Lifeboat Foundation

HBP researchers have trained a large-scale model of the primary visual cortex of the mouse to solve visual tasks in a highly robust way. The model provides the basis for a new generation of neural network models. Due to their versatility and energy-efficient processing, these models can contribute to advances in neuromorphic computing.

Modeling the brain can have a massive impact on artificial intelligence (AI): since the brain processes images in a much more energy-efficient way than artificial networks, scientists take inspiration from neuroscience to create neural networks that function similarly to the biological ones to significantly save energy.

In that sense, brain-inspired neural networks are likely to have an impact on future technology, by serving as blueprints for visual processing in more energy-efficient neuromorphic hardware. Now, a study by Human Brain Project (HBP) researchers from the Graz University of Technology (Austria) showed how a large data-based model can reproduce a number of the brain’s visual processing capabilities in a versatile and accurate way. The results were published in the journal Science Advances.

ARI gene groups (ARI downregulated genes, those highly expressed in BA17 and BA39/40 relative to other regions in controls; ARI upregulated genes, those expressed at low level in BA17 and BA39/40 relative to other regions in controls) were created through taking the union (without duplicates) across all ten identified ASD-attenuated regional comparisons, and sorting genes into the two groups based on gene-expression profiles across regions. The details of this process are described in the Supplementary Methods, along with functional annotation procedures.

Standard workflows using WGCNA¹⁷ were followed as previously described in Parikshak et al.⁵ and Gandal et al.¹ (with minor modifications) to identify gene and transcript co-expression modules. Details regarding network formation, module identification, and module functional characterization are described in the Supplementary Methods.

Frozen brain samples were placed on dry ice in a dehydrated dissection chamber to reduce degradation effects from sample thawing and/or humidity. Approximately 50 mg of cortex was sectioned, ensuring specific grey matter–white matter boundary. The tissue section was homogenized in RNase-free conditions with a light detergent briefly on ice using a dounce homogenizer, filtered through a 40-μM filter and centrifuged at 1,000 g for 8 min at 4 °C. The pelleted nuclei were then filtered through a two-part micro gradient (30%/50%) for 20 min at 4 °C. Clean nuclei were pelleted away from debris. The nuclei were washed two more times with PBS/1%BSA/RNase and spun down at 500 g for 5 min. Cells were inspected for quality (shape, colour and membrane integrity) and counted on a Countess II instrument. They were then loaded onto the 10X Genomics platform to isolate single nuclei and generate libraries for RNA sequencing on the NovaS4 or NovaS2 Illumina machines.

Summary: Bergmann glial cell synaptic engulfing in the cerebellum was enhanced during motor learning in mice.

Source: Tohoku University.

Tohoku University researchers have shown that Bergmann glial cells, astrocyte-like cells in the cerebellum, ‘eat’ their neighboring neuronal elements within healthy living brain tissue.

Although these studies collectively suggest that the DLPFC plays a major role in making risky choices, a question remains as to whether its activity mediates risky choice via probability weighting, via marginal utility (value) of monetary outcomes, or both.

In the present study we causally address the hypothesis that the DLPFC is involved both in the subjective valuation of a monetary reward and in probability weighting. The hypothesis was not preregistered but was formulated prior to the collection of the data. The hypothesis was well grounded in the existing literature on the role of DLPFC in risk taking. Several previous studies mentioned the possible role of the lateral PFC in separate components of choice under risk, such as reward magnitude, reward probability, and expected value^14,25,26. However, previous studies including those exploring causal role of the DLPFC in risky choice with non-invasive brain stimulation were not focusing on the estimation of the risk preference parameters but rather on observing changes purely on a behavioural level. Therefore, in the present study we used an experimental design that is typically employed in economic studies estimating risk preference parameters²⁷. We combined offline repetitive TMS over the left and right DLPFC and sham over the right DLPFC, performed in a randomized and counterbalanced order, with a random lottery pair (RLP) task, which is widely used in economics to estimate the degree of risk aversion as well as the curvature of the probability weighting function on an individual level.

Following offline TMS, subjects completed a computerized task consisting of 96 binary lottery choice questions presented in random order. Using the hierarchical Bayesian modeling approach, we then estimated the structural parameters of risk preferences (degree of risk aversion and the curvature of the probability weighting function) and analyzed the obtained posterior distributions to determine the effect of stimulation on model parameters.

Brain changes in autism are comprehensive throughout the cerebral cortex rather than just particular areas thought to affect social behavior and language, according to a new UCLA-led study that significantly refines scientists’ understanding of how autism spectrum disorder (ASD) progresses at the molecular level.

The study, published today in Nature, represents a comprehensive effort to characterize ASD at the molecular level. While neurological disorders like Alzheimer’s disease or Parkinson’s disease have well-defined pathologies, autism and other psychiatric disorders have had a lack of defining pathology, making it difficult to develop more effective treatments.

The new study finds brain-wide changes in virtually all of the 11 cortical regions analyzed, regardless of whether they are higher critical association regions—those involved in functions such as reasoning, language, social cognition and mental flexibility—or primary sensory regions.

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A single dose of a synthetic version of the mind-altering component of magic mushrooms, psilocybin, improved depression in people with a treatment-resistant form of the disease, a new study found.

Continue reading “Severe depression eased by single dose of synthetic ‘magic mushroom’” »

New images from scientists at Cold Spring Harbor Laboratory (CSHL) reveal for the first time the three-dimensional structures of a set of molecules critical for healthy brain function.

The molecules are members of a family of proteins in the brain known as NMDA receptors, which mediate the passage of essential signals between neurons. The detailed pictures generated by the CSHL team will serve as a valuable blueprint for drug developers working on new treatments for schizophrenia, depression, and other neuropsychiatric conditions.

“This NMDA receptor is such an important drug target,” says Tsung-Han Chou, a postdoctoral researcher in CSHL Professor Hiro Furukawa’s lab. That’s because dysfunctional NMDA receptors are thought to contribute to a wide range of conditions, including not just depression and schizophrenia, but also Alzheimer’s disease, stroke, and seizures. “We hope our images, which visualize the receptor for the first time, will facilitate drug development across the field based on our structural information,” Chou says.

It’s important in sports and in interpersonal relationships—perfect timing. But how does our brain learn to estimate when events might occur and react accordingly? Scientists at MPI CBS in Leipzig together with colleagues from the Kavli Institute at the Norwegian University of Science and Technology in Trondheim were able to demonstrate in an MRI study that our brain learns best in connection with constructive feedback.

Imagine playing a game with friends, where they throw you a ball that you must catch. The first couple of throws you might miss the ball, but as you keep trying, you become better at estimating the time it takes to reach you and catch it more easily. How does your brain do this? “Fundamental to this process are your abilities to learn from past experiences and to extract time-related information from the environment,” explains Ignacio Polti, who conducted the study now published in the journal eLife together with Matthias Nau and Christian Doeller.

“Every throw of your friend will be slightly different from the previous one. Some balls arrive earlier, some arrive later. During the game, your brain learns the distribution of arrival times, and it uses this information to form expectations for future throws. By combining such prior knowledge with specific information of our friend’s current throw, we can thus improve the timing of our catch attempts.”

Researchers examining post-mortem brains confirm a long-held hypothesis explaining neurotransmitter’s connection to a debilitating disorder.

How does the brain chemical dopamine relate to schizophrenia? It is a question that vexed scientists for more than 70 years, and now researchers at the Lieber Institute for Brain Development (LIBD) believe they have solved the challenging riddle. This new understanding may lead to better treatment of schizophrenia, an often-devastating brain disorder characterized by delusional thinking, hallucinations, and other forms of psychosis.

Through their exploration of the expression of genes in the caudate nucleus – a region of the brain linked to emotional decision-making – the scientists uncovered physical evidence that neuronal cells are unable to precisely control levels of dopamine. They also identified the genetic mechanism that controls dopamine flow. Their findings were published today (November 1) in the journal Nature Neuroscience.