Intraoperative Resting-State Functional Connectivity Based on RGB Imaging

Abstract

RGB optical imaging is a marker-free, contactless, and non-invasive technique that is able to monitor hemodynamic brain response following neuronal activation using task-based and resting-state procedures. Magnetic resonance imaging (fMRI) and functional near infra-red spectroscopy (fNIRS) resting-state procedures cannot be used intraoperatively but RGB imaging provides an ideal solution to identify resting-state networks during a neurosurgical operation. We applied resting-state methodologies to intraoperative RGB imaging and evaluated their ability to identify resting-state networks. We adapted two resting-state methodologies from fMRI for the identification of resting-state networks using intraoperative RGB imaging. Measurements were performed in 3 patients who underwent resection of lesions adjacent to motor sites. The resting-state networks were compared to the identifications provided by RGB task-based imaging and electrical brain stimulation. Intraoperative RGB resting-state networks corresponded to RGB task-based imaging (DICE:0.55±0.29). Resting state procedures showed a strong correspondence between them (DICE:0.66±0.11) and with electrical brain stimulation. RGB imaging is a relevant technique for intraoperative resting-state networks identification. Intraoperative resting-state imaging has several advantages compared to functional task-based analyses: data acquisition is shorter, less complex, and less demanding for the patients, especially for those unable to perform the tasks.

Keywords: RGB imaging; functional connectivity; intraoperative imaging; optical imaging; resting-state.

Figure 1

Overview of the algorithm [7] for the computation of concentration changes time curves, the calculation of functional maps (task-based and resting-state maps) and the comparison of functional areas identified by functional maps.

Figure 2

In the first column, the first image of the video sequence acquired for the three patients was represented ($$I\left(0\right)$$). In the second and third columns, the $$Hb{O}_2$$ and $$Hb$$ task-based maps were plotted, respectively. In the fourth and fifth columns, the $$Hb{O}_2$$ and $$Hb$$ resting-state seed maps were plotted, respectively. The seeds used for the computation of the resting-state maps were indicated by white spots and were located at the level of the motor area identified by electrical brain stimulation (letter M). The colorbar indicated the Pearson correlation coefficient values computed for each pixel. The green contours plotted in task-based and resting-state maps delimited the extent of the thresholded images (see Equation (4)). For patient 2 maps, the dotted white circle delimited the patient’s tumor. For patients 1 and 3, tumors were not observable on optical images.

Figure 3

In the first column, the task-based maps were plotted. The spatial distribution of $$\Delta C_{Hb{O}_2}$$ and $$\Delta C_{Hb}$$ fluctuations identified by ICA were represented in other columns. In columns 2 to 6, matrices $$\widetilde{A_1}$$ to $$\widetilde{A_5}$$ were represented for each chromophore and for each patient. Each motor area identified by electrical brain stimulation was indicated by a white spot and by the letter M. The colorbar indicated the range of variation of the Pearson correlation coefficient (task-based maps) and the range of variation of the ICA matrices. The resting-state ICA matrices were normalized with respect to the absolute value of their maximum, keeping zero in the center of the color scale. The green contours plotted in task-based and resting-state maps delimited the extent of the thresholded images (see Equation (4)). For patient 2 maps, the dotted white circle delimited the patient’s tumor. For patients 1 and 3, tumors were not observable on optical images.