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Conti M, Garasto E, Bovenzi R, Ferrari V, Mercuri NB, Di Giuliano F, Cerroni R, Pierantozzi M, Schirinzi T, Stefani A, Rocchi C. Insular and limbic abnormal functional connectivity in early-stage Parkinson's disease patients with autonomic dysfunction. Cereb Cortex 2024; 34:bhae270. [PMID: 38967041 DOI: 10.1093/cercor/bhae270] [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: 04/02/2024] [Revised: 06/05/2024] [Accepted: 06/17/2024] [Indexed: 07/06/2024] Open
Abstract
Autonomic symptoms in Parkinson's disease result from variable involvement of the central and peripheral systems, but many aspects remain unclear. The analysis of functional connectivity has shown promising results in assessing the pathophysiology of Parkinson's disease. This study aims to investigate the association between autonomic symptoms and cortical functional connectivity in early Parkinson's disease patients using high-density EEG. 53 early Parkinson's disease patients (F/M 18/35) and 49 controls (F/M 20/29) were included. Autonomic symptoms were evaluated using the Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score. Data were recorded with a 64-channel EEG system. We analyzed cortical functional connectivity, based on weighted phase-lag index, in θ-α-β-low-γ bands. A network-based statistic was used to perform linear regression between Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score and functional connectivity in Parkinson's disease patients. We observed a positive relation between the Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score and α-functional connectivity (network τ = 2.8, P = 0.038). Regions with higher degrees were insula and limbic lobe. Moreover, we found positive correlations between the mean connectivity of this network and the gastrointestinal, cardiovascular, and thermoregulatory domains of Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction. Our results revealed abnormal functional connectivity in specific areas in Parkinson's disease patients with greater autonomic symptoms. Insula and limbic areas play a significant role in the regulation of the autonomic system. Increased functional connectivity in these regions might represent the central compensatory mechanism of peripheral autonomic dysfunction in Parkinson's disease.
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Affiliation(s)
- Matteo Conti
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133 Rome, Italy
| | - Elena Garasto
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133 Rome, Italy
| | - Roberta Bovenzi
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133 Rome, Italy
| | - Valerio Ferrari
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133 Rome, Italy
| | - Nicola B Mercuri
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133 Rome, Italy
| | - Francesca Di Giuliano
- Neuroradiology Unit, Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Via Montpellier 1, 00133 Rome, Italy
| | - Rocco Cerroni
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133 Rome, Italy
- UOSD Parkinson Centre, Tor Vergata University Hospital, Viale Oxford 81, 00133 Rome, Italy
| | - Mariangela Pierantozzi
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133 Rome, Italy
| | - Tommaso Schirinzi
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133 Rome, Italy
| | - Alessandro Stefani
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133 Rome, Italy
- UOSD Parkinson Centre, Tor Vergata University Hospital, Viale Oxford 81, 00133 Rome, Italy
| | - Camilla Rocchi
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133 Rome, Italy
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Candia-Rivera D, Chavez M, De Vico Fallani F. Measures of the coupling between fluctuating brain network organization and heartbeat dynamics. Netw Neurosci 2024; 8:557-575. [PMID: 38952808 PMCID: PMC11168717 DOI: 10.1162/netn_a_00369] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 02/19/2024] [Indexed: 07/03/2024] Open
Abstract
In recent years, there has been an increasing interest in studying brain-heart interactions. Methodological advancements have been proposed to investigate how the brain and the heart communicate, leading to new insights into some neural functions. However, most frameworks look at the interaction of only one brain region with heartbeat dynamics, overlooking that the brain has functional networks that change dynamically in response to internal and external demands. We propose a new framework for assessing the functional interplay between cortical networks and cardiac dynamics from noninvasive electrophysiological recordings. We focused on fluctuating network metrics obtained from connectivity matrices of EEG data. Specifically, we quantified the coupling between cardiac sympathetic-vagal activity and brain network metrics of clustering, efficiency, assortativity, and modularity. We validate our proposal using open-source datasets: one that involves emotion elicitation in healthy individuals, and another with resting-state data from patients with Parkinson's disease. Our results suggest that the connection between cortical network segregation and cardiac dynamics may offer valuable insights into the affective state of healthy participants, and alterations in the network physiology of Parkinson's disease. By considering multiple network properties, this framework may offer a more comprehensive understanding of brain-heart interactions. Our findings hold promise in the development of biomarkers for diagnostic and cognitive/motor function evaluation.
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Affiliation(s)
- Diego Candia-Rivera
- Sorbonne Université, Paris Brain Institute (ICM), CNRS UMR 7225, INRIA Paris (Nerv Team), INSERM U1127, AP-HP Hôpital Pitié-Salpêtrière, Paris, France
| | - Mario Chavez
- Sorbonne Université, Paris Brain Institute (ICM), CNRS UMR 7225, INRIA Paris (Nerv Team), INSERM U1127, AP-HP Hôpital Pitié-Salpêtrière, Paris, France
| | - Fabrizio De Vico Fallani
- Sorbonne Université, Paris Brain Institute (ICM), CNRS UMR 7225, INRIA Paris (Nerv Team), INSERM U1127, AP-HP Hôpital Pitié-Salpêtrière, Paris, France
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3
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Conti M, Bovenzi R, Palmieri MG, Placidi F, Stefani A, Mercuri NB, Albanese M. Early effect of onabotulinumtoxinA on EEG-based functional connectivity in patients with chronic migraine: A pilot study. Headache 2024. [PMID: 38837259 DOI: 10.1111/head.14750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 03/27/2024] [Accepted: 04/03/2024] [Indexed: 06/07/2024]
Abstract
OBJECTIVE In this pilot prospective cohort study, we aimed to evaluate, using high-density electroencephalography (HD-EEG), the longitudinal changes in functional connectivity (FC) in patients with chronic migraine (CM) treated with onabotulinumtoxinA (OBTA). BACKGROUND OBTA is a treatment for CM. Several studies have shown the modulatory action of OBTA on the central nervous system; however, research on migraine is limited. METHODS This study was conducted at the Neurology Unit of "Policlinico Tor Vergata," Rome, Italy, and included 12 adult patients with CM treated with OBTA and 15 healthy controls (HC). Patients underwent clinical scales at enrollment (T0) and 3 months (T1) from the start of treatment. HD-EEG was recorded using a 64-channel system in patients with CM at T0 and T1. A source reconstruction method was used to identify brain activity. FC in δ-θ-α-β-low-γ bands was analyzed using the weighted phase-lag index. FC changes between HCs and CM at T0 and T1 were assessed using cross-validation methods to estimate the results' reliability. RESULTS Compared to HCs at T0, patients with CM showed hyperconnected networks in δ (p = 0.046, area under the receiver operating characteristic curve [AUC: 0.76-0.98], Cohen's κ [0.65-0.93]) and β (p = 0.031, AUC [0.68-0.95], Cohen's κ [0.51-0.84]), mainly involving orbitofrontal, occipital, temporal pole and orbitofrontal, superior temporal, occipital, cingulate areas, and hypoconnected networks in α band (p = 0.029, AUC [0.80-0.99], Cohen's κ [0.42-0.77]), predominantly involving cingulate, temporal pole, and precuneus. Patients with CM at T1, compared to T0, showed hypoconnected networks in δ band (p = 0.032, AUC [0.73-0.99], Cohen's κ [0.53-0.90]) and hyperconnected networks in α band (p = 0.048, AUC [0.58-0.93], Cohen's κ [0.37-0.78]), involving the sensorimotor, orbitofrontal, cingulate, and temporal cortex. CONCLUSION These preliminary results showed that patients with CM presented disrupted EEG-FC compared to controls restored by a single session of OBTA treatment, suggesting a primary central modulatory action of OBTA.
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Affiliation(s)
- Matteo Conti
- Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Roberta Bovenzi
- Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | | | - Fabio Placidi
- Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Alessandro Stefani
- Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | | | - Maria Albanese
- Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
- Neurology Unit, Regional Referral Headache Center, University of Rome "Tor Vergata", Rome, Italy
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Candia‐Rivera D, Vidailhet M, Chavez M, De Vico Fallani F. A framework for quantifying the coupling between brain connectivity and heartbeat dynamics: Insights into the disrupted network physiology in Parkinson's disease. Hum Brain Mapp 2024; 45:e26668. [PMID: 38520378 PMCID: PMC10960553 DOI: 10.1002/hbm.26668] [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: 10/04/2023] [Revised: 03/07/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024] Open
Abstract
Parkinson's disease (PD) often shows disrupted brain connectivity and autonomic dysfunctions, progressing alongside with motor and cognitive decline. Recently, PD has been linked to a reduced sensitivity to cardiac inputs, that is, cardiac interoception. Altogether, those signs suggest that PD causes an altered brain-heart connection whose mechanisms remain unclear. Our study aimed to explore the large-scale network disruptions and the neurophysiology of disrupted interoceptive mechanisms in PD. We focused on examining the alterations in brain-heart coupling in PD and their potential connection to motor symptoms. We developed a proof-of-concept method to quantify relationships between the co-fluctuations of brain connectivity and cardiac sympathetic and parasympathetic activities. We quantified the brain-heart couplings from electroencephalogram and electrocardiogram recordings from PD patients on and off dopaminergic medication, as well as in healthy individuals at rest. Our results show that the couplings of fluctuating alpha and gamma connectivity with cardiac sympathetic dynamics are reduced in PD patients, as compared to healthy individuals. Furthermore, we show that PD patients under dopamine medication recover part of the brain-heart coupling, in proportion with the reduced motor symptoms. Our proposal offers a promising approach to unveil the physiopathology of PD and promoting the development of new evaluation methods for the early stages of the disease.
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Affiliation(s)
- Diego Candia‐Rivera
- Sorbonne Université, Paris Brain Institute (ICM), Inria Paris, CNRS UMR7225, INSERM U1127, AP‐HP Hôpital Pitié‐SalpêtrièreParisFrance
| | - Marie Vidailhet
- Sorbonne Université, Paris Brain Institute (ICM)—Team “Movement Investigations and Therapeutics” (MOV'IT), CNRS UMR7225, INSERM U1127, AP‐HP Hôpital Pitié‐SalpêtrièreParisFrance
| | - Mario Chavez
- Sorbonne Université, Paris Brain Institute (ICM), Inria Paris, CNRS UMR7225, INSERM U1127, AP‐HP Hôpital Pitié‐SalpêtrièreParisFrance
| | - Fabrizio De Vico Fallani
- Sorbonne Université, Paris Brain Institute (ICM), Inria Paris, CNRS UMR7225, INSERM U1127, AP‐HP Hôpital Pitié‐SalpêtrièreParisFrance
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Ma X, Qi Y, Xu C, Weng Y, Yu J, Sun X, Yu Y, Wu Y, Gao J, Li J, Shu Y, Duan S, Luo B, Pan G. How well do neural signatures of resting-state EEG detect consciousness? A large-scale clinical study. Hum Brain Mapp 2024; 45:e26586. [PMID: 38433651 PMCID: PMC10910334 DOI: 10.1002/hbm.26586] [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: 07/26/2023] [Revised: 12/12/2023] [Accepted: 12/21/2023] [Indexed: 03/05/2024] Open
Abstract
The assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness assessment still remains an intense debate. Through a comprehensive review of the literature, inconsistent findings are observed about the effectiveness of diverse neural signatures. Notably, the majority of existing studies have evaluated neural signatures on a limited number of subjects (usually below 30), which may result in uncertain conclusions due to small data bias. This study presents a systematic evaluation of neural signatures with large-scale clinical resting-state electroencephalography (EEG) signals containing 99 UWS, 129 MCS, 36 emergence from the minimally conscious state, and 32 healthy subjects (296 total) collected over 3 years. A total of 380 EEG-based metrics for consciousness detection, including spectrum features, nonlinear measures, functional connectivity, and graph-based measures, are summarized and evaluated. To further mitigate the effect of data bias, the evaluation is performed with bootstrap sampling so that reliable measures can be obtained. The results of this study suggest that relative power in alpha and delta serve as dependable indicators of consciousness. With the MCS group, there is a notable increase in the phase lag index-related connectivity measures and enhanced functional connectivity between brain regions in comparison to the UWS group. A combination of features enables the development of an automatic detector of conscious states.
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Affiliation(s)
- Xiulin Ma
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, and the Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
| | - Yu Qi
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, and the Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
| | - Chuan Xu
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Sir Run Run Shaw Hospital, Hangzhou, China
| | - Yijie Weng
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Jie Yu
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xuyun Sun
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Yamei Yu
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Sir Run Run Shaw Hospital, Hangzhou, China
| | - Yuehao Wu
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jian Gao
- Department of Rehabilitation, Hangzhou Mingzhou Brain Rehabilitation Hospital, Hangzhou, China
| | - Jingqi Li
- Department of Rehabilitation, Hangzhou Mingzhou Brain Rehabilitation Hospital, Hangzhou, China
| | - Yousheng Shu
- Department of Neurosurgery, Jinshan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institute for Translational Brain Research, Fudan University, Shanghai, China
| | - Shumin Duan
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, and the Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
| | - Benyan Luo
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, and the Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
| | - Gang Pan
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, and the Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
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Bovenzi R, Pierantozzi M, Conti M, Carignani S, Fernandes M, Schirinzi T, Cerroni R, Mercuri NB, Stefani A, Liguori C. Parkinson's disease motor progression in relation to the timing of REM sleep behavior disorder presentation: an exploratory retrospective study. J Neural Transm (Vienna) 2024; 131:239-244. [PMID: 38227218 PMCID: PMC10874311 DOI: 10.1007/s00702-024-02739-5] [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: 09/13/2023] [Accepted: 01/03/2024] [Indexed: 01/17/2024]
Abstract
REM sleep behavior disorder (RBD) is a frequent non-motor symptom of Parkinson's disease (PD), and the timing of its presentation might have a role in the underlying neurodegenerative process. Here, we aimed to define the potential impact of probable RBD (pRBD) on PD motor progression.We conducted a longitudinal retrospective study on 66 PD patients followed up at the University Hospital of Rome Tor Vergata. Patients were divided into three groups: with post-motor pRBD (pRBDpost, n = 25), without pRBD (pRBDwo, n = 20), and with pre-motor pRBD (pRBDpre, n = 21). Hoehn and Yahr (H&Y) scores, Unified PD Rating Scale (UPDRS) motor scores, and levodopa equivalent daily dose were collected at two follow-up visits conducted in a 5-year interval (T0 and T1). pRBDpost patients had a greater rate of motor progression in terms of the H&Y scale compared to pRBDpre and pRBDwo patients, without the influence of anti-parkinsonian treatment.These preliminary findings suggest that the post-motor occurrence of pRBD can be associated with an acceleration in PD motor progression.
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Affiliation(s)
- Roberta Bovenzi
- Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
| | - Mariangela Pierantozzi
- Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
- Parkinson's Disease Unit, University Hospital of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
| | - Matteo Conti
- Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
| | - Silvia Carignani
- Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
| | - Mariana Fernandes
- Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
| | - Tommaso Schirinzi
- Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
- Parkinson's Disease Unit, University Hospital of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
| | - Rocco Cerroni
- Parkinson's Disease Unit, University Hospital of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
| | - Nicola Biagio Mercuri
- Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
| | - Alessandro Stefani
- Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
- Parkinson's Disease Unit, University Hospital of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
| | - Claudio Liguori
- Department of Systems Medicine, University of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy.
- Parkinson's Disease Unit, University Hospital of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy.
- Sleep Medicine Centre, University Hospital of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy.
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7
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Conti M, Guerra A, Pierantozzi M, Bovenzi R, D'Onofrio V, Simonetta C, Cerroni R, Liguori C, Placidi F, Mercuri NB, Di Giuliano F, Schirinzi T, Stefani A. Band-Specific Altered Cortical Connectivity in Early Parkinson's Disease and its Clinical Correlates. Mov Disord 2023; 38:2197-2208. [PMID: 37860930 DOI: 10.1002/mds.29615] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/25/2023] [Accepted: 09/11/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND Functional connectivity (FC) has shown promising results in assessing the pathophysiology and identifying early biomarkers of neurodegenerative disorders, such as Parkinson's disease (PD). OBJECTIVES In this study, we aimed to assess possible resting-state FC abnormalities in early-stage PD patients using high-density electroencephalography (EEG) and to detect their clinical relationship with motor and non-motor PD symptoms. METHODS We enrolled 26 early-stage levodopa naïve PD patients and a group of 20 healthy controls (HC). Data were recorded with 64-channels EEG system and a source-reconstruction method was used to identify brain-region activity. FC was calculated using the weighted phase-lag index in θ, α, and β bands. Additionally, we quantified the unbalancing between β and lower frequencies through a novel index (β-functional ratio [FR]). Statistical analysis was conducted using a network-based statistical approach. RESULTS PD patients showed hypoconnected networks in θ and α band, involving prefrontal-limbic-temporal and frontoparietal areas, respectively, and a hyperconnected network in the β frequency band, involving sensorimotor-frontal areas. The θ FC network was negatively related to Non-Motor Symptoms Scale scores and α FC to the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III gait subscore, whereas β FC and β-FR network were positively linked to the bradykinesia subscore. Changes in θ FC and β-FR showed substantial reliability and high accuracy, precision, sensitivity, and specificity in discriminating PD and HC. CONCLUSIONS Frequency-specific FC changes in PD likely reflect the dysfunction of distinct cortical networks, which occur from the early stage of the disease. These abnormalities are involved in the pathophysiology of specific motor and non-motor PD symptoms, including gait, bradykinesia, mood, and cognition. © 2023 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Matteo Conti
- Parkinson Centre, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Andrea Guerra
- Parkinson and Movement Disorders Unit, Study Centre on Neurodegeneration (CESNE), Department of Neuroscience, University of Padova, Padua, Italy
| | - Mariangela Pierantozzi
- Parkinson Centre, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Roberta Bovenzi
- Parkinson Centre, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Valentina D'Onofrio
- Parkinson and Movement Disorders Unit, Study Centre on Neurodegeneration (CESNE), Department of Neuroscience, University of Padova, Padua, Italy
| | - Clara Simonetta
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Rocco Cerroni
- Parkinson Centre, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Claudio Liguori
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Fabio Placidi
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Nicola Biagio Mercuri
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Francesca Di Giuliano
- Neuroradiology Unit, Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy
| | - Tommaso Schirinzi
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Alessandro Stefani
- Parkinson Centre, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
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8
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Gimenez-Aparisi G, Guijarro-Estelles E, Chornet-Lurbe A, Ballesta-Martinez S, Pardo-Hernandez M, Ye-Lin Y. Early detection of Parkinson's disease: Systematic analysis of the influence of the eyes on quantitative biomarkers in resting state electroencephalography. Heliyon 2023; 9:e20625. [PMID: 37829809 PMCID: PMC10565694 DOI: 10.1016/j.heliyon.2023.e20625] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 07/24/2023] [Accepted: 10/02/2023] [Indexed: 10/14/2023] Open
Abstract
While resting state electroencephalography (EEG) provides relevant information on pathological changes in Parkinson's disease, most studies focus on the eyes-closed EEG biomarkers. Recent evidence has shown that both eyes-open EEG and reactivity to eyes-opening can also differentiate Parkinson's disease from healthy aging, but no consensus has been reached on a discriminatory capability benchmark. The aim of this study was to determine the resting-state EEG biomarkers suitable for real-time application that can differentiate Parkinson's patients from healthy subjects under both eyes closed and open. For this, we analysed and compared the quantitative EEG analyses of 13 early-stage cognitively normal Parkinson's patients with an age and sex-matched healthy group. We found that Parkinson's disease exhibited abnormal excessive theta activity in eyes-closed, which was reflected by a significantly higher relative theta power, a higher time percentage with a frequency peak in the theta band and a reduced alpha/theta ratio, while Parkinson's patients showed a significantly steeper non-oscillatory spectral slope activity than that of healthy subjects. We also found considerably less alpha and beta reactivity to eyes-opening in Parkinson's disease plus a significant moderate correlation between these EEG-biomarkers and the MDS-UPDRS score, used to assesses the clinical symptoms of Parkinson's Disease. Both EEG recordings with the eyes open and reactivity to eyes-opening provided additional information to the eyes-closed condition. We thus strongly recommend that both eyes open and closed be used in clinical practice recording protocols to promote EEG as a complementary non-invasive screening method for the early detection of Parkinson's disease, which would allow clinicians to design patient-oriented treatment and improve the patient's quality of life.
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Affiliation(s)
- G. Gimenez-Aparisi
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022, València, Spain
| | - E. Guijarro-Estelles
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022, València, Spain
| | - A. Chornet-Lurbe
- Servicio de Neurofisiología Clínica, Hospital Lluís Alcanyís, departamento de salud Xàtiva-Ontinyent, 46800, Xàtiva, València, Spain
| | - S. Ballesta-Martinez
- Servicio de Neurofisiología Clínica, Hospital Lluís Alcanyís, departamento de salud Xàtiva-Ontinyent, 46800, Xàtiva, València, Spain
| | - M. Pardo-Hernandez
- Servicio de Neurofisiología Clínica, Hospital Lluís Alcanyís, departamento de salud Xàtiva-Ontinyent, 46800, Xàtiva, València, Spain
| | - Y. Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022, València, Spain
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Wang H, Liu F, Yu D. Complex network of eye movements during rapid automatized naming. Front Neurosci 2023; 17:1024881. [PMID: 37065911 PMCID: PMC10102513 DOI: 10.3389/fnins.2023.1024881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 02/15/2023] [Indexed: 04/03/2023] Open
Abstract
IntroductionAlthough the method of visualizing eye-tracking data as a time-series might enhance performance in the understanding of gaze behavior, it has not yet been thoroughly examined in the context of rapid automated naming (RAN).MethodsThis study attempted, for the first time, to measure gaze behavior during RAN from the perspective of network-domain, which constructed a complex network [referred to as gaze-time-series-based complex network (GCN)] from gaze time-series. Hence, without designating regions of interest, the features of gaze behavior during RAN were extracted by computing topological parameters of GCN. A sample of 98 children (52 males, aged 11.50 ± 0.28 years) was studied. Nine topological parameters (i.e., average degree, network diameter, characteristic path length, clustering coefficient, global efficiency, assortativity coefficient, modularity, community number, and small-worldness) were computed.ResultsFindings showed that GCN in each RAN task was assortative and possessed “small-world” and community architecture. Additionally, observations regarding the influence of RAN task types included that: (i) five topological parameters (i.e., average degree, clustering coefficient, assortativity coefficient, modularity, and community number) could reflect the difference between tasks N-num (i.e., naming of numbers) and N-cha (i.e., naming of Chinese characters); (ii) there was only one topological parameter (i.e., network diameter) which could reflect the difference between tasks N-obj (i.e., naming of objects) and N-col (i.e., naming of colors); and (iii) when compared to GCN in alphanumeric RAN, GCN in non-alphanumeric RAN may have higher average degree, global efficiency, and small-worldness, but lower network diameter, characteristic path length, clustering coefficient, and modularity. Findings also illustrated that most of these topological parameters were largely independent of traditional eye-movement metrics.DiscussionThis article revealed the architecture and topological parameters of GCN as well as the influence of task types on them, and thus brought some new insights into the understanding of RAN from the perspective of complex network.
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Affiliation(s)
- Hongan Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Fulin Liu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Dongchuan Yu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Henan Provincial Medical Key Lab of Child Developmental Behavior and Learning, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- *Correspondence: Dongchuan Yu
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Quantitative High Density EEG Brain Connectivity Evaluation in Parkinson's Disease: The Phase Locking Value (PLV). J Clin Med 2023; 12:jcm12041450. [PMID: 36835985 PMCID: PMC9967371 DOI: 10.3390/jcm12041450] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/01/2023] [Accepted: 02/07/2023] [Indexed: 02/17/2023] Open
Abstract
INTRODUCTION The present study explores brain connectivity in Parkinson's disease (PD) and in age matched healthy controls (HC), using quantitative EEG analysis, at rest and during a motor tasks. We also evaluated the diagnostic performance of the phase locking value (PLV), a measure of functional connectivity, in differentiating PD patients from HCs. METHODS High-density, 64-channels, EEG data from 26 PD patients and 13 HC were analyzed. EEG signals were recorded at rest and during a motor task. Phase locking value (PLV), as a measure of functional connectivity, was evaluated for each group in a resting state and during a motor task for the following frequency bands: (i) delta: 2-4 Hz; (ii) theta: 5-7 Hz; (iii) alpha: 8-12 Hz; beta: 13-29 Hz; and gamma: 30-60 Hz. The diagnostic performance in PD vs. HC discrimination was evaluated. RESULTS Results showed no significant differences in PLV connectivity between the two groups during the resting state, but a higher PLV connectivity in the delta band during the motor task, in HC compared to PD. Comparing the resting state versus the motor task for each group, only HCs showed a higher PLV connectivity in the delta band during motor task. A ROC curve analysis for HC vs. PD discrimination, showed an area under the ROC curve (AUC) of 0.75, a sensitivity of 100%, and a negative predictive value (NPV) of 100%. CONCLUSIONS The present study evaluated the brain connectivity through quantitative EEG analysis in Parkinson's disease versus healthy controls, showing a higher PLV connectivity in the delta band during the motor task, in HC compared to PD. This neurophysiology biomarkers showed the potentiality to be explored in future studies as a potential screening biomarker for PD patients.
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Qiu L, Li J, Pan J. Parkinson’s disease detection based on multi-pattern analysis and multi-scale convolutional neural networks. Front Neurosci 2022; 16:957181. [PMID: 35968382 PMCID: PMC9363757 DOI: 10.3389/fnins.2022.957181] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
Parkinson’s disease (PD) is a complex neurodegenerative disease. At present, the early diagnosis of PD is still extremely challenging, and there is still a lack of consensus on the brain characterization of PD, and a more efficient and robust PD detection method is urgently needed. In order to further explore the features of PD based on brain activity and achieve effective detection of PD patients (including OFF and ON medications), in this study, a multi-pattern analysis based on brain activation and brain functional connectivity was performed on the brain functional activity of PD patients, and a novel PD detection model based on multi-scale convolutional neural network (MCNN) was proposed. Based on the analysis of power spectral density (PSD) and phase-locked value (PLV) features of multiple frequency bands of two independent resting-state electroencephalography (EEG) datasets, we found that there were significant differences in PSD and PLV between HCs and PD patients (including OFF and ON medications), especially in the β and γ bands, which were very effective for PD detection. Moreover, the combined use of brain activation represented by PSD and functional connectivity patterns represented by PLV can effectively improve the performance of PD detection. Furthermore, our proposed MCNN model shows great potential for automatic PD detection, with cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve all above 99%. Our study may help to further understand the characteristics of PD and provide new ideas for future PD diagnosis based on spontaneous EEG activity.
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Ezazi Y, Ghaderyan P. Textural feature of EEG signals as a new biomarker of reward processing in Parkinson’s disease detection. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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