AIMC Topic: Brain Mapping

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MRI-compatible pneumatic stimulator for sensorimotor mapping.

Journal of neuroscience methods
BACKGROUND: Two major concerns with respect to task-based motor functional magnetic resonance imaging (fMRI) are inadequate participants' performance as well as intra- and inter-subject variability in execution of the motor action.

An empirical evaluation of multivariate lesion behaviour mapping using support vector regression.

Human brain mapping
Multivariate lesion behaviour mapping based on machine learning algorithms has recently been suggested to complement the methods of anatomo-behavioural approaches in cognitive neuroscience. Several studies applied and validated support vector regress...

Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. One approach conducts sensitivity analysis on hierarchical 3D image segmentation, and the...

High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology.

Nature communications
Technologies for mapping the spatial and temporal patterns of neural activity have advanced our understanding of brain function in both health and disease. An important application of these technologies is the discovery of next-generation neurotherap...

Predicting chemo-brain in breast cancer survivors using multiple MRI features and machine-learning.

Magnetic resonance in medicine
PURPOSE: Breast cancer (BC) is the most common cancer in women worldwide. There exist various advanced chemotherapy drugs for BC; however, chemotherapy drugs may result in brain damage during treatment. When a patient's brain is changed in response t...

Automatic brain labeling via multi-atlas guided fully convolutional networks.

Medical image analysis
Multi-atlas-based methods are commonly used for MR brain image labeling, which alleviates the burdening and time-consuming task of manual labeling in neuroimaging analysis studies. Traditionally, multi-atlas-based methods first register multiple atla...

Resting-state Functional Connectivity and Deception: Exploring Individualized Deceptive Propensity by Machine Learning.

Neuroscience
Individuals show marked variability in determining to be honest or deceptive in daily life. A large number of studies have investigated the neural substrates of deception; however, the brain networks contributing to the individual differences in dece...

Deep neural network predicts emotional responses of the human brain from functional magnetic resonance imaging.

NeuroImage
An artificial neural network with multiple hidden layers (known as a deep neural network, or DNN) was employed as a predictive model (DNN) for the first time to predict emotional responses using whole-brain functional magnetic resonance imaging (fMRI...

Machine learning multivariate pattern analysis predicts classification of posttraumatic stress disorder and its dissociative subtype: a multimodal neuroimaging approach.

Psychological medicine
BACKGROUND: The field of psychiatry would benefit significantly from developing objective biomarkers that could facilitate the early identification of heterogeneous subtypes of illness. Critically, although machine learning pattern recognition method...