Towards prognostic functional brain biomarkers for cervical myelopathy: A resting-state fMRI study

Abstract: Recently, there has been increasing interest in strategies to predict neurological recovery in cervical myelopathy (CM) based on clinical images of the cervical spine. In this study, we aimed to explore potential preoperative brain biomarkers that can predict postoperative neurological recovery in CM patients by using resting-state functional magnetic resonance imaging (rs-fMRI) and functional connectivity (FC) analysis. Twenty-eight patients with CM and 28 age- and sex-matched healthy controls (HCs) underwent rs-fMRI (twice for CM patients, before and six months after surgery). A seed-to-voxel analysis was performed, and the following three statistical analyses were conducted: (i) FC comparisons between preoperative CM and HC; (ii) correlation analysis between preoperative FCs and clinical scores; and (iii) postoperative FC changes in CM. Our analyses identified three FCs between the visual cortex and the right superior frontal gyrus based on the conjunction of the first two analyses [(i) and (ii)]. These FCs may act as potential biomarkers for postoperative gain in the 10-second test and might be sufficient to provide a prediction formula for potential recovery. Our findings provide preliminary evidence supporting the possibility of novel predictive measures for neurological recovery in CM using rs-fMRI

Tracking the neurodegenerative gradient after spinal cord injury

Objective To quantify neurodegenerative changes along the cervical spinal cord rostral to a spinal cord injury (SCI) by means of quantitative MRI (qMRI) and to determine its relationship with clinical impairment. Methods Thirty chronic SCI patients (15 tetraplegics and 15 paraplegics) and 23 healthy controls underwent a high-resolution T1-weighted and myelin-sensitive magnetization transfer (MT) MRI. We assessed macro- and microstructural changes along the cervical cord from levels C1 to C4, calculating cross-sectional spinal cord area, its anterior-posterior and left-right width and myelin content (i.e. MT). Regression analysis determined associations between qMRI parameters and clinical impairment. Results In SCI patients, cord area decreased by 2.67 mm2 (p=0.004) and left-right width decreased by 0.35 mm (p=0.002) per level in caudal direction when compared to the healthy controls. This gradient of neurodegeneration was greater in tetraplegic than paraplegics in the cord area (by 3.28 mm2, p=0.011), left-right width (by 0.36 mm, p=0.03), and MT (by 0.13%, p=0.04), but independant of lesion severity (p>0.05). Higher lesion level was associated with greater magnitudes of neurodegeneration. Greater loss in myelin content in the dorsal columns and spinothalamic tract was associated with worse light touch (p=0.016) and pin prick score (p=0.024), respectively. Conclusions A gradient of neurodegeneration is evident in the high cervical cord remote from a SCI. Tract-specific associations with appropriate clinical outcomes highlight that remote neurodegenerative changes are clinically eloquent. Monitoring the neurodegenerative gradient could be used to track treatment effects of regenerative and neuroprotective agents, both in trials targeting cervical and thoracic SCI patients

Brain structural and functional alterations in MOG antibody disease.

BACKGROUND: The impact of myelin oligodendrocyte glycoprotein antibody disease (MOGAD) on brain structure and function is unknown. OBJECTIVES: The aim of this study was to study the multimodal brain MRI alterations in MOGAD and to investigate their clinical significance. METHODS: A total of 17 MOGAD, 20 aquaporin-4 antibody seropositive neuromyelitis optica spectrum disorders (AQP4 + NMOSD), and 28 healthy controls (HC) were prospectively recruited. Voxel-wise gray matter (GM) volume, fractional anisotropy (FA), mean diffusivity (MD), and degree centrality (DC) were compared between groups. Clinical associations and differential diagnosis were determined using partial correlation and stepwise logistic regression. RESULTS: In comparison with HC, MOGAD had GM atrophy in frontal and temporal lobe, insula, thalamus, and hippocampus, and WM fiber disruption in optic radiation and anterior/posterior corona radiata; DC decreased in cerebellum and increased in temporal lobe. Compared to AQP4 + NMOSD, MOGAD presented lower GM volume in postcentral gyrus and decreased DC in cerebellum. Hippocampus/parahippocampus atrophy associated with Expanded Disability Status Scale (R = -0.55, p = 0.04) and California Verbal Learning Test (R = 0.62, p = 0.031). The differentiation of MOGAD from AQP4 + NMOSD achieved an accuracy of 95% using FA in splenium of corpus callosum and DC in occipital gyrus. CONCLUSION: Distinct structural and functional alterations were identified in MOGAD. Hippocampus/parahippocampus atrophy associated with clinical disability and cognitive impairment

Machine learning classifies predictive kinematic features in a mouse model of neurodegeneration

Motor deficits are observed in Alzheimer’s disease (AD) prior to the appearance of cognitive symptoms. To investigate the role of amyloid proteins in gait disturbances, we characterized locomotion in APP-overexpressing transgenic J20 mice. We used three-dimensional motion capture to characterize quadrupedal locomotion on a treadmill in J20 and wild-type mice. Sixteen J20 mice and fifteen wild-type mice were studied at two ages (4- and 13-month). A random forest (RF) classification algorithm discriminated between the genotypes within each age group using a leave-one-out cross-validation. The balanced accuracy of the RF classification was 92.3 ± 5.2% and 93.3 ± 4.5% as well as False Negative Rate (FNR) of 0.0 ± 0.0% and 0.0 ± 0.0% for the 4-month and 13-month groups, respectively. Feature ranking algorithms identified kinematic features that when considered simultaneously, achieved high genotype classification accuracy. The identified features demonstrated an age-specific kinematic profile of the impact of APP-overexpression. Trunk tilt and unstable hip movement patterns were important in classifying the 4-month J20 mice, whereas patterns of shoulder and iliac crest movement were critical for classifying 13-month J20 mice. Examining multiple kinematic features of gait simultaneously could also be developed to classify motor disorders in humans

Automatic signal and image-based assessments of spinal cord injury and treatments.

Spinal cord injury (SCI) is one of the most common sources of motor disabilities in humans that often deeply impact the quality of life in individuals with severe and chronic SCI. In this dissertation, we have developed advanced engineering tools to address three distinct problems that researchers, clinicians and patients are facing in SCI research. Particularly, we have proposed a fully automated stochastic framework to quantify the effects of SCI on muscle size and adipose tissue distribution in skeletal muscles by volumetric segmentation of 3-D MRI scans in individuals with chronic SCI as well as non-disabled individuals. We also developed a novel framework for robust and automatic activation detection, feature extraction and visualization of the spinal cord epidural stimulation (scES) effects across a high number of scES parameters to build individualized-maps of muscle recruitment patterns of scES. Finally, in the last part of this dissertation, we introduced an EMG time-frequency analysis framework that implements EMG spectral analysis and machine learning tools to characterize EMG patterns resulting in independent or assisted standing enabled by scES, and identify the stimulation parameters that promote muscle activation patterns more effective for standing. The neurotechnological advancements proposed in this dissertation have greatly benefited SCI research by accelerating the efforts to quantify the effects of SCI on muscle size and functionality, expanding the knowledge regarding the neurophysiological mechanisms involved in re-enabling motor function with epidural stimulation and the selection of stimulation parameters and helping the patients with complete paralysis to achieve faster motor recovery

Management of Degenerative Cervical Myelopathy and Spinal Cord Injury

The present Special Issue is dedicated to presenting current research topics in DCM and SCI in an attempt to bridge gaps in knowledge for both of the two main forms of SCI. The issue consists of fourteen studies, of which the majority were on DCM, the more common pathology, while three studies focused on tSCI. This issue includes two narrative reviews, three systematic reviews and nine original research papers. Areas of research covered include image studies, predictive modeling, prognostic factors, and multiple systemic or narrative reviews on various aspects of these conditions. These articles include the contributions of a diverse group of researchers with various approaches to studying SCI coming from multiple countries, including Canada, Czech Republic, Germany, Poland, Switzerland, United Kingdom, and the United States

Improving longitudinal spinal cord atrophy measurements for clinical trials in multiple sclerosis by using the generalised boundary shift integral (GBSI)

Spinal cord atrophy is a common and clinically relevant feature of multiple sclerosis (MS), and can be used to monitor disease progression and as an outcome measure in clinical trials. Spinal cord atrophy is conventionally estimated with segmentation-based methods (e.g., cross-sectional spinal cord area (CSA)), where spinal cord change is calculated indirectly by numerical difference between timepoints. In this thesis, I validated the generalised boundary shift integral (GBSI), as the first registration-based method for longitudinal spinal cord atrophy measurement. The GBSI registers the baseline and follow-up spinal cord scans in a common half-way space, to directly determine atrophy on the cord edges. First, on a test dataset (9 MS patients and 9 controls), I have found that GBSI presented with lower random measurement error, than CSA, reflected by lower standard deviation, coefficient of variation and median absolute deviation. Then, on multi-centre, multi-manufacturer, and multi–field‐strength scans (282 MS patients and 82 controls), I confirmed that GBSI provided lower measurement variability in all MS subtypes and controls, than CSA, resulting into better separation between MS patients and controls, improved statistical power, and reduced sample size estimates. Finally, on a phase 2 clinical trial (220 primary-progressive MS patients), I demonstrated that spinal cord atrophy measurements on GBSI could be obtained from brain scans, considering their quality and association with corresponding spinal cord MRI-derived measurements. Not least, 1-year spinal cord atrophy measurements on GBSI, but not CSA, were associated with upper and lower limb motor function. In conclusion, spinal cord atrophy on the GBSI had higher measurement precision and stronger clinical correlates, than the segmentation method, and could be derived from high-quality brain acquisitions. Longitudinal spinal cord atrophy on GBSI could become a gold standard for clinical trials including spinal cord atrophy as an outcome measure, but should remain a secondary outcome measure, until further advancements increase the ease of acquisition and processing

Identifying predictors for postoperative clinical outcome in lumbar spinal stenosis patients using smart-shoe technology

BACKGROUND: Approximately 33% of the patients with lumbar spinal stenosis (LSS) who undergo surgery are not satisfied with their postoperative clinical outcomes. Therefore, identifying predictors for postoperative outcome and groups of patients who will benefit from the surgical intervention is of significant clinical benefit. However, many of the studied predictors to date suffer from subjective recall bias, lack fine digital measures, and yield poor correlation to outcomes. METHODS: This study utilized smart-shoes to capture gait parameters extracted preoperatively during a 10 m self-paced walking test, which was hypothesized to provide objective, digital measurements regarding the level of gait impairment caused by LSS symptoms, with the goal of predicting postoperative outcomes in a cohort of LSS patients who received lumbar decompression and/or fusion surgery. The Oswestry Disability Index (ODI) and predominant pain level measured via the Visual Analogue Scale (VAS) were used as the postoperative clinical outcome variables. RESULTS: The gait parameters extracted from the smart-shoes made statistically significant predictions of the postoperative improvement in ODI (RMSE =0.13, r=0.93, and p<3.92×10-7) and predominant pain level (RMSE =0.19, r=0.83, and p<1.28×10-4). Additionally, the gait parameters produced greater prediction accuracy compared to the clinical variables that had been previously investigated. CONCLUSIONS: The reported results herein support the hypothesis that the measurement of gait characteristics by our smart-shoe system can provide accurate predictions of the surgical outcomes, assisting clinicians in identifying which LSS patient population can benefit from the surgical intervention and optimize treatment strategies.ope