Category: 7. Science

Longer thumbs do much more than help primates climb trees. Thumb size evolution tracks with bigger brains, which hints that hands and minds expanded together across our lineage.

A new analysis links thumb length to brain size across many primates and points to the outer brain’s thinking centers as key players. Dr. Joanna Baker of the University of Reading led the work.

Longer thumbs improve grip

Manual dexterity means precise control of the fingers to handle small objects, and it underpins tool use, food processing, and daily tasks.

Longer thumbs increase the area where the thumb meets the fingers, which improves precision grips without needing extra force.

“We’ve always known that our big brains and nimble fingers set us apart, but now we can see they didn’t evolve separately,” said Dr Baker.

These results connect everyday hand skills to the energetic costs of building neural tissue across evolutionary time.

Thumb length and brain size

The team focused on the first and second metacarpal bones, the long bones in the hand that sit between the wrist and the thumb or index finger.

That choice allowed scientists to compare living species with fossils, because bones preserve while behavior does not.

The new study examined 95 fossil and living primate species and tested how relative thumb length tracks with brain size.

The researchers used phylogenetic models that control for shared ancestry. The relationship stayed positive when humans were excluded, which means the signal is primate wide, not only human.

The analysis also checked whether tool-using species looked different from those that have never been recorded using tools.

There was no special offset for tool users after accounting for brain size and hand proportions, which cautions against reading tool use from one bone alone.

Thumb length links to brain’s thinking areas

The big surprise sat in which brain parts matched with longer thumbs. Instead of the cerebellum, which helps coordinate movement, the correlation showed up in the neocortex, the sheet of tissue involved in sensation, planning, and flexible control.

Earlier work documented rapid cerebellar expansion in apes, alongside changes in the neocortex. This means that the new finding does not downplay movement control, it refines where manipulation costs likely landed.

It suggests that sensing, mapping, and planning the contact between fingers and objects scales with hand skill.

In people, primary motor cortex grey matter volume is linked to how well the hand learns a new skilled movement, and disrupting this region can block learning.

That kind of result fits with the new cross-species pattern that ties fine control to cortical resources.

Tool use is not a single trait, and it varies with ecology and learning. Some primates build complex routines that require choosing, transporting, and sequencing objects.

Wild capuchins select and adjust stone hammers as nut properties change, which shows sensitive tuning rather than a one size fits all behavior.

Chimpanzees use different kits for termites versus honey, and group traditions shape how juveniles learn, but none of that maps cleanly onto one hand bone.

This is why the new result matters. It connects a simple, measurable piece of anatomy to overall brain investment while avoiding the trap of over claiming about culture from a single metric.

Unusual thumb and brain pattern

Most primates fell along the same line. Humans and our closest fossil relatives do not sit outside the broader pattern, once brain size is considered.

Australopithecus sediba’s hand stands out as it combines a long thumb with traits linked to climbing and precise grips.

Even here, the message is nuanced, because a long thumb without the full neural and skeletal package may not yield modern, human-like manipulation.

The study also checked where species defied expectations after accounting for brain size and intrinsic hand proportions.

Sediba remained unusual, while others did not, which invites deeper tests that mix simple measures with detailed biomechanics.

What this means

The link between relative thumb length and brain size supports a simple idea.

As species improved their fine control, they paid neural costs in circuits that represent the skin, the joints, and the sequences needed to handle objects.

This does not say that bigger brains came purely from tool use or that dexterity is the only driver.

It shows that everyday actions, like pinching and rotating small items, likely nudged brains to expand in networks that could keep up.

It also sets a baseline for fossils. When researchers add more species and include additional features like joint shape and muscle attachment points, they can test which combinations departed from the primate wide rule and when.

Looking ahead

Future work can map which neocortical fields scale with precision tasks across species. That could include motor and parietal areas that track finger positions in real time and combine touch with vision.

Better fossils and more high resolution scans will help resolve when different hominin lineages shifted their hands and their cortices together.

The same approach can test links between other body parts and neural systems that carry hidden processing costs.

The take home point is clear. Hands and brains changed together, and the bond runs deeper than any single tool tradition or famous species.

The study is published in Communications Biology.

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Gaia has transformed how we see the night sky. What looks calm and unchanging is actually full of motion – stars racing across space, clusters forming and breaking apart, and entire star families stretching thousands of light-years.

For the past decade, the Gaia space observatory has been quietly watching it all unfold with its twin telescopes. Since 2014, Gaia has collected more precise information about our galaxy than any mission before.

The telescopes tracked nearly two billion stars and watched how they move, change, and where they came from. The mission has reshaped our understanding of the Milky Way – and we’re still just scratching the surface.

The accuracy that sets Gaia apart

Gaia didn’t just capture stunning images. It measured the exact positions, movements, brightness, and colors of stars with mind-blowing accuracy. Most telescopes focus on a few objects, but the Gaia mission surveyed billions. And this huge-scale approach has changed everything.

Gaia has caught stars doing strange things like wobbling, expanding, shrinking, or flying into our galaxy after being kicked out of another.

The space observatory achieved its mission of creating the most precise and comprehensive 3D map of the Milky Way, pinpointing exactly where stars live in space, and how fast they’re headed in any direction.

Gaia’s view of star clusters

For decades, scientists believed star clusters were just small, separate groups. There are two main types: open clusters (with hundreds or thousands of stars, mostly near the galaxy’s disc), and globular clusters (which are older and live near the galaxy’s center or edges).

Most stars are born in clusters, but over time, these families break up and drift apart. But Gaia’s data showed that clusters are more connected than we thought.

Some clusters move together in chains or families, stretching far beyond what we could previously detect. The data also revealed that clusters that don’t behave like typical families – some stars fly off in random directions, which is something we never expected.

The mission made it easier to tell which stars actually belong to a cluster and which are just nearby by chance.

“Thanks to Gaia, we can find and remove rogue stars that don’t actually belong to a cluster, making all of our science far more accurate,” said Antonella Vallenari, deputy chair of the Gaia Data Processing and Analysis Consortium (DPAC).

“Gaia can spot and group stars that are born together and moving similarly, even if they’re spread out through space. We’ve used Gaia to find new open clusters ranging from the very small – just a few pairs of co-moving stars! – all the way up those a few thousands strong.”

The neighborhood around the Sun

Gaia didn’t just zoom in on faraway corners of the galaxy. It revolutionized our understanding of the space around our own Sun. It mapped young stars, dark clouds, and even entire star nurseries – places where stars are still being born.

Scientists used to think that some loose groups of young stars, like Orion OB1, were just old clusters that had drifted apart. Gaia proved they were actually born loose, not broken apart. And many of these young stars don’t live in isolation – they’re often part of larger “families” or chains of clusters with common origins.

Gaia’s ability to trace where young stars come from helped scientists study how stars influence their surroundings. As stars explode or throw off gas, they shape the clouds around them and affect how new stars form. This process, called stellar feedback, is messy and complex, and Gaia’s data brought it into focus.

Connections in the Milky Way

Using Gaia’s giant dataset, scientists have discovered that the Milky Way is much more connected than we thought. Star-forming regions and clusters are linked over massive distances.

For instance, a star ring named the Gould Belt proved to be an illusion. Gaia instead revealed stars aligned in long, thin gas structures such as the Radcliffe Wave and the Split.

The mission also taught scientists more about the spiral arms of the Milky Way. It appears young clusters behave differently based on their location in a spiral arm. The arms aren’t as permanent as we assumed – they’re more like temporary features.

Gaia revealed that young clusters often form in unusual shapes – like strings, beads, rings, or filaments. These patterns stick around for millions of years.

Tidal tails: Star clusters unraveling

As star clusters move through the galaxy, they get pulled and stretched by gravity from clouds, dark matter, and other galactic features. This tug-of-war creates long trails of stars called tidal tails.

“Tidal tails aren’t just remnants of a cluster’s past: they’re powerful dynamical tracers that tell the tale of a cluster’s lifetime and place in the galaxy,” said Tereza Jeřábková of Masaryk University.

Before Gaia, these tails were hard to spot – especially in crowded areas of the galaxy. But Gaia’s precision made them clear. It spotted massive tails around the Hyades cluster, which look small to us but stretch thousands of light-years across the sky.

Scientists also used Gaia to confirm that these stars truly broke off from clusters, not just random stars passing by. By studying how the stars in these tails spin, researchers could prove they came from the same place.

Gaia’s methods are now being used to find more tidal tails, even in clusters like Coma Berenices and Praesepe. “Current efforts are focused on detecting more tidal tails, pushing to larger extents and fainter limits, and refining what we know of the constituent stars,” noted Tereza.

Gaia’s legacy and what comes next

Gaia stopped collecting data in March 2025, but its mission is far from over. At this point, less than one-third of its data has been released.

A major data release is coming in December 2026, with the final one expected around the end of 2030. These releases will add even more depth to our understanding of the galaxy.

“Gaia’s datasets are significantly more detailed and precise than any that have come before. It’s no exaggeration to say that the mission has brought about a revolution in Milky Way astronomy, especially when it comes to star clusters,” said Johannes Sahlmann, ESA project scientist for Gaia.

According to Sahlmann, the mission’s discoveries aren’t ending anytime soon. “Gaia’s spacecraft operations may have ended, but its contributions to science are in full swing.”

Image Credit: ESA / Gaia / DPAC / Stefan Payne-Wardenaar.

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Liquid crystals, the same technology found in TV screens, strip thermometers, and mood rings, could soon be used in the next generation of COVID tests. According to scientists at the University of Arkansas and the University of Alabama, such a test could return an accurate result in under two minutes, even when only trace amounts of virus are present.

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You’ve almost certainly interacted with liquid crystals in your life. Even if you weren’t around for the heyday of mood jewelry in the 90s, you’ll have come across a liquid crystal display (LCD) screen on a phone, calculator, or TV. 

As a state of matter, liquid crystals sit somewhere between a solid and a liquid. The crystals themselves are rod-shaped molecules that line up in neat little rows, until something comes along that makes them change their orientation. 

In the LCD screen examples, it’s electricity that does it, leading to light being blocked in different configurations to create the display. In mood jewelry, the crystals respond to temperature, leading to the color changes (and if you didn’t know that we’re sorry to shatter the illusion).

In the new COVID test, it is the binding of the SARS-CoV-2 spike protein to a metallic substrate that triggers the reorientation of the crystals. The reason why it works so well in this context is that liquid crystals like to follow the herd. When a few crystals on a surface start to turn, it triggers a chain reaction among the rest.

“That’s the beauty of liquid crystals. You can capture these events on the surface and transmit them over much larger length-scales,” said corresponding author and assistant professor of chemical engineering Karthik Nayani in a statement.

Nayani and the team tested their sensor with yeast that had been modified to express the COVID spike protein on its surface. It was possible to get a positive result, visible to the naked eye, with around 2,000 copies of the spike protein per milliliter of fluid. A typical saliva sample from an infected person would contain more like 10,000 copies per milliliter at the very least.

They also demonstrated reversibility, something lateral flow tests lack. After introducing anti-SARS-CoV-2 antibodies, the crystals went back to their original configuration. And the sensor was specific, only reacting to the spike protein and not to a range of different control molecules they tried.

Not only could this lead to a cheap, accurate, rapid at-home test for COVID-19 – no more waiting 20 minutes and squinting at a barely visible line – it could also be adapted to other pathogens, even ones we haven’t identified yet.

“The design principles enable the sensor to detect a range of analytes but crucially also novel pathogens for which specific binding interactions are unknown,” the team explain in their paper.

They also say their technology could go further, being used to rapidly detect chemical weapons, nerve agents, pesticides, and harmful gases like formaldehyde. “The dream here is airborne detection,” said Nayani. “Now we’re not even talking about it getting into our body.”

The study is published in the journal Advanced Materials Technologies.

Using the Wide Field Camera 3 (WFC3) instrument aboard the NASA/ESA Hubble Space Telescope, astronomers have captured a stunning new image of the spiral galaxy NGC 7456.

NGC 7456 was discovered by the British astronomer John Herschel on September 4, 1834.

This galaxy lies approximately 51 million light-years away from Earth in the constellation of Grus.

Otherwise known as ESO 346-26, IRAS 22594-3950 or LEDA 70304, it has a diameter of 117,100 light-years.

NGC 7456 is a member of the LDC 1547 galaxy group, a gathering of 16 large galaxies.

“In the Hubble image we see in fine detail the patchy spiral arms of this galaxy, followed by clumps of dark, obscuring dust,” the Webb astronomers said.

“Blossoms of glowing pink are rich reservoirs of gas where new stars are forming, illuminating the clouds around them and causing the gas to emit this tell-tale red light.”

“The Hubble program which collected these data is focused on stellar activity just like this, tracking new stars, clouds of hydrogen and star clusters to learn how the galaxy has evolved through time,” they added.

“Hubble, with its ability to capture visible, ultraviolet and some infrared light, is not the only observatory focused on NGC 7456.”

“ESA’s XMM-Newton satellite has imaged X-rays from the galaxy on multiple occasions, discovering a number of so-called ultraluminous X-ray sources.”

“These small, compact objects emit terrifically powerful X-rays, much more than would be expected for their size.”

“We are still trying to pin down what powers these extreme objects, and NGC 7456 contributes a few more examples.”

“On top of that, the region around the galaxy’s supermassive black hole is spectacularly bright and energetic, making NGC 7456 an active galaxy.”

“Whether looking at its core or its outskirts, at visible light or X-rays, this galaxy has something interesting to show.”

An international team of researchers have successfully derived a quantum version of Bayes’ rule, a cornerstone of probability theory

Their discovery was published on August 28, 2025, in Physical Review Letters and examines how beliefs are updated in the quantum world, where normal physics rules no longer apply

The research was conducted by Professor Valerio Scarani from the Centre for Quantum Technologies and the National University of Singapore, Assistant Professor Ge Bai from the Hong Kong University of Science and Technology, and Professor Francesco Buscemi from Nagoya University in Japan.

Understanding Bayes’ rule

Bayes’ rule was developed in the 18th century by mathematician Thomas Bayes and is a method used to update the probability of a hypothesis based on new evidence. It’s used across a range of fields from medical diagnosis and weather forecasting to data science and machine learning.

Put simply, Bayes’ rule enables individuals to update their expectations in response to new information becoming available. For example, if a person believes they might have the flu and then receives a positive test result, Bayes’ rule helps quantify how much more likely it is that they are actually sick.

This rule is grounded in the idea of conditional probability. It works by updating an individual’s prior belief to a new belief (called the posterior) that takes new information into account.

Bayes’ role in the quantum world

Although the classical Bayes’ rule is well understood, its application in the quantum realm has remained elusive. Quantum systems behave differently from classical ones; they are governed by probabilities and wavefunctions that describe the likelihood of finding a particle in a particular state.

Previously, researchers had proposed several quantum analogues of Bayes’ rule, but none had been derived from a fundamental principle of quantum mechanics. The team adopted a new approach by focusing on how beliefs should adapt in response to new quantum measurements, while maintaining as close a connection as possible to the principle of minimum change.

The principle of minimum change

The principle of minimum change states that when new information is received, beliefs should be adjusted as little as necessary to fit the latest facts. In classical Bayes’ rule, this is reflected mathematically by minimising the distance between the original and updated probability distributions.

To translate this into the quantum domain, the team employed a concept known as quantum fidelity, which measures the closeness of two quantum states to each other. Their goal was to maximise fidelity, or in other words, find the slightest change in belief that still accounts for the observed data.

This led them to derive a quantum Bayes’ rule by maximising fidelity between two mathematical objects representing the forward and reverse processes of measurement and belief update.

Connecting to the Petz map

The team found that in specific scenarios, their newly derived equations matched a well-known mathematical tool in quantum information theory, known as the Petz recovery map. This map, introduced in the 1980s, was considered a promising candidate for a quantum version of Bayes’ rule due to its valuable properties. Still, it had never been derived from first principles before.

Now, with this new research, researchers have not only validated the Petz map’s role in quantum reasoning but also opened the door to new applications, such as quantum error correction and quantum machine learning.

Quantum potential: What does this mean for the future?

The researchers are now exploring whether the principle of minimum change can lead to other quantum analogues by applying it to different mathematical measures. Their findings could further close the gap between classical and quantum reasoning, contributing to the foundation of future quantum technologies.

We performed constrained spherical deconvolution (CSD) and particle-filtering tractography (PFT) with anatomical priors^27,28 on DWI data of 39 healthy participants of the BIL&GIN database (Fig. 1A)²⁹. All tractograms were registered to MNI space³⁰, and the SLS was extracted for each individual leveraging the regions of interest (ROIs) of the JHU cortical atlas (Fig. 1B)³¹. Individual SLS were then concatenated. The N39 SLS was then parceled into sub-SLS using a cortex-to-cortex pairing approach, in which each frontal gyrus was systematically paired with every other gyrus of the same hemisphere as defined by the JHU template (Fig. 1C). sub-SLS from the right hemisphere were flipped and concatenated with their left hemisphere homologous (Fig. 1D). Out of the 119 possible ROI combinations, the cortex-to-cortex pairing approach led to the extraction of 96 non-empty sub-SLS tractograms. Of these, 45 contained more than 200 streamlines and were subsequently processed with QuickBundlesX clustering for the initial exclusion of implausible streamlines based on their geometrical features (Fig. 1D)³². These sub-SLS revealed a defined wiring of connections linking the 4 frontal gyri of the lateral convexity (i.e., IFG, MFG, superior frontal gyrus (SFG), and PrCG) to several regions covering the lateral surface of the parietal (i.e., angular gyrus (AG), postcentral gyrus (PoCG), superior parietal gyrus (SPG) and supramarginal gyrus (SMG)), temporal (i.e., ITG, MTG, STG and temporal pole (T-pole)) and occipital cortices (i.e., inferior (IOG) and middle occipital gyri (MOG)), and a restricted section of the basal temporal cortex (i.e., fusiform gyrus (FuG)) (Fig. 2). Connections involving the FuG, IOG, and T-pole were null when paired with the SFG. On the medial surface, connectivity was confined to the precuneus (PrCu). Conversely, 74 ROI combinations, consisting of 33 empty tractograms (Fig. 2, black circles) and 41 with less than 200 streamlines in both hemispheres (Fig. 2, gray circles), were deemed non-viable. They included the entire connectivity of the fronto-orbital region (lateral fronto-orbital gyrus (LFOG), middle fronto-orbital gyrus (MFOG), and rectus gyrus (RG)) as well as connections to the cuneus (Cu), lingual gyrus (LG), parahippocampal gyrus (PHG), entorhinal cortex (ENT), and superior occipital gyrus (SOG). Additionally, as noted earlier, connections between the SFG and the IOG, FuG, and T-pole were null.

Sub-SLS tractography templates description

This section is dedicated to the anatomical description of the 45 sub-SLS tractography templates obtained with the cortex-to-cortex pairing approach and after the removal of streamlines classified as implausible based on their geometrical features, as represented in Fig. 2. For this description, templates are grouped based on the frontal gyrus from which they originate.

Inferior frontal gyrus

Connections between the IFG and the inferior parietal lobule (IPL), namely AG and SMG, primarily originate from pars opercularis (pOp) and pars triangularis (pTri), with only a few streamlines reaching the dorsal and posterior pars orbitalis (pOrb). These connections course longitudinally within the deep WM of the central region of the hemisphere, running parallel the Sylvian fissure before resurfacing laterally to reach their parietal target. Connections to the AG mostly terminate in its anterior part, while those to the SMG are more evenly distributed. The IFG-PoCG template follows a similar but shorter course, parallelling the Sylvian fissure. For this connection, terminations in the IFG are more densely distributed in pOp and become sparser in pTri, with no streamline reaching pOrb. Posterior terminations cover only the most ventral part of the PoCG. The two templates representing connections to the dorso-medial parietal cortex, namely IFG-SPG and IFG-PrCu, follow a similar anatomical course to the connections described above up to the level of the PoCG, where they shift dorsally to reach their respective targets. These connections arise primarily from pOp, with only a few streamlines reaching pTri, particularly for the IFG-PrCu template, and reach anterior SPG and anterior PrCu. Connections between the IFG and the temporal lobe follow the same longitudinal course as the IFG-parietal connections up to the deep WM at the level of the AG. At this point, streamlines arch around the posterior ramus of the Sylvian fissure, then proceed ventrally with a transverse orientation. After entering the deep WM of the temporal lobe, they proceed towards their target temporal gyrus and start fanning to resurface laterally. While their fanning enlarges both anteriorly and posteriorly for connections to the ITG, MTG, STG, and FuG, connections to the T-pole follow a postero-anterior direction as they traverse the deep WM of the dorsal temporal lobe, parallelling the Sylvian fissure ventrally. Connections reaching the STG and the MTG arise uniformly from both pOp and pTri, with fewer streamlines originating from pOrb, while connections with the FuG, ITG, and T-pole almost exclusively originate from pOp. Posteriorly, connections to the FuG cover only the mid-segment of the ROI. While connections to the ITG, MTG, and STG distribute equally along the posterior ⅔ of the whole gyri, connections to the T-pole correspond to the anterior continuation of these connections. For what concerns connections with the occipital cortex, the IFG-MOG template follows a course similar to IFG-parietal connections, while the IFG-IOG template is more akin to IFG-temporal connections. Indeed, the IFG-MOG template has an overall longitudinal orientation, whereas IFG-IOG fibers arch around the Sylvian fissure, spanning the boundary between the temporal and occipital cortices before fanning out posteriorly to reach the anterior portion of IOG.

Middle frontal gyrus

Connections between the MFG and the IPL originate from the posterior ⅔ of the MFG ROI. After leaving the gyrus, streamlines travel posteriorly within the deep WM of the central region, maintaining a longitudinal orientation along the z-axis (streamlines course at the same height from the posterior end of the MFG to the AG and SMG). Streamlines resurface laterally at the level of the gyrus they terminate into. Posterior terminations are homogeneously distributed across both the AG and SMG ROIs. Templates of connections from the MFG to the PoCG, SPG, and PrCu exhibit a more compact course compared to connections to the IPL, and course slightly dorsal to them. Streamlines connecting to the SPG display more anterior terminations in MFG, covering the posterior ⅔ of the ROI similarly to the IPL connections. In contrast, streamlines to the PoCG and PrCu tend to terminate more posteriorly in the MFG, within the posterior third of the region. Streamlines of the MFG-PoCG template resurface laterally after passing the level of the central sulcus, with posterior terminations covering only the middle third and the inferior part of the dorsal third of the PoCG ROI. Connections from the MFG to the SPG and PrCu shift slightly vertically after passing the level of the postcentral sulcus, reaching the anterior part of the SPG and the entire PrCu ROI. Connections between the MFG and the ITG, MTG, and STG are more widespread, covering the posterior ⅔ of the MFG ROI. In contrast, connections to the FuG and the T-pole tend to arise more posteriorly in MFG, with the MFG-T-pole template covering only the very posterior aspect of the frontal ROI. After coursing longitudinally in the deep WM of the frontal and parietal lobes, MFG-temporal streamlines arch around the Sylvian fissure at the level of the IPL and enter the temporal cortex along a transverse course. Connections to the FuG terminate in the mid-segment of the FuG ROI, while connections to the ITG, MTG, and STG span the posterior ⅔ of the lateral temporal gyri. Connections to the T-pole traverse the deep WM of the superior temporal lobe and extend anteriorly. For what concerns connections with the occipital cortex, the MFG-MOG template resembles in its course MFG-parietal connections, while MFG-IOG conforms with MFG-temporal connections, arching around the Sylvian fissure and coursing ventrally with a transverse orientation before fanning posteriorly to reach the anterior end of the IOG ROI. The MFG-MOG template terminates in the anterior MOG.

Superior frontal gyrus

Connections between the SFG and the parietal lobe originate solely from the posterior third of the SFG. Templates reaching the PoCG, SPG, PrCu, and AG follow a longitudinal course through the deep WM of the dorsal central region. Contrarily, connections reaching the SMG leave the SFG ventrally, follow a main transverse orientation up to the level of the deep WM below the ventral PrCG and abruptly turn longitudinally before traveling posteriorly to the anterior SMG. Terminations in the PoCG cover only the most dorsal portion of the ROI, while connections to the SPG and PrCu cover the entire gyri. The AG is mainly covered in its antero-dorsal part. Connectivity between the SFG and the occipital cortex is represented by the sole SFG-MOG template. It resembles the SFG-AG template in its course, but extends further posteriorly to the anterior MOG. SFG-temporal connections involve only the posterior halves of the ITG, MTG, and STG. These streamlines leave the posterior half of the SFG ventrally and traverse the frontal lobe with a transverse orientation, up to the level of the dorsal IFG. Here, they abruptly switch course to a longitudinal orientation, reaching the deep WM of the IPL before arching into the temporal lobe. Streamlines fan laterally, both posteriorly and anteriorly, at the level of their respective temporal gyrus.

Precentral gyrus

The PrCG-PoCG template represents the only pattern of connectivity between adjacent gyri of the SLS. It consists of U-shaped fibers that span the entire extent of both gyri. Connections between the PrCG and the IPL arise from the ventral half of the PrCG ROI. After coursing briefly through the deep WM of the central region, streamlines resurface laterally, terminating homogeneously along the entire cortical surface of both the AG and SMG. The fanning of these streamlines is larger anteriorly to accommodate for the involvement of the PrCG ROI, which develops primarily along the z-axis. Connections between the PrCG and the SPG or the PrCu originate more dorsally in the PrCG than those to the IPL. Streamlines connecting to the SPG originate from the dorsal half of the PrCG, while those reaching the PrCu originate from the most dorsal third of this region. Streamlines of the PrCG-SPG template group in the deep WM below the PoCG and then turn slightly vertically to reach the anterior half of the SPG, while PrCG-PrCu connections remain dorsal and cover the anterior ⅔ of PrCu. Connections between the PrCG and the temporal lobe arise from the ventral half of the PrCG. Streamlines connecting to the ITG and the MTG are uniformly distributed along this portion of the PrCG ROI, those to the T-pole and the FuG arise more dorsally compared to the others, and those to STG seed only in the most ventral end of the region. Streamlines of the PrCG-temporal templates travel longitudinally up to the deep WM below the IPL, arch around the Sylvian fissure, and follow a transverse course until they reach the deep WM of their target gyrus. Connections to the FuG terminate in the mid-segment of the ROI, while those to the ITG, MTG, and STG cover the posterior ⅔ of the respective gyri. Connections to the T-pole extend more anteriorly, paralleling the Sylvian fissure ventrally, and cover the anterior third of the temporal lobe. As previously described for IFG and MFG, the PrCG-MOG template follows a similar course to PrCG-parietal connections, while streamlines of the PrCG-IOG template arch into the temporal lobe, crossing the boundary between temporal and occipital cortices, and fan posteriorly.

Sub-SLS ex-vivo dissection

Layer-by-layer Klingler microdissection of 6 left and 4 right hemispheres resulted in the identification of 24 dissection layers (henceforth referred to as epochs – abbreviated as “epo” when referring to a specific dissection layer) representing connections of the SLS. In some specimens, (abbreviated as “spc” when referring to a specific specimen) both fronto-parietal and fronto-temporal connectivity were exposed while proceeding with a latero-medial dissection approach (e.g., Fig. 3, spc-01, spc-06, spc-07, spc-10, spc-12, spc-15, and spc-19). Occasionally, fronto-parietal connectivity could be exposed on two subsequent epochs of the same specimen: a lateral one revealing more superficial and shorter fibers, and a medial one revealing deeper and longer ones (e.g., Fig. 3, spc-02 and spc-07). 3D digital models of each epoch were reconstructed with photogrammetry and aligned to the radiological space of each specimen^25,33. A detailed anatomical analysis of the trajectory of the dorsal fibers leaving the frontal cortex and connecting to any other cortex of the same hemisphere was carried out on these models. Each sub-SLS connection visible on the texture of the models was manually annotated with the CloudCompare “Segment Tool” following a cortex-to-cortex pairing approach, compatible with the one adopted for the parcellation of tractography data. The identified sub-SLS annotated on the different epochs are listed and represented in Fig. 3.

Cortex-to-cortex annotation of the SLS connections on the specimens resulted in the identification of 22 sub-SLS, all of which were also previously reconstructed with tractography and represented by sub-SLS templates. These annotations depict connections between the IFG with the IPL (i.e., AG and SMG) as well as with the ITG and MTG. Connections linking the MFG and the PrCG to the lateral aspect of the parietal (i.e., AG, SMG, PoCG, and SPG) and temporal cortices (i.e., STG, MTG, and ITG) were also traced, alongside those to the IOG. The SFG-AG connection was the only sub-SLS identified from the SFG. Fibers reaching the FuG were visible just in one specimen, where they connected to the PrCG.

While the manual annotation of sub-SLS connections on the dissection models confirmed the existence of these connectivity patterns, the concurrent analysis of all the sub-SLS annotations across epochs revealed several organizational principles of the WM fibers of the SLS. Shorter fibers were found to course more superficially, while longer fibers ran more deeply. Specifically, fronto-parietal connections, being shorter by nature, were found on more superficial epochs compared to fronto-temporal fibers, which required the removal of overlaying tissue (e.g., spc-01 and spc-02 epo-06 vs. epo-07 in Fig. 3). While this observation aligns with the classical description of the SLF/AF complex³⁴, we also noted that this organizational principle holds within individual connections defined by specific termination territories. For instance, MFG-AG connection in spc-02 was identified on two consecutive epochs (see Fig. 3, spc-02, epo-05 and epo-06 in orange). On epo-05, MFG-AG appears as a short connection involving only posterior MFG, while on epo-06, deeper fibers extend to reach anterior MFG. This latero-medial organizational pattern contributes to a layered architecture of WM fibers of the SLS, which also respects a ventro-dorsal organization. Ventral regions connect to other ventral regions through fibers coursing ventrally, while dorsal regions are connected to other dorsal regions through fibers coursing dorsally. This is particularly evident in fronto-parietal epochs. For instance, in epo-06 of spc-01 connections between the IFG and the AG course more ventrally compared to those between the MFG and the AG. Conversely, connections between the PrCG and the AG remain dorsal if originating from dorsal PrCG and ventral if arising from ventral PrCG. As seen in epo-07 of spc-02, this organizational layout also applies to fronto-temporal connections. In the case of PrCG-ITG fibers, those arising from the dorsal part of the PrCG after the splitting of the gyrus remain more external as they arch into the temporal lobe (i.e., farther from the posterior end of the Sylvian fissure) compared to fibers originating from the ventral part of the PrCG, which run closer to the Sylvian fissure. Thus, the ventro-dorsal organization in fronto-temporal connections translates into a mirrored pattern relative to the Sylvian fissure. Specifically, connections more ventral in the frontal cortex tend to connect to more dorsal regions in the temporal cortex, following the shape of the Sylvian fissure closely. Interestingly, as fronto-temporal fibers of the SLS arise more dorsally in the frontal cortex and thus course farther from the Sylvian fissure, the shift from a longitudinal course in the frontal and parietal cortices to a transverse course in the temporal cortex becomes less steep. Eventually, this results in an inversion in the fanning of temporal fibers – from more anteriorly directed for internal connections to more posteriorly directed for external connections. This trend culminates in fibers bending posteriorly to reach the occipital cortex, as shown in epo-05 of spc-16.

Sub-SLS templates anatomical evaluation with BraDiPho

All the 45 sub-SLS tractography templates were non-linearly registered to the radiological space of the 10 3D models of ex-vivo dissection for anatomical evaluation (Fig. 1E). Of these, 22 sub-SLS templates obtained with tractography were matched with their corresponding anatomical annotations from ex-vivo dissection, as described in the previous section. Quantitative overlap scores between tractography reconstructions and the corresponding manual annotations on the photogrammetric models of ex-vivo dissection are reported in Supplementary Data 1 and visualized in Supplementary Fig. 1. Across all matched sub-SLS templates, the average overlap between modalities was 88.15% ± 11.60% (mean ± SD; min 67.8%; max 100%). It is important to note that a degree of spatial mismatch may be expected, as tractography is registered to the T1 of the specimens before starting the dissection and minor shifts in tissue conformation occur during the dissection process, particularly as tissues relax following removal of overlying structures.

Based on the representation of the sub-SLS in the ex-vivo dissection models, the tractography templates were further refined. Initially, the selection of plausible streamlines was made without reference to the anatomical features of the sub-SLS, relying instead on plausibility judgment based on the geometrical characteristics of the streamlines. Moreover, this process involved broad clusters encompassing a wide array of streamlines, sometimes intermingling both plausible and implausible ones, possibly leading to the rejection of some plausible streamlines along with implausible ones. While rejecting some plausible streamlines would typically not be problematic in most cases due to the redundancy of tractography, with the correction for density bias in creation of the templates it could result in the loss of relevant reconstructions. Therefore, once anatomical information was obtained through the one-to-one comparison of in-vivo tractography and ex-vivo dissection data, the selection of plausible streamlines was re-performed in Tractome. This allowed for a more selective, connection-specific filtering of implausible streamlines^35,36. The re-evaluation of the sub-SLS templates resulted in a more comprehensive and anatomically informed representation of WM connections, with improved coverage of their anatomical extent as suggested by the dissection data. These anatomically plausible and validated sub-SLS templates are shown in green in Fig. 1. A graphical summary of the anatomical evaluation process with BraDiPho is provided for each template in Supplementary Fig. 1.

The remaining 23 sub-SLS tractography templates were not found in our dissection data. These templates were classified as anatomically plausible but not validated or anatomically implausible based on the information gathered through dissection. The anatomically plausible but not validated templates (Fig. 2, yellow templates) represent connections that were reconstructed with tractography but could not be confirmed in our dissections, although their anatomical course remains consistent with anatomically plausible and validated connections. For instance, the existence of very medial connections, such as those from the MFG/PrCG/SFG to the PrCu, between SFG and dorsal PoCG, and between SFG and SPG, could not be proven, but they could not be excluded either. Indeed, these connections could not be exposed by our latero-medial dissection approach, which was not optimal for exposing such WM pathways. Similarly, connections to the MOG were not found in our dissections, as the dissection of purely longitudinal fibers stopped at the level of the AG. Dissection of the temporal cortex also did not extend to the T-pole, and rarely revealed connections to the basal surface of the hemisphere. The only specimen showing connections to the FuG (i.e., PrCG-FuG, see Fig. 3) was spc-06. Indeed, its unique anatomical configuration showing a “bulge” of the FuG interposing between the ITG and the IOG on the ventral lateral aspect of the convexity allowed the exposure of fibers connecting to this region through a latero-medial dissection approach. Additionally, connections with the IFG were only observed in 3 specimens, leaving anatomically plausible connections such as IFG-PoCG, IFG-STG, and IFG-IOG unproven.

On the other hand, we classified as anatomically implausible those sub-SLS templates showing abrupt changes of direction (Fig. 2, red templates). For instance, sub-SLS templates between the SFG and the temporal cortex (ITG, MTG, and STG) leave the posterior half of the SFG ventrally and traverse the posterior frontal cortex transversally up to the IFG before joining the other fronto-temporal connections of the SLS. Similarly, connections of the SFG-SMG template leave the SFG ventrally, travel vertically up to the level of the IFG, and then abruptly bend longitudinally toward the SMG. A similar but opposed shift in direction is seen in the IFG-SPG and IFG-PrCu templates, which travel longitudinally from the IFG up to the postcentral sulcus, before shifting vertically to reach the dorsal and medial parietal cortices. No such abrupt changes in direction were observed in the ex-vivo dissection of the SLS, therefore, these templates were classified as anatomically implausible. These directional shifts are likely caused by the intersection of longitudinal fibers with the anterior transverse system in the frontal lobe or with projection fibers of the pyramidal tract in the parietal lobe, leading the tractography algorithm to follow undesired directions.

sub-SLS templates replication study

The replication analysis of the sub-SLS tractography templates carried out on the three HCP test-retest tractography datasets distributed on brainlife.io³⁷ with the Fast Streamline Search approach³⁸ revealed a good agreement across tractography reconstructions derived from two different DWI acquisition protocols (i.e., BIL&GIN vs. HCP). Overall, we achieved 0.77 ± 0.27 average wDSC (i.e., dice coefficient on voxels weighted for streamline density) for test and test_run_2 and 0.78 ± 0.26 for retest. When considering only anatomically plausible and validated templates, mean wDSC spiked to 0.92 ± 0.08 for all HCP datasets, while it attested to 0.72 ± 0.29, 0.71 ± 0.3, and 0.71 ± 0.31 for test, test_run_2, and retest respectively, when considering anatomically plausible but not validated templates. On the other hand, wDSC dropped to 0.39 ± 0.22, 0.37 ± 0.19, and 0.43 ± 0.18 when considering sub-SLS templates deemed anatomically implausible. All measurements, including further streamline- and voxel-based similarity measurements on top of wDSC (i.e., bundle adjacency, dice coefficient, density correlation, overlap and overreach) are available in Supplementary Data 2, also reporting mean and standard deviations calculated considering all the templates together as well as filtered according to the classification of the templates (anatomically plausible and validated in green, anatomically plausible but not validated in yellow, and anatomically implausible in red). Single tractography reconstructions and voxel masks showing the overlap between the sub-SLS provided in the present work and sub-SLS extracted from each HCP dataset are displayed in Supplementary Fig. 2 with the respective wDSC. Of note, while on average wDSC dropped for anatomically implausible templates, the visualization provided in Supplementary Fig. 2 shows that implausible streamlines following the exact same course of the ones extracted on the N39 BIL&GIN dataset could also be found in the three HCP datasets. This reinforces the importance of relying on direct anatomical explorations to make tractography-based inferences on the architecture of WM pathways, independently from the characteristic of DWI and tractography.

sub-SLS templates characterization

A numerical description of each anatomically plausible sub-SLS tractography template—both validated and not validated (Fig. 2, green and yellow templates, respectively)—is provided through shape analysis (Supplementary Data 3)³⁹. Additionally, we provide the mean coordinates of both anterior and posterior terminations for each of these templates in MNI space, with “anterior” indicating frontal terminations (Supplementary Data 4).

sub-SLS templates topological organization

The study of the SLS wiring through ex-vivo dissection and the annotation of different sub-SLS on photogrammetric models revealed key organizational principles of the WM fibers of this system. We analyzed the sub-SLS tractography templates to test for the occurrence of the same medio-lateral and dorso-ventral patterns of fiber distribution identified in the dissection study. The combined visualization of centroids of the sub-SLS templates originating in the IFG, MFG, and SFG shows that IFG connections start, course, and terminate more ventrally compared to MFG connections, which in turn are more ventral compared to SFG connections (Fig. 4, middle column, top row, in yellow, orange, and bordeaux, respectively). As previously described in the dissection data, this ordered layout is respected when streamlines bend into the temporal cortex, and defines their distance from the Sylvian fissure when arching around it (i.e., the more dorsal in the frontal cortex, the farther they course from the Sylvian fissure). Connections originating in the PrCG, which parallels the central sulcus and spans the most posterior part of the frontal lobe with a dorso-vental configuration, overlay to those arising from the other frontal gyri (Fig. 4, third column).

The dorso-ventral organization of fibers observed by considering all the frontal gyri together is respected also at the level of the single gyrus (Fig. 4, icons dedicated to IFG, MFG, SFG, and PrCG in the first and second columns). Connections arising in the PrCG provide an illustrative example. As it can be observed in Fig. 4, (second column, PrCG icon) fronto-parietal connections, depicted in green, are organized according to a ventro-dorsal layout, with a sequential organization orderly displaying, from ventral to dorsal, PrCG-SMG, PrCG-AG, PrCG-PoCG (whose centroid lies in the middle of the gyrus, as the connection spans the whole extent of both PrCG and PoCG), PoCG-SPG, and PrCG-PrCu. This organization respects the arrangement of cortical gyri in the parietal cortex, and drives the distribution of the fibers. The same principle applies to connections between the PrCG and both the lateral aspect of the temporal cortex and the occipital lobe. Connections from the PrCG to the STG terminate more ventrally in the PrCG compared to those reaching the MTG, which, in turn, are more ventral than those reaching the ITG. Accordingly, as we proceed from ventral to dorsal in the PrCG – and inversely from dorsal to ventral in the temporal cortex -, streamlines shift from coursing close to the Sylvian fissure to following a more distant trajectory. Similarly, connections to the IOG arise more ventrally in the PrCG compared to those reaching the MOG, and this dorso-ventral organization persists along their entire course. Connections to the T-pole and to the FuG demonstrate a similar organization, forming an additional deeper layer. Indeed, as seen in all the icons of Fig. 4 dedicated to each single gyrus, fronto-parietal connections, depicted in green, are the most superficial, followed by connections to the lateral temporal cortex and to the occipital lobe, which are themselves more superficial compared to connections reaching the anterior temporal lobe (i.e., T-pole) and the basal temporal cortex (i.e., FuG).

This medio-lateral layering reflects the length of streamlines, with shorter connections coursing more superficially and longer connections traveling deeper. This principle is shown in Fig. 5A, B, where streamlines are color-coded based on their distance from the cortex. More superficial fibers (yellow) cover shorter distances, while longer fibers (dark red), travel more deeply. Supplementary Fig. 3 provides the same color-coded representation for each sub-SLS template, further demonstrating that this organizational principle applies broadly across the SLS, even when considering only part of the system. Statistical analysis revealed a significant positive correlation between the length of each streamline and its distance from the cortex (Pearson’s r = 0.689, p < 0.001) (Fig. 5C).

In 2017, astronomers detected ‘Oumuamua, an interstellar object passing through our solar system. Its unusual shape and acceleration patterns defy conventional explanations, leading to various hypotheses, including the possibility of it being an artificial object. However, no consensus has been reached, and ‘Oumuamua remains an unresolved enigma in modern astronomy.

The total lunar eclipse on 7 September 2025 will be visible from the UK as the Moon rises, meaning the Moon will already be eclipsed when it emerges above the horizon.

In fact, totality will have ended about 15 minutes after the Moon pops up above the horizon (as seen from the centre of the UK).

That means, if you want to photograph the 7 September lunar eclipse from the UK, you’ll want to catch it as close to the horizon as possible.

There are many ways to photograph a lunar eclipse and, as you’d expect, some methods work better than others.

Of course, the wild card will be the weather, which caused a lot of issues for much of the UK during the lunar eclipse in March 2025.

All we can do is keep our fingers crossed and hope for crisp, clear, haze-free skies this time around.

Fortunately, with the final partial phase lasting just over an hour, there’s a decent window of opportunity to capture something worthwhile.

Using a smartphone

A smartphone is perfectly acceptable for photographing a lunar eclipse, but the Moon’s apparent diameter of around half a degree will result in a small lunar disc.

Yes, you can zoom in with your phone, but the best results will come from staying within your phone’s optical zoom range.

Beyond that, you’re into digital zooming territory, which is no better than enlarging the image in post-processing using an image editor.

The software increases the number of pixels in the image by interpolating between actual recorded data, filling in gaps with synthetic information.

Get more advice with our guide on how to photograph the Moon with a smartphone.

Camera and lens

A better bet for photographing a lunar eclipse is to use a dedicated camera fitted with a lens of at least 400mm focal length on a full-frame sensor.

This setup will allow you to capture the Moon as a detailed disc with visible surface features.

Mounting the camera on a fixed platform – or, even better, a tracking mount – will help you achieve excellent, sharp images.

The bright lunar surface that is exposed after totality won’t typically require long exposures and so is unlikely to be subject to trailing.

However, if you allow the bright surface to over-expose, you should be able to pull out some of the beautiful umbral colours in the darker part of the shadowed regions.

For this, you’ll need slightly longer exposures, which is where a tracking mount becomes especially useful.

Planetary camera

Another method is to use a planetary camera setup with a wide enough field of view to be able to capture the entire Moon in a single frame.

Planetary setups typically use multi-frame imaging, collecting numerous shots of the same target over a short period of time.

Software such as AutoStakkert! (freeware) can then analyse, quality-assess, register and stack the frames to generate a final image with a higher signal-to-noise ratio than the individual frames.

The trick here is to make sure you don’t extend the capture session too long, otherwise the movement of Earth’s shadow will blur the edge more than usual.

Find out more in our guide to stacking images of the Moon.

6 tips for photographing the September 2025 lunar eclipse

Plan your location

Keep an eye on the weather and, if you’re prepared to put in a little extra effort, identify some alternative locations that might be clear in case you’re clouded out.

Pick sites with unobstructed views in the required direction. From the centre of the UK, the Moon rises at azimuth 100° or just south (right) of east.

Practise, practise, practise

Choose your setup(s) and practise with the Moon on evenings ahead of the eclipse, especially if you intend to relocate on eclipse day.

As a bare minimum, use a DSLR or equivalent with a 400mm or longer lens.

Take your phone for atmospheric shots too, such as the Belt of Venus into which the Moon will rise.

Prepare your camera lens

On evenings before the lunar eclipse, focus your lens as accurately as possible on the Moon.

Then, with the lens set to manual focus, use a length of low-tack electrical tape to secure the focus ring so it can’t rotate.

When the Moon rises on 7 September, the show will already be under way, so you need to be as ready as possible.

Identify where the Moon will rise

To pinpoint exactly where the Moon will appear, visit your chosen location on a night before the eclipse.

Wait for the sky to darken and Saturn to become visible.

Set your camera’s clock accurately (ideally using UT), then take a wide or mid shot of the horizon where you expect the Moon to rise, exposing to capture stars.

Use a stargazing app

Download your images and inspect their EXIF headers using freeware like FastStone.

With a planetarium app, identify stars near the horizon and their azimuths by entering the shot’s date and time. Note the azimuths of visible horizon features.

Using the same app, confirm where the Moon will rise on 7 September.

Give yourself plenty of time

On the night, set up in good time.

Don’t be surprised if the Moon doesn’t show when expected: low haze can easily hide it. Binoculars will very helpful.

Use a low to mid ISO and an aperture around f/8–f/11. Short exposures will capture the bright surface, but try longer ones to reveal colour in the shadowed regions.

If you photograph the 7 September 2025 lunar eclipse, share your images with us by emailing contactus@skyatnightmagazine.com

The joint European Space Agency (ESA) and NASA Solar Orbiter spacecraft has tracked electrons traveling at nearly the speed of light back to the sun, finding they originated in different types of solar outbursts.

Solar Orbiter detected these so-called Solar Energetic Electrons (SEEs) in space after being accelerated to high energies, and researchers were able to pinpoint their source in an attempt to better understand the physics of the sun.