AIMC Topic: Brain

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Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study.

The Lancet. Digital health
BACKGROUND: Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after r...

Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network.

PloS one
High accuracy has been reported in deep learning classification for amyloid brain scans, an important factor in Alzheimer's disease diagnosis. However, the possibility of overfitting should be considered, as this model is fitted with sample data. The...

Support Vector Machine-based Spontaneous Intracranial Hypotension Detection on Brain MRI.

Clinical neuroradiology
BACKGROUND AND PURPOSE: To develop a fully automatic algorithm for the magnetic resonance imaging (MRI) identification of patients with spontaneous intracranial hypotension (SIH).

Rapid Estimation of Entire Brain Strain Using Deep Learning Models.

IEEE transactions on bio-medical engineering
OBJECTIVE: Many recent studies suggest that brain deformation resulting from head impacts are linked to the corresponding clinical outcome, such as mild traumatic brain injury (mTBI). Even if several finite element (FE) head models have been develope...

Meta-learning, social cognition and consciousness in brains and machines.

Neural networks : the official journal of the International Neural Network Society
The intersection between neuroscience and artificial intelligence (AI) research has created synergistic effects in both fields. While neuroscientific discoveries have inspired the development of AI architectures, new ideas and algorithms from AI rese...

Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics.

Human brain mapping
Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification of amyotrophic lateral sclerosis (ALS). While most studies focus on structural data, recent studies assessing functional ...

Characteristics of brain connectivity during verbal fluency test: Convolutional neural network for functional near-infrared spectroscopy analysis.

Journal of biophotonics
Human connectome describes the complicated connection matrix of nervous system among human brain. It also possesses high potential of assisting doctors to monitor the brain injuries and recoveries in patients. In order to unravel the enigma of neuron...

Exploring self-generated thoughts in a resting state with natural language processing.

Behavior research methods
The present study seeks to examine individuals' stream of thought in real time. Specifically, we asked participants to speak their thoughts freely out loud during a typical resting-state condition. We first examined the feasibility and reliability of...

A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks.

IEEE transactions on cybernetics
Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue ...

Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity.

Brain structure & function
Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the unde...