Most cited article - PubMed ID 33192393
Mapping the Scene and Object Processing Networks by Intracranial EEG
The dorsal and ventral visual streams have been considered to play distinct roles in visual processing for action: the dorsal stream is assumed to support real-time actions, while the ventral stream facilitates memory-guided actions. However, recent evidence suggests a more integrated function of these streams. We investigated the neural dynamics and functional connectivity between them during memory-guided actions using intracranial EEG. We tracked neural activity in the inferior parietal lobule in the dorsal stream, and the ventral temporal cortex in the ventral stream as well as the hippocampus during a delayed action task involving object identity and location memory. We found increased alpha power in both streams during the delay, indicating their role in maintaining spatial visual information. In addition, we recorded increased alpha power in the hippocampus during the delay, but only when both object identity and location needed to be remembered. We also recorded an increase in theta band phase synchronization between the inferior parietal lobule and ventral temporal cortex and between the inferior parietal lobule and hippocampus during the encoding and delay. Granger causality analysis indicated dynamic and frequency-specific directional interactions among the inferior parietal lobule, ventral temporal cortex, and hippocampus that varied across task phases. Our study provides unique electrophysiological evidence for close interactions between dorsal and ventral streams, supporting an integrated processing model in which both streams contribute to memory-guided actions.
- Keywords
- Alpha oscillations, Dorsal visual stream, Granger causality analysis, Intracranial EEG, Memory-guided actions, Phase-locking value, Theta oscillations, Ventral visual stream,
- MeSH
- Adult MeSH
- Electroencephalography MeSH
- Electrocorticography MeSH
- Hippocampus * physiology MeSH
- Humans MeSH
- Young Adult MeSH
- Memory * physiology MeSH
- Temporal Lobe * physiology MeSH
- Parietal Lobe * physiology MeSH
- Visual Perception * physiology MeSH
- Visual Pathways * physiology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Antagonistic activity of brain networks likely plays a fundamental role in how the brain optimizes its performance by efficient allocation of computational resources. A prominent example involves externally/internally oriented attention tasks, implicating two anticorrelated, intrinsic brain networks: the default mode network (DMN) and the dorsal attention network (DAN). To elucidate electrophysiological underpinnings and causal interplay during attention switching, we recorded intracranial EEG (iEEG) from 25 epilepsy patients with electrode contacts localized in the DMN and DAN. We show antagonistic network dynamics of activation-related changes in high-frequency (> 50 Hz) and low-frequency (< 30 Hz) power. The temporal profile of information flow between the networks estimated by functional connectivity suggests that the activated network inhibits the other one, gating its activity by increasing the amplitude of the low-frequency oscillations. Insights about inter-network communication may have profound implications for various brain disorders in which these dynamics are compromised.
- MeSH
- Adult MeSH
- Electroencephalography MeSH
- Electrophysiological Phenomena MeSH
- Epilepsy physiopathology MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Brain * physiology physiopathology MeSH
- Nerve Net * physiology MeSH
- Attention * physiology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Stereoelectroencephalography (SEEG) records electrical brain activity with intracerebral electrodes. However, it has an inherently limited spatial coverage. Electrical source imaging (ESI) infers the position of the neural generators from the recorded electric potentials, and thus, could overcome this spatial undersampling problem. Here, we aimed to quantify the accuracy of SEEG ESI under clinical conditions. We measured the somatosensory evoked potential (SEP) in SEEG and in high-density EEG (HD-EEG) in 20 epilepsy surgery patients. To localize the source of the SEP, we employed standardized low resolution brain electromagnetic tomography (sLORETA) and equivalent current dipole (ECD) algorithms. Both sLORETA and ECD converged to similar solutions. Reflecting the large differences in the SEEG implantations, the localization error also varied in a wide range from 0.4 to 10 cm. The SEEG ESI localization error was linearly correlated with the distance from the putative neural source to the most activated contact. We show that it is possible to obtain reliable source reconstructions from SEEG under realistic clinical conditions, provided that the high signal fidelity recording contacts are sufficiently close to the source of the brain activity.
- Keywords
- Electrical source localization (ESL), High-density EEG (HD-EEG), Intracranial EEG (iEEG), Inverse problem, Somatosensory evoked potential (SEP, SSEP), Stereo-EEG (SEEG),
- MeSH
- Electroencephalography methods MeSH
- Electrocorticography * methods MeSH
- Epilepsy * surgery MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Brain Mapping methods MeSH
- Neuroimaging MeSH
- Evoked Potentials, Somatosensory MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Spatial reference frames (RFs) play a key role in spatial cognition, especially in perception, spatial memory, and navigation. There are two main types of RFs: egocentric (self-centered) and allocentric (object-centered). Although many fMRI studies examined the neural correlates of egocentric and allocentric RFs, they could not sample the fast temporal dynamics of the underlying cognitive processes. Therefore, the interaction and timing between these two RFs remain unclear. Taking advantage of the high temporal resolution of intracranial EEG (iEEG), we aimed to determine the timing of egocentric and allocentric information processing and describe the brain areas involved. We recorded iEEG and analyzed broad gamma activity (50-150 Hz) in 37 epilepsy patients performing a spatial judgment task in a three-dimensional circular virtual arena. We found overlapping activation for egocentric and allocentric RFs in many brain regions, with several additional egocentric- and allocentric-selective areas. In contrast to the egocentric responses, the allocentric responses peaked later than the control ones in frontal regions with overlapping selectivity. Also, across several egocentric or allocentric selective areas, the egocentric selectivity appeared earlier than the allocentric one. We identified the maximum number of egocentric-selective channels in the medial occipito-temporal region and allocentric-selective channels around the intraparietal sulcus in the parietal cortex. Our findings favor the hypothesis that egocentric spatial coding is a more primary process, and allocentric representations may be derived from egocentric ones. They also broaden the dominant view of the dorsal and ventral streams supporting egocentric and allocentric space coding, respectively.
- Keywords
- Allocentric, Egocentric, High-frequency gamma activity, Intracranial EEG, Reference frames, Spatial judgment,
- MeSH
- Electrocorticography MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Judgment physiology MeSH
- Spatial Processing * MeSH
- Space Perception * physiology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
INTRODUCTION: Intracranial EEG (iEEG) data is a powerful way to map brain function, characterized by high temporal and spatial resolution, allowing the study of interactions among neuronal populations that orchestrate cognitive processing. However, the statistical inference and analysis of brain networks using iEEG data faces many challenges related to its sparse brain coverage, and its inhomogeneity across patients. METHODS: We review these challenges and develop a methodological pipeline for estimation of network structure not obtainable from any single patient, illustrated on the inference of the interaction among visual streams using a dataset of 27 human iEEG recordings from a visual experiment employing visual scene stimuli. 100 ms sliding window and multiple band-pass filtered signals are used to provide temporal and spectral resolution. For the connectivity analysis we showcase two connectivity measures reflecting different types of interaction between regions of interest (ROI): Phase Locking Value as a symmetric measure of synchrony, and Directed Transfer Function-asymmetric measure describing causal interaction. For each two channels, initial uncorrected significance testing at p < 0.05 for every time-frequency point is carried out by comparison of the data-derived connectivity to a baseline surrogate-based null distribution, providing a binary time-frequency connectivity map. For each ROI pair, a connectivity density map is obtained by averaging across all pairs of channels spanning them, effectively agglomerating data across relevant channels and subjects. Finally, the difference of the mean map value after and before the stimulation is compared to the same statistic in surrogate data to assess link significance. RESULTS: The analysis confirmed the function of the parieto-medial temporal pathway, mediating visuospatial information between dorsal and ventral visual streams during visual scene analysis. Moreover, we observed the anterior hippocampal connectivity with more posterior areas in the medial temporal lobe, and found the reciprocal information flow between early processing areas and medial place area. DISCUSSION: To summarize, we developed an approach for estimating network connectivity, dealing with the challenge of sparse individual coverage of intracranial EEG electrodes. Its application provided new insights into the interaction between the dorsal and ventral visual streams, one of the iconic dualities in human cognition.
