Nested spatiotemporal dynamics organize hierarchical processing in the mouse visual cortex
Poster No:
2072
Submission Type:
Abstract Submission
Authors:
Brendan Harris1, Pulin Gong1,2
Institutions:
1School of Physics, The University of Sydney, Camperdown NSW, Australia, 2ARC Centre of Excellence for Integrative Brain Function, The University of Sydney, Camperdown NSW, Australia
First Author:
Co-Author:
Pulin Gong
School of Physics, The University of Sydney|ARC Centre of Excellence for Integrative Brain Function, The University of Sydney
Camperdown NSW, Australia|Camperdown NSW, Australia
School of Physics, The University of Sydney|ARC Centre of Excellence for Integrative Brain Function, The University of Sydney
Camperdown NSW, Australia|Camperdown NSW, Australia
Introduction:
Neural dynamics bridge anatomy and function over diverse scales (Ni et al., 2024; Senkowski et al., 2024), from microscopic spiking to mesoscale LFPs (Hayden et al., 2023) and macroscopic traveling waves (Aggarwal et al., 2022; Townsend et al., 2018; Xu et al., 2023). We report a new nested dynamical pattern in the mouse visual cortex, comprising: i) large-scale θ waves that propagate across cortical layers and regions; ii) short, localized γ packets that reflect focused processing; and iii) cross-scale coupling between θ waves, γ packets, and spikes (illustrated in Fig. 1A). This flexible dynamical motif aligns with feed-forward/feed-back anatomical features of the visual hierarchy and cortical laminae, while also carrying significant top-down/bottom-up functional information that predicts change-detection performance. Our findings suggest that such distributed cross-scale patterns form a general 'spatiotemporal θ–γ code' for efficiently modulating and multiplexing neural information.
Methods:
We used wave-based methods to analyze multi-region laminar recordings from the mouse visual cortex (AIBS, 2022), illustrated in Fig. 1B. After filtering to 69 high-quality sessions (over 53 mice), we comprehensively mapped LFP spectra across visual areas and laminae, finding prominent θ and γ peaks. We characterized θ as a traveling wave using the instantaneous phase (Fig. 1D) and quantified translaminar propagation with the negative spatial phase gradient (the 'wavenumber', see Fig. 1E). Using the instantaneous amplitude, we detected non-stationary, spatially localized γ packets across layers (Fig. 1F). Finally, we extended classical phase–amplitude coupling methods to measure spatiotemporal interactions between large-scale traveling θ waves, localized γ packets, and neuronal spiking (illustrated in Fig. 1G).
Results:
All regions exhibited clear θ-band (3–10 Hz) and γ-band (30–100 Hz) peaks, shown Fig. 2A. We also discovered a robust spectral gradient along the visual cortical hierarchy (Siegle et al., 2021), with θ strength increasing toward deep layers (median Kendall's τ = 0.37, p < 10-7) and higher regions (0.25 < τ < 0.60 across layers, all p < 0.01). θ displayed striking bidirectional, nonstationary propagation during visual tasks, as shown in Fig. 2B, switching from a deep-to-superficial feed-back mode after onset to a superficial-to-deep feed-forward mode after offset (Fig. 2C). θ also propagates from higher to lower areas after stimulus onset, but in the reverse direction near offset (Fig. 2D). Furthermore, translaminar θ propagation (prior to reaction) predicts 'hit/miss' change-detection performance, with a balanced accuracy of 0.65 ± 0.11 (median ± IQR across sessions, p < 10-13, LDA classifier). Together, these results indicate that θ plays a dual functional role in visual cognition.
We found that γ forms spatiotemporal wave packets, which become spatially localized after stimulus onset (see Fig. 2E). γ packets are also locked to θ waves, particularly in layer 1 of lower-order areas and layer 6 of higher-order regions (see Fig. 2F and Fig. 2G). Moreover, γ packets in superficial layers tended to prefer θ troughs, whereas deep layers preferred the falling edge. This θ–γ locking was mirrored in the relationship between LFP and neuronal spiking, where spikes tracked fluctuations in γ power and exhibited similar layer-specific preferences of θ phase (compare Fig. 2H and Fig. 2I).
We found that γ forms spatiotemporal wave packets, which become spatially localized after stimulus onset (see Fig. 2E). γ packets are also locked to θ waves, particularly in layer 1 of lower-order areas and layer 6 of higher-order regions (see Fig. 2F and Fig. 2G). Moreover, γ packets in superficial layers tended to prefer θ troughs, whereas deep layers preferred the falling edge. This θ–γ locking was mirrored in the relationship between LFP and neuronal spiking, where spikes tracked fluctuations in γ power and exhibited similar layer-specific preferences of θ phase (compare Fig. 2H and Fig. 2I).
Conclusions:
Based on our findings, we introduce the spatiotemporal θ–γ code as a general mechanism for multiplexing and integrating fine-grained top-down and bottom-up information across cortical layers and brain regions. Our wave-based framework ties local cross-scale coupling (Senzai et al., 2019) and inter-areal phase synchrony (Liebe et al., 2012) into a coherent, nested pattern of spikes, γ packets, and θ traveling waves, highlighting the crucial role of large-scale dynamics in shaping the fine-grained processes needed to interpret complex natural stimuli.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Multivariate Approaches
Other Methods
Perception, Attention and Motor Behavior:
Perception: Visual 1
Keywords:
Cognition
Computational Neuroscience
Cortex
Cortical Layers
Data analysis
ELECTROPHYSIOLOGY
Multivariate
Open Data
Perception
Vision
1|2Indicates the priority used for review
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Provide references using APA citation style.
AIBS (2022). Allen Brain Observatory – Neuropixels Visual Behavior [Data set]. http://portal.brain-map.org/circuits-behavior/visual-behavior-neuropixels.
Hayden et al. (2023). Electrophysiological signatures of visual recognition memory across all layers of mouse v1. Journal of Neuroscience, 43(44):7307–7321.
Liebe et al. (2012). Theta coupling between v4 and prefrontal cortex predicts visual short-term memory performance. Nature Neuroscience, 15(3):456–462.
Ni et al. (2024). Distributed and dynamical communication: a mechanism for flexible cortico-cortical interactions and its functional roles in visual attention. Communications Biology, 7(1):550.
Senkowski et al. (2024). Multi-timescale neural dynamics for multisensory integration. Nature Reviews Neuroscience, 25(9):625–642.
Senzai et al. (2019). Layer-specific physiological features and interlaminar interactions in the primary visual cortex of the mouse. Neuron, 101(3):500–513.e5.
Siegle et al. (2021). Survey of spiking in the mouse visual system reveals functional hierarchy. Nature, 592(7852):86–92.
Townsend et al. (2018). Detection and analysis of spatiotemporal patterns in brain activity. PLOS Computational Biology, 14(12):e1006643.
Xu et al. (2023). Interacting spiral wave patterns underlie complex brain dynamics and are related to cognitive processing. Nature Human Behaviour, 7(7):1196–1215.