Simultaneous PET/fMRI revealed increased motor area input to subthalamic nucleus in Parkinson's disease.
Zhenxiang ZangTianbin SongJiping LiBinbin NieShanshan MeiChun ZhangTao WuYuqing ZhangJie LuPublished in: Cerebral cortex (New York, N.Y. : 1991) (2022)
Invasive electrophysiological recordings in patients with Parkinson's disease (PD) are extremely difficult for cross-sectional comparisons with healthy controls. Noninvasive approaches for identifying information flow between the motor area and the subthalamic nucleus (STN) are critical for evaluation of treatment strategy. We aimed to investigate the direction of the cortical-STN hyperdirect pathway using simultaneous 18F-FDG-PET/functional magnetic resonance imaging (fMRI). Data were acquired during resting state on 34 PD patients and 25 controls. The ratio of standard uptake value for PET images and the STN functional connectivity (FC) maps for fMRI data were generated. The metabolic connectivity mapping (MCM) approach that combines PET and fMRI data was used to evaluate the direction of the connectivity. Results showed that PD patients exhibited both increased FDG uptake and STN-FC in the sensorimotor area (PFDR < 0.05). MCM analysis showed higher cortical-STN MCM value in the PD group (F = 6.63, P = 0.013) in the left precentral gyrus. There was a high spatial overlap between the increased glucose metabolism and increased STN-FC in the sensorimotor area in PD. The MCM approach further revealed an exaggerated cortical input to the STN in PD, supporting the precentral gyrus as a target for treatment such as the repetitive transcranial magnetic stimulation.
Keyphrases
- resting state
- functional connectivity
- pet ct
- positron emission tomography
- magnetic resonance imaging
- computed tomography
- transcranial magnetic stimulation
- pet imaging
- end stage renal disease
- high frequency
- ejection fraction
- newly diagnosed
- cross sectional
- chronic kidney disease
- deep brain stimulation
- prognostic factors
- high resolution
- single cell
- parkinson disease
- healthcare
- multiple sclerosis
- big data
- convolutional neural network
- magnetic resonance
- combination therapy
- data analysis
- patient reported outcomes
- health information