Introduction: In chronic stroke, functional MRI (fMRI) is used to map residual motor networks. Standard preprocessing masks the stroke lesion, excluding both the infarct cavity and adjacent T2 ...
Introduction: Accurate preprocessing of functional magnetic resonance imaging (fMRI) data is crucial for effective analysis in preclinical studies. Key steps such as denoising, skull-stripping, and ...
Introduction: Functional brain connectivity measures extracted from resting-state functional magnetic resonance imaging (fMRI) scans have generated wide interest as potential noninvasive biomarkers.
This manuscript provides important information on the neurodynamics of emotional processing while participants were watching movie clips. This work provides convincing results in deciphering the ...
I have recently come across your excellent set of MATLAB scripts for fMRI data preprocessing, which utilize DPARSF and SPM. I am incredibly impressed by the modular and well-structured workflow you've ...
Tools for aiding in the diagnosis of Autism Spectrum Disorder (ASD) using machine learning (ML) and resting-state rs-fMRI (rs-fMRI) must encompass different phases such as data collection, ...
Our study identifies a conserved medullary network, comprising the Lat-RM and CauM, underlying forelimb movement control across species, while also revealing cortical differences potentially driven by ...
I encountered a question when using DeepPrep for fMRI preprocessing. My original BOLD dataset has 600 timepoints. During preprocessing, I set the parameter bold_skip_frame = 10. However, after ...
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