Simultaneous voxel-wise analysis of brain and spinal cord morphometry and microstructure within the SPM framework.

To validate a simultaneous analysis tool for the brain and cervical cord embedded in the statistical parametric mapping (SPM) framework, we compared trauma-induced macro- and microstructural changes in spinal cord injury (SCI) patients to controls. The findings were compared with results obtained from existing processing tools that assess the brain and spinal cord separately. A probabilistic brain-spinal cord template (BSC) was generated using a generative semi-supervised modelling approach. The template was incorporated into the pre-processing pipeline of voxel-based morphometry and voxel-based quantification analyses in SPM. This approach was validated on T1-weighted scans and multiparameter maps, by assessing trauma-induced changes in SCI patients relative to controls and comparing the findings with the outcome from existing analytical tools. Consistency of the MRI measures was assessed using intraclass correlation coefficients (ICC). The SPM approach using the BSC template revealed trauma-induced changes across the sensorimotor system in the cord and brain in SCI patients. These changes were confirmed with established approaches covering brain or cord, separately. The ICC in the brain was high within regions of interest, such as the sensorimotor cortices, corticospinal tracts and thalamus. The simultaneous voxel-wise analysis of brain and cervical spinal cord was performed in a unique SPM-based framework incorporating pre-processing and statistical analysis in the same environment. Validation based on a SCI cohort demonstrated that the new processing approach based on the brain and cord is comparable to available processing tools, while offering the advantage of performing the analysis simultaneously across the neuraxis.

Microstructural plasticity in nociceptive pathways after spinal cord injury.

OBJECTIVE: To track the interplay between (micro-) structural changes along the trajectories of nociceptive pathways and its relation to the presence and intensity of neuropathic pain (NP) after spinal cord injury (SCI). METHODS: A quantitative neuroimaging approach employing a multiparametric mapping protocol was used, providing indirect measures of myelination (via contrasts such as magnetisation transfer (MT) saturation, longitudinal relaxation (R1)) and iron content (via effective transverse relaxation rate (R2*)) was used to track microstructural changes within nociceptive pathways. In order to characterise concurrent changes along the entire neuroaxis, a combined brain and spinal cord template embedded in the statistical parametric mapping framework was used. Multivariate source-based morphometry was performed to identify naturally grouped patterns of structural variation between individuals with and without NP after SCI. RESULTS: In individuals with NP, lower R1 and MT values are evident in the primary motor cortex and dorsolateral prefrontal cortex, while increases in R2* are evident in the cervical cord, periaqueductal grey (PAG), thalamus and anterior cingulate cortex when compared with pain-free individuals. Lower R1 values in the PAG and greater R2* values in the cervical cord are associated with NP intensity. CONCLUSIONS: The degree of microstructural changes across ascending and descending nociceptive pathways is critically implicated in the maintenance of NP. Tracking maladaptive plasticity unravels the intimate relationships between neurodegenerative and compensatory processes in NP states and may facilitate patient monitoring during therapeutic trials related to pain and neuroregeneration.

Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction.

In this paper we present a hierarchical generative model of medical image data, which can capture simultaneously the variability of both signal intensity and anatomical shapes across large populations. Such a model has a direct application for learning average-shaped probabilistic tissue templates in a fully automated manner. While in principle the generality of the proposed Bayesian approach makes it suitable to address a wide range of medical image computing problems, our work focuses primarily on neuroimaging applications. In particular we validate the proposed method on both real and synthetic brain MR scans including the cervical cord and demonstrate that it yields accurate alignment of brain and spinal cord structures, as compared to state-of-the-art tools for medical image registration. At the same time we illustrate how the resulting tissue probability maps can readily be used to segment, bias correct and spatially normalise unseen data, which are all crucial pre-processing steps for MR imaging studies.

Spinal cord grey matter segmentation challenge.

An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication.

Relationship between brainstem neurodegeneration and clinical impairment in traumatic spinal cord injury.

BACKGROUND: Brainstem networks are pivotal in sensory and motor function and in recovery following experimental spinal cord injury (SCI). OBJECTIVE: To quantify neurodegeneration and its relation to clinical impairment in major brainstem pathways and nuclei in traumatic SCI. METHODS: Quantitative MRI data of 30 chronic traumatic SCI patients (15 with tetraplegia and 15 with paraplegia) and 23 controls were acquired. Patients underwent a full neurological examination. We calculated quantitative myelin-sensitive (magnetisation transfer saturation (MT) and longitudinal relaxation rate (R1)) and iron-sensitive (effective transverse relaxation rate (R2*)) maps. We constructed brainstem tissue templates using a multivariate Gaussian mixture model and assessed volume loss, myelin reductions, and iron accumulation across the brainstem pathways (e.g. corticospinal tracts (CSTs) and medial lemniscus), and nuclei (e.g. red nucleus and periaqueductal grey (PAG)). The relationship between structural changes and clinical impairment were assessed using regression analysis. RESULTS: Volume loss was detected in the CSTs and in the medial lemniscus. Myelin-sensitive MT and R1 were reduced in the PAG, the CSTs, the dorsal medulla and pons. No iron-sensitive changes in R2* were detected. Lower pinprick score related to more myelin reductions in the PAG, whereas lower functional independence was related to more myelin reductions in the vestibular and pontine nuclei. CONCLUSION: Neurodegeneration, indicated by volume loss and myelin reductions, is evident in major brainstem pathways and nuclei following traumatic SCI; the magnitude of these changes relating to clinical impairment. Thus, quantitative MRI protocols offer new targets, which may be used as neuroimaging biomarkers in treatment trials.