??? Dynamic Causal Modelling for M/EEG. Electroencephalography (EEG) Dynamic Causal Modelling. M/EEG analysis at sensor level. time.

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Elctroncphalography EEG Dynamc Causal Modllng for M/EEG ampltud μv tm ms tral typ 1 tm channls channls tral typ 2 C.

Kbl M/EEG analyss at snsor lvl Dynamc Causal Modllng tm channls tral typ 1 Approach: Rduc vokd rspons to a fw varabls,.g.: Th avrag ovr a fw channls n pr-stmulus tm.

tral typ 2 Dffrnt approach that tlls us mor about th nuronal dynamcs of localzd bran sourcs?

1 Elctroncphalography EEG Dynamc Causal Modllng for M/EEG ampltud μv tm ms tral typ 1 tm channls channls tral typ 2 C. Phllps, Cntr d Rchrchs du Cyclotron, ULg, Blgum Basd on slds from: S. Kbl M/EEG analyss at snsor lvl Dynamc Causal Modllng tm channls tral typ 1 Approach: Rduc vokd rspons to a fw varabls,.g.: Th avrag ovr a fw channls n pr-stmulus tm.??? Buld a modl for spatotmporal data: Assum that both ERPs ar gnratd by tmporal dynamcs of a ntwork of a fw sourcs A2 channls tral typ 2 Dffrnt approach that tlls us mor about th nuronal dynamcs of localzd bran sourcs? Dynamc Causal Modllng Dscrb tmporal dynamcs by dffrntal quatons Each sourc projcts to th snsors, followng physcal laws Solv for th modl paramtrs usng Baysan modl nvrson x & = f x, u, θ p θ y, m p y m

Msmatch ngatvty MMN mod 1 Modl for msmatch ngatvty Oddball paradgm

2% dvant tons 5 Hz tm prprocssng mod 3 raw data 128 EEG scalp lctrods

/ ERFs data rducton to prncpal spatal mods xplanng most of th varanc

, PNAS, 8 Th gnratv modl Nural mass quatons and connctvty Sourc

2 Msmatch ngatvty MMN mod 1 Modl for msmatch ngatvty Oddball paradgm standards dvants mod 2 psudo-random audtory squnc 8% standard tons Hz 2% dvant tons 5 Hz tm prprocssng mod 3 raw data 128 EEG scalp lctrods convrt to matlab fl fltr poch down sampl artfact corrcton avrag ERPs / ERFs data rducton to prncpal spatal mods xplanng most of th varanc tm ms Garrdo t al., PNAS, 8 Th gnratv modl Nural mass quatons and connctvty Sourc dynamcs f Spatal forward modl g y = g x, θ x & = f x, u, θ stats x paramtrs θ Evokd rspons data y Input u Davd t al., NuroImag, 5.

Nural mass quatons and connctvty Nural mass quatons and connctvty nhbtory ntrnurons spny stllat clls Stat quatons x & = f x,u,θ Extrnsc forward connctons A F S x 7 = x8 H B L 8 = A + A + 2x8 x7 3I S

x1 2 Extrnsc backward A B S x connctons x man trans-mmbran potntals and currnts of th dffrnt nuron populatons.

3 Nural mass quatons and connctvty Nural mass quatons and connctvty nhbtory ntrnurons spny stllat clls Stat quatons x & = f x,u,θ Extrnsc forward connctons A F S x 7 = x8 H B L 8 = A + A + 2x8 x7 3I S x = x4 H F L 2x4 x1 4 = A + A + 1I S x + Cu 2 Intrnsc 1 connctons 2 Extrnsc latral A L S x connctons pyramdal clls x = x5 x6 2 = x5 H B L 5 = A + A S x + 3 = x6 H 2x6 x3 6 = 4S x7 2 2x5 x2 2S x1 2 Extrnsc backward A B S x connctons x man trans-mmbran potntals and currnts of th dffrnt nuron populatons. nuronal sourc modl Spatal modl Baysan modl nvrson x L L θ Dpolarsaton of pyramdal clls Spatal modl Snsor data y Masurd data Expctaton-Maxmzaton algorthm Spcfy gnratv forward modl wth pror dstrbutons of paramtrs Itratv procdur: 1. Comput modl rspons usng currnt st of paramtrs 2. Compar modl rspons wth data 3. Improv paramtrs, f possbl 1. Postror dstrbutons of paramtrs p θ y, m 2. Modl vdnc p y m

4 Modl comparson: Whch modl s th bst? MMN xampl IFG IFG IFG data y Modl 1 Modl 2 p y m1 p θ y, m 1 p y m 2 p θ y, m 2 Modl comparson: Slct modl wth hghst modl vdnc Forward - F Backward - B Forward and Backward - FB bst?... Modl n p y mn p θ y, m n p y m Forward Backward Latral Forward Backward Latral Forward Backward Latral modulaton of ffctv connctvty Group modl comparson Evokd and nducd rsponss Baysan Modl Comparson Group lvl log-vdnc Forward F subjcts Backward B Forward and Backward FB Garrdo t al., 7, NuroImag Trnds Cogn Sc Apr;34:

Modllng of nducd rsponss Fac data EEG: Ntwork of four sourcs

RF x = 36mm y = 31mm z = 18mm Tm-srs data n channl spac

of ach sourc as functon of powr from othr sourcs.

al., Nuromag, 8 Obsrvd powr spctra Sngl subjct rsults:

tral 1: sourc 3 tral 1: sourc 4 LF RF α β α RV - 1-1 - 1-1

5 Modllng of nducd rsponss Fac data EEG: Ntwork of four sourcs Invrson of lctromagntc modl L x = 36mm y = 33mm z = 8mm LF RF x = 36mm y = 31mm z = 18mm Tm-srs data n channl spac Dynamc powr data n sourc spac Am: Explan dynamc powr spctrum of ach sourc as functon of powr from othr sourcs. x = 4mm y = 8mm z = 8mm LV RV x = 4mm y = 8mm z = 1mm Chn t al., Nuromag, 8 Obsrvd powr spctra Sngl subjct rsults: Couplng functons LV RV LF RF tral 1: sourc 1 tral 1: sourc 2 tral 1: sourc 3 tral 1: sourc 4 LF RF α β α RV Tm ms frquncy frquncy frquncy frquncy Frquncy Hz LV RV RF Chn t al., Nuromag, 8

6 Obsrvd and fttd powr spctra Summary LV RV LF RF tral 1: sourc 1 tral 1: sourc 2 tral 1: sourc 3 tral 1: sourc Tm ms DCM combns stat-quatons for nural mass dynamcs wth spatal forward modl. Dffrncs btwn rsponss acqurd undr dffrnt condtons ar modlld as modulaton of connctvty wthn and btwn sourcs. frquncy frquncy frquncy frquncy Frquncy Hz Baysan modl comparson allows on to compar many dffrnt modls and dntfy th bst on. - tral 1: sourc 1 prdctd - tral 1: sourc 2 prdctd - tral 1: sourc 3 prdctd - tral 1: sourc 4 prdctd Mak nfrnc about postror dstrbuton of paramtrs.g., ffctv connctvty, locaton of dpols, tc.. fttd Many xtnsons to DCM for M/EEG ar avalabl n SPM8. channl channl channl channl Thanks to Karl Frston Marta Garrdo CC Chn Jan Daunzau

Dynamic Causal Modelling for EEG and MEG

Dynamic Causal Modelling for EEG and MEG Stefan Kiebel Ma Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany Overview 1 M/EEG analysis 2 Dynamic Causal Modelling Motivation 3 Dynamic