The compensatory phenomenon of the functional connectome related to pathological biomarkers in individuals with subjective cognitive decline.
Adaptation, Physiological
/ physiology
Aged
Aged, 80 and over
Biomarkers
/ cerebrospinal fluid
Brain
/ diagnostic imaging
Cognitive Dysfunction
/ cerebrospinal fluid
Connectome
/ methods
Diagnostic Self Evaluation
Female
Humans
Magnetic Resonance Imaging
/ methods
Male
Nerve Net
/ diagnostic imaging
Compensatory mechanism
Machine learning
Subjective cognitive decline
rs-fMRI
Journal
Translational neurodegeneration
ISSN: 2047-9158
Titre abrégé: Transl Neurodegener
Pays: England
ID NLM: 101591861
Informations de publication
Date de publication:
27 05 2020
27 05 2020
Historique:
received:
14
02
2020
accepted:
20
05
2020
entrez:
29
5
2020
pubmed:
29
5
2020
medline:
20
7
2021
Statut:
epublish
Résumé
Subjective cognitive decline (SCD) is a preclinical stage along the Alzheimer's disease (AD) continuum. However, little is known about the aberrant patterns of connectivity and topological alterations of the brain functional connectome and their diagnostic value in SCD. Resting-state functional magnetic resonance imaging and graph theory analyses were used to investigate the alterations of the functional connectome in 66 SCD individuals and 64 healthy controls (HC). Pearson correlation analysis was computed to assess the relationships among network metrics, neuropsychological performance and pathological biomarkers. Finally, we used the multiple kernel learning-support vector machine (MKL-SVM) to differentiate the SCD and HC individuals. SCD individuals showed higher nodal topological properties (including nodal strength, nodal global efficiency and nodal local efficiency) associated with amyloid-β levels and memory function than the HC, and these regions were mainly located in the default mode network (DMN). Moreover, increased local and medium-range connectivity mainly between the bilateral parahippocampal gyrus (PHG) and other DMN-related regions was found in SCD individuals compared with HC individuals. These aberrant functional network measures exhibited good classification performance in the differentiation of SCD individuals from HC individuals at an accuracy up to 79.23%. The findings of this study provide insight into the compensatory mechanism of the functional connectome underlying SCD. The proposed classification method highlights the potential of connectome-based metrics for the identification of the preclinical stage of AD.
Sections du résumé
BACKGROUND
Subjective cognitive decline (SCD) is a preclinical stage along the Alzheimer's disease (AD) continuum. However, little is known about the aberrant patterns of connectivity and topological alterations of the brain functional connectome and their diagnostic value in SCD.
METHODS
Resting-state functional magnetic resonance imaging and graph theory analyses were used to investigate the alterations of the functional connectome in 66 SCD individuals and 64 healthy controls (HC). Pearson correlation analysis was computed to assess the relationships among network metrics, neuropsychological performance and pathological biomarkers. Finally, we used the multiple kernel learning-support vector machine (MKL-SVM) to differentiate the SCD and HC individuals.
RESULTS
SCD individuals showed higher nodal topological properties (including nodal strength, nodal global efficiency and nodal local efficiency) associated with amyloid-β levels and memory function than the HC, and these regions were mainly located in the default mode network (DMN). Moreover, increased local and medium-range connectivity mainly between the bilateral parahippocampal gyrus (PHG) and other DMN-related regions was found in SCD individuals compared with HC individuals. These aberrant functional network measures exhibited good classification performance in the differentiation of SCD individuals from HC individuals at an accuracy up to 79.23%.
CONCLUSION
The findings of this study provide insight into the compensatory mechanism of the functional connectome underlying SCD. The proposed classification method highlights the potential of connectome-based metrics for the identification of the preclinical stage of AD.
Identifiants
pubmed: 32460888
doi: 10.1186/s40035-020-00201-6
pii: 10.1186/s40035-020-00201-6
pmc: PMC7254770
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
21Subventions
Organisme : National Natural Science Foundation of China
ID : 81822013
Pays : International
Organisme : National Natural Science Foundation of China
ID : 81671665
Pays : International
Organisme : Jiangsu Provincial Key Medical Talents
ID : ZDRCA2016085
Pays : International
Organisme : Key Research and Development Program of Jiangsu Province of China
ID : BE2016610
Pays : International
Organisme : National Key Research and Development Program of China
ID : 2016YFC1300500-504
Pays : International
Organisme : Jiangsu Province Key Medical Discipline
ID : ZDXKA2016020
Pays : International
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