Supporting InformationDaitch et al. 10.1073/pnas.1307947110SI Experimental ProceduresSubject-Specific Methods. Patients 3 and 4. Patients 3 and 4 had vagalnerve stimulators, which are incompatible with the MRI scanner,so presurgical MRIs were not obtained. The CT from each subject(with electrodes) was coregistered with an atlas-representativemagnetization-prepared rapid gradient-echo (MP-RAGE) target,and average functional connectivity maps over a group of 25control subjects were used to define task-relevant and -irrelevantelectrodes.Patient 6. A postsurgical CT was not obtained from patient 6, soelectrode coordinates were estimated based on skull landmarkstaken from saggital and coronal skull X-rays, using the LOC(location on cortex) localization package (1).

Electrocorticography Data Collection/Stimulus Presentation. TheBCI2000 platform (2) was used to synchronize data collection,stimulus presentation, and behavioral response monitoring (e.g.,mouse click) during the task. We asked subjects to maintainfixation on a central crosshair throughout each run of the task,and eye movements were monitored with a Tobii T60 integratedinfrared eye tracker, which was also synchronized with stimuluspresentation and electrocorticography (ECoG) data collectionusing BCI2000. Visual stimuli were presented on a monitor set upby the patient’s hospital bed. We collected between three andfour runs of the task from each subject, each run consisting of120 trials.Each subject was implanted with an electrode grid (8 × 8 or 6 ×

8 configuration) or electrode strips (1 × 4, 1 × 6, or 1 × 8), allplaced subdurally facing the cortical surface. Electrodes, manu-factured by PMT, were made of platinum, each 4 mm in di-ameter with 2.3 mm exposed to the cortical surface and spacedapart by 1 cm. A separate 1 × 4 strip was placed subdurally facingthe skull to use as the amplifier’s ground and reference. Signalswere acquired at 1,200 Hz using optically isolated 16-channel g.USBamp amplifiers (Guger Technologies) and a Dell Precision690 Quad Core computer (Dell) and converted to MATLABfiles for processing and analysis.

ECoG Signal Analysis. All signal processing scripts were customwritten in MATLAB, unless otherwise noted. The signal at eachelectrode was first rereferenced to the common mean of allelectrodes (excluding noisy electrodes) to minimize commonsources of noise from the signals. Spectral decomposition of therereferenced signals was then accomplished using Gabor waveletfiltering (between 1 Hz and 512 Hz), which yields instantaneousamplitude and phase estimates at each time point for each fre-quency, and tailors the temporal resolution for each frequency.Gabor filtering was performed on the entire signal recordedduring the task (i.e., before it was divided into individual trials),such that the amplitude and phase of low frequencies could beestimated accurately. The Gabor output was then divided intotrials for the event-related analyses.The intertrial coherence (ITC) was computed at each time

point within a trial and at all considered frequencies to determinewhat events reset the phase of ongoing oscillations and at whatfrequencies. ITC reflects the consistency of the phase of theoscillation of a particular frequency at a particular time pointwithin a trial, across trials (Fig. S2 A and B). ITC is equivalent tothe magnitude of the mean resultant vector of the oscillatoryphase across trials (computed separately for each time point andfrequency) (3). A Rayleigh uniformity test determined the sig-nificance of each ITC calculation against the null hypothesis that

the phases across all trials came from a uniform circular distri-bution (MATLAB Circular Statistics toolbox).To measure the “phase clustering” between multiple electro-

des (e.g., electrodes within a functional network), we first com-puted the average phase of each electrode across trials (ata single time point and frequency), then computed the meanphase across electrodes, weighting each electrode’s phase by itsITC at that time point and frequency. Average phase differencesbetween networks were calculated as the absolute value of thedifference between the mean phase of each network.Phase-locking values (PLV) were computed between each pair

of electrodes over different epochs in each subject of the task byconcatenating the signal from each 500-ms epoch in a trial, acrosstrials. The PLV is then equal to the magnitude of the meanresultant vector of the phase difference between two electrodes,averaged across all time points within the concatenated signal.Weperformed paired t tests to determine in which network pairsthere was a significant difference between the PLVs in differenttask epochs and the ITI, correcting for number of epochs andnumber of network pairs.Statistical evaluation of differences between physiological

measurements (e.g., ITC) in two experimental conditions (e.g.,ipsilateral vs. contralateral cue; invalid vs. valid targets) was ac-complished with permutation tests, which calculated the likeli-hood of obtaining the given difference in metrics between the twoexperimental conditions if the two conditions were not in factdifferent. To conduct the permutation test, the data wereresampled 1,000 times, each time shuffling the labels for the twoconditions and recomputing the test statistic with these shuffleddatasets. The P value was determined by the percentile of thetest statistic using the correct labels along the distribution of teststatistics using the shuffled labels. When the test statistic relatedto the variance of a measure (e.g., ITC, which is related to thevariance of phases across trials), the distributions from the twoconditions were first shifted such that they had the same meansbefore conducting the permutation test.

Functional MRI Acquisition and Analysis.All scans were collected ona Siemens 3T Tim-Trio scanner. Structural scans consisted ofa saggital MP-RAGE T1-weighted image (TR = 1,950 ms, TE =2.26 ms, flip angle = 9°, voxel size = 1.0 × 1.0 × 1.0 mm) anda transverse turbo spin-echo T2-weighted image (TR = 2,500 ms,TE = 435 ms, voxel size = 1.0 × 1.0 × 1.0 mm). Blood-oxygenlevel-dependent (BOLD) contrast was measured with a gradientecho echo-planar imaging (EPI) sequence (TR = 2,000 ms, TE =27 ms, 32 contiguous 4-mm slices, 4 × 4 in-plane resolution).The resting state functional MRI (fMRI) scans involved be-

tween four and eight 6.7-min scans (200 frames per scan) duringwhich subjects fixated on a centrally presented plus sign. Pre-processing consisted of the following steps: (i) Asynchronous sliceacquisition was compensated by sinc interpolation to align allslices. (ii) Elimination of odd/even slice intensity differencesresulting from interleaved acquisition. (iii) Whole-brain nor-malization corrected for changes in signal intensity across scans.(iv) Data were realigned within and across scans to correct forhead movement. (v) EPI data were coregistered to the subject’sT2-weighted anatomical image, which in turn was coregisteredwith the T1-weighted MP-RAGE, in both cases using a cross-modal procedure based on alignment of image gradients (4). TheMP-RAGE was then transformed to an atlas-space (5) repre-sentative target using a 12-parameter affine transformation.Movement correction and atlas transformation were accomplished

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in one resampling step to minimize blur and noise. (vi) Temporalfiltering that eliminates frequencies above 0.08 Hz. (vi) Removalby linear regression of (a) six parameters for head movement, (b)the signal averaged over the whole brain, excluding the ven-tricles, (c) the signal over a ventricular region, and (d) the signalfrom a white matter region. Temporal derivatives of these re-gressors are also included in the linear model, accounting fortime-shifted versions of spurious variance.Functional connectivity (FC) analyses defined the dorsal at-

tention network (DAN), ventral attention network (VAN),sensorimotor network (SMN), and default-mode network (DMN)within each subject, using the resting state fMRI data. The firstfour frames of each scan were eliminated to allow steady-statemagnetization and the remaining frames were concatenated. Foreach network a set of 6-mm radius spherical seed regions of interst(ROIs) (8, 4, 12, and 10 ROIs, respectively, for DAN, VAN,SMN, and DMN) were first identified from meta-analyses thatwe have previously conducted of task-based activation experi-ments (refs. 6, 7. and 8 as reanalyzed by ref. 9). Then, for eachseed ROI, a voxel-wise FC map was computed indicating thecorrelation of the timeseries of each voxel in the brain with theseed timeseries. The Fisher z-transform was applied to each FCmap, the FC maps were averaged, and the average map wasthresholded at Fisher z = 0.3. The resulting map indicated thevoxels that were most strongly and consistently related toeach network and was used to define task-relevant or -irrelevant

electrodes (see Fig. S1A for variability of functional connectivitymaps across subjects).

Realignment of Subdural Grids with MRI. CT images were acquiredbefore removal of the grid. Preoperative MP-RAGEs were ac-quired using standard clinical protocols. CTs were transformed toatlas space using a cross-modal procedure based on alignment ofimage gradients (4) in which the CT image is aligned to the in-dividual subject MP-RAGE, and the MP-RAGE is then trans-formed to an atlas-space (5) representative target using a 12-parameter affine transformation. Electrodes were segmented inthe CT image by thresholding. Center-of-mass coordinates fromclusters of face-contiguous voxels were isolated using an in-houseclustering algorithm.Because of subdural hygroma after the surgery filling the in-

tracranial space, the locations of the electrodes at the time of CTacquisition are generally displaced inward relative to the locationof the subject’s cortical surface at the time of MRI acquisition.To correct for this displacement, electrode coordinates wereprojected to the surface of the brain along a path normal to thecortical surface. The surface anatomy used in this procedure wasextracted using the average of the first four frames of eachBOLD run, which was then thresholded and blurred modestly(5 mm) such that electrodes arrive at a location reflecting thesmoothed convexity of the brain.

1. Miller KJ, et al. (2007) Cortical electrode localization from X-rays and simple mappingfor electrocorticographic research: The “Location on Cortex” (LOC) package forMATLAB. J Neurosci Methods 162(1-2):303–308.

2. Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, Wolpaw JR (2004) BCI2000: Ageneral-purpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng 51(6):1034–1043.

3. Lakatos P, Chen CM, O’Connell MN, Mills A, Schroeder CE (2007) Neuronal oscillationsand multisensory interaction in primary auditory cortex. Neuron 53(2):279–292.

4. Rowland DJ, Garbow JR, Laforest R, Snyder AZ (2005) Registration of [18F]FDGmicroPET and small-animal MRI. Nucl Med Biol 32(6):567–572.

5. Talairach J, Tournoux P (1988) Co-Planar Stereotaxic Atlas of the Human Brain, transRayport M (Thieme Medical, New York), pp 122.

6. He BJ, et al. (2007) Breakdown of functional connectivity in frontoparietal networksunderlies behavioral deficits in spatial neglect. Neuron 53(6):905–918.

7. Astafiev SV, et al. (2003) Functional organization of human intraparietal and frontalcortex for attending, looking, and pointing. J Neurosci 23(11):4689–4699.

8. Shulman GL, et al. (1997) Common blood flow changes across visual tasks: II. Decreasesin cerebral cortex. J Cogn Neurosci 9(5):648–663.

9. Fox MD, et al. (2005) The human brain is intrinsically organized into dynamic,anticorrelated functional networks. Proc Natl Acad Sci USA 102(27):9673–9678.

4 subjects3 subjects2 subjects1 subject

DAN VAN SMN DMN

PT1 PT2 PT3 PT4 PT5 PT6

Right hemisphere Left hemisphere

A

B

Fig. S1. (A) Conjunction functional connectivity maps. Shown is the degree of overlap between the functional connectivity maps of the four subjects whoreceived fMRI scans. Note that although the general structure is similar across subjects, there is much individual variability. (B) Patients’ grid locations. Shownare the approximate electrode locations for the patients from this study, drawn on atlas-representative brains using the LOC localization package (1).

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Fig. S2. Cognitive events associated with synchronization of ongoing oscillations in task-relevant regions/networks. (A) Simulated local field potential os-cillations at a single site. The yellow vertical line represents an event during the trial that resets the phase of the some oscillations. Before this event, oscillationsexhibit low ITC (shaded in red) and after it, they exhibit high ITC (shaded in blue). We hypothesized that task-relevant regions would become selectively phase-reset by particular events, to modulate the excitability of single sites and the effective connectivity between multiple sites, which are simultaneously phase-reset. (B) ITC measures the consistency of phase values across trials at different points in time. It is equivalent to the magnitude of the mean resultant vector ofthe oscillatory phase across trials (at a single time point and frequency. (C) Phase-resetting may allow two regions to become in-phase (Top and Middle traces),possibly facilitating communication between them, or to become out of phase (Middle and Bottom traces), inhibiting communication.

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Cue

per

iod

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ay p

erio

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ta (1

-3 H

z)

ITC at electrodes with significant ITC

Norm. power at electrodes with significant ITC

Thet

a (3

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z)

Cue

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iod

Distribution of norm. power

Norm. power vs. ITC

-1

1

-0.5

0

0.5

Nor

m. p

ower

Nor

m. p

ower

Nor

m. p

ower

Normalized power (relative to ITI)

r = -0.040265, p = 0.64163

ITC

r = 0.59557, p = 8.1588e-007

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r = -0.089636, p = 0.21875r = -0.089636, p = 0.21875

Mea

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wer

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0

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ons

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E

Fig. S3. (A) Topography and strengh of ITC at electrodes with significant ITC in the δ-band during the cue or delay, or in the θ-band during the cue period. (B)Normalized power [relative to intertrial interval (ITI)] in the corresponding frequency band and task epoch as in the left column, at the same electrodes withsignificant ITC in that frequency band and epoch. Note that many of the electrodes exhibiting significant δ or θ ITC exhibit a power decrease (blue) in that samefrequency/epoch. (C) Distribution of normalized power at the electrodes in the second column. (D) Scatter plot showing the relationship between the nor-malized power and ITC at the electrodes in the first and second column, in the respective frequency band and task epoch. (E) Time-courses of spectral powermodulations (normalized by power during ITI) throughout the Posner task trial, averaged across electrodes within each functional network.

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Hor

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aze

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PT4 PT5 PT6

Valid targets Invalid targets

0.5

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Rig

ht t

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arg

ets

A

B

Fig. S4. (A) Horizontal eye gaze during right vs. left cues. In each trial, the maximum eye gaze deviation (from the center of the monitor) within the 500-mscue period was recorded, and these maximum deviations were averaged across trials. Right and left cue trials were separated to see if subjects saccade to thecued location. Error bars represent SEMs. There was no significant difference between the eye gaze during right versus left cues in any of the three subjects(unpaired t test, P > 0.05). (B) Horizontal eye gaze during valid vs. invalid targets. In each trial, the maximum eye gaze deviation (from the center of themonitor) within the 500 ms following target onset was recorded, and these maximum deviations were averaged across trials. Trials were separated by thosewith right and left targets, and within each of these groups, by invalid and valid targets. Error bars represent SEMs. There were no significant differencesbetween the gaze during invalid vs. valid targets (right valid vs. right invalid and left valid vs. left invalid; unpaired t test, P > 0.05), except for right valid vs.invalid targets in patient 4 (P = 0.0253); however, in this case, the deviation was slightly greater for valid than invalid targets, which is the opposite of whatwould be expected if subjects saccade to unexpected (i.e., invalid) targets.

Fig. S5. Right hemisphere dominance of reorienting response. ITC plots, locked to target onset (dotted line), during valid trials, invalid trials, and the dif-ference between the two conditions, averaged across electrodes in each functional network and across subjects, separately for electrodes over the righthemisphere (Upper; three subjects) and left hemisphere (Lower; three subjects).

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Fig. S6. ITC by frequency. Shown are curves of the average ITC at each frequency, averaged separately over electrodes in each functional network. Note thatin different epochs, ITC is more prominent at different frequencies. For example, during the delay period, electrodes across all networks exhibit a peak in ITCaround 2 Hz.

Fig. S7. Spatial distribution of 2-Hz phase-locking. Shown are 2-Hz phase-locking maps from two example subjects, with seed regions marked in white. Notethat during the ITI, phase-locking is mostly local, whereas during the trial, seed regions become phase-locked with electrodes over many other regions ofinterest. FEF, frontal eye field; IPS, intraparietal sulcus; MT, middle temporal region.

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10

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Inter-electrode distance (mm)20 30 40 50 60 70 80 90100110

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0 20 40 60 80 100 120-200-100

0100200

r = 0.16292, p = 0.00752860 20 40 60 80 100 120

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0100200

r = 0.20593, p = 0.000694330 20 40 60 80 100 120

-200-100

0100200

r = 0.11912, p = 0.0514180 20 40 60 80 100 120

-200-100

0100200

r = 0.030492, p = 0.61921

0 20 40 60 80 100 120-200-100

0100200

r = -0.096592, p = 0.20220 20 40 60 80 100 120

-200-100

0100200

r = -0.15804, p = 0.0361810 20 40 60 80 100 120

-200-100

0100200

r = -0.15482, p = 0.0402060 20 40 60 80 100 120

-200-100

0100200

r = -0.057554, p = 0.44802

0 20 40 60 80 100 120-200-100

0100200

r = 0.11369, p = 0.222280 20 40 60 80 100 120

-200-100

0100200

r = 0.11344, p = 0.223310 20 40 60 80 100 120

-200-100

0100200

r = 0.26336, p = 0.00411920 20 40 60 80 100 120

-200-100

0100200

r = 0.29909, p = 0.0010537

0 20 40 60 80 100 120-200-100

0100200

r = 0.12419, p = 0.118850 20 40 60 80 100 120

-200-100

0100200

r = 0.12348, p = 0.120990 20 40 60 80 100 120

-200-100

0100200

r = 0.12824, p = 0.10720 20 40 60 80 100 120

-200-100

0100200

r = 0.15653, p = 0.048791

0 20 40 60 80 100 120-200-100

0100200

r = 0.044514, p = 9.6988e-0070 20 40 60 80 100 120

-200-100

0100200

r = 0.042528, p = 2.8834e-0060 20 40 60 80 100 120

-200-100

0100200

r = 0.0015564, p = 0.863510 20 40 60 80 100 120

-200-100

0100200

r = -0.01452, p = 0.11036

C

Delta (1-3 Hz) Theta (3-7 Hz) Alpha (7-15 Hz)

% PLV change from ITI

-20%

20%

DANVANSMNDMN

DANVANSMNDMN

Cue - ITI Delay - ITI

Target - ITI Response - ITI

Beta (15-32 Hz) Low gamma (32-55 Hz) High gamma (70-150 Hz)

* p<0.01, Bonferroni corrected

A

Fig. S8. (A) Average phase-locking values within and between networks. Percent change in the average PLV between pairs of electrodes in different networksin different epochs of the task relative to the ITI. Starred squares represent network pairs where there was a significant difference between phase locking inthat epochs versus the ITI (paired t test < 0.01, corrected for number of frequency bands, number of epochs, and number of epoch pairs). (B) Average PLVchange from the ITI to each task epoch, as a function of interelectrode distance, separated by task epoch and network (only electrode pairs within the samenetwork are shown here). Note that many of the largest PLV increases occur between more distant electrode pairs. (C) Relationship between task-related PLVchange and interelectrode distance (raw data shown here that is used to compute average PLV by interelectrode distance in B). Top/first row: Scatterplotsshowing, for all electrode pairs, the relationship between the percent change in PLV in each 500-ms trial epoch relative to the ITI and interelectrode distance.

Legend continued on following page

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Bottom four rows: Scatterplots showing the relationship between percent PLV change from ITI and interelectrode distance, considering only electrode pairswithin each of the four functional brain networks studied here. The line of best fit for each scatterplot is displayed in red, and the r and P values for eachcorrelation (between percent PLV change from ITI and interelectrode distance) are shown at the bottom of each scatterplot.

Mea

n IT

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const. delayvar.delay

const. delayvar.delay

Fig. S9. δ ITC averaged over electrodes in the DAN and SMN following the cue in the constant-delay version of Posner (light lines) and the variable-delayversion (dark lines). For the variable-delay trials, only the first 500 ms of the delay is plotted here. Note that when the delay is variable, δ ITC is not sustainedfollowing the cue, as it is when the delay is constant. Shaded regions represent SEMs.

Table S1. Posner task accuracy across subjects

Subject % Correct (valid trials) % Correct (invalid trials) % Correct (all trials)

PT1 90.8 86.7 90.0PT2 94.8 91.2 94.2PT3 85.2 88.4 85.8PT4 93.4 93.1 93.3PT5 96.9 98.6 97.2PT6 94.5 95.9 94.8

PT, patient.

Table S2. Posner task reaction times across subjects

Patient RT (ms; valid trials) RT (ms; invalid trials) RT (ms; all trials)

PT1 933 978 942PT2 872 941 886PT3 755 892 795PT4 818 865 827PT5 950 960 952PT6 805 810 806

PT, patient; RT, reaction time.

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