Analysis of functional Magnetic Resonance data for neurosurgical planning: subject specific resting state analysis as a complement to task based analysis
Brain and other central nervous system tumors are the 17th most common cancer
type in Europe, being associated with high mortality rate. Neurosurgery has been
the ultimate solution for the treatment of brain tumors. Integration of preoperative
brain mapping in the process is highly recommended in order to preserve fundamental
areas of the brain, especially those believed to be connected to language and movement.
Recently, there has been a growing interest in presurgical planning resorting to restingstate
functional magnetic resonance imaging (fMRI).
The aim of this thesis is to explore strategies to process data of resting-state fMRI
in order to better understand its connection to task brain networks, and to assess their
application to the protocols currently used within clinical institutions that are partners
of the host scientific institution in an ongoing project. A total of 8 subjects were recruited
to participate in this study, all of them previously referred for surgical tumor
resection. An optimal strategy for pre-processing was devised and tested. Task data was
processed using the General Linear Model, while rest data was processed through Independent
Component Analysis. The processed data were then correlated via similarity
coefficients.
The results of similarity tests show a limited coincidence between resting-state networks
and the activation task areas. Further studies will be required in order to improve
these results
