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Exploring Brain Imaging Analysis With Nilearn and Related Python Packages
Abstract
The utilization of brain imaging information holds promise for predicting the prognosis of diverse psychiatric disorders, including schizophrenia, anxiety, attention-deficit hyperactivity disorder, bipolar disorder, and depression. Researchers are displaying growing interest in harnessing Python applications like Nilearn, Nistats, and Nibabel packages to extract and analyze brain imaging data. The Python Nilearn package enables the decoding, analysis, and creation of predictive models using brain imaging data. With Nilearn, users can perform functional connectivity analysis, manipulate brain image volumes, and conduct advanced statistical research on brain images. Furthermore, Nilearn contributes to the development of standardized methods for extracting brain imaging information, addressing potential variations in imaging methodologies across studies. While there is limited literature available on the Nilearn package and similar tools used for similar purposes, it is crucial to promote Nilearn and similar software to facilitate comparisons between studiess.
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