Medical images play a crucial role in predicting and diagnosing neurological disorders. These images, such as MRI, fMRI, rs-fMRI CT scans, and PET scans, provide detailed insights into the structure and function of the brain, allowing healthcare professionals to identify abnormalities, track disease progression, and make informed treatment decisions. By analyzing these images, clinicians can detect early signs of neurological disorders, monitor the effectiveness of interventions, and tailor personalized treatment plans for patients. The use of medical imaging in neuroscience not only enhances diagnostic accuracy but also improves patient outcomes by enabling timely and targeted interventions. Overall, medical images are invaluable tools in neuroscience, facilitating precise prediction and diagnosis of neurological conditions. 

Here, we curate a collection of valuable and essential videos, lectures, and website pages related to navigating the field of neuroimaging while respecting copyright laws and publisher guidelines. This source will be as beneficial to you as it has been.

MRI and fMRI preprocessing for machine learning

Preprocessing MRI and fMRI data is crucial for machine learning applications. It involves noise reduction, motion correction, normalization, and feature extraction to ensure clean, standardized data for analysis. Proper preprocessing leads to accurate predictions and diagnoses in neuroscience research. Here is a collection of lectures, videos, and related web pages.

The fundamental theory of MRI and fMRI analysis


Here is a collection of videos related to analyzing  medical images such as MRI and fMRI:




Hand on code 


Nilearn is a Python library for the analysis of MRI and fMRI data. here you can find the document and guidance on installation. below some related YouTube videos are listed: