Ultraslow fluctuations of brain intrinsic activity in mice
Poster No:
2115
Submission Type:
Abstract Submission
Authors:
Wen-Ju Pan1, Lauren Daley1, Shella Keilholz1
Institutions:
1Emory University/Georgia Tech, Atlanta, GA
First Author:
Co-Author(s):
Introduction:
Resting state fMRI typically investigates the intrinsic activity of the brain in frequencies of 0.01-0.1 Hz (Gohel & Biswal, 2015). The low-pass nature of the hemodynamic response limits signals from higher frequencies, and lower frequencies have rarely been investigated due to the need for long scans. Thus it is currently not known whether the typical frequency range can equally or sufficiently represent the activity of all brain areas (Kajimura et al., 2023). In this report, we compared the time courses of different brain areas in fMRI of mice using two imaging sequences, EPI and ZTE, and found a significant ultraslow component (<0.01 Hz) in some network activity, especially for subcortical nuclei and some high-order cortical areas. The findings suggest the ultraslow activities may be important for some networks in resting state studies.
Methods:
Simultaneous fMRI and wide-field optical imaging (WOI) was obtained from ten mice expressing GCaMP in excitatory neurons (Pan et al., 2022) using EPI or ZTE on a Bruker BioSpin 9.4T scanner with AVANCE NEO console and Paravision360 v3.5. The WOI data was not analyzed for this study. All animals were scanned 20min in resting state under 1% isoflurane EPI and ZTE were set to the same spatial resolution of ~ 400um isotropic voxels with whole brain coverage and temporal sampling rate of 1s per brain volume scan. The FOV saturation pulses were applied at the brain sides for minimizing non-brain signal pickup. Single-shot gradient echo EPI: TR=1000ms/TE=12ms for 16 axial continuous slices. ZTE: TR: 0.673 ms, flip angle 3°, bandwidth 128 kHz, matrix size 54 × 54 × 54, field-of-view 22 × 22 × 22 mm. Functional connectivity analyses were conducted using both seed-based correlation over the typical 0.01-0.1 Hz frequency range and spatial ICA applied without temporal filtering after conventional preprocesses and Allen atlas registration. Power spectra were calculated for each networks by power spectral density estimation via Welch's method.
Results:
1. Higher 0.01-0.1Hz powers in primary sensory areas than others.
As expected, ICA time courses from some cortical areas exhibited good correspondence to a typical 0.01-0.1 Hz range in the resting state, especially primary somatosensory areas, demonstrated in Figure 1 with both EPI and ZTE scans having similar findings. These networks, especially the lateral sensorimotor network (centered at primary somatosensory mouth areas of both hemispheres), appeared relatively stable across scans and subjects. Similar FC networks were detected by the seed-based method with 0.01-0.1 Hz filtering (not shown).
2. Fluctuations at ultraslow frequencies (<0.01Hz) and reduced 0.01-0.1Hz powers in some high-order cortical networks and subcortical networks.
The ICA networks involving thalamus, hippocampus, prelimbic cortex, etc. exhibited some <0.01 Hz large-amplitude fluctuations, Figure 2. Variation across different scans was observed in these networks at these scales. Intriguingly, much of the power of the signal was contained in these low frequencies that are typically discarded.
As expected, ICA time courses from some cortical areas exhibited good correspondence to a typical 0.01-0.1 Hz range in the resting state, especially primary somatosensory areas, demonstrated in Figure 1 with both EPI and ZTE scans having similar findings. These networks, especially the lateral sensorimotor network (centered at primary somatosensory mouth areas of both hemispheres), appeared relatively stable across scans and subjects. Similar FC networks were detected by the seed-based method with 0.01-0.1 Hz filtering (not shown).
2. Fluctuations at ultraslow frequencies (<0.01Hz) and reduced 0.01-0.1Hz powers in some high-order cortical networks and subcortical networks.
The ICA networks involving thalamus, hippocampus, prelimbic cortex, etc. exhibited some <0.01 Hz large-amplitude fluctuations, Figure 2. Variation across different scans was observed in these networks at these scales. Intriguingly, much of the power of the signal was contained in these low frequencies that are typically discarded.
Conclusions:
Coherent fluctuations of intrinsic brain activity in frequencies lower than the typical 0.01 Hz cutoff can be observed in the resting state locally and distantly, constituting networks (Seitzman et al., 2019). ICA provides a useful tool to examine these networks without pre-determining a frequency range for a given network. Our results suggest the ultraslow signals may dominate certainly some subcortical networks.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Connectivity (eg. functional, effective, structural)
Novel Imaging Acquisition Methods:
Non-BOLD fMRI 2
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals 1
Keywords:
ANIMAL STUDIES
Cortex
fMRI CONTRAST MECHANISMS
Neuron
Somatosensory
Sub-Cortical
1|2Indicates the priority used for review
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Provide references using APA citation style.
Kajimura, S., Margulies, D., & Smallwood, J. (2023). Frequency-specific brain network architecture in resting-state fMRI. Scientific Reports, 13(1), 2964. https://doi.org/10.1038/s41598-023-29321-5
Pan, W.-J., Wang, Y., Watters, H., Meyer-Baese, L., Smith, A., Jaeger, D., & Keilholz, S. (2022). (ISMRM 2022) Optimization of wide-field optical imaging method towards fMRI integration in mice. https://archive.ismrm.org/2022/3331.html
Seitzman, B. A., Snyder, A. Z., Leuthardt, E. C., & Shimony, J. S. (2019). The State of Resting State Networks. Topics in Magnetic Resonance Imaging : TMRI, 28(4), 189–196. https://doi.org/10.1097/RMR.0000000000000214