Intraparietal Sulcus

We focused on regions in early visual cortex (EVC; i.e., V1, V2, V3), the lateral occipital complex (LOC), comprising object-selective regions LO and pFs in the ventral stream, and on the posterior intraparietal sulcus (pIPS), comprising the regions IPS0 and IPS1 in the dorsal stream.
To define EVC, we first transformed the subject-specific t maps from the scrambled > objects contrast from the localizer GLM into MNI space. Based on these transformed t-maps, we computed a contrast comparing the group-level activation against zero, which resulted in one t-map across subjects. We then thresholded this t-map at the p < 0.001 level and calculated the overlap between the thresholded t-map and the combined anatomic definition of V1, V2, and V3 from the Glasser Brain Atlas (Glasser et al., 2016 (link)). Finally, we transformed this overlap image back into the native subject space for each subject, resulting in subject-specific EVC masks. A more fine-grained definition of the ROIs V1, V2, V3, and V4 based on the Wang et al. (2015) (link) atlas led to qualitatively similar results as the EVC definition.
To define object-selective cortex, we manually identified the peaks in the subject-specific t-maps of the objects > scrambled contrast from the localizer GLM, which corresponded anatomically to LO and pFS. We then defined spheres with a radius of 6 voxels around both peaks, including only those voxels in the spheres that had t values corresponding to p < 0.0001. This resulted in one ROI mask for LO and pFS, respectively. Initial exploratory analyses revealed that LO and pFS yielded highly comparable results. Therefore, we merged the two ROI masks into one combined LOC mask. This resulted in one object-selective cortex mask for each subject.
To define pIPS, we first combined the probability masks for IPS0 and IPS1 from the Wang et al. (2015) (link) atlas and then thresholded this combined IPS0-1 mask at a value of 20%. Next, we transformed the combined pIPS mask into the individual subject space. Finally, we computed the overlap between the individual pIPS mask and the subject-specific t-map of the contrast from the localizer GLM comparing all objects and scrambled objects against baseline, thresholded at p < 0.0001. This procedure resulted in one pIPS ROI mask for each subject. In case the EVC, object-selective cortex, or pIPS masks overlapped in a given subject, the overlapping voxels were discarded from all masks.

The procedures for the rsEEG cortical source estimations were described in a previous reference article of our Consortium (Babiloni et al. 2019 (link)). We used the official freeware tool called exact LORETA (eLORETA) to linearly estimate the cortical source activity generating scalp-recorded rsEEG rhythms (Pascual 2007 ). The current implementation of eLORETA uses a head volume conductor model composed of the scalp, skull, and brain.
Exploring electrodes can be virtually positioned in the scalp compartment to give EEG data as an input to the source estimation (Pascual 2007 ). The brain model relies on a realistic cerebral shape from a template typically used in neuroimaging studies, namely that of the Montreal Neurological Institute (MNI152 template). The eLORETA freeware solves the so-called EEG inverse problem estimating “neural” current density values at any cortical voxel of the mentioned head volume conductor model. The solutions are computed rsEEG frequency bin-by-frequency bin.
The input for this estimation is the EEG spectral power density computed at scalp electrodes. The output estimates the neural current density at the equivalent current dipoles, each localized into one of the 6239 voxels (5 mm resolution) forming the cortical source space, restricted to the cortical GM of the head volume conductor model. Specifically, eLORETA estimates local neural ionic currents at 3 axes, “z,” “x,” and “y,” of a dipolar source located within each voxel of the cortical source space. The procedure averages those values from the 3 axes to make each dipolar source putatively sensitive to different directions of the local neural ionic currents (https://www.uzh.ch/keyinst/loreta). The eLORETA package provides the Talairach coordinates, lobe, and BA for each voxel.
Following the above procedure, the eLORETA source activities from rsEEG rhythms were estimated in specific ROIs representing the main “hubs” included in the resting-state cortical networks considered in this study (i.e. DMN, SMN, and DAN). In this line, the average of the eLORETA source solutions across the voxels of a given ROI could putatively reflect the local neural currents generated by radial, oblique, and tangential rsEEG sources from near cortical circumvolutions, including gyri, sulci, etc. (Pascual 2007 ).
The selection of the DMN nodes to form the ROIs was performed according to Yeo et al. (2011) (link), while that of the DAN nodes was performed according to Bedini and Baldauf (2021) (link) for the FEFs and to Anderson et al. (2011) (link) for the anterior intraparietal sulcus (aIPS). The correspondence between the network ROIs and the BA is reported in Table 2.
The following procedure normalized eLORETA solutions computed from the rsEEG eyes-closed data. For a given participant, we averaged the eLORETA solutions across all frequency bins from 0.5 to 45 Hz and 6239 voxels of the brain model volume to obtain the eLORETA “mean” solution. Afterward, we computed the ratio between each original eLORETA solution at a given frequency bin/voxel and the eLORETA “mean” solution. As a result, each original eLORETA solution at a given frequency bin/voxel changed to a normalized eLORETA solution.
For the present eLORETA cortical source estimation, we used a 0.5 Hz frequency resolution as the maximum frequency resolution allowed using 2-s artifact-free EEG epochs.