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颞叶癫癎皮质液衰减反转恢复信号的拓扑学原理。

Topographic principles of cortical fluid-attenuated inversion recovery signal in temporal lobe epilepsy.

机构信息

NeuroImaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.

Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.

出版信息

Epilepsia. 2018 Mar;59(3):627-635. doi: 10.1111/epi.14017. Epub 2018 Jan 31.


DOI:10.1111/epi.14017
PMID:29383717
Abstract

OBJECTIVE: In drug-resistant temporal lobe epilepsy (TLE), relative to the large number of whole-brain morphological studies, neocortical T2 changes have not been systematically investigated. The aim of this study was to assess the anatomical principles that govern the distribution of neocortical T2-weighted fluid-attenuated inversion recovery (FLAIR) signal intensity and uncover its topographic principles. METHODS: Using a surface-based sampling scheme, we mapped neocortical FLAIR intensity of 61 TLE patients relative to 38 healthy controls imaged at 3 T. To address topographic principles of the susceptibility to FLAIR signal changes in TLE, we assessed associations with normative data on tissue composition using 2 complementary approaches. First, we evaluated whether the degree of TLE-related FLAIR intensity changes differed across cytoarchitectonic classes as defined by Von Economo-Koskinas taxonomy. Second, as a proxy to map regions with similar intracortical composition, we carried out a FLAIR intensity covariance paradigm in controls by seeding systematically from all cortical regions, and identified those networks that were the best spatial predictors of the between-group FLAIR changes. RESULTS: Increased intensities were observed in bilateral limbic and paralimbic cortices (hippocampus, parahippocampus, cingulate, temporopolar, insular, orbitofrontal). Effect sizes were highest in periallocortical limbic and insular classes as defined by the Von Economo-Koskinas cytoarchitectonic taxonomy. Furthermore, systematic FLAIR intensity covariance analysis in healthy controls revealed that similarity patterns characteristic of limbic cortices, most notably the hippocampus, served as sensitive predictors for the topography of FLAIR hypersignal in patients. FLAIR intensity findings were robust against correction for morphological confounds. Patients with a history of febrile convulsions showed more marked signal changes in parahippocampal and retrosplenial cortices, known to be strongly connected to the hippocampus. SIGNIFICANCE: FLAIR intensity mapping and covariance analysis provide a model of TLE gray matter pathology based on shared vulnerability of periallocortical and limbic cortices.

摘要

目的:在耐药性颞叶癫痫(TLE)中,相对于大量的全脑形态学研究,皮质 T2 变化尚未得到系统研究。本研究旨在评估控制皮质 T2 加权液体衰减反转恢复(FLAIR)信号强度分布的解剖学原理,并揭示其地形学原理。

方法:我们使用基于表面的采样方案,对 61 例 TLE 患者和 38 例健康对照者的皮质 FLAIR 强度进行了成像,扫描均在 3T 磁共振上进行。为了确定 TLE 中 FLAIR 信号变化易感性的地形学原理,我们使用 2 种互补方法评估了与组织成分的正态数据之间的相关性。首先,我们评估了 TLE 相关 FLAIR 强度变化是否因 Von Economo-Koskinas 分类学定义的细胞构筑类型而有所不同。其次,作为映射皮质内组成相似区域的替代方法,我们通过系统地从所有皮质区域播种进行了 FLAIR 强度协方差范式,并确定了那些是组间 FLAIR 变化最佳空间预测者的网络。

结果:双侧边缘和边缘旁皮质(海马体、旁海马体、扣带回、颞极、岛叶、眶额回)观察到强度增加。Von Economo-Koskinas 细胞构筑分类定义的periallocortical 边缘和岛叶类别的效应大小最高。此外,健康对照组的系统性 FLAIR 强度协方差分析表明,边缘皮质(尤其是海马体)的特征相似模式可作为患者 FLAIR 高信号的敏感预测因子。FLAIR 强度结果在纠正形态学混杂因素后仍然可靠。有热性惊厥病史的患者在海马体强烈连接的旁海马体和后扣带回皮质中显示出更明显的信号变化,已知这些区域易受影响。

意义:FLAIR 强度映射和协方差分析提供了一种基于periallocortical 和边缘皮质共享易感性的 TLE 灰质病理学模型。

相似文献

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Topographic principles of cortical fluid-attenuated inversion recovery signal in temporal lobe epilepsy.

Epilepsia. 2018-1-31

[2]
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[3]
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[4]
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Brain. 2016-9

[5]
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[6]
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[7]
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Neurology. 2024-8-13

[8]
SUrface-PRojected FLuid-Attenuation-Inversion-Recovery Analysis: A Novel Tool for Advanced Imaging of Epilepsy.

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[9]
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[10]
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[2]
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Epilepsia Open. 2024-2

[3]
Multiscale neural gradients reflect transdiagnostic effects of major psychiatric conditions on cortical morphology.

Commun Biol. 2022-9-27

[4]
Adolescent development of multiscale structural wiring and functional interactions in the human connectome.

Proc Natl Acad Sci U S A. 2022-7-5

[5]
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Netw Neurosci. 2022-6-1

[6]
Decomposing MRI phenotypic heterogeneity in epilepsy: a step towards personalized classification.

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[7]
Narrative review of epilepsy: getting the most out of your neuroimaging.

Transl Pediatr. 2021-4

[8]
Planning stereoelectroencephalography using automated lesion detection: Retrospective feasibility study.

Epilepsia. 2020-6-13

[9]
Macroscale and microcircuit dissociation of focal and generalized human epilepsies.

Commun Biol. 2020-5-18

[10]
Neuroimaging and connectomics of drug-resistant epilepsy at multiple scales: From focal lesions to macroscale networks.

Epilepsia. 2019-3-19

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