Bulletin of the American Physical Society
2006 APS March Meeting
Monday–Friday, March 13–17, 2006; Baltimore, MD
Session Z7: Synchrony and Complexity in Brain Activity and Function |
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Sponsoring Units: DBP Chair: Michael Zochowski, University of Michigan; Eshel Ben-Jacob, Tel Aviv University Room: Baltimore Convention Center 307 |
Friday, March 17, 2006 11:15AM - 11:51AM |
Z7.00001: Analysis of Direct Recordings from the Surface of the Human Brain Invited Speaker: Recording electrophysiologic signals directly from the cortex of patients with chronically implanted subdural electrodes provides an opportunity to map the functional organization of human cortex. In addition to using direct cortical stimulation, sensory evoked potentials, and electrocorticography (ECoG) can also be used. The analysis of ECoG power spectrums and inter-electrode lateral coherence patterns may be helpful in identifying important eloquent cortical areas and epileptogenic regions in cortical multifocal epilepsy. Analysis of interictal ECoG coherence can reveal pathological cortical areas that are functionally distinct from patent cortex. Subdural ECoGs have been analyzed from 50 medically refractive pediatric epileptic patients as part of their routine surgical work-up. Recording arrays were implanted over the frontal, parietal, occipital or temporal lobes for 4-10 days, depending on the patient's seizure semiology and imaging studies. Segments of interictal ECoG ranging in duration from 5 sec to 45 min were examined to identify areas of increased local coherence. Ictal records were examined to identify the stages and spread of the seizures. Immediately before a seizure began, lateral coherence values decreased, reorganized, and then increased during the late ictal and post-ictal periods. When computed over relatively long interictal periods (45 min) coherence patterns were found to be highly stable (r = 0.97, p $<$ .001), and only changed gradually over days. On the other hand, when calculated over short periods of time (5 sec) coherence patterns were highly dynamic. Coherence patterns revealed a rich topography, with reduced coherence across sulci and major fissures. Areas that participate in receptive and expressive speech can be mapped through event-related potentials and analysis of task-specific changes in power spectrums. Information processing is associated with local increases in high frequency activity, with concomitant changes in coherence, suggestive of a transiently active language network. Our findings suggest that analysis of coherence patterns can supplement visual inspection of conventional records to help identify pathological regions of cortex. With further study, it is hoped that analysis of single channel dynamics, along with analysis of multichannel lateral coherence patterns, and the functional holographic technique may allow determination of the boundaries of epileptic foci based on brief interictal recordings, possibly obviating the current need for extended monitoring of seizures. [Preview Abstract] |
Friday, March 17, 2006 11:51AM - 12:27PM |
Z7.00002: Astrocytes, Synapses and Brain Function: A Computational Approach Invited Speaker: Modulation of synaptic reliability is one of the leading mechanisms involved in long- term potentiation (LTP) and long-term depression (LTD) and therefore has implications in information processing in the brain. A recently discovered mechanism for modulating synaptic reliability critically involves recruitments of astrocytes - star- shaped cells that outnumber the neurons in most parts of the central nervous system. Astrocytes until recently were thought to be subordinate cells merely participating in supporting neuronal functions. New evidence, however, made available by advances in imaging technology has changed the way we envision the role of these cells in synaptic transmission and as modulator of neuronal excitability. We put forward a novel mathematical framework based on the biophysics of the bidirectional neuron-astrocyte interactions that quantitatively accounts for two distinct experimental manifestation of recruitment of astrocytes in synaptic transmission: a) transformation of a low fidelity synapse transforms into a high fidelity synapse and b) enhanced postsynaptic spontaneous currents when astrocytes are activated. Such a framework is not only useful for modeling neuronal dynamics in a realistic environment but also provides a conceptual basis for interpreting experiments. Based on this modeling framework, we explore the role of astrocytes for neuronal network behavior such as synchrony and correlations and compare with experimental data from cultured networks. [Preview Abstract] |
Friday, March 17, 2006 12:27PM - 1:03PM |
Z7.00003: Neuronal Spatiotemporal Pattern Discrimination: The Dynamical Evolution of Seizures Invited Speaker: We developed a modern numerical approach to the multivariate linear discrimination of Fisher from 1936 based upon singular value decomposition that is sufficiently stable to permit widespread application to spatiotemporal neuronal patterns. We demonstrate this approach on an old problem in neuroscience -- whether seizures have distinct dynamical states as they evolve with time. A practical result was the first demonstration that human seizures have distinct initiation and termination dynamics, an important characterization as we seek to better understand how seizures start and stop. Such strategies are also useful in defining the onset of a seizure dynamically, and whether there is a dynamically distinct preseizure state. A proper orthogonal decomposition helps illustrate the changing coherent structures that underlie such activities. Our approach is broadly applicable to a wide variety of spatiotemporal dynamical data, from multichannel EEG or MEG, to sequentially acquired optical imaging data or fMRI. [Preview Abstract] |
Friday, March 17, 2006 1:03PM - 1:39PM |
Z7.00004: Measuring complexity and synchronization phenomena in the human epileptic brain Invited Speaker: The framework of the theory of nonlinear dynamics provides new concepts and powerful algorithms to study complicated dynamics such as the human electroencephalogram (EEG). Although different influencing factors render the use of nonlinear measures (such as measures for complexity, synchronization, or interdependencies) in a strict sense problematic, converging evidence from various investigations now indicates that nonlinear EEG analysis provides a means to reliably characterize different states of normal and pathological brain function and thus, promises to be important for clinical practice. This talk will focus on applications of nonlinear EEG analysis in epileptology. Epilepsy affects more than 50 million individuals worldwide - approximately 1 \% of the world's population. The disease is characterized by a recurrent and sudden malfunction of the brain that is termed seizure. Epileptic seizures are the clinical manifestation of an excessive and hypersynchronous activity of neurons in the brain. It is assumed that seizure activity will be induced when a critical mass of neurons is progressively involved in closely time-linked high frequency discharging. Recent investigations of intracranially recorded EEG involving nonlinear time series analysis techniques indicate that this build up of a critical mass can indeed be tracked over time scales lasting minutes to hours. Future real-time analysis devices may enable both investigations of basic mechanisms leading to seizure initiation in humans and the development of adequate seizure warning and prevention strategies. [Preview Abstract] |
Friday, March 17, 2006 1:39PM - 2:15PM |
Z7.00005: Detection of phase and lag synchrony as an adaptive measure of asymmetric neuronal interactions Invited Speaker: Asymmetric temporal interdependencies between individual neurons and their populations are though to underlie learning and memory formation and can provide information about direction of information transfer in neural systems. We have developed an adaptive measure that detects asymmetries in phase and lag synchrony between activities of individual neurons of synchronized networks. In the first part of the talk I will discuss the properties of the measure on network models of coupled non-linear oscillators and show progression of rapid transitions in temporal patterning in such networks as a function of their topology. In the second part of the talk I will present its application in analysis of normal and pathological neural activity: detection of evolving asymmetry in interactions of hippocampal neurons in freely behaving rats, and characterization of dynamical progression of synchronous seizure-like activity recorded from intact rat hippocampus. [Preview Abstract] |
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