AIMC Topic: Brain

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DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning.

NeuroImage
Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling requirement for DTI leads to long scan times and pos...

Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: Fetal brain abnormalities are some of the most common congenital malformations that may associated with syndromic and chromosomal malformations, and could lead to neurodevelopmental delay and mental retardation. Early prenatal detection of b...

AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction.

NeuroImage. Clinical
The prediction of Mild Cognitive Impairment (MCI) patients who are at higher risk converting to Alzheimer's Disease (AD) is critical for effective intervention and patient selection in clinical trials. Different biomarkers including neuroimaging have...

Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging.

NeuroImage
Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized fr...

Nonlinear ICA of fMRI reveals primitive temporal structures linked to rest, task, and behavioral traits.

NeuroImage
Accumulating evidence from whole brain functional magnetic resonance imaging (fMRI) suggests that the human brain at rest is functionally organized in a spatially and temporally constrained manner. However, because of their complexity, the fundamenta...

A Model for Structured Information Representation in Neural Networks of the Brain.

eNeuro
Humans can reason at an abstract level and structure information into abstract categories, but the underlying neural processes have remained unknown. Recent experimental data provide the hint that this is likely to involve specific subareas of the br...

Classifying creativity: Applying machine learning techniques to divergent thinking EEG data.

NeuroImage
Prior research has shown that greater EEG alpha power (8-13 ​Hz) is characteristic of more creative individuals, and more creative task conditions. The present study investigated the potential for machine learning to classify more and less creative b...

Machine Learning and Multiparametric Brain MRI to Differentiate Hereditary Diffuse Leukodystrophy with Spheroids from Multiple Sclerosis.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: Hereditary diffuse leukoencephalopathy with spheroids (HDLS) and multiple sclerosis (MS) are demyelinating and neurodegenerative disorders that can be hard to distinguish clinically and radiologically. HDLS is a rare disorder ...

Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures.

Scientific reports
Recent studies in brain science and neurological medicine paid a particular attention to develop machine learning-based techniques for the detection and prediction of epileptic seizures with electroencephalogram (EEG). As a noninvasive monitoring met...

Evidence that the Kennedy and polyamine pathways are dysregulated in human brain in cases of dementia with Lewy bodies.

Brain research
Disruptions of brain metabolism are considered integral to the pathogenesis of dementia, but thus far little is known of how dementia with Lewy bodies (DLB) impacts the brain metabolome. DLB is less well known than other neurodegenerative diseases su...