Tag: Cognitive focus and executive control

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Week one of the Musk v. Altman trial: What it was like in the room

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

Two of the most powerful people in AI—Sam Altman and Elon Musk—began their face-off in court in Oakland, California, last week. Musk is suing OpenAI, alleging that the millions he spent to fund it around a decade ago were meant for a nonprofit, not a corporation, and that the company has reneged on that mission since. 

The stakes are high—even a partial win for Musk could set OpenAI back as it reportedly plans to go public this year. But most of the attention comes from the spectacle of a feud on X now playing out in federal court. “Cringey texts, raw diary entries, and endless scheming behind the founding and growth of OpenAI are expected to come to light,” my colleague Michelle Kim wrote before it began. And the trial unfolds as the cultural backlash against AI swells; some of the signs held by protesters outside the courthouse suggest that to a significant number of people, whatever the outcome of Musk v. Altman, we all lose.  

Most of us have had to observe the trial from afar, but Michelle, who also happens to be a lawyer, has been in court each day. I caught up with her to learn what’s unfolded thus far and what might come next.

Can you give us the overview of what this case is actually about? What exactly is being decided, and who is favored right now?

Elon Musk is arguing that Sam Altman and OpenAI president Greg Brockman have breached the company’s charitable trust by effectively converting OpenAI into a for-profit company. Musk alleges that is not what they promised him in the company’s early days. He has asked for several remedies, like a crazy amount of damages and removing Sam Altman. But the main remedy he wants is unwinding OpenAI’s restructuring. [In October 2025 OpenAI struck deals with the attorneys general of California and Delaware that would essentially allow its nonprofit portion to have less day-to-day control of OpenAI. It’s a compromise from what OpenAI originally proposed, but Musk still wants to stop it.] 

OpenAI argues that Elon Musk actually agreed to have the company operate a for-profit arm, because he knew building AI is very expensive. So it’s about proving what Musk knew, what he didn’t know, and whether he really was deceived by Altman and Brockman.

There’s a big debate about when exactly Musk found out about this alleged misconduct. Musk founded OpenAI with Altman and Brockman in 2015, and he brought the suit in 2024. There’s a statute of limitations for charitable trust claims; you need to have brought a claim within three to four years after you find out about the alleged misconduct. So Musk tries to paint a picture that back in the day he was a little suspicious, but that it was really only in 2022 that he realized OpenAI was no longer committed to its original charitable mission, and that he had been scammed. It’s only the first week of trial, but I’m not sure Musk has proved this to the judge and jury.

What were some standout moments thus far?

At one point one of Elon Musk’s lawyers said, “We could all die as a result of AI.” I think a lot of the people in the room were really shaken by this comment, and the judge told Musk’s lawyer: You talk about all these safety risks that OpenAI has when building AI, but Musk is also creating a company that’s in the same exact space. She basically said, I’m sure there’s plenty of people who also don’t want to put the future of humanity in Elon Musk’s hands. 

And then the lawyers just kept going on and on about the catastrophic risks of AI and whether Elon Musk or OpenAI was in the better position to steward AI safety. And the judge sort of snapped. She said very sternly that this trial was not about whether or not artificial intelligence has damaged humanity. And I thought that was a really striking standout moment of the trial that pointed at how even though it is technically just about whether Elon Musk was really deceived by OpenAI, it’s also become a huge discussion about AI safety and some of the practices that the labs are engaging in when building AI. 

Can you give us a look behind the curtain at how getting into this trial works?

There are tons of reporters. This is a very high-profile suit, so I have to wake up around 4:30 a.m. and show up to the Oakland courthouse at 6 a.m. sharp to get in line. And on some days, even 6 a.m. doesn’t get you into the courtroom. There are lots of photographers in front of the courthouse, especially on days when you know Musk or Altman and Brockman are present. And there’s also some concerned citizens who want to watch the trial. I usually have to wait, like, two hours in line to get in to be one of the 30 people who claim the unreserved seats in the courtroom. 

What has it felt like to see Elon Musk testify? How would you describe his demeanor?

He shows up in a crisp black suit. He can be this inflammatory person on X, but in the courtroom, he is calm, cool, collected, and looks very comfortable. He has been in a lot of lawsuits. He knows how to talk to the jury and how to present himself in front of them and the judge. He’s also cracking jokes with his lawyer and even the opposing party’s lawyer and the judge. 

And he can be witty. There was this one moment when OpenAI’s lawyer was asking Musk a question and sort of fed him an answer. And Musk said “That’s not a leading question, that’s a leading answer.” The judge intervened and said, “You’re not a lawyer, Elon.” And then he was like, “Well, I did take Law 101.”

That said, he does get flustered and uncomfortable when OpenAI’s lawyer asks tough, piercing questions. Which he’s been doing.

What are the biggest things we’ve learned that weren’t clear in the earlier phases of this case?

On the fourth day of the trial, Musk admitted during cross-examination that xAI distills OpenAI’s models to train its own models, which was shocking. Musk followed up by saying that this is standard practice among all the labs now and that xAI wasn’t doing anything beyond what others were already doing. But a lot of the journalists started typing away at their laptops as soon as Musk made this comment. 

I also learned that there’s just so much scheming among Big Tech executives. You know about it vaguely, but to hear firsthand accounts and read their emails and text messages is fascinating. 

For example, there was a text message between Musk and Mark Zuckerberg of Meta, where they’re kind of teaming up to stop OpenAI’s restructuring. They’re even trying to make a bid to buy all the assets of OpenAI’s nonprofit. The level of scheming that goes on among these executives is mind-blowing.

What happens next?

OpenAI’s president, Greg Brockman, who was meticulously taking notes during some of Elon Musk’s testimony, is expected to testify next week. And Stuart Russell, a computer scientist at UC Berkeley, will testify about AI safety. I’m expecting that to open the floodgates to this crazy discussion about who can be trusted to build AI. 

A bunch of other high-profile people are expected to testify, like former OpenAI chief scientist Ilya Sutskever, former CTO Mira Murati, and Microsoft CEO Satya Nadella. 

The trial is supposed to last around three weeks. The nine jurors will deliver an advisory verdict that guides the judge on how to decide Musk’s claims against OpenAI. The judge doesn’t have to listen to the jury and can decide however she wants. If she decides OpenAI is liable, then she’ll decide what sort of remedies are appropriate. 

MIT Technology Review will have ongoing coverage of Musk v. Altman until its conclusion. Follow @techreview or @michelletomkim on X for up-to-the-minute reporting.

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Researchers urge study of paternal deaths, though a new paper finds fatherhood is protective

Maternal health is a known crisis in the U.S., where pregnant women and new mothers die at a rate several times higher than in comparable countries. In recent years, increased awareness of the problem has led to interventions at the federal and state level and a strengthening of surveillance and data collection. Even as sizable improvements continue to be elusive, the picture of how many new mothers are dying, and why, is becoming clearer. 

A research letter published on Monday in JAMA Pediatrics argues fathers deserve similar attention. To bolster their assertion, the authors reported the results of a pilot study in Georgia of deaths among fathers of children born in a single year, which found nearly 800 deaths in the first five years of fatherhood. 

Read the rest…

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Big Tech Targets Drug Discovery with Wave of Life Science Platforms

Nvidia CEO, Jensen Huang, asserts that accelerated computing has a missing word: application acceleration. The “vertically integrated” and “horizontally open” chip maker is set on building the infrastructure that delivers AI into real world use. 

“Accelerated computing is not a chip problem,” said Huang when he took the stage for his annual NVIDIA GTC keynote in San Jose in March. “The only way for us to accelerate applications and bring tremendous speed up and cost reduction is through domain specific acceleration.”  

That mission has hit drug discovery, where approval timelines exceed a decade and clinical trial failure rates approach 90%.  

A new wave of platforms from Amazon Web Services (AWS), OpenAI, and Anthropic have customized general-purpose assistants into AI-powered workflows for science research. The trend points to the growing role of cloud infrastructure and agentic AI in unifying fragmented tools, streamlining data management, and making domain expertise more accessible. 

Lab-in-the-loop 

In April, AWS introduced Amazon Bio Discovery, a “lab-in-the-loop” workflow that combines access to more than 40 open-source and proprietary biological foundation models with AI agents that guide experimental design. The platform also integrates CRO partners, including Twist Bioscience, Ginkgo Bioworks, and A-Alpha Bio, for lab validation. The launch was announced at the AWS Life Sciences Symposium at the Javits Center in New York. 

In collaboration with Memorial Sloan Kettering Cancer Center, Amazon Bio Discovery designed nanobodies with nanomolar affinities by generating nearly 300,000 candidates that were narrowed to the top 100,000 for wet lab testing in weeks, a noticeable reduction from the up to one year timeline typical of traditional methods. 

Dan Sheeran, vice president and general manager, healthcare and life science at AWS, explains that while biological AI models have driven breakthroughs in areas, like protein design, their reliance on coding expertise and complex compute infrastructure remains a significant barrier to broader accessibility. 

“Choosing the right model for a given task is itself a significant challenge. Computational biologists, the specialists who bridge AI and biology, are in short supply,” Sheeran told GEN Edge. “The result is a collaboration bottleneck, not because the science isn’t available, but because the tooling doesn’t support how these teams need to work together.” 

David Younger, PhD, co-founder and CEO of A-Alpha Bio, adds that the partnership with AWS highlights a “fundamental gap” in AI-powered drug discovery, the lack of high-quality, experimental data at scale to evaluate protein design models. In silico candidates designed using Amazon Bio Discovery can be rapidly validated in the lab with A-Alpha’s AlphaSeq platform, which quantitatively measures protein-protein interactions by the hundreds to millions. 

“The convergence of technology and life sciences isn’t just about faster compute or better algorithms,” Younger told GEN Edge. “It’s about connecting those advances to real-world, experimental observations.” 

Amazon Bio Discovery is built on the same AWS infrastructure that is currently adopted by 19 of the top 20 global pharmaceutical companies. Each organization’s data is isolated within its application environment, and all proprietary data, models, and designs remain customer-owned. 

Rosalind reasons 

Two days after Amazon Bio Discovery’s launch, OpenAI announced GPT-Rosalind, a specialized reasoning model that supports evidence synthesis, hypothesis generation, and experimental planning for research across biology, drug discovery, and translational medicine. The platform includes a freely accessible life sciences research plugin for Codex that connects to over 50 public multiomics databases, literature repositories, and computational biology tools.  

The model is available through a trusted-access program for qualified enterprise customers in the U.S. Amgen, Moderna, the Allen Institute, and Thermo Fisher Scientific are among GPT‑Rosalind’s customers. 

“Research organizations are actively looking for systems that are built for scientific workflows, not adapted from general-purpose models, and life sciences remains one of the most important areas where better tools could meaningfully accelerate progress,” wrote OpenAI in an email to GEN Edge when describing the motivation for building GPT-Rosalind. 

Named after Rosalind Franklin, PhD, whose work was critical in the discovery of the DNA double helix, the model scored 0.751 on BixBench, a benchmark that evaluates large language model (LLM) performance in bioinformatics and computational biology tasks. The score was a modest lead ahead of GPT-5.4, xAI’s Grok 4.2, and Google’s Gemini 3.1 Pro. 

On LABBench2, a benchmark spanning literature retrieval, database access, sequence manipulation, and protocol design, GPT-Rosalind outperformed GPT-5.4 on six out of 11 tasks. The largest improvement was shown on CloningQA, which requires end-to-end design of DNA constructs and enzyme reagents for molecular cloning workflows. 

GPT-Rosalind is one step in OpenAI’s growing momentum across pharma and healthcare. In recent weeks, the company introduced ChatGPT for Clinicians to support clinical workflows, such as documentation and medical research, alongside partnerships with Novo Nordisk to enhance workforce AI readiness and improve manufacturing and supply chain efficiency, and Massive Bio to expand access to clinical trials. 

Inference inflection 

Anthropic is forging its own path into life sciences, having recently drawn attention for acquiring Coefficient Bio, a roughly 10-person AI drug discovery start-up founded by former Genentech scientists, for $400 million.  

The OpenAI competitor has also been building Claude for Life Sciences, the AI assistant specialized for researchers, clinical coordinators, and regulatory affairs managers, since last fall. 

In an October blog post, Anthropic reported that the customized platform powered by Claude Sonnet 4.5 scored 0.83 in Protocol QA, a benchmark that tests the model’s understanding of laboratory protocols. The score outperformed the human baseline of 0.79 and Sonnet 4’s performance of 0.74. Claude for Life Sciences also incorporates several connectors to scientific platforms, including Benchling’s digital notebooks, PubMed literature, and 10x Genomics tools for single cell and spatial analysis.  

“We want to give scientists the same experience as software engineers of having a brainstorming partner to work with and to delegate tasks,” said Eric Kauderer-Abrams, PhD, head of biology and life sciences at Anthropic, in a video accompanying the product launch. 

In January, Anthropic expanded the platform to Claude for Healthcare, a complementary set of tools that allow healthcare providers, payers, and health tech companies and startups to use Claude for medical purposes through HIPAA-ready products.

When reflecting on these life science releases, Enke Bashllari, PhD, founder and managing director at Arkitekt Ventures, says the three are “playing different games.” OpenAI is selling the “sharpest reasoning engine” with limited access, while AWS is building infrastructure and lab integration. Anthropic is betting on breadth of workflow and making acquisitions to close the specialization gap.  

“For startups, the question isn’t which platform wins. It’s which layer you build on,” wrote Bashllari on LinkedIn. 

Chris Leiter, founder and general partner at Atria Ventures, believes the shift to bioconsumerism will be the “most significant period of disruption for life sciences in the modern era.” 

“Medicine is the use case that justifies the entire buildout,” wrote Leiter on LinkedIn. “The public skepticism starts to erode when the output is a drug that reaches a patient five years early, or a diagnostic that catches a cancer no doctor would have seen.” 

As models increasingly move beyond isolated predictions into complex reasoning across biological systems, the question is no longer whether to adopt, but how quickly the industry can adapt to a new scientific discovery paradigm. 

Huang says it best, “we are now in the beginning of a new platform shift. The inference inflection has arrived.” 

The post Big Tech Targets Drug Discovery with Wave of Life Science Platforms appeared first on GEN – Genetic Engineering and Biotechnology News.

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STAT+: Biotech raises $42 million to run Huntington’s disease trial

Gene therapy startup Latus Bio has raised another $42 million to start its first clinical trials, where it will try to sidestep issues that have set back a more advanced competitor. 

Latus is moving two treatments through clinical trials this year. The first is for a form of Batten disease called CLN2 disease, a fatal genetic condition that causes seizures, vision loss, and cognitive problems. The company anticipates having initial clinical data by the end of the year. 

Now, Latus — founded by Beverly Davidson, chief scientific strategy officer at the Children’s Hospital of Philadelphia — is turning its attention to a second drug candidate, a gene therapy for Huntington’s disease.

Continue to STAT+ to read the full story…

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Meta-analysis of the effects of exercise intervention on physical health in individuals undergoing compulsory isolation

BackgroundPhysical health is the basic indicator to evaluate the health of drug addicts after the process of drug rehabilitation. In order to better improve the deficiency degree of physical health of drug addicts, it is necessary to carry out a systematic review.ObjectiveTo explore the effects of exercise intervention on the physical health of individuals undergoing compulsory drug rehabilitation using Meta-Analysis, aiming to provide evidence-based support for improving their physical health.MethodsRandomized controlled trials (RCTs) published between 2019 and December 2024, examining the impact of exercise intervention on the physical health of compulsory detoxification individuals, were retrieved from databases including Web of Science, PubMed, Cochrane Library, Medline, China National Knowledge Infrastructure (CNKI), Wanfang Data, and VIP Chinese Journal Database. The quality of included studies was assessed using the Cochrane risk-of-bias assessment tool. RevMan 5.4 software was employed for heterogeneity testing, effect size synthesis (using mean difference [MD] and 95% confidence interval [CI]), and generation of forest plots, funnel plots, and quality assessment diagrams. Subgroup analyses were performed to evaluate sensitivity and heterogeneity of the included studies.ResultsExercise intervention effectively improved the physical health of compulsory drug rehabilitation individuals, particularly in physical fitness indicators: sit-and-reach test [MD = 3.92, 95%CI = (3.23, 4.62), P<0.001], single-leg standing with eyes closed [MD = 7.03, 95%CI = (6.05, 8.02), P<0.001], grip strength [MD = 1.23, 95%CI=(0.06, 2.39), P = 0.04], and choice reaction time [MD=-0.03, 95%CI=(-0.05, -0.01), P = 0.002]. Improvements in physical function were also observed; however, the increase in vital capacity [MD = 86.81, 95%CI=(-1.56, 175.17), P = 0.05] did not reach statistical significance.ConclusionThis meta-analysis provides evidence that exercise intervention significantly improves specific physical health deficits—namely flexibility (sit-and-reach), balance (single-leg stance), muscular strength (grip strength), cardiopulmonary function (vital capacity), and sensorimotor coordination (choice reaction time)—in individuals undergoing compulsory rehabilitation. It is recommended to adopt a combination of aerobic and traditional fitness exercises, with at least 3 sessions per week, each lasting no less than 40 minutes, and a duration of over 12 weeks, providing scientific evidence for drug rehabilitation practices. These indicators were selected because they directly reflect the multisystem damage (muscular, neural, and cardiorespiratory) caused by chronic substance use. However, this study acknowledges the limitation that psychological and neurocognitive outcomes (e.g., cravings, mood, executive function), which are crucial in addiction treatment, were not included in the eligibility criteria and systematic analysis. The follow-up research will combine physical and psychological indicators to conduct a comprehensive evaluation of the intervention effect of exercise on drug rehabilitation.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD420251029820.
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Research trends and knowledge mapping of transcranial direct current stimulation in depression: a bibliometric study based on web of science, Scopus, and PubMed (2000-2025)

BackgroundDepressive disorders are clinically heterogeneous and mechanistically complex psychiatric conditions. Transcranial direct current stimulation (tDCS), a key non-invasive neuromodulation technique, has expanded rapidly in both therapeutic application and mechanistic research. However, the field is marked by rapid publication growth, thematic diversity, and variability in evidence quality. A systematic quantitative synthesis is therefore needed to map the research landscape, identify hotspots, and inform future directions.MethodsA systematic search was conducted for English-language publications in the Web of Science Core Collection (WoSCC), Scopus, and PubMed using the terms (“Transcranial direct current stimulation” OR “tDCS”) AND (“depression” OR “major depressive disorder” OR “depressive disorder” OR “MDD”). Only articles and reviews were included. Records from 2026 and non-research publications, including conference abstracts, editorials, letters, news items, and errata, were excluded. Deduplication was performed using DOI-based matching followed by title-assisted matching. Bibliometrix (R), VOSviewer, and CiteSpace were used to analyze publication trends, contributions by countries/regions, institutions, authors, and journals, collaboration networks, keyword co-occurrence, thematic clustering, and burst terms. Citation analysis was based on WoSCC data only.ResultsResearch on tDCS for depression showed sustained growth, with marked acceleration after 2020 and a peak in 2024. The United States, Germany, and Brazil occupied central positions in both productivity and international collaboration, with the United States ranking first in publication volume. Major research hubs included the Universidade de São Paulo, the University of Toronto, and Harvard University, while Brain Stimulation, Journal of Affective Disorders, and Frontiers in Psychiatry were the leading publication venues. Highly cited studies mainly focused on neurophysiological mechanisms, pivotal randomized controlled trials, and evidence-based guidelines. Keyword analyses indicated a shift from early attention to cortical excitability, safety, and short-term efficacy toward a more integrated framework involving prefrontal-targeted stimulation, cognitive function, functional connectivity, treatment outcomes, and cross-disorder applications.ConclusiontDCS research in depression is entering a multidimensional and interdisciplinary phase, with increasing emphasis on network-level mechanisms and precision intervention. Functional connectivity is emerging as a potential biomarker for patient stratification and outcome prediction. Further progress depends on multicenter standardization, reproducible analytic pipelines, and high-quality comparative effectiveness research.
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Current Landscape of Mental Health Conversational Agents From a Trauma-Informed Care Lens: Scoping Review

Background: Conversational agents (CAs) are increasingly used in mental health care to enhance access and engagement. However, their safe, ethical, and user-sensitive design remains a challenge. Despite growing attention to trauma-informed approaches in human-computer interaction, there is limited work on how the trauma-informed care (TIC) framework could be applied in the design of mental health CAs and no comprehensive synthesis to date. Objective: Guided by the Substance Abuse and Mental Health Services Administration’s TIC framework, this scoping review explored how TIC principles (safety; trustworthiness and transparency; collaboration and mutuality; empowerment, voice, and choice; peer support; and cultural, historical, and gender issues) are currently represented in the design and evaluation of mental health conversational agents (MHCAs) and identified gaps and opportunities to promote more trauma-informed design practices. Methods: Online databases, as well as a secondary survey of citation lists from an initial search, were used to identify English-language journal articles and conference proceedings from 2000 to 2024 that empirically evaluated an independent, web- or app-based, unassisted CA used for mental health and included concepts from TIC. Results: Our analysis included 38 publications (n=28, 73.7%, published in 2020 or later) covering 28 distinct MHCAs. Most studies used experimental methods (n=23, 60.6%) or user studies (n=11, 28.9%), with samples skewed toward female (men: mean 34.92%, SD 18.64%), young in age (mean 32.52, SD 14.6 y), and predominantly nonclinical (n=29, 76.3%). MHCAs were largely rule-based prototypes. No studies explicitly referenced the TIC framework as a guiding lens for MHCA design or evaluation. A total of 26 studies referenced terminology from TIC core principles but rarely defined them, while all 38 included language that could be linked to one or more principles. Overall, TIC-related concepts appeared most often within intervention design descriptions, qualitative assessments, or as items embedded in questionnaires evaluating broader constructs. Trustworthiness and transparency, safety, empowerment, voice and choice, and collaboration and mutuality were comparatively well addressed, while peer support and cultural, historical, and gender issues were largely absent. Design recommendations, where present, were relatively broad and emphasized secure, customizable, reliable, human-like, and context-sensitive MHCAs that offered multimodal interaction, goal setting and tracking, and transparency. Conclusions: Studies did not self-identify as using Substance Abuse and Mental Health Services Administration’s framework for TIC, making it more difficult to identify its elements. The fragmented terms, disciplines, and metrics used make it difficult to draw more systematic conclusions about the current research landscape related to TIC, but our analysis indicates TIC to be a descriptive and potentially unifying framework and provides a starting point for the explicit trauma-informed MHCA research and design.
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STAT+: In her own words: Surgeon general nominee Nicole Saphier expresses enthusiasm and caution for MAHA

Now that Casey Means is no longer the Trump administration’s choice for surgeon general, attention is turning to the new nominee for the position. 

Nicole Saphier, whose candidacy was announced Thursday, is a licensed physician — unlike Means, whose license lapsed. A radiologist at the Memorial Sloan Kettering Cancer Center, Saphier (pronounced SAA-fire) is director of breast imaging at MSK Monmouth in New Jersey. She may be more widely known as a regular contributor to Fox Business, where she has said that the overwhelming majority of “good research” disputes the notion that vaccines are linked to autism, but has expressed an openness to alternative childhood vaccine schedules. 

Saphier has weighed in on many other concerns shared by the Make America Healthy Again movement promoted by health secretary Robert F. Kennedy Jr., agreeing with Kennedy on some positions but also clearly questioning others. In her own words, here are her views on vaccines, peptides, Tylenol in pregnancy, dietary guidelines, breast cancer, and also, Casey Means.

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Advanced Neural Probes Reveal Predictable Patterns in Epileptic Brain Activity

In addition to suffering seizures, many people with epilepsy also experience bursts of abnormal brain activity called interictal epileptiform discharges (IEDs). These can happen thousands of times a day and interfere with attention, memory, language, and sleep. New data from a study led by scientists at University of California, San Francisco (UCSF) shows that these brain blips are not random events as once thought. The data shows that they unfold in a predictable pattern that can be detected before they occur, suggesting it may be possible to prevent them. 

Details of their work are published in Nature Neuroscience in a paper titled “Laminar organization of cellular microcircuits modulating human interictal epileptiform discharges.” In it, the scientists explain that they used a high-resolution technology recently adapted for humans that records individual neuron activity to track more than 1000 neurons in four patients undergoing surgery for epilepsy. The so-called Neuropixel probes provide “a view into new ways we might address a debilitating aspect of epilepsy that we haven’t been able to tackle,” said Jon Kleen, MD, PhD, an associate professor of neurology at UCSF and co-senior author of the study. 

Preventing brain blips would be a boon for patients’ quality of life because over time, the effects of these mental disruptions can be significant and may account for some of the cognitive impairment experienced by about half of people with epilepsy. 

Neuropixels probes, which are thin devices lined with hundreds of sensors, are designed to record activity throughout the human cortex. This means that unlike current sensors which are limited to brain signals on the surface of the brain, Neuropixels can provide a three-dimensional view of brain activity. For the study, the scientists implanted the probes seven millimeters deep into the part of the brain where patients’ seizures originate—this is the tissue that surgeons typically remove to reduce epilepsy symptoms. 

Inserting the probes here made it possible to observe what happened in the neurons before, during, and after each IED. While seizures appear as a burst of neurons firing in synchrony, when IEDs occur, they unfold sequentially. Specifically, one set of neurons was active about a second before the IED started followed by another set that generated the sharp electrical spike at its peak, and then a third set became active as the IED faded. “We could see individual neurons that were just microns apart from each other playing different roles in the process,” said Alex Silva, the study’s first author and a medical student and doctoral candidate in the UCSF-UC Berkeley Joint PhD program in bioengineering. “It was really striking.”

Previous studies have demonstrated that most neurons involved in IEDs are used in normal cognitive processing. According to this study, nearly 80% of the neurons involved in IEDs were also involved in language and perception. Current implantable devices for epilepsy may be able to help. They include closed loop neurostimulators that can detect abnormal brain activity and deliver electrical pulses that interrupt it. So in the case of IEDs, devices that monitor single neurons could use the activity of the first set of neurons announcing the arrival of the abnormal pattern as a warning signal. “That would be a major step forward, changing treatment from reactively responding to abnormal brain bursts to proactively preventing them in the first place,” Kleen said.

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