Abstract

Language production requires that semantic representations are mapped to lexical representations on the basis of the ongoing context to select the appropriate words. This mapping is thought to generate two opposing phenomena, “semantic priming,” where multiple word candidates are activated, and “interference,” where these word activities are differentiated to make a goal-relevant selection. In previous neuroimaging and neurophysiological research, priming and interference have been associated to activity in regions of a left frontotemporal network. Most of such studies relied on recordings that either have high temporal or high spatial resolution, but not both. Here, we employed intracerebral EEG techniques to explore with both high resolutions, the neural activity associated with these phenomena. The data came from nine epileptic patients who were stereotactically implanted for presurgical diagnostics. They performed a cyclic picture-naming task contrasting semantically homogeneous and heterogeneous contexts. Of the 84 brain regions sampled, 39 showed task-evoked activity that was significant and consistent across two patients or more. In nine of these regions, activity was significantly modulated by the semantic manipulation. It was reduced for semantically homogeneous contexts (i.e., priming) in eight of these regions, located in the temporal ventral pathway as well as frontal areas. Conversely, it was increased only in the pre-SMA, notably at an early poststimulus temporal window (200–300 msec) and a preresponse temporal window (700–800 msec). These temporal effects respectively suggest the pre-SMA's role in initial conflict detection (e.g., increased response caution) and in preresponse control. Such roles of the pre-SMA are traditional from a history of neural evidence in simple perceptual tasks, yet are also consistent with recent cognitive lexicosemantic theories that highlight top–down processes in language production. Finally, although no significant semantic modulation was found in the ACC, future intracerebral EEG work should continue to inspect ACC with the pre-SMA.

INTRODUCTION

Word selection, or retrieval of a lemma, is a crucial step in the process of language production (Levelt, 1989, Chapter 6), linking semantics with the lexical entries to be articulated. The need for distinguishing and linking such representations is typically motivated by interpretations of tip-of-the-tongue or anomic states (Caramazza & Miozzo, 1997; Vigliocco, Antonini, & Garrett, 1997; Badecker, Miozzo, & Zanuttini, 1995). Word selection has received a lot of attention, both in behavioral research (e.g., Oppenheim, Dell, & Schwartz, 2010; Rapp & Goldrick, 2000; Levelt, Roelofs, & Meyer, 1999) as well as in cognitive neuroscience (for reviews, see Munding, Dubarry, & Alario, 2016; Strijkers & Costa, 2016; Ganushchak, Christoffels, & Schiller, 2011; Indefrey, 2011). Processing models have been thoroughly debated along multiple dimensions, such as the nature of semantic representations driving word selection (features vs. holistic), the structure of lexical entries (e.g., lemmas vs. lexemes), the flow of information between the semantic and the lexical levels (serial vs. interactive), the mechanisms by which individual items are selected (competition vs. threshold), and so forth.

Here, we focus on the previously proposed hypothesis that the links between semantic and lexical representations can generate two opposing phenomena during word production: priming and interference (originally: Roelofs, 1992; La Heij, 1988). Semantic priming is the well-known phenomenon in which processing a given information or item (e.g., the word “doctor”) facilitates the processing of semantically related items (e.g., “nurse”). This has been extensively documented in the word and picture recognition literature (e.g., Neely, 1991). Semantic interference is the phenomenon in which processing two semantically related items leads to impaired performance in selection tasks, compared with unrelated items, and is extensively documented in the word production literature (at least since Glaser & Düngelhoff, 1984; see also MacLeod, 1991).

Semantic priming and interference play a central role in our understanding of the cognitive architecture of word selection processes. In the most consensual views, the activation of an information that has to be expressed overtly leads to the activation of multiple lexical candidates, a process known as semantic priming. Priming is typically conceived of in terms of spreading activation among related representations (Collins & Loftus, 1975). The difficulty of selecting a single item among a cohort increases when the activation levels of alternative, related candidates are higher, a phenomenon known as semantic interference. The actual mechanisms hypothesized to account for semantic interference are diverse (such as differential levels of activation, lateral inhibition, response buffering, and top–down control; Nozari & Hepner, 2018; Belke & Stielow, 2013; Oppenheim et al., 2010; Abdel Rahman & Melinger, 2009; Mahon, Costa, Peterson, Vargas, & Caramazza, 2007; Howard, Nickels, Coltheart, & Cole-Virtue, 2006; Roelofs, 2003; Berg & Schade, 1992).

Semantic priming and interference are not easily teased apart in measures of behavioral performance. This is because the two mechanisms drive opposing effects on performance that may cancel each other out or that may compensate to variable extents. For example, in the picture word interference paradigm, priming is observed for certain semantic relationships, interference is observed for others (e.g., Mahon et al., 2007; Alario, Segui, & Ferrand, 2000; La Heij, Dirkx, & Kramer, 1990), but both are not observed simultaneously (although see Zhang, Feng, Zhu, & Wang, 2016; Collina, Tabossi, & De Simone, 2013; Hantsch, Jescheniak, & Schriefers, 2009; Finkbeiner & Caramazza, 2006, for manipulations that can turn interference into facilitation). In the cyclic picture-naming task used in the present research (Damian, Vigliocco, & Levelt, 2001; see also Kroll & Stewart, 1994), participants are instructed to name pictures presented in blocks that contain a randomly repeated item set (typically four to seven items, repeated four to seven times). The item set in the blocks may be semantically homogeneous (HOM) or heterogeneous (HET), meaning the items either belong to a single category or different semantic categories. In HOM contexts, interference is most frequently observed, yet priming is consistently thought to be an underlying processing component (Navarrete, Del Prato, & Mahon, 2012; Oppenheim et al., 2010).

The general principles of spreading activation and the resulting increase in selection difficulty have been implemented in various theories and models of word selection (Roelofs, 2018; Oppenheim et al., 2010; Howard et al., 2006). For example, Oppenheim et al. (2010) proposed a neural network model with a selection mechanism. In this model, priming originates from a spreading activation network that incrementally learns over productions (e.g. trials) and increasingly coactivates words that share repeatedly elicited semantic properties (i.e., exaggerated in the HOM condition). In this model, increased interference is viewed as a symptom of priming and is present as a result of selection demands. Indeed, with a task instruction in which any of the primed alternatives (e.g., relevant picture synonyms) are considered equally correct in the task, then facilitation is observed instead of interference (Oppenheim, 2017).

In addition, Oppenheim et al.'s (2010) influential cognitive model describes selection interference via an accumulator mechanism (in their terms, a “booster”), where multiple words accumulate activity toward a selection threshold. The threshold is defined as a sufficient activity difference in favor of a target word versus all others. Equivalently, many word activities can be reduced to a single term, referred to as word preference or “inverse conflict” (Nozari & Hepner, 2018), that is, how much higher a word's activity is compared with that of alternative words. When inverse conflict reaches a criterial threshold, that word is selected for production. Empirically, this mechanism where a single word preference value evolves over time can be modeled by an evidence accumulation approach (Anders, Riès, van Maanen, & Alario, 2015, 2017). When fit to behavioral data, empirical modeling successfully separated the two opposing effects of priming and interference. Moreover, in both studies, Anders et al. found the selection criterion to increase during conflict (i.e., during HOM). This result is compatible with Nozari and Hepner's proposal that a criterion is adjusted to preserve accuracy at a cost to speed (response caution, a kind of speed–accuracy trade-off account in language) and is also compliant with the cognitive control account of language interference (Belke & Stielow, 2013), where top–down control mediates HOM interference.

In combination with such current cognitive–behavioral models of semantic priming, interference, and selection dynamics, neurophysiological measures could provide a major source of information on these processes. Word production and selection recruit a broad network of cortical areas that each merit in-depth analyses; these include the inferior, middle, and superior temporal lobe, as well as the inferior and superior frontal lobe (reviewed in Price, 2012; Indefrey, 2011). Within this network, processes such as word selection, lexicosemantic mapping, and the top–down mechanisms needed to regulate their dynamics have been targeted by recording neural activity during various paradigms including, prominently, the cyclic naming task described previously. Table 1 provides a summary of the 18 published studies so far that combined this task with measures of brain function or with stimulation targeting regional brain function. Note that, when reviewing this literature, we disregarded various aspects of each study, including variations in signal processing procedures or secondary task differences, to focus on the functional interpretations given to semantic contrast effects in cyclic naming. This was done for the sake of simplicity and because theoretical discussions have been built on such encompassing functional interpretations.

Table 1.
Summary of Previous Neuroscientific Results in the Cyclic Picture-naming Task, Organized by Process of Interest
ReferenceMethodDiscuss Effect DirectionsDirection of EffectLocusTimingProcess
Aristei et al. (2011EEG Yes HOM < HET L&R temporal electrodes 200–300 msec Semantic priming
de Zubicaray et al. (2017fMRI Yes HOM < HET L MTC N/A Semantic priming
Janssen et al. (2015EEG Yes HOM < HET Frontal and temporal electrodes 250–400 msec Semantic priming
Riès et al. (2017ECoG Yes HOM < HET L frontal and temporal regions 0–350 msec bef. response Semantic priming
de Zubicaray et al (2014fMRI No HOM < HET L hippocampus N/A Incremental learning
Vieth et al. (2015fMRI Yes HOM > HET L hippocampus N/A Incremental learning
Llorens et al. (2016iEEG Yes HOM < HET L&R hippocampus 600 msec Incremental learning
de Zubicaray et al. (2017fMRI Yes HOM > HET L hippocampus N/A Incremental learning
Maess et al. (2002MEG No HOM > HET L temporal region 150–225 msec Semantic interference
Aristei et al. (2011EEG Yes HOM > HET L&R temporal electrodes 200–300 msec Semantic interference
Janssen et al. (2011EEG Yes HOM > HET Anterior electrodes 220–450 msec Semantic interference
Ewald et al. (2012EEG No HOM > HET R frontal–L occipitotemporal connectivity 7 Hz Semantic interference
Python et al. (2018EEG Yes HOM < HET ERP anterior/posterior electrodes 270–315 msec Semantic interference
Riès et al. (2017ECoG Yes HOM > HET L frontal and temporal regions 0–350 msec bef. response Semantic interference
Schnur et al. (2009fMRI No HOM > HET L IFG N/A Conflict resolution
Schnur et al. (2009fMRI No HOM > HET L TC/L MTG N/A Conflict resolution
Aristei et al. (2011EEG Yes HOM > HET Frontal ERP 250–350 msec Conflict resolution
Pisoni et al. (2012tDCS Yes HOM faster L IFG N/A Conflict resolution
Janssen et al. (2015EEG Yes HOM > HET Frontal electrodes 500–750 msec Conflict resolution
de Zubicaray et al. (2017fMRI Yes HOM > HET L IFG N/A Conflict resolution
Hocking et al. (2009fMRI Yes HOM > HET L STC N/A Self-monitoring
Maess et al. (2002MEG No HOM > HET L temporal region 450–475 msec Self-monitoring
Python et al. (2018EEG Yes HOM > HET ERP central electrodes 365–410 msec Self-monitoring
Krieger-Redwood & Jeffries (2014TMS No HOM slower L IFG N/A Top–down control
Krieger-Redwood & Jeffries (2014TMS No HOM slower pMTG N/A Top–down control
Meinzer et al. (2016A-tDCS Yes HOM slower L IFG N/A Top–down control
Hocking et al. (2009fMRI Yes HOM > HET L&R hippocampus N/A Episodic memory
Vieth et al. (2015fMRI Yes HOM < HET L dorsolateral pFC N/A Working memory
Pisoni et al. (2012tDCS Yes HOM slower Overall slower L STF N/A Lexical activation
de Zubicaray et al. (2014fMRI No HOM < HET L M&P lateral TC N/A Lexical interference
de Zubicaray et al. (2017fMRI Yes HOM > HET L mMTC N/A Action processing
de Zubicaray et al. (2017fMRI Yes HOM < HET IPS N/A Action processing
de Zubicaray et al. (2017fMRI Yes HOM < HET L&R visual extrastriate cortices N/A Perceptual processing
Wirth et al. (2011A-tDCS No HOM faster L DPFC N/A Not proposed
Meinzer et al. (2016A-tDCS Yes HOM faster L pMTG/STG, connectivity N/A Not proposed
Ewald et al. (2012EEG No HOM < HET Global field power ERP >280 msec N/A
Llorens et al. (2014EEG No HOM = HET ERP anterior/posterior electrodes N/A N/A
Wirth et al. (2011EEG No HOM < HET Left scalp ERP (ROI) 200–400 msec N/A
ReferenceMethodDiscuss Effect DirectionsDirection of EffectLocusTimingProcess
Aristei et al. (2011EEG Yes HOM < HET L&R temporal electrodes 200–300 msec Semantic priming
de Zubicaray et al. (2017fMRI Yes HOM < HET L MTC N/A Semantic priming
Janssen et al. (2015EEG Yes HOM < HET Frontal and temporal electrodes 250–400 msec Semantic priming
Riès et al. (2017ECoG Yes HOM < HET L frontal and temporal regions 0–350 msec bef. response Semantic priming
de Zubicaray et al (2014fMRI No HOM < HET L hippocampus N/A Incremental learning
Vieth et al. (2015fMRI Yes HOM > HET L hippocampus N/A Incremental learning
Llorens et al. (2016iEEG Yes HOM < HET L&R hippocampus 600 msec Incremental learning
de Zubicaray et al. (2017fMRI Yes HOM > HET L hippocampus N/A Incremental learning
Maess et al. (2002MEG No HOM > HET L temporal region 150–225 msec Semantic interference
Aristei et al. (2011EEG Yes HOM > HET L&R temporal electrodes 200–300 msec Semantic interference
Janssen et al. (2011EEG Yes HOM > HET Anterior electrodes 220–450 msec Semantic interference
Ewald et al. (2012EEG No HOM > HET R frontal–L occipitotemporal connectivity 7 Hz Semantic interference
Python et al. (2018EEG Yes HOM < HET ERP anterior/posterior electrodes 270–315 msec Semantic interference
Riès et al. (2017ECoG Yes HOM > HET L frontal and temporal regions 0–350 msec bef. response Semantic interference
Schnur et al. (2009fMRI No HOM > HET L IFG N/A Conflict resolution
Schnur et al. (2009fMRI No HOM > HET L TC/L MTG N/A Conflict resolution
Aristei et al. (2011EEG Yes HOM > HET Frontal ERP 250–350 msec Conflict resolution
Pisoni et al. (2012tDCS Yes HOM faster L IFG N/A Conflict resolution
Janssen et al. (2015EEG Yes HOM > HET Frontal electrodes 500–750 msec Conflict resolution
de Zubicaray et al. (2017fMRI Yes HOM > HET L IFG N/A Conflict resolution
Hocking et al. (2009fMRI Yes HOM > HET L STC N/A Self-monitoring
Maess et al. (2002MEG No HOM > HET L temporal region 450–475 msec Self-monitoring
Python et al. (2018EEG Yes HOM > HET ERP central electrodes 365–410 msec Self-monitoring
Krieger-Redwood & Jeffries (2014TMS No HOM slower L IFG N/A Top–down control
Krieger-Redwood & Jeffries (2014TMS No HOM slower pMTG N/A Top–down control
Meinzer et al. (2016A-tDCS Yes HOM slower L IFG N/A Top–down control
Hocking et al. (2009fMRI Yes HOM > HET L&R hippocampus N/A Episodic memory
Vieth et al. (2015fMRI Yes HOM < HET L dorsolateral pFC N/A Working memory
Pisoni et al. (2012tDCS Yes HOM slower Overall slower L STF N/A Lexical activation
de Zubicaray et al. (2014fMRI No HOM < HET L M&P lateral TC N/A Lexical interference
de Zubicaray et al. (2017fMRI Yes HOM > HET L mMTC N/A Action processing
de Zubicaray et al. (2017fMRI Yes HOM < HET IPS N/A Action processing
de Zubicaray et al. (2017fMRI Yes HOM < HET L&R visual extrastriate cortices N/A Perceptual processing
Wirth et al. (2011A-tDCS No HOM faster L DPFC N/A Not proposed
Meinzer et al. (2016A-tDCS Yes HOM faster L pMTG/STG, connectivity N/A Not proposed
Ewald et al. (2012EEG No HOM < HET Global field power ERP >280 msec N/A
Llorens et al. (2014EEG No HOM = HET ERP anterior/posterior electrodes N/A N/A
Wirth et al. (2011EEG No HOM < HET Left scalp ERP (ROI) 200–400 msec N/A

L = left; R = right; iEEG = intracerebral stereotactic EEG; MEG = magnetoencephalography; ECoG = electrocorticography; HOM = semantically homogeneous naming context; A-/tDCS = (anodal) transcranial direct current stimulation; HET = semantically heterogeneous naming context; LIFG = left inferior frontal gyrus; MTC = middle temporal cortex; STC = superior temporal cortex.

Akin to the tradition of cognitive–behavioral modeling, which aims link behavioral performance to quantitative cognitive models, a major aim in the field is to also link neurophysiological measures to cognitive processes using insightful computational functions (Friederici & Singer, 2015). However, as this latter practice is only beginning to emerge, it is currently much less developed especially in language production study (e.g., see Strijkers & Costa, 2016; note that Llorens et al., 2016, provide an explicit, if tentative, discussion of the links between cognitive dynamics and neurophysiological modulations). So far in these studies of Table 1, the linking functions postulated to associate the HOM–HET contrast of neural activity to cognitive function have essentially been based on the direction (i.e., “more” activity in HOM or in HET), the location, and the timing of the effect. As in Table 1, semantic priming has been linked to observations of differential HOM < HET activity in the left middle temporal cortex and, more generally, the left frontal and temporal regions, in a broad time window ranging from 200 msec poststimulus to 400 msec before response. In contrast, semantic interference, especially the resolution of conflict, has been associated with differential HOM > HET activity in the left inferior frontal gyrus and the left middle temporal cortex and with neurophysiological components observed in a broad time window ranging from 150 to 450 msec poststimulus (also described as 350 msec before response onset); semantic interference was affected by stimulation of the left inferior frontal gyrus or left temporal gyrus.

Such activity modulations by region in Table 1 can be related to the previously discussed, dominant cognitive theories for word selection (e.g., Oppenheim et al., 2010). For example, bottom–up visual to temporal lobe modulations (e.g., de Zubicaray, Fraser, Ramajoo, & McMahon, 2017) are consistent with spreading activation dynamics after viewing a picture, and hippocampus modulations (Llorens et al., 2016) have been argued to be consistent with incremental learning; these two processes are considered generative to “priming” and in the cited effects, HOM < HET. As for “interference,” recent theories consider that it is introduced by contextual selection demands (Nozari & Hepner, 2018; Oppenheim, 2017) and is mediated by top–down cognitive control (Belke & Stielow, 2013). For example, Nozari and Hepner propose that individuals modulate their response caution as a result of conflict detection (in a paradigm of conflict monitoring). In this respect, Aristei, Melinger, and Rahman (2011) found a frontal ERP of HOM > HET (250–350 msec), suggesting an early onset of top–down activity for conflict detection. However, due to the constraint of using surface EEG, they are unable to specify the region involved. Furthermore, almost no other studies in Table 1 are able to provide high enough spatial resolution (specific brain region[s]) and the pertinent temporal window simultaneously to target the region involved in initial conflict detection, for example (i.e., top–down control). Thus in this study, having greater spatial resolution through intracerebral EEG (iEEG) techniques while preserving also temporal resolution, we aim to further clarify the regions involved in priming and interference during semantically driven word selection.

The Current Study

The purpose of the current study is to contribute to the knowledge in Table 1 by assessing whether variations of neural activity computed with high anatomical and temporal precision are consistent with the joint manifestation of semantic priming and interference during naming. To our knowledge, only one other iEEG study has previously explored the HOM–HET cyclic naming paradigm (Llorens et al., 2016; see Riès et al., 2017, for an electrocorticography exploration), and herein, we include a larger sampling area of brain regions. Based on Table 1 and the previously discussed cognitive models, it was predicted that regions involved in the generative processes of priming will be facilitated (requiring less activity, HOM < HET) due to the effect of preactivation or increased network calibration via incremental learning (the same semantics are more frequently repeated; see Van Turennout, Ellmore, & Martin, 2000) in areas within the temporal cortex (a ventral visual–temporal stream) including hippocampus. Second, increased activity (HOM > HET) should occur in any regions associated with interference control, conflict detection, and response inhibition/management (e.g., Ridderinkhof, Van Den Wildenberg, Segalowitz, & Carter, 2004), such as the pre-SMA, ACC, and, more generally, pFC. As a result of these hypotheses, it should be more likely that regions in a ventral occipitotemporal stream will exhibit HOM < HET activity, whereas regions involved in top–down streams or response control would be more likely to exhibit HOM > HET.

METHODS

Patients

All procedures were performed in accordance with the INSERM institutional review board (no. 0000388), and the patients (or their legal representatives in the case of minors) provided written informed consent. A total of nine epileptic patients volunteered to participate in the experimental protocol. All patients were undergoing presurgical evaluation for pharmacologically intractable epilepsy at the Hôpital de La Timone (in Marseille, France). Their clinical details are provided in Table 2. None of the patients had previously undergone brain surgery. The experiment was conducted only when a patient had been seizure-free for at least the 12 previous hours. Anatomical and functional data were available for all nine patients. Language lateralization was determined by multiple criteria per patient, including (1) the recording of auditory evoked potentials in auditory cortex in response to French voiced and voiceless stop consonants (/ba/, /pa/; detailed methods in Trébuchon-Da Fonseca, Giraud, Badier, Chauvel, and Liégeois-Chauvel (2005); (2) functional mapping of language using direct electrical stimulation, whereby left hemisphere stimulation induced language deficits in all patients; (3) fMRI or WADA tests (Wada, 1949); and (4) patterns of ictal aphasia when seizures involved the left hemisphere.

Table 2.
Participant Clinical Data
PatientAgeSexHandednessLanguage LateralizationEpileptic Zone
33 Male Left Left Left temporal
19 Female Right Left Right temporal
36 Female Right Left Bilateral temporal
40 Male Right Left Right frontotemporal
14 Male Right Bilateral Left temporo-occipital
27 Female Right Left Left prefrontal
21 Male Right Left Bilateral temporal
21 Female Right Left Right temporal lateral
31 Male Right Left Right temporo-occipital
PatientAgeSexHandednessLanguage LateralizationEpileptic Zone
33 Male Left Left Left temporal
19 Female Right Left Right temporal
36 Female Right Left Bilateral temporal
40 Male Right Left Right frontotemporal
14 Male Right Bilateral Left temporo-occipital
27 Female Right Left Left prefrontal
21 Male Right Left Bilateral temporal
21 Female Right Left Right temporal lateral
31 Male Right Left Right temporo-occipital

Materials and Design

Each patient had between 5 and 11 depth-electrodes implanted, which provided for functional stereotactic data acquisition. The locations of the electrode implantations had been strictly guided by clinical indications. Each electrode (0.8 mm in diameter; Alcis) contained 10–15 recording sites (also referred to as “contacts”) along the length of the electrode. Contacts were 2 mm in length and separated by a 1.5-mm distance. Bipolar “channels,” referred to later on, are calculated by subtracting activity recorded at one contact from activity recorded at the next contact on the same electrode (i.e., Channel 1 = Contact 1 − Contact 2, and not Contact 1 − Contact 3). Such computation of (bipolar) channels improves the acquisition of local cortical activity as opposed to far field activity.

The experimental materials consisted of 36 pictures of common objects depicted in black and white, with highly consensual names (Bonin, Peereman, Malardier, Méot, & Chalard, 2003; Alario & Ferrand, 1999). The pictures were drawn from six different semantic categories, with six items per category. The pictures were named in blocks involving six different items, either from the same semantic category (semantically homogeneous blocks, HOM) or from six different semantic categories (semantically heterogeneous blocks, HET). The items were repeated five times within a block, yielding 30 trials per block. There were six homogeneous and six heterogeneous blocks, yielding 360 trials in total. The order of the blocks and the order of the items within each block were pseudorandomized (Van Casteren & Davis, 2006). We created four different block lists by pseudorandomly arranging the order of the blocks to vary the alternation between homogeneous and heterogeneous contexts. Within each block, adjacent trials did not involve the same single item nor items beginning with the same phoneme.

Procedure

The experiment was performed in an electrically shielded room routinely used for experimental tasks.1 Participants were comfortably seated, facing a display monitor. They were first familiarized with the materials by naming them once in a random order and were then tested according to the design described above. The pictures were presented at the center of the screen, subtending an angle of 6° × 6°. Each trial began with a fixation cross, followed by the target item for 1000 msec. Between trials, a fixation cross was presented for 1750 ± 350 msec (random jitter). The patients were instructed to name each object (picture) as fast as possible while avoiding errors. They were asked to remain silent if they did not recognize the object or could not come up with an answer. Responses were recorded with a microphone placed in front of the patients, and the experimenter was present in the room to monitor the patients' performance and mark any erroneous responses. The software used to execute the experiment was E-Prime 1 (Psychology Software Tools, Pittsburgh, PA). Note that as this software did not allow by-trial voice recordings, its automatic voice key was utilized to compute naming latencies relative to picture onset. Although the absence of recordings is suboptimal for detecting millisecond-exact RTs on every single trial, our design compared the very same items across conditions; thus, we could reasonably expect no systematic bias on the RTs averaged by condition.

Anatomical Data

For all patients, both a structural preoperative MRI scan and an intraoperative CT scan were acquired as part of the clinical routine. We used the coregistration between the MRI and CT information, obtained within the Leksell SurgiPlan software (Elekta, Stockholm, Sweden). Visual inspection of the fused images allowed the precise localization of every contact within each patient's anatomy. These locations were then visually classified by the neurologist on the basis of a human brain atlas (Mai, Paxinos, & Voss, 2008), with only minor modifications to its parcellation. The lateral basal and temporal regions, as well as the medial cingular areas, were divided into anterior, middle, and posterior subregions. These subdivisions were intended to capture relevant functional distinctions made in Price's (2012) review of functional imaging studies of language. Finally for visualization purposes, all patients' contacts were mapped onto a common parcellated brain template (ICBM152), as implemented in Brainstorm (Tadel, Baillet, Mosher, Pantazis, & Leahy, 2011). The parcels were either adapted from the predefined Destrieux and Desikan-Killiany atlases in Brainstorm or created from Brainstorm's user interface.

Functional Data Acquisition

iEEG signals were recorded continuously at a sampling rate of 1000 Hz using a 256-channel BrainAmp amplifier system (Brain Products GmbH). An acquisition bandpass filter was used to limit the bandwidth of the output signal to between 0.16 and 200 Hz. A scalp electrode placed in Fz was used as the recording reference.

Signal Processing

Offline preprocessing was conducted using BrainVision Analyzer software (Brain Products GmbH). Time–frequency and statistical analyses were performed using MIA (Multipatient Intracerebral Data Analysis toolbox; freely available at www.neurotrack.fr/neurophysiology/seeg/mia/) and used with MATLAB 2017a (The MathWorks); see also Dubarry et al. (2017) for similar methods. The cortex representations were created in Brainstorm (Tadel et al., 2011), which is also freely available for download, under the GNU general public license (neuroimage.usc.edu/brainstorm).

All analyses performed were based on activity in bipolar channels. When two different regions were involved in a bipolar channel (i.e., the two member contacts belonged to different regions each), we adopted a conservative approach that classified the channel as belonging to both regions. All bipolar channels that were classified as being outside the brain (by at least one of their contacts) or when both contacts were located in white matter were rejected from the analysis. Note that a precise localization of each contact (region, gray/white matter, or outside) was achieved as described in the Anatomical Data subsection. Furthermore, any faulty contacts, such as those with flat or highly noisy activity, were removed.

Epochs were extracted from 1 sec before to 2 sec after the onset of each picture. Epochs with an incorrect or missing behavioral response (4.5% of trials), with epileptic spikes (identified by visual inspection), were removed from the analysis. The mean number of trials left for analysis was 325 (range = 290–353) of 360 total possible. Our analyses focused on activity in the high-gamma range (80–150 Hz range), which has been repeatedly linked to cognitive processing, notably in research on language (Fries, 2015; Lachaux, Axmacher, Mormann, Halgren, & Crone, 2012). The signal was bandpass filtered via the filtfilt() function in MATLAB (using the default setting and the “iir” digital filter option, providing a zero-phase forward and reverse digital infinite impulse response filtering) on consecutive nonoverlapping 10-Hz bands between 80 and 150 Hz. We then extracted the amplitude envelope using the standard Hilbert transform. A z-score normalization was applied separately for each 10-Hz band against baseline (−600 to −50 msec locked on picture onset, so as to exclude edge effects). This provided eight time series per channel and per trial. Then, to create a single high-gamma power band per channel and trial, these eight time series of z scores were summed. Note that, equivalently, the mean could also have been used, because the same number of time series is used for every channel. This procedure was used to compensate for the characteristic that low frequencies have much larger power than high frequencies (Buzsáki, Anastassiou, & Koch, 2012).

Statistical Analysis of Behavioral Data

Behavioral differences between the HOM and HET conditions were tested with an ANOVA on the conditions' RT means by participant, with Participants as a random factor.

Statistical Analysis of Functional Data

To identify task-related activations within each patient's channels, the statistical significance of high-gamma activity was assessed by computing one-sample Student's t tests against zero (α = .001) across trials at each data sample (each channel and each millisecond). These tests were performed in the time window of −400 to 1600 msec after picture onset. The multiple-comparison problem in the time domain was avoided by estimating a minimum duration threshold T for consecutive, significant t values. This bootstrap procedure consisted of randomly selecting the same number of trials as in the original data set with repetitions allowed (see also Guthrie & Buchwald, 1991) and identifying periods with significant activity within the baseline window from −600 to −50 msec. The procedure was repeated 1000 times, where in each iteration, the maximum number of contiguous points passing the significance threshold (corresponding to uncorrected p < .001) was the duration value pooled into a bootstrap distribution. The significance threshold (minimum duration of consecutive significant t values, p < .001) for a region r, as Tr, was defined at the right tail 95% quantile of that bootstrap distribution.

Significant activities of individual patients were then combined in a group analysis. The goal was to compensate for the inevitable variability of implantation sites across patients and for any potential patient idiosyncrasies in their functional activity by identifying functional consistency within each region across patients (see Lachaux et al., 2012, pp. 291–292, for pros and cons). Only regions that were sampled in at least two patients and that showed significant signal (as defined above) were considered. By region and for each patient, we averaged the previously computed t values per millisecond (see paragraph above) of the patient's channels belonging to that region. Thus, each patient contributed one averaged t value time series to that region's analysis.

These averaged t value time series per patient in each region were then used to assess the degree of functional consistency across patients. To do so, for each region, we calculated a Pearson correlation r per each pair of patient's time series. The average of these pairwise correlations $r¯$ for a region was then used to determine functional consistency. In calculating this functional consistency statistic, or $r¯$ value, for every region, an approximately normal distribution of $r¯$ values resulted, in which outliers or a large drop in $r¯$ magnitudes occurred, for example, between $r¯$ = .35 and the next values (i.e., .27, .23; see Table A1 and Figure A1). We hence used $r¯$ ≥ .35 as a criterion for functional consistency. Thus in total, only regions that passed both tests, having sustained significant activity according to the duration bootstrap test, and functional consistency according to the $r¯$ ≥ .35 criterion, were candidates for interpretation in the Results.

We then tested for significant differences related to the HET–HOM contrast. No attempt was made to analyze separately the different repetitions within each block (e.g., Python, Fargier, & Laganaro, 2018). A permutation approach for EEG data was used (Maris, 2012; Maris & Oostenveld, 2007), adapted here for iEEG with trials as a random variable. The method was applied independently to each of the regions that satisfied the criteria described in the previous paragraph (no clustering across regions was sought).

First, for each region, we extracted a matrix including all trials for all channels and patients that were sampled within the region, separated for the two semantic conditions (HOM, HET). This was used to compute the average time courses on Figures 35 and 7. Then differences between the two conditions were identified on each millisecond by calculating a two-tailed unpaired t test, with trials as a random factor and disregarding patient and channel structure. The t test values were grouped in temporal clusters of adjacent/consecutive time-samples with significant (p < .05) differences. The durations Tr of these clusters were extracted, and to control for multiple comparisons within each region, they were tested against a duration threshold obtained from permuted data.

The permutation exchanged randomly the semantic condition assignments (HOM, HET) while preserving channel and patient structure, that is, a trial could only be reassigned to the channel and patient where it was recorded to control for variability across channels and patients. For each of the 1000 permutations, the same t test calculations followed by duration clusters were computed to generate a surrogate distribution of cluster durations that would reflect durations arising from noise. Then p values for the observed duration clusters, Tr, were obtained based on their location (the Trs) in respect to the surrogate distribution of cluster durations arising from the random HOM–HET permutations. Over all regions, the resulting p values of the observed Tr durations were subjected to a false discovery rate (Genovese, Lazar, & Nichols, 2002) test correction for multiple comparisons via the ft_fdr function from Fieldtrip with q = 0.05 (Oostenveld, Fries, Maris, & Schoffelen, 2011). Of 144 uncorrected p values of Tr duration clusters obtained from the permutation test, 11 Tr duration clusters survived the false discovery rate correction.

RESULTS

The intracerebral data stemmed from nine patients having 83 electrodes (mean per patient = 9.0, minimum = 8, maximum = 11) that recorded activity in 1397 cortical sites, 676 located in the left hemisphere and 721 located in the right hemisphere. This led to 84 regions explored, based on the anatomical classification of recording sites described in the Methods section.

RTs

The average RT for word production was 809 msec, with a mean standard error (SE) across patients of 13.4 msec. Responses were significantly faster, F(1, 9) = 11.35, p = .008, ηp2 =.56, in the HET context condition (mean = 786 msec, SE = 18.7 msec) than in the HOM context condition (mean = 833 msec, SE = 19.0 msec), as depicted in the left plot of Figure 1. Furthermore, there was a main effect of faster responses with repetition, F(1, 9) = 7.70, p = .021, ηp2 = .46, but the interaction between context and repetition, as in the right plot of Figure 1, was not statistically significant, F(1, 9) = 1.45, p = .26, ηp2 = .14.

Figure 1.

Behavioral results. Left: mean RTs (786 msec, 833 msec) and mean SE across participants (18.7 msec, 19.0 msec), respectively, for the HET and HOM conditions in the picture-naming paradigm. Right: mean RTs for HET and HOM, respectively, by repetition cycle in the picture-naming paradigm.

Figure 1.

Behavioral results. Left: mean RTs (786 msec, 833 msec) and mean SE across participants (18.7 msec, 19.0 msec), respectively, for the HET and HOM conditions in the picture-naming paradigm. Right: mean RTs for HET and HOM, respectively, by repetition cycle in the picture-naming paradigm.

The criteria for activation and consistency across patients revealed 39 regions with significant power in the high-gamma range. The dynamics of their neural activities are provided in Figure 2, in which the regions are organized anatomically as in Table 3.

Figure 2.

Average neural activity during picture naming. Stimulus-locked time–frequency activity (t value gamma power, 80–150 Hz) of the regions listed in Table 3, with the same abbreviations, and region sampling statistics: the number of significant bipolar channels c and patients p. Picture onset occurred at 0 msec. For improved visualization to compare these time courses, the y-axis limit is specified according to lobe (frontal: 7 units, temporal: 14 units, occipital: 16 units). Because of only one frontal basal region being significant (left middle orbital gyrus, L.mOrG), in the interest of parsimony, this region was grouped into the left frontal medial section. Note only visible regions on the surface projection are depicted on the brain anatomies, for example, in the temporal medial section, the cortical brain atlas can only represent the parahippocampal gyri.

Figure 2.

Average neural activity during picture naming. Stimulus-locked time–frequency activity (t value gamma power, 80–150 Hz) of the regions listed in Table 3, with the same abbreviations, and region sampling statistics: the number of significant bipolar channels c and patients p. Picture onset occurred at 0 msec. For improved visualization to compare these time courses, the y-axis limit is specified according to lobe (frontal: 7 units, temporal: 14 units, occipital: 16 units). Because of only one frontal basal region being significant (left middle orbital gyrus, L.mOrG), in the interest of parsimony, this region was grouped into the left frontal medial section. Note only visible regions on the surface projection are depicted on the brain anatomies, for example, in the temporal medial section, the cortical brain atlas can only represent the parahippocampal gyri.

Table 3.
Summary of the 39 Regions With Significant and Consistent Activity During Word Production in Picture Naming
LobeSubdivisionAbbreviationFull Name
Occipital Basal L.LgG, R.LgG Left and right lingual gyri
Temporal Basal L.aFuG, L.mFUG, L.pFuG Left anterior, middle, and posterior fusiform gyri
R.aFuG Right anterior fusiform gyrus
Medial L.Ent, R.Ent Left and right entorhinal area
R.A Right amygdala
L.Hi, R.Hi Left and right hippocampus
R.pHi Right posterior hippocampus
L.aPHG, R.aPHG Left and right anterior parahippocampus
R.mPHG Right middle parahippocampus
Lateral L.TTG1, R.TTG1 Left and right anterior transverse temporal gyri
R.TTG2 Right posterior transverse temporal gyrus
R.pSTG Right posterior superior temporal gyri
L.aMTG, L.mMTG, L.pMTG, R.mMTG, R.pMTG Left anterior, middle, and posterior middle temporal gyri Right middle and posterior middle temporal gyri
L.aITG, L.pITG Left anterior and posterior inferior temporal gyri
R.aITG, R.mITG, R.pITG Right anterior, middle, and posterior inferior temporal gyri
Frontal Medial L.aCG Left anterior cingulate gyrus
R.MSFG Right medial superior frontal gyrus
L.MOrG, L.mOrG Left medial and middle orbital gyri
Lateral L.IFGOr, R.IFGOr Left and right pars orbitalis
L.IFGTr Left pars triangularis
R.LSFG Right lateral superior frontal gyrus
L.MFG, R.MFG Left and right middle frontal gyri
LobeSubdivisionAbbreviationFull Name
Occipital Basal L.LgG, R.LgG Left and right lingual gyri
Temporal Basal L.aFuG, L.mFUG, L.pFuG Left anterior, middle, and posterior fusiform gyri
R.aFuG Right anterior fusiform gyrus
Medial L.Ent, R.Ent Left and right entorhinal area
R.A Right amygdala
L.Hi, R.Hi Left and right hippocampus
R.pHi Right posterior hippocampus
L.aPHG, R.aPHG Left and right anterior parahippocampus
R.mPHG Right middle parahippocampus
Lateral L.TTG1, R.TTG1 Left and right anterior transverse temporal gyri
R.TTG2 Right posterior transverse temporal gyrus
R.pSTG Right posterior superior temporal gyri
L.aMTG, L.mMTG, L.pMTG, R.mMTG, R.pMTG Left anterior, middle, and posterior middle temporal gyri Right middle and posterior middle temporal gyri
L.aITG, L.pITG Left anterior and posterior inferior temporal gyri
R.aITG, R.mITG, R.pITG Right anterior, middle, and posterior inferior temporal gyri
Frontal Medial L.aCG Left anterior cingulate gyrus
R.MSFG Right medial superior frontal gyrus
L.MOrG, L.mOrG Left medial and middle orbital gyri
Lateral L.IFGOr, R.IFGOr Left and right pars orbitalis
L.IFGTr Left pars triangularis
R.LSFG Right lateral superior frontal gyrus
L.MFG, R.MFG Left and right middle frontal gyri

Beginning with the basal part of the occipital lobe, significant activity was found in the lingual gyri of both the left and right hemispheres. Notable activity occurred in the left lingual gyrus between 100 and 400 msec whereas in the right lingual gyrus between 100 and 200 msec, but at a fraction of the power compared with the left lingual gyrus.

In the basal temporal lobe, the significant regions consisted of the fusiform gyri in both the left (posterior, middle, and anterior) and right hemispheres (anterior). The strongest activation peaks occurred earliest in the left posterior fusiform gyrus (near 100 msec). As one moves to the middle (left) and anterior (left and right) fusiform parts, these activations reduce in magnitude, and their activation peaks occur later in time (e.g., near 200 msec).

In the temporal medial section, the significant regions consisted of the hippocampus, the amygdala, the entorhinal cortices, and the parahippocampal gyri. In the left hemisphere, activity is graduated in power moving from the entorhinal cortex to the hippocampus (e.g., Steward & Scoville, 1976) and to the parahippocampal gyrus, reflecting the role of the entorhinal cortex as a gateway between the neocortex (parahippocampal gyrus) and the hippocampus (Lavenex & Amaral, 2000). In the right hemisphere, strong early activity is observed in the hippocampus (anterior/middle), posterior hippocampus, and parahippocampal gyrus between 100 and 300 msec.

In the lateral part of the temporal lobe, significant activity is mainly observed at the inferior and middle temporal gyri, ranging from the anterior to posterior parts. For example, strong early activity is observed in the left and right posterior inferior temporal and after in the left middle temporal gyri and only marginally in the right. In contrast, later significant activity (after 700 msec) is notably observed in the transverse temporal gyri (TTG) bilaterally and in the right superior temporal gyrus (posterior).

Regarding the frontal lobe, significant average evoked activity was observed in the left orbital gyri (medial, med) shortly after (200 msec) stimulus onset. The left anterior cingulate gyrus demonstrated significant activity near stimulus onset, but subsequent gains in power did not occur until much later (>1000 msec). With respect to the right medial superior frontal gyrus (R.MSFG), two distinct peaks of gamma power emerged near 100 and 200 msec, to which activity then dropped to a low at 400 msec and steady increases then followed.

Finally, in the lateral part of the frontal lobe near 200 msec, steep activity increases occurred in the left pars orbitalis, left pars triangularis, and left middle frontal gyrus from standardized gamma power values of near 1.5 to at least 4. In contrast in the right lateral frontal lobe, more steady increases in power were observed in the right middle frontal gyrus, the right lateral superior frontal gyrus, and the right pars orbitalis.

For these 39 regions discussed, note that Figure 2 provides the number of channels for every region (and patient) that were each found to have significant activity. For the other regions (45) excluded due to lack of sufficient patient numbers, significance, or functional consistency between patients (i.e., an activity correlation $r¯$ < .35), see Table A1 in the Appendix.

Activations Sensitive to the Semantic Context Contrast

As reported in Table 4, the contrast analysis revealed nine regions (of the previous 39) that had significant differences between the HET–HOM conditions in high-gamma band power. Activity in these regions was fairly consistent, as the mean correlation of the power time series between patients sharing channels in a given region was $r¯$ = .64 (SD = .15; range = .42–.78).

Table 4.
The Nine Regions Showing Functional Consistency and Significant Differences in the HET/HOM Contrast
AbbreviationRegionPreviously Associated Cognitive Process
L.aFuG Left anterior fusiform gyrus Semantic activation, retrieval (Price, 2012
L.aITG Left anterior inferior temporal gyrus Semantic activation, comprehension (Price, 2012
L.IFGOr Left pars orbitalis Semantic decision and articulation (Price, 2012
L.MFG Left middle frontal gyrus Word retrieval from semantics (Price, 2012
R.MPHG Right middle parahippocampal gyrus Object recognition (Malach et al., 2002
R.MSFG Right superior medial frontal gyrus Semantic interference (de Zubicaray et al., 2001
R.pSTG Right posterior superior temporal gyrus Self-monitoring and auditory association (Christoffels et al., 2011
R.mITG Right middle inferior temporal gyrus Specificity for object word production (Khader et al., 2010
R.A Right amygdala Word processing: emotion and frequency (Scott et al., 2009
AbbreviationRegionPreviously Associated Cognitive Process
L.aFuG Left anterior fusiform gyrus Semantic activation, retrieval (Price, 2012
L.aITG Left anterior inferior temporal gyrus Semantic activation, comprehension (Price, 2012
L.IFGOr Left pars orbitalis Semantic decision and articulation (Price, 2012
L.MFG Left middle frontal gyrus Word retrieval from semantics (Price, 2012
R.MPHG Right middle parahippocampal gyrus Object recognition (Malach et al., 2002
R.MSFG Right superior medial frontal gyrus Semantic interference (de Zubicaray et al., 2001
R.pSTG Right posterior superior temporal gyrus Self-monitoring and auditory association (Christoffels et al., 2011
R.mITG Right middle inferior temporal gyrus Specificity for object word production (Khader et al., 2010
R.A Right amygdala Word processing: emotion and frequency (Scott et al., 2009

Italic: HOM < HET; bold: HOM > HET.

Nearly all of these regions (eight of nine) were sensitive to the contrast by showing significantly reduced activity in the HOM condition compared with the HET condition. The time courses of activity in these regions broken down into experimental conditions are presented in Figures 35. Figures 3 and 4 correspond to the four left and four right hemisphere regions in which HOM activity was less than HET, and Figure 5 corresponds to the single region in which HOM activity was greater than HET (the R.MSFG). In addition, Table 4 tentatively presents cognitive processes associated with each region in the context of this and similar tasks, to be discussed in more detail after the results.

Figure 3.

Effect of semantic context across left hemisphere regions. Stimulus-locked time series of the high-gamma activity (80–150 Hz) in the four regions of the left hemisphere that exhibited significant differences due to HET–HOM conditions (HET: gray, HOM: black), as determined by a cluster-based permutation analysis (Maris & Oostenveld, 2007). Maris (2012) has clarified that this approach does not exclude the possibility of other potential significant differences existing outside the resulting temporal cluster(s). The number of channels in the region is given by c, number of patients p, and the correlation r between the time series of the patients (the average of their channels). The vertical gray and black lines are the median RTs respectively of the HET and HOM conditions for the patients with significant channels in this region. See Table 4 for full region names and details about the processes associated to each region in previous literature.

Figure 3.

Effect of semantic context across left hemisphere regions. Stimulus-locked time series of the high-gamma activity (80–150 Hz) in the four regions of the left hemisphere that exhibited significant differences due to HET–HOM conditions (HET: gray, HOM: black), as determined by a cluster-based permutation analysis (Maris & Oostenveld, 2007). Maris (2012) has clarified that this approach does not exclude the possibility of other potential significant differences existing outside the resulting temporal cluster(s). The number of channels in the region is given by c, number of patients p, and the correlation r between the time series of the patients (the average of their channels). The vertical gray and black lines are the median RTs respectively of the HET and HOM conditions for the patients with significant channels in this region. See Table 4 for full region names and details about the processes associated to each region in previous literature.

Figure 4.

Effect of semantic context across right hemisphere regions. See Figure 3 for details.

Figure 4.

Effect of semantic context across right hemisphere regions. See Figure 3 for details.

Figure 5.

Effect of semantic manipulation resulting in increased activity for HOM compared with HET conditions. The contacts were located in the R.MSFG, more specifically the pre-SMA (see Figure 6). Data organized as in Figure 3.

Figure 5.

Effect of semantic manipulation resulting in increased activity for HOM compared with HET conditions. The contacts were located in the R.MSFG, more specifically the pre-SMA (see Figure 6). Data organized as in Figure 3.

In the left hemisphere, two significant effects were observed before the mean RT, in the left anterior inferior temporal gyri, this effect was between 240 and 450 msec after picture presentation and in the left anterior fusiform gyrus near 500 msec until 620 msec. The two other effects were in the same time window as the RTs, from 600 to 1000 msec for the left pars orbitalis and near 700 to 1150 msec for the left middle frontal gyrus. In the right hemisphere, the right middle parahippocampus elicited a significant sustained difference from 180 to 650 msec. Later, the right middle inferior temporal gyrus revealed a significant difference from 400 to 1100 msec. The effect in the right posterior superior temporal gyrus surrounded the mean RT from 600 to 850 msec. Finally the right anterior reported a late activity starting at 1400 msec after picture presentation and lasting for 200 msec.

The sole region in which the opposite effect was observed, significantly higher activity in the HOM condition compared with the HET condition, was labeled as the R.MSFG, with contacts more specifically located in the pre-SMA. This localization was based on careful assessment of the MRI scans of the two patients with R.MSFG electrodes (Figure 6). These scans demonstrate that their electrodes were located precisely in the pre-SMA (e.g., see Nachev, Kennard, & Husain, 2008, Figure 1a). The time windows of the two significant differences observed therein were early, from 190 to 350 msec, and late, right before the mean RT, from 700 to 790 msec.

Figure 6.

Location of the contacts classified in the R.MSFG. MRI scans show that the electrodes of the two patients classified in the R.MSFG are indeed located at the pre-SMA: transverse, coronal, and sagittal views of the R.MSFG electrode implantation in Patient 4 (A) and in Patient 8 (B). The blue circles indicate the specific contacts involved (by patient) in the pre-SMA, the three contacts from Patient 4 (A) led to two bipolar channels, and the two contacts from Patient 8 (B) led to one bipolar channel.

Figure 6.

Location of the contacts classified in the R.MSFG. MRI scans show that the electrodes of the two patients classified in the R.MSFG are indeed located at the pre-SMA: transverse, coronal, and sagittal views of the R.MSFG electrode implantation in Patient 4 (A) and in Patient 8 (B). The blue circles indicate the specific contacts involved (by patient) in the pre-SMA, the three contacts from Patient 4 (A) led to two bipolar channels, and the two contacts from Patient 8 (B) led to one bipolar channel.

DISCUSSION

We investigated the neurophysiological correlates of two principal cognitive processes underlying word selection, that is, semantic priming and interference. The evidence presented is an iEEG study that analyzed 84 brain regions sampled from nine epileptic patients while they performed a picture-naming task. The task was set up as a cycling naming protocol, which involved blocks of semantically homogeneous items (HOM), known to induce increased priming and, as a consequence, increased interference during lexical selection, as compared with blocks of semantically heterogeneous items (HET). The main effects observed on the RTs (Figure 1) are typical of the cyclic naming paradigm (e.g., Belke & Stielow, 2013; Oppenheim et al., 2010). Here, the slowing of RTs in the HOM context has been traditionally associated with increased selection interference, perhaps hiding in the overall performance an underlying semantic priming effect.

Semantically Driven Word Production Network

A general network for semantically driven word production (semantics derived from visual input, i.e., picture naming) was revealed in our analyses as a set of 39 brain regions that exhibited task-related activity, which was significant and consistent across at least two patients (Table 3 and Figure 2). These average data can be roughly associated with the relevant task events, i.e., activity evoked by the stimulus; activity sustained throughout the stimulus–response interval; and activity tied to response, including auditory processes, such as for self-monitoring. The regions comprising this network are compatible with much previous work outlining the task-relevant regions, both in their spatial and temporal properties reported (e.g., Munding et al., 2016; Indefrey, 2011; Strijkers & Costa, 2011). Here, we observed these spatial and temporal signatures simultaneously within the same signal.

For example, our 39 regions included the left fusiform gyrus, which is classically associated with semantic activation and word retrieval (anterior part; Price, 2012), as well as its right anterior part, specialized more to semantic activation and less to word retrieval (Mion et al., 2010). Next, the frontotemporal regions in the middle temporal gyrus and inferior frontal gyrus observed herein have been regularly associated to standard subprocesses in language production (e.g., Indefrey, 2011). We also observed involvement of the left and right TTG, known to be sensitive to simple sounds (Mirz et al., 1999), and of the superior temporal gyrus, sensitive to heard words (Zatorre & Belin, 2001). The transverse temporal regions (TTG) also contain primary auditory cortex, therefore being likely to activate when the participant speaks aloud. Significant activity in these regions is consistent with accounts that they are implicated in self-monitoring during word articulation (Christoffels, van de Ven, Waldorp, Formisano, & Schiller, 2011; Schuhmann, Schiller, Goebel, & Sack, 2011; Christoffels, Formisano, & Schiller, 2007).

As this recovered network of 39 task-relevant regions (Table 3) closely corresponds to those found from a review of previous studies, an appropriate stage is set for a detailed interpretation of the 9 of 39 regions (Table 4), which had activity significantly modulated by the HOM–HET contrast. In the following subsections, we elaborate on these nine regions beyond previous literature listed in Table 4 through interpreting their modulation by the HOM–HET contrast. First, we will discuss the eight of nine regions that exhibited HOM < HET activity, which we associate with processes related to priming. Then, we discuss the one region (pre-SMA) exhibiting HOM > HET activity, which we associate with conflict detection and response control.

Disperse Semantic Effects that Can Be Related to Semantic Priming

The earliest temporal activities among the HOM < HET regions occurred in the right parahippocampal and the left inferior temporal gyrus, which can be strongly associated with a bottom–up stream of spreading activation dynamics (i.e., vision to semantics to lexical entries). Specifically, the parahippocampal gyri are known to be involved in object recognition (Malach, Levy, & Hasson, 2002), and the left inferior temporal gyrus is known to be involved in semanticolexical activation (Price, 2012): to link the semantics of the recognized objects to lexical entries. These findings are consistent with the hypotheses (see Introduction section) gleaned from the theoretical frameworks previously discussed. Particularly that ventral, bottom–up streams would be facilitated through priming. In this framework, the dynamic is explained by an increased network calibration through stronger incremental learning in HOM (the same semantics are more frequently repeated in the HOM condition); in turn, words are retrieved with less activity by those same semantics (pictures).

The next-earliest HOM < HET effect may reflect facilitation in the right middle inferior temporal gyrus, which has been described as preferentially active in noun production (see Khader, Jost, Mertens, Bien, & Rösler, 2010). The longer temporal window (observed up to response) and stronger magnitude may also invite an interpretation for facilitation in category-specific processing (Hauk, Davis, Kherif, & Pulvermüller, 2008). As a result, this may be an interesting region for future work in HOM–HET paradigms (which also involve object words) or that contrast categories of object and action words (such as in de Zubicaray et al., 2017, an imaging study that reported contrast effects in the left middle temporal cortex but not the left inferior temporal cortex, though see their Figure 5, lower).

Note that the facilitation hypothesis discussed here is essentially equivalent to that of Aristei et al. (2011), although in their EEG study, they discuss it conversely: HET > HOM occurs as an increased effort due to lack of priming. The neural mechanism proposed to account for the facilitation hypothesis (via network calibration) is known as repetition suppression, where top–down processes suppress incremental learning effects in HOM, leading to reduced activity, hence diminishing the repetition effects of semantic features across the items within these blocks (Gotts, Chow, & Martin, 2012). Repetition suppression is discussed by Llorens et al. (2016), Python et al. (2018), and Riès et al. (2017) in various respects.

Other Processes that Can Be Tied to Reduced Activity in HOM

Some regions elicited a significant HOM < HET window later than the expected time frame, which would make it difficult to directly link them to priming processes. The most pertinent to discuss first are the left anterior fusiform gyrus and the left middle frontal gyrus, which, in similar tasks, have been known to be involved in processes like semantic activation and (word) retrieval from semantics, respectively (Price, 2012). The lack of early window findings herein may be due to the statistical parameters used. For example, each of these regions exhibited an early sustained HOM < HET activity near 200 msec (± 100 msec), but neither time clusters were found to be significant. Maris (2012) clarifies that, upon finding a significant time cluster with this method, it does not exclude the possibility of other time clusters (i.e., these early sustained activities) from also having significant differences. We hence recommend that these regions remain candidates for assessment in future iEEG studies of priming phenomena.

Next, the left pars orbitalis exhibited a significant HOM < HET effect in a temporal window around the response, which is consistent with a facilitation account in its role for articulatory processes in language production (Price, 2012). However, this (interpretation) would be in contrast to previous findings (Janssen, Hernández-Cabrera, van der Meij, & Barber, 2015, surface EEG in Table 1), where a HOM > HET effect was observed around the same timing (around response), and as a result was linked to semantic decision and conflict resolution between words and to the ventrolateral pFC. We propose that these contrasting accounts are likely due to a difference in spatial resolution between surface EEG and iEEG herein (surface EEG is measuring larger synchronous streams, whereas iEEG is measuring local activity) and which algorithms a surface EEG experiment may use to attribute such activity to specific brain regions.

In the right hemisphere, the right posterior superior temporal gyrus exhibited a significant HOM < HET effect, also in a temporal window around the response, and this region is known to be involved during auditory self-monitoring and association (Christoffels et al., 2007, 2011). Notably, an fMRI activation effect of opposite sign (HOM > HET) was revealed in the middle to posterior superior temporal cortex of the left hemisphere (Hocking, McMahon, & de Zubicaray, 2009). Although it would definitely be premature to interpret the opposition of effect signs across hemispheres, these two observations combined suggest modulations of monitoring processes that are typically not considered in barebone lexical networks that account for semantic priming and inhibition (but see Nozari & Hepner, 2018). The timing of the effects that was absent in fMRI but was revealed here with iEEG is compatible with a functional interpretation in terms of monitoring.

Lastly, the right amygdala exhibited a significant HOM < HET time window long after the response. This effect is the most obscure to interpret, due to the timing of the effect and the region involved. In previous research, the amygdala has been mostly associated with emotion. In this respect, disregarding HOM–HET differences, a peak activity in the amygdala occurred for both conditions shortly after stimulus onset (150–200 msec), which suggests an expected role in linking picture semantics to emotional qualities. The amygdala has been linked to lexical processing in an experiment involving written words of low versus high frequency (Scott, O'Donnell, Leuthold, & Sereno, 2009). To our knowledge, because this is the first time that the amygdala was found to be modulated by semantic HOM–HET contrasts, replications would be needed before its functional role in this task can be addressed.

Localized Semantic Effect in the Right Pre-SMA Tied to Semantic Interference

The second principal finding was indeed the converse of the priming results (a single brain region revealed significant HOM > HET activity) and at two time points (first, shortly after picture presentation and later, close to the overt naming response). This region was the R.MSFG. The R.MSFG consists of, from anterior to posterior, the pre-SMA (medial BA 6) and the SMA, each with diverse functional roles (Chauvel, Rey, Buser, & Bancaud, 1996). As per the anatomical localizations of the electrodes in Figure 6, these R.MSFG contacts (blue circles) were all located in the pre-SMA. We note that our sampling and signal processing criteria revealed that there were no electrodes recording activity in the left medial superior frontal gyrus in any patient; hence, we cannot develop a detailed discussion of the lateralization of this effect with the current data.

We will relate the early and late interference effects to, respectively, conflict detection and response control. The HOM > HET activity of the right pre-SMA, a region extensively associated with response and cognitive control (reviewed by Ridderinkhof et al., 2004), suggests that its overactivation during semantic interference (HOM) would contribute to resolve the conflict that arises from multiple words being strongly activated due to shared semantic category features (Piai, Roelofs, Jensen, Schoffelen, & Bonnefond, 2014; de Zubicaray, Wilson, McMahon, & Muthiah, 2001). The timing of the first significant time window suggests an early involvement of the pre-SMA in conflict detection. With regard to the cognitive mechanisms discussed in the Introduction, this would be compatible with raising response caution or raising the activity threshold needed to trigger production of the word (see Nozari & Hepner, 2018; see also models of Anders et al., 2015; Oppenheim et al., 2010). The second significant time window, which is just before response, is compatible with conflict monitoring accounts (Ridderinkhof et al., 2004) and inhibiting incorrect responses (Forstmann et al., 2008, 2010). For example, Forstmann et al. found increased activity in the pre-SMA when participants prepared to stop an ongoing action.

With these interpretations, our study supports previous reports that the pre-SMA may play an important role in conflict management during selection but makes the critical addition that it is involved in language production, particularly in handling interference between highly activated competitors (e.g., HOM). Conflict may also be increased by poorer, rather than more intense, lexicosemantic mapping (see Nozari & Hepner, 2018) such as induced by temporal lobe lesions (Harvey & Schnur, 2015), in which we would predict increased pre-SMA activity. We can also speculate that increased conflict management may be introduced in other situations, for example, when it is highly important for a speaker to choose the best word among alternatives to express a concept. In this view, just before articulatory programming or execution, the pre-SMA would be significantly implicated in the resolution processes between competing lexical items. There are previous findings showing worse performance in prefrontal patients during conflict (Riès, Karzmark, Navarrete, Knight, & Dronkers, 2015), which may be explained by reduced top–down connectivity of the pre-SMA to ventral processing streams, through damage to the frontal aslant tract (Chernoff et al., 2018).

Our study, in combination with those previously discussed, is hence compatible with the idea that the pre-SMA may be involved in the final decision mechanism before a word is produced (second time window), in a ventral pathway that is temporal to frontal to the SMA, and that the pre-SMA also communicates in an early top–down fashion (first time window) for conflict detection and improved response control. These findings imply that the pre-SMA should be considered more carefully during word production paradigms (in line with the discussion by Riès, Dronkers, & Knight, 2016).

Previous works have found a role of the pre-SMA in general selection, without conflict manipulations (Tremblay & Gracco, 2009; Alario, Chainay, Lehericy, & Cohen, 2006), whereas herein we find evidence that the region is increasingly modulated during conflict manipulations. In respect to current theories largely based on behavioral data, these findings are consistent with the proposal that top–down cognitive control processes act to mitigate interference in lexical selection for appropriate response execution (Belke, 2013; Belke & Stielow, 2013; Schnur et al., 2009).

Although word production has indeed been proposed to engage cognitive control (Nozari & Novick, 2017), the pre-SMA/R.MSFG have not been highlighted as critical in this process. However, in other interference or cognitive control tasks such as Stroop, go/no-go, and stopping tasks, the medial superior frontal gyrus (BA 6, notably the pre-SMA), especially in the right hemisphere, has been linked to cognitive control processes such as response selection, inhibition, response switching, and detection or monitoring of conflict (Simmonds, Pekar, & Mostofsky, 2008; Verbruggen & Logan, 2008; Ridderinkhof et al., 2004; George et al., 1994). Our results are hence consistent with these across-domain studies and provide neurophysiological support to a standing hypothesis that cognitive control in language production may utilize domain-general mechanisms (e.g., Riès, Janssen, Dufau, Alario, & Burle, 2011).

Another strong candidate for handling interference was the ACC, herein sampled in the left anterior cingulate gyrus. However, although the left anterior cingulate gyrus was found significant in the general task (Table 3; Figure 2), no significant difference was found in the HOM–HET contrasts (see Figure 7). Hence, these results do not provide support for other cognitive control theories that could link a role of ACC for resolving semantic interference (Janssen et al., 2015; Piai, Roelofs, Acheson, & Takashima, 2013; Botvinick, Cohen, & Carter, 2004; Botvinick, Braver, Barch, Carter, & Cohen, 2001). Though these studies involve different recording techniques (EEG and fMRI), which could obtain different results when, for instance, clustering surface electrodes to make inferences about specific brain regions (EEG) or averaging activities over time (fMRI). Further reproduction of these functional distinctions in the case of word production is needed to assure the interpretation we propose.

Figure 7.

No significant effect of HOM–HET context found in the left anterior cingulate gyrus (L.aCG), namely, the left ACC. Data organized as in Figure 3.

Figure 7.

No significant effect of HOM–HET context found in the left anterior cingulate gyrus (L.aCG), namely, the left ACC. Data organized as in Figure 3.

In respect to the selection mechanism discussed in the Introduction, there are different ways in which one could view the actual dynamic in which the pre-SMA may handle response conflict. For example in a more serial perspective, competitors may race with accumulating activity until a selection deadline (threshold) is activated, which triggers motor preparation and execution (e.g., see Anders et al., 2015; Oppenheim et al., 2010; Roelofs, 1992). Alternatively, the top 2 or 3 closest candidates may be selected and prepared for possible execution, and the response triggering process may continuously switch between these already-prepared responses (which is plausible here due to the small response set, six items per block) until an absolute threshold is crossed. The former paradigm has been one of the most popular approaches in quantitative cognitive modeling of choice behavior (Ratcliff, Gomez, & McKoon, 2004; Ratcliff & Smith, 2004; Busemeyer & Townsend, 1992; Townsend & Ashby, 1983) and has been successfully applied to the word production paradigm with semantic contrasts (Anders et al., 2015, 2017). Support for the latter hypothesis has also been found by Isoda and Hikosaka (2007), who found neurons in the rostral portion of the superior medial wall (or pre-SMA; BA 6) active in Rhesus monkeys during response switching. Although both mechanisms may be able to account for observed performance, further neurophysiological work, which combines also computational modeling, would be interesting to clarify the mechanism that best represents the underlying cognitive operations. With regard to either modeling approach, our hypothesis is that the pre-SMA would be principally implicated in the threshold modulation for final word selection or triggering for production.

Lateralization in the Picture-naming Network

Although not specifically designed to assess lateralization patterns (Riès et al., 2016), the sampling available in our study revealed several noteworthy observations in this respect (Figure 2). In the occipital lobe (Row 1) between 50 and 400 msec, the t values of activity in the left lingual gyrus were multiple times higher than those in the right lingual gyrus (see, however, Tanji, Suzuki, Delorme, Shamoto, & Nakasato, 2005). This is an interesting result that inspires future work to clarify the mechanism, such as to whether top–down language processes may also interact with the left visual gyri for improved lexicosemantic processing.

Second, in the medial-temporal lobe (Row 3), activity is graduated in power as one moves from the posterior to the anterior regions, for example, as one moves from the entorhinal cortex, which is afferent to the hippocampus (e.g., Steward & Scoville, 1976) to the parahippocampal gyrus. In the right hemisphere, however, early activity is also observed in these regions, but without a marked peak in the entorhinal cortex to prime such activity. This result may lead to a speculation that a different mechanism in the right hemisphere may trigger such activity.

In the lateral temporal lobe (Row 4), larger t values of activity were also observed in the left TTG1 than the right TTG1, also known as Heschl's gyri. This result is consistent with studies of this region (Morillon, Liégeois-Chauvel, Arnal, Bénar, & Giraud, 2012) that have found stronger activity in the left hemisphere for higher frequencies (25–45 Hz) versus stronger activity in the right hemisphere for lower frequencies (5–6 Hz). This occurrence, known as asymmetric sampling time (Poeppel, 2003) between hemispheres, has been established as a function of language specialization (Dorsaint-Pierre et al., 2006; Liégeois-Chauvel, de Graaf, Laguitton, & Chauvel, 1999). The contribution of the current study shows that the asymmetric sampling time preference for higher frequencies in the left TTG extends even to the high-gamma range (80–150 Hz).

With regard to a lateralized specialization of the pre-SMA for handling word selection conflict in language production, as we did not have electrodes implanted in the left pre-SMA in any patient, one cannot conclude that these operations are handled more often in the right than the left until such data are readily observed. Support for the right pre-SMA is provided in a previous imaging experiment of picture naming with HET/HOM distractor word conditions (de Zubicaray et al., 2001), as well as in nonlinguistic experiments of cognitive control (e.g., Ridderinkhof et al., 2004). In contrast, during an active lexical selection experiment in which there was no interference condition, the left pre-SMA was much more active than the right (Alario et al., 2006). It is also important to note, however, that there is evidence for bilateral plasticity of the pre-SMA. That is, for example, resection studies of the pre-SMA have found language deficits after removal of a unilateral portion of the pre-SMA, in which language performance recovery was association with increased activity in the contralateral side of the pre-SMA (e.g., Krainik et al., 2003; see also Chainay et al., 2009). Overall, these works and ours provide interesting arguments to further probe the pre-SMA bilaterally in future language experiments, especially those that involve conflict management or cognitive control.

This research has hence led to several lateralization observations and suggests their further investigation through a follow-up study formally designed to assess lateralization.

Limitations

Our study has the regular limitations that are inherent to studies of intracerebral activity in epileptic patients (Lachaux et al., 2012). These populations may show strong interindividual variability. However, our group analyses were anatomically and functionally grounded, compensating to some extent for this aspect. Indeed, fair levels of spatial sampling were achieved, certainly at the expense of the highly specific anatomical details that come with each patient's stereotactic implantation coordinates and with the risk that the cross-patient consistency constraint excludes potentially relevant signal diversity and signal information. Our focus on high-gamma activity was strongly motivated by the current view that it efficiently reflects cognitive processing. Having done this, we did not explore the other potentially meaningful frequency bands (as, e.g., Gaona et al., 2011; Canolty et al., 2007), and subsequent research is needed to broaden the search space to these levels as well, which may reveal further information.

Relatedly, our study assessed significant task activities and semantic effects on trial based statistics, yet what we report and interpret are the averages within and across patients. Such averaging, together with the signal processing procedures, smoothes the signal in time and puts limits on the temporal resolution available. Single trial measures may be better located in time and might be usable in some contexts (Dubarry et al., 2017), but they were not easily adaptable to assess effects across conditions involving different trials, as in the design we used.

Finally, we used an anatomical approach to associate cognitive processes with the brain regions sampled. Such approaches are currently a form of common practice and have been identified as a case of reverse inference (Poldrack, 2006). Although such approaches can be particularly detrimental when inferences are made across cognitively different tasks, the cognitive associations we have made with brain regions were quite specific, came from previously published meta-analyses (e.g., Price, 2012, See Table 2), and involved the same task we used (picture naming) or very similar tasks (e.g., word reading).

Conclusion

We detected a network of 39 brain regions with significant task-related activity recruited during semantically driven word production. These regions are compatible with a much previous work on the picture-naming network, both in their spatial and temporal properties, yet observed here simultaneously within the same signal. Several lateralization contrasts within this network were also identified, offering insights for future work.

The lexicosemantic dynamics of semantic priming and interference resulted in nine of the previous 39 regions being modulated by the semantic context manipulation. Within our sampling, priming appeared to modulate brain activity dispersedly (in eight of nine regions), whereas interference appears to be resolved more locally (one of nine regions), notably in the pre-SMA. These observations are consistent with the hypothesis that both priming and interference underlie semantically driven word retrieval. They further suggest a significant role of the pre-SMA in resolving interference, perhaps as the final mechanism for response selection before articulation. Such interpretation is consistent with previous hypotheses regarding the role of top–down cognitive control in language production, although they did not explicitly consider the pre-SMA. It remains to be determined to what extent the left versus right pre-SMA might be involved in handling interference in language, as the current study did not involve implanted electrodes in the left pre-SMA.

APPENDIX

The following Table A1 provides the regions excluded from interpretation due to either failing the functional consistency criterion (an $r¯$ < .35) or sampling in only one patient. The table includes 45 regions; 6 regions were excluded based the functional consistency criteria (depicted in Figure A1, they are to the left of the vertical line), and 39 due to sampling in only one patient. Conversely, the patient and bipolar channel numbers for the regions satisfying the criteria both for significance and functional consistency are provided in Figure 1.

Table A1.
Summary of Bipolar Channel and Patient Numbers for Brain Regions Not Present in Figure 1
AbbreviationNameChannelsPatientsCorrP
SignSignr
L.pCG Right posterior cingulate gyrus .27
R.mFuG Right mid fusiform gyrus 20 20 .23
R.mSTG Right mid superior temporal gyrus .08
R.pFuG Right posterior fusiform gyrus .02
L.SMG Left supramarginal gyrus .01
L.Pte Left planum temporale NA
L.A Left amygdala NA
L.pHi Left posterior hippocampus NA
L.aSTG Left anterior superior temporal gyrus NA
R.aSTG Right anterior superior temporal gyrus NA
L.mSTG Left mid superior temporal gyrus NA
R.aMTG Right anterior mid temporal gyrus NA
R.aCG Right anterior cingulate gyrus NA
R.pCG Right posterior cingulate gyrus NA
L.IFGOp Left pars opercularis NA
R.IFGOp Right pars opercularis NA
L.Pcun Left posterior cuneous NA
L.17 Left Brodmann's area NA
R.17 Right Brodmann's area NA
L.aCOL Left anterior collateral sulcus NA
L.pCOL Left posterior collateral sulcus NA
L.aCol Left anterior colliculus NA
L.aINS Left anterior insula NA
L.AnG Left angular gyrus NA
L.Cd Left caudate nucleus NA
L.Fop Left frontal operculum NA
L.OcG Left occipital gyrus NA
L.pOP Left posterior pars opercularis NA
L.POTZ Left parietooccipital transition area NA
L.Ppo Left planum polare NA
L.PrG Left precentral gyrus NA
L.SPL Left superior parietal lobule NA
L.SRoG Left superior rostral gyrus NA
R.aINS Right anterior insula NA
R.Fop Right frontal operculum NA
R.mINS Right mid insula NA
R.MOrG Right medial orbital gyrus NA
R.mORG Right mid orbital gyrus NA
R.OcG Right occipital gyrus NA
R.PRC Right perirhinal cortex NA
R.PT Right paratenial thalamic nucleus NA
R.Pte Right planum temporale NA
R.Pu Right putamen NA
R.PuL Right pulvinar NA
R.SMG Right supramarginal gyrus NA
AbbreviationNameChannelsPatientsCorrP
SignSignr
L.pCG Right posterior cingulate gyrus .27
R.mFuG Right mid fusiform gyrus 20 20 .23
R.mSTG Right mid superior temporal gyrus .08
R.pFuG Right posterior fusiform gyrus .02
L.SMG Left supramarginal gyrus .01
L.Pte Left planum temporale NA
L.A Left amygdala NA
L.pHi Left posterior hippocampus NA
L.aSTG Left anterior superior temporal gyrus NA
R.aSTG Right anterior superior temporal gyrus NA
L.mSTG Left mid superior temporal gyrus NA
R.aMTG Right anterior mid temporal gyrus NA
R.aCG Right anterior cingulate gyrus NA
R.pCG Right posterior cingulate gyrus NA
L.IFGOp Left pars opercularis NA
R.IFGOp Right pars opercularis NA
L.Pcun Left posterior cuneous NA
L.17 Left Brodmann's area NA
R.17 Right Brodmann's area NA
L.aCOL Left anterior collateral sulcus NA
L.pCOL Left posterior collateral sulcus NA
L.aCol Left anterior colliculus NA
L.aINS Left anterior insula NA
L.AnG Left angular gyrus NA
L.Cd Left caudate nucleus NA
L.Fop Left frontal operculum NA
L.OcG Left occipital gyrus NA
L.pOP Left posterior pars opercularis NA
L.POTZ Left parietooccipital transition area NA
L.Ppo Left planum polare NA
L.PrG Left precentral gyrus NA
L.SPL Left superior parietal lobule NA
L.SRoG Left superior rostral gyrus NA
R.aINS Right anterior insula NA
R.Fop Right frontal operculum NA
R.mINS Right mid insula NA
R.MOrG Right medial orbital gyrus NA
R.mORG Right mid orbital gyrus NA
R.OcG Right occipital gyrus NA
R.PRC Right perirhinal cortex NA
R.PT Right paratenial thalamic nucleus NA
R.Pte Right planum temporale NA
R.Pu Right putamen NA
R.PuL Right pulvinar NA
R.SMG Right supramarginal gyrus NA

Number of significant channels and patients (Sig) versus total number (n). CorrP = correlation statistic (r) calculated between patients with significant channels to determine if activity is functionally consistent; regions with r < .35 were excluded.

Figure A1.

Distribution of functional consistency or correlations between participants who share significant channels in a region (specific values provided in Table A1). Based on this distribution, a threshold of r ≥ .35 was used (depicted by the blue vertical line) as the inclusion criterion. This resulted in 6 regions excluded and 39 regions retained.

Figure A1.

Distribution of functional consistency or correlations between participants who share significant channels in a region (specific values provided in Table A1). Based on this distribution, a threshold of r ≥ .35 was used (depicted by the blue vertical line) as the inclusion criterion. This resulted in 6 regions excluded and 39 regions retained.

Acknowledgments

This work, carried out within the Labex BLRI (ANR-11-LABX-0036) and the Institut Convergence ILCB (ANR-16-CONV-0002), has benefited from support from the French government, managed by the French National Agency for Research (ANR) and the Excellence Initiative of Aix-Marseille University (A*MIDEX). It was likewise supported by funding from the European Research Council under the European Community's Seventh Framework Program (FP7/2007-2013 grant agreement no. 263575).

Reprint requests should be sent to F.-Xavier Alario, UMR 7290 case D, Aix-Marseille Université & CNRS, 3 Place Victor Hugo, 13331 Marseille cedex 3, France, or via e-mail: francois-xavier.alario@univ-amu.fr.

Note

1.

Experimental tasks involving implanted patients are generally performed at the bedside in many institutions. The epilepsy unit at La Timone Hospital, where these patients were followed, includes a specialized research laboratory in which the patients can be tested collaterally to their medical monitoring.

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Author notes

*

Co-first authors.