Complexity Matching: Brain Signals Mirror Environment Information Patterns during Music Listening and Reward.

Understanding how the human brain integrates information from the environment with intrinsic brain signals to produce individual perspectives is an essential element of understanding the human mind. Brain signal complexity, measured with multiscale entropy, has been employed as a measure of information processing in the brain, and we propose that it can also be used to measure the information available from a stimulus. We can directly assess the correspondence between brain signal complexity and stimulus complexity as an indication of how well the brain reflects the content of the environment in an analysis that we term "complexity matching." Music is an ideal stimulus because it is a multidimensional signal with a rich temporal evolution and because of its emotion- and reward-inducing potential. When participants focused on acoustic features of music, we found that EEG complexity was lower and more closely resembled the musical complexity compared to an emotional task that asked them to monitor how the music made them feel. Music-derived reward scores on the Barcelona Music Reward Questionnaire correlated with less complexity matching but higher EEG complexity. Compared with perceptual-level processing, emotional and reward responses are associated with additional internal information processes above and beyond those linked to the external stimulus. In other words, the brain adds something when judging the emotional valence of music.

Selective Activation of Resting-State Networks following Focal Stimulation in a Connectome-Based Network Model of the Human Brain.

When the brain is stimulated, for example, by sensory inputs or goal-oriented tasks, the brain initially responds with activities in specific areas. The subsequent pattern formation of functional networks is constrained by the structural connectivity (SC) of the brain. The extent to which information is processed over short- or long-range SC is unclear. Whole-brain models based on long-range axonal connections, for example, can partly describe measured functional connectivity dynamics at rest. Here, we study the effect of SC on the network response to stimulation. We use a human whole-brain network model comprising long- and short-range connections. We systematically activate each cortical or thalamic area, and investigate the network response as a function of its short- and long-range SC. We show that when the brain is operating at the edge of criticality, stimulation causes a cascade of network recruitments, collapsing onto a smaller space that is partly constrained by SC. We found both short- and long-range SC essential to reproduce experimental results. In particular, the stimulation of specific areas results in the activation of one or more resting-state networks. We suggest that the stimulus-induced brain activity, which may indicate information and cognitive processing, follows specific routes imposed by structural networks explaining the emergence of functional networks. We provide a lookup table linking stimulation targets and functional network activations, which potentially can be useful in diagnostics and treatments with brain stimulation.

A trade-off between local and distributed information processing associated with remote episodic versus semantic memory.

Episodic memory and semantic memory produce very different subjective experiences yet rely on overlapping networks of brain regions for processing. Traditional approaches for characterizing functional brain networks emphasize static states of function and thus are blind to the dynamic information processing within and across brain regions. This study used information theoretic measures of entropy to quantify changes in the complexity of the brain's response as measured by magnetoencephalography while participants listened to audio recordings describing past personal episodic and general semantic events. Personal episodic recordings evoked richer subjective mnemonic experiences and more complex brain responses than general semantic recordings. Critically, we observed a trade-off between the relative contribution of local versus distributed entropy, such that personal episodic recordings produced relatively more local entropy whereas general semantic recordings produced relatively more distributed entropy. Changes in the relative contributions of local and distributed entropy to the total complexity of the system provides a potential mechanism that allows the same network of brain regions to represent cognitive information as either specific episodes or more general semantic knowledge.

Variability of brain signals processed locally transforms into higher connectivity with brain development.

A number of studies have characterized the changes in variability of brain signals with brain maturation from the perspective of considering the human brain as a complex system. Specifically, it has been shown that complexity of brain signals increases in development. On one hand, such an increase in complexity can be attributed to more specialized and differentiated brain regions able to express a higher repertoire of mental microstates. On the other hand, it can be explained by increased integration between widely distributed neuronal populations and establishment of new connections. The goal of this study was to see which of these two mechanisms is dominant, accounting for the previously observed increase in signal complexity. Using information-theoretic tools based on scalp-recorded EEG measurements, we examined the trade-off between local and distributed variability of brain signals in infants and children separated into age groups of 1-2, 2-8, 9-24, and 24-66 months old. We found that developmental changes were characterized by a decrease in the amount of information processed locally, with a peak in alpha frequency range. This effect was accompanied by an increase in the variability of brain signals processed as a distributed network. Complementary analysis of phase locking revealed an age-related pattern of increased synchronization in the lower part of the spectrum, up to the alpha rhythms. At the same time, we observed the desynchronization effects associated with brain development in the higher beta to lower gamma range.

Exploring transient transfer entropy based on a group-wise ICA decomposition of EEG data.

This paper presents a data-driven pipeline for studying asymmetries in mutual interdependencies between distinct components of EEG signal. Due to volume conductance, estimating coherence between scalp electrodes may lead to spurious results. A group-based independent component analysis (ICA), which is conducted across all subjects and conditions simultaneously, is an alternative representation of the EEG measurements. Within this approach, the extracted components are independent in a global sense while short-lived or transient interdependencies may still be present between the components. In this paper, functional roles of the ICA components are specified through a partial least squares (PLS) analysis of task effects within the time course of the derived components. Functional integration is estimated within the information-theoretic approach using transfer entropy analysis based on asymmetries in mutual interdependencies of reconstructed phase dynamics. A secondary PLS analysis is performed to assess robust task-specific changes in transfer entropy estimates between functionally specific components.

The temporal interaction of modality specific and process specific neural networks supporting simple working memory tasks.

Several theories of brain function emphasize distinctions between sensory and cognitive systems. We hypothesized, instead, that sensory and cognitive systems interact to instantiate the task at the neural level. We tested whether input modality interacts with working memory operations in that, despite similar cognitive demands, differences in the anatomical locations or temporal dynamics of activations following auditory or visual input would not be limited to the sensory cortices. We recorded event-related brain potentials (ERPs) while participants performed simple short-term memory tasks involving visually or auditorily presented bandpass-filtered noise stimuli. Our analyses suggested that working memory operations in each modality had a very similar spatial distribution of current sources outside the sensory cortices, but differed in terms of time course. Specifically, information for visual processing was updated and held online in a manner that was different from auditory processing, which was done mostly after the offset of the final stimulus. Our results suggest that the neural networks that support working memory operations have different temporal dynamics for auditory and visual material, even when the stimuli are matched in term of discriminability, and are designed to undergo very similar transformations when they are encoded and retrieved from memory.

A dual role for prediction error in associative learning.

Confronted with a rich sensory environment, the brain must learn statistical regularities across sensory domains to construct causal models of the world. Here, we used functional magnetic resonance imaging and dynamic causal modeling (DCM) to furnish neurophysiological evidence that statistical associations are learnt, even when task-irrelevant. Subjects performed an audio-visual target-detection task while being exposed to distractor stimuli. Unknown to them, auditory distractors predicted the presence or absence of subsequent visual distractors. We modeled incidental learning of these associations using a Rescorla-Wagner (RW) model. Activity in primary visual cortex and putamen reflected learning-dependent surprise: these areas responded progressively more to unpredicted, and progressively less to predicted visual stimuli. Critically, this prediction-error response was observed even when the absence of a visual stimulus was surprising. We investigated the underlying mechanism by embedding the RW model into a DCM to show that auditory to visual connectivity changed significantly over time as a function of prediction error. Thus, consistent with predictive coding models of perception, associative learning is mediated by prediction-error dependent changes in connectivity. These results posit a dual role for prediction-error in encoding surprise and driving associative plasticity.

Modulation of ventral prefrontal cortex functional connections reflects the interplay of cognitive processes and stimulus characteristics.

Emerging ideas of brain function emphasize the context-dependency of regional contributions to cognitive operations, where the function of a particular region is constrained by its pattern of functional connectivity. We used functional magnetic resonance imaging to examine how modality of input (auditory or visual) affects prefrontal cortex (PFC) functional connectivity for simple working memory tasks. The hypothesis was that PFC would show contextually dependent changes in functional connectivity in relation to the modality of input despite similar cognitive demands. Participants were presented with auditory or visual bandpass-filtered noise stimuli, and performed 2 simple short-term memory tasks. Brain activation patterns independently mapped onto modality and task demands. Analysis of right ventral PFC functional connectivity, however, suggested these activity patterns interact. One functional connectivity pattern showed task differences independent of stimulus modality and involved ventromedial and dorsolateral prefrontal and occipitoparietal cortices. A second pattern showed task differences that varied with modality, engaging superior temporal and occipital association regions. Importantly, these association regions showed nonzero functional connectivity in all conditions, rather than showing a zero connectivity in one modality and nonzero in the other. These results underscore the interactive nature of brain processing, where modality-specific and process-specific networks interact for normal cognitive operations.

Spatiotemporal analysis of auditory "what" and "where" working memory.

Goal-directed attention to sound identity (what) and sound location (where) has been associated with increased neural activity in ventral and dorsal brain regions, respectively. In order to ascertain when such segregation occurs, we measured event-related potentials during an n-back (n = 1, 2) working memory task for sound identity or location, where stimuli selected randomly from 3 semantic categories (human, animal, music) were presented at 3 possible virtual locations. Accuracy and reaction times were comparable in both "what" and "where" tasks, albeit worse for the 2-back than for the 1-back condition. The partial least squares analysis of scalp-recorded and source waveform data revealed domain-specific activity beginning at about 200-ms poststimulus onset, which was best expressed as changes in source activity near Heschl's gyrus, and in central medial, occipital medial, right frontal and right parietal cortex. The effect of working memory load emerged at about 400-ms poststimulus and was expressed maximally over frontocentral scalp region and in sources located in the right temporal, frontal and parietal cortices. The results show that for identical sounds, top-down effects on processing "what" and "where" information is observable at about 200 ms after sound onset and involves a widely distributed neural network.

The interplay of stimulus modality and response latency in neural network organization for simple working memory tasks.

We used functional magnetic resonance imaging to examine how modality of input affects functional network organization beyond the sensory cortices for simple working memory tasks. The stimuli were auditory or visual bandpass-filtered white noise. On a given trial, three stimuli, each with differing center frequencies, were presented in succession. For temporal sequencing tasks, participants indicated when the stimulus with the highest frequency content appeared. For comparison tasks, participants indicated whether the frequency content of the last stimulus was lower, intermediate, or higher than the first two stimuli. Task difficulty was equated by establishing equivalent accuracy thresholds across subjects. We used behavioral spatiotemporal partial-least squares (ST-bPLS) analysis to identify neural patterns capturing the optimal association between brain images and reaction time. Because of statistical instabilities, subjects were divided into a SLOW group and a FAST group based on the median split of reaction times. ST-bPLS identified a significant interaction between stimulus modality and task demands for both groups, indicating that task-dependent brain-behavior correlations changed with stimulus modality. The large-scale activity pattern associated with this effect included prefrontal cortex and parietal cortex for the SLOW group and parietal cortex and cingulate for the FAST group. For the FAST group only, ST-bPLS also identified a significant main effect that differentiated tasks independent of modality. The pattern associated with this effect included prefrontal cortex and parietal cortex. These results confirm that modality of input affects network configuration even outside of the sensory cortices but that network configuration may vary with behavior.

Clustered functional MRI of overt speech production.

To investigate the neural network of overt speech production, event-related fMRI was performed in 9 young healthy adult volunteers. A clustered image acquisition technique was chosen to minimize speech-related movement artifacts. Functional images were acquired during the production of oral movements and of speech of increasing complexity (isolated vowel as well as monosyllabic and trisyllabic utterances). This imaging technique and behavioral task enabled depiction of the articulo-phonologic network of speech production from the supplementary motor area at the cranial end to the red nucleus at the caudal end. Speaking a single vowel and performing simple oral movements involved very similar activation of the cortical and subcortical motor systems. More complex, polysyllabic utterances were associated with additional activation in the bilateral cerebellum, reflecting increased demand on speech motor control, and additional activation in the bilateral temporal cortex, reflecting the stronger involvement of phonologic processing.

Characterizing spatial and temporal features of autobiographical memory retrieval networks: a partial least squares approach.

Conway (Conway, M.A., 1992. A structural model of autobiographical memory. In: Conway, M.A., Spinnler, H., Wagenaar, W.A. (Eds.), Theoretical Perspectives on Autobiological Memory. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 167-194) proposed that two types of autobiographical memories (AMs) exist within a hierarchical AM system: unique, specific events and repeated, general memories. There is little research on whether retrieval of these AMs relies on different neural substrates. To investigate this issue, we used a multivariate image analysis technique, spatiotemporal partial least squares (PLS), to identify distributed patterns of activity most related to AM tasks that we have found to be associated with a medial and left-lateralized network. Using PLS, specific and general memories were more strongly associated with different parts of this retrieval network. Specific AM retrieval was associated more with activation of regions involved in imagery in episodic memory, including the left precuneus, left superior parietal lobule and right cuneus, whereas general AM retrieval was associated with activation of the right inferior temporal gyrus, right medial frontal cortex, and left thalamus. These two patterns emerged at different lags after stimulus onset, with the general AM pattern peaking between 2 and 6 s, and the specific AM pattern between 6 and 8 s. These lag differences are consistent with Conway's theory which posits that general AMs are the preferred level of entry to the AM system. A seed PLS analysis revealed that the regions functionally connected to the hippocampus during retrieval did not differentiate specific from general AM retrieval, which confirms our earlier univariate analysis indicating that some aspects of the memory retrieval network are shared by these memories.