1
|
Jia M, Yang S, Li S, Chen S, Wu L, Li J, Wang H, Wang C, Liu Q, Wu K. Early identification of Parkinson's disease with anxiety based on combined clinical and MRI features. Front Aging Neurosci 2024; 16:1414855. [PMID: 38903898 PMCID: PMC11188332 DOI: 10.3389/fnagi.2024.1414855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 05/15/2024] [Indexed: 06/22/2024] Open
Abstract
Objective To identify cortical and subcortical volume, thickness and cortical area features and the networks they constituted related to anxiety in Parkinson's disease (PD) using structural magnetic resonance imaging (sMRI), and to integrate multimodal features based on machine learning to identify PD-related anxiety. Methods A total of 219 patients with PD were retrospectively enrolled in the study. 291 sMRI features including cortical volume, subcortical volume, cortical thickness, and cortical area, as well as 17 clinical features, were extracted. Graph theory analysis was used to explore structural networks. A support vector machine (SVM) combination model, which used both sMRI and clinical features to identify participants with PD-related anxiety, was developed and evaluated. The performance of SVM models were evaluated. The mean impact value (MIV) of the feature importance evaluation algorithm was used to rank the relative importance of sMRI features and clinical features within the model. Results 17 significant sMRI variables associated with PD-related anxiety was used to build a brain structural network. And seven sMRI and 5 clinical features with statistically significant differences were incorporated into the SVM model. The comprehensive model achieved higher performance than clinical features or sMRI features did alone, with an accuracy of 0.88, a precision of 0.86, a sensitivity of 0.81, an F1-Score of 0.83, a macro-average of 0.85, a weighted-average of 0.92, an AUC of 0.88, and a result of 10-fold cross-validation of 0.91 in test set. The sMRI feature right medialorbitofrontal thickness had the highest impact on the prediction model. Conclusion We identified the brain structural features and networks related to anxiety in PD, and developed and internally validated a comprehensive model with multimodal features in identifying.
Collapse
Affiliation(s)
- Min Jia
- Department of Neurology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, China
| | - Shijun Yang
- Department of Neurology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, China
| | - Shanshan Li
- Department of Medical Ultrasound, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, China
| | - Siying Chen
- Hubei Minzu University, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, China
| | - Lishuang Wu
- Department of Neurology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, China
| | - Jinlan Li
- Department of Neurology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, China
| | - Hanlin Wang
- Department of Medicine, The Xi’an Jiaotong University, Xi’an, Shanxi, China
| | - Congping Wang
- Department of Neurology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, China
| | - Qunhui Liu
- Department of Neurology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, China
| | - Kemei Wu
- Department of Neurology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, Hubei, China
| |
Collapse
|
2
|
Ren Z, Wang B, Yue M, Han J, Chen Y, Zhao T, Wang N, Xu J, Zhao P, Li M, Sun L, Wen B, Zhao Z, Han X. Construction of machine learning models for recognizing comorbid anxiety in epilepsy patients based on their clinical and quantitative EEG features. Epilepsy Res 2024; 201:107333. [PMID: 38422800 DOI: 10.1016/j.eplepsyres.2024.107333] [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: 01/03/2024] [Revised: 02/18/2024] [Accepted: 02/23/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND This study aimed to construct prediction models for the recognizing of anxiety disorders (AD) in patients with epilepsy (PWEs) by combining clinical features with quantitative electroencephalogram (qEEG) features and using machine learning (ML). METHODS Nineteen clinical features and 20-min resting-state EEG were collected from 71 PWEs comorbid with AD and another 60 PWEs without AD who met the inclusion-exclusion criteria of this study. The EEG were preprocessed and 684 Phase Locking Value (PLV) and 76 Lempel-Ziv Complexity (LZC) features on four bands were extracted. The Fisher score method was used to rank all the derived features. We constructed four models for recognizing AD in PWEs, whether PWEs based on different combinations of features using eXtreme gradient boosting (XGboost) and evaluated these models using the five-fold cross-validation method. RESULTS The prediction model constructed by combining the clinical, PLV, and LZC features showed the best performance, with an accuracy of 96.18%, precision of 94.29%, sensitivity of 98.33%, F1-score of 96.06%, and Area Under the Curve (AUC) of 0.96. The Fisher score ranking results displayed that the top ten features were depression, educational attainment, α_P3LZC, α_T6-PzPLV, α_F7LZC, β_Fp2-O1PLV, θ_T4-CzPLV, θ_F7-PzPLV, α_Fp2LZC, and θ_T4-PzPLV. CONCLUSIONS The model, constructed by combining the clinical and qEEG features PLV and LZC, efficiently identified the presence of AD comorbidity in PWEs and might have the potential to complement the clinical diagnosis. Our findings suggest that LZC features in the α band and PLV features in Fp2-O1 may be potential biomarkers for diagnosing AD in PWEs.
Collapse
Affiliation(s)
- Zhe Ren
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province 450003, China; Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, Henan Province 450003, China
| | - Bin Wang
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province 450003, China
| | - Mengyan Yue
- Orthopedic Rehabilitation Department, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, Henan Province 450003, China
| | - Jiuyan Han
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province 450003, China
| | - Yanan Chen
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province 450003, China
| | - Ting Zhao
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province 450003, China
| | - Na Wang
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province 450003, China
| | - Jun Xu
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province 450003, China
| | - Pan Zhao
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province 450003, China
| | - Mingmin Li
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province 450003, China
| | - Lei Sun
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province 450003, China
| | - Bin Wen
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710001, China
| | - Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan Province 453000, China
| | - Xiong Han
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province 450003, China; Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, Henan Province 450003, China.
| |
Collapse
|
3
|
Wang Q, Ren Z, Yue M, Zhao Y, Wang B, Zhao Z, Wen B, Hong Y, Chen Y, Zhao T, Wang N, Zhao P, Hong Y, Han X. A model for the diagnosis of anxiety in patients with epilepsy based on phase locking value and Lempel-Ziv complexity features of the electroencephalogram. Brain Res 2024; 1824:148662. [PMID: 37924926 DOI: 10.1016/j.brainres.2023.148662] [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: 04/27/2023] [Revised: 09/09/2023] [Accepted: 10/30/2023] [Indexed: 11/06/2023]
Abstract
OBJECTIVE Anxiety disorders (AD) are critical factors that significantly (about one-fifth) impact the quality of life (QoL) in patients with epilepsy (PWE). Objective diagnostic methods have contributed to the identification of PWE susceptible to AD. This study aimed to identify AD in PWE by constructing a diagnostic model based on the phase locking value (PLV) and Lempel-Ziv Complexity (LZC) features of the electroencephalogram (EEG). METHODS EEG data from 131 patients with epilepsy (PWE) were enrolled in this study. Patients were divided into two groups, anxiety disorder (AD, n = 61) and non-anxiety disorder (NAD, n = 70), according to the Hamilton Rating Scale for Anxiety (HAM-A). Support vector machine (SVM) and K-Nearest-Neighbor(KNN) algorithms were used to construct three models - the PLVEEG, LZCEEG, and PLVEEG + LZCEEG feature models. Finally, the area under the receiver operating characteristic curve (AUC) and statistical analyses were performed to evaluate the model performance. RESULTS The efficiency of the KNN-based PLCEEG + LZCEEG feature model was the best, and the accuracy, precision, recall, F1-score, and AUC of the model after five-fold cross-validations scores were 87.89 %, 82.27 %, 98.33 %, 88.95 %, and 0.89, respectively. When the model efficiency was optimal, 29 EEG features were suggested. Further analysis of these features indicated 22 EEG features that were significantly different between the two groups, including 50 % features of the alpha (α)-band. CONCLUSIONS The PLVEEG + LZCEEG model features can identify AD in PWE. The PLVEEG and LZCEEG characteristics of the α-band may further be explored as potential biomarkers for AD in PWE.
Collapse
Affiliation(s)
- Qi Wang
- Department of Neurology, Zhengzhou University People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Zhe Ren
- Department of Neurology, Zhengzhou University People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Mengyan Yue
- Department of Rehabilitation, The First Hospital of Shanxi Medical University, Shanxi Province, Taiyuan 030000, China
| | - Yibo Zhao
- Department of Neurology, Zhengzhou University People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Bin Wang
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453000, Henan Province, China
| | - Bin Wen
- School of Life Sciences and Technology, Xi'an Jiaotong University, Xi'an 710000, Shaanxi Province, China
| | - Yang Hong
- Department of Neurology, People's Hospital of Henan University, Zhengzhou 450003, Henan Province, China
| | - Yanan Chen
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Ting Zhao
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Na Wang
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Pan Zhao
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Yingxing Hong
- Department of Neurology, People's Hospital of Henan University, Zhengzhou 450003, Henan Province, China
| | - Xiong Han
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China.
| |
Collapse
|
4
|
Yassine S, Almarouk S, Gschwandtner U, Auffret M, Fuhr P, Verin M, Hassan M. Electrophysiological signatures of anxiety in Parkinson's disease. Transl Psychiatry 2024; 14:66. [PMID: 38280864 PMCID: PMC10821912 DOI: 10.1038/s41398-024-02745-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 12/19/2023] [Accepted: 01/08/2024] [Indexed: 01/29/2024] Open
Abstract
Anxiety is a common non-motor symptom in Parkinson's disease (PD) occurring in up to 31% of the patients and affecting their quality of life. Despite the high prevalence, anxiety symptoms in PD are often underdiagnosed and, therefore, undertreated. To date, functional and structural neuroimaging studies have contributed to our understanding of the motor and cognitive symptomatology of PD. Yet, the underlying pathophysiology of anxiety symptoms in PD remains largely unknown and studies on their neural correlates are missing. Here, we used resting-state electroencephalography (RS-EEG) of 68 non-demented PD patients with or without clinically-defined anxiety and 25 healthy controls (HC) to assess spectral and functional connectivity fingerprints characterizing the PD-related anxiety. When comparing the brain activity of the PD anxious group (PD-A, N = 18) to both PD non-anxious (PD-NA, N = 50) and HC groups (N = 25) at baseline, our results showed increased fronto-parietal delta power and decreased frontal beta power depicting the PD-A group. Results also revealed hyper-connectivity networks predominating in delta, theta and gamma bands against prominent hypo-connectivity networks in alpha and beta bands as network signatures of anxiety in PD where the frontal, temporal, limbic and insular lobes exhibited the majority of significant connections. Moreover, the revealed EEG-based electrophysiological signatures were strongly associated with the clinical scores of anxiety and followed their progression trend over the course of the disease. We believe that the identification of the electrophysiological correlates of anxiety in PD using EEG is conducive toward more accurate prognosis and can ultimately support personalized psychiatric follow-up and the development of new therapeutic strategies.
Collapse
Affiliation(s)
- Sahar Yassine
- MRC Brain Dynamic Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
- University of Rennes, LTSI - U1099, F-35000, Rennes, France.
- Behavior & Basal Ganglia, CIC1414, CIC-IT, CHU Rennes, Rennes, France.
| | - Sourour Almarouk
- University of Rennes, LTSI - U1099, F-35000, Rennes, France
- Behavior & Basal Ganglia, CIC1414, CIC-IT, CHU Rennes, Rennes, France
- Neuroscience Research Centre, Lebanese University, Faculty of Medicine, Beirut, Lebanon
| | - Ute Gschwandtner
- Dept. of Neurology, Hospitals of the University of Basel, Basel, Switzerland
| | - Manon Auffret
- University of Rennes, LTSI - U1099, F-35000, Rennes, France
- Behavior & Basal Ganglia, CIC1414, CIC-IT, CHU Rennes, Rennes, France
- Institut des Neurosciences Cliniques de Rennes (INCR), Rennes, France
- France Développement Electronique, Monswiller, France
| | - Peter Fuhr
- Dept. of Neurology, Hospitals of the University of Basel, Basel, Switzerland
| | - Marc Verin
- University of Rennes, LTSI - U1099, F-35000, Rennes, France
- Behavior & Basal Ganglia, CIC1414, CIC-IT, CHU Rennes, Rennes, France
- Institut des Neurosciences Cliniques de Rennes (INCR), Rennes, France
- Movement Disorders Unit, Neurology Department, Pontchaillou University Hospital, Rennes, France
| | - Mahmoud Hassan
- Behavior & Basal Ganglia, CIC1414, CIC-IT, CHU Rennes, Rennes, France
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
- MINDIG, F-35000, Rennes, France
| |
Collapse
|
5
|
Lai TT, Gericke B, Feja M, Conoscenti M, Zelikowsky M, Richter F. Anxiety in synucleinopathies: neuronal circuitry, underlying pathomechanisms and current therapeutic strategies. NPJ Parkinsons Dis 2023; 9:97. [PMID: 37349373 DOI: 10.1038/s41531-023-00547-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 06/09/2023] [Indexed: 06/24/2023] Open
Abstract
Synucleinopathies are neurodegenerative disorders characterized by alpha-synuclein (αSyn) accumulation in neurons or glial cells, including Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). αSyn-related pathology plays a critical role in the pathogenesis of synucleinopathies leading to the progressive loss of neuronal populations in specific brain regions and the development of motor and non-motor symptoms. Anxiety is among the most frequent non-motor symptoms in patients with PD, but it remains underrecognized and undertreated, which significantly reduces the quality of life for patients. Anxiety is defined as a neuropsychiatric complication with characteristics such as nervousness, loss of concentration, and sweating due to the anticipation of impending danger. In patients with PD, neuropathology in the amygdala, a central region in the anxiety and fear circuitry, may contribute to the high prevalence of anxiety. Studies in animal models reported αSyn pathology in the amygdala together with alteration of anxiety or fear learning response. Therefore, understanding the progression, extent, and specifics of pathology in the anxiety and fear circuitry in synucleinopathies will suggest novel approaches to the diagnosis and treatment of neuropsychiatric symptoms. Here, we provide an overview of studies that address neuropsychiatric symptoms in synucleinopathies. We offer insights into anxiety and fear circuitry in animal models and the current implications for therapeutic intervention. In summary, it is apparent that anxiety is not a bystander symptom in these disorders but reflects early pathogenic mechanisms in the cortico-limbic system which may even contribute as a driver to disease progression.
Collapse
Affiliation(s)
- Thuy Thi Lai
- Department of Pharmacology, Toxicology and Pharmacy, University of Veterinary Medicine, Hannover, Germany
- Center for Systems Neuroscience, Hannover, Germany
| | - Birthe Gericke
- Department of Pharmacology, Toxicology and Pharmacy, University of Veterinary Medicine, Hannover, Germany
- Center for Systems Neuroscience, Hannover, Germany
| | - Malte Feja
- Department of Pharmacology, Toxicology and Pharmacy, University of Veterinary Medicine, Hannover, Germany
- Center for Systems Neuroscience, Hannover, Germany
| | | | | | - Franziska Richter
- Department of Pharmacology, Toxicology and Pharmacy, University of Veterinary Medicine, Hannover, Germany.
- Center for Systems Neuroscience, Hannover, Germany.
| |
Collapse
|
6
|
Espinoza AI, May P, Anjum MF, Singh A, Cole RC, Trapp N, Dasgupta S, Narayanan NS. A pilot study of machine learning of resting-state EEG and depression in Parkinson's disease. Clin Park Relat Disord 2022; 7:100166. [PMID: 36203748 PMCID: PMC9529981 DOI: 10.1016/j.prdoa.2022.100166] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/02/2022] [Accepted: 09/21/2022] [Indexed: 01/11/2023] Open
Abstract
Introduction Depression is a non-motor symptom of Parkinson's disease (PD). PD-related depression is difficult to diagnose, and the neurophysiological basis is poorly understood. Depression can markedly affect cortical function, which suggests that scalp electroencephalography (EEG) may be able to distinguish depression in PD. We conducted a pilot study of depression and resting-state EEG in PD. Methods We recruited 18 PD patients without depression, 18 PD patients with depression, and 12 demographically similar non-PD patients with clinical depression. All patients were on their usual medications. We collected resting-state EEG in all patients and compared cortical brain signal features between patients with and without depression. We used a machine learning algorithm that harnesses the entire power spectrum (linear predictive coding of EEG Algorithm for PD: LEAPD) to distinguish between groups. Results We found differences between PD patients with and without depression in the alpha band (8-13 Hz) globally and in the beta (13-30 Hz) and gamma (30-50 Hz) bands in the central electrodes. From two minutes of resting-state EEG, we found that LEAPD-based machine learning could robustly distinguish between PD patients with and without depression with 97 % accuracy and between PD patients with depression and non-PD patients with depression with 100 % accuracy. We verified the robustness of our finding by confirming that the classification accuracy gracefully declines as data are randomly truncated. Conclusions Our results suggest that resting-state EEG power spectral analysis has the potential to distinguish depression in PD accurately. We demonstrated the efficacy of the LEAPD algorithm in identifying PD patients with depression from PD patients without depression and controls with depression. Our data provide insight into cortical mechanisms of depression and could lead to novel neurophysiological markers for non-motor symptoms of PD.
Collapse
Affiliation(s)
| | - Patrick May
- Department of Electrical and Computer Engineering, University of Iowa, United States
| | - Md Fahim Anjum
- Department of Electrical and Computer Engineering, University of Iowa, United States
| | - Arun Singh
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, United States
| | - Rachel C. Cole
- Department of Neurology, University of Iowa, United States
| | - Nicholas Trapp
- Department of Psychiatry, University of Iowa, United States
| | - Soura Dasgupta
- Department of Electrical and Computer Engineering, University of Iowa, United States
| | - Nandakumar S. Narayanan
- Department of Neurology, University of Iowa, United States,Corresponding author at: 169 Newton Road, Pappajohn Biomedical Discovery Building—5336, University of Iowa, Iowa City 52242, United States.
| |
Collapse
|