Magnetoencephalography Atlas Viewer for Dipole Localization and Viewing.

Magnetoencephalography (MEG) is a noninvasive neuroimaging technique widely recognized for epilepsy and tumor mapping. MEG clinical reporting requires a multidisciplinary team, including expert input regarding each dipole's anatomic localization. Here, we introduce a novel tool, the "Magnetoencephalography Atlas Viewer" (MAV), which streamlines this anatomical analysis. The MAV normalizes the patient's Magnetic Resonance Imaging (MRI) to the Montreal Neurological Institute (MNI) space, reverse-normalizes MNI atlases to the native MRI, identifies MEG dipole files, and matches dipoles' coordinates to their spatial location in atlas files. It offers a user-friendly and interactive graphical user interface (GUI) for displaying individual dipoles, groups, coordinates, anatomical labels, and a tri-planar MRI view of the patient with dipole overlays. It evaluated over 273 dipoles obtained in clinical epilepsy subjects. Consensus-based ground truth was established by three neuroradiologists, with a minimum agreement threshold of two. The concordance between the ground truth and MAV labeling ranged from 79% to 84%, depending on the normalization method. Higher concordance rates were observed in subjects with minimal or no structural abnormalities on the MRI, ranging from 80% to 90%. The MAV provides a straightforward MEG dipole anatomic localization method, allowing a nonspecialist to prepopulate a report, thereby facilitating and reducing the time of clinical reporting.

Synthesizing Contrast-Enhanced MR Images from Noncontrast MR Images Using Deep Learning.

BACKGROUND AND PURPOSE: Recent developments in deep learning methods offer a potential solution to the need for alternative imaging methods due to concerns about the toxicity of gadolinium-based contrast agents. The purpose of the study was to synthesize virtual gadolinium contrast-enhanced T1-weighted MR images from noncontrast multiparametric MR images in patients with primary brain tumors by using deep learning. MATERIALS AND METHODS: We trained and validated a deep learning network by using MR images from 335 subjects in the Brain Tumor Segmentation Challenge 2019 training data set. A held out set of 125 subjects from the Brain Tumor Segmentation Challenge 2019 validation data set was used to test the generalization of the model. A residual inception DenseNet network, called T1c-ET, was developed and trained to simultaneously synthesize virtual contrast-enhanced T1-weighted (vT1c) images and segment the enhancing portions of the tumor. Three expert neuroradiologists independently scored the synthesized vT1c images by using a 3-point Likert scale, evaluating image quality and contrast enhancement against ground truth T1c images (1 = poor, 2 = good, 3 = excellent). RESULTS: The synthesized vT1c images achieved structural similarity index, peak signal-to-noise ratio, and normalized mean square error scores of 0.91, 64.35, and 0.03, respectively. There was moderate interobserver agreement between the 3 raters, regarding the algorithm's performance in predicting contrast enhancement, with a Fleiss kappa value of 0.61. Our model was able to accurately predict contrast enhancement in 88.8% of the cases (scores of 2 to 3 on the 3-point scale). CONCLUSIONS: We developed a novel deep learning architecture to synthesize virtual postcontrast enhancement by using only conventional noncontrast brain MR images. Our results demonstrate the potential of deep learning methods to reduce the need for gadolinium contrast in the evaluation of primary brain tumors.

Dopamine regulates decision thresholds in human reinforcement learning in males.

Dopamine fundamentally contributes to reinforcement learning, but recent accounts also suggest a contribution to specific action selection mechanisms and the regulation of response vigour. Here, we examine dopaminergic mechanisms underlying human reinforcement learning and action selection via a combined pharmacological neuroimaging approach in male human volunteers (n = 31, within-subjects; Placebo, 150 mg of the dopamine precursor L-dopa, 2 mg of the D2 receptor antagonist Haloperidol). We found little credible evidence for previously reported beneficial effects of L-dopa vs. Haloperidol on learning from gains and altered neural prediction error signals, which may be partly due to differences experimental design and/or drug dosages. Reinforcement learning drift diffusion models account for learning-related changes in accuracy and response times, and reveal consistent decision threshold reductions under both drugs, in line with the idea that lower dosages of D2 receptor antagonists increase striatal DA release via an autoreceptor-mediated feedback mechanism. These results are in line with the idea that dopamine regulates decision thresholds during reinforcement learning, and may help to bridge action selection and response vigor accounts of dopamine.

Stromal-induced epithelial-mesenchymal transition induces targetable drug resistance in acute lymphoblastic leukemia.

The bone marrow microenvironment (BME) drives drug resistance in acute lymphoblastic leukemia (ALL) through leukemic cell interactions with bone marrow (BM) niches, but the underlying mechanisms remain unclear. Here, we show that the interaction between ALL and mesenchymal stem cells (MSCs) through integrin ß1 induces an epithelial-mesenchymal transition (EMT)-like program in MSC-adherent ALL cells, resulting in drug resistance and enhanced survival. Moreover, single-cell RNA sequencing analysis of ALL-MSC co-culture identifies a hybrid cluster of MSC-adherent ALL cells expressing both B-ALL and MSC signature genes, orchestrated by a WNT/ß-catenin-mediated EMT-like program. Blockade of interaction between ß-catenin and CREB binding protein impairs the survival and drug resistance of MSC-adherent ALL cells in vitro and results in a reduction in leukemic burden in vivo. Targeting of this WNT/ß-catenin-mediated EMT-like program is a potential therapeutic approach to overcome cell extrinsically acquired drug resistance in ALL.

Establishing language laterality: does resting-state functional MRI help?

OBJECTIVE: Task-based functional MRI (tb-fMRI) is now considered the standard, noninvasive technique in establishing language laterality in children for surgical planning. The evaluation can be limited due to several factors such as age, language barriers, and developmental and cognitive delays. Resting-state functional MRI (rs-fMRI) offers a potential path to establish language dominance without active task participation. The authors sought to compare the ability of rs-fMRI for language lateralization in the pediatric population with conventional tb-fMRI used as the gold standard. METHODS: The authors performed a retrospective evaluation of all pediatric patients at a dedicated quaternary pediatric hospital who underwent tb-fMRI and rs-fMRI from 2019 to 2021 as part of the surgical workup for patients with seizures and brain tumors. Task-based fMRI language laterality was based on a patient's adequate performance on one or more of the following: sentence completion, verb generation, antonym generation, or passive listening tasks. Resting-state fMRI data were postprocessed using statistical parametric mapping, FMRIB Software Library, and FreeSurfer as described in the literature. The laterality index (LI) was calculated from the independent component (IC) with the highest Jaccard Index (JI) for the language mask. Additionally, the authors visually inspected the activation maps for two ICs with the highest JIs. The rs-fMRI LI of IC1 and the authors' image-based subjective interpretation of language lateralization were compared with tb-fMRI, which was considered the gold standard for this study. RESULTS: A retrospective search yielded 33 patients with language fMRI data. Eight patients were excluded (5 with suboptimal tb-fMRI and 3 with suboptimal rs-fMRI data). Twenty-five patients (age range 7-19 years, male/female ratio 15:10) were included in the study. The language laterality concordance between tb-fMRI and rs-fMRI ranged from 68% to 80% for assessment based on LI of independent component analysis with highest JI and for subjective evaluation by visual inspection of activation maps, respectively. CONCLUSIONS: The concordance rates between tb-fMRI and rs-fMRI of 68% to 80% show the limitation of rs-fMRI in determining language dominance. Resting-state fMRI should not be used as the sole method for language lateralization in clinical practice.

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.

Cytoneme delivery of Sonic Hedgehog from ligand-producing cells requires Myosin 10 and a Dispatched-BOC/CDON co-receptor complex.

Morphogens function in concentration-dependent manners to instruct cell fate during tissue patterning. The cytoneme morphogen transport model posits that specialized filopodia extend between morphogen-sending and responding cells to ensure that appropriate signaling thresholds are achieved. How morphogens are transported along and deployed from cytonemes, how quickly a cytoneme-delivered, receptor-dependent signal is initiated, and whether these processes are conserved across phyla are not known. Herein, we reveal that the actin motor Myosin 10 promotes vesicular transport of Sonic Hedgehog (SHH) morphogen in mouse cell cytonemes, and that SHH morphogen gradient organization is altered in neural tubes of Myo10-/- mice. We demonstrate that cytoneme-mediated deposition of SHH onto receiving cells induces a rapid, receptor-dependent signal response that occurs within seconds of ligand delivery. This activity is dependent upon a novel Dispatched (DISP)-BOC/CDON co-receptor complex that functions in ligand-producing cells to promote cytoneme occurrence and facilitate ligand delivery for signal activation.

During development, cells must work together and talk to each other to build the organs and tissues of the growing embryo. To communicate precisely with long-distance targets, cells can project a series of thin finger-like structures known as cytonemes. Cells use these miniature highways to exchange cargo and signals, such as the protein sonic hedgehog (SHH for short). Alterations to the way SHH is exchanged during development predispose to cancer and lead to disorders of the nervous system. Yet, the mechanisms by which cytonemes work in mammals remain to be fully elucidated. In particular, it is still unclear how the structures start to form, and how the proteins are loaded and transported from one end to another. A 'molecular motor' called myosin 10, which can carry cargo along the internal skeleton of cells, may be involved in these processes. To find out, Hall et al. used fluorescent probes to track both myosin 10 and SHH in mouse cells, showing that myosin 10 carries SHH from the core of the signal-producing cell to the tips of cytonemes. There, the protein is passed to the target cell upon contact, triggering a quick response. SHH also appeared to be more than just passive cargo, interacting with another group of proteins in the signal-emitting cell before reaching its target. This mechanism then encourages the signalling cells to produce more cytonemes towards their neighbours. SHH is crucial during development, but also after birth: in fact, changes to SHH transport in adulthood can also disrupt tissue balance and hinder healing. Understanding how healthy tissues send this signal may reveal why and how disease emerges.

Classification of brain tumor isocitrate dehydrogenase status using MRI and deep learning.

Isocitrate dehydrogenase (IDH) mutation status is an important marker in glioma diagnosis and therapy. We propose an automated pipeline for noninvasively predicting IDH status using deep learning and T2-weighted (T2w) magnetic resonance (MR) images with minimal preprocessing (N4 bias correction and normalization to zero mean and unit variance). T2w MR images and genomic data were obtained from The Cancer Imaging Archive dataset for 260 subjects (120 high-grade and 140 low-grade gliomas). A fully automated two-dimensional densely connected model was trained to classify IDH mutation status on 208 subjects and tested on another held-out set of 52 subjects using fivefold cross validation. Data leakage was avoided by ensuring subject separation during the slice-wise randomization. Mean classification accuracy of 90.5% was achieved for each axial slice in predicting the three classes of no tumor, IDH mutated, and IDH wild type. Test accuracy of 83.8% was achieved in predicting IDH mutation status for individual subjects on the test dataset of 52 subjects. We demonstrate a deep learning method to predict IDH mutation status using T2w MRI alone. Radiologic imaging studies using deep learning methods must address data leakage (subject duplication) in the randomization process to avoid upward bias in the reported classification accuracy.

Automatic Identification of Successful Memory Encoding in Stereo EEG of Refractory, Mesial Temporal Lobe Epilepsy.

Surgical resection of portions of the temporal lobe is the standard of care for patients with refractory mesial temporal lobe epilepsy. While this reduces seizures, it often results in an inability to form new memories, which leads to difficulties in social situations, learning, and suboptimal quality of life. Learning about the success or failure to form new memory in such patients is critical if we are to generate neuromodulation-based therapies. To this end, we tackle the many challenges in analyzing memory formation when their brains are recorded using stereoencephalography (sEEG) in a Free Recall task. Our contributions are threefold. First, we compute a rich measure of brain connectivity by computing the phase locking value statistic (synchrony) between pairs of regions, over hundreds of word memorization trials. Second, we leverage the rich information (over 400 values per pair of probed brain regions) to form consistent length feature vectors for classifier training. Third, we train and evaluate seven different types of classifier models and identify which ones achieve the highest accuracy and which brain features are most important for high accuracy. We assess our approach on data from 37 patients pre-resection surgery. We achieve up to 73% accuracy distinguishing successful from unsuccessful memory formation in the human brain from just 1.6 sec epochs of sEEG data.