Shadows of thought: shifting lateralization of human brain electrical patterns during brief visuomotor task

Frontoparietal Traffic Signals: A Fast Optical Imaging Study of Preparatory Dynamics in Response Mode Switching

Coordination between networks of brain regions is important for optimal cognitive performance, especially in attention demanding tasks. With the event-related optical signal (a measure of changes in optical scattering because of neuronal activity) we can characterize rapidly evolving network processes by examining the millisecond-scale temporal correlation of activity in distinct regions during the preparatory period of a response mode switching task. Participants received a precue indicating whether to respond vocally or manually. They then saw or heard the letter “L” or “R,” indicating a “left” or “right” response to be implemented with the appropriate response modality. We employed lagged cross-correlations to characterize the dynamic connectivity of preparatory processes. Our results confirmed coupling of frontal and parietal cortices and the trial-dependent relationship of the right frontal cortex with response preparation areas. The frontal-to-modality-specific cortex cross-correlations revealed a pattern in which first irrelevant regions were deactivated, and then relevant regions were activated. These results provide a window into the subsecond scale network interactions that flexibly tune to task demands.

Neurophysiological pharmacodynamic measures of groups and individuals extended from simple cognitive tasks to more "lifelike" activities

OBJECTIVE

Extend a method to track neurophysiological pharmacodynamics during repetitive cognitive testing to a more complex "lifelike" task.

METHODS

Alcohol was used as an exemplar psychoactive substance. An equation, derived in an exploratory analysis to detect alcohol's EEGs effects during repetitive cognitive testing, was validated in a Confirmatory Study on a new group whose EEGs after alcohol and placebo were recorded during working memory testing and while operating an automobile driving simulator.

RESULTS

The equation recognized alcohol by combining five times beta plus theta power. It worked well (p < .0001) when applied to both tasks in the confirmatory group. The maximum EEG effect occurred 2-2.5 h after drinking (>1 h after peak BAC) and remained at 90% at 3.5-4 h (BAC < 50% of peak). Individuals varied in the magnitude and timing of the EEG effect.

CONCLUSION

The equation tracked the EEG response to alcohol in the Confirmatory Study during both repetitive cognitive testing and a more complex "lifelike" task. The EEG metric was more sensitive to alcohol than several autonomic physiological measures, task performance measures or self-reports.

SIGNIFICANCE

Using EEG as a biomarker to track neurophysiological pharmacodynamics during complex "lifelike" activities may prove useful for assessing how drugs affect integrated brain functioning.

Ambient intelligence for monitoring and research in clinical neurophysiology and medicine: the MIMERICA* project and prototype

Ambient Intelligence (AmI) provides extended but unobtrusive sensing and computing devices and ubiquitous networking for human/environment interaction. It is a new paradigm in information technology compliant with the international Integrating Healthcare Enterprise board (IHE) and eHealth HL7 technological standards in the functional integration of biomedical domotics and informatics in hospital and home care. AmI allows real-time automatic recording of biological/medical information and environmental data. It is extensively applicable to patient monitoring, medicine and neuroscience research, which require large biomedical data sets; for example, in the study of spontaneous or condition-dependent variability or chronobiology. In this respect, AML is equivalent to a traditional laboratory for data collection and processing, with minimal dedicated equipment, staff, and costs; it benefits from the integration of artificial intelligence technology with traditional/innovative sensors to monitor clinical or functional parameters. A prototype AmI platform (MIMERICA*) has been implemented and is operated in a semi-intensive unit for the vegetative and minimally conscious states, to investigate the spontaneous or environment-related fluctuations of physiological parameters in these conditions.

Towards measuring brain function on groups of people in the real world

In three studies, EEGs from three groups of participants were recorded during progressively more real world situations after drinking alcoholic beverages that brought breath alcohol contents near the limit for driving in California 30 minutes after drinking. A simple equation that measured neurophysiological effects of alcohol in the first group of 15 participants performing repetitive cognitive tasks was applied to a second group of 15 operating an automobile driving simulator, and to a third group of 10 ambulatory people recorded simultaneously during a cocktail party. The equation derived from the first group quantified alcohol’s effect by combining measures of higher frequency (beta) and lower frequency (theta) power into a single score. It produced an Area Under the Receiver Operator Characteristic Curve of .73 (p<.05; 67% sensitivity in recognizing alcohol and 87% specificity in recognizing placebo). Applying the same equation to the second group operating the driving simulator, AUC was .95, (p<.0001; 93% sensitivity and 73% specificity), while for the cocktail party group AUC was .87 (p<.01; 80% sensitivity and 80% specificity). EEG scores were significantly related to breath alcohol content in all studies. Some individuals differed markedly from the overall response evident in their respective groups. The feasibility of measuring the neurophysiological effect of a psychoactive substance from an entire group of ambulatory people at a cocktail party suggests that future studies may be able to fruitfully apply brain function measures derived under rigorously controlled laboratory conditions to assess drug effects on groups of people interacting in real world situations.

Spatiotemporal neural interactions underlying continuous drawing movements as revealed by magnetoencephalography

Continuous and sequential movements are controlled by widely distributed brain regions. A series of studies have contributed to understanding the functional role of these regions in a variety of visuomotor tasks. However, little is known about the neural interactions underpinning continuous movements. In the current study, we examine the spatiotemporal neural interactions underlying continuous drawing movements and the association of them with behavioral components. We conducted an experiment in which subjects copied a pentagon continuously for ~45 s using an XY joystick, while neuromagnetic fluxes were recorded from their head using a 248-sensor whole-head magnetoencephalography (MEG) device. Each sensor time series was rendered stationary and non-autocorrelated by applying an autoregressive integrated moving average model and taking the residuals. We used the directional variability of the movement as a behavioral measure of the controls generated. The main objective of this study was to assess the relation between neural interactions and the variability of movement direction. That is, we divided the continuous recordings into consecutive periods (i.e., time-bins) of 51 steps duration and computed the pairwise cross-correlations between the prewhitened time series in each time-bin. The circular standard deviation of the movement direction within each time-bin provides an estimate of the directional variability of the 51-ms trajectory segment. We looked at the association between neural interactions and variability of movement direction, separately for each pair of sensors, by running a cross-correlation analysis between the strength of the MEG pairwise cross-correlations and the circular standard deviations. We identified two types of neuronal networks: in one, the neural interactions are correlated with the directional variability of the movement at negative time-lags (feedforward), and in the other, the neural interactions are correlated with the directional variability of the movement at positive time-lags (feedback). Sensors associated mostly with feedforward processes are distributed in the left hemisphere and the right occipital-temporal junction, whereas sensors related to feedback processes are distributed in the right hemisphere and the left cerebellar hemisphere. These results are in line with findings from a series of previous studies showing that specific brain regions are involved in feedforward and feedback control processes to plan, perform, and correct movements. Additionally, we looked at whether changes in movement direction modulate the neural interactions. Interestingly, we found a preponderance of sensors associated with changes in movement direction over the right hemisphere-ipsilateral to the moving hand. These sensors exhibit stronger coupling with the rest of the sensors for trajectory segments with high rather than low directional movement variability. We interpret these results as evidence that ipsilateral cortical regions are recruited for continuous movements when the curvature of the trajectory increases. To the best of our knowledge, this is the first study that shows how neural interactions are associated with a behavioral control parameter in continuous and sequential movements.

The molecular basis of memory

We propose a tripartite biochemical mechanism for memory. Three physiologic components are involved, namely, the neuron (individual and circuit), the surrounding neural extracellular matrix, and the various trace metals distributed within the matrix. The binding of a metal cation affects a corresponding nanostructure (shrinking, twisting, expansion) and dielectric sensibility of the chelating node (address) within the matrix lattice, sensed by the neuron. The neural extracellular matrix serves as an electro-elastic lattice, wherein neurons manipulate multiple trace metals (n > 10) to encode, store, and decode coginive information. The proposed mechanism explains brains low energy requirements and high rates of storage capacity described in multiples of Avogadro number (N(A) = 6 × 10(23)). Supportive evidence correlates memory loss to trace metal toxicity or deficiency, or breakdown in the delivery/transport of metals to the matrix, or its degradation. Inherited diseases revolving around dysfunctional trace metal metabolism and memory dysfunction, include Alzheimer's disease (Al, Zn, Fe), Wilson's disease (Cu), thalassemia (Fe), and autism (metallothionein). The tripartite mechanism points to the electro-elastic interactions of neurons with trace metals distributed within the neural extracellular matrix, as the molecular underpinning of "synaptic plasticity" affecting short-term memory, long-term memory, and forgetting.

Chaos in induced rhythms of the brain – the value of ERP studies

Event-related potentials (ERPs) – neglected almost entirely by Wright & Liley – allow objective investigation of information processing in the brain. The application of chaos theory to such an analysis broadens this possibility. Through the use of the point correlation dimension (PD2) accurate dimensional analysis of different Event-Related Potential components such as the P3 wave is possible.

Why does the human brain need to be a nonlinear system?

We focus on one aspect of Wright & Liley's target article: the linearity of the EEG. According to the authors, some nonlinear models of the cortex can be reduced (approximated) to the linear case at the millimetric scale. We argue here that the statement about the linear character of EEG is too strong and that EEG exhibits nonlinear features which cannot be ignored.

Nonlinear nonequilibrium nonquantum nonchaotic statistical mechanics of neocortical interactions

The work in progress reported by Wright & Liley shows great promise, primarily because of their experimental and simulation paradigms. However, their tentative conclusion that macroscopic neocortex may be considered (approximately) a linear near-equilibrium system is premature and does not correspond to tentative conclusions drawn from other studies of neocortex.

Levels, models, and brain activities: Neurodynamics is pluralistic

Some dichotomies related to modeling electrocortical activities are analyzed. Attractor neural networks versus biologically motivated models, near-equilibrium versus nonequilibrium processes, linear and nonlinear dynamics, stochastic and chaotic patterns, local and global scale simulation of cortical activities are discussed.

The form of chaos in the noisy brain can manifest function

I would like to emphasize the significance of chaotic dynamics at both local and macroscopic levels in the cortex. The basic notions dealt with in this commentary will be noise-induced order, chaotic “itinerancy” and dissipative structure. Wright & Laley's theory would be partially misleading, since emergent nonlinearity rather than the linearity at even a macroscopic level can actually subserve cortical functions.

Dynamics of the brain at global and microscopic scales: Neural networks and the EEG

There is some complementarity of models for the origin of the electroencephalogram (EEG) and neural network models for information storage in brainlike systems. From the EEG models of Freeman, of Nunez, and of the authors' group we argue that the wavelike processes revealed in the EEG exhibit linear and near-equilibrium dynamics at macroscopic scale, despite extremely nonlinear – probably chaotic – dynamics at microscopic scale. Simulations of cortical neuronal interactions at global and microscopic scales are then presented. The simulations depend on anatomical and physiological estimates of synaptic densities, coupling symmetries, synaptic gain, dendritic time constants, and axonal delays. It is shown that the frequency content, wave velocities, frequency/wavenumber spectra and response to cortical activation of the electrocorticogram (ECoG) can be reproduced by a “lumped” simulation treating small cortical areas as single-function units. The corresponding cellular neural network simulation has properties that include those of attractor neural networks proposed by Amit and by Parisi. Within the simulations at both scales, sharp transitions occur between low and high cell firing rates. These transitions may form a basis for neural interactions across scale. To maintain overall cortical dynamics in the normal low firing-rate range, interactions between the cortex and the subcortical systems are required to prevent runaway global excitation. Thus, the interaction of cortex and subcortex via corticostriatal and related pathways may partly regulate global dynamics by a principle analogous to adiabatic control of artificial neural networks.

The Maltese cross: A new simplistic model for memory

This paper puts forward a general framework for thought about human information processing. It is intended to avoid some of the problems of pipeline or stage models of function. At the same time it avoids the snare of supposing a welter of indefinitely many separate processes. The approach is not particularly original, but rather represents the common elements or presuppositions in a number of modern theories. These presuppositions are not usually explicit, however, and making them so reduces the danger of slipping back into earlier modes of thought.

The key point is to distinguish between persisting representations and the processes that translate one representation into another. Various classes or groups of persisting representations can be distinguished by the experimental treatments that interfere with them. In particular, there now seem to be several kinds of short-term or temporary storage, different from each other as well as from longterm memory; the translating processes also have several different modes or kinds. A particularly important aspect of the current position is that a model of this general type no longer requires some external agent to direct and control long sequences of behaviour.

Comparative reduction of theories — or over-simplification?

To model the organization of levels' of cortical dynamics, at least some general scheme for hierarchy, functional diversity, and proper intrinsic control must be provided. Rhythmic control forces the system to iterate its state by short trajectories, which makes it much more stable and predictable without discarding the desirable ability of chaotic systems to make rapid phase transitions. Rhythmic control provides a fundamentally different systems dynamics, one not provided by models that allow the emergence of continuous trajectories in the systems state space.

Is there chaos in the brain?

For some years there has been a controversy about whether brain state variables such as EEG or neuronal spike trains exhibit chaotic behaviour. Wright & Liley claim that the local dynamics measured by spike trains or local field potentials exhibit chaotic behaviour, but global measures like EEG should be governed by linear dynamics. We propose a different scheme. Based on simulation studies and various experiments, we suggest that the pointwise dimension of EEG time series may provide some valuable information about underlying neuronal generators.

Electrophysiological and auditory behavioral evaluation of individuals with left temporal lobe epilepsy

The purpose of this study was to determine the repercussions of left temporal lobe epilepsy (TLE) for subjects with left mesial temporal sclerosis (LMTS) in relation to the behavioral test-Dichotic Digits Test (DDT), event-related potential (P300), and to compare the two temporal lobes in terms of P300 latency and amplitude. We studied 12 subjects with LMTS and 12 control subjects without LMTS. Relationships between P300 latency and P300 amplitude at sites C3A1,C3A2,C4A1, and C4A2, together with DDT results, were studied in inter-and intra-group analyses. On the DDT, subjects with LMTS performed poorly in comparison to controls. This difference was statistically significant for both ears. The P300 was absent in 6 individuals with LMTS. Regarding P300 latency and amplitude, as a group, LMTS subjects presented trend toward greater P300 latency and lower P300 amplitude at all positions in relation to controls, difference being statistically significant for C3A1 and C4A2. However, it was not possible to determine laterality effect of P300 between affected and unaffected hemispheres.

Time series analysis of magnetoencephalographic data during copying

We used standard time series modeling to analyze magnetoencephalographic (MEG) data acquired during three tasks. Each task lasted 45 s, for a total data acquisition period of 135 s. Ten healthy human subjects fixated their eyes on a central blue point for 45 s (fixation only, "F" task). Then a pentagon (visual template) appeared surrounding the fixation point which simultaneously became red (fixation + template, "FT" task). After 45 s, the fixation point changed to green, which was the "go" signal for the subjects to begin continuously copying the pentagon for 45 s using a joystick and without visual feedback of their movement trajectory (fixation + template + copying, "FTC" task). MEG data were acquired continuously from 248 axial gradiometers at a sampling rate of 1017.25 Hz. After removal of cardiac artifacts and rejection of records with eyeblink artifacts, a Box-Jenkins autoregressive integrative moving average (ARIMA) analysis was applied to the unsmoothed, unaveraged MEG time series for model identification and estimation within 25 time lags (approximately 25 ms). We found that an ARIMA model of 25th order autoregressive, first order differencing, and first order moving average (p=25, d=1, q=1) adequately modeled the series and yielded residuals practically stationary with respect to their mean, variance, and autocorrelation structure. These "prewhitened" residuals were then used for assessing pairwise associations between series using crosscorrelation analysis with +/-25 time lags (approximately +/-25 ms). The cross-correlograms thus obtained revealed rich and consistent patterns of interactions between series with respect to positive and/or negative correlations. The overall prevalence of these patterns was very similar in the three tasks used, and, for particular sensor pairs, they tended to be preserved across tasks.

Propagation of repetitive alpha waves over the scalp in relation to subjective preferences for a flickering light

Paired-comparison tests were performed to examine subjective preferences for a flickering light. Electroencephalograms were then recorded from seven electrodes (10-20 system) during presentations of the most and least preferred flickering-light conditions. As a way of investigating the flow of alpha waves on the scalp over both the left and right hemispheres in relation to subjective preference, the alpha waves were analyzed by means of the cross-correlation function (CCF). The maximum value of the CCF, /phi(tau)/(max), between the alpha waves measured at different electrodes and its delay time, tau(m), were analyzed. Results show that the most preferred flickering light has a significant larger /phi(tau)/(max) than the least preferred flickering light, and that /phi(tau)/(max) decreases with increasing distance between comparison (O(1) or O(2)) and test electrodes. On the other hand, the delay time of the maximum value of the CCF, tau(m), increases with the distance between comparison and test electrodes.

Toward an integrated continuum model of cerebral dynamics: the cerebral rhythms, synchronous oscillation and cortical stability

Continuum models of cerebral cortex with parameters derived from physiological data, provide explanations of the cerebral rhythms, synchronous oscillation, and autonomous cortical activity in the gamma frequency range, and suggest possible mechanisms for dynamic self-organization in the brain. Dispersion relations and derivations of power spectral response for the models, show that a low frequency resonant mode and associated travelling wave solutions of the models' equations of state can account for the predominant 1/f spectral content of the electroencephalogram (EEG). Large scale activity in the alpha, beta, and gamma bands, is accounted for by thalamocortical interaction, under regulation by diffuse cortical excitation. System impulse responses can be used to model Event-Related Potentials. Further classes of local resonance may be generated by rapid negative feedbacks at active synapses. Activity in the gamma band around 40 Hz, associated with large amplitude oscillations of pulse density, appears at higher levels of cortical activation, and is unstable unless compensated by synaptic feedbacks. Control of cortical stability by synaptic feedbacks offers a partial account of the regulation of autonomous activity within the cortex. Synchronous oscillation occurs between concurrently excited cortical sites, and can be explained by analysis of wave motion radiating from each of the co-active sites. These models are suitable for the introduction of learning rules-most notably the coherent infomax rule.

Mutual information analysis of the EEG in patients with Alzheimer's disease

OBJECTIVE

Mutual information provides a measure of both the linear and nonlinear statistical dependencies between two time series. Cross-mutual information (CMI) is used to quantify the information transmitted from one time series to another, while auto mutual information (AMI) in a time series estimates how much on average the value of the time series can be predicted from values of the time series at preceding points. The aim of this study is to assess information transmission between different cortical areas in Alzheimer's disease (AD) patients by estimating the average CMI between EEG electrodes.

METHODS

We recorded the EEG from 16 scale electrodes in 15 AD patients and 15 age-matched normal controls, and estimated the local, distant, and interhemispheric CMIs of the EEG in both groups. The rate of decrease (with increasing delay) of the AMI of the EEG was also measured to evaluate the complexity of the EEG in AD patients.

RESULTS

The local CMI in AD subjects was lower than that in normal controls, especially over frontal and antero-temporal regions. A prominent decrease in information transmission between distant electrodes in the right hemisphere and between corresponding interhemispheric electrodes was detected in the AD patients. In addition, the AMIs throughout the cerebrums of the AD patients decreased significantly more slowly with delay than did the AMIs of normal controls.

CONCLUSIONS

These results are consistent with previous findings that suggest the association of EEG abnormalities in AD patients with functional impairment of information transmission in long cortico-cortical connections.

Brain electrical microstates in subjects with panic disorder

Brain electrical microstates represent spatial configurations of scalp recorded brain electrical activity and are considered to be the basic elements of stepwise processing of information in the brain. In the present study, the hypothesis of a temporo-limbic dysfunction in panic disorder (PD) was tested by investigating the topographic descriptors of brain microstates, in particular the one corresponding to the Late Positive Complex (LPC), an event-related potential (ERP) component with generators in these regions. ERPs were recorded in PD patients and matched healthy subjects during a target detection task, in a central (CC) and a lateral condition (LC). In the CC, a leftward shift of the LPC microstate positive centroid was observed in the patients with PD versus the healthy control subjects. In the LC, the topographic descriptor of the first microstate showed a rightward shift, while those of both the second and the fourth microstate, corresponding to the LPC, revealed a leftward shift in the PD patients versus the healthy control subjects. These findings indicate an overactivation of the right hemisphere networks involved in early visual processing and a hypoactivation of the right hemisphere circuits involved in LPC generators in PD. In line with this interpretation, the abnormal topography of the LPC microstate, observed in the CC, was associated with a worse performance on a test exploring right temporo-hippocampal functioning. Topographical abnormalities found for the LPC microstate in the LC were associated with a higher number of panic attacks, suggesting a pathogenetic role of the right temporo-hippocampal dysfunction in PD.

Neuronal mechanisms of executive control by the prefrontal cortex

Executive function is considered to be a product of the coordinated operation of various processes to accomplish a particular goal in a flexible manner. The mechanism or system responsible for the coordinated operation of various processes is called executive control. Impairments caused by damage to the prefrontal cortex are often called dysexecutive syndromes. Therefore, the prefrontal cortex is considered to play a significant role in executive control. Prefrontal participation to executive control can be partly explained by working memory that includes mechanisms for temporary active storage of information and processing stored information. For the prefrontal cortex to exert executive control, neuronal mechanisms for temporary storage of information and dynamic and flexible interactions among them are necessary. In this article, we present the presence of dynamic and flexible changes in the strength of functional interaction and extensive functional interactions among temporal information-storage processes in the prefrontal cortex. In addition, recent imaging studies show dynamic changes in functional connectivity between the prefrontal cortex and other cortical and subcortical structures depending upon the characteristics or the temporal context of the task. These observations indicate that the examination of dynamic and flexible modulation in neuronal interaction among prefrontal neurons as well as between the prefrontal cortex and other cortical and subcortical areas is important for explaining how the prefrontal cortex exerts executive control.

Electroencephalographic imaging of higher brain function

High temporal resolution is necessary to resolve the rapidly changing patterns of brain activity that underlie mental function. Electroencephalography (EEG) provides temporal resolution in the millisecond range. However, traditional EEG technology and practice provide insufficient spatial detail to identify relationships between brain electrical events and structures and functions visualized by magnetic resonance imaging or positron emission tomography. Recent advances help to overcome this problem by recording EEGs from more electrodes, by registering EEG data with anatomical images, and by correcting the distortion caused by volume conduction of EEG signals through the skull and scalp. In addition, statistical measurements of sub-second interdependences between EEG time-series recorded from different locations can help to generate hypotheses about the instantaneous functional networks that form between different cortical regions during perception, thought and action. Example applications are presented from studies of language, attention and working memory. Along with its unique ability to monitor brain function as people perform everyday activities in the real world, these advances make modern EEG an invaluable complement to other functional neuroimaging modalities.

Magnetoencephalography: applications in psychiatry

Magnetoencephalography (MEG) measures the extracranial magnetic fields produced by intraneuronal ionic current flow within appropriately oriented cortical pyramidal cells. Based upon superconducting quantum interference device technology operating at liquid helium temperatures (4 K), MEG offers excellent temporal and spatial resolution for selected sources, and complements information obtained from electroencephalograms and other functional imaging strategies. Current instrumentation permits recording up to several hundred channels simultaneously with head-shaped dewars, although the cost of such systems is high. The fact that magnetic fields fall off with the square of the distance from the source is both a benefit (when separating activity in the two hemispheres) and a limitation (when attempting to record deep sources). The lack of skin contact facilitates using MEG to record direct current and very high frequency (> 600 Hz) brain activity. The clinical utility of MEG includes presurgical mapping of sensory cortical areas and localization of epileptiform abnormalities, and localization of areas of brain hypoperfusion in stroke patients. MEG studies in psychiatric disorders have contributed materially to improved understanding of anomalous brain lateralization in the psychoses, have suggested that P50 abnormalities may reflect altered gamma band activity, and have provided evidence of hemisphere-specific abnormalities of short-term auditory memory function.

Brain potentials associated with eye fixations during visual tasks under different lighting systems

The variations of eye fixation related potentials (EFRPs) were examined in two tasks under three lighting conditions for assessment of lighting environments. Sixteen subjects participated in two tasks; a difficult and an easy reading task under three lighting conditions: Spot light (S), General light (G) and Mixed light (M). EEG (Oz) and EOG were recorded. EEG epochs time-locked to onset of eye fixations were collected at random and averaged separately in two arrays to obtain a pair of EFRPs. Two wave forms under the S were similar, although those under the G showed the disparity, the largest disparity being in the easy task under the G. Under the S, wave forms of EFRPs were stable in the difficult task. The amplitude changed with the task load. The results suggested that EFRPs might be an index of the work load under lighting conditions.

The future of electroencephalography in assessing neurocognitive functioning

High temporal resolution is necessary to resolve the rapidly changing patterns of brain activity underlying mental function. Additionally, simple, non-intrusive equipment is needed to routinely measure such functions in doctors' offices, at home and work and in other naturalistic contexts as people perform normal everyday activities. When compared with all other modalities for measuring higher brain functions, EEG is unique in that it has both these attributes. Two factors are limiting the further development and application of EEG for measuring cognitive functioning: a technical one that is easy to overcome and a sociological one that is more problematic. The technical limitation is that traditional EEG technology and practice provides insufficient spatial detail to identify relationships between brain electrical events and structures and functions visualized by magnetic resonance imaging (MRI) or other modalities. Recent advances overcome this problem by recording EEGs from more electrodes, by registering EEG data with anatomical information from each subject's MRI, by correcting the distortion caused by volume conduction of EEG signals through the skull and scalp, and by computing hypotheses about the sources of signals recorded at the scalp. The sociological limitation is that clinical EEGs are mostly performed by neurologists with no particular special interest in cognitive brain function, while cognitive research using EEG is largely done by psychology professors and their graduate students with no clinical ambitions. The diminishing clinical role of traditional EEGs in localizing lesions in the brain, and the obvious and insistent medical need for inexpensive and accessible tests of cognitive brain functioning may serve to soon dissipate this sociological obstruction. This will lead to a golden age of EEG in which Hans Berger's vision of the EEG as a window on the mind will be realized. Rather than slowly fading into obsolescence, EEG will retain its role as the primary means of measuring higher brain function when the purpose is not 3D localization per se, and will serve as an invaluable complement to functional MRI in those instances when both high temporal and high spatial resolution are required.

Ganglioside lateralization in the brain of female rats

The ganglioside composition of the cerebral hemispheres of young and adult rats of either sex has been herein assessed for the first time. In females, the total ganglioside content at any age, the content of GM1, GD1a, and GD1b at 8 days, and the content of GM1, GD1b, GT1b, and GQ1b at 60 days were higher in the right than in the left hemisphere. In males, no difference was observed. Concerning the ceramide moiety, a difference was displayed by C18:1 long-chain base in GD1a, whose proportion was higher in the left than in the right hemisphere of females aged 8 days. The comparison between homolateral hemispheres of rats of different sex revealed several differences. On average, in 8-day-old animals, the content of gangliosides was higher in females than in males. At 60 days the amount of gangliosides was on average lower in females than in males, even if with some exception. The data obtained with the current investigation show the existence of a ganglioside lateralization in rat brain, exclusively in females, and almost entirely at charge of the oligosaccharide portion. Moreover, age-dependent changes of ganglioside pattern and content show a dependence on brain lateralization.

EEG-microstates in mild memory impairment and Alzheimer's disease: possible association with disturbed information processing

The only available functional neuroimaging methods reaching the time resolution of human information processing are EEG and MEG. Since spectral analysis implies analysis of longer time epochs, the high temporal resolution of EEG is partly lost. By dividing the EEG in the time-domain into segments of similar spatial distribution on the scalp (microstates) it has been possible to assess patterns of neuronal activity representing the information process currently performed by the brain. In the present study alterations of EEG microstates in subjective (n = 31) and objective (n = 38) memory impairment as well as in probable Alzheimer disease (DAT: n = 64) compared to healthy controls (n = 21) were investigated. The main findings were reduced segment durations and a more anterior center of gravity of the microstate topography in DAT. With more pronounced cognitive dysfunction larger window sizes were found. Shorter microstates and larger windows reflect more rapidly changing spatial activation patterns, and are interpreted as an impaired capability to establish stable brain states necessary for normal brain function. The anteriorization of the microstates is consistent with results in the frequency domain and may reflect neuropathological changes in DAT.

Neuromodulation can significantly change the dynamical state of cortical networks

We present simulation results of an olfactory cortex model complementing the results presented in Wright & Liley's target article. We show how the cortical dynamics as expressed in EEG can be regulated by neuromodulation and discuss how the system can attain global stability without cortical-subcortical interaction, as presumed necessary by Wright & Liley. Network structure is shown to be crucial.

Multiscale modeling of the brain should be validated in more detail against the biological data

Wright & Liley provide an advance in addressing the interaction of multiple scales of processing in the brain. It should address in more detail the biological evidence that underlies the models it proposes to replace.

Dynamics of the brain — from the statistical properties of neural signals to the development of representations

The unification of microscopic and macroscopic models of brain behaviour is of paramount importance and Wright & Liley's target article provides some important groundwork. In this commentary, I propose that a useful approach for the future is to incorporate a developmental perspective into such models. This may be an important constraint, providing a key to understanding the nature of macroscopic measures of brain function such as functional measures like ERP.

Empirical data base for simulation: Firing rates and axonal conduction velocity for cortical neurones

Simulation of brain dynamics requires the use of accurate empirical data. This commentary points out major errors in some of the empirical data used in Wright & Laley's simulation. The simulation is quantitatively very different from the real cortex, and may also have important qualitative differences.

Multiscale modeling of brain dynamics depends upon approximations at each scale

We outline fresh findings that show that our macroscopic electrocorticographic (ECoG) simulations can account for synchronous multiunit pulse oscillations at separate, simultaneously activated cortical sites and the associated gamma-band ECoG activity. We clarify our views on the approximations of dynamic class applicable to neural events at macroscopic and microscopic scales, and the analogies drawn to classes of ANN behaviour. We accept the need to introduce memory processes and detailed anatomical and physiological information into any future developments of our simulations. On the issue of intrinsic cortical stability and the role of extrinsic fibre systems in maintaining stability, we argue that this position is not in extreme contradiction to those of our commentators, and that the mechanisms implicit in our simulations' properties imply rich computational possibilities. We discuss some of the reasons for and against the existence of significant global resonances in the brain and explain why such behaviour appears absent in our simulations. Last, we discuss other phenomena, such as rhythmic driving of the cortex, which have not yet been introduced into our models, and indicate lines for future development of the simulations.

Rhythmicity in the EEG and global stabilization of the average level of excitation in the cerebral cortex

The network model of EEG formation has revealed a unified mechanism for disparate EEG phenomena: for various reactions as well as for ontogenetic and phylogenetic differences. EEG rhythmicity was shown to be an external manifestation of the functioning of the intracortical stabilizing system which provides normal informational operations in the cerebral cortex.

Multiscale neocortical dynamics, experimental EEG measures, and global facilitation of local cell assemblies

Multiscale dynamics, linear approximations, global boundary conditions, experimental verification, and global influences on local cell assemblies are considered in the context of Wright & Liley's work. W&L provide a nice introduction to these issues and a reasonable simulation of intermediate scale dynamics, but the model does not adequately simulate combined local and global processes.

Neural system stability

Two hypotheses concerning nonlinear elements in complex systems are contrasted: that neurons, intrinsically unstable, are stabilized through embedding in networks and populations; and, conversely, that cortical neurons are intrinsically stable, but are destabilized through embedding in cortical populations and corticostriatal feedback systems. Tests are made by piecewise linearization of nonlinear dynamics at nonequilibriumoperating points, followed by linear stability analysis.

The EEGdataindicate stochastic nonlinearity

Wright & Liley contrast their theory that the global dynamics of the EEG are linear with that of Freeman, who hypothesizes an EEG governed by (nonlinear) deterministic-chaotic dynamics. A “call for further discussion” on the part of the authors is made as to how either theory fits with experimental findings indicating that EEG dynamics are non-linear but stochastic.

Is the distribution of coherence a test of the model?

Does the Wright & Liley model predict: (1) that subdural and hippocampal EEGs coherence tend to rise and fall in parallel for many frequencies, (2) that it is locally high or low within 10mm and falls steeply on average or, (3) that it is in constant flux, mostly rising and falling within 5–15 sec?

Modeling for modeling's sake?

Although this is an impressive piece of modeling work, I worry that the two models that Wright & Liley have created do not yet provide us with useful empirical information regarding brain processing.