Black, David F.

Functional magnetic resonance imaging (fMRI) with blood oxygen level-dependent (BOLD) technique is useful for preoperative mapping of brain functional networks in tumor patients, providing reliable in vivo detection of eloquent cortex to help reduce the risk of postsurgical morbidity. BOLD task-based fMRI (tb-fMRI) is the most often used noninvasive method that can reliably map cortical networks, including those associated with sensorimotor, language, and visual functions. BOLD resting-state fMRI (rs-fMRI) is emerging as a promising ancillary tool for visualization of diverse functional networks. Although fMRI is a powerful tool that can be used as an adjunct for brain tumor surgery planning, it has some constraints that should be taken into consideration for proper clinical interpretation. BOLD fMRI interpretation may be limited by neurovascular uncoupling (NVU) induced by brain tumors. Cerebrovascular reactivity (CVR) mapping obtained using breath-hold methods is an effective method for evaluating NVU potential.

In blood-oxygen-level-dependent (BOLD)-based resting-state functional (RS-fMRI) studies, usage of multi-echo echo-planar-imaging (ME-EPI) is limited due to unacceptable late echo times when high spatial resolution is used. Equipped with high-performance gradients, the compact 3T MRI system (C3T) enables a three-echo whole-brain ME-EPI protocol with smaller than 2.5 mm isotropic voxel and shorter than 1 s repetition time, as required in landmark fMRI studies. The performance of the ME-EPI was comprehensively evaluated with signal variance reduction and region-of-interest-, seed- and independent-component-analysis-based functional connectivity analyses and compared with a counterpart of single-echo EPI with the shortest TR possible. Through the multi-echo combination, the thermal noise level is reduced. Functional connectivity, as well as signal intensity, are recovered in the medial orbital sulcus and anterior transverse collateral sulcus in ME-EPI. It is demonstrated that ME-EPI provides superior sensitivity and accuracy for detecting functional connectivity and/or brain networks in comparison with single-echo EPI. In conclusion, the high-performance gradient enabled high-spatial-temporal resolution ME-EPI would be the method of choice for RS-fMRI study on the C3T.

Objective This study investigates a locally low-rank (LLR) denoising algorithm applied to source images from a clinical task-based functional MRI (fMRI) exam before post-processing for improving statistical confidence of task-based activation maps. Methods Task-based motor and language fMRI was obtained in eleven healthy volunteers under an IRB approved protocol. LLR denoising was then applied to raw complex-valued image data before fMRI processing. Activation maps generated from conventional non-denoised (control) data were compared with maps derived from LLR-denoised image data. Four board-certified neuroradiologists completed consensus assessment of activation maps; region-specific and aggregate motor and language consensus thresholds were then compared with nonparametric statistical tests. Additional evaluation included retrospective truncation of exam data without and with LLR denoising; a ROI-based analysis tracked t-statistics and temporal SNR (tSNR) as scan durations decreased. A test-retest assessment was performed; retest data were matched with initial test data and compared for one subject. Results fMRI activation maps generated from LLR-denoised data predominantly exhibited statistically significant ( p = 4.88×10 –4 to p = 0.042; one p = 0.062) increases in consensus t-statistic thresholds for motor and language activation maps. Following data truncation, LLR data showed task-specific increases in t-statistics and tSNR respectively exceeding 20 and 50% compared to control. LLR denoising enabled truncation of exam durations while preserving cluster volumes at fixed thresholds. Test-retest showed variable activation with LLR data thresholded higher in matching initial test data. Conclusion LLR denoising affords robust increases in t-statistics on fMRI activation maps compared to routine processing, and offers potential for reduced scan duration while preserving map quality.

Abstract Background SMART (stroke‐like migraine attacks after radiation therapy) is a rare, delayed complication of brain radiation. In this study, we wanted to review the spectrum of symptoms, neuroradiological findings, autoimmune status, and outcomes in SMART syndrome patients. Methods We conducted a retrospective cohort study of all consecutive adult patients (≥18 years) diagnosed with SMART syndrome at Mayo Clinic, Rochester between January 1995 and December 2018. Results We identified 25 unique patients with SMART syndrome and a total of 31 episodes and 15 (60%) patients were male. The median age at onset was 46 (interquartile range [IQR] 43–55) years and the median latency of onset after the initial radiation was 21.6 (IQR 14.4–28.2) years. Magnetic resonance imaging (MRI) showed gyral edema and enhancement in all cases with the temporal (25, 80.6%) and parietal (23, 74.2%) lobes being the most commonly affected. The median follow‐up of the patients in our cohort was 10 (IQR 6–32) weeks. On univariate analysis, factors associated with an increased risk of recurrent SMART episodes were female gender (odds ratio [OR] 8.1, 95% confidence interval [95% CI] 1.1–52.6, p = 0.019) and absence of electrographic seizure discharges during initial symptoms (OR 7.4, 95% CI 1.1–45.9, p = 0.032). We could not identify an autoimmune etiology. Longer duration of symptoms (>10 weeks) correlated with an older age ( p = 0.049), temporal lobe involvement ( p < 0.001), and diffusion restriction ( p = 0.043). Conclusions SMART is a syndrome with characteristic imaging findings and clinical features. Incomplete recovery by 10 weeks occurred in one‐third of individuals and was associated with older age, temporal lobe involvement, and restricted diffusion on MRI.

Recognition of key concepts of structural and functional anatomy of the cerebellum can facilitate image interpretation and clinical correlation. Recently, the human brain mapping literature has increased our understanding of cerebellar anatomy, function, connectivity with the cerebrum, and significance of lesions involving specific areas. Both the common names and numerically based Schmahmann classifications of cerebellar lobules are illustrated. Anatomic patterns, or signs, of key fissures and white matter branching are introduced to facilitate easy recognition of the major anatomic features. Color-coded overlays of cross-sectional imaging are provided for reference of more complex detail. Examples of exquisite detail of structural and functional cerebellar anatomy at 7 T MRI are also depicted. The functions of the cerebellum are manifold with the majority of areas involved with non-motor association function. Key concepts of lesion–symptom mapping which correlates lesion location to clinical manifestation are introduced, emphasizing that lesions in most areas of the cerebellum are associated with predominantly non-motor deficits. Clinical correlation is reinforced with examples of intrinsic pathologic derangement of cerebellar anatomy and altered functional connectivity due to pathology of the cerebral hemisphere. The purpose of this pictorial review is to illustrate basic concepts of these topics in a cross-sectional imaging-based format that can be easily understood and applied by radiologists.

ABSTRACTPURPOSETo determine interobserver spatial variability in language area localization using three commonly employed language tasks.MATERIALS AND METHODSWith institutional review board approval, 125 fMRI time series from 50 different clinical language cases were retrospectively reviewed by three blinded readers who selected 3‐dimensional points representing the perceived center of Wernicke's and Broca's areas using three language tasks (semantic decision, SD; sentence comprehension, SC; and silent word generation, WG). Point dispersion values were then calculated using the perimeter of the 3‐dimensional triangle defined by the three readers’ selections.RESULTSAfter resolving interobserver laterality disagreements, there was no difference in spatial variability between the three tasks (P = .069). The SD task had the fewest interobserver laterality disagreements (P = .028) and the SC task had fewer failed localizations for Broca's area (P = .050) and Wernicke's area (P = .013).CONCLUSIONWhile there were no differences between interobserver spatial variability in language area localization between the three tasks, language task choice impacts the accuracy of fMRI language area identification because tasks vary in their rates of interobserver laterality disagreements and failed localizations. A combination of tasks including one with low laterality disagreements (eg, SD) and one with few failed localizations (eg, SC) may offer the best combination.