Information flow between hippocampus and related structures during various types of rat's behavior

Assessing functional connectivity across 3D tissue engineered axonal tracts using calcium fluorescence imaging

OBJECTIVE

Micro-tissue engineered neural networks (micro-TENNs) are anatomically-inspired constructs designed to structurally and functionally emulate white matter pathways in the brain. These 3D neural networks feature long axonal tracts spanning discrete neuronal populations contained within a tubular hydrogel, and are being developed to reconstruct damaged axonal pathways in the brain as well as to serve as physiologically-relevant in vitro experimental platforms. The goal of the current study was to characterize the functional properties of these neuronal and axonal networks.

APPROACH

Bidirectional micro-TENNs were transduced to express genetically-encoded calcium indicators, and spontaneous fluorescence activity was recorded using real-time microscopy at 20 Hz from specific regions-of-interest in the neuronal populations. Network activity patterns and functional connectivity across the axonal tracts were then assessed using various techniques from statistics and information theory including Pearson cross-correlation, phase synchronization matrices, power spectral analysis, directed transfer function, and transfer entropy.

MAIN RESULTS

Pearson cross-correlation, phase synchronization matrices, and power spectral analysis revealed high values of correlation and synchronicity between the spatially segregated neuronal clusters connected by axonal tracts. Specifically, phase synchronization revealed high synchronicity of  >0.8 between micro-TENN regions of interest. Normalized directed transfer function and transfer entropy matrices suggested robust information flow between the neuronal populations. Time varying power spectrum analysis revealed the strength of information propagation at various frequencies. Signal power strength was visible at elevated peak levels for dominant delta (1-4 Hz) and theta (4-8 Hz) frequency bands and progressively weakened at higher frequencies. These signal power strength results closely matched normalized directed transfer function analysis where near synchronous information flow was detected between frequencies of 2-5 Hz.

SIGNIFICANCE

To our knowledge, this is the first report using directed transfer function and transfer entropy methods based on fluorescent calcium activity to estimate functional connectivity of distinct neuronal populations via long-projecting, 3D axonal tracts in vitro. These functional data will further improve the design and optimization of implantable neural networks that could ultimately be deployed to reconstruct the nervous system to treat neurological disease and injury.

Directionality indices: Testing information transfer with surrogate correction

Directionality indices can be used as an indicator of the asymmetry in coupling between systems and have found particular application in relation to neurological systems. The directionality index between two systems is a function of measures of information transfer in both directions. Here we illustrate that before inferring the directionality of coupling it is first necessary to consider the use of appropriate tests of significance. We propose a surrogate corrected directionality index which incorporates such testing. We also highlight the differences between testing the significance of the directionality index itself versus testing the individual measures of information transfer in each direction. To validate the approach we compared two different methods of estimating coupling, both of which have previously been used to estimate directionality indices. These were the modeling-based evolution map approach and a conditional mutual information (CMI) method for calculating dynamic information rates. For the CMI-based approach we also compared two different methods for estimating the CMI, an equiquantization-based estimator and a k-nearest neighbors estimator.

Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks

Previous evidence showed a 75.5% best accuracy in the classification of 120 Alzheimer's disease (AD) patients with dementia and 100 matched normal elderly (Nold) subjects based on cortical source current density and linear lagged connectivity estimated by eLORETA freeware from resting state eyes-closed electroencephalographic (rsEEG) rhythms (Babiloni et al., 2016a). Specifically, that accuracy was reached using the ratio between occipital delta and alpha1 current density for a linear univariate classifier (receiver operating characteristic curves). Here we tested an innovative approach based on an artificial neural network (ANN) classifier from the same database of rsEEG markers. Frequency bands of interest were delta (2–4 Hz), theta (4–8 Hz Hz), alpha1 (8–10.5 Hz), and alpha2 (10.5–13 Hz). ANN classification showed an accuracy of 77% using the most 4 discriminative rsEEG markers of source current density (parietal theta/alpha 1, temporal theta/alpha 1, occipital theta/alpha 1, and occipital delta/alpha 1). It also showed an accuracy of 72% using the most 4 discriminative rsEEG markers of source lagged linear connectivity (inter-hemispherical occipital delta/alpha 2, intra-hemispherical right parietal-limbic alpha 1, intra-hemispherical left occipital-temporal theta/alpha 1, intra-hemispherical right occipital-temporal theta/alpha 1). With these 8 markers combined, an accuracy of at least 76% was reached. Interestingly, this accuracy based on 8 (linear) rsEEG markers as inputs to ANN was similar to that obtained with a single rsEEG marker (Babiloni et al., 2016a), thus unveiling their information redundancy for classification purposes. In future AD studies, inputs to ANNs should include other classes of independent linear (i.e., directed transfer function) and non-linear (i.e., entropy) rsEEG markers to improve the classification.

Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach

OBJECTIVE

Combined source-imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a noninvasive fashion. Source-imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source-imaging algorithms to both find the network nodes [regions of interest (ROI)] and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses, and apply Granger analysis on the extracted series to study brain networks under realistic conditions.

METHODS

Source-imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from interictal and ictal signals recorded by EEG and/or Magnetoencephalography (MEG).

RESULTS

Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ∼20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection.

CONCLUSION

Our study indicates that combined source-imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity).

SIGNIFICANCE

The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.

Temporal Information of Directed Causal Connectivity in Multi-Trial ERP Data using Partial Granger Causality

Partial Granger causality (PGC) has been applied to analyse causal functional neural connectivity after effectively mitigating confounding influences caused by endogenous latent variables and exogenous environmental inputs. However, it is not known how this connectivity obtained from PGC evolves over time. Furthermore, PGC has yet to be tested on realistic nonlinear neural circuit models and multi-trial event-related potentials (ERPs) data. In this work, we first applied a time-domain PGC technique to evaluate simulated neural circuit models, and demonstrated that the PGC measure is more accurate and robust in detecting connectivity patterns as compared to conditional Granger causality and partial directed coherence, especially when the circuit is intrinsically nonlinear. Moreover, the connectivity in PGC settles faster into a stable and correct configuration over time. After method verification, we applied PGC to reveal the causal connections of ERP trials of a mismatch negativity auditory oddball paradigm. The PGC analysis revealed a significant bilateral but asymmetrical localised activity in the temporal lobe close to the auditory cortex, and causal influences in the frontal, parietal and cingulate cortical areas, consistent with previous studies. Interestingly, the time to reach a stable connectivity configuration (~250–300 ms) coincides with the deviation of ensemble ERPs of oddball from standard tones. Finally, using a sliding time window, we showed higher resolution dynamics of causal connectivity within an ERP trial. In summary, time-domain PGC is promising in deciphering directed functional connectivity in nonlinear and ERP trials accurately, and at a sufficiently early stage. This data-driven approach can reduce computational time, and determine the key architecture for neural circuit modeling.

Effective connectivity of hippocampal neural network and its alteration in Mg2+-free epilepsy model

Understanding the connectivity of the brain neural network and its evolution in epileptiform discharges is meaningful in the epilepsy researches and treatments. In the present study, epileptiform discharges were induced in rat hippocampal slices perfused with Mg2+-free artificial cerebrospinal fluid. The effective connectivity of the hippocampal neural network was studied by comparing the normal and epileptiform discharges recorded by a microelectrode array. The neural network connectivity was constructed by using partial directed coherence and analyzed by graph theory. The transition of the hippocampal network topology from control to epileptiform discharges was demonstrated. Firstly, differences existed in both the averaged in- and out-degree between nodes in the pyramidal cell layer and the granule cell layer, which indicated an information flow from the pyramidal cell layer to the granule cell layer during epileptiform discharges, whereas no consistent information flow was observed in control. Secondly, the neural network showed different small-worldness in the early, middle and late stages of the epileptiform discharges, whereas the control network did not show the small-world property. Thirdly, the network connectivity began to change earlier than the appearance of epileptiform discharges and lasted several seconds after the epileptiform discharges disappeared. These results revealed the important network bases underlying the transition from normal to epileptiform discharges in hippocampal slices. Additionally, this work indicated that the network analysis might provide a useful tool to evaluate the neural network and help to improve the prediction of seizures.

Resting state cortical EEG rhythms in Alzheimer's disease: toward EEG markers for clinical applications: a review

The human brain contains an intricate network of about 100 billion neurons. Aging of the brain is characterized by a combination of synaptic pruning, loss of cortico-cortical connections, and neuronal apoptosis that provoke an age-dependent decline of cognitive functions. Neural/synaptic redundancy and plastic remodeling of brain networking, also secondary to mental and physical training, promote maintenance of brain activity and cognitive status in healthy elderly subjects for everyday life. However, age is the main risk factor for neurodegenerative disorders such as Alzheimer's disease (AD) that impact on cognition. Growing evidence supports the idea that AD targets specific and functionally connected neuronal networks and that oscillatory electromagnetic brain activity might be a hallmark of the disease. In this line, digital electroencephalography (EEG) allows noninvasive analysis of cortical neuronal synchronization, as revealed by resting state brain rhythms. This review provides an overview of the studies on resting state eyes-closed EEG rhythms recorded in amnesic mild cognitive impairment (MCI) and AD subjects. Several studies support the idea that spectral markers of these EEG rhythms, such as power density, spectral coherence, and other quantitative features, differ among normal elderly, MCI, and AD subjects, at least at group level. Regarding the classification of these subjects at individual level, the most previous studies showed a moderate accuracy (70-80%) in the classification of EEG markers relative to normal and AD subjects. In conclusion, resting state EEG makers are promising for large-scale, low-cost, fully noninvasive screening of elderly subjects at risk of AD.

Electroencephalographic rhythms in Alzheimer's disease

Physiological brain aging is characterized by synapses loss and neurodegeneration that slowly lead to an age-related decline of cognition. Neural/synaptic redundancy and plastic remodelling of brain networking, also due to mental and physical training, promotes maintenance of brain activity in healthy elderly subjects for everyday life and good social behaviour and intellectual capabilities. However, age is the major risk factor for most common neurodegenerative disorders that impact on cognition, like Alzheimer's disease (AD). Brain electromagnetic activity is a feature of neuronal network function in various brain regions. Modern neurophysiological techniques, such as electroencephalography (EEG) and event-related potentials (ERPs), are useful tools in the investigation of brain cognitive function in normal and pathological aging with an excellent time resolution. These techniques can index normal and abnormal brain aging analysis of corticocortical connectivity and neuronal synchronization of rhythmic oscillations at various frequencies. The present review suggests that discrimination between physiological and pathological brain aging clearly emerges at the group level, with suggested applications also at the level of single individual. The possibility of combining the use of EEG together with biological/neuropsychological markers and structural/functional imaging is promising for a low-cost, non-invasive, and widely available assessment of groups of individuals at-risk.

Review of the methods of determination of directed connectivity from multichannel data

The methods applied for estimation of functional connectivity from multichannel data are described with special emphasis on the estimators of directedness such as directed transfer function (DTF) and partial directed coherence. These estimators based on multivariate autoregressive model are free of pitfalls connected with application of bivariate measures. The examples of applications illustrating the performance of the methods are given. Time-varying estimators of directedness: short-time DTF and adaptive methods are presented.

Direction of Information Flow in Alzheimer's Disease and MCI Patients

Is directionality of electroencephalographic (EEG) synchronization abnormal in amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD)? And, do cerebrovascular and AD lesions represent additive factors in the development of MCI as a putative preclinical stage of AD? Here we reported two studies that tested these hypotheses. EEG data were recorded in normal elderly (Nold), amnesic MCI, and mild AD subjects at rest condition (closed eyes). Direction of information flow within EEG electrode pairs was performed by directed transfer function (DTF) at δ (2-4 Hz), θ (4-8 Hz), α1 (8-10 Hz), α2 (10-12 Hz), β1 (13-20 Hz), β2 (20-30 Hz), and γ (30-40 Hz). Parieto-to-frontal direction was stronger in Nold than in MCI and/or AD subjects for α and β rhythms. In contrast, the directional flow within interhemispheric EEG functional coupling did not discriminate among the groups. More interestingly, this coupling was higher at θ, α1, α2, and β1 in MCI with higher than in MCI with lower vascular load. These results suggest that directionality of parieto-to-frontal EEG synchronization is abnormal not only in AD but also in amnesic MCI, supporting the additive model according to which MCI state would result from the combination of cerebrovascular and neurodegenerative lesions.

Quantifying auditory event-related responses in multichannel human intracranial recordings

Multichannel intracranial recordings are used increasingly to study the functional organization of human cortex. Intracranial recordings of event-related activity, or electrocorticography (ECoG), are based on high density electrode arrays implanted directly over cortex, combining good temporal and spatial resolution. Developing appropriate statistical methods for analyzing event-related responses in these high dimensional ECoG datasets remains a major challenge for clinical and systems neuroscience. We present a novel methodological framework that combines complementary, existing methods adapted for statistical analysis of auditory event-related responses in multichannel ECoG recordings. This analytic framework integrates single-channel (time-domain, time–frequency) and multichannel analyses of event-related ECoG activity to determine statistically significant evoked responses, induced spectral responses, and effective (causal) connectivity. Implementation of this quantitative approach is illustrated using multichannel ECoG data from recent studies of auditory processing in patients with epilepsy. Methods described include a time–frequency matching pursuit algorithm adapted for modeling brief, transient cortical spectral responses to sound, and a recently developed method for estimating effective connectivity using multivariate autoregressive modeling to measure brief event-related changes in multichannel functional interactions. A semi-automated spatial normalization method for comparing intracranial electrode locations across patients is also described. The individual methods presented are published and readily accessible. We discuss the benefits of integrating multiple complementary methods in a unified and comprehensive quantitative approach. Methodological considerations in the analysis of multichannel ECoG data, including corrections for multiple comparisons are discussed, as well as remaining challenges in the development of new statistical approaches.

Inter-hemispheric functional coupling of eyes-closed resting EEG rhythms in adolescents with Down syndrome

OBJECTIVE

We tested the hypothesis that inter-hemispheric directional functional coupling of eyes-closed resting EEG rhythms is abnormal in adolescents with Down syndrome (DS).

METHODS

Eyes-closed resting EEG data were recorded in 38 DS adolescents (18.7 years +/-0.67 SE, IQ=49+/-1.9 SE) and in 17 matched normal control subjects (NYoung=19.1 years +/-0.39 SE). The EEG data were recorded from 8 electrodes (Fp1, Fp2, C3, C4, T3, T4, O1, O2) referenced to vertex. EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), and beta 2 (20-30 Hz). Power of EEG rhythms was evaluated by FFT for control purposes, whereas inter-hemispheric directional EEG functional coupling was computed by directed transfer function (DTF).

RESULTS

As expected, alpha, beta, and gamma power was widely higher in NYoung than DS subjects, whereas the opposite was true for delta power. As a novelty, DTF (directionality) values globally prevailed from right to left occipital areas in NYoung subjects and in the opposite direction in DS patients. A control experiment showed that this DTF difference could not be observed in the comparison between DS adults with mild cognitive impairment and normal age-matched adults.

CONCLUSIONS

These results indicate a peculiar abnormal directional inter-hemispheric interplay in visual occipital areas of DS adolescents.

SIGNIFICANCE

Direction of inter-hemispheric EEG functional coupling unveils a new abnormal brain network feature in DS adolescents.

Coupling of mesoscopic brain oscillations: recent advances in analytical and theoretical perspectives

Oscillatory brain activities have been traditionally studied in the context of how oscillations at a single frequency recorded from a single area could reveal functional insights. Recent advances in methodology used in signal analysis have revealed that cross-frequency coupling, within or between functional related areas, is more informative in determining the possible roles played by brain oscillations. In this review, we begin by describing the cellular basis of oscillatory field potentials and its theorized as well as demonstrated role in brain function. The recent development of mathematical tools that allow the investigation of cross-frequency and cross-area oscillation coupling will be presented and discussed in the context of recent advances in oscillation research based on animal data. Particularly, some pitfalls and caveats of methods currently available are discussed. Data generated from the application of examined techniques are integrated back into the theoretical framework regarding the functional role of brain oscillations. We suggest that the coupling of oscillatory activities at different frequencies between brain regions is crucial for understanding the brain from a functional ensemble perspective. Effort should be directed to elucidate how cross-frequency and area coupling are modulated and controlled. To achieve this, only the correct application of analytical tools may shed light on the intricacies of information representation, generation, binding, encoding, storage and retrieval in the brain.

White matter vascular lesions are related to parietal-to-frontal coupling of EEG rhythms in mild cognitive impairment

Do cerebrovascular and Alzheimer's disease (AD) lesions represent additive factors in the development of mild cognitive impairment (MCI) as a putative preclinical stage of AD? Here we tested the hypothesis that directionality of fronto-parietal functional coupling of electroencephalographic (EEG) rhythms is relatively preserved in amnesic MCI subjects in whom the cognitive decline is mainly explained by white-matter vascular load. Resting EEG was recorded in 40 healthy elderly (Nold) and 78 amnesic MCI. In the MCI subjects, white-matter vascular load was quantified based on magnetic resonance images (0-30 visual rating scale). EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), and beta2 (20-30 Hz). Directionality of fronto-parietal functional coupling of EEG rhythms was estimated by directed transfer function software. As main results, (i) fronto-parietal functional coupling of EEG rhythms was higher in magnitude in the Nold than in the MCI subjects; (ii) more interestingly, that coupling was higher at theta, alpha1, alpha2, and beta1 in MCI V+ (high vascular load; N = 42; MMSE = 26) than in MCI V- group (low vascular load; N = 36; MMSE= 26.7). These results are interpreted as supporting the additive model according to which MCI state would result from the combination of cerebrovascular and neurodegenerative lesions.

Directional information flows between brain hemispheres across waking, non-REM and REM sleep states: an EEG study

The present electroencephalographic (EEG) study evaluated the hypothesis of a preferred directionality of communication flows between brain hemispheres across 24 h (i.e., during the whole daytime and nighttime), as an extension of a recent report showing changes in preferred directionality from pre-sleep wake to early sleep stages. Scalp EEGs were recorded in 10 normal volunteers during daytime wakefulness (eyes closed; first period: from 10:00 to 13:00 h; second period: from 14:00 to 18:00 h; third period: from 19:00 to 22:00 h) and nighttime sleep (four NREM-REM cycles). EEG rhythms of interest were delta (1-4 Hz), theta (5-7 Hz), alpha (8-11 Hz), sigma (12-15 Hz) and beta (16-28 Hz). The direction of the inter-hemispheric information flow was evaluated by computing the directed transfer function (DTF) from these EEG rhythms. Inter-hemispheric directional flows varied as a function of the state of consciousness (wake, NREM sleep, REM sleep) and in relation to different cerebral areas. During the daytime, alpha and beta rhythms conveyed inter-hemispheric signals with preferred Left-to-Right hemisphere direction in parietal and central areas, respectively. During the NREM sleep periods of nighttime, the direction of inter-hemispheric DTF information flows conveyed by central beta rhythms was again preponderant from Left-to-Right hemisphere in the stage 2, independent of cortical areas. No preferred direction emerged across the REM periods. These results support the hypothesis that specific directionality of communication flows between brain hemispheres is associated with wakefulness, NREM (particularly stage 2) and REM states during daytime and nighttime. They also reinforce the suggestive hypothesis of a relationship between inter-hemispheric directionality of EEG functional coupling and frequency of the EEG rhythms.

Dynamics of event-related causality in brain electrical activity

A new method (Event-Related Causality, ERC) is proposed for the investigation of functional interactions between brain regions during cognitive processing. ERC estimates the direction, intensity, spectral content, and temporal course of brain activity propagation within a cortical network. ERC is based upon the short-time directed transfer function (SDTF), which is measured in short EEG epochs during multiple trials of a cognitive task, as well as the direct directed transfer function (dDTF), which distinguishes direct interactions between brain regions from indirect interactions via brain regions. ERC uses new statistical methods for comparing estimates of causal interactions during prestimulus "baseline" epochs and during poststimulus "activated" epochs in order to estimate event-related increases and decreases in the functional interactions between cortical network components during cognitive tasks. The utility of the ERC approach is demonstrated through its application to human electrocorticographic recordings (ECoG) of a simple language task. ERC analyses of these ECoG recordings reveal frequency-dependent interactions, particularly in high gamma (>60 Hz) frequencies, between brain regions known to participate in the recorded language task, and the temporal evolution of these interactions is consistent with the putative processing stages of this task. The method may be a useful tool for investigating the dynamics of causal interactions between various brain regions during cognitive task performance.

Clinical neurophysiology of aging brain: from normal aging to neurodegeneration

Physiological brain aging is characterized by a loss of synaptic contacts and neuronal apoptosis that provokes age-dependent decline of sensory processing, motor performance, and cognitive function. Neural redundancy and plastic remodelling of brain networking, also secondary to mental and physical training, promotes maintenance of brain activity in healthy elderly for everyday life and fully productive affective and intellectual capabilities. However, age is the main risk factor for neurodegenerative disorders such as Alzheimer's disease (AD) that impact on cognition. Oscillatory electromagnetic brain activity is a hallmark of neuronal network function in various brain regions. Modern neurophysiological techniques including electroencephalography (EEG), event-related potential (ERP), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) can accurately index normal and abnormal brain aging to facilitate non-invasive analysis of cortico-cortical connectivity and neuronal synchronization of firing and coherence of rhythmic oscillations at various frequencies. The present review provides a perspective of these issues by assaying different neurophysiological methods and integrating the results with functional brain imaging findings. It is concluded that discrimination between physiological and pathological brain aging clearly emerges at the group level, with applications at the individual level also suggested. Integrated approaches utilizing neurophysiological techniques together with biological markers and structural and functional imaging are promising for large-scale, low-cost and non-invasive evaluation of at-risk populations. Practical implications of the methods are emphasized.

Directionality of EEG synchronization in Alzheimer's disease subjects

Is directionality of electroencephalographic (EEG) synchronization abnormal in amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD)? EEG data were recorded in 64 normal elderly (Nold), 69 amnesic MCI, and 73 mild AD subjects at rest condition (closed eyes). Direction of information flux within EEG functional coupling at electrode pairs was performed by directed transfer function (DTF) at delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10 Hz), alpha 2 (10-12 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz). Parietal to frontal direction of the information flux within EEG functional coupling was stronger in Nold than in MCI and/or AD subjects, namely for alpha and beta rhythms. In contrast, the directional flow within inter-hemispheric EEG functional coupling did not discriminate among the three groups. These results suggest that directionality of parieto-to-frontal EEG synchronization is abnormal not only in AD but also in amnesic MCI.

Directional Information Flows between Brain Hemispheres during Presleep Wake and Early Sleep Stages

Neuroscientists' efforts to better understand the underlying processes of human consciousness are growing in a variety of multidisciplinary approaches. Relevant within these are the studies aimed at exploring the physiological substratum of the propagation and reduction of cerebral-namely, corticocortical-communication flows. However, the preferential direction of the information flow between brain hemispheres is as yet largely unknown. It is the aim of the present research to study the communication flows between brain hemispheres, their directionality, and their regional variations across wake-sleep states. A second aim is to investigate the possibility of an association between different brain rhythms and different preferred directions of the information flow. Scalp electroencephalograms (EEGs) were recorded in 10 normal volunteers from wakefulness to early sleep stages (viz., resting wakefulness, sleep stages 2 and 4, and rapid eye movement [REM] of the first sleep cycle). EEG rhythms of interest were delta (1-4 Hz), theta (5-7 Hz), alpha (8-11 Hz), sigma (12-15 Hz), and beta (16-30 Hz). The direction of the interhemispheric information flow was evaluated by computing directed transformation function from these EEG rhythms. Interhemispheric directional flows varied as a function of the state of consciousness (wake and early sleep stages) and in relation to different cerebral areas. Across wake to sleep states, we found that delta and beta rhythms convey interhemispheric signals with opposite directions: preferred right to left hemisphere direction for delta and left to right for beta rhythms. A log correlation confirmed that the trend of low to high EEG frequencies-traditionally associated with an increasing state of vigilance-was significantly related to the direction of the communication flow from the left to right hemisphere. This evidence might open the way for a variety of research lines on different psychophysiological and pathological conditions.

Computational inference of neural information flow networks

Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.

One of the challenges in the area of brain research is to decipher networks describing the flow of information among communicating neurons in the form of electrophysiological signals. These networks are thought to be responsible for perceiving and learning about the environment, as well as producing behavior. Monitoring these networks is limited by the number of electrodes that can be placed in the brain of an awake animal, while inferring and reasoning about these networks is limited by the availability of appropriate computational tools. Here, Smith and Yu and colleagues begin to address these issues by implanting microelectrode arrays in the auditory pathway of freely moving songbirds and by analyzing the data using new computational tools they have designed for deciphering networks. The authors find that a dynamic Bayesian network algorithm they developed to decipher gene regulatory networks from gene expression data effectively infers putative information flow networks in the brain from microelectrode array data. The networks they infer conform to known anatomy and other biological properties of the auditory system and offer new insight into how the auditory system processes natural and synthetic sound. The authors believe that their results represent the first validated study of the inference of information flow networks in the brain.

Linear and nonlinear causality between signals: methods, examples and neurophysiological applications

In this paper, we will present and review the most usual methods to detect linear and nonlinear causality between signals: linear Granger causality test (Geweke in J Am Stat Assoc 77:304-313, 1982) extended to direct causality in multivariate case (LGC), directed coherence (DCOH, Saito and Harashima in Recent advances in EEG and EMG data processing, Elsevier, Amsterdam, 1981), partial directed coherence (PDC, Sameshima and Baccala 1999) and nonlinear Granger causality test of Baek and Brock (in Working Paper University of Iowa, 1992) extended to direct causality in multivariate case (partial nonlinear Granger causality, PNGC). All these methods are tested and compared on several ARX, Poisson and nonlinear models, and on neurophysiological data (depth EEG). The results show that LGC, DCOH and PDC are not very robust in relation to nonlinear linkages but they seem to correctly find linear linkages if only the autoregressive parts are nonlinear. PNGC is extremely dependent on the choice of parameters. Moreover, LGC and PNGC may give misleading results in the case of causality on a spectral band, which is illustrated by our neurophysiological database.

Dynamic changes in the direction of the theta rhythmic drive between supramammillary nucleus and the septohippocampal system

Neurons in the supramammillary nucleus (SUM) of urethane-anesthetized rats fire rhythmically in synchrony with hippocampal theta rhythm. As these neurons project to the septum and hippocampus, it is generally assumed that their role is to mediate ascending activation, leading to the hippocampal theta rhythm. However, the connections between SUM and the septohippocampal system are reciprocal; there is strong evidence that theta remains in the hippocampus after SUM lesions and in the SUM after lesioning the medial septum. The present study examines the dynamics of coupling between rhythmic discharge in the SUM and hippocampal field potential oscillations, using the directionality information carried by the two signals. Using directed transfer function analysis, we demonstrate that during sensory-elicited theta rhythm and also during short episodes of theta acceleration of spontaneous oscillations, the spike train of a subpopulation of SUM neurons contains information predicting future variations in rhythmic field potentials in the hippocampus. In contrast, during slow spontaneous theta rhythm, it is the SUM spike signal that can be predicted from the preceding segment of the electrical signal recorded in the hippocampus. These findings indicate that, in the anesthetized rat, SUM neurons effectively drive theta oscillations in the hippocampus during epochs of sensory-elicited theta rhythm and short episodes of theta acceleration, whereas spontaneous slow theta in the SUM is controlled by descending input from the septohippocampal system. Thus, in certain states, rhythmically firing SUM neurons function to accelerate the septal theta oscillator, and in others, they are entrained by a superordinate oscillatory network.

On the evaluation of information flow in multivariate systems by the directed transfer function

The directed transfer function (DTF) has been proposed as a measure of information flow between the components of multivariate time series. In this paper, we discuss the interpretation of the DTF and compare it with other measures for directed relationships. In particular, we show that the DTF does not indicate multivariate or bivariate Granger causality, but that it is closely related to the concept of impulse response function and can be viewed as a spectral measure for the total causal influence from one component to another. Furthermore, we investigate the statistical properties of the DTF and establish a simple significance level for testing for the null hypothesis of no information flow.

Functional frontoparietal connectivity during encoding and retrieval processes follows HERA model

Recent neuroimaging studies of long-term episodic memory have suggested that left prefrontal cortex predominates in encoding condition, whereas right prefrontal cortex predominates in retrieval condition (hemispheric encoding and retrieval asymmetry, HERA model). The present electroencephalographic (EEG) study investigated the functional coupling of fronto-parietal regions during long-term memorization of visuo-spatial contents (i.e. landscapes, interiors of apartments), to test the predictions of the HERA model. Global fronto-parietal coupling was estimated by spectral coherence, whereas the "direction" of the fronto-parietal information flow was estimated by directed transfer function (DTF). The EEG rhythms of interest were theta (4-7 Hz), alpha (8-13 Hz), beta (14-30 Hz), and gamma (30-45 Hz). Statistically significant coherence in line with the HERA model was obtained at the gamma band. Namely, the fronto-parietal gamma coherence prevailed in the left hemisphere during the encoding condition and in the right hemisphere during the retrieval condition. The DTF estimates of the gamma band showed a dominant parietal-to-frontal directional flow in the right hemisphere during the encoding condition and in the left hemisphere during the retrieval condition (i.e. hemisphere-condition combination not involved by the HERA model). In contrast, a balanced bidirectional flow of the fronto-parietal coupling was observed in the left hemisphere during the encoding condition and in the right hemisphere during the retrieval condition (i.e. hemisphere-condition combination involved by the HERA model). In conclusion, the present encoding-retrieval conditions induced maximal fronto-parietal gamma coupling with bidirectional information flow in the hemisphere-condition combination predicted by the HERA model.

Nonlinear multivariate analysis of neurophysiological signals

Multivariate time series analysis is extensively used in neurophysiology with the aim of studying the relationship between simultaneously recorded signals. Recently, advances on information theory and nonlinear dynamical systems theory have allowed the study of various types of synchronization from time series. In this work, we first describe the multivariate linear methods most commonly used in neurophysiology and show that they can be extended to assess the existence of nonlinear interdependence between signals. We then review the concepts of entropy and mutual information followed by a detailed description of nonlinear methods based on the concepts of phase synchronization, generalized synchronization and event synchronization. In all cases, we show how to apply these methods to study different kinds of neurophysiological data. Finally, we illustrate the use of multivariate surrogate data test for the assessment of the strength (strong or weak) and the type (linear or nonlinear) of interdependence between neurophysiological signals.

Antero-posterior functional coupling at sleep onset: changes as a function of increased sleep pressure

The use of the directed transfer function (DTF), an advanced computational analysis of electroencephalogaphic (EEG) data, which provides an estimation of the direction of the information flow underlying cortico-cortical functional coupling, has shown that the presleep period is characterized by posterior-to-anterior functional cortical coupling, while at sleep onset there is an inversion of that direction. This finding supported the idea that anterior cortical areas first synchronize sleep EEG activity. The aim of the present study was to assess the changes of functional coupling between anterior and posterior midline cortical areas during the sleep onset process when sleep pressure is heightened by a selective slow-wave sleep (SWS) deprivation. The hypothesis was that the anterior-to-posterior direction of the cortical functional coupling at sleep onset is enhanced by SWS deprivation. Ten normal right-handed male students slept for six consecutive nights in the laboratory (1: adaptation, 2: baseline, 3: baseline with awakenings, 4 and 5: SWS deprivations, 6: recovery), with standard polysomnographic recordings. The DTF was computed on data recorded during nights two and six from anterior (Fz(A1)) and posterior (Pz(A1), Oz(A1)) derivations. Results showed that, during the recovery night, the anterior-to-posterior direction of functional cortical coupling is already present in the presleep period, indicating that SWS deprivation advances the shift to an anterior-to-posterior directionality of functional cortical coupling, possibly as a consequence of heightened sleep pressure. These findings support the notion that a spread of synchronizing signals from associative prefrontal to posterior areas play a role in the wake-sleep transition.

Efficient application of Internet databases for new signal processing methods

This paper highlights the ways in which Internet databases may be efficiently used to foster the application of progress in biomedical sciences via data sharing and new algorithms. Employing the Internet to accelerate the pace of interdisciplinary research has significant potential, yet as with all new technologies, the first applications often cause more disappointment than positive outcomes. We discuss examples of solutions to the basic issues: (1) finding the relevant datasets (in portals connected via the Inter-neuro infrastructure), (2) reading the particular format in which the data was stored (using the SignalML language for metadescription of time series), (3) choosing the right method for the data analysis (we provide a brief review of the methods used for the analysis of EEGs, and discuss two of them in detail: Directed Transfer Function and Matching Pursuit), and (4) sharing the software for chosen methods of analysis (via repositories such as the eeg.pl thematic portal).

Decrease of functional coupling between left and right auditory cortices during dichotic listening: An electroencephalography study

The present study focused on functional coupling between human bilateral auditory cortices and on possible influence of right over left auditory cortex during dichotic listening of complex non-verbal tones having near (competing) compared with distant non-competing fundamental frequencies. It was hypothesized that dichotic stimulation with competing tones would induce a decline of functional coupling between the two auditory cortices, as revealed by a decrease of electroencephalography coherence and an increase of directed transfer function from right (specialized for the present stimulus material) to left auditory cortex. Electroencephalograph was recorded from T3 and T4 scalp sites, overlying respectively left and right auditory cortices, and from Cz scalp site (vertex) for control purposes. Event-related coherence between T3 and T4 scalp sites was significantly lower for all electroencephalography bands of interest during dichotic listening of competing than non-competing tone pairs. This was a specific effect, since event-related coherence did not differ in a monotic control condition. Furthermore, event-related coherence between T3 and Cz and between T4 and Cz scalp sites showed no significant effects. Conversely, the directed transfer function results showed negligible influence at group level of right over left auditory cortex during dichotic listening. These results suggest a decrease of functional coupling between bilateral auditory cortices during competing dichotic stimuli as a possible neural substrate for the lateralization of auditory stimuli during dichotic listening.

Assessing cortical functional connectivity by linear inverse estimation and directed transfer function: simulations and application to real data

OBJECTIVE

To test a technique called Directed Transfer Function (DTF) for the estimation of human cortical connectivity, by means of simulation study and human study, using high resolution EEG recordings related to finger movements.

METHODS

The method of the Directed Transfer Function (DTF) is a frequency-domain approach, based on a multivariate autoregressive modeling of time series and on the concept of Granger causality. Since the spreading of the potential from the cortex to the sensors makes it difficult to infer the relation between the spatial patterns on the sensor space and those on the cortical sites, we propose the use of the DTF method on cortical signals estimated from high resolution EEG recordings, which exhibit a higher spatial resolution than conventional cerebral electromagnetic measures. The simulation study was followed by an analysis of variance (ANOVA) of the results obtained for different levels of Signal to Noise Ratio (SNR) and temporal length, as they have been systematically imposed on simulated signals. The whole methodology was then applied to high resolution EEG data recorded during a visually paced finger movement.

RESULTS

The statistical analysis performed returns that during simulations, DTF is able to estimate correctly the imposed connectivity patterns under reasonable operative conditions, i.e. when data exhibit a SNR of at least 3 and a length of at least 75 s of non-consecutive recordings at 64 Hz of sampling rate, equivalent, more generally, to 4800 data samples.

CONCLUSIONS

Functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in any practical EEG recordings, by combining high resolution EEG techniques, linear inverse estimation and the DTF method.

SIGNIFICANCE

The estimation of cortical connectivity can be performed not only with hemodynamic measurements, by using functional MRI recordings, but also with modern EEG recordings treated with advanced computational techniques.

Granger causality and information flow in multivariate processes

The multivariate versus bivariate measures of Granger causality were considered. Granger causality in the application to multivariate physiological time series has the meaning of the information flow between channels. It was shown by means of simulations and by the example of experimental electroencephalogram signals that bivariate estimates of directionality in case of mutually interdependent channels give erroneous results, therefore multivariate measures such as directed transfer function should be used for determination of the information flow.

Determination of EEG activity propagation: pair-wise versus multichannel estimate

Performance of different estimators describing propagation of electroencephalogram (EEG) activity, namely: Granger causality, directed transfer function (DTF), direct DTF (dDTF), short-time DTF (SDTF), bivariate coherence, and partial directed coherence are compared by means of simulations and on the examples of experimental signals. In particular, the differences between pair-wise and multichannel estimates are studied. The results show unequivocally that in most cases, the pair-wise estimates are incorrect and a complete set of signals involved in a given process has to be used to obtain the correct pattern of EEG flows. Different performance of multivariate estimators of propagation depending on their normalization is discussed. Advantages of multivariate autoregressive model are pointed out.

Coupling Between "Hand" Primary Sensorimotor Cortex and Lower Limb Muscles After Ulnar Nerve Surgical Transfer in Paraplegia

Previous neuroimaging evidence revealed an "invasion" of "hand" over "lower limb" primary sensorimotor cortex in paraplegic subjects, with the exception of a rare patient who received a surgical motor reinnervation of hip-thigh muscles by the ulnar nerve. Here, the authors show that a functional reorganization of cortico-muscular and cortico-cortical oscillatory coupling was related to the recovery of the rare patient, as a paradigmatic case of long-term plasticity in human sensorimotor cortex after motor reinnervation of paraplegic muscles. This conclusion was based on electroencephalographic and electromyographic data collected while the patient and normal control subjects performed isometric muscle contraction of the left hand or lower limb. Cortico-muscular and cortico-cortical coupling was estimated by electroencephalographic-electromyographic coherence and directed transfer function of a multivariate autoregressive model.

Functional Frontoparietal Connectivity During Short-Term Memory as Revealed by High-Resolution EEG Coherence Analysis

In this electroencephalographic study, the authors modeled the functional connectivity between frontal and parietal areas during short-term memory (STM) processes by spectral coherence analysis and the directed transfer function, that is, for the estimation of coherence "direction." A no-STM task was used as a reference. STM was characterized by an increased frontoparietal electroencephalograph coherence at high frequencies (beta and gamma, 14-45 Hz). In the control task, parietal-to-frontal flow prevailed at those frequencies. However, the STM task showed a bidirectional frontoparietal flow at the gamma band. In conclusion, frontoparietal connectivity would optimize "representational" memory during STM. In this context, the frontal areas would increase their influence on parietal areas for memory retention.

Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method

A modification of directed transfer function-direct DTF-is proposed for the analysis of direct information transfer among brain structures on the basis of local field potentials (LFP). Comparison of results obtained by the analysis of simulated and experimental data with a new dDTF and DTF method is shown. A new measure to estimate direct causal relations between signals is defined. The present results demonstrate the effectiveness of the new dDTF method and indicate that the dDTF method can be used to obtain the reliable patterns of connections between various brain structures.

Phase and amplitude analysis in time-frequency space--application to voluntary finger movement

Two methods operating in time-frequency space were applied to analysis of EEG activity accompanying voluntary finger movements. The first one, based on matching pursuit approach provided high-resolution distributions of power in time-frequency space. The phenomena of event related desynchronization (ERD) and synchronization (ERS) were investigated without the need of band-pass filtering. Time evolution of mu- and beta-components was observed in a detailed way. The second method was based on a multichannel autoregressive model (MVAR) adapted for investigation of short-time changes in EEG signal. The direction and spectral content of the EEG activity propagation was estimated by means of short-time directed transfer function (SDTF). The evidence of 'cross-talk' between different areas of motor and sensory cortex was found. The earlier known phenomena, connected with voluntary movements, were confirmed and a new evidence concerning focal ERD/surround ERS and beta activity post-movement synchronization was found.

Information flow from the sensorimotor cortex to muscle in humans

OBJECTIVES

To investigate the physiologic mechanism of human electroencephalogram-electromyogram (EEG-EMG) coherence, the directed transfer function (DTF) based on a multivariate autoregressive (MVAR) model was computed.

METHODS

Fifty-six channel EEG and EMG of the right abductor pollicis brevis muscle during a weak tonic contraction were recorded in 6 normal volunteers. The EEG over the left sensorimotor area and the rectified EMG were used to compute coherence and DTF.

RESULTS

EEG-EMG coherence was observed at the peak frequency of 15-29 Hz (mean 18.5 Hz). The peak frequency of DTF from EEG to EMG was 12-27 Hz (mean 17.8 Hz). DTF from EEG to EMG was significantly larger than that from EMG to EEG at 19-30 and 45-50 Hz (P<0.05).

CONCLUSIONS

The present findings suggest that the EEG-EMG coupling mechanism for the 19 Hz or higher frequency might differ from that for the lower frequency. Directional information flow from EEG to EMG in the former frequency range likely reflects the motor control command. The finding of the directional information flow from EEG to EMG within the gamma band indicates that 40 Hz EEG-EMG coherence is not specific to the muscle Piper rhythm which is seen only with strong contraction.

Adaptive phase estimation and its application in EEG analysis of word processing

Oscillations are a general phenomenon of neuronal activity during information processing. Mostly, widespread networks are involved in brain functioning. In order to investigate network activity coherence analysis turned out to be a useful tool for examining the functional relationship between different cortical areas. This parameter allows the investigation of synchronisation phenomena with regard to defined frequencies or frequency bands. Coherence and cross phase are closely connected spectral parameters. Coherence may be understood as a measure of phase stability. Whereas coherence describes the amount of information transfer, the corresponding phase, from which time delays can be computed, hints at the direction of information transfer. Mental processes can be very brief and coupling between different areas may be highly dynamic. For this reason a two-dimensional approach of adaptive filtering was developed to estimate coherence and phase continuously in time. Statistical and dynamic properties of instantaneous phase are discussed. In order to demonstrate the value of this method for studying higher cognitive processes the method was applied to EEG recorded during word processing. During visual presentation of abstract nouns an information transfer from visual areas to frontal association areas in the Alpha1 frequency band could be verified within the first 400 ms. The Alpha1 band predominately seems to reflect sensory processing and attentional processes. In addition to conventional coherence analyses during word processing phase estimations may yield valuable new insights into the physiological mechanisms during word processing.

Interdependence of multiple theta generators in the hippocampus: a partial coherence analysis

The extracellularly recorded theta oscillation reflects a dynamic interaction of various synaptic and cellular mechanisms. Because the spatially overlapping dipoles responsible for the generation of theta field oscillation may represent different mechanisms, their separation might provide clues with regard to their origin and significance. We used a novel approach, partial coherence analysis, to reveal the various components of the theta rhythm and the relationship among its generators. Hippocampal field activity was recorded by a 16-site silicon probe in the CA1-dentate gyrus axis of the awake rat. Field patterns, recorded from various intrahippocampal or entorhinal cortex sites, were used to remove activity caused by a common source by the partialization procedure. The findings revealed highly coherent coupling between theta signals recorded (1) from the hippocampal fissure and stratum (str.) oriens of the CA1 region and (2) between CA1 stratum radiatum and the dentate molecular layer. The results of partial coherence analysis indicated that rhythmic input from the entorhinal cortex explained theta coherence between signals recorded from the hippocampal fissure and str. oriens but not the coherence between signals derived from str. radiatum and the dentate molecular layer. After bilateral lesions of the entorhinal cortex, all signals recorded from both below and above the CA1 hippocampal pyramidal cell layer became highly coherent. These observations indicate the presence of two, relatively independent, theta generators in the hippocampus, which are mediated by the entorhinal cortex and the CA3-mossy cell recurrent circuitry, respectively. The CA3-mossy cell theta generator is partially suppressed by the dentate gyrus interneuronal output in the intact brain. We suggest that timing of the action potentials of pyramidal cells during the theta cycle is determined by the cooperation between the active CA3 neurons and the entorhinal input.