Cortical high-threshold and low-activation characteristics in adolescent depression: a cross-age differential analysis

BackgroundAdolescent depression exhibits distinct neurophysiological features, with marked age heterogeneity particularly in the resting motor threshold (RMT) measured during repetitive transcranial magnetic stimulation (rTMS), and substantial variability in clinical therapeutic efficacy. Functional near-infrared spectroscopy (fNIRS) enables the assessment of cortical excitability levels; however, research investigating the association between RMT and cortical activation in adolescent patients with depression remains limited. This study aims to elucidate the underlying neural mechanisms from the perspective of cortical hemodynamics, which is crucial for further optimizing neuromodulation strategies in patients with depression.MethodsWe collected data from 85 treatment-naive patients with depression who underwent rTMS therapy. All patients completed RMT measurement, fNIRS examination, and Hamilton Depression Rating Scale (HAMD) assessment prior to rTMS treatment. Participants were divided into three groups according to age: the adolescents group (n=31), the young adult group (n=26), and the middle-aged group (n=28). We compared the differences in RMT among the three groups and explored the relationships between RMT, cortical activation (reflected by prefrontal oxyhemoglobin level changes during the verbal fluency task via fNIRS), and depression severity (assessed by HAMD scores).ResultsThe results demonstrated that the adolescents group had a significantly higher RMT than the other age groups (58.00 ± 11.14, P < 0.001), accompanied by the lowest prefrontal Oxy-Hb activation level (0.095 ± 0.06, P < 0.001). A strong negative correlation was observed between RMT and cortical activation (Spearman’s rs= -0.929, P < 0.001), while a strong positive correlation was found between RMT and depression severity (Spearman’s rs = 0.837, P < 0.001). The distinct coupling phenomenon of high threshold-low activation-severe symptoms was most prominently manifested in this age group, which may theoretically reflect an underlying dysregulation in broader emotional networks, though the current direct findings strictly indicate localized alterations in prefrontal activation and motor cortical excitability.ConclusionsThe characteristics of high RMT and low cortical activation in adolescent depression serve as important neurobiological markers for depression severity. This finding provides a novel direction for developing individualized, developmentally tailored neuromodulation strategies (e.g., optimization of rTMS targets and dosages), indicating that interventions for adolescent depression should prioritize promoting the healthy integration of emotional circuits and the functional coordination between the cortex and subcortex.

The shock of seeing your body used in deepfake porn 

When Jennifer got a job doing research for a nonprofit in 2023, she ran her new professional headshot through a facial recognition program. She wanted to see if the tech would pull up the porn videos she’d made more than 10 years before, when she was in her early 20s. It did in fact return some of that content, and also something alarming that she’d never seen before: one of her old videos, but with someone else’s face on her body.

“At first, I thought it was just a different person,” says Jennifer, who is being identified by a pseudonym to protect her privacy. 

But then she recognized a distinctly garish background from a video she’d shot around 2013, and she realized: “Somebody used me in a deepfake.”

Eerily, the facial recognition tech had identified her because the image still contained some of Jennifer’s features—her cheekbones, her brow, the shape of her chin. “It’s like I’m wearing somebody else’s face like a mask,” she says. 

“It’s like I’m wearing somebody else’s face like a mask.”

Conversations about sexualized deepfakes—which fall under the umbrella of nonconsensual intimate imagery, or NCII—most often center on the people whose faces are featured doing something they didn’t really do or on bodies that aren’t really theirs. These are often popular celebrities, though over the past few years more people (mostly women and sometimes youths) have been targeted, sparking alarm, fear, and even legislation. But these discussions and societal responses usually are not concerned with the bodies the faces are attached to in these images and videos.

As Jennifer, now 37 and a psychotherapist working in New York City, says: “There’s never any discussion about Whose body is this?” 

For years, the answer has generally been adult content creators. Deepfakes in fact earned their name back in November 2017, when someone with the Reddit username “deepfakes” uploaded videos showing faces of stars like Scarlett Johansson and Gal Gadot pasted onto porn actors’ bodies. The nonconsensual use of their bodies “happens all the time” in deepfakes, says Corey Silverstein, an attorney specializing in the adult industry. 

But more recently, as generative AI has improved, and as “nudify” apps have begun to proliferate, the issue has grown far more complicated—and, arguably, more dangerous for creators’ futures. 

Porn actors’ bodies aren’t necessarily being taken directly from sexual images and videos anymore, or at least not in an identifiable way. Instead, they are inevitably being used as training data to inform how new AI-generated bodies look, move, and perform. This threatens the livelihood and rights of porn actors as their work is used to train AI nudes that in turn could take away their business. And that’s not all: Advancements in AI have also made it possible for people to wholly re-create these performers’ likenesses without their consent, and the AI copycats may do things the performers wouldn’t do in real life. This could mean their digital doubles are participating in certain sex acts that they haven’t agreed to do, or even that they’re perpetrating scams against fans. 

Adult content creators are already marginalized by a society that largely fails to protect their safety and rights, and these developments put them in an even more vulnerable position. After Jennifer found the deepfake featuring her body, she posted on social media about the psychological effects: “I’ve never seen anyone ask whether that might be traumatic for the person whose body was used without consent too. IT IS!” Several other creators I spoke with shared the mental toll that comes with knowing their bodies have been used nonconsensually, as well as the fear that they’ll suffer financially as other people pirate their work. Silverstein says he hears from adult actors every day who “are concerned that their content is being exploited via AI, and they’re trying to figure out how to protect it.” 

One law professor and expert in violence against women calls these creators the “forgotten victims” of NCII deepfakes. And several of the people I spoke with worry that as the US develops a legal framework to combat nonconsensual sexual content online, adult actors are only at risk of further injury; instead of helping them, the crackdown on deepfakes may provide a loophole through which their content and careers could be stripped from the internet altogether.

How deepfakes cause “embodied harms”

During his preteen years in the 1970s, Spike Irons, now a porn actor and president of the adult content platform XChatFans, was “in love” with Farrah Fawcett. Though Fawcett did not pose nude, Jones managed to get his hands on what looked like pictures of her naked. “People were cutting out faces and pasting them on bodies,” Irons says. “Deepfakes, before AI, had been going around for quite a while. They just weren’t as prolific.”

The early public internet was rife with websites capitalizing on the idea that you could use technology to “see” celebrities naked. “People would just use Microsoft Paint,” says Silverstein, the attorney. It was a simple way to mash up celebrities’ faces with porn. 

People later used software like Adobe After Effects or FakeApp, which was designed to swap two individuals’ faces in images or videos. None of these programs required serious expertise to alter content, so there was a low barrier to entry. That, plus the wealth of porn performers’ videos online, helped make face-swap deepfakes that used real bodies prevalent by the 2010s. When, later in the decade, deepfakes of Gal Gadot and Emma Watson caused something of a broader panic, their faces were allegedly swapped onto the bodies of the porn actors Pepper XO and Mary Moody, respectively.

But it wasn’t just high-profile actors like them whose bodies were being used. Jennifer was “a very minor performer,” she says. “If it happened to me, I feel like it could happen to anybody who’s shot porn.” Since he started his practice in 2006, Silverstein says, “numerous clients” have reached out to report “This is my body on so-and-so.” 

Both people whose faces appear in NCII deepfakes and those whose bodies are used this way can feel serious distress. Experts call this type of damage “embodied harms,” says Anne Craanen, who researches gender-based violence at the UK’s Institute for Strategic Dialogue, an organization that analyzes extremist content, disinformation, and online threats. 

The term reflects the fact that even though the content exists in the virtual realm, it can cause physiological effects, including body dysmorphia. The face-swapped entity occupies the uncanny valley, distorting self-perception. After discovering their faces in sexual deepfakes, many people feel silenced, experts told me; they may “self-censor,” as Craanen puts it, and step back from public-facing life. Allison Mahoney, an attorney who works with abuse survivors, says that people whose faces appear in NCII can experience depression, anxiety, and suicidal ideation: “I’ve had multiple clients tell me that they don’t sleep at night, that they’re losing their hair.” 

Independent creators aren’t just “having sex on camera.” For someone to rip off their work “for their own entertainment or financial gain fucking sucks.”

Though the impact on people whose bodies are used hasn’t been discussed or studied as often, Jennifer says that “it’s just a really terrible feeling, knowing that you are part of somebody else’s abuse.” She sees it as akin to “a new form of sexual violence.”

The uncertainty that comes with not being aware of what your body is doing online can be highly unsettling. Like Jennifer, many adult actors don’t really know what’s out there. But some devoted followers know the actors’ bodies well—often recognizing tattoos, scars, or birthmarks—and “very quickly they bring [deepfakes] to the adult performer’s attention,” says Silverstein. Or performers will stumble upon the content by chance; some 20 years ago, for instance, the first such client to tell Silverstein her body was being used in a deepfake happened to be searching Nicole Kidman online when she found that one of the results showed Kidman’s face on her porn. “She was devastated, obviously, because they took her body,” he says, “and they were monetizing it.” 

Otherwise, this imagery may be found by an organization like Takedown Piracy, one of several copyright enforcement companies serving adult content creators. US copyright violations can be challenging to prove if someone’s body lacks distinguishing features, says Reba Rocket, Takedown Piracy’s chief operating and marketing officer. But Rocket says her team has added digital fingerprinting technology to clients’ material to help flag and remove problematic videos, often finding them before clients realize they’re online. 

By capturing “tens of thousands of tiny little visual data points” from videos, digital fingerprinting creates unique corresponding files that can be used to identify them, Rocket says—kind of like an invisible watermark. The prints remain even if pirates alter the videos or replace performers’ faces. Takedown Piracy has digitally fingerprinted more than half a billion videos and the organization has gotten 130 million copyrighted videos taken down from Google alone (though, of those videos, Rocket hasn’t tracked how many of these specifically include someone else’s face on a performer’s body). 

Besides copyright, a range of legal tools can be used to try and combat NCII, says Eric Goldman, a law professor at Santa Clara University. For example, victims can claim invasion of privacy. But using these tools isn’t particularly straightforward, and they may not even apply when it comes to someone’s body. If there aren’t, for instance, unique markers indicating that a body in a deepfake belongs to the person who says it does, US law “doesn’t really treat [this content] as invasion of privacy,” Goldman says, “because we don’t know who to attribute it to.”

In a 2018 study that reviewed “judicial resolution” of cases involving NCII, Goldman found that one successful way plaintiffs were able to win cases was to assert “intentional affliction of emotional distress.” But again, that hinges on the ability to clearly identify the person in the content. Relevant statutes, he adds, might also require “intent to harm the individual,” which may be hard to show for people whose bodies alone are featured.

“AI girls will do whatever you want”

In the last few years, Silverstein says, it’s become less and less common to see the bodies of real adult content creators in deepfakes, at least in a way that makes them clearly identifiable. 

Sometimes the bodies have been manipulated using AI or simpler editing tools. This can be as basic as erasing a birthmark or changing the size of a body part—minor edits that make it impossible to identify someone’s image beyond a reasonable doubt, so even porn actors who can tell that an altered image used their body as a base won’t get very far in the legal realm. “A lot of people are like, That looks like my body,” says Silverstein, but when he asks them how, they’ll reply, It just does. 

At the same time, other users are now creating NCII with wholly AI-generated bodies. In “nudify” apps, anyone with a minimal grasp of technology can upload a photo of someone’s clothed body and have it replaced with a fake naked one. “So [much] of this content being created is just someone’s face on an AI body,” Silverstein says.

Such apps have drawn a ton of attention recently, from Grok “nudifying” minors to Meta running ads for—and then suing—the nudify app Crushmate. But there’s been relatively little attention paid to the content being used to train them. They almost certainly draw on the more than 10,000 terabytes of online porn, and performers have virtually zero recourse. 

One reason is that creators aren’t able to demonstrate with any certainty that their content is being used to train AI models like those used by nudify apps. “These things are all a black box,” says Hany Farid, a professor at the University of California, Berkeley, who specializes in digital forensics. But “given the ubiquity” of adult content, he adds, it’s a “reasonable assumption” that online porn is being used in AI training. 

“It’s just not at all difficult to come up with pornographic data sets on the internet,” says Stephen Casper, a computer science PhD student at MIT who researches deepfakes. What’s more, he says, plenty of shadowy online communities provide “user guides” on how to use this data to train AI, and in particular programs that generate nudes. 

It’s not certain whether this activity falls within the US legal definition of “fair use”—an issue that’s currently being litigated in several lawsuits from other types of content creators—but Casper argues that even if it does, it’s ethically murky for porn created by consenting adults 10 years ago to wind up in those training data sets. When people “have their stuff used in a way that doesn’t respect or reflect reasonable expectations that they had at that time about what they were creating and how it would be used,” he says, there’s “a legitimate sense in which it’s kind of … nonconsensual.” 

Adult performers who started working years ago couldn’t possibly have consented to AI anything; Jennifer calls AI-related risks “retroactively placed.” Contracts that porn actors signed before AI, adds Silverstein, might provide that “the publisher could do anything with the content using technology that now exists or here and after will be discovered.” That felt more innocuous when producers were talking about the shift from VHS to DVD, because that didn’t change the content itself, just the way it was conveyed. It’s a far different prospect for someone to use your content to train a program to create new content … content that could replace your work altogether. 

Of course, this all affects creators’ bottom line—not unlike the way Google’s AI overviews affect revenue for online publishers who’ve stopped getting clicks when people are content with just reading AI-generated summaries. Performers’ “concern is … it’s another way to pirate [their] content,” says Rocket. 

After all, independent creators aren’t just “having sex on camera,” as the adult content creator Allie Eve Knox says. They’re paying for filming equipment and location rentals, and then spending hours editing and marketing. For someone to then rip off and distort that content “for their own entertainment or financial gain,” she says, “fucking sucks.” 

KIM HOECKELE

Tanya Tate, a longtime adult content creator, tells me about another highly unsettling AI-created situation: She was recently chatting with a fan on Mynx, a sexting app, when he asked her if she knew him. She told him no, and “his eyes just started watering,” Tate says. He was upset because he thought she did know him. Turns out he’d sent $20,000 to a scammer who’d used an AI-generated deepfake of Tate to seduce him. 

Several men, Tate subsequently learned, had been scammed by an AI version of her, and some of them began blaming her for their losses and posting false statements about her online. When she reported one particularly aggressive harasser to the police, they told her he was exercising his “freedom of speech,” she says. Rocket, too, is familiar with situations where AI is used to take advantage of fans. “The actual content creator will get nasty emails from these people who’ve been scammed,” she says.

Other porn actors say they fear that their likenesses have been used without consent to do other things they wouldn’t do. One, Octavia Red, tells me she doesn’t do anal scenes, “but I’m sure there’s tons of deepfake anal videos of me that I didn’t consent to.” That could cost her, she fears, if viewers choose to watch those videos instead of subscribing to her websites. And it could cause fans to develop false expectations about what kind of porn she’ll create.

“I saw one AI creator saying, ‘Well, AI girls will do whatever you want. They don’t say no,’” says Rocket. “That horrifies me … especially if they’re training those AI models on real people. I don’t think they understand the damage to mental health or reputation that that can create. And once it’s on the internet, it’s there forever.” 

Efforts to “scrub adult content from the internet”

As AI technology improves, it’s increasingly difficult for people to discern any type of real video from the best AI-generated ones on their own. In one 2025 study, UC Berkeley’s Farid found that participants correctly identified AI-generated voices about 60% of the time (not much better than random chance), while advances like false heartbeats make AI-generated humans tougher than ever to spot.

Nevertheless, most lawyers and legal experts I spoke with said copyright laws are still adult performers’ best bet in the US legal system, at least for getting their face-swapped content taken down. For his clients, Silverstein says, he tries to figure out the content’s origins and then issue takedown requests under the Digital Millennium Copyright Act, a 1998 law that adapted copyright law for the internet era. “Even recently, I had a performer who has an insanely well-known tattoo,” he says, and with a DMCA subpoena he managed to identify the poster of the content, who voluntarily removed it. 

But this way of working is becoming increasingly rare.

These days it’s nearly “impossible,” Silverstein says, to determine who produced a deepfake, because many platforms that host pirated content operate facelessly. They’re also often based in places that “don’t really care about US law when it comes to copyrights,” says Rocket—places like Russia, the Seychelles, and the Netherlands. 

While governments in the EU, the UK, and Australia have said they will ban or restrict access to nudify apps, it’s not an easily executed proposition. As Craanen notes, when app stores remove these services, they often simply reappear under different names, providing the same services. And social platforms where people share NCII deepfakes, argues Rocket, are slacking in getting them removed. “It’s endless, and it’s ridiculous, because places like Twitter and Facebook have the same technology we do,” Rocket says. “They can identify something as an infringement instantly, but they choose not to.”

(Apple spokesperson Adam Dema emailed, “’nudification’ apps are against our guidelines” in the app store, and it has “proactively rejected many of these apps and removed many others,” flagging a reporting portal for users. A Google spokesperson emailed, “Google Play does not allow apps that contain sexual content,” noting it takes “proactive steps to detect and remove apps with harmful content” and has suspended hundreds of apps for violating its policy. Meta spokesperson shared a blog post about actions it’s taken against nudify apps, but did not respond to follow-up questions about copyrighted material. X did not respond to a request for comment.)

As porn performers are forced to navigate AI-related threats, the only current federal law to address deepfakes may not help them much—and could even make matters worse. The Take It Down Act, which became US law last year, criminalizes publishing NCII and requires websites to remove it within 48 hours. But, as Farid notes, people could weaponize the measure by reporting porn that was made legally and with consent and claiming that it’s NCII. This could result in the content’s removal, which would hurt the performers who made it. Santa Clara’s Goldman points to Project 2025, the Heritage Foundation’s policy blueprint for the second Trump administration, which aims to wipe porn from the web. The Take It Down Act, he argues, “allows for the coordinated effort to scrub adult content from the internet.” 

US lawmakers have a history of hurting sex workers in their attempts to regulate explicit content online. State-level age verification laws are an example; visitors can pretty easily get around these measures, but they can still result in reduced revenue for adult performers (because of lower traffic to those sites and the high price of age-checking services they have to purchase). 

“They’re always doing something to fuck with the porn industry, but not in a way that actually helps sex workers,” says Jennifer. “If they do something, they’re taking away your income again—as opposed to something like giving you more rights to your image, [which] would be tremendously helpful.” 

But as generative AI plays an increasingly large role in NCII deepfakes, the types of images to which adult performers have rights moves deeper into a gray area. Can actors lay claim to AI images likely trained on their bodies? How about AI-generated videos that impersonate them, like the one that tricked Tanya Tate’s fan?

The biggest challenge will be creating “legitimate, effective laws that will absolutely protect content creators from abusing their likeness to train and create AI,” Rocket says. “Absent that, we’re just going to have to keep pulling content down from the internet that’s fake.”

In the meantime, a few porn actors tell me, they’re trying to take advantage of copyright laws that weren’t really made for them; they’ve signed with platforms that host their AI-generated duplicates, with whom fans pay to chat, in part so they’ll have contracts that protect ownership of their AI likenesses. When I spoke with the actor Kiki Daire in September 2025 for a story on adult creators’ “AI twins,” she said she “own[ed] her AI” because she’d signed a contract with Spicey AI, a site that hosted AI duplicates of adult performers. If another company or person created her AI-generated likeness, she added, “I have a leg to stand on, as far as being able to shut that down.”  

Even this, though, is not a sure thing; Spicey AI, for instance, shut down several months after I spoke with Daire, so it’s unlikely that her contract would hold. And when I spoke in October with Rachael Cavalli, another adult actor who had signed with an AI duplicate site in hopes it’d help protect her AI image, she admitted, “I don’t have time to sit around and look for companies that have used my image or turned something into a video that I didn’t actually do … it’s a lot of work.” In other words, having rights to your AI image on paper doesn’t make it easier to track down all the potentially infinite breaches of those rights online.

If she’d known what she knows about technology today, Jennifer says she doesn’t think she would have done porn. The risks have increased too much, and too unpredictably. She now does in-person sex work; it’s “not necessarily safer,” she says, “but it’s a different risk profile that I feel more equipped to manage.” 

Plus, she figures AI is unlikely to replace in-person sex workers the way it could porn actors: “I don’t think there’s going to be stripper robots.” 

Jessica Klein is a Philadelphia-based freelance journalist covering intimate partner violence, cryptocurrency, and other topics.

Targeted Ultrasound Could Offer Alternative to Chronic Pain Medication

A new study has shown that targeting ultrasound stimulation to brain regions involved in processing pain can induce long-lasting changes in brain activity, significantly reducing pain perception. Published in Nature Communications, these findings point at a novel non-invasive strategy to treat chronic pain. 

“Our study represents an important first step in understanding how this technology can non-invasively stimulate deep brain regions involved in pain processing,” said Sam Hughes, PhD, senior lecturer in pain neuroscience at the University of Exeter. “We found that targeting a specific brain region involved in pain processing can alter how pain is perceived and change how this area communicates with other parts of the brain’s pain network. The next stage of our research will be to test whether this approach can help people living with chronic pain.”

Hughes and colleagues used transcranial ultrasound stimulation (TUS), a low-intensity neuromodulation technique, to target the dorsal anterior cingulate cortex (dACC), a brain region implicated in chronic pain. The study recruited a total of 32 healthy volunteers, who were treated either with TUS or a sham while putting their right hand in a cold gel to trigger pain due to the low temperature. All participants were asked to rate the severity of the pain they were feeling and underwent MRI and MRS scans to monitor the physiological changes caused by the treatment. 

Results showed that, while TUS had no immediate effect on pain intensity, participants reported a significant reduction in pain from 28 to 55 minutes after the stimulation, suggesting it can trigger a delayed analgesic effect. At the physiological level, TUS was found to disrupt the relationship between temperature and pain intensity, increasing the connectivity between the dACC and other brain regions involved in pain modulation and changing the concentration of the GABA neurotransmitter within the dCC. 

“The study aimed to characterize how transcranial ultrasound stimulation interacts with—and potentially also alters—the brain’s processing of pain,” said Sophie Clarke, PhD, postdoctoral research fellow at the University of Plymouth and lead author of the study. “Understanding these mechanisms will be very important to support the next steps in understanding whether the stimulation can be effective in helping patients with chronic pain.”

Previous research at the University of Plymouth had shown the potential benefits of TUS for psychiatric conditions including anxiety, depression, and addiction. This study shows these benefits could extend beyond neurological disorders and one day offer a non-invasive treatment option for those experiencing chronic pain due to conditions such as fibromyalgia, back pain, and arthritis, or recovering after cancer treatment.  

“Having shown the use of ultrasound can yield positive results for people with a variety of neurological conditions, we wanted to explore what it could mean for those living with chronic pain,” said Elsa Fouragnan, PhD, director of the University of Plymouth’s Brain Research and Imaging Centre (BRIC) and Centre for Therapeutic Ultrasound (CENTUS). “Most of us know someone experiencing chronic pain, and there are very few treatments that deliver any form of long-term benefit. The findings of this new work are really promising, and we are already building on it to assess whether TUS could be a beneficial and non-invasive therapeutic treatment.”

The post Targeted Ultrasound Could Offer Alternative to Chronic Pain Medication appeared first on Inside Precision Medicine.

Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review

Background: Major depressive disorder (MDD) affects approximately 1 in 6 adults during their lifetime, yet antidepressant selection relies predominantly on trial-and-error, with response rates of only 42% to 53%. While machine learning (ML) models have shown promise in predicting treatment outcomes, most focus on single treatments rather than comparative selection across therapeutic alternatives, limiting their clinical utility for the medication choice decisions that clinicians face in practice. Objective: This systematic review evaluates ML approaches that examine 2 or more pharmacological interventions for predicting treatment outcomes in MDD, with a focus on their capacity to facilitate comparative treatment selection between medications or medication classes for individual patients. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched PubMed, Scopus, and Web of Science for studies published from 2015 to 2025. We included studies involving adults with MDD that used ML models to predict treatment outcomes across 2 or more pharmacological treatments and reported medication-specific prediction outcomes. Risk of bias was assessed using PROBAST-AI (Prediction Model Risk of Bias Assessment Tool for Artificial Intelligence). We conducted a narrative synthesis organized by modeling strategies, data integration approaches, validation methodologies, and performance patterns. Results: From 5370 initial records, 19 studies met the inclusion criteria, with dataset sample sizes ranging from 49 to 77,226 participants. Studies employed 3 distinct modeling strategies: drug-specific supervised models trained independently for each medication, subtype- or trajectory-based approaches using clustering methods to identify differential response patterns, and a unified differential prediction framework generating calibrated cross-treatment predictions. Performance varied substantially, with area under the curve values ranging from 0.59 to 0.95 and classification accuracies between 62% and 95.4%, though high performance was concentrated in studies with small samples, high-dimensional neurobiological features, and internal-only validation. Only 7 studies conducted external validation, which generally yielded more conservative performance estimates. Feature informativeness was more consistently associated with performance variation than algorithm complexity. Most studies did not formally distinguish between prognostic features predicting general outcomes and predictive features identifying differential medication responses, and none applied formal explainability techniques. Conclusions: ML for comparative antidepressant selection remains in an early stage of development. Only 1 study implemented a unified framework directly supporting patient-level treatment ranking. Key barriers to clinical translation include insufficient distinction between prognostic and predictive markers, limited cross-trial validation, near-absent calibration reporting, and absent explainability. Future research should prioritize unified comparative frameworks with calibrated predictions, rigorous external validation on diverse cohorts, explicit modeling of heterogeneous treatment effects, and integration of explainability into model development.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/a59ed16526360bac492b29bed9672c02" />

iCARE Self-Guided Digital Intervention for Postpartum Depression in Danish Mothers: Formative Research Using User-Centered Design

<strong>Background:</strong> Postpartum depression (PPD) is a major public health concern. Despite advancements in treatment, many barriers to accessing care remain. There has been a growing interest in digital interventions for the prevention and treatment of PPD. However, for mothers with mild and moderate symptoms of depression, there is a limited offer of self-guided internet-based interventions developed with user input and with considerations on how to integrate the intervention into stepped care models for PPD. <strong>Objective:</strong> The aim of this study was (1) to describe the process of the design and development of iCARE, a self-guided digital psychological intervention for mothers with mild and moderate symptoms of PPD in Denmark, (2) present the program’s theory illustrated by a logic model, and (3) explore its initial usability and prospective acceptability. <strong>Methods:</strong> Applying user-centered design methods, the intervention development followed six steps: (1) a literature review to identify evidence‑based therapeutic components of self‑guided interventions for PPD, (2) interviews with women with lived experience of PPD and group discussions with mental health experts and home‑visiting providers to identify user needs, (3) iterative design and content development with stakeholder feedback in collaboration with the Department of Digital Psychiatry, (4) prototype testing using think‑aloud usability sessions and interviews with 5 mothers, (5) a group cognitive walkthrough with mental health experts, and (6) final refinement and implementation of the iCARE program with developers and designers. <strong>Results:</strong> Initial interviews with mothers and maternal health care providers emphasized the importance of a digital intervention offering timely psychoeducation, coping strategies, and pathways to in-person care while addressing the diversity of expressions of PPD symptoms. Stakeholders recommended a flexible program, multimodal content, and integration into maternal care systems with community health nurses supporting engagement and participation. The prototype was designed to be user-centered, engaging, and with multiple interactive features. It included components on psychoeducation, cognitive exercises grounded in cognitive behavioral therapy, acceptance and commitment principles, and mood-monitoring. The prototype was designed to be user-centered and engaging, with interactive features and components on psychoeducation, cognitive exercises grounded in cognitive behavioral and acceptance and commitment principles, and mood-monitoring. Prototype testing indicated high prospective acceptability and led to refinements across 6 themes: appropriateness of content; motivation and engagement; inclusivity and gender representation; clarity of instructions and data use; understanding of therapeutic method; and usability, layout, and navigation. <strong>Conclusions:</strong> iCARE is a self-guided internet-based psychological intervention for mothers with mild and moderate symptoms of PPD in Denmark. It was developed with user input by using qualitative methods, user-centered design, and psychological theory. Further research is needed to evaluate the feasibility and effectiveness of the program in a randomized controlled trial and its integration into maternal health care models such as universal PPD screening and home-visiting.

Assessing directional connections between symptoms, cognition, insight, and real-life functioning in schizophrenia: a partial ancestor graphs analysis

IntroductionSchizophrenia is a severe chronic mental disorder causing significant global disability. Understanding the intricate relationships between symptoms, cognitive functions, and real-life outcomes is essential for developing effective interventions. Prior research, while informative, could not often determine the direction of the association between these illness-related factors. This study aimed to investigate the possible causal connections within the interrelationships of these variables. MethodsThis cross-sectional study included 215 clinically stable patients diagnosed with schizophrenia. Comprehensive assessments covered psychopathology, neurocognition, social cognition, metacognition, clinical insight, and real-life functioning. Causal relationships were explored using Partial Ancestral Graphs, a causal discovery framework that accounts for mediators and confounders. The Greedy Fast Causal Inference algorithm was employed with 1,000 bootstrap replications to assign edge orientations.ResultsA central neurocognitive–metacognitive–functional system of directed connection emerged: visual learning was linked to attention/vigilance and working memory. Working memory showed a direct relationship with metacognition, which, in turn, was connected to real-life functioning. Two partly independent contributions to real-life functioning were also identified: conceptual disorganization and experiential negative symptoms, which were directly related to expressive deficits. Positive symptoms, depressive symptoms, and social cognition occupied peripheral positions, showing no significant connection with other variables. Unawareness and misattribution of symptoms showed an indeterminate association disconnected from the main network.DiscussionThe findings show a set of directed associations that start with neurocognitive abilities, pass through working memory and metacognition, and terminate in real-life functioning. Independently, conceptual disorganization and expressive negative symptoms also exert direct influences. These directed systems of connections provide operational guidance for clinical practice, highlighting critical targets for interventions such as cognitive remediation focused on working memory, metacognitive therapies, and strategies addressing disorganization and avolition, all aimed at improving real-life outcomes in schizophrenia.

From cats to cortex: T. gondii and psychosis, depression, and anxiety

This review examines whether cat ownership, via exposure to the neurotropic parasite T. gondii, contributes to vulnerability for psychotic, depressive, and anxiety symptoms. T. gondii establishes lifelong latent infection in the brain and muscle, where it can modulate dopaminergic signaling, neuroinflammation, and tryptophan–kynurenine metabolism, providing a biologically plausible pathway to altered cognition, mood, and behavior. Epidemiological and meta-analytic data indicate small-to-moderate associations between T. gondii seropositivity and schizophrenia, with more variable but suggestive links to depression and anxiety. Evidence for cat ownership as an independent risk factor is inconsistent: some cohorts and recent meta-analyses report elevated odds of schizophrenia-related outcomes in those exposed to cats, whereas rigorously controlled studies frequently find attenuated or null effects. Methodological limitations, alternative explanations, and cultural implications are discussed, and priorities for mechanism-informed, longitudinal and interventional research are outlined.