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原发性进行性失语患者白质中影像组学特征的不对称性。

Asymmetry of radiomics features in the white matter of patients with primary progressive aphasia.

作者信息

Tafuri Benedetta, Filardi Marco, Urso Daniele, Gnoni Valentina, De Blasi Roberto, Nigro Salvatore, Logroscino Giancarlo

机构信息

Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, "Pia Fondazione Cardinale G. Panico", Lecce, Italy.

Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy.

出版信息

Front Aging Neurosci. 2023 May 5;15:1120935. doi: 10.3389/fnagi.2023.1120935. eCollection 2023.

DOI:10.3389/fnagi.2023.1120935
PMID:37213534
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10196268/
Abstract

INTRODUCTION

Primary Progressive Aphasia (PPA) is a neurological disease characterized by linguistic deficits. Semantic (svPPA) and non-fluent/agrammatic (nfvPPA) variants are the two main clinical subtypes. We applied a novel analytical framework, based on radiomic analysis, to investigate White Matter (WM) asymmetry and to examine whether asymmetry is associated with verbal fluency performance.

METHODS

Analyses were performed on T1-weighted images including 56 patients with PPA (31 svPPA and 25 nfvPPA) and 53 age- and sex-matched controls. Asymmetry Index (AI) was computed for 86 radiomics features in 34 white matter regions. The relationships between AI, verbal fluency performance (semantic and phonemic) and Boston Naming Test score (BNT) were explored through Spearman correlation analysis.

RESULTS

Relative to controls, WM asymmetry in svPPA patients involved regions adjacent to middle temporal cortex as part of the inferior longitudinal (ILF), fronto-occipital (IFOF) and superior longitudinal fasciculi. Conversely, nfvPPA patients showed an asymmetry of WM in lateral occipital regions (ILF/IFOF). A higher lateralization involving IFOF, cingulum and forceps minor was found in nfvPPA compared to svPPA patients. In nfvPPA patients, semantic fluency was positively correlated to asymmetry in ILF/IFOF tracts. Performances at BNT were associated with AI values of the middle temporal (ILF/SLF) and parahippocampal (ILF/IFOF) gyri in svPPA patients.

DISCUSSION

Radiomics features depicted distinct pathways of asymmetry in svPPA and nfvPPA involving damage of principal fiber tracts associated with speech and language. Assessing asymmetry of radiomics in PPA allows achieving a deeper insight into the neuroanatomical damage and may represent a candidate severity marker for language impairments in PPA patients.

摘要

引言

原发性进行性失语(PPA)是一种以语言缺陷为特征的神经疾病。语义性(svPPA)和非流利/语法缺失性(nfvPPA)变体是两种主要的临床亚型。我们应用了一种基于放射组学分析的新型分析框架,以研究白质(WM)不对称性,并检查不对称性是否与言语流畅性表现相关。

方法

对T1加权图像进行分析,包括56例PPA患者(31例svPPA和25例nfvPPA)以及53例年龄和性别匹配的对照。计算34个白质区域中86个放射组学特征的不对称指数(AI)。通过Spearman相关性分析探讨AI、言语流畅性表现(语义和音素)与波士顿命名测试分数(BNT)之间的关系。

结果

相对于对照组,svPPA患者的WM不对称涉及颞中皮质附近区域,这些区域是下纵束(ILF)、额枕束(IFOF)和上纵束的一部分。相反,nfvPPA患者在枕外侧区域(ILF/IFOF)表现出WM不对称。与svPPA患者相比,nfvPPA患者在IFOF、扣带和小钳状体方面表现出更高的偏侧化。在nfvPPA患者中,语义流畅性与ILF/IFOF束的不对称呈正相关。svPPA患者的BNT表现与颞中(ILF/SLF)和海马旁(ILF/IFOF)回的AI值相关。

讨论

放射组学特征描绘了svPPA和nfvPPA中不对称的不同途径,涉及与言语和语言相关的主要纤维束损伤。评估PPA中放射组学的不对称性有助于更深入地了解神经解剖损伤,并且可能代表PPA患者语言障碍的候选严重程度标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/892d/10196268/abb74dab72c2/fnagi-15-1120935-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/892d/10196268/dde856825831/fnagi-15-1120935-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/892d/10196268/abb74dab72c2/fnagi-15-1120935-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/892d/10196268/dde856825831/fnagi-15-1120935-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/892d/10196268/abb74dab72c2/fnagi-15-1120935-g002.jpg

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Life (Basel). 2022 Mar 31;12(4):514. doi: 10.3390/life12040514.
3
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Comput Methods Programs Biomed. 2022 Mar;215:106609. doi: 10.1016/j.cmpb.2021.106609. Epub 2021 Dec 27.
4
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Front Aging Neurosci. 2021 Oct 26;13:764872. doi: 10.3389/fnagi.2021.764872. eCollection 2021.
5
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6
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7
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