Brain atrophy measurement over a MRI scanner change in multiple sclerosis

Brain atrophy assessment in multiple sclerosis: technical- and subject-related barriers for translation to real-world application in individual subjects

INTRODUCTION

Brain atrophy is a well-established MRI outcome for predicting clinical progression and monitoring treatment response in persons with multiple sclerosis (pwMS) at the group level. Despite the important progress made, the translation of brain atrophy assessment into clinical practice faces several challenges.

AREAS COVERED

In this review, the authors discuss technical- and subject-related barriers for implementing brain atrophy assessment as part of the clinical routine at the individual level. Substantial progress has been made to understand and mitigate technical barriers behind MRI acquisition. Numerous research and commercial segmentation techniques for volume estimation are available and technically validated, but their clinical value has not been fully established. A systematic assessment of subject-related barriers, which include genetic, environmental, biological, lifestyle, comorbidity, and aging confounders, is critical for the interpretation of brain atrophy measures at the individual subject level. Educating both medical providers and pwMS will help better clarify the benefits and limitations of assessing brain atrophy for disease monitoring and prognosis.

EXPERT OPINION

Integrating brain atrophy assessment into clinical practice for pwMS requires overcoming technical and subject-related challenges. Advances in MRI standardization, artificial intelligence, and clinician education will facilitate this process, improving disease management and potentially reducing long-term healthcare costs.

Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods

Over the past decades, morphometric analysis of brain MRI has contributed substantially to the understanding of healthy brain structure, development and aging as well as to improved characterisation of disease related pathologies. Certified commercial tools based on normative modeling of these metrics are meanwhile available for diagnostic purposes, but they are cost intensive and their clinical evaluation is still in its infancy. Here we have compared the performance of "ScanOMetrics", an open-source research-level tool for detection of statistical anomalies in individual MRI scans, depending on whether it is operated on the output of FreeSurfer or of the deep learning based brain morphometry tool DL + DiReCT. When applied to the public OASIS3 dataset, containing patients with Alzheimer's disease (AD) and healthy controls (HC), cortical thickness anomalies in patient scans were mainly detected in regions that are known as predilection areas of cortical atrophy in AD, regardless of the software used for extraction of the metrics. By contrast, anomaly detections in HCs were up to twenty-fold reduced and spatially unspecific using both DL + DiReCT and FreeSurfer. Progression of the atrophy pattern with clinical dementia rating (CDR) was clearly observable with both methods. DL + DiReCT provided results in less than 25 min, more than 15 times faster than FreeSurfer. This difference in computation time might be relevant when considering application of this or similar methodology as diagnostic decision support for neuroradiologists.

Evoked potentials after autologous hematopoietic stem cell transplantation for multiple sclerosis

OBJECTIVE

To investigate the effect of autologous hematopoietic stem cell transplantation (AHSCT) on functional aspects of the nervous system assessed by visual (VEP), somatosensory (SEP), and motor (MEP) evoked potentials in patients with relapsing-remitting multiple sclerosis.

BACKGROUND

Several studies have demonstrated the efficacy of AHSCT on inflammatory activity and disability progression in patients with multiple sclerosis. However, the impact of AHSCT on evoked potentials has not been evaluated before.

METHODS

Twelve AHSCT-treated patients from Uppsala University Hospital were consecutively recruited. Evoked potentials (EP) were collected at baseline and two follow-up visits, 3 and 12 months post-AHSCT. We calculated a composite EP score for each participant and compared it between different time points.

RESULTS

The median total EP score decreased from 5 at baseline, to 2.5 at 12 months post-ASHCT (p = 0.008). A significant improvement in tibial SEP (tSEP) latencies was observed (42.7 vs 41.5 ms, p < 0.001), with a similar trend for MEP latencies 12 months post-ASHCT. No significant changes in median SEP or VEP latencies were observed.

CONCLUSIONS

Treatment with AHSCT was associated with improved transmission in some central nervous system pathways in multiple sclerosis patients.

Higher longitudinal brain white matter atrophy rate in aquaporin-4 IgG-positive NMOSD compared with healthy controls

We aimed to compare longitudinal brain atrophy in patients with neuromyelitis optica spectrum disorder (NMOSD) with healthy controls (HCs). The atrophy rate in patients with anti-aquaporin-4 antibody-positive NMOSD (AQP4 + NMOSD) was compared with age-sex-matched HCs recruited from the Japanese Alzheimer's Disease Neuroimaging Initiative study and another study performed at Chiba University. Twenty-nine patients with AQP4 + NMOSD and 29 HCs were enrolled in the study. The time between magnetic resonance imaging (MRI) scans was longer in the AQP4 + NMOSD group compared with the HCs (median; 3.2 vs. 2.9 years, P = 0.009). The annualized normalized white matter volume (NWV) atrophy rate was higher in the AQP4 + NMOSD group compared with the HCs (median; 0.37 vs. - 0.14, P = 0.018). The maximum spinal cord lesion length negatively correlated with NWV at baseline MRI in patients with AQP4 + NMOSD (Spearman's rho =  - 0.41, P = 0.027). The annualized NWV atrophy rate negatively correlated with the time between initiation of persistent prednisolone usage and baseline MRI in patients with AQP4 + NMOSD (Spearman's rho =  - 0.43, P = 0.019). Patients with AQP4 + NMOSD had a greater annualized NWV atrophy rate than HCs. Suppressing disease activity may prevent brain atrophy in patients with AQP4 + NMOSD.

Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation

Introduction

Brain atrophy is a critical biomarker of disease progression and treatment response in neurodegenerative diseases such as multiple sclerosis (MS). Confounding factors such as inconsistent imaging acquisitions hamper the accurate measurement of brain atrophy in the clinic. This study aims to develop and validate a robust deep learning model to overcome these challenges; and to evaluate its impact on the measurement of disease progression.

Methods

Voxel-wise pseudo-atrophy labels were generated using SIENA, a widely adopted tool for the measurement of brain atrophy in MS. Deformation maps were produced for 195 pairs of longitudinal 3D T1 scans from patients with MS. A 3D U-Net, namely DeepBVC, was specifically developed overcome common variances in resolution, signal-to-noise ratio and contrast ratio between baseline and follow up scans. The performance of DeepBVC was compared against SIENA using McLaren test-retest dataset and 233 in-house MS subjects with MRI from multiple time points. Clinical evaluation included disability assessment with the Expanded Disability Status Scale (EDSS) and traditional imaging metrics such as lesion burden.

Results

For 3 subjects in test-retest experiments, the median percent brain volume change (PBVC) for DeepBVC and SIENA was 0.105 vs. 0.198% (subject 1), 0.061 vs. 0.084% (subject 2), 0.104 vs. 0.408% (subject 3). For testing consistency across multiple time points in individual MS subjects, the mean (± standard deviation) PBVC difference of DeepBVC and SIENA were 0.028% (± 0.145%) and 0.031% (±0.154%), respectively. The linear correlation with baseline T2 lesion volume were r = -0.288 (p < 0.05) and r = -0.249 (p < 0.05) for DeepBVC and SIENA, respectively. There was no significant correlation of disability progression with PBVC as estimated by either method (p = 0.86, p = 0.84).

Discussion

DeepBVC is a deep learning powered brain volume change estimation method for assessing brain atrophy used T1-weighted images. Compared to SIENA, DeepBVC demonstrates superior performance in reproducibility and in the context of common clinical scan variances such as imaging contrast, voxel resolution, random bias field, and signal-to-noise ratio. Enhanced measurement robustness, automation, and processing speed of DeepBVC indicate its potential for utilisation in both research and clinical environments for monitoring disease progression and, potentially, evaluating treatment effectiveness.