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Khatun S, Morshed BI, Bidelman GM. Monitoring Disease Severity of Mild Cognitive Impairment from Single-Channel EEG Data Using Regression Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:1054. [PMID: 38400211 PMCID: PMC10893543 DOI: 10.3390/s24041054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/28/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024]
Abstract
A deviation in the soundness of cognitive health is known as mild cognitive impairment (MCI), and it is important to monitor it early to prevent complicated diseases such as dementia, Alzheimer's disease (AD), and Parkinson's disease (PD). Traditionally, MCI severity is monitored with manual scoring using the Montreal Cognitive Assessment (MoCA). In this study, we propose a new MCI severity monitoring algorithm with regression analysis of extracted features of single-channel electro-encephalography (EEG) data by automatically generating severity scores equivalent to MoCA scores. We evaluated both multi-trial and single-trail analysis for the algorithm development. For multi-trial analysis, 590 features were extracted from the prominent event-related potential (ERP) points and corresponding time domain characteristics, and we utilized the lasso regression technique to select the best feature set. The 13 best features were used in the classical regression techniques: multivariate regression (MR), ensemble regression (ER), support vector regression (SVR), and ridge regression (RR). The best results were observed for ER with an RMSE of 1.6 and residual analysis. In single-trial analysis, we extracted a time-frequency plot image from each trial and fed it as an input to the constructed convolutional deep neural network (CNN). This deep CNN model resulted an RMSE of 2.76. To our knowledge, this is the first attempt to generate automated scores for MCI severity equivalent to MoCA from single-channel EEG data with multi-trial and single data.
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Affiliation(s)
- Saleha Khatun
- Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN 38152, USA
| | - Bashir I. Morshed
- Compute Science Department, Texas Tech University, Lubbock, TX 79409, USA
| | - Gavin M. Bidelman
- School of Communication Sciences and Disorders, University of Memphis, Memphis, TN 38152, USA
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Chu KT, Lei WC, Wu MH, Fuh JL, Wang SJ, French IT, Chang WS, Chang CF, Huang NE, Liang WK, Juan CH. A holo-spectral EEG analysis provides an early detection of cognitive decline and predicts the progression to Alzheimer's disease. Front Aging Neurosci 2023; 15:1195424. [PMID: 37674782 PMCID: PMC10477374 DOI: 10.3389/fnagi.2023.1195424] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/25/2023] [Indexed: 09/08/2023] Open
Abstract
Aims Our aim was to differentiate patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from cognitively normal (CN) individuals and predict the progression from MCI to AD within a 3-year longitudinal follow-up. A newly developed Holo-Hilbert Spectral Analysis (HHSA) was applied to resting state EEG (rsEEG), and features were extracted and subjected to machine learning algorithms. Methods A total of 205 participants were recruited from three hospitals, with CN (n = 51, MMSE > 26), MCI (n = 42, CDR = 0.5, MMSE ≥ 25), AD1 (n = 61, CDR = 1, MMSE < 25), AD2 (n = 35, CDR = 2, MMSE < 16), and AD3 (n = 16, CDR = 3, MMSE < 16). rsEEG was also acquired from all subjects. Seventy-two MCI patients (CDR = 0.5) were longitudinally followed up with two rsEEG recordings within 3 years and further subdivided into an MCI-stable group (MCI-S, n = 36) and an MCI-converted group (MCI-C, n = 36). The HHSA was then applied to the rsEEG data, and features were extracted and subjected to machine-learning algorithms. Results (a) At the group level analysis, the HHSA contrast of MCI and different stages of AD showed augmented amplitude modulation (AM) power of lower-frequency oscillations (LFO; delta and theta bands) with attenuated AM power of higher-frequency oscillations (HFO; beta and gamma bands) compared with cognitively normal elderly controls. The alpha frequency oscillation showed augmented AM power across MCI to AD1 with a reverse trend at AD2. (b) At the individual level of cross-sectional analysis, implementation of machine learning algorithms discriminated between groups with good sensitivity (Sen) and specificity (Spec) as follows: CN elderly vs. MCI: 0.82 (Sen)/0.80 (Spec), CN vs. AD1: 0.94 (Sen)/0.80 (Spec), CN vs. AD2: 0.93 (Sen)/0.90 (Spec), and CN vs. AD3: 0.75 (Sen)/1.00 (Spec). (c) In the longitudinal MCI follow-up, the initial contrasted HHSA between MCI-S and MCI-C groups showed significantly attenuated AM power of alpha and beta band oscillations. (d) At the individual level analysis of longitudinal MCI groups, deploying machine learning algorithms with the best seven features resulted in a sensitivity of 0.9 by the support vector machine (SVM) classifier, with a specificity of 0.8 yielded by the decision tree classifier. Conclusion Integrating HHSA into EEG signals and machine learning algorithms can differentiate between CN and MCI as well as also predict AD progression at the MCI stage.
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Affiliation(s)
- Kwo-Ta Chu
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Yang-Ming Hospital, Taoyuan, Taiwan
| | - Weng-Chi Lei
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Cognitive Intelligence and Precision Healthcare Center, National Central University, Taoyuan, Taiwan
| | - Ming-Hsiu Wu
- Division of Neurology, Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan
- Department of Long-Term Care and Health Promotion, Min-Hwei Junior College of Health Care Management, Tainan, Taiwan
| | - Jong-Ling Fuh
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shuu-Jiun Wang
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Isobel T. French
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Taiwan International Graduate Program in Interdisciplinary Neuroscience, National Central University and Academia Sinica, Taipei, Taiwan
| | - Wen-Sheng Chang
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
| | - Chi-Fu Chang
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
| | - Norden E. Huang
- Cognitive Intelligence and Precision Healthcare Center, National Central University, Taoyuan, Taiwan
- Key Laboratory of Data Analysis and Applications, First Institute of Oceanography, SOA, Qingdao, China
| | - Wei-Kuang Liang
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Cognitive Intelligence and Precision Healthcare Center, National Central University, Taoyuan, Taiwan
| | - Chi-Hung Juan
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Cognitive Intelligence and Precision Healthcare Center, National Central University, Taoyuan, Taiwan
- Department of Psychology, Kaohsiung Medical University, Kaohsiung, Taiwan
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Morrone CD, Raghuraman R, Hussaini SA, Yu WH. Proteostasis failure exacerbates neuronal circuit dysfunction and sleep impairments in Alzheimer's disease. Mol Neurodegener 2023; 18:27. [PMID: 37085942 PMCID: PMC10119020 DOI: 10.1186/s13024-023-00617-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/29/2023] [Indexed: 04/23/2023] Open
Abstract
Failed proteostasis is a well-documented feature of Alzheimer's disease, particularly, reduced protein degradation and clearance. However, the contribution of failed proteostasis to neuronal circuit dysfunction is an emerging concept in neurodegenerative research and will prove critical in understanding cognitive decline. Our objective is to convey Alzheimer's disease progression with the growing evidence for a bidirectional relationship of sleep disruption and proteostasis failure. Proteostasis dysfunction and tauopathy in Alzheimer's disease disrupts neurons that regulate the sleep-wake cycle, which presents behavior as impaired slow wave and rapid eye movement sleep patterns. Subsequent sleep loss further impairs protein clearance. Sleep loss is a defined feature seen early in many neurodegenerative disorders and contributes to memory impairments in Alzheimer's disease. Canonical pathological hallmarks, β-amyloid, and tau, directly disrupt sleep, and neurodegeneration of locus coeruleus, hippocampal and hypothalamic neurons from tau proteinopathy causes disruption of the neuronal circuitry of sleep. Acting in a positive-feedback-loop, sleep loss and circadian rhythm disruption then increase spread of β-amyloid and tau, through impairments of proteasome, autophagy, unfolded protein response and glymphatic clearance. This phenomenon extends beyond β-amyloid and tau, with interactions of sleep impairment with the homeostasis of TDP-43, α-synuclein, FUS, and huntingtin proteins, implicating sleep loss as an important consideration in an array of neurodegenerative diseases and in cases of mixed neuropathology. Critically, the dynamics of this interaction in the neurodegenerative environment are not fully elucidated and are deserving of further discussion and research. Finally, we propose sleep-enhancing therapeutics as potential interventions for promoting healthy proteostasis, including β-amyloid and tau clearance, mechanistically linking these processes. With further clinical and preclinical research, we propose this dynamic interaction as a diagnostic and therapeutic framework, informing precise single- and combinatorial-treatments for Alzheimer's disease and other brain disorders.
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Affiliation(s)
- Christopher Daniel Morrone
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, M5T 1R8, Canada.
| | - Radha Raghuraman
- Taub Institute, Columbia University Irving Medical Center, 630W 168th Street, New York, NY, 10032, USA
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, 630W 168th Street, New York, NY, 10032, USA
| | - S Abid Hussaini
- Taub Institute, Columbia University Irving Medical Center, 630W 168th Street, New York, NY, 10032, USA.
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, 630W 168th Street, New York, NY, 10032, USA.
| | - Wai Haung Yu
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, M5T 1R8, Canada.
- Geriatric Mental Health Research Services, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, M5T 1R8, Canada.
- Department of Pharmacology and Toxicology, University of Toronto, Medical Sciences Building, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada.
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The Increase of Theta Power and Decrease of Alpha/Theta Ratio as a Manifestation of Cognitive Impairment in Parkinson's Disease. J Clin Med 2023; 12:jcm12041569. [PMID: 36836103 PMCID: PMC9965386 DOI: 10.3390/jcm12041569] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/06/2023] [Accepted: 02/13/2023] [Indexed: 02/18/2023] Open
Abstract
In this study, we aim to assess and examine cognitive functions in Parkinson's Disease patients using EEG recordings, with a central focus on characteristics associated with a cognitive decline. Based on neuropsychological evaluation using Mini-Mental State Examination, Montreal Cognitive Assessment, and Addenbrooke's Cognitive Examination-III, 98 participants were divided into three cognitive groups. All the particpants of the study underwent EEG recordings with spectral analysis. The results revealed an increase in the absolute theta power in patients with Parkinson's disease dementia (PD-D) compared to cognitively normal status (PD-CogN, p=0.00997) and a decrease in global relative beta power in PD-D compared to PD-CogN (p=0.0413). An increase in theta relative power in the left temporal region (p=0.0262), left occipital region (p=0.0109), and right occipital region (p=0.0221) were observed in PD-D compared to PD-N. The global alpha/theta ratio and global power spectral ratio significantly decreased in PD-D compared to PD-N (p = 0.001). In conclusion, the increase in relative theta power and the decrease in relative beta power are characteristic changes in EEG recordings in PD patients with cognitive impairment. Identifying these changes can be a useful biomarker and a complementary tool in the neuropsychological diagnosis of cognitive impairment in Parkinson's Disease.
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Giustiniani A, Danesin L, Bozzetto B, Macina A, Benavides-Varela S, Burgio F. Functional changes in brain oscillations in dementia: a review. Rev Neurosci 2023; 34:25-47. [PMID: 35724724 DOI: 10.1515/revneuro-2022-0010] [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: 02/01/2022] [Accepted: 05/16/2022] [Indexed: 01/11/2023]
Abstract
A growing body of evidence indicates that several characteristics of electroencephalography (EEG) and magnetoencephalography (MEG) play a functional role in cognition and could be linked to the progression of cognitive decline in some neurological diseases such as dementia. The present paper reviews previous studies investigating changes in brain oscillations associated to the most common types of dementia, namely Alzheimer's disease (AD), frontotemporal degeneration (FTD), and vascular dementia (VaD), with the aim of identifying pathology-specific patterns of alterations and supporting differential diagnosis in clinical practice. The included studies analysed changes in frequency power, functional connectivity, and event-related potentials, as well as the relationship between electrophysiological changes and cognitive deficits. Current evidence suggests that an increase in slow wave activity (i.e., theta and delta) as well as a general reduction in the power of faster frequency bands (i.e., alpha and beta) characterizes AD, VaD, and FTD. Additionally, compared to healthy controls, AD exhibits alteration in latencies and amplitudes of the most common event related potentials. In the reviewed studies, these changes generally correlate with performances in many cognitive tests. In conclusion, particularly in AD, neurophysiological changes can be reliable early markers of dementia.
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Affiliation(s)
| | - Laura Danesin
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
| | | | - AnnaRita Macina
- Department of Developmental Psychology and Socialization, University of Padua, via Venezia 8, 35131 Padova, Italy
| | - Silvia Benavides-Varela
- Department of Developmental Psychology and Socialization, University of Padua, via Venezia 8, 35131 Padova, Italy.,Department of Neuroscience, University of Padova, 35128 Padova, Italy.,Padova Neuroscience Center, University of Padova, 35131 Padova, Italy
| | - Francesca Burgio
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
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Morrone CD, Tsang AA, Giorshev SM, Craig EE, Yu WH. Concurrent behavioral and electrophysiological longitudinal recordings for in vivo assessment of aging. Front Aging Neurosci 2023; 14:952101. [PMID: 36742209 PMCID: PMC9891465 DOI: 10.3389/fnagi.2022.952101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 12/12/2022] [Indexed: 01/19/2023] Open
Abstract
Electrophysiological and behavioral alterations, including sleep and cognitive impairments, are critical components of age-related decline and neurodegenerative diseases. In preclinical investigation, many refined techniques are employed to probe these phenotypes, but they are often conducted separately. Herein, we provide a protocol for one-time surgical implantation of EMG wires in the nuchal muscle and a skull-surface EEG headcap in mice, capable of 9-to-12-month recording longevity. All data acquisitions are wireless, making them compatible with simultaneous EEG recording coupled to multiple behavioral tasks, as we demonstrate with locomotion/sleep staging during home-cage video assessments, cognitive testing in the Barnes maze, and sleep disruption. Time-course EEG and EMG data can be accurately mapped to the behavioral phenotype and synchronized with neuronal frequencies for movement and the location to target in the Barnes maze. We discuss critical steps for optimizing headcap surgery and alternative approaches, including increasing the number of EEG channels or utilizing depth electrodes with the system. Combining electrophysiological and behavioral measurements in preclinical models of aging and neurodegeneration has great potential for improving mechanistic and therapeutic assessments and determining early markers of brain disorders.
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Affiliation(s)
- Christopher Daniel Morrone
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada,*Correspondence: Christopher Daniel Morrone,
| | - Arielle A. Tsang
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada,Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON, Canada
| | - Sarah M. Giorshev
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada,Department of Psychology, University of Toronto Scarborough, Toronto, ON, Canada
| | - Emily E. Craig
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Wai Haung Yu
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada,Geriatric Mental Health Research Services, Centre for Addiction and Mental Health, Toronto, ON, Canada,Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada,Wai Haung Yu,
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Iannaccone S, Houdayer E, Spina A, Nocera G, Alemanno F. Quantitative EEG for early differential diagnosis of dementia with Lewy bodies. Front Psychol 2023; 14:1150540. [PMID: 37151310 PMCID: PMC10157484 DOI: 10.3389/fpsyg.2023.1150540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 03/31/2023] [Indexed: 05/09/2023] Open
Abstract
Introduction Differentiating between the two most common forms of dementia, Alzheimer's dementia and dementia with Lewy bodies (DLB) remains difficult and requires the use of invasive, expensive, and resource-intensive techniques. We aimed to investigate the sensitivity and specificity of electroencephalography quantified using the statistical pattern recognition method (qEEG-SPR) for identifying dementia and DLB. Methods Thirty-two outpatients and 16 controls underwent clinical assessment (by two blinded neurologists), EEG recording, and a 6-month follow-up clinical assessment. EEG data were processed using a qEEG-SPR protocol to derive a Dementia Index (positive or negative) and DLB index (positive or negative) for each participant which was compared against the diagnosis given at clinical assessment. Confusion matrices were used to calculate sensitivity, specificity, and predictive values for identifying dementia and DLB specifically. Results Clinical assessment identified 30 cases of dementia, 2 of which were diagnosed clinically with possible DLB, 14 with probable DLB and DLB was excluded in 14 patients. qEEG-SPR confirmed the dementia diagnosis in 26 out of the 32 patients and led to 6.3% of false positives (FP) and 9.4% of false negatives (FN). qEEG-SPR was used to provide a DLB diagnosis among patients who received a positive or inconclusive result of Dementia index and led to 13.6% of FP and 13.6% of FN. Confusion matrices indicated a sensitivity of 80%, a specificity of 89%, a positive predictive value of 92%, a negative predictive value of 72%, and an accuracy of 83% to diagnose dementia. The DLB index showed a sensitivity of 60%, a specificity of 90%, a positive predictive value of 75%, a negative predictive value of 81%, and an accuracy of 75%. Neuropsychological scores did not differ significantly between DLB and non- DLB patients. Head trauma or story of stroke were identified as possible causes of FP results for DLB diagnosis. Conclusion qEEG-SPR is a sensitive and specific tool for diagnosing dementia and differentiating DLB from other forms of dementia in the initial state. This non-invasive, low-cost, and environmentally friendly method is a promising diagnostic tool for dementia diagnosis which could be implemented in local care settings.
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Affiliation(s)
- Sandro Iannaccone
- Department of Rehabilitation and Functional Recovery, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elise Houdayer
- Department of Rehabilitation and Functional Recovery, IRCCS San Raffaele Scientific Institute, Milan, Italy
- *Correspondence: Elise Houdayer,
| | - Alfio Spina
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Gianluca Nocera
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federica Alemanno
- Department of Rehabilitation and Functional Recovery, IRCCS San Raffaele Scientific Institute, Milan, Italy
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Bourdès V, Dogterom P, Aleman A, Parmantier P, Colas D, Lemarchant S, Marie S, Chou T, Abd-Elaziz K, Godfrin Y. Safety, Tolerability, Pharmacokinetics and Initial Pharmacodynamics of a Subcommissural Organ-Spondin-Derived Peptide: A Randomized, Placebo-Controlled, Double-Blind, Single Ascending Dose First-in-Human Study. Neurol Ther 2022; 11:1353-1374. [PMID: 35779189 PMCID: PMC9338184 DOI: 10.1007/s40120-022-00380-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 06/08/2022] [Indexed: 12/30/2022] Open
Abstract
INTRODUCTION This randomized, double-blind, placebo-controlled study in healthy volunteers assessed the safety, tolerability, and pharmacokinetics of single ascending doses of intravenously administered NX210-a linear peptide derived from subcommissural organ-spondin-and explored the effects on blood/urine biomarkers and cerebral activity. METHODS Participants in five cohorts (n = 8 each) were randomized to receive a single intravenous dose of NX210 (n = 6 each) (0.4, 1.25, 2.5, 5, and 10 mg/kg) or placebo (n = 2 each); in total, 10 and 29 participants received placebo and NX210, respectively. Blood samples were collected for pharmacokinetics within 180 min post dosing. Plasma and urine were collected from participants (cohorts: 2.5, 5, and 10 mg/kg) for biomarker analysis and electroencephalography (EEG) recordings within 48 h post dosing. Safety/tolerability and pharmacokinetic data were assessed before ascending to the next dose. RESULTS The study included 39 participants. All dosages were safe and well tolerated. All treatment-emergent adverse events (n = 17) were of mild severity and resolved spontaneously (except one with unknown outcome). Twelve treatment-emergent adverse events (70.6%) were deemed drug related; seven of those (58.3%) concerned nervous system disorders (dizziness, headache, and somnolence). The pharmacokinetic analysis indicated a short half-life in plasma (6-20 min), high apparent volume of distribution (1870-4120 L), and rapid clearance (7440-16,400 L/h). In plasma, tryptophan and homocysteine showed dose-related increase and decrease, respectively. No drug dose effect was found for the glutamate or glutamine plasma biomarkers. Nevertheless, decreased blood glutamate and increased glutamine were observed in participants treated with NX210 versus placebo. EEG showed a statistically significant decrease in beta and gamma bands and a dose-dependent increasing trend in alpha bands. Pharmacodynamics effects were sustained for several hours (plasma) or 48 h (urine and EEG). CONCLUSION NX210 is safe and well tolerated and may exert beneficial effects on the central nervous system, particularly in terms of cognitive processing.
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Affiliation(s)
| | | | - André Aleman
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | | | | | | | | | | | | | - Yann Godfrin
- Axoltis Pharma, 60 Avenue Rockefeller, 69008, Lyon, France
- Godfrin Life-Sciences, Caluire-et-Cuire, France
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Chang KH, French IT, Liang WK, Lo YS, Wang YR, Cheng ML, Huang NE, Wu HC, Lim SN, Chen CM, Juan CH. Evaluating the Different Stages of Parkinson's Disease Using Electroencephalography With Holo-Hilbert Spectral Analysis. Front Aging Neurosci 2022; 14:832637. [PMID: 35619940 PMCID: PMC9127298 DOI: 10.3389/fnagi.2022.832637] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/08/2022] [Indexed: 01/04/2023] Open
Abstract
Electroencephalography (EEG) can reveal the abnormalities of dopaminergic subcortico-cortical circuits in patients with Parkinson's disease (PD). However, conventional time-frequency analysis of EEG signals cannot fully reveal the non-linear processes of neural activities and interactions. A novel Holo-Hilbert Spectral Analysis (HHSA) was applied to reveal non-linear features of resting state EEG in 99 PD patients and 59 healthy controls (HCs). PD patients demonstrated a reduction of β bands in frontal and central regions, and reduction of γ bands in central, parietal, and temporal regions. Compared with early-stage PD patients, late-stage PD patients demonstrated reduction of β bands in the posterior central region, and increased θ and δ2 bands in the left parietal region. θ and β bands in all brain regions were positively correlated with Hamilton depression rating scale scores. Machine learning algorithms using three prioritized HHSA features demonstrated "Bag" with the best accuracy of 0.90, followed by "LogitBoost" with an accuracy of 0.89. Our findings strengthen the application of HHSA to reveal high-dimensional frequency features in EEG signals of PD patients. The EEG characteristics extracted by HHSA are important markers for the identification of depression severity and diagnosis of PD.
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Affiliation(s)
- Kuo-Hsuan Chang
- Department of Neurology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Isobel Timothea French
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Taiwan International Graduate Program in Interdisciplinary Neuroscience, National Central University and Academia Sinica, Taipei, Taiwan
| | - Wei-Kuang Liang
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Cognitive Intelligence and Precision Healthcare Research Center, National Central University, Taoyuan, Taiwan
| | - Yen-Shi Lo
- Department of Neurology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Yi-Ru Wang
- Department of Neurology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Mei-Ling Cheng
- Department of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan
- Metabolomics Core Laboratory, Healthy Aging Research Center, Chang Gung University, Taoyuan, Taiwan
- Clinical Phenome Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Norden E. Huang
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Cognitive Intelligence and Precision Healthcare Research Center, National Central University, Taoyuan, Taiwan
- Data Analysis and Application Laboratory, The First Institute of Oceanography, Qingdao, China
| | - Hsiu-Chuan Wu
- Department of Neurology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Siew-Na Lim
- Department of Neurology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Chiung-Mei Chen
- Department of Neurology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Chi-Hung Juan
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Cognitive Intelligence and Precision Healthcare Research Center, National Central University, Taoyuan, Taiwan
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Li F, Matsumori S, Egawa N, Yoshimoto S, Yamashiro K, Mizutani H, Uchida N, Kokuryu A, Kuzuya A, Kojima R, Hayashi Y, Takahashi R. Predictive Diagnostic Approach to Dementia and Dementia Subtypes Using Wireless and Mobile Electroencephalography: A Pilot Study. Bioelectricity 2022. [DOI: 10.1089/bioe.2021.0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Fangzhou Li
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | | | - Naohiro Egawa
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | | | | | | | - Noriko Uchida
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Atsuko Kokuryu
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Akira Kuzuya
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Ryosuke Kojima
- Department of Human Health Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yu Hayashi
- Department of Human Health Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Ryosuke Takahashi
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Polverino P, Ajčević M, Catalan M, Mazzon G, Bertolotti C, Manganotti P. Brain oscillatory patterns in mild cognitive impairment due to Alzheimer’s and Parkinson’s disease: an exploratory high-density EEG study. Clin Neurophysiol 2022; 138:1-8. [DOI: 10.1016/j.clinph.2022.01.136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 12/08/2021] [Accepted: 01/31/2022] [Indexed: 01/06/2023]
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EEG as a marker of brain plasticity in clinical applications. HANDBOOK OF CLINICAL NEUROLOGY 2022; 184:91-104. [PMID: 35034760 DOI: 10.1016/b978-0-12-819410-2.00029-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Neural networks are dynamic, and the brain has the capacity to reorganize itself. This capacity is named neuroplasticity and is fundamental for many processes ranging from learning and adaptation to new environments to the response to brain injuries. Measures of brain plasticity involve several techniques, including neuroimaging and neurophysiology. Electroencephalography, often used together with other techniques, is a common tool for prognostic and diagnostic purposes, and cortical reorganization is reflected by EEG measurements. Changes of power bands in different cortical areas occur with fatigue and in response to training stimuli leading to learning processes. Sleep has a fundamental role in brain plasticity, restoring EEG bands alterations and promoting consolidation of learning. Exercise and physical inactivity have been extensively studied as both strongly impact brain plasticity. Indeed, EEG studies showed the importance of the physical activity to promote learning and the effects of inactivity or microgravity on cortical reorganization to cope with absent or altered sensorimotor stimuli. Finally, this chapter will describe some of the EEG changes as markers of neural plasticity in neurologic conditions, focusing on cerebrovascular and neurodegenerative diseases. In conclusion, neuroplasticity is the fundamental mechanism necessary to ensure adaptation to new stimuli and situations, as part of the dynamicity of life.
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Yao S, Zhu J, Li S, Zhang R, Zhao J, Yang X, Wang Y. Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021. Front Psychiatry 2022; 13:830819. [PMID: 35677873 PMCID: PMC9167960 DOI: 10.3389/fpsyt.2022.830819] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis. METHODS QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis. RESULTS A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends. CONCLUSION The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.
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Affiliation(s)
- Shun Yao
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jieying Zhu
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Shuiyan Li
- Department of Rehabilitation Medicine, School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China
| | - Ruibin Zhang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jiubo Zhao
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xueling Yang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - You Wang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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14
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Saleh C, Meyer A, Chaturvedi M, Beltrani S, Gschwandtner U, Fuhr P. Does Quantitative Electroencephalography Refine Preoperative Cognitive Assessment in Parkinson's Disease Patients Treated with Deep Brain Stimulation? A Follow-Up Study. Dement Geriatr Cogn Disord 2021; 50:349-356. [PMID: 34569496 DOI: 10.1159/000519053] [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: 06/01/2021] [Accepted: 07/30/2021] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE Deep brain stimulation (DBS) in Parkinson's disease (PD) is associated with an increased risk of post-operative cognitive deterioration. Preoperative neuropsychological testing can be affected and limited by the patient's collaboration in advanced disease. The purpose of this study was to determine whether preoperative quantitative electroencephalography (qEEG) may be a useful complementary examination technique during preoperative assessment to predict cognitive changes in PD patients treated with DBS. METHODS We compared the cognitive performance of 16 PD patients who underwent bilateral subthalamic nucleus DBS to the performance of 15 PD controls (matched for age, sex, and education) at baseline and at 24 months. Cognitive scores were calculated for all patients across 5 domains. A preoperative 256-channel resting EEG was recorded from each patient. We computed the global relative power spectra. Correlation and linear regression models were used to assess associations of preoperative EEG measures with post-operative cognitive scores. RESULTS Slow waves (relative delta and theta band power) were negatively correlated with post-operative cognitive performance, while faster waves (alpha 1) were strongly positively correlated with the same scores (the overall cognitive score, attention, and executive function). Linear models revealed an association of delta power with the overall cognitive score (p = 0.00409, adjusted R2 = 0.6341). Verbal fluency (VF) showed a significant decline after DBS surgery, which was correlated with qEEG measures. CONCLUSIONS To analyse the side effects after DBS in PD patients, the most important parameter is verbal fluency capacity. In addition, correlation with EEG frequency bands might be useful to detect particularly vulnerable patients for cognitive impairment and be supportive in the selection process of patients considered for DBS.
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Affiliation(s)
- Christian Saleh
- Department of Neurophysiology and Neurology, University Hospital Basel, Basel, Switzerland
| | - Antonia Meyer
- Department of Neurophysiology and Neurology, University Hospital Basel, Basel, Switzerland
| | - Menorca Chaturvedi
- Department of Neurophysiology and Neurology, University Hospital Basel, Basel, Switzerland
| | - Selina Beltrani
- Department of Neurophysiology and Neurology, University Hospital Basel, Basel, Switzerland
| | - Ute Gschwandtner
- Department of Neurophysiology and Neurology, University Hospital Basel, Basel, Switzerland
| | - Peter Fuhr
- Department of Neurophysiology and Neurology, University Hospital Basel, Basel, Switzerland
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15
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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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16
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Zinn MA, Jason LA. Cortical autonomic network connectivity predicts symptoms in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Int J Psychophysiol 2021; 170:89-101. [PMID: 34662673 DOI: 10.1016/j.ijpsycho.2021.10.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/17/2021] [Accepted: 10/08/2021] [Indexed: 01/28/2023]
Abstract
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) represents a significant public health challenge given the presence of many unexplained patient symptoms. Research has shown that many features in ME/CFS may result from a dysfunctional autonomic nervous system (ANS). We explored the role of the cortical autonomic network (CAN) involved in higher-order control of ANS functioning in 34 patients with ME/CFS and 34 healthy controls under task-free conditions. All participants underwent resting-state quantitative electroencephalographic (qEEG) scalp recordings during an eyes-closed condition. Source analysis was performed using exact low-resolution electromagnetic tomography (eLORETA), and lagged coherence was used to estimate intrinsic functional connectivity between each node across 7 frequency bands: delta (1-3 Hz), theta (4-7 Hz), alpha-1 (8-10 Hz), alpha-2 (10-12 Hz), beta-1 (13-18 Hz), beta-2 (19-21 Hz), and beta-3 (22-30 Hz). Symptom ratings were measured using the DePaul Symptom Questionnaire and the Short Form (SF-36) health survey. Graph theoretical analysis of weighted, undirected connections revealed significant group differences in baseline CAN organization. Regression results showed that cognitive, affective, and somatomotor symptom cluster ratings were associated with alteration to CAN topology in patients, depending on the frequency band. These findings provide evidence for reduced higher-order homeostatic regulation and adaptability in ME/CFS. If confirmed, these findings address the CAN as a potential therapeutic target for managing patient symptoms.
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Affiliation(s)
- Mark A Zinn
- DePaul University, Center for Community Research, 990 W. Fullerton Ave., Chicago, IL 60614, United States of America.
| | - Leonard A Jason
- DePaul University, Center for Community Research, 990 W. Fullerton Ave., Chicago, IL 60614, United States of America
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17
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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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18
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Rea RC, Berlot R, Martin SL, Craig CE, Holmes PS, Wright DJ, Bon J, Pirtošek Z, Ray NJ. Quantitative EEG and cholinergic basal forebrain atrophy in Parkinson's disease and mild cognitive impairment. Neurobiol Aging 2021; 106:37-44. [PMID: 34233212 DOI: 10.1016/j.neurobiolaging.2021.05.023] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 05/22/2021] [Accepted: 05/31/2021] [Indexed: 02/08/2023]
Abstract
Cholinergic degeneration is a key feature of dementia in neurodegenerative conditions including Alzheimer's disease (AD) and Parkinson's disease (PD). Quantitative electro-encephalography (EEG) metrics are altered in both conditions from early stages, and recent research in people with Lewy body and AD dementia suggests these changes may be associated with atrophy in cholinergic basal forebrain nuclei (cBF). To determine if these relationships exist in predementia stages of neurodegenerative conditions, we studied resting-state EEG and in vivo cBF volumes in 31 people with PD (without dementia), 21 people with mild cognitive impairment (MCI), and 21 age-matched controls. People with PD showed increased power in slower frequencies and reduced alpha reactivity compared to controls. Volumes of cholinergic cell clusters corresponding to the medial septum and vertical and horizontal limb of the diagonal band, and the posterior nucleus basalis of Meynert, correlated positively with; alpha reactivity in people with PD (p< 0.01); and pre-alpha power in people with MCI (p< 0.05). These results suggest that alpha reactivity and pre-alpha power are related to changes in cBF volumes in MCI and PD without dementia.
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Affiliation(s)
- River C Rea
- Department of Psychology, Health, Psychology, and Communities Research Centre, Manchester Metropolitan University, Manchester, UK.
| | - Rok Berlot
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Sarah L Martin
- Department of Psychology, Health, Psychology, and Communities Research Centre, Manchester Metropolitan University, Manchester, UK
| | - Chesney E Craig
- Department of Psychology, Health, Psychology, and Communities Research Centre, Manchester Metropolitan University, Manchester, UK
| | - Paul S Holmes
- Department of Psychology, Health, Psychology, and Communities Research Centre, Manchester Metropolitan University, Manchester, UK
| | - David J Wright
- Department of Psychology, Health, Psychology, and Communities Research Centre, Manchester Metropolitan University, Manchester, UK
| | - Jurij Bon
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, Slovenia; University Psychiatric Clinic Ljubljana, Ljubljana, Slovenia
| | - Zvezdan Pirtošek
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Nicola J Ray
- Department of Psychology, Health, Psychology, and Communities Research Centre, Manchester Metropolitan University, Manchester, UK
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19
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Sultan ZW, Jaeckel ER, Krause BM, Grady SM, Murphy CA, Sanders RD, Banks MI. Electrophysiological signatures of acute systemic lipopolysaccharide-induced inflammation: potential implications for delirium science. Br J Anaesth 2021; 126:996-1008. [PMID: 33648701 PMCID: PMC8132883 DOI: 10.1016/j.bja.2020.12.040] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 12/07/2020] [Accepted: 12/10/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Novel preventive therapies are needed for postoperative delirium, which especially affects older patients. A mouse model is presented that captures inflammation-associated cortical slow wave activity (SWA) observed in patients, allowing exploration of the mechanistic role of prostaglandin-adenosine signalling. METHODS EEG and cortical cytokine measurements (interleukin 6, monocyte chemoattractant protein-1) were obtained from adult and aged mice. Behaviour, SWA, and functional connectivity were assayed before and after systemic administration of lipopolysaccharide (LPS)+piroxicam (cyclooxygenase inhibitor) or LPS+caffeine (adenosine receptor antagonist). To avoid the confounder of inflammation-driven changes in movement which alter SWA and connectivity, electrophysiological recordings were classified as occurring during quiescence or movement, and propensity score matching was used to match distributions of movement magnitude between baseline and post-LPS administration. RESULTS LPS produces increases in cortical cytokines and behavioural quiescence. In movement-matched data, LPS produces increases in SWA (likelihood-ratio test: χ2(4)=21.51, P<0.001), but not connectivity (χ2(4)=6.39, P=0.17). Increases in SWA associate with interleukin 6 (P<0.001) and monocyte chemoattractant protein-1 (P=0.001) and are suppressed by piroxicam (P<0.001) and caffeine (P=0.046). Aged animals compared with adult animals show similar LPS-induced SWA during movement, but exaggerated cytokine response and increased SWA during quiescence. CONCLUSIONS Cytokine-SWA correlations during wakefulness are consistent with observations in patients with delirium. Absence of connectivity effects after accounting for movement changes suggests decreased connectivity in patients is a biomarker of hypoactivity. Exaggerated effects in quiescent aged animals are consistent with increased hypoactive delirium in older patients. Prostaglandin-adenosine signalling may link inflammation to neural changes and hence delirium.
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Affiliation(s)
- Ziyad W Sultan
- Department of Anesthesiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Elizabeth R Jaeckel
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Bryan M Krause
- Department of Anesthesiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Sean M Grady
- Department of Anesthesiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Caitlin A Murphy
- Department of Anesthesiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Robert D Sanders
- Specialty of Anaesthetics, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia; Department of Anaesthetics, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Matthew I Banks
- Department of Anesthesiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
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20
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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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21
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Ahmadi M, Kazemi K, Kuc K, Cybulska-Klosowicz A, Zakrzewska M, Racicka-Pawlukiewicz E, Helfroush MS, Aarabi A. Cortical source analysis of resting state EEG data in children with attention deficit hyperactivity disorder. Clin Neurophysiol 2020; 131:2115-2130. [DOI: 10.1016/j.clinph.2020.05.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 04/03/2020] [Accepted: 05/16/2020] [Indexed: 12/14/2022]
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22
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Massa F, Meli R, Grazzini M, Famà F, De Carli F, Filippi L, Arnaldi D, Pardini M, Morbelli S, Nobili F. Utility of quantitative EEG in early Lewy body disease. Parkinsonism Relat Disord 2020; 75:70-75. [DOI: 10.1016/j.parkreldis.2020.05.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 04/09/2020] [Accepted: 05/05/2020] [Indexed: 10/24/2022]
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23
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Early diagnosis of Alzheimer’s disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts. Clin Neurophysiol 2020; 131:1287-1310. [DOI: 10.1016/j.clinph.2020.03.003] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Revised: 03/01/2020] [Accepted: 03/02/2020] [Indexed: 02/06/2023]
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24
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Waninger S, Berka C, Stevanovic Karic M, Korszen S, Mozley PD, Henchcliffe C, Kang Y, Hesterman J, Mangoubi T, Verma A. Neurophysiological Biomarkers of Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2020; 10:471-480. [PMID: 32116262 PMCID: PMC7242849 DOI: 10.3233/jpd-191844] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
BACKGROUND There is a need for reliable and robust Parkinson's disease biomarkers that reflect severity and are sensitive to disease modifying investigational therapeutics. OBJECTIVE To demonstrate the utility of EEG as a reliable, quantitative biomarker with potential as a pharmacodynamic endpoint for use in clinical assessments of neuroprotective therapeutics for Parkison's disease. METHODS A multi modal study was performed including aquisition of resting state EEG data and dopamine transporter PET imaging from Parkinson's disease patients off medication and compared against age-matched controls. RESULTS Qualitative and test/retest analysis of the EEG data demonstrated the reliability of the methods. Source localization using low resolution brain electromagnetic tomography identified significant differences in Parkinson's patients versus control subjects in the anterior cingulate and temporal lobe, areas with established association to Parkinson's disease pathology. Changes in cortico-cortical and cortico-thalamic coupling were observed as excessive EEG beta coherence in Parkinson's disease patients, and correlated with UPDRS scores and dopamine transporter activity, supporting the potential for cortical EEG coherence to serve as a reliable measure of disease severity. Using machine learning approaches, an EEG discriminant function analysis classifier was identified that parallels the loss of dopamine synapses as measured by dopamine transporter PET. CONCLUSION Our results support the utility of EEG in characterizing alterations in neurophysiological oscillatory activity associated with Parkinson's disease and highlight potential as a reliable method for monitoring disease progression and as a pharmacodynamic endpoint for Parkinson's disease modification therapy.
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Affiliation(s)
- Shani Waninger
- Advanced Brain Monitoring Inc., Carlsbad, CA, USA,Correspondence to: Shani Waninger, Advanced Brain Monitoring, Inc., 2237 Faraday Avenue, Suite 100,
Carlsbad, CA 92008, USA. E-mail:
| | - Chris Berka
- Advanced Brain Monitoring Inc., Carlsbad, CA, USA
| | | | | | | | | | - Yeona Kang
- Weill Cornell Medical College, New York, NY, USA
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25
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Devergnas A, Caiola M, Pittard D, Wichmann T. Cortical Phase-Amplitude Coupling in a Progressive Model of Parkinsonism in Nonhuman Primates. Cereb Cortex 2020; 29:167-177. [PMID: 29190329 DOI: 10.1093/cercor/bhx314] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Indexed: 12/18/2022] Open
Abstract
Parkinson's disease is associated with abnormal oscillatory electrical activities of neurons and neuronal ensembles throughout the basal ganglia-thalamocortical network. It has recently been documented in patients with advanced parkinsonism that the amplitude of gamma-band oscillations (50-200 Hz) in electrocorticogram recordings from the primary motor cortex is abnormally coupled to the phase of beta band oscillations within the same signals. It is not known when in the course of the disease the abnormal phase-amplitude coupling (PAC) arises, and whether it is influenced by arousal or prior exposure to dopaminergic medications. To address these issues, we analyzed the relationship between the severity of parkinsonian motor signs and the extent of PAC in a progressive model of parkinsonism, using primates that were not exposed to levodopa prior to testing. PAC was measured in electrocorticogram signals from the primary motor cortex and the supplementary motor area in 3 monkeys that underwent weekly injections of small doses of the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, rendering them progressively parkinsonian. We found that parkinsonism was associated with increased coupling between the phase of low-frequency (4-10 Hz) oscillations and the amplitude of oscillations in the high gamma band (50-150 Hz). These changes only reached significance when the animals became fully parkinsonian. The increased PAC was normalized after levodopa treatment. We also found a similar increase in PAC during sleep, even in normal animals. The identified PAC was independent of concomitant changes in spectral power in the 2.9-9.8Hz or 49.8-150.4 Hz ranges. We conclude that PAC is predominately a sign of advanced parkinsonism, and is, thus, not essential for the development of parkinsonism. However, increased PAC appears to correlate with the severity of fully developed parkinsonism.
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Affiliation(s)
- Annaelle Devergnas
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA.,Department of Neurology, School of Medicine, Emory University, Atlanta, GA, USA.,Morris K. Udall Center of Excellence in Parkinson's Disease Research, Emory University, Atlanta, GA, USA
| | - M Caiola
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA.,Morris K. Udall Center of Excellence in Parkinson's Disease Research, Emory University, Atlanta, GA, USA
| | - D Pittard
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA.,Morris K. Udall Center of Excellence in Parkinson's Disease Research, Emory University, Atlanta, GA, USA
| | - T Wichmann
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA.,Department of Neurology, School of Medicine, Emory University, Atlanta, GA, USA.,Morris K. Udall Center of Excellence in Parkinson's Disease Research, Emory University, Atlanta, GA, USA
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26
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McMackin R, Muthuraman M, Groppa S, Babiloni C, Taylor JP, Kiernan MC, Nasseroleslami B, Hardiman O. Measuring network disruption in neurodegenerative diseases: New approaches using signal analysis. J Neurol Neurosurg Psychiatry 2019; 90:1011-1020. [PMID: 30760643 PMCID: PMC6820156 DOI: 10.1136/jnnp-2018-319581] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 01/21/2019] [Accepted: 01/21/2019] [Indexed: 12/12/2022]
Abstract
Advanced neuroimaging has increased understanding of the pathogenesis and spread of disease, and offered new therapeutic targets. MRI and positron emission tomography have shown that neurodegenerative diseases including Alzheimer's disease (AD), Lewy body dementia (LBD), Parkinson's disease (PD), frontotemporal dementia (FTD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) are associated with changes in brain networks. However, the underlying neurophysiological pathways driving pathological processes are poorly defined. The gap between what imaging can discern and underlying pathophysiology can now be addressed by advanced techniques that explore the cortical neural synchronisation, excitability and functional connectivity that underpin cognitive, motor, sensory and other functions. Transcranial magnetic stimulation can show changes in focal excitability in cortical and transcortical motor circuits, while electroencephalography and magnetoencephalography can now record cortical neural synchronisation and connectivity with good temporal and spatial resolution.Here we reflect on the most promising new approaches to measuring network disruption in AD, LBD, PD, FTD, MS, and ALS. We consider the most groundbreaking and clinically promising studies in this field. We outline the limitations of these techniques and how they can be tackled and discuss how these novel approaches can assist in clinical trials by predicting and monitoring progression of neurophysiological changes underpinning clinical symptomatology.
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Affiliation(s)
- Roisin McMackin
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin, Ireland
| | - Muthuraman Muthuraman
- Department of Neurology, Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Mainz, Germany
| | - Sergiu Groppa
- Department of Neurology, Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Mainz, Germany
| | - Claudio Babiloni
- Dipartimento di Fisiologia e Farmacologia "Vittorio Erspamer", Università degli Studi di Roma "La Sapienza", Roma, Italy
- Istituto di Ricovero e Cura San Raffaele Cassino, Cassino, Italy
| | - John-Paul Taylor
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Matthew C Kiernan
- Brain & Mind Centre, University of Sydney, Sydney, Sydney, Australia
- Institute of Clinical Neurosciences, Royal Prince Alfred Hospital, Sydney, Sydney, Australia
| | - Bahman Nasseroleslami
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin, Ireland
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity College Dublin, the University of Dublin, Dublin, Ireland
- Beaumont Hospital, Dublin, Ireland
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27
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Chaturvedi M, Bogaarts JG, Kozak Cozac VV, Hatz F, Gschwandtner U, Meyer A, Fuhr P, Roth V. Phase lag index and spectral power as QEEG features for identification of patients with mild cognitive impairment in Parkinson's disease. Clin Neurophysiol 2019; 130:1937-1944. [PMID: 31445388 DOI: 10.1016/j.clinph.2019.07.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 05/18/2019] [Accepted: 07/15/2019] [Indexed: 10/26/2022]
Abstract
OBJECTIVES To identify quantitative EEG frequency and connectivity features (Phase Lag Index) characteristic of mild cognitive impairment (MCI) in Parkinson's disease (PD) patients and to investigate if these features correlate with cognitive measures of the patients. METHODS We recorded EEG data for a group of PD patients with MCI (n = 27) and PD patients without cognitive impairment (n = 43) using a high-resolution recording system. The EEG files were processed and 66 frequency along with 330 connectivity (phase lag index, PLI) measures were calculated. These measures were used to classify MCI vs. MCI-free patients. We also assessed correlations of these features with cognitive tests based on comprehensive scores (domains). RESULTS PLI measures classified PD-MCI from non-MCI patients better than frequency measures. PLI in delta, theta band had highest importance for identifying patients with MCI. Amongst cognitive domains, we identified the most significant correlations between Memory and Theta PLI, Attention and Beta PLI. CONCLUSION PLI is an effective quantitative EEG measure to identify PD patients with MCI. SIGNIFICANCE We identified quantitative EEG measures which are important for early identification of cognitive decline in PD.
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Affiliation(s)
- Menorca Chaturvedi
- Department of Neurology, University Hospital Basel, Basel, Switzerland; Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Jan Guy Bogaarts
- Department of Neurology, University Hospital Basel, Basel, Switzerland; Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Vitalii V Kozak Cozac
- Department of Neurology, University Hospital Basel, Basel, Switzerland; Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Florian Hatz
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Ute Gschwandtner
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Antonia Meyer
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Peter Fuhr
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Volker Roth
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland.
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28
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Auditory brain oscillatory responses in drug-naïve patients with Parkinson’s disease. Neurosci Lett 2019; 701:170-174. [DOI: 10.1016/j.neulet.2019.02.039] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2018] [Revised: 02/11/2019] [Accepted: 02/25/2019] [Indexed: 01/09/2023]
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29
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Electrophysiological assessment methodology of sensory processing dysfunction in schizophrenia and dementia of the Alzheimer type. Neurosci Biobehav Rev 2019; 97:70-84. [DOI: 10.1016/j.neubiorev.2018.09.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 09/04/2018] [Accepted: 09/05/2018] [Indexed: 12/26/2022]
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30
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Song Z, Deng B, Wang J, Wang R. Biomarkers for Alzheimer's Disease Defined by a Novel Brain Functional Network Measure. IEEE Trans Biomed Eng 2019; 66:41-49. [DOI: 10.1109/tbme.2018.2834546] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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31
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Bonanni L. The democratic aspect of machine learning: Limitations and opportunities for Parkinson's disease. Mov Disord 2018; 34:164-166. [DOI: 10.1002/mds.27600] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 12/03/2018] [Accepted: 12/06/2018] [Indexed: 12/13/2022] Open
Affiliation(s)
- Laura Bonanni
- Department of Neuroscience, Imaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐Pescara Chieti Italy
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32
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Time and frequency dependent changes in resting state EEG functional connectivity following lipopolysaccharide challenge in rats. PLoS One 2018; 13:e0206985. [PMID: 30418990 PMCID: PMC6231634 DOI: 10.1371/journal.pone.0206985] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 10/23/2018] [Indexed: 12/20/2022] Open
Abstract
Research has shown that inflammatory processes affect brain function and behavior through several neuroimmune pathways. However, high order brain functions affected by inflammation largely remain to be defined. Resting state functional connectivity of synchronized oscillatory activity is a valid approach to understand network processing and high order brain function under different experimental conditions. In the present study multi-electrode EEG recording in awake, freely moving rats was used to study resting state connectivity after administration of lipopolysaccharides (LPS). Male Wistar rats were implanted with 10 cortical surface electrodes and administered with LPS (2 mg/kg) and monitored for symptoms of sickness at 3, 6 and 24 h. Resting state connectivity and power were computed at baseline, 6 and 24 h. Three prominent connectivity bands were identified using a method resistant to spurious correlation: alpha (5–15 Hz), beta-gamma (20–80 Hz), and high frequency oscillation (150–200 Hz). The most prominent connectivity band, alpha, was strongly reduced 6 h after LPS administration, and returned to baseline at 24 h. Beta-gamma connectivity was also reduced at 6 h and remained reduced at 24 h. Interestingly, high frequency oscillation connectivity remained unchanged at 6 h and was impaired 24 h after LPS challenge. Expected elevations in delta and theta power were observed at 6 h after LPS administration, when behavioral symptoms of sickness were maximal. Notably, gamma and high frequency power were reduced 6 h after LPS and returned to baseline by 24 h, when the effects on connectivity were more evident. Finally, increases in cross-frequency coupling elicited by LPS were detected at 6 h for theta-gamma and at 24 h for theta-high frequency oscillations. These studies show that LPS challenge profoundly affects EEG connectivity across all identified bands in a time-dependent manner indicating that inflammatory processes disrupt both bottom-up and top-down communication across the cortex during the peak and resolution of inflammation.
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Betrouni N, Delval A, Chaton L, Defebvre L, Duits A, Moonen A, Leentjens AFG, Dujardin K. Electroencephalography-based machine learning for cognitive profiling in Parkinson's disease: Preliminary results. Mov Disord 2018; 34:210-217. [PMID: 30345602 DOI: 10.1002/mds.27528] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 08/25/2018] [Accepted: 09/03/2018] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Cognitive symptoms are common in patients with Parkinson's disease. Characterization of a patient's cognitive profile is an essential step toward the identification of predictors of cognitive worsening. OBJECTIVE The aim of this study was to investigate the use of the combination of resting-state EEG and data-mining techniques to build characterization models. METHODS Dense EEG data from 118 patients with Parkinson's disease, classified into 5 different groups according to the severity of their cognitive impairments, were considered. Spectral power analysis within 7 frequency bands was performed on the EEG signals. The obtained quantitative EEG features of 100 patients were mined using 2 machine-learning algorithms to build and train characterization models, namely, support vector machines and k-nearest neighbors models. The models were then blindly tested on data from 18 patients. RESULTS The overall classification accuracies were 84% and 88% for the support vector machines and k-nearest algorithms, respectively. The worst classifications were observed for patients from groups with small sample sizes, corresponding to patients with the severe cognitive deficits. Whereas for the remaining groups for whom an accurate diagnosis was required to plan the future healthcare, the classification was very accurate. CONCLUSION These results suggest that EEG features computed from a daily clinical practice exploration modality in-that it is nonexpensive, available anywhere, and requires minimal cooperation from the patient-can be used as a screening method to identify the severity of cognitive impairment in patients with Parkinson's disease. © 2018 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Nacim Betrouni
- University Lille, Inserm, CHU Lille, Degenerative & Vascular Cognitive Disorders, Lille, France
| | - Arnaud Delval
- University Lille, Inserm, CHU Lille, Degenerative & Vascular Cognitive Disorders, Lille, France.,Centre Hospitalier Universitaire Lille, Clinical Neurophysiology Department, Lille, France
| | - Laurence Chaton
- University Lille, Inserm, CHU Lille, Degenerative & Vascular Cognitive Disorders, Lille, France.,Centre Hospitalier Universitaire Lille, Clinical Neurophysiology Department, Lille, France
| | - Luc Defebvre
- University Lille, Inserm, CHU Lille, Degenerative & Vascular Cognitive Disorders, Lille, France.,Centre Hospitalier Universitaire Lille, Neurology and Movement Disorders Department, Lille, France
| | - Annelien Duits
- Maastricht University Medical Center, Maastricht, The Netherlands
| | - Anja Moonen
- Maastricht University Medical Center, Maastricht, The Netherlands
| | | | - Kathy Dujardin
- University Lille, Inserm, CHU Lille, Degenerative & Vascular Cognitive Disorders, Lille, France.,Centre Hospitalier Universitaire Lille, Neurology and Movement Disorders Department, Lille, France
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34
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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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35
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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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Babiloni C, Del Percio C, Lizio R, Noce G, Cordone S, Lopez S, Soricelli A, Ferri R, Pascarelli MT, Nobili F, Arnaldi D, Famà F, Aarsland D, Orzi F, Buttinelli C, Giubilei F, Onofrj M, Stocchi F, Stirpe P, Fuhr P, Gschwandtner U, Ransmayr G, Caravias G, Garn H, Sorpresi F, Pievani M, D'Antonio F, De Lena C, Güntekin B, Hanoğlu L, Başar E, Yener G, Emek-Savaş DD, Triggiani AI, Franciotti R, Frisoni GB, Bonanni L, De Pandis MF. Abnormalities of Cortical Neural Synchronization Mechanisms in Subjects with Mild Cognitive Impairment due to Alzheimer's and Parkinson's Diseases: An EEG Study. J Alzheimers Dis 2018. [PMID: 28621693 DOI: 10.3233/jad-160883] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The aim of this retrospective and exploratory study was that the cortical sources of resting state eyes-closed electroencephalographic (rsEEG) rhythms might reveal different abnormalities in cortical neural synchronization in groups of patients with mild cognitive impairment due to Alzheimer's disease (ADMCI) and Parkinson's disease (PDMCI) as compared to healthy subjects. Clinical and rsEEG data of 75 ADMCI, 75 PDMCI, and 75 cognitively normal elderly (Nold) subjects were available in an international archive. Age, gender, and education were carefully matched in the three groups. The Mini-Mental State Evaluation (MMSE) was matched between the ADMCI and PDMCI groups. Individual alpha frequency peak (IAF) was used to determine the delta, theta, alpha1, alpha2, and alpha3 frequency band ranges. Fixed beta1, beta2, and gamma bands were also considered. eLORETA estimated the rsEEG cortical sources. Receiver operating characteristic curve (ROC) classified these sources across individuals. Results showed that compared to the Nold group, the posterior alpha2 and alpha3 source activities were more abnormal in the ADMCI than the PDMCI group, while the parietal delta source activities were more abnormal in the PDMCI than the ADMCI group. The parietal delta and alpha sources correlated with MMSE score and correctly classified the Nold and diseased individuals (area under the ROC = 0.77-0.79). In conclusion, the PDMCI and ADMCI patients showed different features of cortical neural synchronization at delta and alpha frequencies underpinning brain arousal and vigilance in the quiet wakefulness. Future prospective cross-validation studies will have to test these rsEEG markers for clinical applications and drug discovery.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy.,Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | | | - Roberta Lizio
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy.,Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Giuseppe Noce
- Department of Integrated Imaging, IRCCS SDN, Naples, Italy
| | - Susanna Cordone
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy
| | - Andrea Soricelli
- Department of Integrated Imaging, IRCCS SDN, Naples, Italy.,Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | - Raffaele Ferri
- Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging, Troina, Enna, Italy
| | - Maria Teresa Pascarelli
- Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging, Troina, Enna, Italy
| | - Flavio Nobili
- Clinical Neurology, dept of Neuroscience (DiNOGMI), University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Dario Arnaldi
- Clinical Neurology, dept of Neuroscience (DiNOGMI), University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Francesco Famà
- Clinical Neurology, dept of Neuroscience (DiNOGMI), University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Dag Aarsland
- Department of Old Age Psychiatry, King's College University, London, UK
| | - Francesco Orzi
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Fabrizio Stocchi
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Paola Stirpe
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Peter Fuhr
- Universitätsspital Basel, Abteilung Neurophysiologie, Basel, Switzerland
| | - Ute Gschwandtner
- Universitätsspital Basel, Abteilung Neurophysiologie, Basel, Switzerland
| | - Gerhard Ransmayr
- Department of Neurology and Psychiatry and Faculty of Medicine, Johannes Kepler University Linz, General Hospital of the City of Linz, Linz, Austria
| | - Georg Caravias
- Department of Neurology and Psychiatry and Faculty of Medicine, Johannes Kepler University Linz, General Hospital of the City of Linz, Linz, Austria
| | - Heinrich Garn
- AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | | | - Michela Pievani
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizia D'Antonio
- Department of Neurology and Psychiatry, Sapienza, University of Rome, Italy
| | - Carlo De Lena
- Department of Neurology and Psychiatry, Sapienza, University of Rome, Italy
| | - Bahar Güntekin
- Department of Biophysics, Istanbul Medipol University, Istanbul, Turkey
| | - Lutfu Hanoğlu
- Department of Neurology, University of Istanbul-Medipol, Istanbul, Turkey
| | - Erol Başar
- Department of Neurosciences and Department of Neurology, Dokuz Eylül University Medical School, Izmir, Turkey
| | - Görsev Yener
- Department of Psychology and Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | - Derya Durusu Emek-Savaş
- Department of Psychology and Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | | | - Raffaella Franciotti
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Giovanni B Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Laura Bonanni
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
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Singh A. Oscillatory activity in the cortico-basal ganglia-thalamic neural circuits in Parkinson's disease. Eur J Neurosci 2018; 48:2869-2878. [DOI: 10.1111/ejn.13853] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Revised: 01/19/2018] [Accepted: 01/21/2018] [Indexed: 12/20/2022]
Affiliation(s)
- Arun Singh
- Department of Neurology; University of Minnesota; Minneapolis MN 55455 USA
- Department of Neurology; University of Iowa; Iowa City IA USA
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Babiloni C, Del Percio C, Lizio R, Noce G, Lopez S, Soricelli A, Ferri R, Nobili F, Arnaldi D, Famà F, Aarsland D, Orzi F, Buttinelli C, Giubilei F, Onofrj M, Stocchi F, Stirpe P, Fuhr P, Gschwandtner U, Ransmayr G, Garn H, Fraioli L, Pievani M, Frisoni GB, D'Antonio F, De Lena C, Güntekin B, Hanoğlu L, Başar E, Yener G, Emek-Savaş DD, Triggiani AI, Franciotti R, Taylor JP, Vacca L, De Pandis MF, Bonanni L. Abnormalities of resting-state functional cortical connectivity in patients with dementia due to Alzheimer's and Lewy body diseases: an EEG study. Neurobiol Aging 2017; 65:18-40. [PMID: 29407464 DOI: 10.1016/j.neurobiolaging.2017.12.023] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Revised: 12/21/2017] [Accepted: 12/21/2017] [Indexed: 11/30/2022]
Abstract
Previous evidence showed abnormal posterior sources of resting-state delta (<4 Hz) and alpha (8-12 Hz) rhythms in patients with Alzheimer's disease with dementia (ADD), Parkinson's disease with dementia (PDD), and Lewy body dementia (DLB), as cortical neural synchronization markers in quiet wakefulness. Here, we tested the hypothesis of additional abnormalities in functional cortical connectivity computed in those sources, in ADD, considered as a "disconnection cortical syndrome", in comparison with PDD and DLB. Resting-state eyes-closed electroencephalographic (rsEEG) rhythms had been collected in 42 ADD, 42 PDD, 34 DLB, and 40 normal healthy older (Nold) participants. Exact low-resolution brain electromagnetic tomography (eLORETA) freeware estimated the functional lagged linear connectivity (LLC) from rsEEG cortical sources in delta, theta, alpha, beta, and gamma bands. The area under receiver operating characteristic (AUROC) curve indexed the classification accuracy between Nold and diseased individuals (only values >0.7 were considered). Interhemispheric and intrahemispheric LLCs in widespread delta sources were abnormally higher in the ADD group and, unexpectedly, normal in DLB and PDD groups. Intrahemispheric LLC was reduced in widespread alpha sources dramatically in ADD, markedly in DLB, and moderately in PDD group. Furthermore, the interhemispheric LLC in widespread alpha sources showed lower values in ADD and DLB than PDD groups. At the individual level, AUROC curves of LLC in alpha sources exhibited better classification accuracies for the discrimination of ADD versus Nold individuals (0.84) than for DLB versus Nold participants (0.78) and PDD versus Nold participants (0.75). Functional cortical connectivity markers in delta and alpha sources suggest a more compromised neurophysiological reserve in ADD than DLB, at both group and individual levels.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy; Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy.
| | | | - Roberta Lizio
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy; Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Giuseppe Noce
- Department of Integrated Imaging, IRCCS SDN, Naples, Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy
| | - Andrea Soricelli
- Department of Integrated Imaging, IRCCS SDN, Naples, Italy; Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | - Raffaele Ferri
- Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging, Troina, Enna, Italy
| | - Flavio Nobili
- Clinical Neurology, Department of Neuroscience (DiNOGMI), University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Dario Arnaldi
- Clinical Neurology, Department of Neuroscience (DiNOGMI), University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Francesco Famà
- Clinical Neurology, Department of Neuroscience (DiNOGMI), University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Dag Aarsland
- Department of Old Age Psychiatry, King's College University, London, UK
| | - Francesco Orzi
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Fabrizio Stocchi
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Paola Stirpe
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Peter Fuhr
- Universitätsspital Basel, Abteilung Neurophysiologie, Basel, Switzerland
| | - Ute Gschwandtner
- Universitätsspital Basel, Abteilung Neurophysiologie, Basel, Switzerland
| | - Gerhard Ransmayr
- Department of Neurology and Psychiatry and Faculty of Medicine, Johannes Kepler University Linz, General Hospital of the City of Linz, Linz, Austria
| | - Heinrich Garn
- AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | | | - Michela Pievani
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Giovanni B Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Fabrizia D'Antonio
- Department of Neurology and Psychiatry, Sapienza, University of Rome, Rome, Italy
| | - Carlo De Lena
- Department of Neurology and Psychiatry, Sapienza, University of Rome, Rome, Italy
| | - Bahar Güntekin
- Department of Biophysics, Istanbul Medipol University, Istanbul, Turkey
| | - Lutfu Hanoğlu
- Department of Neurology, University of Istanbul-Medipol, Istanbul, Turkey
| | - Erol Başar
- IBG, Departments of Neurology and Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | - Görsev Yener
- IBG, Departments of Neurology and Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | - Derya Durusu Emek-Savaş
- Department of Psychology and Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | | | - Raffaella Franciotti
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | | | - Laura Vacca
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy; Casa di Cura Privata del Policlinico (CCPP) Milano SpA, Milan, Italy
| | | | - Laura Bonanni
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
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Ratti E, Waninger S, Berka C, Ruffini G, Verma A. Comparison of Medical and Consumer Wireless EEG Systems for Use in Clinical Trials. Front Hum Neurosci 2017; 11:398. [PMID: 28824402 PMCID: PMC5540902 DOI: 10.3389/fnhum.2017.00398] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Accepted: 07/18/2017] [Indexed: 02/03/2023] Open
Abstract
Objectives: To compare quantitative EEG signal and test-retest reliability of medical grade and consumer EEG systems. Methods: Resting state EEG was acquired by two medical grade (B-Alert, Enobio) and two consumer (Muse, Mindwave) EEG systems in five healthy subjects during two study visits. EEG patterns, power spectral densities (PSDs) and test/retest reliability in eyes closed and eyes open conditions were compared across the four systems, focusing on Fp1, the only common electrode. Fp1 PSDs were obtained using Welch's modified periodogram method and averaged for the five subjects for each visit. The test/retest results were calculated as a ratio of Visit 1/Visit 2 Fp1 channel PSD at each 1 s epoch. Results: B-Alert, Enobio, and Mindwave Fp1 power spectra were similar. Muse showed a broadband increase in power spectra and the highest relative variation across test-retest acquisitions. Consumer systems were more prone to artifact due to eye blinks and muscle movement in the frontal region. Conclusions: EEG data can be successfully collected from all four systems tested. Although there was slightly more time required for application, medical systems offer clear advantages in data quality, reliability, and depth of analysis over the consumer systems. Significance: This evaluation provides evidence for informed selection of EEG systemsappropriate for clinical trials.
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Affiliation(s)
- Elena Ratti
- BiogenCambridge, MA, United States,*Correspondence: Elena Ratti
| | - Shani Waninger
- Advanced Brain Monitoring, Inc.Carlsbad, CA, United States
| | - Chris Berka
- Advanced Brain Monitoring, Inc.Carlsbad, CA, United States
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Babiloni C, Del Percio C, Lizio R, Noce G, Cordone S, Lopez S, Soricelli A, Ferri R, Pascarelli MT, Nobili F, Arnaldi D, Aarsland D, Orzi F, Buttinelli C, Giubilei F, Onofrj M, Stocchi F, Stirpe P, Fuhr P, Gschwandtner U, Ransmayr G, Caravias G, Garn H, Sorpresi F, Pievani M, Frisoni GB, D'Antonio F, De Lena C, Güntekin B, Hanoğlu L, Başar E, Yener G, Emek-Savaş DD, Triggiani AI, Franciotti R, De Pandis MF, Bonanni L. Abnormalities of cortical neural synchronization mechanisms in patients with dementia due to Alzheimer's and Lewy body diseases: an EEG study. Neurobiol Aging 2017; 55:143-158. [PMID: 28454845 DOI: 10.1016/j.neurobiolaging.2017.03.030] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 03/24/2017] [Accepted: 03/26/2017] [Indexed: 12/15/2022]
Abstract
The aim of this retrospective exploratory study was that resting state eyes-closed electroencephalographic (rsEEG) rhythms might reflect brain arousal in patients with dementia due to Alzheimer's disease dementia (ADD), Parkinson's disease dementia (PDD), and dementia with Lewy body (DLB). Clinical and rsEEG data of 42 ADD, 42 PDD, 34 DLB, and 40 healthy elderly (Nold) subjects were available in an international archive. Demography, education, and Mini-Mental State Evaluation score were not different between the patient groups. Individual alpha frequency peak (IAF) determined the delta, theta, alpha 1, alpha 2, and alpha 3 frequency bands. Fixed beta 1, beta 2, and gamma bands were also considered. rsEEG cortical sources were estimated by means of the exact low-resolution brain electromagnetic source tomography and were then classified across individuals, on the basis of the receiver operating characteristic curves. Compared to Nold, IAF showed marked slowing in PDD and DLB and moderate slowing in ADD. Furthermore, all patient groups showed lower posterior alpha 2 source activities. This effect was dramatic in ADD, marked in DLB, and moderate in PDD. These groups also showed higher occipital delta source activities, but this effect was dramatic in PDD, marked in DLB, and moderate in ADD. The posterior delta and alpha sources allowed good classification accuracy (approximately 0.85-0.90) between the Nold subjects and patients, and between ADD and PDD patients. In quiet wakefulness, delta and alpha sources unveiled different spatial and frequency features of the cortical neural synchronization underpinning brain arousal in ADD, PDD, and DLB patients. Future prospective cross-validation studies should test these rsEEG markers for clinical applications and drug discovery.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy; Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy.
| | | | - Roberta Lizio
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy; Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Giuseppe Noce
- Department of Integrated Imaging, IRCCS SDN, Naples, Italy
| | - Susanna Cordone
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy
| | - Andrea Soricelli
- Department of Integrated Imaging, IRCCS SDN, Naples, Italy; Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | - Raffaele Ferri
- Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging, Troina, Italy
| | - Maria Teresa Pascarelli
- Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging, Troina, Italy
| | - Flavio Nobili
- Department of Neuroscience (DiNOGMI), Clinical Neurology, University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Dario Arnaldi
- Department of Neuroscience (DiNOGMI), Clinical Neurology, University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Dag Aarsland
- Department of Old Age Psychiatry, King's College University, London, UK
| | - Francesco Orzi
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Fabrizio Stocchi
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Paola Stirpe
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Peter Fuhr
- Universitätsspital Basel, Abteilung Neurophysiologie, Basel, Switzerland
| | - Ute Gschwandtner
- Universitätsspital Basel, Abteilung Neurophysiologie, Basel, Switzerland
| | - Gerhard Ransmayr
- Department of Neurology and Psychiatry and Faculty of Medicine, Johannes Kepler University Linz, General Hospital of the City of Linz, Linz, Austria
| | - Georg Caravias
- Department of Neurology and Psychiatry and Faculty of Medicine, Johannes Kepler University Linz, General Hospital of the City of Linz, Linz, Austria
| | - Heinrich Garn
- AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | | | - Michela Pievani
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Giovanni B Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Fabrizia D'Antonio
- Department of Neurology and Psychiatry, Sapienza, University of Rome, Rome, Italy
| | - Carlo De Lena
- Department of Neurology and Psychiatry, Sapienza, University of Rome, Rome, Italy
| | - Bahar Güntekin
- Department of Biophysics, Istanbul Medipol University, Istanbul, Turkey
| | - Lutfu Hanoğlu
- Department of Neurology, University of Istanbul-Medipol, Istanbul, Turkey
| | - Erol Başar
- Department of Neurosciences, Dokuz Eylül University Medical School, Izmir, Turkey; Department of Neurology, Dokuz Eylül University Medical School, Izmir, Turkey
| | - Görsev Yener
- Department of Psychology, Dokuz Eylül University, Izmir, Turkey; Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | - Derya Durusu Emek-Savaş
- Department of Psychology, Dokuz Eylül University, Izmir, Turkey; Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | | | - Raffaella Franciotti
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | | | - Laura Bonanni
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
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41
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Hassan M, Chaton L, Benquet P, Delval A, Leroy C, Plomhause L, Moonen AJH, Duits AA, Leentjens AFG, van Kranen-Mastenbroek V, Defebvre L, Derambure P, Wendling F, Dujardin K. Functional connectivity disruptions correlate with cognitive phenotypes in Parkinson's disease. NEUROIMAGE-CLINICAL 2017; 14:591-601. [PMID: 28367403 PMCID: PMC5361870 DOI: 10.1016/j.nicl.2017.03.002] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Revised: 02/28/2017] [Accepted: 03/04/2017] [Indexed: 01/21/2023]
Abstract
Cognitive deficits in Parkinson's disease are thought to be related to altered functional brain connectivity. To date, cognitive-related changes in Parkinson's disease have never been explored with dense-EEG with the aim of establishing a relationship between the degree of cognitive impairment, on the one hand, and alterations in the functional connectivity of brain networks, on the other hand. This study was aimed at identifying altered brain networks associated with cognitive phenotypes in Parkinson's disease using dense-EEG data recorded during rest with eyes closed. Three groups of Parkinson's disease patients (N = 124) with different cognitive phenotypes coming from a data-driven cluster analysis, were studied: G1) cognitively intact patients (63), G2) patients with mild cognitive deficits (46) and G3) patients with severe cognitive deficits (15). Functional brain networks were identified using a dense-EEG source connectivity method. Pairwise functional connectivity was computed for 68 brain regions in different EEG frequency bands. Network statistics were assessed at both global (network topology) and local (inter-regional connections) level. Results revealed progressive disruptions in functional connectivity between the three patient groups, typically in the alpha band. Differences between G1 and G2 (p < 0.001, corrected using permutation test) were mainly frontotemporal alterations. A statistically significant correlation (ρ = 0.49, p < 0.001) was also obtained between a proposed network-based index and the patients' cognitive score. Global properties of network topology in patients were relatively intact. These findings indicate that functional connectivity decreases with the worsening of cognitive performance and loss of frontotemporal connectivity may be a promising neuromarker of cognitive impairment in Parkinson's disease. We test the use of dense-EEG to identify altered brain networks associated with cognitive phenotypes in Parkinson's disease. The functional connectivity decreases with the worsening of cognitive performance The loss of frontotemporal connectivity may be a promising neuromarker of cognitive impairment in Parkinson's disease.
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Affiliation(s)
- M Hassan
- INSERM, U1099, F-35000 Rennes, France; University of Rennes 1, LTSI, F-35000 Rennes, France
| | - L Chaton
- CHU Lille, Clinical Neurophysiology Department, F-59000 Lille, France
| | - P Benquet
- INSERM, U1099, F-35000 Rennes, France; University of Rennes 1, LTSI, F-35000 Rennes, France
| | - A Delval
- University of Lille, U1171 - Degenerative & Vascular Cognitive Disorders, F-59000 Lille, France; INSERM, U1171, F-59000 Lille, France; CHU Lille, Clinical Neurophysiology Department, F-59000 Lille, France
| | - C Leroy
- University of Lille, U1171 - Degenerative & Vascular Cognitive Disorders, F-59000 Lille, France; INSERM, U1171, F-59000 Lille, France; CHU Lille, Clinical Neurophysiology Department, F-59000 Lille, France
| | - L Plomhause
- University of Lille, U1171 - Degenerative & Vascular Cognitive Disorders, F-59000 Lille, France; INSERM, U1171, F-59000 Lille, France; CHU Lille, Clinical Neurophysiology Department, F-59000 Lille, France
| | - A J H Moonen
- Maastricht University Medical Center, Maastricht, The Netherlands
| | - A A Duits
- Maastricht University Medical Center, Maastricht, The Netherlands
| | - A F G Leentjens
- Maastricht University Medical Center, Maastricht, The Netherlands
| | | | - L Defebvre
- University of Lille, U1171 - Degenerative & Vascular Cognitive Disorders, F-59000 Lille, France; INSERM, U1171, F-59000 Lille, France; CHU Lille, Neurology and Movement Disorders Department, F-59000 Lille, France
| | - P Derambure
- University of Lille, U1171 - Degenerative & Vascular Cognitive Disorders, F-59000 Lille, France; INSERM, U1171, F-59000 Lille, France; CHU Lille, Clinical Neurophysiology Department, F-59000 Lille, France
| | - F Wendling
- INSERM, U1099, F-35000 Rennes, France; University of Rennes 1, LTSI, F-35000 Rennes, France
| | - K Dujardin
- University of Lille, U1171 - Degenerative & Vascular Cognitive Disorders, F-59000 Lille, France; INSERM, U1171, F-59000 Lille, France; CHU Lille, Neurology and Movement Disorders Department, F-59000 Lille, France
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42
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Differential diagnosis between patients with probable Alzheimer's disease, Parkinson's disease dementia, or dementia with Lewy bodies and frontotemporal dementia, behavioral variant, using quantitative electroencephalographic features. J Neural Transm (Vienna) 2017; 124:569-581. [PMID: 28243755 PMCID: PMC5399050 DOI: 10.1007/s00702-017-1699-6] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Accepted: 02/14/2017] [Indexed: 12/29/2022]
Abstract
The objective of this work was to develop and evaluate a classifier for differentiating probable Alzheimer’s disease (AD) from Parkinson’s disease dementia (PDD) or dementia with Lewy bodies (DLB) and from frontotemporal dementia, behavioral variant (bvFTD) based on quantitative electroencephalography (QEEG). We compared 25 QEEG features in 61 dementia patients (20 patients with probable AD, 20 patients with PDD or probable DLB (DLBPD), and 21 patients with bvFTD). Support vector machine classifiers were trained to distinguish among the three groups. Out of the 25 features, 23 turned out to be significantly different between AD and DLBPD, 17 for AD versus bvFTD, and 12 for bvFTD versus DLBPD. Using leave-one-out cross validation, the classification achieved an accuracy, sensitivity, and specificity of 100% using only the QEEG features Granger causality and the ratio of theta and beta1 band powers. These results indicate that classifiers trained with selected QEEG features can provide a valuable input in distinguishing among AD, DLB or PDD, and bvFTD patients. In this study with 61 patients, no misclassifications occurred. Therefore, further studies should investigate the potential of this method to be applied not only on group level but also in diagnostic support for individual subjects.
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43
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Babiloni C, Del Percio C, Caroli A, Salvatore E, Nicolai E, Marzano N, Lizio R, Cavedo E, Landau S, Chen K, Jagust W, Reiman E, Tedeschi G, Montella P, De Stefano M, Gesualdo L, Frisoni GB, Soricelli A. Cortical sources of resting state EEG rhythms are related to brain hypometabolism in subjects with Alzheimer's disease: an EEG-PET study. Neurobiol Aging 2016; 48:122-134. [DOI: 10.1016/j.neurobiolaging.2016.08.021] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2014] [Revised: 08/05/2016] [Accepted: 08/24/2016] [Indexed: 11/24/2022]
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Neto E, Biessmann F, Aurlien H, Nordby H, Eichele T. Regularized Linear Discriminant Analysis of EEG Features in Dementia Patients. Front Aging Neurosci 2016; 8:273. [PMID: 27965568 PMCID: PMC5127828 DOI: 10.3389/fnagi.2016.00273] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Accepted: 10/31/2016] [Indexed: 10/24/2022] Open
Abstract
The present study explores if EEG spectral parameters can discriminate between healthy elderly controls (HC), Alzheimer's disease (AD) and vascular dementia (VaD) using. We considered EEG data recorded during normal clinical routine with 114 healthy controls (HC), 114 AD, and 114 VaD patients. The spectral features extracted from the EEG were the absolute delta power, decay from lower to higher frequencies, amplitude, center and dispersion of the alpha power and baseline power of the entire frequency spectrum. For discrimination, we submitted these EEG features to regularized linear discriminant analysis algorithm with a 10-fold cross-validation. To check the consistency of the results obtained by our classifiers, we applied bootstrap statistics. Four binary classifiers were used to discriminate HC from AD, HC from VaD, AD from VaD, and HC from dementia patients (AD or VaD). For each model, we measured the discrimination performance using the area under curve (AUC) and the accuracy of the cross-validation (cv-ACC). We applied this procedure using two different sets of predictors. The first set considered all the features extracted from the 22 channels. For the second set of features, we automatically rejected features poorly correlated with their labels. Fairly good results were obtained when discriminating HC from dementia patients with AD or VaD (AUC = 0.84). We also obtained AUC = 0.74 for discrimination of AD from HC, AUC = 0.77 for discrimination of VaD from HC, and finally AUC = 0.61 for discrimination of AD from VaD. Our models were able to separate HC from dementia patients, and also and to discriminate AD from VaD above chance. Our results suggest that these features may be relevant for the clinical assessment of patients with dementia.
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Affiliation(s)
- Emanuel Neto
- Section for Clinical Neurophysiology, Haukeland University HospitalBergen, Norway; Institute of Biological and Medical Psychology, University of BergenBergen, Norway
| | | | - Harald Aurlien
- Section for Clinical Neurophysiology, Haukeland University Hospital Bergen, Norway
| | - Helge Nordby
- Institute of Biological and Medical Psychology, University of Bergen Bergen, Norway
| | - Tom Eichele
- Section for Clinical Neurophysiology, Haukeland University HospitalBergen, Norway; Institute of Biological and Medical Psychology, University of BergenBergen, Norway; K.G. Jebsen Center for Neuropsychiatric DisordersBergen, Norway
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45
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Cozac VV, Chaturvedi M, Hatz F, Meyer A, Fuhr P, Gschwandtner U. Increase of EEG Spectral Theta Power Indicates Higher Risk of the Development of Severe Cognitive Decline in Parkinson's Disease after 3 Years. Front Aging Neurosci 2016; 8:284. [PMID: 27965571 PMCID: PMC5126063 DOI: 10.3389/fnagi.2016.00284] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 11/11/2016] [Indexed: 11/25/2022] Open
Abstract
Objective: We investigated quantitative electroencephalography (qEEG) and clinical parameters as potential risk factors of severe cognitive decline in Parkinson’s disease. Methods: We prospectively investigated 37 patients with Parkinson’s disease at baseline and follow-up (after 3 years). Patients had no severe cognitive impairment at baseline. We used a summary score of cognitive tests as the outcome at follow-up. At baseline we assessed motor, cognitive, and psychiatric factors; qEEG variables [global relative median power (GRMP) spectra] were obtained by a fully automated processing of high-resolution EEG (256-channels). We used linear regression models with calculation of the explained variance to evaluate the relation of baseline parameters with cognitive deterioration. Results: The following baseline parameters significantly predicted severe cognitive decline: GRMP theta (4–8 Hz), cognitive task performance in executive functions and working memory. Conclusions: Combination of neurocognitive tests and qEEG improves identification of patients with higher risk of cognitive decline in PD.
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Affiliation(s)
- Vitalii V Cozac
- Department of Neurology, Hospital of the University of Basel Basel, Switzerland
| | - Menorca Chaturvedi
- Department of Neurology, Hospital of the University of BaselBasel, Switzerland; Department of Mathematics and Computer Science, University of BaselBasel, Switzerland
| | - Florian Hatz
- Department of Neurology, Hospital of the University of Basel Basel, Switzerland
| | - Antonia Meyer
- Department of Neurology, Hospital of the University of Basel Basel, Switzerland
| | - Peter Fuhr
- Department of Neurology, Hospital of the University of Basel Basel, Switzerland
| | - Ute Gschwandtner
- Department of Neurology, Hospital of the University of Basel Basel, Switzerland
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Chelyapina MV, Sharova EV, Zaytsev OS. [The cholinergic deficiency syndrome in patients with depressed consciousness after severe brain injury]. Zh Nevrol Psikhiatr Im S S Korsakova 2016; 116:17-24. [PMID: 27500871 DOI: 10.17116/jnevro20161167117-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
AIM To determine the clinical and electrophysiological (EEG) signs of cholinergic deficiency in the process of recovery of consciousness in patients with severe brain injury. MATERIAL AND METHODS Thirty-seven people (24 men and 13 women, mean age 32±14 years) were studied. A comprehensive study included assessment of neurological status, mental activity, and EEG. RESULTS AND CONCLUSION A set of neurological symptoms, including reduced muscle tone, autonomic disorders (dry mucous membranes and skin, tachycardia, hypotension, gastrointestinal tract), eye movement disorders, that were,in accordance with the literature, characteristicof the cholinergic deficiency syndrome was found. This syndrome was detected against the background of a comatose state, akinetic mutism and mutism with understanding of speech, disintegration of speech, disorientation and amnestic decline. EEG revealed stable over time (months) characteristic changes: slowing and asymmetric alpha activity, equivalent dipole sources of hippocampal and stem localization, persistent strengthening of intra-hemispheric coherent connections, especially on the left side. The regression of the cholinergic deficiency syndrome was accompanied by an increase of regularity, capacity and frequency of alpha-activity (from 7-8 to 9-10 Hz), prevalence of equivalent dipole sources in the hippocampus with their appearance in the occipital cortex, normalization of connections with right-brain coherence with the preservation of their pathologically high values on the left side.
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Affiliation(s)
- M V Chelyapina
- Institute of Higher Nervous Activity and Neurophysiology, Moscow, Russia
| | - E V Sharova
- Institute of Higher Nervous Activity and Neurophysiology, Moscow, Russia
| | - O S Zaytsev
- Burdenko Research Institute of Neurosurgery, Moscow, Russia
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Babiloni C, Pennica A, Del Percio C, Noce G, Cordone S, Lopez S, Berry K, Muratori C, Ferracuti S, Roma P, Correr V, Di Campli F, Gianserra L, Ciullini L, Aceti A, Soricelli A, Teti E, Viscione M, Limatola C, Onorati P, Capotosto P, Andreoni M. Antiretroviral therapy affects the z-score index of deviant cortical EEG rhythms in naïve HIV individuals. Neuroimage Clin 2016; 12:144-56. [PMID: 27408799 PMCID: PMC4933036 DOI: 10.1016/j.nicl.2016.06.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Revised: 05/12/2016] [Accepted: 06/06/2016] [Indexed: 01/04/2023]
Abstract
OBJECTIVE Here we tested the effect of combined antiretroviral therapy (cART) on deviant electroencephalographic (EEG) source activity in treatment-naïve HIV individuals. METHODS Resting state eyes-closed EEG data were recorded before and after 5 months of cART in 48 male HIV subjects, who were naïve at the study start. The EEG data were also recorded in 59 age- and sex-matched healthy subjects as a control group. Frequency bands of interest included delta, theta, alpha1, alpha2 and alpha3, based on alpha frequency peak specific to each individual. They also included beta1 (13-20 Hz) and beta2 (20-30 Hz). Low-resolution brain electromagnetic tomography (LORETA) estimated EEG cortical source activity in frontal, central, temporal, parietal, and occipital regions. RESULTS Before the therapy, the HIV group showed greater parietal delta source activity and lower spatially diffuse alpha source activity compared to the control group. Thus, the ratio of parietal delta and alpha3 source activity served as an EEG marker. The z-score showed a statistically deviant EEG marker (EEG +) in 50% of the HIV individuals before therapy (p < 0.05). After 5 months of cART, delta source activity decreased, and alpha3 source activity increased in the HIV subjects with EEG + (about 50% of them showed a normalized EEG marker). CONCLUSIONS This procedure detected a deviant EEG marker before therapy and its post-therapy normalization in naïve HIV single individuals. SIGNIFICANCE The parietal delta/alpha3 EEG marker may be used to monitor cART effects on brain function in such individuals.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology, University of Rome “La Sapienza”, Rome, Italy
- IRCCS S. Raffaele Pisana, Rome, Italy
| | - Alfredo Pennica
- Infectious Diseases, Faculty of Medicine and Psychology, University of Rome “La Sapienza”, Rome, Italy
| | | | | | - Susanna Cordone
- Department of Physiology and Pharmacology, University of Rome “La Sapienza”, Rome, Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology, University of Rome “La Sapienza”, Rome, Italy
| | - Ketura Berry
- Department of Physiology and Pharmacology, University of Rome “La Sapienza”, Rome, Italy
| | | | - Stefano Ferracuti
- Psychiatry, Faculty of Medicine and Psychology, University of Rome “La Sapienza”, Rome, Italy
| | - Paolo Roma
- Psychiatry, Faculty of Medicine and Psychology, University of Rome “La Sapienza”, Rome, Italy
| | - Valentina Correr
- Psychiatry, Faculty of Medicine and Psychology, University of Rome “La Sapienza”, Rome, Italy
| | - Francesco Di Campli
- Infectious Diseases, Faculty of Medicine and Psychology, University of Rome “La Sapienza”, Rome, Italy
| | - Laura Gianserra
- Infectious Diseases, Faculty of Medicine and Psychology, University of Rome “La Sapienza”, Rome, Italy
| | - Lorenzo Ciullini
- Infectious Diseases, Faculty of Medicine and Psychology, University of Rome “La Sapienza”, Rome, Italy
| | - Antonio Aceti
- Infectious Diseases, Faculty of Medicine and Psychology, University of Rome “La Sapienza”, Rome, Italy
| | - Andrea Soricelli
- IRCCS SDN, Naples, Italy
- Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | - Elisabetta Teti
- Clinical Infectious Diseases, University of Rome “Tor Vergata”, Rome, Italy
| | - Magdalena Viscione
- Clinical Infectious Diseases, University of Rome “Tor Vergata”, Rome, Italy
| | - Cristina Limatola
- Department of Physiology and Pharmacology, University of Rome “La Sapienza”, Rome, Italy
| | - Paolo Onorati
- Department of Physiology and Pharmacology, University of Rome “La Sapienza”, Rome, Italy
- IRCCS S. Raffaele Pisana, Rome, Italy
| | | | - Massimo Andreoni
- Clinical Infectious Diseases, University of Rome “Tor Vergata”, Rome, Italy
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Babiloni C, Lizio R, Marzano N, Capotosto P, Soricelli A, Triggiani AI, Cordone S, Gesualdo L, Del Percio C. Brain neural synchronization and functional coupling in Alzheimer's disease as revealed by resting state EEG rhythms. Int J Psychophysiol 2016; 103:88-102. [PMID: 25660305 DOI: 10.1016/j.ijpsycho.2015.02.008] [Citation(s) in RCA: 202] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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49
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Quantitative EEG and Cognitive Decline in Parkinson's Disease. PARKINSONS DISEASE 2016; 2016:9060649. [PMID: 27148466 PMCID: PMC4842380 DOI: 10.1155/2016/9060649] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Accepted: 03/14/2016] [Indexed: 12/14/2022]
Abstract
Cognitive decline is common with the progression of Parkinson's disease (PD). Different candidate biomarkers are currently studied for the risk of dementia in PD. Several studies have shown that quantitative EEG (QEEG) is a promising predictor of PD-related cognitive decline. In this paper we briefly outline the basics of QEEG analysis and analyze the recent publications addressing the predictive value of QEEG in the context of cognitive decline in PD. The MEDLINE database was searched for relevant publications from January 01, 2005, to March 02, 2015. Twenty-four studies reported QEEG findings in various cognitive states in PD. Spectral and connectivity markers of QEEG could help to discriminate between PD patients with different level of cognitive decline. QEEG variables correlate with tools for cognitive assessment over time and are associated with significant hazard ratios to predict PD-related dementia. QEEG analysis shows high test-retest reliability and avoids learning effects associated with some neuropsychological testing; it is noninvasive and relatively easy to repeat.
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Bertrand JA, McIntosh AR, Postuma RB, Kovacevic N, Latreille V, Panisset M, Chouinard S, Gagnon JF. Brain Connectivity Alterations Are Associated with the Development of Dementia in Parkinson's Disease. Brain Connect 2016; 6:216-24. [DOI: 10.1089/brain.2015.0390] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
| | | | - Ronald B. Postuma
- Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, Canada
- Department of Neurology, Montreal General Hospital, Montreal, Canada
| | | | - Véronique Latreille
- Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, Canada
- Department of Psychology, Université de Montréal, Montreal, Canada
| | - Michel Panisset
- Unité des troubles du mouvement André Barbeau, Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Sylvain Chouinard
- Unité des troubles du mouvement André Barbeau, Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Jean-François Gagnon
- Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, Canada
- Department of Psychology, Université du Québec à Montréal, Montreal, Canada
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