Associations Between Cardiac Function and Brain Health in Diverse Middle-Aged Adults: The Dallas Heart Study-2.

Background: Previous studies have linked cardiovascular risk factors during midlife to cognitive function in later life. However, few studies have looked at the association between cardiac function, brain structure, and cognitive function and even less have included diverse middle-aged populations. Objectives: The objective of this study was to determine associations between cardiac and brain structure and function in a multiethnic cohort of middle-aged adults. Methods: A cross-sectional study was conducted in participants of the Dallas Heart Study phase 2 (N = 1,919; 46% Black participants). Left ventricular (LV) mass, LV ejection fraction, LV concentricity, and peak systolic strain (LV Ecc) were assessed by cardiac magnetic resonance imaging. White matter hyperintensities (WMH) volume was measured by fluid attenuated inversion recovery magnetic resonance imaging. The Montreal Cognitive Assessment was used to measure cognitive functioning. Associations between cardiac and brain measures were determined using multivariable linear regression after adjusting for cardiovascular risk factors, education level, and physical activity. Results: LV ejection fraction was associated with total Montreal Cognitive Assessment score (ß = 0.06 [95% CI: 0.003-0.12], P = 0.042) and LV Ecc was associated with WMH volume (ß = 0.08 [95% CI: 0.01-0.14], P = 0.025) in the overall cohort without significant interaction by race/ethnicity. Higher LV mass and concentricity were associated with larger WMH volume in the overall cohort (ß = 0.13 [95% CI: 0.03-0.23], P = 0.008 and 0.10 [95% CI: 0.03-0.17], P = 0.005). These associations were more predominant in Black than White participants (ß = 0.17 [95% CI: 0.04-0.30] vs ß = -0.009 [95% CI: -0.16 to 0.14], P = 0.036 and ß = 0.22 [95% CI: 0.13-0.32] vs ß = -0.11 [95% CI: -0.21 to -0.01], P < 0.0001, for LV mass and concentricity, respectively). Conclusions: Subclinical cardiac dysfunction indicated by LVEF was associated with lower cognitive function. Moreover, LV mass and concentric remodeling were associated with higher WMH burden, particularly among Black individuals.

Corrigendum to: A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas.

[This corrects the article DOI: 10.1093/noajnl/vdaa066.].

Neurological emergencies associated with COVID-19: stroke and beyond.

Novel coronavirus disease (COVID-19) was declared a global pandemic on March 1, 2020. Neurological manifestations are now being reported worldwide, including emergent presentation with acute neurological changes as well as a comorbidity in hospitalized patients. There is limited knowledge on the neurologic manifestations of COVID-19 at present, with a wide array of neurological complications reported, ranging from ischemic stroke to acute demyelination and encephalitis. We report five cases of COVID-19 presenting to the ER with acute neurological symptoms, over the course of 1 month. This includes two cases of ischemic stroke, one with large-vessel occlusion and one with embolic infarcts. The remainders of the cases include acute tumefactive demyelination, isolated cytotoxic edema of the corpus callosum with subarachnoid hemorrhage, and posterior reversible encephalopathy syndrome (PRES).

A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas.

BACKGROUND: One of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status as markers for therapy and prognosis. 1p/19q co-deletion is the defining genomic marker for oligodendrogliomas and confers a better prognosis and treatment response than gliomas without it. Our group has previously developed a highly accurate deep-learning network for determining IDH mutation status using T2-weighted (T2w) MRI only. The purpose of this study was to develop a similar 1p/19q deep-learning classification network. METHODS: Multiparametric brain MRI and corresponding genomic information were obtained for 368 subjects from The Cancer Imaging Archive and The Cancer Genome Atlas. 1p/19 co-deletions were present in 130 subjects. Two-hundred and thirty-eight subjects were non-co-deleted. A T2w image-only network (1p/19q-net) was developed to perform 1p/19q co-deletion status classification and simultaneous single-label tumor segmentation using 3D-Dense-UNets. Three-fold cross-validation was performed to generalize the network performance. Receiver operating characteristic analysis was also performed. Dice scores were computed to determine tumor segmentation accuracy. RESULTS: 1p/19q-net demonstrated a mean cross-validation accuracy of 93.46% across the 3 folds (93.4%, 94.35%, and 92.62%, SD = 0.8) in predicting 1p/19q co-deletion status with a sensitivity and specificity of 0.90 ± 0.003 and 0.95 ± 0.01, respectively and a mean area under the curve of 0.95 ± 0.01. The whole tumor segmentation mean Dice score was 0.80 ± 0.007. CONCLUSION: We demonstrate high 1p/19q co-deletion classification accuracy using only T2w MR images. This represents an important milestone toward using MRI to predict glioma histology, prognosis, and response to treatment.

Advanced MRI techniques for transcranial high intensity focused ultrasound targeting.

Magnetic resonance guided high intensity focused ultrasound is a novel, non-invasive, image-guided procedure that is able to ablate intracranial tissue with submillimetre precision. It is currently FDA approved for essential tremor and tremor dominant Parkinson's disease. The aim of this update is to review the limitations of current landmark-based targeting techniques of the ventral intermediate nucleus and demonstrate the role of emerging imaging techniques that are relevant for both magnetic resonance guided high intensity focused ultrasound and deep brain stimulation. A significant limitation of standard MRI sequences is that the ventral intermediate nucleus, dentatorubrothalamic tract, and other deep brain nuclei cannot be clearly identified. This paper provides original, annotated images demarcating the ventral intermediate nucleus, dentatorubrothalamic tract, and other deep brain nuclei on advanced MRI sequences such as fast grey matter acquisition T1 inversion recovery, quantitative susceptibility mapping, susceptibility weighted imaging, and diffusion tensor imaging tractography. Additionally, the paper reviews clinical efficacy of targeting with these novel MRI techniques when compared to current established landmark-based targeting techniques. The paper has widespread applicability to both deep brain stimulation and magnetic resonance guided high intensity focused ultrasound.

A Fully Automated Deep Learning Network for Brain Tumor Segmentation.

We developed a fully automated method for brain tumor segmentation using deep learning; 285 brain tumor cases with multiparametric magnetic resonance images from the BraTS2018 data set were used. We designed 3 separate 3D-Dense-UNets to simplify the complex multiclass segmentation problem into individual binary-segmentation problems for each subcomponent. We implemented a 3-fold cross-validation to generalize the network's performance. The mean cross-validation Dice-scores for whole tumor (WT), tumor core (TC), and enhancing tumor (ET) segmentations were 0.92, 0.84, and 0.80, respectively. We then retrained the individual binary-segmentation networks using 265 of the 285 cases, with 20 cases held-out for testing. We also tested the network on 46 cases from the BraTS2017 validation data set, 66 cases from the BraTS2018 validation data set, and 52 cases from an independent clinical data set. The average Dice-scores for WT, TC, and ET were 0.90, 0.84, and 0.80, respectively, on the 20 held-out testing cases. The average Dice-scores for WT, TC, and ET on the BraTS2017 validation data set, the BraTS2018 validation data set, and the clinical data set were as follows: 0.90, 0.80, and 0.78; 0.90, 0.82, and 0.80; and 0.85, 0.80, and 0.77, respectively. A fully automated deep learning method was developed to segment brain tumors into their subcomponents, which achieved high prediction accuracy on the BraTS data set and on the independent clinical data set. This method is promising for implementation into a clinical workflow.

A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas.

BACKGROUND: Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a highly accurate, MRI-based, voxelwise deep-learning IDH classification network using T2-weighted (T2w) MR images and compare its performance to a multicontrast network. METHODS: Multiparametric brain MRI data and corresponding genomic information were obtained for 214 subjects (94 IDH-mutated, 120 IDH wild-type) from The Cancer Imaging Archive and The Cancer Genome Atlas. Two separate networks were developed, including a T2w image-only network (T2-net) and a multicontrast (T2w, fluid attenuated inversion recovery, and T1 postcontrast) network (TS-net) to perform IDH classification and simultaneous single label tumor segmentation. The networks were trained using 3D Dense-UNets. Three-fold cross-validation was performed to generalize the networks' performance. Receiver operating characteristic analysis was also performed. Dice scores were computed to determine tumor segmentation accuracy. RESULTS: T2-net demonstrated a mean cross-validation accuracy of 97.14% ± 0.04 in predicting IDH mutation status, with a sensitivity of 0.97 ± 0.03, specificity of 0.98 ± 0.01, and an area under the curve (AUC) of 0.98 ± 0.01. TS-net achieved a mean cross-validation accuracy of 97.12% ± 0.09, with a sensitivity of 0.98 ± 0.02, specificity of 0.97 ± 0.001, and an AUC of 0.99 ± 0.01. The mean whole tumor segmentation Dice scores were 0.85 ± 0.009 for T2-net and 0.89 ± 0.006 for TS-net. CONCLUSION: We demonstrate high IDH classification accuracy using only T2-weighted MR images. This represents an important milestone toward clinical translation.