AIMC Topic: Brain

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Toward a unified framework for interpreting machine-learning models in neuroimaging.

Nature protocols
Machine learning is a powerful tool for creating computational models relating brain function to behavior, and its use is becoming widespread in neuroscience. However, these models are complex and often hard to interpret, making it difficult to evalu...

Bootstrapping promotes the RSFC-behavior associations: An application of individual cognitive traits prediction.

Human brain mapping
Resting-state functional connectivity (RSFC) records enormous functional interaction information between any pair of brain nodes, which enriches the individual-phenotypic prediction. To reduce high-dimensional features, correlation analysis is a comm...

Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD.

NeuroImage. Clinical
BACKGROUND: Those with autism spectrum disorder (ASD) and/or attention-deficit-hyperactivity disorder (ADHD) exhibit symptoms of hyperactivity and inattention, causing significant hardships for families and society. A potential mechanism involved in ...

Deep-Learning Detection of Cancer Metastases to the Brain on MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Approximately one-fourth of all cancer metastases are found in the brain. MRI is the primary technique for detection of brain metastasis, planning of radiotherapy, and the monitoring of treatment response. Progress in tumor treatment now ...

Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.

European radiology
BACKGROUND AND PURPOSE: Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features cou...

Machine learning analysis of whole mouse brain vasculature.

Nature methods
Tissue clearing methods enable the imaging of biological specimens without sectioning. However, reliable and scalable analysis of large imaging datasets in three dimensions remains a challenge. Here we developed a deep learning-based framework to qua...

Editorial for "Deep-Learning Detection of Cancer Metastasis to the Brain on MRI".

Journal of magnetic resonance imaging : JMRI
5 TECHNICAL EFFICACY: Stage 1 J. Magn. Reson. Imaging 2020;52:1237-1238.

Probabilistic inference of binary Markov random fields in spiking neural networks through mean-field approximation.

Neural networks : the official journal of the International Neural Network Society
Recent studies have suggested that the cognitive process of the human brain is realized as probabilistic inference and can be further modeled by probabilistic graphical models like Markov random fields. Nevertheless, it remains unclear how probabilis...

Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control.

Communications biology
Efficient action control is indispensable for goal-directed behaviour. Different theories have stressed the importance of either attention or response selection sub-processes for action control. Yet, it is unclear to what extent these processes can b...

Accelerated Acquisition of High-resolution Diffusion-weighted Imaging of the Brain with a Multi-shot Echo-planar Sequence: Deep-learning-based Denoising.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
To accelerate high-resolution diffusion-weighted imaging with a multi-shot echo-planar sequence, we propose an approach based on reduced averaging and deep learning. Denoising convolutional neural networks can reduce amplified noise without requiring...