Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that commonly affects the elderly; early diagnosis and timely treatment are very important to delay the course of the disease. In the past, most brain regions related to AD were identified based on imaging methods, and only some atrophic brain regions could be identified. In this work, the authors used mathematical models to identify the potential brain regions related to AD. In this study, 20 patients with AD and 13 healthy controls (non-AD) were recruited by the neurology outpatient department or the neurology ward of Peking University First Hospital from September 2017 to March 2019. First, diffusion tensor imaging (DTI) was used to construct the brain structural network. Next, the authors set a new local feature index 2hop-connectivity to measure the correlation between different regions. Compared with the traditional graph theory index, 2hop-connectivity exploits the higher-order information of the graph structure. And for this purpose, the authors proposed a novel algorithm called 2hopRWR to measure 2hop-connectivity. Then, a new index global feature score (GFS) based on a global feature was proposed by combing five local features, namely degree centrality, betweenness centrality, closeness centrality, the number of maximal cliques, and 2hop-connectivity, to judge which brain regions are related to AD. As a result, the top ten brain regions identified using the GFS scoring difference between the AD and the non-AD groups were associated to AD by literature verification. The results of the literature validation comparing GFS with the local features showed that GFS was superior to individual local features. Finally, the results of the canonical correlation analysis showed that the GFS was significantly correlated with the scores of the Mini-Mental State Examination (MMSE) scale and the Montreal Cognitive Assessment (MoCA) scale. Therefore, the authors believe the GFS can also be used as a new biomarker to assist in diagnosis and objective monitoring of disease progression. Besides, the method proposed in this paper can be used as a differential network analysis method for network analysis in other domains.
Highlights
Alzheimer’s disease (AD) is a neurodegenerative disease that commonly affects the elderly
The inclusion criteria for the normal control group were as follows: (1) Han nationality, over 18 years old, who were right-handed and agreed to participate in this study, so as to match their ages with the ages of subjects of the AD group; (2) normal cognitive function and not meeting the diagnostic criteria of dementia; (3) no serious white matter lesions found on MRI examination, which meant that the Fazekas scale score was no more than 2
The exclusion criteria were as follows: (1) patients or their family members refusing to participate in the study; (2) unable to complete 3.0Tesla MRI examination due to various reasons; (3) with a history of cerebrovascular diseases, or cognitive impairment caused by toxication, metabolic disease, infection, autoimmune disease, or drug and with a history of demyelination of the central nervous system, white matter lesions, or other diseases that may affect the white matter structure of the brain; (4) with a history of serious mental illness, such as depression, mania, and schizophrenia; (5) having a long-term history of alcoholism or vegetarianism; (6) Patients with other types of dementia other than AD
Summary
Alzheimer’s disease (AD) is a neurodegenerative disease that commonly affects the elderly. It is a continuous process, from the pre-clinical stage to mild cognitive impairment (MCI) to dementia. Effective intervention in the pre-dementia or MCI stage can slow down or reverse the disease process. Early identification of patients with AD in the pre-dementia or MCI stage, as well as early and timely intervention, are of great importance to the prognosis of patients. With the development of imaging technology, the detection of AD is no longer limited to the phenomenon of abnormal protein deposition. Analysis of structural brain network information, such as brain connectome analysis, may be an effective method for early diagnosis and monitoring of disease progression (Fan et al, 2016)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
