In vivo direct imaging of neuronal activity reveals neural circuits in the somatosensory network
Poster No:
1992
Submission Type:
Abstract Submission
Authors:
Jae-Youn Keum1, Phan Tan Toi1, Semi Park1, Heejung Chun2, Jang-Yeon Park1,3
Institutions:
1Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea, 2College of Pharmacy, Yonsei-SL Bigen Institute (YSLI), Yonsei University, Incheon, Republic of Korea, 3Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
First Author:
Jae-Youn Keum
Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University
Suwon, Republic of Korea
Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University
Suwon, Republic of Korea
Co-Author(s):
Phan Tan Toi
Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University
Suwon, Republic of Korea
Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University
Suwon, Republic of Korea
Semi Park
Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University
Suwon, Republic of Korea
Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University
Suwon, Republic of Korea
Heejung Chun
College of Pharmacy, Yonsei-SL Bigen Institute (YSLI), Yonsei University
Incheon, Republic of Korea
College of Pharmacy, Yonsei-SL Bigen Institute (YSLI), Yonsei University
Incheon, Republic of Korea
Jang-Yeon Park
Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University|Department of Biomedical Engineering, Sungkyunkwan University
Suwon, Republic of Korea|Suwon, Republic of Korea
Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University|Department of Biomedical Engineering, Sungkyunkwan University
Suwon, Republic of Korea|Suwon, Republic of Korea
Introduction:
Two years ago, our group reported a novel functional MRI (fMRI) method called direct imaging of neuronal activity (DIANA), enabling direct detection of neural activity on millisecond timescales (Toi et al., 2022). In this study, we report that the DIANA fMRI can reveal mouse forelimb sensory circuits involved in the somatosensory network, including feedback pathways, using forelimb electrical stimulation in medetomidine-anesthetized mice on an 11.7T animal scanner. Additionally, we identified subregions within sensory thalamic nuclei (i.e., VPL and POm) with different DIANA response times.
Methods:
Experiment:
Six adult C57BL/6J medetomidine-anesthetized mice were used for DIANA fMRI at 11.7T (BioSpec, Bruker) (Fig.1A). For DIANA fMRI, 2D gradient-echo line-scan data were acquired (Fig. 1B). To observe signals in the thalamus and forelimb primary somatosensory cortex (S1FL), a single oblique slice covering both thalamus and S1FL was acquired using electrical forelimb stimulation (Fig. 1C). Scan parameters were: TR/TE, 5/2ms; flip angle, 4°; field of view, 25.6×12.8mm2; matrix size, 128×64; and slice thickness, 0.8mm. Sufficient dummy scans (8s, 1600TR) were used to achieve steady-state magnetization prior to the main sequence. Both gradient and RF spoiling were used to suppress the residual transverse magnetization. Electrical stimulation (current, 0.5mA; duration, 1ms; frequency, 5Hz) was delivered to the right forepaw. The stimulus was repeated 64 times, equal to the number of phase-encoding steps, with an interval of 200ms. The stimulation paradigm consisted of 50ms pre-stimulation, 1ms stimulation, and 149ms post-stimulation. 40 trials per mouse were acquired and used for analysis. To minimize neural adaptation, a 120-second rest period was provided every 5 trials.
Data analysis:
DIANA-activated areas were identified using spatiotemporal activation mapping, which exploits temporal information of peak DIANA responses (Keum et al., 2024). Time series were extracted from each activation area of the spatiotemporal activation map, using temporal smoothing with 3-point Gaussian kernel, ROI averaging, signal normalization, and linear detrending. All 40 trials per mouse were averaged and they were averaged across all 5 mice. One mouse with no BOLD response in the thalamus in the pre-BOLD fMRI experiment was excluded from the analysis.
Granger causality tests were performed to confirm the neural information flow within the circuits. The time lag was determined based on Hannan-Quinn criteria (HQC).
Six adult C57BL/6J medetomidine-anesthetized mice were used for DIANA fMRI at 11.7T (BioSpec, Bruker) (Fig.1A). For DIANA fMRI, 2D gradient-echo line-scan data were acquired (Fig. 1B). To observe signals in the thalamus and forelimb primary somatosensory cortex (S1FL), a single oblique slice covering both thalamus and S1FL was acquired using electrical forelimb stimulation (Fig. 1C). Scan parameters were: TR/TE, 5/2ms; flip angle, 4°; field of view, 25.6×12.8mm2; matrix size, 128×64; and slice thickness, 0.8mm. Sufficient dummy scans (8s, 1600TR) were used to achieve steady-state magnetization prior to the main sequence. Both gradient and RF spoiling were used to suppress the residual transverse magnetization. Electrical stimulation (current, 0.5mA; duration, 1ms; frequency, 5Hz) was delivered to the right forepaw. The stimulus was repeated 64 times, equal to the number of phase-encoding steps, with an interval of 200ms. The stimulation paradigm consisted of 50ms pre-stimulation, 1ms stimulation, and 149ms post-stimulation. 40 trials per mouse were acquired and used for analysis. To minimize neural adaptation, a 120-second rest period was provided every 5 trials.
Data analysis:
DIANA-activated areas were identified using spatiotemporal activation mapping, which exploits temporal information of peak DIANA responses (Keum et al., 2024). Time series were extracted from each activation area of the spatiotemporal activation map, using temporal smoothing with 3-point Gaussian kernel, ROI averaging, signal normalization, and linear detrending. All 40 trials per mouse were averaged and they were averaged across all 5 mice. One mouse with no BOLD response in the thalamus in the pre-BOLD fMRI experiment was excluded from the analysis.
Granger causality tests were performed to confirm the neural information flow within the circuits. The time lag was determined based on Hannan-Quinn criteria (HQC).
Results:
First, we identified the forelimb sensory pathway connecting VPL to S1FL via thalamocortical (TC) projection and subsequently to POm via corticothalamic (CT) projection (Circuit I: VPL→S1FL→POm, Fig.1E) (Jung et al., 2021). The feedforward TC projection from dorsolateral VPL (dlVPL) to S1FL and the feedback CT projection to dorsolateral POm (dlPOm) were sequentially observed, with peak latencies of 25.0±3.16 ms, 33.00±3.39 ms, and 53.0±3.00 ms, respectively (n=5). Next, we identified CT projection as a recurrent loop from S1FL back to VPL (Circuit II: VPL→S1FL→VPL, Fig.1F) (Guo et al., 2020), with a peak latency of 80.0±3.16 ms in ventromedial VPL (vmVPL) (n=5). Granger causality tests verified that neural information flow matched the temporal order of DIANA responses: dlVPL Granger-caused S1FL (p=6.9e-6) and S1FL Granger-caused dlPOm (p=1.0e-5) and vmVPL (p=6.4e-5).
·Fig. 1. DIANA fMRI reveals neural circuits in the somatosensory network in response to mouse forelimb stimulation.
Conclusions:
Here, using DIANA fMRI, we revealed neural circuits involved in forelimb sensory processing in mice, including feedback CT projections from S1FL to VPL and POm, and identified corresponding subregions within each VPL and POm. The temporal order of DIANA responses within the circuits was consistent with the Granger causality. DIANA fMRI is expected to make a significant contribution to revealing the brain's neural circuits and creating dynamic brain network models with high spatiotemporal resolution.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling
Novel Imaging Acquisition Methods:
Non-BOLD fMRI 1
Keywords:
FUNCTIONAL MRI
MRI
Neuron
Thalamus
Other - Neural circuits
1|2Indicates the priority used for review
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2. Jung, W. B., Im, G. H., Jiang, H., & Kim, S.-G. (2021). Early fMRI responses to somatosensory and optogenetic stimulation reflect neural information flow. Proceedings of the National Academy of Sciences, 118, e2023265118.
3. Keum, J.-Y., Toi, P. T., Park, S., Chun, H., & Park, J.-Y. (2024). Direct imaging of neural activity reveals neural circuits via spatiotemporal activation mapping. bioRxiv. https://doi.org/10.1101/2024.07.31.606112
4. Toi, P. T., Jang, H. J., Min, K., Kim, S.-P., Lee, S.-K., Lee, J., Kwag, J., & Park, J.-Y. (2022). In vivo direct imaging of neuronal activity at high temporospatial resolution. Science, 378, 160–168.