RT Journal Article
SR Electronic
T1 Inferring Thalamocortical Monosynaptic Connectivity In-Vivo
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.10.09.333930
DO 10.1101/2020.10.09.333930
A1 Yi Juin Liew
A1 Aurélie Pala
A1 Clarissa J Whitmire
A1 William A Stoy
A1 Craig R Forest
A1 Garrett B Stanley
YR 2021
UL http://biorxiv.org/content/early/2021/04/10/2020.10.09.333930.abstract
AB As the tools to simultaneously record electrophysiological signals from large numbers of neurons within and across brain regions become increasingly available, this opens up for the first time the possibility of establishing the details of causal relationships between monosynaptically connected neurons and the patterns of neural activation that underlie perception and behavior. Although recorded activity across synaptically connected neurons has served as the cornerstone for much of what we know about synaptic transmission and plasticity, this has largely been relegated to ex-vivo preparations that enable precise targeting under relatively well-controlled conditions. Analogous studies in-vivo, where image-guided targeting is often not yet possible, rely on indirect, data-driven measures, and as a result such studies have been sparse and the dependence upon important experimental parameters has not been well studied. Here, using in-vivo extracellular single unit recordings in the topographically aligned rodent thalamocortical pathway, we sought to establish a general experimental and computational framework for inferring synaptic connectivity. Specifically, attacking this problem within a statistical signal-detection framework utilizing experimentally recorded data in the ventral-posterior medial (VPm) region of the thalamus and the homologous region in layer 4 of primary somatosensory cortex (S1) revealed a trade-off between network activity levels needed for the data-driven inference and synchronization of nearby neurons within the population that result in masking of synaptic relationships. Taken together, we provide a framework for establishing connectivity in multi-site, multi-electrode recordings based on statistical inference, setting the stage for large-scale assessment of synaptic connectivity within and across brain structures.New & Noteworthy Despite the fact that all brain function relies on the long-range transfer of information across different regions, the tools enabling us to measure connectivity across brain structures are lacking. Here, we provide a statistical framework for identifying and assessing potential monosynaptic connectivity across neuronal circuits from population spiking activity that generalizes to large-scale recording technologies that will help us to better understand the signaling within networks that underlies perception and behavior.Competing Interest StatementThe authors have declared no competing interest.