Utility of independent component analysis for interpretation of intracranial EEG

Transcription

1 HUMAN NEUROSCIENCE Orgnal Research Artcle publshed: 02 November 2010 do: /fnhum Utlty of ndependent component analyss for nterpretaton of ntracranal EEG Dane Whtmer 1 *, Gregory Worrell 2, Matt Stead 2, Il Keun Lee 3 and Scott Makeg 1 1 Swartz Center for Computatonal Neuroscence, Insttute for Neural Computaton, Unversty of Calforna San Dego, La Jolla, CA, USA 2 Mayo Systems Electrophysology Laboratory, Department of Neurology, Mayo Clnc, Rochester, MN, USA 3 Seoul Bran Neurology Clnc, Seoul, Korea Edted by: Olver Bertrand, Insttut Natonal de la Santé et de la Recherche Médcale, France Revewed by: Sara L. Gonzalez Andno, Hôptaux Unverstares de Genève, Swtzerland Pedro Valdes-Sosa, Cuban Neuroscence Center, USA *Correspondence: Dane Whtmer, Starlab Barcelona S.L. C. Teodor Rovralta, , Barcelona, Span. e-mal: dwhtmer@ucsd.edu Electrode arrays are sometmes mplanted n the brans of patents wth ntractable eplepsy to better localze sezure foc before eplepsy surgery. Analyss of ntracranal EEG (EEG) recordngs s typcally performed n the electrode channel doman wthout explct separaton of the sources that generate the sgnals. However, ntracranal EEG sgnals, lke scalp EEG sgnals, could be lnear mxtures of local actvty and volume-conducted actvty arsng n multple source areas. Independent component analyss (ICA) has recently been appled to scalp EEG data, and shown to separate the sgnal mxtures nto ndependently generated bran and non-bran source sgnals. Here, we appled ICA to unmx source sgnals from ntracranal EEG recordngs from four eplepsy patents durng a vsually cued fnger movement task n the presence of background pathologcal bran actvty. Ths ICA decomposton demonstrated that the EEG recordngs were not maxmally ndependent, but rather are lnear mxtures of actvty from multple sources. Many of the ndependent component (IC) projectons to the EEG recordng grd were consstent wth sources from sngle bran regons, ncludng components exhbtng classc movement-related dynamcs. Notably, the largest IC projecton to each channel accounted for no more than 20 80% of the channel sgnal varance, mplyng that n general ntracranal recordngs cannot be accurately nterpreted as recordngs of ndependent bran sources. These results suggest that ICA can be used to dentfy and montor major feld sources of local and dstrbuted functonal networks generatng EEG data. ICA decomposton methods are useful for mprovng the fdelty of source sgnals of nterest, lkely ncludng dstngushng the sources of pathologcal bran actvty. Keywords: ICA, ntracranal, EEG, electrocortcography, ECoG, eplepsy, mu Introducton The goal of cogntve neuroscence s to correlate behavor and experence wth bran functon. Whle anmal studes provde the opportunty to record neural actvty drectly, ether at the level of sngle unts or of local feld potentals, human neuroscence s typcally lmted to non-nvasve, whole-bran magng technques typcally consdered to have lmted spatal and/or temporal resoluton. Scalp electroencephalographc (EEG) recordngs have submllsecond temporal resoluton, but ther spatal resoluton s lmted by the typcal mult-centmeter scale spacng of electrodes on the scalp surface, by the broad pont-spread functons of far-feld potentals generated n cortcal areas, and by the dffculty of estmatng source dstrbutons on the hghly folded bran surface from sparse measurements on the smooth and electrcally dstant scalp surface. Estmaton of the locatons of neurophysologcal current sources that generate the electrc felds recorded by EEG sensors s also nherently a mathematcally underdetermned problem whose soluton requres the use of addtonal physologcal constrants to lmt the nfnte soluton space. Independent component analyss (ICA) s a sgnal decomposton technque that fnds a set of maxmally ndependent sgnals that mx lnearly to produce the recorded data (Bell and Sejnowsk, 1995). An ICA decomposton separates the channel data matrx nto a mxng matrx whose columns each weght the relatve contrbutons of an ndependent component (IC) to the electrodes, and a matrx of IC actvty or actvaton tme seres. ICA has been used to nterpret scalp EEG sgnals recorded durng cogntve and perceptual tasks (Makeg et al., 1996, 1997, 2002, 2004a,b). Addtonally, many IC scalp maps vsualzng ther topographc projectons to the EEG electrode montage exhbt bophyscally smple patterns consstent wth felds generated by dpolar current sources, n the absence of any explct feld pattern constrants n the ICA model (Makeg et al., 1997, 2004a). Intracranal EEG (EEG) s an nvasve technque for recordng the electrcal actvty of the human bran n patents wth medcally ntractable partal eplepsy (Engel, 1996). Intracranal sgnals are recorded from surgcally mplanted subdural arrays on the cortcal surface or from ntraparenchymal depth probes. These typcally use electrodes wth 1 10 mm 2 surface area and 1-cm spacng, and are generally mplctly assumed to record actvty from the proxmal bran tssue (Bullock et al., 1995; Nunez and Srnvasan, 2006). Clncally, EEG has played a crtcal role n the success of eplepsy surgery, and s consdered the gold standard for localzaton of sezure foc (Berger, 1929; Jasper and Penfeld, 1949; Wyler et al., 1984; Engel and Crandall, 1987; Luders et al., 1992; Dewar et al., 1996) and for precse cortcal mappng of sensory, motor, and language Fronters n Human Neuroscence November 2010 Volume 4 Artcle 184 1

2 areas pror to surgery (Penfeld and Krstansen, 1951; Penfeld, 1954; Ojemann, 1982; Burchel et al., 1989; Luders et al., 1995; Tharn and Golby, 2007). Snce for clncal purposes patent ntracranal recordngs are contnued for several days, patents may be allowed to volunteer for cogntve neuroscence studes durng ntracranal montorng. Thus, human ntracranal recordngs have also been analyzed n studes of language (Ojemann et al., 1983, 1989), motor actvty (Arroyo et al., 1993; Crone et al., 1998a,b; Mller et al., 2007a; Ball et al., 2008), vsually guded behavors (Klopp et al., 2001), face recognton (Halgren et al., 1994a,b; Klopp et al., 2000; Quroga et al., 2005), memory (Cameron et al., 2001; Fell et al., 2001; Rzzuto et al., 2006), spatal cognton (Kahana et al., 1999), and attenton (Ray et al., 2008). Intracranal recordngs nvolvng mplantaton of electrodes drectly onto neocortex and nto deep medal temporal structures (amygdala and hppocampus) s often treated as the gold standard to whch clncal scalp EEG results are compared and the estmated locatons of pathologcal actvty verfed (Cooper et al., 1965; Kobayash et al., 2001). However, by smple bophyscs ntracranal recordngs, lke scalp EEG, are mxtures of volume-conducted actvtes of many current sources (Nunez and Srnvasan, 2006). Despte ths, EEG data are frequently examned wthout the applcaton of sgnal separaton methods. Because of volume conducton wthn the bran, sgnals recorded from clncal EEG arrays or strps may nclude actvtes generated dstal as well as proxmal to the electrodes. Actvty generated n tssue proxmal to an electrode (or to the actve reference electrode) may not necessarly domnate each EEG channel sgnal, and typcally the relatve strengths of proxmal and dstal contrbutons to the channel sgnals are not measured. Drect measurements would requre more complex mult-resoluton 3-D electrode arrays. However, snce volume conducton and superposton of bran source sgnals to both EEG and EEG electrodes s lnear and wthout apprecable delay (Nunez and Srnvasan, 2006), we propose that ICA should work just as well for separatng EEG sgnal mxtures as for separatng scalp EEG mxtures, thus provdng a clncally feasble way to separate proxmal and dstal EEG components and, possbly, to better observe ther ndvdual dynamcs and localze ther cortcal dstrbutons. That ntracranal recordngs are from eplepsy and other presurgcal patents means that cogntve research s performed usng brans producng pathologc actvty. Although ctal (sezure) epsodes are typcally not used n the analyss of the cogntve experments, ongong pathologcal bran sgnals may also occur durng nterctal (between-sezure) perods. Thus, abnormal bran sgnals may be mxed wth the task-relevant bran sgnals under nqury n data from cogntve EEG experments performed by epleptc patents. ICA mght be able to separate the volume-conducted contrbutons of abnormal nterctal actvty from normal bran sgnals, f these arse wthn separable source domans. Moreover, ICA may be able to unmx normal bran actvty from epleptc source actvty, thereby revealng more about the locaton and temporal dynamcs of the latter than vsual nspecton of the mxed data channels themselves. For these reasons, we propose that ICA may be useful n the nterpretaton of ntracranal data. Here, we used ICA to unmx ndependent sources of ntracranal EEG data from four patents wth medcally ntractable eplepsy as they performed a vsually cued fnger movement task. We frst tested whether the tme seres of ntracranal recordngs n the channel doman were themselves wholly ndependent and whether ICA could represent the data as a mxture of more ndependent source sgnals. Next, we studed whether the grd projecton maps assocated wth the EEG ndependent components (ICs) may be consstent wth projectons of compact, dpolar source regons or possbly of source networks ncludng more than one tghtly connected bran area. Fnally, we studed whether ICs mght better dentfy bran areas that are functonally lnked to nterctal pathologc actvty (ntermttent rhythmc delta actvty and nterctal spkes) and to subject motor behavor. Materals and methods Four patents wth medcally ntractable eplepsy partcpated n ths Mayo Clnc Internal Revew Board approved study. The research protocol devates from standard clncal practce because of the smultaneous acquston of contnuous, prolonged EEG recordngs from scalp and ntracranal electrodes. Patents gave ther nformed consent after the research protocol was presented to them n detal. Smultaneous scalp and ntracranal EEG recordngs Scalp EEG recordngs were obtaned from at least 21 scalp electrodes placed accordng to the Internatonal system usng gauze, glue and conductve collodon gel. The scalp electrodes were commercally avalable 8-mm gold dsc electrodes (Astro-Med; Grass nstruments). Intracranal depth and/or grd electrodes (Adtech, Inc.) were mplanted accordng to standard pre-surgcal evaluaton protocol (Engel and Crandall, 1987; Luders et al., 1992; Engel, 1996). The ntracranal electrode arrays were composed of 4-mm dameter Platnum/Irdum (Pt/Ir) contacts separated by 10 mm center-tocenter spacng. Scalp and ntracranal EEG recordngs were obtaned usng a scalp suture for ground and reference for one of the patents and the mastod electrode contralateral to the majorty of the ntracranal electrode for three of the patents. The followng table lsts the locatons and numbers of electrodes used from each of the partcpants of the study. Data acquston and pre-processng For eplepsy montorng, contnuous vdeo montorng va scalp EEG and EEG were recorded wth a 128-channel, dgtal 12-bt, XLTEK system (XLTEK Inc.) wth a samplng rate of 500 Hz. Patent 4 was recorded wth a 128-channel, dgtal 24-bt, Neuralynx (Neuralynx Inc.), samplng at 32 khz, and subsequently downsampled to 2 khz. Channels wth sgnfcant artfact and domnated by non-bologcal nose were removed from further analyss. The number of ntracranal and scalp EEG channels used n the analyss of each patent dataset s ndcated n Table 1. Lne nose (60 Hz nose) and ts harmoncs were removed on a sngle channel bass va harmonc analyss (Mtra and Pesaran, 1999; Jarvs and Mtra, 2001) usng Matlab based software from Fronters n Human Neuroscence November 2010 Volume 4 Artcle 184 2

3 Table 1 Numbers and locatons of electrodes. Patent Intracranal electrodes Scalp electrodes* 1 88 total, 87 analyzed 31 recorded, 30 used rght frontal grd frontal strps orbto-frontal strp lateral frontal strps mesal temporal surface 2 52 total, 49 analyzed 21 recorded, 16 used left temporal grd frontal strps mesal temporal depth probes 3 60 total, 60 analyzed 30 recorded, 30 used rght temporal grd 1 8-contact R depth probe 1 8-contact L depth probe 1 8-contact strp frontal strps 4 44 total, 39 analyzed 23 recorded, 16 used rght frontal grd paretal strp *The number of scalp electrodes vared because of dfferences n the surgcal cranotomy sze and locaton. Brefly, mult-taper spectral estmates (Thomson, 1982) were performed on a 1-s sldng wdow wth 50% overlap. Fve tapers were used for each estmate, and a zero paddng factor of 2 10 to ensure hgh resoluton n the frequency doman. A goodness-of-ft F-statstc (Thomson, 1982) was used to determne whch frequences had statstcally sgnfcant peaks, p < The lne nose for Patent 4 was statstcally nsgnfcant. Vsually cued fnger movement task Patents partcpated n a cued fnger movement task for language and motor evaluaton. The subject was presented wth ether the text of one fnger name ( thumb, ndex, etc.) among fve fngers n one hand, or a pcture of one hand wth an arrow desgnatng one fnger (Fgure 1A). Patent volunteers were asked to press the one of 10 keys under each fnger correspondng to the stmulus. Trals were tme lmted to 1.57 s, and falure to respond wthn the allotted tme was consdered an ncorrect response. Audtory feedback was presented n the form of a bref tone 1.24 s after the stmulus presentaton. Two dfferent audo frequences were used for correct versus ncorrect responses. Stmul were presented n a block desgn (Fgure 1B), n the followng order: left hand pcture stmul, rght hand pcture stmul, left hand word stmul, rght hand word stmul, wth the presentaton of specfc fngers n random order wthn a block. There were 400 trals n total for the task, consstng of 20 pcture presentatons and 20 text presentatons for each fnger. The task was performed twce for three of the patents (800 trals) and once for one of the patents. Thereby 400 (Patent 4) or 800 trals (patents 1,2,3) were recorded, yeldng recordngs from 12.5 mn to 25.7 mn n duraton. Electrode localzaton Locatons of mplanted electrodes were estmated for vsualzaton usng the LOC software package of Mller et al. (2007b). Axes n a Talarach coordnate system were dentfed from post-surgcal lateral CT scans of the patent s bran usng glabella and non skull landmarks. Correspondng Brodmann areas were estmated from the Talarach coordnates usng Talarach Daemon software (Lancaster et al., 2000). Data analyss Independent component analyss (ICA) Extended nfomax ICA (Bell and Sejnowsk, 1995; Lee et al., 1999) was performed on the ntracranal data and separately on the scalp EEG from each patent. In the ICA model, a set of recorded tme seres X(t) s the lnear combnaton of a mxng matrx, A, and a set of source sgnals, S(t). Xt () = ASt () (1) The soluton to ICA s an unmxng matrx, W, that when multpled by the orgnal data produce a set of maxmally ndependent tme seres actvatons U(t). WXt () = U() t Multplyng both sdes of the equaton by the pseudo-nverse of W gves a descrpton of the orgnal data as the product of a mxng matrx W ( 1) and the component tme seres or actvatons matrx U(t) * W ( 1) s a square matrx wth ICs represented n columns and electrodes represented n rows. ( 1) Xt () = W U() t Note that the unmxng matrx W s the nverse of A when the actvatons U(t) are the actvtes of the underlyng sources S(t). In practce, U and S may dffer n the order of the components and n ther unt scales and/or polartes, snce an altered scale and/or reversed polarty of a component tme sgnal n U(t) can be cancelled out by an nversely altered scale and/or reversed polarty of the component scalp map n the correspondng column of W ( 1). Par-wse mutual nformaton Mutual nformaton based on dfferental entropy was computed between pars of channels or pars of components. Dfferental entropy, the extenson of nformaton entropy to contnuous random varables, was used here n mutual nformaton calculatons because recorded voltages can take on contnuous values. Mutual nformaton between two random varables X and Y s based on dfferental entropy (Cover and Thomas, 2006) and defned as IXY ( ; ) = h( X) + h( Y) h( XY, ) (4) where h(x) and h(y) are the margnal dfferental entropes for X and Y, respectvely based on the margnal probablty denstes p(x) and p(y), and h(x,y) s the jont dfferental entropy based on jont probablty densty p(x,y). hx ( ) = px ( )ln px ( ) x (5) (2) (3) Fronters n Human Neuroscence November 2010 Volume 4 Artcle 184 3

4 Nx Nx hx ( ) = ln x N x N x (7) when the formula for dfferental entropy from Eq. 7 s substtuted nto Eq. 4, the x and y cancel out, gvng Nx Nx Ny Ny j j IXY (, ) = ln ln + N N j N N Nxy j Nxy j ln. (8) N N j The tme seres data were dvded nto 100 bns for the entropy estmates of all channels and components, whch used a bn sze of s dependng on the length of the patent s dataset. Percent varance accounted for (PVAF) The relatonshp between a channel s actvty and the actvty of ndependent components was quantfed by percent varance. The percent varance accounted for (pvaf) by component j of the sgnal X (t) at channel was computed as: ( 1) X () t = W U () t (9) j, j, pvaf( j,) ( X t X j, t ) var ( ) ( ) = 1 * 100 var ( X ( t) ) (10) Here X (t) s the tme seres for the th channel, W -1 s the ICA mxng matrx, U s the actvaton tme seres of the th component, X j, s the back-projecton of component j to channel, and var() s varance over tme t. Fgure 1 Schematc of vsually cued fnger movement task. (A) One of 20 stmul ndcatng whch fnger to move s presented to the patent. Half of the stmul are words namng a fnger, and the other half are pctures of a hand wth an arrow pontng to a fnger. Stmul are spaced 1.57 s apart. For tral-average spectral changes computed n ths study, the data were parsed nto 2-s epochs centered on the key-press. (ICA was performed on the contnuous data before epochng.) Patents are presented wth an audo tone ndcatng whether ther performance on a gven tral was correct or ncorrect. (B) The study was a block desgn, wth a block for each hand and stmulus type. The presentaton of fnger stmul was randomzed wthn a block. The entre expermental task was run twce for Patents 1,2,3 and once for Patent 4. Note that statstcal ndependence can only be precsely determned on an nfnte dataset. The margnal and jont probablty denstes were approxmated n Matlab usng Remann sums as N px ( )= N x where N s the number of values n the th bn, N s the total number of values, and x s the bn sze. The x factor n the denomnator ensures that the area under the probablty densty functon sums to unty. Dfferental entropy then becomes: (6) Spectral analyss The tme seres of channels and ndependent components were de-trended wth a hgh-pass Butterworth flter wth a pass-band above 2 Hz, and 40-dB attenuaton at the stop band of 1 Hz. Tme seres were subsequently low-pass fltered wth a Butterworth flter wth a 200 Hz pass-band lmt, wth 60 db attenuaton n the stop band at 240 Hz. Baselne-normalzed tral-average event-locked spectral estmates were performed on 2-s trals centered on the key-press. Spectra were estmated as an average over K = 5 ndependent tapers usng the mult-taper method (Thomson, 1982; Percval and Walden, 1993; Mtra and Pesaran, 1999; Jarvs and Mtra, 2001) based on a sngle-tapered sldng 250-ms wndow wth 10-ms overlap. The baselne for a tral was the average spectral change of a gven frequency band over the entre 2-s epoch. Spectral power and coherence estmates for channels and component sgnals exhbtng pathologcal slow wave actvty were estmated as an average over fve ndependent estmates usng the mult-taper method appled to consecutve 10-s segments of tme seres data that was pre-fltered wth a Butterworth low-pass flter wth an edge frequency of 40-Hz and 60-dB attenuaton by 60 Hz. The confdence lmt for the magntude of the coherence across all frequences was estmated by C = 1/( P NK 1) (11) Fronters n Human Neuroscence November 2010 Volume 4 Artcle 184 4

5 n whch P = 0.05 represents the 95% confdence lmt, N s the number of trals or epochs, and NK the number of degrees of freedom (Jarvs and Mtra, 2001; Berg et al., 2006). Results To test whether the EEG and scalp EEG channels were not themselves mutually ndependent, we frst asked whether the tme seres of the ntracranal ICs were more statstcally ndependent than the tme seres of the ntracranal channels themselves. To do ths we computed par-wse mutual nformaton between all pars of channels and between all pars of IC tme seres. The hstograms n Fgure 3 demonstrate that pars of EEG channels have hgher par-wse mutual nformaton than pars of EEG ICs, wth as expected a larger reducton produced by ICA for scalp EEG channels than for EEG channels. Table 2 ndcates the mean and range of values. The quanttatve relatonshp between channels and components was then assessed wth a percent varance accounted for (PVAF) metrc. The percent varance of each EEG channel accounted for by ts maxmum IC (component that accounts for the maxmal percent varance of that channel) s depcted for Patent 1 (Fgure 4A). If ntracranal channel sgnals were ndependent (e.g., f arsng from actvty n wholly separate cortcal domans), ther maxmal ICs would each account for 100% of ther sgnal, and the plotted squares n Fgure 4A would appear whte for all the depcted electrode channels. Instead, as Fgure 4A shows, the varance contrbuted to each ntracranal channel by ts largest-contrbutng IC ranged from only 21.5% to 92.6% (Fgure 4B). For Patents 2, 3, and 4, these lmts were %, %, and %, respectvely. The dstrbuton from all four patents, shown n Fgure 4C, exhbted a broad range wth a mean under 50%. We next examned the maps vsualzed by plottng columns of the ICA mxng (nverse weght) matrx and asked whether they were ordered and consstent wth an orgn n a focal bran area, by.e., resemblng plausble projectons of a sngle dstal or proxmal source area contrbutng to the affected channels through volume conducton. We fnd EEG IC maps to be separable nto a few categores: (a) proxmal ICs, (b) dffuse ICs, (c) complex ICs, and (d) nosy ICs. Fgure 5 shows example IC maps from Patent 1 n the frst three categores. The bar chart n Fgure 6 depcts the fracton of IC maps judged to fall nto each category for each patent. The crtera used were as follows: maps that Fgure 3 Reducton n par-wse mutual nformaton of ndependent components as compared to channels. Hstograms of normalzed par-wse mutual nformaton between channels (top row) and between ndependent components (bottom row) returned from ICA performed on ntracranal data (left) and on scalp EEG (rght). As expected, the par-wse mutual nformaton of ndependent components s greatly reduced as compared to the par-wse mutual nformaton of channel recordngs. The plot combnes results from the recordngs of four patents. Nats are the unts of mutual nformaton usng the natural logarthm. Table 2 Statstcs on hstogram of the par-wse mutual nformaton for Patent 1. Type of pars N Mean MI Medan MI Range MI (nats) (nats) (nats) Fgure 2 Example ndependent component. (A) Schematc of electrode placement for Patent 1 (not to scale). Lateral vew of rght hemsphere on the left, and medal vew of rght hemsphere on the rght. (B) Example map from one ndependent component. Each square represents an electrode from (A). Electrodes referred to n later fgures and text (e.g., Grd9, SOF1, etc.) are labeled here. Intracranal channels Mn: Max: Intracranal components Mn: Max: Scalp EEG channels Mn: Max: Scalp EEG components Mn: Max: Fronters n Human Neuroscence November 2010 Volume 4 Artcle 184 5

6 Fgure 4 Range n percent varance of each ntracranal channel accounted for by ts maxmum component. (A) Quas-topographc dsplay of ntracranal (EEG) channels for a sngle patent, from a medal vew of the rght hemsphere (left) and lateral vew of the rght hemsphere (rght). The percent varance of an EEG channel s actvty accounted for by ts maxmum component ndcated wth the gray scale. Indvdual EEG channels exhbt a wde range n how much of ther varance s accounted for by a sngle component. Channels shown n whte are domnated by a sngle ndependent component, whereas channels n dark gray are accounted for by a lnear combnaton of multple components. (B) Hstogram of maxmum percent varance of EEG channels shown n (A). A range of values s demonstrated. (C) Combned hstogram of the percent varance of maxmal ndependent component projecton to each EEG channel, across all four patents. The range n maxmum percent varance accounted for (PVAF) s smlar for all patents. projected to no more than two electrodes were categorzed as focal ; maps that projected to more than two contguous electrodes were categorzed as dffuse ; maps that projected to multple sets of contguous electrodes were categorzed as complex ; remanng maps from (mostly small) components whose maps appear to project to EEG channels n a dsordered manner were categorzed as nosy. The strongest assessment of the utlty of ICA for analyss of ntracranal EEG data s to examne whether the resultant ICs separate functonally meanngful bran sgnals. Because these data were acqured from eplepsy patents who performed a vsually cued movement task, we could look for both nterctal pathologcal as well as motor task-related bran dynamcs. Patent 1 had a 6 8 EEG grd mplanted over rght frontal cortex plus electrode strps over a varety of cortcal areas ncludng lateral temporal lobe, medal temporal lobe, and medal frontal areas (see Fgure 2 and Table 1). The etology of Patent 1-s partal eplepsy was a structural abnormalty n the medal frontal lobe, wth pathology demonstratng cortcal dysplasa (Kuznecky et al., 1995). Frontal scalp EEG sgnals demonstrated ncreased power n the delta frequency band as compared to other electrodes. More specfcally, the EEG data exhbted epsodes of rhythmc moderate ampltude delta frequency actvty on the followng set of frontally located channels: Grd1, Grd2, Grd9, Grd10, SOF1, and SOF2. Fve of the 87 ICs, totalng 21.8% of the varance of the ntracranal data, projected to some of these sx grd channels. Fgure 7 shows an example of one of these, IC3, whch projects strongly both to a sngle lateral frontal channel (Grd1) as well as to the most anteror two channels of the orbto-frontal strp (SOF1, SOF2). A sample 2-s segment of IC3 actvty, and the whole EEG sgnals at three channels to whch t most strongly projects are shown n Fgure 7B, an example of abnormal delta actvty on ntracranal channels. The log power spectrum of IC3, based on the average of spectral estmates of contguous 10-s segments of the data, contaned a peak at 3 Hz. The two channels to whch the map of IC3 most strongly projects, Grd1 and SOF1, has sgnfcant power n the 2 Hz to 6 Hz band (Fgures 8A and 8B), and the coherence of the two channels was statstcally sgnfcant between 2 and 6 Hz (Fgure 8C). For comparson, the coherences between Grd1 and ts fve next-to-nearest neghbor channels were computed. Before computng coherence, the data were frst hgh-pass fltered at 2 Hz to provde a conservatve estmate of the delta band coherence. These channels dd not exhbt pathologcal delta actvty and were not weghted n any of the IC maps projectng to Grd1. The mean coherence between Grd1 and these other fve channels was nsgnfcant at all frequences (Fgure 8D). ICA also dentfed components exhbtng classc movementrelated dynamcs. Grd24, an EEG channel n or near prmary motor cortex Brodmann area 4 of Patent 1, exhbted classc movement-related spectral changes ncludng per-movement alpha and beta range power decreases and smultaneous gamma band power ncreases. ICA decomposton of ths patent s data revealed one component wth a strong projecton to Grd24. IC18 Fronters n Human Neuroscence November 2010 Volume 4 Artcle 184 6

7 Fgure 5 Examples of ndependent component maps. (A) Four example components wth focal grd maps. (B) Examples of components wth more dffuse grd maps. These component maps could represent the projectons of more focal current sources dstal from the grd, or synchronous actvty of a wder doman of cortex proxmal to the mplanted grd. (C) Set of components that project to wdely separated electrode channels. IC23 (Fgure 10A) separates out the alpha and beta actvty assocated wth mu blockng, but wthout the gamma component. IC23 projects predomnantly to Grd23, whch s n or near Brodmann area 6 correspondng to pre-motor and supplementary motor cortex. Two addtonal components demonstrated event-related spectral changes tme-locked to the movement, but wth slghtly dfferent temporal dynamcs of gamma power and wth maps projectng to dfferent subsets of channels. IC68 shows a strong gamma power ncrease tme-locked to the movement and projects most strongly to Grd15 whch s n or near Brodmann area 6 correspondng to pre-motor and supplementary motor cortex (Fgure 10B). IC63 shows a gamma power ncrease tens of mllseconds after the movement and a strong projecton to Grd8, whch s n or near the superor temporal gyrus (Fgure 10C). Fgure 6 Classes of component maps. Percentage of components for each patent fallng nto each of the four categores based on vsual nspecton and manual classfcaton. accounted for 89% of the actvty on Grd24 (Fgure 9), and demonstrates the same alpha, beta, and gamma band movement-related spectral changes seen on Grd24 (Fgure 9). Dscusson To our knowledge, ths s the frst study n whch ICA has been appled to human ntracranal data for nterpretaton of functonal bran sgnals, although Hu et al. (2007) appled ICA to concurrently recorded EEG and scalp EEG to remove the contrbutons of sources near the scalp reference channel from the EEG channel data. We appled ICA to ntracranal data recorded durng a cogntve task because t has been nstructve for other researchers Fronters n Human Neuroscence November 2010 Volume 4 Artcle 184 7

8 Fgure 8 Low-frequency coherence between channels dentfed by ndependent component 1 (IC1). (A) Mult-taper power spectral estmates ntracranal channel Grd1. (B) Mult-taper power spectral estmate of ntracranal superor orbtal frontal channel SOF1 (Fgure 7A). (C) The two channels, dentfed by IC3 n Fgure 2, are sgnfcantly coherent n the delta band, consstent wth the clncal report descrbng epsodes of concdent rhythmc moderate ampltude delta frequency on these electrodes. (D) By contrast, the coherence of Grd1 wth each of ts next-nearest neghborng channel s not statstcally sgnfcant; the average of these fve coherences s shown here. Fgure 7 Multple ndependent components assocate lateral and medal frontal channels wth nterctal delta actvty. (A) Map from ndependent component 3 (IC3) projectng to Grd1 and to two adjacent channels from the superor orbtal frontal strp: SOF1 and SOF2. (B) Example 2-s tme seres from IC3 and the three channels to whch t projects n varyng degrees. (C) Map of IC1 projectng most strongly to Grd9 (red-colored electrode), but also to Grd1, SOF1, SOF3, and Grd10. (D) Map of IC4 projectng most strongly to SOF2 (red), but also SOF1 (orange), Grd1 (yellow), and SOF2 (yellow). (E) Example 2-s tme seres from Grd 1 and thethree ndependent components wth maps shown n A, C, and D. n decomposng the lnear mxtures recorded by scalp EEG data nto maxmally ndependent model sources whose dynamcs are modulated on a task bass. To an equal extent that the assumptons underlyng ICA are reasonable approxmatons for scalp EEG, they are also reasonable for EEG. ICA s a model based upon the assumpton that the voltages recorded at the level of sensors, are produced by (nearly) ndependent current components that sum lnearly and are spatally statonary over the duraton of the recordng. Lnearty s accepted as a reasonable approxmaton based on the bophyscs of electromagnetsm through bran tssue at the macroscopc level measured n scalp EEG and EEG recordngs (Nunez et al., 2001; Nunez and Srnvasan, 2006). The spatal statonarty of sources at the scale recorded by 1-cm spaced electrodes durng 20-mn recordngs s an emprcal queston that s used here as a plausble gross approxmaton. Travelng waves are observed n cortcal recordngs on much fner spatal scales (Rubno et al., 2006) and spread to a small enough extent that they could appear synchronous on the cm 2 scale of standard EEG recordngs (Freeman et al., 2006). It should be noted that ICA does not exclude movng sources; rather, t accounts for the dynamcs of a movng source as successve, temporally overlappng actvatons of multple spatally adjacent or overlappng components. Although ICA may not capture all the aspects of the mult-scale spatotemporal dynamcs of cortcal sgnals, we propose the use of ICA to spatally flter EEG sgnals wth the goal of mprovng on the standard approach n whch each ntracranal channel s nspected ndvdually. Our results suggest that ICA s useful for the nterpretaton of ntracranal data by unmxng and separatng functonally meanngful sgnals that have bologcally plausble component maps, and by separatng pathologcal actvty from task-based actvty. We frst tested the ndependence of ntracranal recordngs by comparng ther degree of ndependence to that of component sgnals found by nfomax ICA. These are maxmally ndependent sgnals lnearly combnng to produce the data. Both the mutual nformaton hstogram and the PVAF measures demonstrated that ICA decomposed the data nto a set of sgnals wth much greater ndependence than the orgnal sgnals. ICA decomposton can dentfy maxmally ndependent component sgnals n any dataset, and ndependent component sgnals are never less ndependent than the recorded sgnals, but gven ndependent sgnals (such as ts own output), nfomax ICA returns the same sgnals (wthn some numerc lmts). Appled to clncal EEG data, ICA returns components rather dfferent than the nput Fronters n Human Neuroscence November 2010 Volume 4 Artcle 184 8

9 Fgure 9 Mu blockng and movement-related spectral dynamcs. (A) Tral-average event-locked log power spectrum for Grd24, an electrode n or near prmary motor cortex (compare C). (B) Tme seres of Grd24 and IC18 from an example 2-s segment ncludng the fnger movement (at 0 ms). Suppresson of mu began approxmately 1.7 s before the movement and lasted untl 0.5 s after movement onset. (C) Component map and tral-average event-locked log power spectrum for IC18. The grd map of IC18 maxmally back-projects to Grd24 wth smaller projectons to neghborng channels. IC18 shows strong gamma ncrease durng the movement and concurrent power decreases n both the beta and alpha power bands. channel sgnals. Thus, our results demonstrate that ntracranal sgnals from standard clncal EEG electrodes are n fact not ndependent as recorded, and have synchronous features n common to multple electrode channels. Many of the sgnals that ICA fnds n EEG have grd maps that may plausbly be assocated wth a source not located under any sngle EEG electrode. Our results demonstrate that each EEG channel sgnal cannot be regarded as untary and ndependently generated, and suggest use of ICA as a reasonable pre-processng step for the analyss of EEG data. Some researchers re-reference ntracranal data to a bpolar montage or compute a surface Laplacan to remove volume conducton effects from the data. We dd not apply these spatal flters for several reasons. Frst, the data are reduced n dmensonalty by these transformatons. Note that ICA should return an equvalent result when appled to data after dmenson-preservng lnear flterng. Second, and more mportantly, a Laplacan dervaton nherently assumes that the orentaton of the source area s parallel Fgure 10 Independent components capture separable dynamcs n dfferent regons of motor cortex Grd maps and event-locked log power spectra for three ndependent components. (A) The grd map of IC23 projects most strongly to Grd23 (red), located over or near Brodmann Area 6 of the pre-central gyrus, and also projects to neghborng electrodes. IC23 demonstrates an alpha and beta power decrease tme-locked to the fnger movement, followed by a rebound power ncrease. (B) The grd map of IC68 projects most strongly to Grd15 (red), also over or near Brodmann Area 6. As compared to IC23, the movement-locked beta power decrease s smaller n ampltude. IC68 addtonally demonstrates a broadband gamma power ncrease tme-locked to the movement but wth less precson n tme than IC18 (Fgure 9). (C) The grd map for IC63 projects most strongly to Grd8 (red), near the superor temporal gyrus and borderng on Area 43 of the pre-central gyrus. The event-locked spectral dynamcs demonstrate a broadband gamma power ncrease wthn 100 ms after fnger movement onset. to the recordng grd. Akaln Acar et al. (2009) show an example of an ndependent component that could clearly only be accounted for by a sulcal source not orented parallel to the recordng grd. Snce sulcal terrtores are typcally estmated to make up roughly 2/3 of the cortcal surface, Laplacan dervatons are apt to gnore or dstort many recorded source processes. ICA uses a much stronger restrcton (relatve ndependence) on the tme courses of source processes, and by so dong can fnd spatal flters for any locally synchronous source dstrbuton, ncludng sulcal sources. Another shortcomng of Laplacan flterng, at least n straghtforward applcatons, s ts rather crude spatal samplng. ICA can also fnd spatal flters for source areas not centered on a recordng channel, another major advantage relatve to Laplacan decomposton. We then asked how many of the spatal patterns exhbted by the component maps from the ICA decomposton appear bologcally meanngful. We found that only a fracton of the component maps dentfy components projectng to sngle channels, components we categorzed as focal, whle the large majorty Fronters n Human Neuroscence November 2010 Volume 4 Artcle 184 9

10 project to multple electrodes. Another set of component maps, those projectng to multple nearby electrodes, were categorzed as dffuse. Dffuse component maps project to multple channels n bran tssue lkely to be anatomcally connected or that mght represent volume conducton effects wth lmted spatal spread. Of the dffusely projectng component maps, most demonstrated a steep decrement n component weghts from the hghest weghted channel to surroundng channels. Another type of component map projectng to multple electrodes was categorzed as complex because the affected electrodes were not located n contguous bran regons. We hypotheszed that these component maps dentfy functonally untary bran processes, dstnct from that of other components. In many cases, these projectons may be to electrodes located to ether pole of a dpolar source feld. We also found examples of pathologcal components wth synchronous projectons to multple electrodes. We cannot make any concrete clams about the locatons of components based on ICA alone wthout frst buldng ndvdualzed forward head models to predct how current sources generate the electrc felds that appear as voltage changes on the sensors. However, we postulate that the most parsmonous explanaton for focal component maps s that they each represent a bran source doman n close proxmty to the affected electrode. The dffusely projectng component maps mght represent ether, (1) wde (mult-cm) areas of local cortcal feld actvty coupled synchronously, or (2) the projecton through volume conducton of actvty generated n a (smaller) dstal source area to a wde array of sensors. For example, a radally orented focal source on the nferor cortcal surface mght project to a much wder area on the superor cortcal surface. However, the cortcal surface wave propagaton speed (approxmately 2 m/s), would not be compatble wth the appearance of synchronous source actvty at observed EEG frequences across a doman of cortex wth a dameter of >1 cm (possblty 1) above). For example, a propagatng wave (or phase cone, Freeman, 2004) at 10 Hz radatng out from the center of a cortcal patch 4 cm n dameter would requre 10 ms to reach ts edge, a phase lag of 36 ; at 20 Hz, ths would represent a phase lag of 72, whereas ICA models the EEG data as sums of source actvtes each synchronous or nearly synchronous across the recordng grd. However, these two qute dfferent possbltes cannot be defntvely dsambguated wthout a forward model of the current flow through the bran volume conductor takng nto account the complcatons of operatve skull nsults and the non-conductve plastc sheets housng the electrode grds. Usng such a model, Akaln Acar et al. have demonstrated an EEG ndependent component compatble wth radally projectng feld actvty synchronous across a roughly cm-scale patch of neurople located on gyral or sulcal cortex beneath the EEG electrode grd, but not drectly under any of the grd electrodes, and a second, dffuse EEG ndependent component well modeled by a cm-scale source on the wall of a sulcus below the recordng grd. Ths component projected dffusely, wth opposte sgn, to two sets of EEG electrodes, but only mnmally to the electrodes closest to the estmated source (Akaln Acar et al., 2009). Although synchronous actvty across a mult-cm cortcal regon has not been reported n the normal awake bran, n fact few studes have looked n spatal detal at the extent and dynamcs of synchrony across mm to cm spatal scales wthn ether anmal or human cortex. Gven that the dffuse component maps n our data typcally have strong foc surrounded by a more weakly weghted penumbra, representatve of steep voltage gradents, t s most lkely that these components represent actvty n cortex local to the recordng grds. However, more detaled source analyss requres use of a suffcent forward head model, ultmately combned wth use of hgher-densty, multscale recordng grds. The complex component maps n some cases suggested orgns near dfferent sets of electrodes that were functonally (and nearsynchronously) coupled. Agan, the dpolar nature of cortcal felds mean that each component has two opposte projecton drectons (n whch the projected sgnals have opposte sgn). In some cases, complex maps mght thus be generated by a small number of bvalent dpolar projectons to the EEG grd. Fnally, some of the ndependent components were not categorzable as focal, dffuse, or complex and were descrbed as nosy. The ICA algorthm, by desgn, decomposes the data nto the same number of components as there are sensors. The number of potental proxmal and dstal coherent components contrbutng by volume conducton to an EEG data set, however, s lkely larger than the number of electrodes. ICA therefore must mx the contrbutons of small source sgnal contrbutons nto the avalable number of components. Thus, the smallest components returned by ICA may not be domnated by any sngle source sgnal, and all mght not be recovered n multple successve decompostons, snce the (randomzed) order n whch the data are consdered affects detals of nfomax ICA output. An addtonal test of the utlty of ntracranal ICA, beyond the qualtatve assessment of the bologcal plausblty of component maps, was to determne whether the tme seres of ndependent components group sgnals from dsparate areas of the bran that are functonally lnked. The prevalence of stereotyped pathologcal sgnals n these data provded the opportunty to use sgnals that are well characterzed n the channel doman. We dentfed one type of clncally mportant sgnal to test whether ICA can successfully separate pathologcal sgnals from non-pathologcal sgnals. Frontal ntermttent rhythmc delta actvty (FIRDA) s a non-specfc but commonplace pathologcal bran sgnal that can be seen n EEG of patents wth tumors (Kubota and Ohnsh, 1997), ncreased ntracranal pressure, and toxc-metabolc dsorders (Nedermeyer, 2003). ICA decomposton of scalp EEG data has been shown to successfully separate FIRDA from other bran actvty n patents wth Creutzfeldt Jakob dsease, and assocates FIRDA wth perodc lateralzed epleptform dscharges (PLEDs) (Hung et al., 2007). In our ntracranal data, ICA separated FIRDA from other ongong bran actvty. Further, ICA separated 12 components havng maps ncludng projectons to some subset of the sx channels clncally dentfed as exhbtng frontal ntermttent delta actvty. Four of these components showed maxmal spectral peaks at 3 Hz, and the remander exhbted a maxmum at 6 Hz wth a smaller peak near 3 Hz. Fronters n Human Neuroscence November 2010 Volume 4 Artcle

11 Why were the FIRDA sgnals accounted for by multple ndependent components rather than beng aggregated nto a sngle component? ICA models the data as the weghted mxture of ndependent source sgnals that reman spatally statonary through the duraton of the recordng. Dfferent FIRDA trans appearng on dfferent combnatons of channels through the course of the 20-mn recordng wll necessarly be accounted for by more than one ICA component sgnal. Else, the source generators of FIRDA may have been herarchcal rather than ndependent, as s suggested by a novel analyss of ctal eplepsy data (Repucc et al., 2001). Alternatvely, the FIRDA sgnal could be a travelng wave that extends beyond the cm 2 scale over whch ICA spatal statonarty s assumed wth ths methodology. As a further test of whether ICA separates functonally meanngful sgnals n ntracranal data, we also examned how the decomposton parsed event-related sgnals trggered from cued fnger movements. The cortcal dynamcs assocated wth fnger movement are well characterzed and therefore provde a good model for testng ICA. The mu rhythm s a perodc sgnal wth a stereotyped morphology ncludng a sharply contoured wave followed by a rounded phase, observed over rolandc cortex of humans durng perods of stllness and s attenuated wth motor actvty. Mu rhythm, lke posteror alpha actvty, s therefore vewed as an dlng bran rhythm (Jasper and Andrews, 1938; Gastaut et al., 1952; Nedermeyer and Lopes da Slva, 1999). The suppresson of ths rhythm durng movements s a well-known phenomenon frst observed n the tme doman of scalp EEG durng movement (Jasper and Andrews, 1938), and later determned by spectral decomposton to be comprsed of alpha and beta components (Pfurtscheller and Aranbar, 1977, 1980). Ths classcal movement-nduced bran sgnature s robustly evdent across modaltes ncludng EEG (Stancak and Pfurtscheller, 1996), MEG (Salmeln and Har, 1994), and ntracranal recordngs (Arroyo et al., 1993; Crone et al., 1998a,b; Aok et al., 1999; Ohara et al., 2000; Klopp et al., 2001; Mller et al., 2007a), and n response to movements of dfferent modaltes ncludng the tongue, foot, and hand (Pfurtscheller et al., 1994). Hand movement paradgms that have elcted ths stereotyped pattern have ncluded manually squeezng of rubber ball (Pfurtscheller and Aranbar, 1980; Pfurtscheller, 1982), fnger movements from vsual cue (Pfurtscheller et al., 1997; Klopp et al., 2001); fst clenchng and relaxng (Mller et al., 2007a), sustaned muscle contracton (Crone et al., 1998a,b), and more complex tasks nvolvng the hand (Aok et al., 2001; Rektor et al., 2006). Recent studes wth data acqured at hgher samplng rates have demonstrated an ncrease n gamma band power concurrent wth the alpha and beta power decreases assocated wth mu (Crone et al., 1998a,b; Mller et al., 2007a). It was therefore expected that our vsually cued fnger movement task would elct mu rhythm blockng and ts assocated alpha and beta power decreases, concurrent wth and followng movement-related gamma power ncreases over motor cortex. Indeed, ntracranal channels n Patent 1, who had electrodes mplanted over per-rolandc area, exhbted these well- establshed spectral changes tme-algned to fnger movements. ICA decomposton revealed one component (IC18) wth strong projectons to an electrode over pre-central cortex and weaker projectons to the neghborng channels, whch also exhbted salent movement-locked alpha, beta, and gamma band perturbatons. The broader spatal extent of beta dynamcs than gamma dynamcs s consstent wth reports of other researchers (Crone et al., 1998a,b; Mller et al., 2007a). Furthermore, our fndng of gamma power changes n components that project to prmary motor areas (M1) but not nearby supplementary motor area (SMA) s consstent wth the observaton of Ohara et al. (2000) who reported event-related gamma synchronzaton n S1 and M1 but not SMA. The effectveness of ICA to separate predcted event-related dynamcs n our ntracranal data suggests that ICA could be appled to data acqured durng more sophstcated cogntve and perceptual tasks to dentfy further detals of event-related cortcal bran dynamcs wthn functonally connected regons. ICA can dentfy and characterze component sgnals mxed by volume conducton wthout any constrants on where those components are located. The results from ICA decomposton of EEG data may be used n conjuncton wth source localzaton models, such as equvalent sngle dpole or multple dpole modelng wth boundary element (BEM) or fnte element (FEM) head models, to answer both what and where questons about bran functon what source actvtes produce the observed bran electrcal data and where are they generated? The putatve sources of ntracranal data dentfed by ICA can only be used for source localzaton by ncorporatng a sophstcated forward model, as forward models for EEG assume an ntact skull, and are thus nsuffcent for use wth eplepsy patents wth cranotomes. Skull ansotropy has an effect on the accuracy of dpole localzaton to begn wth (Yvert et al., 1997), and the cranotomy has sgnfcant effects on volume conducton (Oostenveld and Oostendorp, 2002). Therefore, a crtcal step n extendng ths work s the development of forward head models of patent-specfc skulls wth cranotomes (Akaln Acar and Makeg, 2008). When used n conjuncton wth patent-specfc forward models, ICA can lkely gve more nformaton about the spatal dstrbuton of both pathologcal and normal bran actvtes recorded by EEG and/or scalp EEG sensors (Akaln Acar et al., 2009). The nfomax ICA algorthm used here has a strong and possbly fallble assumpton of spatal statonarty of the component sgnal areas over the entre length of the recordng. Newer decomposton methods have been developed that explctly account for movng components, such as complex ICA (Anemuller et al., 2003, 2006; Dyrholm et al., 2006, 2007) and an ICA algorthm for fndng multple component mxtures n spatally non-statonary data (Palmer et al., 2006), all of whch have been appled to scalp EEG data. The applcaton of these algorthms to EEG data mght refne and extend the results reported here. Whle ntracranal recordngs from patents wth focal refractory eplepsy provde a unque opportunty for analyss of human bran sgnals wth sub-mllsecond resoluton and mproved spatal resoluton, there may be lmtatons to the extrapolaton of healthy, normal-functonng bran actvty from these studes performed on pathologcal bran tssue. It s therefore crtcal to fnd a robust means for separatng epleptc and otherwse Fronters n Human Neuroscence November 2010 Volume 4 Artcle