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.

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.

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.

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.

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.

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.

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.

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.

Cortical synchrony changes detected by scalp electrode electroencephalograph as traumatic brain injury patients emerge from coma.

BACKGROUND: Recent studies show conscious perception is correlated with firing rate synchronization across multiple neuronal assemblies. This study explores the synchrony between multiple cortical surface sites as brain injury patients emerge from coma. METHODS: Scalp electrode EEG recordings were collected and analyzed from 13 traumatic brain injury patients during their stay in a neurosurgical intensive care unit. Neuronal synchrony was calculated between various electrode pairs during comatose and conscious periods defined by the GCS. Frequency bands from 1 to 30 Hz were evaluated in each patient. RESULTS: As patients emerged from coma at GCS 3 to GCS scores > or =8, synchrony values from all electrode pairs revealed a global decrease in synchrony at higher GCS scores. No significant effects were detected relative to the amount of sedation given, but at higher GCS scores significantly increased neuronal synchrony was observed between occipital lobes and right parietal and temporal lobe sites. Synchrony was decreased between frontal-occipital, frontal-parietal, and parietal-occipital electrodes. CONCLUSIONS: In frequencies from 1 to 30 Hz, synchrony between right parietal and temporal lobes, as well as bilateral occipital lobes, tends to be increased as patients emerge from comatose states. However, synchrony between most intrahemispheric cortical sites is decreased at higher GCS scores in most of the above frequency bands. Thus, brain injury patients demonstrate both increased and decreased cortical surface synchrony between different lobes during emergence from coma.

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.

Preictal state identification by synchronization changes in long-term intracranial EEG recordings.

OBJECTIVE: There is accumulated evidence that mesial temporal lobe seizures are preceded by a preictal transition that evolves over minutes to hours. In the present study, we investigated these possible preictal changes in long-term intracranial recordings of five patients by a measure of phase synchronization. In order to clearly distinguish preictal changes from all the other interictal states, we developed an automatic extraction of representative patterns of interictal synchronization activity. This reference library was used to classify the successive synchronization patterns of long-term recordings into groups of similar patterns. Altered states of brain synchronization were identified as deviating from patterns in the reference library and were evaluated relative to the times of seizure onset in terms of sensitivity and specificity. METHODS: A phase-locking measure was estimated using a sliding window analysis on 15 frequency bands (2Hz steps between 0 and 30Hz), for all pairs of EEG channels in the epileptogenic temporal lobe (14-20 channels), over the entire data sets (total analyzed duration 305h). The preictal identification encompasses three basic stages: (1) a preprocessing stage involving the determination of a reference library of characteristic interictal synchronization patterns using a K-means algorithm, and the identification of discriminant variables differentiating interictal from preictal states, (2) a classification stage of the synchronization pattern via a minimum Mahalanobis distance to the reference patterns, as well as detection of outliers, (3) an evaluation stage of the sensitivity and specificity of the detection by receiver-operating characteristic curves. RESULTS: In most of the cases (36 of 52 seizures, i.e. 70%), a specific state of brain synchronization can be observed several hours before the actual seizure. The changes involved both increases and decreases of the synchronization levels, occurring mostly within the 4-15Hz frequency band, and were often localized near the primary epileptogenic zone. CONCLUSIONS: The analysis of phase synchronization offers a way to distinguish between a preictal state and normal interictal activity. These findings suggest that brain synchronizations are preictally altered in the epileptogenic temporal lobe, inducing a pathological state of higher susceptibility for seizure activity. SIGNIFICANCE: Phase synchronization is capable of extracting information from the EEG that allow the definition of a preictal state. Although the proposed analysis does not constitute genuine seizure anticipation, these changes in neuronal synchronization may provide helpful information for prospective seizure warning.

Exploring the nonlinear dynamics of the brain.

The growing need for a better understanding of large-scale brain dynamics has stimulated in the last decade the development of new and more advanced data analysis techniques. Progress in this domain has greatly benefited from developments in nonlinear time series analysis. This review gives a short overview of some of the nonlinear properties one may wish to infer from brain recordings and presents some examples and recent applications.

Statistical assessment of nonlinear causality: application to epileptic EEG signals.

In this study an information-theoretic test for general Granger causality is used to identify couplings and information transport between different brain areas during epileptic activities. This method can distinguish information that is actually exchanged between two systems from that due to the response to a common signal or past history. This is achieved by an appropriate conditioning of probabilities. Statistical assessment of causality is made from a nonparametric bootstrap test, whereas nonlinearity is assessed by a comparison with a linearized version of the causality index. The framework proposed here provides a useful and model free test to characterize interactions in intracranial electroencephalography (EEG) signals.

Spatio-temporal dynamics prior to neocortical seizures: amplitude versus phase couplings.

The mechanisms underlying the transition of brain activity toward epileptic seizures remain unclear. Based on nonlinear analysis of both intracranial and scalp electroencephalographic (EEG) recordings, different research groups have recently reported dynamical smooth changes in epileptic brain activity several minutes before seizure onset. Such preictal states have been detected in populations of patients with mesial temporal lobe epilepsy (MTLE) and, more recently, with different neocortical partial epilepsies (NPEs). In this paper, we are particularly interested in the spatio-temporal organization of epileptogenic networks prior to seizures in neocortical epilepsies. For this, we characterize the network of two patients with NPE by means of two nonlinear measures of interdependencies. Since the synchronization of neuronal activity is an essential feature of the generation and propagation of epileptic activity, we have analyzed changes in phase synchrony between EEG time series. In order to compare the phase and amplitude dynamics, we have also studied the degree of association between pairs of signals by means of a nonlinear correlation coefficient. Recent findings have suggested changes prior to seizures in a wideband frequency range. Instead, for the examples of this study, we report a significant decrease of synchrony in the focal area several minutes before seizures (>>30 min in both patients) in the frequency band of 10-25 Hz mainly. Furthermore, the spatio-temporal organization of this preictal activity seems to be specifically related to this frequency band. Measures of both amplitude and phase coupling yielded similar results in narrow-band analysis. These results may open new perspectives on the mechanisms of seizure emergence as well as the organization of neocortical epileptogenic networks. The possibility of forecasting the onset of seizures has important implications for a better understanding, diagnosis and a potential treatment of the epilepsy.

Estimating the time-course of coherence between single-trial brain signals: an introduction to wavelet coherence.

This paper introduces the use of wavelet analysis to follow the temporal variations in the coupling between oscillatory neural signals. Coherence, based on Fourier analysis, has been commonly used as a first approximation to track such coupling under the assumption that neural signals are stationary. Yet, stationary neural processing may be the exception rather than the rule. In this context, the recent application to physical systems of a wavelet-based coherence, which does not depend on the stationarity of the signals, is highly relevant. This paper fully develops the method of wavelet coherence and its statistical properties so that it can be practically applied to continuous neural signals. In realistic simulations, we show that, in contrast to Fourier coherence, wavelet coherence can detect short, significant episodes of coherence between non-stationary neural signals. This method can be directly applied for an 'online' quantification of the instantaneous coherence between two signals.

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.

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.

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.

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.