A new AI tool can guess what you're looking at just by analyzing your brain scans--and recreate that image with remarkable precision. It can go the other way too, and predict a person's brain activity based on what they're looking at. In the image above, for example, the left-hand image of each pair is what the user actually saw--and its right-hand counterpart is what the model recreated based on the brain scan. Michal Irani, who developed the tool with her colleagues at the Weizmann Institute of Science in Rehovot, Israel, hopes her "mindreading" tool will ultimately reveal more about how the brain works, and could perhaps be used to help locked-in people communicate, or allow scientists to recreate the content of dreams. Judy Illes, a neuroethicist and professor of neurology at the University of British Columbia in Canada, who was not involved in the research, describes the work as "magnificent." "The idea [of using this approach] to help people with neurologic conditions therapeutically is tremendously exciting," she says. But other scientists warn that a similar approach could be used to reveal the inner thoughts and mental imagery of people, potentially without their consent. "The results seem very impressive," says Tommy Sprague, a neuroscientist at the University of California Santa Barbara. "But if there's a way to surreptitiously extract information about what you're thinking about, then 150 years of sci-fi can come true anytime, and that's worrisome in a lot of ways."

How healthy is your brain? In our efforts to keep our brains healthy, how do we know what is working? It shouldn't have been difficult: 72 x 72. From the back seat, my daughter, newly confident in mental maths, wanted to check her answer. Whether it was because it was the end of the day, I was trying to park or something else, I stalled, cognitively speaking. Lately, though, I have had the sense that my brain isn't firing on all cylinders.

Quantification of brain atrophy currently requires visual rating scales which are time consuming and automated brain image analysis is warranted. We validated our automated deep learning (DL) tool measuring the Global Cerebral Atrophy (GCA) score against trained human raters, and associations with age and cognitive impairment, in representative older (>65 years) patients. CT-brain scans were obtained from patients in acute medicine (ORCHARD-EPR), acute stroke (OCS studies) and a legacy sample. Scans were divided in a 60/20/20 ratio for training, optimisation and testing. CT-images were assessed by two trained raters (rater-1=864 scans, rater-2=20 scans). Agreement between DL tool-predicted GCA scores (range 0-39) and the visual ratings was evaluated using mean absolute error (MAE) and Cohen's weighted kappa. Among 864 scans (ORCHARD-EPR=578, OCS=200, legacy scans=86), MAE between the DL tool and rater-1 GCA scores was 3.2 overall, 3.1 for ORCHARD-EPR, 3.3 for OCS and 2.6 for the legacy scans and half had DL-predicted GCA error between -2 and 2. Inter-rater agreement was Kappa=0.45 between the DL-tool and rater-1, and 0.41 between the tool and rater- 2 whereas it was lower at 0.28 for rater-1 and rater-2. There was no difference in GCA scores from the DL-tool and the two raters (one-way ANOVA, p=0.35) or in mean GCA scores between the DL-tool and rater-1 (paired t-test, t=-0.43, p=0.66), the tool and rater-2 (t=1.35, p=0.18) or between rater-1 and rater-2 (t=0.99, p=0.32). DL-tool GCA scores correlated with age and cognitive scores (both p<0.001). Our DL CT-brain analysis tool measured GCA score accurately and without user input in real-world scans acquired from older patients. Our tool will enable extraction of standardised quantitative measures of atrophy at scale for use in health data research and will act as proof-of-concept towards a point-of-care clinically approved tool.

Shaded regions indicate 95% confidence intervals.

The covid-19 pandemic may have accelerated the ageing of our brains even before we caught the infection. Research suggests that even relatively early on in the outbreak, brains aged by 5.5 months, possibly due to stress or lifestyle changes. We know that many people with long covid experience brain fog, but years after the arrival of covid-19, the pandemic's broader neurological impact is far from fully understood. To get a grasp on this, Ali-Reza Mohammadi-Nejad at Nottingham University, UK, and his colleagues trained a machine learning model on 15,000 brain scans to identify how its structure changes with age. They then fed the model pairs of brain scans from 996 volunteers from the UK Biobank study.

Breakthroughs, discoveries, and DIY tips sent every weekday. That euphoric feeling when a great idea strikes or a challenging puzzle piece fits into place is electric–and also helps our brains. Now, a team of researchers from the United States and Germany have taken a peek inside the brain to see what those so-called aha, lightbulb, or eureka moments look like. The new brain imaging shows that these flashes of insights reshape how the brain represents information and helps burn it into our memory. According to Maxi Becker, a study co-author and cognitive neuroscientist at Humboldt University in Berlin, if you have one of these aha moments when solving a problem, "you're actually more likely to remember the solution.'" The findings are detailed in a study published May 9 in the journal Nature Communications.

"The statistics are frightening: Dementia is the biggest killer in the UK. It has been the leading cause of death for women since 2011," says Hilary Evans, CEO of Alzheimer's Research UK and cochair of the UK Dementia Mission. "One in two of us will be affected by dementia either by caring for someone with the condition or developing it ourselves." There are reasons for optimism, however, with Alzheimer's researchers achieving extraordinary breakthroughs in the treatment of the disease. In May 2023, drugmaker Lilly announced that its new Alzheimer's drug, donanemab, slowed cognitive decline by 35 percent; in 2022, another drug, lecanemab, registered similarly promising results.

Men's brains tend to be larger than women's, which makes them difficult to compare Are men's and women's brains all that different? A new way of investigating this question has concluded that they are – but it takes artificial intelligence (AI) to distinguish between them. The question of whether we can measure differences between men's and women's brains has long been contentious, with previous research coming up with contradictory results. One problem is that men tend to have slightly larger brains than women, probably because they generally have larger bodies, and some previous studies that compared the size of different small regions of the brain failed to adjust for the overall brain volume. However, even doing so hasn't previously resulted in clear-cut findings.

Synthesizing healthy brain scans from diseased brain scans offers a potential solution to address the limitations of general-purpose algorithms, such as tissue segmentation and brain extraction algorithms, which may not effectively handle diseased images. We consider this a 3D inpainting task and investigate the adaptation of 2D inpainting methods to meet the requirements of 3D magnetic resonance imaging (MRI) data. Our contributions encompass potential modifications tailored to MRI-specific needs, and we conducted evaluations of multiple inpainting techniques using the BraTS2023 Inpainting datasets to assess their efficacy and limitations.

Now, there's concern that neuroscientists might be doing the same by developing technologies capable of "decoding" our thoughts and laying bare the hidden contents of our mind. Though neural decoding has been in development for decades, it broke into popular culture earlier this year, thanks to a slew of high-profile papers. In one, researchers used data from implanted electrodes to reconstruct the Pink Floyd song participants were listening to. In another paper, published in Nature, scientists combined brain scans with AI-powered language generators (like those undergirding ChatGPT and similar tools) to translate brain activity into coherent, continuous sentences. This method didn't require invasive surgery, and yet it was able to reconstruct the meaning of a story from purely imagined, rather than spoken or heard, speech.