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The task of identifying and characterizing network structures out of experimentally observed time series is tackled by implementing different solutions, ranging from entropy-based techniques to the evaluation of the significance of observed correlation estimators. Among the metrics that belong to the first class, mutual information is of major importance due to the relative simplicity of implementation and its relying on the crucial concept of entropy. With regard to the second class, a method that allows us to assess the connectivity strength of a link in terms of a time scale of its observability via the significance estimate of measured cross correlation was recently shown to provide a reliable tool to study network structures. In this paper, we investigate the relationship between this last metric and mutual information by simultaneously assessing both metrics on large sets of data extracted from three experimental contexts, human brain magnetoencephalography, human brain electroencephalography, and surface wind measurements carried out on a small regional scale, as well as on simulated coupled, auto-regressive processes. We show that the relationship is well described by a power law and provide a theoretical explanation based on a simple noise and signal model. Besides further upholding the reliability of cross-correlation time scale of observability, the results show that the combined use of this metric and mutual information can be used as a valuable tool to identify and characterize connectivity links in a wide range of experimental contexts.

The oscillatory features of non-REM sleep states have been a subject of intense research over many decades. However, a systematic spatial characterization of the spectral features of cortical activity in each sleep state is not available yet. Here, we used magnetoencephalography (MEG) and electroencephalography (EEG) recordings during night sleep. We performed source reconstruction based on the individual subject's anatomical magnetic resonance imaging (MRI) scans and spectral analysis on each non-REM sleep epoch in eight standard frequency bands, spanning the complete spectrum, and computed cortical source reconstructions of the spectral contrasts between each sleep state in comparison to the resting wakefulness. Despite not distinguishing periods of high and low activity within each sleep stage, our results provide new information about relative overall spectral changes in the non-REM sleep stages.

The oscillatory features of non-REM sleep states have been a subject of intense research over many decades. However, a systematic spatial characterization of the spectral features of cortical activity in each sleep state is not available yet. Here, we used magnetoencephalography (MEG) and electroencephalography (EEG) recordings during night sleep. We performed source reconstruction based on the individual subject’s anatomical magnetic resonance imaging (MRI) scans and spectral analysis on each non-REM sleep epoch in eight standard frequency bands, spanning the complete spectrum, and computed cortical source reconstructions of the spectral contrasts between each sleep state in comparison to the resting wakefulness. Despite not distinguishing periods of high and low activity within each sleep stage, our results provide new information about relative overall spectral changes in the non-REM sleep stages.

Successfully interpreting and navigating our natural visual environment requires us to track its dynamics constantly. Additionally, we focus our attention on behaviorally relevant stimuli to enhance their neural processing. Little is known, however, about how sustained attention affects the ongoing tracking of stimuli with rich natural temporal dynamics. Here, we used MRI-informed source reconstructions of magnetoencephalography (MEG) data to map to what extent various cortical areas track concurrent continuous quasi-rhythmic visual stimulation. Further, we tested how top-down visuo-spatial attention influences this tracking process. Our bilaterally presented quasi-rhythmic stimuli covered a dynamic range of 4 – 20Hz, subdivided into three distinct bands. As an experimental control, we also included strictly rhythmic stimulation (10 vs 12 Hz). Using a spectral measure of brain-stimulus coupling, we were able to track the neural processing of left vs. right stimuli independently, even while fluctuating within the same frequency range. The fidelity of neural tracking depended on the stimulation frequencies, decreasing for higher frequency bands. Both attended and non-attended stimuli were tracked beyond early visual cortices, in ventral and dorsal streams depending on the stimulus frequency. In general, tracking improved with the deployment of visuo-spatial attention to the stimulus location. Our results provide new insights into how human visual cortices process concurrent dynamic stimuli and provide a potential mechanism – namely increasing the temporal precision of tracking – for boosting the neural representation of attended input.

In any network, the dependence of connectivity on physical distance between nodes is a direct consequence of trade-off mechanisms between costs of establishing and sustaining links, processing rates, propagation speed of signals between nodes. Despite its universality, there are still few studies addressing this issue. Here we apply a recently–developed method to infer links between nodes, and possibly subnetwork structures, to determine connectivity strength as a function of physical distance between nodes. The model system we investigate is brain activity reconstructed on the cortex out of magnetoencephalography recordings sampled on a set of healthy subjects in resting state. We found that the dependence of the time scale of observability of a link on its geometric length follows a power–law characterized by an exponent whose extent is inversely proportional to connectivity. Our method provides a new tool to highlight and investigate networks in neuroscience.