Stereotactic neuronavigation currently is routinely utilized during the resection of brain tumors. This technology has been shown to improve the extent of tumor resection and, as a result, improve survival statistics. That said, it is not routine during resections to make use of similar neuronavigation displays that reflect the functional organization of the brain. Hence, the neurosurgeon often has very little insight into what cognitive functions may be compromised by the operative procedure. Task-based fMRI has been employed as a means of preoperatively localizing function. However, task-based fMRI critically depends on the patient’s ability to comply with the task paradigm, which frequently is lacking; consequently, this procedure often does not provide useful information. Moreover, task-based fMRI conventionally is restricted to mapping the representation of motor and speech function, which omits other important functions, e.g., executive function.

During the past several years, it has been shown that the representation of multiple motor, sensory, and cognitive functions can be mapped by analysis of intrinsic brain activity, acquisition of which requires only that the patient hold still during fMRI. Even the waking state during fMRI is not required as essentially the same functional maps are obtained even if the patient is asleep or sedated. Thus, “resting state” fMRI (rsfMRI) provides a much more complete functional map of the brain than does task-based fMRI; moreover, rsfMRI is more reliable and much more time-efficient.
 

In the Leuthardt lab, we are using advanced analytic techniques to create software packages that seamlessly and automatically analyze resting state fMRI data and generate maps of multiple canonical brain networks (i.e., somatomotor, language, ventral attention, dorsal attention, default mode, visual, and frontoparietal control). These maps then can be easily viewed together with anatomical information as the surgeon plans the operative approach prior to surgery and makes ongoing surgical decisions during the resection. Further, these maps can be used to guide numerous types of therapeutic interventions in the future. We anticipate that this technology will lead to improved cognitive status outcomes and decreased morbidity after neurosurgical resections of malignant brain tumors.