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Study Finds a Striking Difference Between Neurons of Humans and Other Mammals



Neurons communicate with each other via electrical impulses, which are produced by ion channels that control the flow of ions such as potassium and sodium. In a surprising new finding, MIT neuroscientists have shown that human neurons have a much smaller number of these channels than expected, compared to the neurons of other mammals.

The researchers hypothesize that this reduction in channel density may have helped the human brain evolve to operate more efficiently, allowing it to divert resources to other energy-intensive processes that are required to perform complex cognitive tasks.

“If the brain can save energy by reducing the density of ion channels, it can spend that energy on other neuronal or circuit processes,” says Mark Harnett, an associate professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

Harnett and his colleagues analyzed neurons from 10 different mammals, the most extensive electrophysiological study of its kind, and identified a “building plan” that holds true for every species they looked at — except for humans. They found that as the size of neurons increases, the density of channels found in the neurons also increases.

However, human neurons proved to be a striking exception to this rule.

“Previous comparative studies established that the human brain is built like other mammalian brains, so we were surprised to find strong evidence that human neurons are special,” says former MIT graduate student Lou Beaulieu-Laroche.

Beaulieu-Laroche is the lead author of the study, which appears today in Nature.

A building plan

Neurons in the mammalian brain can receive electrical signals from thousands of other cells, and that input determines whether or not they will fire an electrical impulse called an action potential. In 2018, Harnett and Beaulieu-Laroche discovered that human and rat neurons differ in some of their electrical properties, primarily in parts of the neuron called dendrites — tree-like antennas that receive and process input from other cells.

One of the findings from that study was that human neurons had a lower density of ion channels than neurons in the rat brain. The researchers were surprised by this observation, as ion channel density was generally assumed to be constant across species. In their new study, Harnett and Beaulieu-Laroche decided to compare neurons from several different mammalian species to see if they could find any patterns that governed the expression of ion channels. They studied two types of voltage-gated potassium channels and the HCN channel, which conducts both potassium and sodium, in layer 5 pyramidal neurons, a type of excitatory neurons found in the brain’s cortex.

They were able to obtain brain tissue from 10 mammalian species: Etruscan shrews (one of the smallest known mammals), gerbils, mice, rats, Guinea pigs, ferrets, rabbits, marmosets, and macaques, as well as human tissue removed from patients with epilepsy during brain surgery. This variety allowed the researchers to cover a range of cortical thicknesses and neuron sizes across the mammalian kingdom.

The researchers found that in nearly every mammalian species they looked at, the density of ion channels increased as the size of the neurons went up. The one exception to this pattern was in human neurons, which had a much lower density of ion channels than expected.

The increase in channel density across species was surprising, Harnett says, because the more channels there are, the more energy is required to pump ions in and out of the cell. However, it started to make sense once the researchers began thinking about the number of channels in the overall volume of the cortex, he says.

In the tiny brain of the Etruscan shrew, which is packed with very small neurons, there are more neurons in a given volume of tissue than in the same volume of tissue from the rabbit brain, which has much larger neurons. But because the rabbit neurons have a higher density of ion channels, the density of channels in a given volume of tissue is the same in both species, or any of the nonhuman species the researchers analyzed.

“This building plan is consistent across nine different mammalian species,” Harnett says. “What it looks like the cortex is trying to do is keep the numbers of ion channels per unit volume the same across all the species. This means that for a given volume of cortex, the energetic cost is the same, at least for ion channels.”

Energy efficiency

The human brain represents a striking deviation from this building plan, however. Instead of increased density of ion channels, the researchers found a dramatic decrease in the expected density of ion channels for a given volume of brain tissue.

The researchers believe this lower density may have evolved as a way to expend less energy on pumping ions, which allows the brain to use that energy for something else, like creating more complicated synaptic connections between neurons or firing action potentials at a higher rate.

“We think that humans have evolved out of this building plan that was previously restricting the size of cortex, and they figured out a way to become more energetically efficient, so you spend less ATP per volume compared to other species,” Harnett says.

He now hopes to study where that extra energy might be going, and whether there are specific gene mutations that help neurons of the human cortex achieve this high efficiency. The researchers are also interested in exploring whether primate species that are more closely related to humans show similar decreases in ion channel density.

The research was funded by the Natural Sciences and Engineering Research Council of Canada, a Friends of the McGovern Institute Fellowship, the National Institute of General Medical Sciences, the Paul and Daisy Soros Fellows Program, the Dana Foundation David Mahoney Neuroimaging Grant Program, the National Institutes of Health, the Harvard-MIT Joint Research Grants Program in Basic Neuroscience, and Susan Haar.

Other authors of the paper include Norma Brown, an MIT technical associate; Marissa Hansen, a former post-baccalaureate scholar; Enrique Toloza, a graduate student at MIT and Harvard Medical School; Jitendra Sharma, an MIT research scientist; Ziv Williams, an associate professor of neurosurgery at Harvard Medical School; Matthew Frosch, an associate professor of pathology and health sciences and technology at Harvard Medical School; Garth Rees Cosgrove, director of epilepsy and functional neurosurgery at Brigham and Women’s Hospital; and Sydney Cash, an assistant professor of neurology at Harvard Medical School and Massachusetts General Hospital.

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New Method Predicts Drug Response of Cancer Patients



Researchers from Delft University of Technology and the Netherlands Cancer Institute (NKI) have developed an algorithm to predict patient response to anti-cancer drugs. This allows us to identify more rapidly if some drugs can have a positive effect on a specific patient, even for complicated medicines such as chemotherapies where response is typically hard to predict. This method is called TRANSACT and makes use of the wealth of data previously collected through research with cell lines. A cell line consists of a strain of human cells, artificially grown in a petri dish. Such cell lines have already been widely used to study the resistance mechanisms of cancer drugs. These findings, however, have so far poorly translated to humans. This is partially due to the fact that cell lines are an artificial model of limited complexity compared to an actual tumor. TRANSACT was developed to bridge this gap between model systems and clinical practice.


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How AI Could Help Screen for Autism in Children



For children with autism spectrum disorder (ASD), receiving an early diagnosis can make a huge difference in improving behavior, skills and language development. But despite being one of the most common developmental disabilities, impacting 1 in 54 children in the U.S., it’s not that easy to diagnose.

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Feast or Forage? Study Finds Circuit That Helps a Brain Decide



MIT neuroscientists have discovered the elegant architecture of a fundamental decision-making brain circuit that allows a C. elegans worm to either forage for food or stop to feast when it finds a source. Capable of integrating multiple streams of sensory information, the circuit employs just a few key neurons to sustain long-lasting behaviors and yet flexibly switch between them as environmental conditions warrant.

“For a foraging worm, the decision to roam or to dwell is one that will strongly impact its survival.” says study senior author Steven Flavell, the Lister Brothers Career Development Associate Professor in the Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT. “We thought that studying how the brain controls this crucial decision-making process could uncover fundamental circuit elements that may be deployed in many animals’ brains.”

This approach of studying simple invertebrates to gain basic insights into how the brain functions has a long tradition in neuroscience, Flavell says. For example, studies of how a squid nerve propagates electrical impulses led to the key insight explaining how brain cells fire in virtually all animals.

Though the critical component of brain circuitry identified by Flavell and colleagues may seem simple now that it has been revealed, finding it was anything but easy. Lead author Ni Ji, a postdoc in Flavell’s lab, used several advanced technologies, including one of the lab’s own inventions, to figure it out. The results of her and her co-authors’ work appear in the journal eLife.

Tracking thinking

C. elegans is a popular model in neuroscience because it only has 302 neurons and the “wiring diagram,” or connectome, has been fully mapped. But even so, the very dense and overlapping interconnectedness among those neurons, plus their ability to signal each other via chemicals called neuromodulators, means that one can hardly just look at the connectome and discern how it switches between different states of behavior.

To identify functional circuitry amid this web of connections, Flavell’s lab developed a new microscope capable of tracking the worms as they move around, thereby constantly imaging the activity of neurons across the worm’s brain, as indicated by calcium-triggered flashes of light. Ji used the scope to focus on 10 interconnected neurons involved in foraging, tracking their patterns of neural activity associated with roaming or dwelling behaviors.

Ji and co-authors trained software that learned the patterns so well that just based on neural activity, it could predict the worm’s behavior with 95 percent accuracy. The analysis revealed a quartet of neurons whose activity was specifically associated with roaming. Another key pattern was that the transition from roaming around to stopping to dwell always followed activation of a neuron called NSM. Flavell’s lab previously showed that NSM can sense the presence of newly ingested food and emit a neuromodulator called serotonin to signal other neurons to slow the worm down to dwell in a nutritive area.

Mutual antagonism

Having identified the activity patterns that changed as the worm switched states, Ji began manipulating neurons in the circuit to understand how they interact. To confirm NSM’s role as the trigger of the dwelling state, Ji engineered it to be artificially activated with a flash of light (a technique called optogenetics). When she flashed the light, it caused the worm to dwell by inhibiting the activity of the roaming-associated neurons. Further experiments showed that this inhibitory power depended on the roaming neurons having an inhibitory serotonin receptor, called MOD-1. If Ji genetically knocked out the MOD-1 receptor, NSM couldn’t inhibit the roaming behavior and quickly stopped trying for lack of feedback.

Similarly, Ji showed that when the worm was roaming, it was because the roaming quartet was using the neuromodulator PDF to inhibit the activity of NSM. Optogenetic activation of PDF-expressing neurons tamped down NSM activity, for instance.

In a normal worm, if the roaming quartet was active NSM was not, and vice versa. But when Ji genetically knocked out the circuit elements that underlie this mutual inhibition both the roaming quartet and NSM could be active at the same time, leaving the worm in a weird state of meandering around at about half of roaming speed.

Sensory inputs

Thus, via an ongoing battle of mutual inhibition, roaming is sustained by the quartet and dwelling is sustained by NSM, but that still begged the question: How does the worm decide to flip the switch? To find out, Ji and colleagues programmed a machine learning algorithm to discern which neurons might work upstream in the broader circuit to influence the serotonin and PDF tug of war. This approach identified a neuron called AIA, which is known for integrating sensory information about food odors. AIA’s activity co-varied with a couple of the roaming neurons during roaming, and with NSM when dwelling began.

In other words, upon becoming activated by the smell of food, AIA could use its input to drive either side of the mutual inhibitory circuit to switch behavior. Remembering that NSM can sense when the worm is actually eating, Ji and Flavell could deduce what AIA and NSM must be doing. If the worm smells food but is not eating, it needs to roam further to that food smell until it is. If the worm smells food and at the same time it begins eating, then it should continue to dwell there.

“To a foraging worm, food odors are an important, but ambiguous, sensory cue. AIA’s ability to detect food odors and to transmit that information to these different downstream circuits, dependent on other incoming cues, allows animals to contextualize the smell and make adaptive foraging decisions,” Flavell said. “If you are looking for circuit elements that could also be operating in bigger brains, this one stands out as a basic motif that might allow for context-dependent behaviors.”

In addition to Ji and Flavell, the paper’s other authors are Gurrein Madan, Guadalupe Fabre, Alyssa Dayan, Casey Baker, Talya Kramer, and Ijeoma Nwabudike.

Funding for the research came from the National Institutes of Health, the National Science Foundation, the JPB Foundation, the Brain and Behavior Research Foundation, NARSAD, the McKnight Foundation, and the Alfred P. Sloan Foundation.

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