VCfMRI: A Matlab Toolbox for Visualization and Conversion of fMRI Data Modalities

Abstract

In this work, a novel conversion and visualization fMRI (VCfMRI) toolbox is proposed. The VCfMRI tool is enabled to read, write 3-D volume data (.dcm, .nii, .img, hdr and .mat format) as well as multi conversion operations between them are performed in the same package. In the current work, real fMRI data are used and all data are acquired by MRI scanner type Siemens/3T in National Magnetic Resonance Research Center (UMRAM)-Bilkent University. About 62 analyses functions have been implemented and incorporated in analysis about 7 GUI tools for multiple conversions of fMRI modalities, reading/writing and viewing in all fMRI data formats, visualizing 3-dimensional (sagittal, coronal and horizontal slices) statistical and non-statistical neuroimaging, thresholding and overlaying viewing. The presented package is a simple tool to address several issues that related to complexity in visualizing and conversion between multi-formats of fMRI data. This work enables the user to visualize and deals with fMRI data in an easy way especially for physicians, healthcare specialists and researchers whose faced challenges about how handling with these type of data.

Independent Component Analysis with Functional Neuroscience Data Analysis

“…Many Graphical User Interface (GUI) software packages are presented for analysis and learning tasks in neurofunctional data analysis [24][25][26][27]. Delorme and Makeig developed Encephalography and local field potentials Analysis (EEGLAB, which is a MATLAB-based GUI package [28] to analyze EEG data for both ordinary and advanced EEG processing.…”

“…EEGLAB [ 28 ] processed EEG signals, while ELAN [ 29 ] processed EEG MEG and LFP signals in the same package. The REST [ 24 ], VCfMRI [ 26 ], SPM [ 31 ], and FSL [ 32 ] dealt with fMRI processing, the FSL used melodic ICA with fMRI analysis.…”

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

“…According to (Manjón, 2017;Park et al, 2019). Also, slice timing correction, motion correction, normalization, smoothing, and filtering are the most important low-level fMRI preprocessing methods (Jaber et al, 2019;Behroozi et al, 2011). Some of the high-level preprocessing methods that have been surveyed in investigations for sMRI and fMRI modalities are segmentation (Makropoulos et al, 2018) and functional connectivity matrix (FCM) (Luo et al, 2011),…”