Treating ADHD With Methylphenidate (Ritalin, Concerta)

Methylphenidate is a stimulant medication used to treat symptoms of ADHD. It helps the brain regulate attention, focus, and impulsive behaviors.

It’s one of the two stimulants widely used in ADHD medications. Methylphenidate is the active ingredient in Ritalin and Concerta, among others. The other commonly used stimulant, amphetamine, is the active ingredient in Adderall and Vyvanse, among others. Both stimulants work by increasing levels of dopamine and norepinephrine, chemicals in the brain that control attention, focus, and impulsivity. If a child doesn’t do well on the first stimulant medication they try, they may respond better to a different formulation of that type or the other type of stimulant.

How is methylphenidate different from amphetamine?

Methylphenidate is somewhat less powerful than amphetamine and tends to have milder side effects.

If your child is under 12 and has just been diagnosed with ADHD, a doctor is likely to prescribe a methylphenidate medication first, to see how well the medication reduces their ADHD symptoms, and whether the side effects are problematic.

Methylphenidate is also many doctors’ first choice for younger children because it has been used to treat ADHD much longer than amphetamine. Ritalin (methylphenidate-based) was FDA approved in 1955, while Adderall (amphetamine-based) wasn’t approved until 1996. In countries outside the United States, amphetamine-based ADHD medications are less widely approved than those based on methylphenidate.

How methylphenidate works vs amphetamine

The two stimulants target the same brain chemicals but work slightly differently, says Paul Mitrani, MD, PhD, a child and adolescent psychiatrist at the Child Mind Institute. Methylphenidate increases the levels of dopamine and norepinephrine by blocking what’s called reuptake — the process by which nerve cells reabsorb these chemicals after they’ve been released. As Dr. Mitrani describes it, methylphenidate “enhances” the norepinephrine and dopamine the brain naturally releases by making the chemicals stay around longer. It boosts the stimulation the brain is already getting from whatever activity the child is engaged in.

Amphetamine, on the other hand, not only blocks reuptake but stimulates the release of more dopamine and norepinephrine, which is why it’s considered stronger. “Adding stimulation with amphetamine sometimes helps,” he notes. “But sometimes that added stimulation is too much, and it increases side effects the child experiences.”

Kids vary in how they respond to methylphenidate vs amphetamine

There is individual variation in how children respond to the two stimulants. So if methylphenidate doesn’t give the desired symptom relief or produces problematic side effects, it’s recommended practice to try amphetamine, or vice versa. Research shows that 70 percent of children with ADHD respond to a trial of methylphenidate. More than 90 percent will have a beneficial response to one of the stimulants if both methylphenidate and amphetamine are tried. Studies also show that approximately 41 percent respond equally well to both types of stimulant.

Children can also vary in their response to different formulations of the same stimulant, which affect the rate at which the medication goes into the bloodstream.  For instance, a short-acting form of Ritalin will kick in quickly and last for 3-4 hours, while Concerta, a delayed-release formula, lasts as long as 10-12 hours. It’s very common for kids to try several before finding the best fit.

What are the side effects of stimulant medications?

Methylphenidate and amphetamine have the same side effects, though they may be less intense with the former.

Appetite suppression

The most common side effect of stimulants is appetite suppression. It can be especially concerning with long-acting forms of the medication, which are often preferred to get better coverage through the school day. Kids who take a long-acting stimulant in the morning tend to lose their appetite for lunch and may not be interested in eating until after dinnertime.

When this is a problem, Dr. Mitrani notes that taking a shorter-acting form of the medication can help. “For instance, Concerta is a methylphenidate medication that lasts for a long time and can suppress appetite for 10–12 hours.” An alternative might be a medication that lasts for 6–8 hours, such as Metadate CD or Ritalin LA. Some children with more pronounced problems with appetite will do better on a short-acting dose in the morning and then another after lunch, he adds, since it gives them a break during the day where they can eat better.

Sleep issues

Kids who take stimulant medication can have trouble falling asleep. This can happen when a long-acting medication or an afternoon dose of a short-acting medication wears off and they get restless or hyperactive around bedtime. Difficulty falling asleep can get better after a few weeks, but if it doesn’t, it may be helpful to change either the timing or the type of the medication that is given. It’s also important to explore whether there are other contributors to sleep challenges, such as worry, screen time too close to bedtime, or lack of a consistent evening routine that helps kids calm down.

Irritability

Stimulant medications can generate agitation and irritability, which can be especially problematic in kids who are already anxious. For children with anxiety, this can be another reason to start treatment with methylphenidate, because amphetamines can feel more activating.

But Dr. Mitrani notes that treating ADHD can also reduce anxiety: “Some kids are so stressed about school — because they can’t pay attention or arealways getting in trouble — that when you treat the ADHD, they are better able to manage the demands of school and become less anxious.”

That reduction in school anxiety can also affect what happens when they get home from school. “When there is anxiety, it’s like kids are holding it together at school, and then they come home after a stressful day and just let it out,” he says. “So if the school day is less stressful, you may also see that come down at the end of the day.”

Mood changes

Some children report that stimulant medications seem to dull their personality. Dr. Mitrani suggests that this may be connected to the medication stimulating the prefrontal cortex, the part of the brain that not only manages attention and focus, but also helps regulate emotions and impulse control in other brain areas. “Enhanced control of the emotional part of the brain can cause this feeling of dullness,” he notes. “Some people will even say they feel depressed, that they’re just not like themselves because they don’t have the same energy or personality.”

If this happens to a child on methylphenidate, Dr. Mitrani will recommend trying an amphetamine or a non-stimulant medication.

Rebound effects

Some families report that their child is irritable or emotional after school or at the end of the day, when the stimulant medication is wearing off. Dr. Mitrani notes that this can coincide with the child being hungry after missing lunch. It can also be connected to the medication level dropping too quickly, and strategies that create a more gradual decrease may help take it away. For example, he might suggest adding a small dose of  short-acting form of the stimulant a half hour before the morning medication wears off.

Starting children on methylphenidate

Dr. Mitrani usually starts a child on a short-acting form of methylphenidate for two reasons: as a quick test to see if the child will experience side effects and to have an opportunity to try it twice in a day, to have more chances to assess for positive changes.

He recommends starting the medication on a weekend or a break from school and giving the child some tasks that are challenging for them because of their ADHD, like reading or something else that requires concentration, such as cleaning their room or doing household chores. “After lunch you want to try it again, to have another time point to check on. Because if you only give one dose of the medication, you don’t know if the child’s behavior was a result of the medication or some other factor. The more data points that we have, or more trials, the more information we get.”

He recommends keeping the child on short-acting doses for at least several days before trying a longer-acting formula.

Starting children on a low dose

Practice guidelines for psychiatrists recommend starting children on a low dose to assess any side effects the child might experience and gradually increasing it over 1-2 weeks with careful monitoring of response until you reach the minimum dose that will give the best symptom relief.

There is a great deal of variation in how children respond to these medications, so starting with an “average” effective dose, even adjusted by body weight, would be under-medicating some kids and overmedicating others.

For instance, for a 6- or 7-year-old child, a common starting dose of a short-acting medication might be about 2.5 mg, going up to 5 mg if more is needed for symptom relief and side effects are not an issue, Dr. Mitrani says. 

Liquid versions of either stimulant have an advantage when it comes to getting exactly the right dose, he notes: “You can do, 1 milliliter, 1.5, 1.6, depending on the syringe.”

Long-acting formulations that come in capsules can be especially frustrating, he adds — since they come in set doses and can’t be opened and divided effectively, because the beads inside are made to be triggered at different time periods.

Trying different formulations

Dr. Mitrani stresses that small differences in the formulation of a medication can make a difference in a child’s reaction.

For instance, Focalin (dexmethylphenidate) is a refined form of methylphenidate. Standard methylphenidate medications contain two mirror-image forms, or isomers, but most of the benefit comes from one of them. Focalin contains only this more active isomer. For some children, it works better, causes fewer side effects, or feels smoother.

He also notes that variations in the release patterns among long-acting formulations can affect a child’s experience. “Take Concerta, which has a unique mechanism for the extended release,” he explains. “There are three phases: a really immediate phase, then a regular Ritalin kind of phase and, then a slow extrusion of the remaining methylphenidate throughout the day that helps it last as long as 12 hours.”

By contrast, he describes Ritalin LA, which tends to last for 6-8 hours, as “50-50” — 50 percent of the dose is immediate released and the other half is delayed release. Other formulations are “40-60” or “30-70.” “These subtle differences can result in some kids responding better to one than the other, while other kids can do well on any of them.”

So even within the methylphenidate group, there may be reason to try a child on number of different formulations to get the best fit. And, of course, other reasons for trying different versions are limits on what insurance covers —which can change suddenly — and what’s available because of shortages. “And that can be really frustrating for families,” he says. “What I hear is, ‘My child was on Concerta or on Metadate CD and they made me switch to this one and now my kid’s not doing as well.’ “


When families cannot get a medication that has been working, finding another medication that’s available, that’s effective, and that insurance will approve can be a lot of hoops to jump through, he adds.

The post Treating ADHD With Methylphenidate (Ritalin, Concerta) appeared first on Child Mind Institute.

AI agents are not your “coworkers”

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Imagine coming in to work to learn that a new underling will report to you. The worker is not a person but an AI tool—one that your company nonetheless calls Alex, an “employee” with a title and defined responsibilities. How well do you think you would work with Alex?

If you’re anything like the managers recently studied by Emma Wiles, a Boston University business professor, treating Alex as a “coworker” and not a software tool would lead you to do a worse job. Wiles found that people caught 18% fewer errors when the work was said to have come from an agentic “AI employee” rather than a chatbot. It turns out that what’s in a name matters. A lot. 

This is an alarming glimpse of the future Silicon Valley is hurling us toward. Last year Nvidia’s CEO, Jensen Huang, talked about workplaces of “digital humans.” Since April, Microsoft, OpenAI, Anthropic, and Google have all released new tools oriented toward managing teams of AI agents, many of which are explicitly advertised as digital colleagues with the flexibility and cognitive power of actual humans. And nearly a third of the 1,261 managers who participated in Wiles’s study said their companies already frame AI agents as employees (23% even list them on org charts).

The technical progress of agentic AI is not all hot air, of course. Agents, which can effectively be thought of as AI tools programmed to work in a loop until they achieve a goal, have become measurably better at more complicated tasks. But it’s a huge leap to refer to these tools as coworkers or employees, and doing so will set unrealistic expectations for what AI can do while leaving the human employees supposedly responsible for them worse off.

That’s partially because, Wiles’s research suggests, it inverts our sense of who’s in charge. When an AI tool was framed as an employee, participants in the study saw themselves as less responsible for its output. They were also 44% more likely to escalate its questionable work to a manager for further review rather than trusting their own corrections (thus negating the time-saving purpose of using the AI agent in the first place). 

That matters far beyond office culture: As AI agents are embedded into health care, warfare, education, and government, there’s a growing risk they’ll become a convenient place to dump blame for failures that are instead the product of bad human decisions, incentives, and oversight (recall how the bomb strike on a girls’ school in Iran was popularly blamed on Claude, when all signs point to a cascade of human errors).

“AI agents right now are being marketed as things that can replace humans, and I think that’s just a losing proposition,” says Daron Acemoglu, an economist at MIT who won the Nobel Prize in 2024 and studies AI’s impact on the economy. “They should instead be optimized so that they can improve human capabilities, which is not what they have [been] at the moment.”

What could that look like? Consider a new effort at Stanford, where researchers presented 1,500 workers in 104 jobs with information about what tasks AI could potentially do in their work and then asked what would actually be most helpful and productive. Workers did want automation in certain areas: Law clerks thought AI could help ensure that adequate progress was being made across cases, for example. But often the tasks that tech experts deemed most suitable for AI—like verifying customer credit ratings for sales reps—were what the actual workers said they definitely did not want or need an agent to do. 

Which brings us back to Alex. Calling Alex an employee is easy—and convenient, especially when something goes wrong—but it’s a branding exercise. It doesn’t make the tool more fit for the job, and as Wiles’s research shows, it makes the humans around it worse at theirs. And recall that they are the ones with the agency that AI is trying to replicate. They deserve better than Alex. 

Agent confidence on the technical frontier

Enterprise investment in AI is booming. Gartner is calling 2026 an “inflection year” for organizations to align their AI projects with strategic business objectives. As the pressure to prove ROI mounts, executives and technology leaders are looking to agentic AI to drive the measurable financial outcomes their businesses seek.

A prime opportunity for AI agents exists in the tech function, where IT infrastructure costs are projected to grow two to three times by 2030, even as budgets remain unchanged, according to McKinsey. And in the last 18 months, tech teams—the engineers, developers, architects, and other practitioners who are building, deploying, and continually improving their organizations’ infrastructure and applications—are clearly putting agents to work.

The ultimate promise of agents is not only to automate tasks but to manage and coordinate entire workflows, pursuing business goals in a way that allows humans and agents to work together. Given the risks involved in automated decision-making, teams cannot delegate the work that agents do without confidence that they are fully capable of performing the task and that it will do so in a safe, reliable, and secure manner.

Among technology experts, our research shows that teams are exceedingly confident about using agentic AI across a significant amount of AI, data, and cloud tasks.

Where agent readiness drops is largely due to a lack of business context being supplied to agentic systems. The more complex the task, the more reasoning capability an agent requires and the greater its need for business context. Such context-generation capabilities for agents are still at an early stage of development, especially in situations where enterprise data is difficult to wrangle and connect into the agent lifecycle at the speed and quality in which developers and executives need it. Human oversight is a key factor of success in deploying agentic AI.

Knowing that tech teams are in a pivotal position to lead this transformation, the experts we interviewed expect agent confidence to accelerate as experience with agents deepens and business environments mature. “As we design agents to operate within the same operational boundaries, identity systems, and governance models that teams already use, they start to behave more like the systems organizations already trust,” says Jeremy Winter, corporate vice president and chief product officer at Microsoft Azure Platform.

This report, based on a survey of 300 global technology experts, ranks 101 tasks across AI, data, and cloud workflows based on respondents’ confidence in agents acting on their behalf. It also examines how technology teams view the opportunities and challenges related to agentic AI, along with the potential for the technology to enhance their careers.

Key findings from the report include:

Confidence in agents is surging for measurable tasks and growing in areas of complex judgment. Technology experts overwhelmingly believe agents help with everyday work including streamlining processes, improving performance, and reducing repetitive tasks. Confidence is highest for processes like generating reports and boilerplate code, and there is clear opportunity where tasks involve multistep workflows and advanced reasoning to make decisions.

Data workflows are the breakthrough domain. Tech teams trust agents most where structure can provide a reliable foundation for decisions. This includes areas such as data quality monitoring, visualization anomaly detection, real-time data stream monitoring, and data profiling. This is where domain experts closest to the point of data generation can provide context to allow agents to act and deliver trusted outcomes.

Download the full report.

Read the Microsoft Cloud blog by Amanda Silver, corporate vice president of Microsoft 365 Core and Work IQ, which underscores the importance of keeping humans in the loop and how systems thinking advances careers. And for a deeper dive into data workflows as a breakthrough use case for agents, check out the Fabric blog to hear from Kim Manis, corporate vice president of Product for Microsoft Fabric.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

The Download: metric weaknesses and AI elephant warnings

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

The inevitable weakness of metrics

There are plenty of useful things a metric can reveal. There are even more that it can obscure or corrupt.

Like a lot of people bitten by the self-quantifying bug, I started gathering personal data to pursue a nebulous collection of goals and desires. I wanted to feel better physically and emotionally, get outside more, and bring order to the messiness and uncertainty of my daily existence.

But external metrics and data can never capture what’s truly important. Worse, they inevitably redefine your core sense of what’s important, whether you’re aware of the trap or not.

Dive into the dangers of quantifying our lives with metrics.

—Bryan Gardiner

This story is from the next edition of our magazine, which is all about engineering. Subscribe now to get a copy when it lands!

Elephant alert! AI warning systems aim to avoid deadly clashes

India is home to about 60% of the world’s wild Asian elephants, and around 80% of their habitat lies outside protected areas. That brings them into close contact with people, and clashes can turn lethal: there have been some 3,000 human casualties in the last five years and over 1,000 elephant deaths since 2014.

In response, state forest departments, NGOs, and locals are designing, testing, and deploying a range of AI systems that cut response and warning times to minutes—or even seconds. They range from wildlife eyes in Maharashtra to infrared drones in Chhattisgarh.

Find out how they work in our interactive map.

—Kanika Gupta

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The US has allowed Anthropic to release Mythos 5 to “trusted” orgs
About 100 US companies and federal agencies now have access. (Semafor)
+ The White House said appropriate safeguards were now in place. (WSJ $)
+ The US had restricted both models over national security concerns. (BBC)
+ Which raised new questions about AI safety. (MIT Technology Review) 
 
2 A Chinese AI model has matched Mythos in finding security bugs
Security researchers say Zhipu AI is poised to reset the AI race. (WSJ $)
+ It’s sparked alarm that US restrictions are boosting China’s progress. (NYT $)
+ Although it still can’t match Anthropic or OpenAI on general tasks. (Verge)
+ In the AI race, China is eyeing a come-from-behind victory. (WP $)
 
3 Apple is seeking approval to buy chips from a blacklisted Chinese firm
It’s lobbying the White House for clearance to buy from ChangXin. (FT $)
+ ChangXin is on a Pentagon list of firms with Chinese military ties. (WP $)
+ Chipmakers are profiting off AI at the expense of everyone else. (WSJ $)
+ The US is banning imports of more Chinese technology. (Reuters $)
+ But Chinese tech companies feel optimistic. (MIT Technology Review)
 
4. South Korea plans to train its entire military as “drone warriors”
It wants to train all 500,000 personnel. (Reuters $)
+ And produce 110,000 drones by 2029. (Ars Technica)
 
5 Google has limited Meta’s use of its Gemini AI models
Meta wanted more compute than Google could provide. (FT $)
+ The cap has disrupted and delayed some Meta AI projects. (Bloomberg $)

6 Zuckerberg wants Meta to work with Polymarket and Kalshi
Meta wants its own prediction market, but without real-money bets. (NYT $)
+ The partnerships could hedge risks and accelerate development. (Reuters $)
 
7 Extreme heat is putting already hot data centers under pressure
Severe weather is now the leading cause of loss for data centers. (CNBC)
+ Heat waves also mess with your brain. (MIT Technology Review)

8 Android phones alerted millions moments before Venezuela’s earthquakes
They gave users between seconds and up to two minutes’ notice. (NYT $)

9 Scientists think Uranus and Neptune may not be the icy giants we imagined
They may have a magma ocean brewing on the inside. (Gizmodo)

10 Too much sleep may be as harmful as too little
A new study suggests 6.4–7.8 hours is the sweet spot. (Economist $)

Quote of the day

“This kind of powerful weapon that can alter the landscape of cyberwarfare can’t remain solely in American hands.” 

—360 Security CEO Zhou Hongyi tells a cybersecurity conference in Beijing why Chinese AI firms need to match the capabilities of their rivals in the US, The Wall Street Journal reports.

One More Thing

teenage girls on their phones

GETTY


Why Generation Z falls for online misinformation

Research shows that young people are more likely to believe and pass on misinformation if they feel a sense of common identity with the person who shared it in the first place. 

Offline, teenagers are likely to draw on the context that their communities provide. Social media, however, promotes credibility based on identity rather than community. And when trust is built on identity, authority shifts to influencers.

As young people participate in more political discussions online, those who have successfully cultivated identity-based credibility could become de facto community leaders, attracting like-minded people and steering the conversation. While that has the potential to empower marginalized groups, it also exacerbates the threat of misinformation.

Find out what we can all learn about how young people evaluate truth online.

—Jennifer Neda John

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ The Euclid space telescope has captured the most detailed image yet of the Milky Way.
+ Here’s a lovely, lilting medieval bardcore cover of Daft Punk’s electronic classic Veridis Quo.
+ A toilet plunger becomes an unlikely engineering breakthrough in this quest to build a better blowgun.

Association of MAP2 gene polymorphisms and altered expression with schizophrenia risk in a Chinese Han population

BackgroundSchizophrenia (SCZ) is a highly heritable primary psychotic disorder. The microtubule-associated protein 2 (MAP2) gene is essential for dendritic integrity and synaptic plasticity, positioning it as a key candidate for bridging genetic risk and neuropathology. Nevertheless, the role of common genetic variations within MAP2 in SCZ susceptibility remains to be elucidated.MethodsWe conducted a candidate gene association study of MAP2 in a Han Chinese cohort comprising 418 SCZ patients and 418 matched healthy controls. Targeted sequencing was used to genotype single nucleotide polymorphisms (SNPs). MAP2 mRNA levels were quantified by RT-qPCR and correlated with genotypes and clinical symptoms. Bioinformatic tools (such as GTEx, BrainSeq, 3DSNP, HaploReg, RegulomeDB and SNP2TFBS database) were employed for functional annotation of risk loci.ResultsWe identified multiple MAP2 SNPs associated with SCZ risk in a Han Chinese cohort. Specifically, the AA genotype of rs288057 and the GG genotype of rs288087 were significantly associated with increased disease risk (OR = 2.393 and 2.258, respectively). Expression analysis revealed a marked reduction in peripheral MAP2 mRNA levels in patients compared to controls. This downregulation was genotype-dependent: the risk AA at rs288057 and GG at rs288087 were correlated with lower mRNA levels, a finding supported by its significant eQTL effect in the GTEx and BrainSeq database. In silico annotation suggested rs288087 resides within a putative enhancer region, while rs288057 may affect a promoter-proximal regulatory site. Clinically, MAP2 expression showed a significant positive correlation with the severity of negative symptoms (SANS score). Furthermore, ROC analysis indicated that MAP2 expression levels distinguished patients from controls with an AUC of 0.728.ConclusionThis study identifies MAP2 as a schizophrenia risk gene, wherein non-coding variants likely reduce its expression via distinct regulatory mechanisms, linking this downregulation to core negative symptoms. These findings highlight MAP2’s pathophysiological and translational relevance.

Association between objective sleep structure and suicidal ideation in patients with depression: a study based on polysomnographic regression and cluster analysis

BackgroundDepression is a common mental disorder, and current suicidal ideation (SI) is a clinically important symptom. Sleep disturbance is common in depression, but the relationship between objective sleep architecture and current SI remains unclear. This study examined associations between objective sleep structure and current SI and explored whether sleep-clinical clustering could support exploratory SI stratification.Methods287 patients meeting DSM-5 criteria for depressive disorder underwent clinical assessment and overnight polysomnography (PSG). Current SI was assessed using item 3 of the HAMD-17. Binary logistic regression evaluated factors associated with current SI. K-Means clustering (K = 3) was performed using standardized HAMD score, PSQI score, AHI, total awakenings, N3%, R%, and sleep efficiency. Quantitative cluster validation, sensitivity analyses excluding HAMD from clustering, and ROC-based performance metrics were additionally conducted.ResultsCurrent SI was present in 50.52% (145/287) of patients. In the individual-variable regression model, only HAMD total score was independently associated with current SI (OR = 1.683, p < 0.001). Cluster analysis identified three subgroups with distinct current SI rates: Cluster 1 (deep-sleep dominant, 37.04%), Cluster 2 (high-arousal-apnea, 70.37%), and Cluster 3 (low-arousal-subjective insomnia, 60.80%). The K = 3 solution showed WSS = 1450.71, mean silhouette = 0.204, and Davies-Bouldin index = 1.679. In the demographic-adjusted model, membership in Clusters 2 + 3 was associated with current SI (OR = 3.152, 95% CI: 1.883-5.274, p < 0.001; AUC = 0.700, 95% CI: 0.639-0.760). When HAMD was excluded from clustering, the association remained (OR = 2.669, 95% CI: 1.598-4.458, p < 0.001), whereas additional adjustment for HAMD total score attenuated the primary cluster association (OR = 0.842, 95% CI: 0.411-1.725, p = 0.639).ConclusionsDepression severity was the primary factor associated with current SI. PSG-derived sleep-clinical clusters may help characterize heterogeneous presentations of current SI, but their incremental value beyond depressive severity should be interpreted as exploratory and validated in larger longitudinal samples.

Subjective sleepiness and objective sleep propensity in adults with attention-deficit/hyperactivity disorder referred for multiple sleep latency testing

IntroductionAdults with attention-deficit/hyperactivity disorder (ADHD) often report excessive daytime sleepiness, but the relationship between subjective sleepiness and objective sleep propensity remains unclear. We examined this relationship in adults referred for Multiple Sleep Latency Test (MSLT) evaluation, using a clinical comparison group with excessive daytime sleepiness (EDS) but without ADHD.MethodsIn this retrospective cross-sectional study, we analyzed medical records of 130 adults aged 18 years or older who underwent MSLT between January and December 2021, including 68 adults in the ADHD group and 62 in the EDS-only group. Subjective sleepiness was assessed by the Epworth Sleepiness Scale (ESS) and objective sleep propensity by mean MSLT sleep latency, with MSLT positivity defined as mean sleep latency ≤ 480 s. Associations between ESS scores and mean sleep latency were assessed within each group, and correlation coefficients were compared between groups using Fisher’s r-to-z transformation.ResultsESS scores did not differ significantly between groups, with median scores of 14.0 in the ADHD group and 13.0 in the EDS-only group. In contrast, objective sleep propensity differed significantly: median mean sleep latency was longer in the ADHD group than in the EDS-only group (432.0 s vs 322.0 s, p = 0.008), and MSLT positivity was less frequent in the ADHD group (61.8% vs 87.1%, p = 0.001). Within the ADHD group, ESS scores were not significantly correlated with mean sleep latency, including among MSLT-positive cases. A significant inverse correlation was observed in the MSLT-positive EDS-only subgroup, although formal comparison of correlation coefficients did not demonstrate a statistically significant between-group difference in the ESS–MSLT relationship. SOREMP frequencies were numerically higher in the EDS-only group but did not differ significantly between groups.DiscussionThese findings suggest that subjective sleepiness complaints and objective sleep propensity may not closely align in adults with ADHD referred for sleep evaluation, and support the need for integrated psychiatric and sleep-medicine assessment when such patients present with excessive daytime sleepiness.

Associations of TNF-α, MIF, and cortisol with cognitive function in patients with bipolar disorder during acute manic episodes: a short-term follow-up study

BackgroundBipolar disorder (BD) is frequently accompanied by cognitive impairment, and growing evidence suggests that immune-inflammatory activation and hypothalamic-pituitary-adrenal axis dysregulation may contribute to its pathophysiology. This study aimed to examine the associations of tumor necrosis factor-α (TNF-α), macrophage migration inhibitory factor (MIF), and cortisol (COR) with cognitive function in patients with BD during manic episodes and to characterize their short-term changes.MethodsIn this short-term follow-up study, 53 patients with BD during manic episodes and 53 healthy controls (HCs) were enrolled. Plasma TNF-α, MIF, and COR levels were measured using enzyme-linked immunosorbent assay. Cognitive function was assessed using the Chinese Brief Cognitive Test, including information processing speed (IPS), executive function (EF), sustained attention (SAT), and working memory (WM). Patients were evaluated at baseline and after 8 weeks of treatment, whereas HCs were assessed once at baseline. Group comparisons and biomarker–cognition correlation analyses were performed. Multiple testing in the correlation analyses was controlled using the Benjamini–Hochberg false discovery rate (FDR) procedure.ResultsAt both baseline and follow-up, patients with BD had significantly lower IPS, EF, SAT, and WM scores, but significantly higher plasma TNF-α, MIF, and COR levels, than HCs. After 8 weeks of treatment, cognitive scores in the BD group improved significantly, whereas reductions in TNF-α, MIF, and COR did not reach statistical significance. In exploratory unadjusted Pearson analyses, several biomarker–cognition associations survived FDR correction. However, in the primary adjusted partial correlation analyses, only the negative association between TNF-α and WM remained significant after adjustment for covariates and FDR correction at both baseline and follow-up.ConclusionPatients with BD during manic episodes exhibited widespread cognitive impairment accompanied by elevated inflammatory and neuroendocrine markers. TNF-α showed the most robust association with working memory after adjustment for covariates and correction for multiple comparisons. Associations involving MIF or cortisol and executive function should be interpreted as exploratory and require validation in larger longitudinal studies.