Detection of cerebral reorganization associated with degenerative cervical myelopathy using diffusion spectral imaging (DSI)

Patterns of cortical thickness alterations in degenerative cervical myelopathy: associations with dexterity and gait dysfunctions

Degenerative cervical myelopathy (DCM) can lead to significant brain structural reorganization. The association between the cortical changes and specific motor symptoms in DCM has yet to be fully elucidated. We investigated the associations between cortical thickness changes with neurological symptoms, such as dexterity and gait abnormalities, in patients with DCM in a case-control study. A 3 Tesla MRI scanner was used to acquire high-resolution T1-weighted structural scans from 30 right-handed patients with DCM and 22 age-matched healthy controls. Pronounced cortical thinning was observed in DCM patients relative to healthy controls, particularly in the bilateral precentral and prefrontal gyri, left pars triangularis, left postcentral gyrus, right transverse temporal and visual cortices (P ≤ 0.04). Notably, cortical thickness in these regions showed strong correlations with objective motor deficits (P < 0.0001). Specifically, the prefrontal cortex, premotor area and supplementary motor area exhibited significant thickness reductions correlating with diminished dexterity (R2 = 0.33, P < 0.0007; R2 = 0.34, P = 0.005, respectively). Similarly, declines in gait function were associated with reduced cortical thickness in the visual motor and frontal eye field cortices (R2 = 0.39, P = 0.029, R2 = 0.33, P = 0.04, respectively). Interestingly, only the contralateral precuneus thickness was associated with the overall modified Japanese Orthopaedic Association (mJOA) scores (R2 = 0.29, P = 0.003). However, the upper extremity subscore of mJOA indicated an association with the visual cortex and the anterior prefrontal (R2 = 0.48, P = 0.002, R2 = 0.33, P = 0.0034, respectively). In conclusion, our findings reveal patterns of cortical changes correlating with motor deficits, highlighting the significance of combining objective clinical and brain imaging assessments for understanding motor network dysfunction in DCM.

Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis

OBJECT

Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are well-known and powerful imaging techniques for MRI. Although DTI evaluation has evolved continually in recent years, there are still struggles regarding quantitative measurements that can benefit brain areas that are consistently difficult to measure via diffusion-based methods, e.g., gray matter (GM). The present study proposes a new image processing technique based on diffusion distribution evaluation of López-Ruiz, Mancini and Calbet (LMC) complexity called diffusion complexity (DC).

MATERIALS AND METHODS

The OASIS-3 and TractoInferno open-science databases for healthy individuals were used, and all the codes are provided as open-source materials.

RESULTS

The DC map showed relevant signal characterization in brain tissues and structures, achieving contrast-to-noise ratio (CNR) gains of approximately 39% and 93%, respectively, compared to those of the FA and ADC maps.

DISCUSSION

In the special case of GM tissue, the DC map obtains its maximum signal level, showing the possibility of studying cortical and subcortical structures challenging for classical DTI quantitative formalism. The ability to apply the DC technique, which requires the same imaging acquisition for DTI and its potential to provide complementary information to study the brain's GM structures, can be a rich source of information for further neuroscience research and clinical practice.

A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

A Predictive Model for the Risk of Recurrence of Cervical Spondylotic Radiculopathy After Surgery

INTRODUCTION

This study aimed to analyze the risk factors affecting the recurrence of cervical spondylotic radiculopathy after surgery, construct a nomogram predictive model, and validate the model's predictive performance using a calibration plot.

METHODS

In this study, 304 cervical spondylotic radiculopathy patients who underwent computed tomography (CT)-guided radiofrequency ablation (RFA) of cervical intervertebral discs or low-temperature plasma RFA for cervical radiculopathy were enrolled at the Pain Department of Jiaxing College Affiliated Hospital from January 2019 to March 2022. The patients were randomly divided into training (n = 213) and testing (n = 91) groups in a 7:3 ratio. Lasso regression analysis was used to screen for independent predictors of recurrence 1 year after surgery. A nomogram predictive model was established based on the selected factors using multiple logistic regression analysis.

RESULTS

One year after surgery, 250 of the 304 cervical spondylotic radiculopathy patients did not have recurrences, while 54 had recurrences. Lasso regression combined with multiple logistic regression analysis revealed that duration, numbness, and the Numeric Rating Scale (NRS) were significant predictors of recurrence 1 year after surgery (P < 0.05). A nomogram predictive model was established using these variables. The area under the curve (AUC) of the nomogram predictive model for predicting recurrence in the training group was 0.918 [95% confidence interval (CI) 0.866-0.970], and the AUC in the testing group was 0.892 (95% CI 0.806-0.978). The Hosmer-Lemeshow goodness-of-fit test exhibited a good model fit (P > 0.05). Decision curve analysis (DCA) indicated that the nomogram predictive model had a higher net benefit for predicting the risk of postoperative recurrence in cervical radiculopathy patients when the threshold probability was between 0 and 0.603.

CONCLUSION

This study successfully developed and validated a high-precision nomogram prediction model (predictive variables include duration, numbness, and NRS) for predicting the risk of postoperative recurrence in cervical radiculopathy patients. The model can help improve the early identification of high-risk patients and screening for postoperative recurrence.

The role of neuronal plasticity in cervical spondylotic myelopathy surgery: functional assessment and prognostic implication

Cervical spondylotic myelopathy (CSM) is a degenerative disease representing the most common spinal cord disorder in the adult population. It is characterized by chronic compression leading to neurological dysfunction due to static and dynamic injury of the spinal cord in cervical spine. These insidious damage mechanisms can result in the reorganization of cortical and subcortical areas. The cerebral cortex can reorganize due to spinal cord injury and may play a role in preserving neurological function. To date, the gold standard treatment of cervical myelopathy is surgery, comprising anterior, posterior, and combined approaches. However, the complex physiologic recovery processes involving cortical and subcortical neural reorganization following surgery are still inadequately understood. It has been demonstrated that diffusion MRI and functional imaging and techniques, such as transcranial magnetic stimulation (TMS) or functional magnetic resonance imaging (fMRI), can provide new insights into the diagnosis and prognosis of CSM. This review aims to shed light on the state-of-the-art regarding the pattern of cortical and subcortical areas reorganization and recovery before and after surgery in CSM patients, underlighting the critical role of neuroplasticity.

Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of twenty-six participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n=20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

Structural Relationship between Cerebral Gray and White Matter Alterations in Degenerative Cervical Myelopathy

Patients with degenerative cervical myelopathy (DCM) undergo adaptive supraspinal changes. However, it remains unknown how subcortical white matter changes reflect the gray matter loss. The current study investigated the interrelationship between gray matter and subcortical white matter alterations in DCM patients. Cortical thickness of gray matter, as well as the intra-cellular volume fraction (ICVF) of subcortical whiter matter, were assessed in a cohort of 44 patients and 17 healthy controls (HCs). The results demonstrated that cortical thinning of sensorimotor and pain related regions is associated with more severe DCM symptoms. ICVF values of subcortical white matter underlying the identified regions were significantly lower in study patients than in HCs. The left precentral gyrus (r = 0.5715, p < 0.0001), the left supramarginal gyrus (r = 0.3847, p = 0.0099), the left postcentral gyrus (r = 0.5195, p = 0.0003), the right superior frontal gyrus (r = 0.3266, p = 0.0305), and the right caudal (r = 0.4749, p = 0.0011) and rostral anterior cingulate (r = 0.3927, p = 0.0084) demonstrated positive correlations between ICVF and cortical thickness in study patients, but no significant correlations between ICVF and cortical thickness were observed in HCs. Results from the current study suggest that DCM may cause widespread gray matter alterations and underlying subcortical neurite loss, which may serve as potential imaging biomarkers reflecting the pathology of DCM.

Neck Disability at Presentation Influences Long Term Clinical Improvement for Neck Pain, Arm Pain, Disability and Physical Function in Patients Undergoing Anterior Cervical Discectomy and Fusion

STUDY DESIGN

Retrospective cohort PURPOSE: To compare perioperative characteristics, patient-reported outcome measures (PROMs) and minimum clinical important difference (MCID) achievement following anterior cervical discectomy and fusion (ACDF) in patients stratified by preoperative neck disability.

OVERVIEW OF LITERATURE

The Neck Disability Index (NDI) assesses a patient's self-perceived neck disability and is often utilized to assess the efficacy of cervical surgical intervention. Our study evaluates how preoperative severity of patient neck disability influences postoperative clinical improvement following ACDF.

METHODS

Primary, single or multi-level ACDF procedures were included. PROMs were administered at preoperative/6-week/12-week/6-month/1-year/2-year timepoints and included PROMIS-PF, VAS for neck and arm pain, NDI, and SF-12 PCS. Patients were grouped: preoperative NDI score<50 (mild to moderate neck disability) or NDI score≥50 (Severe neck disability). Demographics/perioperative characteristics/postoperative complications/mean PROMs/MCID achievement rates were compared using chi-squared or Student's t-test. Postoperative improvement from preoperative baseline within each cohort was assessed with paired samples t-test. MCID achievement was determined by comparing ΔPROMs to established thresholds.

RESULTS

225 patients were included, 150 NDI<50 and 75 NDI≥50. NDI≥50 cohort was significantly younger(p=0.002). Cohorts did not differ for spinal pathology/operative duration/estimated blood loss/postoperative length of stay/postoperative narcotic consumption/adjacent segment disease rate/1-year arthrodesis rate/6-month pseudoarthrosis rate. Postoperative VAS pain score on POD0 and 1 was significantly increased in NDI≥50 cohort(p<0.048, all). Postoperative complication rates did not differ. All mean PROMs differed at all timepoints(p<0.043, all). NDI<50 patient cohort significantly improved from preoperative baseline for all PROMs and timepoints except SF-12 PCS/PROMIS-PF at 6-weeks. NDI≥50 cohort significantly improved for all PROMs and timepoints except SF-12 PCS at 6-weeks. NDI≥50 cohort demonstrated greater proportion achieving MCID for NDI at 6-weeks/2-years/overall(p<0.037, all).

CONCLUSION

Both cohorts demonstrated significant long-term clinical improvement for neck pain/arm pain/physical function/neck disability, though patients with severe preoperative neck disability reported inferior mean scores for these outcomes at all timepoints.