MRI biomarkers of disease progression in multiple sclerosis: old  dog, new tricks?

Barnett, Yael; Garber, Justin Y.; Barnett, Michael

doi:10.21037/qims.2020.01.04

Cited by 13 publications

(13 citation statements)

References 32 publications

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“…One of the challenges in MS evaluation and the prediction of its stages is that the symptoms vary widely and as the disease worsens, new lesions become less frequent ( 57 ). In addition, the MS course in patients, even from the early stages, is characterized by a slow progression of disabilities independent of relapses ( 58 ).…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images

et al. 2021

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Multiple sclerosis (MS) is one of the most common autoimmune diseases which is commonly diagnosed and monitored using magnetic resonance imaging (MRI) with a combination of clinical manifestations. The purpose of this review is to highlight the main applications of Machine Learning (ML) models and their performance in the MS field using MRI. We reviewed the articles of the last decade and grouped them based on the applications of ML in MS using MRI data into four categories: 1) Automated diagnosis of MS, 2) Prediction of MS disease progression, 3) Differentiation of MS stages, 4) Differentiation of MS from similar disorders.

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Section: Resultsmentioning

confidence: 99%

Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images

et al. 2021

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“…The integrity of the central nervous system, as well as progression of disease activity, is often probed using MRI [ 8 , 9 ]. However, accumulating evidence suggests inflammatory central nervous system lesions are transient on brain imaging [ 9 ] and symptoms relate more to disruption in functional brain networks that do not always localize well to individual brain structures [ 119 ].…”

Section: Discussionmentioning

confidence: 99%

“…This is critical information needed to determine how neurorehabilitation may alter neuroplasticity in heterogeneous disorders of the central nervous system such as stroke and multiple sclerosis (MS) [ 2 , 3 , 4 , 5 ]. Some biomarkers can be collected and assayed from biological fluid such blood [ 6 , 7 ] or cerebrospinal fluid [ 7 ], while others could involve sophisticated imaging techniques such as positron emission tomography or magnetic resonance imaging (MRI) [ 3 , 8 , 9 ]. One potential method to gather biomarkers of central nervous system (dys)function and neuroplasticity is via Transcranial Magnetic Stimulation (TMS) [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Probing the Brain–Body Connection Using Transcranial Magnetic Stimulation (TMS): Validating a Promising Tool to Provide Biomarkers of Neuroplasticity and Central Nervous System Function

et al. 2021

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Transcranial magnetic stimulation (TMS) is a non-invasive method used to investigate neurophysiological integrity of the human neuromotor system. We describe in detail, the methodology of a single pulse TMS protocol that was performed in a large cohort of people (n = 110) with multiple sclerosis (MS). The aim was to establish and validate a core-set of TMS variables that predicted typical MS clinical outcomes: walking speed, hand dexterity, fatigue, and cognitive processing speed. We provide a brief and simple methodological pipeline to examine excitatory and inhibitory corticospinal mechanisms in MS that map to clinical status. Delayed and longer ipsilateral silent period (a measure of transcallosal inhibition; the influence of one brain hemisphere’s activity over the other), longer cortical silent period (suggestive of greater corticospinal inhibition via GABA) and higher resting motor threshold (lower corticospinal excitability) most strongly related to clinical outcomes, especially when measured in the hemisphere corresponding to the weaker hand. Greater interhemispheric asymmetry (imbalance between hemispheres) correlated with poorer performance in the greatest number of clinical outcomes. We also show, not surprisingly, that TMS variables related more strongly to motor outcomes than non-motor outcomes. As it was validated in a large sample of patients with varying severities of central nervous system dysfunction, the protocol described herein can be used by investigators and clinicians alike to investigate the role of TMS as a biomarker in MS and other central nervous system disorders.

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“…It is well established that MS brain lesions are characterized by numerous pathological processes, such as demyelination, inflammation, and gliosis 5 . As of now, though, the underlying pathology that characterizes lesional tissue that go on to develop into atrophied T2‐LV remains poorly understood.…”

Section: Introductionmentioning

confidence: 99%

Diffusion tensor imaging reveals greater microstructure damage in lesional tissue that shrinks into cerebrospinal fluid in multiple sclerosis

Bergsland

Dwyer

Jakimovski

et al. 2021

Journal of Neuroimaging

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Background and Purpose Atrophied T2 lesion volume (LV), reflecting the complete transformation of lesions into cerebrospinal fluid (CSF), has been associated with disease progression in multiple sclerosis (MS). The underlying damage leading to lesion destruction remains poorly understood. The objective of this study was to use diffusion tensor imaging (DTI) to investigate the extent of microstructural tissue damage at baseline in lesions that subsequently transform into CSF. Methods Ninety‐nine MS patients (67 relapsing‐remitting MS [RRMS] and 32 progressive PMS [PMS]) were imaged at baseline and after an average of 5.3 ± 0.6 years of follow‐up. Assessments included T2 LV and DTI at baseline and atrophied T2 LV over follow‐up. Lesioned areas that became atrophied T2 LV were compared to those that did not. Baseline lesional DTI metrics were compared between RRMS versus PMS patients and between patients with disability progression (DP, n = 35) versus non‐DP (n = 64), using ANCOVA models. Results Lesion tissue that developed into atrophied T2 LV had significantly different baseline DTI parameters compared to nonatrophied T2‐LV tissue (p<0.001), with the largest effect for free‐water (d = 2.739). Baseline tissue characteristics of future atrophied T2 LV were not significantly different between groups. However, DP patients developed greater atrophied T2 LV (377 vs. 83 mm3, p < 0.001). Conclusions Extensive microstructural damage characterizes lesions replaced by CSF, independently of disease phenotype or future DP. Greater atrophied T2 LV predicts DP.

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MRI biomarkers of disease progression in multiple sclerosis: old dog, new tricks?

Cited by 13 publications

References 32 publications

Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images

Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images

Probing the Brain–Body Connection Using Transcranial Magnetic Stimulation (TMS): Validating a Promising Tool to Provide Biomarkers of Neuroplasticity and Central Nervous System Function

Diffusion tensor imaging reveals greater microstructure damage in lesional tissue that shrinks into cerebrospinal fluid in multiple sclerosis

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