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Front Neurol. 2019 May 29;10:566. doi: 10.3389/fneur.2019.00566. eCollection 2019.

Frequency-Dependent Relationship Between Resting-State fMRI and Glucose Metabolism in the Elderly.

Jiao F1, Gao Z2,3, Shi K4, Jia X2,3, Wu P5, Jiang C5, Ge J5, Su H6, Guan Y5, Shi S7, Zang YF2,3, Zuo C5,8,9.

Author information

1
Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China.
2
Center for Cognition and Brain Disorders, Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.
3
Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou, China.
4
Department of Nuclear Medicine, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany.
5
PET Center, Huashan Hospital, Fudan University, Shanghai, China.
6
Shanghai Mental Health Center, Shanghai Jiaotong University, Shanghai, China.
7
Psychiatry Department, Huashan Hospital, Fudan University, Shanghai, China.
8
Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
9
Human Phenome Institute, Fudan University, Shanghai, China.

Abstract

Both glucose metabolism and resting-state fMRI (RS-fMRI) signal reflect hemodynamic features. The objective of this study was to investigate their relationship in the resting-state in healthy elderly participants (n = 18). For RS-fMRI signal, regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), and degree of centrality (DC) maps were generated in multiple frequency bands. Glucose uptake was acquired with 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). Linear correlation of each pair of the FDG-PET and RS-fMRI metrics was explored both in across-voxel way and in across-subject way. We found a significant across-voxel correlation between the FDG-PET and BOLD-fMRI metrics. However, only a small portion of voxels showed significant across-subject correlation between FDG-PET and BOLD-fMRI metrics. All these results were similar across all frequency bands of RS-fMRI data. The current findings indicate that FDG-PET and RS-fMRI metrics share similar spatial pattern (significant across-voxel correlation) but have different underlying physiological importance (non-significant across-subject correlation). Specifically, FDG-PET measures the mean glucose metabolism over tens of minutes, while RS-fMRI measures the dynamic characteristics. The combination of FDG-PET and RS-fMRI provides complementary information to reveal the underlying mechanisms of the brain activity and may enable more comprehensive interpretation of clinical PET-fMRI studies. Future studies would attempt to reduce the artifacts of RS-fMRI and to analyze the dynamic feature of PET signal.

KEYWORDS:

FDG; PET; fMRI; frequency-dependent; multi-modalities

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