Lesions in deep gray nuclei after severe traumatic brain injury predict neurologic outcome.

PURPOSE: This study evaluates the correlation between injuries to deep gray matter nuclei, as quantitated by lesions in these nuclei on MR T2 Fast Spin Echo (T2 FSE) images, with 6-month neurological outcome after severe traumatic brain injury (TBI). MATERIALS AND METHODS: Ninety-five patients (80 males, mean age = 36.7y) with severe TBI were prospectively enrolled. All patients underwent a MR scan within the 45 days after the trauma that included a T2 FSE acquisition. A 3D deformable atlas of the deep gray matter was registered to this sequence; deep gray matter lesions (DGML) were evaluated using a semi-quantitative classification scheme. The 6-month outcome was dichotomized into unfavorable (death, vegetative or minimally conscious state) or favorable (minimal or no neurologic deficit) outcome. RESULTS: Sixty-six percent of the patients (63/95) had both satisfactory registration of the 3D atlas on T2 FSE and available clinical follow-up. Patients without DGML had an 89% chance (P = 0.0016) of favorable outcome while those with bilateral DGML had an 80% risk of unfavorable outcome (P = 0.00008). Multivariate analysis based on DGML accurately classified patients with unfavorable neurological outcome in 90.5% of the cases. CONCLUSION: Lesions in deep gray matter nuclei may predict long-term outcome after severe TBI with high sensitivity and specificity.

Neurophysiological Evidence for a Cortical Contribution to the Wakefulness-Related Drive to Breathe Explaining Hypocapnia-Resistant Ventilation in Humans.

Spontaneous ventilation in mammals is driven by automatic brainstem networks that generate the respiratory rhythm and increase ventilation in the presence of increased carbon dioxide production. Hypocapnia decreases the drive to breathe and induces apnea. In humans, this occurs during sleep but not during wakefulness. We hypothesized that hypocapnic breathing would be associated with respiratory-related cortical activity similar to that observed during volitional breathing, inspiratory constraints, or in patients with defective automatic breathing (preinspiratory potentials). Nineteen healthy subjects were studied under passive (mechanical ventilation, n = 10) or active (voluntary hyperventilation, n = 9) profound hypocapnia. Ventilatory and electroencephalographic recordings were performed during voluntary sniff maneuvers, normocapnic breathing, hypocapnia, and after return to normocapnia. EEG recordings were analyzed with respect to the ventilatory flow signal to detect preinspiratory potentials in frontocentral electrodes and to construct time-frequency maps. After passive hyperventilation, hypocapnia was associated with apnea in 3 cases and ventilation persisted in 7 cases (3 and 6 after active hyperventilation, respectively). No respiratory-related EEG activity was observed in subjects with hypocapnia-related apneas. In contrast, preinspiratory potentials were present at vertex recording sites in 12 of the remaining 13 subjects (p < 0.001). This was corroborated by time-frequency maps. This study provides direct evidence of a cortical substrate to hypocapnic breathing in awake humans and fuels the notion of corticosubcortical cooperation to preserve human ventilation in a variety of situations. Of note, maintaining ventilatory activity at low carbon dioxide levels is among the prerequisites to speech production insofar as speech often induces hypocapnia. SIGNIFICANCE STATEMENT: Human ventilatory activity persists, during wakefulness, even when hypocapnia makes it unnecessary. This peculiarity of human breathing control is important to speech and speech-breathing insofar as speech induces hypocapnia. This study evidences a specific respiratory-related cortical activity. This suggests that human hypocapnic breathing is driven, at least in part, by cortical mechanisms similar to those involved in volitional breathing, in breathing against mechanical constraints or with weak inspiratory muscle, and in patients with defective medullary breathing pattern generators. This fuels the notion that the human ventilatory drive during wakefulness often results from a corticosubcortical cooperation, and opens new avenues to study certain ventilatory and speech disorders.

Electroencephalographic detection of respiratory-related cortical activity in humans: from event-related approaches to continuous connectivity evaluation.

The presence of a respiratory-related cortical activity during tidal breathing is abnormal and a hallmark of respiratory difficulties, but its detection requires superior discrimination and temporal resolution. The aim of this study was to validate a computational method using EEG covariance (or connectivity) matrices to detect a change in brain activity related to breathing. In 17 healthy subjects, EEG was recorded during resting unloaded breathing (RB), voluntary sniffs, and breathing against an inspiratory threshold load (ITL). EEG were analyzed by the specially developed covariance-based classifier, event-related potentials, and time-frequency (T-F) distributions. Nine subjects repeated the protocol. The classifier could accurately detect ITL and sniffs compared with the reference period of RB. For ITL, EEG-based detection was superior to airflow-based detection (P < 0.05). A coincident improvement in EEG-airflow correlation in ITL compared with RB (P < 0.05) confirmed that EEG detection relates to breathing. Premotor potential incidence was significantly higher before inspiration in sniffs and ITL compared with RB (P < 0.05), but T-F distributions revealed a significant difference between sniffs and RB only (P < 0.05). Intraclass correlation values ranged from poor (-0.2) to excellent (1.0). Thus, as for conventional event-related potential analysis, the covariance-based classifier can accurately predict a change in brain state related to a change in respiratory state, and given its capacity for near "real-time" detection, it is suitable to monitor the respiratory state in respiratory and critically ill patients in the development of a brain-ventilator interface.

Revisiting mu suppression in autism spectrum disorder.

Two aspects of the EEG literature lead us to revisit mu suppression in Autism Spectrum Disorder (ASD). First and despite the fact that the mu rhythm can be functionally segregated in two discrete sub-bands, 8-10 Hz and 10-12/13 Hz, mu-suppression in ASD has been analyzed as a homogeneous phenomenon covering the 8-13 Hz frequency. Second and although alpha-like activity is usually found across the entire scalp, ASD studies of action observation have focused on the central electrodes (C3/C4). The present study was aimed at testing on the whole brain the hypothesis of a functional dissociation of mu and alpha responses to the observation of human actions in ASD according to bandwidths. Electroencephalographic (EEG) mu and alpha responses to execution and observation of hand gestures were recorded on the whole scalp in high functioning subjects with ASD and typical subjects. When two bandwidths of the alpha-mu 8-13 Hz were distinguished, a different mu response to observation appeared for subjects with ASD in the upper sub-band over the sensorimotor cortex, whilst the lower sub-band responded similarly in the two groups. Source reconstructions demonstrated that this effect was related to a joint mu-suppression deficit over the occipito-parietal regions and an increase over the frontal regions. These findings suggest peculiarities in top-down response modulation in ASD and question the claim of a global dysfunction of the MNS in autism. This research also advocates for the use of finer grained analyses at both spatial and spectral levels for future directions in neurophysiological accounts of autism.

Beta, but not gamma, band oscillations index visual form-motion integration.

Electrophysiological oscillations in different frequency bands co-occur with perceptual, motor and cognitive processes but their function and respective contributions to these processes need further investigations. Here, we recorded MEG signals and seek for percept related modulations of alpha, beta and gamma band activity during a perceptual form/motion integration task. Participants reported their bound or unbound perception of ambiguously moving displays that could either be seen as a whole square-like shape moving along a Lissajou's figure (bound percept) or as pairs of bars oscillating independently along cardinal axes (unbound percept). We found that beta (15-25 Hz), but not gamma (55-85 Hz) oscillations, index perceptual states at the individual and group level. The gamma band activity found in the occipital lobe, although significantly higher during visual stimulation than during base line, is similar in all perceptual states. Similarly, decreased alpha activity during visual stimulation is not different for the different percepts. Trial-by-trial classification of perceptual reports based on beta band oscillations was significant in most observers, further supporting the view that modulation of beta power reliably index perceptual integration of form/motion stimuli, even at the individual level.

Response-specific slowing in older age revealed through differential stimulus and response effects on P300 latency and reaction time.

Older age produces numerous changes in cognitive processes, including slowing in the rate of mental processing speed. There has been controversy over the past three decades about whether this slowing is generalized or process-specific. A growing literature indicates that it is process-specific and suggests it is most dramatic at the interface where a stimulus input is translated into a response output. We tested this hypothesis using a task in which young and older adult males made either compatible or incompatible responses to the word LEFT or RIGHT shown briefly and variously located in a 4 row × 6 column matrix surrounded by # signs or by letters chosen randomly from the sets A-G or A-Z. Processing speed was measured using P300 latency and reaction time. Experimental effects on these two measures provided support for the hypothesis in revealing that stimulus identification processes were preserved, whereas processes related to translating a stimulus input into a designated response output and then selecting that response were compromised in the elderly.

Anatomical connectivity influences both intra- and inter-brain synchronizations.

Recent development in diffusion spectrum brain imaging combined to functional simulation has the potential to further our understanding of how structure and dynamics are intertwined in the human brain. At the intra-individual scale, neurocomputational models have already started to uncover how the human connectome constrains the coordination of brain activity across distributed brain regions. In parallel, at the inter-individual scale, nascent social neuroscience provides a new dynamical vista of the coupling between two embodied cognitive agents. Using EEG hyperscanning to record simultaneously the brain activities of subjects during their ongoing interaction, we have previously demonstrated that behavioral synchrony correlates with the emergence of inter-brain synchronization. However, the functional meaning of such synchronization remains to be specified. Here, we use a biophysical model to quantify to what extent inter-brain synchronizations are related to the anatomical and functional similarity of the two brains in interaction. Pairs of interacting brains were numerically simulated and compared to real data. Results show a potential dynamical property of the human connectome to facilitate inter-individual synchronizations and thus may partly account for our propensity to generate dynamical couplings with others.

Does the brain know who is at the origin of what in an imitative interaction?

Brain correlates of the sense of agency have recently received increased attention. However, the explorations remain largely restricted to the study of brains in isolation. The prototypical paradigm used so far consists of manipulating visual perception of own action while asking the subject to draw a distinction between self- versus externally caused action. However, the recent definition of agency as a multifactorial phenomenon combining bottom-up and top-down processes suggests the exploration of more complex situations. Notably there is a need of accounting for the dynamics of agency in a two-body context where we often experience the double faceted question of who is at the origin of what in an ongoing interaction. In a dyadic context of role switching indeed, each partner can feel body ownership, share a sense of agency and altogether alternate an ascription of the primacy of action to self and to other. To explore the brain correlates of these different aspects of agency, we recorded with dual EEG and video set-ups 22 subjects interacting via spontaneous versus induced imitation (II) of hand movements. The differences between the two conditions lie in the fact that the roles are either externally attributed (induced condition) or result from a negotiation between subjects (spontaneous condition). Results demonstrate dissociations between self- and other-ascription of action primacy in delta, alpha and beta frequency bands during the condition of II. By contrast a similar increase in the low gamma frequency band (38-47 Hz) was observed over the centro-parietal regions for the two roles in spontaneous imitation (SI). Taken together, the results highlight the different brain correlates of agency at play during live interactions.

Human gamma oscillations during slow wave sleep.

Neocortical local field potentials have shown that gamma oscillations occur spontaneously during slow-wave sleep (SWS). At the macroscopic EEG level in the human brain, no evidences were reported so far. In this study, by using simultaneous scalp and intracranial EEG recordings in 20 epileptic subjects, we examined gamma oscillations in cerebral cortex during SWS. We report that gamma oscillations in low (30-50 Hz) and high (60-120 Hz) frequency bands recurrently emerged in all investigated regions and their amplitudes coincided with specific phases of the cortical slow wave. In most of the cases, multiple oscillatory bursts in different frequency bands from 30 to 120 Hz were correlated with positive peaks of scalp slow waves ("IN-phase" pattern), confirming previous animal findings. In addition, we report another gamma pattern that appears preferentially during the negative phase of the slow wave ("ANTI-phase" pattern). This new pattern presented dominant peaks in the high gamma range and was preferentially expressed in the temporal cortex. Finally, we found that the spatial coherence between cortical sites exhibiting gamma activities was local and fell off quickly when computed between distant sites. Overall, these results provide the first human evidences that gamma oscillations can be observed in macroscopic EEG recordings during sleep. They support the concept that these high-frequency activities might be associated with phasic increases of neural activity during slow oscillations. Such patterned activity in the sleeping brain could play a role in off-line processing of cortical networks.

Improving quantification of functional networks with EEG inverse problem: evidence from a decoding point of view.

Decoding experimental conditions from single trial Electroencephalographic (EEG) signals is becoming a major challenge for the study of brain function and real-time applications such as Brain Computer Interface. EEG source reconstruction offers principled ways to estimate the cortical activities from EEG signals. But to what extent it can enhance informative brain signals in single trial has not been addressed in a general setting. We tested this using the minimum norm estimate solution (MNE) to estimate spectral power and coherence features at the cortical level. With a fast implementation, we computed a support vector machine (SVM) classifier output from these quantities in real-time, without prior on the relevant functional networks. We applied this approach to single trial decoding of ongoing mental imagery tasks using EEG data recorded in 5 subjects. Our results show that reconstructing the underlying cortical network dynamics significantly outperforms a usual electrode level approach in terms of information transfer and also reduces redundancy between coherence and power features, supporting a decrease of volume conduction effects. Additionally, the classifier coefficients reflect the most informative features of network activity, showing an important contribution of localized motor and sensory brain areas, and of coherence between areas up to 6cm distance. This study provides a computationally efficient and interpretable strategy to extract information from functional networks at the cortical level in single trial. Moreover, this sets a general framework to evaluate the performance of EEG source reconstruction methods by their decoding abilities.

Shifting visual attention away from fixation is specifically associated with alpha band activity over ipsilateral parietal regions.

We studied brain activity during the displacement of attention in a modified visuo-spatial orienting paradigm. Using a behaviorally relevant no-shift condition as a control, we asked whether ipsi- or contralateral parietal alpha band activity is specifically related to covert shifts of attention. Cue-related event-related potentials revealed an attention directing anterior negativity (ADAN) contralateral to the shift of attention and P3 and contingent negative variation waveforms that were enhanced in both shift conditions as compared to the no-shift task. When attention was shifted away from fixation, alpha band activity over parietal regions ipsilateral to the attended hemifield was enhanced relative to the control condition, albeit with different dynamics in the upper and lower alpha subbands. Contralateral-to-attended parietal alpha band activity was indistinguishable from the no-shift task.

Inter-brain synchronization during social interaction.

During social interaction, both participants are continuously active, each modifying their own actions in response to the continuously changing actions of the partner. This continuous mutual adaptation results in interactional synchrony to which both members contribute. Freely exchanging the role of imitator and model is a well-framed example of interactional synchrony resulting from a mutual behavioral negotiation. How the participants' brain activity underlies this process is currently a question that hyperscanning recordings allow us to explore. In particular, it remains largely unknown to what extent oscillatory synchronization could emerge between two brains during social interaction. To explore this issue, 18 participants paired as 9 dyads were recorded with dual-video and dual-EEG setups while they were engaged in spontaneous imitation of hand movements. We measured interactional synchrony and the turn-taking between model and imitator. We discovered by the use of nonlinear techniques that states of interactional synchrony correlate with the emergence of an interbrain synchronizing network in the alpha-mu band between the right centroparietal regions. These regions have been suggested to play a pivotal role in social interaction. Here, they acted symmetrically as key functional hubs in the interindividual brainweb. Additionally, neural synchronization became asymmetrical in the higher frequency bands possibly reflecting a top-down modulation of the roles of model and imitator in the ongoing interaction.

Ten years of Nature Reviews Neuroscience: insights from the highly cited.

To celebrate the first 10 years of Nature Reviews Neuroscience, we invited the authors of the most cited article of each year to look back on the state of their field of research at the time of publication and the impact their article has had, and to discuss the questions that might be answered in the next 10 years. This selection of highly cited articles provides interesting snapshots of the progress that has been made in diverse areas of neuroscience. They show the enormous influence of neuroimaging techniques and highlight concepts that have generated substantial interest in the past decade, such as neuroimmunology, social neuroscience and the 'network approach' to brain function. These advancements will pave the way for further exciting discoveries that lie ahead.

Mapping interictal oscillations greater than 200 Hz recorded with intracranial macroelectrodes in human epilepsy.

Interictal high-frequency oscillations over 200 Hz have been recorded with microelectrodes in the seizure onset zone of epileptic patients suffering from mesial temporal lobe epilepsy. Recent work suggests that similar high-frequency oscillations can be detected in the seizure onset zone using standard diagnostic macroelectrodes. However, only a few channels were examined in these studies, so little information is available on the spatial extent of high-frequency oscillations. Here, we present data on high-frequency oscillations recorded from a larger number of intracerebral contacts spatial (mean 38) in 16 patients. Data were obtained from 1 h of interictal recording sampled at 1024 Hz and was analysed using a new semi-automatic detection procedure based on a wavelet decomposition. A detailed frequency analysis permitted a rapid and reliable discrimination of high-frequency oscillations from other high-frequency events. A total of 1932 high-frequency oscillations were detected with an average frequency of 261 +/- 53 Hz, amplitude of 11.9 +/- 6.7 microV and duration of 22.7 +/- 11.6 ms. Records from a patient often showed several different high-frequency oscillation patterns. We classified 24 patterns from 11 patients. Usually (20/24 patterns) high-frequency oscillations were nested in an epileptic paroxysm, such as a spike or a sharp wave, and typically high-frequency oscillations (19/24) were recorded from just one recording contact. Unexpectedly in other cases, high-frequency oscillations (5/24) were detected simultaneously on two or three contacts, sometimes separated by large distances. This large spatial extent suggests that high-frequency oscillations may sometimes result from a neuronal synchrony manifest on a scale of centimetres. High-frequency oscillations were almost always recorded in seizure-generating structures of patients suffering from mesial (9/9) or polar (1/3) temporal lobe epilepsy. They were never found in the epileptic or healthy basal, lateral temporal or extra temporal neocortex nor in the healthy amygdalo-hippocampal complex. These findings confirm that the generation of oscillations at frequencies higher that 200 Hz is, at this scale, a specific, intrinsic property of seizure-generating networks in medial and polar temporal lobes, which have a common archaic phylogenetic origin. We show that this activity can be detected and its spatial extent determined with conventional intracranial electroencephalography electrodes in records from patients with temporal lobe epilepsy. It is a reliable marker of the seizure onset zone that should be considered in decisions on surgical treatment.

Assessment of bursting activity and interspike intervals variability: a case study for methodological comparison.

The detection and characterization of bursting activity remains a topic where no consensual definition has been reached so far. We compare here three different approaches of spike trains variability: statistical characterization (average frequency, coefficient of variation), burst detection (Poisson and rank surprise) and multi-scale analysis (detrended fluctuations analysis). Using both real and simulated data, we show that Poisson surprise provides information closely related to the coefficient of variation and that rank surprise detects significant bursts which are associated with long-range correlations. Since these long-range correlations are only adequately characterized with multi-scale analysis, this study emphasizes the complementarity of these approaches for the complete characterization of spike trains.

Simultaneous MEG and intracranial EEG recordings during attentive reading.

The relationship between neural oscillations recorded at various spatial scales remains poorly understood partly due to an overall dearth of studies utilizing simultaneous measurements. In an effort to study quantitative markers of attention during reading, we performed simultaneous magnetoencephalography (MEG) and intracranial electroencephalography (iEEG) recordings in four epileptic patients. Patients were asked to attend to a specific color when presented with an intermixed series of red words and green words, with words of a given color forming a cohesive story. We analyzed alpha, beta, and gamma band oscillatory responses to the word presentation and compared the strength and spatial organization of those responses in both electrophysiological recordings. Time-frequency analysis of iEEG revealed a network of clear attention-modulated high gamma band (50-150 Hz) power increases and alpha/beta (9-25 Hz) suppressions in response to the words. In addition to analyses at the sensor level, MEG time-frequency analysis was performed at the source level using a sliding window beamformer technique. Strong alpha/beta suppressions were observed in MEG reconstructions, in tandem with iEEG effects. While the MEG counterpart of high gamma band enhancement was difficult to interpret at the sensor level in two patients, MEG time-frequency source reconstruction revealed additional activation patterns in accordance with iEEG results. Importantly, iEEG allowed us to confirm that several sources of gamma band modulation observed with MEG were indeed of cortical origin rather than EMG muscular or ocular artifact.

Cortical local and long-range synchronization interplay in human absence seizure initiation.

Brain activity relies on transient, fluctuating interactions between segregated neuronal populations. Synchronization within a single and between distributed neuronal clusters reflects the dynamics of these cooperative patterns. Thus absence epilepsy can be used as a model for integrated, large-scale investigation of the emergence of pathological collective dynamics in the brain. Indeed, spike-wave discharges (SWD) of an absence seizure are thought to reflect abnormal cortical hypersynchronization. In this paper, we address two questions: how and where do SWD arise in the human brain? Therefore, we explored the spatio-temporal dynamics of interactions within and between widely distributed cortical sites using magneto-encephalographic recordings of spontaneous absence seizures. We then extracted, from their time-frequency analysis, local synchronization of cortical sources and long-range synchronization linking distant sites. Our analyses revealed a reproducible sequence of 1) long-range desynchronization, 2) increased local synchronization and 3) increased long-range synchronization. Although both local and long-range synchronization displayed different spatio-temporal profiles, their cortical projection within an initiation time window overlap and reveal a multifocal fronto-central network. These observations contradict the classical view of sudden generalized synchronous activities in absence epilepsy. Furthermore, they suggest that brain states transition may rely on multi-scale processes involving both local and distant interactions.

Enter feelings: somatosensory responses following early stages of visual induction of emotion.

In a MEG experiment, we imaged the early dynamics of the human cerebral cortex during the induction of emotion by visual stimuli. We tested the hypothesis that early cortical responses would correlate with the emotional competence of visual stimuli and subsequent subjective ratings of feeling in a set of specific target regions important for somatosensory, attentional, and motivational functions, just after initial visual and appraisal related cortical responses to picture presentation. Relative to the neutral condition, cortical responses, during the 350-500 ms phase of the MEG evoked response, were stronger for both pleasant and unpleasant stimuli in the orbitofrontal cortex, ventromedial prefrontal cortex, anterior cingulate and somatosensory cortices. These responses, which correlated with subjective ratings of arousal, emerged after an initial spreading of cortical activity from early visual cortex (70-200 ms) to the ventral visual stream, temporopolar and orbitofrontal regions (200-350 ms), higher for emotionally competent stimuli than for neutral in the 200-350 ms window, in a manner compatible with an appraisal function. Heart beats occurring during the first 500 ms post stimulus showed longer intervals for unpleasant than for neutral stimuli relative to the preceding beat. The results support the hypothesis of a sequence of processing regarding the emergence of feelings and suggest that, even in the early phase of feeling induction, actual body responses to the inducing stimuli could be represented in the brain.

Rapid interactions between the ventral visual stream and emotion-related structures rely on a two-pathway architecture.

Visual attention can be driven by the affective significance of visual stimuli before full-fledged processing of the stimuli. Two kinds of models have been proposed to explain this phenomenon: models involving sequential processing along the ventral visual stream, with secondary feedback from emotion-related structures ("two-stage models"); and models including additional short-cut pathways directly reaching the emotion-related structures ("two-pathway models"). We tested which type of model would best predict real magnetoencephalographic responses in subjects presented with arousing visual stimuli, using realistic models of large-scale cerebral architecture and neural biophysics. The results strongly support a "two-pathway" hypothesis. Both standard models including the retinotectal pathway and nonstandard models including cortical-cortical long-range fasciculi appear plausible.

Prediction of performance level during a cognitive task from ongoing EEG oscillatory activities.

OBJECTIVE: Tracking the level of performance in cognitive tasks may be useful in environments, such as aircraft, in which the awareness of the pilots is critical for security. In this paper, the usefulness of EEG for the prediction of performance is investigated. METHODS: We present a new methodology that combines various ongoing EEG measurements to predict performance level during a cognitive task. We propose a voting approach that combines the outputs of elementary support vector machine (SVM) classifiers derived from various sets of EEG parameters in different frequency bands. The spectral power and phase synchrony of the oscillatory activities are used to classify the periods of rapid reaction time (RT) versus the slow RT responses of each subject. RESULTS: The voting algorithm significantly outperforms classical SVM and gives a good average classification accuracy across 12 subjects (71%) and an average information transfer rate (ITR) of 0.49bit/min. The main discriminating activities are laterally distributed theta power and anterio-posterior alpha synchronies, possibly reflecting the role of a visual-attentional network in performance. CONCLUSIONS: Power and synchrony measurements enable the discrimination between periods of high average reaction time versus periods of low average reaction time in a same subject. Moreover, the proposed approach is easy to interpret as it combines various types of measurements for classification, emphasizing the most informative. SIGNIFICANCE: Ongoing EEG recordings can predict the level of performance during a cognitive task. This can lead to real-time EEG monitoring devices for the anticipation of human mistakes.