The analysis of human brain fMRI subjects can research neuro-related diseases and explore the related rules of human brain activity. In this paper, we proposed an algorithm framework to analyze the functional connectivity network of the whole brain and to distinguish Alzheimer’s disease (AD), mild cognitive impairment (MCI), and cognitively normal (CN). In other studies, they use algorithms to select features or extract abstract features, or even manually select features based on prior information. Then, a classifier is constructed to classify the selected features. We designed a concise algorithm framework that uses whole-brain functional connectivity for classification without feature selection. The algorithm framework is a two-hidden-layer neural network based on extreme learning machine (ELM), which overcomes the instability of classical ELM in high-dimensional data scenarios. We use this method to conduct experiments for AD, MCI, and CN data and perform 10-fold cross-validation. We found that it has several advantages: (1) the proposed method has excellent classification accuracy with high speed. The classification accuracy of AD vs. CN is 96.85%, and the accuracy of MCI vs. CN is 95.05%. Their AUC (area under curve) of ROC (receiver operating characteristic curve) reached 0.9891 and 0.9888, respectively. Their sensitivities are 97.1% and 94.7%, and specificities are 96.3% and 95.3%, respectively. (2) Compared with other studies, the proposed method is concise. Construction of a two-hidden-layer neural network is to learn features of the whole brain for the diagnosis of AD and MCI, without the feature screening. It avoids the negative effects of feature screening by algorithm or prior information. (3) The proposed method is suitable for small sample and high-dimensional data. It meets the requirements of medical image analysis. In other studies, its classifiers usually deal with several to dozens of feature dimensions. The proposed method deals with 4005 feature dimensions.

Jia Lu

Weiming Zeng

Lu Zhang

Yuhu Shi

Concepts in Magnetic Resonance Part B

磁共振概念 B 部分汇集了参与设计和开发用于磁共振技术的硬件和软件的工程师和物理学家。该杂志欢迎主要来自磁共振成像 (MRI)、核磁共振 (NMR) 和电子顺磁共振 (EPR) 领域的投稿，但也鼓励提交与不太常见的磁共振成像和分析方法相关的投稿。学术界和工业界，报告仪器和计算机编程发展的最新进展，以支持医学、非医学和分析磁共振技术。

Concepts in Magnetic Resonance Part B: Magnetic Resonance Engineering brings together engineers and physicists involved in the design and development of hardware and software for magnetic resonance technology. The journal welcomes submissions primarily from the fields of Magnetic Resonance Imaging (MRI), Nuclear Magnetic Resonance (NMR), and Electron Paramagnetic Resonance (EPR), but also encourages contributions related to less common magnetic resonance imaging and analysis methods. Both academia and industry are invited to report the latest advancements in instrument and computer programming developments to support medical, non-medical, and analytical magnetic resonance technologies.

《Concepts in Magnetic Resonance Part B: Magnetic Resonance Engineering》汇集了参与设计和开发磁共振技术硬件和软件的工程师和物理学家。该杂志欢迎来自磁共振成像 (MRI)、核磁共振 (NMR) 和电子顺磁共振 (EPR) 领域的投稿，同时也鼓励与不太常见的磁共振成像和分析方法相关的研究。无论是学术界还是工业界，均可报告仪器和计算机编程发展的最新进展，以支持医学、非医学和分析磁共振技术。

Category	Quartile
医学	4
医学, 物理化学	4
医学, 仪器仪表	4
医学, 物理原子、分子和化学物理	4
医学, 光谱学	4

Diagnosis of Alzheimer’s Disease with Extreme Learning Machine on Whole-Brain Functional Connectivity

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