Biology subjects

Guan, J.-S.

Publications and source records attributed to Guan, J.-S..

5 recordsLinked to original sources

bioRxiv2025

Short Excitatory Projections Promote Burst Activity and Modular Functional Organization in Neural Networks

Physical constraints in the brain favor short, distance-dependent connections. How-ever, short-range projections are not merely a compromise imposed by these constraints; they also serve essential functional roles. Here, we explore networks with balanced excitation and inhibition, demonstrating that short excitatory projections fundamentally increase the number of common presynaptic inputs, thereby enhancing the correlations of neural responses. Strengthened recurrent correlations can lead to bursting states, amplifying postsynaptic neuronal activity and supporting signal propagation over long distances within the network. Moreover, even without an explicitly imposed modular structure, short excitatory connections can give rise to functional clustering and support a broad spectrum of modular functions. Therefore, this structural principle provides a mechanistic basis for functional column formation and dynamic burst propagation, suggesting that the brain transforms structural wiring constraints into opportunities for complex computation and distributed processing. These findings pave the way for a deeper understanding of how the brain transforms structural limitations into opportunities for complex computation.

neuroscience↗

bioRxiv2024

Polarized ATP synthase in synaptic mitochondria induced by learning and plasticity signals

MINFLUX represents a state-of-the-art single-molecule localization microscopy technology that surpasses the conventional diffraction limit, enabling the visualization and analysis of nanostructures with exceptional precision. Despite its potential, the utilization of MINFLUX has been primarily confined to in vitro cultured cell environments. In this research, we have refined the sample preparation protocols to facilitate 3D MINFLUX imaging in fixed brain tissue sections, focusing on the mitochondrial distribution within dendritic spines and engram cells of the dentate gyrus. Probing single molecules in vivo reveals an interesting finding: the reorganization of mitochondrial inner membrane proteins during synaptic plasticity within dendritic spines of engram cells. Utilizing 3D MINFLUX nanoscopy, we identified a significant redistribution of -F1-ATP synthase, which correlates with learning related activities. This redistribution implies a pivotal function for polarized ATP synthesis in the vicinity of the postsynaptic zone, which may modulate both short-term and long-term synaptic modifications, thereby influencing synaptic plasticity and memory consolidation. Furthermore, dual-color 3D MINFLUX imaging has uncovered distinct patterns of mitochondrial reorganization involving both the inner and outer membranes within dendritic spines. These patterns, induced by plasticity signals, persist for up to 12 hours in neuronal cultures. This distinction highlights the presence of distinct regulatory mechanisms governing mitochondrial proteins during synaptic plasticity. These results offer new insights into the molecular mechanisms underlying synaptic plasticity and underscore the transformative potential of 3D MINFLUX imaging for studying neuronal processes in the brain.

neuroscience↗

bioRxiv2021

Single Image-based Vignetting Correction for Improving the Consistency of Neural Activity Analysis in 2-Photon Functional Microscopy

High-resolution functional 2-photon microscopy of neural activity is a cornerstone technique in current neuroscience, enabling, for instance, the image-based analysis of relations of the organization of local neuron populations and their temporal neural activity patterns. Interpreting local image intensity as a direct quantitative measure of neural activity presumes, however, a consistent within- and across-image relationship between the image intensity and neural activity, which may be subject to interference by illumination artifacts. In particular, the so-called vignetting artifact - the decrease of image intensity towards the edges of an image - is, at the moment, widely neglected in the context of functional microscopy analyses of neural activity, but potentially introduces a substantial center-periphery bias of derived functional measures. In the present report, we propose a straightforward protocol for single image-based vignetting correction. Using immediate-early-gene-based 2-photon microscopic neural image data of the mouse brain, we show the necessity of correcting both image brightness and contrast to improve within- and across-image intensity consistency and demonstrate the plausibility of the resulting functional data.

neuroscience↗

bioRxiv2019

Multimodal memory components and their long-term dynamics identified in cortical layers II/III but not layer Vb

Activity patterns of cerebral cortical regions represent the present environment in which animals receive multi-modal inputs. They are also shaped by the history of previous activity that reflects learned information on past multimodal exposures. We studied the long-term dynamics of cortical activity patterns during the formation of multimodal memories by analysing in vivo high-resolution 2-photon mouse brain imaging of Immediate Early Gene expression, resolved by cortical layers. Strikingly, in layers II/III, the patterns showed similar dynamics across functional distinct cortical areas and the consistency of dynamic patterns lasts for one to several days. In contrast, in layer Vb, the activity dynamics varied across functional distinct areas, and the present activities are sensitive to the previous activities at different time depending on the cortical locations, indicating that the information stored in the cortex at different time points is distributed across different cortical areas. These results suggest different roles of layer II/III and layer Vb neurons in the long-term multimodal perception of the environment.

neuroscience↗

bioRxiv2018

Discrimination of the hierarchical structure of cortical layers in 2-photon microscopy data by combined unsupervised and supervised machine learning

The laminar organization of the cerebral cortex is a fundamental characteristic of the brain, with essential implications for cortical function. Due to the rapidly growing amount of high-resolution brain imaging data, a great demand arises for automated and flexible methods for discriminating the laminar texture of the cortex. Here, we propose a combined approach of unsupervised and supervised machine learning to discriminate the hierarchical cortical laminar organization in high-resolution 2-photon microscopic neural image data without observer bias, that is, without the prerequisite of manually labeled training data. For local cortical foci, we modify an unsupervised clustering approach to identify and represent the laminar cortical structure. Subsequently, supervised machine learning is applied to transfer the resulting layer labels across different locations and image data, to ensure the existence of a consistent layer label system. By using neurobiologically meaningful features, the discrimination results are shown to be consistent with the layer classification of the classical Brodmann scheme, and provide additional insight into the structure of the cerebral cortex and its hierarchical organization. Thus, our work paves a new way for studying the anatomical organization of the cerebral cortex, and potentially its functional organization.

neuroscience↗