Research reportCompensatory brainstem functional and structural connectivity in patients with degenerative cervical myelopathy by probabilistic tractography and functional MRI
Introduction
Degenerative cervical myelopathy (DCM) is the most common cause of spinal cord impairment in adults, and results from spinal cord injury secondary to the progressive degeneration of cervical osseous and soft tissue structural components (Kalsi-Ryan et al., 2013, Tracy and Bartleson, 2010). The classic myelopathic symptoms include gait dysfunction, hand incoordination, upper extremity dysesthesias, and bladder disturbance, and these findings are often used to evaluate the severity of DCM. Early surgical intervention is often advocated as prolonged duration of symptoms may be associated with poorer outcomes (Holly et al., 2009). Previous studies have also evaluated the association between T2 and T1 weighted signal changes of the spinal cord, and the relationship between these findings with baseline severity and neurological outcome (Nouri et al., 2017). However, limitations still exist in our ability to monitor the progression of this disorder and predict the potential for recovery by conventional imaging modalities.
Magnetic resonance imaging (MRI) has been critical in the diagnosis and management of patients with cervical degenerative changes, including those with DCM and neurologically asymptomatic patients with evidence of spinal cord compression, and recent enhancements have allowed for increased sensitivity to detect spinal cord injury induced neural tissue changes. Quantifiable metrics obtained from different MR imaging modalities have been used to reveal both spinal and cerebral changes in DCM patients. Cervical spinal cord compression induces upstream alteration in both white and gray brain matter, manifested in part by cortical representational changes and regions of atrophy (Bernabeu-Sanz et al., 2019, Holly et al., 2019, Sun et al., 2017, Woodworth et al., 2018, Yoon et al., 2013, Zdunczyk et al., 2018). Resting-state functional MRI (rs-fMRI) has revealed that cerebral connectivity is associated with altered sensorimotor function and neck disability in DCM patients (Holly et al., 2019, Woodworth et al., 2018). Further, the application of diffusion weighted imaging (DWI) has provided reliable biomarkers for detecting both cerebral and spinal white matter changes associated with DCM (Banaszek et al., 2014, Dong et al., 2008, Holly et al., 2007, Hrabalek et al., 2015, Hrabalek et al., 2018, Mamata et al., 2005). More recently, probabilistic tractography was proposed to facilitate fiber tract-specific comparisons (Raffelt et al., 2017). By characterizing the specific fiber population within a voxel, referring to fixel-based analysis (FBA) (Raffelt et al., 2015), total intra-axonal volume of white matter axons can be measured regardless of direction. This technique not only allows for estimation of differences in fiber density (FD), but also for cross-sectional fiber bundles (FDC), thus providing a combinational method to detect degeneration of white matter tracts.
The brainstem has been understudied in DCM patients, as neural plasticity research has primarily been focused on cerebral connectivity and the spinal cord microstructural reorganization in this patient population. Functioning as the nexus of the brain and the spinal cord, the brainstem plays a critical role in conveying sensorimotor signals. One of the major goals of this investigation was to elucidate how brainstem connectivity responds to changing cerebral and spinal networks in DCM patients, as this critical information is unknown, and has yet to be investigated.
In this present study, group connectometry analysis using probabilistic tractography was first performed on a cohort of 26 patients with spinal cord compression and 32 neurologically intact, healthy controls (HCs) to detect the cerebral white matter changes associated with spinal cord injury. Statistical differences in voxel-wise FD measurement were explored, and correlated with the degree of neurological impairment as measured by the modified Japanese Orthopedic Association (mJOA) scale, and neck disability as measured by the neck disability index (NDI). By seeding the brainstem, we also tested for the difference in FD and FDC in areas of the brain associated with integration of sensory information and pain modulation. Functional connectivity (FC) of the brainstem in the study cohort was further correlated with the mJOA and NDI scales. We hypothesized that the brainstem of the study cohort would demonstrate altered neurological connectivity depending on the severity of neurological impairment and neck disability. Such changes were not only localized to the primary sensorimotor network, but also in regions responsible for sensorimotor regulation and visual processing to compensate for insufficient input sensory information. We aimed to define a distinct set of imaging biomarkers based on brainstem neuronal connections that clinicians may use to successfully diagnose and treat DCM patients.
Section snippets
Correlation of white matter with symptom scores
The mean mJOA score for the study cohort was 16.1 (range from 12 to 18). Of the 26 study patients with spinal cord compression, 8 were neurologically asymptomatic (mJOA = 18), and 18 were symptomatic (mean mJOA = 15.3). High quality whole brain FD images were obtained from all 58 subjects that participated in the current study. To test the hypothesis that the extent of specific microstructural changes within the brain may correlate with degree of neurological impairment and neck disability, we
Discussion
DCM is the leading cause of progressive spinal cord impairment and neurological deficit in elderly patients (Kalsi-Ryan et al., 2013, Tracy and Bartleson, 2010). While the vast majority of DCM research has focused on the spinal cord (Avadhani et al., 2010, Ellingson et al., 2014, Ellingson et al., 2015, Nouri et al., 2016, Nouri et al., 2017, Tetreault et al., 2013), recent interest has moved to the upstream microstructural and functional changes within the sensorimotor network in DCM patients (
Patient population
The study cohort consisted of 26 subjects, including 18 DCM patients and 8 patients with cervical spine degenerative changes and spinal cord compression that were neurologically asymptomatic. These patients were prospectively enrolled in a cross-sectional study involving observational MRI and evaluation of neurological and neck disability from 2016 to 2018. The patients were recruited from an outpatient neurosurgery clinic, and each had spinal cord compression with evidence of spinal cord
Grant Funding
Funding was received through the following NIH/NINDS grants: 1R01NS078494-01A1 (to LTH, NS, and BME), and 2R01NS078494-06 (to LTH, NS, and BME).
CRediT authorship contribution statement
Chencai Wang: Methodology, Software, Formal analysis, Data curation, Writing - original draft. Azim Laiwalla: Data curation. Noriko Salamon: Data curation, Writing - review & editing. Benjamin M. Ellingson: Conceptualization, Methodology, Software, Writing - review & editing, Supervision. Langston T. Holly: Conceptualization, Writing - review & editing, Project administration, Funding acquisition.
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2022, eBioMedicineCitation Excerpt :Using this approach, the ROC AUC for transitions between symptomatic DCM stages ranged from 0.884 to 0.927, with the highest AUC for predicting mild from asymptomatic SCC (Table 2 and Figure 3). In the past decade, upstream morphological abnormalities,16 and functional connectivity within sensorimotor regions as a response to spinal cord injury have been widely studied.2-7 Consistent with these previous findings, we were able to confirm that large-scale functional network reorganization occurs in patients with DCM by characterizing the connectivity patterns associated with varying degrees of functional impairment in patients with DCM.
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2021, NeuroImageCitation Excerpt :The adoption of the FBA framework has seen a stark increase over time (Fig. 1). For convenience, we categorized all 75 FBA studies as follows: healthy ageing and healthy adults (Adab et al., 2020; Choy et al., 2020; Honnedevasthana Arun et al., 2021; Kelley et al., 2019; Kelley et al., 2021; Mizuguchi et al., 2019; Park et al., 2021; Radhakrishnan et al., 2020; Verhelst et al., 2021), typical and atypical childhood development (Barendse et al., 2020; Bleker et al., 2019; Bleker et al., 2020; Blommaert et al., 2020; Burley et al., 2021; Chahal et al., 2021a; Chahal et al., 2021b; Dimond et al., 2019; Dimond et al., 2020; Fuelscher et al., 2021; Genc et al., 2017; Genc et al., 2018; Genc et al., 2020a; Genc et al., 2020b; Grazioplene et al., 2020; Hyde et al., 2021; Kirkovski et al., 2020; Lugo-Candelas et al., 2020), fetal and neonatal development (Kelly et al., 2018; Kelly et al., 2020; Malhotra et al., 2019; Pannek et al., 2018; Pannek et al., 2020; Pecheva et al., 2019; Wu et al., 2020), psychiatric disorders (Grazioplene et al., 2018; Lyon et al., 2019), neurodegenerative and demyelinating disorders (Adanyeguh et al., 2018; Adanyeguh et al., 2021; Al-Amin et al., 2020; Boonstra et al., 2020; Carandini et al., 2021; Gajamange et al., 2018; Janssen et al., 2020; Li et al., 2020; Luo et al., 2020; Mito et al., 2018; Palmer et al., 2021; Park et al., 2020; Raffelt et al., 2015; Rau et al., 2019; Sakamoto et al., 2020; Sanchez et al., 2020; Savard et al., 2020; Storelli et al., 2020; Wang et al., 2020; Xiao et al., 2021; Zarkali et al., 2020; Zarkali et al., 2021; Zeun et al., 2021), brain injury and insult (Egorova et al., 2020; Fekonja et al., 2021; Friedman et al., 2019; Gottlieb et al., 2020; Verhelst et al., 2019; Wallace et al., 2020; Zamani et al., 2021), epilepsy (Bauer et al., 2020; Raffelt et al., 2017; Vaughan et al., 2017), and other disorders (Bishop et al., 2018; Haykal et al., 2019; Haykal et al., 2020; Mu et al., 2018; Sleurs et al., 2018; Zanin et al., 2020). We summarized the main results and conclusions of each study in Supplementary Document 3.