Neural Correlates of Resting State Functional Networks in Cortex
Presented During: Poster Session 3
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Presented During: Poster Session 4
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Poster No:
1186
Submission Type:
Abstract Submission
Authors:
Lisa Meyer-Baese1, Dieter Jaeger2, Shella Keilholz3
Institutions:
1Georgia Institute of Technology and Emory University, Atlanta, GA, 2Emory University, Atlanta, GA, 3Emory, Atlanta, GA
First Author:
Co-Author(s):
Introduction:
Resting-state fMRI (rsfMRI) functional connectivity (FC) is a popular technique for studying how brain regions are intrinsically organized. FC networks are inferred to be functionally connected and are thought to involve the synchrony of neuronal populations involved in a common function (Stampanoni Bassi, Iezzi, Gilio, Centonze, & Buttari, 2019). Yet to what degree rsfMRI FC reflects underlying neuronal activity, and how this varies as a function of space and time is still poorly understood. Here we use wide-field optical imaging to look at resting state FC as measured with a novel fast voltage fluorescent sensor (Lu et al., 2023) while also tracking changes in hemodynamics. This allows us to probe to what degree hemodynamic FC networks are represented in the simultaneously acquired neural networks.
Methods:
Four EMX1-Cre pups expressing pan-cortical membrane-bound green JEDI-1P, a voltage sensor, and the cytosolic red-emitting reference fluorescent protein (mCherry) were imaged (Lu et al., 2023). The imaging set-up uses two CMOS high-speed imaging cameras (MiCAM ULTIMA. SciMedia) (Fig1A). Preprocessing included image alignment, background subtraction, photobleaching correction, hemodynamic correction, and alignment to the Allen atlas. The full dataset consists of 5 sessions acquired per mouse, with a single session consisting of 10 40.96-second-long trials sampled at 200FPS. The imaging FOV spans 3 different canonical functional networks in cortex: the default mode network (DMN), the somatomotor network, and the visual network (VIS). Due to the prominence of DMN activity at rest, all regions corresponding to the DMN were assigned to that network (Whitesell et al., 2021). The remaining areas were then subdivided into the somatomotor and VIS network (Fig1B). Time-varying FC matrices were computed by calculating the Pearson correlation coefficients (zero-lag correlation) between the time series of each ROI pair, resulting in symmetric 4450 x 4459 FC matrices that can be further subdivided into the three networks (Fig1C).
Results:
Static time-averaged FC maps for the broadband voltage signal and the slow hemodynamic signal (Fig1C) both exhibit similar structure. To explore the frequency-dependence of FC in the voltage signal, we calculated pairwise interhemispheric correlations for 4 ROIs shown in Fig1D. The strongest interhemispheric correlation is driven by the slowest frequency bands. The drop-off in correlation in the somatomotor regions is much steeper than in the DMN (Fig1D). To further explore differences in FC across frequency bands, the voltage signal was bandpass filtered into 4 ranges and the static time averaged FC maps were calculated (Fig2A). The FC of the voltage activity between the slow 1-6Hz and faster frequencies was compared by calculating the Euclidean distance for network-specific FC matrices. The theta band is most like the delta FC, while there is a much larger difference between alpha and beta bands. This trend holds across all three networks. There is little to no significant difference between the FC in alpha and beta (Fig2B). A similar analysis was done comparing the Euclidean distance between the hemodynamic FC matrix to the four different bandpass voltage signals. The hemodynamic FC networks are most like the slowest 1-6Hz voltage FC. There is an increasing difference as a function of frequency. Across the three networks, the smallest differences across all frequency bands were in the somatomotor network. While the DMN exhibits the largest FC difference.
Conclusions:
Voltage-derived FC spans 1-22Hz, with interareal FC varying by frequency and network. The strongest FC occurs in the slow voltage signal (1-6Hz), which is most correlated with the slow hemodynamic signal. A comparison of voltage and hemodynamic FC shows the highest similarity in the somatomotor network. These results indicate that hemodynamic derived FC relates to voltage FC in a frequency and region-specific manner.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Methods Development
Task-Independent and Resting-State Analysis
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
Imaging Methods Other 2
Keywords:
Cortex
Data analysis
Neuron
Optical Imaging Systems (OIS)
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Stampanoni Bassi, M., Iezzi, E., Gilio, L., Centonze, D., & Buttari, F. (2019). Synaptic Plasticity Shapes Brain Connectivity: Implications for Network Topology. International Journal of Molecular Sciences, 20(24). doi:10.3390/ijms20246193
Whitesell, J. D., Liska, A., Coletta, L., Mihalas, S., Gozzi, A., & Harris, J. A. (2021). Regional, Layer, and Cell-Type-Specific Connectivity of the Mouse Default Mode Network. Neuron.