Articles tagged with Human Brain Models
Researchers used computational models to analyze thousands of hours of transcribed audio recordings of children and adults interacting. The findings suggest that adults' ability to make context-based interpretations provides crucial feedback for babies acquiring language. These interpretations are critical for understanding what small ...
Researchers found that deep neural networks often respond the same way to images with no resemblance to the target, generating unnatural signals. The models develop unique invariances that are different from human perceptual systems, causing them to perceive pairs of stimuli as similar despite their differences.
Researchers assembled an atlas of hundreds of cell types that make up a human brain in unprecedented detail. The study uses techniques originally developed for mice to identify brain cell subtypes in human brains.
Researchers at Mount Sinai Hospital developed an algorithm called HistoAge that predicts age at death based on human brain tissue specimens using artificial intelligence, revealing insights into aging mechanisms and neurodegenerative diseases like Alzheimer's disease.
GlowTrack, a non-invasive movement tracking method using fluorescent dye markers, improves the capture of diverse movements in laboratories. This technique enables easier comparison of movement data between studies, increasing scientific discovery and advancing fields like biology, robotics, and medicine.
Researchers used new software to compare MRI scans from 300 babies and found that myelin, a fatty sheath around axons, develops much slower after birth. The study supports the idea that gestational age at birth and scan time matter for brain development.
Researchers developed a computational model to analyze glucose-responsive insulin (GRI) performance in human patients. The model predicted that differences in sugar receptor behavior between humans and lab animals led to the drug's poor effect in clinical trials. This breakthrough helps researchers design better GRIs, potentially reduc...
The study identified two relevant circuits in patients with disorders of consciousness (DoC), including the thalamo-frontotemporal region and the posterior cortical region. These findings bring a new understanding of brain networks and could improve diagnosis and treatment for people suffering from DoC.
A new study suggests that simple limitations in neural responses explain visual illusions, rather than deeper psychological processes. Researchers developed a model that combines information on neural firing speeds, pattern perception, and natural scene assumptions to predict how animals see colour and understand visual illusions.
Researchers at MIT have designed a computational model that can predict other people's emotions, including joy, gratitude, and regret. The model uses insights into human intuition, incorporating factors such as desires, expectations, and observation of actions to make predictions.
Researchers built personalized brain network models to simulate brain dynamics and found that people with higher fluid intelligence took more time to solve difficult tasks compared to those with lower FI. This suggests that a synchronized brain is better at solving problems but not necessarily faster.
Researchers found that higher intelligence scores are only associated with quicker performance on simple tasks, while more difficult problems require more time and lead to better results. The brain models also showed that slower brains make fewer errors due to reduced synchrony between brain areas.
Researchers create a human-brain-like environment to study microglia development and function for the first time in living human-derived tissue. The findings suggest that brain environment influences microglia development and function, particularly in diseases such as autism spectrum disorder and Alzheimer's disease.
A novel stroke model in pigs has been established to mimic human cerebral artery occlusion and study mechanisms of ischemic stroke. The model's translational features make it suitable for testing new therapeutic compounds and devices.
A machine learning model helps explain how brains recognize the meaning of communication sounds, such as animal calls or spoken words. The study models sound-processing networks in social animals' brains and demonstrates their ability to distinguish between different sound categories.
HBP researchers develop a robotic platform capable of learning as humans do while navigating around a space. The simulated hippocampus alters its own synaptic connections to remember paths, a marked improvement over current methods relying on deep learning.
Biyu J. He's groundbreaking research on perceptual cognition and subjective experience has challenged traditional views of consciousness. Her work explores how past experiences shape individual perception and understanding, using innovative techniques like fMRI and EEG.
Researchers at the Human Brain Project present novel clinical uses of advanced brain modelling methods, enabling clinicians to simulate epileptic seizures and identify target areas. The approach has broad applicability in neuroscience, medicine, and neurotechnology, with potential improvements in data resolution and patient specificity.
Researchers have created a high-resolution, single-cell model of the human hippocampus CA1 area using data from the BigBrain Atlas. The model replicates the structure and architecture of the neurons, as well as their connectivity. This advancement in neuroscience has potential applications for modeling other brain regions.
Researchers collected electrophysiological recordings from prefrontal cortical regions in three human subjects with severe treatment-resistant depression. They found lower depression severity correlated with decreased low-frequency neural activity and increased high-frequency activity.
A personalized brain modeling approach is being tested in a large-scale clinical trial for drug-resistant epilepsy, aiming to improve surgery preparation and patient specificity. The technique uses virtual models of patients' brains to simulate abnormal activity during seizures, helping clinicians identify target areas more effectively.
A Rutgers-led study found that a gene mutation associated with autism causes an overstimulation of brain cells, disrupting the normal information flow. The researchers used human stem cells and transplanting them into mouse brains to understand how the mutation affects brain development.
Researchers found positive correlations between lipoxin A4 and cognitive brain functions, as well as its potential to potentiate the endocannabinoid system. The study suggests that reducing levels of this substance may weaken the body's defenses against neurodegeneration.
A new neurocomputational model introduces a three-level information processing framework to understand brain development and cognition. The model focuses on Hebbian learning and reinforcement learning, highlighting two fundamental mechanisms for multilevel cognitive ability development in biological neural networks.
Researchers at Cedars-Sinai created complex computer models of individual brain cells using artificial intelligence. These models capture the electrical signals that neurons fire to communicate with each other, allowing researchers to replicate brain activity at the single-cell level.
Researchers at UCLA have developed a new framework for studying the organization of the human brain by combining data simulation and experimental observation. The study reveals that a small number of spatiotemporal patterns can unify various observations, providing a description of global functional brain organization.
A review of over 200 publications suggests that fish perceive quantities using similar brain regions as mammals and birds. This finding could lead to the development of new treatments for human diseases affecting math skills, such as developmental dyscalculia.
Researchers used microcircuit models of basal ganglia and thalamus areas to create multiscale models of Parkinson's patient and healthy control brain. They found that in-silico deep brain stimulation could normalize decreased firing rates in subcortical regions, but also caused differential activity in the motor cortex.
A study led by Tel Aviv University researchers reveals a common mechanism underlying genetic mutations associated with autism, schizophrenia, and other neurological disorders. The discovery points to an experimental drug developed by the team as a potential treatment for these conditions.
Researchers have successfully processed sequences with a large neural network while consuming significantly less energy on neuromorphic hardware. This breakthrough showcases the potential of neuromorphic technology to improve the energy efficiency of AI workloads.
Researchers have developed a multi-dimensional approach to quantify cognitive decline in aging dogs, which may also inform human Alzheimer's disease treatment. The method combines physical exams, lab tests, and cognitive assessments to provide a more complete picture of canine cognitive function.
Researchers found a correlation between Parkinson's gene alpha-synuclein and vocal production in zebra finches. The study suggests that this link could lead to earlier diagnoses and treatments for Parkinson's patients.
Researchers created patient-derived models of brain metastases that recapitulate human disseminated disease, reflecting clinical manifestations and biological characteristics. These models can be used to test therapeutic value of different treatments and explore new approaches tailored to each patient.
A study by Tokyo University of Science researchers has demonstrated that a computationally-light model can simulate complex brain cell responses, including periodic and quasi-periodic responses. The Izhikevich neuron model was found to be capable of reproducing both types of responses at lower computational cost.
Researchers developed personalised brain models to simulate patient response to deep brain stimulation for depression, improving efficacy by 50%. The models use individual EEG and MRI data to replicate brain response and pave the way for tailored treatment approaches.
Scientists at ISTA used brain organoids to study the effects of CHD8 mutations on brain development. The study found that mutant organoids produced larger amounts of neurons, leading to brain overgrowth and macrocephaly similar to patient symptoms.
Researchers studied how diverse neural network training datasets impact generalization. They found that data diversity is key to overcoming bias, but also degrade performance when neural networks are trained for multiple tasks simultaneously. The study highlights the importance of designing diverse and controlled datasets in machine le...
Neuroscientists have designed neural organoids with both mature neurons and astrocytic glial cells to study interactions between brain cells. The new technology enables the emulation of brain activity during healthy and disease states, opening doors to rapid drug screening for neurological diseases.
Researchers at Radboud University Medical Center have created reference models for brain development and aging, providing personalized predictions for various brain conditions. These 'brain charts' enable early detection of mental disorders and help stratify subtypes, ultimately improving treatment outcomes.
Breakthrough research reveals Tuberous Sclerosis Complex arises from human-specific progenitor cells, explaining its pathology. Human-derived cerebral organoid models shed light on complex brain development and potential mechanisms for other diseases.
The MIT team developed a computer model that can perform sound localization tasks as well as humans, and adapts to real-world environments. The model uses convolutional neural networks and was trained on over 400 sounds, including human voices and animal sounds.
Professor Alexander Ecker is awarded a Starting Grant to develop machine-learning methods to describe neurons' shape and function, leveraging a large dataset from the US Brain Initiative. The research aims to uncover how a neuron's shape relates to its role in sensory information processing.
Researchers found that CBD shrinks glioblastoma tumors by reducing inflammation and restoring immune balance. The compound also suppresses key proteins involved in tumor growth and spread, making it a potential novel adjunct therapy for glioblastoma patients.
Researchers at the University of Bern have developed an approach called 'evolving-to-learn' (E2L) that enables computers to discover mechanisms of synaptic plasticity, leading to improved learning capabilities. The algorithm was tested in three scenarios and successfully solved new tasks by mimicking biological evolution.
A new study reveals that high-performing AI next-word prediction models resemble the function of language-processing centers in the human brain. The models' activity patterns closely match those seen in the brain during language tasks, suggesting a potential connection between AI and human language processing.
Researchers at University of Cambridge have developed lab-grown mini brains that can mimic the development of motor neurone disease and frontotemporal dementia. The models, grown for up to 340 days, provide valuable insights into early stages of these devastating conditions.
Researchers have created a human disease model of FCMD using stem cells from a patient, which successfully mimicked the disorder's brain defects. The study found that a small compound called Mannan-007 can restore αDG glycosylation and reduce FCMD-related defects.
The UTSA Brain Health Consortium will study genetic brain disorders using transgenic marmosets, which could impact treating or eliminating neurological diseases such as epilepsy and Alzheimer’s disease. The team aims to develop novel methods for producing sperm with genetic changes that can be used to study human brain disease.
A machine learning analysis has identified 50 genes strongly associated with neurological aging in both Drosophila fruit flies and humans. The study suggests that fruit flies could be used as a model organism to further investigate aging-related processes.
A new experimental model using human stem cells successfully tested the impact of a next-generation anti-inflammatory drug on the brain's blood-brain barrier. The results showed that the breakdown of the barrier is more complex than previously thought and can be prevented with antioxidants.
The eNeuro special collection explores the documentary In Silico, featuring reactions from leading neuroscientists on brain modelling and massive research collaborations. The commentaries discuss next steps for large-scale initiatives, charismatic leadership's impact, and the history of building brain models.
Using human brain organoid models, researchers demonstrate various viruses cause microcephaly through different mechanisms. ZIKV and HSV-1 infections impair neuroepithelial identity and attenuate Type I interferon responses.
Research reveals that processing factual information elicits stronger brain activity compared to uncertain information in narratives. The brain adds new information to a model of the developing situation, with stronger activity in frontal medial areas for updating one's own beliefs.
Recent advances using lab-grown brain organoids have improved understanding of human brain development and diseases. However, questions remain about how well these brain models mirror the complexity of the cortex. Studies show that organoids can reproduce a rich diversity of cell types found in the cerebral cortex, but fail to capture ...
Researchers have used a new imaging method to analyze brain activity in live pigs, discovering that pig brains are better platforms for studying human neurological conditions. The study suggests a promising approach to determining biomarkers or brain signatures that lead to CTE diagnosis while patients are still alive.
Researchers employed machine learning to analyze fMRI data and infer cellular properties of different brain regions without surgery. The approach could lead to personalized treatment plans for neurological disorders.
A new study using computational models shows promise in understanding how brain structure impacts performance on language-related tasks. The research found relationships between brain characteristics and task speed, potentially leading to advancements in treating diseases and enhancing human performance.
Researchers at Wake Forest Institute for Regenerative Medicine have created an advanced brain organoid that closely resembles human brain anatomy. This innovation enables faster drug discovery and screening for neurological conditions, as well as improved understanding of the blood-brain barrier.
Researchers trained a deep neural network to perform two auditory tasks, including speech and music identification. The model accurately replicated human performance and provided evidence for the hierarchical organization of the auditory cortex.
A new computer model, Recursive Cortical Network (RCN), has been developed that can solve CAPTCHA and identify handwritten digits with comparable accuracy to state-of-the-art deep learning approaches but using significantly less training data.