Intraoperative localization of spatially and spectrally distinct resting-state networks in Parkinson’s disease

2020 ◽  
Vol 132 (4) ◽  
pp. 1234-1242 ◽  
Author(s):  
Paolo Belardinelli ◽  
Ramin Azodi-Avval ◽  
Erick Ortiz ◽  
Georgios Naros ◽  
Florian Grimm ◽  
...  

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for symptomatic Parkinson’s disease (PD); the clinical benefit may not only mirror modulation of local STN activity but also reflect consecutive network effects on cortical oscillatory activity. Moreover, STN-DBS selectively suppresses spatially and spectrally distinct patterns of synchronous oscillatory activity within cortical-subcortical loops. These STN-cortical circuits have been described in PD patients using magnetoencephalography after surgery. This network information, however, is currently not available during surgery to inform the implantation strategy.The authors recorded spontaneous brain activity in 3 awake patients with PD (mean age 67 ± 14 years; mean disease duration 13 ± 7 years) during implantation of DBS electrodes into the STN after overnight withdrawal of dopaminergic medication. Intraoperative propofol was discontinued at least 30 minutes prior to the electrophysiological recordings. The authors used a novel approach for performing simultaneous recordings of STN local field potentials (LFPs) and multichannel electroencephalography (EEG) at rest. Coherent oscillations between LFP and EEG sensors were computed, and subsequent dynamic imaging of coherent sources was performed.The authors identified coherent activity in the upper beta range (21–35 Hz) between the STN and the ipsilateral mesial (pre)motor area. Coherence in the theta range (4–6 Hz) was detected in the ipsilateral prefrontal area.These findings demonstrate the feasibility of detecting frequency-specific and spatially distinct synchronization between the STN and cortex during DBS surgery. Mapping the STN with this technique may disentangle different functional loops relevant for refined targeting during DBS implantation.

2021 ◽  
Author(s):  
Feng Han ◽  
Gregory L. Brown ◽  
Yalin Zhu ◽  
Aaron E. Belkin‐Rosen ◽  
Mechelle M. Lewis ◽  
...  

2015 ◽  
Vol 9 ◽  
pp. 300-309 ◽  
Author(s):  
Erik S. te Woerd ◽  
Robert Oostenveld ◽  
Bastiaan R. Bloem ◽  
Floris P. de Lange ◽  
Peter Praamstra

Neuroscience ◽  
2020 ◽  
Vol 436 ◽  
pp. 170-183 ◽  
Author(s):  
Zhi-yao Tian ◽  
Long Qian ◽  
Lei Fang ◽  
Xue-hua Peng ◽  
Xiao-hu Zhu ◽  
...  

2018 ◽  
Vol 52 ◽  
pp. 102-106 ◽  
Author(s):  
Muhammad Nazmuddin ◽  
D.L.Marinus Oterdoom ◽  
J. Marc C. van Dijk ◽  
Jonathan C. van Zijl ◽  
Anne K. Kampman ◽  
...  

2008 ◽  
Vol 211 (1) ◽  
pp. 59-66 ◽  
Author(s):  
Alexandros G. Androulidakis ◽  
Paolo Mazzone ◽  
Vladimir Litvak ◽  
Will Penny ◽  
Michele Dileone ◽  
...  

2021 ◽  
Author(s):  
Xiangnan Xu ◽  
Michal Lubomski ◽  
Andrew J Holmes ◽  
Carolyn M Sue ◽  
Ryan L Davis ◽  
...  

The microbiome plays a fundamental role in human health and diet is one of the strongest modulators of the gut microbiome. However, interactions between microbiota and host health are complex and diverse. Understanding the interplay between diet, the microbiome and health state could enable the design of personalized intervention strategies and improve the health and wellbeing of affected individuals. A common approach to this is to divide the study population into smaller cohorts based on dietary preferences in the hope of identifying specific microbial signatures. However, classification of patients based solely on diet is unlikely to reflect the microbiome-host health relationship or the taxonomic microbiome makeup. To this end, we present a novel approach, the Nutrition-Ecotype Mixture of Experts (NEMoE) model, for establishing associations between gut microbiota and health state that accounts for diet-specific cohort variability using a regularized mixture of experts model framework with an integrated parameter sharing strategy to ensure data driven diet-cohort identification consistency across taxonomic levels. The success of our approach was demonstrated through a series of simulation studies, in which NEMoE showed robustness with regard to parameter selection and varying degrees of data heterogeneity. Further application to real-world microbiome data from a Parkinson's disease cohort revealed that NEMoE is capable of not only improving predictive performance for Parkinson's Disease but also for identifying diet-specific microbiome markers of disease. Our results indicate that NEMoE can be used to uncover diet-specific relationships between nutritional-ecotype and patient health and to contextualize precision nutrition for different diseases.


2020 ◽  
Vol 120 (12) ◽  
pp. 18
Author(s):  
D.V. Pokhabov ◽  
D.D. Pokhabov ◽  
V.G. Abramov ◽  
M.E. Tunik ◽  
K.O. Tutsenko ◽  
...  

Author(s):  
Eva M. Navarro-López ◽  
Utku Çelikok ◽  
Neslihan S. Şengör

AbstractWe propose to investigate brain electrophysiological alterations associated with Parkinson’s disease through a novel adaptive dynamical model of the network of the basal ganglia, the cortex and the thalamus. The model uniquely unifies the influence of dopamine in the regulation of the activity of all basal ganglia nuclei, the self-organised neuronal interdependent activity of basal ganglia-thalamo-cortical circuits and the generation of subcortical background oscillations. Variations in the amount of dopamine produced in the neurons of the substantia nigra pars compacta are key both in the onset of Parkinson’s disease and in the basal ganglia action selection. We model these dopamine-induced relationships, and Parkinsonian states are interpreted as spontaneous emergent behaviours associated with different rhythms of oscillatory activity patterns of the basal ganglia-thalamo-cortical network. These results are significant because: (1) the neural populations are built upon single-neuron models that have been robustly designed to have eletrophysiologically-realistic responses, and (2) our model distinctively links changes in the oscillatory activity in subcortical structures, dopamine levels in the basal ganglia and pathological synchronisation neuronal patterns compatible with Parkinsonian states, this still remains an open problem and is crucial to better understand the progression of the disease.


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