期刊
JOURNAL OF MOLECULAR NEUROSCIENCE
卷 72, 期 4, 页码 888-899出版社
SPRINGERNATURE
DOI: 10.1007/s12031-021-01958-9
关键词
Spinal cord injury; Diabetes; Motoneuron; Spike activity; Depression; Plasticity
资金
- State Committee of Science RA [19YR-1F010]
This study assessed the short-term plasticity of lumbar spinal cord motoneurons in conditions of spinal cord injury and diabetes. The findings suggest that under traumatic and/or metabolic pathology, synaptic inputs exhibit opposing plasticity for homeostatic control of neurotransmission. These results are important for understanding the impact of spinal cord injury and diabetes on motor impairment.
Spinal cord injury (SCI) causes motor impairment and the proper excitation/inhibition balance in motoneurons is important for recovery. Diabetes mellitus impairs regenerative capacity following SCI. The purpose of this study was to assess the short-term plasticity (STP) of lumbar spinal cord motoneurons in conditions of (1) lateral hemisection (SCI), (2) fructose-induced diabetes (D), and (3) diabetes associated with hemisection (D + SCI). We show that in the cases of SCI, D, and D + SCI, the ratio of percentage share of excitatory and inhibitory combinations of motoneurons responses to high-frequence stimulation of sciatic nerve is multidirectional. In the SCI and D + SCI groups, the cumulative changes in generalized baseline frequencies decreased significantly. When we compared the cumulative changes in the intensity of excitatory and inhibitory responses relative to baseline during high-frequency stimulation (tetanization epoch), we found that there was a significant intensification in tetanic depression in the D + SCI groups versus SCI, as well as an intensification in tetanic potentiation in the D + SCI vs. D and D + SCI vs. SCI groups. Thus, in conditions of traumatic and/or metabolic pathology, the distinct synaptic inputs exhibit opposing plasticity for homeostatic control of neurotransmission and these integral changes most likely shape postsynaptic STP in the spinal motor network.
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