Diffusion Assessment of Cortical Changes, Induced by Traumatic Spinal Cord Injury

Abstract

Promising treatments are being developed to promote functional recovery after spinal cord injury (SCI). Magnetic resonance imaging, specifically Diffusion Tensor Imaging (DTI) has been shown to non-invasively measure both axonal and myelin integrity following traumatic brain and SCI. A novel data-driven model-selection algorithm known as Diffusion Basis Spectrum Imaging (DBSI) has been proposed to more accurately delineate white matter injury. The objective of this study was to investigate whether DTI/DBSI changes that extend to level of the cerebral peduncle and internal capsule following a SCI could be correlated with clinical function. A prospective non-randomized cohort of 23 patients with chronic spinal cord injuries and 17 control subjects underwent cranial diffusion weighted imaging, followed by whole brain DTI and DBSI computations. Region-based analyses were performed on cerebral peduncle and internal capsule. Three subgroups of patients were included in the region-based analysis. Tract-Based Spatial Statistics (TBSS) was also applied to allow whole-brain white matter analysis between controls and all patients. Functional assessments were made using International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) as modified by the American Spinal Injury Association (ASIA) Scale. Whole brain white matter analysis using TBSS finds no statistical difference between controls and all patients. Only cervical ASIA A/B patients in cerebral peduncle showed differences from controls in DTI and DBSI results with region-based analysis. Cervical ASIA A/B SCI patients had higher levels of axonal injury and edema/tissue loss as measured by DBSI at the level of the cerebral peduncle. DTI Fractional Anisotropy (FA), Axial Diffusivity (AD) and Radial Diffusivity (RD) was able to detect differences in cervical ASIA A/B patients, but were non-specific to pathologies. Increased water fraction indicated by DBSI non-restricted isotropic diffusion fraction in the cerebral peduncle, explains the simultaneously increased DTI AD and DTI RD values. Our results further demonstrate the utility of DTI to detect disruption in axonal integrity in white matter, yet a clear shortcoming in differentiating true axonal injury from inflammation/tissue loss. Our results suggest a preservation of axonal integrity at the cortical level and has implications for future regenerative clinical trials.

Design and Validation of Diffusion MRI Models of White Matter

“…DBSI has been applied to a variety of pathologies (Chiang et al, 2014; Lin et al, 2017; Murphy et al, 2016; Sun et al, 2017; Wang et al, 2014c), but has not been extensively validated outside of the originating group. Given the unfavorable fitting landscape in a comparatively much simpler model such as NODDIDA (Jelescu et al, 2016a), especially in the presence of limited signal-to-noise ratio (SNR) (Figure 2), the reliability of DBSI model estimates remains to be clarified.…”

The usage of tractography of the spinal cord as a predictor of neurological disorders regression in patients with severe cervical spine and spinal cord injury

“…Так, Peng Sun і співавт. зареєстрували значне зниження FA і підвищення AD і RD у ніжках мозку та внутрішній капсулі у пацієнтів із ХСМТ ШВХ з неврологічним дефіцитом ASIA A і B [39].…”

Diffusion Basis Spectrum Imaging with Deep Neural Network Differentiates Distinct Histology in Pediatric Brain Tumors

“…Pediatric brain cancer has unfortunately surpassed leukemia to become the most common cause of death from cancer in children in the US (2). It is estimated that 2,940 new cases of childhood (0-14 age group) and adolescent (15)(16)(17)(18)(19) age group) primary malignant and non-malignant central nervous system (CNS) tumors will be diagnosed in the United States (US) in 2020 (3).…”

“…We developed diffusion basis spectrum imaging (DBSI) (11) and demonstrated its ability to quantitatively characterize pathologies in multiple central nervous system diseases, including glioblastoma (12), multiple sclerosis (13)(14)(15), spinal cord injury (16), and epilepsy (17). We modified DBSI to better characterize these pathology-directed structural changes to ultimately incorporate DBSI-derived diffusion metrics with a deep neural network (DNN) algorithm to detect and differentiate various tumor histologic components in pediatric high-grade brain tumors.…”