Harnessing functional segregation across brain rhythms as a means to detect EEG oscillatory multiplexing during music listening.

OBJECTIVE: Music, being a multifaceted stimulus evolving at multiple timescales, modulates brain function in a manifold way that encompasses not only the distinct stages of auditory perception, but also higher cognitive processes like memory and appraisal. Network theory is apparently a promising approach to describe the functional reorganization of brain oscillatory dynamics during music listening. However, the music induced changes have so far been examined within the functional boundaries of isolated brain rhythms. APPROACH: Using naturalistic music, we detected the functional segregation patterns associated with different cortical rhythms, as these were reflected in the surface electroencephalography (EEG) measurements. The emerged structure was compared across frequency bands to quantify the interplay among rhythms. It was also contrasted against the structure from the rest and noise listening conditions to reveal the specific components stemming from music listening. Our methodology includes an efficient graph-partitioning algorithm, which is further utilized for mining prototypical modular patterns, and a novel algorithmic procedure for identifying 'switching nodes' (i.e. recording sites) that consistently change module during music listening. MAIN RESULTS: Our results suggest the multiplex character of the music-induced functional reorganization and particularly indicate the dependence between the networks reconstructed from the δ and ß H rhythms. This dependence is further justified within the framework of nested neural oscillations and fits perfectly within the context of recently introduced cortical entrainment to music. SIGNIFICANCE: Complying with the contemporary trends towards a multi-scale examination of the brain network organization, our approach specifies the form of neural coordination among rhythms during music listening. Considering its computational efficiency, and in conjunction with the flexibility of in situ electroencephalography, it may lead to novel assistive tools for real-life applications.

Greater Repertoire and Temporal Variability of Cross-Frequency Coupling (CFC) Modes in Resting-State Neuromagnetic Recordings among Children with Reading Difficulties.

Cross-frequency, phase-to-amplitude coupling (PAC) between neuronal oscillations at rest may serve as the substrate that supports information exchange between functionally specialized neuronal populations both within and between cortical regions. The study utilizes novel algorithms to identify prominent instantaneous modes of cross-frequency coupling and their temporal stability in resting state magnetoencephalography (MEG) data from 25 students experiencing severe reading difficulties (RD) and 27 age-matched non-impaired readers (NI). Phase coherence estimates were computed in order to identify the prominent mode of PAC interaction for each sensor, sensor pair, and pair of frequency bands (from δ to γ) at successive time windows of the continuous MEG record. The degree of variability in the characteristic frequency-pair PAC(f1-f2) modes over time was also estimated. Results revealed a wider repertoire of prominent PAC interactions in RD as compared to NI students, suggesting an altered functional substrate for information exchange between neuronal assemblies in the former group. Moreover, RD students showed significant variability in PAC modes over time. This temporal instability of PAC values was particularly prominent: (a) within and between right hemisphere temporo-parietal and occipito-temporal sensors and, (b) between left hemisphere frontal, temporal, and occipito-temporal sensors and corresponding right hemisphere sites. Altered modes of neuronal population coupling may help account for extant data revealing reduced, task-related neurophysiological and hemodynamic activation in left hemisphere regions involved in the reading network in RD. Moreover, the spatial distribution of pronounced instability of cross-frequency coupling modes in this group may provide an explanation for previous reports suggesting the presence of inefficient compensatory mechanisms to support reading.

A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses.

The detection of mild cognitive impairment (MCI), the transitional stage between normal cognitive changes of aging and the cognitive decline caused by AD, is of paramount clinical importance, since MCI patients are at increased risk of progressing into AD. Electroencephalographic (EEG) alterations in the spectral content of brainwaves and connectivity at resting state have been associated with early-stage AD. Recently, cognitive event-related potentials (ERPs) have entered into the picture as an easy to perform screening test. Motivated by the recent findings about the role of cross-frequency coupling (CFC) in cognition, we introduce a relevant methodological approach for detecting MCI based on cognitive responses from a standard auditory oddball paradigm. By using the single trial signals recorded at Pz sensor and comparing the responses to target and non-target stimuli, we first demonstrate that increased CFC is associated with the cognitive task. Then, considering the dynamic character of CFC, we identify instances during which the coupling between particular pairs of brainwave frequencies carries sufficient information for discriminating between normal subjects and patients with MCI. In this way, we form a multiparametric signature of impaired cognition. The new composite biomarker was tested using data from a cohort that consists of 25 amnestic MCI patients and 15 age-matched controls. Standard machine-learning algorithms were employed so as to implement the binary classification task. Based on leave-one-out cross-validation, the measured classification rate was found reaching very high levels (95%). Our approach compares favorably with the traditional alternative of using the morphology of averaged ERP response to make the diagnosis and the usage of features from spectro-temporal analysis of single-trial responses. This further indicates that task-related CFC measurements can provide invaluable analytics in AD diagnosis and prognosis.

Spontaneous Up states in vitro: a single-metric index of the functional maturation and regional differentiation of the cerebral cortex.

Understanding the development and differentiation of the neocortex remains a central focus of neuroscience. While previous studies have examined isolated aspects of cellular and synaptic organization, an integrated functional index of the cortical microcircuit is still lacking. Here we aimed to provide such an index, in the form of spontaneously recurring periods of persistent network activity -or Up states- recorded in mouse cortical slices. These coordinated network dynamics emerge through the orchestrated regulation of multiple cellular and synaptic elements and represent the default activity of the cortical microcircuit. To explore whether spontaneous Up states can capture developmental changes in intracortical networks we obtained local field potential recordings throughout the mouse lifespan. Two independent and complementary methodologies revealed that Up state activity is systematically modified by age, with the largest changes occurring during early development and adolescence. To explore possible regional heterogeneities we also compared the development of Up states in two distinct cortical areas and show that primary somatosensory cortex develops at a faster pace than primary motor cortex. Our findings suggest that in vitro Up states can serve as a functional index of cortical development and differentiation and can provide a baseline for comparing experimental and/or genetic mouse models.

Cognitive workload assessment based on the tensorial treatment of EEG estimates of cross-frequency phase interactions.

The decoding of conscious experience, based on non-invasive measurements, has become feasible by tailoring machine learning techniques to analyse neuroimaging data. Recently, functional connectivity graphs (FCGs) have entered into the picture. In the related decoding scheme, FCGs are treated as unstructured data and, hence, their inherent format is overlooked. To alleviate this, tensor subspace analysis (TSA) is incorporated for the parsimonious representation of connectivity data. In addition to the particular methodological innovation, this work also makes a contribution at a conceptual level by encoding in FCGs cross-frequency coupling apart from the conventional frequency-specific interactions. Working memory related tasks, supported by networks oscillating at different frequencies, are good candidates for assessing the novel approach. We employed surface EEG recordings when the subjects were repeatedly performing a mental arithmetic task of five cognitive workload levels. For each trial, an FCG was constructed based on phase interactions within and between Frontal (θ) and Parieto-Occipital (α2) neural activities, which are considered to reflect the function of two distinct working memory subsystems. Based on the TSA representation, a remarkably high correct-recognition-rate (96%) of the task difficulties was achieved using a standard classifier. The overall scheme is computational efficient and therefore potentially useful for real-time and personalized applications.

Functional aspects of the EGF-induced MAP kinase cascade: a complex self-organizing system approach.

The EGF-induced MAP kinase cascade is one of the most important and best characterized networks in intracellular signalling. It has a vital role in the development and maturation of living organisms. However, when deregulated, it is involved in the onset of a number of diseases. Based on a computational model describing a "surface" and an "internalized" parallel route, we use systems biology techniques to characterize aspects of the network's functional organization. We examine the re-organization of protein groups from low to high external stimulation, define functional groups of proteins within the network, determine the parameter best encoding for input intensity and predict the effect of protein removal to the system's output response. Extensive functional re-organization of proteins is observed in the lower end of stimulus concentrations. As we move to higher concentrations the variability is less pronounced. 6 functional groups have emerged from a consensus clustering approach, reflecting different dynamical aspects of the network. Mutual information investigation revealed that the maximum activation rate of the two output proteins best encodes for stimulus intensity. Removal of each protein of the network resulted in a range of graded effects, from complete silencing to intense activation. Our results provide a new "vista" of the EGF-induced MAP kinase cascade, from the perspective of complex self-organizing systems. Functional grouping of the proteins reveals an organizational scheme contrasting the current understanding of modular topology. The six identified groups may provide the means to experimentally follow the dynamics of this complex network. Also, the vulnerability analysis approach may be used for the development of novel therapeutic targets in the context of personalized medicine.

On the discovery of group-consistent graph substructure patterns from brain networks.

Complex networks constitute a recurring issue in the analysis of neuroimaging data. Recently, network motifs have been identified as patterns of interconnections since they appear in a significantly higher number than in randomized networks, in a given ensemble of anatomical or functional connectivity graphs. The current approach for detecting and enumerating motifs in brain networks requires a predetermined motif repertoire and can operate only with motifs of small size (consisting of few nodes). There is a growing interest in methodologies for frequent graph-based pattern mining in large graph datasets that can facilitate adaptive design of motifs. The results presented in this paper are based on the graph-based Substructure pattern mining (gSpan) algorithm and introduce a manifold of ways to exploit it for data-driven motif extraction in connectomics research. Functional connectivity graphs from electroencephalographic (EEG) recordings during resting state and mental calculations are used to demonstrate our approach. Relying on either time-invariant or time-evolving graphs, characteristic motifs associated with various frequency bands were derived and compared. With a suitable manipulation, the gSpan discovers motifs which are specific to performing mental arithmetics. Finally, the subject-dependent temporal signatures of motifs' appearance revealed the transient nature of the evolving functional connectivity (math-related motifs "come and go").

Source space analysis of event-related dynamic reorganization of brain networks.

How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

Surface EEG shows that functional segregation via phase coupling contributes to the neural substrate of mental calculations.

Multichannel EEG traces from healthy subjects are used to investigate the brain's self-organisation tendencies during two different mental arithmetic tasks. By making a comparison with a control-state in the form of a classification problem, we can detect and quantify the changes in coordinated brain activity in terms of functional connectivity. The interactions are quantified at the level of EEG sensors through descriptors that differ over the nature of functional dependencies sought (linear vs. nonlinear) and over the specific form of the measures employed (amplitude/phase covariation). Functional connectivity graphs (FCGs) are analysed with a novel clustering algorithm, and the resulting segregations enter an appropriate discriminant function. The magnitude of the contrast function depends on the frequency-band (θ, α(1), α(2), ß and γ) and the neural synchrony descriptor. We first show that the maximal-contrast corresponds to a phase coupling descriptor and then identify the corresponding spatial patterns that represent best the task-induced changes for each frequency band. The principal finding of this study is that, during mental calculations, phase synchrony plays a crucial role in the segregation into distinct functional domains, and this segregation is the most prominent feature of the brain's self-organisation as this is reflected in sensor space.

In quest of the missing neuron: spike sorting based on dominant-sets clustering.

Spike sorting algorithms aim at decomposing complex extracellular signals to independent events from single neurons in the electrode's vicinity. The decision about the actual number of active neurons is still an open issue, with sparsely firing neurons and background activity the most influencing factors. We introduce a graph-theoretical algorithmic procedure that successfully resolves this issue. Dimensionality reduction coupled with a modern, efficient and progressively executable clustering routine proved to achieve higher performance standards than popular spike sorting methods. Our method is validated extensively using simulated data for different levels of SNR.

An EEG study of brain connectivity dynamics at the resting state.

We investigated the dynamical behavior of resting state functional connectivity using EEG signals. Employing a recently introduced methodology that considers the time variations of phase coupling among signals from different channels, a sequence of functional connectivity graphs (FCGs) was constructed for different frequency bands and analyzed based on graph theoretic tools. In the first stage of analysis, hubs were detected in the FCGs based on local and global efficiency. The probability of each node to be identified as a hub was estimated. This defined a topographic function that showed widespread distribution with prominence over the frontal brain regions for both local and global efficiency. Hubs consistent across time were identified via a summarization technique and found to locate over forehead. In the second stage of analysis, the modular structure of each single FCG was delineated. The derived time-dependent signatures of functional structure were compared in a systematic way revealing fluctuations modulated by frequency. Interestingly, the evolution of functional connectivity can be described via abrupt transitions between states, best described as short-lasting bimodal functional segregations. Based on a distance function that compares clusterings, we discovered that these segregations are recurrent. Entropic measures further revealed that the apparent fluctuations are subject to intrinsic constraints and that order emerges from spatially extended interactions.

A novel symbolization scheme for multichannel recordings with emphasis on phase information and its application to differentiate EEG activity from different mental tasks.

UNLABELLED: Symbolic dynamics is a powerful tool for studying complex dynamical systems. So far many techniques of this kind have been proposed as a means to analyze brain dynamics, but most of them are restricted to single-sensor measurements. Analyzing the dynamics in a channel-wise fashion is an invalid approach for multisite encephalographic recordings, since it ignores any pattern of coordinated activity that might emerge from the coherent activation of distinct brain areas. We suggest, here, the use of neural-gas algorithm (Martinez et al. in IEEE Trans Neural Netw 4:558-569, 1993) for encoding brain activity spatiotemporal dynamics in the form of a symbolic timeseries. A codebook of k prototypes, best representing the instantaneous multichannel data, is first designed. Each pattern of activity is then assigned to the most similar code vector. The symbolic timeseries derived in this way is mapped to a network, the topology of which encapsulates the most important phase transitions of the underlying dynamical system. Finally, global efficiency is used to characterize the obtained topology. We demonstrate the approach by applying it to EEG-data recorded from subjects while performing mental calculations. By working in a contrastive-fashion, and focusing in the phase aspects of the signals, we show that the underlying dynamics differ significantly in their symbolic representations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11571-011-9186-5) contains supplementary material, which is available to authorized users.

Tracking brain dynamics via time-dependent network analysis.

Complex network analysis is currently employed in neuroscience research to describe the neuron pathways in the brain with a small number of computable measures that have neurobiological meaning. Connections in biological neural networks might fluctuate over time; therefore, surveillance can provide a more useful picture of brain dynamics than the standard approach that relies on a static graph to represent functional connectivity. Using the application of well-known measures of neural synchrony over short segments of brain activity in a time series, we attempted a time-dependent characterization of brain connectivity by investigating functional segregation and integration. In our implementation, a frequency-dependent time window was employed and regularly spaced (defined as overlapping segments), and a novel, parameter-free method was introduced to derive the required adjacency matrices. The resulting characterization was compared against conventional approaches that rely on static and time-evolving graphs, which are constructed from non-overlapping segments of arbitrarily defined durations. Our approach is demonstrated using EEG recordings during mental calculations. The derived consecutive values of network metrics were then compared with values from randomized networks. The results revealed the dynamic small-world character of the brain's functional connectivity, which otherwise can be hidden from estimators that rely on either long or stringent time-windows. Moreover, by involving a network-metric time series (NMTS) in a summarizing procedure that was based on replicator dynamics, consistent hubs that facilitated communication in the underlying networks were identified. Finally, the scale-free character of brain networks was also demonstrated based on the significant edges selected with the introduced approach.

What does delta band tell us about cognitive processes: a mental calculation study.

Multichannel EEG recordings from 18 healthy subjects were used to investigate brain activity in four delta subbands during two mental arithmetic tasks (number comparison and two-digit multiplication) and a control condition. The spatial redistribution of signal-power (SP) was explored based on four consecutives subbands of the delta rhythm. Additionally, network analysis was performed, independently for each subband, and the related graphs reflecting functional connectivity were characterized in terms of local structure (i.e. the clustering coefficient), overall integration (i.e. the path length) and the optimality of network organization (i.e. the "small-worldness"). EEG delta activity showed a widespread increase in all subbands during the performance of both arithmetic tasks. The inter-task comparison of the two arithmetic tasks revealed significant differences, in terms of signal-power, for the two subbands of higher frequency over left hemisphere (frontal, temporal, parietal and occipital) regions. The estimated brain networks exhibited small-world characteristics in the case of all subbands. On the contrary, lower frequency subbands were found to operate differently than the higher frequency subbands, with the latter featuring nodal organization and poor remote interconnectivity. These findings possibly reflect the deactivation of default mode network and could be attributed to inhibitory mechanisms activated during mental tasks.

NASS: an empirical approach to spike sorting with overlap resolution based on a hybrid noise-assisted methodology.

Background noise and spike overlap pose problems in contemporary spike-sorting strategies. We attempted to resolve both issues by introducing a hybrid scheme that combines the robust representation of spike waveforms to facilitate the reliable identification of contributing neurons with efficient data learning to enable the precise decomposition of coactivations. The isometric feature mapping (ISOMAP) technique reveals the intrinsic data structure, helps with recognising the involved neurons and, simultaneously, identifies the overlaps. Exemplar activation patterns are first estimated for all detected neurons and consecutively used to build a synthetic database in which spike overlaps are systematically varied and realistic noise is added. An Extreme Learning Machine (ELM) is then trained with the ISOMAP representation of this database and learns to associate the synthesised waveforms with the corresponding source neurons. The trained ELM is finally applied to the actual overlaps from the experimental data and this completes the entire spike-sorting process. Our approach is better characterised as semi-supervised, noise-assisted strategy of an empirical nature. The user's engagement is restricted at recognising the number of active neurons from low-dimensional point-diagrams and at deciding about the complexity of overlaps. Efficiency is inherited from the incorporation of well-established algorithms. Moreover, robustness is guaranteed by adaptation to the actual noise properties of a given data set. The validity of our work has been verified via extensive experimentation, using realistically simulated data, under different levels of noise.

Using conditional FCM to mine event-related brain dynamics.

We introduce a framework for mining event related dynamics based on conditional FCM (CFCM). For a given set of responses, the variation in the data is summarized by means of a small set of meaningful prototypes accompanied with a low-dimensional graph capturing their relative relationships. CFCM enables prototyping in a principled manner. User-defined constraints, which are imposed by the nature of experimental data and/or dictated by the neuroscientist's intuition, direct the process of knowledge extraction and can robustify single-trial analysis. The method is introduced using simulated data and demonstrated using actual encephalographic data.

Characterizing dynamic functional connectivity across sleep stages from EEG.

Following a nonlinear dynamics approach, we investigated the emergence of functional clusters which are related with spontaneous brain activity during sleep. Based on multichannel EEG traces from 10 healthy subjects, we compared the functional connectivity across different sleep stages. Our exploration commences with the conjecture of a small-world patterning, present in the scalp topography of the measured electrical activity. The existence of such a communication pattern is first confirmed for our data and then precisely determined by means of two distinct measures of non-linear interdependence between time-series. A graph encapsulating the small-world network structure along with the relative interdependence strength is formed for each sleep stage and subsequently fed to a suitable clustering procedure. Finally the delineated graph components are comparatively presented for all stages revealing novel attributes of sleep architecture. Our results suggest a pivotal role for the functional coupling during the different stages and indicate interesting dynamic characteristics like its variable hemispheric asymmetry and the isolation between anterior and posterior cortical areas during REM.

Timing and connectivity in the human somatosensory cortex from single trial mass electrical activity.

Parallel-distributed processing is ubiquitous in the brain but often ignored by experimental designs and methods of analysis, which presuppose sequential and stereotypical brain activations. We introduce here a methodology that can effectively deal with sequential and distributed activity. Regional brain activations elicited by electrical median nerve stimulation are identified in tomographic estimates extracted from single trial magnetoencephalographic signals. Habituation is identified in both primary somatosensory cortex (SI) and secondary somatosensory cortex (SII), often interrupted by resurgence of strong activations. Pattern analysis is used to identify single trials with homogeneous regional brain activations. Common activity patterns with well-defined connectivity are identified within each homogeneous group of single trials across the subjects studied. On the contralateral side one encounters distinct sets of single trials following identical stimuli. We observe in one set of trials sequential activation from SI to SII and insula with onset of SII at 60 msec, whereas in the other set simultaneous early co-activations of the same two areas.