AIMC Topic: Magnetic Resonance Imaging

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A brain-based account of "basic-level" concepts.

NeuroImage
This study provides a brain-based account of how object concepts at an intermediate (basic) level of specificity are represented, offering an enriched view of what it means for a concept to be a basic-level concept, a research topic pioneered by Rosc...

Quantitative Testing of fMRI-Compatibility of an Electrically Active Mechatronic Device for Robot-Assisted Sensorimotor Protocols.

IEEE transactions on bio-medical engineering
OBJECTIVE: To develop a quantitative set of methods for testing the functional magnetic resonance imaging (fMRI) compatibility of an electrically-active mechatronic device developed to support sensorimotor protocols during fMRI.

Multi-Connection Pattern Analysis: Decoding the representational content of neural communication.

NeuroImage
The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection...

An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement.

NeuroImage
This paper presents an open, multi-vendor, multi-field strength magnetic resonance (MR) T1-weighted volumetric brain imaging dataset, named Calgary-Campinas-359 (CC-359). The dataset is composed of images of older healthy adults (29-80 years) acquire...

Learning and combining image neighborhoods using random forests for neonatal brain disease classification.

Medical image analysis
It is challenging to characterize and classify normal and abnormal brain development during early childhood. To reduce the complexity of heterogeneous data population, manifold learning techniques are increasingly applied, which find a low-dimensiona...

Decoding naturalistic experiences from human brain activity via distributed representations of words.

NeuroImage
Natural visual scenes induce rich perceptual experiences that are highly diverse from scene to scene and from person to person. Here, we propose a new framework for decoding such experiences using a distributed representation of words. We used functi...

Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences.

Medical image analysis
Dynamic contrast-enhanced MRI (DCE-MRI) is an imaging protocol where MRI scans are acquired repetitively throughout the injection of a contrast agent. The analysis of dynamic scans is widely used for the detection and quantification of blood-brain ba...

Machine Learning of Functional Magnetic Resonance Imaging Network Connectivity Predicts Substance Abuse Treatment Completion.

Biological psychiatry. Cognitive neuroscience and neuroimaging
BACKGROUND: Successfully treating illicit drug use has become paramount, yet elusive. Devising specialized treatment interventions could increase positive outcomes, but it is necessary to identify risk factors of poor long-term outcomes to develop sp...

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker.

NeuroImage
Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the creden...

Convolutional Neural Network for the Detection of End-Diastole and End-Systole Frames in Free-Breathing Cardiac Magnetic Resonance Imaging.

Computational and mathematical methods in medicine
Free-breathing cardiac magnetic resonance (CMR) imaging has short examination time with high reproducibility. Detection of the end-diastole and the end-systole frames of the free-breathing cardiac magnetic resonance, supplemented by visual identifica...