Statistical learning is a cognitive process of great importance for the detection and representation of environmental regularities. Complex cognitive processes such as statistical learning usually emerge as a result of the activation of widespread cortical areas functioning in dynamic networks. The present study investigated the cortical large-scale network supporting statistical learning of tone sequences in humans. The reorganization of this network related to musical expertise was assessed via a cross-sectional comparison of a group of musicians to a group of non-musicians. The cortical responses to a statistical learning paradigm incorporating an oddball approach were measured via Magnetoencephalographic (MEG) recordings. Large-scale connectivity of the cortical activity was calculated via a statistical comparison of the estimated transfer entropy in the sources’ activity. Results revealed the functional architecture of the network supporting the processing of statistical learning, highlighting the prominent role of informational processing pathways that bilaterally connect superior temporal and intraparietal sources with the left IFG. Musical expertise is related to extensive reorganization of this network, as the group of musicians showed a network comprising of more widespread and distributed cortical areas as well as enhanced global efficiency and increased contribution of additional temporal and frontal sources in the information processing pathway.

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Previous neuroimaging studies have shown that sounds can be discriminated due to living-related or man-made-related characteristics and involve different brain regions. However, these studies have mainly provided source space analyses, which offer simple maps of activated brain regions but do not explain how regions of a distributed system are functionally organized under a specific task. In the present study, we aimed to further examine the functional connectivity of the auditory processing pathway across different categories of non-speech sounds in healthy adults, by means of MEG. Our analyses demonstrated significant activation and interconnection differences between living and man-made object sounds, in the prefrontal areas, anterior-superior temporal gyrus (aSTG), posterior cingulate cortex (PCC), and supramarginal gyrus (SMG), occurring within 80–120 ms post-stimulus interval. Current findings replicated previous ones, in that other regions beyond the auditory cortex are involved during auditory processing. According to the functional connectivity analysis, differential brain networks across the categories exist, which proposes that sound category discrimination processing relies on distinct cortical networks, a notion that has been strongly argued in the literature also in relation to the visual system.

The constant increase in the graying population is the result of a great expansion of life expectancy. A smaller expansion of healthy cognitive and brain functioning diminishes the gains achieved by longevity. Music training, as a special case of multisensory learning, may induce restorative neuroplasticity in older ages. The current study aimed to explore aging effects on the cortical network supporting multisensory cognition and to define aging effects on the network’s neuroplastic attributes. A computer-based music reading protocol was developed and evaluated via electroencephalography measurements pre- and post-training on young and older adults. Results revealed that multisensory integration is performed via diverse strategies in the two groups: Older adults employ higher-order supramodal areas to a greater extent than lower level perceptual regions, in contrast to younger adults, indicating an age-related shift in the weight of each processing strategy. Restorative neuroplasticity was revealed in the left inferior frontal gyrus and right medial temporal gyrus, as a result of the training, while task-related reorganization of cortical connectivity was obstructed in the group of older adults, probably due to systemic maturation mechanisms. On the contrary, younger adults significantly increased functional connectivity among the regions supporting multisensory integration.

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Analyses of cerebro-peripheral connectivity aim to quantify ongoing coupling between brain activity (measured by MEG/EEG) and peripheral signals such as muscle activity, continuous speech, or physiological rhythms (such as pupil dilation or respiration). Due to the distinct rhythmicity of these signals, undirected connectivity is typically assessed in the frequency domain. This leaves the investigator with two critical choices, namely a) the appropriate measure for spectral estimation (i.e., the transformation into the frequency domain) and b) the actual connectivity measure. As there is no consensus regarding best practice, a wide variety of methods has been applied. Here we systematically compare combinations of six standard spectral estimation methods (comprising fast Fourier and continuous wavelet transformation, bandpass filtering, and short-time Fourier transformation) and six connectivity measures (phase-locking value, Gaussian-Copula mutual information, Rayleigh test, weighted pairwise phase consistency, magnitude squared coherence, and entropy). We provide performance measures of each combination for simulated data (with precise control over true connectivity), a single-subject set of real MEG data, and a full group analysis of real MEG data. Our results show that, overall, wppc and gcmi tend to outperform other connectivity measures, while entropy was the only measure sensitive to bimodal deviations from a uniform phase distribution. For group analysis, choosing the appropriate spectral estimation method appeared to be more critical than the connectivity measure. We discuss practical implications (sampling rate, SNR, computation time, and data length) and aim to provide recommendations tailored to particular research questions.

Recent advancements in the field of network science allow us to quantify inter-network information exchange and model the interaction within and between task-defined states of large-scale networks. Here, we modeled the inter- and intra- network interactions related to multisensory statistical learning. To this aim, we implemented a multifeatured statistical learning paradigm and measured evoked magnetoencephalographic responses to estimate task-defined state of functional connectivity based on cortical phase interaction. Each network state represented the whole-brain network processing modality-specific (auditory, visual and audiovisual) statistical learning irregularities embedded within a multisensory stimulation stream. The way by which domain-specific expertise re-organizes the interaction between the networks was investigated by a comparison of musicians and non-musicians. Between the modality-specific network states, the estimated connectivity quantified the characteristics of a supramodal mechanism supporting the identification of statistical irregularities that are compartmentalized and applied in the identification of uni-modal irregularities embedded within multisensory stimuli. Expertise-related re-organization was expressed by an increase of intra- and a decrease of inter-network connectivity, showing increased compartmentalization.

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