Progressive neurodegeneration following spinal cord injury

Objective To quantify atrophy, demyelination, and iron accumulation over 2 years following acute spinal cord injury and to identify MRI predictors of clinical outcomes and determine their suitability as surrogate markers of therapeutic intervention. Methods We assessed 156 quantitative MRI datasets from 15 patients with spinal cord injury and 18 controls at baseline and 2, 6, 12, and 24 months after injury. Clinical recovery (including neuropathic pain) was assessed at each time point. Between-group differences in linear and nonlinear trajectories of volume, myelin, and iron change were estimated. Structural changes by 6 months were used to predict clinical outcomes at 2 years. Results The majority of patients showed clinical improvement with recovery stabilizing at 2 years. Cord atrophy decelerated, while cortical white and gray matter atrophy progressed over 2 years. Myelin content in the spinal cord and cortex decreased progressively over time, while cerebellar loss decreases decelerated. As atrophy progressed in the thalamus, sustained iron accumulation was evident. Smaller cord and cranial corticospinal tract atrophy, and myelin changes within the sensorimotor cortices, by 6 months predicted recovery in lower extremity motor score at 2 years. Whereas greater cord atrophy and microstructural changes in the cerebellum, anterior cingulate cortex, and secondary sensory cortex by 6 months predicted worse sensory impairment and greater neuropathic pain intensity at 2 years. Conclusion These results draw attention to trauma-induced neuroplastic processes and highlight the intimate relationships among neurodegenerative processes in the cord and brain. These measurable changes are sufficiently large, systematic, and predictive to render them viable outcome measures for clinical trials.

[1]  D. Cadotte,et al.  Will imaging biomarkers transform spinal cord injury trials? , 2013, The Lancet Neurology.

[2]  A. Geurts,et al.  A clinical prediction rule for ambulation outcomes after traumatic spinal cord injury: a longitudinal cohort study , 2011, The Lancet.

[3]  A. Curt,et al.  Association of pain and CNS structural changes after spinal cord injury , 2016, Scientific Reports.

[4]  M. Fehlings,et al.  Secondary injury mechanisms of spinal cord trauma: a novel therapeutic approach for the management of secondary pathophysiology with the sodium channel blocker riluzole. , 2002, Progress in brain research.

[5]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[6]  Julien Cohen-Adad,et al.  Spinal cord grey matter segmentation challenge , 2017, NeuroImage.

[7]  F. Biering-Sørensen,et al.  Independent spinal cord atrophy measures correlate to motor and sensory deficits in individuals with spinal cord injury , 2011, Spinal Cord.

[8]  Karl J. Friston,et al.  MRI investigation of the sensorimotor cortex and the corticospinal tract after acute spinal cord injury: a prospective longitudinal study , 2013, The Lancet Neurology.

[9]  Simon Hametner,et al.  Iron and neurodegeneration in the multiple sclerosis brain , 2013, Annals of neurology.

[10]  Karl J. Friston,et al.  Embodied neurology: an integrative framework for neurological disorders , 2016, Brain : a journal of neurology.

[11]  J. Connor,et al.  Iron, brain ageing and neurodegenerative disorders , 2004, Nature Reviews Neuroscience.

[12]  David H. Miller,et al.  Imaging outcomes for neuroprotection and repair in multiple sclerosis trials , 2009, Nature Reviews Neurology.

[13]  Bingbing Song,et al.  Recovery of supraspinal control of stepping via indirect propriospinal relay connections after spinal cord injury , 2008, Nature Medicine.

[14]  D. McTigue,et al.  Systemic iron chelation results in limited functional and histological recovery after traumatic spinal cord injury in rats , 2013, Experimental Neurology.

[15]  Gerard R. Ridgway,et al.  Symmetric Diffeomorphic Modeling of Longitudinal Structural MRI , 2013, Front. Neurosci..

[16]  Thomas H. B. FitzGerald,et al.  Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging , 2014, Neurobiology of Aging.

[17]  Serge Rossignol,et al.  Spinal Cord Injury: Time to Move? , 2007, The Journal of Neuroscience.

[18]  Allan R. Martin,et al.  Translating state-of-the-art spinal cord MRI techniques to clinical use: A systematic review of clinical studies utilizing DTI, MT, MWF, MRS, and fMRI , 2015, NeuroImage: Clinical.

[19]  Mert R. Sabuncu,et al.  Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models , 2013, NeuroImage.

[20]  Mary Jane Mulcahey,et al.  Reference for the 2011 revision of the international standards for neurological classification of spinal cord injury , 2011, The journal of spinal cord medicine.

[21]  Anil Chandra,et al.  Retrograde Wallerian degeneration of cranial corticospinal tracts in cervical spinal cord injury patients using diffusion tensor imaging , 2008, Journal of neuroscience research.

[22]  S. Ropele,et al.  Quantitative MR imaging of brain iron: a postmortem validation study. , 2010, Radiology.

[23]  G. Slobogean,et al.  Cerebrospinal fluid inflammatory cytokines and biomarkers of injury severity in acute human spinal cord injury. , 2010, Journal of neurotrauma.

[24]  J. Cohen-Adad,et al.  Demyelination and degeneration in the injured human spinal cord detected with diffusion and magnetization transfer MRI , 2011, NeuroImage.

[25]  M. Schwab,et al.  Heterogeneous spine loss in layer 5 cortical neurons after spinal cord injury. , 2012, Cerebral cortex.

[26]  Simon B. Eickhoff,et al.  A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data , 2005, NeuroImage.

[27]  Karl J. Friston,et al.  Computational neuroimaging strategies for single patient predictions , 2017, NeuroImage.

[28]  Marc Bolliger,et al.  Relationship between structural brainstem and brain plasticity and lower-limb training in spinal cord injury: a longitudinal pilot study , 2015, Front. Hum. Neurosci..

[29]  B. Todorich,et al.  Oligodendrocytes and myelination: The role of iron , 2009, Glia.

[30]  Sébastien Ourselin,et al.  Estimating anatomical trajectories with Bayesian mixed-effects modeling , 2015 .

[31]  Martin E Schwab,et al.  The injured spinal cord spontaneously forms a new intraspinal circuit in adult rats , 2004, Nature Neuroscience.

[32]  Jörn Diedrichsen,et al.  A spatially unbiased atlas template of the human cerebellum , 2006, NeuroImage.

[33]  J. Fawcett,et al.  Guidelines for the conduct of clinical trials for spinal cord injury as developed by the ICCP panel: spontaneous recovery after spinal cord injury and statistical power needed for therapeutic clinical trials , 2007, Spinal Cord.

[34]  J. Ashburner,et al.  Tracking sensory system atrophy and outcome prediction in spinal cord injury , 2015, Annals of neurology.

[35]  S. Bauer,et al.  Alteration of Forebrain Neurogenesis after Cervical Spinal Cord Injury in the Adult Rat , 2012, Front. Neurosci..

[36]  J. Noth,et al.  Sequential loss of myelin proteins during Wallerian degeneration in the human spinal cord. , 2004, Brain : a journal of neurology.

[37]  Thomas E. Nichols,et al.  Simple group fMRI modeling and inference , 2009, NeuroImage.

[38]  B. Zörner,et al.  Anti‐Nogo on the go: from animal models to a clinical trial , 2010, Annals of the New York Academy of Sciences.

[39]  J. Fawcett,et al.  Guidelines for the conduct of clinical trials for spinal cord injury (SCI) as developed by the ICCP panel: clinical trial outcome measures , 2007, Spinal Cord.

[40]  David H. Miller,et al.  Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain , 2004, Annals of neurology.

[41]  Nikolaus Weiskopf,et al.  Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation , 2013, Front. Neurosci..