Unveiling the Distinctive Brain Dynamics in Parkinson's Disease and Progressive Supranuclear Palsy
Poster No:
199
Submission Type:
Abstract Submission
Authors:
Jiahui Shi1, Ziyan Deng1, Chen Ran1, Chenfei Ye2, Jinghong Ma3, Ting Ma2
Institutions:
1Harbin Institute of Technology (Shenzhen), Shenzhen, GuangDong, 2School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, GuangDong, 3Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China, Beijing, Beijing
First Author:
Co-Author(s):
Chenfei Ye
School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen)
Shenzhen, GuangDong
School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen)
Shenzhen, GuangDong
Jinghong Ma
Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
Beijing, Beijing
Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
Beijing, Beijing
Ting Ma
School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen)
Shenzhen, GuangDong
School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen)
Shenzhen, GuangDong
Introduction:
As a rare neurodegenerative disease with a range of motor and behavioral syndromes, progressive supranuclear palsy (PSP) is distinguished by presence of tau inclusion bodies within both neuronal and glial cells. Its symptoms is closely resemble those of Parkinson's disease (PD), leading to frequent misdiagnoses (Tinaz et al., 2021). Disruptions to resting-state functional connectivity in PSP may affect cortical areas associated with cognitive functioning and motor behavior (Johannes et al., 2017). Besides, recent evidence indicate abnormal temporal dynamics of brain networks may serve as a biomarker in PSP (Whiteside et al., 2021). Despite the progress achieved, a substantial gap in the current body of literature is evident, particularly in understanding and evaluation of characteristics of brain states that are crucial for differentiating PSP from PD. In this study we aim to elucidate the underlying biological mechanisms of PSP and provide new insights for distinguishing PSP and PD from the dynamic brain temporal features of cortical networks.
Methods:
Study included 29 PSP patients [19: 10; mean age (MA): 67.24 years, standard deviation (SD):5.66], 82 PD [41: 41; MA: 59.96, SD: 8.6] and 69 healthy adults [40: 30; MA: 59.79, SD: 9.39]. Neuropsychological assessments conducted by Movement Disorders Clinic at Xuanwu Hospital, Capital Medical University with no PD medications taken. Using fMRIPrep software (Esteban O et al., 2019) for preprocessing and Hidden Markov Models distinct hidden internal states (Vidaurre D et al., 2017). Optimal cluster numbers of 7 was determined based on Dunn and Davies-Bouldin indices. Spatial configurations were aligned to Yeo-7 cortical networks, determine specific activate regions (Yeo B T et al., 2011). Transition probability, occupancy and dwell time were calculated (see Fig. 1A). Partial correlation analysis examined population-wise relationship between these functional dynamic characteristics and behavioral symptom scores, with adjustments for age and sex. To validate the robustness of identified brain states, we compared spatial configurations of all states across different clustering numbers and atlases. Results were corrected for multiple comparisons at a 0.05 level using false discovery rate.
Results:
Compared to PD, PSP exhibited a significant reduced dwell time in State 6 (activated by dorsal attention network (DAN), T=-2.82, p=0.005, see Fig. 2A), associated worsen UPDRS3 scores of motor impairments in PSP (r=-0.42, p=0.04, see Fig. 2C). Additionally, transition from State 3 activated by sensorimotor network (SMN) to State 6 increased (T=2.21, p=0.03, Fig. 2B) with serve UPDRS1 scores of non-motor symptoms in PSP (r=0.39, p=0.04, Fig. 2C). Furthermore, transition from State 1 activated by default mode network (DMN) to State 7 (global activation) reduced in PSP (T= -2.96, p=0.003, Fig.2B) with increased scores of UPDRS1 (r=-0.38, p=0.03, Fig. 2C). Sensitivity analysis revealed spatial profiles of above brain states were consistent across diverse brain atlases and various clustering numbers, confirming the robustness of our results (Fig. 1B).
Conclusions:
Our research uncovers distinct abnormal functional brain dynamics between PSP and PD. PD exhibit an increased dwell time in DAN state, a pattern not observed in PSP patients. In PSP, it is negatively correlated with severity of motor impairments, a finding not present in PD. This is further supported by studies showing that PD patients with freezing of gait display an altered organization within the DAN, as reported by (Inbal Maidan, 2019). Additionally, PSP is marked by an increased transition probability from the SMN state to the DAN state and a decreased transition probability from the DMN state to the global signal state. These transition probabilities are significantly associated with the severity of non-motor impairments in PSP patients. Our findings offer novel insights into brain dynamic biomarkers for PSP, which may facilitate differential diagnosis from PD.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Cortex
Degenerative Disease
FUNCTIONAL MRI
Movement Disorder
1|2Indicates the priority used for review
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2. Rosskopf, J. (2017). Intrinsic functional connectivity alterations in progressive supranuclear palsy: Differential effects in frontal cortex, motor, and midbrain networks. Movement Disorders, 32(7), 1006–1015.
3. Tinaz, S. (2021). Functional connectome in Parkinson’s disease and parkinsonism. Current neurology and neuroscience reports, 21(6), 24.
4. Vidaurre, D. (2017). Brain network dynamics are hierarchically organized in time. Proceedings of the National Academy of Sciences, 114 (48), 12827–12832.
5. Whiteside, D. (2021). Altered network stability in progressive supranuclear palsy. Neurobiology of Aging, 107, 109–117.
6. Yeo, B. T. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165.
7. Inbal Maidan. (2019). Altered organization of the dorsal attention network is associated with freezing of gait in Parkinson's disease. Parkinsonism & related disorders, 63, 77–82.