Comput. Biol. Med. - Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG.

Tópicos

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{ framework(1458) process(801) describ(734) }
{ model(3480) simul(1196) paramet(876) }
{ network(2748) neural(1063) input(814) }
{ signal(2180) analysi(812) frequenc(800) }
{ estim(2440) model(1874) function(577) }
{ measur(2081) correl(1212) valu(896) }
{ research(1085) discuss(1038) issu(1018) }
{ import(1318) role(1303) understand(862) }
{ extract(1171) text(1153) clinic(932) }
{ case(1353) use(1143) diagnosi(1136) }
{ model(3404) distribut(989) bayesian(671) }
{ method(1557) propos(1049) approach(1037) }
{ first(2504) two(1366) second(1323) }
{ time(1939) patient(1703) rate(768) }
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{ system(1976) rule(880) can(841) }
{ imag(2830) propos(1344) filter(1198) }
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{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
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{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
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Resumo

Brain connectivity can be modeled and quantified with a large number of techniques. The main objective of this paper is to present the most modern and widely established mathematical methods for calculating connectivity that is commonly applied to functional high resolution multichannel neurophysiological signals, including electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. A historical timeline of each technique is outlined along with some illustrative applications. The most crucial underlying assumptions of the presented methodologies are discussed in order to help the reader understand where each technique fits into the bigger picture of measuring brain connectivity. In this endeavor, linear, nonlinear, causality-assessing and information-based techniques are summarized in the framework of measuring functional and effective connectivity. Model based vs. data-driven techniques and bivariate vs. multivariate methods are also discussed. Finally, certain important caveats (i.e. stationarity assumption) pertaining to the applicability of the methods are also illustrated along with some examples of clinical applications.

Resumo Limpo

brain connect can model quantifi larg number techniqu main object paper present modern wide establish mathemat method calcul connect common appli function high resolut multichannel neurophysiolog signal includ electroencephalograph eeg magnetoencephalograph meg signal histor timelin techniqu outlin along illustr applic crucial under assumpt present methodolog discuss order help reader understand techniqu fit bigger pictur measur brain connect endeavor linear nonlinear causalityassess informationbas techniqu summar framework measur function effect connect model base vs datadriven techniqu bivari vs multivari method also discuss final certain import caveat ie stationar assumpt pertain applic method also illustr along exampl clinic applic

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