while newer methods like tRNS (random noise) and tACS (alternating current) lacked enough studies to be included in the analysis.
Methods
Only randomized controlled trials (RCTs)—considered the gold standard in clinical research—were included in the review. For tDCS, the results were promising:
-A meta-analysis of 12 studies (582 participants) showed small but statistically significant improvements in inhibitory control (the ability to stop or delay responses).
-Nine studies (390 participants) showed small-to-medium improvements in working memory.
-Two smaller studies (94 participants) hinted at improvement in cognitive flexibility, but the results were not strong enough to be considered reliable.
-Seven studies (277 participants) found medium-to-large improvements in linattention, though results varied significantly between studies.
Hyperactivity and impulsivity showed some improvement, but again, the number of studies was too small to draw firm conclusions.
For rTMS, however, the results were not as encouraging. A meta-analysis of three studies (137 participants) found no significant improvement in ADHD symptoms.
Conclusion
While the results suggest that tDCS may offer some benefit for executive functions and attention in people with ADHD—especially when targeting specific brain areas like the F3/F4 regions (roughly over the dorsolateral prefrontal cortex)—the authors emphasize the need for further research. Most studies didn’t include long-term follow-up, and there’s still a lack of consistency in how stimulation is applied across studies. Moreover, even when positive findings emerged for executive functions is not clear how these translate into changes that are meaningful for the patient.
Importantly, this study doesn’t suggest that NIBS should replace standard treatments. Although the paper highlights challenges with medication adherence and side effects, ADHD medications and behavior therapies remain the most well-established and effective treatments for most patients. The improvements seen with NIBS so far are relatively small and preliminary in comparison.
Instead, the findings support the idea that NIBS could one day serve as a complementary tool—especially for individuals who don’t respond well to existing treatments. But until more rigorous and long-term studies are done, NIBS should be viewed as an experimental approach, not a substitute.
Yao Yin, Xueke Wang, and Tingyong Feng, “Noninvasive Brain Stimulation for Improving Cognitive Deficits and Clinical Symptoms in Attention-Deficit/Hyperactivity Disorder: A Systematic Review and Meta-Analysis,” Brain Sciences (2024), 14, 1237, https://doi.org/10.3390/brainsci14121237.
It sounds like science fiction, but scientists have been testing computerized methods to train the brains of ADHD people to reduce both ADHD symptoms and cognitive deficits such as difficulties with memory or attention.
Two main approaches have been used: cognitive training and neurofeedback. Cognitive training methods ask patients to practice tasks aimed at teaching specific skills, such as retaining information in memory or inhibiting impulsive responses.
Currently, results from ADHD brain studies suggest that the ADHD brain is not very different from the non-ADHD brain, but that ADHD leads to small differences in the structure, organization, and functioning of the brain. The idea behind cognitive training is that the brain can be reorganized to accomplish tasks through a structured learning process. Cognitive retraining helps people who have suffered brain damage, so it was logical to think it might help the types of brain differences seen in ADHD people. Several software packages have been created to deliver cognitive training sessions to ADHD people.
Neurofeedback was applied to ADHD after it had been observed, in many studies, that people with ADHD have unusual brain waves as measured by the electroencephalogram (EEG). We believe that these unusual brain waves are caused by the different ways that the ADHD brain processes information. Because these differences lead to problems with memory, attention, inhibiting responses, and other areas of cognition and behavior, it was believed that normalizing the brain waves might reduce ADHD symptoms.
In a neurofeedback session, patients sit with a computer that reads their brain waves via wires connected to their heads. The patient is asked to do a task on the computer that is known to produce a specific type of brain wave. The computer gives feedback via sound or a visual on the computer screen that tells the patient how 'normal' their brainwaves are. By modifying their behavior, patients learn to change their brain waves. The method is called neurofeedback because it gives patients direct feedback about how their brains are processing information.
Both cognitive training and neurofeedback have been extensively studied. If you've been reading my blogs about ADHD, you know that I play by the rules of evidence-based medicine. My view is that the only way to be sure that a treatment works is to see what researchers have published in scientific journals. The highest level of evidence is a meta-analysis of randomized controlled clinical trials. This ensures that many rigorous studies have been conducted and summarized with a sophisticated mathematical method.
Although both cognitive training and neurofeedback are rational methods based on good science, meta-analyses suggest that they do not help reduce ADHD symptoms. They may be helpful for specific problems, such as problems with memory, but more work is needed to be certain if that is true. The future may bring better news about these methods if they are modified and become more effective. You can learn more about non-pharmacologic treatment for ADHD from a book I recently edited: Faraone, S. V. &Antshel, K. M. (2014). ADHD: Non-Pharmacologic Interventions. Child Adolesc Psychiatr Clin N Am 23, xiii-xiv.
ADHD is hypothesized to arise from 1) poor inhibitory control resulting from impaired executive functions which are associated with reduced activation in the dorsolateral prefrontal cortex and increased activation of some subcortical regions; and 2)hyperarousal to environmental stimuli, hampering the ability of the executive functioning system, particularly the medial frontal cortex, orbital and ventromedial prefrontal areas, and subcortical regions such as the caudate nucleus, amygdala, nucleus accumbens, and thalamus, to control the respective stimuli.
These brain anomalies, rendered visible through magnetic resonance imaging, have led researchers to try new means of treatment to directly address the deficits. Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that uses a weak electrical current to stimulate specific regions of the brain.
Efficacy:
A team of researchers from Europe and ran performed a systematic search of the literature and identified fourteen studies exploring the safety and efficacy of tDCS. Three of these studies examined the effects on ADHD symptoms. They found a large effect size for the inattention subscale and a medium effect size for the hyperactivity/impulsivity. Yet, as the authors cautioned, "a definite conclusion concerning the clinical efficacy of tDCS based on the results of these three studies is not possible."
The remaining studies investigated the effects on specific neuropsychological and cognitive deficits in ADHD:
The fact that heterogeneity in the methodology of these studies made meta-analysis impossible means these results, while promising, cannot be seen as in any way definitive.
Safety:
Ten studies examined childhood ADHD. Three found no adverse effects either during or after tDCS. One study reported a feeling of "shock" in a few patients during tDCS. Several more reported skin tingling and itching during tDCS. Several also reported mild headaches.
The four studies of adults with ADHD reported no major adverse events. One study reported a single incident of acute mood change, sadness, diminished motivation, and tension five hours after stimulation. Another reported mild instances of skin tingling and burning sensations.
To address side effects such as tingling and itching, the authors suggested reducing the intensity of the electrical current and increasing the duration. They also suggested placing electrodes at least 6 cm apart to reduce current shunting through the ski. For children, they recommended the use of smaller electrodes for better focus in smaller brains.
The authors concluded, "The findings of this systematic review suggest at least a partial improvement of symptoms and cognitive deficits in ADHD by tDCS. They further suggest that stimulation parameters such as polarity and site are relevant to the efficacy of tDCS in ADHD. Compared to cathodal stimulation, Anodal tDCS seems to have a superior effect on both the clinical symptoms and cognitive deficits. However, the routine clinical application of this method as an efficient therapeutic intervention cannot yet be recommended based on these studies ..."
A two-year study examined the effect of digital media use on ADHD symptoms in over 2500 adolescents. An earlier meta-analysis found that traditional media use (TV and video console games) was modestly associated with ADHD-like behaviors (Nikkelen et al 2014). The current study extends the examination to a large sample, with modern digital media delivery of high-intensity stimuli, including mobile platforms.
The authors used the Current Symptom Self-Report Scale (Barkley R 1998) to establish ADHD symptoms at baseline and six-month assessments over 24 months. None of the subjects reported having ADHD, study entry. Subjects were considered to be ADHD symptom-positive (the primary binary outcome) if they had greater than or equal to six inattentive and/or hyperactive-impulsive symptoms rated on this frequency-based scale (0-3). Modern digital media use was surveyed on a frequency basis for 14 media activities(including checking social media sites, texting, browsing, downloading or streaming music, posting pictures, online chatting, playing games, online shopping, and video chatting). The most common media activity was the high-frequency checking of social media. Of note, high-frequency engagement in each of the digital media activities was significantly, but moderately, associated with having ADHD symptoms at each six-month follow-up (OR 1.10), even after adjusting for covariates. High-frequency media use at baseline seemed to be associated with the development of ADHD symptoms.
Among the 495 students who reported no high-frequency media use at baseline, 4.6% met ADHD symptom criteria at follow-up. Among 114 students scoring 7 for high-frequency media use at baseline, 9.5% met the symptoms criteria. For the 51 students with a score of 14 for high-frequency media use at baseline, the rate was 10.5% (both comparisons were statistically significant).
This study is important in that it notes that an association between high-frequency digital media use (in current platforms and modalities) may be associated with the development of ADHD-like symptoms. A significant limitation of the study, as noted by the authors, is that ADHD-like symptoms do not establish a diagnosis of ADHD and do not assess impairment; therefore, these results must be interpreted with some caution. It does highlight that even with the current level of understanding, it might be prudent for clinicians to recommend limiting high-frequency media use for adolescent patients.
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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