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Permezel F, Alty J, Harding IH, Thyagarajan D. Brain Networks Involved in Sensory Perception in Parkinson's Disease: A Scoping Review. Brain Sci 2023; 13:1552. [PMID: 38002513 PMCID: PMC10669548 DOI: 10.3390/brainsci13111552] [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: 10/12/2023] [Revised: 10/26/2023] [Accepted: 10/31/2023] [Indexed: 11/26/2023] Open
Abstract
Parkinson's Disease (PD) has historically been considered a disorder of motor dysfunction. However, a growing number of studies have demonstrated sensory abnormalities in PD across the modalities of proprioceptive, tactile, visual, auditory and temporal perception. A better understanding of these may inform future drug and neuromodulation therapy. We analysed these studies using a scoping review. In total, 101 studies comprising 2853 human participants (88 studies) and 125 animals (13 studies), published between 1982 and 2022, were included. These highlighted the importance of the basal ganglia in sensory perception across all modalities, with an additional role for the integration of multiple simultaneous sensation types. Numerous studies concluded that sensory abnormalities in PD result from increased noise in the basal ganglia and increased neuronal receptive field size. There is evidence that sensory changes in PD and impaired sensorimotor integration may contribute to motor abnormalities.
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Affiliation(s)
- Fiona Permezel
- Department of Neuroscience, Monash University, Melbourne 3004, Australia; (F.P.); (I.H.H.)
- Department of Neurology, Mayo Clinic, Rochester, MN 55901, USA
| | - Jane Alty
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart 7001, Australia;
| | - Ian H. Harding
- Department of Neuroscience, Monash University, Melbourne 3004, Australia; (F.P.); (I.H.H.)
| | - Dominic Thyagarajan
- Department of Neuroscience, Monash University, Melbourne 3004, Australia; (F.P.); (I.H.H.)
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Darbin O, Indriani DW, Ardalan A, Eghbalnia HR, Assadi A, Nambu A, Montgomery E. Spectrum dependency to Rate and Spike Timing in neuronal spike trains. J Neurosci Methods 2022; 372:109532. [PMID: 35182602 DOI: 10.1016/j.jneumeth.2022.109532] [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: 10/15/2021] [Revised: 02/11/2022] [Accepted: 02/14/2022] [Indexed: 10/19/2022]
Abstract
BACKGROUND Spike trains are series of interspike intervals in a specific order that can be characterized by their probability distributions and order in time which refer to the concepts of rate and spike timing features. Periodic structure in the spike train can be reflected in oscillatory activities. Thus, there is a direct link between oscillator activities and the spike train. The proposed methods are to investigate the dependency of emerging oscillatory activities to the rate and the spike timing features. METHOD First, the circular statistics methods were compared to Fast Fourier Transform for best estimation of spectra. Second, two statistical tests were introduced to help make decisions regarding the dependency of spectrum, or individual frequencies, onto rate and spike timing. Third, the methodology is applied to in-vivo recordings of basal ganglia neurons in mouse, primate, and human. Finally, this novel framework is shown to allow the investigation of subsets of spikes contributing to individual oscillators. RESULTS Use of circular statistical methods, in comparison to FFT, minimizes spectral leakage. Using virtual spike trains, the Rate versus Timing Dependency Spectrum Test (or RTDs-Test) permits identifying spectral spike trains solely dependent on the rate feature from those that are also dependent on the spike timing feature. Similarly, the Rate versus Timing Dependency Frequency Test (or RTDf-Test), allows to identify individual oscillators with partial dependency on spike timing. Dependency on spike timing was found for all in-vivo recordings but only in few frequencies. The mapping in frequency and time of dependencies showed a dynamical process that may be organizing the basal ganglia function. CONCLUSIONS The methodology may improve our understanding of the emergence of oscillatory activities and, possibly, the relation between oscillatory activities and circuitry functions.
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Affiliation(s)
- Olivier Darbin
- Department of Neurology, University South Alabama, 307 University Blvd, Mobile, AL 36688, USA; Division of System Neurophysiology, National Institute for Physiological Sciences, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, JAPAN.
| | - Dwi Wahyu Indriani
- Division of System Neurophysiology, National Institute for Physiological Sciences, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, JAPAN; Department of Physiological Sciences, SOKENDAI, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, JAPAN
| | - Adel Ardalan
- Zuckerman Mind Brain Behavior Institute, Columbia University, 3227 Broadway, New York, NY, 10027, USA
| | - Hamid R Eghbalnia
- Department of Molecular Biology and Biophysics. UConn Health. 263 Farmington Avenue Farmington, CT 06030, USA
| | - Amir Assadi
- Department of Mathematics, University of Wisconsin Madison, 480 Lincoln Drive, 213 Van Vleck Hall, Madison, WI 53706 5, USA
| | - Atsushi Nambu
- Division of System Neurophysiology, National Institute for Physiological Sciences, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, JAPAN; Department of Physiological Sciences, SOKENDAI, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, JAPAN
| | - Erwin Montgomery
- Department of Medicine (Neurology), Health Sciences, McMaster University, Hamilton, ON L8L 2X2, CA, USA
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Santaniello S, Gale JT, Sarma SV. Systems approaches to optimizing deep brain stimulation therapies in Parkinson's disease. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2018; 10:e1421. [PMID: 29558564 PMCID: PMC6148418 DOI: 10.1002/wsbm.1421] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Revised: 01/29/2018] [Accepted: 02/01/2018] [Indexed: 01/17/2023]
Abstract
Over the last 30 years, deep brain stimulation (DBS) has been used to treat chronic neurological diseases like dystonia, obsessive-compulsive disorders, essential tremor, Parkinson's disease, and more recently, dementias, depression, cognitive disorders, and epilepsy. Despite its wide use, DBS presents numerous challenges for both clinicians and engineers. One challenge is the design of novel, more efficient DBS therapies, which are hampered by the lack of complete understanding about the cellular mechanisms of therapeutic DBS. Another challenge is the existence of redundancy in clinical outcomes, that is, different DBS programs can result in similar clinical benefits but very little information (e.g., predictive models, longitudinal data, metrics, etc.) is available to select one program over another. Finally, there is high variability in patients' responses to DBS, which forces clinicians to carefully adjust the stimulation settings to each patient via lengthy programming sessions. Researchers in neural engineering and systems biology have been tackling these challenges over the past few years with the specific goal of developing novel DBS therapies, design methodologies, and computational tools that optimize the therapeutic effects of DBS in each patient. Furthermore, efforts are being made to automatically adapt the DBS treatment to the fluctuations of disease symptoms. A review of the quantitative approaches currently available for the treatment of Parkinson's disease is presented here with an emphasis on the contributions that systems theoretical approaches have provided to understand the global dynamics of complex neuronal circuits in the brain under DBS. This article is categorized under: Translational, Genomic, and Systems Medicine > Therapeutic Methods Analytical and Computational Methods > Computational Methods Analytical and Computational Methods > Dynamical Methods Physiology > Mammalian Physiology in Health and Disease.
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Affiliation(s)
- Sabato Santaniello
- Biomedical Engineering Department and CT Institute for the Brain and Cognitive Sciences, University of Connecticut; ORCID-ID: 0000-0002-2133-9471
| | - John T. Gale
- Department of Neurosurgery, Emory University School of Medicine
| | - Sridevi V. Sarma
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University
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Deng C, Sun T, Gale JT, Montgomery EB, Santaniello S. Effects of the temporal pattern of subthalamic deep brain stimulation on the neuronal complexity in the globus pallidus. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:3352-3355. [PMID: 29060615 DOI: 10.1109/embc.2017.8037574] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Deep brain stimulation (DBS) is a surgical treatment for Parkinson's disease (PD) but, despite clinical efficacy, the mechanisms of DBS still require investigation. Recent evidence suggests that the temporal pattern of the electrical pulses may be critical to the therapeutic merit of DBS and carefully-designed, non-regular patterns could ameliorate some of the motor symptoms in PD. It is unclear, though, how different stimulation patterns affect the neural activity in the basal ganglia and whether this is related to the pathophysiology of PD. In this study, a non-human primate was treated with DBS of the subthalamic nucleus while single-unit recordings were collected in the animal's globus pallidus internus (GPi). Three stimulation patterns were applied (one regular, two non-regular) and the stimulation effects on the GPi spike trains were assessed via point process modeling. On a preliminary set of 23 GPi neurons, we show that regular DBS maximized the neuronal complexity, which is a measure of the amount of information that a single neuron can encode, and significantly increased the dependency of the neurons' spike trains on the background ensemble activity through an articulated balance of excitation and inhibition. Overall, regular DBS caused the largest modulation in the neurons' spiking pattern and the largest increment in encoding capabilities. Both results may be relevant to the mechanisms of therapeutic DBS.
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Blumenfeld Z, Brontë-Stewart H. High Frequency Deep Brain Stimulation and Neural Rhythms in Parkinson's Disease. Neuropsychol Rev 2015; 25:384-97. [PMID: 26608605 DOI: 10.1007/s11065-015-9308-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Accepted: 11/09/2015] [Indexed: 01/28/2023]
Abstract
High frequency (HF) deep brain stimulation (DBS) is an established therapy for the treatment of Parkinson's disease (PD). It effectively treats the cardinal motor signs of PD, including tremor, bradykinesia, and rigidity. The most common neural target is the subthalamic nucleus, located within the basal ganglia, the region most acutely affected by PD pathology. Using chronically-implanted DBS electrodes, researchers have been able to record underlying neural rhythms from several nodes in the PD network as well as perturb it using DBS to measure the ensuing neural and behavioral effects, both acutely and over time. In this review, we provide an overview of the PD neural network, focusing on the pathophysiological signals that have been recorded from PD patients as well as the mechanisms underlying the therapeutic benefits of HF DBS. We then discuss evidence for the relationship between specific neural oscillations and symptoms of PD, including the aberrant relationships potentially underlying functional connectivity in PD as well as the use of different frequencies of stimulation to more specifically target certain symptoms. Finally, we briefly describe several current areas of investigation and how the ability to record neural data in ecologically-valid settings may allow researchers to explore the relationship between brain and behavior in an unprecedented manner, culminating in the future automation of neurostimulation therapy for the treatment of a variety of neuropsychiatric diseases.
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Affiliation(s)
- Zack Blumenfeld
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Helen Brontë-Stewart
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94305, USA.
- Department of Neurosurgery, Stanford University, Stanford, CA, 94305, USA.
- Stanford University School of Medicine, Rm A343, 300 Pasteur Drive, Stanford, CA, 94305, USA.
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