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R-fMRI 指标的生理学意义:功能指标能否区分结构性病变(脑肿瘤)?

Physiological significance of R-fMRI indices: Can functional metrics differentiate structural lesions (brain tumors)?

机构信息

Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai Neurosurgical Clinical Center, Shanghai, China.

CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.

出版信息

Neuroimage Clin. 2019;22:101741. doi: 10.1016/j.nicl.2019.101741. Epub 2019 Mar 1.

Abstract

Resting-state functional MRI (R-fMRI) research has recently entered the era of "big data", however, few studies have provided a rigorous validation of the physiological underpinnings of R-fMRI indices. Although studies have reported that various neuropsychiatric disorders exhibit abnormalities in R-fMRI measures, these "biomarkers" have not been validated in differentiating structural lesions (brain tumors) as a concept proof. We enrolled 60 patients with intracranial tumors located in the unilateral cranialcavity and 60 matched normal controls to test whether R-fMRI indices can differentiate tumors, which represents a prerequisite for adapting such indices as biomarkers for neuropsychiatric disorders. Common R-fMRI indices of tumors and their counterpart control regions, which were defined as the contralateral normal areas (for amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo) and degree centrality (DC)) and ipsilateral regions surrounding the tumors (for voxel-mirrored homotopic connectivity (VMHC)), were comprehensively assessed. According to robust paired t-tests with a Bonferroni correction, only VMHC (Fisher's r-to-z transformed) could successfully differentiate substantial tumors from their counterpart normal regions in patients. Furthermore, ALFF and DC were not able to differentiate tumor from normal unless Z-standardization was employed. To validate the lower power of the between-subject design compared to the within-subject design, each metric was calculated in a matched control group, and robust two-sample t-tests were used to compare the patient tumors and the normal controls at the same place. Similarly, only VMHC succeeded in differentiating significant differences between tumors and the sham tumor areas of normal controls. This study tested the premise of R-fMRI biomarkers for differentiating lesions, and brings a new understanding to physical significance of the Z-standardization.

摘要

静息态功能磁共振成像(R-fMRI)研究最近已进入“大数据”时代,然而,很少有研究对 R-fMRI 指标的生理基础进行严格验证。尽管研究报告称各种神经精神疾病的 R-fMRI 测量值存在异常,但这些“生物标志物”尚未在区分结构性病变(脑瘤)方面得到验证,无法作为概念验证。我们纳入了 60 例单侧颅腔颅内肿瘤患者和 60 例匹配的正常对照,以测试 R-fMRI 指标是否可以区分肿瘤,这是将此类指标作为神经精神疾病生物标志物的前提。我们综合评估了肿瘤及其对应对照区域的常见 R-fMRI 指标(对于低频波动幅度(ALFF)、分数 ALFF(fALFF)、局部一致性(ReHo)和度中心性(DC),定义为对侧正常区域;对于体素镜像同伦连接性(VMHC),定义为肿瘤同侧区域)。根据稳健的配对 t 检验和 Bonferroni 校正,只有 VMHC(Fisher r 到 z 转换)能够成功区分患者的实质性肿瘤与其对应正常区域。此外,除非进行 Z 标准化,否则 ALFF 和 DC 无法区分肿瘤与正常。为了验证组间设计相对于组内设计的较低功效,在匹配的对照组中计算了每个指标,并使用稳健的两样本 t 检验比较了同一部位的患者肿瘤和正常对照。同样,只有 VMHC 成功区分了肿瘤与正常对照的假肿瘤区域之间的显著差异。本研究检验了 R-fMRI 生物标志物用于区分病变的前提,并为 Z 标准化的物理意义带来了新的认识。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac16/6423471/b17875be88af/gr1.jpg

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