Background <p>Degenerative cervical myelopathy (DCM) represents a prevalent etiology of neurological dysfunction, for which cervical decompression surgery (CDS) constitutes the principal therapeutic intervention. Advanced magnetic resonance imaging (MRI) techniques are crucial in elucidating the cerebral alterations associated with neuroinjury resulting from DCM.</p> Methods <p>In this prospective cohort study, we conducted an analysis of data from 54 patients with DCM both before and after surgery, as well as 57 healthy controls (HC), using functional MRI in combination with high-resolution structural MRI. The primary metrics included the z score transformation amplitude of low-frequency fluctuations (zALFF), functional connectivity (FC), and gray matter volume (GMV). Neurological function was evaluated through standardized clinical scores. Statistical analyses were employed to compare preoperative and postoperative changes, as well as to examine correlations with patient recovery outcome.</p> Results <p>Here we show that DCM patients exhibit significant preoperative alterations in zALFF, FC, and GMV within critical brain regions associated with sensory processing, motor control, and cognitive integration. Postoperatively, an increase in zALFF within Postcentral_R region, along with increased FC with motor-related areas, which correlates positively with neurological recovery. Moreover, GMV shows a widespread reduction before and after surgery.</p> Conclusions <p>Our study reveals functional alterations within the brain are closely associated with effective surgical recovery, especially concerning the remodeling of the ascending and descending pathways along the brain–spinal cord axis. Moreover, macrostructural changes manifest more gradually, with the recovery of brain function depending more on compensation and remodeling within neural networks than solely on structural restoration.</p>

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Functional and structural brain remodeling in patients with degenerative cervical myelopathy following cervical decompression surgery

  • Bingyong Xie,
  • Haoyu Ni,
  • Jiyuan Yao,
  • Zhibin Xu,
  • Sicheng Bian,
  • Haoxiang Wang,
  • Kun Zhu,
  • Xianyong Wu,
  • Peiwen Song,
  • Yuanyuan Wu,
  • Yongqiang Yu,
  • Fulong Dong

摘要

Background

Degenerative cervical myelopathy (DCM) represents a prevalent etiology of neurological dysfunction, for which cervical decompression surgery (CDS) constitutes the principal therapeutic intervention. Advanced magnetic resonance imaging (MRI) techniques are crucial in elucidating the cerebral alterations associated with neuroinjury resulting from DCM.

Methods

In this prospective cohort study, we conducted an analysis of data from 54 patients with DCM both before and after surgery, as well as 57 healthy controls (HC), using functional MRI in combination with high-resolution structural MRI. The primary metrics included the z score transformation amplitude of low-frequency fluctuations (zALFF), functional connectivity (FC), and gray matter volume (GMV). Neurological function was evaluated through standardized clinical scores. Statistical analyses were employed to compare preoperative and postoperative changes, as well as to examine correlations with patient recovery outcome.

Results

Here we show that DCM patients exhibit significant preoperative alterations in zALFF, FC, and GMV within critical brain regions associated with sensory processing, motor control, and cognitive integration. Postoperatively, an increase in zALFF within Postcentral_R region, along with increased FC with motor-related areas, which correlates positively with neurological recovery. Moreover, GMV shows a widespread reduction before and after surgery.

Conclusions

Our study reveals functional alterations within the brain are closely associated with effective surgical recovery, especially concerning the remodeling of the ascending and descending pathways along the brain–spinal cord axis. Moreover, macrostructural changes manifest more gradually, with the recovery of brain function depending more on compensation and remodeling within neural networks than solely on structural restoration.