ROCHESTER, Minn. — Mayo Clinic researchers have created a detailed map of the pulvinar, a deep brain region that could help doctors more precisely target brain stimulation therapies for people with drug-resistant epilepsy. The findings, published in the Journal of Neuroscience, reveal that brain regions separated by only a few millimeters connect to entirely different
According to the Cleveland Clinic, a quarter of cancer deaths can be attributed to one source: cachexia. Cachexia is a syndrome that accompanies underlying chronic illness and causes unwanted muscle and fat loss, reducing quality of life and sometimes even limiting treatment options.
A new study led by Thales Papagiannakopoulos, Ph.D., an incoming Salk professor, published in Science, points to a potential new target for preventing cachexia.
The researchers found that a common genetic subset of lung cancer is more prone to cachexia and that tumors from this subtype talk to the brain through sensory neurons in the lung. Silencing these sensory nerves to disrupt the tumor-to-brain connection reduced cachexia, as did blocking the production of the lipid signaling molecule prostaglandin E2 (PGE2) through dietary changes.
Information continuously flows between regions of the human brain, forming patterns that shift across states of consciousness, cognitive modes, and neuropsychiatric conditions. While functional magnetic resonance imaging (fMRI) reveals large-scale activity changes over seconds, the electrophysiological dynamics governing sub-second reconfiguration remain poorly understood. Here, relative phase analysis (RPA), a method leveraging phase lead/lag relationships, is introduced to capture whole-brain dynamics with millisecond precision in real time from electroencephalography (EEG). RPA reveals sub-second alternations, occurring approximately every 200 ms, between two dominant modes of information flow: a top-down mode, where anterior regions drive posterior activity, and a bottom-up mode, characterized by reverse directionality. These dynamics are most prominent during wakefulness, gradually diminish under anesthesia, and exhibit pathological imbalance in attention-deficit/hyperactivity disorder (ADHD). Simultaneous EEG-fMRI recordings demonstrate that top-down dynamics coincide with increased activity of higher-order cognitive networks, whereas bottom-up dynamics correspond to heightened activity in sensory networks. A connectome-based coupled-oscillator model reproduces these transitions, indicating that sub-second fluctuations emerge naturally from inter-regional interactions shaped by underlying structural connectivity. This study establishes RPA as a framework for tracking whole-brain dynamics precisely in real time and identifies sub-second top-down/bottom-up alternations as a fundamental organizing principle of human brain function and consciousness.
Keywords: ADHD; Kuramoto model; cortical traveling waves; coupled-oscillator model; general anesthesia; human brain dynamics; relative phase analysis; simultaneous EEG-fMRI; sub-second transitions; top-down versus bottom-up modes.
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Line up the brains of a fish, bird and a mammal, and something unexpected comes up. You do not see three different answers to the problem of making sense of the world. You see one answer, tilted three different ways. “You can really see it’s almost like a continuum,” says Emre Yaksi, a professor at the Kavli Institute for Systems Neuroscience in Trondheim.
Read across decades of anatomy, the same two ancient pathways carry the world into the forebrain of all these animals. What changes from one to the next is mainly which route does more of the work. Evolution built these brains from different parts, in creatures that parted ways hundreds of millions of years ago. It kept arriving at the same answer anyway.
That is the puzzle the Yaksi lab set out to chase. If animals this far apart on the tree of life keep landing on the same arrangement, perhaps the arrangement is no accident. Perhaps there are organizational rules deep enough that a fish and a person, for all the differences between them, are bound by the same ones.
In 2023, scientists at the Paris Brain Institute investigated one of the most fascinating and unsettling transitions in neuroscience: what happens to the cortex when the brain is deprived of oxygen.
In a rat model of systemic anoxia, researchers found that the dying brain does not simply “shut off” all at once. Instead, cortical activity follows a structured sequence: brief high-frequency activity, slowing oscillations, electrical silence, and then a massive wave of anoxic depolarization — often called the “wave of death.”
This wave appeared to begin deep in the neocortex, especially around layer 5 pyramidal neurons, before spreading upward toward the cortical surface and downward toward the white matter. These neurons are large, metabolically demanding projection cells, which may make them especially vulnerable when oxygen and ATP collapse.
But the most important part of the study is that this wave did not always represent an absolute point of no return. When oxygenation was restored within a critical window, researchers observed a “wave of resuscitation,” followed by partial recovery of synaptic activity.
That does not mean death has been “reversed” in a simple or sensational sense. But it does suggest something scientifically powerful: the boundary between life and death in the brain may be more dynamic, layered, and measurable than we often imagine.
This is where the implications become fascinating.
If the “wave of death” is an organized biophysical event, future neurocritical care may one day be able to detect the brain’s approach toward irreversible injury in real time. Instead of relying only on broad markers like heartbeat, oxygen saturation, or flat EEG, clinicians may eventually use more detailed brain-state monitoring to identify whether the cortex is entering a reversible, borderline, or irreversible phase.
Imagine opening a difficult book in a quiet room. The first page is dense. You read one paragraph, then reread it. Nothing “clicks” yet. Your brain is doing what learning often requires: spending effort before the reward arrives. Then your phone lights up. One thumb movement, and the situation changes completely. A joke, a message, a clip, a tiny social reward: all available instantly, all requiring almost no effort. The book has not become harder and, definitely, your intelligence has not disappeared. But the book now feels more expensive, because another activity nearby offers a much better bargain: reward now, effort almost zero.
That is the central idea of the paper “An Effort Recalibration Framework for Digital Media Use and Cognition” that just appeared in Nature Human Behavior. It argues that the most important effect of social media might be that repeated exposure to effortless digital rewards changes how we value effort itself. Over time, the authors suggest, digital media may recalibrate our internal sense of what effort is worth. Difficult work then begins to feel less attractive, not because we can no longer do it, but because our everyday decision system has learned to expect faster returns.
It is because the phantom primal eye is centrally evoked by the cellular as generic APS identically with all the contents of special sense information that Leibniz’s “like can only interact with like” condition is satisfied by the non-physical primal eye “monad”—as opposed to Descartes’s cellular pineal gland.
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Nuttida Rungratsameetaweemana is challenging a story neuroscience has told for decades. According to the conventional account, our eyes collect raw information and relay it through a series of nerves and waystations that lead deep into the brain, eventually reaching the cortex. There, the thinking begins as information is processed and put to use for higher tasks such as reasoning, judgment and decision-making.
Her group’s work is complicating that account. Last year, the team published fMRI scans showing unexpected levels of activity in the earliest visual areas of the cortex, the regions that first receive visual signals. Rather than passively relaying what the eyes take in, those early areas seemed to process the same information differently depending on what the research participant was doing. When asked to sort shapes by one set of rules, a participant’s early visual system behaved one way. When asked to apply a different set of rules to the same shape, it behaved differently.
In a new paper published today in PLOS Biology, Rungratsameetaweemana and her team at Columbia Engineering show how the brain might pull this off. They built a simple neural network that follows many of the rules that govern real brains. Like the brain, their model contained one class of neurons that drive other neurons to fire and another class that suppress firing.
