AIMC Topic: Brain

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Interpretable Multimodal Fusion Networks Reveal Mechanisms of Brain Cognition.

IEEE transactions on medical imaging
The combination of multimodal imaging and genomics provides a more comprehensive way for the study of mental illnesses and brain functions. Deep network-based data fusion models have been developed to capture their complex associations, resulting in ...

Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder.

Scientific reports
We investigated the differential spatial covariance pattern of blood oxygen level-dependent (BOLD) responses to single-task and multitask functional magnetic resonance imaging (fMRI) between patients with psychophysiological insomnia (PI) and healthy...

How Blue is the Sky?

eNeuro
The recent trend toward an industrialization of brain exploration and the technological prowess of artificial intelligence algorithms and high-performance computing has caught the imagination of the public. These impressive advances are fueling an un...

A generic deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI.

Magnetic resonance in medicine
PURPOSE: With rising safety concerns over the use of gadolinium-based contrast agents (GBCAs) in contrast-enhanced MRI, there is a need for dose reduction while maintaining diagnostic capability. This work proposes comprehensive technical solutions f...

Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition.

The Journal of neuroscience : the official journal of the Society for Neuroscience
In humans, impaired response inhibition is characteristic of a wide range of psychiatric diseases and of normal aging. It is hypothesized that the right inferior frontal cortex (rIFC) plays a key role by inhibiting the motor cortex via the basal gang...

Prediction of brain age from routine T2-weighted spin-echo brain magnetic resonance images with a deep convolutional neural network.

Neurobiology of aging
Our study investigated the feasibility and clinical relevance of brain age prediction using axial T2-weighted images (T2-WIs) with a deep convolutional neural network (CNN) algorithm. The CNN model was trained by 1,530 scans in our institution. The p...

Detection of Negative Stress through Spectral Features of Electroencephalographic Recordings and a Convolutional Neural Network.

Sensors (Basel, Switzerland)
In recent years, electroencephalographic (EEG) signals have been intensively used in the area of emotion recognition, partcularly in distress identification due to its negative impact on physical and mental health. Traditionally, brain activity has b...

Growth strategy determines the memory and structural properties of brain networks.

Neural networks : the official journal of the International Neural Network Society
The interplay between structure and function affects the emerging properties of many natural systems. Here we use an adaptive neural network model that couples activity and topological dynamics and reproduces the experimental temporal profiles of syn...

Image Quality Assessment of Fetal Brain MRI Using Multi-Instance Deep Learning Methods.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Due to random motion of fetuses and maternal respirations, image quality of fetal brain MRIs varies considerably. To address this issue, visual inspection of the images is performed during acquisition phase and after 3D-reconstruction, an...