AIMC Topic: Brain Mapping
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Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features.
Medical image analysis
Jun 15, 2018
Sub-cortical brain structure segmentation in Magnetic Resonance Images (MRI) has attracted the interest of the research community for a long time as morphological changes in these structures are related to different neurodegenerative disorders. Howev...
A supervised learning approach for diffusion MRI quality control with minimal training data.
NeuroImage
Jun 5, 2018
Quality control (QC) is a fundamental component of any study. Diffusion MRI has unique challenges that make manual QC particularly difficult, including a greater number of artefacts than other MR modalities and a greater volume of data. The gold stan...
Multicenter validation of [F]-FDG PET and support-vector machine discriminant analysis in automatically classifying patients with amyotrophic lateral sclerosis versus controls.
Amyotrophic lateral sclerosis & frontotemporal degeneration
Jun 4, 2018
OBJECTIVE: F-Fluorodeoxyglucose (F-FDG) positron emission tomography (PET) single-center studies using support vector machine (SVM) approach to differentiate amyotrophic lateral sclerosis (ALS) from controls have shown high overall accuracy on an ind...
Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach.
Journal of psychiatric research
May 26, 2018
Neuroimaging studies have been steadily explored in Bipolar Disorder (BD) in the last decades. Neuroanatomical changes tend to be more pronounced in patients with repeated episodes. Although the role of such changes in cognition and memory is well es...
Concussion classification via deep learning using whole-brain white matter fiber strains.
PloS one
May 24, 2018
Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses exp...
Integrating spatial-anatomical regularization and structure sparsity into SVM: Improving interpretation of Alzheimer's disease classification.
NeuroImage
May 23, 2018
In recent years, machine learning approaches have been successfully applied to the field of neuroimaging for classification and regression tasks. However, many approaches do not give an intuitive relation between the raw features and the diagnosis. T...
Decoding natural images from evoked brain activities using encoding models with invertible mapping.
Neural networks : the official journal of the International Neural Network Society
May 21, 2018
Recent studies have built encoding models in the early visual cortex, and reliable mappings have been made between the low-level visual features of stimuli and brain activities. However, these mappings are irreversible, so that the features cannot be...
Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis.
NeuroImage
May 18, 2018
Dynamic Causal Modeling (DCM) is an advanced biophysical model which explicitly describes the entire process from experimental stimuli to functional magnetic resonance imaging (fMRI) signals via neural activity and cerebral hemodynamics. To conduct a...
Experimental Comparisons of Sparse Dictionary Learning and Independent Component Analysis for Brain Network Inference From fMRI Data.
IEEE transactions on bio-medical engineering
May 17, 2018
In this work, we conduct comprehensive comparisons between four variants of independent component analysis (ICA) methods and three variants of sparse dictionary learning (SDL) methods, both at the subject-level, by using synthesized fMRI data with gr...
Sharpening of Hierarchical Visual Feature Representations of Blurred Images.
eNeuro
May 8, 2018
The robustness of the visual system lies in its ability to perceive degraded images. This is achieved through interacting bottom-up, recurrent, and top-down pathways that process the visual input in concordance with stored prior information. The inte...
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