functional magnetic resonance imaging
PulseAugur coverage of functional magnetic resonance imaging — every cluster mentioning functional magnetic resonance imaging across labs, papers, and developer communities, ranked by signal.
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Study finds shared geometry in human brain's visual representations
Researchers have demonstrated that a shared geometric structure exists within human brain representations of visual stimuli. By analyzing functional magnetic resonance imaging (fMRI) data from the Natural Scenes Dataset…
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New QS-TCN method orders time series using volatility memory
Researchers have developed QS-TCN, a novel method for ordering multivariate time series by analyzing volatility memory. This approach combines statistical estimates of volatility persistence with temporal convolutional …
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BrainBind framework maps EEG and fMRI to video embeddings
Researchers have developed BrainBind, a novel framework designed to map synchronized electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data into shared video embedding spaces. This approach t…
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New research links component separation to Riesz energy, validated by simulations
A new arXiv paper explores the conditions under which independent components can be recovered, even when they are Gaussian. The research proposes that component separation is related to a Riesz 2-energy, which depends o…
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New Vis-Lens framework generates preferred images for AI and human brains
Researchers have developed a novel framework called Vis-Lens that generates preferred images by modifying latent codes, offering insights into both biological and artificial neural networks. This approach integrates a v…
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Self-supervised learning aligns fMRI data across participants
Researchers have adapted a self-supervised learning architecture, a joint embedding predictive architecture (JEPA), to decode sensory-evoked functional magnetic resonance imaging (fMRI) data. This approach trains encode…
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AI model attribution reliability questioned in Alzheimer's prediction study
A new study published on arXiv investigates the reliability of individualized functional-network attribution in predicting cognitive decline in Alzheimer's Disease Neuroimaging Initiative (ADNI) participants using resti…
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New method improves fMRI visual reconstruction with limited data
Researchers have developed a novel training procedure called Adapter Alignment for reconstructing visual images from functional magnetic resonance imaging (fMRI) data. This method is particularly effective in low-data s…
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New DRAFT method reconstructs 3D room layouts from fMRI and camera data
Researchers have developed a novel method called DRAFT that can reconstruct the 3D layout of rooms using only functional magnetic resonance imaging (fMRI) signals and camera trajectories, without relying on visual frame…
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New framework stabilizes graph filters for improved signal generation
Researchers have developed a new framework for designing graph filters to improve the generation of graph signals, particularly when the underlying graph structure is noisy or imperfectly estimated. The proposed method …
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New algorithm identifies dependent components in multi-domain data
Researchers have developed a new algorithm called Multi-Domain Covariance Matching (MuDo-CoM) to identify dependent latent components within linear mixing models across multiple data domains. Unlike previous methods tha…
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New framework aligns brain encoding models with human perception
Researchers have developed a new framework to fine-tune brain encoding models, aiming to better align their internal representations with individual human perceptual similarity judgments. By adapting a generic model (Dr…
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Brain-Aligned LLMs Show Promise in Understanding Cartoon Movie Emotions
Researchers have developed Brain-Aligned Multimodal Large Language Models (MLLMs) capable of understanding the social and emotional content of cartoon movies. These models were evaluated against human judgments and neur…
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New decoder reconstructs speech from fMRI brain activity
Researchers have developed a new temporal convolutional decoder called NOEMA to reconstruct perceived speech from functional magnetic resonance imaging (fMRI) data. This decoder aligns windows of fMRI measurements with …
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BrainDyn: Novel Graph ODE Network Models and Forecasts Brain Dynamics
Researchers have developed BrainDyn, a novel graph ordinary differential equation (ODE) network designed to model and forecast brain activity. This network utilizes a cellular sheaf with dynamic restriction maps to medi…
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New AI models reconstruct visual experiences from fMRI data
Researchers have developed new generative frameworks for decoding visual experiences from fMRI data. MultiFlow and StimFlow utilize conditional flow matching techniques to reconstruct neural activity, addressing the inh…
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New fMRI foundation model CoFlu-Brain shows strong transfer learning
Researchers have developed CoFlu-Brain, a new foundation model for functional magnetic resonance imaging (fMRI) data. Unlike traditional methods that use a single correlation matrix, CoFlu-Brain organizes framewise cofl…
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New framework BrainATCL learns adaptive temporal brain connectivity from fMRI data
Researchers have developed BrainATCL, a novel unsupervised framework designed to learn adaptive temporal brain connectivity from functional magnetic resonance imaging (fMRI) data. This method addresses the limitations o…
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New MovieSTAGE framework improves ADHD classification using fMRI data
Researchers have developed MovieSTAGE, a novel framework designed to improve the classification of attention deficit hyperactivity disorder (ADHD) using fMRI data collected during movie viewing. This multiscale approach…
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New fMRI dataset released for decoding naturalistic speech
Researchers have released Cephalonauts One, a comprehensive fMRI dataset designed for decoding naturalistic speech in the human brain. The dataset, collected from three subjects listening to audio podcasts, offers 30 ho…