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Evaluation of brain functional connectome in tuberous sclerosis complex with neuropsychological disorders using resting-state fMRI1Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan, 2Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan, 3Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan, 4Institute of Medicine and Department of Medical Imaging, Chung Shan Medical University and Hospital, Taichung, Taiwan, 5School of Medicine and Department of Pediatrics, Chung Shan Medical University and Hospital, Taichung, Taiwan
Synopsis
We aimed to use resting-state fMRI to provide the first findings on disrupted functional brain networks in tuberous sclerosis complex (TSC) patients with graph theoretical analysis (GTA) and network-based statistic (NBS) analysis. We found several topological parameters including clustering coefficient, local efficiency, transitivity, and modularity in the healthy control were better than those in the TSC patients. One subnetwork showed more edges in the healthy control compared with the TSC, including the connections from the frontal lobe to the parietal lobe. Our findings may help better understand the variable clinical phenotypes of TSC and the underlying physiological mechanisms.
Introduction
Tuberous sclerosis complex (TSC) is a rare genetic disorder with multisystem involvement. TSC is characterized by benign hamartomas in multiple organs including the brain and its clinical phenotypes may be associated with abnormal functional connections. We aimed to use resting-state fMRI to provide the first findings on disrupted functional brain networks in TSC patients with graph theoretical analysis (GTA) and network-based statistic (NBS) analysis 1.Methods
Twenty TSC patients (age = 28.5 ± 21.5 y/o) and 18 age-matched healthy controls (HC, age 30 ± 7 y/o) were recruited in this study. All the participants underwent rs-fMRI scan (GRE-EPI, TR/TE = 2000ms / 30ms, voxel size= 2.7×2.7×4.0 mm3, 28 axial slices per volume, total 240 volumes per scan) using 3 T MRI scanner (Skyra, Siemens Medical Systems, Germany) with a standard 20-channel head coil.
In data analysis, Statistical Parametric Mapping (SPM, Wellcome Department of Cognitive Neurology, London, UK) was used to perform pre-processing for images, including slice-timing, realignment, normalization, and smoothness. The functional connectivity analysis was performed after removing physiological noises. The graph theoretical analysis (GTA) was used to calculate the topological parameters of the brain network, including clustering coefficient (C), normalized clustering coefficient (γ), local efficiency (Elocal), characteristic path length (L), normalized characteristic path length (λ), global Efficiency (Eglobal), small-worldness (σ), transitivity, modularity, and assortativity. The network-based statistic (NBS) analysis was then applied to find the difference of cerebrum functional connectivity between each group. The age and gender were used as covariates, and a p-value less than 0.05 was considered statistically significant.
Results
In the GTA (Fig. 1), we found several topological parameters including clustering coefficient, local efficiency, transitivity, and modularity in the HC were better than those in the TSC patients (p < 0.05). However, no significant difference was found in the characteristic path length, global efficiency, assortativity and small-worldness. Although all participants maintained the small-worldness functional brain network according to the σ calculation, the network was more like a random network in the TSC patients 1. In the NBS analysis (Fig. 2), we compared the edges of the brain networks between the TSC and the HC groups. One subnetwork showed more edges in the HC compared with the TSC (p < 0.05), including the connections from the frontal lobe to the parietal lobe.Discussion
We found worse local segregation of the functional network in the TSC patients compared with HC, especially in the frontal lobe and the parietal lobe, and the functional network in the TSC patients was more like a random network. The presence of tubers in the temporal lobe of TSC patients has been linked to the risk of autism and epilepsy. The degree of functional connectivity is thought to be associated with the severity of neuropsychological impairment. Therefore, TSC patients are more frequently associated with worse outcomes, earlier age at seizure onset, and more severe intellectual disability 2, 3.Conclusion
We provided the first findings on disrupted functional connectivity and organization in TSC compared with HC. Our findings may help better understand the variable clinical phenotypes of TSC and the underlying physiological mechanisms. Future neuropsychological testing is needed to better understand the neuropsychological consequences.Acknowledgements
This study was supported by the research program MOST107-2221-E-182-054-MY3, that was sponsored by the Ministry of Science and Technology, Taipei, Taiwan. This study was also supported by the grant (BMRPD1H0101) of Chang Gung University, Taoyuan, Taiwan.References
1. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience. 2009; 10(3): 186–198.
2. Sarikaya I. PET studies in epilepsy. American Journal of Nuclear Medicine and Molecular Imaging. 2005; 5(5): 416–430.
3. Peters JM, Taquet M, Prohl AK, et al. Diffusion tensor imaging and related techniques in tuberous sclerosis complex: review and future directions. Future Neurol. 2013; 8(5): 583–597.
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