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Lu J, Sorooshyari SK. Machine Learning Identifies a Rat Model of Parkinson's Disease via Sleep-Wake Electroencephalogram. Neuroscience 2023; 510:1-8. [PMID: 36470477 DOI: 10.1016/j.neuroscience.2022.11.035] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 11/25/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022]
Abstract
Alpha-synuclein induced degeneration of the midbrain substantia nigra pars compact (SNc) dopaminergic neurons causes Parkinson's disease (PD). Rodent studies demonstrate that nigrostriatal dopamine stimulates pallidal neurons which, via the topographical pallidocortical pathway, regulate cortical activity and functions. We hypothesize that nigrostriatal dopamine acting at the basal ganglia regulates cortical activity in sleep and wake state, and its depletion systemically alters electroencephalogram (EEG) across frequencies during sleep-wake state. Compared to control rats, 6-hydroxydopamine induced selective SNc lesions increased overall EEG power (positive synchronization) across 0.5-60 Hz during wake, NREM (non-rapid eye movement) sleep, and REM sleep. Application of machine learning (ML) to seven EEG features computed at a single or combined spectral bands during sleep-wake differentiated SNc lesions from controls at high accuracy. ML algorithms construct a model based on empirical data to make predictions on subsequent data. The accuracy of the predictive results indicate that nigrostriatal dopamine depletion increases global EEG spectral synchronization in wake, NREM sleep, and REM sleep. The EEG changes can be exploited by ML to identify SNc lesions at a high accuracy.
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Affiliation(s)
- Jun Lu
- Stroke Center, Department of Neurology, 1st Hospital of Jilin University, Changchun 120021, China.
| | - Siamak K Sorooshyari
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720, USA.
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Verma AK, Yu Y, Acosta-Lenis SF, Havel T, Sanabria DE, Molnar GF, MacKinnon CD, Howell MJ, Vitek JL, Johnson LA. Parkinsonian daytime sleep-wake classification using deep brain stimulation lead recordings. Neurobiol Dis 2023; 176:105963. [PMID: 36521781 PMCID: PMC9869648 DOI: 10.1016/j.nbd.2022.105963] [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: 09/30/2022] [Revised: 12/01/2022] [Accepted: 12/10/2022] [Indexed: 12/14/2022] Open
Abstract
Excessive daytime sleepiness is a recognized non-motor symptom that adversely impacts the quality of life of people with Parkinson's disease (PD), yet effective treatment options remain limited. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for PD motor signs. Reliable daytime sleep-wake classification using local field potentials (LFPs) recorded from DBS leads implanted in STN can inform the development of closed-loop DBS approaches for prompt detection and disruption of sleep-related neural oscillations. We performed STN DBS lead recordings in three nonhuman primates rendered parkinsonian by administrating neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Reference sleep-wake states were determined on a second-by-second basis by video monitoring of eyes (eyes-open, wake and eyes-closed, sleep). The spectral power in delta (1-4 Hz), theta (4-8 Hz), low-beta (8-20 Hz), high-beta (20-35 Hz), gamma (35-90 Hz), and high-frequency (200-400 Hz) bands were extracted from each wake and sleep epochs for training (70% data) and testing (30% data) a support vector machines classifier for each subject independently. The spectral features yielded reasonable daytime sleep-wake classification (sensitivity: 90.68 ± 1.28; specificity: 88.16 ± 1.08; accuracy: 89.42 ± 0.68; positive predictive value; 88.70 ± 0.89, n = 3). Our findings support the plausibility of monitoring daytime sleep-wake states using DBS lead recordings. These results could have future clinical implications in informing the development of closed-loop DBS approaches for automatic detection and disruption of sleep-related neural oscillations in people with PD to promote wakefulness.
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Affiliation(s)
- Ajay K Verma
- Department of Neurology, University of Minnesota, Minneapolis, United States of America
| | - Ying Yu
- Department of Neurology, University of Minnesota, Minneapolis, United States of America
| | - Sergio F Acosta-Lenis
- Department of Neurology, University of Minnesota, Minneapolis, United States of America
| | - Tyler Havel
- Department of Neurology, University of Minnesota, Minneapolis, United States of America
| | | | - Gregory F Molnar
- Department of Neurology, University of Minnesota, Minneapolis, United States of America
| | - Colum D MacKinnon
- Department of Neurology, University of Minnesota, Minneapolis, United States of America
| | - Michael J Howell
- Department of Neurology, University of Minnesota, Minneapolis, United States of America
| | - Jerrold L Vitek
- Department of Neurology, University of Minnesota, Minneapolis, United States of America
| | - Luke A Johnson
- Department of Neurology, University of Minnesota, Minneapolis, United States of America.
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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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Bore JC, Toth C, Campbell BA, Cho H, Pucci F, Hogue O, Machado AG, Baker KB. Consistent Changes in Cortico-Subthalamic Directed Connectivity Are Associated With the Induction of Parkinsonism in a Chronically Recorded Non-human Primate Model. Front Neurosci 2022; 16:831055. [PMID: 35310095 PMCID: PMC8930827 DOI: 10.3389/fnins.2022.831055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/31/2022] [Indexed: 11/23/2022] Open
Abstract
Parkinson’s disease is a neurological disease with cardinal motor signs including bradykinesia and tremor. Although beta-band hypersynchrony in the cortico-basal ganglia network is thought to contribute to disease manifestation, the resulting effects on network connectivity are unclear. We examined local field potentials from a non-human primate across the naïve, mild, and moderate disease states (model was asymmetric, left-hemispheric dominant) and probed power spectral density as well as cortico-cortical and cortico-subthalamic connectivity using both coherence and Granger causality, which measure undirected and directed effective connectivity, respectively. Our network included the left subthalamic nucleus (L-STN), bilateral primary motor cortices (L-M1, R-M1), and bilateral premotor cortices (L-PMC, R-PMC). Results showed two distinct peaks (Peak A at 5–20 Hz, Peak B at 25–45 Hz) across all analyses. Power and coherence analyses showed widespread increases in power and connectivity in both the Peak A and Peak B bands with disease progression. For Granger causality, increases in Peak B connectivity and decreases in Peak A connectivity were associated with the disease. Induction of mild disease was associated with several changes in connectivity: (1) the cortico-subthalamic connectivity in the descending direction (L-PMC to L-STN) decreased in the Peak A range while the reciprocal, ascending connectivity (L-STN to L-PMC) increased in the Peak B range; this may play a role in generating beta-band hypersynchrony in the cortex, (2) both L-M1 to L-PMC and R-M1 to R-PMC causalities increased, which may either be compensatory or a pathologic effect of disease, and (3) a decrease in connectivity occurred from the R-PMC to R-M1. The only significant change seen between mild and moderate disease was increased right cortical connectivity, which may reflect compensation for the left-hemispheric dominant moderate disease state.
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Affiliation(s)
- Joyce Chelangat Bore
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Carmen Toth
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Brett A. Campbell
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Hanbin Cho
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Francesco Pucci
- Center for Neurological Restoration, Cleveland Clinic, Neurological Institute, Cleveland, OH, United States
- Department of Neurosurgery, Cleveland Clinic, Neurological Institute, Cleveland, OH, United States
| | - Olivia Hogue
- Center for Neurological Restoration, Cleveland Clinic, Neurological Institute, Cleveland, OH, United States
| | - Andre G. Machado
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
- Center for Neurological Restoration, Cleveland Clinic, Neurological Institute, Cleveland, OH, United States
- Department of Neurosurgery, Cleveland Clinic, Neurological Institute, Cleveland, OH, United States
| | - Kenneth B. Baker
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
- Center for Neurological Restoration, Cleveland Clinic, Neurological Institute, Cleveland, OH, United States
- *Correspondence: Kenneth B. Baker,
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