The regularity of the physical world and the biomechanics of the human body movements generate distributions of highly probable states that are internalized by the brain in the course of a lifetime. In Bayesian terms, the brain exploits prior knowledge, especially under conditions when sensory input is unavailable or uncertain, to predictively anticipate the most likely outcome of upcoming stimuli and movements. These internal models, formed during development, yet still malleable in adults, continuously adapt through the learning of novel stimuli and movements. Traditionally, neural beta (β) oscillations are considered essential for maintaining sensorimotor and cognitive representations, and for temporal coding of expectations. However, recent findings show that fluctuations of β band power in the resting state strongly correlate between cortical association regions. Moreover, central (hub) regions form strong interactions over time with different brain regions/networks (dynamic core). β band centrality fluctuations of regions of the dynamic core predict global efficiency peaks suggesting a mechanism for network integration. Furthermore, this temporal architecture is surprisingly stable, both in topology and dynamics, during the observation of ecological natural visual scenes, whereas synthetic temporally scrambled stimuli modify it. We propose that spontaneous β rhythms may function as a long-term “prior” of frequent environmental stimuli and behaviors.
Spontaneous activity is correlated across brain regions in large scale networks (RSN) closely resembling those recruited during several behavioral tasks and characterized by functional specialization and dynamic integration. Specifically, MEG studies revealed a set of central regions (dynamic core) possibly facilitating communication among differently specialized brain systems. However, source projected MEG signals, due to the fundamentally ill-posed inverse problem, are affected by spatial leakage, leading to the estimation of spurious, blurred connections that may affect the topological properties of brain networks and their integration. To reduce leakage effects, several correction schemes have been proposed including the Geometric Correction Scheme (GCS) whose theory, simulations and empirical results on topography of a few RSNs were already presented. However, its impact on the estimation of fundamental graph measures used to describe the architecture of interactions among brain regions has not been investigated yet. Here, we estimated dense, MEG band-limited power connectomes in theta, alpha, beta, and gamma bands from 13 healthy subjects (all young adults). We compared the connectivity and topology of MEG uncorrected and GCS-corrected connectomes. The use of GCS considerably reorganized the topology of connectivity, reducing the local, within-hemisphere interactions mainly in the beta and gamma bands and increasing across-hemisphere interactions mainly in the alpha and beta bands. Moreover, the number of hubs decreased in the alpha and beta bands, but the centrality of some fundamental regions such as the Posterior Cingulate Cortex (PCC), Supplementary Motor Area (SMA) and Middle Prefrontal Cortex (MPFC) remained strong in all bands, associated to an increase of the Global Efficiency and a decrease of Modularity. As a comparison, we applied orthogonalization on connectomes and ran the same topological analyses. The correlation values were considerably reduced, and orthogonalization mainly decreased local within-hemisphere interactions in all bands, similarly to GCS. Notably, the centrality of the PCC, SMA and MPFC was preserved in all bands, as for GCS, together with other hubs in the posterior parietal regions. Overall, leakage correction removes spurious local connections, but confirms the role of dynamic hub regions, specifically the anterior and posterior cingulate, in integrating information in the brain at rest.
Abstract-This study presents NMR signal detection by means of a superconducting channel consisting of a Nb surface detection coil inductively coupled to a YBCO mixed sensor. The NMR system operates at a low-field (8.9 mT) in a magnetically shielded room suitable for magnetoencephalographic (MEG) recordings. The main field is generated by a compact solenoid and the geometry of the pick-up coil has been optimized to provide high spatial sensitivity in the NMR field of view. The Nb detection coil is coupled to the mixed sensor through a Nb input coil. The mixed sensor consists of a superconducting YBCO loop with 2-µm constriction above which two Giant MagnetoResistance sensors are placed in a half-bridge configuration to detect changes of the bridge voltage as a function of the flux through the YBCO loop. The sensitivity of the receiving channel is calibrated experimentally. The measured spatial sensitivity is in agreement with the simulations and is ∼10 times better than that of the stand-alone mixed sensor. A NMR echo at 375 kHz shows a SNR only a factor 4 smaller than a tuned room temperature coil tightly wound around the sample, with a noise level which is a factor 3 better than for the volume coil. Our results suggest that mixed sensors are suitable for the integration of low-field MRI and MEG in a hybrid apparatus, where MEG and MRI would be recorded by SQUIDs and mixed sensors, respectively.
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