Focal multiple sclerosis lesions abound in ‘normal appearing white matter’

2011 ◽  
Vol 17 (11) ◽  
pp. 1313-1323 ◽  
Author(s):  
Niraj Mistry ◽  
Emma C Tallantyre ◽  
Jennifer E Dixon ◽  
Nicolas Galazis ◽  
Tim Jaspan ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
High Field ◽  
White Matter Lesions ◽  
7 Tesla ◽  
Ultra High Field ◽  
Rapid Acquisition ◽  
Normal Appearing White Matter ◽  
Mri Scans ◽  
Image Type

Background: The ‘normal appearing white matter’ (NAWM) in multiple sclerosis (MS) is known to be abnormal using quantitative magnetic resonance (MR) techniques. The aetiology of the changes in NAWM remains debatable. Objective: To investigate whether high-field and ultra high-field T1-weighted magnetization prepared rapid acquisition gradient echo (MPRAGE) MRI enables detection of MS white matter lesions in areas defined as NAWM using high-field T2-weighted fluid attenuation inversion recovery (FLAIR) MRI; that is, to ascertain whether undetected lesions are likely contributors to the burden of abnormality in similarly defined NAWM. Methods: Fourteen MS patients underwent MRI scans using 3T FLAIR and MPRAGE and 7 Tesla (7T) MPRAGE sequences. Independent observers identified lesions on 3T FLAIR and (7T and 3T) MPRAGE images. The detection of every individual lesion was then compared for each image type. Results: We identified a total of 812 white matter lesions on 3T FLAIR. Using 3T MPRAGE, 186 additional lesions were detected that were not detected using 3T FLAIR. Using 7T MPRAGE, 231 additional lesions were detected that were not detected using 3T FLAIR. Conclusions: MRI with 3T and 7T MPRAGE enables detection of MS lesions in areas defined as NAWM using 3T FLAIR. Focal MS lesions contribute to the abnormalities known to exist in the NAWM.

scholarly journals Ultra-high-field imaging distinguishes MS lesions from asymptomatic white matter lesions

Neurology ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. 534-539 ◽  
Author(s):  
E. C. Tallantyre ◽  
J. E. Dixon ◽  
I. Donaldson ◽  
T. Owens ◽  
P. S. Morgan ◽  
...  
Keyword(s):  
White Matter ◽  
High Field ◽  
White Matter Lesions ◽  
Ultra High Field ◽  
High Field Imaging ◽  
Field Imaging

scholarly journals Blood-brain barrier breakdown in non-enhancing multiple sclerosis lesions detected by 7-Tesla MP2RAGE ΔT1 mapping

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249973
Author(s):  
Seongjin Choi ◽  
Margaret Spini ◽  
Jun Hua ◽  
Daniel M. Harrison
Keyword(s):  
Multiple Sclerosis ◽  
Relaxation Time ◽  
White Matter ◽  
Blood Brain Barrier ◽  
White Matter Lesions ◽  
Brain Barrier ◽  
7 Tesla ◽  
T1 Relaxation Time ◽  
T1 Relaxation ◽  
Blood Brain

Although the blood-brain barrier (BBB) is altered in most multiple sclerosis (MS) lesions, gadolinium enhancement is seen only in acute lesions. In this study, we aimed to investigate gadolinium-induced changes in T1 relaxation time in MS lesions on 7-tesla (7T) MRI as a means to quantify BBB breakdown in non-enhancing MS lesions. Forty-seven participants with MS underwent 7T MRI of the brain with a magnitude-prepared rapid acquisition of 2 gradient echoes (MP2RAGE) sequence before and after contrast. Subtraction of pre- and post-contrast T1 maps was used to measure T1 relaxation time change (ΔT1) from gadolinium. ΔT1 values were interrogated in enhancing white matter lesions (ELs), non-enhancing white matter lesions (NELs), and normal appearing white matter (NAWM) and metrics were compared to clinical data. ΔT1 was measurable in NELs (median: -0.139 (-0.304, 0.174) seconds; p < 0.001) and was negligible in NAWM (median: -0.001 (-0.036, 0.155) seconds; p = 0.516). Median ΔT1 in NELs correlated with disability as measured by Expanded Disability Status Scale (EDSS) (rho = -0.331, p = 0.026). Multiple measures of NEL ΔT1 variability also correlated with EDSS. NEL ΔT1 values were greater and more variable in patients with progressive forms of MS and greater in those not on MS treatment. Measurement of the changes in T1 relaxation time caused by contrast on 7T MP2RAGE reveals clinically relevant evidence of BBB breakdown in NELs in MS. This data suggests that NEL ΔT1 should be evaluated further as a biomarker for disease severity and treatment effect in MS.

scholarly journals Functional activation changes at ultra-high field related to upper and lower limb impairments in multiple sclerosis

2020 ◽  
Author(s):  
Myrte Strik ◽  
Camille Shanahan ◽  
Anneke Van der Walt ◽  
Frederique Boonstra ◽  
Rebecca Glarin ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Lower Limb ◽  
High Field ◽  
Premotor Cortex ◽  
Network Activity ◽  
7 Tesla ◽  
Ultra High Field ◽  
Sensorimotor Network ◽  
Force Matching ◽  
Early Stages

Upper and lower limb impairments are common in people with multiple sclerosis (pwMS), yet difficult to clinically identify in early stages of disease progression. Tasks involving complex motor control can potentially reveal more subtle deficits in early stages, and can be performed during functional MRI acquisition, to investigate underlying neural mechanisms, providing markers for early motor progression. We investigated brain activation during visually-guided force-matching of hand or foot in 28 minimally disabled pwMS and 17 healthy controls (HC) using ultra-high field 7-Tesla fMRI, allowing us to visualise sensorimotor network activity in high detail. Task activations and performance (tracking lag and error) were compared between groups, and correlations were performed. PwMS showed delayed (+124 s, p=0.002) and more erroneous (+0.15 N, p=0.001) lower limb tracking, together with higher primary motor and premotor cortex activation, and lower cerebellar activation compared to HC. No differences were seen in upper limb performance or activation. Functional activation levels of cerebellar, visual and motor areas correlated with task performance. These results demonstrate that ultra-high field fMRI during complex hand and foot tracking can identify subtle impairments in movement and brain activity, and differentiates upper and lower limb impairments in minimally disabled pwMS.

scholarly journals Cortical axonal loss is associated with both gray matter demyelination and white matter tract pathology in progressive multiple sclerosis: Evidence from a combined MRI-histopathology study

2020 ◽  
pp. 135245852091897 ◽  
Author(s):  
Svenja Kiljan ◽  
Paolo Preziosa ◽  
Laura E Jonkman ◽  
Wilma DJ van de Berg ◽  
Jos Twisk ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
White Matter Lesions ◽  
Progressive Multiple Sclerosis ◽  
Axonal Loss ◽  
Normal Appearing White Matter ◽  
Axonal Density ◽  
Neuroaxonal Degeneration

Background: Neuroaxonal degeneration is one of the hallmarks of clinical deterioration in progressive multiple sclerosis (PMS). Objective: To elucidate the association between neuroaxonal degeneration and both local cortical and connected white matter (WM) tract pathology in PMS. Methods: Post-mortem in situ 3T magnetic resonance imaging (MRI) and cortical tissue blocks were collected from 16 PMS donors and 10 controls. Cortical neuroaxonal, myelin, and microglia densities were quantified histopathologically. From diffusion tensor MRI, fractional anisotropy, axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) were quantified in normal-appearing white matter (NAWM) and white matter lesions (WML) of WM tracts connected to dissected cortical regions. Between-group differences and within-group associations were investigated through linear mixed models. Results: The PMS donors displayed significant axonal loss in both demyelinated and normal-appearing (NA) cortices ( p < 0.001 and p = 0.02) compared with controls. In PMS, cortical axonal density was associated with WML MD and AD ( p = 0.003; p = 0.02, respectively), and NAWM MD and AD ( p = 0.04; p = 0.049, respectively). NAWM AD and WML AD explained 12.6% and 22.6%, respectively, of axonal density variance in NA cortex. Additional axonal loss in demyelinated cortex was associated with cortical demyelination severity ( p = 0.002), explaining 34.4% of axonal loss variance. Conclusion: Reduced integrity of connected WM tracts and cortical demyelination both contribute to cortical axonal loss in PMS.

Non-invasive quantification of inflammation, axonal and myelin injury in multiple sclerosis

Brain ◽  
2020 ◽  
Author(s):  
Simona Schiavi ◽  
Maria Petracca ◽  
Peng Sun ◽  
Lazar Fleysher ◽  
Sirio Cocozza ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Black Holes ◽  
White Matter ◽  
Corpus Callosum ◽  
Fractional Anisotropy ◽  
White Matter Lesions ◽  
Healthy Controls ◽  
Normal Appearing White Matter ◽  
Relapsing Remitting ◽  
Spectrum Imaging

Abstract The aim of this study was to determine the feasibility of diffusion basis spectrum imaging in multiple sclerosis at 7 T and to investigate the pathological substrates of tissue damage in lesions and normal-appearing white matter. To this end, 43 patients with multiple sclerosis (24 relapsing-remitting, 19 progressive), and 21 healthy control subjects were enrolled. White matter lesions were classified in T1-isointense, T1-hypointense and black holes. Mean values of diffusion basis spectrum imaging metrics (fibres, restricted and non-restricted fractions, axial and radial diffusivities and fractional anisotropy) were measured from whole brain white matter lesions and from both lesions and normal appearing white matter of the corpus callosum. Significant differences were found between T1-isointense and black holes (P ranging from 0.005 to &lt;0.001) and between lesions’ centre and rim (P &lt; 0.001) for all the metrics. When comparing the three subject groups in terms of metrics derived from corpus callosum normal appearing white matter and T2-hyperintense lesions, a significant difference was found between healthy controls and relapsing-remitting patients for all metrics except restricted fraction and fractional anisotropy; between healthy controls and progressive patients for all metrics except restricted fraction and between relapsing-remitting and progressive multiple sclerosis patients for all metrics except fibres and restricted fractions (P ranging from 0.05 to &lt;0.001 for all). Significant associations were found between corpus callosum normal-appearing white matter fibres fraction/non-restricted fraction and the Symbol Digit Modality Test (respectively, r = 0.35, P = 0.043; r = −0.35, P = 0.046), and between black holes radial diffusivity and Expanded Disability Status Score (r = 0.59, P = 0.002). We showed the feasibility of diffusion basis spectrum imaging metrics at 7 T, confirmed the role of the derived metrics in the characterization of lesions and normal appearing white matter tissue in different stages of the disease and demonstrated their clinical relevance. Thus, suggesting that diffusion basis spectrum imaging is a promising tool to investigate multiple sclerosis pathophysiology, monitor disease progression and treatment response.

scholarly journals Automated Detection of Multiple Sclerosis Lesions in Normal Appearing White Matter from Brain MRI: A Survey

2021 ◽  
Vol 10 (1) ◽  
pp. 38-44
Author(s):  
Manoj V. Khatokar ◽  
M. Hemanth Kumar ◽  
K. Chandrahas ◽  
M. D. Swetha ◽  
Preeti Satish
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Soft Tissues ◽  
Diffusion Tensor ◽  
Brain Mri ◽  
The Body ◽  
Normal Appearing White Matter ◽  
Mri Scans ◽  
The Central Nervous System ◽  
Magnetic Resonance Imaging Mri

Multiple Sclerosis is an inoperable disease of the Central Nervous System (CNS) that irritates the myelin sheath by forming lesions. This affects all organs of the CNS; the vital of them is the brain. This disease can be detected by diagnosis like Magnetic Resonance Imaging (MRI). It is a non-invasive diagnostic test that provides detailed images of the soft tissues of the body. Out of the different variations of MRI, MS lesions are predominantly visible in the DTI (Diffusion Tensor Imaging) variant of MRI. DTI gives enhanced visualization of normal-appearing white matter tracts of the organs, hence providing a better image of the MS lesion. In this paper, the latest methodologies regarding the identification of the MS lesions in MRI scans like T2 FLAIR or DTI, using automated techniques like deep learning, computer vision, neural network and many more are surveyed. Furthermore, this paper consists of a proposed model which would focus on correlating the lesions found in DTI scan with the basic MRI scan like T2. It would identify the MS lesion in DTI scan and eventually highlight that lesion position in the T2 image scan. This would help radiologist in a way to effectively handle multiple MRI scans.

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