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Category: neuroscience – Page 10

UC Davis Health is pleased to announce that Neurosurgeon David Brandman and his team at UC Davis Neuroprosthetics Lab were selected for a 2025 Top Ten Clinical Research Achievement Award. The Clinical Research Forum presents this award to honor 10 outstanding clinical research studies published in peer-reviewed journals in the previous year. This year’s Top 10 Awards ceremony will be held on April 14 in Washington, D.C.

Brandman and his team are recognized for their groundbreaking work in developing a new brain-computer interface (BCI) that translates brain signals into speech with up to 97% accuracy — the most accurate system of its kind. Their work was published in the New England Journal of Medicine.

“Our team is very honored that our study was selected among the nation’s best published clinical research studies. Our work demonstrates the most accurate speech neuroprosthesis (device) ever reported,” said Brandman, co-director of the Neuroprosthetics Lab. He is an assistant professor in the UC Davis Department of Neurological Surgery.

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Musa, A., Khan, S., Mujahid, M. et al. The shallow cognitive map hypothesis: A hippocampal framework for thought disorder in schizophrenia. Schizophr 8, 34 (2022). https://doi.org/10.1038/s41537-022-00247-7

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The latent circuit model captures task-related neural activity in the low-dimensional subspace spanned by the columns of Q, with dynamics within this subspace generated by the neural circuit Eq. (2). We infer the latent circuit parameters (Q, wrec, win and wout) from neural activity y by minimizing the loss function L = ∑k,t ∥ y − Qx ∥ 2 + ∥ z − woutx ∥ 2, where k and t index trials and time within a trial, respectively (Methods).

In the latent circuit model, the heterogeneity of single-neuron responses has three possible sources: mixing of task inputs to the latent circuit via win, recurrent interactions among latent nodes via wrec and linear mixing of representations in single neurons via the embedding Q. The orthonormality constraint on Q implies that the projection defined by the transpose matrix QT is a dimensionality reduction in which projection onto the i th column of Q correlates with the activity of the i th node in the latent circuit. Conversely, the image of each latent node i is a high-dimensional activity pattern given by the column qi of the matrix Q. Thus, the latent circuit provides a dimensionality reduction that incorporates an explicit mechanistic hypothesis for how the resulting low-dimensional dynamics are generated.

In general, it is not obvious under what circumstances we can satisfactorily fit a latent circuit model to the responses of a high-dimensional system. If, for example, solutions to cognitive tasks that emerge in large systems are qualitatively different from mechanisms operating in small circuits, then we should not be able to adequately fit task-related dynamics of the large system with a low-dimensional circuit model. However, the existence of a low-dimensional circuit solution that accurately captures dynamics of the large system would suggest that this circuit mechanism may be latent in the high-dimensional system.

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Sylvain Lesné, a neuroscientist accused of image manipulation in a seminal Alzheimer’s disease paper in, resigned last week from his tenured professorship at the University of Minnesota Twin Cities (UMN). The move follows a 2.5-year investigation in which the university found problems with several other papers on which Lesné is an author. The study has already been pulled, but the school has asked that four more of Lesné’s papers be retracted.

The resignation, effective 1 March, was first reported by the. Lesné did not respond to a request for comment. UMN spokesperson Jake Ricker said, “The university has identified data integrity concerns impacting several publications and has been in touch with those journals to recommend retraction of the publications, where appropriate.”

As a postdoc, Lesné worked in the lab of neuroscientist Karen Ashe. In 2006, they published a study in that seemed to show a cause-effect relationship between a protein—amyloid-beta *56—and memory loss in rats. Those symptoms seemed to resemble the memory problems that afflict Alzheimer’s patients.

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Imagine a world where your thoughts aren’t confined to the boundaries of your skull, where your consciousness is intimately connected to the universe around you, and where the neurons in your brain communicate instantly across vast distances.

This isn’t science fiction — it’s the intriguing possibility suggested by applying the principle of quantum entanglement to the realm of consciousness.

Quantum entanglement, often described as the “spooky action at a distance,” is a phenomenon that baffled even Einstein. In essence, it describes a scenario where two particles become so deeply linked that they share the same fate, regardless of the distance separating them. Measuring the state of one instantly reveals the state of its partner, even if they are light-years apart.

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Scientists explored Human Accelerated Regions (HARs), genetic regulators that tweak existing genes rather than introducing new ones. Using cutting-edge techniques, they mapped nearly all HAR interactions, revealing their role in brain development and neurological disorders like autism and schizophrenia.

Decoding the Genetic Evolution of the Human Brain

A new Yale study offers a deeper understanding of the genetic changes that shaped human brain evolution and how this process differed from that of chimpanzees.

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Our guts are home to trillions of bacteria, and research over the last few decades has established how essential they are to our physiology—in health and disease. A new study from EMBL Heidelberg researchers shows that gut bacteria can bring about profound molecular changes in one of our most critical organs—the brain.

The new study, published in the journal Nature Structural & Molecular Biology, is the first to show that bacteria living in the gut can influence how proteins in the brain are modified by carbohydrates—a process called glycosylation. The study was made possible by a new method the scientists developed—DQGlyco—which allows them to study glycosylation at a much higher scale and resolution than previous studies.

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