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原发性进行性多发性硬化症中脑容量随时间的变化映射。

Mapping brain volume change across time in primary-progressive multiple sclerosis.

机构信息

Multiple Sclerosis Center, Sheba Medical Center, Ramat-Gan, Israel.

Arrow Program for Medical Research Education, Sheba Medical Center, Ramat-Gan, Israel.

出版信息

Neuroradiology. 2024 Jul;66(7):1189-1197. doi: 10.1007/s00234-024-03354-7. Epub 2024 Apr 13.


DOI:10.1007/s00234-024-03354-7
PMID:38609687
Abstract

PURPOSE: Detection and prediction of the rate of brain volume loss with age is a significant unmet need in patients with primary progressive multiple sclerosis (PPMS). In this study we construct detailed brain volume maps for PPMS patients. These maps compare age-related changes in both cortical and sub-cortical regions with those in healthy individuals. METHODS: We conducted retrospective analyses of brain volume using T1-weighted Magnetic Resonance Imaging (MRI) scans of a large cohort of PPMS patients and healthy subjects. The volume of brain parenchyma (BP), cortex, white matter (WM), deep gray matter, thalamus, and cerebellum were measured using the robust SynthSeg segmentation tool. Age- and gender-related regression curves were constructed based on data from healthy subjects, with the 95% prediction interval adopted as the normality threshold for each brain region. RESULTS: We analyzed 495 MRI scans from 169 PPMS patients, aged 20-79 years, alongside 563 exams from healthy subjects aged 20-86. Compared to healthy subjects, a higher proportion of PPMS patients showed lower than expected brain volumes in all regions except the cerebellum. The most affected areas were BP, WM, and thalamus. Lower brain volumes correlated with longer disease duration for BP and WM, and higher disability for BP, WM, cortex, and thalamus. CONCLUSIONS: Constructing age- and gender-related brain volume maps enabled identifying PPMS patients at a higher risk of brain volume loss. Monitoring these high-risk patients may lead to better treatment decisions and improve patient outcomes.

摘要

目的:在原发性进展型多发性硬化症(PPMS)患者中,检测和预测脑容量随年龄的损失率是一项重大未满足的需求。在本研究中,我们构建了 PPMS 患者的详细脑容量图。这些图谱将皮质和皮质下区域的与年龄相关的变化与健康个体进行了比较。

方法:我们对大量 PPMS 患者和健康受试者的 T1 加权磁共振成像(MRI)扫描进行了脑容量的回顾性分析。使用稳健的 SynthSeg 分割工具测量脑实质(BP)、皮质、白质(WM)、深部灰质、丘脑和小脑的体积。根据健康受试者的数据构建了年龄和性别相关的回归曲线,将 95%预测区间作为每个脑区的正常性阈值。

结果:我们分析了 169 名 20-79 岁的 PPMS 患者的 495 次 MRI 扫描,以及 563 名 20-86 岁的健康受试者的检查。与健康受试者相比,除小脑外,所有区域的 PPMS 患者脑容量均低于预期的比例更高。受影响最严重的区域是 BP、WM 和丘脑。脑容量较低与 BP 和 WM 的疾病持续时间较长以及 BP、WM、皮质和丘脑的残疾程度较高相关。

结论:构建与年龄和性别相关的脑容量图可以识别出脑容量损失风险较高的 PPMS 患者。监测这些高危患者可能会做出更好的治疗决策并改善患者预后。

相似文献

[1]
Mapping brain volume change across time in primary-progressive multiple sclerosis.

Neuroradiology. 2024-7

[2]
Brain atrophy and physical disability in primary progressive multiple sclerosis: A volumetric study.

Neuroradiol J. 2015-6

[3]
Exploring the relationship between white matter and gray matter damage in early primary progressive multiple sclerosis: an in vivo study with TBSS and VBM.

Hum Brain Mapp. 2009-9

[4]
Gray Matter Atrophy in the Cortico-Striatal-Thalamic Network and Sensorimotor Network in Relapsing-Remitting and Primary Progressive Multiple Sclerosis.

Neuropsychol Rev. 2021-12

[5]
A multiparametric evaluation of regional brain damage in patients with primary progressive multiple sclerosis.

Hum Brain Mapp. 2009-9

[6]
Cortical lesions in primary progressive multiple sclerosis: a 2-year longitudinal MR study.

Neurology. 2009-4-14

[7]
Grey and white matter volume changes in early primary progressive multiple sclerosis: a longitudinal study.

Brain. 2005-6

[8]
Progressive change in primary progressive multiple sclerosis normal-appearing white matter: a serial diffusion magnetic resonance imaging study.

Mult Scler. 2004-4

[9]
Cognitive impairment and magnetic resonance imaging correlates in primary progressive multiple sclerosis.

Acta Neurol Scand. 2017-8

[10]
Extent of cerebellum, subcortical and cortical atrophy in patients with MS: a case-control study.

J Neurol Sci. 2009-7-15

本文引用的文献

[1]
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining.

Med Image Anal. 2023-5

[2]
Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets.

Proc Natl Acad Sci U S A. 2023-2-28

[3]
Personalized maps of T1 relaxometry abnormalities provide correlates of disability in multiple sclerosis patients.

Neuroimage Clin. 2023

[4]
Association of Brain Atrophy With Disease Progression Independent of Relapse Activity in Patients With Relapsing Multiple Sclerosis.

JAMA Neurol. 2022-7-1

[5]
Magnetic Resonance Imaging in Primary Progressive Multiple Sclerosis Patients : Review.

Clin Neuroradiol. 2022-9

[6]
Diagnosis of Progressive Multiple Sclerosis From the Imaging Perspective: A Review.

JAMA Neurol. 2021-3-1

[7]
Changes in the intracranial volume from early adulthood to the sixth decade of life: A longitudinal study.

Neuroimage. 2020-10-15

[8]
Brain atrophy in multiple sclerosis: mechanisms, clinical relevance and treatment options.

Auto Immun Highlights. 2019-8-10

[9]
SciPy 1.0: fundamental algorithms for scientific computing in Python.

Nat Methods. 2020-2-3

[10]
: An open source Python package for producing summary statistics for research papers.

JAMIA Open. 2018-5-23

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