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Direct electrical stimulation (DES) at 50 Hz is used as a gold standard to map cognitive functions but little is known about its ability to map large-scale networks and specific subnetwork. In the present study, we aim to propose a new methodological approach to evaluate the specific hypothesis suggesting that language errors/dysfunction induced by DES are the result of large-scale network modification rather than of a single cortical region, which explains that similar language symptoms may be observed after stimulation of different cortical regions belonging to this network. We retrospectively examined 29 patients suffering from focal drug-resistant epilepsy who benefitted from stereo-electroencephalographic (SEEG) exploration and exhibited language symptoms during a naming task following 50 Hz DES. We assessed the large-scale language network correlated with behavioral DES-induced responses (naming errors) by quantifying DES-induced changes in high frequency activity (HFA, 70-150 Hz) outside the stimulated cortical region. We developed a probabilistic approach to report the spatial pattern of HFA modulations during DES-induced language errors. Similarly, we mapped the pattern of after-discharges (3-35 Hz) occurring after DES. HFA modulations concurrent to language symptoms revealed a brain network similar to our current knowledge of language gathered from standard brain mapping. In addition, specific subnetworks could be identified within the global language network, related to different language processes, generally described in relation to the classical language regions. Spatial patterns of after-discharges were similar to HFA induced during DES. Our results suggest that this new methodological DES-HFA mapping is a relevant approach to map functional networks during SEEG explorations, which would allow to shift from "local" to "network" perspectives., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Objective: The authors assessed the clinical relevance of preoperative task-induced high-frequency activity (HFA) for language mapping in patients with refractory epilepsy during stereoelectroencephalography recording. Although HFA evaluation was described as a putative biomarker of cognition, its clinical relevance for mapping language networks was assessed predominantly by studies using electrocorticography (ECOG)., Methods: Forty-two patients with epilepsy who underwent intracranial electrode implantation during both task-induced HFA and direct cortical stimulation (DCS) language mapping were evaluated. The spatial and functional relevance of each method in terms of specificity and sensitivity were evaluated., Results: The results showed that the two methods were able to map classic language regions, and a large and bilateral language network was obtained with induced HFA. At a regional level, differences were observed between methods for parietal and temporal lobes: HFA recruited a larger number of cortical parietal sites, while DCS involved more cortical temporal sites. Importantly, the results showed that HFA predicts language interference induced by DCS with high specificity (92.4%; negative predictive value 95.9%) and very low sensitivity (8.9%; positive predictive value 4.8%)., Conclusions: DCS language mapping appears to be more appropriate for an extensive temporal mapping than induced HFA mapping. Furthermore, induced HFA should be used as a complement to DCS to preselect the number of stimulated sites during DCS, by omitting those reported as HFA-. This may be a considerable advantage because it allows a reduction in the duration of the stimulation procedure. Several parameters to be used for each method are discussed and the results are interpreted in relation to previous results reported in ECOG studies.

The ability to monitor our own errors is mediated by a network that includes dorsomedial prefrontal cortex (dmPFC) and anterior insula (AI). However, the dynamics of the underlying neurophysiological processes remain unclear. In particular, whether AI is on the receiving or driving end of the error-monitoring network is unresolved. Here, we recorded intracerebral electroencephalography signals simultaneously from AI and dmPFC in epileptic patients while they performed a stop-signal task. We found that errors selectively modulated broadband neural activity in human AI. Granger causality estimates revealed that errors were immediately followed by a feedforward influence from AI onto anterior cingulate cortex and, subsequently, onto presupplementary motor area. The reverse pattern of information flow was observed on correct responses. Our findings provide the first direct electrophysiological evidence indicating that the anterior insula rapidly detects and conveys error signals to dmPFC, while the latter might use this input to adapt behavior following inappropriate actions., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Temporal lobe epilepsy can induce functional plasticity in temporoparietal networks involved in language and long-term memory processing. Previous studies in healthy subjects have revealed the relative difficulty for this network to respond effectively across different experimental designs, as compared to more reactive regions such as frontal lobes. For a protocol to be optimal for clinical use, it has to first show robust effects in a healthy cohort. In this study, we developed a novel experimental paradigm entitled NEREC, which is able to reveal the robust participation of temporoparietal networks in a uniquely combined language and memory task, validated in an fMRI study with healthy subjects. Concretely, NEREC is composed of two runs: (a) an intermixed language-memory task (confrontation naming associated with encoding in nonverbal items, NE) to map language (i.e., word retrieval and lexico-semantic processes) combined with simultaneous long-term verbal memory encoding (NE items named but also explicitly memorized) and (b) a memory retrieval task of items encoded during NE (word recognition, REC) intermixed with new items. Word recognition is based on both perceptual-semantic familiarity (feeling of 'know') and accessing stored memory representations (remembering). In order to maximize the remembering and recruitment of medial temporal lobe structures, we increased REC difficulty by changing the modality of stimulus presentation (from nonverbal during NE to verbal during REC). We report that (a) temporoparietal activation during NE was attributable to both lexico-semantic (language) and memory (episodic encoding and semantic retrieval) processes; that (b) encoding activated the left hippocampus, bilateral fusiform, and bilateral inferior temporal gyri; and that (c) task recognition (recollection) activated the right hippocampus and bilateral but predominant left fusiform gyrus. The novelty of this protocol consists of (a) combining two tasks in one (language and long-term memory encoding/recall) instead of applying isolated tasks to map temporoparietal regions, (b) analyzing NE data based on performances recorded during REC, (c) double-mapping networks involved in naming and in long-term memory encoding and retrieval, (d) focusing on remembering with hippocampal activation and familiarity judgment with lateral temporal cortices activation, and (e) short duration of examination and feasibility. These aspects are of particular interest in patients with TLE, who frequently show impairment of these cognitive functions. Here, we show that the novel protocol is suited for this clinical evaluation., (Copyright © 2015 Elsevier Inc. All rights reserved.)

This functional magnetic resonance imaging (fMRI) study aimed at examining the cerebral regions involved in the auditory perception of prosodic focus using a natural focus detection task. Two conditions testing the processing of simple utterances in French were explored, narrow-focused versus broad-focused. Participants performed a correction detection task. The utterances in both conditions had exactly the same segmental, lexical, and syntactic contents, and only differed in their prosodic realization. The comparison between the two conditions therefore allowed us to examine processes strictly associated with prosodic focus processing. To assess the specific effect of pitch on hemispheric specialization, a parametric analysis was conducted using a parameter reflecting pitch variations specifically related to focus. The comparison between the two conditions reveals that brain regions recruited during the detection of contrastive prosodic focus can be described as a right-hemisphere dominant dual network consisting of (a) ventral regions which include the right posterosuperior temporal and bilateral middle temporal gyri and (b) dorsal regions including the bilateral inferior frontal, inferior parietal and left superior parietal gyri. Our results argue for a dual stream model of focus perception compatible with the asymmetric sampling in time hypothesis. They suggest that the detection of prosodic focus involves an interplay between the right and left hemispheres, in which the computation of slowly changing prosodic cues in the right hemisphere dynamically feeds an internal model concurrently used by the left hemisphere, which carries out computations over shorter temporal windows., (Copyright © 2012 Wiley Periodicals, Inc.)

Many high-level visual regions exhibit complex patterns of stimulus selectivity that make their responses difficult to explain in terms of a single cognitive mechanism. For example, the parahippocampal place area (PPA) responds maximally to environmental scenes during fMRI studies but also responds strongly to nonscene landmark objects, such as buildings, which have a quite different geometric structure. We hypothesized that PPA responses to scenes and buildings might be driven by different underlying mechanisms with different temporal profiles. To test this, we examined broadband γ (50-150 Hz) responses from human intracerebral electroencephalography recordings, a measure that is closely related to population spiking activity. We found that the PPA distinguished scene from nonscene stimuli in ∼80 ms, suggesting the operation of a bottom-up process that encodes scene-specific visual or geometric features. In contrast, the differential PPA response to buildings versus nonbuildings occurred later (∼170 ms) and may reflect a delayed processing of spatial or semantic features definable for both scenes and objects, perhaps incorporating signals from other cortical regions. Although the response preferences of high-level visual regions are usually interpreted in terms of the operation of a single cognitive mechanism, these results suggest that a more complex picture emerges when the dynamics of recognition are considered.

As you might experience it while reading this sentence, silent reading often involves an imagery speech component: we can hear our own "inner voice" pronouncing words mentally. Recent functional magnetic resonance imaging studies have associated that component with increased metabolic activity in the auditory cortex, including voice-selective areas. It remains to be determined, however, whether this activation arises automatically from early bottom-up visual inputs or whether it depends on late top-down control processes modulated by task demands. To answer this question, we collaborated with four epileptic human patients recorded with intracranial electrodes in the auditory cortex for therapeutic purposes, and measured high-frequency (50-150 Hz) "gamma" activity as a proxy of population level spiking activity. Temporal voice-selective areas (TVAs) were identified with an auditory localizer task and monitored as participants viewed words flashed on screen. We compared neural responses depending on whether words were attended or ignored and found a significant increase of neural activity in response to words, strongly enhanced by attention. In one of the patients, we could record that response at 800 ms in TVAs, but also at 700 ms in the primary auditory cortex and at 300 ms in the ventral occipital temporal cortex. Furthermore, single-trial analysis revealed a considerable jitter between activation peaks in visual and auditory cortices. Altogether, our results demonstrate that the multimodal mental experience of reading is in fact a heterogeneous complex of asynchronous neural responses, and that auditory and visual modalities often process distinct temporal frames of our environment at the same time.

Integration of phonological and lexicosemantic processes is essential for visual word recognition. Here we used dynamic causal modeling of event-related potentials, combined with group source reconstruction, to estimate how those processes translate into context-dependent modulation of effective connectivity within the temporal-frontal language network. Fifteen healthy human subjects performed a phoneme detection task in pseudo-words and a semantic categorization task in words. Cortical current densities revealed the sequential activation of temporal regions, from the occipital-temporal junction toward the anterior temporal lobe, before reaching the inferior frontal gyrus. A difference of activation between phonology and semantics was identified in the anterior temporal lobe within the 240-300 ms peristimulus time window. Dynamic causal modeling indicated this increase of activation of the anterior temporal lobe in the semantic condition as a consequence of an increase of forward connectivity from the posterior inferior temporal lobe to the anterior temporal lobe. In addition, fast activation of the inferior frontal region, which allowed a feedback control of frontal regions on the superior temporal and posterior inferior temporal cortices, was found to be likely. Our results precisely describe spatiotemporal network mechanisms occurring during integration of phonological and semantic processes. In particular, they support the hypothesis of multiple pathways within the temporal lobe for language processing, where frontal regions would exert a top-down control on temporal regions in the recruitment of the anterior temporal lobe for semantic processing.

Our goal was to apply a statistical approach to allow the identification of atypical language patterns and to differentiate patients with epilepsy from healthy subjects, based on their cerebral activity, as assessed by functional MRI (fMRI). Patients with focal epilepsy show reorganization or plasticity of brain networks involved in cognitive functions, inducing 'atypical' (compared to 'typical' in healthy people) brain profiles. Moreover, some of these patients suffer from drug-resistant epilepsy, and they undergo surgery to stop seizures. The neurosurgeon should only remove the zone generating seizures and must preserve cognitive functions to avoid deficits. To preserve functions, one should know how they are represented in the patient's brain, which is in general different from that of healthy subjects. For this purpose, in the pre-surgical stage, robust and efficient methods are required to identify atypical from typical representations. Given the frequent location of regions generating seizures in the vicinity of language networks, one important function to be considered is language. The risk of language impairment after surgery is determined pre-surgically by mapping language networks. In clinical settings, cognitive mapping is classically performed with fMRI. The fMRI analyses allowing the identification of atypical patterns of language networks in patients are not sufficiently robust and require additional statistic approaches. In this study, we report the use of a statistical nonlinear machine learning classification, the Extreme Gradient Boosting (XGBoost) algorithm, to identify atypical patterns and classify 55 participants as healthy subjects or patients with epilepsy. XGBoost analyses were based on neurophysiological features in five language regions (three frontal and two temporal) in both hemispheres and activated with fMRI for a phonological (PHONO) and a semantic (SEM) language task. These features were combined into 135 cognitively plausible subsets and further submitted to selection and binary classification. Classification performance was scored with the Area Under the receiver operating characteristic curve (AUC). Our results showed that the subset SEM_LH BA_47-21 (left fronto-temporal activation induced by the SEM task) provided the best discrimination between the two groups (AUC of 91 ± 5%). The results are discussed in the framework of the current debates of language reorganization in focal epilepsy. [ABSTRACT FROM AUTHOR]

Abstract: This fMRI study aims to assess the effect of two variables on the cerebral substrate of phonological processing during visual phoneme detection: (a) the difficulty level (type) of grapheme-to-phoneme conversion (GPC, letter-sound mapping) with two modalities, simple (S) and complex (C); and (b) the gender of participants, females (F) vs. males (M). The behavioral results have shown that simple items were processed more accurately than complex ones. At the cerebral level, phoneme detection activated the left-hemisphere phonological network and several regions of this network were modulated by the GPC type. Specifically, the activity of the superior posterior temporal gyrus was significantly higher for simple grapheme detection and suggests automatic activation of phonological representations; the activity of the inferior temporal gyrus was significantly higher for complex grapheme detection, suggesting greater demands of the integrative processes for solving competitive and inhibitory processes induced by the visual and phonological properties of stimuli. With respect to gender variable, we obtained significant interaction between GPC and gender. This effect showed higher accuracy for simple graphemes in females and suggests that female participants were more proficient than males for detecting simple items. This effect suggests easier and more rapid activation of phonological codes, probably based on a specific visual strategy, different from males. This is supported by the additional activation of the lingual gyrus in females for processing simple graphemes, although the exact explanation of this effect is not clear yet and requires supplementary experimentation and evidence. Overall, our results indicate that the cognitive mechanisms and cerebral correlates of phonological processing may depend on intrinsic and extrinsic variables, such as GPC and gender. [Copyright &y& Elsevier]