Mirror Visual Feedback Therapy (MVFT) offers efficient non-invasive treatment for patients suffering from phantom limb pain. It is hypothesized to cause functional remodeling of neural networks in the patient's brain, what induces a relief in phantom pain. However, details about the functional remodeling of the brain are not yet fully understood and are a current topic of research.
In this thesis, subject-specific parcellation of functional Magnetic Resonance Imaging (fMRI) data is utilized and subsequent model map analysis is employed to quantify changes of functional connectivity patterns related to MVFT success.
Subject-specific functional parcellation is employed in order to form functionally homogeneous working regions of interest, which adapt to the functional and anatomical characteristics of every subject in the study population. Moreover, a dimensionality reduction of the datasets is achieved, facilitating further computationally intensive analysis. The functional connectivities between the obtained parcels are quantified by mapping their affinities in a new metric space of Euclidean distances.
Thus, the functional relations between the fMRI signals are translated into spatial distances in the functional geometry. Applying subject-specific functional parcellation and furthermore the quantification of the parcel-wise functional connectivities on a group of interest, comprising pre- and post-MVFT data, allows to identify differentiating functional connectivity patterns related to MVFT success. The results obtained endorse the hypothesis of functional reorganization of the brain. The results show that brain regions, responsible for the movement of the amputated leg, are involved with the brain's functioning during the movement of the healthy leg after the MVFT. However, due to the small dataset (a maximum of 5 pre- and 5 post-MVFT data for left leg amputees) the results can only be hypothesized.