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Yin N, Wang H, Wang Z, Feng K, Xu G, Yin S. A study of brain networks associated with Freezing of gait in Parkinson's disease using transfer entropy analysis. Brain Res 2023; 1821:148610. [PMID: 37783260 DOI: 10.1016/j.brainres.2023.148610] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/22/2023] [Accepted: 09/27/2023] [Indexed: 10/04/2023]
Abstract
BACKGROUND Parkinson's disease (PD) is a common neurodegenerative disease in the elderly. Freezing of Gait (FOG) is one of the common motor symptoms of PD, but the potential mechanism remains unclear. This study aimed to investigate the changes of brain functional network topology in PD patients with FOG. METHODS The resting electroencephalogram (EEG) were acquired from15 PD patients with FOG (PD-FOG), 13 PD patients without FOG (PD-nFOG), and 16 healthy control (HC). Cognitive and motor functions were assessed using subjective scales. The whole-brain functional networks were constructed based on transfer entropy. Transfer entropy was used to analyse the information flow and causality in the network and the network connectivity was analyzed by graph theory. The characteristics of PD-FOG and PD-nFOG were compared by receiver operator characteristic (ROC) curve analysis. RESULTS The θ bands brain network of PD-FOG, PD-nFOG and HC group was significantly different (P < 0.05). The average characteristic path length of the θ bands brain network was positively correlated with FOG Questionnaire (FOGQ). PD-FOG and PD-nFOG get high classification accuracy according to this feature. The information inflow in the frontal and occipital lobes and information outflow in the temporal lobe of PD-FOG patients in the θ bands increased significantly. CONCLUSIONS The whole-brain functional network characteristics of PD-FOG in the θ bands can serve as potential biomarkers for early diagnosis of PD-FOG. Abnormal information flow of the frontal, occipital, and temporal lobes in the θ bands may be an important factor leading to FOG.
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Affiliation(s)
- Ning Yin
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Haili Wang
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Zhaoya Wang
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Keke Feng
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China.
| | - Shaoya Yin
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin 300350, China.
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Chang H, Liu B, Zong Y, Lu C, Wang X. EEG-Based Parkinson's Disease Recognition via Attention-Based Sparse Graph Convolutional Neural Network. IEEE J Biomed Health Inform 2023; 27:5216-5224. [PMID: 37405893 DOI: 10.1109/jbhi.2023.3292452] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Abstract
Parkinson's disease (PD) is a complicated neurological ailment that affects both the physical and mental wellness of elderly individuals which makes it problematic to diagnose in its initial stages. Electroencephalogram (EEG) promises to be an efficient and cost-effective method for promptly detecting cognitive impairment in PD. Nevertheless, prevailing diagnostic practices utilizing EEG features have failed to examine the functional connectivity among EEG channels and the response of associated brain areas causing an unsatisfactory level of precision. Here, we construct an attention-based sparse graph convolutional neural network (ASGCNN) for diagnosing PD. Our ASGCNN model uses a graph structure to represent channel relationships, the attention mechanism for selecting channels, and the L1 norm to capture channel sparsity. We conduct extensive experiments on the publicly available PD auditory oddball dataset, which consists of 24 PD patients (under ON/OFF drug status) and 24 matched controls, to validate the effectiveness of our method. Our results show that the proposed method provides better results compared to the publicly available baselines. The achieved scores for Recall, Precision, F1-score, Accuracy and Kappa measures are 90.36%, 88.43%, 88.41%, 87.67%, and 75.24%, respectively. Our study reveals that the frontal and temporal lobes show significant differences between PD patients and healthy individuals. In addition, EEG features extracted by ASGCNN demonstrate significant asymmetry in the frontal lobe among PD patients. These findings can offer a basis for the establishment of a clinical system for intelligent diagnosis of PD by using auditory cognitive impairment features.
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Alves CL, Toutain TGLDO, Porto JAM, Aguiar PMDC, de Sena EP, Rodrigues FA, Pineda AM, Thielemann C. Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia. J Neural Eng 2023; 20:056025. [PMID: 37673060 DOI: 10.1088/1741-2552/acf734] [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: 11/25/2022] [Accepted: 09/06/2023] [Indexed: 09/08/2023]
Abstract
Objective. Schizophrenia(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals.Approach.For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature.Main results.When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ.Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.
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Affiliation(s)
- Caroline L Alves
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
- BioMEMS Lab, Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany
| | | | | | - Patrícia Maria de Carvalho Aguiar
- Hospital Israelita Albert Einstein, São Paulo, Brazil
- Federal University of São Paulo, Department of Neurology and Neurosurgery, São Paulo, Brazil
| | | | - Francisco A Rodrigues
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
| | - Aruane M Pineda
- University of São Paulo (USP), Institute of Mathematical and Computer Sciences (ICMC), São Paulo, Brazil
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4
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Marquez JS, Bartsch RP, Günther M, Hasan SMS, Koren O, Plotnik M, Bai O. Supplementary Motor Area Activity Differs in Parkinson's Disease with and without Freezing of Gait. PARKINSON'S DISEASE 2023; 2023:5033835. [PMID: 37701070 PMCID: PMC10495228 DOI: 10.1155/2023/5033835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 07/18/2023] [Accepted: 08/07/2023] [Indexed: 09/14/2023]
Abstract
The study aimed to investigate the neural changes that differentiate Parkinson's disease patients with freezing of gait and age-matched controls, using ambulatory electroencephalography event-related features. Compared to controls, definite freezers exhibited significantly less alpha desynchronization at the motor cortex about 300 ms before and after the start of overground walking and decreased low-beta desynchronization about 300 ms before and about 300 and 700 ms after walking onset. The late slope of motor potentials also differed in the sensory and motor areas between groups of controls, definite, and probable freezers. This difference was found both in preparation and during the execution of normal walking. The average frontal peak of motor potential was also found to be largely reduced in the definite freezers compared with the probable freezers and controls. These findings provide valuable insights into the underlying structures that are affected in patients with freezing of gait, which could be used to tailor drug development and personalize drug care for disease subtypes. In addition, the study's findings can help in the evaluation and validation of nonpharmacological therapies for patients with Parkinson's disease.
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Affiliation(s)
- J. Sebastian Marquez
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida, USA
| | - Ronny P. Bartsch
- Department of Physics, Bar-Ilan University, Ramat Gan 5290002, Israel
| | | | - S. M. Shafiul Hasan
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida, USA
| | - Or Koren
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
| | - Meir Plotnik
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
- Department of Physiology and Pharmacology, School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Ou Bai
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida, USA
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5
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Yamada Y, Inaba K. Detecting partial synchrony in a complex oscillatory network using pseudovortices. Phys Rev E 2023; 108:024307. [PMID: 37723738 DOI: 10.1103/physreve.108.024307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 06/23/2023] [Indexed: 09/20/2023]
Abstract
Partial synchronization is an important dynamical process of coupled oscillators on various natural and artificial networks, which can remain undetected due to the system complexity. With an analogy between pairwise asynchrony of oscillators and topological defects, i.e., vortices, in the two-dimensional XY model, we propose a robust and data-driven method to identify the partial synchronization on complex networks. The proposed method is based on an integer matrix whose element is pseudovorticity that discretely quantifies asynchronous phase dynamics in every two oscillators, which results in graphical and entropic representations of partial synchrony. As a first trial, we apply our method to 200 FitzHugh-Nagumo neurons on a complex small-world network. Partially synchronized chimera states are revealed by discriminating synchronized states even with phase lags. Such phase lags also appear in partial synchronization in chimera states. Our topological, graphical, and entropic method is implemented solely with measurable phase dynamics data, which will lead to a straightforward application to general oscillatory networks including neural networks in the brain.
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Affiliation(s)
- Yasuhiro Yamada
- NTT Basic Research Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Kensuke Inaba
- NTT Basic Research Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan
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Heimler B, Koren O, Inzelberg R, Rosenblum U, Hassin-Baer S, Zeilig G, Bartsch RP, Plotnik M. Heart-rate variability as a new marker for freezing predisposition in Parkinson's disease. Parkinsonism Relat Disord 2023:105476. [PMID: 37321936 DOI: 10.1016/j.parkreldis.2023.105476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/22/2023] [Accepted: 05/29/2023] [Indexed: 06/17/2023]
Abstract
INTRODUCTION Freezing of gait (FoG) is a debilitating symptom of advanced Parkinson's disease (PD) characterized by a sudden, episodic stepping arrest despite the intention to continue walking. The etiology of FoG is still unknown, but accumulating evidence unraveled physiological signatures of the autonomic nervous system (ANS) around FoG episodes. Here we aim to investigate for the first time whether detecting a predisposition for upcoming FoG events from ANS activity measured at rest is possible. METHODS We recorded heart-rate for 1-min while standing in 28 persons with PD with FoG (PD + FoG), while OFF, and in 21 elderly controls (EC). Then, PD + FoG participants performed walking trials containing FoG-triggering events (e.g., turns). During these trials, n = 15 did experience FoG (PD + FoG+), while n = 13 did not (PD + FoG-). Most PD participants (n = 20: 10 PD + FoG+ and 10 PD + FoG-) repeated the experiment 2-3 weeks later, while ON, and none experienced FoG. We then analyzed heart-rate variability (HRV), i.e., the fluctuations in time intervals between adjacent heartbeats, mainly generated by brain-heart interactions. RESULTS During OFF, HRV was significantly lower in PD + FoG + participants, reflecting imbalanced sympathetic/parasympathetic activity and disrupted self-regulatory capacity. PD + FoG- and EC participants showed comparable (higher) HRV. During ON, HRV did not differ among groups. HRV values did not correlate with age, PD duration, levodopa consumption, nor motor -symptoms severity scores. CONCLUSIONS Overall, these results document for the first time a relation between HRV at rest and FoG presence/absence during gait trials, expanding previous evidence regarding the involvement of ANS in FoG.
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Affiliation(s)
- Benedetta Heimler
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel.
| | - Or Koren
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
| | - Rivka Inzelberg
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel; Department of Neurology and Neurosurgery, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Uri Rosenblum
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
| | - Sharon Hassin-Baer
- Department of Neurology and Neurosurgery, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Department of Neurology, Movement Disorders Institute, Sheba Medical Center, Ramat Gan, Israel
| | - Gabi Zeilig
- Department of Neurological Rehabilitation, Sheba Medical Center, Ramat Gan, Israel; Department of Physical and Rehabilitation Medicine, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; School of Health Professions, Ono Academic College, Kiryat Ono, Israel
| | - Ronny P Bartsch
- Department of Physics, Bar-Ilan University, Ramat Gan, Israel
| | - Meir Plotnik
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel; Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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7
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Bogachev M, Sinitca A, Grigarevichius K, Pyko N, Lyanova A, Tsygankova M, Davletshin E, Petrov K, Ageeva T, Pyko S, Kaplun D, Kayumov A, Mukhamedshina Y. Video-based marker-free tracking and multi-scale analysis of mouse locomotor activity and behavioral aspects in an open field arena: A perspective approach to the quantification of complex gait disturbances associated with Alzheimer's disease. Front Neuroinform 2023; 17:1101112. [PMID: 36817970 PMCID: PMC9932053 DOI: 10.3389/fninf.2023.1101112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 01/12/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction Complex gait disturbances represent one of the prominent manifestations of various neurophysiological conditions, including widespread neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Therefore, instrumental measurement techniques and automatic computerized analysis appears essential for the differential diagnostics, as well as for the assessment of treatment effectiveness from experimental animal models to clinical settings. Methods Here we present a marker-free instrumental approach to the analysis of gait disturbances in animal models. Our approach is based on the analysis of video recordings obtained with a camera placed underneath an open field arena with transparent floor using the DeeperCut algorithm capable of online tracking of individual animal body parts, such as the snout, the paws and the tail. The extracted trajectories of animal body parts are next analyzed using an original computerized methodology that relies upon a generalized scalable model based on fractional Brownian motion with parameters identified by detrended partial cross-correlation analysis. Results We have shown that in a mouse model representative movement patterns are characterized by two asymptotic regimes characterized by integrated 1/f noise at small scales and nearly random displacements at large scales separated by a single crossover. More detailed analysis of gait disturbances revealed that the detrended cross-correlations between the movements of the snout, paws and tail relative to the animal body midpoint exhibit statistically significant discrepancies in the Alzheimer's disease mouse model compared to the control group at scales around the location of the crossover. Discussion We expect that the proposed approach, due to its universality, robustness and clear physical interpretation, is a promising direction for the design of applied analysis tools for the diagnostics of various gait disturbances and behavioral aspects in animal models. We further believe that the suggested mathematical models could be relevant as a complementary tool in clinical diagnostics of various neurophysiological conditions associated with movement disorders.
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Affiliation(s)
- Mikhail Bogachev
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
- Institute for Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Aleksandr Sinitca
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
| | - Konstantin Grigarevichius
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
| | - Nikita Pyko
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
| | - Asya Lyanova
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
| | - Margarita Tsygankova
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
| | - Eldar Davletshin
- Institute for Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Konstantin Petrov
- FRC Kazan Scientific Center of RAS, Arbuzov Institute of Organic and Physical Chemistry, Kazan, Russia
| | - Tatyana Ageeva
- Institute for Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Svetlana Pyko
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
| | - Dmitrii Kaplun
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
| | - Airat Kayumov
- Institute for Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Yana Mukhamedshina
- Institute for Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
- Department of Histology, Cytology and Embryology, Kazan State Medical University, Kazan, Russia
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Costa TDDC, Machado CBDS, Lemos Segundo RP, Silva JPDS, Silva ACT, Andrade RDS, Rosa MRD, Smaili SM, Morya E, Costa-Ribeiro A, Lindquist ARR, Andrade SM, Machado DGDS. Are the EEG microstates correlated with motor and non-motor parameters in patients with Parkinson's disease? Neurophysiol Clin 2023; 53:102839. [PMID: 36716585 DOI: 10.1016/j.neucli.2022.102839] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/05/2022] [Accepted: 12/17/2022] [Indexed: 01/30/2023] Open
Abstract
OBJECTIVES This study compared electroencephalography microstates (EEG-MS) of patients with Parkinson's disease (PD) to healthy controls and correlated EEG-MS with motor and non-motor aspects of PD. METHODS This cross-sectional exploratory study was conducted with patients with PD (n = 10) and healthy controls (n = 10) matched by sex and age. We recorded EEG-MS using 32 channels during eyes-closed and eyes-open conditions and analyzed the four classic EEG-MS maps (A, B, C, D). Clinical information (e.g., disease duration, medications, levodopa equivalent daily dose), motor (Movement Disorder Society - Unified Parkinson Disease Rating Scale II and III, Timed Up and Go simple and dual-task, and Mini-Balance Evaluation Systems Test) and non-motor aspects (Mini-Mental State Exam [MMSE], verbal fluency, Hospital Anxiety and Depression Scale, and Parkinson's Disease Questionnaire-39 [PDQ-39]) were assessed in the PD group. Mann-Whitney U test was used to compare groups, and Spearman's correlation coefficient to analyze the correlations between coverage of EEG-MS and clinical aspects of PD. RESULTS The PD group showed a shorter duration of EEG-MS C in the eyes-closed condition than the control group. We observed correlations (rho = 0.64 to 0.82) between EEG-MS B, C, and D and non-motor aspects of PD (MMSE, verbal fluency, PDQ-39, and levodopa equivalent daily dose). CONCLUSION Alterations in EEG-MS and correlations between topographies and cognitive aspects, quality of life, and medication dose indicate that EEG could be used as a PD biomarker. Future studies should investigate these associations using a longitudinal design.
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Affiliation(s)
- Thaísa Dias de Carvalho Costa
- Aging and Neuroscience Laboratory, Federal University of Paraíba, João Pessoa, Brazil; Graduate Program in Cognitive and Behavioural Neuroscience, Federal University of Paraíba, João Pessoa, Brazil
| | | | | | | | | | - Rafael de Souza Andrade
- Division of Neurology, Lauro Wanderley University Hospital, Federal University of Paraíba, João Pessoa, Brazil
| | - Marine Raquel Diniz Rosa
- Graduate Program in Cognitive and Behavioural Neuroscience, Federal University of Paraíba, João Pessoa, Brazil
| | | | - Edgard Morya
- Edmond and Lily Safra International Institute of Neurosciences, Santos Dumont Institute, Natal, Brazil
| | - Adriana Costa-Ribeiro
- NeuroMove Laboratory, Department of Physiotherapy, Federal University of Paraíba, Joao Pessoa, Brazil
| | - Ana Raquel Rodrigues Lindquist
- Laboratory of Intervention and Analysis of Movement, Department of Physiotherapy, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Suellen Marinho Andrade
- Aging and Neuroscience Laboratory, Federal University of Paraíba, João Pessoa, Brazil; Graduate Program in Cognitive and Behavioural Neuroscience, Federal University of Paraíba, João Pessoa, Brazil
| | - Daniel Gomes da Silva Machado
- Research Group in Neuroscience of Human Movement (NeuroMove), Department of Physical Education, Federal University of Rio Grande do Norte, Natal, RN, Brazil.
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Jiang L, He J, Pan H, Wu D, Jiang T, Liu J. Seizure detection algorithm based on improved functional brain network structure feature extraction. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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10
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Alves CL, Cury RG, Roster K, Pineda AM, Rodrigues FA, Thielemann C, Ciba M. Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments. PLoS One 2022; 17:e0277257. [PMID: 36525422 PMCID: PMC9757568 DOI: 10.1371/journal.pone.0277257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/23/2022] [Indexed: 12/23/2022] Open
Abstract
Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by the inhabitants of this region for hundreds of years. Furthermore, this plant has been demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments have found specific brain regions that changed significantly due to ayahuasca. Here, we used an EEG dataset to investigate the ability to automatically detect changes in brain activity using machine learning and complex networks. Machine learning was applied at three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of the EEG time series, and (C) the complex network measures calculated from (B). Further, at the abstraction level of (C), we developed new measures of complex networks relating to community detection. As a result, the machine learning method was able to automatically detect changes in brain activity, with case (B) showing the highest accuracy (92%), followed by (A) (88%) and (C) (83%), indicating that connectivity changes between brain regions are more important for the detection of ayahuasca. The most activated areas were the frontal and temporal lobe, which is consistent with the literature. F3 and PO4 were the most important brain connections, a significant new discovery for psychedelic literature. This connection may point to a cognitive process akin to face recognition in individuals during ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assortativity were the most important complex network measures. These two measures are also associated with diseases such as Alzheimer's disease, indicating a possible therapeutic mechanism. Moreover, the new measures were crucial to the predictive model and suggested larger brain communities associated with the use of ayahuasca. This suggests that the dissemination of information in functional brain networks is slower when this drug is present. Overall, our methodology was able to automatically detect changes in brain activity during ayahuasca consumption and interpret how these psychedelics alter brain networks, as well as provide insights into their mechanisms of action.
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Affiliation(s)
- Caroline L. Alves
- BioMEMS Lab, Aschaffenburg University of Applied Sciences (UAS), Aschaffenburg, Germany
- Institute of Mathematical and Computer Sciences, University of São Paulo (USP), São Paulo, Brazil
- * E-mail:
| | - Rubens Gisbert Cury
- Department of Neurology, Movement Disorders Center, University of São Paulo (USP), São Paulo, Brazil
| | - Kirstin Roster
- Institute of Mathematical and Computer Sciences, University of São Paulo (USP), São Paulo, Brazil
| | - Aruane M. Pineda
- Institute of Mathematical and Computer Sciences, University of São Paulo (USP), São Paulo, Brazil
| | - Francisco A. Rodrigues
- Institute of Mathematical and Computer Sciences, University of São Paulo (USP), São Paulo, Brazil
| | - Christiane Thielemann
- BioMEMS Lab, Aschaffenburg University of Applied Sciences (UAS), Aschaffenburg, Germany
| | - Manuel Ciba
- BioMEMS Lab, Aschaffenburg University of Applied Sciences (UAS), Aschaffenburg, Germany
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11
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Huang CY, Chen YA, Wu RM, Hwang IS. Dual-task walking improvement with enhanced kinesthetic awareness in Parkinson’s disease with mild gait impairment: EEG connectivity and clinical implication. Front Aging Neurosci 2022; 14:1041378. [DOI: 10.3389/fnagi.2022.1041378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 11/10/2022] [Indexed: 12/02/2022] Open
Abstract
Due to basal ganglia dysfunction, short step length is a common gait impairment in Parkinson’s disease (PD), especially in a dual-task walking. Here, we use electroencephalography (EEG) functional connectivity to investigate neural mechanisms of a stride awareness strategy that could improve dual-task walking in PD. Eighteen individuals with PD who had mild gait impairment walked at self-paced speed while keeping two interlocking rings from touching each other. During the dual-task walking trial, the participants received or did not receive awareness instruction to take big steps. Gait parameters, ring-touching time, and EEG connectivity in the alpha and beta bands were analyzed. With stride awareness, individuals with PD exhibited greater gait velocity and step length, along with a significantly lower mean EEG connectivity strength in the beta band. The awareness-related changes in the EEG connectivity strength of the beta band positively correlated with the awareness-related changes in gait velocity, cadence, and step length, but negatively correlated with the awareness-related change in step-length variability. The smaller reduction in beta connectivity strength was associated with greater improvement in locomotion control with stride awareness. This study is the first to reveal that a stride awareness strategy modulates the beta band oscillatory network and is related to walking efficacy in individuals with PD in a dual-task condition.
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12
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Bosch TJ, Espinoza AI, Mancini M, Horak FB, Singh A. Functional Connectivity in Patients With Parkinson’s Disease and Freezing of Gait Using Resting-State EEG and Graph Theory. Neurorehabil Neural Repair 2022; 36:715-725. [DOI: 10.1177/15459683221129282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Background Although many studies have shown abnormalities in brain structure and function in people with Parkinson’s disease (PD), we still have a poor understanding of how brain structure and function relates to freezing of gait (FOG). Graph theory analysis of electroencephalography (EEG) can explore the relationship between brain network structure and gait function in PD. Methods Scalp EEG signals of 83 PD (42 PDFOG+ and 41 PDFOG−) and 42 healthy controls were recorded in an eyes-opened resting-state. The phase lag index was calculated for each electrode pair in different frequency bands, but we focused our analysis on the theta-band and performed global analyses along with nodal analyses over a midfrontal channel. The resulting connectivity matrices were converted to weighted graphs, whose structure was characterized using strength and clustering coefficient measurements, our main outcomes. Results We observed increased global strength and increased global clustering coefficient in people with PD compared to healthy controls in the theta-band, though no differences were observed in midfrontal nodal strength and midfrontal clustering coefficient. Furthermore, no differences in global nor midfrontal nodal strength nor global clustering coefficients were observed between PDFOG+ and PDFOG− in the theta-band. However, PDFOG+ exhibited a significantly diminished midfrontal nodal clustering coefficient in the theta-band compared to PDFOG−. Furthermore, FOG scores were negatively correlated with midfrontal nodal clustering coefficient in the theta-band. Conclusion The present findings support the involvement of midfrontal theta oscillations in FOG symptoms in PD and the sensitivity of graph metrics to characterize functional networks in PDFOG+.
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Affiliation(s)
- Taylor J. Bosch
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, USA
| | | | - Martina Mancini
- Department of Neurology, School of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Fay B. Horak
- Department of Neurology, School of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Arun Singh
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, USA
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Multigroup recognition of dementia patients with dynamic brain connectivity under multimodal cortex parcellation. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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14
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Koren O, Bartsch RP, Katzir Z, Rosenblum U, Hassin-Baer S, Inzelberg R, Plotnik M. Dopaminergic medication reduces interhemispheric hyper-synchronization in Parkinson's disease. Parkinsonism Relat Disord 2022; 97:39-46. [PMID: 35299069 DOI: 10.1016/j.parkreldis.2022.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 02/13/2022] [Accepted: 02/14/2022] [Indexed: 11/15/2022]
Abstract
INTRODUCTION We previously reported on interhemispheric cortical hyper synchronization in PD. The aim of the present study was to address the hypothesis that increased interhemispheric cortical synchronization in PD is related to dopamine deficiency and is correlated with motor function. METHODS We studied participants with PD and characterized cortical synchronization with reference to brain regions. Electroencephalography (EEG) was recorded from 20 participants with PD while OFF and ON their dopaminergic medications (two separate visits), during quiet standing and straight-line walking. Cortical interactions in the theta, alpha, beta, and gamma brain wave frequency bands were evaluated using interhemispheric phase synchronization (inter-PS). RESULTS Inter-PS values were found to be significantly higher during the OFF state as compared to the ON state in standing and walking trials for theta, alpha and beta bands. In addition, inter-PS reduction from OFF to ON was associated with mobility improvement evaluated by the Timed Up and Go test, and with daily levodopa equivalent dose across individuals. Higher differences in inter-PS values between OFF and ON states were evident mainly in the occipital-parietal cortex. CONCLUSIONS Persons with PD have increased inter-PS during the OFF state compared to their ON state, and this increase in inter-PS is associated with the clinical improvement between OFF and ON. We speculate that these findings, together with previous evidence of higher inter-PS in PD as compared to healthy older adults, reflect neuronal processes consequential to asymmetric subcortical dopamine deficiency.
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Affiliation(s)
- Or Koren
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
| | - Ronny P Bartsch
- Department of Physics, Bar-Ilan University, Ramat Gan, Israel
| | - Zoya Katzir
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel; Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Uri Rosenblum
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel; Department of Physical Therapy, Recanati School for Community Health Professions, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Sharon Hassin-Baer
- Movement Disorders Institute, Department of Neurology, Sheba Medical Center, Ramat Gan, Israel; Department of Neurology and Neurosurgery, Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | - Rivka Inzelberg
- Department of Neurology and Neurosurgery, Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Meir Plotnik
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel; Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
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Bayot M, Gérard M, Derambure P, Dujardin K, Defebvre L, Betrouni N, Delval A. Functional networks underlying freezing of gait: a resting-state electroencephalographic study. Neurophysiol Clin 2022; 52:212-222. [PMID: 35351387 DOI: 10.1016/j.neucli.2022.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 03/09/2022] [Accepted: 03/09/2022] [Indexed: 10/18/2022] Open
Abstract
INTRODUCTION The pathophysiology of freezing of gait in people with Parkinson's disease (PD) remains unclear, despite its association with motor, cognitive, limbic and sensory-perceptual impairments. Resting-state electroencephalography (EEG) may provide functional information for a better understanding of freezing of gait by studying spectral power and connectivity between brain regions in different frequency bands. METHODS High-resolution EEG was recorded in 36 patients with PD (18 freezers, 18 non-freezers), and 18 healthy controls during a 5-min resting-state protocol with eyes open, followed by a basic spectral analysis in the sensor space and a more advanced analysis of functional connectivity at the source level. RESULTS Freezers showed a diffusely higher theta-band relative spectral power than controls. This increased power was correlated with a deficit in executive control. Concerning resting-state functional connectivity, connectivity strength within a left fronto-parietal network appeared to be higher in freezers than in controls in the theta band, and to be correlated with freezing severity and a history of falls. CONCLUSION We have shown that spectral power and connectivity analyses of resting-state EEG provide useful and complementary information to better understand freezing of gait in PD. The higher connectivity strength seen within the left ventral attention network in freezers is in keeping with an excessive guidance of behavior by external cues, due to executive dysfunction, and spectral analysis also found changes in freezers that was closely correlated with executive control deficits. This exaggerated influence of the external environment might result in behavioral consequences that contribute to freezing of gait episodes. These findings should be further investigated with a longitudinal study.
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Affiliation(s)
- Madli Bayot
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Department of Clinical Neurophysiology, F-59000 Lille, France
| | - Morgane Gérard
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Department of Clinical Neurophysiology, F-59000 Lille, France
| | - Philippe Derambure
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Department of Clinical Neurophysiology, F-59000 Lille, France
| | - Kathy Dujardin
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Department of Neurology and Movement Disorders, F-59000 Lille, France
| | - Luc Defebvre
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Department of Neurology and Movement Disorders, F-59000 Lille, France
| | - Nacim Betrouni
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Department of Clinical Neurophysiology, F-59000 Lille, France
| | - Arnaud Delval
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Department of Clinical Neurophysiology, F-59000 Lille, France.
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Liu DF, Zhao BT, Zhu GY, Liu YY, Bai YT, Liu HG, Jiang Y, Zhang X, Lin-Shi, Zhang H, Yang AC, Zhang JG. Synchronized Intracranial Electrical Activity and Gait Recording in Parkinson’s Disease Patients With Freezing of Gait. Front Neurosci 2022; 16:795417. [PMID: 35310098 PMCID: PMC8927080 DOI: 10.3389/fnins.2022.795417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 02/10/2022] [Indexed: 11/29/2022] Open
Abstract
Background This study aimed to describe a synchronized intracranial electroencephalogram (EEG) recording and motion capture system, which was designed to explore the neural dynamics during walking of Parkinson’s disease (PD) patients with freezing of gait (FOG). Preliminary analysis was performed to test the reliability of this system. Methods A total of 8 patients were enrolled in the study. All patients underwent bilateral STN-DBS surgery and were implanted with a right subdural electrode covering premotor and motor area. Synchronized electrophysiological and gait data were collected using the Nihon Kohden EEG amplifier and Codamotion system when subjects performed the Timed Up and Go (TUG) test. To verify the reliability of the acquisition system and data quality, we calculated and compared the FOG index between freezing and non-freezing periods during walking. For electrophysiological data, we first manually reviewed the scaled (five levels) quality during waking. Spectra comprising broadband electrocorticography (ECoG) and local field potential (LFP) were also compared between the FOG and non-FOG states. Lastly, connectivity analysis using coherence between cortical and STN electrodes were conducted. In addition, we also use machine learning approaches to classified FOG and non-FOG. Results A total of 8 patients completed 41 walking tests, 30 of which had frozen episodes, and 21 of the 30 raw data were level 1 or 2 in quality (70%). The mean ± SD walking time for the TUG test was 85.94 ± 47.68 s (range: 38 to 190.14 s); the mean ± SD freezing duration was 12.25 ± 7.35 s (range: 1.71 to 27.50 s). The FOG index significantly increased during the manually labeled FOG period (P < 0.05). The beta power of STN LFP in the FOG period was significantly higher than that in the non-FOG period (P < 0.05), while the band power of ECoG did not exhibit a significant difference between walking states. The coherence between the ECoG and STN LFP was significantly greater in high beta and gamma bands during the FOG period compared with the shuffled surrogates (P < 0.05). Lastly, STN-LFP band power features showed above-chance performance (p < 0.01, permutation test) in identifying FOG epochs. Conclusion In this study, we established and verified the synchronized ECoG/LFP and gait recording system in PD patients with FOG. Further neural substrates underlying FOG could be explored using the current system.
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Bohnen NI, Costa RM, Dauer WT, Factor SA, Giladi N, Hallett M, Lewis SJ, Nieuwboer A, Nutt JG, Takakusaki K, Kang UJ, Przedborski S, Papa SM. Discussion of Research Priorities for Gait Disorders in Parkinson's Disease. Mov Disord 2021; 37:253-263. [PMID: 34939221 PMCID: PMC10122497 DOI: 10.1002/mds.28883] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/08/2021] [Accepted: 11/10/2021] [Indexed: 12/18/2022] Open
Abstract
Gait and balance abnormalities develop commonly in Parkinson's disease and are among the motor symptoms most disabling and refractory to dopaminergic or other treatments, including deep brain stimulation. Efforts to develop effective therapies are challenged by limited understanding of these complex disorders. There is a major need for novel and appropriately targeted research to expedite progress in this area. The Scientific Issues Committee of the International Parkinson and Movement Disorder Society has charged a panel of experts in the field to consider the current knowledge gaps and determine the research routes with highest potential to generate groundbreaking data. © 2021 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Nicolaas I. Bohnen
- Departments of Radiology and Neurology University of Michigan and VA Ann Arbor Healthcare System Ann Arbor Michigan USA
| | - Rui M. Costa
- Departments of Neuroscience and Neurology, Zuckerman Mind Brain Behavior Institute Columbia University New York New York USA
| | - William T. Dauer
- Departments of Neurology and Neuroscience The Peter O'Donnell Jr. Brain Institute, UT Southwestern Dallas Texas USA
| | - Stewart A. Factor
- Jean and Paul Amos Parkinson's Disease and Movement Disorders Program Emory University School of Medicine Atlanta Georgia USA
| | - Nir Giladi
- Movement Disorders Unit, Department of Neurology, Tel‐Aviv Sourasky Medical Center, Sackler School of Medicine and Sagol School of Neuroscience Tel Aviv University Tel Aviv Israel
| | - Mark Hallett
- Human Motor Control Section National Institute of Neurological Disorders and Stroke, National Institutes of Health Bethesda Maryland USA
| | - Simon J.G. Lewis
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences University of Sydney Sydney New South Wales Australia
| | - Alice Nieuwboer
- Department of Rehabilitation Sciences KU Leuven Leuven Belgium
| | - John G. Nutt
- Movement Disorder Section, Department of Neurology Oregon Health & Science University Portland Oregon USA
| | - Kaoru Takakusaki
- Department of Physiology, Section of Neuroscience Asahikawa Medical University Asahikawa Japan
| | - Un Jung Kang
- Departments of Neurology, Neuroscience, and Physiology Neuroscience Institute, The Marlene and Paolo Fresco Institute for Parkinson's and Movement Disorders, The Parekh Center for Interdisciplinary Neurology, New York University Grossman School of Medicine New York New York USA
| | - Serge Przedborski
- Departments of Pathology and Cell Biology, Neurology, and Neuroscience Columbia University New York New York USA
| | - Stella M. Papa
- Department of Neurology, School of Medicine, and Yerkes National Primate Research Center Emory University Atlanta Georgia USA
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