Clinical Personal Connectomics Using Hybrid PET/MRI
10.1007/s13139-019-00572-3
- Author:
Dong Soo LEE
1
Author Information
1. Department of Nuclear Medicine, College of Medicine, Seoul National University, Seoul, Republic of Korea. dsl@snu.ac.kr
- Publication Type:Review
- Keywords:
Connectivity;
PET/MRI;
Classification;
Persistent homology;
Permutation;
Connectomics
- MeSH:
Brain;
Classification;
Connectome;
Glucose;
Humans;
Magnetic Resonance Imaging;
Metabolism;
Perfusion
- From:Nuclear Medicine and Molecular Imaging
2019;53(3):153-163
- CountryRepublic of Korea
- Language:English
-
Abstract:
Brain connectivity can now be studied with topological analysis using persistent homology. It overcame the arbitrariness of thresholding to make binary graphs for comparison between disease and normal control groups. Resting-state fMRI can yield personal interregional brain connectivity based on perfusion signal on MRI on individual subject bases and FDG PET produces the topography of glucose metabolism. Assuming metabolism perfusion coupling and disregarding the slight difference of representing time of metabolism (before image acquisition) and representing time of perfusion (during image acquisition), topography of brain metabolism on FDG PET and topologically analyzed brain connectivity on resting-state fMRI might be related to yield personal connectomics of individual subjects and even individual patients. The work of association of FDG PET/resting-state fMRI is yet to be warranted; however, the statistics behind the group comparison of connectivity on FDG PET or resting-state MRI was already developed. Before going further into the connectomics construction using directed weighted brain graphs of FDG PET or resting-state fMRI, I detailed in this review the plausibility of using hybrid PET/MRI to enable the interpretation of personal connectomics which can lead to the clinical use of brain connectivity in the near future.
