A Bayesian changepoint analysis on spatio-temporal point processes

Changepoint analysis is a well established area of statistical research, but in the context of spatio-temporal point processes it is as yet relatively unexplored. Some substantial differences with regard to standard changepoint analysis have to be taken into account: firstly, at every time point the datum is an irregular pattern of points; secondly, in real situations issues of spatial dependence between points and temporal dependence within time segments raise. Our motivating example consists of data concerning the monitoring and recovery of radioactive particles from Sandside beach, North of Scotland; there have been two major changes in the equipment used to detect the particles, representing known potential changepoints in the number of retrieved particles. In addition, offshore particle retrieval campaigns are believed may reduce the particle intensity onshore with an unknown temporal lag; in this latter case, the problem concerns multiple unknown changepoints. We therefore propose a Bayesian approach for detecting multiple changepoints in the intensity function of a spatio-temporal point process, allowing for spatial and temporal dependence within segments. We use Log-Gaussian Cox Processes, a very flexible class of models suitable for environmental applications that can be implemented using integrated nested Laplace approximation (INLA), a computationally efficient alternative to Monte Carlo Markov Chain methods for approximating the posterior distribution of the parameters. Once the posterior curve is obtained, we propose a few methods for detecting significant change points. We present a simulation study, which consists in generating spatio-temporal point pattern series under several scenarios; the performance of the methods is assessed in terms of type I and II errors, detected changepoint locations and accuracy of the segment intensity estimates. We finally apply the above methods to the motivating dataset and find good and sensible results about the presence and quality of changes in the process.

Temporal variation of genetic diversity and differentiation of albacore, Thunnus alalunga, in the Mediterranean Sea

This study is on albacore (Thunnus alalunga, Bonnaterre 1788), an epi- and mesopelagic oceanic tuna species cosmopolitan in the tropical and temperate waters of all oceans including the Mediterranean Sea, extending in a broad band between 40°N and 40°S. What it’s known about albacore population structure is based on different studies that used fisheries data, RFLP, mtDNA control region and nuDNA markers, blood lectins analysis, individual tags and microsatellite. At the moment, for T. alalunga six management units are recognized: the North Pacific, South Pacific, Indian, North Atlantic, South Atlantic and Mediterranean stocks. In this study I have done a temporal and spatial comparison of genetic variability between different Mediterranean populations of Thunnus alalunga matching an historical dataset ca. from 1920s composed of 43 individuals divided in 3 populations (NADR, SPAIN and CMED) with a modern dataset composed of 254 individuals and 7 populations (BAL, CYP, LIG, TYR, TUR, ADR, ALB). The investigation was possible using a panel of 94 nuclear SNPs, built specifically for the target species at the University of Basque Country UPV/EHU. First analysis done was the Hardy-Weinberg, then the number of clusters (K) was determined using STRUCTURE and to assess the genetic variability, allele frequencies, the average number of alleles per locus, expected (He) and observed (Ho) heterozygosis, and the index of polymorphism (P) was used the software Genetix. Historical and modern samples gives different results, showing a clear loss of genetic diversity over time leading to a single cluster in modern albacore instead of the two found in historical samples. What this study reveals is very important for conservation concerns, and additional research endeavours are needed.

Spatio-temporal diversity of Megistozoobenthos in the Antalya Gulf and relationships with environmental features

Antalya Gulf is situated in the Levantine Sea, the second biggest and most eastern basin in the Mediterranean Sea. This area is an ultra-oligotrophic basin, strongly affected by anthropogenic inputs, in particular in the fishing areas. For this characteristic, in the Levantine Sea, there is a strong pressure on the natural resources and benthic assemblages. Furthermore, many alien species enter from Suez Canal and are well established in the area. All these pressures are leading to a degradation of the Levantine Sea. For this reason it is important to have tools to study and monitoring the functioning of the marine ecosystem. Benthic organisms are superior to many other biological groups for their response to environmental stresses. The variability of benthic assemblages on a site can reflect, in an integrative mode, the entire functioning of the marine ecosystem. In this study, that wants to analyze the spatial and temporal distribution of the benthic macrofaunal assemblages of Antalya Gulf, 90 benthic species divided in 8 taxa (Annelida, Cnidaria, Echinodermata, Echiura, Mollusca, Porifera, Sipunculida and Tunicata) were found. All the analyses conducted on the entire benthic class and later on Mollusca and Echinodermata separately highlighted the importance of depth on structuring benthic community.

Analyzing spatio-temporal patterns of genuine cross-correlations

In multivariate time series analysis, the equal-time cross-correlation is a classic and computationally efficient measure for quantifying linear interrelations between data channels. When the cross-correlation coefficient is estimated using a finite amount of data points, its non-random part may be strongly contaminated by a sizable random contribution, such that no reliable conclusion can be drawn about genuine mutual interdependencies. The random correlations are determined by the signals' frequency content and the amount of data points used. Here, we introduce adjusted correlation matrices that can be employed to disentangle random from non-random contributions to each matrix element independently of the signal frequencies. Extending our previous work these matrices allow analyzing spatial patterns of genuine cross-correlation in multivariate data regardless of confounding influences. The performance is illustrated by example of model systems with known interdependence patterns. Finally, we apply the methods to electroencephalographic (EEG) data with epileptic seizure activity.

Single-shot electron bunch length measurements using a spatial electro-optical autocorrelation interferometer

A spatial, electro-optical autocorrelation (EOA) interferometer using the vertically polarized lobes of coherent transition radiation (CTR) has been developed as a single-shot electron bunch length monitor at an optical beam port downstream the 100 MeV preinjector LINAC of the Swiss Light Source. This EOA monitor combines the advantages of step-scan interferometers (high temporal resolution) [D. Mihalcea et al., Phys. Rev. ST Accel. Beams 9, 082801 (2006) and T. Takahashi and K. Takami, Infrared Phys. Technol. 51, 363 (2008)] and terahertz-gating technologies [U. Schmidhammer et al., Appl. Phys. B: Lasers Opt. 94, 95 (2009) and B. Steffen et al., Phys. Rev. ST Accel. Beams 12, 032802 (2009)] (fast response), providing the possibility to tune the accelerator with an online bunch length diagnostics. While a proof of principle of the spatial interferometer was achieved by step-scan measurements with far-infrared detectors, the single-shot capability of the monitor has been demonstrated by electro-optical correlation of the spatial CTR interference pattern with fairly long (500 ps) neodymium-doped yttrium aluminum garnet (Nd:YAG) laser pulses in a ZnTe crystal. In single-shot operation, variations of the bunch length between 1.5 and 4 ps due to different phase settings of the LINAC bunching cavities have been measured with subpicosecond time resolution.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.

Spatio-temporal credit assignment in population learning

We present a model for plasticity induction in reinforcement learning which is based on a cascade of synaptic memory traces. In the cascade of these so called eligibility traces presynaptic input is first corre lated with postsynaptic events, next with the behavioral decisions and finally with the external reinforcement. A population of leaky integrate and fire neurons endowed with this plasticity scheme is studied by simulation on different tasks. For operant co nditioning with delayed reinforcement, learning succeeds even when the delay is so large that the delivered reward reflects the appropriateness, not of the immediately preceeding response, but of a decision made earlier on in the stimulus - decision sequence . So the proposed model does not rely on the temporal contiguity between decision and pertinent reward and thus provides a viable means of addressing the temporal credit assignment problem. In the same task, learning speeds up with increasing population si ze, showing that the plasticity cascade simultaneously addresses the spatial problem of assigning credit to the different population neurons. Simulations on other task such as sequential decision making serve to highlight the robustness of the proposed sch eme and, further, contrast its performance to that of temporal difference based approaches to reinforcement learning.

Spatial and functional relationships between air conduits and blood capillaries in the pulmonary gas exchange tissue of adult and developing chickens

The documented data regarding the three-dimensional structure of the air capillaries (ACs), the ultimate sites of gas exchange in the avian lung is contradictory. Further, the mode of gas exchange, described as cross-current has not been clearly elucidated. We studied the temporal and spatial arrangement of the terminal air conduits of the chicken lung and their relationship with the blood capillaries (BCs) in embryos as well as the definitive architecture in adults. Several visualization techniques that included corrosion casting, light microscopy as well as scanning and transmission electron microscopy were used. Two to six infundibulae extend from each atrium and give rise to numerous ACs that spread centrifugally. Majority of the ACs are tubular structures that give off branches, which anastomose with their neighboring cognates. Some ACs have globular shapes and a few are blind-ending tapering tubes. During inauguration, the luminal aspects of the ACs are characterized by numerous microvillus-like microplicae, which are formed during the complex processes of cell attenuation and canalization of the ACs. The parabronchial exchange BCs, initially inaugurated as disorganized meshworks, are reoriented via pillar formation to lie predominantly orthogonal to the long axes of the ACs. The remodeling of the retiform meshworks by intussusceptive angiogenesis essentially accomplishes a cross-current system at the gas exchange interface in the adults, where BCs form ring-like patterns around the ACs, thus establishing a cross-current system. Our findings clarify the mode of gas exchange in the parabronchial mantle and illuminate the basis for the functional efficiency of the avian lung.

Socio-Spatial Relationships in Dairy Cows

Farm animals may serve as models for evaluating social networks in a controlled environment. We used an automated system to track, at fine temporal and spatial resolution (once per minute, +/- 50 cm) every individual in six herds of dairy cows (Bos taurus). We then analysed the data using social network analyses. Relationships were based on non-random attachment and avoidance relationships in respect to synchronous use and distances observed in three different functional areas (activity, feeding and lying). We found that neither synchrony nor distance between cows was strongly predictable among the three functional areas. The emerging social networks were tightly knit for attachment relationships and less dense for avoidance relationships. These networks loosened up from the feeding and lying area to the activity area, and were less dense for relationships based on synchronicity than on median distance with respect to node degree, relative size of the largest cluster, density and diameter of the network. In addition, synchronicity was higher in dyads of dairy cows that had grown up together and shared their last dry period. This last effect disappeared with increasing herd size. Dairy herds can be characterized by one strongly clustered network including most of the herd members with many non-random attachment and avoidance relationships. Closely synchronous dyads were composed of cows with more intense previous contact. The automatic tracking of a large number of individuals proved promising in acquiring the data necessary for tackling social network analyses.

Spatio-Temporal Credit Assignment in Neuronal Population Learning

n learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.

Coastal upwelling is linked to temporal genetic variability in the acorn barnacle Balanus glandula

Dispersal and recruitment are central processes that shape the geographic and temporal distributions of populations of marine organisms. However, significant variability in factors such as reproductive output, larval transport, survival, and settlement success can alter the genetic identity of recruits from year to year. We designed a temporal and spatial sampling protocol to test for genetic heterogeneity among adults and recruits from multiple time points along a similar to 400 km stretch of the Oregon (USA) coastline. In total, 2824 adult and recruiting Balanus glandula were sampled between 2001 and 2008 from 9 sites spanning the Oregon coast. Consistent with previous studies, we observed high mitochondrial DNA diversity at the cytochrome oxidase I locus (884 unique haplotypes) and little to no spatial genetic population structure among the 9 sites (Phi(ST) = 0.00026, p = 0.170). However, subtle but significant temporal shifts in genetic composition were observed among year classes (Phi(ST) = 0.00071, p = 0.035), and spatial Phi(ST) varied from year to year. These temporal shifts in genetic structure were correlated with yearly differences in the strength of coastal upwelling (p = 0.002), with greater population structure observed in years with weaker upwelling. Higher levels of barnacle settlement were also observed in years with weaker upwelling (p < 0.001). These data suggest the hypothesis that low upwelling intensity maintains more local larvae close to shore, thereby shaping the genetic structure and settlement rate of recruitment year classes.

Widespread grey matter changes and hemodynamic correlates to interictal epileptiform discharges in pharmacoresistant mesial temporal epilepsy

Focal onset epilepsies most often occur in the temporal lobes. To improve diagnosis and therapy of patients suffering from pharmacoresistant temporal lobe epilepsy it is highly important to better understand the underlying functional and structural networks. In mesial temporal lobe epilepsy (MTLE) widespread functional networks are involved in seizure generation and propagation. In this study we have analyzed the spatial distribution of hemodynamic correlates (HC) to interictal epileptiform discharges on simultaneous EEG/fMRI recordings and relative grey matter volume (rGMV) reductions in 10 patients with MTLE. HC occurred beyond the seizure onset zone in the hippocampus, in the ipsilateral insular/operculum, temporo-polar and lateral neocortex, cerebellum, along the central sulcus and bilaterally in the cingulate gyrus. rGMV reductions were detected in the middle temporal gyrus, inferior temporal gyrus and uncus to the hippocampus, the insula, the posterior cingulate and the anterior lobe of the cerebellum. Overlaps between HC and decreased rGMV were detected along the mesolimbic network ipsilateral to the seizure onset zone. We conclude that interictal epileptic activity in MTLE induces widespread metabolic changes in functional networks involved in MTLE seizure activity. These functional networks are spatially overlapping with areas that show a reduction in relative grey matter volumes.

Point Process Methodology for On-line Spatio-temporal Disease Surveillance

The AEGISS (Ascertainment and Enhancement of Gastrointestinal Infection Surveillance and Statistics) project aims to use spatio-temporal statistical methods to identify anomalies in the space-time distribution of non-specific, gastrointestinal infections in the UK, using the Southampton area in southern England as a test-case. In this paper, we use the AEGISS project to illustrate how spatio-temporal point process methodology can be used in the development of a rapid-response, spatial surveillance system. Current surveillance of gastroenteric disease in the UK relies on general practitioners reporting cases of suspected food-poisoning through a statutory notification scheme, voluntary laboratory reports of the isolation of gastrointestinal pathogens and standard reports of general outbreaks of infectious intestinal disease by public health and environmental health authorities. However, most statutory notifications are made only after a laboratory reports the isolation of a gastrointestinal pathogen. As a result, detection is delayed and the ability to react to an emerging outbreak is reduced. For more detailed discussion, see Diggle et al. (2003). A new and potentially valuable source of data on the incidence of non-specific gastro-enteric infections in the UK is NHS Direct, a 24-hour phone-in clinical advice service. NHS Direct data are less likely than reports by general practitioners to suffer from spatially and temporally localized inconsistencies in reporting rates. Also, reporting delays by patients are likely to be reduced, as no appointments are needed. Against this, NHS Direct data sacrifice specificity. Each call to NHS Direct is classified only according to the general pattern of reported symptoms (Cooper et al, 2003). The current paper focuses on the use of spatio-temporal statistical analysis for early detection of unexplained variation in the spatio-temporal incidence of non-specific gastroenteric symptoms, as reported to NHS Direct. Section 2 describes our statistical formulation of this problem, the nature of the available data and our approach to predictive inference. Section 3 describes the stochastic model. Section 4 gives the results of fitting the model to NHS Direct data. Section 5 shows how the model is used for spatio-temporal prediction. The paper concludes with a short discussion.

How does spatial extent of fMRI datasets affect independent component analysis decomposition?

Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to different spatial or temporal readouts of brain activation phenomena. By reducing and increasing a volume of interest (VOI), we applied sICA to different datasets from real activation experiments with multislice acquisition and single or multiple sensory-motor task-induced blood oxygenation level-dependent (BOLD) signal sources with different spatial and temporal structure. Using receiver operating characteristics (ROC) methodology for accuracy evaluation and multiple regression analysis as benchmark, we compared sICA decompositions of reduced and increased VOI fMRI time-series containing auditory, motor and hemifield visual activation occurring separately or simultaneously in time. Both approaches yielded valid results; however, the results of the increased VOI approach were spatially more accurate compared to the results of the decreased VOI approach. This is consistent with the capability of sICA to take advantage of extended samples of statistical observations and suggests that sICA is more powerful with extended rather than reduced VOI datasets to delineate brain activity.