Past, present and future of topographic mapping

EEG Microstate Features as an Automatic Recognition Model of High-Density Epileptic EEG Using Support Vector Machine

Epilepsy is one of the most serious nervous system diseases; it can be diagnosed accurately by video electroencephalogram. In this study, we analyzed microstate epileptic electroencephalogram (EEG) to aid in the diagnosis and identification of epilepsy. We recruited patients with focal epilepsy and healthy participants from the Third Xiangya Hospital and recorded their resting EEG data. In this study, the EEG data were analyzed by microstate analysis, and the support vector machine (SVM) classifier was used for automatic epileptic EEG classification based on features of the EEG microstate series, including microstate parameters (duration, occurrence, and coverage), linear features (median, second quartile, mean, kurtosis, and skewness) and non-linear features (Petrosian fractal dimension, approximate entropy, sample entropy, fuzzy entropy, and Lempel-Ziv complexity). In the gamma sub-band, the microstate parameters as a model were the best for interictal epilepsy recognition, with an accuracy of 87.18%, recall of 70.59%, and an area under the curve of 94.52%. There was a recognition effect of interictal epilepsy through the features extracted from the EEG microstate, which varied within the 4~45 Hz band with an accuracy of 79.55%. Based on the SVM classifier, microstate parameters and EEG features can be effectively used to classify epileptic EEG, and microstate parameters can better classify epileptic EEG compared with EEG features.

The Brain Structure and Intrinsic Characters of Falsification Thinking in Conditional Proposition Testing

Wason's selection task (WST) as a representative of the field of conditional proposition testing has been explored by multiple disciplines for more than 50 years, but the neural basis of its key falsification thinking remains unclear. Considering that the accuracy of individuals in WST has stability over time, we believe that falsification thinking has a specific brain structural basis and intrinsic neural characteristics. To test this hypothesis, we studied individuals who were able to complete the WST using T1-weighted MRI (using voxel-based morphology (VBM) analysis) and resting electroencephalogram (EEG) (using microstate analysis, which can reflect stable cognitive characteristics of individuals) techniques. First, VBM analysis found that, compared with the verification group, the gray matter volume (GMV) of the left inferior temporal gyrus and the right superior temporal region of the falsification group was larger, whereas the GMV in the cerebellum of the verification group was significantly larger than that of the falsification group. Subsequently, the results of the microstate analysis of the resting EEG data showed that the contribution of class A of the falsification group, which is closely related to the language network, is significantly higher than that of the verification group. Our structural MRI and resting EEG results consistently show that the structure and intrinsic activity pattern of the temporal lobe in individuals with falsification thinking are specific. Furthermore, the findings may provide potential insights into the role of the temporal lobe (which is also a brain region of language processing) in thought.

Excitatory-inhibitory balance within EEG microstates and resting-state fMRI networks: assessed via simultaneous trimodal PET-MR-EEG imaging

The symbiosis of neuronal activities and glucose energy metabolism is reflected in the generation of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) signals. However, their association with the balance between neuronal excitation and inhibition (E/I-B), which is closely related to the activities of glutamate and γ-aminobutyric acid (GABA) and the receptor availability (RA) of GABAA and mGluR5, remains unexplored. This research investigates these associations during the resting state (RS) condition using simultaneously recorded PET/MR/EEG (trimodal) data. The trimodal data were acquired from three studies using different radio-tracers such as, [11C]ABP688 (ABP) (N = 9), [11C]Flumazenil (FMZ) (N = 10) and 2-[18F]fluoro-2-deoxy-D-glucose (FDG) (N = 10) targeted to study the mGluR5, GABAA receptors and glucose metabolism respectively. Glucose metabolism and neuroreceptor binding availability (non-displaceable binding potential (BPND)) of GABAA and mGluR5 were found to be significantly higher and closely linked within core resting-state networks (RSNs). The neuronal generators of EEG microstates and the fMRI measures were most tightly associated with the BPND of GABAA relative to mGluR5 BPND and the glucose metabolism, emphasising a predominance of inhibitory processes within in the core RSNs at rest. Changes in the neuroreceptors leading to an altered coupling with glucose metabolism may render the RSNs vulnerable to psychiatric conditions. The paradigm employed here will likely help identify the precise neurobiological mechanisms behind these alterations in fMRI functional connectivity and EEG oscillations, potentially benefitting individualised healthcare treatment measures.

The spontaneous brain activity of disgust: Perspective from resting state fMRI and resting state EEG

In recent years, more and more studies on disgust have shown the association between disgust and various psychopathologies. Revealing the spontaneous brain activity patterns associated with disgust sensitivity from the perspective of individual differences will give us an insight into the neurologic nature of disgust and its psychopathological vulnerability. Here, we used two modal brain imaging techniques (resting fMRI and resting EEG) to reveal spontaneous brain activity patterns closely related to disgust sensitivity. The amplitude of low-frequency fluctuation results showed that disgust sensitivity is negatively correlated with the spontaneous activity of the right cerebellum crus II and positively correlated with the spontaneous activity of the right superior frontal cortex, which are inhibition-related brain regions. Furthermore, the microstate results of rest EEG indicated that the corrected duration, occurrence rate, and contribution of Class C, which is related to the anterior default mode network and is considered to be related to subjective representation of one' own body by combining interoceptive information with affective salience, were significantly positively correlated with the disgust sensitivity level. This data-driven approach provides the first evidence on the intrinsic brain features of disgust sensitivity based on two resting-state brain modalities. The results represent an initial effort to uncover the neurological basis of disgust sensitivity and its connection to psychopathology.

Noisy network attractor models for transitions between EEG microstates

The brain is intrinsically organized into large-scale networks that constantly re-organize on multiple timescales, even when the brain is at rest. The timing of these dynamics is crucial for sensation, perception, cognition, and ultimately consciousness, but the underlying dynamics governing the constant reorganization and switching between networks are not yet well understood. Electroencephalogram (EEG) microstates are brief periods of stable scalp topography that have been identified as the electrophysiological correlate of functional magnetic resonance imaging defined resting-state networks. Spatiotemporal microstate sequences maintain high temporal resolution and have been shown to be scale-free with long-range temporal correlations. Previous attempts to model EEG microstate sequences have failed to capture this crucial property and so cannot fully capture the dynamics; this paper answers the call for more sophisticated modeling approaches. We present a dynamical model that exhibits a noisy network attractor between nodes that represent the microstates. Using an excitable network between four nodes, we can reproduce the transition probabilities between microstates but not the heavy tailed residence time distributions. We present two extensions to this model: first, an additional hidden node at each state; second, an additional layer that controls the switching frequency in the original network. Introducing either extension to the network gives the flexibility to capture these heavy tails. We compare the model generated sequences to microstate sequences from EEG data collected from healthy subjects at rest. For the first extension, we show that the hidden nodes 'trap' the trajectories allowing the control of residence times at each node. For the second extension, we show that two nodes in the controlling layer are sufficient to model the long residence times. Finally, we show that in addition to capturing the residence time distributions and transition probabilities of the sequences, these two models capture additional properties of the sequences including having interspersed long and short residence times and long range temporal correlations in line with the data as measured by the Hurst exponent.

Dynamic state with covarying brain activity-connectivity: On the pathophysiology of schizophrenia

The human brain is a dynamic system that incorporates the evolution of local activities and the reconfiguration of brain interactions. Reoccurring brain patterns, regarded as "brain states", have revealed new insights into the pathophysiology of brain disorders, particularly schizophrenia. However, previous studies only focus on the dynamics of either brain activity or connectivity, ignoring the temporal co-evolution between them. In this work, we propose to capture dynamic brain states with covarying activity-connectivity and probe schizophrenia-related brain abnormalities. We find that the state-based activity and connectivity show high correspondence, where strong and antagonistic connectivity is accompanied with strong low-frequency fluctuations across the whole brain while weak and sparse connectivity co-occurs with weak low-frequency fluctuations. In addition, graphical analysis shows that connectivity network efficiency is associated with the fluctuation of brain activities and such associations are different across brain states. Compared with healthy controls, schizophrenia patients spend more time in weakly-connected and -activated brain states but less time in strongly-connected and -activated brain states. schizophrenia patients also show lower efficiency in thalamic regions within the "strong" states. Interestingly, the atypical fractional occupancy of one brain state is correlated with individual attention performance. Our findings are replicated in another independent dataset and validated using different brain parcellation schemes. These converging results suggest that the brain spontaneously reconfigures with covarying activity and connectivity and such co-evolutionary property might provide meaningful information on the mechanism of brain disorders which cannot be observed by investigating either of them alone.

Tinnitus and neuropathic pain share a common neural substrate in the form of specific brain connectivity and microstate profiles

Tinnitus and neuropathic pain share similar pathophysiological, clinical, and treatment characteristics. In this EEG study, a group of tinnitus (n = 100) and neuropathic pain (n = 100) patients are compared to each other and to a healthy control group (n = 100). Spectral analysis demonstrates gamma band activity within the primary auditory and somatosensory cortices in patients with tinnitus and neuropathic pain, respectively. A conjunction analysis further demonstrates an overlap of tinnitus and pain related activity in the anterior and posterior cingulate cortex as well as in the dorsolateral prefrontal cortex in comparison to healthy controls. Further analysis reveals that similar states characterize tinnitus and neuropathic pain patients, two of which differ from the healthy group and two of which are shared. Both pain and tinnitus patients spend half of the time in one specific microstate. Seed-based functional connectivity with the source within the predominant microstate shows delta, alpha1, and gamma lagged phase synchronization overlap with multiple brain areas between pain and tinnitus. These data suggest that auditory and somatosensory phantom perceptions share an overlapping brain network with common activation and connectivity patterns and are differentiated by specific sensory cortex gamma activation.

Comparison of EEG microstates with resting state fMRI and FDG-PET measures in the default mode network via simultaneously recorded trimodal (PET/MR/EEG) data

Simultaneous trimodal positron emission tomography/magnetic resonance imaging/electroencephalography (PET/MRI/EEG) resting state (rs) brain data were acquired from 10 healthy male volunteers. The rs-functional MRI (fMRI) metrics, such as regional homogeneity (ReHo), degree centrality (DC) and fractional amplitude of low-frequency fluctuations (fALFFs), as well as 2-[18F]fluoro-2-desoxy-d-glucose (FDG)-PET standardised uptake value (SUV), were calculated and the measures were extracted from the default mode network (DMN) regions of the brain. Similarly, four microstates for each subject, showing the diverse functional states of the whole brain via topographical variations due to global field power (GFP), were estimated from artefact-corrected EEG signals. In this exploratory analysis, the GFP of microstates was nonparametrically compared to rs-fMRI metrics and FDG-PET SUV measured in the DMN of the brain. The rs-fMRI metrics (ReHO, fALFF) and FDG-PET SUV did not show any significant correlations with any of the microstates. The DC metric showed a significant positive correlation with microstate C (r_s  = 0.73, p = .01). FDG-PET SUVs indicate a trend for a negative correlation with microstates A, B and C. The positive correlation of microstate C with DC metrics suggests a functional relationship between cortical hubs in the frontal and occipital lobes. The results of this study suggest further exploration of this method in a larger sample and in patients with neuropsychiatric disorders. The aim of this exploratory pilot study is to lay the foundation for the development of such multimodal measures to be applied as biomarkers for diagnosis, disease staging, treatment response and monitoring of neuropsychiatric disorders.

Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia

The human brain is a highly dynamic system with non-stationary neural activity and rapidly-changing neural interaction. Resting-state dynamic functional connectivity (dFC) has been widely studied during recent years, and the emerging aberrant dFC patterns have been identified as important features of many mental disorders such as schizophrenia (SZ). However, only focusing on the time-varying patterns in FC is not enough, since the local neural activity itself (in contrast to the inter-connectivity) is also found to be highly fluctuating from research using high-temporal-resolution imaging techniques. Exploring the time-varying patterns in brain activity and their relationships with time-varying brain connectivity is important for advancing our understanding of the co-evolutionary property of brain network and the underlying mechanism of brain dynamics. In this study, we introduced a framework for characterizing time-varying brain activity and exploring its associations with time-varying brain connectivity, and applied this framework to a resting-state fMRI dataset including 151 SZ patients and 163 age- and gender matched healthy controls (HCs). In this framework, 48 brain regions were first identified as intrinsic connectivity networks (ICNs) using group independent component analysis (GICA). A sliding window approach was then adopted for the estimation of dynamic amplitude of low-frequency fluctuation (dALFF) and dFC, which were used to measure time-varying brain activity and time-varying brain connectivity respectively. The dALFF was further clustered into six reoccurring states by the k-means clustering method and the group difference in occurrences of dALFF states was explored. Lastly, correlation coefficients between dALFF and dFC were calculated and the group difference in these dALFF-dFC correlations was explored. Our results suggested that 1) ALFF of brain regions was highly fluctuating during the resting-state and such dynamic patterns are altered in SZ, 2) dALFF and dFC were correlated in time and their correlations are altered in SZ. The overall results support and expand prior work on abnormalities of brain activity, static FC (sFC) and dFC in SZ, and provide new evidence on aberrant time-varying brain activity and its associations with brain connectivity in SZ, which might underscore the disrupted brain cognitive functions in this mental disorder.

Altered Brain Microstate Dynamics in Adolescents with Narcolepsy

Narcolepsy is a chronic sleep disorder caused by a loss of hypocretin-1 producing neurons in the hypothalamus. Previous neuroimaging studies have investigated brain function in narcolepsy during rest using positron emission tomography (PET) and single photon emission computed tomography (SPECT). In addition to hypothalamic and thalamic dysfunction they showed aberrant prefrontal perfusion and glucose metabolism in narcolepsy. Given these findings in brain structure and metabolism in narcolepsy, we anticipated that changes in functional magnetic resonance imaging (fMRI) resting state network (RSN) dynamics might also be apparent in patients with narcolepsy. The objective of this study was to investigate and describe brain microstate activity in adolescents with narcolepsy and correlate these to RSNs using simultaneous fMRI and electroencephalography (EEG). Sixteen adolescents (ages 13-20) with a confirmed diagnosis of narcolepsy were recruited and compared to age-matched healthy controls. Simultaneous EEG and fMRI data were collected during 10 min of wakeful rest. EEG data were analyzed for microstates, which are discrete epochs of stable global brain states obtained from topographical EEG analysis. Functional MRI data were analyzed for RSNs. Data showed that narcolepsy patients were less likely than controls to spend time in a microstate which we found to be related to the default mode network and may suggest a disruption of this network that is disease specific. We concluded that adolescents with narcolepsy have altered resting state brain dynamics.

Time course of EEG oscillations during repeated listening of a well-known aria

While previous studies have analyzed mean neurophysiological responses to musical stimuli, the current study aimed to identify specific time courses of electroencephalography (EEG) oscillations, which are associated with dynamic changes in the acoustic features of the musical stimulus. In addition, we were interested in whether these time courses change during a repeated presentation of the same musical piece. A total of 16 subjects repeatedly listened to the well-known aria “Nessun dorma,” sung by Paul Potts, while continuous 128-channel EEG and heart rate, as well as electrodermal responses, were recorded. The time courses for the EEG oscillations were calculated using a time resolution of 1 second for several frequency bands, on the basis of individual alpha-peak frequencies (theta, low alpha-1, low alpha-2, upper alpha, and beta). For all frequency bands, we identified a more or less continuous increase in power relative to a baseline period, indicating strong event-related synchronization (ERS) during music listening. The ERS time courses, however, did not correlate strongly with the time courses of the acoustic features of the aria. In addition, we did not observe changes in EEG oscillations after repeated presentation of the same musical piece. Aside from this distinctive feature, we identified a remarkable variability in EEG oscillations, both within and between the repeated presentations of the aria. We interpret the continuous increase in ERS observed in all frequency bands during music listening as an indicator of a particular neurophysiological and psychological state evoked by music listening. We suggest that this state is characterized by increased internal attention (accompanied by reduced external attention), increased inhibition of brain networks not involved in the generation of this internal state, the maintenance of a particular level of general alertness, and a type of brain state that can be described as “mind wandering.” The overall state can be categorized as a psychological process that may be seen as a “drawing in” to the musical piece. However, this state is not stable and varies considerably throughout the music listening session and across subjects. Most important, however, is the finding that the neurophysiological activations occurring during music listening are dynamic and not stationary.

Luria revisited: cognitive research in schizophrenia, past implications and future challenges

Contemporary psychiatry is becoming more biologically oriented in the attempt to elicit a biological rationale of mental diseases. Although mental disorders comprise mostly functional abnormalities, there is a substantial overlap between neurology and psychiatry in addressing cognitive disturbances. In schizophrenia, the presence of cognitive impairment prior to the onset of psychosis and early after its manifestation suggests that some neurocognitive abnormalities precede the onset of psychosis and may represent a trait marker. These cognitive alterations may arise from functional disconnectivity, as no significant brain damage has been found. In this review we aim to revise A.R. Luria's systematic approach used in the neuropsychological evaluation of cognitive functions, which was primarily applied in patients with neurological disorders and in the cognitive evaluation in schizophrenia and other related disorders. As proposed by Luria, cognitive processes, associated with higher cortical functions, may represent functional systems that are not localized in narrow, circumscribed areas of the brain, but occur among groups of concertedly working brain structures, each of which makes its own particular contribution to the organization of the functional system. Current developments in neuroscience provide evidence of functional connectivity in the brain. Therefore, Luria's approach may serve as a frame of reference for the analysis and interpretation of cognitive functions in general and their abnormalities in schizophrenia in particular. Having said that, modern technology, as well as experimental evidence, may help us to understand the brain better and lead us towards creating a new classification of cognitive functions. In schizophrenia research, multidisciplinary approaches must be utilized to address specific cognitive alterations. The relationships among the components of cognitive functions derived from the functional connectivity of the brain may provide an insight into cognitive machinery.

Inner and Outer Horizons of Time Experience

Human experience of temporal durations exhibits a multi-regional structure, with more or less distinct boundaries, or horizons, on the scale of physical duration. The inner horizons are imposed by perceptual thresholds for simultaneity (≈ 3 ms) and temporal order (≈ 30 ms), and are determined by the dynamical properties of the neural substrate integrating sensory information. Related to the inner horizon of experienced time are perceptual or cognitive “moments.” Comparative data on autokinetic times suggest that these moments may be relatively invariant (≈ 102 ms) across a wide range of species. Extension of the “sensible present” (≈ 3 s) defines an intermediate horizon, beyond which the generic experience of duration develops. The domain of immediate duration experience is delimited by the ultimate outer horizon at about ≈102 s, as evidenced by analysis of duration reproduction experiments (reproducibility horizon), probably determined by relaxation times of “neural accumulators.” Beyond these phenomenal horizons, time is merely cognitively (re)constructed, not actually experienced or “perceived,” a fact that is frequently ignored by contemporary time perception research. The nyocentric organization of time experience shows an interesting analogy with the egocentric organization of space, suggesting that structures of subjective space and time are derived from active motion as a common experiential basis.

EEG correlates of self-referential processing

Self-referential processing has been principally investigated using functional magnetic resonance imaging (fMRI). However, understanding of the brain functioning is not possible without careful comparison of the evidence coming from different methodological domains. This paper aims to review electroencephalographic (EEG) studies of self-referential processing and to evaluate how they correspond, complement, or contradict the existing fMRI evidence. There are potentially two approaches to the study of EEG correlates of self-referential processing. Firstly, because simultaneous registration of EEG and fMRI has become possible, the degree of overlap between these two signals in brain regions related to self-referential processing could be determined. Second and more direct approach would be the study of EEG correlates of self-referential processing per se. In this review, I discuss studies, which employed both these approaches and show that in line with fMRI evidence, EEG correlates of self-referential processing are most frequently found in brain regions overlapping with the default network, particularly in the medial prefrontal cortex. In the time domain, the discrimination of self- and others-related information is mostly associated with the P300 ERP component, but sometimes is observed even earlier. In the frequency domain, different frequency oscillations have been shown to contribute to self-referential processing, with spontaneous self-referential mentation being mostly associated with the alpha frequency band.

Linking brain connectivity across different time scales with electroencephalogram, functional magnetic resonance imaging, and diffusion tensor imaging

Structural and functional connectivity are intrinsic properties of the human brain and represent the amount of cognitive capacities of individual subjects. These connections are modulated due to development, learning, and disease. Momentary adaptations in functional connectivity alter the structural connections, which in turn affect the functional connectivity. Thus, structural and functional connectivity interact on a broad timescale. In this study, we aimed to explore distinct measures of connectivity assessed by functional magnetic resonance imaging and diffusion tensor imaging and their association to the dominant electroencephalogram oscillatory property at rest: the individual alpha frequency (IAF). We found that in 21 healthy young subjects, small intraindividual temporal IAF fluctuations were correlated to increased blood oxygenation level-dependent signal in brain areas associated to working memory functions and to the modulation of attention. These areas colocalized with functionally connected networks supporting the respective functions. Furthermore, subjects with higher IAF show increased fractional anisotropy values in fascicles connecting the above-mentioned areas and networks. Hence, due to a multimodal approach a consistent functionally and structurally connected network related to IAF was observed.

Perspective and agency during video gaming influences spatial presence experience and brain activation patterns

Background

The experience of spatial presence (SP), i.e., the sense of being present in a virtual environment, emerges if an individual perceives himself as 1) if he were actually located (self-location) and 2) able to act in the virtual environment (possible actions). In this study, two main media factors (perspective and agency) were investigated while participants played a commercially available video game.

Methods

The differences in SP experience and associated brain activation were compared between the conditions of game play in first person perspective (1PP) and third person perspective (3PP) as well as between agency, i.e., active navigation of the video game character (active), and non-agency, i.e., mere passive observation (passive). SP was assessed using standard questionnaires, and brain activation was measured using electroencephalography (EEG) and sLORETA source localisation (standard low-resolution brain electromagnetic tomography).

Results

Higher SP ratings were obtained in the 1PP compared with the 3PP condition and in the active compared with the passive condition. On a neural level, we observed in the 1PP compared with the 3PP condition significantly less alpha band power in the parietal, the occipital and the limbic cortex. In the active compared with the passive condition, we uncovered significantly more theta band power in frontal brain regions.

Conclusion

We propose that manipulating the factors perspective and agency influences SP formation by either directly or indirectly modulating the ego-centric visual processing in a fronto-parietal network. The neuroscientific results are discussed in terms of the theoretical concepts of SP.

Spatiotemporal dynamics of the brain at rest--exploring EEG microstates as electrophysiological signatures of BOLD resting state networks

Neuroimaging research suggests that the resting cerebral physiology is characterized by complex patterns of neuronal activity in widely distributed functional networks. As studied using functional magnetic resonance imaging (fMRI) of the blood-oxygenation-level dependent (BOLD) signal, the resting brain activity is associated with slowly fluctuating hemodynamic signals (~10s). More recently, multimodal functional imaging studies involving simultaneous acquisition of BOLD-fMRI and electroencephalography (EEG) data have suggested that the relatively slow hemodynamic fluctuations of some resting state networks (RSNs) evinced in the BOLD data are related to much faster (~100 ms) transient brain states reflected in EEG signals, that are referred to as "microstates". To further elucidate the relationship between microstates and RSNs, we developed a fully data-driven approach that combines information from simultaneously recorded, high-density EEG and BOLD-fMRI data. Using independent component analysis (ICA) of the combined EEG and fMRI data, we identified thirteen microstates and ten RSNs that are organized independently in their temporal and spatial characteristics, respectively. We hypothesized that the intrinsic brain networks that are active at rest would be reflected in both the EEG data and the fMRI data. To test this hypothesis, the rapid fluctuations associated with each microstate were correlated with the BOLD-fMRI signal associated with each RSN. We found that each RSN was characterized further by a specific electrophysiological signature involving from one to a combination of several microstates. Moreover, by comparing the time course of EEG microstates to that of the whole-brain BOLD signal, on a multi-subject group level, we unraveled for the first time a set of microstate-associated networks that correspond to a range of previously described RSNs, including visual, sensorimotor, auditory, attention, frontal, visceromotor and default mode networks. These results extend our understanding of the electrophysiological signature of BOLD RSNs and demonstrate the intrinsic connection between the fast neuronal activity and slow hemodynamic fluctuations.

Native language experience influences the topography of the mismatch negativity to speech

The ability to learn second language speech sound categories declines during development. We examined this phenomenon by studying the mismatch negativity (MMN) to the /r/ - /l/ distinction in native English speakers and learners of English as a second language who are native speakers of Japanese. Previous studies have suggested that the MMN is remarkably plastic when evaluated as a waveform at a central electrode. We replicated this finding: analyses of the MMN at a typical electrode location (Fz) revealed only small, non-significant differences between groups, despite large behavioral differences in the ability to discriminate these sounds from one another. Topographic analyses, however, revealed reliable differences in lateralization of the MMN, such that native English speakers' responses were left-lateralized relative to native Japanese speakers' responses.

BOLD correlates of EEG topography reveal rapid resting-state network dynamics

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 10s. 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.

An automated method for micro-state segmentation of evoked potentials

We present a method for segmenting evoked potentials into functional micro-states. The method is based on measuring the similarity between all the topographic maps in the evoked potential and grouping them into functional micro-states based on minimizing an error function. The similarity is measured as the normalized cross-correlation coefficient. The method was validated on simulated data and tested on its ability to segment a visual evoked potential. On simulated data the method missed from 1% to 8.5% of the micro-state boundaries for evoked potentials with a signal-to-noise ratio of 20-1dB, respectively. The proposed segmentation method was compared with segmentation based on K-mean clustering. It was found that the proposed method was better at detecting the correct number of micro-states and was computationally more efficient. The automatic segmentation of the visual evoked potential was compared to the manual segmentation performed by eleven EEG specialists. No significant difference in the deviation of micro-state boundaries was observed between two random EEG specialists and between a random EEG specialist and the automatic method. Thus it was found that the method could reliably segment evoked potentials into their functional micro-states.

Coarse neural tuning for print peaks when children learn to read

Adult readers exhibit increased fast N1 activity to wordlike strings in their event-related brain potential. This increase has been linked to visual expertise for print, implying a protracted monotonic development. We investigated the development of coarse neural tuning for print by studying children longitudinally before and after learning to read, and comparing them to skilled adults. The coarse N1 tuning, which had been absent in nonreading kindergarten children, emerged in less than 2 years after the same children had mastered basic reading skills in 2nd grade. The N1 became larger for words than symbol strings in every child, and this coarse tuning was stronger for faster readers. Fast brain processes thus specialize rapidly for print when children learn to read, and play an important functional role in the fluency of early reading. Comparing 2nd graders with adults revealed a further decrease of the coarse N1 tuning in adults, presumably reflecting further reading practice. This constitutes a prominent nonlinear development of coarse neurophysiological specialization for print. The maximum tuning in novice readers possibly reflects the high sensitivity of their neural network for visual aspects of print, and a more selective tuning in expert adult readers.

Neural correlate of spatial presence in an arousing and noninteractive virtual reality: an EEG and psychophysiology study

Using electroencephalography (EEG), psychophysiology, and psychometric measures, this is the first study which investigated the neurophysiological underpinnings of spatial presence. Spatial presence is considered a sense of being physically situated within a spatial environment portrayed by a medium (e.g., television, virtual reality). Twelve healthy children and 11 healthy adolescents were watching different virtual roller coaster scenarios. During a control session, the roller coaster cab drove through a horizontal roundabout track. The following realistic roller coaster rides consisted of spectacular ups, downs, and loops. Low-resolution brain electromagnetic tomography (LORETA) and event-related desynchronization (ERD) were used to analyze the EEG data. As expected, we found that, compared to the control condition, experiencing a virtual roller coaster ride evoked in both groups strong SP experiences, increased electrodermal reactions, and activations in parietal brain areas known to be involved in spatial navigation. In addition, brain areas that receive homeostatic afferents from somatic and visceral sensations of the body were strongly activated. Most interesting, children (as compared to adolescents) reported higher spatial presence experiences and demonstrated a different frontal activation pattern. While adolescents showed increased activation in prefrontal areas known to be involved in the control of executive functions, children demonstrated a decreased activity in these brain regions. Interestingly, recent neuroanatomical and neurophysiological studies have shown that the frontal brain continues to develop to adult status well into adolescence. Thus, the result of our study implies that the increased spatial presence experience in children may result from the not fully developed control functions of the frontal cortex.

Emerging Neurophysiological Specialization for Letter Strings

In adult readers, printed words and other letter strings activate specialized visual functions within 200 msec, as evident from neurophysiological recordings of brain activity. These fast, specialized responses to letter strings are thought to develop through plastic changes in the visual system. However, it is unknown whether this specialization emerges only with the onset of word reading, or represents a precursor of literacy. We compared 6-year-old kindergarten children who could not yet read words to adult readers. Both age groups detected immediate repetitions of visually presented words, pseudo-words, symbol strings, and pictures during event-related potential (ERP) mapping. Maps from seven corresponding ERP segments in children and adults were analyzed regarding fast (<250 msec) and slow (>300 msec) specialization for letter strings. Adults reliably differentiated words through increased fast (<150 msec) occipito-temporal N1 activity from symbols. Children showed a later, more mid-occipital N1 with marginal word-symbol differences, which were absent in those children with low letter knowledge. Children with high letter knowledge showed some fast sensitivity to letter strings, which was confined to right occipito-temporal sites, unlike the stronger adult N1 specialization. This suggests that a critical degree of early literacy induces some immature, but fast, specialization for letter strings before word reading becomes possible. Children also differentiated words from symbols in later segments through increased right occipito-temporal negativity for words. This slow specialization for letter strings was not modulated by letter knowledge and was absent in adults, possibly reflecting a visual precursor of literacy due to visual familiarity with letter strings.

Development of the automatic mismatch response: from frontal positivity in kindergarten children to the mismatch negativity

OBJECTIVE

The automatic event-related potential (ERP) response to auditory deviance typically consists of a frontocentral mismatch negativity (MMN), which has been shown to be quite stable during development. Whereas in some infant studies, positive frontal mismatch responses have been reported instead of a MMN; to date, such positivities have not been reported for older children.

METHODS

Oddball sequences with small frequency and phoneme deviance (standard: 1000 Hz, 'ba'; larger deviance: 1060 Hz, 'ta'; smaller deviance: 1030 Hz, 'da') and short intervals (every 0.38 s) were presented to 6-7-year-old children and adults during 43-channel ERP recordings.

RESULTS

Children showed a consistent frontal positive mismatch response with posterior negativity (179-207 ms), and adults a frontocentral MMN with mastoid positivity (129-199 ms). This map polarity reversal was reflected by significantly different 3D centroid distributions. Low-resolution electromagnetic tomography (LORETA) revealed temporal mismatch response sources for both age groups and conditions.

CONCLUSIONS

Major developmental changes characterise the automatic mismatch response for the small deviances and short intervals used. Source localisation suggests that children's and adults' mismatch responses originated from superior temporal plane generators with similar localisation but opposite polarity. This indicates qualitatively different neurophysiological functioning of the automatic bi-temporal auditory change detectors in children and adults.

Correspondence of event-related potential tomography and functional magnetic resonance imaging during language processing

Combining event-related potentials (ERP) and functional magnetic resonance imaging (fMRI) may provide sufficient temporal and spatial resolution to clarify the functional connectivity of neural processes, provided both methods represent the same neural networks. The current study investigates the statistical correspondence of ERP tomography and fMRI within the common activity volume and time range in a complex visual language task. The results demonstrate that both methods represent similar neural networks within the bilateral occipital gyrus, lingual gyrus, precuneus and middle frontal gyrus, and the left inferior and superior parietal lobe, middle and superior temporal gyrus, cingulate gyrus, superior frontal gyrus and precentral gyrus. The mean correspondence of both methods over subjects was significant. On an individual basis, only half of the subjects showed significantly corresponding activity patterns, suggesting that a one-to-one correspondence between individual fMRI activation patterns and ERP source tomographies integrated over microstates cannot be assumed in all cases.

Human alpha oscillations in wakefulness, drowsiness period, and REM sleep: different electroencephalographic phenomena within the alpha band

Cortical oscillations in the range of alpha activity (8-13 Hz) are one of the fundamental electrophysiological phenomena of the human electroencephalogram (EEG). Evidence from quantitative EEG data has shown that their electrophysiological features, cortical generation mechanisms, and therefore, their functional correlates vary along the sleep-wake continuum. Specifically, spectral microstructure and EEG coherence levels between anterior and posterior cortical regions permit to differentiate among alpha activity spontaneously appearing in relaxed wakefulness with eyes closed, drowsiness period, and REM sleep, by reflecting distinct properties of neural networks involved in its cortical generation as well as a different interplay between cortical generators, respectively. Besides, the dissimilar spatiotemporal features of brain electrical microstates within the alpha range reveals a different geometry of active neural structures underlying each alpha variant or, simply, changes in the stability level of neural networks during each brain state. Studies reviewed in this paper support the hypothesis that two different alpha variants occur during human REM sleep: 'background responsive alpha activity', blocked over occipital regions when rapid eye movements are present, and 'REM-alpha bursts', non modulated by the alteration of tonic and phasic periods. Altogether, evidence suggests that electrophysiological features of human cortical oscillations in the alpha frequency range vary across different behavioural states, as well as within state, reflecting different cerebral phenomena with probably dissimilar functional meaning.

Towards a quantitative characterisation of functional states of the brain: from the non-linear methodology to the global linear description

The paper traces the development of a global approach to the electric activity of the brain, from its roots in non-linear dynamical approach to the current state of art. The rationale of a three-dimensional system of global multichannel EEG descriptors (sigma, phi and omega) is provided and results obtained by means of the global descriptors in various application areas are summarised. Finally, arguments in favour of a global, 'holistic' assessment of brain functional states are presented. Definitions and properties of the global EEG descriptors are summarised in the Appendix.

Three-dimensional filtering approach to brain potential mapping

The spatial distribution of electroencephalogram (EEG) features on the scalp surface, both in time or frequency, is of great importance in clinical applications and medical research. Traditionally, mathematical methods based on interpolation algorithms have been widely applied to obtain the EEG mappings. This paper presents an innovative approach to reconstructing the brain potential mappings from multichannel EEG's. The three-dimensional (3-D) filtering approach, differing from the numerical interpolating methods, considers the spatial distribution of brain potentials as a 3-D signal, which is processed and interpolated according to its spatial frequency characteristics. The performance of the 3-D filtering method evaluated on simulated brain potentials is shown to be comparable to the four-nearest-neighbors method. Moreover, the 3-D filtering method is superior to the spherical splines method in efficiency. Two main advantages of this method are: the prospect of developing real-time, animated EEG mappings utilizing powerful digital signal processors and its capability of processing and interpolating the brain potentials on the realistic irregular scalp surface.

[Guidelines for recording/analyzing quantitative EEG and evoked potentials. Part II: Clinical aspects]

Digital EEG (DEEG) and quantitative EEG (QEEG) are recently developed tools present in many clinical situations. Besides showing didactic and research utility, they may also have a clinical role. Although a considerable amount of scientific literature has been published related to QEEG, many controversies still subsist regarding its clinical utilization. Clinical applications are: 1. DEEG is already an established substitute for conventional EEG, representing a clear technical advance. 2. Certain QEEG techniques are an established addition to DEEG for: 2a) screening for epileptic spikes or seizures in long-term recordings; 2b) Operation room and intensive care unit EEG monitoring. 3. Certain QEEG techniques are considered possible useful additions to DEEG: 3a) topographic voltage and dipole analysis in epilepsy evaluations; 3b) frequency analysis in cerebrovascular disease and dementia, mostly when other tests have been inconclusive. 4. QEEG remains investigational for clinical use in postconcussion syndrome, learning disability, attention disorders, schizophrenia, depression, alcoholism and drug abuse. EEG brain mapping and other QEEG techniques should be clinically used only by physicians highly skilled in clinical EEG interpretation and as an adjunct to traditional EEG work.

P300 delay and attenuation in schizophrenia: reversal by neuroleptic medication

BACKGROUND

P300 amplitude reduction in schizophrenia has been found by many investigators, but P300 latency generally has been reported to be normal; however, conflicting findings are present in the literature, and interpretation has been confounded by medication effects and methodological differences.

METHODS

This study used a standard auditory oddball paradigm to compare the latency, amplitude, and topographic distribution of P300s in neuroleptic-free schizophrenic patients with those of healthy controls. The patients then were treated for 6 weeks with either remoxipride or haloperidol, and their P300s were reassessed.

RESULTS

P300s were attenuated and delayed among neuroleptic-free patients. There was no evidence of peak lateralization or amplitude asymmetry over temporal areas. Subsequent neuroleptic medication normalized P300 latencies and increased P300 amplitudes, but the latter remained below normal limits over all except frontal areas. There were no correlations between P300 latency or amplitude and clinical symptomatology either before or after treatment.

CONCLUSIONS

The finding of a P300 delay in neuroleptic-free schizophrenics that is normalized by neuroleptic medication has not been reported previously. Neuroleptic effects on P300 amplitude and latency appear to be independent of effects on clinical symptoms, and cannot be attributed to anticholinergic activity.

Enlarged frontal P300 to stimulus change in panic disorder

This study investigated event-related potential (ERP) indices of information processing in sufferers of panic disorder (PD). ERPs were recorded from 14 PD patients and 15 controls during an auditory target detection task. The task required subjects to discriminate infrequent target tones (p = .14; 2000 Hz) from frequent (p = .72; 1000 Hz) and infrequent (p = .14; 500 Hz) distractor tones. A frontal P300 (P3a) identified in the PD group was characteristic of activity that would be expected to novel, task-irrelevant stimuli and is consistent with junctional pathology involving the prefrontal-limbic pathways. This study provides psychophysiological evidence of an abnormality in PD of the brain's processing of physical changes in the stimulus field that occurs even under conditions of low stimulus load. It may assist in helping to understand the breakdown in information processing that occurs in PD under high load conditions such as crowds and supermarkets.

Non-linear forecasting measurements of multichannel EEG dynamics

This work presents a new method for studying the underlying dynamics of multichannel EEG on the basis of the mathematical theory of dynamical systems. It computes the local loss of predictability and Kolmogorov entropy of the dynamics reconstructed from brain electrical activity. This reconstruction uses multichannel recordings in order to quantify an equivalent of spatio-temporal mapping. Five experimental conditions have been studied: closed eyes at rest, closed eyes and counting even numbers, staring at a spotlight, passive and active auditive odd-ball tasks. The entropy is positive for all the experimental conditions which proves that the underlying EEG dynamics are chaotic. Moreover, on the basis of the dynamical signature it is possible to differentiate 3 types of EEG activity: the rest closed eyes activity, the task closed eyes activity (counting and odd-ball tasks) and the open eyes activity (staring at a spotlight). It is inferred that this index could characterize task-related changes in brain activity.

Dimensional analysis of human EEG during experimental affective experience

The present investigation was designed to study whether dimensional complexity of EEG discriminates between internal and external attentional demands during affective experience. In the first task Ss were waiting for a slight electric shock (affective intake condition). In the second task Ss were engaged in the controlled imagery involving the emotional experience 'programmed' during intake task (affective rejection condition). Both affective tasks were compared to the controlled rest condition. The results of imagery trial replicated previous demonstration of the increased dimension of EEG during imagery in comparison to perceptual processing, with differences, confined to frontal and partly to central sites. Affective intake trial in comparison to rest condition was characterized by significantly higher dimensional estimates over the central and more posterior (parietal and occipital) areas.

Adaptive segmentation of spontaneous EEG map series into spatially defined microstates

Space-oriented segmentation can decompose multi-channel EEG map series into time segments characterized by quasi-stationary field map configurations. This assesses the dynamics of the underlying processes as activities of different neural generator ensembles. Our method of space-oriented segmentation describes the scalp field at times of maximal field strength (Global Field Power) by the locations of the centroids of positive and negative map areas. A quantitative measure of the simultaneous distance of the centroid locations evaluates the similarity between consecutive maps. A segment is defined as a sequence of maps that do not differ from each other by more than a present value. Finally, the average centroid locations for each segment are entered into an agglomerative clustering procedure to obtain a set of distinct classes of field configurations. Four records of 16 s of 42-channel resting EEG (band-pass filtered 2-16 Hz) from six subjects were analyzed. Average segment duration was 157.9 ms. Most segments belonged to a small number of classes (from 2 to 6, mean 3.7 classes for 90% of analysis time). The most frequent class showed an anterior-posterior field orientation and covered from 45 to 74% (mean 55% across subjects) of total time, with an average duration of 265 ms. The procedure was also tested using temporally and spatially unstructured data (white noise and randomly shuffled EEG) to ascertain that the methods reflect the spatio-temporal structure of the EEG processes.

Dimensional complexity of EEG brain mechanisms in untreated schizophrenia

The dimensional complexity of left temporal-parietal and parietal-occipital electroencephalographic (EEG) recordings was assessed by computing the correlation dimension during 20 sec in six recording conditions from 15 first-episode acute schizophrenics before medication, 12 other medication-free individuals clinically and socially remitted after a first schizophrenic episode, 17 medication-free neurotics and 17 controls. The correlation dimension of the temporal-parietal EEG differed between groups [analysis of variance (ANOVA)] (p < 0.004), whereas neurotics (different from schizophrenics at p < 0.002) and remitted schizophrenics showed intermediate values. There was no overall significant difference between groups in the parietal-occipital EEG. Differences of the correlation dimension of the temporal-parietal versus the parietal-occipital EEG were significant between groups (ANOVA p < 0.05); first-episode schizophrenics differed from controls (p < 0.002) and remitted patients (p < 0.08). Increased dimensional complexity of schizophrenic EEG was found in one of two examined brain regions. The higher dimensional complexity of functional brain mechanisms in schizophrenics versus normals is reminiscent of the loosened organization of thought, and of suggestions of certain superior abilities in the patients.

Global dimensional complexity of multi-channel EEG indicates change of human brain functional state after a single dose of a nootropic drug

Viewing the multi-channel EEG as a sequence of momentary field maps corresponds to the concept of a trajectory in K-dimensional state space (K = number of channels). This approach permits a quantitative, single value measure of complexity of the brain state trajectory, the global correlation dimension that describes the ensemble characteristics of all recorded channels. In 5 normal volunteers, 4 records of 16-channel resting EEG were obtained during each of 4 randomized sessions (double blind design) after a single dose of placebo or 2.9 g or 4.8 g or 9.6 g piracetam. The global correlation dimension of a 40 sec epoch from each record was estimated, using 50 computational runs with 8192 point pairs. The results were combined for the two intermediate doses and averaged over repeated records. The dimensionality decreased from placebo (median = 5.89) to low dose (median = 5.72) to high dose (median = 5.59), significant in a Friedman ANOVA at P < 0.02, with significant differences between placebo vs. high and low vs. high dose. Thus, the subtle change of brain global functional state after a single dose of piracetam is reflected by the non-linear measure of global dimensional complexity of the multi-channel EEG.