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Large-scale brain activity evolves dynamically over time across multiple time-scales. The structural connectome imposes a spatial network constraint since two structurally connected brain regions are more likely to coordinate their activity. It also imposes a temporal network constraint by virtue of time delays via signal transmission, which has modulatory effects on oscillatory signals. Specifically, the lengths of the structural bundles, their widths, myelination, and the topology of the structural connectome influence the timing of the interactions across the brain network. Together, they define a space-time structure (topochronic map) spanned by connection strengths (space) and signal transmission delays (time), which together establish the deterministic scaffold underlying the evolution of brain dynamics. We introduce a novel in vivo approach for directly measuring functional delays across the whole brain using magneto/electroencephalography and integrating them with the structural connectome derived from magnetic resonance imaging. We developed a map of the functional delays characterizing the connections across the human brain and a map of the corresponding functional velocities. This yields a topochronic map of the human brain dynamics. The functional delays are tightly regulated, with a trend showing, as expected, that larger structural bundles had faster velocities, with the delays varying much less than expected if they only depended upon distance. Then, we estimated the delays from magnetoencephalography (MEG) data in a cohort of multiple sclerosis patients, who have damaged myelin sheaths, and demonstrated that patients showed greater delays across the whole network than a matched control group. Furthermore, within each patient, individual lesioned connections were slowed down more than unaffected ones. Our technique provides a practical approach for estimating functional transmission delays in vivo at the single-subject and single-fiber level and, thus, opens the possibility for novel diagnostic and curative interventions as well as providing empirical, subject-specific constraints to tailor brain models.

The current paper proposes a method to estimate phase to phase cross-frequency coupling between brain areas, applied to broadband signals, without any a priori hypothesis about the frequency of the synchronized components. N:m synchronization is the only form of cross-frequency synchronization that allows the exchange of information at the time resolution of the faster signal, hence likely to play a fundamental role in large-scale coordination of brain activity. The proposed method, named cross-frequency phase linearity measurement (CF-PLM), builds and expands upon the phase linearity measurement, an iso-frequency connectivity metrics previously published by our group. The main idea lies in using the shape of the interferometric spectrum of the two analyzed signals in order to estimate the strength of cross-frequency coupling. Here, we demonstrate that the CF-PLM successfully retrieves the (different) frequencies of the original broad-band signals involved in the connectivity process. Furthermore, if the broadband signal has some frequency components that are synchronized in iso-frequency and some others that are synchronized in cross-frequency, our methodology can successfully disentangle them and describe the behaviour of each frequency component separately. We first provide a theoretical explanation of the metrics. Then, we test the proposed metric on simulated data from coupled oscillators synchronized in iso- and cross-frequency (using both Rössler and Kuramoto oscillator models), and subsequently apply it on real data from brain activity, using source-reconstructed Magnetoencephalography (MEG) data. In the synthetic data, our results show reliable estimates even in the presence of noise and limited sample sizes. In the real signals, components synchronized in cross-frequency are retrieved, together with their oscillation frequencies. All in all, our method is useful to estimate n:m synchronization, based solely on the phase of the signals (independently of the amplitude), and no a-priori hypothesis is available about the expected frequencies. Our method can be exploited to more accurately describe patterns of cross-frequency synchronization and determine the central frequencies involved in the coupling.

Stimulus perception is assumed to involve the (fast) detection of sensory inputs and their (slower) integration. The capacity of the brain to quickly adapt, at all times, to unexpected stimuli suggests that the interplay between the slow and fast processes happens at short timescales. We hypothesised that, even during resting-state, the flow of information across the brain regions should evolve quickly, but not homogeneously in time. Applying edge-wise connectivity to high temporal-resolution Magnetoencephalography (MEG) signals, we estimate the persistence of the information in functional interactions across the brain. The characterization of brain network edges according to their information storage capability naturally partitions the brain into two anatomically distinct networks. The short storage network (SSN) is localized in the regions that typically belong to the dorsal and ventral streams during perceptive tasks, while the long storage network (LSN) hinges on classically associative areas. Finally, we show that only a subset of the brain regions, which we name the multi-storage core (MSC), belong to both networks. The MSC is hypothesized to play a role in the communication between the (otherwise) segregated subnetworks.

Rapid reconfigurations of brain activity support efficient neuronal communication and flexible behaviour. Suboptimal brain dynamics impair this adaptability, possibly leading to functional deficiencies. We hypothesize that impaired flexibility in brain activity can lead to motor and cognitive symptoms of Parkinson’s disease (PD). To test this hypothesis, we studied the ‘functional repertoire’ – the number of distinct configurations of neural activity – using source-reconstructed magnetoencephalography in PD patients and controls. We found stereotyped brain dynamics and reduced flexibility in PD. The intensity of this reduction was proportional to symptoms severity, which can be explained by beta-band hyper-synchronization. Moreover, the basal ganglia were prominently involved in the abnormal patterns of brain activity. Our findings support the hypotheses that: symptoms in PD reflect impaired brain flexibility, this impairment preferentially involves the basal ganglia, and beta-band hypersynchronization is associated with reduced brain flexibility. These findings highlight the importance of extensive functional repertoires for behaviour and motor.