Comparison of Time-lapse GPR and Resistivity over Simulated Clandestine Graves

Forensic geophysics should be an invaluable tool to assist search teams to detect and locate clandestine graves of buried murder victims. At present however, geophysics is under-utilised and currently used techniques may not be optimal for specific targets or sites. There is a need for geophysical datasets to be collected over known burial sites for varying time periods post-burial. A study site was created with a naked and wrapped pig cadaver. The dimensions are based on available statistics of discovered burials. Monthly surveys using resistivity, Electrical Resistivity Tomography (ERT) and Ground Penetrating Radar (GPR) were performed post-burial. Resistivity results show low anomalies over the naked pig and a smaller high anomaly over the wrapped pig with respect to background values. ERT time-lapse data shows optimum survey periods for the naked and wrapped pigs to be 9 and 3 months respectively. GPR 2D profiles detected both burials, with the wrapped pig exhibiting stronger reflection events. Lower frequency (110 MHz) antennae were found to be the optimal frequency to detect pig burials

Using soil and groundwater to understand resistivity surveys over a simulated clandestine grave

Geophysical electrical resistivity surveys have been used in a number of attempts to locate clandestine ‘shallow’ graves, based on the valid assumption that a grave may represent a contrast in the electrical properties of the ground compared to ‘background’ values. However, the exact causes of measurable geophysical signals associated with graves are not well understood, particularly for electrical methods. In this study, soil and groundwater samples have been obtained from a simulated grave containing a domestic pig (Sus domestica) carcass, in order to better understand how the presence of a grave may influence the bulk electrical properties of the soil. This information is used to explain observations based on repeat resistivity surveys over a period of 6 months over a second simulated grave at the same site. An area of low resistivity values was observed at the grave location in the survey data obtained from 4 to 20 weeks post-burial, with the grave being difficult to identify in survey data collected outside of this interval. The low resistivity grave anomaly appeared to be caused by highly conductive fluids released by the actively decomposing carcass and this is consistent with the relatively short timescale during which the grave was detectable. It is then suggested that the most appropriate time to use resistivity surveys in the search for a grave is during the period in which the cadaver is most likely to be undergoing active decomposition. However, other authors have observed low resistivity anomalies over much older graves and it is possible that, for graves in different environments, other factors may contribute to a detectable change in the bulk electrical properties of the soil

Information transmission in oscillatory neural activity

Periodic neural activity not locked to the stimulus or to motor responses is usually ignored. Here, we present new tools for modeling and quantifying the information transmission based on periodic neural activity that occurs with quasi-random phase relative to the stimulus. We propose a model to reproduce characteristic features of oscillatory spike trains, such as histograms of inter-spike intervals and phase locking of spikes to an oscillatory influence. The proposed model is based on an inhomogeneous Gamma process governed by a density function that is a product of the usual stimulus-dependent rate and a quasi-periodic function. Further, we present an analysis method generalizing the direct method (Rieke et al, 1999; Brenner et al, 2000) to assess the information content in such data. We demonstrate these tools on recordings from relay cells in the lateral geniculate nucleus of the cat.Comment: 18 pages, 8 figures, to appear in Biological Cybernetic

Variability Measures of Positive Random Variables

During the stationary part of neuronal spiking response, the stimulus can be encoded in the firing rate, but also in the statistical structure of the interspike intervals. We propose and discuss two information-based measures of statistical dispersion of the interspike interval distribution, the entropy-based dispersion and Fisher information-based dispersion. The measures are compared with the frequently used concept of standard deviation. It is shown, that standard deviation is not well suited to quantify some aspects of dispersion that are often expected intuitively, such as the degree of randomness. The proposed dispersion measures are not entirely independent, although each describes the interspike intervals from a different point of view. The new methods are applied to common models of neuronal firing and to both simulated and experimental data

Investigating large-scale brain dynamics using field potential recordings: Analysis and interpretation

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (EEG, MEG, ECoG and LFP) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide best-practice recommendations for the analyses and interpretations using a forward model and an inverse model. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems