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Diffusion-weighted imaging (DWI) thermometry is a magnetic resonance-based imaging tool that allows the noninvasive measurement of brain core temperature. Although only applicable to cerebrospinal fluid, it is thought to be potentially useful in assessing the thermal pathophysiology of the brain in both patients and healthy subjects. The objective of this article is to provide a concise but thorough review of the basic physical principles and the principal applications of DWI thermometry as a potential method to elucidate the pathophysiology of several brain diseases and neurologic syndromes.

Purpose: High-grade glioma surgery has evolved around the principal belief that a safe maximal tumor resection improves symptoms, quality of life, and survival. Mapping brain function has been recently improved by resting-state functional magnetic resonance imaging (rest-fMRI), a novel imaging technique that explores networks connectivity at “rest.” Methods: This prospective study analyzed 10 patients with high-grade glioma in whom rest-fMRI connectivity was assessed both in single-subject and in group analysis before and after surgery. Seed-based functional connectivity analysis was performed with CONN toolbox. Network identification focused on 8 major functional connectivity networks. A voxel-wise region of interest (ROI) to ROI correlation map to assess functional connectivity throughout the whole brain was computed from a priori seeds ROI in specific resting-state networks before and after surgical resection in each patient. Results: Reliable topography of all 8 resting-state networks was successfully identified in each participant before surgical resection. Single-subject functional connectivity analysis showed functional disconnection for dorsal attention and salience networks, whereas the language network demonstrated functional connection either in the case of left temporal glioblastoma. Functional connectivity in group analysis showed wide variations of functional connectivity in the default mode, salience, and sensorimotor networks. However, salience and language networks, salience and default mode networks, and salience and sensorimotor networks showed a significant correlation (P uncorrected