Computational modelling and analysis of thermal characteristics of DBS electrode in application to Parkinson's disease

2014 ◽  
Author(s):  
M Vidya ◽  
M. Sharat Divya ◽  
N. Priyadarshini ◽  
E.R Rajkumar
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Computational Modelling ◽  
Thermal Characteristics

scholarly journals Phase-dependent suppression of beta oscillations in Parkinson’s disease patients

2018 ◽  
Author(s):  
Abbey B. Holt ◽  
Eszter Kormann ◽  
Alessandro Gulberti ◽  
Monika Pötter-Nerger ◽  
Colin G. McNamara ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Time Course ◽  
Computational Modelling ◽  
Brain Diseases ◽  
Functional Neurosurgery ◽  
Selective Targeting ◽  
Beta Frequency ◽  
Normal Processing ◽  
Specific Phase

AbstractSynchronized oscillations within and between brain areas facilitate normal processing, but are often amplified in disease. A prominent example is the abnormally sustained beta-frequency (~20Hz) oscillations recorded from the cortex and subthalamic nucleus of Parkinson’s Disease patients. Computational modelling suggests that the amplitude of such oscillations could be modulated by applying stimulation at a specific phase. Such a strategy would allow selective targeting of the oscillation, with relatively little effect on other activity parameters. Here we demonstrate in awake, parkinsonian patients undergoing functional neurosurgery, that electrical stimulation arriving on consecutive cycles of a specific phase of the subthalamic oscillation can suppress its amplitude and coupling to cortex. Stimulus-evoked changes in spiking did not have a consistent time course, suggesting that the oscillation was modulated independently of net output. Phase-dependent stimulation could thus be a valuable strategy for treating brain diseases and probing the function of oscillations in the healthy brain.

scholarly journals Reversing cognitive–motor impairments in Parkinson’s disease patients using a computational modelling approach to deep brain stimulation programming

Brain ◽  
2010 ◽  
Vol 133 (3) ◽  
pp. 746-761 ◽  
Author(s):  
Anneke M. M. Frankemolle ◽  
Jennifer Wu ◽  
Angela M. Noecker ◽  
Claudia Voelcker-Rehage ◽  
Jason C. Ho ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Computational Modelling ◽  
Motor Impairments ◽  
Deep Brain ◽  
Modelling Approach

scholarly journals Neural signatures of hyperdirect pathway activity in Parkinson’s disease

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ashwini Oswal ◽  
Chunyan Cao ◽  
Chien-Hung Yeh ◽  
Wolf-Julian Neumann ◽  
James Gratwicke ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Basal Ganglia ◽  
Supplementary Motor Area ◽  
Computational Modelling ◽  
Motor Area ◽  
Internal Globus Pallidus ◽  
Pathway Activity ◽  
High Beta ◽  
Beta Frequency

AbstractParkinson’s disease (PD) is characterised by the emergence of beta frequency oscillatory synchronisation across the cortico-basal-ganglia circuit. The relationship between the anatomy of this circuit and oscillatory synchronisation within it remains unclear. We address this by combining recordings from human subthalamic nucleus (STN) and internal globus pallidus (GPi) with magnetoencephalography, tractography and computational modelling. Coherence between supplementary motor area and STN within the high (21–30 Hz) but not low (13-21 Hz) beta frequency range correlated with ‘hyperdirect pathway’ fibre densities between these structures. Furthermore, supplementary motor area activity drove STN activity selectively at high beta frequencies suggesting that high beta frequencies propagate from the cortex to the basal ganglia via the hyperdirect pathway. Computational modelling revealed that exaggerated high beta hyperdirect pathway activity can provoke the generation of widespread pathological synchrony at lower beta frequencies. These findings suggest a spectral signature and a pathophysiological role for the hyperdirect pathway in PD.

scholarly journals Insights into Parkinson’s disease from computational models of the basal ganglia

2018 ◽  
Author(s):  
Mark D. Humphries ◽  
Jose Obeso ◽  
Jakob Kisbye Dreyer
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Basal Ganglia ◽  
Computational Models ◽  
Neural Circuits ◽  
Computational Modelling ◽  
Beta Band ◽  
Complex Interplay ◽  
Deep Brain

AbstractMovement disorders arise from the complex interplay of multiple changes to neural circuits. Successful treatments for these disorders could interact with these complex changes in myriad ways, and as a consequence their mechanisms of action and their amelioration of symptoms are incompletely understood. Using Parkinson’s disease as a case-study, we review here how computational models are a crucial tool for taming this complexity, across causative mechanisms, consequent neural dynamics, and treatments. For mechanisms, we review models that capture the effects of losing dopamine on basal ganglia function; for dynamics, we discuss models that have transformed our understanding of how beta-band (15-30 Hz) oscillations arise in the parkinsonian basal ganglia. For treatments, we touch on the breadth of computational modelling work trying to understand the therapeutic actions of deep brain stimulation. Collectively, models from across all levels of description are providing a compelling account of the causes, symptoms, and treatments for Parkinson’s disease.

scholarly journals Reducing cognitive-motor declines associated with bilateral subthalamic deep brain stimulation through computational modelling in a parkinson's disease patient

2010 ◽  
Vol 81 (10) ◽  
pp. 1170-1172 ◽  
Author(s):  
J. L. Alberts ◽  
K. Hallahan ◽  
A. Thota ◽  
A. M. Noecker ◽  
J. L. Vitek ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Disease Patient ◽  
Computational Modelling ◽  
Deep Brain

scholarly journals Insights into Parkinson’s disease from computational models of the basal ganglia

2018 ◽  
Vol 89 (11) ◽  
pp. 1181-1188 ◽  
Author(s):  
Mark D Humphries ◽  
Jose Angel Obeso ◽  
Jakob Kisbye Dreyer
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Basal Ganglia ◽  
Computational Models ◽  
Neural Circuits ◽  
Computational Modelling ◽  
Beta Band ◽  
Complex Interplay ◽  
Deep Brain

Movement disorders arise from the complex interplay of multiple changes to neural circuits. Successful treatments for these disorders could interact with these complex changes in myriad ways, and as a consequence their mechanisms of action and their amelioration of symptoms are incompletely understood. Using Parkinson’s disease as a case study, we review here how computational models are a crucial tool for taming this complexity, across causative mechanisms, consequent neural dynamics and treatments. For mechanisms, we review models that capture the effects of losing dopamine on basal ganglia function; for dynamics, we discuss models that have transformed our understanding of how beta-band (15–30 Hz) oscillations arise in the parkinsonian basal ganglia. For treatments, we touch on the breadth of computational modelling work trying to understand the therapeutic actions of deep brain stimulation. Collectively, models from across all levels of description are providing a compelling account of the causes, symptoms and treatments for Parkinson’s disease.

scholarly journals Structural and functional alterations associated with the LRRK2 G2019S mutation revealed in structured human neural networks

2020 ◽  
Author(s):  
Vibeke Devold Valderhaug ◽  
Ola Huse Ramstad ◽  
Rosanne van de Wijdeven ◽  
Kristine Heiney ◽  
Stefano Nichele ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Current Knowledge ◽  
Computational Modelling ◽  
Network Activity ◽  
Microfluidic Chips ◽  
G2019s Mutation ◽  
Lrrk2 Gene ◽  
And Function

AbstractMutations in the LRRK2 gene have been widely linked to Parkinson’s disease. The G2019S variant has been shown to contribute uniquely to both familial and sporadic forms of the disease. LRRK2-related mutations have been extensively studied, yet the wide variety of cellular and network events directly or indirectly related to these mutations remain poorly understood. In this study, we structured multi-nodal human neural networks carrying the G2019S mutation using custom-designed microfluidic chips coupled to microelectrode-arrays. By applying live imaging approaches, immunocytochemistry and computational modelling, we have revealed alterations in both the structure and function of the resulting neural networks when compared to controls. We provide first evidence of increased neuritic density associated with the G2019S LRRK2 mutation, while previous studies have found either a strong decrease, or no change, compared to controls. Additionally, we corroborate previous findings regarding increased baseline network activity compared to control neural networks. Furthermore, we can reveal additional network alterations attributable to the specific mutation by selectively inducing transient overexcitation to confined parts of the structured multi-nodal networks. These alterations, which we were able to capture both at the micro- and mesoscale manifested as differences in relative network activity and correlation, as well as in mitochondria activation, neuritic remodelling, and synaptic alterations. Our study thus provides important new insights into early signs of neural network pathology significantly expanding upon the current knowledge relating to the G2019S Parkinson’s disease mutation.

scholarly journals Computational Modelling of Deep Brain Stimulation for Parkinson's Disease: A Critical Review

2020 ◽  
Vol 05 (02) ◽  
pp. 1-1
Author(s):  
Kristen W. Carlson ◽  
◽  
Jay L. Shils ◽  
Sahil Patel ◽  
Longzhi Mei ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Computational Models ◽  
Mean Field ◽  
Computational Modelling ◽  
Model Categories ◽  
Study Results ◽  
Deep Brain

We review the use of numerical and computational models to explore deep brain stimulation for Parkinson’s disease (DBS PD). It is a review for the modeler and those interested in PD DBS modelling methods and their value. The main model categories of active fiber, mean field, driving force, and volume of tissue activated are described as well as many modelling techniques. We give the basic requirements for a DBS PD model and current theories of DBS mechanism of action, PD etiology, and movement selection. The emphasis is on providing the reader with a representative sample of the variety of models and the range of techniques that have been applied to DBS PD, describing and critiquing them, and less so on study results. However, an extensive set of data and results that can be used for model calibration, validation, and comparison is provided in a Supplement.

scholarly journals Neural signatures of pathological hyperdirect pathway activity in Parkinson’s disease

2020 ◽  
Author(s):  
Ashwini Oswal ◽  
Chien-Hung Yeh ◽  
Wolf-Julian Neumann ◽  
James Gratwicke ◽  
Harith Akram ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Computational Modelling ◽  
Spectral Signature ◽  
Fibre Tract ◽  
Cortical Inputs ◽  
High Beta ◽  
Oscillatory Network ◽  
Network Disruption ◽  
Beta Frequency

AbstractParkinson’s disease (PD) is characterised by the emergence of pathological patterns of oscillatory synchronisation across the cortico-basal-ganglia circuit. The relationship between anatomical connectivity and oscillatory synchronisation within this system remains poorly understood. We address this by integrating evidence from invasive electrophysiology, magnetoencephalography, tractography and computational modelling in patients. Coupling between supplementary motor area (SMA) and subthalamic nucleus (STN) within the high beta frequency (21-30 Hz) range correlated with fibre tract densities between these two structures. Additionally within the STN, non-linear waveform features suggestive of cortical synchronisation correlated with cortico-STN fibre densities. Finally, computational modelling revealed that exaggerated hyperdirect cortical inputs to the STN in the upper beta frequency range can provoke the generation of widespread pathological synchrony at lower beta (13-20 Hz) frequencies. These observations reveal a spectral signature of the hyperdirect pathway at high beta frequencies and provide evidence for its pathophysiological role in oscillatory network dysfunction in PD.One sentence summarySignatures of the hyperdirect pathway and its likely role in pathological network disruption in Parkinson’s disease are identified.

Computational Modelling of TNFα Pathway in Parkinson’s Disease – A Systemic Perspective

2019 ◽  
pp. 762-773 ◽  
Author(s):  
Hemalatha Sasidharakurup ◽  
Lakshmi Nair ◽  
Kanishka Bhaskar ◽  
Shyam Diwakar
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Computational Modelling
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