Biomedical subjects

Steven L Bressler

Publications and source records attributed to Steven L Bressler.

15 recordsLinked to original sources

PubMed2006

Operational principles of neurocognitive networks.

Large-scale neural networks are thought to be an essential substrate for the implementation of cognitive function by the brain. If so, then a thorough understanding of cognition is not possible without knowledge of how the large-scale neural networks of cognition (neurocognitive networks) operate. Of necessity, such understanding requires insight into structural, functional, and dynamical aspects of network operation, the intimate interweaving of which may be responsible for the intricacies of cognition. Knowledge of anatomical structure is basic to understanding how neurocognitive networks operate. Phylogenetically and ontogenetically determined patterns of synaptic connectivity form a structural network of brain areas, allowing communication between widely distributed collections of areas. The function of neurocognitive networks depends on selective activation of anatomically linked cortical and subcortical areas in a wide variety of configurations. Large-scale functional networks provide the cooperative processing which gives expression to cognitive function. The dynamics of neurocognitive network function relates to the evolving patterns of interacting brain areas that express cognitive function in real time. This article considers the proposition that a basic similarity of the structural, functional, and dynamical features of all neurocognitive networks in the brain causes them to function according to common operational principles. The formation of neural context through the coordinated mutual constraint of multiple interacting cortical areas, is considered as a guiding principle underlying all cognitive functions. Increasing knowledge of the operational principles of neurocognitive networks is likely to promote the advancement of cognitive theories, and to seed strategies for the enhancement of cognitive abilities.

Animals↗

PubMed2006

Differentially variable component analysis: Identifying multiple evoked components using trial-to-trial variability.

Electric potentials and magnetic fields generated by ensembles of synchronously active neurons, either spontaneously or in response to external stimuli, provide information essential to understanding the processes underlying cognitive and sensorimotor activity. Interpreting recordings of these potentials and fields is difficult because detectors record signals simultaneously generated by various regions throughout the brain. We introduce a novel approach to this problem, the differentially variable component analysis (dVCA) algorithm, which relies on trial-to-trial variability in response amplitude and latency to identify multiple components. Using simulations we demonstrate the importance of response variability to component identification, the robustness of dVCA to noise, and its ability to characterize single-trial data. We then compare the source-separation capabilities of dVCA with those of principal component analysis and independent component analysis. Finally, we apply dVCA to neural ensemble activity recorded from an awake, behaving macaque-demonstrating that dVCA is an important tool for identifying and characterizing multiple components in the single trial.

Animals↗

PubMed2006

Large-scale visuomotor integration in the cerebral cortex.

Efficient visuomotor behavior depends on integrated processing by the visual and motor systems of the cerebral cortex. Yet, many previous cortical neurophysiology studies have examined the visual and motor modalities in isolation, largely ignoring questions of large-scale cross-modal integration. To address this issue, we analyzed event-related local field potentials simultaneously recorded from multiple visual, motor, and executive cortical sites in monkeys performing a visuomotor pattern discrimination task. The timing and cortical location of four aspects of event-related activities were examined: stimulus-evoked activation onset, stimulus-specific processing, stimulus category-specific processing, and response-specific processing. Activations appeared earliest in striate cortex and rapidly thereafter in other visual areas. Stimulus-specific processing began early in most visual cortical areas, some at activation onset. Early onset latencies were also observed in motor, premotor, and prefrontal areas, some as early as in striate cortex, but these early-activating frontal sites did not show early stimulus-specific processing. Response-specific processing began around 150 ms poststimulus in widespread cortical areas, suggesting that perceptual decision formation and response selection arose through concurrent processes of visual, motor, and executive areas. The occurrence of stimulus-specific and stimulus category-specific differences after the onset of response-specific processing suggests that sensory and motor stages of visuomotor processing overlapped in time.

Algorithms↗

PubMed2006

Overexpression of hyaluronan synthases alters vascular smooth muscle cell phenotype and promotes monocyte adhesion.

Hyaluronan (HA) accumulates in vascular disease but its functional role is not fully understood. To investigate the impact of HA enriched extracellular matrices (ECM) on cell phenotype, arterial smooth muscle cells (ASMCs) were transduced with retroviral constructs (LXSN) encoding murine has-1, has-2, and has-3. HA synthesis was significantly elevated in has transduced ASMCs. Metabolically labeled HA from has-1 and has-2 transduced cells was present mostly in high molecular weight (HWA) fractions (2-10x10(6) Da), whereas HA produced by has-3 and control cells was present in lower molecular weight fractions (approximately 2x10(6) Da). Both has-1 and has-3 transduced ASMCs accumulated more pericellular HA than has-2 transduced ASMCs. All has transduced ASMCs had attenuated growth and migration rates, and a decreased detachment response. Affinity histochemistry revealed that has-1 transduced ASMCs accumulated the greatest amount of HA containing ECM than the other transduced ASMCs. This ECM was hyaluronidase sensitive and bound a significantly greater number of monocytes than the ECM generated by has-2 or has-3 transduced ASMCs. Confocal microscopy showed CD44 positive monocytes bound to hyaluronidase sensitive ECM in has-1 transduced ASMCs. These data implicate specific has isoforms in the formation of HA enriched pro-inflammatory ECMs.

Animals↗

PubMed2006

Stochastic modeling of neurobiological time series: power, coherence, Granger causality, and separation of evoked responses from ongoing activity.

In this article we consider the stochastic modeling of neurobiological time series from cognitive experiments. Our starting point is the variable-signal-plus-ongoing-activity model. From this model a differentially variable component analysis strategy is developed from a Bayesian perspective to estimate event-related signals on a single trial basis. After subtracting out the event-related signal from recorded single trial time series, the residual ongoing activity is treated as a piecewise stationary stochastic process and analyzed by an adaptive multivariate autoregressive modeling strategy which yields power, coherence, and Granger causality spectra. Results from applying these methods to local field potential recordings from monkeys performing cognitive tasks are presented.

Action Potentials↗

PubMed2005

Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data.

It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method.

Algorithms↗

PubMed2005

Empirical mode decomposition of field potentials from macaque V4 in visual spatial attention.

Empirical mode decomposition (EMD) has recently been introduced as a local and fully data-driven technique for the analysis of non-stationary time-series. It allows the frequency and amplitude of a time-series to be evaluated with excellent time resolution. In this article we consider the application of EMD to the analysis of neuronal activity in visual cortical area V4 of a macaque monkey performing a visual spatial attention task. We show that, by virtue of EMD, field potentials can be resolved into a sum of intrinsic components with different degrees of oscillatory content. Low-frequency components in single-trial recordings contribute to the average visual evoked potential (AVEP), whereas high-frequency components do not, but are identified as gamma-band (30-90 Hz) oscillations. The magnitude of time-varying gamma activity is shown to be enhanced when the monkey attends to a visual stimulus as compared to when it is not attending to the same stimulus. Comparison with Fourier analysis shows that EMD may offer better temporal and frequency resolution. These results support the idea that the magnitude of gamma activity reflects the modulation of V4 neurons by visual spatial attention. EMD, coupled with instantaneous frequency analysis, is demonstrated to be a useful technique for the analysis of neurobiological time-series.

Algorithms↗

PubMed2004

Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by Granger causality.

Previous studies have shown that synchronized beta frequency (14-30 Hz) oscillations in the primary motor cortex are involved in maintaining steady contractions of contralateral arm and hand muscles. However, little is known about the role of postcentral cortical areas in motor maintenance and their patterns of interaction with motor cortex. We investigated the functional relations of beta-synchronized neuronal assemblies in pre- and postcentral areas of two monkeys as they pressed a hand lever during the wait period of a visual discrimination task. By using power and coherence spectral analysis, we identified a beta-synchronized large-scale network linking pre- and postcentral areas. We then used Granger causality spectra to measure directional influences among recording sites. In both monkeys, strong Granger causal influences were observed from primary somatosensory cortex to both motor cortex and inferior posterior parietal cortex, with the latter area also exerting Granger causal influences on motor cortex. Granger causal influences from motor cortex to postcentral sites, however, were weak in one monkey and not observed in the other. These results are the first, to our knowledge, to demonstrate in awake monkeys that synchronized beta oscillations bind multiple sensorimotor areas into a large-scale network during motor maintenance behavior and carry Granger causal influences from primary somatosensory and inferior posterior parietal cortices to motor cortex.

Animals↗

PubMed2004

Neural dynamics and the fundamental mechanisms of event-related brain potentials.

Event-related potentials (ERPs) provide a critical link between the hemodynamic response, as measured by functional magnetic resonance imaging, and the dynamics of the underlying neuronal activity. Single-trial ERP recordings capture the oscillatory activity that are hypothesized to underlie both communication between brain regions and amplified processing of behaviorally relevant stimuli. However, precise interpretations of ERPs are precluded by uncertainty about their neural mechanisms. One influential theory holds that averaged sensory ERPs are generated by partial phase resetting of ongoing electroencephalographic oscillations, while another states that ERPs result from stimulus-evoked neural responses. We formulated critical predictions of each theory and tested these using direct, intracortical analyses of neural activity in monkeys. Our findings support a predominant role for stimulus-evoked activity in sensory ERP generation, and they outline both logic and methodology necessary for differentiating evoked and phase resetting contributions to cognitive and motor ERPs in future studies.

Animals↗

PubMed2004

Inferential constraint sets in the organization of visual expectation.

Many lines of evidence indicate that considering visual perception as a passive, stimulus-driven, feedforward decoding process is no longer tenable. Visual perception naturally occurs within the context of an integrated array of ongoing cognitive processes involving memory, perception in other modalities, and motor control. In many situations, these processes allow expectations to be formed for likely visual events. This article explores the idea that the formation of visual expectations involves the active organization of visual cortical areas, providing a framework of contextual information within which expected events are interpreted. Retinal inputs are treated as constraints that feed into a complex system of interacting visual cortical areas and thalamic nuclei, which are concurrently imposing constraints on one another. Although the nature of expectational organization in the visual cortex is not well-understood, a reasonable hypothesis is that expectation involves the mutual constraint of spatiotemporal activity patterns in multiple visual cortical areas. In this scenario, expectation is instantiated by a set of activity patterns in high-level visual cortical areas that impose constraints on one another as well as on low-level areas according to the partial information that is available about expected retinal inputs. One approach to testing this proposal is through the analysis of simultaneously recorded local field potentials (LFPs) from local neuronal assemblies in multiple visual cortical areas. Analysis of LFPs by multivariate autoregressive modeling is showing promise in revealing the organization of expectation in visual cortex.

Animals↗

PubMed2004

The regulated synthesis of versican, decorin, and biglycan: extracellular matrix proteoglycans that influence cellular phenotype.

The principal extracellular matrix (ECM) chondroitin/dermatan sulfate proteoglycans include members of two gene families--the large aggregating chondroitin sulfate proteoglycans (lecticans) and the small leucine-rich proteoglycans (SLRPs). These families of proteoglycans are widely distributed within the interstitial matrix, where they are known to bind a variety of both soluble and insoluble ligands. Extensive structural studies and data concerning the synthesis of these proteoglycans have been published over the last few years. This review focuses on the regulation of the expression of the lectican, versican, and the SLRPs--decorin and biglycan, as well--studied and widely distributed examples of these families of ECM proteoglycans. In addition, the effects of these proteoglycans on the formation of the ECM and the response of cells to growth factors and cytokines are examined as mechanisms by which versican, decorin and biglycan, both directly and indirectly influence cellular proliferation, migration, and phenotype.

Animals↗

PubMed2003

Estimation of single-trial multicomponent ERPs: differentially variable component analysis (dVCA).

A Bayesian inference framework for estimating the parameters of single-trial, multicomponent, event-related potentials is presented. Single-trial recordings are modeled as the linear combination of ongoing activity and multicomponent waveforms that are relatively phase-locked to certain sensory or motor events. Each component is assumed to have a trial-invariant waveform with trial-dependent amplitude scaling factors and latency shifts. A Maximum a Posteriori solution of this model is implemented via an iterative algorithm from which the component's waveform, single-trial amplitude scaling factors and latency shifts are estimated. Multiple components can be derived from a single-channel recording based on their differential variability, an aspect in contrast with other component analysis techniques (e.g., independent component analysis) where the number of components estimated is equal to or smaller than the number of recording channels. Furthermore, we show that, by subtracting out the estimated single-trial components from each of the single-trial recordings, one can estimate the ongoing activity, thus providing additional information concerning task-related brain dynamics. We test this approach, which we name differentially variable component analysis (dVCA), on simulated data and apply it to an experimental dataset consisting of intracortically recorded local field potentials from monkeys performing a visuomotor pattern discrimination task.

Algorithms↗

PubMed2003

Cortical coordination dynamics and the disorganization syndrome in schizophrenia.

There has been a long history of investigation in the fields of neuropsychology and cognitive psychology into the question of functional integration in the brain. Each of the several dominant themes in that history can be interpreted as representing an important feature of a unitary general mechanism that integrates distributed processes in the cerebral cortex. This mechanism must allow local areas to function within the large-scale anatomical structure of the cortex so as to satisfy competing requirements for stability and flexibility. Each specialized cortical area must perform a unique role by expressing its own form of information, yet must have its performance constrained by interactions with other areas to which it is connected. In order to generate adaptive behavior within changing and not fully predictable environments, the cortex as a whole must be able to rapidly coordinate the activities of variable assemblages of areas that can collectively express consensual information that is appropriate for the functional requirements engendered by each successive stage of behavioral performance. This paper proposes that the phase synchronization of neuronal population activity from different cortical areas may serve a role in large-scale coordination. Theoretical studies suggest that the cortex normally operates in a metastable dynamic regime in which groups of areas are able to coordinate rapidly and reversibly their activities through changes in their degree of phase synchronization. A disruption of phase synchronization, leading to an excess of local information expression by cortical areas, is proposed as a contributing factor to the disorganization syndrome in schizophrenia.

Animals↗

PubMed2002

Synchronized activity in prefrontal cortex during anticipation of visuomotor processing.

It is commonly presumed, though not well established, that the prefrontal cortex exerts top-down control of sensory processing. One aspect of this control is thought to be a facilitation of sensory pathways in anticipation of such processing. To investigate the possible involvement of prefrontal cortex in anticipatory top-down control, we studied the statistical relations between prefrontal activity, recorded while a macaque monkey waited for presentation of a visual stimulus, and subsequent sensory and motor events. Local field potentials were simultaneously recorded from prefrontal, motor, occipital and temporal cortical sites in the left cerebral hemisphere. Spectral power and coherence analysis revealed that during stimulus anticipation three of five prefrontal sites participated in a coherent oscillatory network synchronized in the beta-frequency range. Pre-stimulus network power and coherence were highly correlated with the amplitude and latency of early visual evoked potential components in visual cortical areas, and with response time. The results suggest that synchronized oscillatory networks in prefrontal cortex are involved in top-down anticipatory mechanisms that facilitate subsequent sensory processing in visual cortex. They further imply that stronger top-down control leads to larger and faster sensory responses, and a subsequently faster motor response.

Animals↗

PubMed2002

Trial-to-trial variability of cortical evoked responses: implications for the analysis of functional connectivity.

OBJECTIVES: The time series of single trial cortical evoked potentials typically have a random appearance, and their trial-to-trial variability is commonly explained by a model in which random ongoing background noise activity is linearly combined with a stereotyped evoked response. In this paper, we demonstrate that more realistic models, incorporating amplitude and latency variability of the evoked response itself, can explain statistical properties of cortical potentials that have often been attributed to stimulus-related changes in functional connectivity or other intrinsic neural parameters. METHODS: Implications of trial-to-trial evoked potential variability for variance, power spectrum, and interdependence measures like cross-correlation and spectral coherence, are first derived analytically. These implications are then illustrated using model simulations and verified experimentally by the analysis of intracortical local field potentials recorded from monkeys performing a visual pattern discrimination task. To further investigate the effects of trial-to-trial variability on the aforementioned statistical measures, a Bayesian inference technique is used to separate single-trial evoked responses from the ongoing background activity. RESULTS: We show that, when the average event-related potential (AERP) is subtracted from single-trial local field potential time series, a stimulus phase-locked component remains in the residual time series, in stark contrast to the assumption of the common model that no such phase-locked component should exist. Two main consequences of this observation are demonstrated for statistical measures that are computed on the residual time series. First, even though the AERP has been subtracted, the power spectral density, computed as a function of time with a short sliding window, can nonetheless show signs of modulation by the AERP waveform. Second, if the residual time series of two channels co-vary, then their cross-correlation and spectral coherence time functions can also be modulated according to the shape of the AERP waveform. Bayesian estimation of single-trial evoked responses provides further proof that these time-dependent statistical changes are due to remnants of the evoked phase-locked component in the residual time series. CONCLUSIONS: Because trial-to-trial variability of the evoked response is commonly ignored as a contributing factor in evoked potential studies, stimulus-related modulations of power spectral density, cross-correlation, and spectral coherence measures is often attributed to dynamic changes of the connectivity within and among neural populations. This work demonstrates that trial-to-trial variability of the evoked response must be considered as a possible explanation of such modulation.

Animals↗