Travelling nature of cortical spontaneous neural activities in different cell types
Poster No:
1222
Submission Type:
Abstract Submission
Authors:
Liang Shi1, Jinling Lu2, Pengcheng Li2
Institutions:
1Hainan University, Sanya, Hainan, 2Huazhong University of Science and Technology, Wuhan, Hubei
First Author:
Co-Author(s):
Introduction:
Mounting evidence has revealed the presence of global structures in spontaneous neural activity, unveiling large-scale nonstationary synchronizations in the form of traveling waves. Their regional specificity may not align with brain regions or the structural connectome. It's still unclear physiological significances of such phenomenon. Moreover, functional connectivity (FC), as a factor of stationary synchronization, is influenced by nonstationary synchronization. The fine-tuning of large-scale neural activity relies on the intricate interplay between different neuronal types, chiefly glutamatergic and GABAergic neurons, which remains unclear.
Methods:
Our focus lies on one type of glutamatergic excitatory neuron expressing the second vesicular glutamate transporter (VGLUT2) and three types of GABAergic inhibitory interneurons expressing parvalbumin (PV), somatostatin (SOM, or SST), and vasoactive intestinal peptide (VIP) (Fan et al., 2015; Fremeau et al., 2004; Taniguchi et al., 2011). To achieve this, we use calcium fluorescence imaging with neuron-type-specific Cre driver lines (Hippenmeyer et al., 2005) and genetically encoded calcium indicators (Dana et al., 2019). We collect calcium fluorescence data from the mouse cortex at three developmental time points (P14, P28, and P56) and anesthesia/awake states (at rest, spontaneous moving and under anesthesia). We analyze "standing" and "travelling" waves identified using complex principal component analysis (CPCA) and compared them with FC (Bolt et al., 2022).
Results:
A set of standing and traveling waves can be extracted from spontaneous neural activity. These waves vary in proportion within the original signal and exhibit different strengths and time delays across brain regions. They are broadly distributed throughout the cortex and recur in a quasi-periodic manner. The first wave is a global mode of oscillation with minimal spatial phase variation, corresponding to a "standing wave." Standing wave accounts for the largest portion of the signal, approximately 35%, with minimal time-lag across the cortex. In contrast, other waves show substantial spatial phase variation, indicating they are "traveling" across the cortex.
FC is a crucial measure of neural activity synchronization. Our analysis reveals that the standing and traveling waves identified by CPCA can also be viewed as FC decompositions-specifically, FC is the linear superposition of reconstructed FC matrices derived from traveling waves. Moreover, the small and negative correlations observed in these reconstructed FC matrices arise from time-lags between different brain regions. Both FC and the standing/traveling waves capture the synchronization features of the brain's neural activity.
FC is a crucial measure of neural activity synchronization. Our analysis reveals that the standing and traveling waves identified by CPCA can also be viewed as FC decompositions-specifically, FC is the linear superposition of reconstructed FC matrices derived from traveling waves. Moreover, the small and negative correlations observed in these reconstructed FC matrices arise from time-lags between different brain regions. Both FC and the standing/traveling waves capture the synchronization features of the brain's neural activity.
·Large-scale neural activity is organized by standing and traveling waves.
Conclusions:
Our findings provide new insights into the global synchronizing structure of spontaneous neural activities and the potential origin and function of large-scale time-lag synchronizations within the brain.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Task-Independent and Resting-State Analysis 2
Keywords:
Cortex
Data analysis
GABA
1|2Indicates the priority used for review
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Provide references using APA citation style.
Dana, H. (2019). High-performance calcium sensors for imaging activity in neuronal populations and microcompartments. Nature Methods, 16(7), 7.
Fan, J. (2015). Vasoactive intestinal polypeptide (VIP)-expressing neurons in the suprachiasmatic nucleus provide sparse GABAergic outputs to local neurons with circadian regulation occurring distal to the opening of postsynaptic GABAA ionotropic receptors. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 35(5), 1905–1920.
Fremeau, R. T. (2004). VGLUTs define subsets of excitatory neurons and suggest novel roles for glutamate. Trends in Neurosciences, 27(2), 98–103. https://doi.org/10.1016/j.tins.2003.11.005
Hippenmeyer, S. (2005). A developmental switch in the response of DRG neurons to ETS transcription factor signaling. PLoS Biology, 3(5), e159.
Shi, L. (2024). Global spatiotemporal synchronizing structures of spontaneous neural activities in different cell types. Nature Communications, 15(1), 2884.
Taniguchi, H. (2011). A Resource of Cre Driver Lines for Genetic Targeting of GABAergic Neurons in Cerebral Cortex. Neuron, 71(6), 995–1013.