Intermediate Gray Matter Interneurons in the Lumbar Spinal Cord Play a Critical and Necessary Role in Coordinated Locomotion

Abstract

AbstractLocomotion is a complex task involving excitatory and inhibitory circuitry in spinal gray matter. While genetic knockouts examine the function of unique spinal interneuron (SpIN) subtypes, the phenotype of combined premotor interneuron loss remains to be explored. We modified a kainic acid lesion to damage intermediate gray matter (laminae V-VII) in the lumbar spinal enlargement (spinal L2-L4) in female rats. A thorough, tailored behavioral evaluation revealed deficits in gross hindlimb function, skilled walking, coordination, balance and gait two-weeks post-injury. Using a Random Forest algorithm, we combined these behavioral assessments into a highly predictive binary classification system which strongly correlated with structural deficits in the rostro-caudal axis. Machine-learning quantification confirmed interneuronal damage to laminae V-VII in spinal L2-L4 correlates with hindlimb dysfunction. White matter damage and lower motoneuron loss did not correlate with behavioral deficits. Animals do not regain lost sensorimotor function three months after injury, indicating that natural recovery of the spinal cord cannot compensate for loss of laminae V-VII neurons. As spinal cord injuries are often located at spinal enlargements, this research lays the groundwork for new neuroregenerative therapies to replace these lost neuronal pools vital to sensorimotor function.HighlightsFunctional deficits in coordination, balance, rhythmic walking and gait follow two weeks after a lumbar (L2-L4) intermediate (V-VII) gray matter spinal cord injury in ratsDeficits correlate with neuronal loss in laminae V-VII in spinal levels L2-L4 but do not correlate with lower motoneuron loss or white matter damage nor do animals show signs of sensory dysfunction due to spinal cord injuryCoordination deficits remain after three months, indicating that natural recovery cannot compensate for this interneuronal lossNewly developed machine-learning models non-invasively fully classify injured animals by functional readouts equivalent to time-intensive endpoint histological analysis