Radiology

Annual meeting of the Organization for Human Brain Mapping at the Metro Toronto Convention Centre, bringing together researchers to share groundbreaking research, engage in educational forums, and foster collaboration across neuroimaging methods and applications.

Provide up to USD 5,000 for a six-month neuroradiology research project during January–June 2027. The award supports eligible residents, fellows and postdoctoral researchers while they remain in the required training programme. Funds can support direct project needs such as imaging, analysis software and related research resources, but cannot pay the trainee, mentor or collaborators’ salaries. Applicants require ASNR membership and appropriate mentorship or institutional support. Apply by 30 October 2026 at 9 a.m. Central Time.

Support women developing leadership and management responsibilities in neuroradiology through attendance at the fall 2027 Radiology Leadership Institute Summit. The scholarship provides a USD 2,000 travel stipend and directly paid event registration. It targets applicants approaching associate professorship, recently appointed associate professors, or private-practice clinicians with demonstrated leadership potential. Membership in ASNR, ACR and AAWR is required. Applications close on 30 October 2026 at 9 a.m. Central Time.

Identifying a structural brain lesion on MRI is the most important factor that correlates with seizure freedom after surgery in patients suffering from drug-resistant focal epilepsy. By providing better image contrast and higher spatial resolution, structural MRI at 7 Tesla (7T) can lead to lesion detection in about 25% of patients presenting with negative MRI at lower fields. In addition to a better detection/delineation/phenotyping of epileptogenic lesions, higher signal at ultra-high field also facilitates more detailed analyses of several functional and molecular alterations of tissues, susceptible to detect epileptogenic properties even in absence of visible lesions. These advantages but also the technical challenges of 7T MRI in practice will be presented and discussed.

Recognizing the causes of ischemic stroke is crucial for defining secondary preventive strategies. Etiology-based classification systems integrate clinical, imaging, and laboratory findings to assign stroke etiology[1-4]. However, this requires significant time and resources, and these systems still fail to assign etiology in up to 53% of cases[5]. This project aims to develop a Machine Learning (ML) model to predict etiology directly from neuroimaging data of ischemic stroke patients, in order to automate and expedite etiology assignment, and potentially outperform existing systems by identifying etiology in undetermined cases. Data for our project were collected through the PROMISE study, which includes neuroimaging (T1w MRI or NCCT), clinical data, laboratory findings, and etiology assignments (based on the TOAST classification system) for 504 ischemic stroke patients. We extracted two types of features from manually segmented stroke lesions: radiomics features (with pyradiomics) and lesion location features (crafted using published human brain atlases)[6-11]. In total, we extracted 1047 features, which were used to train a Random Forest (RF) model (with scikit-learn[12]). This model is designed to correctly classify stroke cases with known etiology and, specifically, distinguish cardioembolic cases (75% of samples) from others. We selected cases with known etiology (55% of samples), and the resulting dataset was then split into a learning and a test set (70% / 30%) using stratified random sampling. Both sets were median-centered and scaled to the 5th-95th percentile (calculated on the learning set only to prevent data leakage). We used Out-Of-Bag (OOB) estimates on the learning set for hyperparameter optimization, focusing on the number of trees in the forest and the number of features per tree. The highest balanced accuracy was achieved with 500 trees and the square root of the total number of features. With these settings, the model was trained on the learning set using 10-fold stratified cross-validation, achieving an average balanced accuracy of 0.56 ± 0.1. To improve these results, we plan to examine the influence of other hyperparameters on model performance, and consider feature selection or dimensionality reduction before training. Additionally, we may explore other classifiers and implement an unsupervised model to investigate latent structures within our data.

On Wednesday, November 30th, at noon ET / 6PM CET, we will host Alexandre Boutet and Erik H. Middlebrooks. Alexandre Boutet, MD, PhD, is a neuroradiology fellow at the University of Toronto, and will tell us about “How to become an integral part of the functional neurosurgery team as a radiologist”. Erik H. Middlebrooks, MD, is a Professor and Consultant of Neuroradiology and Neurosurgery and the Neuroradiology Program Director at Mayo Clinic. Beside his scientific presentation about “Bridging the Gap from Research to Clinical Decision Making in Epilepsy Neuromodulation”, he will also give us a glimpse at the “Person behind the science”. The talks will be followed by a shared discussion. You can register via talks.stimulatingbrains.org to receive the (free) Zoom link!