#Brain Mapping and the Connectome

[thetendertongue]

connectome — a comprehensive map of neural connections in the brain, and may be thought of as its "wiring diagram." an organism's nervous system is made up of neurons which communicate through synapses. a connectome is constructed by tracing the neuron in a nervous system and mapping where neurons are connected through synapses

image from wikipedia, a rendering of a group connectome based on 20 different subjects.

[bpod-bpod]

Total Connection

The wiring diagram of a whole adult female fruit fly brain. This map or connectome represents the 50 million chemical synapses or connections between 139,255 neurons and is helping advance understanding of the brain functioning as a whole

Read the published research article here

Image from work by Sven Dorkenwald and colleagues

Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA

Image originally published with a Creative Commons Attribution 4.0 International (CC BY 4.0)

Published in Nature, October 2024

You can also follow BPoD on Instagram, Twitter and Facebook

[usa-journal]

Breakthrough in Fly Brain Research Paves Way for Understanding Human Cognition

Scientists have achieved a monumental breakthrough by mapping the fly brain, revealing the position, shape, and connections of all its 130,000 cells and 50 million intricate connections. This research represents the most detailed analysis of an adult animal's brain to date and is being hailed as a "huge leap" in understanding human cognition.

The fly's brain, though tiny, supports a range of complex behaviors, including walking, hovering, and even producing mating songs. Dr. Gregory Jefferis, a leader in the research from the Medical Research Council's Laboratory of Molecular Biology in Cambridge, emphasizes that this mapping could illuminate the mechanisms behind thought processes in humans. He noted the lack of understanding about how brain cell networks facilitate our interactions with the world.

Despite humans having a million times more neurons than the fruit fly, the new wiring diagram, or connectome, will aid scientists in deciphering cognitive functions. Published in the journal Nature, the imagery showcases a stunningly complex structure that reveals how a small organ can perform powerful computational tasks.

Dr. Mala Murthy, co-leader of the project from Princeton University, stated that this connectome will be transformative for neuroscientists, allowing for a better understanding of healthy brain function and the potential to compare it with malfunctioning brains.

Dr. Lucia Prieto Godino from the Francis Crick Institute supports this view, highlighting that while simpler organisms like worms and maggots have had their connectomes mapped, the fly’s intricate wiring is a significant achievement. This success paves the way for mapping larger brains, potentially leading to a human connectome in the future.

The research team has successfully identified separate circuits for various functions, illustrating how movement-related circuits are positioned at the base of the brain, while those responsible for vision are located on the sides. The study not only identifies these circuits but also explains their connections, enhancing our understanding of neural processing.

Interestingly, researchers are already applying these circuit diagrams to understand why flies are so hard to catch. The wiring related to vision quickly processes incoming threats, sending signals to the fly's legs to jump away faster than conscious thought.

To create the wiring diagram, researchers used a technique involving slicing the fly brain into 7,000 incredibly thin pieces, photographing each slice, and digitally reconstructing the whole. They employed artificial intelligence to analyze neuron shapes and connections, correcting over three million errors manually.

Dr. Philipp Schlegel from the Medical Research Council highlights that this data serves as a comprehensive map of brain connectivity, akin to a detailed Google Maps for the neural networks. This combined information will facilitate countless discoveries in neuroscience in the coming years.

While a human connectome remains elusive due to the complexity of the human brain, researchers believe that advancements in technology may allow for such mapping in about three decades. The fly brain research marks a significant step toward unlocking the mysteries of human cognition and understanding our own minds better.

The study was conducted by the FlyWire Consortium, an international collaboration of scientists dedicated to advancing neuroscience.

[jcmarchi]

Neuroscientists create a comprehensive map of the cerebral cortex

New Post has been published on https://thedigitalinsider.com/neuroscientists-create-a-comprehensive-map-of-the-cerebral-cortex/

Neuroscientists create a comprehensive map of the cerebral cortex

By analyzing brain scans taken as people watched movie clips, MIT researchers have created the most comprehensive map yet of the functions of the brain’s cerebral cortex.

Using functional magnetic resonance imaging (fMRI) data, the research team identified 24 networks with different functions, which include processing language, social interactions, visual features, and other types of sensory input.

Many of these networks have been seen before but haven’t been precisely characterized using naturalistic conditions. While the new study mapped networks in subjects watching engaging movies, previous works have used a small number of specific tasks or examined correlations across the brain in subjects who were simply resting.

“There’s an emerging approach in neuroscience to look at brain networks under more naturalistic conditions. This is a new approach that reveals something different from conventional approaches in neuroimaging,” says Robert Desimone, director of MIT’s McGovern Institute for Brain Research. “It’s not going to give us all the answers, but it generates a lot of interesting ideas based on what we see going on in the movies that’s related to these network maps that emerge.”

The researchers hope that their new map will serve as a starting point for further study of what each of these networks is doing in the brain.

Desimone and John Duncan, a program leader in the MRC Cognition and Brain Sciences Unit at Cambridge University, are the senior authors of the study, which appears today in Neuron. Reza Rajimehr, a research scientist in the McGovern Institute and a former graduate student at Cambridge University, is the lead author of the paper.

Precise mapping

The cerebral cortex of the brain contains regions devoted to processing different types of sensory information, including visual and auditory input. Over the past few decades, scientists have identified many networks that are involved in this kind of processing, often using fMRI to measure brain activity as subjects perform a single task such as looking at faces.

In other studies, researchers have scanned people’s brains as they do nothing, or let their minds wander. From those studies, researchers have identified networks such as the default mode network, a network of areas that is active during internally focused activities such as daydreaming.

“Up to now, most studies of networks were based on doing functional MRI in the resting-state condition. Based on those studies, we know some main networks in the cortex. Each of them is responsible for a specific cognitive function, and they have been highly influential in the neuroimaging field,” Rajimehr says.

However, during the resting state, many parts of the cortex may not be active at all. To gain a more comprehensive picture of what all these regions are doing, the MIT team analyzed data recorded while subjects performed a more natural task: watching a movie.

“By using a rich stimulus like a movie, we can drive many regions of the cortex very efficiently. For example, sensory regions will be active to process different features of the movie, and high-level areas will be active to extract semantic information and contextual information,” Rajimehr says. “By activating the brain in this way, now we can distinguish different areas or different networks based on their activation patterns.”

The data for this study was generated as part of the Human Connectome Project. Using a 7-Tesla MRI scanner, which offers higher resolution than a typical MRI scanner, brain activity was imaged in 176 people as they watched one hour of movie clips showing a variety of scenes.

The MIT team used a machine-learning algorithm to analyze the activity patterns of each brain region, allowing them to identify 24 networks with different activity patterns and functions.

Some of these networks are located in sensory areas such as the visual cortex or auditory cortex, as expected for regions with specific sensory functions. Other areas respond to features such as actions, language, or social interactions. Many of these networks have been seen before, but this technique offers more precise definition of where the networks are located, the researchers say.

“Different regions are competing with each other for processing specific features, so when you map each function in isolation, you may get a slightly larger network because it is not getting constrained by other processes,” Rajimehr says. “But here, because all the areas are considered together, we are able to define more precise boundaries between different networks.”

The researchers also identified networks that hadn’t been seen before, including one in the prefrontal cortex, which appears to be highly responsive to visual scenes. This network was most active in response to pictures of scenes within the movie frames.

Executive control networks

Three of the networks found in this study are involved in “executive control,” and were most active during transitions between different clips. The researchers also observed that these control networks appear to have a “push-pull” relationship with networks that process specific features such as faces or actions. When networks specific to a particular feature were very active, the executive control networks were mostly quiet, and vice versa.

“Whenever the activations in domain-specific areas are high, it looks like there is no need for the engagement of these high-level networks,” Rajimehr says. “But in situations where perhaps there is some ambiguity and complexity in the stimulus, and there is a need for the involvement of the executive control networks, then we see that these networks become highly active.”

Using a movie-watching paradigm, the researchers are now studying some of the networks they identified in more detail, to identify subregions involved in particular tasks. For example, within the social processing network, they have found regions that are specific to processing social information about faces and bodies. In a new network that analyzes visual scenes, they have identified regions involved in processing memory of places.

“This kind of experiment is really about generating hypotheses for how the cerebral cortex is functionally organized. Networks that emerge during movie watching now need to be followed up with more specific experiments to test the hypotheses. It’s giving us a new view into the operation of the entire cortex during a more naturalistic task than just sitting at rest,” Desimone says.

The research was funded by the McGovern Institute, the Cognitive Science and Technology Council of Iran, the MRC Cognition and Brain Sciences Unit at the University of Cambridge, and a Cambridge Trust scholarship.

[goldislops]

Largest Brain Map Ever Reveals Fruit Fly’s Neurons in Exquisite Detail

Wiring diagram lays out connections between nearly 140,000 neurons and reveals new types of nerve cell

50 largest neurons of the fly brain connectome.

50 largest neurons of the fly brain connectome.

Tyler Sloan and Amy Sterling for FlyWire, Princeton University, (Dorkenwald et al., Nature, 2024)

Wiring diagram lays out connections between nearly 140,000 neurons and reveals new types of nerve cell

A fruit fly might not be the smartest organism, but scientists can still learn a lot from its brain. Researchers are hoping to do that now that they have a new map — the most complete for any organism so far — of the brain of a single fruit fly (Drosophila melanogaster). The wiring diagram, or ‘connectome’, includes nearly 140,000 neurons and captures more than 54.5 million synapses, which are the connections between nerve cells.

“This is a huge deal,” says Clay Reid, a neurobiologist at the Allen Institute for Brain Science in Seattle, Washington, who was not involved in the project but has worked with one of the team members who was. “It’s something that the world has been anxiously waiting for, for a long time.”

The map is described in a package of nine papers about the data published in Nature today. Its creators are part of a consortium known as FlyWire, co-led by neuroscientists Mala Murthy and Sebastian Seung at Princeton University in New Jersey.

On supporting science journalism

If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.

A long road

Seung and Murthy say that they’ve been developing the FlyWire map for more than four years, using electron microscopy images of slices of the fly’s brain. The researchers and their colleagues stitched the data together to form a full map of the brain with the help of artificial-intelligence (AI) tools.

But these tools aren’t perfect, and the wiring diagram needed to be checked for errors. The scientists spent a great deal of time manually proofreading the data — so much time that they invited volunteers to help. In all, the consortium members and the volunteers made more than 3 million manual edits, according to co-author Gregory Jefferis, a neuroscientist at the University of Cambridge, UK. (He notes that much of this work took place in 2020, when fly researchers were at loose ends and working from home during the COVID-19 pandemic.)

But the work wasn’t finished: the map still had to be annotated, a process in which the researchers and volunteers labelled each neuron as a particular cell type. Jefferis compares the task to assessing satellite images: AI software might be trained to recognize lakes or roads in such images, but humans would have to check the results and name the specific lakes or roads themselves. All told, the researchers identified 8,453 types of neuron — much more than anyone had expected. Of these, 4,581 were newly discovered, which will create new research directions, Seung says. “Every one of those cell types is a question,” he adds.

The team was surprised by some of the ways in which the various cells connect to one another, too. For instance, neurons that were thought to be involved in just one sensory wiring circuit, such as a visual pathway, tended to receive cues from multiple senses, including hearing and touch1. “It’s astounding how interconnected the brain is,” Murthy says.

Exploring the map

The FlyWire map data have been available for the past few years for researchers to explore. This has enabled scientists to learn more about the brain and about fruit flies — findings that are captured in some of the papers published in Nature today.

In one paper, for example, researchers used the connectome to create a computer model of the entire fruit-fly brain, including all the connections between neurons. They tested it by activating neurons that they knew either sense sweet or bitter tastes. These neurons then launched a cascade of signals through the virtual fly’s brain, ultimately triggering motor neurons tied to the fly’s proboscis — the equivalent of the mammalian tongue. When the sweet circuit was activated, a signal for extending the proboscis was transmitted, as if the insect was preparing to feed; when the bitter circuit was activated, this signal was inhibited. To validate these findings, the team activated the same neurons in a real fruit fly. The researchers learnt that the simulation was more than 90% accurate at predicting which neurons would respond and therefore how the fly would behave.

In another study, researchers describe two wiring circuits that signal a fly to stop walking. One of these contains two neurons that are responsible for halting ‘walk’ signals sent from the brain when the fly wants to stop and feed. The other circuit includes neurons in the nerve cord, which receives and processes signals from the brain. These cells create resistance in the fly’s leg joints, allowing the insect to stop while it grooms itself.

One limitation of the new connectome is that it was created from a single female fruit fly. Although fruit-fly brains are similar to each other, they are not identical. Until now, the most complete connectome for a fruit-fly brain was a map of a ‘hemibrain’ — a portion of a fly’s brain containing around 25,000 neurons. In one of the Nature papers out today, Jefferis, Davi Bock, a neurobiologist at the University of Vermont in Burlington, and their colleagues compared the FlyWire brain with the hemibrain.

Some of the differences were striking. The FlyWire fly had almost twice as many neurons in a brain structure called the mushroom body, which is involved in smell, compared with the fly used in the hemibrain-mapping project. Bock thinks the discrepancy could be because the hemibrain fly might have starved while it was still growing, which harmed its brain development.

The FlyWire researchers say that much work remains to be done to fully understand the fruit-fly brain. For instance, the latest connectome shows only how neurons connect through chemical synapses, across which molecules called neurotransmitters send information. It doesn’t offer any information about electrical connectivity between neurons or about how neurons chemically communicate outside synapses. And Murthy hopes to eventually have a male fly connectome, too, which would allow researchers to study male-specific behaviours such as singing. “We’re not done, but it’s a big step,” Bock says.

This article is reproduced with permission and was first published on October 2, 2024.

[in-sightjournal]

Ask A Genius 986: Getting Fired From Jobs

Scott Douglas Jacobsen: I’ve only ever been fired from one job. I was 15 years old, working at a bistro owned by a family friend in my hometown. I remember being quite unpleasant at the time. One day, the dishwasher said they didn’t think it would work out. It reminded me of that Chris Rock joke about hating a job so much that he would sit on the toilet to make more time pass. I did the same…

[transgenderer]

Researchers are hoping to do that now that they have a new map — the most complete for any organism so far — of the brain of a single fruit fly (Drosophila melanogaster). The wiring diagram, or ‘connectome’, includes nearly 140,000 neurons and captures more than 54.5 million synapses, which are the connections between nerve cells.

n one paper2, for example, researchers used the connectome to create a computer model of the entire fruit-fly brain, including all the connections between neurons. They tested it by activating neurons that they knew either sense sweet or bitter tastes. These neurons then launched a cascade of signals through the virtual fly’s brain, ultimately triggering motor neurons tied to the fly’s proboscis — the equivalent of the mammalian tongue. When the sweet circuit was activated, a signal for extending the proboscis was transmitted, as if the insect was preparing to feed; when the bitter circuit was activated, this signal was inhibited. To validate these findings, the team activated the same neurons in a real fruit fly. The researchers learnt that the simulation was more than 90% accurate at predicting which neurons would respond and therefore how the fly would behave.

In another study3, researchers describe two wiring circuits that signal a fly to stop walking. One of these contains two neurons that are responsible for halting ‘walk’ signals sent from the brain when the fly wants to stop and feed. The other circuit includes neurons in the nerve cord, which receives and processes signals from the brain. These cells create resistance in the fly’s leg joints, allowing the insect to stop while it grooms itself.

is there some like deceptive hype language here or is this just like. absolutely bonkers. full fly brain in the computer

[compneuropapers]

Interesting Papers for Week 3, 2025

Synaptic weight dynamics underlying memory consolidation: Implications for learning rules, circuit organization, and circuit function. Bhasin, B. J., Raymond, J. L., & Goldman, M. S. (2024). Proceedings of the National Academy of Sciences, 121(41), e2406010121.

Characterization of the temporal stability of ToM and pain functional brain networks carry distinct developmental signatures during naturalistic viewing. Bhavna, K., Ghosh, N., Banerjee, R., & Roy, D. (2024). Scientific Reports, 14, 22479.

Connectomic reconstruction predicts visual features used for navigation. Garner, D., Kind, E., Lai, J. Y. H., Nern, A., Zhao, A., Houghton, L., … Kim, S. S. (2024). Nature, 634(8032), 181–190.

Socialization causes long-lasting behavioral changes. Gil-Martí, B., Isidro-Mézcua, J., Poza-Rodriguez, A., Asti Tello, G. S., Treves, G., Turiégano, E., … Martin, F. A. (2024). Scientific Reports, 14, 22302.

Neural pathways and computations that achieve stable contrast processing tuned to natural scenes. Gür, B., Ramirez, L., Cornean, J., Thurn, F., Molina-Obando, S., Ramos-Traslosheros, G., & Silies, M. (2024). Nature Communications, 15, 8580.

Lack of optimistic bias during social evaluation learning reflects reduced positive self-beliefs in depression and social anxiety, but via distinct mechanisms. Hoffmann, J. A., Hobbs, C., Moutoussis, M., & Button, K. S. (2024). Scientific Reports, 14, 22471.

Causal involvement of dorsomedial prefrontal cortex in learning the predictability of observable actions. Kang, P., Moisa, M., Lindström, B., Soutschek, A., Ruff, C. C., & Tobler, P. N. (2024). Nature Communications, 15, 8305.

A transient high-dimensional geometry affords stable conjunctive subspaces for efficient action selection. Kikumoto, A., Bhandari, A., Shibata, K., & Badre, D. (2024). Nature Communications, 15, 8513.

Presaccadic Attention Enhances and Reshapes the Contrast Sensitivity Function Differentially around the Visual Field. Kwak, Y., Zhao, Y., Lu, Z.-L., Hanning, N. M., & Carrasco, M. (2024). eNeuro, 11(9), ENEURO.0243-24.2024.

Transformation of neural coding for vibrotactile stimuli along the ascending somatosensory pathway. Lee, K.-S., Loutit, A. J., de Thomas Wagner, D., Sanders, M., Prsa, M., & Huber, D. (2024). Neuron, 112(19), 3343-3353.e7.

Inhibitory plasticity supports replay generalization in the hippocampus. Liao, Z., Terada, S., Raikov, I. G., Hadjiabadi, D., Szoboszlay, M., Soltesz, I., & Losonczy, A. (2024). Nature Neuroscience, 27(10), 1987–1998.

Third-party punishment-like behavior in a rat model. Mikami, K., Kigami, Y., Doi, T., Choudhury, M. E., Nishikawa, Y., Takahashi, R., … Tanaka, J. (2024). Scientific Reports, 14, 22310.

The morphospace of the brain-cognition organisation. Pacella, V., Nozais, V., Talozzi, L., Abdallah, M., Wassermann, D., Forkel, S. J., & Thiebaut de Schotten, M. (2024). Nature Communications, 15, 8452.

A Drosophila computational brain model reveals sensorimotor processing. Shiu, P. K., Sterne, G. R., Spiller, N., Franconville, R., Sandoval, A., Zhou, J., … Scott, K. (2024). Nature, 634(8032), 210–219.

Decision-making shapes dynamic inter-areal communication within macaque ventral frontal cortex. Stoll, F. M., & Rudebeck, P. H. (2024). Current Biology, 34(19), 4526-4538.e5.

Intrinsic Motivation in Dynamical Control Systems. Tiomkin, S., Nemenman, I., Polani, D., & Tishby, N. (2024). PRX Life, 2(3), 033009.

Coding of self and environment by Pacinian neurons in freely moving animals. Turecek, J., & Ginty, D. D. (2024). Neuron, 112(19), 3267-3277.e6.

The role of training variability for model-based and model-free learning of an arbitrary visuomotor mapping. Velázquez-Vargas, C. A., Daw, N. D., & Taylor, J. A. (2024). PLOS Computational Biology, 20(9), e1012471.

Rejecting unfairness enhances the implicit sense of agency in the human brain. Wang, Y., & Zhou, J. (2024). Scientific Reports, 14, 22822.

Impaired motor-to-sensory transformation mediates auditory hallucinations. Yang, F., Zhu, H., Cao, X., Li, H., Fang, X., Yu, L., … Tian, X. (2024). PLOS Biology, 22(10), e3002836.

[eternal-echoes]

“To be fair, critics are correct that it is an oversimplification to speak of a "male brain" versus a "female brain." A close examination reveals extensive overlap in the structures and functions of the brains of men and women. Like their bodies, the brains of males and females have much in common, and much that is distinct.(32) Therefore, rather than speaking of a male or female brain, it is more precise to speak of the brain of a male or female. It is the person who is sexually dimorphic, not one's brain itself.

However, there are also profound sexual differences in the brain.(33) Whereas males have a greater number of white matter connections running from the front to the rear of their brain, women have more connections between the two hemispheres.(34) High levels of fetal testosterone results in a smaller corpus collosum in brains of men, which serves to transmit information from one side of the brain to the other.(35)

When performing identical activities, different regions of the brain are activated, based upon one's sex. For example, a specific region of the left brain is activated during speech for males, whereas women experience activation in different regions on both sides of the brain.(36) This explains why men take longer to recover speech following a stroke.(37) It also explains why a man might become mute after being injured on the left side of his head, whereas a woman struck in the same location is likely to keep on talking!(38)”

-Jason Evert, Male, Female, or Other: A Catholic Guide to Understanding Gender

—

Work cited:

32) Cf. M. Hines, "Sex-Related Variation in Human Behavior and the Brain," Trends in Cognitive Science 14:10 (2010), 448-56; M. Hines, "Gender Development and the Human Brain," Annual Review of Neuroscience 34 (2011), 69-88; McCarthy, Sex and the Developing Brain; Marianne J. Legato, MD, ed., Principles of Gender-Specific Medicine: Gender in the Genomic Era, 3rd ed. (Amsterdam: Academic Press, 2017).

33) Cf. Larry Cahill, "His Brain, Her Brain," Scientific American (October 1, 2012).

34) Cf. M. Ingalhalikar et al., "Sex Differences in the Structural Connectome of the Human Brain," Proceedings of the National Academy of Sciences of The United States of America 11:2 (2003), 823-828; Larry Cahill, "Fundamental Sex Difference in Human Brain Architecture," Proceedings of the National Academy of Sciences of the United States of America 111:2 (2014): 577-578.

35) Cf. Glezerman, Gender Medicine, 64.

36) Cf. B. Shaywitz, et al. "Sex Differences in the Functional Organization of the Brain for Language," Nature 373 (2004), 607-609.

37) Cf. D. Kent et al., "Sex-Based Differences in response to Recombinant Tissue Plasminogen Activator in Acute Ischemic Stroke," Stroke 36 (2005), 62-65.

38) Cf. B. and A. Pease, Why Men Don't Listen & Women Can't Read Maps, 65.

—

For more recommended resources on gender dysphoria, click here.

[jackoshadows]

Fly brain breakthrough 'huge leap' to unlock human mind

Whole-brain annotation and multi-connectome cell typing of Drosophila

A fruit fly's brain is smaller than a poppy seed, but it packs tremendous complexity into that tiny space. Over 140,000 neurons are joined together by more than 490 feet of wiring, as long as four blue whales placed end to end. Hundreds of scientists mapped out those connections in stunning detail in a series of papers published on Wednesday in the journal Nature. The wiring diagram will be a boon to researchers who have studied the nervous system of the fly species, Drosophila melanogaster, for generations. Previously, a tiny worm was the only adult animal to have had its brain entirely reconstructed, with just 385 neurons in its entire nervous system. The new fly map is "the first time we've had a complete map of any complex brain," said Mala Murthy, a neurobiologist at Princeton who helped lead the effort.

The mapping began in 2013, when Davi Bock, a neuroscientist then at the Janelia Research Campus of the Howard Hughes Medical Institute in Virginia, and his colleagues dunked the brain of an adult fly in a chemical bath, hardening it into a solid block. They shaved an exquisitely thin layer off the top of the block and used a microscope to take pictures of it. Then the scientists shaved another layer and took a new picture. To capture the entire brain, they imaged 7,050 sections and produced about 21 million pictures. Dr. Seung and his colleagues also developed software to interpret these images. They programmed computers to recognize the cross-sections of neurons in each picture and stack them into the 3-D shapes of the cells.

Dr. Shiu's team tested the simulated brain by seeing how it responded to food. A fly's tongue-like proboscis is covered in neurons that are sensitive to sugar. The researchers activated them and watched the signals race through the fly's brain. The simulated brain did what a real brain would: It commanded the proboscis to stick out so that the fly could eat. And if the virtual fly tasted sugar only on the right side of its proboscis, the brain sent commands to bend it toward the right.

There's more at the link including a visualization video of the different systems of the brain. Very interesting stuff!

[nuadox]

Researchers release the most extensive dataset of neural connections ever recorded

- By Nuadox Crew -

Harvard and Google researchers, led by Jeff Lichtman of Harvard, have published a groundbreaking study in the journal Science detailing the largest 3D brain reconstruction ever created.

The study showcases a high-resolution map of a cubic millimeter of human cortex, which astonishingly contains 57,000 cells, 230 millimeters of blood vessels, 150 million synapses, and equates to about 1,400 terabytes of data.

This work is part of a nearly decade-long collaboration that merges advanced electron microscopy and AI algorithms to color-code and map the intricate neural wiring of mammals.

The ultimate objective of this ongoing research, which is part of the National Institutes of Health BRAIN Initiative, is to develop a detailed neural wiring map of a mouse's brain, projected to contain 1,000 times more data than the current human cortex sample.

The study not only advances our understanding of brain structure, including rare neural formations seen in epilepsy patients but also enhances tools available for connectomics—a field dedicated to the comprehensive mapping of brain connections to better understand brain function and disease.

Moving forward, the team plans to focus on mapping the mouse hippocampal formation, key in studying memory and neurological diseases.

--

Header image: Six layers of excitatory neurons, color-coded according to depth. Credit: Google Research and Lichtman Lab.

Source: Anne J. Manning, The Harvard Gazette

Full study: Alexander Shapson-Coe et al, A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution, Science (2024). DOI: 10.1126/science.adk4858. www.science.org/doi/10.1126/science.adk4858

Read Also

3D mapping a fruit fly’s brain (video)

[perdvivly]

Complete drosophila connectome actually seems like an incredibly important step forwards in my im-not-particularly-educated-in-this-field onion

[govindhtech]

Geospatial Reasoning: New Approach To AI Spatial Analysis

How artificial intelligence is improving scientific research for practical benefit.

Geospatial reasoning gives intelligent maps a voice and cognitive process. You can ask it simple questions like a friend.

Google research teams employ AI to answer basic scientific questions and study quantum computing, geospatial science, biological science, and neurology.

Artificial intelligence has historically driven scientific progress at Google, and the current rate is unmatched. AI's advancements have accelerated and expanded the "magic cycle" of research from breakthrough to practical impact.

AI boosts human creativity. Google teams are using AI to answer basic scientific questions and broaden the range of possibility, yielding new insights into life and innovative solutions to humanity's biggest issues. To accelerate scientific discoveries, collaborate with academics and industry. It also gives partners all technologies and instruments for study.

Recent Google Research findings in these four areas have major scientific and social ramifications.

Improving disease treatment with biological science

AI's promise to democratise science, personalise medicine, and expand biological and medical research fascinates us. This AI co-scientist wants to accelerate biomedical therapy discovery. This multi-agent system uses AI's ability to synthesise information and perform complex reasoning to help scientists produce fresh research ideas and hypotheses using plain language.

A multimodal version of AMIE, a medical diagnostic dialogue AI agent, was published in Nature. It can interpret visual medical data for more accurate diagnosis. AMIE is based on MedPaLM and other medical language models.

Using embedding models for digital pathology, dermatology, and chest x-rays, the developers created TxGemma, a set of open models to improve therapeutic development. To help developers build medical AI apps, it keeps supporting Health AI Developer Foundations.

Genomic research is also used to diagnose rare disorders and investigate genetic susceptibilities. Researchers can correlate genomic variants with REGLE, an unsupervised deep learning algorithm. In collaboration with Personalised Pangenome References, researchers released new DeepVariant models. When analysing genomes with many ancestries, these models reduce errors by 30%.

Neuroscience research is improving brain understanding

Have also progressed brain science and connectomics in the previous decade. The first method for mapping neurones and their connections in brain tissue using commonly available light microscopes, LICONN, was published in Nature yesterday by Google Research and ISTA. LICONN will enable connect omics research in more labs worldwide.

In collaboration with Harvard and HHMI Janelia, the developers released the Zebrafish Activity Prediction Benchmark (ZAPBench) beyond neural connections. Over 70,000 larval zebrafish brain neurones were recorded for this benchmark. Scientists can now study the relationship between dynamic neural activity and structural circuitry in a vertebrate brain. The dataset and benchmark are publicly available to help neuroscientists model brain activity more accurately.

In collaboration with Princeton University, NYU, and HUJI, investigations examined the similarities and differences between deep language models and the human brain in natural language processing. This study suggests that deep learning models may offer a new computational framework for brain neuronal code decipherment.

Addressing global concerns via geospatial reasoning

Google Research speeds up geographical problem-solving by making important information more accessible. The first FireSat satellite was launched to fight wildfires. The high-resolution data, updated globally every 20 minutes to build the constellation to over 50 satellites, will help scientists comprehend wildfire propagation and emergency responders notice flames early. Companies have improved climate resilience and crisis response with Flood Forecasting and WeatherNext AI models.

The new Geospatial Reasoning research project uses generative AI and geospatial foundation models to locate actionable knowledge using a conversational interface. It builds on prior Population Dynamics and trajectory-based mobility core models, as well as weather, floods, wildfires, Open Buildings, and SKAI models. Geospatial reasoning benefits public health, integrated business planning, urban planning, climate research, and others.

Quantum computing nears practicality

For over ten years, it has been working towards constructing huge quantum computers that can solve impossible problems. This Willow chip, with mistake correction and cutting-edge performance, is a milestone. On World Quantum Day, stressed its progress towards practical applications.

In collaboration with Sandia National Laboratories, researchers found that a quantum algorithm might better simulate sustained fusion processes. This could enable fusion energy with its large-scale clean energy potential. It demonstrated a breakthrough hybrid method to quantum simulation that opens the door to future scientific discoveries that will advance quantum research.

AI's potential is being realised in several sciences. Developers will keep asking the most critical questions and solving intractable issues to find scientific discoveries that can aid billions.

[in-sightjournal]

Ask A Genius 985: Gish Gallops Can Work

Scott Douglas Jacobsen: Do you have any further opinions on what was called a debate? Rick Rosner: Some people have calmed down, or the initial reaction that Biden had lost the election, which was a knee-jerk reaction on CNN and MSNBC, has subsided. An hour after the debate, CNN finally fact-checked the debaters, something they did not do during the discussion. In 38 minutes of speaking, Trump…

[esquizo3214378]

🧠 Inquiry Return: Cluster 1 – Brain Connectivity Mapping

🧾 Contextual Impression:

A recent wave of neuroscience research is leveraging AI, especially deep learning and graph neural networks, to decode the brain's connectome with greater resolution and functional interpretation. The central thrust is moving from static structural maps to dynamic, predictive models of neural interaction. Tools like diffusion MRI, calcium imaging, and high-density electrophysiology are being coupled with AI to reveal previously obscured patterns of synchronization and modularity. A 2024 paper in Nature Neuroscience discusses how AI-enhanced models are revealing transient brain states—fleeting patterns of connectivity that may underpin attention, memory formation, and consciousness. AI is particularly useful in compressing noisy, high-dimensional data into functionally meaningful networks, which may soon allow for individualized connectomic fingerprints.

There is also increasing interest in temporal connectomics—understanding how brain connectivity changes over time, both in health and in disease. Real-time AI-assisted mapping may become central in detecting subtle transitions in mental state, early markers of neurodegeneration, or adaptive brain plasticity.

🔖 Tagged Keyword List:

connectome

graph neural networks

dynamic brain states

temporal connectomics

individualized mapping

fMRI decoding

functional connectivity

AI-driven parcellation

brain fingerprints

non-linear dimensionality reduction

📜 Conceptually Resonant Excerpt:

“Artificial intelligence has transitioned from mapping the brain’s roads to anticipating its traffic patterns—forecasting the transient synchronies that may constitute thought itself.” — Nature Neuroscience, 2024

Shall I continue with the next cluster on early diagnosis of neurological diseases?