Electrophysiological activity in thalamus associated with cortical network anticorrelation
Poster No:
763
Submission Type:
Abstract Submission
Authors:
Emily Davidson1, Dian Lyu2, Ethan Yan3, Zoe Lusk2, Josef Parvizi2, Nathan Spreng1
Institutions:
1McGill University, Montreal, Quebec, 2Stanford University, Palo Alto, CA, 3McGill, Montreal, Quebec
First Author:
Co-Author(s):
Introduction:
Anticorrelations between the Default Network (DN) and Dorsal Attention Network (DAN) are robustly observed in fMRI BOLD signal, and correspond to cognitive activity, potentially reflecting a dynamic balance between internal and external states (Fox et al., 2005; Raichle, 2015). Dynamic analyses have revealed DN-DAN coupling to fluctuate over time (Dixon et al., 2017). The drivers and significance of these fluctuations remain largely unknown. The thalamus may mediate anticorrelation given its regulation of information flow across cortex (Sherman & Guillery, 2002), and in particular, the way in which different thalamic nuclei associate with different cortical networks preferentially. One such nucleus, the Pulvinar, is often associated with attentional fluctuation and has been linked to the DAN (Benarroch, 2015). Here, we assessed intracranial stereotaxic EEG (sEEG) of thalamic nuclei, and their temporal relation to DN-DAN connectivity dynamics.
Methods:
We collected intracranial stereotactic EEG (sEEG) from seven epilepsy patients while they watched animated films ("Partly Cloudy" and "The Present"). Data was preprocessed using notch filtering (60/120/180 Hz) and average re-referencing. Contacts within the DN and DAN were identified from resting state fMRI using the Yeo7 Network Parcellation (Yeo et al., 2011); electrodes within four thalamic nuclei-Mediodorsal (MD), Pulvinar, Anterior, Medial Pulvinar-were identified using the Brainstorm thalamic atlas. A single, averaged signal from all corresponding channels within each network and nuclei was generated for each participant session. From the sEEG timeseries, we then extracted event markers for peaks in DN-DAN correlation and anticorrelation in the high-frequency broadband (HFB) signal (70–170 Hz), which approximates BOLD (Nir et al., 2007). Correlation and anticorrelation peaks and troughs were identified using the SciPy Signal Processing Toolbox and identified as distinct timeseries events. For analysis, a 5s epoch (-2.5 to +2.5 s) was generated for each correlation and anticorrelation event. A Morlet wavelet transform (center=1 Hz, step=0.5 Hz) was used to ascertain power data at each epoch timepoint for DN, DAN and thalamic nuclei (see figure 2). A two-tailed t-test was performed to compare the overall ERPs across correlation and anticorrelation conditions.
Results:
Two concurrent ERPs in the DAN and pulvinar were identified, approximately 50ms before anticorrelation peak onset at time 0. These ERPS were significantly different between the correlation and anticorrelation conditions (see figure 1). To determine whether these ERPS were tied to the correlation or anticorrelation condition, a time-frequency analysis was performed using a Morlet wavelet (1Hz, 0.5s) analysis for each condition (see figure 2). The anticorrelation condition showed a large increase in alpha power for both the DAN and pulvinar roughly 50ms before anticorrelation peak onset at time 0. This alpha power peak was not present in the correlation condition. Taken together, these results indicate that preceding anticorrelation, an ERP, largely dominant in the alpha band, occurs 50ms before the DN and DAN are maximally in anticorrelation.
·Between-Condition T-tests
·Time-Spectral Analysis - Alpha Band
Conclusions:
We show a temporal role for thalamus in shaping DN-DAN anticorrelation over time. The DAN-Pulvinar synchronicity aligns with previous static work (Benarroch, 2015; Saalmann et al., 2012). Our data suggest a dynamic relationship, potentially based on attentional demands. By differentially modulating temporal shifts in DN-DAN coupling, these pulvinar effects offer a deeper understanding of how subcortical circuits influence large-scale functional dynamics important for cognition. Future directions include examining phase amplitude coupling and predictive modeling of subcortical-cortical network dynamics.
Higher Cognitive Functions:
Higher Cognitive Functions Other 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
EEG/MEG Modeling and Analysis 2
Keywords:
Cognition
Cortex
ELECTROPHYSIOLOGY
Thalamus
1|2Indicates the priority used for review
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Provide references using APA citation style.
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Saalmann, Y. B., Pinsk, M. A., Wang, L., Li, X., & Kastner, S. (2012). The Pulvinar Regulates Information Transmission Between Cortical Areas Based on Attention Demands. Science, 337(6095), 753–756. https://doi.org/10.1126/science.1223082
Sherman, S. M., & Guillery, R. W. (2002). The role of the thalamus in the flow of information to the cortex. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 357(1428), 1695–1708. https://doi.org/10.1098/rstb.2002.1161