Altered large‐scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study

Author:

Jing Rixing1ORCID,Chen Pindong23ORCID,Wei Yongbin4,Si Juanning1,Zhou Yuying5,Wang Dawei6,Song Chengyuan7,Yang Hongwei8,Zhang Zengqiang9,Yao Hongxiang10,Kang Xiaopeng23,Fan Lingzhong2,Han Tong11,Qin Wen12ORCID,Zhou Bo13,Jiang Tianzi23ORCID,Lu Jie8ORCID,Han Ying141516,Zhang Xi13,Liu Bing17ORCID,Yu Chunshui12ORCID,Wang Pan5,Liu Yong234ORCID,

Affiliation:

1. School of Instrument Science and Opto‐Electronics Engineering Beijing Information Science and Technology University Beijing China

2. Brainnetome Center & National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences Beijing China

3. School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

4. School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

5. Department of Neurology Tianjin Huanhu Hospital, Tianjin University Tianjin China

6. Department of Radiology Qilu Hospital of Shandong University Ji'nan China

7. Department of Neurology Qilu Hospital of Shandong University Ji'nan China

8. Department of Radiology Xuanwu Hospital of Capital Medical University Beijing China

9. Branch of Chinese PLA General Hospital Sanya China

10. Department of Radiology, the Second Medical Centre National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital Beijing China

11. Department of Radiology Tianjin Huanhu Hospital Tianjin China

12. Department of Radiology Tianjin Medical University General Hospital Tianjin China

13. Department of Neurology the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital Beijing China

14. Department of Neurology Xuanwu Hospital of Capital Medical University Beijing China

15. Beijing Institute of Geriatrics Beijing China

16. National Clinical Research Center for Geriatric Disorders Beijing China

17. State Key Laboratory of Cognition Neuroscience & Learning Beijing Normal University Beijing China

Abstract

AbstractAlzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting‐state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding‐window method to estimate the subject‐specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave‐one‐site‐out cross‐validation. Alterations in connectivity strength, fluctuation, and inter‐synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.

Funder

Beijing Natural Science Foundation

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

National Institutes of Health

U.S. Department of Defense

National Institute on Aging

National Institute of Biomedical Imaging and Bioengineering

AbbVie

Alzheimer's Association

Alzheimer's Drug Discovery Foundation

BioClinica

Biogen

Eisai Inc.

Eli Lilly and Company

Genentech

Fujirebio

GE Healthcare

H. Lundbeck A/S

Merck

Meso Scale Diagnostics

Novartis Pharmaceuticals Corporation

Pfizer

Servier

Takeda Pharmaceutical Company

Canadian Institutes of Health Research

Publisher

Wiley

Subject

Neurology (clinical),Neurology,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology,Anatomy

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