Plasticity of the injured human spinal cord: insights revealed by spinal cord functional MRI.

INTRODUCTION: While numerous studies have documented evidence for plasticity of the human brain there is little evidence that the human spinal cord can change after injury. Here, we employ a novel spinal fMRI design where we stimulate normal and abnormal sensory dermatomes in persons with traumatic spinal cord injury and perform a connectivity analysis to understand how spinal networks process information. METHODS: Spinal fMRI data was collected at 3 Tesla at two institutions from 38 individuals using the standard SEEP functional MR imaging techniques. Thermal stimulation was applied to four dermatomes in an interleaved timing pattern during each fMRI acquisition. SCI patients were stimulated in dermatomes both above (normal sensation) and below the level of their injury. Sub-group analysis was performed on healthy controls (n = 20), complete SCI (n = 3), incomplete SCI (n = 9) and SCI patients who recovered full function (n = 6). RESULTS: Patients with chronic incomplete SCI, when stimulated in a dermatome of normal sensation, showed an increased number of active voxels relative to controls (p = 0.025). There was an inverse relationship between the degree of sensory impairment and the number of active voxels in the region of the spinal cord corresponding to that dermatome of abnormal sensation (R(2) = 0.93, p<0.001). Lastly, a connectivity analysis demonstrated a significantly increased number of intraspinal connections in incomplete SCI patients relative to controls suggesting altered processing of afferent sensory signals. CONCLUSIONS: In this work we demonstrate the use of spinal fMRI to investigate changes in spinal processing of somatosensory information in the human spinal cord. We provide evidence for plasticity of the human spinal cord after traumatic injury based on an increase in the average number of active voxels in dermatomes of normal sensation in chronic SCI patients and an increased number of intraspinal connections in incomplete SCI patients relative to healthy controls.

Proton magnetic resonance spectroscopic imaging can predict length of survival in patients with supratentorial gliomas.

OBJECTIVE: We compared the ability of proton magnetic resonance spectroscopic imaging ((1)H-MRSI) measures with that of standard clinicopathological measures to predict length of survival in patients with supratentorial gliomas. METHODS: We developed two sets of leave-one-out logistic regression models based on either 1) intratumoral (1)H-MRSI features, including maximum values of a) choline and b) lactate-lipid, c) number of (1)H-MRSI voxels with low N-acetyl group values, and d) number of (1)H-MRSI voxels with high lactate-lipid values, all (a-d) of which were normalized to creatine in normal-appearing brain, or 2) standard clinicopathological features, including a) tumor histopathological grade, b) patient age, c) performance of surgical debulking, and d) tumor diagnosis (i.e., oligodendroglioma, astrocytoma). We assessed the accuracy of these two models in predicting patient survival for 6, 12, 24, and 48 months by performing receiver operating characteristic curve analysis. Cox proportional hazards analysis was performed to assess the extent to which patient survival could be explained by the above predictors. We then performed a series of leave-one-out linear multiple regression analyses to determine how well patient survival could be predicted in a continuous fashion. RESULTS: The results of using the models based on (1)H-MRSI and clinicopathological features were equally good, accounting for 81 and 64% of the variability (r(2)) in patients' actual survival durations. All features except number of (1)H-MRSI voxels with lactate-lipid/creatine values of at least 1 were significant predictors of survival in the (1)H-MRSI model. Two features (tumor grade and debulking) were found to be significant predictors in the clinicopathological model. Survival as a continuous variable was predicted accurately on the basis of the (1)H-MRSI data (r = 0.77, P < 0.001; median prediction error, 1.7 mo). CONCLUSION: Our results suggest that appropriate analysis of (1)H-MRSI data can predict survival in patients with supratentorial gliomas at least as accurately as data derived from more invasive clinicopathological features.