Postoperative adhesive arachnoiditis is an inflammatory response of the spinal leptomeninges that occurs after surgery, resulting in scar formation in the avascular nature of the arachnoid layer. Clinical manifestations of this condition include pain, sensory deficits, motor dysfunction, reflex abnormalities, and bladder/bowel impairments. In MRI scans, the signs of postoperative adhesive arachnoiditis can vary, but some specific indicators can assist surgeons in locating the lesion and planning surgical interventions more accurately. In this report, we reported a 37-year-old man with postoperative adhesive arachnoiditis after two surgeries for Chiari I malformation, demonstrating that the gradually-forming the delta cord sign with sequential syringomyelia. During the intraoperative examination, we identified the presence of the delta cord sign, which was formed by an arachnoid scar that was tethering the dorsal spinal cord to the dura. This discovery allowed us to precisely pinpoint the location of the arachnoid scar, thereby providing valuable guidance to avoid any unnecessary exploration of innocent structures during the procedure. Other localization signs were also reviewed.

Concussion, also known as mild traumatic brain injury (mTBI), commonly causes transient neurocognitive symptoms, but in some cases, it causes cognitive impairment, including working memory (WM) deficit, which can be long-lasting and impede a patient’s return to work. The predictors of long-term cognitive outcomes following mTBI remain unclear because abnormality is often absent in structural imaging findings. The purpose of the study was to determine whether machine learning-based models using functional magnetic resonance imaging (fMRI) biomarkers and demographic or neuropsychological measures at baseline could effectively predict 1-year cognitive outcomes of concussion. We conducted a prospective, observational study of patients with mTBI who were compared with demographically-matched healthy controls enrolled between September 2015 to August 2020. Baseline assessments were collected within the first week of injury, and follow-ups were conducted at 6 weeks, 3 months, 6 months, and 1 year. Potential demographic, neuropsychological, and fMRI features were selected according to the significance of correlation with the estimated changes in WM ability. The support vector machine classifier was trained using these potential features and estimated changes in WM between the predefined time periods. Patients demonstrated significant cognitive recovery at the third month, followed by worsened performance after 6 months, which persisted until 1 year after concussion. Approximately half of the patients experienced prolonged cognitive impairment at 1-year follow up. Satisfactory predictions were achieved for patients whose WM function did not recover at 3 months (accuracy=87.5%), 6 months (accuracy=83.3%), 1 year (accuracy=83.3%), and performed worse at 1-year follow-up compared to baseline assessment (accuracy=83.3%). This study demonstrated the feasibility of personalized prediction for long-term postconcussive WM outcomes based on baseline fMRI and demographic features, opening a new avenue for early rehabilitation intervention in selected individuals with possible poor long-term cognitive outcomes.