Inference of brain pathway activities for Alzheimer's disease classification

BMC Medical Informatics and Decision Making

Research Article

Kwang H Lee¹  Yong Jeong¹  Doheon Lee¹  Jong-Won Kim²  Jongan Lee²  Younghoon Kim²  Duk L Na³ 

[1] Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea;Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea;Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea;

关键词: Functional Connectivity;    Default Mode Network;    Support Vector Machine Model;    Cognitively Normal;    aMCI Patient;   

DOI  :  10.1186/1472-6947-15-S1-S1

来源: Springer

【 摘 要 】

BackgroundAlzheimer's disease (AD) is a neurodegenerative and progressive disorder that results in brain malfunctions. Resting-state (RS) functional magnetic resonance imaging (fMRI) techniques have been successfully applied for quantifying brain activities of both Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) patients. Region-based approaches are widely utilized to classify patients from cognitively normal subjects (CN). Nevertheless, region-based approaches have a few limitations, reproducibility owing to selection of disease-specific brain regions, and heterogeneity of brain activities during disease progression. For coping with these issues, network-based approaches have been suggested in the field of molecular bioinformatics. In comparison with individual gene-based approaches, they acquired more accurate results in diverse disease classification, and reproducibility was confirmed by replication studies. In our work, we applied a similar methodology integrating brain pathway information into pathway activity inference, and permitting classification of both aMCI and AD patients based on pathway activities rather than single region activities.ResultsAfter aggregating the 59 brain pathways from literature, we estimated brain pathway activities by using exhaustive search algorithms between patients and cognitively normal subjects, and identified discriminatory pathways according to disease progression. We used three different data sets and each data set consists of two different groups. Our results show that the pathway-based approach (AUC = 0.89, 0.9, 0.75) outperformed the region-based approach (AUC = 0.69, 0.8, 0.68). Also, our approach provided enhanced diagnostic power achieving higher accuracy, sensitivity, and specificity (pathway-based approach: accuracy = 83%; sensitivity = 86%; specificity = 78%, region-based approach: accuracy = 74%; sensitivity = 78%; specificity = 76%).ConclusionsWe proposed a novel method inferring brain pathway activities for disease classification. Our approach shows better classification performance than region-based approach in four classification models. We expect that brain pathway-based approach would be helpful for precise classification of brain disorders, and provide new opportunities for uncovering disrupted brain pathways caused by disease. Moreover, discriminatory pathways between patients and cognitively normal subjects may facilitate the interpretation of functional alterations during disease progression.

【 授权许可】

CC BY   
© Lee et al.; licensee BioMed Central Ltd. 2015