Mapping the Scene and Object Processing Networks by Intracranial EEG

Abstract

Human perception and cognition are based predominantly on visual information processing. Much of the information regarding neuronal correlates of visual processing has been derived from functional imaging studies, which have identified a variety of brain areas contributing to visual analysis, recognition, and processing of objects and scenes. However, only two of these areas, namely the parahippocampal place area (PPA) and the lateral occipital complex (LOC), were verified and further characterized by intracranial electroencephalogram (iEEG). iEEG is a unique measurement technique that samples a local neuronal population with high temporal and anatomical resolution. In the present study, we aimed to expand on previous reports and examine brain activity for selectivity of scenes and objects in the broadband high-gamma frequency range (50-150 Hz). We collected iEEG data from 27 epileptic patients while they watched a series of images, containing objects and scenes, and we identified 375 bipolar channels responding to at least one of these two categories. Using K-means clustering, we delineated their brain localization. In addition to the two areas described previously, we detected significant responses in two other scene-selective areas, not yet reported by any electrophysiological studies; namely the occipital place area (OPA) and the retrosplenial complex. Moreover, using iEEG we revealed a much broader network underlying visual processing than that described to date, using specialized functional imaging experimental designs. Here, we report the selective brain areas for scene processing include the posterior collateral sulcus and the anterior temporal region, which Abbreviations: ATC, anterior temporal cortex brain region; AUC, the area under the curve; BGA, broadband gamma activity; CC, cingulate and paracingulate cortex brain region; FC, frontal cortex brain region; FDR, false discovery rate; FLPG, fusiform, lingual and parahippocampal gyri brain region; HIP, hippocampus brain region; iEEG, intracranial electroencephalography; INS, insula brain region; LOC, lateral occipital complex; LO, a lateral occipital portion of LOC; LTC, lateral temporal cortex brain region; MPA, medial place area; OC, occipital cortex brain region; OPA, occipital place area; PC, parietal cortex brain region; pFs, posterior fusiform sulcus portion of LOC; RSC, retrosplenial cortex and precuneus brain region; PPA, parahippocampal place area; SF, spatial frequency.

Tackling the challenges of group network inference from intracranial EEG data

“…Next, the resting state fMRI data suggest the PPA area connects with more anterior temporal regions including the anterior hippocampus (ROI 7) (Boccia et al, 2017). In our previous study (Vlcek et al, 2020), we documented late anterior hippocampal scene selectivity starting at around 300-350ms and even earlier responses at about 200-250ms around anterior collateral sulcus, which is positioned anatomically between the PPA and hippocampus. We added this region, located between ROI 3 and 7, as ROI 4.…”

“…Because the implantation is decided by clinical requirements, it naturally includes many areas not involved in visual processing. Thus, to limit computational costs and improve statistical power, only those channels with significant responses to demonstrated pictures were analyzed (see Vlcek et al (2020) for statistical analysis details). Moreover, for the purposes of connectivity analysis, only those subjects, who had implanted at least two of the initially defined seven regions of interest (ROIs) relevant for the cognitive tasks studied (described in Sec.…”

“…A previous study on the same dataset (Vlcek et al, 2020) has defined 7 clusters of channels that were of particular interest as they have shown a stronger reaction to the visual stimulation by scenes than by objects.…”

“…Below, we develop a pipeline for the analysis of the interaction of the two cortical visual processing streams, applied to a recorded iEEG task-based dataset used by Vlcek et al (2020). In this analysis, we illustrate the considerations outlined above, that need to be taken into account while analysing the causal structure of such complex neuroimaging datasets.…”

“…For the purpose of our current analysis, we developed a hypothesis about the connectivity between seven ROIs (Figure 2), based on our previous iEEG study (Vlcek et al, 2020) and other electrophysiological and anatomical published results (Kravitz et al, 2011; Henriksson et al, 2019; Baldassano et al, 2016; Bastin et al, 2013; Boccia et al, 2017; Cavanna and Trimble, 2006; Epstein and Baker, 2019; Byrne et al, 2007).…”