TY - GEN
T1 - Identification and Evaluation of Multimodal Connectomics in Early Alzheimer’s Dementia
AU - Chen, Yu
AU - Fan, Yingwei
AU - Tang, Xiaoying
AU - Zhang, Jian
AU - Yan, Tianyi
AU - Wu, Jinglong
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Alzheimer’s disease is a common neurodegenerative disease with a long disease course and is one of the main causes of dementia. To investigate the longitudinal disorder of multimodal brain networks in the process of cognitive decline and construct a connectome-based identification model for questionable dementia, we construct functional and structural connectivity of brain networks by using magnetic resonance imaging (MRI) data from the OASIS database. The discovery group of longitudinal database includes 113 healthy aging individuals, 65 healthy subjects at the time of scanning but diagnosed as questionable dementia during follow-up, and 39 subjects with questionable dementia at the time of scanning. The results showed that the default mode network of the potential dementia group had structural and functional abnormalities as early as the cognitive normal stage. Furthermore, a logistic regression model was further constructed using the connectomics features of 455 subjects in identification group to construct an objective mapping between the imaging biomarkers and the clinical dementia scale. The average classification accuracy was about 85%. The above findings may indicate the potential changes of the central nervous system in the early stage of dementia, and serve as an auxiliary and reference for clinicians in the early diagnosis of dementia.
AB - Alzheimer’s disease is a common neurodegenerative disease with a long disease course and is one of the main causes of dementia. To investigate the longitudinal disorder of multimodal brain networks in the process of cognitive decline and construct a connectome-based identification model for questionable dementia, we construct functional and structural connectivity of brain networks by using magnetic resonance imaging (MRI) data from the OASIS database. The discovery group of longitudinal database includes 113 healthy aging individuals, 65 healthy subjects at the time of scanning but diagnosed as questionable dementia during follow-up, and 39 subjects with questionable dementia at the time of scanning. The results showed that the default mode network of the potential dementia group had structural and functional abnormalities as early as the cognitive normal stage. Furthermore, a logistic regression model was further constructed using the connectomics features of 455 subjects in identification group to construct an objective mapping between the imaging biomarkers and the clinical dementia scale. The average classification accuracy was about 85%. The above findings may indicate the potential changes of the central nervous system in the early stage of dementia, and serve as an auxiliary and reference for clinicians in the early diagnosis of dementia.
KW - Alzheimer’s dementia
KW - clinical dementia rating scale
KW - connectomics
KW - early assessment of dementia
KW - multimodal magnetic resonance imaging
UR - http://www.scopus.com/inward/record.url?scp=105002442727&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-3294-7_7
DO - 10.1007/978-981-96-3294-7_7
M3 - Conference contribution
AN - SCOPUS:105002442727
SN - 9789819632930
T3 - Lecture Notes in Computer Science
SP - 81
EP - 93
BT - Brain Informatics - 17th International Conference, BI 2024, Proceedings
A2 - Itthipuripat, Sirawaj
A2 - Ascoli, Giorgio A.
A2 - Li, Anan
A2 - Pat, Narun
A2 - Kuai, Hongzhi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Conference on Brain Informatics, BI 2024
Y2 - 13 December 2024 through 15 December 2024
ER -