Articles tagged with Human Brain Models
Researchers at Cold Spring Harbor Laboratory created the first cross-species brain map to link mouse and marmoset regions. The study found that 43% of mouse brain regions match with marmoset regions, shedding light on human brain function and potential disease insights.
A NIH-funded research project has generated a comprehensive collection of patient-derived tumor models, covering 25 types of cancer from thousands of donor tissues. These models closely match their parental tumors in terms of genetic alterations and gene expression signatures, enabling researchers to explore tumor evolution and diversity.
The new section in JMIR Neurotechnology focuses on the responsible and ethical use of neurotechnology. Submissions welcome for original research, case series, theoretical viewpoints, and data-driven analyses on ethics and policy in neurotechnology.
Biocomputing harnesses human brain cells to perform computational functions, with applications in energy-efficient computing, drug discovery, and neuromorphic engineering. Early developments in biocomputing hold promise for biomedical research, despite ongoing bioethical concerns.
Dr. Sara Simblett brings expertise in digital health innovation, patient engagement, and technology integration to JMIR Neurotechnology. Her research focuses on improving access, quality, and outcomes of care through digital interventions.
A new study in Biological Psychiatry found that emotional pain (guilt, shame, sadness, loss of joy) weighs more heavily on individuals with PTSD, impairing daily functioning more than fear. The research identified two distinct PTSD profiles: one centered on fear and the other on emotional pain.
Researchers at Virginia Tech found that adjusting molecular processes can improve memory in older subjects. They used CRISPR-dCas13 and CRISPR-dCas9 to target age-related changes in K63 polyubiquitination and IGF2, two genes linked to memory formation.
Scientists at RIKEN Center for Brain Science found that fruit flies use separate circuits to compute pleasant and unpleasant odors, challenging the idea that 'good' is the opposite of 'bad'. The discovery may contribute to a better understanding of human brain's flavor appreciation mechanisms.
Professor John O'Brien's research focuses on understanding the impact of electrical synapses and gap junction plasticity on the retina and other neurological functions. The study aims to identify proteins that control electrical synapse strength, shedding light on their role in human disorders such as autism and seizures.
Gene coexpression analysis reveals optimal markers of cell types and states, providing opportunities for developing novel biomarkers and targeted treatment strategies for glioma patients. Dr. Oldham's work tackles the reproducibility crisis in science, emphasizing data metadata standardization.
Terrence Sejnowski has been elected to the Royal Society and the American Philosophical Society, a testament to his groundbreaking contributions to computational neuroscience. His pioneering work includes inventing the Boltzmann machine and NETtalk program, which revolutionized artificial neural networks and speech recognition.
A study published in Communications Psychology reveals how the brain forms a sense of agency when learning new motor skills. Researchers used a data glove to control a cursor on screen, finding that motor exploration plays a crucial role in developing self-agency.
A study published in PLOS Biology found that individuals who receive dynamic facial-expression feedback from an avatar's face tend to gamble more than those receiving real human feedback. The amygdala plays a key role in this facilitation, with increased valuation of uncertainty contributing to increased risk-taking behavior.
A team of researchers developed Lp-Convolution, a novel method that uses multivariate p-generalized normal distribution to reshape CNN filters dynamically. This breakthrough improves the accuracy and efficiency of image recognition systems while reducing computational burden.
Researchers investigated how brains process language during real-life conversations, employing AI to analyze neural activity and linguistic features. They found that speaking and listening engage widespread networks of brain areas in the frontal and temporal lobes, with highly specific patterns changing depending on words and context.
A University of Ottawa-led research team has deciphered the message that serotonin conveys to the brain, discovering a 'prospective code for value' that explains why neurons are activated by rewards and punishments. This finding has implications across multiple fields, including neuroscience, psychology, and psychiatry.
A new study has revealed that Beagle dogs carrying mutations in the Shank3 gene exhibit face processing abnormalities, similar to those observed in human ASD patients. The research provides direct experimental evidence that mutations in Shank3 lead to ASD-like deficits in face processing, contributing to social impairments.
Scientists at ISTA create a new brain organoid model that incorporates microglia to study viral infections, such as Rubella, and test the effectiveness of ibuprofen. The results show that microglia play a crucial role in inflammatory reactions and that ibuprofen exerts its protective effects by inhibiting two inflammatory enzymes.
A new open-source model of brain metabolism has shown how altering key chemicals can restore aged cells to their youthful activity and resilience. The detailed simulation provides a glimpse into the impact of aging on brain metabolism and highlights potential drug targets.
Researchers from Virginia Tech have published a visionary paper on fusing wireless technologies and AI to create human-like common sense. The team aims to develop a network that can think, plan, and imagine like humans, enabling seamless merging of physical, virtual, and digital dimensions.
A new model of brain computational analysis, called CHARM, reveals that the human brain can surpass machines in critical decision-making. The CHARM model applies quantum mechanics to analyze long-distance brain connections, enabling precise modeling of the brain's dynamics.
Researchers at UC San Francisco have enabled a paralyzed man to control a robotic arm through a device that relays signals from his brain to a computer. The device, known as a brain-computer interface (BCI), worked for a record 7 months without needing to be adjusted.
Researchers at Technion-Israel Institute of Technology discovered how the brain handles large text units. The brain creates a summary while reading, helping it understand what comes next, unlike artificial language models that process long texts as a whole.
Researchers at WashU created a new model that uncovers brain mechanisms behind focusing amid distractions, finding people ignore past distractions but stay open to other inputs. The model demonstrates attention control by modulating focus on distractors and target tasks.
Researchers at Penn State developed a new method to analyze bird songs, revealing context dependence and underlying neurobiology. The study suggests that auditory feedback plays a key role in creating context dependence, and may hold insights into the neural mechanisms of human language.
The collaboration aims to develop advanced 3D mini-brain models for replicating human brain architecture, enabling researchers to explore neurological diseases and screen drug candidates. The platform offers a high-throughput screening method for rapid testing of potential drug candidates.
Neuroscientists at WashU develop a novel method to create personalized brain models, shedding light on individual variations in brain dynamics and their impact on cognition and behavior. The study reveals new insights into the mechanisms underlying person-specific differences in brain electrophysiology.
Researchers developed a preclinical model of brain stimulation therapy that enhances the brain's ability to process conflicting evidence. This improvement in cognitive flexibility could lead to more effective treatments for conditions like depression, ADHD, and addiction.
Researchers used machine learning to predict multiple types of intelligence from brain connections, with general intelligence performing best. The model's accuracy improved when trained on theory-driven connections, suggesting there are still unknown aspects of intelligence to discover.
A new brain mapping technique has revealed fresh insights into how higher-order functions like language, thought, and attention are organised. Researchers used data from fMRI scans to create accurate models of brain activity and interactions between regions.
Researchers observed distinct growth patterns in brain functional networks during the birth transition, with some areas showing minimal changes and others undergoing dramatic reorganization. The subcortical network was found to exhibit significant increases in communication efficiency at birth.
Researchers at the University of Kansas have developed atomically tunable memory resistors, dubbed 'memristors,' to enable brain-inspired advanced computing. The innovation enables precise atomic-scale tuning of oxide semiconductor memristors for high-speed and high-energy efficiency.
A new study by MIT researchers found that debunking election misinformation is not always effective, but can be successful if people are less certain of their beliefs and believe the authority is unbiased or motivated by accuracy. The model analyzed over 200 scenarios and identified factors that make debunking efforts more successful.
Mayo Clinic scientists created mini brain models in a dish that closely match key features seen in the brains of patients with Lewy body dementia. The team identified four potential drug compounds that may offer approaches to treating the disease.
Researchers developed an AI-driven approach to model complex hand movements, overcoming current limitations in neuroscience and biomedical engineering. The model achieved a 100% success rate in controlling virtual Baoding balls, showcasing its strength in various challenging situations.
A research team has successfully recreated wrinkle structures in biological tissue in vitro, revealing the mechanisms behind their formation. The study found that compressive forces and dehydration play a crucial role in wrinkle formation, mirroring aging skin effects.
Researchers at WVU created a motion-compatible brain scanner that allows patients to move around during imaging. The Ambulatory Motion-enabling PET (AMPET) scanner can help study human behaviors, balance, and emotions, and may be used to monitor brain activity for PTSD treatment and mindfulness meditation.
Researchers discovered that mutations in the SYNGAP1 gene disrupt prolonged human neuron development, leading to accelerated maturation and altered cognitive function. This finding has implications for understanding and treating intellectual disabilities and autism.
Researchers at Georgia Tech developed a neural network that makes decisions similar to humans, using Bayesian neural networks and evidence accumulation processes. The model outperformed rival deterministic models and showed improved performance in high-speed scenarios, demonstrating its potential for more accurate decision-making.
A new model developed by Flatiron Institute researchers proposes that individual neurons exert more control over their surroundings, which could be replicated in artificial neural networks. This updated model treats neurons as tiny 'controllers' and may lead to better AI performance and efficiency.
A new study reveals that praising and petting dogs enhances their learning success by reducing stress. Dogs who received a 'permissive' training style with praise and petting performed better than those in a 'controlling' style, which induced greater stress.
Researchers at Duke University developed an assistive machine learning model that greatly improves the ability of medical professionals to read EEG charts. The model, which provides visual explanations and decision support, has been shown to almost double medical professionals' accuracy in identifying seizure-like events, potentially s...
A new NIH study using human cerebral organoids suggests a substantial species barrier preventing the transmission of chronic wasting disease from cervids to people. Researchers found no infection in healthy human cerebral organoids exposed to CWD prions for up to six months.
Researchers discovered that rats without prior knowledge of numbers can develop a sense of numbers when trained with sounds representing two or three numbers. The study found that the brain has a specific area for dealing with numbers, which is separate from the processing of magnitude.
Weill Cornell Medicine scientists developed a human neuron model that simulates tau protein aggregate spread, a process driving cognitive decline in Alzheimer's disease. The model identified novel therapeutic targets, including the UFMylation cascade, which can potentially block tau spread.
Researchers at Kobe University identified differences in synaptic protein production between mice and marmosets during development. The study found that these differences may relate to evolutionary differences between rodent and primate brains, as well as their relevance to autism spectrum disorders.
Dogs have an implicit understanding of object words, activating a matching mental representation when hearing their name. Research found that brain activity patterns differ between matching and mismatched objects, supporting the notion that dogs comprehend object words.
Researchers developed chronological age prediction models by analyzing gene expression changes in the prefrontal cortex, identifying genes associated with aging and potential mechanisms. The models showed high correlation with age and demonstrated female and male-specific differences.
Researchers at Salk Institute have created a novel organoid model of the human brain that includes mature, functional astrocytes. This allows for the study of inflammation and stress in aging and diseases like Alzheimer's with greater clarity, revealing a relationship between astrocyte dysfunction and inflammation.
A new neuroscientific paradigm, the synergistic model, allows better understanding of brain-body-environment interactions. This framework enables more accurate personalized diagnoses and treatments of brain diseases.
A recent study published in PLOS Biology found that the human brain can segregate direct speech from its echo, allowing for reliable recognition of echoic speech. This neural separation is essential for understanding conversations in noisy environments and is supported by magnetoencephalography recordings.
Researchers at McGill University propose a new biological classification system for Parkinson's disease, considering alpha-synuclein, neurodegeneration, and genetics. This shift in thinking aims to diagnose the disease earlier, targeting specific patient groups with more common biology, improving treatment development success.
A new study using generative AI models simulated how the brain learns and remembers events, revealing how memories are re-constructed in our minds. The model showed how the hippocampus and neocortex work together to create efficient 'conceptual' representations of scenes, enabling us to both recall past experiences and imagine new ones.
A novel human brain organoid model generates all major cell types of the cerebellum, including functional Purkinje neurons. This breakthrough provides a new way to explore cerebellar development and disorders, advancing therapeutic interventions.
A new study finds that complex and unfamiliar sentences generate stronger responses in the brain's language processing centers. Sentences with higher surprisal and linguistic complexity evoke more activation, while extremely simple or nonsensical sequences elicit little response.
Research reveals that the midbrain of midshipman fish initiates and patterns sounds used in vocal communication. The findings suggest a functional comparable periaqueductal gray node between fish and mammals, which can influence acoustic structure of social context-specific vocal signals.
Researchers discovered a striking similarity between AI models and the human brain's hippocampus, which enables powerful AI systems. By mimicking the NMDA receptor's gating process, they improved long-term memory in Transformer models.
A new study from MIT shows that computational models trained on auditory tasks display an internal organization similar to the human auditory cortex. Models trained on diverse tasks and background noise more closely mimic brain activation patterns.
A recent study published in the Proceedings of the National Academy of Sciences found that AI's deep convolutional neural networks can identify faces but struggle to capture other important information like emotional state and trustworthiness. Brain activity scans revealed a weak correlation between AI's codes and human brain represent...
The UTSA MATRIX AI Consortium has received a $2 million grant to create new AI models that rapidly learn, adapt, and operate in uncertain conditions. The team aims to bridge the gap between human brain processing efficiency and current AI limitations, enabling more efficient and adaptive AI systems.