In vitro characterization of intrinsic properties and local synaptic inputs to pyramidal neurons in macaque primary motor cortex.
Ontology highlight
ABSTRACT: Primates (including humans) have a highly developed corticospinal tract, and specialized motor cortical areas which differ in key ways from rodents. Much work on motor cortex has therefore used macaque monkeys as a good animal model for human motor control. However, there is a paucity of data describing the fundamental functional architecture of primate primary motor cortex, which is best addressed with in vitro approaches. In this study we examined the cellular properties and the micro-circuitry of the adult macaque primary motor cortex by carrying out in-vitro intracellular recordings. We aimed to characterize the basic properties of the cortical circuitry by studying the intrinsic properties of its pyramidal neurons and their physiological interconnectivity. We studied the passive and active electrophysiological properties of pyramidal neurons in both superficial and deep cortical layers. Both superficial and deep pyramidal neurons exhibited bursting behaviour that could act as powerful excitation for downstream targets. Synaptic connections were lamina specific. Neurons in the deep layers had convergent excitatory inputs from all cortical layers whereas superficial neurons had only significant inputs from superficial layers. This sheds light on the functional architecture of the primate primary motor cortex and how its output is shaped. We also took the unique opportunity in our recording technique to characterize the relationship between intracellular and extracellular spike waveforms, with implications for cell-type identification in studies in awake behaving monkey. Our results will aid the interpretation of primate studies into motor control involving extracellular spike recordings and electrical stimulation in primary motor cortex.
SUBMITTER: Xu W
PROVIDER: S-EPMC6175011 | biostudies-literature | 2018 Aug
REPOSITORIES: biostudies-literature
ACCESS DATA