A ground-breaking study shows the most detailed map of a mammal's brain to date. The 3D blueprints display more than two miles of neural wiring, close to 100,000 nerve cells, and about 500 million synapses -- all contained in a piece of mouse brain no bigger than a grain of sand. Dr Clay Reid of the Allen Institute for Brain Science in Seattle said: 'Inside this tiny speck is an exquisite forest of connections, filled with rules we're only beginning to understand.' The sample comes from an outer part of the brain - known as the cortex - a region which is involved in sight, the Times reports. Dr Forrest Collman, of the same Institute, said: 'By studying how the cortex functions in the mouse brain, we can generate better ideas and hypotheses about how our own brains work.'

Sex comprises an intricate tangle of impulses and interactions between partners. Neuroscientists have learned a great deal about the neural mechanisms underlying sex, but questions about the processes that control the sequence of events during sex remain unanswered. While past research has identified the regions of the brain that control how mice initiate sex, other steps of copulation are still mysteries. A team of researchers in China and Japan have investigated which brain regions and neurotransmitters are responsible for different phases during sex. A paper published March 19 in the journal Neuron describes what exactly goes on in a mouse brain during sex.

Holistic 3D modeling of molecularly defined brain structures is crucial for understanding complex brain functions. Emerging tissue profiling technologies enable the construction of a comprehensive atlas of the mammalian brain with sub-cellular resolution and spatially resolved gene expression data. However, such tera-scale volumetric datasets present significant computational challenges in understanding complex brain functions within their native 3D spatial context. Here, we propose the novel generative approach $\textbf{Tera-MIND}$, which can simulate $\textbf{Tera}$-scale $\textbf{M}$ouse bra$\textbf{IN}$s in 3D using a patch-based and boundary-aware $\textbf{D}$iffusion model. Taking spatial transcriptomic data as the conditional input, we generate virtual mouse brains with comprehensive cellular morphological detail at teravoxel scale. Through the lens of 3D $gene$-$gene$ self-attention, we identify spatial molecular interactions for key transcriptomic pathways in the murine brain, exemplified by glutamatergic and dopaminergic neuronal systems. Importantly, these $in$-$silico$ biological findings are consistent and reproducible across three tera-scale virtual mouse brains. Therefore, Tera-MIND showcases a promising path toward efficient and generative simulations of whole organ systems for biomedical research. Project website: https://musikisomorphie.github.io/Tera-MIND.html

An experimental brain implant containing tens of thousands of living neurons can form cell connections with the brains of mice. Such a device could eventually enable sophisticated control over millions of neurons on the level of individual cells – but without relying on surgically implanted electrodes that penetrate and destroy brain tissue. The biohybrid implant, developed by California-based start-up Science Corporation, differs from many other brain-computer interface devices, which usually contain arrays of electrodes that penetrate the brain and sometimes damage cells. In comparison, Science Corporation's implant is…

Mouse and human brains have different functions that depend on their neuronal networks. In this study, we analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained results indicated that mouse neuronal somata are smaller and neurites are thinner than those of human neurons. These structural features allow mouse neurons to be integrated in the limited space of the brain, though thin neurites should suppress distal connections according to cable theory. We implemented this mouse-mimetic constraint in convolutional layers of a generative adversarial network (GAN) and a denoising diffusion implicit model (DDIM), which were then subjected to image generation tasks using photo datasets of cat faces, cheese, human faces, and birds. The mouse-mimetic GAN outperformed a standard GAN in the image generation task using the cat faces and cheese photo datasets, but underperformed for human faces and birds. The mouse-mimetic DDIM gave similar results, suggesting that the nature of the datasets affected the results. Analyses of the four datasets indicated differences in their image entropy, which should influence the number of parameters required for image generation. The preferences of the mouse-mimetic AIs coincided with the impressions commonly associated with mice. The relationship between the neuronal network and brain function should be investigated by implementing other biological findings in artificial neural networks.

The most detailed cellular map of a mouse's brain to date could deepen our understanding of how the organ evolved in mammals and what goes wrong in certain neurological conditions. Various research groups have previously mapped hundreds of cell types across the mouse brain, but these were often based on a relatively small sample of cells.

Abstract: MRI connectomics is an emerging approach to study the brain as a network of interconnected brain regions. Understanding and mapping the development of the MRI connectome may offer new insights into the development of brain connectivity and plasticity, ultimately leading to improved understanding of normal development and to more effective diagnosis and treatment of developmental disorders. In this review, we describe the attempts made to date to map the whole-brain structural MRI connectome in the developing brain and pay a special attention to the challenges associated with the rapid changes that the brain is undergoing during maturation. The two main steps in constructing a structural brain network are (i) choosing connectivity measures that will serve as the network "edges" and (ii) finding an appropriate way to divide the brain into regions that will serve as the network "nodes". We will discuss how these two steps are usually performed in developmental studies and the rationale behind different strategies.

In 2008, the journalist Jonah Lehrer paid a visit to a lab in Lausanne, Switzerland, to profile Henry Markram, a world-renowned neuroscientist. Markram, a South African, had trained at a series of élite institutions in Israel, the United States, and Germany; in the nineties, he published foundational papers on neural connections and synaptic activity. Markram's work in the laboratory, which involved piercing neural membranes with what Lehrer described as an "invisibly sharp glass pipette," was known for its painstaking precision. Lehrer's visit, however, had been occasioned not by Markram's incremental contributions to the field--it's not easy to sell a colorful profile on the basis of such publications as "The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability"--but by Markram's pivot, in the early two-thousands, to brain simulation. Neuroscience, Markram declaimed to Lehrer, had reached an impasse. Researchers had generated an enormous wealth of fine-grained data, but the marginal returns had begun to diminish.

In order to scrutinize the internal features of a living organism using light, it is necessary to A) deliver sufficient light energy to the sample and B) accurately measure the signal reflected from the target tissue. However, in living tissues multiple scattering effects and severe aberration1 tend to occur when light hits the cells, which makes it difficult to obtain sharp images. In complex structures such as living tissue, light undergoes multiple scattering, which causes the photons to randomly change their direction several times as they travel through the tissue. Because of this process, much of the image information carried by the light becomes ruined. However, even if it is a very small amount of reflected light, it is possible to observe the features located relatively deep within the tissues by correcting the wavefront2 distortion of the light that was reflected from the target to be observed. However, the above-mentioned multiple scattering effects interfere with this correction process.

Neurons communicate via synapses--tiny, button-like protrusions that sprout from one neuron and connect it to the next. These minuscule structures are thought to be the backbone of learning and memory, changing in strength and number as we learn. At about 1/5,000th the width of a human hair, synapses can be hard to visualize, and researchers are just beginning to develop the tools necessary to do so. In a study published in Cell Reports on August 2, researchers at the Chinese Academy of Sciences and Shanghai University used a combination of deep learning algorithms and high-resolution electron microscopy to map out how frightful experiences rearrange brain connections. They found that when mice learn to fear the sound of a buzzer, neurons in their hippocampus form more connections with other neurons downstream and shuttle more mitochondria to synaptic sites.