scholarly journals Reconstruction of post-synaptic potentials by reverse modeling of local field potentials

2019 ◽  
Vol 16 (2) ◽  
pp. 026023 ◽  
Author(s):  
Maxime Yochum ◽  
Julien Modolo ◽  
David J Mogul ◽  
Pascal Benquet ◽  
Fabrice Wendling
2018 ◽  
Author(s):  
Maxime Yochum ◽  
Julien Modolo ◽  
Pascal Benquet ◽  
Fabrice Wendling

AbstractAmong electrophysiological signals, Local Field Potentials (LFPs) are extensively used to study brain activity, either in vivo or in vitro. LFPs are recorded with extracellular electrodes implanted in brain tissue. They reflect intermingled excitatory and inhibitory processes in neuronal assemblies. In cortical structures, LFPs mainly originate from the summation of post-synaptic potentials (PSPs), either excitatory (ePSPs) and inhibitory (iPSPs) generated at the level of pyramidal cells. The challenging issue, addressed in this paper, is to estimate, from a single extracellularly-recorded signal, both ePSP and iPSP components of the LFP. The proposed method is based on a model-based reverse engineering approach in which the measured LFP is fed into a physiologically-grounded neural mass model (mesoscopic level) in order to estimate the synaptic activity of a sub-population of pyramidal cells interacting with local GABAergic interneurons. The method was first validated using simulated LFPs for which excitatory and inhibitory components are known a priori and can thus serve as a ground truth. It was then evaluated on in vivo data (PTZ-induced seizures, rat; PTZ-induced excitability increase, mouse; epileptiform discharges, mouse) and on in clinico data (human seizures recorded with depth-EEG electrodes). Under these various conditions, results showed that the proposed reverse engineering method provides a reliable estimation of the average excitatory and inhibitory post-synaptic potentials at the origin of the measured LFPs. They also indicated that the method allows for monitoring of the excitation/inhibition ratio. The method has potential for multiple applications in neuroscience, typically when a time tracking of local excitability changes is required.


2021 ◽  
Vol 11 (7) ◽  
pp. 882
Author(s):  
Yeon Hee Yu ◽  
Seong-Wook Kim ◽  
Dae-Kyoon Park ◽  
Ho-Yeon Song ◽  
Duk-Soo Kim ◽  
...  

Increased prevalence of chronic kidney disease (CKD) and neurological disorders including cerebrovascular disease, cognitive impairment, peripheral neuropathy, and dysfunction of central nervous system have been reported during the natural history of CKD. Psychological distress and depression are serious concerns in patients with CKD. However, the relevance of CKD due to decline in renal function and the pathophysiology of emotional deterioration is not clear. Male Sprague Dawley rats were divided into three groups: sham control, 5/6 nephrectomy at 4 weeks, and 5/6 nephrectomy at 10 weeks. Behavior tests, local field potentials, and histology and laboratory tests were conducted and investigated. We provided direct evidence showing that CKD rat models exhibited anxiogenic behaviors and depression-like phenotypes, along with altered hippocampal neural oscillations at 1–12 Hz. We generated CKD rat models by performing 5/6 nephrectomy, and identified higher level of serum creatinine and blood urea nitrogen (BUN) in CKD rats than in wild-type, depending on time. In addition, the level of α-smooth muscle actin (α-SMA) and collagen I for renal tissue was markedly elevated, with worsening fibrosis due to renal failures. The level of anxiety and depression-like behaviors increased in the 10-week CKD rat models compared with the 4-week rat models. In the recording of local field potentials, the power of delta (1–4 Hz), theta (4–7 Hz), and alpha rhythm (7–12 Hz) was significantly increased in the hippocampus of CKD rats compared with wild-type rats. Together, our findings indicated that anxiogenic behaviors and depression can be induced by CKD, and these abnormal symptoms can be worsened as the onset of CKD was prolonged. In conclusion, our results show that the hippocampus is vulnerable to uremia.


Sign in / Sign up

Export Citation Format

Share Document