Supplementary Figure S1: Confirmation of MEG source estimates with LFP. We considered FF>CS as well as CS>FF LFPs to be confirmatory of localization

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1 Supplementary Figure S1: Confirmation of MEG source estimates with LFP. We considered FF>CS as well as CS>FF LFPs to be confirmatory of localization of the MEG signal, provided they were recorded in the putative letterform area, because either LFP response could cause the observed MEG dynamics. LFPs from many cortical patches add and subtract to produce the net signal at a given MEG sensor. Two are schematically illustrated here as red and blue ovals, with the MEG sensor represented as a black square. Some cortical patches projecting to a given sensor change between CS and FF (red oval) while others do not (blue oval). A. In the simplest case, the MEG signals from the changing and unchanging cortical patches would be of the same polarity and in the changing cortical patches the LFP would be CS>FF, resulting in the MEG also being CS>FF. B. However if the MEG signals from the changing and unchanging cortical patches were of opposite polarity and in the changing cortical patches the LFP were FF>CS, then the MEG might again be CS>FF. Thus, the critical issue in confirming an MEG source estimate is whether there is an LFP in the critical anatomical location that changes between the same conditions, not the direction of that change.

2 Supplementary Figure S2: Cortical locations with greater BOLD response to FF than CS. (p<0.05, cluster corrected, see Supplementary Methods above for details). Ventral view of the left hemisphere.

3 Patient Age Sex Wada VCI POI WMI WTAR Vocab MRI Sz focus Path Lang stim A 22 m L normal LH MTL dysplasia normal B 53 m L LH OL LH OL dysplasia normal, LF hit C 39 F L LH MTS LH MTL heterotopia normal D 44 f L n/a 12 normal LH MTL dysplasia Normal E 53 f L LH MTS LH MTL dysplasia Normal F 26 m L LH MTS LH MTL dysplasia Normal G 47 f L normal RH MTL n/a n/a H 26 f L normal LH MTL dysplasia Normal J 53 m L LH OL LH OL dysplasia Normal Supplementary Table S1: Intracranial implant patient demographics. Wada: language lateralization as determined by Wada procedure; Wechsler Adult Intelligence Scale III indices: VCI: Verbal Comprehension Index; POI: Perceptual Organization Index; WMI: Working Memory Index; WTAR: Wechsler Test of Adult Reading (standard score); Vocab: WAIS-III Vocabulary subtest (standard score); MRI: presurgical MRI findings, LH=left hemisphere, RH=right hemisphere, MTL=mesial temporal lobe, OL=occipital lobe; Sz focus: seizure focus as determined by intracranial EEG; Path: pathology findings of resected tissue in seizure onset zone; Lang stim: results from cortical stimulation for receptive language mapping testing normal or abnormal language localization, In patient B, language stim was performed at VOC electrodes showing visual confrontation naming disruption. Patient G received bilateral strip implants to determine seizure lateralization without undergoing a resection, hence no cortical stimulation and pathology data are available.

4 Supplementary Methods Structural and functional MRI acquisition. MRI data were acquired using a 3T Siemens Allegra head-only scanner (TE = 3.25 ms, TR = 2530 ms, TI = 1100 ms, flip angle = 7 deg, FOV = 256 mm, matrix = 256 x 256 x 171, slice thickness = 1.3 mm). Two T1-weighted images were acquired, rigid body registered to each other, and reoriented into a common space, roughly similar to alignment based on the AC-PC line. Images were corrected for non-linear warping caused by non-uniform fields created by the gradient coils. Image intensities were further normalized and made uniform with the FreeSurfer (3.0.5) software package ( Functional BOLD data images were acquired using a T2*-sensitive echo planar imaging sequence (TR = 3000ms, TE = 25 ms, flip angle 90 degrees; 3x3x3 voxels; FOV = 192mm). Thirty-nine interleaved transverse slices (3mm width with no gap) were obtained during each TR, covering the entire cortex. The image files in DICOM format were transferred to a Linux workstation for morphometric and functional BOLD analysis. Structural MRI analysis. Geometric representations of the cortical surface were constructed from the T1-weighted structural volumetric images using procedures described previously [52-54]. First, segmentation of cortical white matter was performed and the estimated border between gray and white matter was tessellated, providing a topographically correct representation of the surface. This representation of the folded cortical surface was used to derive the locations of the dipoles used in the analysis of the MEG data and visualization of the BOLD response for fmri, as well as localization of subdural electrode contacts in each individual patient using his or her own MRI. For MEG and fmri intersubject averaging, the reconstructed surface for each subject was morphed into an average spherical representation, optimally aligning sulcal and gyral features across subjects while minimizing metric distortion [55]. This surface-based deformation procedure results in a substantial reduction in anatomical and functional variability across subjects relative to volume-based normalization approaches [55]. fmri statistical analysis. Normalized beta-weights (relating to the BOLD amplitude) for the Real Words > Consonant Strings, and Consonant Strings > False Fonts contrasts were examined across the cortical surface and within group comparisons were performed for each contrast (supplementary fig. 2). T-stat maps were generated from the beta-weights, and cluster-based thresholding was performed according to procedures described by Hagler et al. [56]. This yielded significant clusters of fmri activity for the group contrasts of interest (FWHM = 8mm, with t-statistics thresholded at p<.001 and cortical surface clusters smaller than 94 mm 2 excluded, achieving a corrected cluster p < 0.05). False fonts have basic visual features matched to regular letters, and thus letter-specific activation was defined as an increased BOLD response to Consonant Strings compared to False Fonts. Consonant Strings made from letters of the Roman alphabet were matched in length to Real Words. The main difference is that the combination of letters in the Real Word condition represents a high-frequency word of the English alphabet. Thus, word-specific activity was defined as an increased BOLD response to Real Words compared to Consonant Strings. Larger responses to FF are common, with EEG as well as hemodynamic measures, especially in the right hemisphere [57-60]. Since such responses are thought to reflect the novelty of the FF rather than template-matching [61], we omitted them from our study. MEG acquisition and analysis. Twelve healthy subjects (7 males, mean age: 25, range: 21-38) underwent MEG testing. Each subject was right-handed [46] and free of neurological impairments. MEG signals were recorded from 306 channels using a Neuromag-Vectorview instrument (Elekta, Stockholm) with a magnetometer and orthogonal pairs of planar gradiometers at each of 102 locations over the entire scalp. Pairs of EOG electrodes were used to detect eye blinks and movements. To ensure head stability, foam wedges were inserted between the subject s head and the inside of the unit and a Velcro strap was placed under the subject s chin. The translation between the MEG coordinate systems and each participant s structural MRI was made using three head position coils placed on the scalp and fiducial landmarks [62]. Signals were recorded continuously with 1000 Hz sampling rate and minimal on-line filtering ( Hz). Data were then low-pass filtered off-line at 40 Hz (transition band = 4 Hz),

5 high-pass filtered at.2 Hz (transition band =.4 Hz), detrended, baseline corrected, and downsampled by a factor of four before separate averages were created for each subject. Epochs containing EOG amplitudes exceeding 280 microvolts in the EOG electrode or magnetometer amplitudes exceeding approximately 3000 ft were excluded from the averages. Grand averages for each stimulus type were created by averaging across the two runs. To estimate the time courses of cortical activity, distributed source estimates were calculated from gradiometer data using dynamic statistical parametric mapping (dspm) [17]. This method is based on the observation that the main generators of MEG and EEG signals are localized in the gray matter. Once the exact shape of the cortical surface is known, this information can be used to reduce the MEG solution space. Furthermore, normalization procedures are used that take into account the noise sensitivity at each spatial location, allowing for statistical parametric maps that provide information about the estimated signal at each location relative to the noise. First, the cortical surface was subsampled to about 2500 dipole locations per hemisphere [17]. Second, the forward solution at each location was calculated using boundary element method [63]. Third, dipole power was estimated at each cortical location every 4 ms and divided by the predicted noise power obtained from all conditions for each individual. This method generates statistical maps that are F distributed and represent the mean activity for each participant throughout the time course. The square roots of these values were then averaged on the cortical surface across individuals after aligning their sulcal-gyral patterns [18]. From the mean group activity maps, thresholded t-stat maps were calculated that take into account withingroup variability and the same cluster based-thresholding approach used for the fmri analysis was performed on selected time frames for the MEG data (FWHM = 22mm; t-statistics thresholded at p<.001; corrected cluster p < 0.05). ROI analyses were performed using four ROIs derived from the fmri surface activation maps of RW>CS CS>FF, and their conjunction in the left ventral occipitotemporal cortex, plus a ventral prefrontal site (Figure 1B). MEG waveforms across the entire to 600ms timecourse were extracted from the cortical surface-based ROIs. Based on studies reviewed in the main text, we hypothesized that early peaks reflecting low-level letter and word processing would occur in the ventral occipitotemporal region between ms, with later peaks in the same and more anterior regions. To examine the time course of letterform and wordform effects, we first inspected data from these ventral occipitotemporal ROIs for major event-related field peaks within this time window. We also expected a second large peak in the VWFA at ~350 to 400ms latency, as previously observed with intracranial recordings [41]. Finally, the initial peak in the posteroventral prefrontal cortex was expected to be later, between 250 and 300ms. Repeated measures ANOVAs were performed on the average source estimates between conditions, 10 ms around the maxima, within these ROIs (Figure 1B). MEG, fmri and intracranial EEG in the same subjects. Prior to implantation, Patients A and B underwent fmri scanning using the same paradigm and scanner as described above. Patient B also underwent MEG recording using the paradigm described above and recorded on a 4D 148 magnetometer MEG system housed in a magnetically shielded room. ieeg data analysis. Data were downsampled to 400 Hz if necessary and epoched from -500 ms to 1000 ms relative to stimulus onset. Data were analyzed in Matlab using the Fieldtrip software package ( and custom analysis and visualization routines. For ERP analysis, post-processing steps included a bandpass-filter at.1 30Hz, detrending and baseline correction (-200 to -50 ms). Epochs containing artifacts were identified by visual inspection of band-pass filtered data and were excluded from further time- and frequency-domain analysis. Raw data were transformed from the time-domain to the time-frequency domain using the complex Morlet wavelet transform. Constant temporal and frequency resolution across target frequencies was obtained by adjusting the wavelet widths according to the target frequency. The wavelet widths increase linearly from 14 to 38 as frequency increases from 70 to 190Hz, resulting in a constant temporal resolution (σ t ) of 16ms and frequency resolution (σ f ) of 10Hz. For each epoch, spectral power was calculated from the wavelet spectra, normalized by the inverse square frequency to adjust for the rapid drop-off in the EEG power

6 spectrum with frequency, and averaged from 70 to 190Hz, excluding line noise harmonics. Visual inspection of the resulting high gamma power waveforms revealed additional artifacts not apparent in the time-domain signal, and the artifactual epochs were excluded from the gamma power analysis. Both LFP and gamma waveforms were compared across stimulus types from 0 to 600ms using a nonparametric randomization test with temporal clustering to correct for multiple comparisons [64]. HGP was not calculated in Patient B due to technical problems. Relation of response to the number of elements in the visual stimulus. High gamma power (HGP) was measured using wavelets as described above. Two contacts, from different patients, were chosen for analysis because they recorded the letter-selective HGP responses with the highest SNR. In one analysis, trials were averaged according to the stimulus category (consonant strings, words and false fonts), and the number of elements (letters in consonant strings and words, pseudoletters in false fonts). The number of elements was 4, 5, 6, 7, or 8. The results from this analysis (correlations between five pairs of numbers for each task condition and subject) are reported in the main text. In a second analysis, we plotted the average HGP from ms against the number of elements in individual trials, and obtained similar results. The number of trials per condition-element number bin for both analyses ranged from 47 to 153. Specifically, in individual trials a significant correlation was found between HGP and the number of letters in consonant strings (r=.27,.18 all p<.001) and words (.24,.13 all p<.001), but not the number of pseudoletters in FF (r=.003,.008 all p>0.8). Across single trials of words we also tested if there was a correlation with the frequency that words are used. However, since shorter words tend to be more frequent, we calculated the effect on HGP of word frequency after removing the effect of word length. These partial correlations were low, negative, and not significant (r=-.051, all p>0.08). Phase Locking Analysis. The phase locking value (PLV) was first calculated as described by Lachaux [23]. However, the PLV is only defined on a set of trials and therefor it is not appropriate to define phase-locking on a single trial, as would be required for statistical comparison of phase-locking in different conditions. Although Lachaux has also described a phase locking statistic on individual trials [65], this is an estimate of the stability of phase across time, rather than a measure of the stability and strength of phase-locking on a particular trial. We thus defined an individual trial PLV as the similarity of the phase relations between two sites to the average task-evoked phase relation across all trials. Specifically, we calculated the first (circular) moment of the phase difference between two sensors. This is a vector in the complex plane whose angle gives the mean phase difference across both conditions and whose amplitude quantifies the concentration onto that mean. This amplitude is the PLV between two sensors at a particular latency and frequency across both conditions. For each trial, we then calculated the projection of the phase difference onto the first moment in the complex plane. This projection represents how similar a particular phase difference in one condition is to the mean across both conditions. This PLVi was summed across a latency-frequency window to arrive at a single number for each trial, and these values were entered into a 2-tailed t-test. For the specific comparisons reported here, a single value of phase-locking (PLVi) was calculated on each trial for a given contact, and the PLVi values from word trials (n= ~450) were compared to those from false font trials (n= ~450). Cortical Mapping of Language with Electrical Stimulation. Cortical mapping was performed at ventral occipito-temporal contacts in one patient (Patient B, see Table S1 (supplements) and Figure 2.B3) utilizing biphasic balanced stimulation of adjacent electrodes with a Grass S-12 Stimulator (Grass Instrument Co, Quincy, MA). Stimulation parameters were: Maximum train duration 5 seconds, pulse width 500 µm, pulse frequency 50 Hertz, current 12.5 mamp. The patient engaged in a visual confrontation naming task that requires the verbal identification of objects (black and white line drawings) from the Boston Naming battery, as well as a simple counting and phrase recitation tasks. A baseline performance level without stimulation was established to ensure that the patient was able to perform the tasks. Speech arrest or paraphasic errors were seen as indicators of performance disruption. Only visual confrontation naming was disrupted.

7 Supplementary References 52. Dale, A.M., B. Fischl, and M.I. Sereno, Cortical surface-based analysis I: Segmentation and surface reconstruction. NeuroImage, (2): p Fischl, B., M.I. Sereno, and A.M. Dale, Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage, (2): p Fischl, B., et al., Whole brain segmentation. Automated labeling of neuroanatomical structures in the human brain. Neuron, (3): p Fischl, B., et al., High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp, (4): p Hagler, D.J., Jr., A.P. Saygin, and M.I. Sereno, Smoothing and cluster thresholding for cortical surface-based group analysis of fmri data. Neuroimage, (4): p Liotti, M., C.T. Gay, and P.T. Fox, Functional imaging and language: evidence from positron emission tomography. J Clin Neurophysiol, (2): p Bentin, S., et al., ERP manifestations of processing printed words at different psycholinguistic levels: time course and scalp distribution. J Cogn Neurosci, (3): p Appelbaum, L.G., et al., The temporal dynamics of implicit processing of non-letter, letter, and word-forms in the human visual cortex. Front Hum Neurosci, : p Maurer, U., et al., Emerging neurophysiological specialization for letter strings. J Cogn Neurosci, (10): p Tagamets, M.A., et al., A parametric approach to orthographic processing in the brain: an fmri study. J Cogn Neurosci, (2): p Hamalainen, M., et al., Magnetoencephalography - theory, instrumentation, and application to noninvasive studies of the working human brain. Reviews of Modern Physics, : p Oostendorp, T.F. and A. Van Oosterom, Source parameter estimation using realistic geometry in bioelectricity and biomagnetism, in Biomagnetic Localization and 3D Modeling, J. Nenonen, H.M. Rajala, and T. Katila, Editors. 1992, Helsinky Univ. of Technology, Report TKK-F-A689: Helsinki. 64. Maris, E. and R. Oostenveld, Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods, (1): p Lachaux, J.P., et al., A quantitative study of gamma-band activity in human intracranial recordings triggered by visual stimuli. Eur J Neurosci, (7): p