#cerebral cortex

[biologist4ever]

Illuminating the brain through art and science

[quotesfrommyreading]

One study concluded that humans have five times the information-processing capacity of cetaceans, whom they placed beneath chimps, monkeys, and some birds. But in the same study, horses—with smaller brains than chimps—were found to have five times the number of cortical neurons. Does this mean horses are smarter than chimps? A major confounding factor in these types of comparisons appears to be that every factor is itself quite confounding. Estimating numbers of neurons is a very rough science, so the raw number comparisons are crude. There are lots of different kinds of neurons, and they are arranged in different configurations and proportions in different species. We know all these variations mean something, that they will determine what brains are capable of, but we don’t know yet quite what, or how that might change from one moment to the next in different parts of the brain. There are a lot of assumptions at play, and it can be misleading to extrapolate from one brain to another.

This also applies to comparing cognitive ability. Trying to infer from brains and their structures which animals are “better” at cognition and ranking animal brains in order of “intelligence” is as treacherous as it is tempting. Stan Kuczaj, who spent his lifetime studying the cognition and behavior of different animals, put it bluntly: “We suck at being able to validly measure intelligence in humans. We’re even worse when we try to compare species.” Intelligence is a slippery concept and perhaps unmeasurable. As mentioned earlier, many biologists conceive of it as an animal’s ability to solve problems. But because different animals live in different environments with different problems, you can’t really translate scores of how well their brains perform. A brain attribute is not simply “good” or “bad” for thinking, but rather varies depending on the situation and the thinking that brain needs to undertake. Intelligence is a moving target.

What confounds this dilemma further is that individual animals within a species have varying cognitive abilities. To quote the Yosemite National Park ranger who, when asked why it was proving so hard to make a garbage can that bears couldn’t break into, said, “There is considerable overlap between the intelligence of the smartest bears and the dumbest tourists.”

 —   In the Mind of a Whale

[er-cryptid]

[yoan-le-grall]

[sallygcronin]

Smorgasbord Health Column 2023 - The Body our Greatest Asset - The Brain- Introduction and Anatomy by Sally Cronin

Smorgasbord Health Column 2023 – The Body our Greatest Asset – The Brain- Introduction and Anatomy by Sally Cronin

I have featured this series over the last ten years on a regular basis for new readers who might have joined the blog. Our bodies are are greatest asset. It has a long road ahead of if from birth, through the teen years, work life, parenthood, middle age and then into our 70s and beyond. At every stage of our life healthy nutrition is essential to help the body develop and remain as disease free…

[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.

[anauthorslife]

This week, on Living with Disabilities, I had the opportunity to sit down with a friend of mine Tracy Jackson. When she was younger, she earned the title of champion. She was a horse trainer back in the day.

[covenawhite66]

Outer layers of the brain produce high frequency brain ways associated with sensory stimulation

Deeper regions of the brain produce low frequency alpha waves associated with control signals.

Studying six layers of the brain's Cerebral Cortex in 14 regions.

The Cerebral Cortex is responsible for higher cognitive function.

Cognitive function is a broad term that refers to mental processes involved in the acquisition of knowledge, manipulation of information, and reasoning.

Lamination is the biological process by which cells are arranged in layers within a tissue during development

[wendalyz]

[thatwriterwiththeblock]

Created a God out of a human brain at the laundromat today

[biologist4ever]

Illuminating the brain through art and science!

[margotricordeau]

[quotesfrommyreading]

I’d learned by this point that comparing brains is a difficult business in general. In explaining how clever humans are, we often point out the extraordinarily large size of our thinking organs. Their bulk is the bane of childbirth and consumes 90 percent of the glucose in our blood. But size itself is not a clear guide for comparing animal intelligences, as some bigger animals with larger brains seem to lack the cognitive abilities of smaller ones. Size, as the saying goes, isn’t everything. Relative brain-to-body size, how wrinkled and complex brains are, the thickness of their layers, the structures within them, and the types of neurons these are made of are all helpful—though our human brains are, naturally, the yardstick that other brains are measured against. And yet it is impossible to look at a whale brain and not be surprised by its size. When Hof first saw one, despite knowing they were big, its mass still shocked him. The human brain is about 1,350 grams, three times larger than our big-brained relative, the chimpanzee. A sperm whale or killer whale brain can be 10 kilograms. These are the biggest brains on Earth and possibly the biggest brains ever, anywhere. It’s perhaps not a fair comparison: in relation to the size of our bodies, our brains are bigger than those of whales. Ours are similar in proportion to our body mass, as are the brains of some rodents; mice and men both invest a lot of themselves in their thinking organs. But we both lag far behind small birds and ants, which have much bigger brains compared to their body size than any big animals.

The outer layer of a mammal’s brain is called the cerebral cortex. In cross section, it looks a little like a wraparound bicycle helmet sitting on top of the other parts of the brain. This is the most recently evolved part of our brains, and it was by using their own cerebral cortexes that brain scientists have learned that this area is responsible for rational, conscious thought.

It handles tasks like perceiving senses, thinking, movement, figuring out how you relate to the space around you, and language. You are using yours now to read and think about this sentence. Many biologists define “intelligence” as something along the lines of the mental and behavioral flexibility of an organism to solve problems and come up with novel solutions. In humans, the cerebral cortex, acting with other bits of the brain (the basal ganglia, basal forebrain, and dorsal thalamus), appears to be the seat of this form of “intelligence.” The more cortex you have and the more wrinkled it is, the more surface area available for making connections—and voila! More thinking.

Humans have a really large neocortex surface area, but it’s still just over half that of a common dolphin, and miles behind the sperm whale. Even if you divide the cortex area by the total weight of the brain to remove the cetacean size advantage, humans still lag behind dolphins and killer whales. But there are other measurements in the cortex that seem to be associated with intelligence, and here, dolphins and whales lag behind humans.

The more neurons are packed in, how closely and effectively they are wired, and how fast they transmit impulses are also extremely important in brain function. Just as the composition and layout of the chipset in your tiny, cheap cellphone allows it to pack more computing power than a five-tonne room-sized 1970s supercomputer. Both cetaceans and elephants, the biggest mammals on sea and land, seem to have large distances between their neurons and slower conduction speeds. In raw numbers of neurons, humans here, too, have the edge, with a human cortex containing an estimated 15 billion neurons. Given the larger size of cetacean brains, you’d think they’d have more, but in fact their cerebral cortex is thinner, and the neurons are fatter, taking up more room.

Nevertheless, some cetaceans such as the false killer whale are close behind human levels with 10.5 billion cerebral neurons, about the same as an elephant. Chimps have 6.2 billion and gorillas 4.3 billion. Further complicating comparisons, whales have huge numbers of other kinds of cells, called glia, packing their cortexes. Until recently, we believed these glial cells to be an unthinking filler, but we’ve now discovered that they actually seem important for cognition, too. I don’t know about you, but all this cortex measurement and comparison makes my own feeble organ hurt.

 —   In the Mind of a Whale

[biglisbonnews]

Study finds a connection between fructose and Alzheimer's disease Scientists from the University of Colorado have found a link between fructose and Alzheimer's disease. They say the sugar shuts down parts of the brain that deal with memory and attention to time and causes people to focus on finding food. — Read the rest https://boingboing.net/2023/02/16/study-finds-a-connection-between-fructose-and-alzheimers-disease.html

[indefinite-pitch]

Neural Scape - Pineal

Buy & Support: Neural Scape - Cerebral Cortex

[jcmarchi]

A fast and flexible approach to help doctors annotate medical scans

New Post has been published on https://thedigitalinsider.com/a-fast-and-flexible-approach-to-help-doctors-annotate-medical-scans/

A fast and flexible approach to help doctors annotate medical scans

To the untrained eye, a medical image like an MRI or X-ray appears to be a murky collection of black-and-white blobs. It can be a struggle to decipher where one structure (like a tumor) ends and another begins. 

When trained to understand the boundaries of biological structures, AI systems can segment (or delineate) regions of interest that doctors and biomedical workers want to monitor for diseases and other abnormalities. Instead of losing precious time tracing anatomy by hand across many images, an artificial assistant could do that for them.

The catch? Researchers and clinicians must label countless images to train their AI system before it can accurately segment. For example, you’d need to annotate the cerebral cortex in numerous MRI scans to train a supervised model to understand how the cortex’s shape can vary in different brains.

Sidestepping such tedious data collection, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts General Hospital (MGH), and Harvard Medical School have developed the interactive “ScribblePrompt” framework: a flexible tool that can help rapidly segment any medical image, even types it hasn’t seen before. 

Instead of having humans mark up each picture manually, the team simulated how users would annotate over 50,000 scans, including MRIs, ultrasounds, and photographs, across structures in the eyes, cells, brains, bones, skin, and more. To label all those scans, the team used algorithms to simulate how humans would scribble and click on different regions in medical images. In addition to commonly labeled regions, the team also used superpixel algorithms, which find parts of the image with similar values, to identify potential new regions of interest to medical researchers and train ScribblePrompt to segment them. This synthetic data prepared ScribblePrompt to handle real-world segmentation requests from users.

“AI has significant potential in analyzing images and other high-dimensional data to help humans do things more productively,” says MIT PhD student Hallee Wong SM ’22, the lead author on a new paper about ScribblePrompt and a CSAIL affiliate. “We want to augment, not replace, the efforts of medical workers through an interactive system. ScribblePrompt is a simple model with the efficiency to help doctors focus on the more interesting parts of their analysis. It’s faster and more accurate than comparable interactive segmentation methods, reducing annotation time by 28 percent compared to Meta’s Segment Anything Model (SAM) framework, for example.”

ScribblePrompt’s interface is simple: Users can scribble across the rough area they’d like segmented, or click on it, and the tool will highlight the entire structure or background as requested. For example, you can click on individual veins within a retinal (eye) scan. ScribblePrompt can also mark up a structure given a bounding box.

Then, the tool can make corrections based on the user’s feedback. If you wanted to highlight a kidney in an ultrasound, you could use a bounding box, and then scribble in additional parts of the structure if ScribblePrompt missed any edges. If you wanted to edit your segment, you could use a “negative scribble” to exclude certain regions.

These self-correcting, interactive capabilities made ScribblePrompt the preferred tool among neuroimaging researchers at MGH in a user study. 93.8 percent of these users favored the MIT approach over the SAM baseline in improving its segments in response to scribble corrections. As for click-based edits, 87.5 percent of the medical researchers preferred ScribblePrompt.

ScribblePrompt was trained on simulated scribbles and clicks on 54,000 images across 65 datasets, featuring scans of the eyes, thorax, spine, cells, skin, abdominal muscles, neck, brain, bones, teeth, and lesions. The model familiarized itself with 16 types of medical images, including microscopies, CT scans, X-rays, MRIs, ultrasounds, and photographs.

“Many existing methods don’t respond well when users scribble across images because it’s hard to simulate such interactions in training. For ScribblePrompt, we were able to force our model to pay attention to different inputs using our synthetic segmentation tasks,” says Wong. “We wanted to train what’s essentially a foundation model on a lot of diverse data so it would generalize to new types of images and tasks.”

After taking in so much data, the team evaluated ScribblePrompt across 12 new datasets. Although it hadn’t seen these images before, it outperformed four existing methods by segmenting more efficiently and giving more accurate predictions about the exact regions users wanted highlighted.

“​​Segmentation is the most prevalent biomedical image analysis task, performed widely both in routine clinical practice and in research — which leads to it being both very diverse and a crucial, impactful step,” says senior author Adrian Dalca SM ’12, PhD ’16, CSAIL research scientist and assistant professor at MGH and Harvard Medical School. “ScribblePrompt was carefully designed to be practically useful to clinicians and researchers, and hence to substantially make this step much, much faster.”

“The majority of segmentation algorithms that have been developed in image analysis and machine learning are at least to some extent based on our ability to manually annotate images,” says Harvard Medical School professor in radiology and MGH neuroscientist Bruce Fischl, who was not involved in the paper. “The problem is dramatically worse in medical imaging in which our ‘images’ are typically 3D volumes, as human beings have no evolutionary or phenomenological reason to have any competency in annotating 3D images. ScribblePrompt enables manual annotation to be carried out much, much faster and more accurately, by training a network on precisely the types of interactions a human would typically have with an image while manually annotating. The result is an intuitive interface that allows annotators to naturally interact with imaging data with far greater productivity than was previously possible.”

Wong and Dalca wrote the paper with two other CSAIL affiliates: John Guttag, the Dugald C. Jackson Professor of EECS at MIT and CSAIL principal investigator; and MIT PhD student Marianne Rakic SM ’22. Their work was supported, in part, by Quanta Computer Inc., the Eric and Wendy Schmidt Center at the Broad Institute, the Wistron Corp., and the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health, with hardware support from the Massachusetts Life Sciences Center.

Wong and her colleagues’ work will be presented at the 2024 European Conference on Computer Vision and was presented as an oral talk at the DCAMI workshop at the Computer Vision and Pattern Recognition Conference earlier this year. They were awarded the Bench-to-Bedside Paper Award at the workshop for ScribblePrompt’s potential clinical impact.