Unravelling the multisensory learning advantage: Different patterns of within and across frequency-specific interactions drive uni- and multisensory neuroplasticity.

In the field of learning theory and practice, the superior efficacy of multisensory learning over uni-sensory is well-accepted. However, the underlying neural mechanisms at the macro-level of the human brain remain largely unexplored. This study addresses this gap by providing novel empirical evidence and a theoretical framework for understanding the superiority of multisensory learning. Through a cognitive, behavioral, and electroencephalographic assessment of carefully controlled uni-sensory and multisensory training interventions, our study uncovers a fundamental distinction in their neuroplastic patterns. A multilayered network analysis of pre- and post- training EEG data allowed us to model connectivity within and across different frequency bands at the cortical level. Pre-training EEG analysis unveils a complex network of distributed sources communicating through cross-frequency coupling, while comparison of pre- and post-training EEG data demonstrates significant differences in the reorganizational patterns of uni-sensory and multisensory learning. Uni-sensory training primarily modifies cross-frequency coupling between lower and higher frequencies, whereas multisensory training induces changes within the beta band in a more focused network, implying the development of a unified representation of audiovisual stimuli. In combination with behavioural and cognitive findings this suggests that, multisensory learning benefits from an automatic top-down transfer of training, while uni-sensory training relies mainly on limited bottom-up generalization. Our findings offer a compelling theoretical framework for understanding the advantage of multisensory learning.

Interaction within and between cortical networks subserving multisensory learning and its reorganization due to musical expertise.

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.

Comparison of undirected frequency-domain connectivity measures for cerebro-peripheral analysis.

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 appears 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.

Computerized Music-Reading Intervention Improves Resistance to Unisensory Distraction Within a Multisensory Task, in Young and Older Adults.

Incoming information from multiple sensory channels compete for attention. Processing the relevant ones and ignoring distractors, while at the same time monitoring the environment for potential threats, is crucial for survival, throughout the lifespan. However, sensory and cognitive mechanisms often decline in aging populations, making them more susceptible to distraction. Previous interventions in older adults have successfully improved resistance to distraction, but the inclusion of multisensory integration, with its unique properties in attentional capture, in the training protocol is underexplored. Here, we studied whether, and how, a 4-week intervention, which targets audiovisual integration, affects the ability to deal with task-irrelevant unisensory deviants within a multisensory task. Musically naïve participants engaged in a computerized music reading game and were asked to detect audiovisual incongruences between the pitch of a song's melody and the position of a disk on the screen, similar to a simplistic music staff. The effects of the intervention were evaluated via behavioral and EEG measurements in young and older adults. Behavioral findings include the absence of age-related differences in distraction and the indirect improvement of performance due to the intervention, seen as an amelioration of response bias. An asymmetry between the effects of auditory and visual deviants was identified and attributed to modality dominance. The electroencephalographic results showed that both groups shared an increase in activation strength after training, when processing auditory deviants, located in the left dorsolateral prefrontal cortex. A functional connectivity analysis revealed that only young adults improved flow of information, in a network comprised of a fronto-parietal subnetwork and a multisensory temporal area. Overall, both behavioral measures and neurophysiological findings suggest that the intervention was indirectly successful, driving a shift in response strategy in the cognitive domain and higher-level or multisensory brain areas, and leaving lower level unisensory processing unaffected.

Aging Effects on the Neuroplastic Attributes of Multisensory Cortical Networks as Triggered by a Computerized Music Reading Training Intervention.

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.

Auditory Categorization of Man-Made Sounds Versus Natural Sounds by Means of MEG Functional Brain Connectivity.

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.

Statistical learning of multisensory regularities is enhanced in musicians: An MEG study.

The present study used magnetoencephalography (MEG) to identify the neural correlates of audiovisual statistical learning, while disentangling the differential contributions of uni- and multi-modal statistical mismatch responses in humans. The applied paradigm was based on a combination of a statistical learning paradigm and a multisensory oddball one, combining an audiovisual, an auditory and a visual stimulation stream, along with the corresponding deviances. Plasticity effects due to musical expertise were investigated by comparing the behavioral and MEG responses of musicians to non-musicians. The behavioral results indicated that the learning was successful for both musicians and non-musicians. The unimodal MEG responses are consistent with previous studies, revealing the contribution of Heschl's gyrus for the identification of auditory statistical mismatches and the contribution of medial temporal and visual association areas for the visual modality. The cortical network underlying audiovisual statistical learning was found to be partly common and partly distinct from the corresponding unimodal networks, comprising right temporal and left inferior frontal sources. Musicians showed enhanced activation in superior temporal and superior frontal gyrus. Connectivity and information processing flow amongst the sources comprising the cortical network of audiovisual statistical learning, as estimated by transfer entropy, was reorganized in musicians, indicating enhanced top-down processing. This neuroplastic effect showed a cross-modal stability between the auditory and audiovisual modalities.

Functional connectivity of the cortical network supporting statistical learning in musicians and non-musicians: an MEG study.

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.