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The authors aimed to identify brain MR imaging characteristics obtained from fetal and early neonatal scans that can predict the clinical presentation. A total of 32 neonatal patients (21 patients in the neonatal at-risk cohort, 11 in the infantile treatment cohort) were identified. Maximal mediolateral diameter and cross-sectional area at the narrowest point of the straight/falcine sinus were most predictive of clinical evolution into the neonatal at-risk cohort. This measurement clearly and unambiguously differentiated between high- and low-risk cohorts. The ability to accurately predict clinical evolution after birth based on fetal MR imaging can be of help for both caregivers and families, enabling better preparedness for urgent treatment and better planning for allocation of resources.

Two-hundred fifty-four conventional fluoroscopy-guided selective cervical nerve root blocks were performed via an anterolateral approach with an average fluoroscopy time of 24.3 seconds for all cases. There were no aborted procedures and no major or permanent complications. There were 14 minor complications; 12 of these were periprocedural and resolved by the 2-week follow-up visit. One-hundred eighty-five patients (75.2%) reported pain improvement of >50% from baseline at 15 minutes postinjection. The authors conclude that this study confirms that conventional fluoroscopy is as safe and effective as CT for the guidance of selective cervical nerve root block when using a meticulous technique and a nonparticulate steroid and performed by an experienced proceduralist.

The authors used resting-state fMRI in a prospective study to compare whole-brain functional connectivity between patients with good and poor outcomes, implementing support vector machine learning. They automatically predicted coma outcome using resting-state fMRI and also compared the prediction based on resting-state fMRI with the outcome prediction based on DWI. Of 17 eligible patients who completed the study procedure (among 351 patients screened), 9 regained consciousness and 8 remained comatose. They found higher functional connectivity in patients recovering consciousness, with greater changes occurring within and between the occipitoparietal and temporofrontal regions. Coma outcome prognostication based on resting-state fMRI machine learning was very accurate, notably for identifying patients with good outcome. They conclude that resting-state fMRI might bridge the gap left in early prognostication of postanoxic patients in a coma by identifying those with both good and poor outcomes.