Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience.

Encoding models have been used to assess how the human brain represents concepts in language and vision.

Pathophysiolpgical modelling of brain systems from microscale to macroscale remains difficult in group comparisons partly because of the infeasibility of modelling the interactions of thousands of neurons at the scales involved. Here, to address the challenge, we present a novel approach to construct differential causal networks directly from electroencephalogram (EEG) data. The proposed network is based on conditionally coupled neuronal circuits which describe the average behaviour of interacting neuron populations that contribute to observed EEG data. In the network, each node represents a parameterised local neural system while directed edges stand for node-wise connections with transmission parameters. The network is hierarchically structured in the sense that node and edge parameters are varying in subjects but follow a mixed-effects model. A novel evolutionary optimisation algorithm for parameter inference in the proposed method is developed using a loss function derived from Chen-Fliess expansions of stochastic differential equations. The method is demonstrated by application to the fitting of coupled Jansen-Rit local models. The performance of the proposed method is evaluated on both synthetic and real EEG data. In the real EEG data analysis, we track changes in the parameters that characterise dynamic causality within brains that demonstrate epileptic activity. We show evidence of network functional disruptions, due to imbalance of excitatory-inhibitory interneurons and altered epileptic brain connectivity, before and during seizure periods.

Gestala is the latest company to emerge from China's burgeoning brain-computer interface industry. It plans to access the brain with noninvasive ultrasound technology. China's brain-computer interface industry is growing fast, and the newest company to emerge from the country is aiming to access the brain without the use of invasive implants . Gestala, newly founded in Chengdu with offices in Shanghai and Hong Kong, plans to use ultrasound technology to stimulate--and eventually read from--the brain, according to CEO and cofounder Phoenix Peng. It's the second company to launch in recent weeks with the aim of tapping into the brain with ultrasound.

New research has discovered that a neural circuit may explain procrastination. Scientists were able to disrupt this connection using a drug. The brain avoids unpleasant tasks even if they promise reward, according to a recent study. The reason you decide to postpone household chores and spend your time browsing social media could be explained by the workings of a brain circuit. Recent research has identified a neural connection responsible for delaying the start of activities associated with unpleasant experiences, even when these activities offer a clear reward.

We're about to simulate a human brain on a supercomputer The world's most powerful supercomputers can now run simulations of billions of neurons, and researchers hope such models will offer unprecedented insights into how our brains work What would it mean to simulate a human brain? Today's most powerful computing systems now contain enough computational firepower to run simulations of billions of neurons, comparable to the sophistication of real brains. We increasingly understand how these neurons are wired together, too, leading to brain simulations that researchers hope will reveal secrets of brain function that were previously hidden. Researchers have long tried to isolate specific parts of the brain, modelling smaller regions with a computer to explain particular brain functions. But "we have never been able to bring them all together into one place, into one larger brain model where we can check whether these ideas are at all consistent", says Markus Diesmann at the Jülich Research Centre in Germany.

Aligning neural dynamics with movements is a fundamental goal in neuroscience and brain-machine interfaces. However, there is still a lack of dimensionality reduction methods that can effectively align low-dimensional latent dynamics with movements. To address this gap, we propose Neural Embeddings Rank (NER), a technique that embeds neural dynamics into a 3D latent space and contrasts the embeddings based on movement ranks. NER learns to regress continuous representations of neural dynamics (i.e., embeddings) on continuous movements. We apply NER and six other dimensionality reduction techniques to neurons in the primary motor cortex (M1), dorsal premotor cortex (PMd), and primary somatosensory cortex (S1) as monkeys perform reaching tasks.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/.

Merge Labs, a brain-computer interface startup that seeks to read brain activity using ultrasound, is being spun out of Forest Neurotech, a Los Angeles nonprofit. Samuel Altman, CEO of OpenAI, testifies in Washington, DC, on May 16, 2023. OpenAI CEO Sam Altman's new brain-computer interface startup, Merge Labs, is being spun out of the Los Angeles-based nonprofit Forest Neurotech, according to a source with direct knowledge of the plans. It will focus on using ultrasound to read brain activity. Along with Altman, WIRED has learned, Forest Neurotech's CEO Sumner Norman and chief scientific officer Tyson Aflalo are among the cofounders of Merge Labs, which is still in stealth mode.