Connectivity measures applied to human brain electrophysiological data.

Connectivity measures are (typically bivariate) statistical measures that may be used to estimate interactions between brain regions from electrophysiological data. We review both formal and informal descriptions of a range of such measures, suitable for the analysis of human brain electrophysiological data, principally electro- and magnetoencephalography. Methods are described in the space-time, space-frequency, and space-time-frequency domains. Signal processing and information theoretic measures are considered, and linear and nonlinear methods are distinguished. A novel set of cross-time-frequency measures is introduced, including a cross-time-frequency phase synchronization measure.

Inferring spatiotemporal network patterns from intracranial EEG data.

OBJECTIVE: The characterization of spatial network dynamics is desirable for a better understanding of seizure physiology. The goal of this work is to develop a computational method for identifying transient spatial patterns from intracranial electroencephalographic (iEEG) data. METHODS: Starting with bivariate synchrony measures, such as phase correlation, a two-step clustering procedure is used to identify statistically significant spatial network patterns, whose temporal evolution can be inferred. We refer to this as the composite synchrony profile (CSP) method. RESULTS: The CSP method was verified with simulated data and evaluated using ictal and interictal recordings from three patients with intractable epilepsy. Application of the CSP method to these clinical iEEG datasets revealed a set of distinct CSPs with topographies consistent with medial temporal/limbic and superior parietal/medial frontal networks thought to be involved in the seizure generation process. CONCLUSIONS: By combining relatively straightforward multivariate signal processing techniques, such as phase synchrony, with clustering and statistical hypothesis testing, the methods we describe may prove useful for network definition and identification. SIGNIFICANCE: The network patterns we observe using the CSP method cannot be inferred from direct visual inspection of the raw time series data, nor are they apparent in voltage-based topographic map sequences.

Hidden Markov modelling of spike propagation from interictal MEG data.

For patients with partial epilepsy, automatic spike detection techniques applied to interictal MEG data often discover several potentially epileptogenic brain regions. An important determination in treatment planning is which of these detected regions are most likely to be the primary sources of epileptogenic activity. Analysis of the patterns of propagation activity between the detected regions may allow for detection of these primary epileptic foci. We describe the use of hidden Markov models (HMM) for estimation of the propagation patterns between several spiking regions from interictal MEG data. Analysis of the estimated transition probability matrix allows us to make inferences regarding the propagation pattern of the abnormal activity and determine the most likely region of its origin. The proposed HMM paradigm allows for a simple incorporation of the spike detector specificity and sensitivity characteristics. We develop bounds on performance for the case of perfect detection. We also apply the technique to simulated data sets in order to study the robustness of the method to the non-ideal specificity-sensitivity characteristics of the event detectors and compare results with the lower bounds. Our study demonstrates robustness of the proposed technique to event detection errors. We conclude with an example of the application of this method to a single patient.

Regional resolving power of combined MEG/EEG.

Different modeling frameworks (such as error analyses for dipole localization [Fuchs, 1998] [Huizenga, 2001]; crosstalk and point spread analyses for linear estimators [Liu, 2002]; etc.) have demonstrated improved three-dimensional (3D) resolution for combined MEG/EEG (or EMEG) source estimation. Complementary to these, an empirical analysis of 2D surface data suggested that MEG and EEG information content could be superadditive [Pflieger, 2000]. Taking a hybrid approach in the present study, we made simulations within a regional activity estimation (REGAE, [Pflieger, 2001]) framework, which quantifies the ability of EMEG to discriminate brain activity originating within a 3D region of interest (ROI) from simultaneous non-ROI activity. Two metrics were employed: Kullback-Leibler divergence (KLD) and area under the receiver operator characteristic curve (AUROC). High-density sensor configurations (248 magnetometers, 256 electrodes) were combined with a gray matter source space model (7931 dipole triples, maximum entropy activities), assuming magnetic 3-shell sphere and electric BEM head models. Superadditive KLD was observed frequently across 89 representative brain ROIs and 3 ROI sizes (5, 10, and 15 mm radii), especially for regions already fairly visible to each modality. We also report an observed functional relationship between AUROC and KLD.

The structure of the voltage-sensitive sodium channel. Inferences derived from computer-aided analysis of the Electrophorus electricus channel primary structure.

A variety of computer-aided analyses was applied to the recently derived amino acid sequence of the Electrophorus electricus sodium channel protein in order to extract structural information such as hydrophobicity, periodicity, and secondary structure predictors. We propose a schematic model for the arrangement and folding of the polypeptide chain within the bilayer. The model consists of 4 homologous regions, each containing 8 membrane-spanning (probably alpha-helical) structures. Several of these structures are amphipathic with a repeat of 3.5 residues, 4 of which (one from each homologous region) are postulated to form a negatively charged channel lining. Gating currents are proposed to arise from voltage-dependent separation of multiple ion pairs buried within the hydrophobic, intramembranous protein interior.

Adapting lights and lowered extracellular free calcium desensitize toad photoreceptors by differing mechanisms.

Extracellular recordings were made across the outer segment layer of isolated, superfused toad retinas. Under these recording conditions, the photovoltage reflects primarily the current flowing through the outer-segment membrane of red rods. In normal toad Ringer solution, a dim conditioning flash desensitized a test flash response. The desensitization reached a peak 1.8-2.0 s after the conditioning flash and then declined approximately as an exponential with time constant 6 s. Lowered extracellular calcium, [Ca2+]o, desensitized the photoresponse. It required approximately ten times more light to reach a half-maximal response for each ten-fold change in [Ca2+]o from 10(-6) to 10(-9) M. When [Ca2+]o was less than 10(-7) M, substitution of Li+ for Na+ as the predominant monovalent cation in the superfusate permitted responses to continue and a resensitization of up to approximately 1 log unit was observed. The effects of lowered [Ca2+]o on response kinetics were markedly different from the effects of background lights producing a comparable desensitization. Low [Ca2+]o increased absolute latency and time-to-peak of the flash response. Background lights decreased time-to-peak, leaving latency unchanged. The effects of background lights and lowered [Ca2+]o are not additive. Moderate backgrounds had little effect on the intensity/response function in low [Ca2+]o. Conditioning flashes facilitated the test flash response in 10(-7) M-[Ca2+]o superfusate. These results can be understood in terms of the Ca2+ hypothesis of transduction (Hagins & Yoshikami, 1974) if it is assumed that lowered [Ca2+]o exposes an endogenous Ca2+ buffer. The data also provide evidence for a role of Na+/Ca2+ exchange in regulating intracellular Ca2+ concentration in the toad photoreceptor. A quantitative model based on these assumptions is derived and compared with the experimental data.