Posttraumatic stress disorder (PTSD) is a psychiatric condition that develops after a person experiences one or more traumatic events, characterized by intrusive recollection, avoidance of trauma‐related events, hyperarousal, and negative cognitions and mood. Neurophysiological evidence suggests that the development of PTSD is ascribed to functional abnormalities in fear learning, threat detection, executive function and emotional regulation, and contextual processing. Magnetic resonance imaging (MRI) plays a primary role in both structural and functional neuroimaging for PTSD, demonstrating focal atrophy of the gray matter, altered fractional anisotropy, and altered focal neural activity and functional connectivity. MRI findings have implicated that brain regions associated with PTSD pathophysiology include the medial and dorsolateral prefrontal cortex, orbitofrontal cortex, insula, lentiform nucleus, amygdala, hippocampus and parahippocampus, anterior and posterior cingulate cortex, precuneus, cuneus, fusiform and lingual gyri, and the white matter tracts connecting these brain regions. Of these, alterations in the anterior cingulate, amygdala, hippocampus, and insula are highly reproducible across structural and functional MRI, supporting the hypothesis that abnormalities in fear learning and reactions to threat play an important role in the development of PTSD. In addition, most of these structures have been known to belong to one or more intrinsic brain networks regulating autobiographical memory retrieval and self‐thought, salience detection and autonomic responses, or attention and emotional control. Altered functional brain networks have been shown in PTSD. Therefore, in PTSD MRI is expected to reflect disequilibrium among functional brain networks, malfunction within an individual network, and impaired brain structures closely interacting with the networks.Level of Evidence: 3Technical Efficacy Stage: 3J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:380–396.
Purpose:To elucidate differences between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) with MR image-based texture features.Methods:This was an Institutional Review Board (IRB)-approved retrospective study. Consecutive, pathologically proven, initially treated 44 patients with GBM and 16 patients with PCNSL were enrolled. We calculated a total of 67 image texture features on the largest contrast-enhancing lesion in each patient on post-contrast T1-weighted images. Texture analyses included first-order features (histogram) and second-order features calculated with gray level co-occurrence matrix, gray level run length matrix (GLRLM), gray level size zone matrix, and multiple gray level size zone matrix. All texture features were measured by two neuroradiologists independently and the intraclass correlation coefficients were calculated. Reproducible features with the intraclass correlation coefficients of greater than 0.7 were used for hierarchical clustering between the cases and the features along with unpaired t statistics-based comparisons under the control of false discovery rate (FDR) < 0.05. Principal component analysis (PCA) was performed to find the predominant features in evaluating the differences between GBM and PCNSL.Results:Twenty-one out of the 67 features satisfied the acceptable intraclass correlation coefficient and the FDR constraints. PCA suggested first-order entropy, median, GLRLM-based run length non-uniformity, and run percentage as the distinguished features. Compared with PCNSL, run percentage and median were significantly lower, and entropy and run length non-uniformity were significantly higher in GBM.Conclusions:Among MR image-based textures, first-order entropy, median, GLRLM-based run length non-uniformity, and run percentage are considered to enhance differences between GBM and PCNSL.
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