Now showing 1 - 2 of 2
ItemBottom-of-sulcus dysplasia: the role of F-18-FDG PET in identifying a focal surgically remedial epileptic lesionBerlangieri, SU ; Mito, R ; Semmelroch, M ; Pedersen, M ; Jackson, G (SPRINGERNATURE, 2020-12-15)PURPOSE: Bottom-of-sulcus dysplasia (BOSD) is a type of focal cortical dysplasia and an important cause of intractable epilepsy. While the MRI features of BOSD have been well documented, the contribution of PET to the identification of these small lesions has not been widely explored. The aim of this study was to investigate the role of F-18 fluorodeoxyglucose (18F-FDG) PET in the identification of BOSD. METHODS: Twenty patients with BOSD underwent both 18F-FDG PET and structural MRI scans as part of preoperative planning for surgery. Visual PET analysis was performed, and patients were classified as positive if they exhibited a focal or regional hypometabolic abnormality, or negative in the absence of a hypometabolic abnormality. MRI data were reviewed to determine if any structural abnormality characteristic of BOSD were observed before and after co-registration with PET findings. RESULTS: PET detected hypometabolic abnormalities consistent with the seizure focus location in 95% (19/20) of cases. Focal abnormalities were detected on 18F-FDG PET in 12/20 (60%) patients, while regional hypometabolism was evident in 7/20 (35%). BOSD lesions were missed in 20% (4/20) of cases upon initial review of MRI scans. Co-registration of 18F-FDG PET with MRI enabled detection of the BOSD in all four cases where the lesion was initially missed. CONCLUSION: Our findings show that 18F-FDG PET provides additional clinical value in the localisation and detection of BOSD lesions, when used in conjunction with MRI.
ItemModeling the respiratory central pattern generator with resonate-and-fire Izhikevich-NeuronsTolmachev, P ; Dhingra, RR ; Pauley, M ; Dutschmann, M ; Manton, JH ; Cheng, L ; Leung, ACS ; Ozawa, S (Springer Nature, 2018-01-01)Computational models of the respiratory central pattern generator (rCPG) are usually based on biologically-plausible Hodgkin Huxley neuron models. Such models require numerous parameters and thus are prone to overfitting. The HH approach is motivated by the assumption that the biophysical properties of neurons determine the network dynamics. Here, we implement the rCPG using simpler Izhikevich resonate-and-fire neurons. Our rCPG model generates a 3-phase respiratory motor pattern based on established connectivities and can reproduce previous experimental and theoretical observations. Further, we demonstrate the flexibility of the model by testing whether intrinsic bursting properties are necessary for rhythmogenesis. Our simulations demonstrate that replacing predicted mandatory bursting properties of pre-inspiratory neurons with spike adapting properties yields a model that generates comparable respiratory activity patterns. The latter supports our view that the importance of the exact modeling parameters of specific respiratory neurons is overestimated.