The sixth sense: how much does interictal intracranial EEG add to determining the focality of epileptic networks?

Intracranial EEG is used for two main purposes: to determine (i) if epileptic networks are amenable to focal treatment and (ii) where to intervene. Currently, these questions are answered qualitatively and differently across centres. There is a need to quantify the focality of epileptic networks systematically, which may guide surgical decision-making, enable large-scale data analysis and facilitate multi-centre prospective clinical trials.

A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma.

Vestibular schwannomas (VS) are the most common tumor of the skull base with available treatment options that carry a risk of iatrogenic injury to the facial nerve, which can significantly impact patients' quality of life. As facial nerve outcomes remain challenging to prognosticate, we endeavored to utilize machine learning to decipher predictive factors relevant to facial nerve outcomes following microsurgical resection of VS. A database of patient-, tumor- and surgery-specific features was constructed via retrospective chart review of 242 consecutive patients who underwent microsurgical resection of VS over a 7-year study period.

Mapping the Relationship of White Matter Lesions to Depression in Multiple Sclerosis.

Background: Multiple sclerosis (MS) is an immune-mediated neurological disorder, and up to 50% of patients experience depression. We investigated how white matter network disruption is related to depression in MS.

Methods: Using electronic health records, 380 participants with MS were identified.

Quantifying interictal intracranial EEG to predict focal epilepsy.

Introduction: Intracranial EEG (IEEG) is used for 2 main purposes, to determine: (1) if epileptic networks are amenable to focal treatment and (2) where to intervene. Currently these questions are answered qualitatively and sometimes differently across centers. There is a need for objective, standardized methods to guide surgical decision making and to enable large scale data analysis across centers and prospective clinical trials.

Mapping the relationship of white matter lesions to depression in multiple sclerosis.

Importance: Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects nearly one million people in the United States. Up to 50% of patients with MS experience depression.

Objective: To investigate how white matter network disruption is related to depression in MS.

Association of brain tissue cerebrospinal fluid fraction with age in healthy cognitively normal adults.

Background And Purpose: Our objective was to apply multi-compartment T2 relaxometry in cognitively normal individuals aged 20-80 years to study the effect of aging on the parenchymal CSF fraction (CSFF), a potential measure of the subvoxel CSF space.

Materials And Methods: A total of 60 volunteers (age range, 22-80 years) were enrolled. Voxel-wise maps of short-T2 myelin water fraction (MWF), intermediate-T2 intra/extra-cellular water fraction (IEWF), and long-T2 CSFF were obtained using fast acquisition with spiral trajectory and adiabatic T2prep (FAST-T2) sequence and three-pool non-linear least squares fitting.

Quantitative Water Permeability Mapping of Blood-Brain-Barrier Dysfunction in Aging.

Blood-brain-barrier (BBB) dysfunction is a hallmark of aging and aging-related disorders, including cerebral small vessel disease and Alzheimer's disease. An emerging biomarker of BBB dysfunction is BBB water exchange rate (k) as measured by diffusion-weighted arterial spin labeling (DW-ASL) MRI. We developed an improved DW-ASL sequence for Quantitative Permeability Mapping and evaluated whole brain and region-specific k in a cohort of 30 adults without dementia across the age spectrum.

Quantitative susceptibility mapping versus phase imaging to identify multiple sclerosis iron rim lesions with demyelination.

Background And Purpose: To compare quantitative susceptibility mapping (QSM) and high-pass-filtered (HPF) phase imaging for (1) identifying chronic active rim lesions with more myelin damage and (2) distinguishing patients with increased clinical disability in multiple sclerosis.

Methods: Eighty patients were scanned with QSM for paramagnetic rim detection and Fast Acquisition with Spiral Trajectory and T2prep for myelin water fraction (MWF). Chronic lesions were classified based on the presence/absence of rim on HPF and QSM images.

QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps.

Background And Purpose: Chronic active multiple sclerosis (MS) lesions are characterized by a paramagnetic rim at the edge of the lesion and are associated with increased disability in patients. Quantitative susceptibility mapping (QSM) is an MRI technique that is sensitive to chronic active lesions, termed rim + lesions on the QSM. We present QSMRim-Net, a data imbalance-aware deep neural network that fuses lesion-level radiomic and convolutional image features for automated identification of rim + lesions on QSM.

ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation.

Accurate detection and segmentation of multiple sclerosis (MS) brain lesions on magnetic resonance images are important for disease diagnosis and treatment. This is a challenging task as lesions vary greatly in size, shape, location, and image contrast. The objective of our study was to develop an algorithm based on deep convolutional neural network integrated with anatomic information and lesion-wise loss function (ALL-Net) for fast and accurate automated segmentation of MS lesions.

Fully automated detection of paramagnetic rims in multiple sclerosis lesions on 3T susceptibility-based MR imaging.

Background And Purpose: The presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis lesion. Increased prevalence of these paramagnetic rim lesions is associated with a more severe disease course in MS, but manual identification is time-consuming. We present APRL, a method to automatically detect paramagnetic rim lesions on 3T T2*-phase images.

Functional connectome reorganization relates to post-stroke motor recovery and structural and functional disconnection.

Motor recovery following ischemic stroke is contingent on the ability of surviving brain networks to compensate for damaged tissue. In rodent models, sensory and motor cortical representations have been shown to remap onto intact tissue around the lesion site, but remapping to more distal sites (e.g.

Diagnostic accuracy of shuttle CT angiography (CTA) and helical CTA in the diagnosis of vasospasm.

Purpose: To evaluate the diagnostic accuracy of computed tomography angiography (CTA) acquired with shuttle technique (CTAs) and helical CTA (CTAh) for vasospasm, using digital subtraction angiography (DSA) obtained within 24 h as reference standard.

Methods: Thirty-six patients with suspected vasospasm in the setting of aneurysmal subarachnoid hemorrhage (ASAH, 30/36) or acute inflammatory/infectious conditions (6/36) who underwent CTAs (17/36) or CTAh (19/36) followed by DSA within 24 h were identified retrospectively. Presence of vasospasm in the proximal cerebral arterial segments was assessed qualitatively and semi-quantitatively on CTA and subsequent DSA.

Predictors of acute deep venous thrombosis in patients hospitalized for COVID-19.

Deep venous thrombosis (DVT) is associated with high mortality in coronavirus disease 2019 (COVID-19) but there remains uncertainty about the benefit of anti-coagulation prophylaxis and how to decide when ultrasound screening is indicated. We aimed to determine parameters predicting which COVID-19 patients are at risk of DVT and to assess the benefit of prophylactic anti-coagulation.Adult hospitalized patients with positive severe acute respiratory syndrome coronavirus-2 reverse transcription-polymerase chain reaction (RT-PCR) undergoing venous duplex ultrasound for DVT assessment (n = 451) were retrospectively reviewed.

Brain oxygen extraction fraction mapping in patients with multiple sclerosis.

We aimed to demonstrate the feasibility of whole brain oxygen extraction fraction (OEF) mapping for measuring lesion specific and regional OEF abnormalities in multiple sclerosis (MS) patients. In 22 MS patients and 11 healthy controls (HC), OEF and neural tissue susceptibility () maps were computed from MRI multi-echo gradient echo data. In MS patients, 80 chronic active lesions with hyperintense rim on quantitative susceptibility mapping were identified, and the mean OEF and within the rim and core were compared using linear mixed-effect model analysis.

Heuristic scoring method utilizing FDG-PET statistical parametric mapping in the evaluation of suspected Alzheimer disease and frontotemporal lobar degeneration.

Distinguishing frontotemporal lobar degeneration (FTLD) and Alzheimer Disease (AD) on FDG-PET based on qualitative review alone can pose a diagnostic challenge. SPM has been shown to improve diagnostic performance in research settings, but translation to clinical practice has been lacking. Our purpose was to create a heuristic scoring method based on statistical parametric mapping z-scores.

Global cerebrospinal fluid as a zero-reference regularization for brain quantitative susceptibility mapping.

Background And Purpose: The objective ofthis study was to demonstrate a global cerebrospinal fluid (CSF) method for a consistent and automated zero referencing of brain quantitative susceptibility mapping (QSM).

Methods: Whole brain CSF mask was automatically segmented by thresholding the gradient echo transverse relaxation ( map, and regularization was employed to enforce uniform susceptibility distribution within the CSF volume in the field-to-susceptibility inversion. This global CSF regularization method was compared with a prior ventricular CSF regularization.

Differential frequency in imaging-based outcome measurement: Bias in real-world oncology comparative-effectiveness studies.

Background: Comparative-effectiveness studies using real-world data (RWD) can be susceptible to surveillance bias. In solid tumor oncology studies, analyses of endpoints such as progression-free survival (PFS) are based on progression events detected by imaging assessments. This study aimed to evaluate the potential bias introduced by differential imaging assessment frequency when using electronic health record (EHR)-derived data to investigate the comparative effectiveness of cancer therapies.

Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging.

We introduce the first-ever statistical framework for estimating the age of Multiple Sclerosis (MS) lesions from magnetic resonance imaging (MRI). Estimating lesion age is an important step when studying the longitudinal behavior of MS lesions and can be used in applications such as studying the temporal dynamics of chronic active MS lesions. Our lesion age estimation models use first order radiomic features over a lesion derived from conventional T1 (T1w) and T2 weighted (T2w) and fluid attenuated inversion recovery (FLAIR), T1w with gadolinium contrast (T1w+c), and Quantitative Susceptibility Mapping (QSM) MRI sequences as well as demographic information.

PItcHPERFeCT: Primary Intracranial Hemorrhage Probability Estimation using Random Forests on CT.

Introduction: Intracerebral hemorrhage (ICH), where a blood vessel ruptures into areas of the brain, accounts for approximately 10-15% of all strokes. X-ray computed tomography (CT) scanning is largely used to assess the location and volume of these hemorrhages. Manual segmentation of the CT scan using planimetry by an expert reader is the gold standard for volume estimation, but is time-consuming and has within- and across-reader variability.

A LAG FUNCTIONAL LINEAR MODEL FOR PREDICTION OF MAGNETIZATION TRANSFER RATIO IN MULTIPLE SCLEROSIS LESIONS.

We propose a lag functional linear model to predict a response using multiple functional predictors observed at discrete grids with noise. Two procedures are proposed to estimate the regression parameter functions: (1) an approach that ensures smoothness for each value of time using generalized cross-validation; and (2) a global smoothing approach using a restricted maximum likelihood framework. Numerical studies are presented to analyze predictive accuracy in many realistic scenarios.

PREVAIL: Predicting Recovery through Estimation and Visualization of Active and Incident Lesions.

Objective: The goal of this study was to develop a model that integrates imaging and clinical information observed at lesion incidence for predicting the recovery of white matter lesions in multiple sclerosis (MS) patients.

Methods: Demographic, clinical, and magnetic resonance imaging (MRI) data were obtained from 60 subjects with MS as part of a natural history study at the National Institute of Neurological Disorders and Stroke. A total of 401 lesions met the inclusion criteria and were used in the study.

Removing inter-subject technical variability in magnetic resonance imaging studies.

Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across subjects. However, we show that unwanted inter-scan variability associated with imaging site, scanner effect, and other technical artifacts is still present after standard intensity normalization in large multi-site neuroimaging studies.

Clinical 3-tesla FLAIR* MRI improves diagnostic accuracy in multiple sclerosis.

Objective: To evaluate clinical fluid-attenuated inversion recovery (FLAIR)* 3T magnetic resonance imaging (MRI), which is sensitive to perivenular inflammatory demyelinating lesions, in diagnosing multiple sclerosis (MS).

Background: Central veins may be a distinguishing feature of MS lesions. FLAIR*, a combined contrast derived from clinical MRI scans, has not been studied as a clinical tool for diagnosing MS.

Statistical estimation of T1 relaxation times using conventional magnetic resonance imaging.

Quantitative T1 maps estimate T1 relaxation times and can be used to assess diffuse tissue abnormalities within normal-appearing tissue. T1 maps are popular for studying the progression and treatment of multiple sclerosis (MS). However, their inclusion in standard imaging protocols remains limited due to the additional scanning time and expert calibration required and susceptibility to bias and noise.