Dynamic reconfiguration of the language network preceding onset of speech in picture naming

Tracking Components of Bilingual Language Control in Speech Production: An fMRI Study Using Functional Localizers

When bilingual speakers switch back to speaking in their native language (L1) after having used their second language (L2), they often experience difficulty in retrieving words in their L1. This phenomenon is referred to as the L2 after-effect. We used the L2 after-effect as a lens to explore the neural bases of bilingual language control mechanisms. Our goal was twofold: first, to explore whether bilingual language control draws on domain-general or language-specific mechanisms; second, to investigate the precise mechanism(s) that drive the L2 after-effect. We used a precision fMRI approach based on functional localizers to measure the extent to which the brain activity that reflects the L2 after-effect overlaps with the language network (Fedorenko et al., 2010) and the domain-general multiple demand network (Duncan, 2010), as well as three task-specific networks that tap into interference resolution, lexical retrieval, and articulation. Forty-two Polish-English bilinguals participated in the study. Our results show that the L2 after-effect reflects increased engagement of domain-general but not language-specific resources. Furthermore, contrary to previously proposed interpretations, we did not find evidence that the effect reflects increased difficulty related to lexical access, articulation, and the resolution of lexical interference. We propose that difficulty of speech production in the picture naming paradigm-manifested as the L2 after-effect-reflects interference at a nonlinguistic level of task schemas or a general increase of cognitive control engagement during speech production in L1 after L2.

Beta- and gamma-band cortico-cortical interactions support naturalistic reading of continuous text

Large-scale integration of information across cortical structures, building on neural connectivity, has been proposed to be a key element in supporting human cognitive processing. In electrophysiological neuroimaging studies of reading, quantification of neural interactions has been limited to the level of isolated words or sentences due to artefacts induced by eye movements. Here, we combined magnetoencephalography recording with advanced artefact rejection tools to investigate both cortico-cortical coherence and directed neural interactions during naturalistic reading of full-page texts. Our results show that reading versus visual scanning of text was associated with wide-spread increases of cortico-cortical coherence in the beta and gamma bands. We further show that the reading task was linked to increased directed neural interactions compared to the scanning task across a sparse set of connections within a wide range of frequencies. Together, the results demonstrate that neural connectivity flexibly builds on different frequency bands to support continuous natural reading.

Macaque monkeys and humans sample temporal regularities in the acoustic environment

Many animal species show comparable abilities to detect basic rhythms and produce rhythmic behavior. Yet, the capacities to process complex rhythms and synchronize rhythmic behavior appear to be species-specific: vocal learning animals can, but some primates might not. This discrepancy is of high interest as there is a putative link between rhythm processing and the development of sophisticated sensorimotor behavior in humans. Do our closest ancestors show comparable endogenous dispositions to sample the acoustic environment in the absence of task instructions and training? We recorded EEG from macaque monkeys and humans while they passively listened to isochronous equitone sequences. Individual- and trial-level analyses showed that macaque monkeys' and humans' delta-band neural oscillations encoded and tracked the timing of auditory events. Further, mu- (8-15 Hz) and beta-band (12-20 Hz) oscillations revealed the superimposition of varied accentuation patterns on a subset of trials. These observations suggest convergence in the encoding and dynamic attending of temporal regularities in the acoustic environment, bridging a gap in the phylogenesis of rhythm cognition.

Spatiotemporal dynamics characterise spectral connectivity profiles of continuous speaking and listening

Speech production and perception are fundamental processes of human cognition that both rely on intricate processing mechanisms that are still poorly understood. Here, we study these processes by using magnetoencephalography (MEG) to comprehensively map connectivity of regional brain activity within the brain and to the speech envelope during continuous speaking and listening. Our results reveal not only a partly shared neural substrate for both processes but also a dissociation in space, delay, and frequency. Neural activity in motor and frontal areas is coupled to succeeding speech in delta band (1 to 3 Hz), whereas coupling in the theta range follows speech in temporal areas during speaking. Neural connectivity results showed a separation of bottom-up and top-down signalling in distinct frequency bands during speaking. Here, we show that frequency-specific connectivity channels for bottom-up and top-down signalling support continuous speaking and listening. These findings further shed light on the complex interplay between different brain regions involved in speech production and perception.

Vowel and formant representation in the human auditory speech cortex

Vowels, a fundamental component of human speech across all languages, are cued acoustically by formants, resonance frequencies of the vocal tract shape during speaking. An outstanding question in neurolinguistics is how formants are processed neurally during speech perception. To address this, we collected high-density intracranial recordings from the human speech cortex on the superior temporal gyrus (STG) while participants listened to continuous speech. We found that two-dimensional receptive fields based on the first two formants provided the best characterization of vowel sound representation. Neural activity at single sites was highly selective for zones in this formant space. Furthermore, formant tuning is adjusted dynamically for speaker-specific spectral context. However, the entire population of formant-encoding sites was required to accurately decode single vowels. Overall, our results reveal that complex acoustic tuning in the two-dimensional formant space underlies local vowel representations in STG. As a population code, this gives rise to phonological vowel perception.

Time-course of phonetic (motor speech) encoding in utterance production

Speaking involves the preparation of the linguistic content of an utterance and of the motor programs leading to articulation. The temporal dynamics of linguistic versus motor-speech (phonetic) encoding is highly debated: phonetic encoding has been associated either to the last quarter of an utterance preparation time (∼150ms before articulation), or to virtually the entire planning time, simultaneously with linguistic encoding. We (i) review the evidence on the time-course of motor-speech encoding based on EEG/MEG event-related (ERP) studies and (ii) strive to replicate the early effects of phonological-phonetic factors in referential word production by reanalysing a large EEG/ERP dataset. The review indicates that motor-speech encoding is engaged during at least the last 300ms preceding articulation (about half of a word planning lag). By contrast, the very early involvement of phonological-phonetic factors could be replicated only partially and is not as robust as in the second half of the utterance planning time-window.

The relationship between electrophysiological and hemodynamic measures of neural activity varies across picture naming tasks: A multimodal magnetoencephalography-functional magnetic resonance imaging study

Different neuroimaging methods can yield different views of task-dependent neural engagement. Studies examining the relationship between electromagnetic and hemodynamic measures have revealed correlated patterns across brain regions but the role of the applied stimulation or experimental tasks in these correlation patterns is still poorly understood. Here, we evaluated the across-tasks variability of MEG-fMRI relationship using data recorded during three distinct naming tasks (naming objects and actions from action images, and objects from object images), from the same set of participants. Our results demonstrate that the MEG-fMRI correlation pattern varies according to the performed task, and that this variability shows distinct spectral profiles across brain regions. Notably, analysis of the MEG data alone did not reveal modulations across the examined tasks in the time-frequency windows emerging from the MEG-fMRI correlation analysis. Our results suggest that the electromagnetic-hemodynamic correlation could serve as a more sensitive proxy for task-dependent neural engagement in cognitive tasks than isolated within-modality measures.

Localization of beta power decrease as measure for lateralization in pre-surgical language mapping with magnetoencephalography, compared with functional magnetic resonance imaging and validated by Wada test

Objective: Atypical patterns of language lateralization due to early reorganizational processes constitute a challenge in the pre-surgical evaluation of patients with pharmaco-resistant epilepsy. There is no consensus on an optimal analysis method used for the identification of language dominance in MEG. This study examines the concordance between MEG source localization of beta power desynchronization and fMRI with regard to lateralization and localization of expressive and receptive language areas using a visual verb generation task. Methods: Twenty-five patients with pharmaco-resistant epilepsy, including six patients with atypical language lateralization, and ten right-handed controls obtained MEG and fMRI language assessment. Fourteen patients additionally underwent the Wada test. We analyzed MEG beta power desynchronization in sensor (controls) and source space (patients and controls). Beta power decrease between 13 and 35 Hz was localized applying Dynamic Imaging of Coherent Sources Beamformer technique. Statistical inferences were grounded on cluster-based permutation testing for single subjects. Results: Event-related desynchronization of beta power in MEG was seen within the language-dominant frontal and temporal lobe and within the premotor cortex. Our analysis pipeline consistently yielded left language dominance with high laterality indices in controls. Language lateralization in MEG and Wada test agreed in all 14 patients for inferior frontal, temporal and parietal language areas (Cohen's Kappa = 1, p < 0.001). fMRI agreed with Wada test in 12 out of 14 cases (85.7%) for Broca's area (Cohen's Kappa = 0.71, p = 0.024), while the agreement for temporal and temporo-parietal language areas were non-significant. Concordance between MEG and fMRI laterality indices was highest within the inferior frontal gyrus, with an agreement in 19/24 cases (79.2%), and non-significant for Wernicke's area. Spatial agreement between fMRI and MEG varied considerably between subjects and brain regions with the lowest Euclidean distances within the inferior frontal region of interest. Conclusion: Localizing the desynchronization of MEG beta power using a verb generation task is a promising tool for the identification of language dominance in the pre-surgical evaluation of epilepsy patients. The overall agreement between MEG and fMRI was lower than expected and might be attributed to differences within the baseline condition. A larger sample size and an adjustment of the experimental designs are needed to draw further conclusions.

Correcting MEG Artifacts Caused by Overt Speech

Recording brain activity during speech production using magnetoencephalography (MEG) can help us to understand the dynamics of speech production. However, these measurements are challenging due to the induced artifacts coming from several sources such as facial muscle activity, lower jaw and head movements. Here, we aimed to characterize speech-related artifacts, focusing on head movements, and subsequently present an approach to remove these artifacts from MEG data. We recorded MEG from 11 healthy participants while they pronounced various syllables in different loudness. Head positions/orientations were extracted during speech production to investigate its role in MEG distortions. Finally, we present an artifact rejection approach using the combination of regression analysis and signal space projection (SSP) in order to correct the induced artifact from MEG data. Our results show that louder speech leads to stronger head movements and stronger MEG distortions. Our proposed artifact rejection approach could successfully remove the speech-related artifact and retrieve the underlying neurophysiological signals. As the presented artifact rejection approach was shown to remove artifacts arising from head movements, induced by overt speech in the MEG, it will facilitate research addressing the neural basis of speech production with MEG.

White matter microstructural differences underlying beta oscillations during speech in adults who stutter

The basal ganglia-thalamocortical (BGTC) loop may underlie speech deficits in developmental stuttering. In this study, we investigated the relationship between abnormal cortical neural oscillations and structural integrity alterations in adults who stutter (AWS) using a novel magnetoencephalography (MEG) guided tractography approach. Beta oscillations were analyzed using sensorimotor speech MEG, and white matter pathways were examined using tract-based spatial statistics (TBSS) and probabilistic tractography in 11 AWS and 11 fluent speakers. TBSS analysis revealed overlap between cortical regions of increased beta suppression localized to the mouth motor area and a reduced fractional anisotropy (FA) in the AWS group. MEG-guided tractography showed reduced FA within the BGTC loop from left putamen to subject-specific MEG peak. This is the first study to provide evidence that structural abnormalities may be associated with functional deficits in stuttering and reflect a network deficit within the BGTC loop that includes areas of the left ventral premotor cortex and putamen.

Oscillatory and structural signatures of language plasticity in brain tumor patients: A longitudinal study

Recent evidence suggests that damage to the language network triggers its functional reorganization. Yet, the spectro‐temporal fingerprints of this plastic rearrangement and its relation to anatomical changes is less well understood. Here, we combined magnetoencephalographic recordings with a proxy measure of white matter to investigate oscillatory activity supporting language plasticity and its relation to structural reshaping. First, cortical dynamics were acquired in a group of healthy controls during object and action naming. Results showed segregated beta (13–28 Hz) power decreases in left ventral and dorsal pathways, in a time‐window associated to lexico‐semantic processing (~250–500 ms). Six patients with left tumors invading either ventral or dorsal regions performed the same naming task before and 3 months after surgery for tumor resection. When longitudinally comparing patients' responses we found beta compensation mimicking the category‐based segregation showed by controls, with ventral and dorsal damage leading to selective compensation for object and action naming, respectively. At the structural level, all patients showed preoperative changes in white matter tracts possibly linked to plasticity triggered by tumor growth. Furthermore, in some patients, structural changes were also evident after surgery and showed associations with longitudinal changes in beta power lateralization toward the contralesional hemisphere. Overall, our findings support the existence of anatomo‐functional dependencies in language reorganization and highlight the potential role of oscillatory markers in tracking longitudinal plasticity in brain tumor patients. By doing so, they provide valuable information for mapping preoperative and postoperative neural reshaping and plan surgical strategies to preserve language function and patient's quality of life.

Picture naming yields highly consistent cortical activation patterns: Test-retest reliability of magnetoencephalography recordings

Reliable paradigms and imaging measures of individual-level brain activity are paramount when reaching from group-level research studies to clinical assessment of individual patients. Magnetoencephalography (MEG) provides a direct, non-invasive measure of cortical processing with high spatiotemporal accuracy, and is thus well suited for assessment of functional brain damage in patients with language difficulties. This MEG study aimed to identify, in a delayed picture naming paradigm, source-localized evoked activity and modulations of cortical oscillations that show high test-retest reliability across measurement days in healthy individuals, demonstrating their applicability in clinical settings. For patients with a language disorder picture naming can be a challenging task. Therefore, we also determined whether a semantic judgment task ('Is this item living?') with a spoken response ("yes"/"no") would suffice to induce comparably consistent activity within brain regions related to language production. The MEG data was collected from 19 healthy participants on two separate days. In picture naming, evoked activity was consistent across measurement days (intraclass correlation coefficient (ICC)>0.4) in the left frontal (400-800 ms after image onset), sensorimotor (200-800 ms), parietal (200-600 ms), temporal (200-800 ms), occipital (400-800 ms) and cingulate (600-800 ms) regions, as well as the right temporal (600-800 ms) region. In the semantic judgment task, consistent evoked activity was spatially more limited, occurring in the left temporal (200-800 ms), sensorimotor (400-800 ms), occipital (400-600 ms) and subparietal (600-800 ms) regions, and the right supramarginal cortex (600-800 ms). The delayed naming task showed typical beta oscillatory suppression in premotor and sensorimotor regions (800-1200 ms) but other consistent modulations of oscillatory activity were mostly observed in posterior cortical regions that have not typically been associated with language processing. The high test-retest consistency of MEG evoked activity in the picture naming task testifies to its applicability in clinical evaluations of language function, as well as in longitudinal MEG studies of language production in clinical and healthy populations.

Dynamic Language Network in Early and Late Cantonese-Mandarin Bilinguals

The brain representation of language in bilinguals is sculptured by several factors, such as age of acquisition (AoA) and proficiency level (PL) in second language. Although the effect of AoA-L2 on brain function and structure has been studied, little attention is devoted to dynamic properties of the language network and their differences between early and late bilinguals. In this study, we acquired resting-state fMRI data from early and late Cantonese (L1)–Mandarin (L2) bilinguals with high PLs of verbal fluency in both languages. We then analyzed dynamic functional connectivity (dFC) by using the sliding-windows approach, estimated the dFC states by using the k-means clustering algorithm, and calculated the dynamic topological properties of the language network for the early and late bilinguals. We detected four dFC states, State 1, State 2, State 3, and State 4, which may be related to phonetic processing, semantic processing, language control, and syntactic processing, respectively. Compared to the late bilinguals, the early bilinguals showed higher dFC between the inferior frontal area and the temporal area in State 1 and State 2, while higher dFC between the cerebellum and other regions in State 3. The early bilinguals showed a higher clustering coefficient and local and global efficiency in State 1 and State 3, but lower characteristic path length in State 1, than the late bilinguals. Together, these results suggested that AoA-L2 affects temporal neural activation and dynamic topological properties of the language network. These findings provide new information to understand the effect of experience of L2 acquisition on language network in bilinguals.

Frequency-Dependent Intrinsic Electrophysiological Functional Architecture of the Human Verbal Language Network

Functional magnetic resonance imaging (fMRI) allowed the spatial characterization of the resting-state verbal language network (vLN). While other resting-state networks (RSNs) were matched with their electrophysiological equivalents at rest and could be spectrally defined, such correspondence is lacking for the vLN. This magnetoencephalography (MEG) study aimed at defining the spatio-spectral characteristics of the neuromagnetic intrinsic functional architecture of the vLN. Neuromagnetic activity was recorded at rest in 100 right-handed healthy adults (age range: 18-41 years). Band-limited power envelope correlations were performed within and across frequency bands (θ, α, β, and low γ) from a seed region placed in the left Broca's area, using static orthogonalization as leakage correction. K-means clustering was used to segregate spatio-spectral clusters of resting-state functional connectivity (rsFC). Remarkably, unlike other RSNs, within-frequency long-range rsFC from the left Broca's area was not driven by one main carrying frequency but was characterized by a specific spatio-spectral pattern segregated along the ventral (predominantly θ and α) and dorsal (β and low-γ bands) vLN streams. In contrast, spatial patterns of cross-frequency vLN functional integration were spectrally more widespread and involved multiple frequency bands. Moreover, the static intrinsic functional architecture of the neuromagnetic human vLN involved clearly left-hemisphere-dominant vLN interactions as well as cross-network interactions with the executive control network and postero-medial nodes of the DMN. Overall, this study highlighted the involvement of multiple modes of within and cross-frequency power envelope couplings at the basis of long-range electrophysiological vLN functional integration. As such, it lays the foundation for future works aimed at understanding the pathophysiology of language-related disorders.

Task-Modulated Corticocortical Synchrony in the Cognitive-Motor Network Supporting Handwriting

Both motor and cognitive aspects of behavior depend on dynamic, accurately timed neural processes in large-scale brain networks. Here, we studied synchronous interplay between cortical regions during production of cognitive-motor sequences in humans. Specifically, variants of handwriting that differed in motor variability, linguistic content, and memorization of movement cues were contrasted to unveil functional sensitivity of corticocortical connections. Data-driven magnetoencephalography mapping (n = 10) uncovered modulation of mostly left-hemispheric corticocortical interactions, as quantified by relative changes in phase synchronization. At low frequencies (~2–13 Hz), enhanced frontoparietal synchrony was related to regular handwriting, whereas premotor cortical regions synchronized for simple loop production and temporo-occipital areas for a writing task substituting normal script with loop patterns. At the beta-to-gamma band (~13–45 Hz), enhanced synchrony was observed for regular handwriting in the central and frontoparietal regions, including connections between the sensorimotor and supplementary motor cortices and between the parietal and dorsal premotor/precentral cortices. Interpreted within a modular framework, these modulations of synchrony mainly highlighted interactions of the putative pericentral subsystem of hand coordination and the frontoparietal subsystem mediating working memory operations. As part of cortical dynamics, interregional phase synchrony varies depending on task demands in production of cognitive-motor sequences.

Involvement of the Cortico-Basal Ganglia-Thalamocortical Loop in Developmental Stuttering

Stuttering is a complex neurodevelopmental disorder that has to date eluded a clear explication of its pathophysiological bases. In this review, we utilize the Directions Into Velocities of Articulators (DIVA) neurocomputational modeling framework to mechanistically interpret relevant findings from the behavioral and neurological literatures on stuttering. Within this theoretical framework, we propose that the primary impairment underlying stuttering behavior is malfunction in the cortico-basal ganglia-thalamocortical (hereafter, cortico-BG) loop that is responsible for initiating speech motor programs. This theoretical perspective predicts three possible loci of impaired neural processing within the cortico-BG loop that could lead to stuttering behaviors: impairment within the basal ganglia proper; impairment of axonal projections between cerebral cortex, basal ganglia, and thalamus; and impairment in cortical processing. These theoretical perspectives are presented in detail, followed by a review of empirical data that make reference to these three possibilities. We also highlight any differences that are present in the literature based on examining adults versus children, which give important insights into potential core deficits associated with stuttering versus compensatory changes that occur in the brain as a result of having stuttered for many years in the case of adults who stutter. We conclude with outstanding questions in the field and promising areas for future studies that have the potential to further advance mechanistic understanding of neural deficits underlying persistent developmental stuttering.

Mapping critical hubs of receptive and expressive language using MEG: A comparison against fMRI

The complexity of the widespread language network makes it challenging for accurate localization and lateralization. Using large-scale connectivity and graph-theoretical analyses of task-based magnetoencephalography (MEG), we aimed to provide robust representations of receptive and expressive language processes, comparable with spatial profiles of corresponding functional magnetic resonance imaging (fMRI). We examined MEG and fMRI data from 12 healthy young adults (age 20-37 years) completing covert auditory word-recognition task (WRT) and covert auditory verb-generation task (VGT). For MEG language mapping, broadband (3-30 Hz) beamformer sources were estimated, voxel-level connectivity was quantified using phase locking value, and highly connected hubs were characterized using eigenvector centrality graph measure. fMRI data were analyzed using a classic general linear model approach. A laterality index (LI) was computed for 20 language-specific frontotemporal regions for both MEG and fMRI. MEG network analysis showed bilateral and symmetrically distributed hubs within the left and right superior temporal gyrus (STG) during WRT and predominant hubs in left inferior prefrontal gyrus (IFG) during VGT. MEG and fMRI localization maps showed high correlation values within frontotemporal regions during WRT and VGT (r = 0.63, 0.74, q < 0.05, respectively). Despite good concordance in localization, notable discordances were observed in lateralization between MEG and fMRI. During WRT, MEG favored a left-hemispheric dominance of left STG (LI = 0.25 ± 0.22) whereas fMRI supported a bilateral representation of STG (LI = 0.08 ± 0.2). Laterality of MEG and fMRI during VGT consistently showed a strong asymmetry in left IFG regions (MEG-LI = 0.45 ± 0.35 and fMRI-LI = 0.46 ± 0.13). Our results demonstrate the utility of a large-scale connectivity and graph theoretical analyses for robust identification of language-specific regions. MEG hubs are in great agreement with the literature in revealing with canonical and extra-canonical language sites, thus providing additional support for the underlying topological organization of receptive and expressive language cortices. Discordances in lateralization may emphasize the need for multimodal integration of MEG and fMRI to obtain an excellent predictive value in a heterogeneous healthy population and patients with neurosurgical conditions.

Correlates of anomia in non-semantic variants of primary progressive aphasia converge over time

To track neural correlates of naming performance with disease progression, we estimated key areas affected in nonfluent/agrammatic (nfvPPA) and logopenic (lvPPA) primary progressive aphasia variants over time and changes in naming correlates over time. Twenty-nine non-semantic PPA participants (17 nfvPPA and 12 lvPPA) were selected based upon current diagnostic criteria and PiB-PET status and conducted a confrontation-naming task and a structural MRI. Linear mixed-effect models implemented in FreeSurfer were used for tracking cortical thickness and epicenters of atrophy over time. Using averaged cortical thickness of epicenters and naming performance as variables of interest, two sets of multivariate analyses were conducted to compare atrophy progression and naming correlates across groups. While all PPA participants demonstrated naming deterioration and progressive cortical thinning in the left temporal lobe and the left inferior frontal gyrus, the lvPPA cohort showed greater naming deterioration and thinning in the left posterior inferior parietal cortex over time than it did the nfvPPA cohort. The multivariate analyses confirmed a widespread cortical thinning in lvPPA over time, but a more rapid thinning in the right superior frontal gyrus of nfvPPA participants. Impaired naming correlated with common cortical regions in both groups. These regions included the left anterior superior temporal gyrus and the posterior middle temporal gyrus, which was primarily affected in lvPPA. Non-semantic PPA variants initially present with separate epicenters of atrophy and different spatial-temporal patterns of neurodegeneration over time, but the common involvement in key cortical regions of the left temporal lobe accounts for naming deterioration in both groups.

Analysis of Functional Connectivity and Oscillatory Power Using DICS: From Raw MEG Data to Group-Level Statistics in Python

Communication between brain regions is thought to be facilitated by the synchronization of oscillatory activity. Hence, large-scale functional networks within the brain may be estimated by measuring synchronicity between regions. Neurophysiological recordings, such as magnetoencephalography (MEG) and electroencephalography (EEG), provide a direct measure of oscillatory neural activity with millisecond temporal resolution. In this paper, we describe a full data analysis pipeline for functional connectivity analysis based on dynamic imaging of coherent sources (DICS) of MEG data. DICS is a beamforming technique in the frequency-domain that enables the study of the cortical sources of oscillatory activity and synchronization between brain regions. All the analysis steps, starting from the raw MEG data up to publication-ready group-level statistics and visualization, are discussed in depth, including methodological considerations, rules of thumb and tradeoffs. We start by computing cross-spectral density (CSD) matrices using a wavelet approach in several frequency bands (alpha, theta, beta, gamma). We then provide a way to create comparable source spaces across subjects and discuss the cortical mapping of spectral power. For connectivity analysis, we present a canonical computation of coherence that facilitates a stable estimation of all-to-all connectivity. Finally, we use group-level statistics to limit the network to cortical regions for which significant differences between experimental conditions are detected and produce vertex- and parcel-level visualizations of the different brain networks. Code examples using the MNE-Python package are provided at each step, guiding the reader through a complete analysis of the freely available openfMRI ds000117 “familiar vs. unfamiliar vs. scrambled faces” dataset. The goal is to educate both novice and experienced data analysts with the “tricks of the trade” necessary to successfully perform this type of analysis on their own data.

Dynamic reshaping of functional brain networks during visual object recognition

OBJECTIVE

Emerging evidence shows that the modular organization of the human brain allows for better and efficient cognitive performance. Many of these cognitive functions are very fast and occur in a sub-second time scale such as the visual object recognition.

APPROACH

Here, we investigate brain network modularity while controlling stimuli meaningfulness and measuring a participant's reaction time. We particularly raised two questions: i) does the dynamic brain network modularity change during the recognition of meaningful and meaningless visual images? And (ii) is there a correlation between network modularity and the reaction time of the participants? To tackle these issues, we collected dense-electroencephalography (EEG, 256 channels) data from 20 healthy human subjects performing a cognitive task consisting of naming meaningful (tools, animals…) and meaningless (scrambled) images. Functional brain networks in both categories were estimated at the sub-second time scale using the EEG source connectivity method. By using multislice modularity algorithms, we tracked the reconfiguration of functional networks during the recognition of both meaningful and meaningless images.

MAIN RESULTS

Results showed a difference in the module's characteristics of both conditions in term of integration (interactions between modules) and occurrence (probability on average of any two brain regions to fall in the same module during the task). Integration and occurrence were greater for meaningless than for meaningful images. Our findings revealed also that the occurrence within the right frontal regions and the left occipito-temporal can help to predict the ability of the brain to rapidly recognize and name visual stimuli.

SIGNIFICANCE

We speculate that these observations are applicable not only to other fast cognitive functions but also to detect fast disconnections that can occur in some brain disorders.

Large-scale functional networks connect differently for processing words and symbol strings

Reconfigurations of synchronized large-scale networks are thought to be central neural mechanisms that support cognition and behavior in the human brain. Magnetoencephalography (MEG) recordings together with recent advances in network analysis now allow for sub-second snapshots of such networks. In the present study, we compared frequency-resolved functional connectivity patterns underlying reading of single words and visual recognition of symbol strings. Word reading emphasized coherence in a left-lateralized network with nodes in classical perisylvian language regions, whereas symbol processing recruited a bilateral network, including connections between frontal and parietal regions previously associated with spatial attention and visual working memory. Our results illustrate the flexible nature of functional networks, whereby processing of different form categories, written words vs. symbol strings, leads to the formation of large-scale functional networks that operate at distinct oscillatory frequencies and incorporate task-relevant regions. These results suggest that category-specific processing should be viewed not so much as a local process but as a distributed neural process implemented in signature networks. For words, increased coherence was detected particularly in the alpha (8-13 Hz) and high gamma (60-90 Hz) frequency bands, whereas increased coherence for symbol strings was observed in the high beta (21-29 Hz) and low gamma (30-45 Hz) frequency range. These findings attest to the role of coherence in specific frequency bands as a general mechanism for integrating stimulus-dependent information across brain regions.

A preliminary study on the neural oscillatory characteristics of motor preparation prior to dysfluent and fluent utterances in adults who stutter

PURPOSE

Recent literature on speech production in adults who stutter (AWS) has begun to investigate the neural mechanisms characterizing speech-motor preparation prior to speech onset. Compelling evidence has suggested that stuttering is associated with atypical processing within cortical and sub-cortical motor networks, particularly in the beta frequency range, that is effective before speech production even begins. Due to low stuttering frequency in experimental settings, however, the literature has so far predominantly reported on fluent speech production in AWS. Consequently, we have limited understanding of the way in which fluent speech processing in AWS is disturbed leading to a dysfluency. This preliminary study aims to characterize neural motor preparation prior to stuttered utterances in AWS.

METHODS

Eight AWS participated in the study. A total of 336 stuttered utterances were compared to the participants' own fluent utterance productions. Beta oscillatory activity was analyzed with magnetoencephalography (MEG) and localized using minimum-variance beamforming.

RESULTS

Preparation for speech production induced beta suppression in the bilateral premotor and motor cortex prior to speech onset. Although the data revealed some interesting trends, no significant differences between fluent and stuttered utterances were present. This may be due to a relatively low and variable number of stuttered trials analyzed in individual subjects.

CONCLUSION

While the lack of significant differences may have resulted from the relatively low numbers of stuttered utterances across subjects, the observed trends demonstrated that the proposed methodology and experimental paradigm is a promising approach for future studies aiming to characterize differences between stuttered and fluent speech.

Sensorimotor Oscillations Prior to Speech Onset Reflect Altered Motor Networks in Adults Who Stutter

Adults who stutter (AWS) have demonstrated atypical coordination of motor and sensory regions during speech production. Yet little is known of the speech-motor network in AWS in the brief time window preceding audible speech onset. The purpose of the current study was to characterize neural oscillations in the speech-motor network during preparation for and execution of overt speech production in AWS using magnetoencephalography (MEG). Twelve AWS and 12 age-matched controls were presented with 220 words, each word embedded in a carrier phrase. Controls were presented with the same word list as their matched AWS participant. Neural oscillatory activity was localized using minimum-variance beamforming during two time periods of interest: speech preparation (prior to speech onset) and speech execution (following speech onset). Compared to controls, AWS showed stronger beta (15–25 Hz) suppression in the speech preparation stage, followed by stronger beta synchronization in the bilateral mouth motor cortex. AWS also recruited the right mouth motor cortex significantly earlier in the speech preparation stage compared to controls. Exaggerated motor preparation is discussed in the context of reduced coordination in the speech-motor network of AWS. It is further proposed that exaggerated beta synchronization may reflect a more strongly inhibited motor system that requires a stronger beta suppression to disengage prior to speech initiation. These novel findings highlight critical differences in the speech-motor network of AWS that occur prior to speech onset and emphasize the need to investigate further the speech-motor assembly in the stuttering population.

Identification of Interictal Epileptic Networks from Dense-EEG

Epilepsy is a network disease. The epileptic network usually involves spatially distributed brain regions. In this context, noninvasive M/EEG source connectivity is an emerging technique to identify functional brain networks at cortical level from noninvasive recordings. In this paper, we analyze the effect of the two key factors involved in EEG source connectivity processing: (i) the algorithm used in the solution of the EEG inverse problem and (ii) the method used in the estimation of the functional connectivity. We evaluate four inverse solutions algorithms (dSPM, wMNE, sLORETA and cMEM) and four connectivity measures (r ², h ², PLV, and MI) on data simulated from a combined biophysical/physiological model to generate realistic interictal epileptic spikes reflected in scalp EEG. We use a new network-based similarity index to compare between the network identified by each of the inverse/connectivity combination and the original network generated in the model. The method will be also applied on real data recorded from one epileptic patient who underwent a full presurgical evaluation for drug-resistant focal epilepsy. In simulated data, results revealed that the selection of the inverse/connectivity combination has a significant impact on the identified networks. Results suggested that nonlinear methods (nonlinear correlation coefficient, phase synchronization and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The wMNE inverse solution showed higher performance than dSPM, cMEM and sLORETA. In real data, the combination (wMNE/PLV) led to a very good matching between the interictal epileptic network identified from noninvasive EEG recordings and the network obtained from connectivity analysis of intracerebral EEG recordings. These results suggest that source connectivity method, when appropriately configured, is able to extract highly relevant diagnostic information about networks involved in interictal epileptic spikes from non-invasive dense-EEG data.

From Structure to Circuits: The Contribution of MEG Connectivity Studies to Functional Neurosurgery

New advances in structural neuroimaging have revealed the intricate and extensive connections within the brain, data which have informed a number of ambitious projects such as the mapping of the human connectome. Elucidation of the structural connections of the brain, at both the macro and micro levels, promises new perspectives on brain structure and function that could translate into improved outcomes in functional neurosurgery. The understanding of neuronal structural connectivity afforded by these data now offers a vista on the brain, in both healthy and diseased states, that could not be seen with traditional neuroimaging. Concurrent with these developments in structural imaging, a complementary modality called magnetoencephalography (MEG) has been garnering great attention because it too holds promise for being able to shed light on the intricacies of functional brain connectivity. MEG is based upon the elemental principle of physics that an electrical current generates a magnetic field. Hence, MEG uses highly sensitive biomagnetometers to measure extracranial magnetic fields produced by intracellular neuronal currents. Put simply then, MEG is a measure of neurophysiological activity, which captures the magnetic fields generated by synchronized intraneuronal electrical activity. As such, MEG recordings offer exquisite resolution in the time and oscillatory domain and, as well, when co-registered with magnetic resonance imaging (MRI), offer excellent resolution in the spatial domain. Recent advances in MEG computational and graph theoretical methods have led to studies of connectivity in the time-frequency domain. As such, MEG can elucidate a neurophysiological-based functional circuitry that may enhance what is seen with MRI connectivity studies. In particular, MEG may offer additional insight not possible by MRI when used to study complex eloquent function, where the precise timing and coordination of brain areas is critical. This article will review the traditional use of MEG for functional neurosurgery, describe recent advances in MEG connectivity analyses, and consider the additional benefits that could be gained with the inclusion of MEG connectivity studies. Since MEG has been most widely applied to the study of epilepsy, we will frame this article within the context of epilepsy surgery and functional neurosurgery for epilepsy.

The Relationship between Frontotemporal Effective Connectivity during Picture Naming, Behavior, and Preserved Cortical Tissue in Chronic Aphasia

While several studies of task-based effective connectivity of normal language processing exist, little is known about the functional reorganization of language networks in patients with stroke-induced chronic aphasia. During oral picture naming, activation in neurologically intact individuals is found in "classic" language regions involved with retrieval of lexical concepts [e.g., left middle temporal gyrus (LMTG)], word form encoding [e.g., left posterior superior temporal gyrus, (LpSTG)], and controlled retrieval of semantic and phonological information [e.g., left inferior frontal gyrus (LIFG)] as well as domain-general regions within the multiple demands network [e.g., left middle frontal gyrus (LMFG)]. After stroke, lesions to specific parts of the left hemisphere language network force reorganization of this system. While individuals with aphasia have been found to recruit similar regions for language tasks as healthy controls, the relationship between the dynamic functioning of the language network and individual differences in underlying neural structure and behavioral performance is still unknown. Therefore, in the present study, we used dynamic causal modeling (DCM) to investigate differences between individuals with aphasia and healthy controls in terms of task-induced regional interactions between three regions (i.e., LIFG, LMFG, and LMTG) vital for picture naming. The DCM model space was organized according to exogenous input to these regions and partitioned into separate families. At the model level, random effects family wise Bayesian Model Selection revealed that models with driving input to LIFG best fit the control data whereas models with driving input to LMFG best fit the patient data. At the parameter level, a significant between-group difference in the connection strength from LMTG to LIFG was seen. Within the patient group, several significant relationships between network connectivity parameters, spared cortical tissue, and behavior were observed. Overall, this study provides some preliminary findings regarding how neural networks for language reorganize for individuals with aphasia and how brain connectivity relates to underlying structural integrity and task performance.

Task-sensitive reconfiguration of corticocortical 6-20 Hz oscillatory coherence in naturalistic human performance

Electrophysiological oscillatory coherence between brain regions has been proposed to facilitate functional long‐range connectivity within neurocognitive networks. This notion is supported by intracortical recordings of coherence in singled‐out corticocortical connections in the primate cortex. However, the manner in which this operational principle manifests in the task‐sensitive connectivity that supports human naturalistic performance remains undercharacterized. Here, we demonstrate task‐sensitive reconfiguration of global patterns of coherent connectivity in association with a set of easier and more demanding naturalistic tasks, ranging from picture comparison to speech comprehension and object manipulation. Based on whole‐cortex neuromagnetic recording in healthy behaving individuals, the task‐sensitive component of long‐range corticocortical coherence was mapped at spectrally narrow‐band oscillatory frequencies between 6 and 20 Hz (theta to alpha and low‐beta bands). This data‐driven cortical mapping unveiled markedly distinct and topologically task‐relevant spatiospectral connectivity patterns for the different tasks. The results demonstrate semistable oscillatory states relevant for neurocognitive processing. The present findings decisively link human behavior to corticocortical coherence at oscillatory frequencies that are widely thought to convey long‐range, feedback‐type neural interaction in cortical functional networks. Hum Brain Mapp 36:2455–2469, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc..