Automated Neurons Recognition and Sorting for Diamond Based Microelectrode Arrays Recording: A Feasibility Study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985823%3A_____%2F19%3A00504888" target="_blank" >RIV/67985823:_____/19:00504888 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68378271:_____/19:00504888 RIV/68407700:21460/19:00326474

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-981-10-9038-7_52" target="_blank" >http://dx.doi.org/10.1007/978-981-10-9038-7_52</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-10-9038-7_52" target="_blank" >10.1007/978-981-10-9038-7_52</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automated Neurons Recognition and Sorting for Diamond Based Microelectrode Arrays Recording: A Feasibility Study

Popis výsledku v původním jazyce

Microelectrode arrays (MEA) are extensively used for recording and stimulating neural activity in vitro and in vivo. Depositing nanostructured boron doped diamond (BDD) onto the neuroelectrodes makes it possible to obtain dual mode low-noise neuroelectrical and neurochemical information simultaneously. The signal processing procedure requires finding and distinguishing individual neurons spikes in the recordings. Spike identification is usually done manually which is inaccurate and inappropriate for complex datasets. In this paper, we present a methodology and two algorithms for neurons recognition and evaluation based on unsupervised learning. Forty-five extracellular randomly selected signals from 26 unique measurements of embryonic hippocampal rat neurons (20 kHz, 6 min) were recorded on the commercial 60 TiN channels MEA. The signals were filtered in the 300-3000 Hz band and an amplitude detector (4x std of the background noise) was used for spike detection. WaveClus features were computed and its 3 PCA components were extracted for every spike. The optimal number of clusters were evaluated by an expert rater. K-means + gap criterion (alg. 1) and the Gaussian Mixture Model + Bayesian Information Criterion (alg. 2) were implemented and compared. The total IntraClass Correlation showed a significant inter-rater agreement for all 3 rater procedures (ICC = 0.69, p < 0.001), when post hoc weighted Cohen's Kappas for 2 raters were 0.85 (expert vs. alg. 1, p < 0.001) and 0.62 (expert vs. alg. 2, p < 0.001). This will contribute to the objective definition of dual mode BDD MEA performance criteria and for a comparison with the current system.

Název v anglickém jazyce

Automated Neurons Recognition and Sorting for Diamond Based Microelectrode Arrays Recording: A Feasibility Study

Popis výsledku anglicky

Microelectrode arrays (MEA) are extensively used for recording and stimulating neural activity in vitro and in vivo. Depositing nanostructured boron doped diamond (BDD) onto the neuroelectrodes makes it possible to obtain dual mode low-noise neuroelectrical and neurochemical information simultaneously. The signal processing procedure requires finding and distinguishing individual neurons spikes in the recordings. Spike identification is usually done manually which is inaccurate and inappropriate for complex datasets. In this paper, we present a methodology and two algorithms for neurons recognition and evaluation based on unsupervised learning. Forty-five extracellular randomly selected signals from 26 unique measurements of embryonic hippocampal rat neurons (20 kHz, 6 min) were recorded on the commercial 60 TiN channels MEA. The signals were filtered in the 300-3000 Hz band and an amplitude detector (4x std of the background noise) was used for spike detection. WaveClus features were computed and its 3 PCA components were extracted for every spike. The optimal number of clusters were evaluated by an expert rater. K-means + gap criterion (alg. 1) and the Gaussian Mixture Model + Bayesian Information Criterion (alg. 2) were implemented and compared. The total IntraClass Correlation showed a significant inter-rater agreement for all 3 rater procedures (ICC = 0.69, p < 0.001), when post hoc weighted Cohen's Kappas for 2 raters were 0.85 (expert vs. alg. 1, p < 0.001) and 0.62 (expert vs. alg. 2, p < 0.001). This will contribute to the objective definition of dual mode BDD MEA performance criteria and for a comparison with the current system.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

<a href="/cs/project/GA17-15319S" target="_blank" >GA17-15319S: Diamantová mikroelektrodová pole pro duální monitorování nervových signálů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

World Congress on Medical Physics and Biomedical Engineering 2018

ISBN

978-981-10-9037-0

ISSN

1680-0737

e-ISSN

1433-9277

Počet stran výsledku

6

Strana od-do

281-286

Název nakladatele

Springer

Místo vydání

Singapore

Místo konání akce

Praha

Datum konání akce

3. 6. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000449742700052