A review of EEG and MEG for brainnetome research
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paper_title: Functional imaging and localization of electromagnetic brain activity

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Functional imaging of electric brain activity requires specific models to transform the signals recorded at the surface of the human head into an image. Two categories of model are available: single-time-point and spatio-temporal methods. The instantaneous methods rely only on the few voltage differences measured at one sampling point. To create a spatial image from this limited information, they require strict assumptions that rarely conform with the underlying physiology. Spatio-temporal models create two kinds of images: first, a spatial image of discrete equivalent multiple dipoles or regional sources, and second, an image of source current waveforms that reflect the temporal dynamics of the brain activity in circumscribed areas. The accuracy of the spatial image is model dependent and limited, but it can be validated from the spatio-temporal data by the "regional source imaging" technique, introduced here. The source waveforms are linear combinations of the scalp waveforms, and thus, specific derivations which image local brain activities at a macroscopic level. Brain source imaging of somatosensory evoked potentials revealed temporally overlapping activities from the brainstem, thalamus and from multiple sources in the region of the contralateral somatosensory projection areas.
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paper_title: BOLD correlates of EEG topography reveal rapid resting-state network dynamics

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article i nfo Resting-state functional connectivity studies with fMRI showed that the brain is intrinsically organized into large-scale functional networks for which the hemodynamic signature is stable for about 10 s. Spatial analyses of the topography of the spontaneous EEG also show discrete epochs of stable global brain states (so-called microstates), but they remain quasi-stationary for only about 100 ms. In order to test the relationship between the rapidly fluctuating EEG-defined microstates and the slowly oscillating fMRI- defined resting states, we recorded 64-channel EEG in the scanner while subjects were at rest with their eyes closed. Conventional EEG-microstate analysis determined the typical four EEG topographies that dominated across all subjects. The convolution of the time course of these maps with the hemodynamic response function allowed to fit a linear model to the fMRI BOLD responses and revealed four distinct distributed networks. These networks were spatially correlated with four of the resting-state networks (RSNs) that were found by the conventional fMRI group-level independent component analysis (ICA). These RSNs have previously been attributed to phonological processing, visual imagery, attention reorientation, and subjective interoceptive-autonomic processing. We found no EEG-correlate of the default mode network. Thus, the four typical microstates of the spontaneous EEG seem to represent the neurophysiological correlate of four of the RSNs and show that they are fluctuating much more rapidly than fMRI alone suggests.
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paper_title: Resonant cell assemblies: a new approach to cognitive functions and neuronal synchrony.

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This paper presents a novel reading of ideas on temporal binding as a key for cognitive operations by means of fast (gamma band) phase synchrony. We advocate a view of binding of widely distributed cell assemblies transiently locked in a neural hypergraph which serves as a reference point to incorporate or interpret other less coherent concurrent neural events. The paper traces in some detail the empirical evidence concerning the gamma binding process and presents some implications for the constitution of a unified cognitive-mental space.
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paper_title: A multiresolution framework to MEG/EEG source imaging

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A new method based on a multiresolution approach for solving the ill-posed problem of brain electrical activity reconstruction from EEG/MEG signals is proposed in a distributed source model. At each step of the algorithm, a regularized solution to the inverse problem is used to constrain the source space on the cortical surface to be scanned at higher spatial resolution. We present the iterative procedure together with an extension of the ST-maximum a posteriori method that integrates spatial and temporal a priori information in an estimator of the brain electrical activity. Results from EEG in a phantom head experiment with a real human skull and from real MEG data on a healthy human subject are presented. The performances of the multiresolution method combined with a nonquadratic estimator are compared with commonly used dipolar methods, and to minimum-norm method with and without multiresolution. In all cases, the proposed approach proved to be more efficient both in terms of computational load and result quality, for the identification of sparse focal patterns of cortical current density, than the fixed scale imaging approach.
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paper_title: Mapping human brain function with MEG and EEG: methods and validation

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We survey the field of magnetoencephalography (MEG) and electroencephalography (EEG) source estimation. These modalities offer the potential for functional brain mapping with temporal resolution in the millisecond range. However, the limited number of spatial measurements and the ill-posedness of the inverse problem present significant limits to our ability to produce accurate spatial maps from these data without imposing major restrictions on the form of the inverse solution. Here we describe approaches to solving the forward problem of computing the mapping from putative inverse solutions into the data space. We then describe the inverse problem in terms of low dimensional solutions, based on the equivalent current dipole (ECD), and high dimensional solutions, in which images of neural activation are constrained to the cerebral cortex. We also address the issue of objective assessment of the relative performance of inverse procedures by the free-response receiver operating characteristic (FROC) curve. We conclude with a discussion of methods for assessing statistical significance of experimental results through use of the bootstrap for determining confidence regions in dipole-fitting methods, and random field (RF) and permutation methods for detecting significant activation in cortically constrained imaging studies.
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paper_title: Thinking about brain cell assemblies

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Neurobiologists from Sherrington to Hebb (1 ) have struggled with the problem of how perception, cognition, and memory are coded by assemblies of neurons within the cerebral cortex of the brain. We can now simultaneously monitor the activity of hundreds of neighboring cells (2) and have identified functional properties of ensembles of neurons in the cortex and hippocampus (3-7). In this issue of Science, Wilson and McNauehton ( 8 ) reDort one of the . . . first of these studies on the hippocampus, a phylogenetically old part of the brain that is a final stage of information processing for some kinds of memory. They are able to monitor the activity of neural ensembles and show that, when it comes to functional coding, many neurons are better than one. These kinds of studies-plus insights from computational neuroscience-are beginning to show how the hierarchical organization of the cerebral cortex can be coupled with distributed coding. We may soon understand the nature of representation in the "thinking" parts of the brain. A comprehensive understanding of representation by neural ensembles will require reconciliation of two extreme views of information coding in the cortex. One view espouses a systematic organization com~osed of a hierarchv of "filters" or "detectors" that encode simple stimulus features and com~lex events bv the activitv of single neuron' (9). A cont;asting view'espouses a fully distributed representation that encodes each item by distinct spatiotemporal activity patterns of homogeneous arrays of neurons (1 0). These opposing views may in fact be reconcilable; in the brain, both are employed to different extents at successive stages of information processing (see fieure). ~viYdence in support of the idea that the brain codes information with feature detectors followed closely the development of the extracellular microelectrode in the late 1950s. This technology unexpectedly revealed that cortical firing patterns could readily be related to recognizable dimensions of stimuli and actions ( I I). Many such observations, perhaps most widely promoted by Hubel and Wiesel's studies on the visual cortex, revealed an organized topo-
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paper_title: Topography, independent component analysis and dipole source analysis of movement related potentials

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The objective of this study was to test, in single subjects, the hypothesis that the signs of voluntary movement-related neural activity would first appear in the prefrontal region, then move to both the medial frontal and posterior parietal regions, progress to the medial primary motor area, lateralize to the contralateral primary motor area and finally involve the cerebellum (where feedback-initiated error signals are computed). Six subjects performed voluntary finger movements while DC coupled EEG was recorded from 64 scalp electrodes. Event-related potentials (ERPs) averaged on the movements were analysed both before and after independent component analysis (ICA) combined with dipole source analysis (DSA) of the independent components. Both a simple topographic analysis of undecomposed ERPs and the ICA/DSA analysis suggested that the original hypothesis was inadequate. The major departure from its predictions was that, while activity over many brain regions did appear at the expected times, it also appeared at unexpected times. Overall, the results suggest that the neuroscientific ‘standard model’, in which neural activity occurs sequentially in a series of discrete local areas each specialized for a particular function, may reflect the true situation less well than models in which large areas of brain shift simultaneously into and out of common activity states.
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paper_title: Brainnetome: A new -ome to understand the brain and its disorders

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The human brain can be studied as a hierarchy of complex networks on different temporal and spatial scales. On each scale, from gene, protein, synapse, neuron and microcircuit, to area, pathway and the whole brain, many advances have been made with the development of related techniques. Brain network studies on different temporal and spatial scales are booming. However, such studies have focused on single levels, and can only reflect limited aspects of how the brain is formed and how it works. Therefore, it is increasingly urgent to integrate a variety of techniques, methods and models, and to merge fragmented findings into a uniform research framework or platform. To this end, we have proposed the concept of the brainnetome and several related programs/projects have been launched in China. In this paper, we offer a brief review on the methodologies of the brainnetome, which include techniques on different scales, the brainnetome atlas, and methods of brain network analysis. We then take Alzheimer's disease and schizophrenia as examples to show how the brainnetome can be studied in neurological and psychiatric disorders. We also review the studies of how risk genes for brain diseases affect the brain networks. Finally, we summarize the challenges for the brainnetome, and what actions and measures have been taken to address these challenges in China. It is envisioned that the brainnetome will open new avenues and some long-standing issues may be solved by combining the brainnetome with other "omes". (C) 2013 Elsevier Inc. All rights reserved.
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paper_title: Clinical Electroencephalography and Event-Related Potentials

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This chapter focuses on clinical applications of electroencephalography (EEG) and event-related potentials (ERPs). There are two basic types of neuroelectric examinations: (1) EEG studies that involve inspection of spontaneous brain activity and (2) ERP studies that use signal-averaging techniques to extract neuroelectric activity that is time-locked to specific sensory, motor, or cognitive events. There are two types of brain cells, namely, neurons and glia, and both contribute to the EEG. Nerve cells display two distinct classes of neuroelectric events: (1) graded synaptic potentials and (2) action potentials. Glia are known to show slow changes in their resting membrane potentials. The extracellular current sinks and sources cause an electrical potential distribution in the conductive media, with the magnitude of the potential decreasing with increasing distance from the neuron. The exact manner in which the potential changes within the media partly depends on the configuration of the relevant currents. Currents associated with individual action potentials are generally much stronger than synaptic currents, but postsynaptic potentials generally make the more significant contribution to the EEG.
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paper_title: Event-related desynchronization during anticipatory attention for an upcoming stimulus: a comparative EEG/MEG study

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Objectives: Our neurophysiological model of anticipatory behaviour (e.g. Acta Psychol 101 (1999) 213; Bastiaansen et al., 1999a) predicts an activation of (primary) sensory cortex during anticipatory attention for an upcoming stimulus. In this paper we attempt to demonstrate this by means of event-related desynchronization (ERD). Methods: Five subjects performed a time estimation task, and were informed about the quality of their time estimation by either visual or auditory stimuli providing Knowledge of Results (KR). EEG and MEG were recorded in separate sessions, and ERD was computed in the 8‐ 10 and 10‐12 Hz frequency bands for both datasets. Results: Both in the EEG and the MEG we found an occipitally maximal ERD preceding the visual KR for all subjects. Preceding the auditory KR, no ERD was present in the EEG, whereas in the MEG we found an ERD over the temporal cortex in two of the 5 subjects. These subjects were also found to have higher levels of absolute power over temporal recording sites in the MEG than the other subjects, which we consider to be an indication of the presence of a ‘tau’ rhythm (e.g. Neurosci Lett 222 (1997) 111). Conclusions: It is concluded that the results are in line with the predictions of our neurophysiological model. q 2001 Elsevier Science Ireland Ltd. All rights reserved.
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paper_title: Diffusion magnetic resonance imaging for Brainnetome: A critical review

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Increasing evidence shows that the human brain is a highly self-organized system that shows attributes of smallworldness, hierarchy and modularity. The “connectome” was conceived several years ago to identify the underpinning physical connectivities of brain networks. The need for an integration of multi-spatial and -temporal approaches is becoming apparent. Therefore, the “Brainnetome” (brain-net-ome) project was proposed. Diffusion magnetic resonance imaging (dMRI) is a non-invasive way to study the anatomy of brain networks. Here, we review the principles of dMRI, its methodologies, and some of its clinical applications for the Brainnetome. Future research in this field is discussed.
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paper_title: Source connectivity analysis with MEG and EEG.

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Interactions between functionally specialized brain regions are crucial for normal brain func- tion. Magnetoencephalography (MEG) and electroencephalography (EEG) are techniques suited to cap- ture these interactions, because they provide whole head measurements of brain activity in the millisec- ond range. More than one sensor picks up the activity of an underlying source. This field spread severely limits the utility of connectivity measures computed directly between sensor recordings. Con- sequentially, neuronal interactions should be studied on the level of the reconstructed sources. This ar- ticle reviews several methods that have been applied to investigate interactions between brain regions in source space. We will mainly focus on the different measures used to quantify connectivity, and on the different strategies adopted to identify regions of interest. Despite various successful accounts of MEG and EEG source connectivity, caution with respect to the interpretation of the results is still war- ranted. This is due to the fact that effects of field spread can never be completely abolished in source space. However, in this very exciting and developing field of research this cautionary note should not discourage researchers from further investigation into the connectivity between neuronal sources. Hum Brain Mapp 30:1857-1865, 2009. V C 2009 Wiley-Liss, Inc.
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paper_title: Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets

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The distributed brain systems associated with performance of a verbal fluency task were identified in a nondirected correlational analysis of neurophysiological data obtained with positron tomography. This analysis used a recursive principal-component analysis developed specifically for large data sets. This analysis is interpreted in terms of functional connectivity, defined as the temporal correlation of a neurophysiological index measured in different brain areas. The results suggest that the variance in neurophysiological measurements, introduced experimentally, was accounted for by two independent principal components. The first, and considerably larger, highlighted an intentional brain system seen in previous studies of verbal fluency. The second identified a distributed brain system including the anterior cingulate and Wernicke's area that reflected monotonic time effects. We propose that this system has an attentional bias.
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paper_title: Brain-Body-Mind in the Nebulous Cartesian System: A Holistic Approach by Oscillations

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PART I: Foundations: History and Fundamental Processes in Nature.- Introduction.- Chapter 1: Brain-Body-Mind Problem: A Short Historical and Interdisciplinary Survey.- Chapter 2: Frameworks in the Integration of the Sciences.- PART II: Whole Brain Work and a Holistic Approach to Brain-Body Integration.- Introduction.- Chapter 3: Brain Structures, Transmitters and Analyzing Strategies.- Chapter 4: Autonomus Nervous System, Cardiovascular System and Smooth Muscles.- Chapter 5: Overall Myogenic-Coordination:Building Stones in the Whole-Body-Integration and Tuning.- Chapter 6: Dynamics of Sensory and Cognitive Processing.- Chapter 7: Dynamic Memory.- Chapter 8: Whole-Brain-Work.- Chapter 9: Does the Brain-Body-Mind Work as Dynamic Syncytium.- PART III: Brain in Different States: Evolution, Maturing, Emotion and Pathology.- Introduction.- Chapter 10: The Brain in Evolution of Species and Darwin's Theory.- Chapter 11: The Maturing Brain: Micro Evolution.- Chapter 12: Oscillatory Dynamics of the Emotional Brain: Links of Emotion to Episodic Memory.- Chapter 13: Pathologic Brain: Impairment of Mind Bases on Break of Oscillations and Modulation of Neurotransmitter Release.- PART IV: The Physics of Biology Interface: "A Cartesian System for the 21st Century".-Introduction.- Chapter 14: Chaos and Quantum Approach: Gateway to a 21st Century Cartesian System.- Chapter 15: The Brain in Probabilistic Hyperspace.- Chapter 16: Quantum Brain and the Nebulous Cartesian System.- PART V: Metaphysics of the Brain: Cutting Edges of Philosophy.- Introduction.- Chapter 17: Darwinism, Bergsonism, Concept of Entropy and Creative Thinking.- Chapter 18: Bergson's Intuition Memory and Episodic Memory.- Chapter 19: Towards Metaphysics, Conscient and Unconcient States.- Chapter 20: Mysteries of Mind Conscient and Unconcient States in Creativity and Sleep.- PART VI: Essentials and Unifying Trends in Brain and Body.- Introduction.- Chapter 21: Leitmotives and Common Concepts: An Interim Description.- Chapter 22: Oscillations and Transmitters are Quasi-Invariants in Brain-Body-Mind Integration. What is Break of Mind?.- Chapter 23: Unifying Trends: Globally Coupled Oscillators in Brain-Body.- Chapter 24: Unifying Concepts: Brain's String Theory.- Chapter 25: Unifying Concepts: Brain-Body-Mind Integration and Intuitive Processes.- Chapter 26: Need for a Paradigm Shift and a New Cartesian System
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paper_title: A measure for brain complexity: relating functional segregation and integration in the nervous system

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In brains of higher vertebrates, the functional segregation of local areas that differ in their anatomy and physiology contrasts sharply with their global integration during perception and behavior. In this paper, we introduce a measure, called neural complexity (CN), that captures the interplay between these two fundamental aspects of brain organization. We express functional segregation within a neural system in terms of the relative statistical independence of small subsets of the system and functional integration in terms of significant deviations from independence of large subsets. CN is then obtained from estimates of the average deviation from statistical independence for subsets of increasing size. CN is shown to be high when functional segregation coexists with integration and to be low when the components of a system are either completely independent (segregated) or completely dependent (integrated). We apply this complexity measure in computer simulations of cortical areas to examine how some basic principles of neuroanatomical organization constrain brain dynamics. We show that the connectivity patterns of the cerebral cortex, such as a high density of connections, strong local connectivity organizing cells into neuronal groups, patchiness in the connectivity among neuronal groups, and prevalent reciprocal connections, are associated with high values of CN. The approach outlined here may prove useful in analyzing complexity in other biological domains such as gene regulation and embryogenesis.
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paper_title: A review of functional magnetic resonance imaging for Brainnetome

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The functional brain network using blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) has revealed the potentials for probing brain architecture, as well as for identifying clinical biomarkers for brain diseases. In the general context of Brainnetome, this review focuses on the development of approaches for modeling and analyzing functional brain networks with BOLD fMRI. The prospects for these approaches are also discussed.
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paper_title: The brainweb: phase synchronization and large-scale integration.

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The emergence of a unified cognitive moment relies on the coordination of scattered mosaics of functionally specialized brain regions. Here we review the mechanisms of large-scale integration that counterbalance the distributed anatomical and functional organization of brain activity to enable the emergence of coherent behaviour and cognition. Although the mechanisms involved in large-scale integration are still largely unknown, we argue that the most plausible candidate is the formation of dynamic links mediated by synchrony over multiple frequency bands.
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paper_title: Concepts of Connectivity and Human Epileptic Activity

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This review attempts to place the concept of connectivity from increasingly sophisticated neuroimaging data analysis methodologies within the field of epilepsy research. We introduce the more principled connectivity terminology developed recently in neuroimaging and review some of the key concepts related to the characterisation of propagation of epileptic activity using what may be called traditional correlation-based studies based on EEG. We then show how essentially similar methodologies, and more recently models addressing causality, have been used to characterise whole-brain and regional networks using functional MRI data. Following a discussion of our current understanding of the neuronal system aspects of the onset and propagation of epileptic discharges and seizures, we discuss the most advanced and ambitious framework to attempt to fully characterise epileptic networks based on neuroimaging data.
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paper_title: A hybrid model for the neural representation of complex mental processing in the human brain

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In the present conceptual review several theoretical and empirical sources of information were integrated, and a hybrid model of the neural representation of complex mental processing in the human brain was proposed. Based on empirical evidence for strategy-related and inter-individually different task-related brain activation networks, and further based on empirical evidence for a remarkable overlap of fronto-parietal activation networks across different complex mental processes, it was concluded by the author that there might be innate and modular organized neuro-developmental starting regions, for example, in intra-parietal, and both medial and middle frontal brain regions, from which the neural organization of different kinds of complex mental processes emerge differently during individually shaped learning histories. Thus, the here proposed model provides a hybrid of both massive modular and holistic concepts of idiosyncratic brain physiological elaboration of complex mental processing. It is further concluded that 3-D information, obtained by respective methodological approaches, are not appropriate to identify the non-linear spatio-temporal dynamics of complex mental process-related brain activity in a sufficient way. How different participating network parts communicate with each other seems to be an indispensable aspect, which has to be considered in particular to improve our understanding of the neural organization of complex cognition.
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paper_title: Simultaneously Recorded Trains of Action Potentials: Analysis and Functional Interpretation

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A new kind of statistical display, the joint peri-stimulus-time scatter diagram, facilitates the analysis and interpretation of two or more simultaneously recorded trains of action potentials. The display is a generalization of the cross correlation and the peri-stimulus-time histogram, and it reflects specific underlying neuronal interactions. The technique yields quantitative measures of interaction in terms of effectiveness of synaptic connections.
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paper_title: Towards the utilization of EEG as a brain imaging tool

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Recent advances in signal analysis have engendered EEG with the status of a true brain mapping and brain imaging method capable of providing spatio-temporal information regarding brain (dys)function. Because of the increasing interest in the temporal dynamics of brain networks, and because of the straightforward compatibility of the EEG with other brain imaging techniques, EEG is increasingly used in the neuroimaging community. However, the full capability of EEG is highly underestimated. Many combined EEG-fMRI studies use the EEG only as a spike-counter or an oscilloscope. Many cognitive and clinical EEG studies use the EEG still in its traditional way and analyze grapho-elements at certain electrodes and latencies. We here show that this way of using the EEG is not only dangerous because it leads to misinterpretations, but it is also largely ignoring the spatial aspects of the signals. In fact, EEG primarily measures the electric potential field at the scalp surface in the same way as MEG measures the magnetic field. By properly sampling and correctly analyzing this electric field, EEG can provide reliable information about the neuronal activity in the brain and the temporal dynamics of this activity in the millisecond range. This review explains some of these analysis methods and illustrates their potential in clinical and experimental applications.
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paper_title: Theoretical Neuroanatomy: Relating Anatomical and Functional Connectivity in Graphs and Cortical Connection Matrices

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A pneumatic control instrument includes a nozzle-flapper system having improved dynamic response characteristics under vibrational conditions. A light-mass, compliant flapper having a high natural frequency covers the nozzle and serves to cushion the impact of the nozzle striking the flapper by moving at the same amplitude of vibration as the nozzle so as to precisely track the nozzle over a wide range of vibrational frequencies and amplitudes.
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paper_title: MAKING COMPLEXITY SIMPLER: MULTIVARIABILITY AND METASTABILITY IN THE BRAIN

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This article provides a retrospective, current, and prospective overview on developments in brain research and neuroscience. Both theoretical and empirical studies are considered, with emphasis in the concept of multivariability and metastability in the brain. In this new view on the human brain, the potential multivariability of the neuronal networks appears to be far from continuous in time, but confined by the dynamics of short-term local and global metastable brain states. The article closes by suggesting some of the implications of this view in future multidisciplinary brain research.
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paper_title: Brain-Body-Mind in the Nebulous Cartesian System: A Holistic Approach by Oscillations

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PART I: Foundations: History and Fundamental Processes in Nature.- Introduction.- Chapter 1: Brain-Body-Mind Problem: A Short Historical and Interdisciplinary Survey.- Chapter 2: Frameworks in the Integration of the Sciences.- PART II: Whole Brain Work and a Holistic Approach to Brain-Body Integration.- Introduction.- Chapter 3: Brain Structures, Transmitters and Analyzing Strategies.- Chapter 4: Autonomus Nervous System, Cardiovascular System and Smooth Muscles.- Chapter 5: Overall Myogenic-Coordination:Building Stones in the Whole-Body-Integration and Tuning.- Chapter 6: Dynamics of Sensory and Cognitive Processing.- Chapter 7: Dynamic Memory.- Chapter 8: Whole-Brain-Work.- Chapter 9: Does the Brain-Body-Mind Work as Dynamic Syncytium.- PART III: Brain in Different States: Evolution, Maturing, Emotion and Pathology.- Introduction.- Chapter 10: The Brain in Evolution of Species and Darwin's Theory.- Chapter 11: The Maturing Brain: Micro Evolution.- Chapter 12: Oscillatory Dynamics of the Emotional Brain: Links of Emotion to Episodic Memory.- Chapter 13: Pathologic Brain: Impairment of Mind Bases on Break of Oscillations and Modulation of Neurotransmitter Release.- PART IV: The Physics of Biology Interface: "A Cartesian System for the 21st Century".-Introduction.- Chapter 14: Chaos and Quantum Approach: Gateway to a 21st Century Cartesian System.- Chapter 15: The Brain in Probabilistic Hyperspace.- Chapter 16: Quantum Brain and the Nebulous Cartesian System.- PART V: Metaphysics of the Brain: Cutting Edges of Philosophy.- Introduction.- Chapter 17: Darwinism, Bergsonism, Concept of Entropy and Creative Thinking.- Chapter 18: Bergson's Intuition Memory and Episodic Memory.- Chapter 19: Towards Metaphysics, Conscient and Unconcient States.- Chapter 20: Mysteries of Mind Conscient and Unconcient States in Creativity and Sleep.- PART VI: Essentials and Unifying Trends in Brain and Body.- Introduction.- Chapter 21: Leitmotives and Common Concepts: An Interim Description.- Chapter 22: Oscillations and Transmitters are Quasi-Invariants in Brain-Body-Mind Integration. What is Break of Mind?.- Chapter 23: Unifying Trends: Globally Coupled Oscillators in Brain-Body.- Chapter 24: Unifying Concepts: Brain's String Theory.- Chapter 25: Unifying Concepts: Brain-Body-Mind Integration and Intuitive Processes.- Chapter 26: Need for a Paradigm Shift and a New Cartesian System
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paper_title: Dynamic patterns: The self-organization of brain and behavior

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Part 1 How nature handles complexity: what is a pattern? kinds of patterns principles of dynamic pattern formation the messages of self-organized patterns new laws to be expected in the organism matters of mind and matter the mind revealed? or, what this book's about. Part 2 Self-organization of behaviour - the basic picture: some historical remarks about the science of psychology are actions self-organized? if so, how? from synergies to synergetics requirements of a theory of self-organized behaviour. Part 3 Self-organization of behaviour - first steps of generalization: Hubris tempered? on Harvard horses and Russian cats coordination between components of an organism coordination between organisms on coupling. Part 4 Extending the basic picture - breaking away: relative coordination relative coordination explained absolute and relative coordination unified related models - fireflies, lampreys, and lasers instability and the nature of life - the intermittency mechanism exposed postscript. Part 5 Intentional dynamics: goal-directness in biology the second cornerstone of biological self-organization - informational specificity intentional behaviourial change related views - termites, predator-prey cycles, and quantum mechanics summing up. Part 6 Learning dynamics: issues in learning the main concepts the 'seagull effect' - competition and cooperation questions of learning transfer and generalization - symmetry again behaviourial development evolution and morphogenesis summary and conclusions. Part 7 perceptual dynamics: the barrier of meaning - perceptual dynamics I the barrier of meaning - perceptual dynamics II metastability of mind principles of perceiving - calculating, settling, resonating, and twinkling. Part 8 Self-organizing dynamics of the nervous system: microscale events mesoscale events macroscale events extending the basic picture...again postscript on etymology. Part 9 Self-organization of the human brain: prolegomenon obstacles to understanding the brain is not a static machine the 'brain dynamics' approach - fractural dimension spatiotemporal patterns of the brain models of brain behaviour - coupled modes and Sil'nikov chaos summary and conclusions - brain behaviour.
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paper_title: Complex brain networks: graph theoretical analysis of structural and functional systems

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In recent years, the principles of network science have increasingly been applied to the study of the brain's structural and functional organization. Bullmore and Sporns review this growing field of research and discuss its contributions to our understanding of brain function.
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paper_title: Multimodal Functional Network Connectivity: An EEG-fMRI Fusion in Network Space

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EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.
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paper_title: Time domain measures of inter-channel EEG correlations: a comparison of linear, nonparametric and nonlinear measures

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Correlations between ten-channel EEGs obtained from thirteen healthy adult participants were investigated. Signals were obtained in two behavioral states: eyes open no task and eyes closed no task. Four time domain measures were compared: Pearson product moment correlation, Spearman rank order correlation, Kendall rank order correlation and mutual information. The psychophysiological utility of each measure was assessed by determining its ability to discriminate between conditions. The sensitivity to epoch length was assessed by repeating calculations with 1, 2, 3, …, 8 s epochs. The robustness to noise was assessed by performing calculations with noise corrupted versions of the original signals (SNRs of 0, 5 and 10 dB). Three results were obtained in these calculations. First, mutual information effectively discriminated between states with less data. Pearson, Spearman and Kendall failed to discriminate between states with a 1 s epoch, while a statistically significant separation was obtained with mutual information. Second, at all epoch durations tested, the measure of between-state discrimination was greater for mutual information. Third, discrimination based on mutual information was more robust to noise. The limitations of this study are discussed. Further comparisons should be made with frequency domain measures, with measures constructed with embedded data and with the maximal information coefficient.
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paper_title: A comparative study of different references for EEG default mode network: The use of the infinity reference

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Objective: The choice of electroencephalograph (EEG) reference is a critical issue for the study of brain activity. The present study addressed the use of the infinity reference obtained by the reference electrode standardisation technique (REST) in the study of EEG default mode network (DMN). Methods: A total of 100 randomly positioned source configurations, each consisting of two dipoles with coherent waveforms, were adopted for simulating EEG networks. Dense (129-channel), eyes-closed EEG was recorded from 15 subjects. Simulated data with infinity as reference and the real data were re-referenced to reconstructed infinity (REST), their average (AR), linked mastoids (LM) and left mastoid (L) references. For simulated data, the effects of different references on coherence and network were investigated. For real data, spectral properties of seven conventional EEG frequency bands were first analysed and then DMN was constructed based on the coherence. Results: The simulation showed that REST can exactly recover the true EEG network configuration. For real EEG data, significant differences among references were found for the power spectra, coherence and DMN configuration. Compared with REST, the long-distance connectivity between anterior and posterior areas was strengthened by AR, and the connectivity over posterior areas was destroyed when LM and L were employed. Moreover, all comparisons demonstrated frequency-dependent reference effects. Conclusions: Non-neutral reference influences the power spectra, coherence as well as the network analysis. REST demonstrates its validity in data referencing, and meanwhile, AR is much closer to REST than the other references in terms of spectra and coherence. However, the DMN alters a great deal with AR. Significance: The results underscore the importance of considering EEG reference effects in the functional connectivity studies. REST is a promising reference technique for objective comparison in cross-laboratory studies and clinical practices.
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paper_title: Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space

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The imaginary part of coherency is a measure to investigate the synchronization of brain sources on the EEG/MEG sensor level, robust to artifacts of volume conduction meaning that independent sources cannot generate a significant result. It does not mean, however, that volume conduction is irrelevant when true interactions are present. Here, we analyze in detail the possibilities to construct measures of true brain interactions which are strictly invariant to linear spatial transformations of the sensor data. Specifically, such measures can be constructed from maximization of imaginary coherency in virtual channels, bivariate measures as a corrected variate of imaginary coherence, and global measures indicating the total interaction contained within a space or between two spaces. A complete theoretic framework on this question is provided for second order statistical moments. Relations to existing linear and nonlinear approaches are presented. We applied the methods to resting state EEG data, showing clear interactions at all bands, and to a combined measurement of EEG and MEG during rest condition and a finger tapping task. We found that MEG was capable of observing brain interactions which were not observable in the EEG data.
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paper_title: Gaussian source model based iterative algorithm for EEG source imaging

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Estimation of the neural active sources from the scalp electroencephalogram (EEG) is an ill-posed inverse problem. In this paper, we propose a new source model: Gaussian distributed Source Model (GSM), to model the activations in brain. GSM may imitate an Isolated Source Model (ISM) or a Distributed Source Model (DSM) by adopting different supporting range parameter of the Gaussian function. Using GSM, an iterative Gaussian source Imaging Algorithm (GIA) is developed to detect the EEG sources. As GIA dynamically reduces the solution space, the solution may gradually converge to a desired distribution. A comparative evaluation among LORETA, FOCUSS and GIA was conducted for both isolated point sources and distributed sources, the results demonstrate that GIA is more flexible and efficient for various actual sources configurations. Finally, GSM was applied to real recordings obtained from a visual spatial attention task; the corresponding source activation areas of the early component are localized in contralateral occipital cortices, consistent with the retinotopic organization of early visual spatial attention effects.
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paper_title: Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach

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We describe a comprehensive linear approach to the problem of imaging brain activity with high temporal as well as spatial resolution based on combining EEG and MEG data with anatomical constraints derived from MRI images. The "inverse problem" of estimating the distribution of dipole strengths over the cortical surface is highly underdetermined, even given closely spaced EEG and MEG recordings. We have obtained much better solutions to this problem by explicitly incorporating both local cortical orientation as well as spatial covariance of sources and sensors into our formulation. An explicit polygonal model of the cortical manifold is first constructed as follows: (1) slice data in three orthogonal planes of section (needle-shaped voxels) are combined with a linear deblurring technique to make a single high-resolution 3-D image (cubic voxels), (2) the image is recursively flood-filled to determine the topology of the gray-white matter border, and (3) the resulting continuous surface is refined by relaxing it against the original 3-D gray-scale image using a deformable template method, which is also used to computationally flatten the cortex for easier viewing. The explicit solution to an error minimization formulation of an optimal inverse linear operator (for a particular cortical manifold, sensor placement, noise and prior source covariance) gives rise to a compact expression that is practically computable for hundreds of sensors and thousands of sources. The inverse solution can then be weighted for a particular (averaged) event using the sensor covariance for that event. Model studies suggest that we may be able to localize multiple cortical sources with spatial resolution as good as PET with this technique, while retaining a much finer grained picture of activity over time.
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paper_title: Equivalent dipole estimation of spontaneous EEG alpha activity: two-moving dipole approach

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A method of estimating equivalent moving and fixed dipoles from the scalp-recorded EEG alpha waves, with the realistic geometry of the head taken into account, is presented. Twenty-one silver electrodes were used to collect spontaneous EEG alpha waves on the scale. Four models, the single-moving dipole model, the single-fixed dipole model, the two-moving dipole model and the two-fixed dipole model were applied to approximate the EEG alpha field on the scalp. The algorithm, based on a least-squares fit for estimating the moving and the fixed dipoles by using a realistically shaped head model, is described. The numerical accuracy of the algorithm is also evaluated by a computer simulation. It is found that the spontaneous EEG alpha activity observed on the scalp can be represented by two equivalent moving dipoles, simultaneously located separately in the occipital regions of the right and the left hemisphere, at a depth of 4–6 cm beneath the scalp, with a goodness-of-fit of up to 97 per cent for all subjects examined. The excellent fit of the two-moving dipole model to the EEG human alpha activity is also compared with the single-dipole fit.
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paper_title: Review on solving the inverse problem in EEG source analysis

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In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided.This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources. Furthermore the computationally intensive solution given by SLF was not found to give any additional benefits under such simulated conditions.
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paper_title: A Comparative Study Of Global Optimization Approaches To Meg Source Localization

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It is well-known that the problem of MEG source localization can be cast as an optimization problem. So far, there have been many works in which various optimization methods were adopted for source localization. In this paper, we compare the performance of three typical and widely used optimization techniques for a specific MEG source localization problem. We first introduce a hybrid algorithm by combining genetic and local search strategies to overcome disadvantages of conventional genetic algorithms. Second, we apply the tabu search, a widely used optimization method in combinational optimization and discrete mathematics, to source localization. To the best of our knowledge, this is the first attempt in the literature to apply tabu search to MEG/EEG source localization. Third, in order to further compare the performance of the above algorithms, simulated annealing is also applied to MEG source localization problem. The computer simulation results show that our local genetic algorithm is the most effective approach to dipole localization, and the tabu search method is also a very good strategy for this problem.
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paper_title: Electric potential produced by a dipole in a homogeneous conducting sphere

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The potential produced by a dipole in a homogeneous conducting sphere is useful in a simulation study. The current available solutions still suffer from some shortcomings. In this communication, a closed solution is developed for the precise calculation of the potential anywhere in the spherical model.
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paper_title: Lp Norm Iterative Sparse Solution for EEG Source Localization

paper_content:

How to localize the neural electric activities effectively and precisely from the scalp EEG recordings is a critical issue for clinical neurology and cognitive neuroscience. In this paper, based on the spatial sparse assumption of brain activities, proposed is a novel iterative EEG source imaging algorithm, Lp norm iterative sparse solution (LPISS). In LPISS, the lp(ples1) norm constraint for sparse solution is integrated into the iterative weighted minimum norm solution of the underdetermined EEG inverse problem, and it is the constraint and the iteratively renewed weight that forces the inverse problem to converge to a sparse solution effectively. The conducted simulation studies with comparison to LORETA and FOCUSS for various dipoles configurations confirmed the validation of LPISS for sparse EEG source localization. Finally, LPISS was applied to a real evoked potential collected in a study of inhibition of return (IOR), and the result was consistent with the previously suggested activated areas involved in an IOR process
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paper_title: Estimation of Cortical Connectivity From EEG Using State-Space Models

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A state-space formulation is introduced for estimating multivariate autoregressive (MVAR) models of cortical connectivity from noisy, scalp-recorded EEG. A state equation represents the MVAR model of cortical dynamics, while an observation equation describes the physics relating the cortical signals to the measured EEG and the presence of spatially correlated noise. We assume that the cortical signals originate from known regions of cortex, but the spatial distribution of activity within each region is unknown. An expectation-maximization algorithm is developed to directly estimate the MVAR model parameters, the spatial activity distribution components, and the spatial covariance matrix of the noise from the measured EEG. Simulation and analysis demonstrate that this integrated approach is less sensitive to noise than two-stage approaches in which the cortical signals are first estimated from EEG measurements, and next, an MVAR model is fit to the estimated cortical signals. The method is further demonstrated by estimating conditional Granger causality using EEG data collected while subjects passively watch a movie.
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paper_title: Assessment of EEG synchronization based on state-space analysis

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Cortical computation involves the formation of cooperative neuronal assemblies characterized by synchronous oscillatory activity. A traditional method for the identification of synchronous neuronal assemblies has been the coherence analysis of EEG signals. Here, we suggest a new method called S estimator, whereby cortical synchrony is defined from the embedding dimension in a state-space. We first validated the method on clusters of chaotic coupled oscillators and compared its performance to that of other methods for assessing synchronization. Then nine adult subjects were studied with high-density EEG recordings, while they viewed in the two hemifields (hence with separate hemispheres) identical sinusoidal gratings either arranged collinearly and moving together, or orthogonally oriented and moving at 90 degrees . The estimated synchronization increased with the collinear gratings over a cluster of occipital electrodes spanning both hemispheres, whereas over temporo-parietal regions of both hemispheres, it decreased with the same stimulus and it increased with the orthogonal gratings. Separate calculations for different EEG frequencies showed that the occipital clusters involved synchronization in the beta band and the temporal clusters in the alpha band. The gamma band appeared to be insensitive to stimulus diversity. Different stimulus configurations, therefore, appear to cause a complex rearrangement of synchronous neuronal assemblies distributed over the cortex, in particular over the visual cortex.
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paper_title: Reduced functional connectivity between cortical sources in five meditation traditions detected with lagged coherence using EEG tomography

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Abstract Brain functional states are established by functional connectivities between brain regions. In experienced meditators (13 Tibetan Buddhists, 15 QiGong, 14 Sahaja Yoga, 14 Ananda Marga Yoga, 15 Zen), 19-channel EEG was recorded before, during and after that meditation exercise which their respective tradition regards as route to the most desirable meditative state. The head surface EEG data were recomputed (sLORETA) into 19 cortical regional source model time series. All 171 functional connectivities between regions were computed as ‘lagged coherence’ for the eight EEG frequency bands (delta through gamma). This analysis removes ambiguities of localization, volume conduction-induced inflation of coherence, and reference-dependence. All significant differences (corrected for multiple testing) between meditation compared to no-task rest before and after meditation showed lower coherence during meditation, in all five traditions and eight (inhibitory as well as excitatory) frequency bands. Conventional coherence between the original head surface EEG time series very predominantly also showed reduced coherence during meditation. The topography of the functional connectivities was examined via PCA-based computation of principal connectivities. When going into and out of meditation, significantly different connectivities revealed clearly different topographies in the delta frequency band and minor differences in the beta-2 band. The globally reduced functional interdependence between brain regions in meditation suggests that interaction between the self process functions is minimized, and that constraints on the self process by other processes are minimized, thereby leading to the subjective experience of non-involvement, detachment and letting go, as well as of all-oneness and dissolution of ego borders during meditation.
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paper_title: Equivalent Charge Source Model Based Iterative Maximum Neighbor Weight for Sparse EEG Source Localization

paper_content:

How to localize the neural electric activities within brain effectively and precisely from the scalp electroencephalogram (EEG) recordings is a critical issue for current study in clinical neurology and cognitive neuroscience. In this paper, based on the charge source model and the iterative re-weighted strategy, proposed is a new maximum neighbor weight based iterative sparse source imaging method, termed as CMOSS (Charge source model based Maximum neighbOr weight Sparse Solution). Different from the weight used in focal underdetermined system solver (FOCUSS) where the weight for each point in the discrete solution space is independently updated in iterations, the new designed weight for each point in each iteration is determined by the source solution of the last iteration at both the point and its neighbors. Using such a new weight, the next iteration may have a bigger chance to rectify the local source location bias existed in the previous iteration solution. The simulation studies with comparison to FOCUSS and LORETA for various source configurations were conducted on a realistic 3-shell head model, and the results confirmed the validation of CMOSS for sparse EEG source localization. Finally, CMOSS was applied to localize sources elicited in a visual stimuli experiment, and the result was consistent with those source areas involved in visual processing reported in previous studies.
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paper_title: EEG source analysis and fMRI reveal two electrical sources in the fronto-parietal operculum during subepidermal finger stimulation

paper_content:

Abstract Using functional magnetic resonance imaging (fMRI) and electroencephalographic (EEG) source dipole analysis in 10 normal subjects, two electrical source dipoles in the contralateral fronto-parietal operculum were identified during repetitive painful subepidermal stimulation of the right index finger. The anterior source dipole peaking at 79 ± 8 ms (mean ± SD) was located in the frontal operculum, and oriented tangentially toward the cortical surface. The posterior source dipole peaking at 118 ± 12 ms was located in the upper bank of the Sylvian fissure corresponding to the second somatosensory cortex (S2). The orientations of the posterior source dipoles displayed large variability, but differed significantly (P
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paper_title: fMRI functional networks for EEG source imaging

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r r Abstract: The brain exhibits temporally coherent networks (TCNs) involving numerous cortical and sub-cortical regions both during the rest state and during the performance of cognitive tasks. TCNs represent the interactions between different brain areas, and understanding such networks may facili- tate electroencephalography (EEG) source estimation. We propose a new method for examining TCNs using scalp EEG in conjunction with data obtained by functional magnetic resonance imaging (fMRI). In this approach, termed NEtwork based SOurce Imaging (NESOI), multiple TCNs derived from fMRI with independent component analysis (ICA) are used as the covariance priors of the EEG source reconstruction using Parametric Empirical Bayesian (PEB). In contrast to previous applications of PEB in EEG source imaging with smoothness or sparseness priors, TCNs play a fundamental role among the priors used by NESOI. NESOI achieves an efficient integration of the high temporal resolution EEG and TCN derived from the high spatial resolution fMRI. Using synthetic and real data, we directly compared the performance of NESOI with other distributed source inversion methods, with and with- out the use of fMRI priors. Our results indicated that NESOI is a potentially useful approach for EEG source imaging. Hum Brain Mapp 00:000-000, 2010. V C 2010 Wiley-Liss, Inc.
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paper_title: Imaging functional brain connectivity patterns from high-resolution EEG and fMRI via graph theory

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We describe a set of computational tools able to estimate cortical activity and connectivity from high-resolution EEG and fMRI recordings in humans. These methods comprise the estimation of cortical activity using realistic geometry head volume conductor models and distributed cortical source models, followed by the evaluation of cortical connectivity between regions of interest coincident with the Brodmann areas via the use of Partial Directed Coherence. Connectivity patterns estimated on the cortical surface in different frequency bands are then imaged and interpreted with measures based on graph theory. These computational tools were applied on a set of EEG and fMRI data from a Stroop task to demonstrate the potential of the proposed approach. The present findings suggest that the methodology is able to identify differences in functional connectivity patterns elicited by different experimental tasks or conditions.
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paper_title: Dynamic Statistical Parametric Mapping Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity

paper_content:

Abstract Functional magnetic resonance imaging (fMRI) can provide maps of brain activation with millimeter spatial resolution but is limited in its temporal resolution to the order of seconds. Here, we describe a technique that combines structural and functional MRI with magnetoencephalography (MEG) to obtain spatiotemporal maps of human brain activity with millisecond temporal resolution. This new technique was used to obtain dynamic statistical parametric maps of cortical activity during semantic processing of visually presented words. An initial wave of activity was found to spread rapidly from occipital visual cortex to temporal, parietal, and frontal areas within 185 ms, with a high degree of temporal overlap between different areas. Repetition effects were observed in many of the same areas following this initial wave of activation, providing evidence for the involvement of feedback mechanisms in repetition priming.
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paper_title: A Parametric Empirical Bayesian framework for fMRI-constrained MEG/EEG source reconstruction

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We describe an asymmetric approach to fMRI and MEG/EEG fusion in which fMRI data are treated as empirical priors on electromagnetic sources, such that their influence depends on the MEG/EEG data, by virtue of maximizing the model evidence. This is important if the causes of the MEG/EEG signals differ from those of the fMRI signal. Furthermore, each suprathreshold fMRI cluster is treated as a separate prior, which is important if fMRI data reflect neural activity arising at different times within the EEG/MEG data. We present methodological considerations when mapping from a 3D fMRI Statistical Parametric Map to a 2D cortical surface and thence to the covariance components used within our Parametric Empirical Bayesian framework. Our previous introduction of a canonical (inverse-normalized) cortical mesh also allows deployment of fMRI priors that live in a template space; for example, from a group analysis of different individuals. We evaluate the ensuing scheme with MEG and EEG data recorded simultaneously from 12 participants, using the same face-processing paradigm under which independent fMRI data were obtained. Because the fMRI priors become part of the generative model, we use the model evidence to compare (i) multiple versus single, (ii) valid versus invalid, (iii) binary versus continuous, and (iv) variance versus covariance fMRI priors. For these data, multiple, valid, binary, and variance fMRI priors proved best for a standard Minimum Norm inversion. Interestingly, however, inversion using Multiple Sparse Priors benefited little from additional fMRI priors, suggesting that they already provide a sufficiently flexible generative model. Hum Brain Mapp, 2010. © 2010 Wiley-Liss, Inc.
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paper_title: Automatic fMRI-guided MEG multidipole localization for visual responses

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Previously, we introduced the use of individual cortical location and orientation constraints in the spatiotemporal Bayesian dipole analysis setting proposed by Jun et al. ((2005); Neuroimage 28:84-98). However, the model's performance was limited by slow convergence and multimodality of the numerically estimated posterior distribution. In this paper, we present an intuitive way to exploit functional magnetic resonance imaging (fMRI) data in the Markov chain Monte Carlo sampling -based inverse estimation of magnetoencephalographic (MEG) data. We used simulated MEG and fMRI data to show that the convergence and localization accuracy of the method is significantly improved with the help of fMRI-guided proposal distributions. We further demonstrate, using an identical visual stim- ulation paradigm in both fMRI and MEG, the usefulness of this type of automated approach when investigating activation patterns with several spatially close and temporally overlapping sources. Theo- retically, the MEG inverse estimates are not biased and should yield the same results even without fMRI information, however, in practice the multimodality of the posterior distribution causes problems due to the limited mixing properties of the sampler. On this account, the algorithm acts perhaps more as a stochastic optimizer than enables a full Bayesian posterior analysis. Hum Brain Mapp 30:1087-1099, 2009. V C 2008 Wiley-Liss, Inc.
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paper_title: Estimation of Effective and Functional Cortical Connectivity From Neuroelectric and Hemodynamic Recordings

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In this paper, different linear and nonlinear methodologies for the estimation of cortical connectivity from neuroelectric and hemodynamic measurements are reviewed and applied on common data set in order to highlight similarities and differences in the results. Different effective and functional connectivity methods were applied to motor and cognitive data sets, including structural equation modeling (SEM), directed transfer function (DTF), partial directed coherence (PDC), and direct directed transfer function (dDTF). Comparisons were made between the results in order to understand if, for a same dataset, effective and functional connectivity estimators can return the same cortical connectivity patterns. An application of a nonlinear method [phase synchronization index (PSI)] to similar executed and imagined movements was also reviewed. Connectivity patterns estimated with the use of the neuroelectric information and of the information from the multimodal integration of neuroelectric and hemodynamic data were also compared. Results suggests that the estimation of the cortical connectivity patterns performed with the linear methods (SEM, DTF, PDC, dDTF) or with the nonlinear method (PSI) on movement related potentials returned similar cortical networks. Differences in cortical connectivity were noted between the patterns estimated with the use of multimodal integration and those estimated by using only the neuroelectric data.
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paper_title: The use of functional constraints for the neuroelectromagnetic inverse problem: alternatives and caveats.

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The use of functional neuroimages as a constraint for the solution of the neuroelectromagnetic inverse problem (NIP) constitutes an appealing alternative to deal with the non uniqueness of the solution. Among the functional techniques it is probably the fMRI the most attractive one due to its high spatial resolution. A limitation to this integration is that the relationships between neuronal activity and BOLD responses are poorly defined. This paper starts discussing some alternatives to integrate functional information as constraints for the inverse solution. Concrete examples of situations where functional images substantially diverge from electrophysiological methods are presented to promote the discussion about the most reasonable alternatives to combine these image modalities. The results of an anatomically constrained inverse solution that employs a sound physical model are compared with the EEG triggered fMRI in an epileptic patient. This example serves to show that the spatial resolution attainable with inverse solutions is comparable in some situations with that of functional images. Finally, some concrete strategies to ameliorate the quality and reliability of linear inverse solutions maps in more general situations are briefly described. The main conclusion of this paper is that integration of functional modalities into the solution of the NIP should be cautiously considered until a more tight coupling between BOLD effects and electrophysiological measurements could be established.
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paper_title: Comparison of different cortical connectivity estimators for high-resolution EEG recordings

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The aim of this work is to characterize quantitatively the performance of a body of techniques in the frequency domain for the estimation of cortical connectivity from high-resolution EEG recordings in different operative conditions commonly encountered in practice. Connectivity pattern estimators investigated are the Directed Transfer Function (DTF), its modification known as direct DTF (dDTF) and the Partial Directed Coherence (PDC). Predefined patterns of cortical connectivity were simulated and then retrieved by the application of the DTF, dDTF, and PDC methods. Signal-to-noise ratio (SNR) and length (LENGTH) of EEG epochs were studied as factors affecting the reconstruction of the imposed connectivity patterns. Reconstruction quality and error rate in estimated connectivity patterns were evaluated by means of some indexes of quality for the reconstructed connectivity pattern. The error functions were statistically analyzed with analysis of variance (ANOVA). The whole methodol- ogy was then applied to high-resolution EEG data recorded during the well-known Stroop paradigm. Simulations indicated that all three methods correctly estimated the simulated connectivity patterns under reasonable condi- tions. However, performance of the methods differed somewhat as a function of SNR and LENGTH factors. The methods were generally equivalent when applied to the Stroop data. In general, the amount of available EEG affected the accuracy of connectivity pattern estimations. Analysis of 27 s of nonconsecutive recordings with an SNR of 3 or more ensured that the connectivity pattern could be accurately recovered with an error below 7% for the PDC and 5% for the DTF. In conclusion, functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in most EEG recordings by combining high-resolution EEG techniques, linear inverse estimation of the cortical activity, and frequency domain multivariate methods such as PDC, DTF, and dDTF. Hum
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paper_title: A new interpretation of P300 responses upon analysis of coherences

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Previous studies on cognitive dynamics showed that oscillatory responses of P300 are composed of mainly delta and theta responses. In the present study, for the first time, the long-distance intra-hemispheric event related coherence (auditory oddball paradigm) and evoked coherence (simple sound) were compared in order to evaluate the effects of cognitive tasks on the long-distance coherences. Seventeen healthy subjects (8 female, 9 male) were included in the study. The coherence was analyzed for delta (1–3.5 Hz), theta (4–7.5 Hz) and alpha (8–13 Hz) frequency ranges for (F3-P3, F4-P4, F3-T7, F4-T8, F3-O1, F4-O2) electrode pairs. The coherence to target responses were higher than the non-target and simple auditory response coherence. This difference is significant for the delta coherence for both hemispheres and for theta coherences over the left hemisphere. The highest coherences were recorded at fronto-temporal locations for all frequency bands (delta, theta, alpha). Furthermore, fronto-parietal coherences were higher than the fronto-occipital coherences for all frequency bands (delta, theta, alpha).These results show that the fronto-temporal and fronto-parietal connections are most relevant for the identification of the target signal. This analysis open the way for a new interpretation of dynamic localization results during cognitive tasks.
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paper_title: Dynamic causal modelling for EEG and MEG

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Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnetic resonance imaging (fMRI) to quantify effective connectivity between brain areas. Recently, this framework has been extended and established in the magneto/encephalography (M/EEG) domain. DCM for M/EEG entails the inversion a full spatiotemporal model of evoked responses, over multiple conditions. This model rests on a biophysical and neurobiological generative model for electrophysiological data. A generative model is a prescription of how data are generated. The inversion of a DCM provides conditional densities on the model parameters and, indeed on the model itself. These densities enable one to answer key questions about the underlying system. A DCM comprises two parts; one part describes the dynamics within and among neuronal sources, and the second describes how source dynamics generate data in the sensors, using the lead-field. The parameters of this spatiotemporal model are estimated using a single (iterative) Bayesian procedure. In this paper, we will motivate and describe the current DCM framework. Two examples show how the approach can be applied to M/EEG experiments.
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paper_title: Functional connectivity in the brain—is it an elusive concept?

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Even though functional brain connectivity is an influential concept in modern cognitive neuroscience, it is a very controversial notion. This is why further theoretical and methodological clarification are needed to help define precisely what is meant by functional connectivity and to help frame-associated issues. In this review we present the neurophysiological concept of functional connectivity, which utilizes in a plausible manner the notion of neural assemblies, as well as local and large-scale levels of description. Here functional connectivity is the mechanism for the coordination of activity between different neural assemblies in order to achieve a complex cognitive task or perceptual process. Our theoretical and empirical findings offer new insights into possible implications of the concept of functional connectivity for cognitive neuroscience.
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paper_title: Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets

paper_content:

The distributed brain systems associated with performance of a verbal fluency task were identified in a nondirected correlational analysis of neurophysiological data obtained with positron tomography. This analysis used a recursive principal-component analysis developed specifically for large data sets. This analysis is interpreted in terms of functional connectivity, defined as the temporal correlation of a neurophysiological index measured in different brain areas. The results suggest that the variance in neurophysiological measurements, introduced experimentally, was accounted for by two independent principal components. The first, and considerably larger, highlighted an intentional brain system seen in previous studies of verbal fluency. The second identified a distributed brain system including the anterior cingulate and Wernicke's area that reflected monotonic time effects. We propose that this system has an attentional bias.
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paper_title: Brain-Mind Operational Architectonics Imaging: Technical and Methodological Aspects

paper_content:

This review paper deals with methodological and technical foundations of the Operational Architectonics framework of brain and mind functioning. This theory provides a framework for mapping and understanding important aspects of the brain mechanisms that constitute perception, cognition, and eventually consciousness. The methods utilized within Operational Architectonics framework allow analyzing with an incredible detail the operational behavior of local neuronal assemblies and their joint activity in the form of unified and metastable operational modules, which constitute the whole hierarchy of brain operations, operations of cognition and phenomenal consciousness.
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paper_title: Cortical network dynamics with time delays reveals functional connectivity in the resting brain

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In absence of all goal-directed behavior, a characteristic network of cortical regions involving prefrontal and cingulate cortices consistently shows temporally coherent fluctuations. The origin of these fluctuations is unknown, but has been hypothesized to be of stochastic nature. In the present paper we test the hypothesis that time delays in the network dynamics play a crucial role in the generation of these fluctuations. By tuning the propagation velocity in a network based on primate connectivity, we scale the time delays and demonstrate the emergence of the resting state networks for biophysically realistic parameters.
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paper_title: Dynamic imaging of coherent sources : Studying neural interactions in the human brain

paper_content:

Functional connectivity between cortical areas may appear as correlated time behavior of neural activity. It has been suggested that merging of separate features into a single percept (“binding”) is associated with coherent gamma band activity across the cortical areas involved. Therefore, it would be of utmost interest to image cortico-cortical coherence in the working human brain. The frequency specificity and transient nature of these interactions requires time-sensitive tools such as magneto- or electroencephalography (MEG/EEG). Coherence between signals of sensors covering different scalp areas is commonly taken as a measure of functional coupling. However, this approach provides vague information on the actual cortical areas involved, owing to the complex relation between the active brain areas and the sensor recordings. We propose a solution to the crucial issue of proceeding beyond the MEG sensor level to estimate coherences between cortical areas. Dynamic imaging of coherent sources (DICS) uses a spatial filter to localize coherent brain regions and provides the time courses of their activity. Reference points for the computation of neural coupling may be based on brain areas of maximum power or other physiologically meaningful information, or they may be estimated starting from sensor coherences. The performance of DICS is evaluated with simulated data and illustrated with recordings of spontaneous activity in a healthy subject and a parkinsonian patient. Methods for estimating functional connectivities between brain areas will facilitate characterization of cortical networks involved in sensory, motor, or cognitive tasks and will allow investigation of pathological connectivities in neurological disorders.
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paper_title: Measuring Phase Synchrony in Brain Signals

paper_content:

This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long-range neural integration during cognitive tasks. The method, called phase-locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time-resolution (,100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large-scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long-scale effects do reflect cognitive processing, short-scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony. Hum Brain Mapping 8:194-208, 1999. r 1999 Wiley-Liss, Inc.
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paper_title: A method for the estimation of functional brain connectivity from time-series data

paper_content:

A central issue in cognitive neuroscience is which cortical areas are involved in managing information processing in a cognitive task and to understand their temporal interactions. Since the transfer of information in the form of electrical activity from one cortical region will in turn evoke electrical activity in other regions, the analysis of temporal synchronization provides a tool to understand neuronal information processing between cortical regions. We adopt a method for revealing time-dependent functional connectivity. We apply statistical analyses of phases to recover the information flow and the functional connectivity between cortical regions for high temporal resolution data. We further develop an evaluation method for these techniques based on two kinds of model networks. These networks consist of coupled Rossler attractors or of coupled stochastic Ornstein–Uhlenbeck systems. The implemented time-dependent coupling includes uni- and bi-directional connectivities as well as time delayed feedback. The synchronization dynamics of these networks are analyzed using the mean phase coherence, based on averaging over phase-differences, and the general synchronization index. The latter is based on the Shannon entropy. The combination of these with a parametric time delay forms the basis of a connectivity pattern, which includes the temporal and time lagged dynamics of the synchronization between two sources. We model and discuss potential artifacts. We find that the general phase measures are remarkably stable. They produce highly comparable results for stochastic and periodic systems. Moreover, the methods proves useful for identifying brief periods of phase coupling and delays. Therefore, we propose that the method is useful as a basis for generating potential functional connective models.
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paper_title: Measures of Conditional Linear Dependence and Feedback between Time Series

paper_content:

Abstract Measures of linear dependence and feedback for two multiple time series conditional on a third are defined. The measure of conditional linear dependence is the sum of linear feedback from the first to the second conditional on the third, linear feedback from the second to the first conditional on the third, and instantaneous linear feedback between the first and second series conditional on the third. The measures are non-negative and may be expressed in terms of measures of unconditional feedback between various combinations of the three series. The measures of conditional linear feedback can be additively decomposed by frequency. Estimates of these measures are straightforward to compute, and their distribution can be routinely approximated by bootstrap methods. An empirical example involving real output, money, and interest rates is presented.
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paper_title: Granger Causality: Basic Theory and Application to Neuroscience

paper_content:

Multi-electrode neurophysiological recordings produce massive quantities of data. Multivariate time series analysis provides the basic framework for analyzing the patterns of neural interactions in these data. It has long been recognized that neural interactions are directional. Being able to assess the directionality of neuronal interactions is thus a highly desired capability for understanding the cooperative nature of neural computation. Research over the last few years has shown that Granger causality is a key technique to furnish this capability. The main goal of this article is to provide an expository introduction to the concept of Granger causality. Mathematical frameworks for both bivariate Granger causality and conditional Granger causality are developed in detail with particular emphasis on their spectral representations. The technique is demonstrated in numerical examples where the exact answers of causal influences are known. It is then applied to analyze multichannel local field potentials recorded from monkeys performing a visuomotor task. Our results are shown to be physiologically interpretable and yield new insights into the dynamical organization of large-scale oscillatory cortical networks.
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paper_title: Investigating Causal Relations by Econometric Models and Cross-Spectral Methods

paper_content:

There occurs on some occasions a difficulty in deciding the direction of causality between two related variables and also whether or not feedback is occurring. Testable definitions of causality and feedback are proposed and illustrated by use of simple two-variable models. The important problem of apparent instantaneous causality is discussed and it is suggested that the problem often arises due to slowness in recording information or because a sufficiently wide class of possible causal variables has not been used. It can be shown that the cross spectrum between two variables can be decomposed into two parts, each relating to a single causal arm of a feedback situation. Measures of causal lag and causal strength can then be constructed. A generalisation of this result with the partial cross spectrum is suggested.
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paper_title: Noninvasive dynamic imaging of seizures in epileptic patients

paper_content:

Epileptic seizures are due to abnormal synchronized neuronal discharges. Techniques measuring electrical changes are commonly used to analyze seizures. Neuronal activity can be also defined by concomitant hemodynamic and metabolic changes. Simultaneous electroencephalogram (EEG)-functional MRI (fMRI) measures noninvasively with a high-spatial resolution BOLD changes during seizures in the whole brain. Until now, only a static image representing the whole seizure was provided. We report in 10 focal epilepsy patients a new approach to dynamic imaging of seizures including the BOLD time course of seizures and the identification of brain structures involved in seizure onset and discharge propagation. The first activation was observed in agreement with the expected location of the focus based on clinical and EEG data (three intracranial recordings), thus providing validity to this approach. The BOLD signal preceded ictal EEG changes in two cases. EEG-fMRI may detect changes in smaller and deeper structures than scalp EEG, which can only record activity form superficial cortical areas. This method allowed us to demonstrate that seizure onset zone was limited to one structure, thus supporting the concept of epileptic focus, but that a complex neuronal network was involved during propagation. Deactivations were also found during seizures, usually appearing after the first activation in areas close or distant to the activated regions. Deactivations may correspond to actively inhibited regions or to functional disconnection from normally active regions. This new noninvasive approach should open the study of seizure generation and propagation mechanisms in the whole brain to groups of patients with focal epilepsies.
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paper_title: A method for functional network connectivity among spatially independent resting-state components in schizophrenia

paper_content:

Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in healthy individuals as well as in patients with brain disorders. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subject's ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients.
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paper_title: Multimodal Functional Network Connectivity: An EEG-fMRI Fusion in Network Space

paper_content:

EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.
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paper_title: Investigation of relationships between fMRI brain networks in the spectral domain using ICA and Granger causality reveals distinct differences between schizophrenia patients and healthy controls

paper_content:

Abstract Functional network connectivity (FNC) is an approach that examines the relationships between brain networks (as opposed to functional connectivity (FC) that focuses upon the relationships between single voxels). FNC may help explain the complex relationships between distributed cerebral sites in the brain and possibly provide new understanding of neurological and psychiatric disorders such as schizophrenia. In this paper, we use independent component analysis (ICA) to extract the time courses of spatially independent components and then use these in Granger causality test (GCT) to investigate causal relationships between brain activation networks. We present results using both simulations and fMRI data of 155 subjects obtained during two different tasks. Unlike previous research, causal relationships are presented over different portions of the frequency spectrum in order to differentiate high and low-frequency effects and not merged in a scalar. The results obtained using Sternberg item recognition paradigm (SIRP) and auditory oddball (AOD) tasks showed FNC differentiations between schizophrenia and control groups, and explained how the two groups differed during these tasks. During the SIRP task, secondary visual and cerebellum activation networks served as hubs and included most complex relationships between the activated regions. Secondary visual and temporal lobe activations replaced these components during the AOD task.
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paper_title: EEG/fMRI fusion based on independent component analysis: Integration of data-driven and model-driven methods

paper_content:

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary noninvasive information of brain activity, and EEG/fMRI fusion can achieve higher spatiotemporal resolution than each modality separately. This focuses on independent component analysis (ICA)-based EEG/fMRI fusion. In order to appreciate the issues, we first describe the potential and limitations of the developed fusion approaches: fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and symmetric fusion. We then outline some newly developed hybrid fusion techniques using ICA and the combination of data-/model-driven methods, with special mention of the spatiotemporal EEG/fMRI fusion (STEFF). Finally, we discuss the current trend in methodological development and the existing limitations for extrapolating neural dynamics.
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paper_title: Functional and effective frontotemporal connectivity and genetic risk for schizophrenia

paper_content:

Abstract Background Functional neuroimaging and electrophysiologic studies have found disturbed frontotemporal interaction in schizophrenia. We sought to determine whether abnormalities of frontotemporal connectivity are trait markers of genetic risk for schizophrenia. Methods We investigated 64 schizophrenia patients, 79 of their clinically unaffected siblings, and 88 unrelated normal controls with an auditory oddball electroencephalogram (EEG) evoked potential paradigm. We measured: 1) frontotemporal event-related EEG-coherence (i.e. a measure of functional connectivity); and 2) we performed structural equation modeling of the effective connectivity between the frontal P300 and temporoparietal P300-amplitude. Results Schizophrenic patients and their siblings showed a reduction of frontotemporal coherence. At peak activation during the P300 time-window, a negative (“inhibitory”) frontotemporal path coefficient was found in normal controls, whereas a positive coefficient was seen in schizophrenic patients with siblings being intermediate. Intra-class correlations between sib-pairs and relative risk estimates of the applied connectivity measures were non-significant. Topographic correlation matrix analyses suggested that the altered functional and effective frontotemporal connectivity indirectly reflect regional abnormalities of increased activation variance. Conclusions Impaired interaction of the frontotemporal macro-circuit indirectly reflects genetically determined abnormalities of frontal and temporoparietal microcircuits. The reasons why frontotemporal connectivity appears to be a poor predictor of genetic risk for schizophrenia are discussed.
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paper_title: Sensory evoked and event related oscillations in Alzheimer’s disease: a short review

paper_content:

Diagnosis and treatment of Alzheimer’s disease (AD) depend on clinical evaluation and there is a strong need for an objective tool as a biomarker. Our group has investigated brain oscillatory responses in a small group of AD subjects. We found that the de novo (untreated) AD group differs from both the cholinergically-treated AD group and aged-matched healthy controls in theta and delta responses over left frontal-central areas after cognitive stimulation. On the contrary, the difference observed in AD groups upon a sensory visual stimulation includes response increase over primary or secondary visual sensorial areas compared to controls. These findings imply at least two different neural networks, depending on type of stimulation (i.e. cognitive or sensory). The default mode defined as activity in resting state in AD seems to be affected electrophysiologically. Coherences are also very valuable in observing the group differences, especially when a cognitive stimulus is applied. In healthy controls, higher coherence values are elicited after a cognitive stimulus than after a sensory task. Our findings support the notion of disconnectivity of cortico-cortical connections in AD. The differences in comparison of oscillatory responses upon sensory and cognitive stimulations and their role as a biomarker in AD await further investigation in series with a greater number of subjects.
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paper_title: Abnormal functional connectivity in Alzheimer’s disease: intrahemispheric EEG coherence during rest and photic stimulation

paper_content:

Electroencephalography (EEG) coherence provides a measure of functional correlations between two EEG signals. The present study was conducted to examine intrahemispheric EEG coherence at rest and during photic stimulation (PS; 5, 10 and 15 Hz) in ten unmedicated patients with presenile dementia of the Alzheimer type (AD; mean age at onset 56 years). In the resting EEG, the AD patients had significantly lower coherence than gender- and age-matched control subjects in the alpha-1, alpha-2 and beta-1 frequency bands. The EEG analysis during PS also showed that the patients had significantly lower coherence in the frequency corresponding to PS at 10 and 15 Hz. In this study, the changes in coherence from the resting state to the stimulus condition (i.e. PS-related coherence reactivity) were examined. The patients were found to show significantly smaller coherence reactivity to PS at 5 and 15 Hz. These findings suggest that, in addition to the resting state, AD patients have an impairment of intrahemispheric functional connectivity during PS. They also suggest that AD shows a failure of PS-related functional reorganization.
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paper_title: Brain-Body-Mind in the Nebulous Cartesian System: A Holistic Approach by Oscillations

paper_content:

PART I: Foundations: History and Fundamental Processes in Nature.- Introduction.- Chapter 1: Brain-Body-Mind Problem: A Short Historical and Interdisciplinary Survey.- Chapter 2: Frameworks in the Integration of the Sciences.- PART II: Whole Brain Work and a Holistic Approach to Brain-Body Integration.- Introduction.- Chapter 3: Brain Structures, Transmitters and Analyzing Strategies.- Chapter 4: Autonomus Nervous System, Cardiovascular System and Smooth Muscles.- Chapter 5: Overall Myogenic-Coordination:Building Stones in the Whole-Body-Integration and Tuning.- Chapter 6: Dynamics of Sensory and Cognitive Processing.- Chapter 7: Dynamic Memory.- Chapter 8: Whole-Brain-Work.- Chapter 9: Does the Brain-Body-Mind Work as Dynamic Syncytium.- PART III: Brain in Different States: Evolution, Maturing, Emotion and Pathology.- Introduction.- Chapter 10: The Brain in Evolution of Species and Darwin's Theory.- Chapter 11: The Maturing Brain: Micro Evolution.- Chapter 12: Oscillatory Dynamics of the Emotional Brain: Links of Emotion to Episodic Memory.- Chapter 13: Pathologic Brain: Impairment of Mind Bases on Break of Oscillations and Modulation of Neurotransmitter Release.- PART IV: The Physics of Biology Interface: "A Cartesian System for the 21st Century".-Introduction.- Chapter 14: Chaos and Quantum Approach: Gateway to a 21st Century Cartesian System.- Chapter 15: The Brain in Probabilistic Hyperspace.- Chapter 16: Quantum Brain and the Nebulous Cartesian System.- PART V: Metaphysics of the Brain: Cutting Edges of Philosophy.- Introduction.- Chapter 17: Darwinism, Bergsonism, Concept of Entropy and Creative Thinking.- Chapter 18: Bergson's Intuition Memory and Episodic Memory.- Chapter 19: Towards Metaphysics, Conscient and Unconcient States.- Chapter 20: Mysteries of Mind Conscient and Unconcient States in Creativity and Sleep.- PART VI: Essentials and Unifying Trends in Brain and Body.- Introduction.- Chapter 21: Leitmotives and Common Concepts: An Interim Description.- Chapter 22: Oscillations and Transmitters are Quasi-Invariants in Brain-Body-Mind Integration. What is Break of Mind?.- Chapter 23: Unifying Trends: Globally Coupled Oscillators in Brain-Body.- Chapter 24: Unifying Concepts: Brain's String Theory.- Chapter 25: Unifying Concepts: Brain-Body-Mind Integration and Intuitive Processes.- Chapter 26: Need for a Paradigm Shift and a New Cartesian System
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paper_title: Concepts of Connectivity and Human Epileptic Activity

paper_content:

This review attempts to place the concept of connectivity from increasingly sophisticated neuroimaging data analysis methodologies within the field of epilepsy research. We introduce the more principled connectivity terminology developed recently in neuroimaging and review some of the key concepts related to the characterisation of propagation of epileptic activity using what may be called traditional correlation-based studies based on EEG. We then show how essentially similar methodologies, and more recently models addressing causality, have been used to characterise whole-brain and regional networks using functional MRI data. Following a discussion of our current understanding of the neuronal system aspects of the onset and propagation of epileptic discharges and seizures, we discuss the most advanced and ambitious framework to attempt to fully characterise epileptic networks based on neuroimaging data.
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paper_title: Perceptual and response interference in Alzheimer’s disease and mild cognitive impairment

paper_content:

Objectives: The ability to resolve conflicts is indispensable to the function of daily life and decreases with cognitive decline. We hypothesized that subjects with different levels of cognitive impairment exhibit different conflict resolution performances and may be susceptible to interference effects at different stages.
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paper_title: Directionality of EEG synchronization in Alzheimer's disease subjects

paper_content:

Abstract 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.
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paper_title: Functional Connectivity and Working Memory in Schizophrenia: An Eeg Study

paper_content:

A leading hypothesis suggests that schizophrenic patients suffer from a disconnection syndrome. A failure in functional connectivity curtails the cortical integration and network activation needed to perform working memory tasks. Simulations with neural network models also indicate that connectivity is crucial for simulation of working memory asks. Multichannel EEG correlation-coefficient estimations are considered as a reliable measurement of connectivity patterns among cortical regions. in this study EEG samples are obtained selectively at the delay epochs of a delayed response working memory task. Results of correlation-coefficient estimations indicate a lack of statistically significant changes between non-task and task conditions in frontal, certain parietal, temporal and central channels. These findings propose that schizophrenics probably “fail” to activate the neural networks of the fronto-temporal regions. These are the networks involved in computation of the working memory task. Interestingly al...
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paper_title: Graph theoretical analysis of complex networks in the brain

paper_content:

Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the 'synchronizability' of complex networks of coupled oscillators can be determined by graph spectral analysis. These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer's disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern.
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paper_title: Functional connectivity patterns of human magnetoencephalographic recordings: a ‘small-world’ network?

paper_content:

EEG and MEG (magnetoencephalography) are widely used to study functional connectivity between different brain regions. We address the question whether such connectivity patterns display an optimal organization for information processing. MEG recordings of five healthy human subjects were converted to sparsely connected graphs (N=126; k=15) by applying a suitable threshold to the N * N matrix of synchronization strengths. For intermediate frequencies (8-30 Hz) the synchronization patterns were similar to those of an ordered graph with a consistent drop of synchronization strength as a function of distance. For low ( 30 Hz) frequency bands the synchronization patterns displayed the features of a so-called 'small-world' network. This might reflect an optimal organization pattern for information processing, connecting any two brain area by only a small number of intermediate steps.
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paper_title: Small-world network organization of functional connectivity of EEG slow-wave activity during sleep

paper_content:

Abstract Objective To analyze the functional connectivity patterns of the EEG slow-wave activity during the different sleep stages and Cyclic Alternating Pattern (CAP) conditions, using concepts derived from Graph Theory. Methods We evaluated spatial patterns of EEG slow-wave synchronization between all possible pairs of electrodes (19) placed over the scalp of 10 sleeping healthy young normal subjects using two graph theoretical measures: the clustering coefficient (Cp) and the characteristic path length (Lp). The measures were obtained during the different sleep stages and CAP conditions from the real EEG connectivity networks and randomized control (surrogate) networks (Cp-s and Lp-s). Results Cp and Cp/Cp-s increased significantly from wakefulness to sleep while Lp and Lp/Lp-s did not show changes. Cp/Cp-s was higher for A1 phases, compared to B phases of CAP. Conclusions The network organization of the EEG slow-wave synchronization during sleep shows features characteristic of small-world networks (high Cp combined with low Lp); this type of organization is slightly but significantly more evident during the CAP A1 subtypes. Significance Our results show feasibility of using graph theoretical measures to characterize the complexity of brain networks during sleep and might indicate sleep, and the A1 phases of CAP in particular, as a period during which slow-wave synchronization shows optimal network organization for information processing.
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paper_title: Shadows of artistry: cortical synchrony during perception and imagery of visual art

paper_content:

Functional and topographical differences between two groups, artists and non-artists, during the performances of visual perception and imagery of paintings were presented by means of EEG phase synchrony analysis. In artists as compared with non-artists, significantly higher phase synchrony was found in the high frequency beta and gamma bands during the perception of the paintings; in the low frequency bands (primarily delta), phase synchrony was mostly enhanced during imagery. Strong decreases in phase synchrony of alpha were found primarily in artists for both tasks. The right hemisphere was found to present higher synchrony than the left in artists, whereas hemispheric asymmetry was less significant in non-artists. In the artists, enhanced synchrony in the high frequency band is most likely due to their enhanced binding capabilities of numerous visual attributes, and enhanced synchrony in the low frequency band seems to be due to the higher involvement of long-term visual memory mostly in imagery. Thus, the analysis of phase synchrony from EEG signals yields new information about the dynamical co-operation between neuronal assemblies during the cognition of visual art.
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paper_title: Brain-Body-Mind in the Nebulous Cartesian System: A Holistic Approach by Oscillations

paper_content:

PART I: Foundations: History and Fundamental Processes in Nature.- Introduction.- Chapter 1: Brain-Body-Mind Problem: A Short Historical and Interdisciplinary Survey.- Chapter 2: Frameworks in the Integration of the Sciences.- PART II: Whole Brain Work and a Holistic Approach to Brain-Body Integration.- Introduction.- Chapter 3: Brain Structures, Transmitters and Analyzing Strategies.- Chapter 4: Autonomus Nervous System, Cardiovascular System and Smooth Muscles.- Chapter 5: Overall Myogenic-Coordination:Building Stones in the Whole-Body-Integration and Tuning.- Chapter 6: Dynamics of Sensory and Cognitive Processing.- Chapter 7: Dynamic Memory.- Chapter 8: Whole-Brain-Work.- Chapter 9: Does the Brain-Body-Mind Work as Dynamic Syncytium.- PART III: Brain in Different States: Evolution, Maturing, Emotion and Pathology.- Introduction.- Chapter 10: The Brain in Evolution of Species and Darwin's Theory.- Chapter 11: The Maturing Brain: Micro Evolution.- Chapter 12: Oscillatory Dynamics of the Emotional Brain: Links of Emotion to Episodic Memory.- Chapter 13: Pathologic Brain: Impairment of Mind Bases on Break of Oscillations and Modulation of Neurotransmitter Release.- PART IV: The Physics of Biology Interface: "A Cartesian System for the 21st Century".-Introduction.- Chapter 14: Chaos and Quantum Approach: Gateway to a 21st Century Cartesian System.- Chapter 15: The Brain in Probabilistic Hyperspace.- Chapter 16: Quantum Brain and the Nebulous Cartesian System.- PART V: Metaphysics of the Brain: Cutting Edges of Philosophy.- Introduction.- Chapter 17: Darwinism, Bergsonism, Concept of Entropy and Creative Thinking.- Chapter 18: Bergson's Intuition Memory and Episodic Memory.- Chapter 19: Towards Metaphysics, Conscient and Unconcient States.- Chapter 20: Mysteries of Mind Conscient and Unconcient States in Creativity and Sleep.- PART VI: Essentials and Unifying Trends in Brain and Body.- Introduction.- Chapter 21: Leitmotives and Common Concepts: An Interim Description.- Chapter 22: Oscillations and Transmitters are Quasi-Invariants in Brain-Body-Mind Integration. What is Break of Mind?.- Chapter 23: Unifying Trends: Globally Coupled Oscillators in Brain-Body.- Chapter 24: Unifying Concepts: Brain's String Theory.- Chapter 25: Unifying Concepts: Brain-Body-Mind Integration and Intuitive Processes.- Chapter 26: Need for a Paradigm Shift and a New Cartesian System
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paper_title: A hybrid model for the neural representation of complex mental processing in the human brain

paper_content:

In the present conceptual review several theoretical and empirical sources of information were integrated, and a hybrid model of the neural representation of complex mental processing in the human brain was proposed. Based on empirical evidence for strategy-related and inter-individually different task-related brain activation networks, and further based on empirical evidence for a remarkable overlap of fronto-parietal activation networks across different complex mental processes, it was concluded by the author that there might be innate and modular organized neuro-developmental starting regions, for example, in intra-parietal, and both medial and middle frontal brain regions, from which the neural organization of different kinds of complex mental processes emerge differently during individually shaped learning histories. Thus, the here proposed model provides a hybrid of both massive modular and holistic concepts of idiosyncratic brain physiological elaboration of complex mental processing. It is further concluded that 3-D information, obtained by respective methodological approaches, are not appropriate to identify the non-linear spatio-temporal dynamics of complex mental process-related brain activity in a sufficient way. How different participating network parts communicate with each other seems to be an indispensable aspect, which has to be considered in particular to improve our understanding of the neural organization of complex cognition.
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paper_title: Structure and tie strengths in mobile communication networks

paper_content:

ncovering the structure and function of communication networks has always been constrained by the practical difficulty of mapping out interactions among a large number of individuals. Indeed, most of our current understanding of com- munication and social networks is based on questionnaire data, reaching typically a few dozen individuals and relying on the individual's opinion to reveal the nature and the strength of the ties. The fact that currently an increasing fraction of human interactions are recorded, from e-mail (1-3) to phone records (4), offers unprecedented opportunities to uncover and explore the large scale characteristics of communication and social networks (5). Here we take a first step in this direction by exploiting the widespread use of mobile phones to construct a map of a society-wide communication network, capturing the mobile interaction patterns of millions of individuals. The data set allows us to explore the relationship between the topology of the network and the tie strengths between individuals, informa- tion that was inaccessible at the societal level before. We demonstrate a local coupling between tie strengths and network topology, and show that this coupling has important conse- quences for the network's global stability if ties are removed, as well as for the spread of news and ideas within the network. A significant portion of a country's communication network wasreconstructedfrom18weeksofallmobilephonecallrecords among 20% of the country's entire population, 90% of whose
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paper_title: Genetic Components of Functional Connectivity in the Brain: The Heritability of Synchronization Likelihood

paper_content:

Cognitive functions require the integrated activity of multiple specialized, distributed brain areas. Such functional coupling depends on the existence of anatomical connections between the various brain areas as well as physiological processes whereby the activity in one area influences the activity in another area. Recently, the Synchronization Likelihood (SL) method was developed as a general method to study both linear and nonlinear aspects of coupling. In the present study the genetic architecture of the SL in different frequency bands was investigated. Using a large genetically informative sample of 569 subjects from 282 extended twin families we found that the SL is moderately to highly heritable (41- 67%) especially in the alpha frequency (8 -13 Hz) range. This index of functional connectivity of the brain has been associated with a number of pathological states of the brain. The significant heritability found here suggests that SL can be used to examine the genetic susceptibility to these conditions. Hum Brain Mapp 26: 191-198, 2005. © 2005 Wiley-Liss, Inc.
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paper_title: The brainweb: phase synchronization and large-scale integration.

paper_content:

The emergence of a unified cognitive moment relies on the coordination of scattered mosaics of functionally specialized brain regions. Here we review the mechanisms of large-scale integration that counterbalance the distributed anatomical and functional organization of brain activity to enable the emergence of coherent behaviour and cognition. Although the mechanisms involved in large-scale integration are still largely unknown, we argue that the most plausible candidate is the formation of dynamic links mediated by synchrony over multiple frequency bands.
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paper_title: Resting oscillations and cross-frequency coupling in the human posteromedial cortex

paper_content:

Abstract Using rare intracranial recordings from the posterior interhemispheric region of the human brain, we explored the oscillatory properties of the posteromedial cortex (PMC) during rest. The PMC is a core structure of the default mode network, which is known for its higher activity during the resting state. We found that resting PMC spectral power peaked in the theta band range (4–7 Hz) and was clearly distinguishable from adjacent cortical sites in the occipital lobe displaying peaks in the alpha band range (8–12 Hz). Additionally, the phase of PMC theta oscillations modulated the amplitude of ongoing high gamma (70–180 Hz) activity during the resting state. The magnitude of this cross-frequency modulation was shown to fluctuate at time scales comparable to those observed in functional neuroimaging studies of intrinsic functional connectivity networks (~ 0.1 Hz). The difference of canonical oscillations in the PMC compared to its adjacent cortical sites conforms to functional specialization across anatomical boundaries. Such differences may reflect separate oscillatory preferences between networks that are functionally connected.
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paper_title: Dynamic cross-frequency couplings of local field potential oscillations in rat striatum and hippocampus during performance of a T-maze task

paper_content:

Oscillatory rhythms in different frequency ranges mark different behavioral states and are thought to provide distinct temporal windows that coherently bind cooperating neuronal assemblies. However, the rhythms in different bands can also interact with each other, suggesting the possibility of higher-order representations of brain states by such rhythmic activity. To explore this possibility, we analyzed local field potential oscillations recorded simultaneously from the striatum and the hippocampus. As rats performed a task requiring active navigation and decision making, the amplitudes of multiple high-frequency oscillations were dynamically modulated in task-dependent patterns by the phase of cooccurring theta-band oscillations both within and across these structures, particularly during decision-making behavioral epochs. Moreover, the modulation patterns uncovered distinctions among both high- and low-frequency subbands. Cross-frequency coupling of multiple neuronal rhythms could be a general mechanism used by the brain to perform network-level dynamical computations underlying voluntary behavior.
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Title: A Review of EEG and MEG for Brainnetome Research
Section 1: Introduction
Description 1: Introduce the background and importance of brainnetome research, and the role of EEG and MEG in this field.

Section 2: Usefulness of EEG/MEG in brainnetome analysis
Description 2: Discuss the advantages and usefulness of EEG and MEG in brainnetome analysis.

Section 3: Graph theory
Description 3: Introduce the basics of graph theory and its relevance to brainnetome research.

Section 4: The construction of EEG/MEG networks
Description 4: Explain the process of constructing networks using EEG and MEG data.

Section 5: Definition of nodes
Description 5: Detail the methods and considerations for defining nodes in EEG/MEG networks.

Section 6: Source reconstruction model
Description 6: Discuss the models used for source reconstruction in EEG/MEG analysis.

Section 7: Source estimation methods
Description 7: Present the various methods for estimating sources within EEG/MEG data.

Section 8: Spatial a priori assumptions
Description 8: Explain the role of spatial a priori assumptions in improving source estimation.

Section 9: Definition of edges
Description 9: Describe the criteria and methods for defining edges in EEG/MEG networks.

Section 10: Synchronization likelihood
Description 10: Explain the synchronization likelihood method for assessing functional connectivity.

Section 11: Granger causality
Description 11: Discuss the use of Granger causality for constructing effective connectivity in EEG/MEG networks.

Section 12: Frequency-domain method
Description 12: Describe the methods for analyzing EEG/MEG data in the frequency domain for network construction.

Section 13: Fusing the brainnetome of EEG/MEG with other modalities
Description 13: Explore the integration of EEG/MEG data with other neuroimaging modalities.

Section 14: Fusion of EEG and fMRI based on neural networks
Description 14: Discuss the fusion of EEG and fMRI data using neural network approaches.

Section 15: A systematic perspective for network integration
Description 15: Present a systematic perspective for integrating EEG/MEG networks with other modalities.

Section 16: Applications of the EEG/MEG network
Description 16: Outline the applications of EEG/MEG networks in neuroscience research and clinical settings.

Section 17: The EEG/MEG network in brain disorders
Description 17: Introduce the applications of EEG/MEG networks in the study and diagnosis of brain disorders.

Section 18: The EEG/MEG network in cognition
Description 18: Detail how EEG/MEG networks contribute to understanding cognitive processes.

Section 19: Methodological issues
Description 19: Discuss the methodological challenges in constructing and analyzing EEG/MEG networks.

Section 20: Conceptual issues
Description 20: Address conceptual challenges and future directions for EEG/MEG network research.

Section 21: Relationships between brain rhythms
Description 21: Explore the interplay between different brain rhythms and their significance in brainnetome research.

Section 22: Conclusions
Description 22: Summarize the key findings, significance, and future directions of EEG/MEG for brainnetome research.