Prediction Algorithm of the Cat Spinal Segments Lengths and Positions in Relation to the Vertebrae

• Published in: Anatomical record (Hoboken, N.J. : 2007) N.J. : 2007), 2019-09, Vol.302 (9), p.1628-1637
• Main Authors: Shkorbatova, Polina.Y., Lyakhovetskii, Vsevolod A., Merkulyeva, Natalia S., Veshchitskii, Alexandr A., Bazhenova, Elena Y., Laurens, Jean, Pavlova, Natalia V., Musienko, Pavel E.
• Format: Article
• Language: English
• Subjects: Algorithms; cat; dorsal roots; Neurosurgery; prediction algorithm; segments; Sensorimotor system; Spinal cord; Spine; Vertebrae
• ISSN: 1932-8486; 1932-8494
• DOI: 10.1002/ar.24054

ABSTRACT Detailed knowledge of the topographic organization and precise access to the spinal cord segments is crucial for the neurosurgical manipulations as well as in vivo neurophysiological investigations of the spinal networks involved in sensorimotor and visceral functions. Because of high individual variability, accurate identification of particular portion of the lumbosacral enlargement is normally possible only during postmortem dissection. Yet, it is often necessary to determine the precise location of spinal segments prior to in vivo investigation, targeting spinal cord manipulations, neurointerface implantations, and neuronal activity recordings. To solve this problem, we have developed an algorithm to predict spinal segments locations based on their relation to vertebral reference points. The lengths and relative positions of the spinal cord segments (T13‐S3) and the vertebrae (VT13‐VL7) were measured in 17 adult cats. On the basis of these measurements, we elaborated the estimation procedure: the cubic regression of the ratio of the segment's length to the lengths of the VL2 vertebra was used for the determination of segment's length; and the quadratic regression of the ratio of their positions in relation to the VL2 rostral part was used to determine the position of the segments. The coefficients of these regressions were calculated at the training sample (nine cats) and were then confirmed at the testing sample (eight cats). Although the quality of the prediction is decreased in the caudal direction, we found high correlations between the regressions and real data. The proposed algorithm can be further translated to other species including human. Anat Rec, 302:1628–1637, 2019. © 2018 American Association for Anatomy