Traditional fMRI utilizes blood oxygenation level dependent (BOLD) contrast to map brain activity. BOLD signal is sensitive to the hemodynamic changes associated with brain activity, and gives an indirect measure of brain activity. Low frequency fluctuations (LFFs) have been observed in the BOLD signal even in the absence of any anesthetic agent, and the correlations between the fluctuations from different brain regions has been used to map functional connectivity in the brain. Most studies involving spontaneous fluctuations in the BOLD signal extract connectivity patterns that show relationships between brain areas that are maintained over the length of the scanning session. The research presented in this document investigates the spatiotemporal dynamics of the BOLD fluctuations to identify common spatiotemporal patterns within a scan. First, the presence of a visually detectable spatiotemporal propagation pattern is demonstrated by utilizing single-slice data with high spatial and temporal resolution. The pattern consists of lateral-medial propagation of BOLD signal, demonstrating the presence of time-varying features in spontaneous BOLD fluctuations. Further, a novel pattern finding algorithm is developed for detecting repeated spatiotemporal patterns in BOLD fMRI data. The algorithm is applied to high temporal resolution T2*-weighted multislice images obtained from rats and humans in the absence of any task or stimulation. In rats, the primary pattern consists of waves of high signal intensity, propagating in a lateral-medial direction across the cortex, replicating the results obtained using visual observation. In humans, the most common spatiotemporal pattern consisted of an alteration between activation of areas comprising the "default-mode" (e.g., posterior cingulate and anterior medial prefrontal cortices) and the "task-positive" (e.g., superior parietal and premotor cortices) networks. Signal propagation from focal starting points is also observed. The pattern finding algorithm is shown to be reasonably insensitive to the variation in user-defined parameters, and the results are consistent within and between subjects. This novel approach for probing the spontaneous network activity of the brain has implications for the interpretation of conventional functional connectivity studies, and may increase the amount of information that can be obtained from neuroimaging data.

L'ancienne mine de plomb et de zinc de Carnoulès (Gard, France) a généré 1.5 Mt de déchets d'où émerge un drainage de mine aux eaux acides alimentant un ruisseau, le Reigous. Ce site fournit un exceptionnel exemple d'adaptation à un environnement extrême en raison des eaux acides (pH~3) et de très fortes concentrations en métaux et métalloïdes, particulièrement en As. Dans les 30 premiers mètres du ruisseau, l'activité bactérienne conduit à un phénomène de remédiation naturelle avec la co-précipitation de 20 à 60% de l'As dissous avec du fer. Les bactéries présentes dans les sédiments du ruisseau ont dans un premier temps été décrites par clonage/séquençage du gène de l'ARNr 16S, puis les membres actifs des communautés bactériennes ont été révélés par une approche de métaprotéomique. L'étude des Archaea au sein des sédiments a révélé la présence de groupes impliqués dans la méthanogénèse ou dans l'oxydation de l'ammoniac qui pourraient participer au cycle du carbone ou de l'azote. Les eucaryotes ont été caractérisés pour la première fois sur ce site par pyroséquençage, mettant en évidence une forte proportion de champignons (60%). Enfin, l'étude des variations spatiotemporelles des populations bactériennes dans les eaux a conduit à l'identification de 6801 OTUs dont des phyla encore jamais identifiés sur ce site. La concentration en arsenic, la température et le potentiel redox semblent jouer un rôle dans la structuration de ces communautés. Ce travail de thèse a ainsi contribué à une meilleure connaissance des microorganismes présents (Bactéries, Archaea, Eucaryotes) et de leurs dynamiques spatiotemporelles en relation avec les paramètres physicochimiques du milieu. / Acidic mining drainage generated at Carnoulès, a former Pb-Zn mine (Gard, France) coincides with the spring of the Reigous Creek. This site provides an exceptional example of adaptation to extreme environments due to its acidic water (pH~3) and very high concentrations of metals and metalloids, particularly arsenic. During the first 30 m of downflow in Reigous Creek, natural remediation occurred, with co-precipitation of 20 to 60% of the dissolved arsenic with iron, mediated by bacteria. Bacterial communities inhabiting the creek sediments were first described by cloning/sequencing of the 16S rRNA genes and the active members were identified by a metaproteomic approach. A survey of the archaeal community in the sediment highlighted the presence of sequences phylogenetically related to methanogenic Archaea and to ammonia oxidizers, which could be involved in carbon and nitrogen biochemical cycling. The Eukaryotic communities were studied for the first time at this site by pyrosequencing, revealing that around 60% of the sequences belonged to Fungi. Finally, the study of the spatiotemporal variations of the water bacterial communities allowed the identification of 6801 OTUs including sequences of taxa never detected before. The environmental variables significantly correlated with bacterial community dynamics appear to be arsenic concentration, temperature and Eh. This PhD work has contributed to a better understanding of the spatiotemporal dynamics of microorganisms (Bacteria, Archaea, Eukaryotes) in relation with the physicochemical parameters of their environment.

Large scale functional brain networks, defined by synchronized spontaneous oscillations between spatially distinct anatomical regions, are essential to brain function and have been implicated in disease states, cognitive capacity, and many sensing and motor processes. In this work, we sever the corpus callosum in the rodent model to determine if structural connectivity (specifically the primary interhemispheric pathway) organizes and influences bilateral functional connectivity and brain-wide spatiotemporal dynamic activity patterns. Prior to the callosotomy work, resting state brain networks were evaluated using blood oxygen level dependent (BOLD) and cerebral blood volume (CBV) magnetic resonance imaging contrast mechanisms, and revealed that BOLD and CBV provide highly similar spatial maps of functional connectivity; however, the amplitude of BOLD connectivity was generally stronger. The effects of extended anesthetic durations on functional connectivity were also evaluated revealing extended isoflurane anesthetic periods prior to the switch to dexmedetomidine attenuates functional activity for a longer duration as compared to a shorter isoflurane paradigm. We also observed a secondary significant evolution of functional metrics occurring during long durations of dexmedetomidine use under the currently accepted and refined dexmedetomidine sedation paradigm. Taking these previous findings into account, we moved forward with the callosotomy study. Functional network integrity was evaluated in sham and full callosotomy groups using BOLD and electrophysiology. Functional connectivity analysis indicated a similar significant reduction in bilateral connectivity in the full callosotomy group as compared to the sham group across both recording modalities. Spatiotemporal dynamic analysis revealed bilaterally symmetric propagating waves of activity in the sham data, but none were present in the full callosotomy data; however, the emergence of unilateral spatiotemporal patterns became prominent following the callosotomy. This finding suggests that the corpus callosum could be largely responsible for maintaining bilateral network integrity, but non-bilaterally symmetric propagating waves occur in the absence of the corpus callosum, suggesting a possible subcortical driver of the dynamic cascading event. This work represents a robust finding indicating the corpus callosum's influence on maintaining integrity in bilateral functional networks.

An extended open system such as traffic flow is said to be convectively unstable if perturbations of the stationary state grow but propagate in only one direction, so they eventually leave the system. By means of data analysis, simulations, and analytical calculations, we give evidence that this concept is relevant for instabilities of congested traffic flow. We analyze detector data from several hundred traffic jams and propose estimates for the linear growth rate, the wavelength, the propagation velocity, and the severity of the associated bottleneck that can be evaluated semi-automatically. Scatter plots of these quantities reveal systematic dependencies. On the theoretical side, we derive, for a wide class of microscopic and macroscopic traffic models, analytical criteria for convective and absolute linear instabilities. Based on the relative positions of the stability limits in the fundamental diagram, we divide these models into five stability classes which uniquely determine the set of possible elementary spatiotemporal patterns in open systems with a bottleneck. Only two classes, both dominated by convective instabilities, are compatible wiqth observations. By means of approximate solutions of convectively unstable systems with sustained localized noise, we show that the observed spatiotemporal phenomena can also be described analytically. The parameters of the analytical expressions can be inferred from observations, and also (analytically) derived from the model equations.

The study of spatiotemporal dynamics of fish populations is important for both stock assessment and fishery management. I explored the impacts of environmental and anthropogenic factors on spatiotemporal patterns of fish populations, and contributed to stock assessment and management by incorporating the inherent spatial structure. Hierarchical models were developed to specify spatial and temporal variations, and Bayesian methods were adopted to fit the models. Yellow perch (Perca flavescens) is one of the most important commercial and recreational fisheries in Lake Erie, which is currently managed using four management units (MUs), with each assessed by a spatially-independent stock-specific assessment model. The current spatially-independent stock-specific assessment assumes that movement of yellow perch among MUs in Lake Erie is statistically negligible and biologically insignificant. I investigated whether the assumption is violated and the effect this assumption has on assessment. I first explored the spatiotemporal patterns of yellow perch abundance in Lake Erie based on data from a 27-year gillnet survey, and analyzed the impacts of environmental factors on spatiotemporal dynamics of the population. I found that yellow perch relative biomass index displayed clear temporal variation and spatial heterogeneity, however the two middle MUs displayed spatial similarities. I then developed a state-space model based on a 7-year tag-recovery data to explore movements of yellow perch among MUs, and performed a simulation analysis to evaluate the impacts of sample size on movement estimates. The results suggested substantial movement between the two stocks in the central basin, and the accuracy and precision of movement estimates increased with increasing sample size. These results demonstrate that the assumption on movements among MUs is violated, and it is necessary to incorporate regional connectivity into stock assessment. I thus developed a tag-integrated multi-region model to incorporate movements into a spatial stock assessment by integrating the tag-recovery data with 45-years of fisheries data. I then compared population projections such as recruitment and abundance derived from the tag-integrated multi-region model and the current spatial-independent stock-specific assessment model to detect the influence of hypotheses on with/without movements among MUs. Differences between the population projections from the two models suggested that the integration of regional stock dynamics has significant influence on stock estimates. American Shad (Alosa sapidissima), Hickory Shad (A. mediocris) and river herrings, including Alewife (A. pseudoharengus) and Blueback Herring (A. aestivalis), are anadromous pelagic fishes that spend most of the annual cycle at sea and enter coastal rivers in spring to spawn. Alosa fisheries were once one of the most valuable along the Atlantic coast, but have declined in recent decades due to pollution, overfishing and dam construction. Management actions have been implemented to restore the populations, and stocks in different river systems have displayed different recovery trends. I developed a Bayesian hierarchical spatiotemporal model to identify the population trends of these species among rivers in the Chesapeake Bay basin and to identify environmental and anthropogenic factors influencing their distribution and abundance. The results demonstrated river-specific heterogeneity of the spatiotemporal dynamics of these species and indicated the river-specific impacts of multiple factors including water temperature, river flow, chlorophyll a concentration and total phosphorus concentration on their population dynamics. Given the importance of these two case studies, analyses to diagnose the factors influencing population dynamics and to develop models to consider spatial complexity are highly valuable to practical fisheries management. Models incorporating spatiotemporal variation describe population dynamics more accurately, improve the accuracy of stock assessments, and would provide better recommendations for management purposes. / Doctor of Philosophy / Many fish populations exhibit complex spatial structure, but the spatial patterns have been incorporated into stock assessment only in few cases. A full understanding of spatial structure of fish populations is needed to better manage the populations. Stock assessment and management strategies should depend on the inherent spatial structure of the target fish population. There have been many approaches developed to analyze spatial structure of fish populations. In this dissertation, I developed quantitative models to analyze fish demographic data and tagging data to explore spatial structure of fish populations. Yellow perch (Perca flavescens) in Lake Erie and Alosa group including American Shad (Alosa sapidissima), Hickory Shad (A. mediocris) and river herrings (Alewife A. pseudoharengus and Blueback Herring A. aestivalis) in selected tributaries of the Chesapeake Bay were taken as examples. Fishery-independent data for yellow perch displayed spatial similarities in the central basin of Lake Erie. Distinct temporal trends were observed in relative abundance data for Alosa sp. in different tributaries of the Chesapeake Bay. Substantial yellow perch movement among the central basin of the Lake was observed in tagging data. Ignoring the inherent spatial structure may cause fish to be overfished in some regions and underfished in others. To maximize the effectiveness of management in all regions for fish populations, I highly recommend incorporating spatial structure into stock assessment and management such as the ones developed in this dissertation.

Conservation of wood-decaying fungi requires improved knowledge about the long-term effects of forest management; regarding habitat loss, fragmentation and fire suppression. To better understand such effects, I examined the influence of area, isolation, fire history and forest stand characteristics on communities of wood-decaying fungi. Species richness and composition were studied along a gradient of 22 forested islands varying in size (0.16 to 17.58 ha) and fire history (spanning 5000 years) in a boreal forest archipelago in northern Sweden. A total of 490 records of 41 polypore species were found in 33 circular plots, each 0.1 ha in size. Species richness and the number of red-listed species were analyzed using generalized linear models (GLMs), while species composition was examined using non-metric multidimensional scaling (NMDS) ordination. The species composition was clearly different between recent-fire (< 300 years since last fire) and old-fire (≥ 300 years since last fire) islands, mirroring the shift in tree species composition as pine-associated fungal species were replaced by spruce-associated fungal species. The volume of logs was the only variable influencing the species richness, although the diversity of logs showed a clear trend of also influencing species richness positively. The results demonstrate the importance of having both recent-fire and old-fire forests as landscape-level habitats and species pools, where fire naturally would constitute a key role for maintaining forest biodiversity in the boreal forest landscape. The results also stress the importance of dead wood for species richness at the individual forest stands.

Orientador: Maria de Lourdes Bueno Trindade Galo / Resumo: O represamento de rios para a produção de energia elétrica usualmente provoca atividades antrópicas que impactam um ecossistema aquático fortemente. Uma das consequências de se instalar pequenos reservatórios em regiões sujeitas à intensos processos de urbanização e industrialização é a abundância de macrófitas, resultante do despejo de nutrientes em grandes concentrações no ecossistema aquático. Recentemente, o grande volume de images multitemporais de sensoriamento remoto disponíveis em bancos de dados gratuitos, bem como a alta performance computacional que permite a mineração de grandes volumes de dados, fazem com que o monitoramento de fenômenos ambientais seja um objeto de estudo recorrente. O propósito desse estudo é desenvolver uma metodologia baseada na integração de dados heterogêneos, fornecidos por séries temporais de coleções de imagens multiespectrais e multitemporais Landsat e coleções de dados climáticos históricos, para investigar a evolução e comportamento espacial de macrófitas aquáticas em lagos e reservatórios eutrofizados. A extensa coleção temporal de imagens de superfície de reflectância Landsat disponível e também dados de variáveis ambientais permitiram a construção e análise de séries temporais para investigar a recorrente abundância de macrófitas no reservatório de Salto Grande, localizado na região metropolitana de Campinas, São Paulo, Brasil. Inicialmente, foi encontrado que as imagens Landsat possuem a qualidade radiométrica necessária para se r... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: River damming for electric power production usually triggers anthropic activities that strongly impact on aquatic ecosystem. One of the consequences of installing small reservoirs in regions subject to an intense process of urbanization and industrialization is the overabundance of macrophytes, resulting from the input of nutrients in high concentration into the aquatic ecosystem. Currently, the large volume of multitemporal remote sensing images available in open data sources, as well as the high computational performance that allow the mining of large volumes of data has made the monitoring of environmental phenomena a recurrent object of analysis. The aim of this study is to develop a methodology based on the integration of heterogeneous data, provided by time series of multispectral and multitemporal Landsat images and collections of historical climatic data, to investigate the evolution and spatial behavior of aquatic macrophytes in lakes and eutrophic reservoirs. So, the extensive temporal collection of the Landsat surface reflectance images made available as well as environmental variables data permitted the construction and analysis of time series to investigate the recurrent over-abundance of macrophytes in Salto Grande reservoir, located in the metropolitan region of Campinas, São Paulo, Brazil. Initially, it was found that the the Landsat images have the necessary radiometric quality to perform the time series analyses, through an assessment based on information ab... (Complete abstract click electronic access below) / Doutor

While task-based functional magnetic resonance imaging (fMRI) has helped us understand the functional role of many regions in the human brain, many diseases and complex behaviors defy explanation. Alternatively, if no task is performed, the fMRI signal between distant, anatomically connected, brain regions is similar over time. These correlations in “resting state” fMRI have been strongly linked to behavior and disease. Previous work primarily calculated correlation in entire fMRI runs of six minutes or more, making understanding the neural underpinnings of these fluctuations difficult. Recently, coordinated dynamic activity on shorter time scales has been observed in resting state fMRI: correlation calculated in comparatively short sliding windows and quasi-periodic (periodic but not constantly active) spatiotemporal patterns. However, little relevance to behavior or underlying neural activity has been demonstrated. This dissertation addresses this problem, first by using 12.3 second windows to demonstrate a behavior-fMRI relationship previously only observed in entire fMRI runs. Second, simultaneous recording of fMRI and electrical signals from the brains of anesthetized rats is used to demonstrate that both types of dynamic activity have strong correlates in electrophysiology. Very slow neural signals correspond to the quasi-periodic patterns, supporting the idea that low-frequency activity organizes large scale information transfer in the brain. This work both validates the use of dynamic analysis of resting state fMRI, and provides a starting point for the investigation of the systemic basis of many neuropsychiatric diseases.