Defining reperfusion post endovascular therapy in ischemic stroke using MR-dynamic contrast enhanced perfusion.

Objectives: Cerebral blood flow (CBF) measurements after endovascular therapy (EVT) for acute ischemic stroke are important to distinguish early secondary injury related to persisting ischemia from that related to reperfusion when considering clinical response and infarct growth.

Methods: We compare reperfusion quantified by the modified Thrombolysis in Cerebral Infarction Score (mTICI) with perfusion measured by MRI dynamic contrast-enhanced perfusion within 5 h of EVT anterior circulation stroke. MR perfusion (rCBF, rCBV, rTmax, rT0) and mTICI scores were included in a predictive model for change in NIHSS at 24 h and diffusion-weighted imaging (DWI) lesion growth (acute to 24 h MRI) using a machine learning RRELIEFF feature selection coupled with a support vector regression.

The Association of Saliva Cytokines and Pediatric Sports-Related Concussion Outcomes.

Objectives: This study aimed to explore cytokine alterations following pediatric sports-related concussion (SRC) and whether a specific cytokine profile could predict symptom burden and time to return to sports (RTS).

Setting: Sports Medicine Clinic.

Participants: Youth ice hockey participants (aged 12-17 years) were recruited prior to the 2013-2016 hockey season.

Building an Otoscopic screening prototype tool using deep learning.

Background: Otologic diseases are often difficult to diagnose accurately for primary care providers. Deep learning methods have been applied with great success in many areas of medicine, often outperforming well trained human observers. The aim of this work was to develop and evaluate an automatic software prototype to identify otologic abnormalities using a deep convolutional neural network.

Automatic classification of major depression disorder using arterial spin labeling MRI perfusion measurements.

Aim: Neuroimaging-based multivariate pattern-recognition methods have been successfully used to develop diagnostic algorithms to distinguish patients with major depressive disorder (MDD) from healthy controls (HC). We developed and evaluated the accuracy of a multivariate classification method for the differentiation of MDD and HC using cerebral blood flow (CBF) features measured by non-invasive arterial spin labeling (ASL) MRI.

Methods: Twenty-two medication-free patients with the diagnosis of MDD based on DSM-IV criteria and 22 HC underwent pseudo-continuous 3-D-ASL imaging to assess CBF.

Widespread diffusion changes differentiate Parkinson's disease and progressive supranuclear palsy.

Background: Parkinson's disease (PD) and progressive supranuclear palsy - Richardson's syndrome (PSP-RS) are often represented by similar clinical symptoms, which may challenge diagnostic accuracy. The objective of this study was to investigate and compare regional cerebral diffusion properties in PD and PSP-RS subjects and evaluate the use of these metrics for an automatic classification framework.

Material And Methods: Diffusion-tensor MRI datasets from 52 PD and 21 PSP-RS subjects were employed for this study.

Improved Automatic Morphology-Based Classification of Parkinson's Disease and Progressive Supranuclear Palsy.

Objectives: The overlapping symptoms of Parkinson's disease (PD) and progressive supranuclear palsy-Richardson's syndrome (PSP-RS) often make a correct clinical diagnosis difficult. The volume of subcortical brain structures derived from high-resolution T1-weighted magnetic resonance imaging (MRI) datasets is frequently used for individual level classification of PD and PSP-RS patients. The aim of this study was to evaluate the benefit of including additional morphological features beyond the simple regional volume, as well as clinical features, and morphological features of cortical structures for an automatic classification of PD and PSP-RS patients.