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Ysbæk-Nielsen AT. Connectome-based predictive modelling estimates individual cognitive status in Parkinson's disease. Parkinsonism Relat Disord 2024; 123:106020. [PMID: 38579439 DOI: 10.1016/j.parkreldis.2024.106020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 01/30/2024] [Accepted: 01/30/2024] [Indexed: 04/07/2024]
Abstract
INTRODUCTION The progressive nature of Parkinson's disease (PD) affords emphasis on accurate early-stage individual-level assessment of risk and intervention appropriateness. In PD, cognitive impairment (CI) may follow or precede motor symptoms but are generally underdetected. In addition to impeding daily functioning and quality of life, CIs increase the risk for later conversion to dementia, providing a pressing need to develop novel tools to detect and interpret them. Connectome-based predictive modelling (CPM) is an emerging machine-learning approach to individual prediction that holds translational promise due to its noninvasiveness and simple implementation. The aim of this study was to investigate CPM's potential to predict and understand CIs in PD. METHODS Resting-state functional connectivity from 58 patients with PD of varying cognitive status was used to train a CPM-model to predict a global cognitive composite (GCC) score. The model was validated using cross-validation, permutation testing, and internal stability analyses. The combined predictive strength of two brain connectivity networks, positive and negative, directly and inversely correlated with GCC, respectively, was assessed. RESULTS The model significantly predicted individual GCC scores, r = 0.63, pperm < .05. Separately, the positive and negative networks were similar in performance, rs ≥ .58, ps < .05, but varied in anatomical distribution. CONCLUSIONS This study identified a connectome predictive of cognitive scores in PD, with features overlapping with established and emerging evidence on aberrant connectivity in PD-related CIs. Overall, CPM appears promising for clinical translation in this population, but longitudinal studies with out-of-sample validation are needed.
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Dahmani L, Bai Y, Zhang W, Ren J, Li S, Hu Q, Fu X, Ma J, Wei W, Wang M, Liu H, Wang D. Individualized functional connectivity markers associated with motor and mood symptoms of Parkinson's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.31.578238. [PMID: 38352322 PMCID: PMC10862849 DOI: 10.1101/2024.01.31.578238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Parkinson's disease (PD) is a complex neurological disorder characterized by many motor and non-motor symptoms. While most studies focus on the motor symptoms of the disease, it is important to identify markers that underlie different facets of the disease. In this case-control study, we sought to discover reliable, individualized functional connectivity markers associated with both motor and mood symptoms of PD. Using functional MRI, we extensively sampled 166 patients with PD (64 women, 102 men; mean age=61.8 years, SD=7.81) and 51 healthy control participants (32 women, 19 men; mean age=55.68 years, SD=7.62). We found that a model consisting of 44 functional connections predicted both motor (UPDRS-III: Pearson r=0.21, FDR-adjusted p=0.006) and mood symptoms (HAMD: Pearson r=0.23, FDR-adjusted p=0.006; HAMA: Pearson r=0.21, FDR-adjusted p=0.006). Two sets of connections contributed differentially to these predictions. Between-network connections, mainly connecting the sensorimotor and visual large-scale functional networks, substantially contributed to the prediction of motor measures, while within-network connections in the insula and sensorimotor network contributed more so to mood prediction. The middle to posterior insula region played a particularly important role in predicting depression and anxiety scores. We successfully replicated and generalized our findings in two independent PD datasets. Taken together, our findings indicate that sensorimotor and visual network markers are indicative of PD brain pathology, and that distinct subsets of markers are associated with motor and mood symptoms of PD.
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Affiliation(s)
- Louisa Dahmani
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA, 02129
| | - Yan Bai
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Wei Zhang
- Changping Laboratory, Beijing, China
| | | | - Shiyi Li
- Changping Laboratory, Beijing, China
| | - Qingyu Hu
- Changping Laboratory, Beijing, China
| | | | - Jianjun Ma
- Department of Neurology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Wei Wei
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Hesheng Liu
- Changping Laboratory, Beijing, China
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA, 02129
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Wang Y, Zhang Y, Xu T, Han X, Ge X, Chen F. Finger motor representation supports the autonomy in arithmetic: neuroimaging evidence from abacus training. Cereb Cortex 2024; 34:bhad524. [PMID: 38186011 DOI: 10.1093/cercor/bhad524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 12/09/2023] [Accepted: 12/11/2023] [Indexed: 01/09/2024] Open
Abstract
Researches have reported the close association between fingers and arithmetic. However, it remains unclear whether and how finger training can benefit arithmetic. To address this issue, we used the abacus-based mental calculation (AMC), which combines finger training and mental arithmetic learning, to explore the neural correlates underlying finger-related arithmetic training. A total of 147 Chinese children (75 M/72 F, mean age, 6.89 ± 0.46) were recruited and randomly assigned into AMC and control groups at primary school entry. The AMC group received 5 years of AMC training, and arithmetic abilities and resting-state functional magnetic resonance images data were collected from both groups at year 1/3/5. The connectome-based predictive modeling was used to find the arithmetic-related networks of each group. Compared to controls, the AMC's positively arithmetic-related network was less located in the control module, and the inter-module connections between somatomotor-default and somatomotor-control modules shifted to somatomotor-visual and somatomotor-dorsal attention modules. Furthermore, the positive network of the AMC group exhibited a segregated connectivity pattern, with more intra-module connections than the control group. Overall, our results suggested that finger motor representation with motor module involvement facilitated arithmetic-related network segregation, reflecting increased autonomy of AMC, thus reducing the dependency of arithmetic on higher-order cognitive functions.
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Affiliation(s)
- Yanjie Wang
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou 310058, China
| | - Yi Zhang
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou 310058, China
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310058, China
| | - Tianyong Xu
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou 310058, China
| | - Xiao Han
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou 310058, China
| | - Xuelian Ge
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou 310058, China
| | - Feiyan Chen
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou 310058, China
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Rabini G, Pierotti E, Meli C, Dodich A, Papagno C, Turella L. Connectome-based fingerprint of motor impairment is stable along the course of Parkinson's disease. Cereb Cortex 2023; 33:9896-9907. [PMID: 37455441 DOI: 10.1093/cercor/bhad252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/18/2023] Open
Abstract
Functional alterations in brain connectivity have previously been described in Parkinson's disease, but it is not clear whether individual differences in connectivity profiles might be also linked to severity of motor-symptom manifestation. Here we investigated the relevance of individual functional connectivity patterns measured with resting-state fMRI with respect to motor-symptom severity in Parkinson's disease, through a whole-brain, data-driven approach (connectome-based predictive modeling). Neuroimaging and clinical data of Parkinson's disease patients from the Parkinson's Progression Markers Initiative were derived at baseline (session 1, n = 81) and at follow-up (session 2, n = 53). Connectome-based predictive modeling protocol was implemented to predict levels of motor impairment from individual connectivity profiles. The resulting predictive model comprised a network mainly involving functional connections between regions located in the cerebellum, and in the motor and frontoparietal networks. The predictive power of the model was stable along disease progression, as the connectivity within the same network could predict levels of motor impairment, even at a later stage of the disease. Finally, connectivity profiles within this network could be identified at the individual level, suggesting the presence of individual fingerprints within resting-state fMRI connectivity associated with motor manifestations in Parkinson's disease.
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Affiliation(s)
- Giuseppe Rabini
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
| | - Enrica Pierotti
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
| | - Claudia Meli
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
| | - Alessandra Dodich
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
| | - Costanza Papagno
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
| | - Luca Turella
- Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy
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Huang P, Zhang M. Magnetic Resonance Imaging Studies of Neurodegenerative Disease: From Methods to Translational Research. Neurosci Bull 2023; 39:99-112. [PMID: 35771383 PMCID: PMC9849544 DOI: 10.1007/s12264-022-00905-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/07/2022] [Indexed: 01/22/2023] Open
Abstract
Neurodegenerative diseases (NDs) have become a significant threat to an aging human society. Numerous studies have been conducted in the past decades to clarify their pathologic mechanisms and search for reliable biomarkers. Magnetic resonance imaging (MRI) is a powerful tool for investigating structural and functional brain alterations in NDs. With the advantages of being non-invasive and non-radioactive, it has been frequently used in both animal research and large-scale clinical investigations. MRI may serve as a bridge connecting micro- and macro-level analysis and promoting bench-to-bed translational research. Nevertheless, due to the abundance and complexity of MRI techniques, exploiting their potential is not always straightforward. This review aims to briefly introduce research progress in clinical imaging studies and discuss possible strategies for applying MRI in translational ND research.
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Affiliation(s)
- Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
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