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Zhao D, Zhang X, Jin WF, Huang P, Wan H, He Y. Efficacy of Astragalus membranaceus-Carthamus tinctorius in cerebral ischemia/reperfusion injury: Insights from metabolomics and mass spectrometry imaging. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 133:155881. [PMID: 39059267 DOI: 10.1016/j.phymed.2024.155881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 06/04/2024] [Accepted: 07/13/2024] [Indexed: 07/28/2024]
Abstract
BACKGROUND The combination of Astragalus membranaceus and Carthamus tinctorius (AC) exhibits significant therapeutic effects in cerebral ischemia/reperfusion injury (CIRI). Understanding the metabolic characteristics of brain microregions and disturbances in tissues and systemic circulation is crucial for elucidating the mechanisms of CIRI and the therapeutic benefits of AC. However, in situ metabolic regulation of the complex brain structure has not been adequately studied, and the therapeutic mechanism of AC requires immediate clarification. PURPOSE The present study aimed to unveil the specific metabolic reprogramming of CIRI at systemic and microregional levels, identify key metabolic pathways and metabolites, and elucidate the therapeutic mechanisms of AC. METHODS Air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI), a newly developed technique, was used to investigate metabolites in brain microregions. Hematoxylin-eosin, Nissl, and immunofluorescence staining were performed to visualize the microscopic changes associated with spatial metabolism. A comprehensive metabolomics study was conducted on serum, brain tissue, and microregions, along with neurological assessments, cerebral infarction measurements, and Evans blue experiments, to assess the systemic and local metabolic effects of AC treatment for CIRI. RESULTS AC significantly reduced neurological damage, minimized infarct size, and repaired blood-brain barrier damage in CIRI rats. AFADESI-MSI demonstrated that the metabolic imbalance caused by CIRI primarily occurs in the cerebral cortex, hippocampus, caudate putamen, thalamus, cerebellar cortex, and fiber tract regions. Significant changes in 16 metabolites were observed in these regions, corresponding to neuron damage, glial cell activation, and neural repair. 20 metabolites from serum and 4 from brain tissue varied significantly with the sham group. Comprehensive metabolomics analysis indicated a close relationship among serum, tissue, and microregional metabolism. CIRI-induced systemic and localized metabolic disorders involve 14 metabolic pathways. AC conferred therapeutic benefits in CIRI by reversing various metabolic imbalances. CONCLUSION AFADESI-MSI efficiently visualized brain microregion metabolism. Comprehensive metabolomics analysis revealed detailed insights into the specific metabolic reprogramming in CIRI and the therapeutic impacts of AC. AC demonstrated significant clinical potential as an adjunct therapy to existing CIRI treatments.
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Affiliation(s)
- Di Zhao
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xian Zhang
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Wei-Feng Jin
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Ping Huang
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Haitong Wan
- School of Basic Medicine Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
| | - Yu He
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
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Chu HY, Smith Y, Lytton WW, Grafton S, Villalba R, Masilamoni G, Wichmann T. Dysfunction of motor cortices in Parkinson's disease. Cereb Cortex 2024; 34:bhae294. [PMID: 39066504 PMCID: PMC11281850 DOI: 10.1093/cercor/bhae294] [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: 02/18/2024] [Revised: 06/26/2024] [Accepted: 07/19/2024] [Indexed: 07/28/2024] Open
Abstract
The cerebral cortex has long been thought to be involved in the pathophysiology of motor symptoms of Parkinson's disease. The impaired cortical function is believed to be a direct and immediate effect of pathologically patterned basal ganglia output, mediated to the cerebral cortex by way of the ventral motor thalamus. However, recent studies in humans with Parkinson's disease and in animal models of the disease have provided strong evidence suggesting that the involvement of the cerebral cortex is much broader than merely serving as a passive conduit for subcortical disturbances. In the present review, we discuss Parkinson's disease-related changes in frontal cortical motor regions, focusing on neuropathology, plasticity, changes in neurotransmission, and altered network interactions. We will also examine recent studies exploring the cortical circuits as potential targets for neuromodulation to treat Parkinson's disease.
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Affiliation(s)
- Hong-Yuan Chu
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, United States
- Department of Pharmacology and Physiology, Georgetown University Medical Center, 3900 Reservoir Rd N.W., Washington D.C. 20007, United States
| | - Yoland Smith
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, United States
- Department of Neurology, School of Medicine, Emory University, 12 Executive Drive N.E., Atlanta, GA 30329, United States
- Emory National Primate Research Center, 954 Gatewood Road N.E., Emory University, Atlanta, GA 30329, United States
| | - William W Lytton
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, United States
- Department of Physiology & Pharmacology, SUNY Downstate Medical Center, 450 Clarkson Avenue, Brooklyn, NY 11203, United States
- Department of Neurology, Kings County Hospital, 451 Clarkson Avenue,Brooklyn, NY 11203, United States
| | - Scott Grafton
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, United States
- Department of Psychological and Brain Sciences, University of California, 551 UCEN Road, Santa Barbara, CA 93106, United States
| | - Rosa Villalba
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, United States
- Emory National Primate Research Center, 954 Gatewood Road N.E., Emory University, Atlanta, GA 30329, United States
| | - Gunasingh Masilamoni
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, United States
- Emory National Primate Research Center, 954 Gatewood Road N.E., Emory University, Atlanta, GA 30329, United States
| | - Thomas Wichmann
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, United States
- Department of Neurology, School of Medicine, Emory University, 12 Executive Drive N.E., Atlanta, GA 30329, United States
- Emory National Primate Research Center, 954 Gatewood Road N.E., Emory University, Atlanta, GA 30329, United States
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Steina A, Sure S, Butz M, Vesper J, Schnitzler A, Hirschmann J. Mapping Subcortico-Cortical Coupling-A Comparison of Thalamic and Subthalamic Oscillations. Mov Disord 2024; 39:684-693. [PMID: 38380765 DOI: 10.1002/mds.29730] [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: 06/27/2023] [Revised: 11/29/2023] [Accepted: 01/08/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND The ventral intermediate nucleus of the thalamus (VIM) is an effective target for deep brain stimulation in tremor patients. Despite its therapeutic importance, its oscillatory coupling to cortical areas has rarely been investigated in humans. OBJECTIVES The objective of this study was to identify the cortical areas coupled to the VIM in patients with essential tremor. METHODS We combined resting-state magnetoencephalography with local field potential recordings from the VIM of 19 essential tremor patients. Whole-brain maps of VIM-cortex coherence in several frequency bands were constructed using beamforming and compared with corresponding maps of subthalamic nucleus (STN) coherence based on data from 19 patients with Parkinson's disease. In addition, we computed spectral Granger causality. RESULTS The topographies of VIM-cortex and STN-cortex coherence were very similar overall but differed quantitatively. Both nuclei were coupled to the ipsilateral sensorimotor cortex in the high-beta band; to the sensorimotor cortex, brainstem, and cerebellum in the low-beta band; and to the temporal cortex, brainstem, and cerebellum in the alpha band. High-beta coherence to sensorimotor cortex was stronger for the STN (P = 0.014), whereas low-beta coherence to the brainstem was stronger for the VIM (P = 0.017). Although the STN was driven by cortical activity in the high-beta band, the VIM led the sensorimotor cortex in the alpha band. CONCLUSIONS Thalamo-cortical coupling is spatially and spectrally organized. The overall similar topographies of VIM-cortex and STN-cortex coherence suggest that functional connections are not necessarily unique to one subcortical structure but might reflect larger frequency-specific networks involving VIM and STN to a different degree. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Alexandra Steina
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Sarah Sure
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Markus Butz
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Jan Vesper
- Department of Functional Neurosurgery and Stereotaxy, Neurosurgical Clinic, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Jan Hirschmann
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
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Gohil C, Huang R, Roberts E, van Es MWJ, Quinn AJ, Vidaurre D, Woolrich MW. osl-dynamics, a toolbox for modeling fast dynamic brain activity. eLife 2024; 12:RP91949. [PMID: 38285016 PMCID: PMC10945565 DOI: 10.7554/elife.91949] [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] [Indexed: 01/30/2024] Open
Abstract
Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes.
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Affiliation(s)
- Chetan Gohil
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Rukuang Huang
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Evan Roberts
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Mats WJ van Es
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Andrew J Quinn
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
- Centre for Human Brain Health, School of Psychology, University of BirminghamBirminghamUnited Kingdom
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus UniversityAarhusDenmark
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
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Mertiens S, Sure M, Schnitzler A, Florin E. Alterations of PAC-based resting state networks in Parkinson's disease are partially alleviated by levodopa medication. Front Syst Neurosci 2023; 17:1219334. [PMID: 37588811 PMCID: PMC10427244 DOI: 10.3389/fnsys.2023.1219334] [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: 05/08/2023] [Accepted: 07/10/2023] [Indexed: 08/18/2023] Open
Abstract
Introduction Parkinson's disease (PD) is a neurodegenerative disorder affecting the whole brain, leading to several motor and non-motor symptoms. In the past, it has been shown that PD alters resting state networks (RSN) in the brain. These networks are usually derived from fMRI BOLD signals. This study investigated RSN changes in PD patients based on maximum phase-amplitude coupling (PAC) throughout the cortex. We also tested the hypothesis that levodopa medication shifts network activity back toward a healthy state. Methods We recorded 23 PD patients and 24 healthy age-matched participants for 30 min at rest with magnetoencephalography (MEG). PD patients were measured once in the dopaminergic medication ON and once in the medication OFF state. A T1-MRI brain scan was acquired from each participant for source reconstruction. After correcting the data for artifacts and performing source reconstruction using a linearly constrained minimum variance beamformer, we extracted visual, sensorimotor (SMN), and frontal RSNs based on PAC. Results We found significant changes in all networks between healthy participants and PD patients in the medication OFF state. Levodopa had a significant effect on the SMN but not on the other networks. There was no significant change in the optimal PAC coupling frequencies between healthy participants and PD patients. Discussion Our results suggest that RSNs, based on PAC in different parts of the cortex, are altered in PD patients. Furthermore, levodopa significantly affects the SMN, reflecting the clinical alleviation of motor symptoms and leading to a network normalization compared to healthy controls.
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Affiliation(s)
- Sean Mertiens
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Matthias Sure
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Masaracchia L, Fredes F, Woolrich MW, Vidaurre D. Dissecting unsupervised learning through hidden Markov modeling in electrophysiological data. J Neurophysiol 2023; 130:364-379. [PMID: 37403598 PMCID: PMC10625837 DOI: 10.1152/jn.00054.2023] [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: 02/01/2023] [Revised: 06/28/2023] [Accepted: 06/29/2023] [Indexed: 07/06/2023] Open
Abstract
Unsupervised, data-driven methods are commonly used in neuroscience to automatically decompose data into interpretable patterns. These patterns differ from one another depending on the assumptions of the models. How these assumptions affect specific data decompositions in practice, however, is often unclear, which hinders model applicability and interpretability. For instance, the hidden Markov model (HMM) automatically detects characteristic, recurring activity patterns (so-called states) from time series data. States are defined by a certain probability distribution, whose state-specific parameters are estimated from the data. But what specific features, from all of those that the data contain, do the states capture? That depends on the choice of probability distribution and on other model hyperparameters. Using both synthetic and real data, we aim to better characterize the behavior of two HMM types that can be applied to electrophysiological data. Specifically, we study which differences in data features (such as frequency, amplitude, or signal-to-noise ratio) are more salient to the models and therefore more likely to drive the state decomposition. Overall, we aim at providing guidance for the appropriate use of this type of analysis on one- or two-channel neural electrophysiological data and an informed interpretation of its results given the characteristics of the data and the purpose of the analysis.NEW & NOTEWORTHY Compared with classical supervised methods, unsupervised methods of analysis have the advantage to be freer of subjective biases. However, it is not always clear what aspects of the data these methods are most sensitive to, which complicates interpretation. Focusing on the hidden Markov model, commonly used to describe electrophysiological data, we explore in detail the nature of its estimates through simulations and real data examples, providing important insights about what to expect from these models.
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Affiliation(s)
- Laura Masaracchia
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Felipe Fredes
- Center for Proteins in Memory, Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Mark W Woolrich
- Psychiatry Department, Oxford Centre for Human Brain Activity, Oxford University, Oxford, United Kingdom
| | - Diego Vidaurre
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Psychiatry Department, Oxford Centre for Human Brain Activity, Oxford University, Oxford, United Kingdom
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DBS-evoked cortical responses index optimal contact orientations and motor outcomes in Parkinson's disease. NPJ Parkinsons Dis 2023; 9:37. [PMID: 36906723 PMCID: PMC10008535 DOI: 10.1038/s41531-023-00474-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 02/13/2023] [Indexed: 03/13/2023] Open
Abstract
Although subthalamic deep brain stimulation (DBS) is a highly-effective treatment for alleviating motor dysfunction in patients with Parkinson's disease (PD), clinicians currently lack reliable neurophysiological correlates of clinical outcomes for optimizing DBS parameter settings, which may contribute to treatment inefficacies. One parameter that could aid DBS efficacy is the orientation of current administered, albeit the precise mechanisms underlying optimal contact orientations and associated clinical benefits are not well understood. Herein, 24 PD patients received monopolar stimulation of the left STN during magnetoencephalography and standardized movement protocols to interrogate the directional specificity of STN-DBS current administration on accelerometer metrics of fine hand movements. Our findings demonstrate that optimal contact orientations elicit larger DBS-evoked cortical responses in the ipsilateral sensorimotor cortex, and importantly, are differentially predictive of smoother movement profiles in a contact-dependent manner. Moreover, we summarize traditional evaluations of clinical efficacy (e.g., therapeutic windows, side effects) for a comprehensive review of optimal/non-optimal STN-DBS contact settings. Together, these data suggest that DBS-evoked cortical responses and quantitative movement outcomes may provide clinical insight for characterizing the optimal DBS parameters necessary for alleviating motor symptoms in patients with PD in the future.
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Cortical network formation based on subthalamic beta bursts in Parkinson's disease. Neuroimage 2022; 263:119619. [PMID: 36087901 DOI: 10.1016/j.neuroimage.2022.119619] [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: 06/03/2022] [Revised: 08/02/2022] [Accepted: 09/06/2022] [Indexed: 11/21/2022] Open
Abstract
Recent evidence suggests that beta bursts in subthalamic nucleus (STN) play an important role in Parkinsonian pathophysiology. We studied the spatio-temporal relationship between STN beta bursts and cortical activity in 26 Parkinson's disease (PD) patients undergoing deep brain stimulation (DBS) surgery. Postoperatively, we simultaneously recorded STN local field potentials (LFP) from externalized DBS leads and cortical activity using whole-brain magnetoencephalography. Event-related magnetic fields (ERF) were averaged time-locked to STN beta bursts and subjected to source localization. Our results demonstrate that ERF exhibiting activity significantly different from baseline activity were localized within areas functionally related to associative, limbic, and motor systems as well as regions pertinent for visual and language processing. Our data suggest that STN beta bursts are involved in network formation between STN and cortex. This interaction is in line with the idea of parallel processing within the basal ganglia-cortex loop, specifically within the functional subsystems of the STN (i.e., associative, limbic, motor, and the related cortical areas). ERFs within visual and language-related cortical areas indicate involvement of beta bursts in STN-cortex networks beyond the associative, limbic, and motor loops. In sum, our results highlight the involvement of STN beta bursts in the formation of multiple STN - cortex loops in patients with PD.
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Hirschmann J, Steina A, Vesper J, Florin E, Schnitzler A. Neuronal oscillations predict deep brain stimulation outcome in Parkinson's disease. Brain Stimul 2022; 15:792-802. [PMID: 35568311 DOI: 10.1016/j.brs.2022.05.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 05/06/2022] [Accepted: 05/07/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Neuronal oscillations are linked to symptoms of Parkinson's disease. This relation can be exploited for optimizing deep brain stimulation (DBS), e.g. by informing a device or human about the optimal location, time and intensity of stimulation. Whether oscillations predict individual DBS outcome is not clear so far. OBJECTIVE To predict motor symptom improvement from subthalamic power and subthalamo-cortical coherence. METHODS We applied machine learning techniques to simultaneously recorded magnetoencephalography and local field potential data from 36 patients with Parkinson's disease. Gradient-boosted tree learning was applied in combination with feature importance analysis to generate and understand out-of-sample predictions. RESULTS A few features sufficed for making accurate predictions. A model operating on five coherence features, for example, achieved correlations of r > 0.8 between actual and predicted outcomes. Coherence comprised more information in less features than subthalamic power, although in general their information content was comparable. Both signals predicted akinesia/rigidity reduction best. The most important local feature was subthalamic high-beta power (20-35 Hz). The most important connectivity features were subthalamo-parietal coherence in the very high frequency band (>200 Hz) and subthalamo-parietal coherence in low-gamma band (36-60 Hz). Successful prediction was not due to the model inferring distance to target or symptom severity from neuronal oscillations. CONCLUSION This study demonstrates for the first time that neuronal oscillations are predictive of DBS outcome. Coherence between subthalamic and parietal oscillations are particularly informative. These results highlight the clinical relevance of inter-areal synchrony in basal ganglia-cortex loops and might facilitate further improvements of DBS in the future.
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Affiliation(s)
- Jan Hirschmann
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany.
| | - Alexandra Steina
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Jan Vesper
- Functional Neurosurgery and Stereotaxy, Department of Neurosurgery, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany; Center for Movement Disorders and Neuromodulation, Department of Neurology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany
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Zhang J, Villringer A, Nikulin VV. Dopaminergic Modulation of Local Non-oscillatory Activity and Global-Network Properties in Parkinson’s Disease: An EEG Study. Front Aging Neurosci 2022; 14:846017. [PMID: 35572144 PMCID: PMC9106139 DOI: 10.3389/fnagi.2022.846017] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Dopaminergic medication for Parkinson’s disease (PD) modulates neuronal oscillations and functional connectivity (FC) across the basal ganglia-thalamic-cortical circuit. However, the non-oscillatory component of the neuronal activity, potentially indicating a state of excitation/inhibition balance, has not yet been investigated and previous studies have shown inconsistent changes of cortico-cortical connectivity as a response to dopaminergic medication. To further elucidate changes of regional non-oscillatory component of the neuronal power spectra, FC, and to determine which aspects of network organization obtained with graph theory respond to dopaminergic medication, we analyzed a resting-state electroencephalography (EEG) dataset including 15 PD patients during OFF and ON medication conditions. We found that the spectral slope, typically used to quantify the broadband non-oscillatory component of power spectra, steepened particularly in the left central region in the ON compared to OFF condition. In addition, using lagged coherence as a FC measure, we found that the FC in the beta frequency range between centro-parietal and frontal regions was enhanced in the ON compared to the OFF condition. After applying graph theory analysis, we observed that at the lower level of topology the node degree was increased, particularly in the centro-parietal area. Yet, results showed no significant difference in global topological organization between the two conditions: either in global efficiency or clustering coefficient for measuring global and local integration, respectively. Interestingly, we found a close association between local/global spectral slope and functional network global efficiency in the OFF condition, suggesting a crucial role of local non-oscillatory dynamics in forming the functional global integration which characterizes PD. These results provide further evidence and a more complete picture for the engagement of multiple cortical regions at various levels in response to dopaminergic medication in PD.
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Affiliation(s)
- Juanli Zhang
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- *Correspondence: Juanli Zhang,
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
| | - Vadim V. Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Neurophysics Group, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Vadim V. Nikulin,
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Novak K, Chase BA, Narayanan J, Indic P, Markopoulou K. Quantitative Electroencephalography as a Biomarker for Cognitive Dysfunction in Parkinson's Disease. Front Aging Neurosci 2022; 13:804991. [PMID: 35046794 PMCID: PMC8761986 DOI: 10.3389/fnagi.2021.804991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 11/25/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Quantitative electroencephalography (qEEG) has been suggested as a biomarker for cognitive decline in Parkinson's disease (PD). Objective: Determine if applying a wavelet-based qEEG algorithm to 21-electrode, resting-state EEG recordings obtained in a routine clinical setting has utility for predicting cognitive impairment in PD. Methods: PD subjects, evaluated by disease stage and motor score, were compared to healthy controls (N = 20 each). PD subjects with normal (PDN, MoCA 26-30, N = 6) and impaired (PDD, MoCA ≤ 25, N = 14) cognition were compared. The wavelet-transform based time-frequency algorithm assessed the instantaneous predominant frequency (IPF) at 60 ms intervals throughout entire recordings. We then determined the relative time spent by the IPF in the four standard EEG frequency bands (RTF) at each scalp location. The resting occipital rhythm (ROR) was assessed using standard power spectral analysis. Results: Comparing PD subjects to healthy controls, mean values are decreased for ROR and RTF-Beta, greater for RTF-Theta and similar for RTF-Delta and RTF-Alpha. In logistic regression models, arithmetic combinations of RTF values [e.g., (RTF-Alpha) + (RTF-Beta)/(RTF-Delta + RTF-Theta)] and RTF-Alpha values at occipital or parietal locations are most able to discriminate between PD and controls. A principal component (PC) from principal component analysis (PCA) using RTF-band values in all subjects is associated with PD status (p = 0.004, β = 0.31, AUC = 0.780). Its loadings show positive contribution from RTF-Theta at all scalp locations, and negative contributions from RTF-Beta at occipital, parietal, central, and temporal locations. Compared to cognitively normal PD subjects, cognitively impaired PD subjects have lower median RTF-Alpha and RTF-Beta values, greater RTF-Theta values and similar RTF-Delta values. A PC from PCA using RTF-band values in PD subjects is associated with cognitive status (p = 0.002, β = 0.922, AUC = 0.89). Its loadings show positive contributions from RTF-Theta at all scalp locations, negative contributions from RTF-Beta at central locations, and negative contributions from RTF-Delta at central, frontal and temporal locations. Age, disease duration and/or sex are not significant covariates. No PC was associated with motor score or disease stage. Significance: Analyzing standard EEG recordings obtained in a community practice setting using a wavelet-based qEEG algorithm shows promise as a PD biomarker and for predicting cognitive impairment in PD.
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Affiliation(s)
- Kevin Novak
- Department of Neurology, NorthShore University HealthSystem, Evanston, IL, United States.,Department of Neurology, Pritzker School of Medicine, University of Chicago, Chicago, IL, United States
| | - Bruce A Chase
- Department of Neurology, NorthShore University HealthSystem, Evanston, IL, United States.,Department of Health Information Technology, Clinical Analytics, NorthShore University HealthSystem, Evanston, IL, United States
| | - Jaishree Narayanan
- Department of Neurology, NorthShore University HealthSystem, Evanston, IL, United States.,Department of Neurology, Pritzker School of Medicine, University of Chicago, Chicago, IL, United States
| | - Premananda Indic
- Department of Electrical Engineering, The University of Texas at Tyler, Tyler, TX, United States
| | - Katerina Markopoulou
- Department of Neurology, NorthShore University HealthSystem, Evanston, IL, United States.,Department of Neurology, Pritzker School of Medicine, University of Chicago, Chicago, IL, United States
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