State of Practice on Transcranial MR-Guided Focused Ultrasound: A Report from the ASNR Standards and Guidelines Committee and ACR Commission on Neuroradiology Workgroup.

Transcranial focused ultrasound (FUS) is a versatile, MR-guided, incisionless intervention with diagnostic and therapeutic applications for neurologic and psychiatric diseases. It is currently FDA-approved as a thermoablative treatment of essential tremor and Parkinson disease. However, other applications of FUS including BBB opening for diagnostic and therapeutic applications, sonodynamic therapy, histotripsy, and low-intensity focused ultrasound neuromodulation are all in clinical trials.

Repeatability and Reproducibility of pseudo-Continuous Arterial Spin Labeling Measured Brain Perfusion in Healthy Volunteers and Glioblastoma Patients.

Background And Purpose: Arterial spin labeled (ASL) MRI has gained recognition as a quantitative perfusion imaging method for managing patients with brain tumors. Limited studies have so far investigated the reproducibility of ASL-derived perfusion in patients with brain tumors. This study aims to evaluate intrasession repeatability and intersession reproducibility of perfusion measurements using 3D pseudo-continuous ASL (pCASL) with Cartesian TSE (TSE-CASPR) in healthy volunteers (HV) and glioblastoma (GBM) patients at 3 Tesla and compare against 3D pCASL with GRASE.

Reduced Cortical Excitability is Associated with Cognitive Symptoms in Concussed Adolescent Football Players.

Background: American tackle football is associated with high rates of concussion, leading to neurophysiological disturbances and debilitating clinical symptoms. Previous investigations of the neurophysiological effects of concussion have largely ignored aperiodic neurophysiological activity, which is a marker of cortical excitability.

Purpose: We examined whether concussion during a season of high school football is related to changes in aperiodic and periodic neurophysiological activity and whether any such changes are associated with clinical outcomes.

Cognitive and Salience Network Connectivity Changes following a Single Season of Repetitive Head Impact Exposure in High School Football.

Background And Purpose: During a season of high school football, adolescents with actively developing brains experience a considerable number of head impacts. Our aim was to determine whether repetitive head impacts in the absence of a clinically diagnosed concussion during a season of high school football produce changes in cognitive performance or functional connectivity of the salience network and its central hub, the dorsal anterior cingulate cortex.

Materials And Methods: Football players were instrumented with the Head Impact Telemetry System during all practices and games, and the helmet sensor data were used to compute a risk-weighted exposure metric (RWEcp), accounting for the cumulative risk during the season.

GAN-Based Motion Artifact Correction of 3D MR Volumes Using an Image-to-Image Translation Algorithm.

The quality of brain MRI volumes is often compromised by motion artifacts arising from intricate respiratory patterns and involuntary head movements, manifesting as blurring and ghosting that markedly degrade imaging quality. In this study, we introduce an innovative approach employing a 3D deep learning framework to restore brain MR volumes afflicted by motion artifacts. The framework integrates a densely connected 3D U-net architecture augmented by generative adversarial network (GAN)-informed training with a novel volumetric reconstruction loss function tailored to 3D GAN to enhance the quality of the volumes.

Simultaneous MEG and EEG source imaging of electrophysiological activity in response to acute transcranial photobiomodulation.

Introduction: Transcranial photobiomodulation (tPBM) is a non-invasive neuromodulation technique that improves human cognition. The effects of tPBM of the right forehead on neurophysiological activity have been previously investigated using EEG in sensor space. However, the spatial resolution of these studies is limited.

Antidepressant-related microstructural changes in the external capsule.

Several magnetic resonance imaging (MRI) studies have reported that antidepressant medications are strongly linked to brain microstructural alterations. Notably, external capsule alterations have been reported to be a biological marker for therapeutic response. However, prior studies did not investigate whether a change in the neurite density or directional coherence of white matter (WM) fibers underlies the observed microstructural alterations.

Two-Stage Training Framework Using Multicontrast MRI Radiomics for Mutation Status Prediction in Glioma.

Purpose To develop a radiomics framework for preoperative MRI-based prediction of isocitrate dehydrogenase () mutation status, a crucial glioma prognostic indicator. Materials and Methods Radiomics features (shape, first-order statistics, and texture) were extracted from the whole tumor or the combination of nonenhancing, necrosis, and edema regions. Segmentation masks were obtained via the federated tumor segmentation tool or the original data source.

Synthesizing 3D Multi-Contrast Brain Tumor MRIs Using Tumor Mask Conditioning.

Data scarcity and data imbalance are two major challenges in training deep learning models on medical images, such as brain tumor MRI data. The recent advancements in generative artificial intelligence have opened new possibilities for synthetically generating MRI data, including brain tumor MRI scans. This approach can be a potential solution to mitigate the data scarcity problem and enhance training data availability.

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.

Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.

Background And Purpose: Artificial intelligence models in radiology are frequently developed and validated using data sets from a single institution and are rarely tested on independent, external data sets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multicenter artificial intelligence competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency.

Materials And Methods: In total, 1201 anonymized, full-head NCCT clinical scans from 5 institutions were pooled to form the data set.

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.

Magnetoencephalography (MEG) Data Processing in Epilepsy Patients with Implanted Responsive Neurostimulation (RNS) Devices.

Drug-resistant epilepsy (DRE) is often treated with surgery or neuromodulation. Specifically, responsive neurostimulation (RNS) is a widely used therapy that is programmed to detect abnormal brain activity and intervene with tailored stimulation. Despite the success of RNS, some patients require further interventions.

MRI-Based Deep Learning Method for Classification of IDH Mutation Status.

Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. This study sought to develop deep learning networks for non-invasive IDH classification using T2w MR images while comparing their performance to a multi-contrast network. Methods: Multi-contrast brain tumor MRI and genomic data were obtained from The Cancer Imaging Archive (TCIA) and The Erasmus Glioma Database (EGD).

ELectronic Archimedes spiral Neural Network (ELANN).

The Archimedes spiral is a clinical tool that aids in the diagnosis and monitoring of essential tremor. However, spiral ratings may vary based on experience and training of the rating physician. This study sought to generate an objective standard model for tremor evaluation using convolutional neural networks.

Improving tremor response to focused ultrasound thalamotomy.

MRI-guided high-intensity focused ultrasound thalamotomy is an incisionless therapy for essential tremor. To reduce adverse effects, the field has migrated to treating at 2 mm above the anterior commissure-posterior commissure plane. We perform MRI-guided high-intensity focused ultrasound with an advanced imaging targeting technique, four-tract tractography.

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.

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.

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.].

MEG measured delta waves increase in adolescents after concussion.

Introduction: The purpose of this study is to determine if delta waves, measured by magnetoencephalography (MEG), increase in adolescents due to a sports concussion.

Methods: Twenty-four adolescents (age 14-17) completed pre- and postseason MRI and MEG scanning. MEG whole-brain delta power was calculated for each subject and normalized by the subject's total power.

Relationship between Aggressiveness, Self-Confidence, and Perceived Coach Support and Head Impact Exposure in Youth Football.

This study evaluated head impact exposure (HIE) metrics in relation to individual-level determinants of HIE. Youth (n = 13) and high school (n = 21) football players were instrumented with the Head Impact Telemetry (HIT) system during one season. Players completed the Trait-Robustness of Self-Confidence Inventory (TROSCI), Sports Climate Questionnaire (SCQ), and Competitive Aggressiveness and Anger Scale (CAAS), measuring self-confidence, perceived coach support, and competitive aggressiveness, respectively.

Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction.

: Deep learning has shown promise for predicting the molecular profiles of gliomas using MR images. Prior to clinical implementation, ensuring robustness to real-world problems, such as patient motion, is crucial. The purpose of this study is to perform a preliminary evaluation on the effects of simulated motion artifact on glioma marker classifier performance and determine if motion correction can restore classification accuracies.