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 ...

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 affine ...

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 ...

This study aggregates across five fMRI datasets and reports that a network of brain areas previously associated with response inhibition processes, including several in the basal ganglia, are more ...

Abstract: Functional magnetic resonance imaging (fMRI) has ended up the most famous technique for imaging brain functions. Currently, there is a giant range of software packages for the analysis of ...

Objects that belong to the same category tend to elicit similar patterns of brain activity. Here, we reverse this mapping and ask whether neural similarity is sufficient to induce increased perceptual ...

This project uses data science (NumPy, Pandas, Matplotlib), machine learning (Scikit-Learn), and deep learning (TensorFlow, Keras) tools on brain data obtained with Electroencephalography (EEG) and ...

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