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Choi A, Zhang N, Adler CH, Beach TG, Shill HA, Driver-Dunckley E, Mehta S, Belden C, Atri A, Sabbagh MN, Caviness JN. Resting-state EEG predicts cognitive decline in a neuropathologically diagnosed longitudinal community autopsied cohort. Parkinsonism Relat Disord 2024; 128:107120. [PMID: 39236511 DOI: 10.1016/j.parkreldis.2024.107120] [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: 02/11/2024] [Revised: 08/02/2024] [Accepted: 08/30/2024] [Indexed: 09/07/2024]
Abstract
OBJECTIVE To assess correlative strengths of quantitative electroencephalography (qEEG) and visual rating scale EEG features on cognitive outcomes in only autopsied cases from the Arizona Study of Neurodegenerative Disorders (AZSAND). We hypothesized that autopsy proven Parkinson Disease will show distinct EEG features from Alzheimer's Disease prior to dementia (mild cognitive impairment). BACKGROUND Cognitive decline is debilitating across neurodegenerative diseases. Resting-state EEG analysis, including spectral power across frequency bins (qEEG), has shown significant associations with neurodegenerative disease classification and cognitive status, with autopsy confirmed diagnosis relatively lacking. METHODS Biannual EEG was analyzed from autopsied cases in AZSAND who had at least one rsEEG (>1 min eyes closed±eyes open). Analysis included global relative spectral power and a previously described visual rating scale (VRS). Linear mixed regression was performed for neuropsychological assessment and testing within 2 years of death (n = 236, 594 EEG exams) in a mixed linear regression model. RESULTS The cohort included cases with final clinicopathologic diagnoses of Parkinson's disease (n = 73), Alzheimer disease (n = 65), and tauopathy not otherwise specified (n = 56). A VRS score of 3 diffuse or frequent generalized slowing) over the study duration was associated with an increase in consensus diagnosis cognitive worsening at 4.9 (3.1) years (HR 2.02, CI 1.05-3.87). Increases in global theta power% and VRS were the most consistently associated with large regression coefficients inversely with cognitive performance measures. CONCLUSION Resting-state EEG analysis was meaningfully related to cognitive performance measures in a community-based autopsy cohort. EEG deserves further study and use as a cognitive biomarker.
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Affiliation(s)
- Alexander Choi
- Banner Sun Health Research Institute, 10515 W Santa Fe Dr, Sun City, AZ, 85351, USA.
| | - Nan Zhang
- Department of Neurology, Mayo Clinic College of Medicine, Mayo Clinic Arizona, 13400 E Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Charles H Adler
- Department of Neurology, Mayo Clinic College of Medicine, Mayo Clinic Arizona, 13400 E Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Thomas G Beach
- Banner Sun Health Research Institute, 10515 W Santa Fe Dr, Sun City, AZ, 85351, USA
| | - Holly A Shill
- Barrow Neurological Institute, 240 W. Thomas Suite 301, Phoenix, AZ, 85013, USA
| | - Erika Driver-Dunckley
- Department of Neurology, Mayo Clinic College of Medicine, Mayo Clinic Arizona, 13400 E Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Shyamal Mehta
- Department of Neurology, Mayo Clinic College of Medicine, Mayo Clinic Arizona, 13400 E Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Christine Belden
- Banner Sun Health Research Institute, 10515 W Santa Fe Dr, Sun City, AZ, 85351, USA
| | - Alireza Atri
- Banner Sun Health Research Institute, 10515 W Santa Fe Dr, Sun City, AZ, 85351, USA
| | - Marwan N Sabbagh
- Barrow Neurological Institute, 240 W. Thomas Suite 301, Phoenix, AZ, 85013, USA
| | - John N Caviness
- Department of Neurology, Mayo Clinic College of Medicine, Mayo Clinic Arizona, 13400 E Shea Blvd, Scottsdale, AZ, 85259, USA
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van der Plas MC, Rasing I, Geraedts VJ, Tromp SC, Terwindt GM, van Dort R, Kaushik K, van Zwet EW, Tannemaat MR, Wermer MJH. Quantitative electroencephalography in cerebral amyloid angiopathy. Clin Neurophysiol 2024; 164:111-118. [PMID: 38861875 DOI: 10.1016/j.clinph.2024.05.013] [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: 07/20/2023] [Revised: 04/14/2024] [Accepted: 05/22/2024] [Indexed: 06/13/2024]
Abstract
OBJECTIVE We investigated whether quantitative electroencephalography (qEEG) correlates with cognition and cortical superficial siderosis (cSS) in cerebral amyloid angiopathy. METHODS We included patients with sporadic (sCAA) and hereditary Dutch-type CAA (D-CAA). Spectral measures and the phase lag index (PLI) were analyzed on qEEG. Cognition was assessed with the MoCA and cSS presence was scored on 3T-MRI. Linear regression analyses were performed to investigate these qEEG measures and cognition. Independent samples T-tests were used to analyze the qEEG measure differences between participants with and without cSS. RESULTS We included 92 participants (44 D-CAA; 48 sCAA). A lower average peak frequency (β[95 %CI] = 0.986[0.252-1.721]; P = 0.009) and a higher spectral ratio (β[95 %CI] = -0.918[-1.761--0.075]; P = 0.033) on qEEG correlated with a lower MoCA score, irrespective of a history of symptomatic intracerebral hemorrhage (sICH). The PLI showed no correlation to the MoCA. qEEG slowing was not different in those with or without cSS. CONCLUSIONS Spectral qEEG (but not PLI) reflects cognitive performance in patients with CAA with and without a history of sICH. We found no association between qEEG slowing and cSS. SIGNIFICANCE qEEG could be a valuable biomarker, especially in challenging cognitive testing situations in CAA, and a potential predictive tool in future studies.
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Affiliation(s)
- M C van der Plas
- Department of Neurology, Leiden University Medical Center, Albinusdreef 2, 2300 RC, Leiden, the Netherlands.
| | - I Rasing
- Department of Neurology, Leiden University Medical Center, Albinusdreef 2, 2300 RC, Leiden, the Netherlands
| | - V J Geraedts
- Department of Neurology, Leiden University Medical Center, Albinusdreef 2, 2300 RC, Leiden, the Netherlands
| | - S C Tromp
- Department of Neurology, Leiden University Medical Center, Albinusdreef 2, 2300 RC, Leiden, the Netherlands
| | - G M Terwindt
- Department of Neurology, Leiden University Medical Center, Albinusdreef 2, 2300 RC, Leiden, the Netherlands
| | - R van Dort
- Department of Neurology, Leiden University Medical Center, Albinusdreef 2, 2300 RC, Leiden, the Netherlands
| | - K Kaushik
- Department of Neurology, Leiden University Medical Center, Albinusdreef 2, 2300 RC, Leiden, the Netherlands
| | - E W van Zwet
- Department of Biomedical Data Sciences, Leiden University Medical Center, Albinusdreef 2, 2300 RC Leiden, the Netherlands
| | - M R Tannemaat
- Department of Neurology, Leiden University Medical Center, Albinusdreef 2, 2300 RC, Leiden, the Netherlands
| | - M J H Wermer
- Department of Neurology, Leiden University Medical Center, Albinusdreef 2, 2300 RC, Leiden, the Netherlands; Department of Neurology, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands
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Qian S, Yang Q, Cai C, Dong J, Cai S. Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study. Brain Sci 2024; 14:507. [PMID: 38790485 PMCID: PMC11118919 DOI: 10.3390/brainsci14050507] [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: 04/22/2024] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.
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Affiliation(s)
| | | | | | | | - Shuhui Cai
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen 361005, China; (S.Q.); (Q.Y.); (C.C.); (J.D.)
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Luo Z, Yin E, Zeng LL, Shen H, Su J, Peng L, Yan Y, Hu D. Frequency-specific segregation and integration of human cerebral cortex: An intrinsic functional atlas. iScience 2024; 27:109206. [PMID: 38439977 PMCID: PMC10910261 DOI: 10.1016/j.isci.2024.109206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/24/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
The cognitive and behavioral functions of the human brain are supported by its frequency multiplexing mechanism. However, there is limited understanding of the dynamics of the functional network topology. This study aims to investigate the frequency-specific topology of the functional human brain using 7T rs-fMRI data. Frequency-specific parcellations were first performed, revealing frequency-dependent dynamics within the frontoparietal control, parietal memory, and visual networks. An intrinsic functional atlas containing 456 parcels was proposed and validated using stereo-EEG. Graph theory analysis suggested that, in addition to the task-positive vs. task-negative organization observed in static networks, there was a cognitive control system additionally from a frequency perspective. The reproducibility and plausibility of the identified hub sets were confirmed through 3T fMRI analysis, and their artificial removal had distinct effects on network topology. These results indicate a more intricate and subtle dynamics of the functional human brain and emphasize the significance of accurate topography.
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Affiliation(s)
- Zhiguo Luo
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Erwei Yin
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Jianpo Su
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Limin Peng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Ye Yan
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, China
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
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Bar-On M, Baharav S, Katzir Z, Mirelman A, Sosnik R, Maidan I. Task-Related Reorganization of Cognitive Network in Parkinson's Disease Using Electrophysiology. Mov Disord 2023; 38:2031-2040. [PMID: 37553881 DOI: 10.1002/mds.29571] [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: 04/15/2023] [Revised: 07/03/2023] [Accepted: 07/17/2023] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Cognitive deficits in Parkinson's disease (PD) patients are well described, however, their underlying neural mechanisms as assessed by electrophysiology are not clear. OBJECTIVES To reveal specific neural network alterations during the performance of cognitive tasks in PD patients using electroencephalography (EEG). METHODS Ninety participants, 60 PD patients and 30 controls underwent EEG recording while performing a GO/NOGO task. Source localization of 16 regions of interest known to play a pivotal role in GO/NOGO task was performed to assess power density and connectivity within this cognitive network. The connectivity matrices were evaluated using a graph-theory approach that included measures of cluster-coefficient, degree, and global-efficiency. A mixed-model analysis, corrected for age and levodopa equivalent daily dose was performed to examine neural changes between PD patients and controls. RESULTS PD patients performed worse in the GO/NOGO task (P < 0.001). The power density was higher in δ and θ bands, but lower in α and β bands in PD patients compared to controls (interaction group × band: P < 0.001), indicating a general slowness within the network. Patients had more connections within the network (P < 0.034) than controls and these were used for graph-theory analysis. Differences between groups in graph-theory measures were found only in cluster-coefficient, which was higher in PD compared to controls (interaction group × band: P < 0.001). CONCLUSIONS Cognitive deficits in PD are underlined by alterations at the brain network level, including higher δ and θ activity, lower α and β activity, increased connectivity, and segregated network organization. These findings may have important implications on future adaptive deep brain stimulation. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- May Bar-On
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Faculty of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Shaked Baharav
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Faculty of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Zoya Katzir
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Neurology, School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Anat Mirelman
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Neurology, School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Ronen Sosnik
- Faculty of Engineering, Holon Institute of Technology (HIT), Holon, Israel
| | - Inbal Maidan
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Neurology, School of Medicine, Tel Aviv University, Tel-Aviv, Israel
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Germany E, Teixeira I, Danthine V, Santalucia R, Cakiroglu I, Torres A, Verleysen M, Delbeke J, Nonclercq A, Tahry RE. Functional brain connectivity indexes derived from low-density EEG of pre-implanted patients as VNS outcome predictors. J Neural Eng 2023; 20:046039. [PMID: 37595607 DOI: 10.1088/1741-2552/acf1cd] [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: 03/14/2023] [Accepted: 08/18/2023] [Indexed: 08/20/2023]
Abstract
Objective. In 1/3 of patients, anti-seizure medications may be insufficient, and resective surgery may be offered whenever the seizure onset is localized and situated in a non-eloquent brain region. When surgery is not feasible or fails, vagus nerve stimulation (VNS) therapy can be used as an add-on treatment to reduce seizure frequency and/or severity. However, screening tools or methods for predicting patient response to VNS and avoiding unnecessary implantation are unavailable, and confident biomarkers of clinical efficacy are unclear.Approach. To predict the response of patients to VNS, functional brain connectivity measures in combination with graph measures have been primarily used with respect to imaging techniques such as functional magnetic resonance imaging, but connectivity graph-based analysis based on electrophysiological signals such as electroencephalogram, have been barely explored. Although the study of the influence of VNS on functional connectivity is not new, this work is distinguished by using preimplantation low-density EEG data to analyze discriminative measures between responders and non-responder patients using functional connectivity and graph theory metrics.Main results. By calculating five functional brain connectivity indexes per frequency band upon partial directed coherence and direct transform function connectivity matrices in a population of 37 refractory epilepsy patients, we found significant differences (p< 0.05) between the global efficiency, average clustering coefficient, and modularity of responders and non-responders using the Mann-Whitney U test with Benjamini-Hochberg correction procedure and use of a false discovery rate of 5%.Significance. Our results indicate that these measures may potentially be used as biomarkers to predict responsiveness to VNS therapy.
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Affiliation(s)
- Enrique Germany
- IoNS, Universite Catholique de Louvain, Brussels, Belgium
- WELBIO Department, WEL Research Institute, Wavre, Belgium
| | - Igor Teixeira
- IoNS, Universite Catholique de Louvain, Brussels, Belgium
| | | | | | - Inci Cakiroglu
- IoNS, Universite Catholique de Louvain, Brussels, Belgium
| | - Andres Torres
- IoNS, Universite Catholique de Louvain, Brussels, Belgium
| | | | - Jean Delbeke
- IoNS, Universite Catholique de Louvain, Brussels, Belgium
| | - Antoine Nonclercq
- Bio-Electro-and Mechanical Systems (BEAMS), Université Libre de Bruxelles, Brussels, Belgium
| | - Riëm El Tahry
- IoNS, Universite Catholique de Louvain, Brussels, Belgium
- WELBIO Department, WEL Research Institute, Wavre, Belgium
- Cliniques Universitaires Saint-Luc, Brussels, Belgium
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Yassine S, Gschwandtner U, Auffret M, Duprez J, Verin M, Fuhr P, Hassan M. Identification of Parkinson's Disease Subtypes from Resting-State Electroencephalography. Mov Disord 2023; 38:1451-1460. [PMID: 37310340 DOI: 10.1002/mds.29451] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/11/2023] [Accepted: 05/05/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Parkinson's disease (PD) patients present with a heterogeneous clinical phenotype, including motor, cognitive, sleep, and affective disruptions. However, this heterogeneity is often either ignored or assessed using only clinical assessments. OBJECTIVES We aimed to identify different PD sub-phenotypes in a longitudinal follow-up analysis and their electrophysiological profile based on resting-state electroencephalography (RS-EEG) and to assess their clinical significance over the course of the disease. METHODS Using electrophysiological features obtained from RS-EEG recordings and data-driven methods (similarity network fusion and source-space spectral analysis), we have performed a clustering analysis to identify disease sub-phenotypes and we examined whether their different patterns of disruption are predictive of disease outcome. RESULTS We showed that PD patients (n = 44) can be sub-grouped into three phenotypes with distinct electrophysiological profiles. These clusters are characterized by different levels of disruptions in the somatomotor network (Δ and β band), the frontotemporal network (α2 band) and the default mode network (α1 band), which consistently correlate with clinical profiles and disease courses. These clusters are classified into either moderate (only-motor) or mild-to-severe (diffuse) disease. We showed that EEG features can predict cognitive evolution of PD patients from baseline, when the cognitive clinical scores were overlapped. CONCLUSIONS The identification of novel PD subtypes based on electrical brain activity signatures may provide a more accurate prognosis in individual patients in clinical practice and help to stratify subgroups in clinical trials. Innovative profiling in PD can also support new therapeutic strategies that are brain-based and designed to modulate brain activity disruption. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Sahar Yassine
- LTSI - INSERM U1099, University of Rennes, Rennes, France
- NeuroKyma, Rennes, France
- Behavior and Basal Ganglia, CIC1414, CIC-IT, CHU Rennes, Rennes, France
| | - Ute Gschwandtner
- Department of Neurology, Hospitals of the University of Basel, Basel, Switzerland
| | - Manon Auffret
- LTSI - INSERM U1099, University of Rennes, Rennes, France
- Behavior and Basal Ganglia, CIC1414, CIC-IT, CHU Rennes, Rennes, France
- Institut des Neurosciences Cliniques de Rennes (INCR), Rennes, France
- France Développement Electronique, Monswiller, France
| | - Joan Duprez
- LTSI - INSERM U1099, University of Rennes, Rennes, France
| | - Marc Verin
- LTSI - INSERM U1099, University of Rennes, Rennes, France
- Behavior and 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
| | - Peter Fuhr
- Department of Neurology, Hospitals of the University of Basel, Basel, Switzerland
| | - Mahmoud Hassan
- Behavior and Basal Ganglia, CIC1414, CIC-IT, CHU Rennes, Rennes, France
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
- MINDIG, Rennes, France
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Stylianou O, Kaposzta Z, Czoch A, Stefanovski L, Yabluchanskiy A, Racz FS, Ritter P, Eke A, Mukli P. Scale-Free Functional Brain Networks Exhibit Increased Connectivity, Are More Integrated and Less Segregated in Patients with Parkinson's Disease following Dopaminergic Treatment. FRACTAL AND FRACTIONAL 2022; 6:737. [PMID: 38106971 PMCID: PMC10723163 DOI: 10.3390/fractalfract6120737] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Dopaminergic treatment (DT), the standard therapy for Parkinson's disease (PD), alters the dynamics of functional brain networks at specific time scales. Here, we explore the scale-free functional connectivity (FC) in the PD population and how it is affected by DT. We analyzed the electroencephalogram of: (i) 15 PD patients during DT (ON) and after DT washout (OFF) and (ii) 16 healthy control individuals (HC). We estimated FC using bivariate focus-based multifractal analysis, which evaluated the long-term memory ( H ( 2 ) ) and multifractal strength ( Δ H 15 ) of the connections. Subsequent analysis yielded network metrics (node degree, clustering coefficient and path length) based on FC estimated by H ( 2 ) or Δ H 15 . Cognitive performance was assessed by the Mini Mental State Examination (MMSE) and the North American Adult Reading Test (NAART). The node degrees of the Δ H 15 networks were significantly higher in ON, compared to OFF and HC, while clustering coefficient and path length significantly decreased. No alterations were observed in the H ( 2 ) networks. Significant positive correlations were also found between the metrics of H ( 2 ) networks and NAART scores in the HC group. These results demonstrate that DT alters the multifractal coupled dynamics in the brain, warranting the investigation of scale-free FC in clinical and pharmacological studies.
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Affiliation(s)
- Orestis Stylianou
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
- Institute of Translational Medicine, Semmelweis University, 1094 Budapest, Hungary
- Berlin Institute of Health at Charité, University Hospital Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité-University Hospital Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Zalan Kaposzta
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
| | - Akos Czoch
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
| | - Leon Stefanovski
- Berlin Institute of Health at Charité, University Hospital Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité-University Hospital Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Andriy Yabluchanskiy
- Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, 975 NE 10th Street, BRC, Oklahoma City, OK 73104, USA
| | - Frigyes Samuel Racz
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, TX 78712, USA
| | - Petra Ritter
- Berlin Institute of Health at Charité, University Hospital Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité-University Hospital Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Einstein Center Digital Future, Wilhelmstraße 67, 10117 Berlin, Germany
| | - Andras Eke
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar Street, New Haven, CT 06520, USA
| | - Peter Mukli
- Department of Physiology, Semmelweis University, 1094 Budapest, Hungary
- Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, 975 NE 10th Street, BRC, Oklahoma City, OK 73104, USA
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Rana A, Dumka A, Singh R, Panda MK, Priyadarshi N. A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives. Diagnostics (Basel) 2022; 12:2708. [PMID: 36359550 PMCID: PMC9689408 DOI: 10.3390/diagnostics12112708] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 10/30/2022] [Accepted: 11/02/2022] [Indexed: 08/03/2023] Open
Abstract
According to the World Health Organization (WHO), Parkinson's disease (PD) is a neurodegenerative disease of the brain that causes motor symptoms including slower movement, rigidity, tremor, and imbalance in addition to other problems like Alzheimer's disease (AD), psychiatric problems, insomnia, anxiety, and sensory abnormalities. Techniques including artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been established for the classification of PD and normal controls (NC) with similar therapeutic appearances in order to address these problems and improve the diagnostic procedure for PD. In this article, we examine a literature survey of research articles published up to September 2022 in order to present an in-depth analysis of the use of datasets, various modalities, experimental setups, and architectures that have been applied in the diagnosis of subjective disease. This analysis includes a total of 217 research publications with a list of the various datasets, methodologies, and features. These findings suggest that ML/DL methods and novel biomarkers hold promising results for application in medical decision-making, leading to a more methodical and thorough detection of PD. Finally, we highlight the challenges and provide appropriate recommendations on selecting approaches that might be used for subgrouping and connection analysis with structural magnetic resonance imaging (sMRI), DaTSCAN, and single-photon emission computerized tomography (SPECT) data for future Parkinson's research.
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Affiliation(s)
- Arti Rana
- Computer Science & Engineering, Veer Madho Singh Bhandari Uttarakhand Technical University, Dehradun 248007, Uttarakhand, India
| | - Ankur Dumka
- Department of Computer Science and Engineering, Women Institute of Technology, Dehradun 248007, Uttarakhand, India
- Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun 248001, Uttarakhand, India
| | - Rajesh Singh
- Division of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, Uttarakhand, India
- Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
| | - Manoj Kumar Panda
- Department of Electrical Engineering, G.B. Pant Institute of Engineering and Technology, Pauri 246194, Uttarakhand, India
| | - Neeraj Priyadarshi
- Department of Electrical Engineering, JIS College of Engineering, Kolkata 741235, West Bengal, India
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10
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Deb R, An S, Bhat G, Shill H, Ogras UY. A Systematic Survey of Research Trends in Technology Usage for Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2022; 22:5491. [PMID: 35897995 PMCID: PMC9371095 DOI: 10.3390/s22155491] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/17/2022] [Accepted: 07/20/2022] [Indexed: 06/15/2023]
Abstract
Parkinson's disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist's subjective assessment of clinical scales. A significant amount of recent research has explored new cost-effective and subjective assessment methods pertaining to PD symptoms to address this challenge. This article analyzes the application areas and use of mobile and wearable technology in PD research using the PRISMA methodology. Based on the published papers, we identify four significant fields of research: diagnosis, prognosis and monitoring, predicting response to treatment, and rehabilitation. Between January 2008 and December 2021, 31,718 articles were published in four databases: PubMed Central, Science Direct, IEEE Xplore, and MDPI. After removing unrelated articles, duplicate entries, non-English publications, and other articles that did not fulfill the selection criteria, we manually investigated 1559 articles in this review. Most of the articles (45%) were published during a recent four-year stretch (2018-2021), and 19% of the articles were published in 2021 alone. This trend reflects the research community's growing interest in assessing PD with wearable devices, particularly in the last four years of the period under study. We conclude that there is a substantial and steady growth in the use of mobile technology in the PD contexts. We share our automated script and the detailed results with the public, making the review reproducible for future publications.
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Affiliation(s)
| | - Sizhe An
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA;
| | - Ganapati Bhat
- School of Electrical Engineering & Computer Science, Washington State University, Pullman, WA 99164, USA;
| | - Holly Shill
- Lonnie and Muhammad Ali Movement Disorder Center, Phoenix, AZ 85013, USA;
| | - Umit Y. Ogras
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA;
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11
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Avvaru S, Parhi KK. Betweenness Centrality in Resting-State Functional Networks Distinguishes Parkinson's Disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4785-4788. [PMID: 36086073 DOI: 10.1109/embc48229.2022.9870988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The goal of this paper is to use graph theory network measures derived from non-invasive electroencephalography (EEG) to develop neural decoders that can differentiate Parkinson's disease (PD) patients from healthy controls (HC). EEG signals from 27 patients and 27 demographically matched controls from New Mexico were analyzed by estimating their functional networks. Data recorded from the patients during ON and OFF levodopa sessions were included in the analysis for comparison. We used betweenness centrality of estimated functional networks to classify the HC and PD groups. The classifiers were evaluated using leave-one-out cross-validation. We observed that the PD patients (on and off medication) could be distinguished from healthy controls with 89% accuracy - approximately 4% higher than the state-of-the-art on the same dataset. This work shows that brain network analysis using extracranial resting-state EEG can discover patterns of interactions indicative of PD. This approach can also be extended to other neurological disorders.
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12
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The frontostriatal subtype of mild cognitive impairment in Parkinson’s disease, but not the posterior cortical one, is associated with specific EEG alterations. Cortex 2022; 153:166-177. [DOI: 10.1016/j.cortex.2022.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/27/2022] [Accepted: 04/07/2022] [Indexed: 11/22/2022]
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13
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Zhang J, Villringer A, Nikulin VV. Dopaminergic Modulation of Local Non-oscillatory Activity and Global-Network Properties in Parkinson's Disease: An EEG Study. Front Aging Neurosci 2022; 14:846017. [PMID: 35572144 PMCID: PMC9106139 DOI: 10.3389/fnagi.2022.846017] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Dopaminergic medication for Parkinson's disease (PD) modulates neuronal oscillations and functional connectivity (FC) across the basal ganglia-thalamic-cortical circuit. However, the non-oscillatory component of the neuronal activity, potentially indicating a state of excitation/inhibition balance, has not yet been investigated and previous studies have shown inconsistent changes of cortico-cortical connectivity as a response to dopaminergic medication. To further elucidate changes of regional non-oscillatory component of the neuronal power spectra, FC, and to determine which aspects of network organization obtained with graph theory respond to dopaminergic medication, we analyzed a resting-state electroencephalography (EEG) dataset including 15 PD patients during OFF and ON medication conditions. We found that the spectral slope, typically used to quantify the broadband non-oscillatory component of power spectra, steepened particularly in the left central region in the ON compared to OFF condition. In addition, using lagged coherence as a FC measure, we found that the FC in the beta frequency range between centro-parietal and frontal regions was enhanced in the ON compared to the OFF condition. After applying graph theory analysis, we observed that at the lower level of topology the node degree was increased, particularly in the centro-parietal area. Yet, results showed no significant difference in global topological organization between the two conditions: either in global efficiency or clustering coefficient for measuring global and local integration, respectively. Interestingly, we found a close association between local/global spectral slope and functional network global efficiency in the OFF condition, suggesting a crucial role of local non-oscillatory dynamics in forming the functional global integration which characterizes PD. These results provide further evidence and a more complete picture for the engagement of multiple cortical regions at various levels in response to dopaminergic medication in PD.
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Affiliation(s)
- Juanli Zhang
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
| | - Vadim V. Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Neurophysics Group, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
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14
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Yassine S, Gschwandtner U, Auffret M, Achard S, Verin M, Fuhr P, Hassan M. Functional Brain Dysconnectivity in Parkinson's Disease: A 5-Year Longitudinal Study. Mov Disord 2022; 37:1444-1453. [PMID: 35420713 PMCID: PMC9543227 DOI: 10.1002/mds.29026] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/19/2022] [Accepted: 03/24/2022] [Indexed: 11/29/2022] Open
Abstract
Background Tracking longitudinal functional brain dysconnectivity in Parkinson's disease (PD) is a key element to decoding the underlying physiopathology and understanding PD progression. Objectives The objectives of this follow‐up study were to explore, for the first time, the longitudinal changes in the functional brain networks of PD patients over 5 years and to associate them with their cognitive performance and the lateralization of motor symptoms. Methods We used a 5‐year longitudinal cohort of PD patients (n = 35) who completed motor and non‐motor assessments and sequent resting state (RS) high‐density electroencephalography (HD‐EEG) recordings at three timepoints: baseline (BL), 3 years follow‐up (3YFU) and 5 years follow‐up (5YFU). We assessed disruptions in frequency‐dependent functional networks over the course of the disease and explored their relation to clinical symptomatology. Results In contrast with HC (n = 32), PD patients showed a gradual connectivity impairment in α2 (10‐13 Hz) and β (13–30 Hz) frequency bands. The deterioration in the global cognitive assessment was strongly correlated with the disconnected networks. These disconnected networks were also associated with the lateralization of motor symptoms, revealing a dominance of the right hemisphere in terms of impaired connections in the left‐affected PD patients in contrast to dominance of the left hemisphere in the right‐affected PD patients. Conclusions Taken together, our findings suggest that with disease progression, dysconnectivity in the brain networks in PD can reflect the deterioration of global cognitive deficits and the lateralization of motor symptoms. RS HD‐EEG may be an early biomarker of PD motor and non‐motor progression. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society
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Affiliation(s)
- Sahar Yassine
- Univ Rennes, Inserm, LTSI - U1099, Rennes, F-35000, France.,NeuroKyma, Rennes, F-35000, France
| | - Ute Gschwandtner
- Department of Neurology, Hospitals of the University of Basel, Basel, Switzerland
| | - Manon Auffret
- Comportement et noyaux gris centraux, EA 4712, CHU Rennes, Rennes, France
| | - Sophie Achard
- CNRS, Grenoble INP, GIPSA-Lab, University of Grenoble Alpes, Grenoble, France
| | - Marc Verin
- Univ Rennes, Inserm, LTSI - U1099, Rennes, F-35000, France.,Comportement et noyaux gris centraux, EA 4712, CHU Rennes, Rennes, France.,Movement Disorders Unit, Neurology Department, Pontchaillou University Hospital, Rennes, France.,Institut des Neurosciences Cliniques de Rennes (INCR), Rennes, France
| | - Peter Fuhr
- Department of Neurology, Hospitals of the University of Basel, Basel, Switzerland
| | - Mahmoud Hassan
- MINDig, Rennes, F-35000, France.,School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
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15
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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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16
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Mano T, Kinugawa K, Ozaki M, Kataoka H, Sugie K. Neural synchronization analysis of electroencephalography coherence in patients with Parkinson's disease-related mild cognitive impairment. Clin Park Relat Disord 2022; 6:100140. [PMID: 35308256 PMCID: PMC8928128 DOI: 10.1016/j.prdoa.2022.100140] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 02/13/2022] [Accepted: 03/02/2022] [Indexed: 11/28/2022] Open
Abstract
We studied brain functional connectivity in 20 patients with PD-MCI and 10 MCI patients without Parkinsonism. Cognitive impairment was related to decreased coherence in the alpha range [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19]. Regional coherence in left FP had a higher correlation with cognitive function. Differences in EEG coherence may reflect a compensatory response to PD-MCI.
Introduction The underlying pathophysiology of slight cognitive dysfunction in Parkinson’s disease-related mild cognitive impairment (PD-MCI) is yet to be elucidated. Our study aimed to evaluate the association between cognitive function and brain functional connectivity (FC) in patients with PD-MCI. Methods Twenty patients with sporadic PD-MCI were evaluated for FC in the brain network. Further, electroencephalography (EEG) coherence analysis in the whole-brain and quantified regional coherence using phase coupling were performed for each frequency, and motor and cognitive function were assessed in the whole-brain. Results The degree of cognitive impairment was related to a decrease in the coherence in the alpha ranges. The regional coherence in the left frontal-left parietal region rather than the right frontal-right parietal region showed a higher correlation with the cognitive function scores. Conclusion The differences in EEG coherence across different types of cognitive dysfunction reflect a compensatory response to the heterogeneous and complex clinical presentation of PD-MCI. Our findings indicate decreased brain efficiency and impaired neural synchronization in PD-MCI; these results may be crucial in elucidating the pathological exacerbation of PD-MCI.
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Key Words
- Coherence analysis
- EEG, electroencephalography
- Electroencephalography
- FAB, Frontal Assessment Battery
- FC, functional connectivity
- FF, frontal-frontal
- FP, frontal-parietal
- FPL, left frontal-left parietal
- FPR, right frontal-right parietal
- FT, frontal-temporal
- HDS-R, Revised Hasegawa Dementia Score
- LEDD, levodopa-equivalent daily dose
- MCI, Mild Cognitive Impairment
- MCI, mild cognitive impairment
- MDS-UPDRS, Movement Disorder Society Unified Parkinson's Disease Rating Scale
- MMSE, Mini-Mental State Examination
- Mild cognitive impairment
- PD, Parkinson’s disease
- PO, parietal-occipital
- PT, parietal-temporal
- Parkinson's disease
- RBD, rapid eye movement sleep behavior disorder
- TT, temporal-temporal
- Time–frequency analysis
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Affiliation(s)
- Tomoo Mano
- Department of Neurology, Nara Medical University, 840 Shijo-cho, Kashihara, Nara 634-8521, Japan.,Department of Rehabilitation Medicine, Nara Medical University, 840 Shijo-cho, Kashihara, Nara 634-8521, Japan
| | - Kaoru Kinugawa
- Department of Neurology, Nara Medical University, 840 Shijo-cho, Kashihara, Nara 634-8521, Japan
| | - Maki Ozaki
- Department of Neurology, Nara Medical University, 840 Shijo-cho, Kashihara, Nara 634-8521, Japan
| | - Hiroshi Kataoka
- Department of Neurology, Nara Medical University, 840 Shijo-cho, Kashihara, Nara 634-8521, Japan
| | - Kazuma Sugie
- Department of Neurology, Nara Medical University, 840 Shijo-cho, Kashihara, Nara 634-8521, Japan
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17
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Brain Connectivity and Graph Theory Analysis in Alzheimer’s and Parkinson’s Disease: The Contribution of Electrophysiological Techniques. Brain Sci 2022; 12:brainsci12030402. [PMID: 35326358 PMCID: PMC8946843 DOI: 10.3390/brainsci12030402] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/08/2022] [Accepted: 03/16/2022] [Indexed: 12/31/2022] Open
Abstract
In recent years, applications of the network science to electrophysiological data have increased as electrophysiological techniques are not only relatively low cost, largely available on the territory and non-invasive, but also potential tools for large population screening. One of the emergent methods for the study of functional connectivity in electrophysiological recordings is graph theory: it allows to describe the brain through a mathematic model, the graph, and provides a simple representation of a complex system. As Alzheimer’s and Parkinson’s disease are associated with synaptic disruptions and changes in the strength of functional connectivity, they can be well described by functional connectivity analysis computed via graph theory. The aim of the present review is to provide an overview of the most recent applications of the graph theory to electrophysiological data in the two by far most frequent neurodegenerative disorders, Alzheimer’s and Parkinson’s diseases.
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18
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Blomsma N, de Rooy B, Gerritse F, van der Spek R, Tewarie P, Hillebrand A, Otte WM, Stam CJ, van Dellen E. Minimum spanning tree analysis of brain networks: A systematic review
of network size effects, sensitivity for neuropsychiatric pathology and disorder
specificity. Netw Neurosci 2022; 6:301-319. [PMID: 35733422 PMCID: PMC9207994 DOI: 10.1162/netn_a_00245] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 03/10/2022] [Indexed: 11/05/2022] Open
Abstract
Brain network characteristics’ potential to serve as a neurological and psychiatric pathology biomarker has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. It is yet unknown whether this approach leads to more consistent findings across studies and converging outcomes of either disease-specific biomarkers or transdiagnostic effects. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies (N = 43) to study consistency of MST metrics between different network sizes and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. Analysis of data from control groups (12 studies) showed that MST leaf fraction but not diameter decreased with increasing network size. Studies showed a broad range in metric values, suggesting that specific processing pipelines affect MST topology. Contradicting findings remain in the inconclusive literature of MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders across pathologies, and is associated with symptom severity and disease progression; (2) neurophysiological studies in epilepsy show frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology in alpha band is found across disorders associated with attention impairments. The potential of brain network characteristics to serve as biomarker of neurological and psychiatric pathology has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. MST leaf fraction but not diameter decreased with increasing network size. Contradicting findings remain in the literature on MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders; (2) in epilepsy there are frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology is found across disorders associated with attention impairments.
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Affiliation(s)
- Nicky Blomsma
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Bart de Rooy
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Frank Gerritse
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Rick van der Spek
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Prejaas Tewarie
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Wim M. Otte
- University Medical Center Utrecht, Department of Child Neurology, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Cornelis Jan Stam
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Edwin van Dellen
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
- University Medical Center Utrecht, Department of Intensive Care Medicine, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
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19
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Utianski RL, Botha H, Caviness JN, Worrell GA, Duffy JR, Clark HM, Whitwell JL, Josephs KA. A Preliminary Report of Network Electroencephalographic Measures in Primary Progressive Apraxia of Speech and Aphasia. Brain Sci 2022; 12:brainsci12030378. [PMID: 35326334 PMCID: PMC8946002 DOI: 10.3390/brainsci12030378] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/09/2022] [Accepted: 03/10/2022] [Indexed: 02/04/2023] Open
Abstract
The objective of this study was to characterize network-level changes in nonfluent/agrammatic Primary Progressive Aphasia (agPPA) and Primary Progressive Apraxia of Speech (PPAOS) with graph theory (GT) measures derived from scalp electroencephalography (EEG) recordings. EEGs of 15 agPPA and 7 PPAOS patients were collected during relaxed wakefulness with eyes closed (21 electrodes, 10–20 positions, 256 Hz sampling rate, 1–200 Hz bandpass filter). Eight artifact-free, non-overlapping 1024-point epochs were selected. Via Brainwave software, GT weighted connectivity and minimum spanning tree (MST) measures were calculated for theta and upper and lower alpha frequency bands. Differences in GT and MST measures between agPPA and PPAOS were assessed with Wilcoxon rank-sum tests. Of greatest interest, Spearman correlations were computed between behavioral and network measures in all frequency bands across all patients. There were no statistically significant differences in GT or MST measures between agPPA and PPAOS. There were significant correlations between several network and behavioral variables. The correlations demonstrate a relationship between reduced global efficiency and clinical symptom severity (e.g., parkinsonism, AOS). This preliminary, exploratory study demonstrates potential for EEG GT measures to quantify network changes associated with degenerative speech–language disorders.
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Affiliation(s)
- Rene L. Utianski
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA; (H.B.); (G.A.W.); (J.R.D.); (H.M.C.); (K.A.J.)
- Correspondence:
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA; (H.B.); (G.A.W.); (J.R.D.); (H.M.C.); (K.A.J.)
| | - John N. Caviness
- Department of Neurology, Mayo Clinic, Scottsdale, AZ 85259, USA;
| | - Gregory A. Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA; (H.B.); (G.A.W.); (J.R.D.); (H.M.C.); (K.A.J.)
| | - Joseph R. Duffy
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA; (H.B.); (G.A.W.); (J.R.D.); (H.M.C.); (K.A.J.)
| | - Heather M. Clark
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA; (H.B.); (G.A.W.); (J.R.D.); (H.M.C.); (K.A.J.)
| | | | - Keith A. Josephs
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA; (H.B.); (G.A.W.); (J.R.D.); (H.M.C.); (K.A.J.)
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20
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Jia H, Wu X, Wu Z, Wang E. Aberrant dynamic minimal spanning tree parameters within default mode network in patients with autism spectrum disorder. Front Psychiatry 2022; 13:860348. [PMID: 36186871 PMCID: PMC9524021 DOI: 10.3389/fpsyt.2022.860348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 07/07/2022] [Indexed: 11/13/2022] Open
Abstract
The altered functional connectivity (FC) level and its temporal characteristics within certain cortical networks, such as the default mode network (DMN), could provide a possible explanatory framework for Autism spectrum disorder (ASD). In the current study, we hypothesized that the topographical organization along with its temporal dynamics of the autistic brain measured by temporal mean and variance of complex network measures, respectively, were significantly altered, which may further explain the autistic symptom severity in patients with ASD. To validate these hypotheses, the precise FCs between DMN regions at each time point were calculated using the resting-state functional magnetic resonance imaging (fMRI) datasets from the Autism Brain Imaging Data Exchange (ABIDE) project. Then, the minimal spanning tree (MST) technique was applied to construct a time-varying complex network of DMN. By analyzing the temporal mean and variance of MST parameters and their relationship with autistic symptom severity, we found that in persons with ASD, the information exchange efficiencies between cortical regions within DMN were significantly lower and more volatile compared with those in typical developing participants. Moreover, these alterations within DMN were closely associated with the autistic symptom severity of the ASD group.
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Affiliation(s)
- Huibin Jia
- Institute of Psychology and Behavior, Henan University, Kaifeng, China.,School of Psychology, Henan University, Kaifeng, China
| | - Xiangci Wu
- Institute of Psychology and Behavior, Henan University, Kaifeng, China.,School of Psychology, Henan University, Kaifeng, China
| | - Zhiyu Wu
- Huaxian People's Hospital of Henan Province, Anyang, China
| | - Enguo Wang
- Institute of Psychology and Behavior, Henan University, Kaifeng, China.,School of Psychology, Henan University, Kaifeng, China
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21
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Bočková M, Výtvarová E, Lamoš M, Klimeš P, Jurák P, Halámek J, Goldemundová S, Baláž M, Rektor I. Cortical network organization reflects clinical response to subthalamic nucleus deep brain stimulation in Parkinson's disease. Hum Brain Mapp 2021; 42:5626-5635. [PMID: 34448523 PMCID: PMC8559467 DOI: 10.1002/hbm.25642] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/03/2021] [Accepted: 08/16/2021] [Indexed: 12/19/2022] Open
Abstract
The degree of response to subthalamic nucleus deep brain stimulation (STN‐DBS) is individual and hardly predictable. We hypothesized that DBS‐related changes in cortical network organization are related to the clinical effect. Network analysis based on graph theory was used to evaluate the high‐density electroencephalography (HDEEG) recorded during a visual three‐stimuli paradigm in 32 Parkinson's disease (PD) patients treated by STN‐DBS in stimulation “off” and “on” states. Preprocessed scalp data were reconstructed into the source space and correlated to the behavioral parameters. In the majority of patients (n = 26), STN‐DBS did not lead to changes in global network organization in large‐scale brain networks. In a subgroup of suboptimal responders (n = 6), identified according to reaction times (RT) and clinical parameters (lower Unified Parkinson's Disease Rating Scale [UPDRS] score improvement after DBS and worse performance in memory tests), decreased global connectivity in the 1–8 Hz frequency range and regional node strength in frontal areas were detected. The important role of the supplementary motor area for the optimal DBS response was demonstrated by the increased node strength and eigenvector centrality in good responders. This response was missing in the suboptimal responders. Cortical topologic architecture is modified by the response to STN‐DBS leading to a dysfunction of the large‐scale networks in suboptimal responders.
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Affiliation(s)
- Martina Bočková
- Central European Institute of Technology (CEITEC), Brain and Mind Research Program, Masaryk University, Brno, Czech Republic.,Movement Disorders Center, First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Brno, Czech Republic
| | - Eva Výtvarová
- Central European Institute of Technology (CEITEC), Brain and Mind Research Program, Masaryk University, Brno, Czech Republic.,Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Martin Lamoš
- Central European Institute of Technology (CEITEC), Brain and Mind Research Program, Masaryk University, Brno, Czech Republic
| | - Petr Klimeš
- Institute of Scientific Instruments of the Czech Academy of Sciences, v.v.i., Brno, Czech Republic
| | - Pavel Jurák
- Institute of Scientific Instruments of the Czech Academy of Sciences, v.v.i., Brno, Czech Republic
| | - Josef Halámek
- Institute of Scientific Instruments of the Czech Academy of Sciences, v.v.i., Brno, Czech Republic
| | - Sabina Goldemundová
- Central European Institute of Technology (CEITEC), Brain and Mind Research Program, Masaryk University, Brno, Czech Republic
| | - Marek Baláž
- Central European Institute of Technology (CEITEC), Brain and Mind Research Program, Masaryk University, Brno, Czech Republic.,Movement Disorders Center, First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Brno, Czech Republic
| | - Ivan Rektor
- Central European Institute of Technology (CEITEC), Brain and Mind Research Program, Masaryk University, Brno, Czech Republic.,Movement Disorders Center, First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Brno, Czech Republic
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22
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Li Z, Liu C, Wang Q, Liang K, Han C, Qiao H, Zhang J, Meng F. Abnormal Functional Brain Network in Parkinson's Disease and the Effect of Acute Deep Brain Stimulation. Front Neurol 2021; 12:715455. [PMID: 34721258 PMCID: PMC8551554 DOI: 10.3389/fneur.2021.715455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/26/2021] [Indexed: 01/21/2023] Open
Abstract
Objective: The objective of this study was to use functional connectivity and graphic indicators to investigate the abnormal brain network topological characteristics caused by Parkinson's disease (PD) and the effect of acute deep brain stimulation (DBS) on those characteristics in patients with PD. Methods: We recorded high-density EEG (256 channels) data from 21 healthy controls (HC) and 20 patients with PD who were in the DBS-OFF state and DBS-ON state during the resting state with eyes closed. A high-density EEG source connectivity method was used to identify functional brain networks. Power spectral density (PSD) analysis was compared between the groups. Functional connectivity was calculated for 68 brain regions in the theta (4-8 Hz), alpha (8-13 Hz), beta1 (13-20 Hz), and beta2 (20-30 Hz) frequency bands. Network estimates were measured at both the global (network topology) and local (inter-regional connection) levels. Results: Compared with HC, PSD was significantly increased in the theta (p = 0.003) frequency band and was decreased in the beta1 (p = 0.009) and beta2 (p = 0.04) frequency bands in patients with PD. However, there were no differences in any frequency bands between patients with PD with DBS-OFF and DBS-ON. The clustering coefficient and local efficiency of patients with PD showed a significant decrease in the alpha, beta1, and beta2 frequency bands (p < 0.001). In addition, edgewise statistics showed a significant difference between the HC and patients with PD in all analyzed frequency bands (p < 0.005). However, there were no significant differences between the DBS-OFF state and DBS-ON state in the brain network, except for the functional connectivity in the beta2 frequency band (p < 0.05). Conclusion: Compared with HC, patients with PD showed the following characteristics: slowed EEG background activity, decreased clustering coefficient and local efficiency of the brain network, as well as both increased and decreased functional connectivity between different brain areas. Acute DBS induces a local response of the brain network in patients with PD, mainly showing decreased functional connectivity in a few brain regions in the beta2 frequency band.
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Affiliation(s)
- Zhibao Li
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Chong Liu
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Qiao Wang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Kun Liang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Chunlei Han
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Hui Qiao
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Fangang Meng
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing, China.,Chinese Institute for Brain Research, Beijing (CIBR), Beijing, China
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23
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Pardo-Rodriguez M, Bojorges-Valdez E, Yanez-Suarez O. Disruption of the Cortical-Vagal Communication Network in Parkinson's Disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5842-5845. [PMID: 34892448 DOI: 10.1109/embc46164.2021.9630751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Parkinson's disease (PD) is a neuropathy characterized by motor disorders, but it has also been associated with the presence of autonomic alterations as a result of degradation of the dopaminergic system. Studying the relation between Band Power time series (BPts) and Heart Rate Variability (HRV), has been proposed as a tool to explore the bidirectional communication pathways between cortex and autonomic control. This work presents a primer analysis on study brain ↔ heart interaction on a databse of PD patients under two conditions: without and after levadopa (L-dopa) intake. Additionally a healthy control population was also analyzed, and used as comparison level between both conditions. Results show PD affects pathways by reducing the number of connections, specially association of beta and power and the second faster component of HRV seems to be more sensitive to L-dopa administration.
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24
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Graph Theory on Brain Cortical Sources in Parkinson's Disease: The Analysis of 'Small World' Organization from EEG. SENSORS 2021; 21:s21217266. [PMID: 34770573 PMCID: PMC8587014 DOI: 10.3390/s21217266] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 11/17/2022]
Abstract
Parkinson’s disease (PD) is the second most common neurodegenerative disease in the elderly population. Similarly to other neurodegenerative diseases, the early diagnosis of PD is quite difficult. The current pilot study aimed to explore the differences in brain connectivity between PD and NOrmal eLDerly (Nold) subjects to evaluate whether connectivity analysis may speed up and support early diagnosis. A total of 26 resting state EEGs were analyzed from 13 PD patients and 13 age-matched Nold subjects, applying to cortical reconstructions the graph theory analyses, a mathematical representation of brain architecture. Results showed that PD patients presented a more ordered structure at slow-frequency EEG rhythms (lower value of SW) than Nold subjects, particularly in the theta band, whereas in the high-frequency alpha, PD patients presented more random organization (higher SW) than Nold subjects. The current results suggest that PD could globally modulate the cortical connectivity of the brain, modifying the functional network organization and resulting in motor and non-motor signs. Future studies could validate whether such an approach, based on a low-cost and non-invasive technique, could be useful for early diagnosis, for the follow-up of PD progression, as well as for evaluating pharmacological and neurorehabilitation treatments.
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25
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Simon OB, Buard I, Rojas DC, Holden SK, Kluger BM, Ghosh D. A novel approach to understanding Parkinsonian cognitive decline using minimum spanning trees, edge cutting, and magnetoencephalography. Sci Rep 2021; 11:19704. [PMID: 34611218 PMCID: PMC8492620 DOI: 10.1038/s41598-021-99167-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 09/21/2021] [Indexed: 11/11/2022] Open
Abstract
Graph theory-based approaches are efficient tools for detecting clustering and group-wise differences in high-dimensional data across a wide range of fields, such as gene expression analysis and neural connectivity. Here, we examine data from a cross-sectional, resting-state magnetoencephalography study of 89 Parkinson’s disease patients, and use minimum-spanning tree (MST) methods to relate severity of Parkinsonian cognitive impairment to neural connectivity changes. In particular, we implement the two-sample multivariate-runs test of Friedman and Rafsky (Ann Stat 7(4):697–717, 1979) and find it to be a powerful paradigm for distinguishing highly significant deviations from the null distribution in high-dimensional data. We also generalize this test for use with greater than two classes, and show its ability to localize significance to particular sub-classes. We observe multiple indications of altered connectivity in Parkinsonian dementia that may be of future use in diagnosis and prediction.
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Affiliation(s)
- Olivier B Simon
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Isabelle Buard
- Department of Neurology, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Donald C Rojas
- Department of Psychology, Colorado State University, Fort Collins, CO, USA
| | - Samantha K Holden
- Department of Neurology, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Benzi M Kluger
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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26
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Revankar GS, Kajiyama Y, Hattori N, Shimokawa T, Nakano T, Mihara M, Mori E, Mochizuki H. Prestimulus Low-Alpha Frontal Networks Are Associated with Pareidolias in Parkinson's Disease. Brain Connect 2021; 11:772-782. [PMID: 33858200 DOI: 10.1089/brain.2020.0992] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background: Pareidolias are visual phenomena wherein ambiguous, abstract forms or shapes appear meaningful due to incorrect perception. In Parkinson's disease (PD), patients susceptible to visual hallucinations experience visuo-perceptual deficits in the form of pareidolias. Although pareidolias necessitate top-down modulation of visual processing, the cortical dynamics of internally generated perceptual priors on these visual misperceptions is unknown. Objectives: To study prestimulus-related electroencephalography (EEG) spectral and network abnormalities in PD patients experiencing pareidolias. Methods: Twenty-one PD in-patients and 10 age-matched controls were evaluated. Neuropsychological assessments included tests for cognition, attention, and executive functions. Pareidolias were quantified by using the "noise pareidolia test" with simultaneous EEG recording. The PD patients were subdivided into two groups-those with high pareidolia counts (n = 10) and those without (n = 11). The EEG was analyzed 1000 msec before stimulus presentation in the spectral domain (theta, low-alpha, and high-alpha frequencies) with corresponding graph networks to evaluate network properties. Statistical analysis included analysis of variance and multiple regression to evaluate the differences. Results: The PD patients with high pareidolia counts were older with lower scores on neuropsychological tests. Their prestimulus EEG low-alpha band showed a tendency toward higher frontal activity (p = 0.07). Graph networks showed increased normalized clustering coefficient (p = 0.05) and lower frontal degree centrality (p = 0.005). These network indices correlated positively to patients' pareidolia scores. Discussion: We suggest that pareidolias in PD are a consequence of an abnormal top-down modulation of visual processing; they are defined by their frontal low-alpha spectral and network alterations in the prestimulus phase due to a dissonance between patients' internally generated mental processing with external stimuli. Impact statement Pareidolias in Parkinson's disease (PD) are considered to be promising early markers of visual hallucinations and an indicator of PD prognosis. In certain susceptible PD patients, pareidolias can be evoked and studied. Here, via electroencephalography, we aimed at understanding this visual phenomenon by studying how neural information is processed before stimulus presentation in such patients. Using spectral and graph network measures, we revealed how top-down modulated internally generated processes affect visual perception in patients with pareidolias. Our findings highlight how prestimulus network alterations in the frontal cortex shape poststimulus pareidolic manifestations in PD.
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Affiliation(s)
- Gajanan S Revankar
- Department of Neurology, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yuta Kajiyama
- Department of Neurology, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Noriaki Hattori
- Department of Neurology, Graduate School of Medicine, Osaka University, Osaka, Japan.,Department of Rehabilitation, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama, Japan
| | - Tetsuya Shimokawa
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Osaka, Japan
| | - Tomohito Nakano
- Department of Neurology, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Masahito Mihara
- Department of Neurology, Graduate School of Medicine, Osaka University, Osaka, Japan.,Department of Neurology, Kawasaki Medical College, Okayama, Japan
| | - Etsuro Mori
- Department of Behavioral Neurology and Neuropsychiatry, Osaka University, Osaka, Japan
| | - Hideki Mochizuki
- Department of Neurology, Graduate School of Medicine, Osaka University, Osaka, Japan
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27
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Altered directed connectivity during processing of implicit versus explicit predictive stimuli in Parkinson's disease patients. Brain Cogn 2021; 152:105773. [PMID: 34225173 DOI: 10.1016/j.bandc.2021.105773] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 05/06/2021] [Accepted: 06/25/2021] [Indexed: 11/22/2022]
Abstract
The study investigated the role of top-down versus bottom-up connectivity, during the processing of implicit or explicit predictive information, in Parkinson's disease (PD). EEG was recorded during the performance of a task, which evaluated the ability to utilize either implicit or explicit predictive contextual information in order to facilitate the detection of predictable versus random targets. Thus, subjects performed an implicit and explicit session, where subjects were either unaware or made aware of a predictive sequence that signals the presentation of a subsequent target, respectively. We evaluated EEG event-related directed connectivity, in PD patients compared with healthy age-matched controls, using phase transfer entropy. PD patients showed increased top-down frontal-parietal connectivity, compared to control subjects, during the processing of the last (most informative) stimulus of the predictive sequence and of random standards, in the implicit and explicit session, respectively. These findings suggest that PD is associated with compensatory top-down connectivity, specifically during the processing of implicit predictive stimuli. During the explicit session, PD patients seem to allocate more attentional resources to non-informative standard stimuli, compared to controls. These connectivity changes shed further light on the cognitive deficits, associated with the processing of predictive contextual information, that are observed in PD patients.
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28
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Yin Z, Wang Y, Dong M, Ren S, Hu H, Yin K, Liang J. Special Patterns of Dynamic Brain Networks Discriminate Between Face and Non-face Processing: A Single-Trial EEG Study. Front Neurosci 2021; 15:652920. [PMID: 34177446 PMCID: PMC8221185 DOI: 10.3389/fnins.2021.652920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 05/17/2021] [Indexed: 12/03/2022] Open
Abstract
Face processing is a spatiotemporal dynamic process involving widely distributed and closely connected brain regions. Although previous studies have examined the topological differences in brain networks between face and non-face processing, the time-varying patterns at different processing stages have not been fully characterized. In this study, dynamic brain networks were used to explore the mechanism of face processing in human brain. We constructed a set of brain networks based on consecutive short EEG segments recorded during face and non-face (ketch) processing respectively, and analyzed the topological characteristic of these brain networks by graph theory. We found that the topological differences of the backbone of original brain networks (the minimum spanning tree, MST) between face and ketch processing changed dynamically. Specifically, during face processing, the MST was more line-like over alpha band in 0-100 ms time window after stimuli onset, and more star-like over theta and alpha bands in 100-200 and 200-300 ms time windows. The results indicated that the brain network was more efficient for information transfer and exchange during face processing compared with non-face processing. In the MST, the nodes with significant differences of betweenness centrality and degree were mainly located in the left frontal area and ventral visual pathway, which were involved in the face-related regions. In addition, the special MST patterns can discriminate between face and ketch processing by an accuracy of 93.39%. Our results suggested that special MST structures of dynamic brain networks reflected the potential mechanism of face processing in human brain.
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Affiliation(s)
- Zhongliang Yin
- School of Electronic Engineering, Xidian University, Xi'an, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Yue Wang
- School of Electronic Engineering, Xidian University, Xi'an, China
| | - Minghao Dong
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Shenghan Ren
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Haihong Hu
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Kuiying Yin
- Nanjing Research Institute of Electronics Technology, Nanjing, China
| | - Jimin Liang
- School of Electronic Engineering, Xidian University, Xi'an, China
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29
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Machine learning for automated EEG-based biomarkers of cognitive impairment during Deep Brain Stimulation screening in patients with Parkinson’s Disease. Clin Neurophysiol 2021; 132:1041-1048. [DOI: 10.1016/j.clinph.2021.01.021] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 01/09/2021] [Accepted: 01/12/2021] [Indexed: 11/19/2022]
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30
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Maggioni E, Arienti F, Minella S, Mameli F, Borellini L, Nigro M, Cogiamanian F, Bianchi AM, Cerutti S, Barbieri S, Brambilla P, Ardolino G. Effective Connectivity During Rest and Music Listening: An EEG Study on Parkinson's Disease. Front Aging Neurosci 2021; 13:657221. [PMID: 33994997 PMCID: PMC8113619 DOI: 10.3389/fnagi.2021.657221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 03/31/2021] [Indexed: 11/30/2022] Open
Abstract
Music-based interventions seem to enhance motor, sensory and cognitive functions in Parkinson’s disease (PD), but the underlying action mechanisms are still largely unknown. This electroencephalography (EEG) study aimed to investigate the effective connectivity patterns characterizing PD in the resting state and during music listening. EEG recordings were obtained from fourteen non-demented PD patients and 12 healthy controls, at rest and while listening to three music tracks. Theta- and alpha-band power spectral density and multivariate partial directed coherence were computed. Power and connectivity measures were compared between patients and controls in the four conditions and in music vs. rest. Compared to controls, patients showed enhanced theta-band power and slightly enhanced alpha-band power, but markedly reduced theta- and alpha-band interactions among EEG channels, especially concerning the information received by the right central channel. EEG power differences were partially reduced by music listening, which induced power increases in controls but not in patients. Connectivity differences were slightly compensated by music, whose effects largely depended on the track. In PD, music enhanced the frontotemporal inter-hemispheric communication. Our findings suggest that PD is characterized by enhanced activity but reduced information flow within the EEG network, being only partially normalized by music. Nevertheless, music capability to facilitate inter-hemispheric communication might underlie its beneficial effects on PD pathophysiology and should be further investigated.
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Affiliation(s)
- Eleonora Maggioni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Federica Arienti
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Stella Minella
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Francesca Mameli
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Linda Borellini
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Martina Nigro
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Filippo Cogiamanian
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Anna Maria Bianchi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Sergio Cerutti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Sergio Barbieri
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Gianluca Ardolino
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
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31
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Peláez Suárez AA, Berrillo Batista S, Pedroso Ibáñez I, Casabona Fernández E, Fuentes Campos M, Chacón LM. EEG-Derived Functional Connectivity Patterns Associated with Mild Cognitive Impairment in Parkinson's Disease. Behav Sci (Basel) 2021; 11:40. [PMID: 33806841 PMCID: PMC8005012 DOI: 10.3390/bs11030040] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 03/17/2021] [Accepted: 03/18/2021] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE To evaluate EEG-derived functional connectivity (FC) patterns associated with mild cognitive impairment (MCI) in Parkinson's disease (PD). METHODS A sample of 15 patients without cognitive impairment (PD-WCI), 15 with MCI (PD-MCI), and 26 healthy subjects were studied. The EEG was performed in the waking functional state with eyes closed, for the functional analysis it was used the synchronization likelihood (SL) and graph theory (GT). RESULTS PD-MCI patients showed decreased FC in frequencies alpha, in posterior regions, and delta with a generalized distribution. Patients, compared to the healthy people, presented a decrease in segregation (lower clustering coefficient in alpha p = 0.003 in PD-MCI patients) and increased integration (shorter mean path length in delta (p = 0.004) and theta (p = 0.002) in PD-MCI patients). There were no significant differences in the network topology between the parkinsonian groups. In PD-MCI patients, executive dysfunction correlated positively with global connectivity in beta (r = 0.47) and negatively with the mean path length at beta (r = -0.45); alterations in working memory were negatively correlated with the mean path length at beta r = -0.45. CONCLUSIONS PD patients present alterations in the FC in all frequencies, those with MCI show less connectivity in the alpha and delta frequencies. The neural networks of the patients show a random topology, with a similar organization between patients with and without MCI. In PD-MCI patients, alterations in executive function and working memory are related to beta integration.
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Affiliation(s)
- Alejandro Armando Peláez Suárez
- Movement Disorders and Neurodegeneration Clinic, International Center for Neurological Restoration, Playa, Havana 11300, Cuba; (I.P.I.); (E.C.F.)
| | - Sheila Berrillo Batista
- Department of Clinical Neurophysiology, International Center for Neurological Restoration, Playa, Havana 11300, Cuba;
| | - Ivonne Pedroso Ibáñez
- Movement Disorders and Neurodegeneration Clinic, International Center for Neurological Restoration, Playa, Havana 11300, Cuba; (I.P.I.); (E.C.F.)
| | - Enrique Casabona Fernández
- Movement Disorders and Neurodegeneration Clinic, International Center for Neurological Restoration, Playa, Havana 11300, Cuba; (I.P.I.); (E.C.F.)
| | | | - Lilia Morales Chacón
- Department of Clinical Neurophysiology, International Center for Neurological Restoration, Playa, Havana 11300, Cuba;
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32
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Gutierrez Nuno RA, Chung CHR, Maharatna K. Hardware architecture for real-time EEG-based functional brain connectivity parameter extraction. J Neural Eng 2020; 18. [PMID: 33326940 DOI: 10.1088/1741-2552/abd462] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 12/16/2020] [Indexed: 11/11/2022]
Abstract
In this work, we proposed a novel architecture for real-time quantitative characterization of functional brain connectivity networks derived from Electroencephalogram (EEG). It consists of two main parts - calculation of Phase Lag Index (PLI) to form the functional connectivity networks and the extraction of a set of graph-theoretic parameters to quantitatively characterize these networks. The architecture was developed for a 19-channel EEG system. The system can calculate all the functional connectivity parameters in a total time of 131µs, utilizes 71% logic resources, and shows 51.84 mW dynamic power consumption at 22.16 MHz operation frequency when implemented in a Stratix IV EP4SGX230K FPGA. Our analysis also showed that the system occupies an area equivalent to approximately 937K 2-input NAND gates, with an estimated power consumption of 39.3 mW at 0.9 V supply using a 90 nm CMOS Application Specific Integrated Circuit (ASIC) technology.
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Affiliation(s)
- Rafael Angel Gutierrez Nuno
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, University of Southampton, Southampton, SO17 1BJ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Chi Hang Raphael Chung
- University of Southampton, Southampton, SO17 1BJ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Koushik Maharatna
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, University of Southampton, Southampton, SO17 1BJ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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Mihaescu AS, Kim J, Masellis M, Graff-Guerrero A, Cho SS, Christopher L, Valli M, Díez-Cirarda M, Koshimori Y, Strafella AP. Graph theory analysis of the dopamine D2 receptor network in Parkinson's disease patients with cognitive decline. J Neurosci Res 2020; 99:947-965. [PMID: 33271630 DOI: 10.1002/jnr.24760] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 11/14/2020] [Indexed: 12/30/2022]
Abstract
Cognitive decline in Parkinson's disease (PD) is a common sequela of the disorder that has a large impact on patient well-being. Its physiological etiology, however, remains elusive. Our study used graph theory analysis to investigate the large-scale topological patterns of the extrastriatal dopamine D2 receptor network. We used positron emission tomography with [11 C]FLB-457 to measure the binding potential of cortical dopamine D2 receptors in two networks: the meso-cortical dopamine network and the meso-limbic dopamine network. We also investigated the application of partial volume effect correction (PVEC) in conjunction with graph theory analysis. Three groups were investigated in this study divided according to their cognitive status as measured by the Montreal Cognitive Assessment score, with a score ≤25 considered cognitively impaired: (a) healthy controls (n = 13, 11 female), (b) cognitively unimpaired PD patients (PD-CU, n = 13, 5 female), and (c) PD patients with mild cognitive impairment (PD-MCI, n = 17, 4 female). In the meso-cortical network, we observed increased small-worldness, normalized clustering, and local efficiency in the PD-CU group compared to the PD-MCI group, as well as a hub shift in the PD-MCI group. Compensatory reorganization of the meso-cortical dopamine D2 receptor network may be responsible for some of the cognitive preservation observed in PD-CU. These results were found without PVEC applied and PVEC proved detrimental to the graph theory analysis. Overall, our findings demonstrate how graph theory analysis can be used to detect subtle changes in the brain that would otherwise be missed by regional comparisons of receptor density.
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Affiliation(s)
- Alexander S Mihaescu
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, ON, Canada
| | - Jinhee Kim
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Mario Masellis
- Institute of Medical Science, University of Toronto, ON, Canada.,LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Ariel Graff-Guerrero
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, ON, Canada
| | - Sang Soo Cho
- Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Leigh Christopher
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Mikaeel Valli
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, ON, Canada
| | - María Díez-Cirarda
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, ON, Canada.,Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
| | - Yuko Koshimori
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Antonio P Strafella
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, ON, Canada.,Morton and Gloria Shulman Movement Disorder Unit & E.J. Safra Program in Parkinson Disease, Neurology Division, Department of Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, ON, Canada
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Ghaderi AH, Baltaretu BR, Andevari MN, Bharmauria V, Balci F. Synchrony and Complexity in State-Related EEG Networks: An Application of Spectral Graph Theory. Neural Comput 2020; 32:2422-2454. [DOI: 10.1162/neco_a_01327] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The brain may be considered as a synchronized dynamic network with several coherent dynamical units. However, concerns remain whether synchronizability is a stable state in the brain networks. If so, which index can best reveal the synchronizability in brain networks? To answer these questions, we tested the application of the spectral graph theory and the Shannon entropy as alternative approaches in neuroimaging. We specifically tested the alpha rhythm in the resting-state eye closed (rsEC) and the resting-state eye open (rsEO) conditions, a well-studied classical example of synchrony in neuroimaging EEG. Since the synchronizability of alpha rhythm is more stable during the rsEC than the rsEO, we hypothesized that our suggested spectral graph theory indices (as reliable measures to interpret the synchronizability of brain signals) should exhibit higher values in the rsEC than the rsEO condition. We performed two separate analyses of two different datasets (as elementary and confirmatory studies). Based on the results of both studies and in agreement with our hypothesis, the spectral graph indices revealed higher stability of synchronizability in the rsEC condition. The k-mean analysis indicated that the spectral graph indices can distinguish the rsEC and rsEO conditions by considering the synchronizability of brain networks. We also computed correlations among the spectral indices, the Shannon entropy, and the topological indices of brain networks, as well as random networks. Correlation analysis indicated that although the spectral and the topological properties of random networks are completely independent, these features are significantly correlated with each other in brain networks. Furthermore, we found that complexity in the investigated brain networks is inversely related to the stability of synchronizability. In conclusion, we revealed that the spectral graph theory approach can be reliably applied to study the stability of synchronizability of state-related brain networks.
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Affiliation(s)
- Amir Hossein Ghaderi
- Centre for Vision Research and Canada Vision: Science to Applications Program, York University, Toronto M3J 1P3, Canada, and Iranian Neuro-Wave Lab., No. 32, Vilashahr, Isfahan, Iran
| | | | | | - Vishal Bharmauria
- Centre for Vision Research, York University, Toronto M3J 1P3, Canada
| | - Fuat Balci
- Department of Psychology and Research Center for Translational Medicine, Koç University, Istanbul, Turkey
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Lejko N, Larabi DI, Herrmann CS, Aleman A, Ćurčić-Blake B. Alpha Power and Functional Connectivity in Cognitive Decline: A Systematic Review and Meta-Analysis. J Alzheimers Dis 2020; 78:1047-1088. [PMID: 33185607 PMCID: PMC7739973 DOI: 10.3233/jad-200962] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Background: Mild cognitive impairment (MCI) is a stage between expected age-related cognitive decline and dementia. Dementias have been associated with changes in neural oscillations across the frequency spectrum, including the alpha range. Alpha is the most prominent rhythm in human EEG and is best detected during awake resting state (RS). Though several studies measured alpha power and synchronization in MCI, findings have not yet been integrated. Objective: To consolidate findings on power and synchronization of alpha oscillations across stages of cognitive decline. Methods: We included studies published until January 2020 that compared power or functional connectivity between 1) people with MCI and cognitively healthy older adults (OA) or people with a neurodegenerative dementia, and 2) people with progressive and stable MCI. Random-effects meta-analyses were performed when enough data was available. Results: Sixty-eight studies were included in the review. Global RS alpha power was lower in AD than in MCI (ES = –0.30; 95% CI = –0.51, –0.10; k = 6), and in MCI than in OA (ES = –1.49; 95% CI = –2.69, –0.29; k = 5). However, the latter meta-analysis should be interpreted cautiously due to high heterogeneity. The review showed lower RS alpha power in progressive than in stable MCI, and lower task-related alpha reactivity in MCI than in OA. People with MCI had both lower and higher functional connectivity than OA. Publications lacked consistency in MCI diagnosis and EEG measures. Conclusion: Research indicates that RS alpha power decreases with increasing impairment, and could—combined with measures from other frequency bands—become a biomarker of early cognitive decline.
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Affiliation(s)
- Nena Lejko
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, Groningen, The Netherlands
| | - Daouia I Larabi
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, Groningen, The Netherlands.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | | | - André Aleman
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, Groningen, The Netherlands
| | - Branislava Ćurčić-Blake
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, Groningen, The Netherlands
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Wang Q, Meng L, Pang J, Zhu X, Ming D. Characterization of EEG Data Revealing Relationships With Cognitive and Motor Symptoms in Parkinson's Disease: A Systematic Review. Front Aging Neurosci 2020; 12:587396. [PMID: 33240076 PMCID: PMC7683572 DOI: 10.3389/fnagi.2020.587396] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 10/06/2020] [Indexed: 01/08/2023] Open
Abstract
Recent research regards the electroencephalogram (EEG) as a promising method to study real-time brain dynamic changes in patients with Parkinson's disease (PD), but a deeper understanding is needed to discern coincident pathophysiology, patterns of changes, and diagnosis. This review summarized recent research on EEG characterization related to the cognitive and motor functions in PD patients and discussed its potential to be used as diagnostic biomarkers. Thirty papers out of 220 published from 2010 to 2020 were reviewed. Movement abnormalities and cognitive decline are related to changes in EEG spectrum and event-related potentials (ERPs) during typical oddball paradigms and/or combined motor tasks. Abnormalities in β and δ frequency bands are, respectively the main manifestation of dyskinesia and cognitive decline in PD. The review showed that PD patients have noteworthy changes in specific EEG characterizations, however, the underlying mechanism of the interrelation between gait and cognitive is still unclear. Understanding the specific nature of the relationship is essential for development of novel invasive clinical diagnostic and therapeutic methods.
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Affiliation(s)
- Qing Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Lin Meng
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Jun Pang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Xiaodong Zhu
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
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Prajapati R, Emerson IA. Construction and analysis of brain networks from different neuroimaging techniques. Int J Neurosci 2020; 132:745-766. [DOI: 10.1080/00207454.2020.1837802] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Rutvi Prajapati
- Bioinformatics Programming Laboratory, Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Isaac Arnold Emerson
- Bioinformatics Programming Laboratory, Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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Zanin M, Belkoura S, Gomez J, Alfaro C, Cano J. Uncertainty in Functional Network Representations of Brain Activity of Alcoholic Patients. Brain Topogr 2020; 34:6-18. [PMID: 33044705 DOI: 10.1007/s10548-020-00799-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 10/04/2020] [Indexed: 11/30/2022]
Abstract
In spite of the large attention received by brain activity analyses through functional networks, the effects of uncertainty on such representations have mostly been neglected. We here elaborate the hypothesis that such uncertainty is not just a nuisance, but that on the contrary is condition-dependent. We test this hypothesis by analysing a large set of EEG brain recordings corresponding to control subjects and patients suffering from alcoholism, through the reconstruction of the corresponding Maximum Spanning Trees (MSTs), the assessment of their topological differences, and the comparison of two frequentist and Bayesian reconstruction approaches. A machine learning model demonstrates that the Bayesian reconstruction encodes more information than the frequentist one, and that such additional information is related to the uncertainty of the topological structures. We finally show how the Bayesian approach is more effective in the validation of generative models, over and above the frequentist one, by proposing and disproving two models based on additive noise.
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Affiliation(s)
- Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122, Palma de Mallorca, Spain.
| | - Seddik Belkoura
- Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | - Javier Gomez
- Department of Computer Science and Statistics, Universidad Rey Juan Carlos, Madrid, Spain
| | - César Alfaro
- Department of Computer Science and Statistics, Universidad Rey Juan Carlos, Madrid, Spain
| | - Javier Cano
- Department of Computer Science and Statistics, Universidad Rey Juan Carlos, Madrid, Spain.,Department of Statistics, University of Auckland, Auckland, New Zealand
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39
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Human brain connectivity: Clinical applications for clinical neurophysiology. Clin Neurophysiol 2020; 131:1621-1651. [DOI: 10.1016/j.clinph.2020.03.031] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 03/13/2020] [Accepted: 03/17/2020] [Indexed: 12/12/2022]
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40
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Guan X, Guo T, Zeng Q, Wang J, Zhou C, Liu C, Wei H, Zhang Y, Xuan M, Gu Q, Xu X, Huang P, Pu J, Zhang B, Zhang MM. Oscillation-specific nodal alterations in early to middle stages Parkinson's disease. Transl Neurodegener 2019; 8:36. [PMID: 31807287 PMCID: PMC6857322 DOI: 10.1186/s40035-019-0177-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 11/07/2019] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND Different oscillations of brain networks could carry different dimensions of brain integration. We aimed to investigate oscillation-specific nodal alterations in patients with Parkinson's disease (PD) across early stage to middle stage by using graph theory-based analysis. METHODS Eighty-eight PD patients including 39 PD patients in the early stage (EPD) and 49 patients in the middle stage (MPD) and 36 controls were recruited in the present study. Graph theory-based network analyses from three oscillation frequencies (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz; slow-3: 0.073-0.198 Hz) were analyzed. Nodal metrics (e.g. nodal degree centrality, betweenness centrality and nodal efficiency) were calculated. RESULTS Our results showed that (1) a divergent effect of oscillation frequencies on nodal metrics, especially on nodal degree centrality and nodal efficiency, that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed, which visually showed that network was perturbed in PD; (2) PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities, which was consistently detected within all three oscillation frequencies; (3) the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients; (4) logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level; (5) occipital disruption within high frequency (slow-3) made a significant influence on motor impairment which was dominated by akinesia and rigidity. CONCLUSIONS Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages.
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Affiliation(s)
- Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Qiaoling Zeng
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Jiaqiu Wang
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Chunlei Liu
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA USA
| | - Hongjiang Wei
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA USA
| | - Yuyao Zhang
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA USA
| | - Min Xuan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Quanquan Gu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Jiali Pu
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Min-Ming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
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Carmona Arroyave JA, Tobón Quintero CA, Suárez Revelo JJ, Ochoa Gómez JF, García YB, Gómez LM, Pineda Salazar DA. Resting functional connectivity and mild cognitive impairment in Parkinson’s disease. An electroencephalogram study. FUTURE NEUROLOGY 2019. [DOI: 10.2217/fnl-2018-0048] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Objective: Parkinson’s disease (PD) is characterized by cognitive deficits. There is not clarity about electroencephalogram (EEG) connectivity related to the cognitive profile of patients. Our objective was to evaluate connectivity over resting EEG in nondemented PD. Methods: PD subjects with and without mild cognitive impairment (MCI) were assessed using coherence from resting EEG for local, intra and interhemispheric connectivity. Results: PD subjects without MCI (PD-nMCI) had lower intra and interhemispheric coherence in alpha2 compared with controls. PD with MCI (PD-MCI) showed higher intra and posterior interhemispheric coherence in alpha2 and beta1, respectively, in comparison to PD-nMCI. PD-MCI presented lower frontal coherence in beta frequencies compared with PD-nMCI. Conclusion: EEG coherence measures indicate distinct cortical activity in PD with and without MCI.
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Affiliation(s)
- Jairo Alexander Carmona Arroyave
- Neuroscience Group, Medical School, University of Antioquia, SIU, Calle 62 No. 52–59, Medellín, Colombia
- Neuropsychology & Behavior Group (GRUNECO), Medical School, University of Antioquia, SIU – Área Asistencial, Calle 62 No. 52–59, Medellín, Colombia
| | - Carlos Andrés Tobón Quintero
- Neuroscience Group, Medical School, University of Antioquia, SIU, Calle 62 No. 52–59, Medellín, Colombia
- Neuropsychology & Behavior Group (GRUNECO), Medical School, University of Antioquia, SIU – Área Asistencial, Calle 62 No. 52–59, Medellín, Colombia
| | - Jasmín Jimena Suárez Revelo
- Neuroscience Group, Medical School, University of Antioquia, SIU, Calle 62 No. 52–59, Medellín, Colombia
- Bioinstrumentation & Clinical Engineering Research Group (GIBIC), Bioengineering Program, University of Antioquia, Calle 70 No. 52–21, Medellín, Colombia
| | - John Fredy Ochoa Gómez
- Neuroscience Group, Medical School, University of Antioquia, SIU, Calle 62 No. 52–59, Medellín, Colombia
- Bioinstrumentation & Clinical Engineering Research Group (GIBIC), Bioengineering Program, University of Antioquia, Calle 70 No. 52–21, Medellín, Colombia
| | - Yamile Bocanegra García
- Neuroscience Group, Medical School, University of Antioquia, SIU, Calle 62 No. 52–59, Medellín, Colombia
- Neuropsychology & Behavior Group (GRUNECO), Medical School, University of Antioquia, SIU – Área Asistencial, Calle 62 No. 52–59, Medellín, Colombia
| | - Leonardo Moreno Gómez
- Neurology Unit, Pablo Tobón Uribe Hospital, Calle 78B No. 69–240, Medellín, Colombia
| | - David Antonio Pineda Salazar
- Neuroscience Group, Medical School, University of Antioquia, SIU, Calle 62 No. 52–59, Medellín, Colombia
- Neuropsychology & Behavior Group (GRUNECO), Medical School, University of Antioquia, SIU – Área Asistencial, Calle 62 No. 52–59, Medellín, Colombia
- Neuropsychology & Behavior Group (GRUNECO), Psychology Department, University of San Buenaventura, Carrera 56 C No. 51–110, Medellín, Colombia
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Chen D, Jiang J, Lu J, Wu P, Zhang H, Zuo C, Shi K. Brain Network and Abnormal Hemispheric Asymmetry Analyses to Explore the Marginal Differences in Glucose Metabolic Distributions Among Alzheimer's Disease, Parkinson's Disease Dementia, and Lewy Body Dementia. Front Neurol 2019; 10:369. [PMID: 31031697 PMCID: PMC6473028 DOI: 10.3389/fneur.2019.00369] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 03/25/2019] [Indexed: 12/17/2022] Open
Abstract
Facilitating accurate diagnosis and ensuring appropriate treatment of dementia subtypes, including Alzheimer's disease (AD), Parkinson's disease dementia (PDD), and Lewy body dementia (DLB), is clinically important. However, the differences in glucose metabolic distribution among these three dementia subtypes are minor, which can result in difficulties in diagnosis by visual assessment or traditional quantification methods. Here, we explored this issue using novel approaches, including brain network and abnormal hemispheric asymmetry analyses. We generated 18F-labeled fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) images from patients with AD, PDD, and DLB, and healthy control (HC) subjects (n = 22, 18, 22, and 22, respectively) from Huashan hospital, Shanghai, China. Brain network properties were measured and between-group differences evaluated using graph theory. We also calculated and explored asymmetry indices for the cerebral hemispheres in the four groups, to explore whether differences between the two hemispheres were characteristic of each group. Our study revealed significant differences in the network properties of the HC and AD groups (small-world coefficient, 1.36 vs. 1.28; clustering coefficient, 1.48 vs. 1.59; characteristic path length, 1.57 vs. 1.64). In addition, differing hub regions were identified in the different dementias. We also identified rightward asymmetry in the hemispheric brain networks of patients with AD and DLB, and leftward asymmetry in the hemispheric brain networks of patients with PDD, which were attributable to aberrant topological properties in the corresponding hemispheres.
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Affiliation(s)
- Danyan Chen
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.,Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai, China
| | - Jiaying Lu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Huiwei Zhang
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Kuangyu Shi
- Department Nuclear Medicine, University of Bern, Bern, Switzerland.,Department of Informatics, Technical University of Munich, Munich, Germany
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Mostile G, Giuliano L, Monastero R, Luca A, Cicero CE, Donzuso G, Dibilio V, Baschi R, Terranova R, Restivo V, Sofia V, Zappia M, Nicoletti A. Electrocortical networks in Parkinson's disease patients with Mild Cognitive Impairment. The PaCoS study. Parkinsonism Relat Disord 2019; 64:156-162. [PMID: 30981665 DOI: 10.1016/j.parkreldis.2019.03.027] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2018] [Revised: 03/27/2019] [Accepted: 03/30/2019] [Indexed: 01/03/2023]
Abstract
INTRODUCTION Parkinson's Disease (PD) is frequently associated with cognitive dysfunction ranging from Mild Cognitive Impairment (PD-MCI) to dementia. Few electrophysiological studies are available evaluating potential pathogenetic mechanisms linked to cognitive impairment in PD since its initial phases. The objective of the study is to analyze electrocortical networks related with cognitive decline in PD-MCI for identifying possible early electrophysiological markers of cognitive impairment in PD. METHODS From the PaCoS (Parkinson's disease Cognitive impairment Study) cohort, a sample of 102 subjects including 46 PD-MCI and 56 PD with normal cognition (PD-NC) was selected based on the presence of a neuropsychological assessment and at least one EEG recording. EEG signal epochs were analysed using Independent Component Analysis LORETA and spectral analysis by computing the Power Spectral Density (PSD) of site-specific signal epochs. RESULTS LORETA analysis revealed significant differences in PD-MCI patients compared to PD-NC, with a decreased network involving alpha activity over the occipital lobe, an increased network involving beta activity over the frontal lobe associated with a reduction over the parietal lobe, an increased network involving theta and delta activity over the frontal lobe and a reduction of networks involving theta and delta activity in the parietal lobe. Quantitative EEG analysis showed a significant decrease of alpha PSD over the occipital regions and an increase of delta PSD over the left temporal region in PD-MCI as compared to PD-NC. CONCLUSION Electrocortical abnormalities detected in PD-MCI patients may represent the instrumental counterpart of early cognitive decline in PD.
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Affiliation(s)
- Giovanni Mostile
- Department "G.F. Ingrassia", Section of Neurosciences, University of Catania, Via S. Sofia 78, 95123, Catania, Italy
| | - Loretta Giuliano
- Department "G.F. Ingrassia", Section of Neurosciences, University of Catania, Via S. Sofia 78, 95123, Catania, Italy
| | - Roberto Monastero
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, Section of Neurology, University of Palermo, Via La Loggia 1, 90129, Palermo, Italy
| | - Antonina Luca
- Department "G.F. Ingrassia", Section of Neurosciences, University of Catania, Via S. Sofia 78, 95123, Catania, Italy
| | - Calogero Edoardo Cicero
- Department "G.F. Ingrassia", Section of Neurosciences, University of Catania, Via S. Sofia 78, 95123, Catania, Italy
| | - Giulia Donzuso
- Department "G.F. Ingrassia", Section of Neurosciences, University of Catania, Via S. Sofia 78, 95123, Catania, Italy
| | - Valeria Dibilio
- Department "G.F. Ingrassia", Section of Neurosciences, University of Catania, Via S. Sofia 78, 95123, Catania, Italy
| | - Roberta Baschi
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, Section of Neurology, University of Palermo, Via La Loggia 1, 90129, Palermo, Italy
| | - Roberta Terranova
- Department "G.F. Ingrassia", Section of Neurosciences, University of Catania, Via S. Sofia 78, 95123, Catania, Italy
| | - Vincenzo Restivo
- Department of Sciences for Health Promotion and Mother-Child Care, University of Palermo, Via Del Vespro 133, 90127, Palermo, Italy
| | - Vito Sofia
- Department "G.F. Ingrassia", Section of Neurosciences, University of Catania, Via S. Sofia 78, 95123, Catania, Italy
| | - Mario Zappia
- Department "G.F. Ingrassia", Section of Neurosciences, University of Catania, Via S. Sofia 78, 95123, Catania, Italy
| | - Alessandra Nicoletti
- Department "G.F. Ingrassia", Section of Neurosciences, University of Catania, Via S. Sofia 78, 95123, Catania, Italy.
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Boon LI, Geraedts VJ, Hillebrand A, Tannemaat MR, Contarino MF, Stam CJ, Berendse HW. A systematic review of MEG-based studies in Parkinson's disease: The motor system and beyond. Hum Brain Mapp 2019; 40:2827-2848. [PMID: 30843285 PMCID: PMC6594068 DOI: 10.1002/hbm.24562] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 01/27/2019] [Accepted: 02/13/2019] [Indexed: 01/29/2023] Open
Abstract
Parkinson's disease (PD) is accompanied by functional changes throughout the brain, including changes in the electromagnetic activity recorded with magnetoencephalography (MEG). An integrated overview of these changes, its relationship with clinical symptoms, and the influence of treatment is currently missing. Therefore, we systematically reviewed the MEG studies that have examined oscillatory activity and functional connectivity in the PD‐affected brain. The available articles could be separated into motor network‐focused and whole‐brain focused studies. Motor network studies revealed PD‐related changes in beta band (13–30 Hz) neurophysiological activity within and between several of its components, although it remains elusive to what extent these changes underlie clinical motor symptoms. In whole‐brain studies PD‐related oscillatory slowing and decrease in functional connectivity correlated with cognitive decline and less strongly with other markers of disease progression. Both approaches offer a different perspective on PD‐specific disease mechanisms and could therefore complement each other. Combining the merits of both approaches will improve the setup and interpretation of future studies, which is essential for a better understanding of the disease process itself and the pathophysiological mechanisms underlying specific PD symptoms, as well as for the potential to use MEG in clinical care.
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Affiliation(s)
- Lennard I Boon
- Amsterdam UMC, location VUmc, Department of Neurology, Amsterdam Neuroscience, Amsterdam, the Netherlands.,Amsterdam UMC, location VUmc, Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Victor J Geraedts
- Amsterdam UMC, location VUmc, Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam Neuroscience, Amsterdam, the Netherlands.,Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Arjan Hillebrand
- Amsterdam UMC, location VUmc, Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Martijn R Tannemaat
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Maria Fiorella Contarino
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands.,Department of Neurology, Haga Teaching Hospital, The Hague, The Netherlands
| | - Cornelis J Stam
- Amsterdam UMC, location VUmc, Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Henk W Berendse
- Amsterdam UMC, location VUmc, Department of Neurology, Amsterdam Neuroscience, Amsterdam, the Netherlands
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45
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Wang Y, Tao F, Zuo C, Kanji M, Hu M, Wang D. Disrupted Resting Frontal-Parietal Attention Network Topology Is Associated With a Clinical Measure in Children With Attention-Deficit/Hyperactivity Disorder. Front Psychiatry 2019; 10:300. [PMID: 31156474 PMCID: PMC6530394 DOI: 10.3389/fpsyt.2019.00300] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Accepted: 04/16/2019] [Indexed: 11/13/2022] Open
Abstract
Purpose: Although alterations in resting-state functional connectivity between brain regions have been reported in children with attention-deficit/hyperactivity disorder (ADHD), the spatial organization of these changes remains largely unknown. Here, we studied frontal-parietal attention network topology in children with ADHD, and related topology to a clinical measure of disease progression. Methods: Resting-state fMRI scans were obtained from New York University Child Study Center, including 119 children with ADHD (male n = 89; female n = 30) and 69 typically developing controls (male n = 33; female n = 36). We characterized frontal-parietal functional networks using standard graph analysis (clustering coefficient and shortest path length) and the construction of a minimum spanning tree, a novel approach that allows a unique and unbiased characterization of brain networks. Results: Clustering coefficient and path length in the frontal-parietal attention network were similar in children with ADHD and typically developing controls; however, diameter was greater and leaf number, tree hierarchy, and kappa were lower in children with ADHD, and were significantly correlated with ADHD symptom score. There were significant alterations in nodal eccentricity in children with ADHD, involving prefrontal and occipital cortex regions, which are compatible with the results of previous ADHD studies. Conclusions: Our results indicate the tendency to deviate from a more centralized organization (star-like topology) towards a more decentralized organization (line-like topology) in the frontal-parietal attention network of children with ADHD. This represents a more random network that is associated with impaired global efficiency and network decentralization. These changes appear to reflect clinically relevant phenomena and hold promise as markers of disease progression.
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Affiliation(s)
- Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,College of Educational Science, Anhui Normal University, Wuhu, China
| | - Fuxiang Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chenyi Zuo
- College of Educational Science, Anhui Normal University, Wuhu, China
| | - Maihefulaiti Kanji
- The Key Laboratory of Mental Development and Learning Science, Xinjiang Normal University, Urumqi, China
| | - Mingming Hu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Daoyang Wang
- College of Educational Science, Anhui Normal University, Wuhu, China
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46
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Geraedts VJ, Boon LI, Marinus J, Gouw AA, van Hilten JJ, Stam CJ, Tannemaat MR, Contarino MF. Clinical correlates of quantitative EEG in Parkinson disease: A systematic review. Neurology 2018; 91:871-883. [PMID: 30291182 DOI: 10.1212/wnl.0000000000006473] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
OBJECTIVE To assess the relevance of quantitative EEG (qEEG) measures as outcomes of disease severity and progression in Parkinson disease (PD). METHODS Main databases were systematically searched (January 2018) for studies of sufficient methodologic quality that examined correlations between clinical symptoms of idiopathic PD and cortical (surface) qEEG metrics. RESULTS Thirty-six out of 605 identified studied were included. Results were classified into 4 domains: cognition (23 studies), motor function (13 studies), responsiveness to interventions (7 studies), and other (10 studies). In cross-sectional studies, EEG slowing correlated with global cognitive impairment and with diffuse deterioration in other domains. In longitudinal studies, decreased dominant frequency and increased θ power, reflecting EEG slowing, were biomarkers of cognitive deterioration at an individual level. Results on motor dysfunction and treatment yielded contrasting findings. Studies on functional connectivity at an individual level and longitudinal studies on other domains or on connectivity measures were lacking. CONCLUSION qEEG measures reflecting EEG slowing, particularly decreased dominant frequency and increased θ power, correlate with cognitive impairment and predict future cognitive deterioration. qEEG could provide reliable and widely available biomarkers for nonmotor disease severity and progression in PD, potentially promoting early diagnosis of nonmotor symptoms and an objective monitoring of progression. More studies are needed to clarify the role of functional connectivity and network analyses.
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Affiliation(s)
- Victor J Geraedts
- From the Department of Neurology (V.J.G., J.M., J.J.v.H., M.R.T., M.F.C.), Leiden University Medical Center; Department of Clinical Neurophysiology and MEG Center (V.J.G., L.I.B., A.A.G., C.J.S.) and Alzheimer Center, Department of Neurology (A.A.G.), VU University Medical Center, Amsterdam; and Department of Neurology (M.F.C.), Haga Teaching Hospital, The Hague, the Netherlands
| | - Lennard I Boon
- From the Department of Neurology (V.J.G., J.M., J.J.v.H., M.R.T., M.F.C.), Leiden University Medical Center; Department of Clinical Neurophysiology and MEG Center (V.J.G., L.I.B., A.A.G., C.J.S.) and Alzheimer Center, Department of Neurology (A.A.G.), VU University Medical Center, Amsterdam; and Department of Neurology (M.F.C.), Haga Teaching Hospital, The Hague, the Netherlands
| | - Johan Marinus
- From the Department of Neurology (V.J.G., J.M., J.J.v.H., M.R.T., M.F.C.), Leiden University Medical Center; Department of Clinical Neurophysiology and MEG Center (V.J.G., L.I.B., A.A.G., C.J.S.) and Alzheimer Center, Department of Neurology (A.A.G.), VU University Medical Center, Amsterdam; and Department of Neurology (M.F.C.), Haga Teaching Hospital, The Hague, the Netherlands
| | - Alida A Gouw
- From the Department of Neurology (V.J.G., J.M., J.J.v.H., M.R.T., M.F.C.), Leiden University Medical Center; Department of Clinical Neurophysiology and MEG Center (V.J.G., L.I.B., A.A.G., C.J.S.) and Alzheimer Center, Department of Neurology (A.A.G.), VU University Medical Center, Amsterdam; and Department of Neurology (M.F.C.), Haga Teaching Hospital, The Hague, the Netherlands
| | - Jacobus J van Hilten
- From the Department of Neurology (V.J.G., J.M., J.J.v.H., M.R.T., M.F.C.), Leiden University Medical Center; Department of Clinical Neurophysiology and MEG Center (V.J.G., L.I.B., A.A.G., C.J.S.) and Alzheimer Center, Department of Neurology (A.A.G.), VU University Medical Center, Amsterdam; and Department of Neurology (M.F.C.), Haga Teaching Hospital, The Hague, the Netherlands
| | - Cornelis J Stam
- From the Department of Neurology (V.J.G., J.M., J.J.v.H., M.R.T., M.F.C.), Leiden University Medical Center; Department of Clinical Neurophysiology and MEG Center (V.J.G., L.I.B., A.A.G., C.J.S.) and Alzheimer Center, Department of Neurology (A.A.G.), VU University Medical Center, Amsterdam; and Department of Neurology (M.F.C.), Haga Teaching Hospital, The Hague, the Netherlands
| | - Martijn R Tannemaat
- From the Department of Neurology (V.J.G., J.M., J.J.v.H., M.R.T., M.F.C.), Leiden University Medical Center; Department of Clinical Neurophysiology and MEG Center (V.J.G., L.I.B., A.A.G., C.J.S.) and Alzheimer Center, Department of Neurology (A.A.G.), VU University Medical Center, Amsterdam; and Department of Neurology (M.F.C.), Haga Teaching Hospital, The Hague, the Netherlands.
| | - Maria Fiorella Contarino
- From the Department of Neurology (V.J.G., J.M., J.J.v.H., M.R.T., M.F.C.), Leiden University Medical Center; Department of Clinical Neurophysiology and MEG Center (V.J.G., L.I.B., A.A.G., C.J.S.) and Alzheimer Center, Department of Neurology (A.A.G.), VU University Medical Center, Amsterdam; and Department of Neurology (M.F.C.), Haga Teaching Hospital, The Hague, the Netherlands
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Mehrali M, Bagherifard S, Akbari M, Thakur A, Mirani B, Mehrali M, Hasany M, Orive G, Das P, Emneus J, Andresen TL, Dolatshahi‐Pirouz A. Blending Electronics with the Human Body: A Pathway toward a Cybernetic Future. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2018; 5:1700931. [PMID: 30356969 PMCID: PMC6193179 DOI: 10.1002/advs.201700931] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 05/24/2018] [Indexed: 05/22/2023]
Abstract
At the crossroads of chemistry, electronics, mechanical engineering, polymer science, biology, tissue engineering, computer science, and materials science, electrical devices are currently being engineered that blend directly within organs and tissues. These sophisticated devices are mediators, recorders, and stimulators of electricity with the capacity to monitor important electrophysiological events, replace disabled body parts, or even stimulate tissues to overcome their current limitations. They are therefore capable of leading humanity forward into the age of cyborgs, a time in which human biology can be hacked at will to yield beings with abilities beyond their natural capabilities. The resulting advances have been made possible by the emergence of conformal and soft electronic materials that can readily integrate with the curvilinear, dynamic, delicate, and flexible human body. This article discusses the recent rapid pace of development in the field of cybernetics with special emphasis on the important role that flexible and electrically active materials have played therein.
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Affiliation(s)
- Mehdi Mehrali
- Technical University of DenmarkDTU NanotechCenter for Nanomedicine and Theranostics2800KgsDenmark
| | - Sara Bagherifard
- Department of Mechanical EngineeringPolitecnico di Milano20156MilanItaly
| | - Mohsen Akbari
- Laboratory for Innovations in MicroEngineering (LiME)Department of Mechanical EngineeringUniversity of VictoriaVictoriaBCV8P 5C2Canada
- Center for Biomedical ResearchUniversity of VictoriaVictoriaV8P 5C2Canada
- Center for Advanced Materials and Related Technologies (CAMTEC)University of VictoriaVictoriaV8P 5C2Canada
| | - Ashish Thakur
- Technical University of DenmarkDTU NanotechCenter for Nanomedicine and Theranostics2800KgsDenmark
| | - Bahram Mirani
- Laboratory for Innovations in MicroEngineering (LiME)Department of Mechanical EngineeringUniversity of VictoriaVictoriaBCV8P 5C2Canada
- Center for Biomedical ResearchUniversity of VictoriaVictoriaV8P 5C2Canada
- Center for Advanced Materials and Related Technologies (CAMTEC)University of VictoriaVictoriaV8P 5C2Canada
| | - Mohammad Mehrali
- Process and Energy DepartmentDelft University of TechnologyLeeghwaterstraat 392628CBDelftThe Netherlands
| | - Masoud Hasany
- Technical University of DenmarkDTU NanotechCenter for Nanomedicine and Theranostics2800KgsDenmark
| | - Gorka Orive
- NanoBioCel GroupLaboratory of PharmaceuticsSchool of PharmacyUniversity of the Basque Country UPV/EHUPaseo de la Universidad 701006Vitoria‐GasteizSpain
- Biomedical Research Networking Centre in Bioengineering, Biomaterials, and Nanomedicine (CIBER‐BBN)Vitoria‐Gasteiz28029Spain
- University Institute for Regenerative Medicine and Oral Implantology—UIRMI (UPV/EHU‐Fundación Eduardo Anitua)Vitoria01007Spain
| | - Paramita Das
- School of Chemical and Biomedical EngineeringNanyang Technological University62 Nanyang DriveSingapore637459Singapore
| | - Jenny Emneus
- Technical University of DenmarkDTU Nanotech2800KgsDenmark
| | - Thomas L. Andresen
- Technical University of DenmarkDTU NanotechCenter for Nanomedicine and Theranostics2800KgsDenmark
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Caviness JN, Beach TG, Hentz JG, Shill HA, Driver-Dunckley ED, Adler CH. Association Between Pathology and Electroencephalographic Activity in Parkinson's Disease. Clin EEG Neurosci 2018; 49:321-327. [PMID: 29161906 DOI: 10.1177/1550059417696179] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
INTRODUCTION The key mechanisms that connect Parkinson's disease pathology with dementia are unclear. We tested the hypothesis that the quantitative spectral electroencephalographic measure, delta bandpower, correlates with Lewy type synucleinopathy on pathological examination in Parkinson's disease. As a corollary hypothesis, we analyzed whether there would be delta bandpower electroencephalographic differences between Parkinson's disease dementia cases with and without pathological criteria for Alzheimer's disease. METHODS We used pathological examination results from 44 Parkinson's disease subjects from our brain bank with various degrees of cognitive decline, who had undergone electroencephalography. Pathological grading for Lewy type synucleinopathy, plaques, tangles, and indications of vascular pathology in subcortical and cortical areas were correlated with the most associated electroencephalographic biomarker with Parkinson's disease dementia in our laboratory, delta bandpower. Group differences for all spectral electroencephalographic measures were also analyzed between cases with and without pathological criteria for Alzheimer's disease. RESULTS Findings revealed significant correlations between delta bandpower with Lewy type synucleinopathy, whereas indications of Alzheimer's disease or vascular pathology had nonsignificant correlation. The strongest association was with delta bandpower and Lewy type synucleinopathy in the anterior cingulate region. Mean delta bandpower was higher in the group for Parkinson's disease dementia with Alzheimer's disease pathology criteria than without. CONCLUSIONS Lewy type synucleinopathy severity appears to be more associated with increased delta bandpower than with Alzheimer's disease pathology or indications of vascular pathology over all cases. However, the presence of Alzheimer's pathology may associate with more cortex physiological disruption in a subset of cases.
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Affiliation(s)
| | - Thomas G Beach
- 2 Civin Laboratory for Neuropathology, Banner-Sun Health Research Institute, Sun City, AZ, USA
| | - Joseph G Hentz
- 3 Department of Biostatistics, Mayo Clinic, Scottsdale, AZ, USA
| | - Holly A Shill
- 4 Barrow Neurological Institute, St Joseph's Hospital, Phoenix, AZ, USA
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Quantitative EEG reflects non-dopaminergic disease severity in Parkinson’s disease. Clin Neurophysiol 2018; 129:1748-1755. [DOI: 10.1016/j.clinph.2018.04.752] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 04/13/2018] [Accepted: 04/26/2018] [Indexed: 11/21/2022]
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50
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Time estimation and beta segregation: An EEG study and graph theoretical approach. PLoS One 2018; 13:e0195380. [PMID: 29624619 PMCID: PMC5889177 DOI: 10.1371/journal.pone.0195380] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 03/21/2018] [Indexed: 11/28/2022] Open
Abstract
Elucidation of the neural correlates of time perception constitutes an important research topic in cognitive neuroscience. The focus to date has been on durations in the millisecond to seconds range, but here we used electroencephalography (EEG) to examine brain functional connectivity during much longer durations (i.e., 15 min). For this purpose, we conducted an initial exploratory experiment followed by a confirmatory experiment. Our results showed that those participants who overestimated time exhibited lower activity of beta (18–30 Hz) at several electrode sites. Furthermore, graph theoretical analysis indicated significant differences in the beta range (15–30 Hz) between those that overestimated and underestimated time. Participants who underestimated time showed higher clustering coefficient compared to those that overestimated time. We discuss our results in terms of two aspects. FFT results, as a linear approach, are discussed within localized/dedicated models (i.e., scalar timing model). Second, non-localized properties of psychological interval timing (as emphasized by intrinsic models) are addressed and discussed based on results derived from graph theory. Results suggested that although beta amplitude in central regions (related to activity of BG-thalamocortical pathway as a dedicated module) is important in relation to timing mechanisms, the properties of functional activity of brain networks; such as the segregation of beta network, are also crucial for time perception. These results may suggest subjective time may be created by vector units instead of scalar ticks.
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