Search

A user ready, portable, documented software package, NFTsim, is presented to facilitate numerical simulations of a wide range of brain systems using continuum neural field modeling. NFTsim enables users to simulate key aspects of brain activity at multiple scales. At the microscopic scale, it incorporates characteristics of local interactions between cells, neurotransmitter effects, synaptodendritic delays and feedbacks. At the mesoscopic scale, it incorporates information about medium to large scale axonal ranges of fibers, which are essential to model dissipative wave transmission and to produce synchronous oscillations and associated cross-correlation patterns as observed in local field potential recordings of active tissue. At the scale of the whole brain, NFTsim allows for the inclusion of long range pathways, such as thalamocortical projections, when generating macroscopic activity fields. The multiscale nature of the neural activity produced by NFTsim has the potential to enable the modeling of resulting quantities measurable via various neuroimaging techniques. In this work, we give a comprehensive description of the design and implementation of the software. Due to its modularity and flexibility, NFTsim enables the systematic study of an unlimited number of neural systems with multiple neural populations under a unified framework and allows for direct comparison with analytic and experimental predictions. The code is written in C++ and bundled with Matlab routines for a rapid quantitative analysis and visualization of the outputs. The output of NFTsim is stored in plain text file enabling users to select from a broad range of tools for offline analysis. This software enables a wide and convenient use of powerful physiologically-based neural field approaches to brain modeling. NFTsim is distributed under the Apache 2.0 license.

This paper demonstrates a method for analyzing target evoked potentials in an auditory oddball task, using Wiener deconvolution to separate the brain's task-dependent properties from its task-invariant response. It is shown that a target response can be deconvolved, and the result contains two delta-like peaks separated by approximately 100 ms, implying that targets resemble a superposition of two standard responses. The latencies and areas of these delta-like peaks give quantitative measures of the evoked potential, providing a method of analysis that is simpler and more physiologically meaningful than peak scoring. This deconvolution method is applied to both synthetic and experimental evoked potential data, and is demonstrated to be applicable even when normal evoked potential features are not clearly visible.

Although there is substantial evidence indicating that patients with first-episode schizophrenia exhibit both anatomical and electrophysiological abnormalities, there has been little research investigating the relationship between these two indices. We acquired structural magnetic resonance images and resting electroencephalographic recordings from 19 patients with schizophrenia, both at the time of their first presentation to mental health services and 2-3 years subsequently. Patients' grey matter images were parcellated into four brain lobes, and slow-wave, alpha- and beta-electroencephalographic power was calculated in four corresponding cortical regions. Although grey matter volume decreased longitudinally, particularly fronto-parietally, electroencephalographic power increased in the slow-wave and beta-frequency bands. These results suggest that first-episode schizophrenia may be associated with abnormally elevated levels of neural synchrony.

Adolescence to early adulthood is a period of dramatic transformation in the healthy human brain. However, the relationship between the concurrent structural and functional changes remains unclear. We investigated the impact of age on both neuroanatomy and neurophysiology in the same healthy subjects (n = 138) aged 10 to 30 years using magnetic resonance imaging (MRI) and resting electroencephalography (EEG) recordings. MRI data were segmented into gray and white matter images and parcellated into large‐scale regions of interest. Absolute EEG power was quantified for each lobe for the slow‐wave, alpha and beta frequency bands. Gray matter volume was found to decrease across the age bracket in the frontal and parietal cortices, with the greatest change occurring in adolescence. EEG activity, particularly in the slow‐wave band, showed a similar curvilinear decline to gray matter volume in corresponding cortical regions. An inverse pattern of curvilinearly increasing white matter volume was observed in the parietal lobe. We suggest that the reduction in gray matter primarily reflects a reduction of neuropil, and that the corresponding elimination of active synapses is responsible for the observed reduction in EEG power. Hum Brain Mapp, 2007. © 2006 Wiley‐Liss, Inc.

This paper presents a new method for the identification of individual event related potential (ERP) components in both frequency and time domains. Using the similar basis function (SBF) algorithm the method provides a time to frequency transform, representing a frequency domain equivalent of the component waveform. Notable features of the SBF algorithm are that it allows for unevenly spaced sampled functions in both the time and frequency domains, and estimates of spectral densities are obtained by numerical computation of finite Fourier integrals. Application of this method to ERP data from 20 normal subjects demonstrated a similar shape of component amplitude frequency characteristics for traditional late component waveforms (N1, P2, N2 and P3). On this basis, a low-frequency band was found where the component amplitude frequency characteristic was described by a Gaussian function, while the component phase frequency characteristic was a linear function of frequency. These relationships are interpreted as frequency domain equivalents of the component. Transformed to the time domain, they provided an analytical description of the ERP as the sum of positive- and negative-going monopolar waves. The study points to similar mechanisms underlying these component waveforms, and analytically defines dynamic properties for the components both in the frequency and time domains.

The dynamics of a cortex driven by a finite number of white-noise point sources is studied using a recently developed wave-equation formulation. Green's functions, power spectra, fluctuation levels, and two-point correlation functions are computed analytically and numerically. It is shown that a range of observed properties of so-called synchronous oscillations in the cerebral cortex can be correctly reproduced using the wave equation that involves only excitatory interactions between neurons. In particular, the observed existence of a maximal correlation at zero temporal lag between spatially separated points is reproduced and explained for a cortex driven by two white-noise sources.

Nonlinear equations are introduced to model the behavior of the waves of cortical electrical activity that are responsible for signals observed in electroencephalography. These equations incorporate nonlinearities, axonal and dendritic lags, excitatory and inhibitory neuronal populations, and the two-dimensional nature of the cortex, while rendering nonlinear features far more tractable than previous formulations, both analytically and numerically. The model equations are first used to calculate steady-state levels of cortical activity for various levels of stimulation. Dispersion equations for linear waves are then derived analytically and an analytic expression is found for the linear stability boundary beyond which a seizure will occur. The effects of boundary conditions in determining global eigenmodes are also studied in various geometries and the corresponding eigenfrequencies are found. Numerical results confirm the analytic ones, which are also found to reproduce existing results in the relevant limits, thereby elucidating the limits of validity of previous approximations.

The averaging of individual late component event related potential (ERP) responses, particularly P300, has revealed significant differences between schizophrenic patients and normal subjects. However, the averaging process removes the variability of the individual epochs that constitute that average. The response-variance-curve (RVC) method quantifies the variability of the individual epochs and allows examinations of windows of maximum variance. In this study, we examine the complementary nature of the RVC method to the traditional averaging approach. The averaged N200 and P300 ERP components differed significantly between the schizophrenic and normal groups, but not between the unmedicated and medicated schizophrenic patients. The RVC measure, on the other hand, revealed systematic differences in variability, maximal between 190 and 240 ms, between the unmedicated and medicated schizophrenic patients. The RVC measure therefore provides a focused time frame in which to examine dysfunctions in information processing and macroscopic scale changes in brain function due to medication.

The response-variance-curve (RVC) method quantifies the variability of the individual epochs that constitute the average event related potential (ERP), providing complementary information to that offered by ERPs. Numerous studies have found that average ERP late components of an auditory "oddball" paradigm can differentiate schizophrenic patients from normal subjects. Our previous study of the RVC measure revealed significant differences between medicated and unmedicated schizophrenic patients in the maximum ERP variability from 190 to 240 ms. In the present study of unmedicated schizophrenic patients and normal control subjects, we examined the influence of intertarget intervals (generated by pseudorandom stimulus sequences in an auditory oddball paradigm) on the intratrial effects of ERP variability measured by the RVC. The ERPs of unmedicated schizophrenic patients were characterized by an instability in a latency window corresponding to the N200 component. The effect was particularly large at an intertarget interval of 7.8 s and was significantly reduced on either side of this intertarget interval.

The spatiotemporal behavior of human EEG oscillations is investigated. Traveling waves in the alpha and theta ranges are found to be common in both prestimulus and poststimulus EEG activity. The dynamical properties of these waves, including their speeds, directions, and durations, are systematically characterized for the first time, and the results show that there are significant changes of prestimulus spontaneous waves in the presence of an external stimulus. Furthermore, the functional relevance of these waves is examined by studying how they are correlated with reaction times on a single trial basis; prestimulus alpha waves traveling in the frontal-to-occipital direction are found to be most correlated to reaction speeds. These findings suggest that propagating waves of brain oscillations might be involved in mediating long-range interactions between widely distributed parts of human cortex.

Auditory event-related potentials (ERPs) have been extensively studied in patients with depression, but most studies have focused on purely phenomenological analysis methods, such as component scoring. In contrast, this study applies two recently developed physiology-based methods-fitting using a thalamocortical model of neuronal activity and waveform deconvolution - to data from a selective-attention task in four subject groups (49 patients with melancholic depression, 34 patients with non-melancholic depression, 111 participants with subclinical depressed mood, and 98 healthy controls), to yield insight into physiological differences in attentional processing between participants with major depression and controls. This approach found evidence that: participants with depressed mood, regardless of clinical status, shift from excitation in the thalamocortical system towards inhibition; that clinically depressed participants have decreased relative response amplitude between target and standard waveforms; and that patients with melancholic depression also have increased thalamocortical delays. These findings suggest possible physiological mechanisms underlying different depression subtypes, and may eventually prove useful in motivating new physiology-based diagnostic methods.

This study examined the N100 component of the event related potential in a habituation paradigm with shorrt interstimulus intervals. The paradigm was designed to be relatively brief in duration (approx. 4 min for each of two conditions), so that it could be used for clinical populations with cognitive dysfunction, in which compliance may be a problem with long paradigms. Two conditions — Ignore and Attend — were employed with normal subjects. In each condition, 15 stimulus trains, each consisting of 10 innocuous tones, were presented. The eighth tone was a change stimulus. There was a fixed interstimulus interval of 1.1 s and an inter-train interval of 5 s. From the perspective of traditional Orienting Response theory, evidence was sought for within-train habituation in terms of diminished N100 amplitude to repeated stimuli, response recovery to the change stimulus, and dishabituation of the response to the following standard stimuli. Habituation was suggested by significant decreases of approx. 50% with stimulus repetition, and response recovery to the change stimulus in both conditions. However, there was no evidence of dishabituation following the change stimulus. These results confirm that N100 fails to meet the formal requirements of response habituation, suggesting instead that it may index an earlier process than the Orienting Response.

Objective This study examines developmental and aging trends in auditory evoked potentials (AEPs) by applying two analysis methods to a large database of healthy subjects. Methods AEPs and reaction times were recorded from 1498 healthy subjects aged 6–86 years using an auditory oddball paradigm. AEPs were analyzed using a recently published deconvolution method and conventional component scoring. Age trends in the resultant data were determined using smooth median-based fits. Results Component latencies generally decreased during development and increased during aging. Deconvolution showed the emergence of a new feature during development, corresponding to improved differentiation between standard and target tones. The latency of this feature provides similar information as the target component latencies, while its amplitude provides a marker of cognitive development. Conclusions Age trends in component scores can be related to physiological changes in the brain. However, component scores show a high degree of redundancy, which limits their information content, and are often invalid when applied to young children. Deconvolution provides additional information on development not available through other methods. Significance This is the largest study of AEP age trends to date. It provides comprehensive statistics on conventional component scores and shows that deconvolution is a simple and informative alternative.

This study was undertaken using the INTEGRATE Model of brain organization, which is based on a temporal continuum of emotion, thinking and self regulation. In this model, the key organizing principle of self adaption is the motivation to minimize danger and maximize reward. This principle drives brain organization across a temporal continuum spanning milliseconds to seconds, minutes and hours. The INTEGRATE Model comprises three distinct processes across this continuum. Emotion is defined by automatic action tendencies triggered by signals that are significant due to their relevance to minimizing danger-maximizing reward (such as abrupt, high contrast stimuli). Thinking represents cognitive functions and feelings that rely on brain and body feedback emerging from around 200 ms post-stimulus onwards. Self regulation is the modulation of emotion, thinking and feeling over time, according to more abstract adaptions to minimize danger-maximize reward. Here, we examined the impact of dispositional factors, age and genetic variation, on this temporal continuum. Brain Resource methodology provided a standardized platform for acquiring genetic, brain and behavioral data in the same 1000 healthy subjects. Results showed a "paradox" of declining function in the "thinking" time scale over the lifespan (6 to 80+ years), but a corresponding preservation or even increase in automatic functions of "emotion" and "self regulation". This paradox was paralleled by a greater loss of grey matter in cortical association areas (assessed using MRI) over age, but a relative preservation of subcortical grey matter. Genetic polymorphisms associated with both healthy function and susceptibility to disorder (including the BDNFVal(66)Met, COMTVal(158/108)Met, MAOA and DRD4 tandem repeat and 5HTT-LPR polymorphisms) made specific contributions to emotion, thinking and self regulatory functions, which also varied according to age.

Variable contributions of state and trait to the electroencephalographic (EEG) signal affect the stability over time of EEG measures, quite apart from other experimental uncertainties. The extent of intraindividual and interindividual variability is an important factor in determining the statistical, and hence possibly clinical significance of observed differences in the EEG. This study investigates the changes in classical quantitative EEG (qEEG) measures, as well as of parameters obtained by fitting frequency spectra to an existing continuum model of brain electrical activity. These parameters may have extra variability due to model selection and fitting. Besides estimating the levels of intraindividual and interindividual variability, we determined approximate time scales for change in qEEG measures and model parameters. This provides an estimate of the recording length needed to capture a given percentage of the total intraindividual variability. Also, if more precise time scales can be obtained in future, these may aid the characterization of physiological processes underlying various EEG measures. Heterogeneity of the subject group was constrained by testing only healthy males in a narrow age range (mean = 22.3 years, sd = 2.7). Eyes-closed EEGs of 32 subjects were recorded at weekly intervals over an approximately six-week period, of which 13 subjects were followed for a year. QEEG measures, computed from Cz spectra, were powers in five frequency bands, alpha peak frequency, and spectral entropy. Of these, theta, alpha, and beta band powers were most reproducible. Of the nine model parameters obtained by fitting model predictions to experiment, the most reproducible ones quantified the total power and the time delay between cortex and thalamus. About 95% of the maximum change in spectral parameters was reached within minutes of recording time, implying that repeat recordings are not necessary to capture the bulk of the variability in EEG spectra.

The electrical activity of the brain has been observed for over a century and is widely used to probe brain function and disorders, chiefly through the electroencephalogram (EEG) recorded by electrodes on the scalp. However, the connections between physiology and EEGs have been chiefly qualitative until recently, and most uses of the EEG have been based on phenomenological correlations. A quantitative mean-field model of brain electrical activity is described that spans the range of physiological and anatomical scales from microscopic synapses to the whole brain. Its parameters measure quantities such as synaptic strengths, signal delays, cellular time constants, and neural ranges, and are all constrained by independent physiological measurements. Application of standard techniques from wave physics allows successful predictions to be made of a wide range of EEG phenomena, including time series and spectra, evoked responses to stimuli, dependence on arousal state, seizure dynamics, and relationships to functional magnetic resonance imaging (fMRI). Fitting to experimental data also enables physiological parameters to be infered, giving a new noninvasive window into brain function, especially when referenced to a standardized database of subjects. Modifications of the core model to treat mm-scale patchy interconnections in the visual cortex are also described, and it is shown that resulting waves obey the Schroedinger equation. This opens the possibility of classical cortical analogs of quantum phenomena.

A recently developed quantitative model of cortical activity is used that permits data comparison with experiment using a quantitative and standardized means. The model incorporates properties of neurophysiology including axonal transmission delays, synaptodendritic rates, range-dependent connectivities, excitatory and inhibitory neural populations, and intrathalamic, intracortical, corticocortical and corticothalamic pathways. This study tests the ability of the model to determine unique physiological properties in a number of different data sets varying in mean age and pathology. The model is used to fit individual electroencephalographic (EEG) spectra from post-traumatic stress disorder (PTSD), schizophrenia, first episode schizophrenia (FESz), attention deficit hyperactivity disorder (ADHD), and their age/sex matched controls. The results demonstrate that the model is able to distinguish each group in terms of a unique cluster of abnormal parameter deviations. The abnormal physiology inferred from these parameters is also consistent with known theoretical and experimental findings from each disorder. The model is also found to be sensitive to the effects of medication in the schizophrenia and FESz group, further supporting the validity of the model.

This paper presents the results from using electroencephalographic (EEG) data to estimate the values of key neurophysiological parameters using a detailed biophysical model of brain activity. The model incorporates spatial and temporal aspects of cortical function including axonal transmission delays, synapto-dendritic rates, range-dependent connectivities, excitatory and inhibitory neural populations, and intrathalamic, intracortical, corticocortical and corticothalamic pathways. Parameter estimates were obtained by fitting the model's theoretical spectrum to EEG spectra from each of 100 healthy human subjects. Statistical analysis was used to infer significant parameter variations occurring between eyes-closed and eyes-open states, and a correlation matrix was used to investigate links between the parameter variations and traditional measures of quantitative EEG (qEEG). Accurate fits to all experimental spectra were observed, and both inter-subject and between-state variability were accounted for by the variance in the fitted biophysical parameters, which were in turn consistent with known independent experimental and theoretical estimates. These values thus provide physiological information regarding the state. transitions (eyes-closed vs. eyes-open) and phenomena including cortical idling and alpha desynchronization. The parameters are also consistent with traditional qEEG, but are more informative, since they provide links to underlying physiological processes. To our knowledge, this is the first study where a detailed biophysical model of the brain is used to estimate neurophysiological parameters underlying the transitions in a broad range (0.25-50 Hz) of EEG spectra obtained from a large set of human data.

Simulation of electrocortical activity requires (a) determination of the most crucial features to be modelled, (b) specification of state equations with parameters that can be determined against independent measurements, and (c) explanation of electrical events in the brain at several scales. We report our attempts to address these problems, and show that mutually consistent explanations, and simulation of experimental data can be achieved for cortical gamma activity, synchronous oscillation, and the main features of the EEG power spectrum including the cerebral rhythms and evoked potentials. These simulations include consideration of dendritic and synaptic dynamics, AMPA, NMDA, and GABA receptors, and intracortical and cortical/subcortical interactions. We speculate on the way in which Hebbian learning and intrinsic reinforcement processes might complement the brain dynamics thus explained, to produce elementary cognitive operations.

Evoked potentials -- the brain's transient electrical responses to discrete stimuli -- are modeled as impulse responses using a continuum model of brain electrical activity. Previous models of ongoing brain activity are refined by adding an improved model of thalamic connectivity and modulation, and by allowing for two populations of excitatory cortical neurons distinguished by their axonal ranges. Evoked potentials are shown to be modelable as an impulse response that is a sum of component responses. The component occurring about 100 ms poststimulus is attributed to sensory activation, and this, together with positive and negative feedback pathways between the cortex and thalamus, results in subsequent peaks and troughs that semiquantitatively reproduce those of observed evoked potentials. Modulation of the strengths of positive and negative feedback, in ways consistent with psychological theories of attentional focus, results in distinct responses resembling those seen in experiments involving attentional changes. The modeled impulse responses reproduce key features of typical experimental evoked response potentials: timing, relative amplitude, and number of peaks. The same model, with further modulation of feedback, also reproduces experimental spectra. Together, these results mean that a broad range of ongoing and transient electrocortical activity can be understood within a common framework, which is parameterized by values that are directly related to physiological and anatomical quantities.

A continuum model of the electrical activity of the cerebral cortex is described which predicts the occurrence of a resonance in the gamma range near 40 Hz. The emergence of this resonance is due to two refinements to a previous model, namely the inclusion of a modulation of synaptic strength due to finite reversal potentials, and use of parameters that better match physiological measurements. Analytical expressions for the fixed points of the system and for its linear dynamics are found in terms of average neuronal properties, and together explain the occurrence and modulation of the gamma-like resonance. The analytical results are confirmed by a numerical simulation.

One limitation of averaging individual late component event related potential (ERP) responses is that a single average ERP cannot reflect the variability of responses from epoch to epoch. In this article, we describe a method to quantify this variability and determine if any part of the overall ERP reflects a maximum variance through the use of response variance curves. We then apply this method to one disorder, schizophrenia, in which variability of information processing is hypothesized to underlie aspects of the symptomatology. Response variance curves in a group of unmedicated schizophrenic patients reveal systematic differences, maximal between 190 and 250 ms, compared with those in a group of medicated schizophrenic patients and normal control subjects.