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Adversarial AI framework reveals mechanisms behind impaired consciousness and a potential therapy

Consciousness, and the ways in which it can become impaired after certain brain injuries, are not well understood, making disorders of consciousness (DOC), like coma, vegetative states and minimally conscious states difficult to treat. But a new study, published in Nature Neuroscience, indicates that AI might be able to help researchers gain some traction with this problem. The research team involved in the new study has developed an adversarial AI framework to help them determine what exactly is going on in states of reduced consciousness and how to approach a solution.

To better understand the mechanisms behind impaired consciousness, the researchers developed two types of AI models and had them play a kind of game where one model determined different levels of consciousness based on EEGs simulated to look like those of real unconscious and conscious brains. The AI agents guessing consciousness levels, called deep convolutional neural networks (DCNNs), were first trained on 680,000 ten-second recordings of brain activity from conscious and unconscious humans, monkeys, bats and rats to detect which neural signals related to differing levels of consciousness. The AI showing EEG data was a biologically plausible simulation of the human brain.

“To decode consciousness from these signals, we trained three separate DCNNs, each specialized for a different brain region, to output a continuous score from 0 (unconscious) to 1 (fully conscious): a cortical consciousness detector (ctx-DCNN), a thalamic consciousness detector (th-DCNN) and a pallidal consciousness detector (pal-DCNN). The ctx-DCNN was trained on continuous consciousness levels derived from clinical scales (GCS and CRS-R), enabling it to recognize graded states of consciousness,” the study authors explain.

Brain computer interface enables rapid communication for two people with paralysis

Researchers from Brown University and Mass General Brigham have developed an implantable brain-computer interface that allowed two people with paralysis — one with ALS and one with a spinal cord injury — to communicate through rapid, accurate typing. The system uses microelectrode sensors in the motor cortex, maps letters to attempted finger movements on a QWERTY keyboard, and decodes those neural signals into text.

In the study, one participant reached a top speed of 110 characters per minute (about 22 words per minute) with a 1.6% word error rate, and both participants were able to use the system from home after calibration with as few as 30 sentences. The results were published in Nature Neuroscience.

This is the kind of neurotechnology that starts to close the gap between thought and communication.

Implantable device research from the BrainGate clinical trial enables communication through rapid typing for a patient with ALS and a patient with a spinal cord injury.

Anatomical 3D Visualization Powered by Three.js for Scientific Studies

It’s impressive when 3D visualization work finds use in diverse fields, including science and education. One example of such a project is a 3D visualization of a cranium showcased by a Physiotherapist.

The 3D model is a web-based 3D morphometry and simulation platform for the cranium and cranio-cervical junction, which performs landmark-based measurements on STL/GLB models, applies localized mesh deformation, and generates simulations using patient-derived CSV data.

The real-time 3D visualization is powered by Three.js. The model allows for a wide range of cross-sectional analysis and measurement workflows. According to the creator, the visualization might be used in academic research, anatomy studies, simulations for surgical planning, education, and more. You can find more details in Physiotherapist’s X/Twitter thread.

Reduce Energy Consumption In Unity Games With This Plug-In

Over the past few months, we’ve covered plug-ins for both Unreal Engine and Godot that optimize power use, making games more energy-efficient and helping players get more out of their battery life. They work by detecting when a player goes idle, then lowering the frame rate and rendering resolution, and during longer periods of inactivity, even pausing rendering entirely.

Now, thanks to Oliver Stock, who felt like somebody should step up and do the same for Unity, there’s a similar plug-in available for developers. It’s free and open-source, and you can get it by clicking here. It monitors player input, and when nothing’s happening, it automatically switches between different energy profiles. These profiles control which settings are adjusted, like frame rate, resolution, or physics updates. You can easily tweak or create your own profiles to suit your project’s needs.

Oliver recommends using Unity 2022.3.62f2 or newer. The plug-in currently only works with Unity’s URP or HDRP.

A foundation model of vision, audition, and language for in-silico neuroscience

‘The present results strengthen the possibility of a paradigm shift in neuroscience… moving from the fragmented mapping of isolated cognitive tasks toward the use of unified, predictive foundation models of brain and cognitive functions By aligning the representations of Al systems to those of the human brain, we demonstrate that a single architecture can integrate a vast range of fMRI responses across hundreds of individuals, extending the framework that led the 2025 Algonauts competition. The observed log-linear scaling of encoding accuracy mirroring power laws in both artificial intelligence and neuroscience suggests that the ceiling for predicting human brain activity is yet to be reached.’

Cognitive neuroscience is fragmented into specialized models, each tailored to specific experimental paradigms, hence preventing a unified model of cognition in the human brain. Here, we introduce TRIBE v2, a tri-modal (video, audio and language) foundation model capable of predicting human brain activity in a variety of naturalistic and experimental conditions. Leveraging a unified dataset of over 1,000 hours of fMRI across 720 subjects, we demonstrate that our model accurately predicts high-resolution brain responses for novel stimuli, tasks and subjects, superseding traditional linear encoding models, delivering several-fold improvements in accuracy. Critically, TRIBE v2 enables in silico experimentation: tested on seminal visual and neuro-linguistic paradigms, it recovers a variety of results established by decades of empirical research.

RNase L regulates the antiviral proteome by accelerating mRNA decay, inhibiting nuclear mRNA export, and repressing transcription

Watkins et al. show that RNase L dampens the expression of interferon-stimulated genes by accelerating mRNA decay, inhibiting nuclear mRNA export block, and repressing transcription.

EBV Dysregulation Is Associated With Immune Imbalance in Multiple SclerosisEvidence From Integrated Viral and Host Analyses

EBV dysregulation is associated with immune imbalance in multiple sclerosis: evidence from integrated viral and host analyses.

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Characterization of a Splice Variant in FLNA Associated With Periventricular Nodular Heterotopia

This study broadens the phenotypic and genetic spectrum of PNH, demonstrating a dual PNH phenotype associated with a bi‑transcript mechanism and mosaic inheritance, including tissue‑specific mosaicism.

PNH is a neurodevelopmental brain malformation characterized by failure of the gray matter to properly migrate to the cerebral cortex during embryonic development. This results in ectopic localization around the ventricular ependyma.1 MRI serves as the primary diagnostic tool, showing bilateral periventricular gray matter nodules with a signal intensity similar to that of normal cortical gray matter.2,3 Its primary clinical manifestation is epilepsy, which is often accompanied by intellectual disabilities and learning difficulties.2,3 PNH is genetically heterogeneous and is linked to variants in multiple genes, including ARFGEF2, ERMARD, NEDD4L, ARF1, and MAP1B, as well as abnormalities in chromosome 5. Among these, pathogenic variants in FLNA are the most common genetic causes.4

FLNA is located at Xq28 and comprises 47 exons,5 encoding a 280 kDa actin binding protein, called filamin A. The N-terminal region contains an actin binding domain (ABD) and a rod-like structure composed of 24 immunoglobulin-like repeats. ABD interacts with actin to stabilize the cytoskeletal architecture and plays crucial roles in maintaining cell shape, migration, and transmitting mechanical force. FLNA regulates cellular migration and extension processes via interactions with several signaling proteins, including small GTPases Rac/Rho, TRAF2, integrins, and BRCA2.6–8 This gene possesses at least 2 transcription initiation sites (ENST00000369850.8 and ENST00000610817.4) that use distinct promoters and demonstrate tissue-specific expression.6–8 Rat FLNA-knockdown models exhibit impaired neuronal migration and elevated epileptic susceptibility.

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