This page lists the publicly available datasets from the Open Data Commons for Spinal Cord Injury. Additional data is available to researchers as part of the ODC-SCI Data Commons. To become a part of the Commons please register your lab.
Cervical (C5), unilateral spinal cord injury with diverse injury modalities, multiple behavioral outcomes, and histopathology
Ferguson, A.R., Irvine,K.-A., Gensel, J.C., Nielson, J.L., Lin, A., Ly, J., Segal, M.R., Ratan, R.R., Bresnahan, J.C., Beattie, M.S. (2018) Cervical (C5), unilateral spinal cord injury with diverse injury modalities, multiple behavioral outcomes, and histopathology. Open Data Commons for Spinal Cord Injury. ODC-SCI:26 http://doi.org/10.7295/W9T72FMZ
STUDY PURPOSE: This project was a multivariate validation study of unilateral cervical contusion (hemi-contusion) model-development efforts.
DATA COLLECTED: The dataset includes N=159 rats with hemisections (n=9), NYU MASCIS weight drop contusions: sham (n=10), 6.25 mm (n=10), 12.5 mm (n=32); Infinite Horizon Impactor: sham (n=6), 75 kdyn (n=58), 100 kdyn (n=34). Behavioral recovery was monitored over 6 weeks using mutiple assements taken from the same subjects: Grooming Score, Paw Placement in a Cylinder, BBB locomotor subscore, Forelimb Open Field Score, and numerous Catwalk footprint analysis metrics. Histopathology metrics included lesion epicenter gray matter sparing, white matter sparing, total tissue sparing, and motorneuron numbers.
PRIMARY CONCLUSION: Principal component analysis (PCA)-based multidimensional pattern detectors can resolve the ‘syndrome space’ across multiple models of SCI, allowing direct comparison of subjects with diverse injury types, outcome sets and therapeutics.
T10 lateral hemisection spinal cord injury with multiple histological and behavioral outcomes
Yuanyuan Liu, Xuhua Wang, Wenlei Li, Qian Zhang, Yi Li, Zicong Zhang, Junjie Zhu, Bo Chen, Philip R. Williams, Yiming Zhang, Bin Yu, Xiaosong Gu, Zhigang He (2018). T10 lateral hemisection spinal cord injury with multiple histological and behavioral outcomes. Open Data Commons for Spinal Cord Injury. ODC-SCI:NN http://doi.org/10.7295/W9HQ3X20
STUDY PURPOSE: A major hurdle for functional recovery after spinal cord injury is the limited re- growth of the axons in the corticospinal tract (CST) that originate in the motor cortex and innervate the spinal cord. In this study, we tested whether post-lesional AAV-assisted co- expression of two soluble proteins, namely insulin-like growth factor 1 (IGF1) and osteopontin (OPN), in cortical neurons leads to CST regrowth and CST-dependent functional recovery.
DATA COLLECTED: With an incomplete spinal cord injury model (T10 lateral hemisection), we tested the sensitizing effect of OPN/IGF1 on corticospinal neurons, the extent of axon regrowth both in the injured side (axon regeneration) and the intact side (collateral axon sprouting). In addition, we assessed behavioral performance for both gross and skilled locomotion in mice treated with OPN/IGF1 post T10 lateral hemisection.
PRIMARY CONCLUSION: Our results demonstrate a potentially translatable strategy for restoring cortical dependent function after injury in the adult.
Spinal cord injury (SCI) produces a complex syndrome characterized by loss of motor control and mobility; loss of bladder, bowel and sexual function; pathological pain; and loss of autonomy. The multifaceted nature of SCI presents a challenge for the SCI translational therapeutic pipeline. This problem of SCI complexity can be conceptualized as a big-data issue: the field needs a forum for large-scale data-indexing, data-sharing, and application of advanced analytics to catalyze SCI discoveries. To address this challenge our team has begun assembling a large multicenter, multispecies database of SCI research data spanning across a wide spectrum of SCI severities, treatments and outcomes. To fully harness the potential of SCI big-data we are building a digital infrastructure to democratize SCI data science, allowing outside researchers to access existing SCI big-data, contribute their own data, and enable access to user-friendly tools for big-data analytics for knowledge discovery within SCI research.