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