TY - GEN
T1 - Differential Identification of Prodromal Stages of Alzheimer's Disease Using Tissue Probability Map (TPM) based Network
AU - Adebisi, Abdulyekeen T.
AU - Gonuguntla, Venkateswarlu
AU - Lee, Ho Won
AU - Veluvolu, Kalyana C.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - A lot of efforts have been made by researchers for easy detection of the prodromal phase of Alzheimer's disease (AD) and other dementia to enable curative measures. Among the leading approaches that show promising results is the use of complex network theory on neuroimaging data such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), magnetoencephalogram (MEG), electroencephalogram (EEG) etc. However, exploring the network theory using the tissue probability Map (TPM) of magnetic resonance imaging (MRI) data has been quite unexplored. Therefore, in this paper, we developed the generalized improved multiscale permutation entropy (GIMPE) for the computation of complexity of grey matter (GM) TPM for all the considered region of interests (ROIs). In order to formulate a well defined network, the vectors, GIMPEs of all ROIs are taken as nodes and the edges between the nodes are defined by the Euclidean distance between the corresponding vectors (GIMPEs). The validation of our approach on MRI data accentuates the importance of the proposed approach as well as the significance of TPM based brain networks for the discrimination and differential diagnosis of normal aging, prodromal phase and the later phase of AD.
AB - A lot of efforts have been made by researchers for easy detection of the prodromal phase of Alzheimer's disease (AD) and other dementia to enable curative measures. Among the leading approaches that show promising results is the use of complex network theory on neuroimaging data such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), magnetoencephalogram (MEG), electroencephalogram (EEG) etc. However, exploring the network theory using the tissue probability Map (TPM) of magnetic resonance imaging (MRI) data has been quite unexplored. Therefore, in this paper, we developed the generalized improved multiscale permutation entropy (GIMPE) for the computation of complexity of grey matter (GM) TPM for all the considered region of interests (ROIs). In order to formulate a well defined network, the vectors, GIMPEs of all ROIs are taken as nodes and the edges between the nodes are defined by the Euclidean distance between the corresponding vectors (GIMPEs). The validation of our approach on MRI data accentuates the importance of the proposed approach as well as the significance of TPM based brain networks for the discrimination and differential diagnosis of normal aging, prodromal phase and the later phase of AD.
KW - Alzheimer's Disease (AD)
KW - Complex Network Theory
KW - Dementia
KW - Magnetic Resonance Imaging (MRI)
KW - Minimum Spanning Tree (MST)
KW - Permutation Entropy
UR - https://www.scopus.com/pages/publications/85125178410
U2 - 10.1109/BIBM52615.2021.9669847
DO - 10.1109/BIBM52615.2021.9669847
M3 - Conference contribution
AN - SCOPUS:85125178410
T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
SP - 3705
EP - 3712
BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
A2 - Huang, Yufei
A2 - Kurgan, Lukasz
A2 - Luo, Feng
A2 - Hu, Xiaohua Tony
A2 - Chen, Yidong
A2 - Dougherty, Edward
A2 - Kloczkowski, Andrzej
A2 - Li, Yaohang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Y2 - 9 December 2021 through 12 December 2021
ER -