EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI

Tensor Analysis and Fusion of Multimodal Brain Images

Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks that underpin human cognitive processes by analysis of different neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the multimodal, multi-scale nature of neuroimaging data is well reflected by a multi-way (tensor) structure where the underlying processes can be summarized by a relatively small number of components or "atoms". We introduce Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network notation in order to analyze these models. These diagrams not only clarify matrix and tensor EEG and fMRI time/frequency analysis and inverse problems, but also help understand multimodal fusion via Multiway Partial Least Squares and Coupled Matrix-Tensor Factorization. We show here, for the first time, that Granger causal analysis of brain networks is a tensor regression problem, thus allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI recordings shows the potential of the methods and suggests their use in other scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE

Guidelines for the recording and evaluation of pharmaco-EEG data in man: the International Pharmaco-EEG Society (IPEG)

The International Pharmaco-EEG Society (IPEG) presents updated guidelines summarising the requirements for the recording and computerised evaluation of pharmaco-EEG data in man. Since the publication of the first pharmaco-EEG guidelines in 1982, technical and data processing methods have advanced steadily, thus enhancing data quality and expanding the palette of tools available to investigate the action of drugs on the central nervous system (CNS), determine the pharmacokinetic and pharmacodynamic properties of novel therapeutics and evaluate the CNS penetration or toxicity of compounds. However, a review of the literature reveals inconsistent operating procedures from one study to another. While this fact does not invalidate results per se, the lack of standardisation constitutes a regrettable shortcoming, especially in the context of drug development programmes. Moreover, this shortcoming hampers reliable comparisons between outcomes of studies from different laboratories and hence also prevents pooling of data which is a requirement for sufficiently powering the validation of novel analytical algorithms and EEG-based biomarkers. The present updated guidelines reflect the consensus of a global panel of EEG experts and are intended to assist investigators using pharmaco-EEG in clinical research, by providing clear and concise recommendations and thereby enabling standardisation of methodology and facilitating comparability of data across laboratories

Comparing ICA-based and Single-Trial Topographic ERP Analyses

Single-trial analysis of human electroencephalography (EEG) has been recently proposed for better understanding the contribution of individual subjects to a group-analyis effect as well as for investigating single-subject mechanisms. Independent Component Analysis (ICA) has been repeatedly applied to concatenated single-trial responses and at a single-subject level in order to extract those components that resemble activities of interest. More recently we have proposed a single-trial method based on topographic maps that determines which voltage configurations are reliably observed at the event-related potential (ERP) level taking advantage of repetitions across trials. Here, we investigated the correspondence between the maps obtained by ICA versus the topographies that we obtained by the single-trial clustering algorithm that best explained the variance of the ERP. To do this, we used exemplar data provided from the EEGLAB website that are based on a dataset from a visual target detection task. We show there to be robust correpondence both at the level of the activation time courses and at the level of voltage configurations of a subset of relevant maps. We additionally show the estimated inverse solution (based on low-resolution electromagnetic tomography) of two corresponding maps occurring at approximately 300ms post-stimulus onset, as estimated by the two aforementioned approaches. The spatial distribution of the estimated sources significantly correlated and had in common a right parietal activation within Brodmann's Area (BA) 40. Despite their differences in terms of theoretical bases, the consistency between the results of these two approaches shows that their underlying assumptions are indeed compatibl

Multimodal imaging of human brain activity: rational, biophysical aspects and modes of integration

Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship

Knowledge-Driven Contrast Gain Control is Characterized by Two Distinct Electrocortical Markers

Sensitivity to variations in luminance (contrast) is fundamental to perception because contrasts define the edges and textures of visual objects. Recent research has shown that contrast sensitivity, in addition to being controlled by purely stimulus-driven mechanisms, is also affected by expectations and prior knowledge about the contrast of upcoming stimuli. The ability to adjust contrast sensitivity based on expectations and prior knowledge could help to maximize the information extracted when scanning familiar visual scenes. In the present study we used the event-related potentials (ERP) technique to resolve the stages that mediate knowledge-driven aspects of contrast gain control. Using groupwise independent components analysis and multivariate partial least squares, we isolated two robust spatiotemporal patterns of electrical brain activity associated with preparation for upcoming targets whose contrast was predicted by a cue. The patterns were sensitive to the informative value of the cue. When the cues were informative, these patterns were also able to differentiate among cues that predicted low-contrast targets and cues that predicted high-contrast targets. Both patterns were localized to parts of occipitotemporal cortex, and their morphology, latency, and topography resembled P2/N2 and P3 potentials. These two patterns provide electrophysiological markers of knowledge-driven preparation for impending changes in contrast and shed new light on the manner in which top-down factors modulate sensory processing

Prefrontal Cortex Based Sex Differences in Tinnitus Perception: Same Tinnitus Intensity, Same Tinnitus Distress, Different Mood

BACKGROUND: Tinnitus refers to auditory phantom sensation. It is estimated that for 2% of the population this auditory phantom percept severely affects the quality of life, due to tinnitus related distress. Although the overall distress levels do not differ between sexes in tinnitus, females are more influenced by distress than males. Typically, pain, sleep, and depression are perceived as significantly more severe by female tinnitus patients. Studies on gender differences in emotional regulation indicate that females with high depressive symptoms show greater attention to emotion, and use less anti-rumination emotional repair strategies than males. METHODOLOGY: The objective of this study was to verify whether the activity and connectivity of the resting brain is different for male and female tinnitus patients using resting-state EEG. CONCLUSIONS: Females had a higher mean score than male tinnitus patients on the BDI-II. Female tinnitus patients differ from male tinnitus patients in the orbitofrontal cortex (OFC) extending to the frontopolar cortex in beta1 and beta2. The OFC is important for emotional processing of sounds. Increased functional alpha connectivity is found between the OFC, insula, subgenual anterior cingulate (sgACC), parahippocampal (PHC) areas and the auditory cortex in females. Our data suggest increased functional connectivity that binds tinnitus-related auditory cortex activity to auditory emotion-related areas via the PHC-sgACC connections resulting in a more depressive state even though the tinnitus intensity and tinnitus-related distress are not different from men. Comparing male tinnitus patients to a control group of males significant differences could be found for beta3 in the posterior cingulate cortex (PCC). The PCC might be related to cognitive and memory-related aspects of the tinnitus percept. Our results propose that sex influences in tinnitus research cannot be ignored and should be taken into account in functional imaging studies related to tinnitus

Covariance-domain Dictionary Learning for Overcomplete EEG Source Identification

We propose an algorithm targeting the identification of more sources than channels for electroencephalography (EEG). Our overcomplete source identification algorithm, Cov-DL, leverages dictionary learning methods applied in the covariance-domain. Assuming that EEG sources are uncorrelated within moving time-windows and the scalp mixing is linear, the forward problem can be transferred to the covariance domain which has higher dimensionality than the original EEG channel domain. This allows for learning the overcomplete mixing matrix that generates the scalp EEG even when there may be more sources than sensors active at any time segment, i.e. when there are non-sparse sources. This is contrary to straight-forward dictionary learning methods that are based on the assumption of sparsity, which is not a satisfied condition in the case of low-density EEG systems. We present two different learning strategies for Cov-DL, determined by the size of the target mixing matrix. We demonstrate that Cov-DL outperforms existing overcomplete ICA algorithms under various scenarios of EEG simulations and real EEG experiments