AIMC Topic: Brain Mapping
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Neural mechanisms supporting the relationship between dispositional mindfulness and pain.
Pain
Dec 1, 2018
Interindividual differences in pain sensitivity vary as a function of interactions between sensory, cognitive-affective, and dispositional factors. Trait mindfulness, characterized as the innate capacity to nonreactively sustain attention to the pres...
Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision.
Cerebral cortex (New York, N.Y. : 1991)
Dec 1, 2018
Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-stream areas. Here, we further showed that such CNN could reliably predict and decode functional magn...
Isometry Invariant Shape Descriptors for Abnormality Detection on Brain Surfaces Affected by Alzheimer's Disease.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
Alzheimer's disease (AD), a progressive brain disorder, is the most common neurodegenerative disease in older adults. There is a need for brain structural magnetic resonance imaging (MRI) biomarkers to help assess AD progression and intervention effe...
Biophysically interpretable recurrent neural network for functional magnetic resonance imaging analysis and sparsity based causal architecture discovery.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
Recent efforts use state-of-the-art Recurrent Neural Networks (RNN) to gain insight into neuroscience. A limitation of these works is that the used generic RNNs lack biophysical meaning, making the interpretation of the results in a neuroscience cont...
Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.
Medical image analysis
Jul 1, 2018
fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional...
Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI.
Journal of digital imaging
Apr 1, 2018
Schizophrenia has been proposed to result from impairment of functional connectivity. We aimed to use machine learning to distinguish schizophrenic subjects from normal controls using a publicly available functional MRI (fMRI) data set. Global and lo...
Machine Learning Applications to Resting-State Functional MR Imaging Analysis.
Neuroimaging clinics of North America
Nov 1, 2017
Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classificati...
Distributed Intrinsic Functional Connectivity Patterns Predict Diagnostic Status in Large Autism Cohort.
Brain connectivity
Oct 1, 2017
Diagnosis of autism spectrum disorder (ASD) currently relies on behavioral observations because brain markers are unknown. Machine learning approaches can identify patterns in imaging data that predict diagnostic status, but most studies using functi...
[Computational neuroanatomy and microstructure imaging using magnetic resonance imaging].
Der Nervenarzt
Aug 1, 2017
BACKGROUND: Current computational neuroanatomy focuses on morphological measurements of the brain using standard magnetic resonance imaging (MRI) techniques. In comparison quantitative MRI (qMRI) typically provides a better tissue contrast and also g...
Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.
Journal of integrative neuroscience
Jan 1, 2017
Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of f...
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