Use of Network Analysis to Establish Neurosurgical Parameters in Gliomas and Epilepsy

Abstract

Cutting-edge neuroimaging technologies can facilitate preoperative evaluation in various neurosurgical settings. Surgery for gliomas and epilepsy requires precise localization for resection due to the need to preserve (or perhaps improve) higher cognitive functions. Accordingly, a hodological approach should be taken that considers subcortical networks as well as cortical functions within various functional domains. Resting state functional magnetic resonance imaging (fMRI) has the potential to provide new insights that are valuable for this approach. In this review, we describe recent developments in network analysis using resting state fMRI related to factors in glioma and epilepsy surgery: the identification of functionally dominant areas, evaluation of cognitive function by alteration of resting state networks (RSNs), glioma grading, and epileptic focus detection. One particular challenge that is close to realization is using fMRI for the identification of sensorimotor- and language-dominant areas during a task-free resting state. Various RSNs representative of the default mode network demonstrated at least some alterations in both patient groups, which correlated with behavioral changes including cognition, memory, and attention, and the development of psychosis. Still challenging is the detection of epileptic foci and propagation pathways when using only network analysis with resting state fMRI; however, a combined method with simultaneous electroencephalography has produced promising results. Consequently, network analysis is expected to continue to advance as neuroimaging technology improves in the next decade, and preoperative evaluation for neurosurgical parameters through these techniques should improve parallel with them.

Fig. 1.

Representative resting state networks identified by independent component analysis (ICA) with resting state functional magnetic resonance imaging. Resting state functional images were obtained in 125 healthy controls (range of age: 20–59) using a 3.0 Tesla scanner at Nagoya University’s Brain and Mind Research Center. After preprocessing, group ICA was performed using the MELODIC software from the FSL package (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/). We extracted 30 independent components, and identified representative networks. DMN: default mode network, ECN: executive control network.

Fig. 2.

The comparison of the language networks in resting state fMRI and task fMRI in a patient with glioma. The patient was a 35-year-old female who had anaplastic astrocytoma in the left insula. Individual independent component analysis was performed with the datasets of resting state fMRI (red) and fMRI with a verb generation task (green). Although the affected hemisphere was distorted, the language networks with both methods appeared in the appropriate language-related regions, including Broca’s and Wernicke’s areas, with overlapping regions in both areas (yellow) (unpublished data). fMRI: functional magnetic resonance imaging.

Fig. 3.

An interictal epileptic discharge (IED)-related cluster in the EEG-fMRI of a patient with focal epilepsy. The patient was a 19-year-old female who had medically refractory epilepsy with a lesion in the left anterior cingulate gyrus. The IEDs were observed maximally in the CZ and FZ electrodes during the recording of the EEG simultaneously with MRI scanning (left upper panel); the event-related analysis was performed at the times of occurrence of 25 observed IEDs (right upper panel). An IED-related cluster was successfully identified in and near the lesion (lower panel). EEG-fMRI: electroencephalography functional magnetic resonance imaging, PSTH: Peri-Stimulus Time Histogram.