Objective
To evaluate the association between the damage to the anterior and posterior visual pathway as evidence of the presence of retrograde and anterograde trans‐synaptic degeneration in multiple ...sclerosis (MS).
Methods
We performed a longitudinal evaluation on a cohort of 100 patients with MS, acquiring retinal optical coherence tomography to measure anterior visual pathway damage (peripapillary retinal nerve fiber layer RNFL thickness and macular volume) and 3T brain magnetic resonance imaging (MRI) for posterior visual pathway damage (volumetry and spectroscopy of visual cortex, lesion volume within optic radiations) at inclusion and after 1 year. Freesurfer and SPM8 software was used for MRI analysis. We evaluated the relationships between the damage in the anterior and posterior visual pathway by voxel‐based morphometry (VBM), multiple linear regressions, and general linear models.
Results
VBM analysis showed that RNFL thinning was specifically associated with atrophy of the visual cortex and with lesions in optic radiations at study inclusion (p < 0.05). Visual cortex volume (β = +0.601, 95% confidence interval CI = +0.04 to +1.16), N‐acetyl aspartate in visual cortex (β = +1.075, 95% CI = +0.190 to +1.961), and lesion volume within optic radiations (β = −2.551, 95% CI = −3.910 to −1.192) significantly influenced average RNFL thinning at study inclusion independently of other confounders, especially optic neuritis (ON). The model indicates that a decrease of 1cm3 in visual cortex volume predicts a reduction of 0.6μm in RNFL thickness. This association was also observed after 1 year of follow‐up. Patients with severe prior ON (adjusted difference = −3.01, 95% CI = −5.08 to −0.95) and mild prior ON (adjusted difference = −1.03, 95% CI = −3.02 to +0.95) had a lower adjusted mean visual cortex volume than patients without ON.
Interpretation
Our results suggest the presence of trans‐synaptic degeneration as a contributor to chronic axon damage in MS. ANN NEUROL 2014;75:98–107
Uncontrolled studies of mesenchymal stem cells (MSCs) in multiple sclerosis suggested some beneficial effect. In this randomized, double-blind, placebo-controlled, crossover phase II study we ...investigated their safety and efficacy in relapsing-remitting multiple sclerosis patients. Efficacy was evaluated in terms of cumulative number of gadolinium-enhancing lesions (GEL) on magnetic resonance imaging (MRI) at 6 months and at the end of the study.
Patients unresponsive to conventional therapy, defined by at least 1 relapse and/or GEL on MRI scan in past 12 months, disease duration 2 to 10 years and Expanded Disability Status Scale (EDSS) 3.0-6.5 were randomized to receive IV 1-2×10(6) bone-marrow-derived-MSCs/Kg or placebo. After 6 months, the treatment was reversed and patients were followed-up for another 6 months. Secondary endpoints were clinical outcomes (relapses and disability by EDSS and MS Functional Composite), and several brain MRI and optical coherence tomography measures. Immunological tests were explored to assess the immunomodulatory effects.
At baseline 9 patients were randomized to receive MSCs (n = 5) or placebo (n = 4). One patient on placebo withdrew after having 3 relapses in the first 5 months. We did not identify any serious adverse events. At 6 months, patients treated with MSCs had a trend to lower mean cumulative number of GEL (3.1, 95% CI = 1.1-8.8 vs 12.3, 95% CI = 4.4-34.5, p = 0.064), and at the end of study to reduced mean GEL (-2.8±5.9 vs 3±5.4, p = 0.075). No significant treatment differences were detected in the secondary endpoints. We observed a non-significant decrease of the frequency of Th1 (CD4+ IFN-γ+) cells in blood of MSCs treated patients.
Bone-marrow-MSCs are safe and may reduce inflammatory MRI parameters supporting their immunomodulatory properties. ClinicalTrials.gov NCT01228266.
The consequences of extremely intense long‐term exercise for brain health remain unknown. We studied the effects of strenuous exercise on brain structure and function, its dose‒response relationship, ...and mechanisms in a rat model of endurance training. Five‐week‐old male Wistar rats were assigned to moderate (MOD) or intense (INT) exercise or a sedentary (SED) group for 16 weeks. MOD rats showed the highest motivation and learning capacity in operant conditioning experiments; SED and INT presented similar results. In vivo MRI demonstrated enhanced global and regional connectivity efficiency and clustering as well as a higher cerebral blood flow (CBF) in MOD but not INT rats compared with SED. In the cortex, downregulation of oxidative phosphorylation complex IV and AMPK activation denoted mitochondrial dysfunction in INT rats. An imbalance in cortical antioxidant capacity was found between MOD and INT rats. The MOD group showed the lowest hippocampal brain‐derived neurotrophic factor levels. The mRNA and protein levels of inflammatory markers were similar in all groups. In conclusion, strenuous long‐term exercise yields a lesser improvement in learning ability than moderate exercise. Blunting of MOD‐induced improvements in CBF and connectivity efficiency, accompanied by impaired mitochondrial energetics and, possibly, transient local oxidative stress, may underlie the findings in intensively trained rats.
The consequences of extremely intense long‐term exercise for brain health remain unknown. We studied the effects of strenuous exercise on brain structure and function in rats that were assigned to moderate (MOD) or intense (INT) exercise or a sedentary (SED) group. INT yields a lesser improvement in learning ability compared to MOD and blunts MOD‐induced improvements in cerebral blood flow and connectivity efficiency.
Quadrantanopia caused by inadvertent severing of Meyer's Loop of the optic radiation is a well‐recognised complication of temporal lobectomy for conditions such as epilepsy. Dissection studies ...indicate that the anterior extent of Meyer's Loop varies considerably between individuals. Quantifying this for individual patients is thus an important step to improve the safety profile of temporal lobectomies. Previous attempts to delineate Meyer's Loop using diffusion MRI tractography have had difficulty estimating its full anterior extent, required manual ROI placement, and/or relied on advanced diffusion sequences that cannot be acquired routinely in most clinics. Here we present CONSULT: a pipeline that can delineate the optic radiation from raw DICOM data in a completely automated way via a combination of robust pre‐processing, segmentation, and alignment stages, plus simple improvements that bolster the efficiency and reliability of standard tractography. We tested CONSULT on 696 scans of predominantly healthy participants (539 unique brains), including both advanced acquisitions and simpler acquisitions that could be acquired in clinically acceptable timeframes. Delineations completed without error in 99.4% of the scans. The distance between Meyer's Loop and the temporal pole closely matched both averages and ranges reported in dissection studies for all tested sequences. Median scan‐rescan error of this distance was 1 mm. When tested on two participants with considerable pathology, delineations were successful and realistic. Through this, we demonstrate not only how to identify Meyer's Loop with clinically feasible sequences, but also that this can be achieved without fundamental changes to tractography algorithms or complex post‐processing methods.
Quadrantanopia caused by inadvertent severing of Meyer's Loop of the optic radiation is a well‐recognised complication of temporal lobectomy. We demonstrated a fully automated pipeline that could delineate this structure reliably and realistically on more than 500 unique brains, including both advanced and more clinically‐accessible acquisitions. Results were in line with historical dissections studies and median scan‐rescan error was 1 mm.
Purpose
To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep ...learning.
Methods
MRF using echo‐planar imaging (EPI) scans with varying repetition and echo times were acquired for whole brain quantification of T1 and T2∗ in 50 subjects with multiple sclerosis (MS) and 10 healthy volunteers along 2 centers. MRF T1 and T2∗ parametric maps were distortion corrected and denoised. A CNN was trained to reconstruct the T1 and T2∗ parametric maps, and the WM and GM probability maps.
Results
Deep learning‐based postprocessing reduced reconstruction and image processing times from hours to a few seconds while maintaining high accuracy, reliability, and precision. Mean absolute error performed the best for T1 (deviations 5.6%) and the logarithmic hyperbolic cosinus loss the best for T2∗ (deviations 6.0%).
Conclusions
MRF is a fast and robust tool for quantitative T1 and T2∗ mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning.
Abstract In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease ...pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Machine learning (ML) models analyse various data sources, including magnetic resonance imaging (MRI), genetic, and clinical data, to distinguish MS from other conditions, predict disease progression, and personalize treatment strategies. Additionally, AI models have been extensively applied to lesion segmentation, identification of biomarkers, and prediction of outcomes, disease monitoring, and management. Despite the big promises of AI solutions, model interpretability and transparency remain critical for gaining clinician and patient trust in these methods. The future of AI in MS holds potential for open data initiatives that could feed ML models and increasing generalizability, the implementation of federated learning solutions for training the models addressing data sharing issues, and generative AI approaches to address challenges in model interpretability, and transparency. In conclusion, AI presents an opportunity to advance our understanding and management of MS. AI promises to aid clinicians in MS diagnosis and prognosis improving patient outcomes and quality of life, however ensuring the interpretability and transparency of AI-generated results is going to be key for facilitating the integration of AI into clinical practice.
Multiple sclerosis (MS) is a chronic autoimmune disease characterized by demyelinating lesions that are often visible on magnetic resonance imaging (MRI). Segmentation of these lesions can provide ...imaging biomarkers of disease burden that can help monitor disease progression and the imaging response to treatment. Manual delineation of MRI lesions is tedious and prone to subjective bias, while automated lesion segmentation methods offer objectivity and speed, the latter being particularly important when analysing large datasets. Lesion segmentation can be broadly categorised into two groups: cross-sectional methods, which use imaging data acquired at a single time-point to characterise MRI lesions; and longitudinal methods, which use imaging data from the same subject acquired at two or more different time-points to characterise lesions over time. The main objective of longitudinal segmentation approaches is to more accurately detect the presence of new MS lesions and the growth or remission of existing lesions, which may be effective biomarkers of disease progression and treatment response. This paper reviews articles on longitudinal MS lesion segmentation methods published over the past 10 years. These are divided into traditional machine learning methods and deep learning techniques. PubMed articles using longitudinal information and comparing fully automatic two time point segmentations in any step of the process were selected. Nineteen articles were reviewed. There is an increasing number of deep learning techniques for longitudinal MS lesion segmentation that are promising to help better understand disease progression.
The optic radiation (OR) is one of the major components of the visual system and a key structure at risk in white matter diseases such as multiple sclerosis (MS). However, it is challenging to ...perform track reconstruction of the OR using diffusion MRI due to a sharp change of direction in the Meyer's loop and the presence of kissing and crossing fibers along the pathway. As such, we aimed to provide a highly precise and reproducible framework for tracking the OR from thalamic and visual cortex masks. The framework combined the generation of probabilistic streamlines by high order fiber orientation distributions estimated with constrained spherical deconvolution and an automatic post-processing based on anatomical exclusion criteria (AEC) to compensate for the presence of anatomically implausible streamlines. Specifically, those ending in the contralateral hemisphere, cerebrospinal fluid or grey matter outside the visual cortex were automatically excluded. We applied the framework to two distinct high angular resolution diffusion-weighted imaging (HARDI) acquisition protocols on one cohort, comprised of ten healthy volunteers and five MS patients. The OR was successfully delineated in both HARDI acquisitions in the healthy volunteers and MS patients. Quantitative evaluation of the OR position was done by comparing the results with histological reference data. Compared with histological mask, the OR reconstruction into a template (OR-TCT) was highly precise (percentage of voxels within the OR-TCT correctly defined as OR), ranging from 0.71 to 0.83. The sensitivity (percentage of voxels in histological reference mask correctly defined as OR in OR-TCT) ranged from 0.65 to 0.81 and the accuracy (measured by F1 score) was 0.73 to 0.77 in healthy volunteers. When AEC was not applied the precision and accuracy decreased. The absolute agreement between both HARDI datasets measured by the intraclass correlation coefficient was 0.73. This improved framework allowed us to reconstruct the OR with high reliability and accuracy independently of the acquisition parameters. Moreover, the reconstruction was possible even in the presence of tissue damage due to MS. This framework could also be applied to other tracts with complex configuration.
To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning.
MRF ...using echo-planar imaging (EPI) scans with varying repetition and echo times were acquired for whole brain quantification of
and
in 50 subjects with multiple sclerosis (MS) and 10 healthy volunteers along 2 centers. MRF
and
parametric maps were distortion corrected and denoised. A CNN was trained to reconstruct the
and
parametric maps, and the WM and GM probability maps.
Deep learning-based postprocessing reduced reconstruction and image processing times from hours to a few seconds while maintaining high accuracy, reliability, and precision. Mean absolute error performed the best for
(deviations 5.6%) and the logarithmic hyperbolic cosinus loss the best for
(deviations 6.0%).
MRF is a fast and robust tool for quantitative
and
mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning.
Background:
Cognitive reserve (CR) could attenuate the impact of the brain burden on the cognition in people with multiple sclerosis (PwMS).
Objective:
To explore the relationship between CR and ...structural brain connectivity and investigate their role on cognition in PwMS cognitively impaired (PwMS-CI) and cognitively preserved (PwMS-CP).
Methods:
In this study, 181 PwMS (71% female; 42.9 ± 10.0 years) were evaluated using the Cognitive Reserve Questionnaire (CRQ), Brief Repeatable Battery of Neuropsychological tests, and MRI. Brain lesion and gray matter volumes were quantified, as was the structural network connectivity. Patients were classified as PwMS-CI (
z
scores = −1.5 SD in at least two tests) or PwMS-CP. Linear and multiple regression analyses were run to evaluate the association of CRQ and structural connectivity with cognition in each group. Hedges's effect size was used to compute the strength of associations.
Results:
We found a very low association between CRQ scores and connectivity metrics in PwMS-CP, while in PwMS-CI, this relation was low to moderate. The multiple regression model, adjusted for age, gender, mood, lesion volume, and graph metrics (local and global efficiency, and transitivity), indicated that the CRQ (β = 0.26, 95% CI: 0.17–0.35) was associated with cognition (adj
R
2
= 0.34) in PwMS-CP (55%). In PwMS-CI, CRQ (β = 0.18, 95% CI: 0.07–0.29), age, and network global efficiency were independently associated with cognition (adj
R
2
= 0.55). The age- and gender-adjusted association between CRQ score and global efficiency on having an impaired cognitive status was −0.338 (OR: 0.71,
p
= 0.036) and −0.531 (OR: 0.59,
p
= 0.002), respectively.
Conclusions:
CR seems to have a marginally significant effect on brain structural connectivity, observed in patients with more severe clinical impairment. It protects PwMS from cognitive decline regardless of their cognitive status, yet once cognitive impairment has set in, brain damage and aging are also influencing cognitive performance.
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