Research findings help explain why symptoms present so differently from one child to the next, and why individualized supports and interventions are essential.
Autism can look very different from person to person. One child might differ from another in how they learn, process sensory information, and experience social and communication challenges. Scientists have long suspected these differences stem from distinct biology, but proving it has been challenging — until now.
A recent study published in Nature Neuroscience has identified two biological subtypes of autism linked to different pathways in the brain.
Researchers from the Child Mind Institute, the Istituto Italiano di Tecnologia, and other international partners analyzed brain connection patterns in nearly 2,000 individuals, including 940 autistic people from the Autism Brain Imaging Data Exchange (ABIDE). By combining human brain-imaging datasets with complementary biological data, they identified two consistent patterns in how different brain regions communicate.
One subtype showed reduced communication, or hypoconnectivity, among brain regions linked to pathways that help brain cells send signals to one another. The other showed increased communication, or hyperconnectivity, among brain regions linked to pathways associated with the immune system. The two subtypes exhibited differences in functional brain structure and modest differences on standardized autism assessments, with the hyperconnectivity subtype scoring moderately higher on autism severity measures.
These findings give scientists the first empirically biology-based framework for understanding autism’s complexities over time. This type of work could move the field closer to more precise, personalized approaches to medicine and care. However, this does not mean autism can now be divided into just two categories, nor does it create a new diagnostic framework. Autism is complex, and these two subtypes are likely part of a much larger picture.
The study also highlights the importance of open science. Through shared datasets like ABIDE, researchers can tackle questions too large for a single lab to answer alone.
Background: Adolescent asthma is a significant contributor to youth morbidity and is known to be best managed through consistent medication use and symptom management. However, adolescents often struggle to perceive their symptoms accurately and consistently use their medication at the recommended rate, risking worsened symptoms and impaired quality of life. The Responsive Asthma Care for Teens (ReACT) system is a project aimed at identifying and and providing supporting for several barriers adolescents may face in asthma management. By integrating both software and hardware to monitor medication adherence, ReACT provides a personalized support plan to improve asthma management and, subsequently, quality of life. Objective: The objective of this study is to conduct a proof-of-concept assessment of the ReACT system following an initial pilot study and adjusting for the feedback received. In addition to assessing the acceptability and usability of the current version, this study aims to assess whether the proposed ReACT system leads to indications of improvement in medication adherence because of the personalized support plans. Methods: Participants in the study were 5 adolescents aged 15 to 17 years recruited using a combination of consent-to-contact forms delivered via an in-person asthma clinic and Qualtrics panels. As a part of this study, participants met with the study team 3 times over 1 month. After completing initial surveys on stress, problem-solving, and asthma-related quality of life, we oriented the participants to the ReACT platform and asked them to interact with it as normal. After the month, the participants were interviewed, and they discussed the system and completed surveys assessing their opinions on acceptability and usability. Results: On a 4-point scale, participants reported high acceptability of ReACT (mean score 3, SD 0.32), willingness to use it again (mean score 4, SD 0.89), and willingness to recommend it to a friend (mean score 3.75, SD 0.55), and they considered it to be helpful (mean score 3.2, SD 0.84). Conclusions: Our findings suggest that ReACT is an acceptable and usable mobile health intervention to improve asthma self-management among adolescents, and it had promising results for improving self-regulation, problem-solving, and asthma control. The system continues improving based on feedback from a larger sample size of participants, and we hope that ReACT will aid adolescent development while delivering highly personalized support for each user.
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Background: Returning migrants face a variety of challenges that limit their ability to integrate and adapt to Mexico. This represents a break in their life trajectory, with effects on family dynamics, mid- and long-term projects, and uncertainty about short-term plans. Objective: This study describes the coproduction approach used to design and develop a WhatsApp-based psychoeducational program entitled “Here Again: Coping With Return,” which aims to promote the adoption of self-care behaviors to reduce the risk of mental health and substance use problems among returning migrants. Methods: The process included four phases: (1) a situational diagnosis of the needs of migrants in preventing mental health problems and reducing the risks associated with alcohol use, (2) the design and development of content, (3) evaluation by a group of experts in mental health and substance use, and (4) pilot testing. Results: The study identified 4 intervention pillars: emotional risk factors, coping strategies, barriers to care, and technological feasibility. Eighty WhatsApp messages were developed, focusing on mental health (n=52, 65%) and alcohol use (n=20, 25%) through a sequence of motivation, instruction, and reinforcement. Following an expert evaluation that simplified technical language, a pilot study with 14 migrants showed a 78.6% completion rate. Participants reported the successful application of emotional management tools and a preference for text-based messages over audiovisual content to conserve mobile data. Conclusions: This study describes the development of a psychoeducational program for returning migrants based on coproduction, integrating user needs and expert experience. The intervention addresses emotional management, self-care, and substance use prevention, using WhatsApp for its accessibility and low cost. The pilot results demonstrated high acceptability and a 78.6% retention rate over 16 weeks, highlighting that the culturally sensitive approach and accessible language enabled participants to apply mental health tools autonomously and effectively.
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Research on artificial intelligence (AI) and mental health has focused largely on harms at deployment, including chatbot safety, sycophancy, and AI-associated delusions. Less attention has been paid to a prior question: whether the human-generated text and preference judgments that shape large language models are themselves clinically reliable, particularly when self-report may be distorted. This Viewpoint aims to develop the clinical psychiatric construct of collusion—the uncritical acceptance of an unreliable account—as an analytic lens for AI training and deployment, and to argue that the clinical reliability of training and preference data should be treated as an explicit trustworthy-AI criterion in mental-health–relevant systems. A conceptual synthesis of psychiatry, clinical psychology, and AI safety literature was undertaken. The analysis distinguishes three pipeline layers: pretraining corpora, preference data and posttraining methods, and deployment-time interaction. It maps the clinical construct of collusion against adjacent technical concepts, including sycophancy, reward overoptimization, grounding, refusal training, red-teaming, and live monitoring. The synthesis suggests that collusion-like dynamics are least applicable at the pretraining layer and most applicable at the preference-data and deployment layers, where unassessed user or labeler input can be reinforced without corroboration. Existing mitigations, including data curation, Constitutional AI, reward-model evaluation, grounded generation, refusal training, red-teaming, and postdeployment monitoring, address parts of this problem. However, these approaches are not yet organized around a clinically informed account of when self-report is unreliable. The central novelty is therefore not a generic claim about bias, but the proposal that clinical self-report reliability should be assessed as a distinct data-quality and governance dimension. Trustworthy-AI frameworks for mental-health–relevant applications should incorporate clinical expertise in self-report reliability into preference-data design, red-teaming, and postmarket surveillance. Adding the clinical reliability of training and preference data as an explicit criterion could complement existing technical safeguards while leaving empirical evaluation of clinician involvement as an open research agenda.
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In Uganda’s fishing villages along lakes and rivers, wearable GPS devices are offering a new, detailed picture of how schistosomiasis spreads, helping refine control strategies for a disease affecting about 250 million people globally, mostly in rural sub-Saharan Africa.
A Nature Health study by researchers at the University of Oxford’s Big Data Institute shows that simple models using GPS-tracked movement can accurately predict which open-water sites people use, how often they visit them, and which sites are most likely to drive transmission of Schistosoma mansoni, the parasite that causes schistosomiasis.
“Snail fever”
Schistosomiasis, or “snail fever,” is caused by Schistosoma mansoni flatworms that parasitize freshwater snails. Infection occurs when people contact contaminated water where larval forms of the flatworm penetrate the skin. Repeated exposure leads to reinfection and chronic disease, including liver damage, portal hypertension, bladder fibrosis, kidney damage, and increased cancer risk.
While praziquantel can cure infection, mass drug administration (MDA) with the antiparasitic medication has failed to interrupt transmission of the parasite. The World Health Organization (WHO) notes that MDA alone is insufficient, since transmission persists in localized hotspots. Focal interventions are needed, but researchers have limited them due to poor data on where water contact actually occurs.
Conventional assumptions have limited modeling of human contact with open-water sites. Though open-water contact is heterogeneous within villages and households, it is often assumed that people use only the site closest to their household or village. However, how mobility affects site usage patterns and whether assignment rules beyond nearest distance can be more realistic are unclear.
Along with praziquantel MDA, the 2022 WHO schistosomiasis control guidelines recommend safe water, sanitation, and hygiene (WASH) as the main intervention, but evidence is mixed. Lack of reliable data has made it difficult to determine why the intervention did not improve biannual MDA. Most water contact and WASH studies use self-reported data or household distance to sites and taps, which lacks objective, spatially granular data to characterize fine-scale water usage patterns and quantify WASH’s impact on water contact and (re)infection.
Focal exposure
Lead author Fabian Reitzug, PhD, and colleagues from Goylette F. Chami’s lab tracked 452 people using GPS loggers in three Ugandan districts for 10 days. A total of 8,200 water contact events occurred at 69 open-water sites and 32 improved sources like taps and boreholes—deep drilling to groundwater. Of the participants, 63.9% used open water and 33.2% improved sources.
Reitzug and colleagues found, unsurprisingly, that distance strongly predicted behavior: usage dropped sharply with distance. Open-water contact was ~70% at 20 meters from home and 11% at 500 meters. Nearly all tap and borehole use occurred within 1 km of home, and over 99.5% of open-water contact occurred within 3 km, showing highly localized exposure.
Adding mobility metrics (such as “radius of gyration”) did not improve predictions. This simple finding challenges the assumption that mobile phone tracking can reliably estimate infection risk. Schistosomiasis exposure appears to be caused by local, routine movements, not long-distance travel.
The study found little evidence that safe water infrastructure reduces risky water contact. Taps and boreholes rarely replaced open-water use; fewer than 2% of people fully substituted safe water for natural sources. Daily activities like bathing, fishing, and washing still require lake or river contact. Behavior varied by location. In the Western Ugandan district of Buliisa, nearly 90% visited open water, compared with 44% in the Eastern Ugandan district of Mayuge.
When incorporated into transmission models, GPS-informed movement patterns closely reproduced observed reinfection rates. Simple “nearest-water-source” assumptions overestimated risk. The improved model also identified high-risk water sites by combining human use with ecological suitability for snail habitats.
Targeted interventions at key sites
These findings suggest control programs could shift toward targeted interventions at key transmission sites, such as focal snail control, environmental modification, or localized treatment. The study also indicates that a 1 km intervention radius may be more realistic than the current 500 m guideline. Importantly, reliable spatial patterns emerged from as few as 15 participants over 10 days, suggesting the approach is scalable. Key limitations include using proximity as a proxy for water contact and limited seasonal coverage.
Overall, the study reframes schistosomiasis transmission as a highly local, measurable process, enabling more precise, data-driven control strategies. Future research should examine whether similar models apply to other waterborne diseases. Identifying pathogen-specific exposure pathways and collecting GPS logger data from various locations could test this approach’s generalizability.
In a new study published in Nature Methods titled, “A multimodal adaptive optical microscope for in vivo imaging from molecules to organisms,” researchers from University of California, Berkeley present high-powered microscopes that can track the development of live specimens, including cell movement through tissue, the evolution of internal cellular structures, and shuttling of proteins and other molecules within the cell. The system, namedMultimodal Optical Scope with Adaptive Imaging Correction (MOSAIC), has been implemented in more than a dozen worldwide labs over the past six years.
“Life has to be studied in living tissue, holistically, and over fast timescales and for long periods of time,” said Eric Betzig, PhD, professor of molecular and cell biology at UC Berkeley, 2014 Nobel Prize in Chemistry, and co-corresponding author on the study. “You can’t study something as complex as a cell or organism just by looking at the parts individually—there are something like 40 million protein molecules alone of 20,000 different types.”
The microscope uses a large “vision” language model (LVLM), similar to ChatGPT, to measure petabytes of data, the equivalent of about 500 billion pages of text.
Betzig, who is also a Howard Hughes Medical Institute (HHMI) investigator, refers to the imaging data as five-dimensional (5D) composed of three spatial dimensions, plus time and color. The color comes from fluorescent labels that allow scientists to track multiple subcellular structures simultaneously, such as organelles, membranes, the cytoskeleton and more, as they migrate, change shape, divide and interact over time.
In one video, the authors capture a zebrafish regrowing its tail fin. The video revealed tiny events inside living tissue that are normally difficult to visualize, such as cells near the wound releasing small communication packets, microscopic fibers beneath the skin shifting as the tissue repaired itself, two repair cells fusing together and a red blood cell briefly getting trapped as new blood vessels were remodeled.
Ian Swinburne, PhD, assistant professor of molecular and cell biology at UC Berkeley and collaborator on the work, emphasizes that there’s a wealth of information in these large movies across scales, but it can be difficult for a very well-trained biologist to interrogate the data.
“AI can help us interface with the data and ask or answer questions more easily. Like, ‘How many macrophages are crawling into my tissue during an infection?’ or ‘Can I predict when a cell’s going to start leaving its organ?’ That happens in development but also in cancer during metastasis,” said Swinburne.
Building an LVLM or AI that can handle petabytes of imaging data is a main focus of Berkeley’s Advanced Bioimaging Center, which hopes to create a first-of-its-kind Cell Observatory.
“The impact of MOSAIC will be minimal until we build an AI model that can deal with the data that comes out of those systems. We basically have a gold mine, but we have no ability to get the gold out,” said Srigokul “Gokul” Upadhyayula, PhD, assistant professor in residence of cell biology, development and physiology at UC Berkeley. “The primary output of our Cell Observatory Initiative will be an AI mind that’s able to be our scientific partner in extracting these observations.”
Amid rapidly growing adoption of enterprise-level AI agents, there’s a disconnect emerging between ambition and execution.
Although 85% of organizations say they want to be agentic within the next three years, 76% say their current operations and infrastructure can’t support that change. They cite a lack of readiness across people, processes, and workflows.
The sticky tape problem
The challenge is that many organisations are often layering AI agents onto existing operations, rather than reimagine the operating model and how work will need to be rewired, explains Prasun Shah, global CTO for workforce consulting and chief AI officer at PwC UK Consulting. “They’re embedding AI employees into what is a human operating model,” layering on AI agents to existing workplace structures when “this is like adding sticky tapes to parts of an operating model that is breaking.”
Doing so may be preventing organizations from unlocking the full value agentic AI offers, creating circumstances where disillusionment can quickly creep in. That full value lies in agents’ capacity to execute entire workflows with limited human input. They can coordinate complex tasks, make independent decisions, adjust to changing conditions, and iterate performance.
In early proving grounds that span customer service, HR, and sales, it’s already estimated that AI agents could accelerate business processes by as much as 30% to 50% and low-value work time by 25% to 40% when deployed at scale. But with this capability comes greater complexity and the need for an enterprise-wide change.
Growing the AI vocabulary
Enterprise agentic AI platform Ema describes this change as agentic business transformation (ABT), a term it coined last year in partnership with HFS Research, in an attempt to plug what it sees as a gap in the existing lexicon about AI agents, and to provide enterprises with a new framework with which to think about their own adoption of the technology.
“None of the existing vocabulary captures the full scope of the change,” explains Ema CEO and founder Surojit Chatterjee. “Digital transformation was about moving from paper to software. AI transformation was about adding artificial intelligence to existing processes. Co-pilot is about AI assisting in various human tasks. But ABT is something categorically different: It’s the integration of AI agents into the fabric of the organization.”
For Shah, the dedicated term (ABT) “helps drive the need to redesign an organization in its entirety: its operating model, its workflows, decision rights, and performance management systems.” He emphasizes that “everything that’s needed to ensure those agents are actually active participants in value creation, rather than just point tools or productivity aids.”
According to Ema, ABT encompasses three core pillars: an organization’s technology stack, its workforce, and the metrics used for success.
AI agents as connective tissue
The first pillar of ABT is the technology stack. “Your existing tech stack was designed for human-operated, application-centric workflows,” says Chatterjee. “It needs to be reconsidered when the actor is an AI agent operating at machine speed across multiple systems simultaneously.”
As AI agents are integrated into an organization, enterprises will need to pivot from a set of linear processes and steps, to rewiring work in a very different way, explains Shah. That’s because the value in AI agents isn’t as another layer in an existing technology stack but as a connective tissue, he explains, moving between or across layers to coordinate a high-level task or retrieve and interpret data from multiple discrete applications. AI agents can create “a true competitive differentiation for an enterprise” by making decisions based on this capacity to contextualize, he says. “That is where the next battleground will be.”
To build this connective tissue, leaders need to adapt their technology stack to surface higher quality decisions from AI agents, prioritizing access to multiple datasets and applications simultaneously to develop tacit knowledge. “Organizations that make this architectural shift become genuinely more adaptive,” says Chatterjee. “When a new business requirement emerges, you don’t wait six months for a software vendor to build a feature. You configure an AI employee using natural language and connect it to the systems it needs. The time from business to production workflow drops from months to days.”
The workforce, redesigned
As AI agents are deployed for more use cases, enterprise leaders must consider what this means for dynamics across their workforce, the second pillar of ABT.
Workforce structures today deviate little from the hierarchical model of the early days of industrialization. To maximize efficiency and scale, processes are standardized, tasks are clearly delineated between strategic business units (SBUs), and employees progress up through an organization based on their capacity to optimize output from teams below them. But with AI agents that can execute, coordinate, and optimize tasks—often without managerial coordination—the lines of that established hierarchy become blurred.
In a workforce that blends AI agents and human employees, managers will be freed up from many execution-based tasks but take on new responsibilities associated with managing hybrid teams. Managers “will need to be able to manage issues around trust, explainability, psychological safety, and even status dynamics” to navigate new tensions that could arise in a hybrid workforce, says Shah.
The impact of agentic AI on existing workforce structures goes far beyond the management layer, too. McKinsey predicts that by 2030, three-quarters of current jobs will require redesign, upskilling, or redeployment, and organizations will need to act swiftly to amend recruitment, retention, and remuneration.
From output to outcome
Success metrics are the third and final pillar of ABT.
As AI agents assume greater ownership of core enterprise processes, taking on collaborative roles alongside human employees, traditional workforce metrics that focus on activity or output—such as calls handled or reports filed—no longer make sense.
“When you add AI employees into the workforce, activity metrics become meaningless or actively misleading,” says Chatterjee. “An AI employee can handle a thousand customer interactions in the time it takes a human to handle ten. If you measure success by interactions handled, you’ll conclude the AI is working brilliantly while missing whether any of those interactions actually drove customer satisfaction, retention, or revenue.” To correct this, enterprises must develop a new set of metrics that focus on outcome rather than output. That is, metrics on the broader benefits or changes achieved, rather than individual deliverables.
For example, when one of Ema’s large enterprise customers overhauled its own metrics, switching from tool metrics like cost per query and AI accuracy, to outcomes like the percentage of contracts reviewed without human escalation, the measured ROI from agentic AI tripled within two quarters. The changes meant “this customer stopped building point solutions in high-volume, low-complexity workflows and started deploying AI employees where the outcome value was highest,” says Chatterjee.
Integrating new metrics may also require a complete reconfiguration of reward and talent management processes, as well as accountability and ownership within organizations, points out Shah. In human-AI teams, for example, although ethical and fiduciary responsibilities will likely remain with human employees, operational accountability will become significantly more diffused to reflect the systemic role of AI agents.
This change will raise new questions that senior leadership teams will need to wrestle with, Shah adds. They’ll need to consider: Who is accountable when an AI employee makes a mistake? What happens when AI and humans disagree? What guardrails should be erected to safeguard customers?
Laying the groundwork for systems-level change
Systems-level change is gradual. These are complex lines of inquiry that experts continue to grapple with. But in kickstarting internal dialogue about the core pillars of ABT—the workforce, the technology stack, and the metrics by which success can be gauged—leaders can lay the groundwork for an enterprise better poised to embrace AI agents at a systems level and start to close the gap between their ambition and execution.
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.
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.
A reality check on the AI jobs hysteria
Despite the growing hysteria over AI’s threat to white-collar jobs, there’s still scant evidence that the technology has had a large-scale impact on the labor market.
Analysis of US labor data shows that unemployment in occupations most exposed to AI is actually lower than in less-exposed jobs. There are also no signs that large numbers of workers are shifting from AI-threatened professions into supposedly safer manual-labor jobs.
Opinion: It’s time to address the looming crisis in entry-level work
—Georgios Petropoulos, an assistant professor at the USC Marshall School of Business
AI has not yet produced mass unemployment. But it may be quietly weakening the first rung of the career ladder.
A recent Stanford study found that young workers in AI-exposed occupations suffered a sharp decline in employment after the spread of generative AI. The same pattern didn’t appear in low-exposure jobs, suggesting AI is replacing junior tasks that once gave young workers their first foothold.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 The Pope has called for governments to regulate AI In his first major teaching document, Pope Leo said AI must be “disarmed.” (BBC) + He warned that AI fuels war and misinformation. (CNN) + But could also “open up a horizon extending in all directions.” (Engadget) + Anthropic cofounder Chris Olah also spoke at the event. (Reuters $)
2 SpaceX has launched its biggest and most powerful rocket The Starship V3 made its test flight debut two days after Elon Musk announced SpaceX’s IPO.(Guardian)+ SpaceX pulled off the launch, but not the landing. (Ars Technica) + The rocket could be key to SpaceX’s valuation. (Fortune $) + But rivals to the company are rising. (MIT Technology Review)
3 Huawei says it can make industry-leading chips within five years The Chinese tech giant announced a breakthrough in chip design. (Reuters $) + Its progress underscores Beijing’s push to neutralize US sanctions. (NBC) + Chinese chip stocks rallied after the announcement. (Bloomberg $)
4 A new vaccine may protect against the Ebola strain behind the current crisis Tests have shown promising results for the mRNA vaccine. (New Scientist) + Another Ebola vaccine that could be ready for trials in months. (BBC) + But vaccines face a new problem: their name. (MIT Technology Review)
5 A swimmer broke a world record at the ‘Steroid Olympics’ Athletes at the Enhance Games were encouraged to take dope. (Wired $) + Silicon Valley elites have backed the competition. (WP $) + Which fits right into 2026’s longevity vibes. (MIT Technology Review)
6 The EU plans to fine Google a massive antitrust penalty For allegedly favoring its own services in search results. (CNBC) + It would be the largest penalty for breaching the Digital Markets Act. (Reuters $)
7 US quantum computing subsidies may not be legal Congressional critics say the funding has been misused. (Ars Technica)
8 AI is minting new billionaires—and workers want their share The Samsung labor showdown reflects global concerns. (Rest of World)
9 China has launched artificial human embryos into orbit To find out whether we can reproduce beyond Earth. (Gizmodo)
10 Jony Ives has designed Ferrari’s first fully-electric car The legendary Apple designer has created a polarizing aesthetic. (FT $)
Quote of the day
“Technology is never neutral, because it takes on the characteristics of those who devise, finance, regulate, and use it.”
—Pope Leo issues a warning about AI in his first encyclical letter, entitled ‘Magnifica humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence.”
One More Thing
ALYSSA SCHUKAR
How climate vulnerability and the digital divide are linked
In Anacostia, a historic African-American section of Washington, DC, Monica Sanders is measuring Wi-Fi speeds. It’s below the FCC’s minimum to qualify as a broadband service. She then checks the temperature: 46.9 °F.
Sanders, an adjunct professor of law at Georgetown University, frequently records this combination of weak internet access and environmental conditions. Her work shows how underinvestment in infrastructure can leave underserved communities more exposed to climate risks like extreme heat and flooding.
Hypertensive Chronic Kidney Disease (CKD) constitutes a significant global health burden, characterized by a vicious cycle of hypertension and progressive renal decline. Autonomic imbalance, specifically sympathetic overactivity and parasympathetic withdrawal, is increasingly recognized as a central driver of this pathophysiology, interacting with traditional hemodynamic factors such as RAAS activation and volume overload. This review aims to elucidate the deep-seated mechanisms by which autonomic imbalance induces vascular injury and renal progression, and to evaluate the clinical potential of ultrasound-guided sympathetic blockade as a novel therapeutic strategy. We conducted a comprehensive narrative review of recent basic research and clinical evidence regarding the “Kidney-Brain-Vascular Axis” and neuromodulation therapies in the context of hypertensive CKD. The pathogenesis involves a maladaptive “Neuro-Immune-Vascular Axis.” Sympathetic overactivity not only induces hemodynamic stress but also disrupts the Th1/Th2 immune balance, accelerating vascular calcification and fibrosis. Ultrasound-guided sympathetic blockade offers a reversible, minimally invasive neuromodulation approach. Preliminary clinical evidence suggests it may lower blood pressure, improves vascular endothelial function, and potentially delays renal progression, particularly in patients with resistant hypertension, with a superior safety profile compared to renal denervation. Ultrasound-guided sympathetic blockade represents an emerging and investigational adjunct, constrained by the lack of large-scale randomized controlled trials and long-term outcome data.
BackgroundCognitive control deficits are a core feature of adolescent major depressive disorder (MDD), yet the associated task-state neurophysiological mechanisms remain poorly characterized. This study investigated electrophysiological alterations in MDD using a Stroop color-word task.MethodsTwenty-two adolescents with MDD and fifteen age- and sex-matched healthy controls (HC) completed the task during 32-channel EEG recording. We analyzed P300 amplitude and latency, 1-30Hz power spectral density (PSD) in key cortical regions, and task-based functional connectivity using the phase locking value (PLV). A support vector machine (SVM) classifier with leave-one-out cross-validation was employed to assess the diagnostic utility of the multimodal features.ResultsRelative to HC, the MDD group exhibited significantly prolonged incongruent trial reaction times, reduced P300 amplitude at centro-parietal electrodes (Oz, PO7, O2), and enhanced alpha/beta-band PSD in occipitotemporal regions. Functional connectivity analysis revealed a task-state shift from a frontoparietal to an occipitotemporal network. The multimodal SVM model achieved 86.49% classification accuracy (AUC = 0.86).ConclusionTask-specific P300 hypoactivity, aberrant oscillatory dynamics, and functional network reorganization collectively distinguish adolescent MDD from HC. These findings provide convergent neurophysiological evidence for impaired cognitive control in MDD and highlight the potential of preliminary candidate EEG biomarkers for early identification, prognostic assessment, and monitoring treatment response in adolescent MDD.
