Inter-subject FDG PET Brain Networks Exhibit Multi-scale Community Structure with Different Normalization Techniques
Inter-subject networks are used to model correlations between brain regions and are particularly useful for metabolic imaging techniques, like 18F-2-deoxy-2-(18F)fluoro-d-glucose (FDG) positron emission tomography (PET). Since FDG PET typically produces a single image, correlations cannot be calculated over time. Little focus has been placed on the basic properties of inter-subject networks and if they are affected by group size and image normalization. FDG PET images were acquired from rats (n = 18), normalized by whole brain, visual cortex, or cerebellar FDG uptake, and used to construct correlation matrices. Group size effects on network stability were investigated by systematically adding rats and evaluating local network connectivity (node strength and clustering coefficient). Modularity and community structure were also evaluated in the differently normalized networks to assess meso-scale network relationships. Local network properties are stable regardless of normalization region for groups of at least 10. Whole brain-normalized networks are more modular than visual cortex- or cerebellum-normalized network (p < 0.00001); however, community structure is similar at network resolutions where modularity differs most between brain and randomized networks. Hierarchical analysis reveals consistent modules at different scales and clustering of spatially-proximate brain regions. Findings suggest inter-subject FDG PET networks are stable for reasonable group sizes and exhibit multi-scale modularity.
KeywordsNetworks Brain FDG PET Modularity
The authors thank Eric Blankemeyer for his assistance with PET imaging, Dr. Danielle Bassett for helpful conversations regarding modularity, and Dr. Blythe Phillips for consultation when developing the dexmedetomidine sedation protocol. This project was supported by funding from the Catherine D. Sharpe Foundation, the Oral and Maxillofacial Surgery Foundation, the Oral and Maxillofacial Surgery Schoenleber Research Fund from the University of Pennsylvania School of Dental Medicine, and a training grant from NIH/NIAMS (T32-AR007132).
Conflict of interest
The authors have no conflicts of interest to disclose.
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