This is a Work In Progress (WIP) project. It is part of my undergraduate studies in Biomedical Engineering at Ostbayerische Technische Hochschule in Germany. This repository incorporates the code and results of my participation in the course Data Science Projects: Train you own Machine Learning Model. The goal of this project is to demonstrate that clustering of Resting State fMRI data to distinguish between different gender of subjects is possible. This should pave the way for a future diagnosis of different diseases that are connected to reorganisation of the human brain.
MRI (Magnetic Resonance Imaging) is a procedure in medical diagnostics which aims to produce three-dimensional images of the body. In comparison to X-Ray and Computertomography it does not incorporate the usage of ionizing radiation and is primarily used for evaluating anatomy and physiology of soft tissue. It's based on the nuclear spin of hydrogen atoms, which can be measured by applying different techniques regarding magnetic fields. An advancement of this process is called fMRI which means functional MRI. This procedure uses the so called BOLD-contrast which stands for blood-oxygen-level dependent contrast. Due to the difference in the magneticity of oxygenrich blood and the one low in oxygen neural activity in the brain can be measured. The HCP (Human Connectom Project) is researching human connectomics, the connections in the brain, on different scales (e.g. between regions or individual neurons). MEG, EEG and MRI has been performed on about 1200 patients. In addition, genetic sequencing and several sensory and motoric test as well as cognitive evaluation took place. At this point this work is using only parts of the resting state fMRI data (specifically Resting State fMRI 1 FIX-Denoised Extended) in the HCP Unrelated 100 dataset. This is due to considerations regarding storage space as well as processing time and power. Therefore, only the first session in the downloaded data has been used until now.
Open this notebook.
Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
Please note that due to the implementation of the "Intel® Extension for Scikit-learn" package only Intel processors and no AMD processors can be used for the execution of the code.
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