AI Medical Compendium Journal:
NeuroImage

Showing 91 to 100 of 380 articles

Deep neural networks learn general and clinically relevant representations of the ageing brain.

NeuroImage
The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data - the brain age delta - has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in ...

Self-supervised Natural Image Reconstruction and Large-scale Semantic Classification from Brain Activity.

NeuroImage
Reconstructing natural images and decoding their semantic category from fMRI brain recordings is challenging. Acquiring sufficient pairs of images and their corresponding fMRI responses, which span the huge space of natural images, is prohibitive. We...

Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning.

NeuroImage
The quantification of subcortical volume development from 3D fetal ultrasound can provide important diagnostic information during pregnancy monitoring. However, manual segmentation of subcortical structures in ultrasound volumes is time-consuming and...

SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI.

NeuroImage
Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the acc...

Deep learning identifies brain structures that predict cognition and explain heterogeneity in cognitive aging.

NeuroImage
Specific brain structures (gray matter regions and white matter tracts) play a dominant role in determining cognitive decline and explain the heterogeneity in cognitive aging. Identification of these structures is crucial for screening of older adult...

FastSurferVINN: Building resolution-independence into deep learning segmentation methods-A solution for HighRes brain MRI.

NeuroImage
Leading neuroimaging studies have pushed 3T MRI acquisition resolutions below 1.0 mm for improved structure definition and morphometry. Yet, only few, time-intensive automated image analysis pipelines have been validated for high-resolution (HiRes) s...

Volumetric segmentation of white matter tracts with label embedding.

NeuroImage
Convolutional neural networks have achieved state-of-the-art performance for white matter (WM) tract segmentation based on diffusion magnetic resonance imaging (dMRI). However, the segmentation can still be difficult for challenging WM tracts with th...

Predicting individual traits from unperformed tasks.

NeuroImage
Relating individual differences in cognitive traits to brain functional organization is a long-lasting challenge for the neuroscience community. Individual intelligence scores were previously predicted from whole-brain connectivity patterns, extracte...

Evidence for distinct neuro-metabolic phenotypes in humans.

NeuroImage
Advances in magnetic resonance imaging have shown how individual differences in the structure and function of the human brain relate to health and cognition. The relationship between individual differences and the levels of neuro-metabolites, however...

Unsupervised learning of brain state dynamics during emotion imagination using high-density EEG.

NeuroImage
This study applies adaptive mixture independent component analysis (AMICA) to learn a set of ICA models, each optimized by fitting a distributional model for each identified component process while maximizing component process independence within som...