Recommended Resting-State fMRI Acquisition and Preprocessing Steps for Preoperative Mapping of Language and Motor and Visual Areas in Adult and Pediatric Patients with Brain Tumors and Epilepsy.

Resting-state (rs) fMRI has been shown to be useful for preoperative mapping of functional areas in patients with brain tumors and epilepsy. However, its lack of standardization limits its widespread use and hinders multicenter collaboration. The American Society of Functional Neuroradiology, American Society of Pediatric Neuroradiology, and the American Society of Neuroradiology Functional and Diffusion MR Imaging Study Group recommend specific rs-fMRI acquisition approaches and preprocessing steps that will further support rs-fMRI for future clinical use. A task force with expertise in fMRI from multiple institutions provided recommendations on the rs-fMRI steps needed for mapping of language, motor, and visual areas in adult and pediatric patients with brain tumor and epilepsy. These were based on an extensive literature review and expert consensus.Following rs-fMRI acquisition parameters are recommended: minimum 6-minute acquisition time; scan with eyes open with fixation; obtain rs-fMRI before both task-based fMRI and contrast administration; temporal resolution of ≤2 seconds; scanner field strength of 3T or higher. The following rs-fMRI preprocessing steps and parameters are recommended: motion correction (seed-based correlation analysis [SBC], independent component analysis [ICA]); despiking (SBC); volume censoring (SBC, ICA); nuisance regression of CSF and white matter signals (SBC); head motion regression (SBC, ICA); bandpass filtering (SBC, ICA); and spatial smoothing with a kernel size that is twice the effective voxel size (SBC, ICA).The consensus recommendations put forth for rs-fMRI acquisition and preprocessing steps will aid in standardization of practice and guide rs-fMRI program development across institutions. Standardized rs-fMRI protocols and processing pipelines are essential for multicenter trials and to implement rs-fMRI as part of standard clinical practice.

Post-COVID-19 Brain [18F] FDG-PET Findings: A Retrospective Single-Center Study in the United States.

BACKGROUND AND PURPOSE: The pathophysiology of neurologic manifestations of postacute sequelae of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infection is not clearly understood. Our aim was to investigate brain metabolic activity on [18F] FDG-PET/CT scans in patients with a history of coronavirus disease 2019 (COVID-19) infection before imaging. MATERIALS AND METHODS: This retrospective study included 45 patients who underwent [18F] FDG-PET/CT imaging for any reason and had, at least once, tested positive for COVID-19 at any time before imaging. Fifteen patients had available [18F] FDG-PET scans obtained under identical conditions before the infection. A group of 52 patients with melanoma or multiple myeloma who underwent [18F] FDG-PET/CT were used as controls. Whole-brain 2-sample t test analysis was performed using SPM software to identify clusters of hypo- and hypermetabolism and compare brain metabolic activity between patients with COVID-19 and controls. Paired sample t test comparison was also performed for 15 patients, and correlations between metabolic values of clusters and clinical data were measured. RESULTS: Compared with the control group, patients with a history of COVID-19 infection exhibited focal areas of hypometabolism in the bilateral frontal, parietal, occipital, and posterior temporal lobes and cerebellum (P = .05 uncorrected at the voxel level, family-wise error-corrected at the cluster level) that peaked during the first 2 months, improved to near-complete recovery around 6 months, and disappeared at 12 months. Hypermetabolism involving the brainstem, cerebellum, limbic structures, frontal cortex, and periventricular white matter was observed only at 2-6 months after infection. Older age, neurologic symptoms, and worse disease severity scores positively correlated with the metabolic changes. CONCLUSIONS: This study demonstrates a profile of time-dependent brain PET hypo- and hypermetabolism in patients with confirmed SARS-CoV-2 infection.

Identification of the Language Network from Resting-State fMRI in Patients with Brain Tumors: How Accurate Are Experts?

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.

Preoperative Mapping of the Supplementary Motor Area in Patients with Brain Tumor Using Resting-State fMRI with Seed-Based Analysis.

BACKGROUND AND PURPOSE: The supplementary motor area can be a critical region in the preoperative planning of patients undergoing brain tumor resection because it plays a role in both language and motor function. While primary motor regions have been successfully identified using resting-state fMRI, there is variability in the literature regarding the identification of the supplementary motor area for preoperative planning. The purpose of our study was to compare resting-state fMRI to task-based fMRI for localization of the supplementary motor area in a large cohort of patients with brain tumors presenting for preoperative brain mapping. MATERIALS AND METHODS: Sixty-six patients with brain tumors were evaluated with resting-state fMRI using seed-based analysis of hand and orofacial motor regions. Rates of supplementary motor area localization were compared with those in healthy controls and with localization results by task-based fMRI. RESULTS: Localization of the supplementary motor area using hand motor seed regions was more effective than seeding using orofacial motor regions for both patients with brain tumor (95.5% versus 34.8%, P < .001) and controls (95.2% versus 45.2%, P < .001). Bilateral hand motor seeding was superior to unilateral hand motor seeding in patients with brain tumor for either side (95.5% versus 75.8%/75.8% for right/left, P < .001). No difference was found in the ability to identify the supplementary motor area between patients with brain tumors and controls. CONCLUSIONS: 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.

American Society of Functional Neuroradiology-Recommended fMRI Paradigm Algorithms for Presurgical Language Assessment.

INTRODUCTION: Functional MR imaging is increasingly being used for presurgical language assessment in the treatment of patients with brain tumors, epilepsy, vascular malformations, and other conditions. The inherent complexity of fMRI, which includes numerous processing steps and selective analyses, is compounded by institution-unique approaches to patient training, paradigm choice, and an eclectic array of postprocessing options from various vendors. Consequently, institutions perform fMRI in such markedly different manners that data sharing, comparison, and generalization of results are difficult. The American Society of Functional Neuroradiology proposes widespread adoption of common fMRI language paradigms as the first step in countering this lost opportunity to advance our knowledge and improve patient care. LANGUAGE PARADIGM REVIEW PROCESS: A taskforce of American Society of Functional Neuroradiology members from multiple institutions used a broad literature review, member polls, and expert opinion to converge on 2 sets of standard language paradigms that strike a balance between ease of application and clinical usefulness. ASFNR RECOMMENDATIONS: The taskforce generated an adult language paradigm algorithm for presurgical language assessment including the following tasks: Sentence Completion, Silent Word Generation, Rhyming, Object Naming, and/or Passive Story Listening. The pediatric algorithm includes the following tasks: Sentence Completion, Rhyming, Antonym Generation, or Passive Story Listening. DISCUSSION: Convergence of fMRI language paradigms across institutions offers the first step in providing a "Rosetta Stone" that provides a common reference point with which to compare and contrast the usefulness and reliability of fMRI data. From this common language task battery, future refinements and improvements are anticipated, particularly as objective measures of reliability become available. Some commonality of practice is a necessary first step to develop a foundation on which to improve the clinical utility of this field.

Presurgical Brain Mapping of the Ventral Somatomotor Network in Patients with Brain Tumors Using Resting-State fMRI.

BACKGROUND AND PURPOSE: Resting-state fMRI readily identifies the dorsal but less consistently the ventral somatomotor network. Our aim was to assess the relative utility of resting-state fMRI in the identification of the ventral somatomotor network via comparison with task-based fMRI in patients with brain tumor. MATERIALS AND METHODS: We identified 26 surgically naïve patients referred for presurgical fMRI brain mapping who had undergone both satisfactory ventral motor activation tasks and resting-state fMRI. Following standard preprocessing for task-based fMRI and resting-state fMRI, general linear model analysis of the ventral motor tasks and independent component analysis of resting-state fMRI were performed with the number of components set to 20, 30, 40, and 50. Visual overlap of task-based fMRI and resting-state fMRI at different component levels was assessed and categorized as full match, partial match, or no match. Rest-versus-task-fMRI concordance was calculated with Dice coefficients across varying fMRI thresholds before and after noise removal. Multithresholded Dice coefficient volume under the surface was calculated. RESULTS: The ventral somatomotor network was identified in 81% of patients. At the subject level, better matches between resting-state fMRI and task-based fMRI were seen with an increasing order of components (53% of cases for 20 components versus 73% for 50 components). Noise-removed group-mean volume under the surface improved as component numbers increased from 20 to 50, though ANOVA demonstrated no statistically significant difference among the 4 groups. CONCLUSIONS: In most patients, the ventral somatomotor network can be identified with an increase in the probability of a better match at a higher component number. There is variable concordance of the ventral somatomotor network at the single-subject level between resting-state and task-based fMRI.

In vivo amyloid imaging in Alzheimer's disease.

Targeted approaches to therapy for Alzheimer's disease have evolved based on detailed understanding of the genetic, molecular biologic, and neuropathologic basis of the disease. Given the potential for greater treatment efficacy in the earlier stages of the disease, the notion of early diagnosis has become more relevant. Current clinical and imaging diagnostic approaches lack reliability in the preclinical and prodromal phases of the disease. We review emerging studies on imaging of the molecular substrate of the disease, most notably the amyloid peptide, which hope to increase early diagnostic efficacy. We offer a brief overview of the demographics, diagnostic criteria, and current imaging tests, followed by a review of amyloid biology and developments in cerebral amyloid imaging yielded by recent in vitro, in vivo and human studies.