Estimation of the effective and functional human cortical connectivity with structural equation modeling and directed transfer function applied to high-resolution EEG

Directional information flow analysis in memory retrieval: a comparison between exaggerated and normal pictures

Working memory plays an important role in cognitive science and is a basic process for learning. While working memory is limited in regard to capacity and duration, different cognitive tasks are designed to overcome these difficulties. This study investigated information flow during a novel visual working memory task in which participants respond to exaggerated and normal pictures. Ten healthy men (mean age 28.5 ± 4.57 years) participated in two stages of the encoding and retrieval tasks. The electroencephalogram (EEG) signals are recorded. Moreover, the adaptive directed transfer function (ADTF) method is used as a computational tool to investigate the dynamic process of visual working memory retrieval on the extracted event-related potentials (ERPs) from the EEG signal. Network connectivity and P300 sub-components (P3a, P3b, and LPC) are also extracted during visual working memory retrieval. Then, the nonparametric Wilcoxon test and five classifiers are applied to network properties for features selection and classification between exaggerated-old and normal-old pictures. The Z-values of Ge is more distinctive rather than other network properties. In terms of the machine learning approach, the accuracy, F1-score, and specificity of the k-nearest neighbors (KNN), classifiers are 81%, 77%, and 81%, respectively. KNN classifier ranked first compared with other classifiers. Furthermore, the results of in-degree/out-degree matrices show that the information flows continuously in the right hemisphere during the retrieval of exaggerated pictures, from P3a to P3b. During the retrieval of visual working memory, the networks associated with attentional processes show greater activation for exaggerated pictures compared to normal pictures. This suggests that the exaggerated pictures may have captured more attention and thus required greater cognitive resources for retrieval.

A Graph Fourier Transform Based Bidirectional Long Short-Term Memory Neural Network for Electrophysiological Source Imaging

Electrophysiological source imaging (ESI) refers to the process of reconstructing underlying activated sources on the cortex given the brain signal measured by Electroencephalography (EEG) or Magnetoencephalography (MEG). Due to the ill-posed nature of ESI, solving ESI requires the design of neurophysiologically plausible regularization or priors to guarantee a unique solution. Recovering focally extended sources is more challenging, and traditionally uses a total variation regularization to promote spatial continuity of the activated sources. In this paper, we propose to use graph Fourier transform (GFT) based bidirectional long-short term memory (BiLSTM) neural network to solve the ESI problem. The GFT delineates the 3D source space into spatially high, medium and low frequency subspaces spanned by corresponding eigenvectors. The low frequency components can naturally serve as a spatially low-band pass filter to reconstruct extended areas of source activation. The BiLSTM is adopted to learn the mapping relationship between the projection of low-frequency graph space and the recorded EEG. Numerical results show the proposed GFT-BiLSTM outperforms other benchmark algorithms in synthetic data under varied signal-to-noise ratios (SNRs). Real data experiments also demonstrate its capability of localizing the epileptogenic zone of epilepsy patients with good accuracy.

Brain functional and effective connectivity based on electroencephalography recordings: A review

Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.

Dynamic brain effective connectivity analysis based on low-rank canonical polyadic decomposition: application to epilepsy

In this paper, a new method to track brain effective connectivity networks in the context of epilepsy is proposed. It relies on the combination of partial directed coherence with a constrained low-rank canonical polyadic tensor decomposition. With such combination being established, the most dominating directed graph structures underlying each time window of intracerebral electroencephalographic signals are optimally inferred. Obtained time and frequency signatures of inferred brain networks allow respectively to track the time evolution of these networks and to define frequency bands on which they are operating. Besides, the proposed method allows also to track brain connectivity networks through several epileptic seizures of the same patient. Understanding the most dominating directed graph structures over epileptic seizures and investigating their behavior over time and frequency plans are henceforth possible. Since only few but the the most important directed connections in the graph structure are of interest and also for a meaningful interpretation of obtained signatures to be guaranteed, the low-rank canonical polyadic tensor decomposition is prompted respectively by the sparsity and the non-negativity constraints on the tensor loading matrices. The main objective of this contribution is to propose a new way of tracking brain networks in the context of epileptic iEEG data by identifying the most dominant effective connectivity patterns underlying the observed iEEG signals at each time window. The performance of the proposed method is firstly evaluated on simulated data imitating brain activities and secondly on real intracerebral electroencephalographic signals obtained from an epileptic patient. The partial directed coherence-based tensor has been decomposed into space, time, and frequency signatures in accordance with the expected ground truth for each consecutive sequence of the simulated data. The method is also in accordance with the clinical expertise for iEEG epileptic signals, where the signatures were investigated through a simultaneous multi-seizure analysis.

Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography

The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.

What type of inhibition underpins performance on Luria's Fist-Edge-Palm task?

OBJECTIVE

The Fist-Edge-Palm task is a motor sequencing task believed to be sensitive to frontal lobe impairment. The present study aimed to investigate the inhibitory processes underlying successful execution of this task.

METHOD

Seventy-two healthy participants were asked to perform the Fist-Edge-Palm task paced at 120 bpms, 60 bpms and self-paced. They also completed assessments sensitive to recently dissociated forms of inhibition (the Hayling Sentence Completion Test and the Stroop Color-Word Test) that have recently been shown to be differentially lateralized (the right and left Prefrontal Cortex, respectively), and Cattell's Culture Fair Intelligence test.

RESULTS

Analysis revealed that performance on the Hayling Sentence Completion Test predicted the amount of crude errors and the overall score on the Fist-Edge-Palm task, and that pacing condition had no effect on this outcome. Neither the Stroop Color-Word Test nor Cattell's Culture Fair Intelligence Test predicted performance on the Fist-Edge-Palm task.

CONCLUSIONS

Consistent with some previous neuroimaging findings, the present findings suggest that Fist-Edge-Palm task performance relies on right lateralized inhibitory processes.

EEGSourceSim: A framework for realistic simulation of EEG scalp data using MRI-based forward models and biologically plausible signals and noise

BACKGROUND

Electroencephalography (EEG) is widely used to investigate human brain function. Simulation studies are essential for assessing the validity of EEG analysis methods and the interpretability of results.

NEW METHOD

Here we present a simulation environment for generating EEG data by embedding biologically plausible signal and noise into MRI-based forward models that incorporate individual-subject variability in structure and function.

RESULTS

The package includes pipelines for the evaluation and validation of EEG analysis tools for source estimation, functional connectivity, and spatial filtering. EEG dynamics can be simulated using realistic noise and signal models with user specifiable signal-to-noise ratio (SNR). We also provide a set of quantitative metrics tailored to source estimation, connectivity and spatial filtering applications.

COMPARISON WITH EXISTING METHOD(S)

We provide a larger set of forward solutions for individual MRI-based head models than has been available previously. These head models are surface-based and include two sets of regions-of-interest (ROIs) that have been brought into registration with the brain of each individual using surface-based alignment - one from a whole brain and the other from a visual cortex atlas. We derive a realistic model of noise by fitting different model components to measured resting state EEG. We also provide a set of quantitative metrics for evaluating source-localization, functional connectivity and spatial filtering methods.

CONCLUSIONS

The inclusion of a larger number of individual head-models, combined with surface-atlas based labeling of ROIs and plausible models of signal and noise, allows for simulation of EEG data with greater realism than previous packages.

Quantifying the Effect of Demixing Approaches on Directed Connectivity Estimated Between Reconstructed EEG Sources

Electrical activity recorded on the scalp using electroencephalography (EEG) results from the mixing of signals originating from different regions of the brain as well as from artifactual sources. In order to investigate the role of distinct brain areas in a given experiment, the signal recorded on the sensors is typically projected back into the brain (source reconstruction) using algorithms that address the so-called EEG "inverse problem". Once the activity of sources located inside of the brain has been reconstructed, it is often desirable to study the statistical dependencies among them, in particular to quantify directional dynamical interactions between brain areas. Unfortunately, even when performing source reconstruction, the superposition of signals that is due to the propagation of activity from sources to sensors cannot be completely undone, resulting in potentially biased estimates of directional functional connectivity. Here we perform a set of simulations involving interacting sources to quantify source connectivity estimation performance as a function of the location of the sources, their distance to each other, the noise level, the source reconstruction algorithm, and the connectivity estimator. The generated source activity was projected onto the scalp and projected back to the cortical level using two source reconstruction algorithms, linearly constrained minimum variance beamforming and 'Exact' low-resolution tomography (eLORETA). In source space, directed connectivity was estimated using multi-variate Granger causality and time-reversed Granger causality, and compared with the imposed ground truth. Our results demonstrate that all considered factors significantly affect the connectivity estimation performance.

Epileptic source connectivity analysis based on estimating of dynamic time series of regions of interest

We propose a new source connectivity method by focusing on estimating time courses of the regions of interest (ROIs). To this aim, it is necessary to consider the strong inherent non-stationary behavior of neural activity. We develop an iterative dynamic approach to extract a single time course for each ROI encoding the temporal non-stationary features. The proposed approach explicitly includes dynamic constraints by taking into account the evolution of the sources activities for further dynamic connectivity analysis. We simulated an epileptic network with a non-stationary structure; accordingly, EEG source reconstruction using LORETA is performed. Using the reconstructed sources, the spatially compact ROIs are selected. Then, a single time course encoding the temporal non-stationarity is extracted for each ROI. An adaptive directed transfer function (ADTF) is applied to measure the information flow of underlying brain networks. Obtained results demonstrate that the contributed approach is more efficient to estimate the ROI time series and ROI to ROI information flow in comparison with existing methods. Our work is validated in three drug-resistance epilepsy patients. The proposed ROI time series estimation directly affects the quality of connectivity analysis, leading to the best possible seizure onset zone (SOZ) localization verified by electrocorticography and post-operational results.

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.

The absence of resting-state high-gamma cross-frequency coupling in patients with tinnitus

Tinnitus is a psychoacoustic phantom perception of currently unknown neuropathology. Despite a growing number of post-stimulus tinnitus studies, uncertainty still exists regarding the neural signature of tinnitus in the resting-state brain. In the present study, we used high-gamma cross-frequency coupling and a Granger causality analysis to evaluate resting-state electroencephalographic (EEG) data in healthy participants and patients with tinnitus. Patients with tinnitus lacked robust frontal delta-phase/central high-gamma-amplitude coupling that was otherwise clearly observed in healthy participants. Since low-frequency phase and high-frequency amplitude coupling reflects inter-regional communication during cognitive processing, and given the absence of frontal modulation in patients with tinnitus, we hypothesized that tinnitus might be related to impaired prefrontal top-down inhibitory control. A Granger causality analysis consistently showed abnormally pronounced functional connectivity of low-frequency activity in patients with tinnitus, possibly reflecting a deficiency in large-scale communication during the resting state. Moreover, different causal neurodynamics were characterized across two subgroups of patients with tinnitus; the T1 group (with higher P300 amplitudes) showed abnormal frontal-to-auditory cortical information flow, whereas the T2 group (with lower P300 amplitudes) exhibited abnormal auditory-to-frontal cortical information control. This dissociation in resting-state low-frequency causal connectivity is consistent with recent post-stimulus observations. Taken together, our findings suggest that maladaptive neuroplasticity or abnormal reorganization occurs in the auditory default mode network of patients with tinnitus. Additionally, our data highlight the utility of resting-state EEG for the quantitative diagnosis of tinnitus symptoms and the further characterization of tinnitus subtypes.

A Critical Assessment of Directed Connectivity Estimates with Artificially Imposed Causality in the Supramammillary-Septo-Hippocampal Circuit

Algorithms for estimating directed connectivity have become indispensable to further understand the neurodynamics between functionally coupled brain areas. The evaluation of directed connectivity on the propagation of brain activity has largely been based on simulated data or toy models, where various hidden properties of neurophysiological data may not be fully recapitulated. In this study, directionality was unequivocally manipulated in the freely moving rat in a unique dataset, where normal oscillatory interactions between the supramammillary nucleus (SuM) and hippocampus (HPC) were attenuated by temporary medial septal (MS) inactivation, and replaced by electrical stimulation of the fornix to evaluate the performance of several directed connectivity assessment methods. The directed transfer function, partial directed coherence, directed coherence, pair-wise Geweke-Granger causality, phase slope index, and phase transfer entropy, all found SuM to HPC theta propagation when the MS is inactivated, and HPC activity was driven by peaks of simultaneously recorded SuM theta. As expected from theoretical expectations and simulated data, signal features including coupling strength, signal-to-noise ratio, and stationarity all weakly affected directed connectivity measures. We conclude that all the examined directed connectivity estimates correctly identify artificially imposed uni-directionality of brain oscillations in freely moving animals. Non-auto-regressive modeling based methods appear to be the most robust, and are least affected by inherent features in data such as signal-to-noise ratio and stationarity.

Changes in Effective Connectivity Network Patterns in Drug Abusers, Treated With Different Methods

Introduction:

Various treatment methods for drug abusers will result in different success rates. This is partly due to different neural assumptions and partly due to various rate of relapse in abusers because of different circumstances. Investigating the brain activation networks of treated subjects can reveal the hidden mechanisms of the therapeutic methods.

Methods:

We studied three groups of subjects: heroin abusers treated with abstinent based therapy (ABT) method, heroin abusers treated with Methadone Maintenance Therapy (MMT) method, and a control group. They were all scanned with functional magnetic resonance imaging (fMRI), using a 6-block task, where each block consisted of the rest-craving-rest-neutral sequence. Using the dynamic causal modeling (DCM) algorithm, brain effective connectivity network (caused by the drug craving stimulation) was quantified for all groups. In this regard, 4 brain areas were selected for this analysis based on previous findings: ventromedial prefrontal cortex (VMPFC), dorsolateral prefrontal cortex (DLPFC), amygdala, and ventral striatum.

Results:

Our results indicated that the control subjects did not show significant brain activations after craving stimulations, but the two other groups showed significant brain activations in all 4 regions. In addition, VMPFC showed higher activations in the ABT group compared to the MMT group. The effective connectivity network suggested that the control subjects did not have any direct input from drug-related cue indices, while the other two groups showed reactions to these cues. Also, VMPFC displayed an important role in ABT group. In encountering the craving pictures, MMT subjects manifest a very simple mechanism compared to other groups.

Conclusion:

This study revealed an activation network similar to the emotional and inhibitory control networks observed in drug abusers in previous works. The results of DCM analysis also support the regulatory role of frontal regions on bottom regions. Furthermore, this study demonstrates the different effective connectivity patterns after drug abuse treatment and in this way helps the experts in the field.

The influence of corticospinal activity on TMS-evoked activity and connectivity in healthy subjects: A TMS-EEG study

Combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) can be used to analyze cortical reactivity and connectivity. However, the effects of corticospinal and peripheral muscle activity on TMS-evoked potentials (TEPs) are not well understood. The aim of this paper is to evaluate the relationship between cortico-spinal activity, in the form of peripheral motor-evoked potentials (MEPs), and the TEPs from motor areas, along with the connectivity among activated brain areas. TMS was applied to left and right motor cortex (M1), separately, at motor threshold while multi-channel EEG responses were recorded in 17 healthy human subjects. Cortical excitability and source imaging analysis were performed for all trials at each stimulation location, as well as comparing trials resulting in MEPs to those without. Connectivity analysis was also performed comparing trials resulting in MEPs to those without. Cortical excitability results significantly differed between the MEP and no-MEP conditions for left M1 TMS at 60 ms (CP1, CP3, C1) and for right M1 TMS at 54 ms (CP6, C6). Connectivity analysis revealed higher outflow and inflow between M1 and somatosensory cortex bi-directionally for trials with MEPs than those without for both left M1 TMS (at 60, 100, 164 ms) and right M1 TMS (at 54, 100, and 164 ms). Both TEP amplitudes and connectivity measures related to motor and somatosensory areas ipsilateral to the stimulation were shown to correspond with peripheral MEP amplitudes. This suggests that cortico-spinal activation, along with the resulting somatosensory feedback, affects the cortical activity and dynamics within motor areas reflected in the TEPs. The findings suggest that TMS-EEG, along with adaptive connectivity estimators, can be used to evaluate the cortical dynamics associated with sensorimotor integration and proprioceptive manipulation along with the influence of peripheral muscle feedback.

Comparison of Local Information Indices Applied in Resting State Functional Brain Network Connectivity Prediction

Anatomical distance has been widely used to predict functional connectivity because of the potential relationship between structural connectivity and functional connectivity. The basic implicit assumption of this method is “distance penalization.” But studies have shown that one-parameter model (anatomical distance) cannot account for the small-worldness, modularity, and degree distribution of normal human brain functional networks. Two local information indices–common neighbor (CN) and preferential attachment index (PA), are introduced into the prediction model as another parameter to emulate many key topological of brain functional networks in the previous study. In addition to these two indices, many other local information indices can be chosen for investigation. Different indices evaluate local similarity from different perspectives. Currently, we still have no idea about how to select local information indices to achieve higher predicted accuracy of functional connectivity. Here, seven local information indices are chosen, including CN, hub depressed index (HDI), hub promoted index (HPI), Leicht-Holme-Newman index (LHN-I), Sørensen index (SI), PA, and resource allocation index (RA). Statistical analyses were performed on eight network topological properties to evaluate the predictions. Analysis shows that different prediction models have different performances in terms of simulating topological properties and most of the predicted network properties are close to the real data. There are four topological properties whose average relative error is less than 5%, including characteristic path length, clustering coefficient, global efficiency, and local efficiency. CN model shows the most accurate predictions. Statistical analysis reveals that five properties within the CN-predicted network do not differ significantly from the real data (P > 0.05, false-discovery rate method corrected for seven comparisons). PA model shows the worst prediction performance which was first applied in models of growth networks. Our results suggest that PA is not suitable for predicting connectivity in a small-world network. Furthermore, in order to evaluate the predictions rapidly, prediction power was proposed as an evaluation metric. The current study compares the predictions of functional connectivity with seven local information indices and provides a reference of method selection for construction of prediction models.

Alterations of Intrinsic Brain Connectivity Patterns in Depression and Bipolar Disorders: A Critical Assessment of Magnetoencephalography-Based Evidence

Despite being the object of a thriving field of clinical research, the investigation of intrinsic brain network alterations in psychiatric illnesses is still in its early days. Because the pathological alterations are predominantly probed using functional magnetic resonance imaging (fMRI), many questions about the electrophysiological bases of resting-state alterations in psychiatric disorders, particularly among mood disorder patients, remain unanswered. Alongside important research using electroencephalography (EEG), the specific recent contributions and future promise of magnetoencephalography (MEG) in this field are not fully recognized and valued. Here, we provide a critical review of recent findings from MEG resting-state connectivity within major depressive disorder (MDD) and bipolar disorder (BD). The clinical MEG resting-state results are compared with those previously reported with fMRI and EEG. Taken together, MEG appears to be a promising but still critically underexploited technique to unravel the neurophysiological mechanisms that mediate abnormal (both hyper- and hypo-) connectivity patterns involved in MDD and BD. In particular, a major strength of MEG is its ability to provide source-space estimations of neuromagnetic long-range rhythmic synchronization at various frequencies (i.e., oscillatory coupling). The reviewed literature highlights the relevance of probing local and interregional rhythmic synchronization to explore the pathophysiological underpinnings of each disorder. However, before we can fully take advantage of MEG connectivity analyses in psychiatry, several limitations inherent to MEG connectivity analyses need to be understood and taken into account. Thus, we also discuss current methodological challenges and outline paths for future research. MEG resting-state studies provide an important window onto perturbed spontaneous oscillatory brain networks and hence supply an important complement to fMRI-based resting-state measurements in psychiatric populations.

Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness

It is becoming increasingly clear that combining multi-modal brain imaging data is able to provide more information for individual subjects by exploiting the rich multimodal information that exists. However, the number of studies that do true multimodal fusion (i.e. capitalizing on joint information among modalities) is still remarkably small given the known benefits. In part, this is because multi-modal studies require broader expertise in collecting, analyzing, and interpreting the results than do unimodal studies. In this paper, we start by introducing the basic reasons why multimodal data fusion is important and what it can do, and importantly how it can help us avoid wrong conclusions and help compensate for imperfect brain imaging studies. We also discuss the challenges that need to be confronted for such approaches to be more widely applied by the community. We then provide a review of the diverse studies that have used multimodal data fusion (primarily focused on psychosis) as well as provide an introduction to some of the existing analytic approaches. Finally, we discuss some up-and-coming approaches to multi-modal fusion including deep learning and multimodal classification which show considerable promise. Our conclusion is that multimodal data fusion is rapidly growing, but it is still underutilized. The complexity of the human brain coupled with the incomplete measurement provided by existing imaging technology makes multimodal fusion essential in order to mitigate against misdirection and hopefully provide a key to finding the missing link(s) in complex mental illness.

Why more is better: Simultaneous modeling of EEG, fMRI, and behavioral data

The need to test a growing number of theories in cognitive science has led to increased interest in inferential methods that integrate multiple data modalities. In this manuscript, we show how a method for integrating three data modalities within a single framework provides (1) more detailed descriptions of cognitive processes and (2) more accurate predictions of unobserved data than less integrative methods. Specifically, we show how combining either EEG and fMRI with a behavioral model can perform substantially better than a behavioral-data-only model in both generative and predictive modeling analyses. We then show how a trivariate model - a model including EEG, fMRI, and behavioral data - outperforms bivariate models in both generative and predictive modeling analyses. Together, these results suggest that within an appropriate modeling framework, more data can be used to better constrain cognitive theory, and to generate more accurate predictions for behavioral and neural data.

Effective connectivity of visual word recognition and homophone orthographic errors

The study of orthographic errors in a transparent language like Spanish is an important topic in relation to writing acquisition. The development of neuroimaging techniques, particularly functional magnetic resonance imaging (fMRI), has enabled the study of such relationships between brain areas. The main objective of the present study was to explore the patterns of effective connectivity by processing pseudohomophone orthographic errors among subjects with high and low spelling skills. Two groups of 12 Mexican subjects each, matched by age, were formed based on their results in a series of ad hoc spelling-related out-scanner tests: a high spelling skills (HSSs) group and a low spelling skills (LSSs) group. During the f MRI session, two experimental tasks were applied (spelling recognition task and visuoperceptual recognition task). Regions of Interest and their signal values were obtained for both tasks. Based on these values, structural equation models (SEMs) were obtained for each group of spelling competence (HSS and LSS) and task through maximum likelihood estimation, and the model with the best fit was chosen in each case. Likewise, dynamic causal models (DCMs) were estimated for all the conditions across tasks and groups. The HSS group's SEM results suggest that, in the spelling recognition task, the right middle temporal gyrus, and, to a lesser extent, the left parahippocampal gyrus receive most of the significant effects, whereas the DCM results in the visuoperceptual recognition task show less complex effects, but still congruent with the previous results, with an important role in several areas. In general, these results are consistent with the major findings in partial studies about linguistic activities but they are the first analyses of statistical effective brain connectivity in transparent languages.

EEG source connectivity analysis: from dense array recordings to brain networks

The recent past years have seen a noticeable increase of interest for electroencephalography (EEG) to analyze functional connectivity through brain sources reconstructed from scalp signals. Although considerable advances have been done both on the recording and analysis of EEG signals, a number of methodological questions are still open regarding the optimal way to process the data in order to identify brain networks. In this paper, we analyze the impact of three factors that intervene in this processing: i) the number of scalp electrodes, ii) the combination between the algorithm used to solve the EEG inverse problem and the algorithm used to measure the functional connectivity and iii) the frequency bands retained to estimate the functional connectivity among neocortical sources. Using High-Resolution (hr) EEG recordings in healthy volunteers, we evaluated these factors on evoked responses during picture recognition and naming task. The main reason for selection this task is that a solid literature background is available about involved brain networks (ground truth). From this a priori information, we propose a performance criterion based on the number of connections identified in the regions of interest (ROI) that belong to potentially activated networks. Our results show that the three studied factors have a dramatic impact on the final result (the identified network in the source space) as strong discrepancies were evidenced depending on the methods used. They also suggest that the combination of weighted Minimum Norm Estimator (wMNE) and the Phase Synchronization (PS) methods applied on High-Resolution EEG in beta/gamma bands provides the best performance in term of topological distance between the identified network and the expected network in the above-mentioned cognitive task.

Increasing fMRI sampling rate improves Granger causality estimates

Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI) is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD) contrast based whole-head inverse imaging (InI). Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain.

Neuroelectrical imaging investigation of cortical activity during listening to music in prelingually deaf children with cochlear implants

OBJECTIVE

To date, no objective measure of the pleasantness of music perception by children with cochlear implants has been reported. The EEG alpha asymmetries of pre-frontal cortex activation are known to relate to emotional/affective engagement in a perceived stimulus. More specifically, according to the "withdrawal/approach" model, an unbalanced de-synchronization of the alpha activity in the left prefrontal cortex has been associated with a positive affective state/approach toward a stimulus, and an unbalanced de-synchronization of the same activity in the right prefrontal cortex with a negative affective state/withdrawal from a stimulus. In the present study, High-Resolution EEG with Source Reconstruction was used to compare the music-induced alpha asymmetries of the prefrontal cortex in a group of prelingually deaf implanted children and in a control group of normal-hearing children.

METHODS

Six normal-hearing and six age-matched deaf children using a unilateral cochlear implants underwent High-Resolution EEG recordings as they were listening to a musical cartoon. Musical stimuli were delivered in three versions: Normal, Distort (reverse audio flow) and Mute. The EEG alpha rhythm asymmetry was analyzed: Power Spectral Density was calculated for each Region of Interest, together with a right-left imbalance index. A map of cortical activation was then reconstructed on a realistic cortical model.

RESULTS

Asymmetries of EEG alpha rhythm in the prefrontal cortices were observed in both groups. In the normal-hearing children, the asymmetries were consistent with the withdrawal/approach model, whereas in cochlear implant users they were not. Moreover, in implanted children a different pattern of alpha asymmetries in extrafrontal cortical areas was noticed as compared to normal-hearing subjects.

CONCLUSIONS

The peculiar pattern of alpha asymmetries in implanted children's prefrontal cortex in response to musical stimuli suggests an inability by these subjects to discriminate normal from dissonant music and to appreciate the pleasantness of normal music. High-Resolution EEG may prove to be a promising tool for objectively measuring prefrontal cortex alpha asymmetries in child cochlear implant users.

Inhibition of propofol anesthesia on functional connectivity between LFPs in PFC during rat working memory task

Working memory (WM) refers to the temporary storage and manipulation of information necessary for performance of complex cognitive tasks. There is a growing interest in whether and how propofol anesthesia inhibits WM function. The aim of this study is to investigate the possible inhibition mechanism of propofol anesthesia based on the functional connections of multi-local field potentials (LFPs) and behavior during WM tasks. Adult SD rats were randomly divided into 3 groups: pro group (0.5 mg·kg⁻¹·min⁻¹,2 h), PRO group (0.9 mg·kg⁻¹·min⁻¹, 2 h) and control group. The experimental data were 16-channel LFPs obtained at prefrontal cortex with implanted microelectrode array in SD rats during WM tasks in Y-maze at 24, 48, 72, 96, 120 hours (day 1-day 5) after propofol anesthesia, and the behavior results of WM were recoded at the same time. Directed transfer function (DTF) method was applied to analyze the connections among LFPs directly. Furthermore, the causal networks were identified by DTF. The clustering coefficient (C), network density (D) and global efficiency (E_global) were selected to describe the functional connectivity quantitatively. The results show that: comparing with the control group, the LFPs functional connectivity in pro group were no significantly difference (p>0.05); the connectivity in PRO group were significantly decreased (p<0.05 at 24 hours, p<0.05 at 48 hours), while no significant difference at 72, 96 and 120 hours for rats (p>0.05), which were consistent with the behavior results. These findings could lead to improved understanding the mechanism of inhibition of anesthesia on WM functions from the view of connections among LFPs.

EEG frontal asymmetry related to pleasantness of music perception in healthy children and cochlear implanted users

Interestingly, the international debate about the quality of music fruition for cochlear implanted users does not take into account the hypothesis that bilateral users could perceive music in a more pleasant way with respect to monolateral users. In this scenario, the aim of the present study was to investigate if cerebral signs of pleasantness during music perception in healthy child are similar to those observed in monolateral and in bilateral cochlear implanted users. In fact, previous observations in literature on healthy subjects have indicated that variations of the frontal EEG alpha activity are correlated with the perceived pleasantness of the sensory stimulation received (approach-withdrawal theory). In particular, here we described differences between cortical activities estimated in the alpha frequency band for a healthy child and in patients having a monolateral or a bilateral cochlear implant during the fruition of a musical cartoon. The results of the present analysis showed that the alpha EEG asymmetry patterns observed in a healthy child and that of a bilateral cochlear implanted patient are congruent with the approach-withdrawal theory. Conversely, the scalp topographic distribution of EEG power spectra in the alpha band resulting from the monolateral cochlear user presents a different EEG pattern from the normal and bilateral implanted patients. Such differences could be explained at the light of the approach-withdrawal theory. In fact, the present findings support the hypothesis that a monolateral cochlear implanted user could perceive the music in a less pleasant way when compared to a healthy subject or to a bilateral cochlear user.

Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness

This paper reviews published papers related to neurophysiological measurements (electroencephalography: EEG, electrooculography EOG; heart rate: HR) in pilots/drivers during their driving tasks. The aim is to summarise the main neurophysiological findings related to the measurements of pilot/driver's brain activity during drive performance and how particular aspects of this brain activity could be connected with the important concepts of "mental workload", "mental fatigue" or "situational awareness". Review of the literature suggests that exists a coherent sequence of changes for EEG, EOG and HR variables during the transition from normal drive, high mental workload and eventually mental fatigue and drowsiness. In particular, increased EEG power in theta band and a decrease in alpha band occurred in high mental workload. Successively, increased EEG power in theta as well as delta and alpha bands characterise the transition between mental workload and mental fatigue. Drowsiness is also characterised by increased blink rate and decreased HR values. The detection of such mental states is actually performed "offline" with accuracy around 90% but not online. A discussion on the possible future applications of findings provided by these neurophysiological measurements in order to improve the safety of the vehicles will be also presented.

How the statistical validation of functional connectivity patterns can prevent erroneous definition of small-world properties of a brain connectivity network

The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes.

A selective review of multimodal fusion methods in schizophrenia

Schizophrenia (SZ) is one of the most cryptic and costly mental disorders in terms of human suffering and societal expenditure (van Os and Kapur, 2009). Though strong evidence for functional, structural, and genetic abnormalities associated with this disease exists, there is yet no replicable finding which has proven accurate enough to be useful in clinical decision making (Fornito et al., 2009), and its diagnosis relies primarily upon symptom assessment (Williams et al., 2010a). It is likely in part that the lack of consistent neuroimaging findings is because most models favor only one data type or do not combine data from different imaging modalities effectively, thus missing potentially important differences which are only partially detected by each modality (Calhoun et al., 2006a). It is becoming increasingly clear that multimodal fusion, a technique which takes advantage of the fact that each modality provides a limited view of the brain/gene and may uncover hidden relationships, is an important tool to help unravel the black box of schizophrenia. In this review paper, we survey a number of multimodal fusion applications which enable us to study the schizophrenia macro-connectome, including brain functional, structural, and genetic aspects and may help us understand the disorder in a more comprehensive and integrated manner. We also provide a table that characterizes these applications by the methods used and compare these methods in detail, especially for multivariate models, which may serve as a valuable reference that helps readers select an appropriate method based on a given research question.

Assessment of mental fatigue during car driving by using high resolution EEG activity and neurophysiologic indices

Driving tasks are vulnerable to the effects of sleep deprivation and mental fatigue, diminishing driver's ability to respond effectively to unusual or emergent situations. Physiological and brain activity analysis could help to understand how to provide useful feedback and alert signals to the drivers for avoiding car accidents. In this study we analyze the insurgence of mental fatigue or drowsiness during car driving in a simulated environment by using high resolution EEG techniques as well as neurophysiologic variables such as heart rate (HR) and eye blinks rate (EBR). Results suggest that it is possible to introduce a EEG-based cerebral workload index that it is sensitive to the mental efforts of the driver during drive tasks of different levels of difficulty. Workload index was based on the estimation of increase of EEG power spectra in the theta band over prefrontal areas and the simultaneous decrease of EEG power spectra over parietal areas in alpha band during difficult drive conditions. Such index could be used in a future to assess on-line the mental state of the driver during the drive task.

Seizure source imaging by means of FINE spatio-temporal dipole localization and directed transfer function in partial epilepsy patients

OBJECTIVE

To investigate the usage of a high-density EEG recording system and source imaging technique for localizing seizure activity in patients with medically intractable partial epilepsy.

METHODS

High-density, 76-channel scalp EEG signals were recorded in 10 patients with partial epilepsy. The patients underwent routine clinical pre-surgical evaluation and all had resective surgery with seizure free outcome. After applying a FINE (first principle vectors) spatio-temporal source localization and DTF (directed transfer function) connectivity analysis approach, ictal sources were imaged. Effects of number of scalp EEG electrodes on the seizure localization were also assessed using 76, 64, 48, 32, and 21 electrodes, respectively.

RESULTS

Surgical resections were used to assess the source imaging results. Results from the 76-channel EEG in the 10 patients showed high correlation with the surgically resected brain regions. The localization of seizure onset zone from 76-channel EEG showed improved source detection accuracy compared to other EEG configurations with fewer electrodes.

CONCLUSIONS

FINE together with DTF was able to localize seizure onset zones of partial epilepsy patients. High-density EEG recording can help achieve improved seizure source imaging.

SIGNIFICANCE

The present results suggest the promise of high-density EEG and electrical source imaging for noninvasively localizing seizure onset zones.

Recent advances in understanding the neural bases of autism spectrum disorder

PURPOSE OF REVIEW

This article reviews current work investigating the neural bases of autism spectrum disorder (ASD) within the discipline of electrophysiological brain research. The manuscript focuses primarily on advances in understanding related to social information processing and interconnectivity among brain systems in ASD.

RECENT FINDINGS

Recent research indicates anomalous function of social brain regions in ASD and highlights the specificity of processing problems to these systems. Atypical activity in this circuitry may reflect genetic susceptibility for ASD, with increased activity in compensatory areas marking the distinction between developing and not developing the disorder. Advances in understanding connectivity in ASD are highlighted by novel work providing initial evidence of atypical interconnectivity in infancy.

SUMMARY

Emerging understanding of neural dysfunction in ASD indicates consistent but heterogeneous dysfunction across brain systems in ASD. Key objectives for the immediate future include the use of multimethod approaches that encompass temporal and spatial imaging; behavioral phenotyping carried out in developmental context to reveal subgroups defined uniquely by trajectories; and individual-specific profiles of behavioral performance and brain function.

A review of multivariate methods for multimodal fusion of brain imaging data

The development of various neuroimaging techniques is rapidly improving the measurements of brain function/structure. However, despite improvements in individual modalities, it is becoming increasingly clear that the most effective research approaches will utilize multi-modal fusion, which takes advantage of the fact that each modality provides a limited view of the brain. The goal of multi-modal fusion is to capitalize on the strength of each modality in a joint analysis, rather than a separate analysis of each. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions from high dimensional data with a limited number of subjects. Numerous research efforts have been reported in the field based on various statistical approaches, e.g. independent component analysis (ICA), canonical correlation analysis (CCA) and partial least squares (PLS). In this review paper, we survey a number of multivariate methods appearing in previous multimodal fusion reports, mostly fMRI with other modality, which were performed with or without prior information. A table for comparing optimization assumptions, purpose of the analysis, the need of priors, dimension reduction strategies and input data types is provided, which may serve as a valuable reference that helps readers understand the trade-offs of the 7 methods comprehensively. Finally, we evaluate 3 representative methods via simulation and give some suggestions on how to select an appropriate method based on a given research.

Functional connectivity analysis of steady-state visual evoked potentials

In this paper, functional connectivity of steady-state visual evoked potential (SSVEP) was investigated. Directed transfer function (DTF) was applied to cortical signals recorded from electroencephalography (EEG) in order to obtain connectivity patterns. Flow gain was proposed to assess the role of the specific brain region involved in the information transmission process. We found network connections exist in many regions beyond occipital region. Flow gain mapping both in 8-12Hz and 13-30Hz showed that parietal region seemed to serve as the sole hub of information transmission. Further studies of flow gain obtained from channel Pz showed two distinct peaks centered at about 12Hz low frequency and 20Hz medium frequency respectively. The low frequency region had a larger value of flow gain. The present study introduced functional connectivity into SSVEP. Furthermore, we put forward the concept of flow gain for the first time to explore the exchange and processing of brain information during SSVEP.

eConnectome: A MATLAB toolbox for mapping and imaging of brain functional connectivity

We have developed a MATLAB-based toolbox, eConnectome (electrophysiological connectome), for mapping and imaging functional connectivity at both the scalp and cortical levels from the electroencephalogram (EEG), as well as from the electrocorticogram (ECoG). Graphical user interfaces were designed for interactive and intuitive use of the toolbox. Major functions of eConnectome include EEG/ECoG preprocessing, scalp spatial mapping, cortical source estimation, connectivity analysis, and visualization. Granger causality measures such as directed transfer function and adaptive directed transfer function were implemented to estimate the directional interactions of brain functional networks, over the scalp and cortical sensor spaces. Cortical current density inverse imaging was implemented using a generic realistic geometry brain-head model from scalp EEGs. Granger causality could be further estimated over the cortical source domain from the inversely reconstructed cortical source signals as derived from the scalp EEG. Users may implement other connectivity estimators in the framework of eConnectome for various applications. The toolbox package is open-source and freely available at http://econnectome.umn.edu under the GNU general public license for noncommercial and academic uses.

Hippocampal-neocortical networks differ during encoding and retrieval of relational memory: functional and effective connectivity analyses

Encoding and retrieval of relational information requires interaction between the hippocampus and various neocortical regions, but it is unknown whether the connectivity of hippocampal-neocortical networks is different at input and output stages. To examine this, we conducted a network analysis of event-related fMRI data collected during a face-recognition, remember/know paradigm. Directed analyses in the medial temporal lobe identified a small region in the left hippocampus that showed differential activation for encoding and retrieval of recollected versus familiar items. Multivariate seed partial least squares (PLS) analysis was used to identify brain regions that were functionally connected to this hippocampal region at encoding and retrieval of 'remembered' items. Anatomically based structural equation modeling (SEM) was then used to test for differences in effective connectivity of network nodes between these two memory stages. The SEM analysis revealed a reversal of directionality between the left hippocampus (LHC) and left inferior parietal cortex (LIPC) at encoding and retrieval. During encoding, activation of the LHC had a positive influence on the LIPC, whereas during retrieval the reverse pattern was found, i.e., the LIPC activation positively influenced LHC activation. These findings emphasize the importance of hippocampal-parietal connections and underscore the complexity of their interactions in initial binding and retrieval/reintegration of relational memory. We also found that, during encoding, the right hippocampus had a positive influence on the right retrospenial cortex, whereas during retrieval this influence was significantly weaker. We submit that examining patterns of connectivity can be important both to elaborate and constrain models of memory involving hippocampal-neocortical interactions.

EEG-fMRI reciprocal functional neuroimaging

OBJECTIVE

Integration of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has been pursued in an effort to achieve greater spatio-temporal resolution of imaging dynamic brain activity. We report a data-driven approach to image spatio-temporal features of neural oscillatory activity and event-related activity from continuously recorded EEG and fMRI signals.

METHODS

This approach starts with using the independent component analysis (ICA) to decompose the spatio-temporal EEG data into a linear combination of scalp potential maps and time courses. The time course of each independent component (IC) is used to construct a regressor to fit the fMRI time series. The resultant fMRI map then feeds back as a spatial constraint to the estimation of the source distribution underlying the corresponding IC's scalp map. The estimated source distributions multiplied by the corresponding IC time courses are summed across all ICs, giving rise to the reconstructed spatio-temporal brain activity. Functional connectivity between cortical areas can be further revealed from the imaged source signals using phase synchrony measures. We tested the method using both simulated oscillatory activity and event-related neural activity at various cortical regions. We also used this method to study the alpha-band EEG modulations in an eyes-open-eyes-closed human experiment.

RESULTS

In the simulation study, reliable reconstruction of the localization, time-frequency feature and cortical functional connection were achieved for the simulated oscillatory and event-related activities. In the experimental study, the alpha rhythmic modulation was localized mainly in the occipital visual area and the parieto-occipital sulcus. Within these regions, time-frequency analysis and phase-synchronization analysis indicated increased alpha power and alpha-band phase-synchronization in eyes-closed condition versus eyes-open condition.

CONCLUSION

Our results suggest that the proposed approach is well suited to image continuously oscillatory activities and their functional connectivity.

SIGNIFICANCE

Such ability promises to facilitate the investigation of the long-term neural behaviors and large-scale cortical interactions involved in spontaneous brain activity and cognitive tasks.

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.