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PubMed1994

A possible neural mechanism underlying consciousness based on the pattern processing capabilities of pyramidal neurons in the cerebral cortex.

This paper examines a possible neural mechanism underlying the phenomenon of consciousness by exploring the ability of cortical pyramidal cells to process patterns of information. A numerical model of a section of a neuron is described that enables the transient membrane potential distribution to be predicted following inputs to the cell. The neuron model incorporates learning by modelling the ability of dendritic spine receptors to undergo changes in their sensitivity if they receive inputs when their local membrane is depolarized. Simulations show the cell to be capable of recognizing patterns amongst its inputs, and to be able to extend its repertoire of learnt patterns by associating one pattern of inputs with another. These pattern processing capabilities can take place within the apical dendritic tree, whilst the soma sees a much attenuated and slower response that reflects the general level of pattern recognition taking place in the dendrites. It is argued here that the distribution of patterns stored in the apical dendrites of all the pyramidal cells represents the cortical knowledge base. Incoming patterns of information are compared with those stored in the cortical knowledge base to pick out components that have been experienced before. The resulting distribution of soma responses represents the way the incoming patterns of information are perceived. Most of the patterns learnt arise from other pyramidals and represent information about cortical behaviour itself. The distribution of soma responses therefore represents a perception of the self as well as a perception of the environment. It is argued here that this intimate perception of the self could underlie the phenomenon of consciousness.

Cerebral Cortex↗
PubMed1996

High-resolution random mesh algorithms for creating a probabilistic 3D surface atlas of the human brain.

Striking variations exist, across individuals, in the internal and external geometry of the brain. Such normal variations in the size, orientation, topology, and geometric complexity of cortical and subcortical structures have complicated the problem of quantifying deviations from normal anatomy and of developing standardized neuroanatomical atlases. This paper describes the design, implementation, and results of a technique for creating a three-dimensional (3D) probabilistic surface atlas of the human brain. We have developed, implemented, and tested a new 3D statistical method for assessing structural variations in a data-base of anatomic images. The algorithm enables the internal surface anatomy of new subjects to be analyzed at an extremely local level. The goal was to quantify subtle and distributed patterns of deviation from normal anatomy by automatically generating detailed probability maps of the anatomy of new subjects. Connected systems of parametric meshes were used to model the internal course of the following structures in both hemispheres: the parieto-occipital sulcus, the anterior and posterior rami of the calcarine sulcus, the cingulate and marginal sulci, and the supracallosal sulcus. These sulci penetrate sufficiently deeply into the brain to introduce an obvious topological decomposition of its volume architecture. A family of surface maps was constructed, encoding statistical properties of local anatomical variation within individual sulci. A probability space of random transformations, based on the theory of Gaussian random fields, was developed to reflect the observed variability in stereotaxic space of the connected system of anatomic surfaces. A complete system of probability density functions was computed, yielding confidence limits on surface variation. The ultimate goal of brain mapping is to provide a framework for integrating functional and anatomical data across many subjects and modalities. This task requires precise quantitative knowledge of the variations in geometry and location of intracerebral structures and critical functional interfaces. The surface mapping and probabilistic techniques presented here provide a basis for the generation of anatomical templates and expert diagnostic systems which retain quantitative information on intersubject variations in brain architecture.

Algorithms↗
PubMed1999

Plurality and resemblance in fMRI data analysis.

We apply nine analytic methods employed currently in imaging neuroscience to simulated and actual BOLD fMRI signals and compare their performances under each signal type. Starting with baseline time series generated by a resting subject during a null hypothesis study, we compare method performance with embedded focal activity in these series of three different types whose magnitudes and time courses are simple, convolved with spatially varying hemodynamic responses, and highly spatially interactive. We then apply these same nine methods to BOLD fMRI time series from contralateral primary motor cortex and ipsilateral cerebellum collected during a sequential finger opposition study. Paired comparisons of results across methods include a voxel-specific concordance correlation coefficient for reproducibility and a resemblance measure that accommodates spatial autocorrelation of differences in activity surfaces. Receiver-operating characteristic curves show considerable model differences in ranges less than 10% significance level (false positives) and greater than 80% power (true positives). Concordance and resemblance measures reveal significant differences between activity surfaces in both data sets. These measures can assist researchers by identifying groups of models producing similar and dissimilar results, and thereby help to validate, consolidate, and simplify reports of statistical findings. A pluralistic strategy for fMRI data analysis can uncover invariant and highly interactive relationships between local activity foci and serve as a basis for further discovery of organizational principles of the brain. Results also suggest that a pluralistic empirical strategy coupled formally with substantive prior knowledge can help to uncover new brain-behavior relationships that may remain hidden if only a single method is employed.

Brain↗
PubMed1999

Ambiguous results in functional neuroimaging data analysis due to covariate correlation.

In this note we draw attention to a source of potential ambiguity in functional neuroimaging results when data analysis is based on the resolution of a linear model. This ambiguity arises whenever there exists correlation between the model covariates. A single-subject PET activation experiment helps to illustrate to what extent correlation can affect statistical results interpretation, possibly leading to misinterpretation of part of the activation pattern. This note is intended to clarify this point and to suggest the use of a simple and well-known procedure to deal with these situations. In the Appendix, we suggest a convenient mathematical formulation for statistical tests particularly useful in such cases.

Brain↗
PubMed2001

Locally regularized spatiotemporal modeling and model comparison for functional MRI.

In this work we treat fMRI data analysis as a spatiotemporal system identification problem and address issues of model formulation, estimation, and model comparison. We present a new model that includes a physiologically based hemodynamic response and an empirically derived low-frequency noise model. We introduce an estimation method employing spatial regularization that improves the precision of spatially varying noise estimates. We call the algorithm locally regularized spatiotemporal (LRST) modeling. We develop a new model selection criterion and compare our model to the SPM-GLM method. Our findings suggest that our method offers a better approach to identifying appropriate statistical models for fMRI studies.

Arousal↗
PubMed1996

A computational model of cholinergic disruption of septohippocampal activity in classical eyeblink conditioning.

A previous neurocomputational model of corticohippocampal interaction (Gluck & Myers, 1993) can provide a framework for examining the behavioral effects of septohippocampal modulation during classical conditioning. The model assumes that the hippocampal region is necessary for forming new stimulus representations during learning, but not for the formation of simple associations. This paper considers how septohippocampal interaction could affect this function. The septal nuclei provide several modulatory inputs to the hippocampus, including a cholinergic input which Hasselmo (1995) has suggested may function to regulate hippocampal dynamics on a continuum between two states: a storage state in which incoming information is encoded as an intermediate-term memory and a recall state when this information is reactivated. In this theory, anticholinergic drugs such as scopolamine should disrupt learning by selectively reducing the hippocampus's ability to store new information. An approximation of Hasselmo's idea can be implemented in the corticohippocampal model by a simple manipulation of hippocampal learning rate; this manipulation is formally equivalent to adjusting the amount of time the hippocampus spends in learning and recall states. With this manipulation, the model successfully accounts for the effects of scopolamine in retarding classical conditioning in humans (Solomon, Groccia-Ellison, Flynn, Mirak, Edwards, Dunehew, & Stanton, 1993) and animals (Solomon, Soloman, van der Schaaf, & Perry, 1983). The model further predicts that although cholinergic agonists (such as Tacrine) may improve learning in subjects with artificially depressed brain acetylcholine levels, there may be limited memory improvement in normal subjects from such cholinergic therapy. This is consistent with the general finding of a U-shaped dose response curve for cholinergic drugs in normal subjects: low to moderate doses may improve learning, but higher doses are ineffective or even degrade learning (e.g., Ennaceur & Meliani, 1992; Dumery, Derer, & Blozovski, 1988; etc.).

Animals↗
PubMed2005

Discriminative analysis of brain function at resting-state for attention-deficit/hyperactivity disorder.

In this work, a discriminative model of attention deficit hyperactivity disorder (ADHD) is presented on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model consists of two parts, a classifier and an intuitive representation of discriminative pattern of brain function between patients and normal controls. Regional homogeneity (ReHo), a measure of brain function at resting-state, is used here as a feature of classification. Fisher discriminative analysis (FDA) is performed on the features of training samples and a linear classifier is generated. Our initial experimental results show a successful classification rate of 85%, using leave-one-out cross validation. The classifier is also compared with linear support vector machine (SVM) and Batch Perceptron. Our classifier outperforms the alternatives significantly. Fisher brain, the optimal projective-direction vector in FDA, is used to represent the discriminative pattern. Some abnormal brain regions identified by Fisher brain, like prefrontal cortex and anterior cingulate cortex, are well consistent with that reported in neuroimaging studies on ADHD. Moreover, some less reported but highly discriminative regions are also identified. We conclude that the discriminative model has potential ability to improve current diagnosis and treatment evaluation of ADHD.

Algorithms↗
PubMed2003

Localization Estimation Algorithm (LEA): a supervised prior-based approach for solving the EEG/MEG inverse problem.

Localizing and quantifying the sources of ElectroEncephaloGraphy (EEG) and MagnetoEncephaloGraphy (MEG) measurements is an ill-posed inverse problem, whose solution requires a spatial regularization involving both anatomical and functional priors. The distributed source model enables the introduction of such constraints. However, the resulting solution is unstable since the equation system one has to solve is badly conditioned and under-determined. We propose an original approach for solving the inverse problem, that allows to deal with a better-determined system and to temper the influence of priors according to their consistency with the measured EEG/MEG data. This Localization Estimation Algorithm (LEA) estimates the amplitude of a selected subset of sources, which are localized based on a prior distribution of activation probability. LEA is evaluated through numerical simulations and compared to a classical Weighted Minimum Norm estimation.

Algorithms↗
PubMed2003

Multivariate group effect analysis in functional Magnetic Resonance Imaging.

In functional MRI (fMRI), analysis of multisubject data typically involves spatially normalizing (i.e. co-registering in a common standard space) all data sets and summarizing results in a single group activation map. This widely used approach does not explicitely account for between-subject anatomo-functional variability. Therefore, we propose a group effect analysis method which makes use of a multivariate model to select the main signal variations that are common to all subjects, while allowing final statistical inference on the individual scale. The normalization step is thus avoided and individual anatomo-functional features are preserved. The approach is evaluated by using simulated data and it is shown that sensitivity is drastically improved compared to more conventional individual analysis.

Algorithms↗
PubMed1997

A phosphorylation cascade in the basal ganglia of the mammalian brain: regulation by the D-1 dopamine receptor. A mathematical model of known biochemical reactions.

Stimulation of the dopamine D-1 receptor in the corpus striatum initiates a cascade of biochemical events. These events include: activation of adenylate cyclase, stimulation of cAMP-dependent protein kinase, protein phosphorylation and inhibition of phosphoprotein phosphotase-1. This article presents and discusses a mathematical model of these biochemical events (and their dependence upon the concentration of cytosolic calcium). According to this model, the activity of calcineurin (which is regulated by the concentration of cytosolic calcium ions) counterbalances the activity of the "D-1 cascade". The combined activity of the "D-1 cascade" and calcineurin can regulate the activity of calcium- and calmodulin-dependent protein kinase II.

Adenylyl Cyclases↗
PubMed1991

Glutamate receptor antagonism: neurotoxicity, anti-akinetic effects, and psychosis.

There is evidence to suggest that glutamate and other excitatory amino acids play an important role in the regulation of neuronal excitation. Glutamate receptor stimulation leads to a non-physiological increase of intracellular free Ca2+. Disturbed Ca2+ homeostasis and subsequent radical formation may be decisive factors in the pathogenesis of neurodegenerative diseases. Decreased glutamatergic activity appears to contribute to paranoid hallucinatory psychosis in schizophrenia and pharmacotoxic psychosis in Parkinson's disease. It has been suggested that a loss of glutamatergic function causes dopaminergic over-activity. Imbalances of glutamatergic and dopaminergic systems in different brain regions may result in anti-akinetic effects or the occurrence of psychosis. The simplified hypothesis of a glutamatergic-dopaminergic (im)-balance may lead to a better understanding of motor behaviour and psychosis.

Animals↗
PubMed1992

The dorsolateral prefrontal cortex, schizophrenia and PET.

Central neurophysiology can be measured with PET. These measurements are providing insights into the regional abnormalities associated with schizophrenia. Cohorts of schizophrenic subjects have been studied cross-sectionally in attempts to identify common regional deficits. More recently the advent of fast dynamic measurements of regional cerebral blood flow have allowed rapid serial measurements in the same subject in different brain states (activation studies). These complementary approaches are based upon, and are interpreted with reference to, a number of methodological considerations and underlying hypotheses. The key hypotheses underpinning cross-sectional and activation studies are discussed within the framework of the lesion model and functional anatomy models of brain function. This brief review of some assumptions, ideas and methodological constraints is illustrated with empirical data implicating the dorsolateral prefrontal cortex in schizophrenic symptoms.

Behavior↗
PubMed1991

Organizational levels of the cerebral cortex: an integrated model.

We propose a theoretical model of the cerebral cortex which is based on its cellular components and integrates its different levels of organization: (1) cells have general adaptive and memorization properties; (2) cortical columns are repetitive interneuronal circuits which determine an adaptive processing specific to the cerebral cortex; (3) cortical maps effect selective combinations which are very efficient to learn basic behavioural adaptations such as invariant recognition of forms, visually-guided hand movements, or execution of structured motor programs; (4) the network between cortical areas has a global architecture which integrates successive learning experiences into coherent functions such as the human language.

Cerebral Cortex↗
PubMed1994

The role of a transient potassium current in a bursting neuron model.

Presented here is a biophysical cell model which can exhibit low-frequency repetitive activity and bursting behavior. The model is developed from previous models (Av-Ron et al. 1991, 1993) for excitability, oscillations and bursting. A stepwise development of the present model shows the contribution of a transient potassium current (IA) to the overall dynamics. By changing a limited set of model parameters one can describe different firing patterns; oscillations with frequencies ranging from 2-200 Hz and a wide range of bursting behaviors in terms of the durations of bursting and quiescence, peak firing frequency and rate of change of the firing frequency.

Animals↗
PubMed1995

Anti-phase solutions in relaxation oscillators coupled through excitatory interactions.

Relaxation oscillators interacting via models of excitatory chemical synapses with sharp thresholds can have stable anti-phase as well as in-phase solutions. The mechanism for anti-phase demonstrated in this paper relies on the fact that, in a large class of neural models, excitatory input slows down the receiving oscillator over a portion of its trajectory. We analyze the effect of this "virtual delay" in an abstract model, and then show that the hypotheses of that model hold for widely used descriptions of bursting neurons.

Animals↗
PubMed1995

Analysis of an autonomous phase model for neuronal parabolic bursting.

An understanding of the nonlinear dynamics of bursting is fundamental in unraveling structure-function relations in nerve and secretory tissue. Bursting is characterized by alternations between phases of rapid spiking and slowly varying potential. A simple phase model is developed to study endogenous parabolic bursting, a class of burst activity observed experimentally in excitable membrane. The phase model is motivated by Rinzel and Lee's dissection of a model for neuronal parabolic bursting (J. Math. Biol 25, 653-675 (1987)). Rapid spiking is represented canonically by a one-variable phase equation that is coupled bi-directionally to a two-variable slow system. The model is analyzed in the slow-variable phase plane, using quasi steady-state assumptions and formal averaging. We derive a reduced system to explore where the full model exhibits bursting, steady-states, continuous and modulated spiking. The relative speed of activation and inactivation of the slow variables strongly influences the burst pattern as well as other dynamics. We find conditions of the bistability of solutions between continuous spiking and bursting. Although the phase model is simple, we demonstrate that it captures many dynamical features of more complex biophysical models.

Animals↗