A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse

Age- and sex-dependent alterations in primary somatosensory cortex neuronal calcium network dynamics during locomotion

Over the past 30 years, the calcium (Ca2+ ) hypothesis of brain aging has provided clear evidence that hippocampal neuronal Ca2+ dysregulation is a key biomarker of aging. Age-dependent Ca2+ -mediated changes in intrinsic excitability, synaptic plasticity, and activity have helped identify some of the mechanisms engaged in memory and cognitive decline based on work done mostly at the single-cell level and in the slice preparation. Recently, our lab identified age- and Ca2+ -related neuronal network dysregulation in the cortex of the anesthetized animal. Still, investigations in the awake animal are needed to test the generalizability of the Ca2+ hypothesis of brain aging. Here, we used in vigilo two-photon imaging in ambulating mice, to image GCaMP8f in the primary somatosensory cortex (S1), during ambulation and at rest. We investigated aging- and sex-related changes in neuronal networks in the C56BL/6J mouse. Following imaging, gait behavior was characterized to test for changes in locomotor stability. During ambulation, in both young adult and aged mice, an increase in network connectivity and synchronicity was noted. An age-dependent increase in synchronicity was seen in ambulating aged males only. Additionally, females displayed increases in the number of active neurons, Ca2+ transients, and neuronal activity compared to males, particularly during ambulation. These results suggest S1 Ca2+ dynamics and network synchronicity are likely contributors of locomotor stability. We believe this work raises awareness of age- and sex-dependent alterations in S1 neuronal networks, perhaps underlying the increase in falls with age.

V-NeuroStack: Open-source 3D time stack software for identifying patterns in neuronal data

Understanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large-scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualization challenges. We developed V-NeuroStack, a novel network visualization tool to visualize data obtained using calcium imaging of spontaneous activity of neurons in a mouse brain slice as well as in vivo using two-photon imaging. V-NeuroStack creates 3D time stacks by stacking 2D time frames for a time-series dataset. It provides a web interface to explore and analyze data using both 3D and 2D visualization techniques. Previous attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V-NeuroStack's 3D view is used to explore patterns in dynamic large-scale correlations between neurons over time. The 2D view is used to examine any timestep of interest in greater detail. Furthermore, a dual-line graph provides the ability to explore the raw and first-derivative values of activity from an individual or a functional cluster of neurons. V-NeuroStack can scale to datasets with at least a few thousand temporal snapshots. It can potentially support future advancements in in vitro and in vivo data capturing techniques to bring forth novel hypotheses by allowing unambiguous visualization of massive patterns in neuronal activity data.