MRIVIEW: A Software Package for the Analysis and Visualization of Brain Imaging Data

2019 ◽  
pp. 1-18
Author(s):  
Doug Ranken
Keyword(s):  
Brain Imaging ◽  
Software Package ◽  
Imaging Data ◽  
Brain Imaging Data

scholarly journals The Open Brain Consent : Informing research participants and obtaining consent to share brain imaging data

2021 ◽  
Author(s):  
Elise Bannier ◽  
Gareth Barker ◽  
Valentina Borghesani ◽  
Nils Broeckx ◽  
Patricia Clement ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Imaging Data ◽  
Research Participants ◽  
Brain Imaging Data

Learning from Enhanced Contextual Similarity in Brain Imaging Data for Classification of Schizophrenia

2017 ◽  
pp. 265-275 ◽  
Author(s):  
Tewodros Mulugeta Dagnew ◽  
Letizia Squarcina ◽  
Massimo W. Rivolta ◽  
Paolo Brambilla ◽  
Roberto Sassi
Keyword(s):  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data

scholarly journals Chronic subdural hematomas: challenges and solutions. Part I. Clinical variants and diagnosis

2021 ◽  
Vol 2 (2) ◽  
pp. 21-27
Author(s):  
Leonid B. Likhterman ◽  
◽  
Aleksandr D. Kravchuk ◽  
Vladimir A. Okhlopkov ◽  
◽  
...  
Keyword(s):  
Brain Imaging ◽  
Intracranial Hemorrhage ◽  
Subdural Hematoma ◽  
Clinical Course ◽  
Chronic Subdural Hematoma ◽  
Imaging Data ◽  
Clinical Variants ◽  
Brain Compression ◽  
Brain Imaging Data ◽  
Ct And Mri

Chronic subdural hematoma (cSDH) is a multifactorial extensive intracranial hemorrhage, causing the local and/or general brain compression. Hematoma has a delimiting capsule, which defines all pathophysiological features, clinical course and treatment tactics. The paper reports contemporary views on ethiology and clinical course of cSDH. Emphasis is placed on the diagnosis. Based on the analysis of 558 verified cSDH observations, the phasal course and brain imaging data are reported. CT and MRI signs of cSDH are defined.

scholarly journals A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Jinlong Hu ◽  
Yuezhen Kuang ◽  
Bin Liao ◽  
Lijie Cao ◽  
Shoubin Dong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Imaging ◽  
Motor Task ◽  
Data Classification ◽  
Fmri Data ◽  
Imaging Data ◽  
Whole Brain ◽  
Human Connectome Project ◽  
Brain Imaging Data ◽  
3D Cnn

Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data. The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks. We experimentally compared the proposed M2D CNN against several widely used models including SVM, 1D CNN, 2D CNN, 3D CNN, and 3D separable CNN with respect to their performance in classifying task-based fMRI data. We tested M2D CNN against six models as benchmarks to classify a large number of time-series whole-brain imaging data based on a motor task in the Human Connectome Project (HCP). The results of our experiments demonstrate the following: (i) convolution operations in the CNN models are advantageous for high-dimensional whole-brain imaging data classification, as all CNN models outperform SVM; (ii) 3D CNN models achieve higher accuracy than 2D CNN and 1D CNN model, but 3D CNN models are computationally costly as any extra dimension is added in the input; (iii) the M2D CNN model proposed in this study achieves the highest accuracy and alleviates data overfitting given its smaller number of parameters as compared with 3D CNN.

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