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da Silva Castanheira J, Wiesman AI, Hansen JY, Misic B, Baillet S. The neurophysiological brain-fingerprint of Parkinson's disease. EBioMedicine 2024; 105:105201. [PMID: 38908100 PMCID: PMC11253223 DOI: 10.1016/j.ebiom.2024.105201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 05/30/2024] [Accepted: 05/30/2024] [Indexed: 06/24/2024] Open
Abstract
BACKGROUND Research in healthy young adults shows that characteristic patterns of brain activity define individual "brain-fingerprints" that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson's disease (PD). METHODS We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N = 424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4 min and as short as 30 s. FINDINGS The arrhythmic spectral components of cortical activity in patients with Parkinson's disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson's brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson's symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology. INTERPRETATION The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson's disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets. FUNDING Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311).
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Affiliation(s)
| | - Alex I Wiesman
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Justine Y Hansen
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sylvain Baillet
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
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McKeown DJ, Jones M, Pihl C, Finley AJ, Kelley N, Baumann O, Schinazi VR, Moustafa AA, Cavanagh JF, Angus DJ. Medication-invariant resting aperiodic and periodic neural activity in Parkinson's disease. Psychophysiology 2024; 61:e14478. [PMID: 37937898 DOI: 10.1111/psyp.14478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 10/08/2023] [Accepted: 10/16/2023] [Indexed: 11/09/2023]
Abstract
Parkinson's disease (PD) has been associated with greater total power in canonical frequency bands (i.e., alpha, beta) of the resting electroencephalogram (EEG). However, PD has also been associated with a reduction in the proportion of total power across all frequency bands. This discrepancy may be explained by aperiodic activity (exponent and offset) present across all frequency bands. Here, we examined differences in the eyes-open (EO) and eyes-closed (EC) resting EEG of PD participants (N = 26) on and off medication, and age-matched healthy controls (CTL; N = 26). We extracted power from canonical frequency bands using traditional methods (total alpha and beta power) and extracted separate parameters for periodic (parameterized alpha and beta power) and aperiodic activity (exponent and offset). Cluster-based permutation tests over spatial and frequency dimensions indicated that total alpha and beta power, and aperiodic exponent and offset were greater in PD participants, independent of medication status. After removing the exponent and offset, greater alpha power in PD (vs. CTL) was only present in EO recordings and no reliable differences in beta power were observed. Differences between PD and CTL in the resting EEG are likely driven by aperiodic activity, suggestive of greater relative inhibitory neural activity and greater neuronal spiking. Our findings suggest that resting EEG activity in PD is characterized by medication-invariant differences in aperiodic activity which is independent of the increase in alpha power with EO. This highlights the importance of considering aperiodic activity contributions to the neural correlates of brain disorders.
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Affiliation(s)
- Daniel J McKeown
- Faculty of Society and Design, School of Psychology, Bond University, Gold Coast, Queensland, Australia
| | - Manon Jones
- Faculty of Society and Design, School of Psychology, Bond University, Gold Coast, Queensland, Australia
| | - Camilla Pihl
- Faculty of Society and Design, School of Psychology, Bond University, Gold Coast, Queensland, Australia
| | - Anna J Finley
- Institute on Aging, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Nicholas Kelley
- School of Psychology, University of Southampton, Southampton, UK
| | - Oliver Baumann
- Faculty of Society and Design, School of Psychology, Bond University, Gold Coast, Queensland, Australia
| | - Victor R Schinazi
- Faculty of Society and Design, School of Psychology, Bond University, Gold Coast, Queensland, Australia
| | - Ahmed A Moustafa
- Faculty of Society and Design, School of Psychology, Bond University, Gold Coast, Queensland, Australia
| | - James F Cavanagh
- Department of Psychology, University of New Mexico, Albuquerque, New Mexico, USA
| | - Douglas J Angus
- Faculty of Society and Design, School of Psychology, Bond University, Gold Coast, Queensland, Australia
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Peng C, Wang Z, Sun Y, Mo Y, Hu K, Li Q, Hou X, Zhu Z, He X, Xue S, Zhang S. Subthalamic nucleus dynamics track microlesion effect in Parkinson's disease. Front Cell Dev Biol 2024; 12:1370287. [PMID: 38434618 PMCID: PMC10906266 DOI: 10.3389/fcell.2024.1370287] [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: 01/14/2024] [Accepted: 02/06/2024] [Indexed: 03/05/2024] Open
Abstract
Parkinson's Disease (PD) is characterized by the temporary alleviation of motor symptoms following electrode implantation (or nucleus destruction), known as the microlesion effect (MLE). Electrophysiological studies have explored different PD stages, but understanding electrophysiological characteristics during the MLE period remains unclear. The objective was to examine the characteristics of local field potential (LFP) signals in the subthalamic nucleus (STN) during the hyperacute period following implantation (within 2 days) and 1 month post-implantation. 15 patients diagnosed with PD were enrolled in this observational study, with seven simultaneous recordings of bilateral STN-LFP signals using wireless sensing technology from an implantable pulse generator. Recordings were made in both on and off medication states over 1 month after implantation. We used a method to parameterize the neuronal power spectrum to separate periodic oscillatory and aperiodic components effectively. Our results showed that beta power exhibited a significant increase in the off medication state 1 month after implantation, compared to the postoperative hyperacute period. Notably, this elevation was effectively attenuated by levodopa administration. Furthermore, both the exponents and offsets displayed a decrease at 1 month postoperatively when compared to the hyperacute postoperative period. Remarkably, levodopa medication exerted a modulatory effect on these aperiodic parameters, restoring them back to levels observed during the hyperacute period. Our findings suggest that both periodic and aperiodic components partially capture distinct electrophysiological characteristics during the MLE. It is crucial to adequately evaluate such discrepancies when exploring the mechanisms of MLE and optimizing adaptive stimulus protocols.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Sha Xue
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Shizhong Zhang
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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Chung H, Wilkinson CL, Job Said A, Tager-Flusberg H, Nelson CA. Evaluating early EEG correlates of restricted and repetitive behaviors for toddlers with or without autism. RESEARCH SQUARE 2024:rs.3.rs-3871138. [PMID: 38313269 PMCID: PMC10836096 DOI: 10.21203/rs.3.rs-3871138/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Background Restricted and repetitive behaviors (RRB) are among the primary characteristics of autism spectrum disorder (ASD). Despite the potential impact on later developmental outcomes, our understanding of the neural underpinnings of RRBs is limited. Alterations in EEG alpha activity have been observed in ASD and implicated in RRBs, however, developmental changes within the alpha band requires careful methodological considerations when studying its role in brain-behavior relationships during infancy and early childhood. Novel approaches now enable the parameterization of the power spectrum into periodic and aperiodic components. This study aimed to characterize the neural correlates of RRBs in infancy by (1) comparing infant resting-state measures (periodic alpha and aperiodic activity) between infants who develop ASD, elevated likelihood infants without ASD, and low likelihood infants without ASD, and (2) evaluate whether these infant EEG measures are associated with frequency of RRBs measured at 24 months. Methods Baseline non-task related EEG data were collected from 12-to-14-month-old infants with and without elevated likelihood of autism (N=160), and periodic alpha activity (periodic alpha power, individual peak alpha frequency and amplitude), and aperiodic activity measures (aperiodic exponent) were calculated. Parent-reported RRBs were obtained at 24 months using the Repetitive Behavior Scale-Revised questionnaire. Group differences in EEG measures were evaluated using ANCOVA, and multiple linear regressions were conducted to assess relationships between EEG and RRB measures. Results No group-level differences in infant EEG measures were observed. Marginal effects analysis of linear regressions revealed significant associations within the ASD group, such that higher periodic alpha power, lower peak alpha frequency, and lower aperiodic exponent, were associated with elevated RRBs at 24 months. No significant associations were observed for non-ASD outcome groups. Limitations The sample size for ASD (N=19) was modest for examining brain-behavior relations. Larger sample sizes are needed to increase statistical power. Conclusion For infants with later ASD diagnoses, measures of alpha and aperiodic activity measured at 1-year of age were associated with later manifestation of RRBs at 2-years. Longitudinal studies are needed to elucidate whether the early trajectory of these EEG measures and their dynamic relations in development influence manifestations of RRBs in ASD.
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Kopčanová M, Tait L, Donoghue T, Stothart G, Smith L, Flores-Sandoval AA, Davila-Perez P, Buss S, Shafi MM, Pascual-Leone A, Fried PJ, Benwell CSY. Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes. Neurobiol Dis 2024; 190:106380. [PMID: 38114048 DOI: 10.1016/j.nbd.2023.106380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 11/30/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023] Open
Abstract
Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimer's disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD < HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasize the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.
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Affiliation(s)
- Martina Kopčanová
- Division of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, Dundee, UK.
| | - Luke Tait
- Centre for Systems Modelling and Quantitative Biomedicine, School of Medical and Dental Sciences, University of Birmingham, UK; Cardiff University Brain Research Imaging Centre, Cardiff, UK
| | - Thomas Donoghue
- Department of Biomedical Engineering, Columbia University, New York, USA
| | | | - Laura Smith
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Aimee Arely Flores-Sandoval
- Charité - Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, 10117 Berlin, Germany; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Paula Davila-Perez
- Rey Juan Carlos University Hospital (HURJC), Department of Clinical Neurophysiology, Móstoles, Madrid, Spain; Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Stephanie Buss
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Mouhsin M Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Alvaro Pascual-Leone
- Department of Neurology, Harvard Medical School, Boston, MA, USA; Hinda and Arthur Marcus Institute for Aging Research and Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Boston, MA, United States of America
| | - Peter J Fried
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Christopher S Y Benwell
- Division of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, Dundee, UK
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Pardo-Valencia J, Fernández-García C, Alonso-Frech F, Foffani G. Oscillatory vs. non-oscillatory subthalamic beta activity in Parkinson's disease. J Physiol 2024; 602:373-395. [PMID: 38084073 DOI: 10.1113/jp284768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 11/13/2023] [Indexed: 01/16/2024] Open
Abstract
Parkinson's disease is characterized by exaggerated beta activity (13-35 Hz) in cortico-basal ganglia motor loops. Beta activity includes both periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuations reflecting spiking activity and excitation/inhibition balance (i.e. non-oscillatory activity). However, the relative contribution, dopamine dependency and clinical correlations of oscillatory vs. non-oscillatory beta activity remain unclear. We recorded, modelled and analysed subthalamic local field potentials in parkinsonian patients at rest while off or on medication. Autoregressive modelling with additive 1/f noise clarified the relationships between measures of beta activity in the time domain (i.e. amplitude and duration of beta bursts) or in the frequency domain (i.e. power and sharpness of the spectral peak) and oscillatory vs. non-oscillatory activity: burst duration and spectral sharpness are specifically sensitive to oscillatory activity, whereas burst amplitude and spectral power are ambiguously sensitive to both oscillatory and non-oscillatory activity. Our experimental data confirmed the model predictions and assumptions. We subsequently analysed the effect of levodopa, obtaining strong-to-extreme Bayesian evidence that oscillatory beta activity is reduced in patients on vs. off medication, with moderate evidence for absence of modulation of the non-oscillatory component. Finally, specifically the oscillatory component of beta activity correlated with the rate of motor progression of the disease. Methodologically, these results provide an integrative understanding of beta-based biomarkers relevant for adaptive deep brain stimulation. Biologically, they suggest that primarily the oscillatory component of subthalamic beta activity is dopamine dependent and may play a role not only in the pathophysiology but also in the progression of Parkinson's disease. KEY POINTS: Beta activity in Parkinson's disease includes both true periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuations reflecting spiking activity and synaptic balance (i.e. non-oscillatory activity). The relative contribution, dopamine dependency and clinical correlations of oscillatory vs. non-oscillatory beta activity remain unclear. Burst duration and spectral sharpness are specifically sensitive to oscillatory activity, while burst amplitude and spectral power are ambiguously sensitive to both oscillatory and non-oscillatory activity. Only the oscillatory component of subthalamic beta activity is dopamine-dependent. Stronger beta oscillatory activity correlates with faster motor progression of the disease.
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Affiliation(s)
- Jesús Pardo-Valencia
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | - Carla Fernández-García
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
| | - Fernando Alonso-Frech
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- Department of Neurology, San Carlos Research Health Intitute (IdISSC), Hospital Clínico San Carlos, Madrid, Spain
| | - Guglielmo Foffani
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- Hospital Nacional de Parapléjicos, SESCAM, Toledo, Spain
- Instituto de Salud Carlos III, CIBERNED, Madrid, Spain
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da Silva Castanheira J, Wiesman AI, Hansen JY, Misic B, Baillet S. The neurophysiological brain-fingerprint of Parkinson's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.03.23285441. [PMID: 36798232 PMCID: PMC9934726 DOI: 10.1101/2023.02.03.23285441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
In this study, we investigate the clinical potential of brain-fingerprints derived from electrophysiological brain activity for diagnostics and progression monitoring of Parkinson's disease (PD). We obtained brain-fingerprints from PD patients and age-matched healthy controls using short, task-free magnetoencephalographic recordings. The rhythmic components of the individual brain-fingerprint distinguished between patients and healthy participants with approximately 90% accuracy. The most prominent cortical features of the Parkinson's brain-fingerprint mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also show that Parkinson's disease stages can be decoded directly from cortical neurophysiological activity. Additionally, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology. We further demonstrate that the arrhythmic components of cortical activity are more variable over short periods of time in patients with Parkinson's disease than in healthy controls, making individual differentiation between patients based on these features more challenging and explaining previous negative published results. Overall, we outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and clinical staging of Parkinson's disease. For this reason, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification and to the improved identification and testing of therapeutic neurostimulation targets.
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Affiliation(s)
| | - Alex I. Wiesman
- Montreal Neurological Institute, McGill University, Montreal QC, Canada
| | - Justine Y. Hansen
- Montreal Neurological Institute, McGill University, Montreal QC, Canada
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, Montreal QC, Canada
| | - Sylvain Baillet
- Montreal Neurological Institute, McGill University, Montreal QC, Canada
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Averna A, Coelli S, Ferrara R, Cerutti S, Priori A, Bianchi AM. Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer's and Parkinson's disease. J Neural Eng 2023; 20:051001. [PMID: 37746822 DOI: 10.1088/1741-2552/acf8fa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 09/12/2023] [Indexed: 09/26/2023]
Abstract
Brain-related neuronal recordings, such as local field potential, electroencephalogram and magnetoencephalogram, offer the opportunity to study the complexity of the human brain at different spatial and temporal scales. The complex properties of neuronal signals are intrinsically related to the concept of 'scale-free' behavior and irregular dynamic, which cannot be fully described through standard linear methods, but can be measured by nonlinear indexes. A remarkable application of these analysis methods on electrophysiological recordings is the deep comprehension of the pathophysiology of neurodegenerative diseases, that has been shown to be associated to changes in brain activity complexity. In particular, a decrease of global complexity has been associated to Alzheimer's disease, while a local increase of brain signals complexity characterizes Parkinson's disease. Despite the recent proliferation of studies using fractal and entropy-based analysis, the application of these techniques is still far from clinical practice, due to the lack of an agreement about their correct estimation and a conclusive and shared interpretation. Along with the aim of helping towards the realization of a multidisciplinary audience to approach nonlinear methods based on the concepts of fractality and irregularity, this survey describes the implementation and proper employment of the mostly known and applied indexes in the context of Alzheimer's and Parkinson's diseases.
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Affiliation(s)
- Alberto Averna
- Department of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Stefania Coelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Rosanna Ferrara
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Sergio Cerutti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Alberto Priori
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Anna Maria Bianchi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
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Kopčanová M, Tait L, Donoghue T, Stothart G, Smith L, Sandoval AAF, Davila-Perez P, Buss S, Shafi MM, Pascual-Leone A, Fried PJ, Benwell CS. Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.11.544491. [PMID: 37398162 PMCID: PMC10312609 DOI: 10.1101/2023.06.11.544491] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimer's disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD < HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasise the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.
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Affiliation(s)
- Martina Kopčanová
- Division of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, Dundee, UK
| | - Luke Tait
- Centre for Systems Modelling and Quantitative Biomedicine, School of Medical and Dental Sciences, University of Birmingham, UK
- Cardiff University Brain Research Imaging Centre, Cardiff, UK
| | - Thomas Donoghue
- Department of Biomedical Engineering, Columbia University, New York, USA
| | | | - Laura Smith
- School of Psychology, University of Kent, Kent, UK
| | - Aimee Arely Flores Sandoval
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, 10117, Berlin, Germany
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Paula Davila-Perez
- Rey Juan Carlos University Hospital (HURJC), Department of Clinical Neurophysiology, Móstoles, Madrid, Spain
- Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Stephanie Buss
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Mouhsin M. Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Alvaro Pascual-Leone
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Hinda and Arthur Marcus Institute for Aging Research and Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Boston MA
| | - Peter J. Fried
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Christopher S.Y. Benwell
- Division of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, Dundee, UK
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Wiest C, Torrecillos F, Pogosyan A, Bange M, Muthuraman M, Groppa S, Hulse N, Hasegawa H, Ashkan K, Baig F, Morgante F, Pereira EA, Mallet N, Magill PJ, Brown P, Sharott A, Tan H. The aperiodic exponent of subthalamic field potentials reflects excitation/inhibition balance in Parkinsonism. eLife 2023; 12:e82467. [PMID: 36810199 PMCID: PMC10005762 DOI: 10.7554/elife.82467] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 02/22/2023] [Indexed: 02/24/2023] Open
Abstract
Periodic features of neural time-series data, such as local field potentials (LFPs), are often quantified using power spectra. While the aperiodic exponent of spectra is typically disregarded, it is nevertheless modulated in a physiologically relevant manner and was recently hypothesised to reflect excitation/inhibition (E/I) balance in neuronal populations. Here, we used a cross-species in vivo electrophysiological approach to test the E/I hypothesis in the context of experimental and idiopathic Parkinsonism. We demonstrate in dopamine-depleted rats that aperiodic exponents and power at 30-100 Hz in subthalamic nucleus (STN) LFPs reflect defined changes in basal ganglia network activity; higher aperiodic exponents tally with lower levels of STN neuron firing and a balance tipped towards inhibition. Using STN-LFPs recorded from awake Parkinson's patients, we show that higher exponents accompany dopaminergic medication and deep brain stimulation (DBS) of STN, consistent with untreated Parkinson's manifesting as reduced inhibition and hyperactivity of STN. These results suggest that the aperiodic exponent of STN-LFPs in Parkinsonism reflects E/I balance and might be a candidate biomarker for adaptive DBS.
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Affiliation(s)
- Christoph Wiest
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Flavie Torrecillos
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Alek Pogosyan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Manuel Bange
- Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University MainzMainzGermany
| | - Muthuraman Muthuraman
- Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University MainzMainzGermany
| | - Sergiu Groppa
- Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University MainzMainzGermany
| | - Natasha Hulse
- Department of Neurosurgery, King's College LondonLondonUnited Kingdom
| | - Harutomo Hasegawa
- Department of Neurosurgery, King's College LondonLondonUnited Kingdom
| | - Keyoumars Ashkan
- Department of Neurosurgery, King's College LondonLondonUnited Kingdom
| | - Fahd Baig
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George' s, University of LondonLondonUnited Kingdom
| | - Francesca Morgante
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George' s, University of LondonLondonUnited Kingdom
| | - Erlick A Pereira
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George' s, University of LondonLondonUnited Kingdom
| | - Nicolas Mallet
- Institut des Maladies Neurodégénératives, CNRS UMR5293, Université de BordeauxBordeauxFrance
| | - Peter J Magill
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Peter Brown
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Andrew Sharott
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Huiling Tan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
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11
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Darmani G, Drummond NM, Ramezanpour H, Saha U, Hoque T, Udupa K, Sarica C, Zeng K, Cortez Grippe T, Nankoo JF, Bergmann TO, Hodaie M, Kalia SK, Lozano AM, Hutchison WD, Fasano A, Chen R. Long-Term Recording of Subthalamic Aperiodic Activities and Beta Bursts in Parkinson's Disease. Mov Disord 2023; 38:232-243. [PMID: 36424835 DOI: 10.1002/mds.29276] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 10/19/2022] [Accepted: 10/25/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Local field potentials (LFPs) represent the summation of periodic (oscillations) and aperiodic (fractal) signals. Although previous studies showed changes in beta band oscillations and burst characteristics of the subthalamic nucleus (STN) in Parkinson's disease (PD), how aperiodic activity in the STN is related to PD pathophysiology is unknown. OBJECTIVES The study aimed to characterize the long-term effects of STN-deep brain stimulation (DBS) and dopaminergic medications on aperiodic activities and beta bursts. METHODS A total of 10 patients with PD participated in this longitudinal study. Simultaneous bilateral STN-LFP recordings were conducted in six separate visits during a period of 18 months using the Activa PC + S device in the off and on dopaminergic medication states. We used irregular-resampling auto-spectral analysis to separate oscillations and aperiodic components (exponent and offset) in the power spectrum of STN-LFP signals in beta band. RESULTS Our results revealed a systematic increase in both the exponent and the offset of the aperiodic spectrum over 18 months following the DBS implantation, independent of the dopaminergic medication state of patients with PD. In contrast, beta burst durations and amplitudes were stable over time and were suppressed by dopaminergic medications. CONCLUSIONS These findings indicate that oscillations and aperiodic activities reflect at least partially distinct yet complementary neural mechanisms, which should be considered in the design of robust biomarkers to optimize adaptive DBS. Given the link between increased gamma-aminobutyric acidergic (GABAergic) transmission and higher aperiodic activity, our findings suggest that long-term STN-DBS may relate to increased inhibition in the basal ganglia. © 2022 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Ghazaleh Darmani
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Neil M Drummond
- Krembil Research Institute, University Health Network, Toronto, Canada
| | | | - Utpal Saha
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Tasnuva Hoque
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Kaviraja Udupa
- Department of Neurophysiology, National Institute of Mental Health & Neurosciences, Bengaluru, India
| | - Can Sarica
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Ke Zeng
- Krembil Research Institute, University Health Network, Toronto, Canada
| | | | | | - Til Ole Bergmann
- Neuroimaging Center, Johannes Gutenberg University Medical Center, Mainz, Germany
- Leibniz Institute for Resilience Research, Mainz, Germany
| | - Mojgan Hodaie
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
| | - Suneil K Kalia
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
| | - Andres M Lozano
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
| | - William D Hutchison
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
| | - Alfonso Fasano
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, Canada
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Toronto, Canada
| | - Robert Chen
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, Canada
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Toronto, Canada
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12
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A systematic review of local field potential physiomarkers in Parkinson's disease: from clinical correlations to adaptive deep brain stimulation algorithms. J Neurol 2023; 270:1162-1177. [PMID: 36209243 PMCID: PMC9886603 DOI: 10.1007/s00415-022-11388-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/16/2022] [Indexed: 02/03/2023]
Abstract
Deep brain stimulation (DBS) treatment has proven effective in suppressing symptoms of rigidity, bradykinesia, and tremor in Parkinson's disease. Still, patients may suffer from disabling fluctuations in motor and non-motor symptom severity during the day. Conventional DBS treatment consists of continuous stimulation but can potentially be further optimised by adapting stimulation settings to the presence or absence of symptoms through closed-loop control. This critically relies on the use of 'physiomarkers' extracted from (neuro)physiological signals. Ideal physiomarkers for adaptive DBS (aDBS) are indicative of symptom severity, detectable in every patient, and technically suitable for implementation. In the last decades, much effort has been put into the detection of local field potential (LFP) physiomarkers and in their use in clinical practice. We conducted a research synthesis of the correlations that have been reported between LFP signal features and one or more specific PD motor symptoms. Features based on the spectral beta band (~ 13 to 30 Hz) explained ~ 17% of individual variability in bradykinesia and rigidity symptom severity. Limitations of beta band oscillations as physiomarker are discussed, and strategies for further improvement of aDBS are explored.
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13
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Maria Pani S, Saba L, Fraschini M. Clinical applications of EEG power spectra aperiodic component analysis: a mini-review. Clin Neurophysiol 2022; 143:1-13. [DOI: 10.1016/j.clinph.2022.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 08/09/2022] [Accepted: 08/11/2022] [Indexed: 11/03/2022]
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14
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Wang Z, Mo Y, Sun Y, Hu K, Peng C, Zhang S, Xue S. Separating the aperiodic and periodic components of neural activity in Parkinson's disease. Eur J Neurosci 2022; 56:4889-4900. [PMID: 35848719 DOI: 10.1111/ejn.15774] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 06/23/2022] [Accepted: 07/11/2022] [Indexed: 11/27/2022]
Abstract
Most studies on electrophysiology have not separated aperiodic activity from the spectra but have rather evaluated a combined periodic oscillatory component and the aperiodic component. As the understanding of aperiodic activity gradually deepens, its potential physiological significance has acquired increased appreciation. Herein, we investigated the two components in scalp electroencephalogram in 16 healthy controls and 15 patients with Parkinson's disease (PD); the results revealed that aperiodic parameters were approximately symmetrically distributed in topography in patients with PD and were significantly modulated by dopaminergic medication in channels C4, C3, CP5 and FC5. In sum, our findings might provide indicators for evaluating treatment response in PD and highlight the importance of re-evaluating the neuronal power spectra parameterization.
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Affiliation(s)
- Zhuyong Wang
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yixiang Mo
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yujia Sun
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Kai Hu
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Chunkai Peng
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Shizhong Zhang
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Sha Xue
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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15
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Lai HJ, Deng CR, Wang RW, Lee LHN, Kuo CC. The genesis and functional consequences of cortico-subthalamic beta augmentation and excessive subthalamic burst discharges after dopaminergic deprivation. Exp Neurol 2022; 356:114153. [PMID: 35752209 DOI: 10.1016/j.expneurol.2022.114153] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 05/19/2022] [Accepted: 06/18/2022] [Indexed: 11/24/2022]
Abstract
The cardinal electrophysiological signs in Parkinson's disease (PD) include augmented beta oscillations in the motor cortex-subthalamic nucleus (MC-STN) axis and excessive burst discharges in STN. We have shown that excessive STN burst discharges have a direct causal relation with the locomotor deficits in PD. To investigate the correlation between the two cardinal signs, we characterized the courses of development of the electrophysiological abnormalities in the hemiparkinsonian rat model. The loss of dopaminergic neurons develops fast, and is histologically completed within 4-7 days of the lesion. The increase in STN burst discharges is limited to the lesioned side, and follows a very similar course. In contrast, beta augmentation has a bilateral presentation, and requires 14-21 days for full development. Behaviorally, the gross locomotor deficits in open field test and limb akinesia in stepping test match the foregoing fast and slow time courses, respectively. A further look into the spike entrainment shows that the oscillations in local field potential (LFP) of the MC effectively entrain the multi-unit (MU) spikes of MC, STN and entopeduncular nucleus (EPN), a rat homolog of human globus pallidus interna (GPi), whereas the LFP of STN or EPN (GPi) cannot entrain the spikes in MC. We conclude that excessive STN burst discharges are a direct consequence, whereas beta augmentation is probably a secondary or adaptive changes in the cortico-subcortical re-entrant loops, to dopaminergic deprivation. Beta augmentation is therefore not so consistently present as excessive STN burst discharges, but could signal more delicate derangements at the level of cortical programming in PD.
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Affiliation(s)
- Hsing-Jung Lai
- Institute of Physiology, National Taiwan University College of Medicine, Taipei, Taiwan.; Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan; National Taiwan University Hospital, Jin-Shan Branch, New Taipei, Taiwan
| | - Chuan-Rou Deng
- Institute of Physiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Ren-Wei Wang
- Institute of Physiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Lan-Hsin Nancy Lee
- Institute of Physiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chung-Chin Kuo
- Institute of Physiology, National Taiwan University College of Medicine, Taipei, Taiwan..
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16
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Dopamine depletion can be predicted by the aperiodic component of subthalamic local field potentials. Neurobiol Dis 2022; 168:105692. [DOI: 10.1016/j.nbd.2022.105692] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 03/06/2022] [Accepted: 03/11/2022] [Indexed: 12/19/2022] Open
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17
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EEG spectral exponent as a synthetic index for the longitudinal assessment of stroke recovery. Clin Neurophysiol 2022; 137:92-101. [PMID: 35303540 PMCID: PMC9038588 DOI: 10.1016/j.clinph.2022.02.022] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 01/02/2022] [Accepted: 02/22/2022] [Indexed: 12/20/2022]
Abstract
The Spectral Exponent (SE) indexes power-law features of the resting EEG in stroke patients. SE is consistently steeper in the affected hemisphere of patients after middle cerebral artery stroke. SE is linked to clinical status and seems to be a good predictor of clinical outcome.
Objective Quantitative Electroencephalography (qEEG) can capture changes in brain activity following stroke. qEEG metrics traditionally focus on oscillatory activity, however recent findings highlight the importance of aperiodic (power-law) structure in characterizing pathological brain states. We assessed neurophysiological alterations and recovery after mono-hemispheric stroke by means of the Spectral Exponent (SE), a metric that reflects EEG slowing and quantifies the power-law decay of the EEG Power Spectral Density (PSD). Methods Eighteen patients (n = 18) with mild to moderate mono-hemispheric Middle Cerebral Artery (MCA) ischaemic stroke were retrospectively enrolled for this study. Patients underwent EEG recording in the sub-acute phase (T0) and after 2 months of physical rehabilitation (T1). Sixteen healthy controls (HC; n = 16) matched by age and sex were enrolled as a normative group. SE values and narrow-band PSD were estimated for each recording. We compared SE and band-power between patients and HC, and between the affected (AH) and unaffected hemisphere (UH) at T0 and T1 in patients. Results At T0, stroke patients showed significantly more negative SE values than HC (p = 0.003), reflecting broad-band EEG slowing. Most important, in patients SE over the AH was consistently more negative compared to the UH and showed a renormalization at T1. This SE renormalization significantly correlated with National Institute of Health Stroke Scale (NIHSS) improvement (R = 0.63, p = 0.005). Conclusions SE is a reliable readout of the neurophysiological and clinical alterations occurring after an ischaemic cortical lesion. Significance SE promise to be a robust method to monitor and predict patients’ functional outcome.
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18
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Smith Y, Bolam JP, Boraud T. Special Issue Editorial: Basal Ganglia/Movement Disorders. Eur J Neurosci 2021; 53:2045-2048. [PMID: 33759243 DOI: 10.1111/ejn.15204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 03/17/2021] [Accepted: 03/22/2021] [Indexed: 11/29/2022]
Affiliation(s)
- Yoland Smith
- Yerkes National Primate Research Center and Department of Neurology, Emory University, Atlanta, GA, USA
| | - John Paul Bolam
- MRC Brain Network Dynamics Unit and Department of Pharmacology, University of Oxford, Oxford, UK
| | - Thomas Boraud
- Institut des Maladies Neurodégénératives, CNRS, URM:5293, Université de Bordeaux, Bordeaux, France
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19
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Godinho F, Fim Neto A, Bianqueti BL, de Luccas JB, Varjão E, Terzian Filho PR, Figueiredo EG, Almeida TP, Yoneyama T, Takahata AK, Rocha MS, Soriano DC. Spectral characteristics of subthalamic nucleus local field potentials in Parkinson's disease: Phenotype and movement matter. Eur J Neurosci 2021; 53:2804-2818. [PMID: 33393163 DOI: 10.1111/ejn.15103] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 12/23/2020] [Accepted: 12/24/2020] [Indexed: 02/07/2023]
Abstract
Parkinson's disease (PD) is clinically heterogeneous across patients and may be classified in three motor phenotypes: tremor dominant (TD), postural instability and gait disorder (PIGD), and undetermined. Despite the significant clinical characterization of motor phenotypes, little is known about how electrophysiological data, particularly subthalamic nucleus local field potentials (STN-LFP), differ between TD and PIGD patients. This is relevant since increased STN-LFP bandpower at α-β range (8-35 Hz) is considered a potential PD biomarker and, therefore, a critical setpoint to drive adaptive deep brain stimulation. Acknowledging STN-LFP differences between phenotypes, mainly in rest and movement states, would better fit DBS to clinical and motor demands. We studied this issue through spectral analyses on 35 STN-LFP in TD and PIGD patients during rest and movement. We demonstrated that higher β2 activity (22-35 Hz) was observed in PIGD only during rest. Additionally, bandpower differences between rest and movement occurred at the α-β range, but with different patterns as per phenotypes: movement-induced desynchronization concerned lower frequencies in TD (10-20 Hz) and higher frequencies in PIGD patients (21-28 Hz). Finally, when supervised learning algorithms were employed aiming to discriminate PD phenotypes based on STN-LFP bandpower features, movement information had improved the classification accuracy, achieving peak performances when TD and PIGD movement-induced desynchronization ranges were considered. These results suggest that STN-LFP β-band encodes phenotype-movement dependent information in PD patients.
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Affiliation(s)
- Fabio Godinho
- Center of Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo, Brazil.,Department of Functional Neurosurgery, Santa Marcelina Hospital, São Paulo, Brazil.,Division of Functional Neurosurgery of Institute of Psychiatry, Department of Neurology, University of São Paulo Medical School, São Paulo, Brazil
| | - Arnaldo Fim Neto
- Center of Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo, Brazil.,Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil.,Department of Cosmic Rays and Chronology, Institute of Physics, University of Campinas, Campinas, Brazil
| | - Bruno Leonardo Bianqueti
- Center of Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo, Brazil.,Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Julia Baldi de Luccas
- Center of Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo, Brazil.,Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Eduardo Varjão
- Department of Functional Neurosurgery, Santa Marcelina Hospital, São Paulo, Brazil
| | | | | | - Tiago Paggi Almeida
- Center of Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo, Brazil.,Division of Electronic Engineering, Technological Institute of Aeronautics, São José dos Campos, Brazil.,Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Takashi Yoneyama
- Division of Electronic Engineering, Technological Institute of Aeronautics, São José dos Campos, Brazil
| | - André Kazuo Takahata
- Center of Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo, Brazil.,Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | | | - Diogo Coutinho Soriano
- Center of Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo, Brazil.,Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
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