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In addition to task-based fMRI, seed-based analysis of resting-state fMRI represents an equally effective method for supplementary motor area localization in patients with brain tumors, with the best results obtained with bilateral hand motor region seeding.

Pulmonary metastases from benign-appearing smooth muscle tumors of the uterus are rare, and are termed benign metastasizing leiomyoma (BML). Affected patients usually present with single or multiple lung nodules and are usually women who have undergone hysterectomy. Only a few cases of BML with lung cysts have been reported, with 2 patients presenting with spontaneous pneumothoraces. We report a case of BML in a 29-year-old woman with an abnormal preoperative chest radiograph who several years after hysterectomy developed spontaneous bilateral pneumothoraces.

BACKGROUND AND PURPOSE: Resting-state fMRI helps identify neural networks in presurgical patients who may be limited in their ability to undergo task-fMRI. The purpose of this study was to determine the accuracy of identifying the language network from resting-state-fMRI independent component analysis (ICA) maps.MATERIALS AND METHODS: Through retrospective analysis, patients who underwent both resting-state-fMRI and task-fMRI were compared by identifying the language network from the resting-state-fMRI data by 3 reviewers. Blinded to task-fMRI maps, these investigators independently reviewed resting-state-fMRI ICA maps to potentially identify the language network. Reviewers ranked up to 3 top choices for the candidate resting-state-fMRI language map. We evaluated associations between the probability of correct identification of the language network and some potential factors.RESULTS: Patients included 29 men and 14 women with a mean age of 41 years. Reviewer 1 (with 17 years' experience) demonstrated the highest overall accuracy with 72%; reviewers 2 and 3 (with 2 and 7 years' experience, respectively) had a similar percentage of correct responses (50% and 55%). The highest accuracy used ICA50 and the top 3 choices (81%, 65%, and 60% for reviewers 1, 2, and 3, respectively). The lowest accuracy used ICA50, limiting each reviewer to the top choice (58%, 35%, and 42%). CONCLUSIONS:We demonstrate variability in the accuracy of blinded identification of resting-state-fMRI language networks across reviewers with different years of experience. ABBREVIATIONS:BOLD ¼ blood oxygen level-dependent; ICA ¼ independent component analysis; rs ¼ resting-state R esting-state (rs) fMRI has emerged as a novel tool to analyze brain function. In contrast to traditional task-fMRI, no explicit task is required in rs-fMRI while blood oxygen level-dependent (BOLD) images are acquired. Owing to an assortment of naturally occurring fluctuations of BOLD activity in various regions of the brain, a set of intrinsic brain networks can be identified by examining spatially distinct, however temporally synchronous, BOLD signals at rest. The number of discrete brain networks is somewhat