Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review.
Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review.
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DOI：
10.1002/hbm.25369
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发表时间：
2021-06-15
影响因子：
4.8
通讯作者：
Saripan MI
中科院分区：
文献类型：
--
作者：
Ibrahim B;Suppiah S;Ibrahim N;Mohamad M;Hassan HA;Nasser NS;Saripan MI
静息状态功能磁共振成像（rs - fMRI）检测阿尔茨海默病（AD）和轻度认知障碍（MCI）患者大脑中发生的功能连接（FC）异常。DMN （default mode network）的FC在AD和MCI中普遍受损。我们进行了一项系统综述，旨在利用机器学习（ML）方法确定rs - fMRI在识别AD或MCI患者DMN中FC异常与健康对照组（hc）相比的诊断能力。多模态支持向量机（SVM）算法是最常用的机器学习方法。多核方法可以通过结合各种区分特征来帮助分类，例如基于“节点”和“边缘”的FC图，以及基于结构MRI的区域皮层厚度和灰质体积。其他多模式特征包括神经精神测试分数、DTI特征和局部脑血流量。在AD患者中，后扣带皮层(PCC)/楔前叶被认为是DMN的一个高度受影响的中枢，显示出整体的FC减少。而在MCI患者中，PCC和前扣带皮层（ACC）之间的DMN FC减少。有证据表明，DMN的节点可以提供中等到高的诊断能力来区分AD和MCI患者。然而，基于患者选择、扫描仪效果以及分类器和算法的不同使用，对数据同质性的各种担忧，对基于ML的rs - fMRI数据集图像解释提出了挑战，使其成为诊断AD和预测HC/MCI向AD转化的主流选择。静息状态功能磁共振成像（rs - fMRI）可以帮助早期发现阿尔茨海默病（AD）和MCI。深度机器学习（ML）方法可以为评估rs - fMRI对AD的诊断准确性提供一个平台。在ML成为临床实践中使用的主流算法之前，要实现研究的同质性还有很多工作要做。
Resting‐state fMRI (rs‐fMRI) detects functional connectivity (FC) abnormalities that occur in the brains of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). FC of the default mode network (DMN) is commonly impaired in AD and MCI. We conducted a systematic review aimed at determining the diagnostic power of rs‐fMRI to identify FC abnormalities in the DMN of patients with AD or MCI compared with healthy controls (HCs) using machine learning (ML) methods. Multimodal support vector machine (SVM) algorithm was the commonest form of ML method utilized. Multiple kernel approach can be utilized to aid in the classification by incorporating various discriminating features, such as FC graphs based on “nodes” and “edges” together with structural MRI‐based regional cortical thickness and gray matter volume. Other multimodal features include neuropsychiatric testing scores, DTI features, and regional cerebral blood flow. Among AD patients, the posterior cingulate cortex (PCC)/Precuneus was noted to be a highly affected hub of the DMN that demonstrated overall reduced FC. Whereas reduced DMN FC between the PCC and anterior cingulate cortex (ACC) was observed in MCI patients. Evidence indicates that the nodes of the DMN can offer moderate to high diagnostic power to distinguish AD and MCI patients. Nevertheless, various concerns over the homogeneity of data based on patient selection, scanner effects, and the variable usage of classifiers and algorithms pose a challenge for ML‐based image interpretation of rs‐fMRI datasets to become a mainstream option for diagnosing AD and predicting the conversion of HC/MCI to AD. Resting state fMRI (rs‐fMRI) can aid in the early detection of Alzheimer's disease (AD) and MCI. Deep machine learning (ML) methods can provide a platform for the assessment of the diagnostic accuracy of rs‐fMRI in AD. Much work is yet to be done to achieve homogeneity of the studies before ML can become a mainstream algorithm utilized in clinical practice.
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DOI：
10.1111/ejn.12177
发表时间：
2013-06
期刊：
The European journal of neuroscience
影响因子：
--
作者：
Cha J;Jo HJ;Kim HJ;Seo SW;Kim HS;Yoon U;Park H;Na DL;Lee JM
通讯作者：
Lee JM
影响因子：
4.8
作者：
Chen X;Zhang H;Zhang L;Shen C;Lee SW;Shen D
通讯作者：
Shen D
影响因子：
2.6
作者：
Beynon R;Sterne JA;Wilcock G;Likeman M;Harbord RM;Astin M;Burke M;Bessell A;Ben-Shlomo Y;Hawkins J;Hollingworth W;Whiting P
通讯作者：
Whiting P
影响因子：
9.9
作者：
Du, AT;Schuff, N;Weiner, MW
通讯作者：
Weiner, MW
影响因子：
5.7
作者：
Dai, Zhengjia;Yan, Chaogan;He, Yong
通讯作者：
He, Yong