Building better biomarkers: brain models in translational neuroimaging
Despite its great promise, neuroimaging has yet to substantially impact clinical practice and
public health. However, a develo** synergy between emerging analysis techniques and …
public health. However, a develo** synergy between emerging analysis techniques and …
The distributed nature of working memory
Studies in humans and non-human primates have provided evidence for storage of working
memory contents in multiple regions ranging from sensory to parietal and prefrontal cortex …
memory contents in multiple regions ranging from sensory to parietal and prefrontal cortex …
Decoding speech perception from non-invasive brain recordings
Decoding speech from brain activity is a long-awaited goal in both healthcare and
neuroscience. Invasive devices have recently led to major milestones in this regard: deep …
neuroscience. Invasive devices have recently led to major milestones in this regard: deep …
Reconstructing the mind's eye: fMRI-to-image with contrastive learning and diffusion priors
We present MindEye, a novel fMRI-to-image approach to retrieve and reconstruct viewed
images from brain activity. Our model comprises two parallel submodules that are …
images from brain activity. Our model comprises two parallel submodules that are …
Similarity of neural network representations revisited
Recent work has sought to understand the behavior of neural networks by comparing
representations between layers and between different trained models. We examine methods …
representations between layers and between different trained models. We examine methods …
Natural scene reconstruction from fMRI signals using generative latent diffusion
In neural decoding research, one of the most intriguing topics is the reconstruction of
perceived natural images based on fMRI signals. Previous studies have succeeded in re …
perceived natural images based on fMRI signals. Previous studies have succeeded in re …
fMRIPrep: a robust preprocessing pipeline for functional MRI
Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to
clean and standardize the data before statistical analysis. Generally, researchers create ad …
clean and standardize the data before statistical analysis. Generally, researchers create ad …
Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns
Contextual embeddings, derived from deep language models (DLMs), provide a continuous
vectorial representation of language. This embedding space differs fundamentally from the …
vectorial representation of language. This embedding space differs fundamentally from the …
[HTML][HTML] Identification of autism spectrum disorder using deep learning and the ABIDE dataset
The goal of the present study was to apply deep learning algorithms to identify autism
spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the …
spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the …
Improving the accuracy of single-trial fMRI response estimates using GLMsingle
Advances in artificial intelligence have inspired a paradigm shift in human neuroscience,
yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide …
yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide …