Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill posed problem that commonly arises when modelling real world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of (ie, hypotheses about) network architectures and implicit coupling functions in terms of their Bayesian model evidence. These methods are collectively referred to as dynamical casual modelling (DCM). We focus on a relatively new approach that is proving remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems

Conservation of the critically endangered frog Telmatobufo bullocki in fragmented temperate forests of Chile : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Conservation Biology at Massey University, Albany, New Zealand

Amphibians are currently facing several threats and are suffering severe population declines and extinction worldwide. Telmatobufo bullocki (Anura: Calyptocephalellidae) is one of the rarest and most endangered amphibian species in Chile's temperate forests. It is the fifth most evolutionarily distinct and globally endangered (EDGE) amphibian in the world, and one of the world's top 100 priority species for conservation (Zoological Society of London, 2011).This stream-breeding frog is micro-endemic to the coastal Nahuelbuta mountain range in central-south Chile (37°C - 38°50' S), a hot-spot for conservation. This area has suffered severe loss and fragmentation of native forest, which has been replaced by extensive commercial plantations of exotic pines and eucalyptus. Despite its potential detrimental effects, the impact of native forest loss on this species has not been studied before. Furthermore, few historical observations exist, and the ecology and behaviour of the species is poorly known. In addition, current status and location of extant populations are uncertain, which makes conservation and targeted habitat protection difficult. Through the use of different approaches and modern conservation tools this thesis aims to make a significant contribution to the conservation of T.bullocki and its habitat. Historical and new locations were surveyed to identify extant populations. A distribution modeling approach (i.e. Maxent) was used to infer the species’ distribution within Nahuelbuta, generate a predictive habitat suitability map, identify important environmental associations, and assess the impact of main environmental threats (i.e. native forest loss, climate change).Field-based research (e.g. surveys, radio-tracking) was done to extend the ecological and behavioural knowledge of the species (e.g. movement patterns and habitat use), and identify critical aquatic and terrestrial habitat for protection (i.e. core habitat). Mitochondrial and specifically developed microsatellite genetic markers were used to measure levels of intra-specific genetic variability, define genetic population structure and connectivity, infer evolutionary history (phylogeography), estimate effective population size and detect demographic changes (e.g. bottlenecks). Finally, a landscape genetics approach was used to relate landscape characteristics to contemporary patterns of gene flow, and identify important landscape features facilitating (i.e. corridors) or hindering (i.e. barriers) genetic connectivity between populations. Telmatobufo bullocki was found in nine basins within Nahuelbuta, including historic and new locations. Presence of T. bullocki was positively related to the amount of native forests in the landscape. However, some populations persist in areas dominated by exotic plantations. Some frogs were found living under mature pine plantation adjacent to native forest, but no frogs were found in core plantation areas.T. bullocki makes extensive use of terrestrial habitat adjacent to breeding streams during the post-breeding season, moving up to 500 m away from streams. A core terrestrial habitat of at least 220 m from streams is proposed for the protection of populations. Population genetics and phylogeography revealed significant population structure. The northernmost and disjunct population of Chivilingo is geographically and genetically isolated from all other sampled populations and was identified as a separate evolutionary significant unit (ESU). The population of Los Lleulles was also identified as a separate management unit, while the remaining populations were grouped into two clusters forming a larger and more connected metaC population. Connectivity within groups was high, suggesting individuals are able to disperse between neighbouring basins. Levels of genetic diversity were not homogeneous, and were lowest at Los Lleulles and highest at Caramávida. Results suggest disjunct populations are at highest risk and should be prioritised for restoration and habitat protection, while management of metaCpopulations should aim at maintaining and improving connectivity among basins. Landscape genetic results identified streams and riparian habitat as dispersal pathways, and least-cost-path analysis was used to identify a potential connectivity network

Attentional Enhancement of Auditory Mismatch Responses: a DCM/MEG Study.

Despite similar behavioral effects, attention and expectation influence evoked responses differently: Attention typically enhances event-related responses, whereas expectation reduces them. This dissociation has been reconciled under predictive coding, where prediction errors are weighted by precision associated with attentional modulation. Here, we tested the predictive coding account of attention and expectation using magnetoencephalography and modeling. Temporal attention and sensory expectation were orthogonally manipulated in an auditory mismatch paradigm, revealing opposing effects on evoked response amplitude. Mismatch negativity (MMN) was enhanced by attention, speaking against its supposedly pre-attentive nature. This interaction effect was modeled in a canonical microcircuit using dynamic causal modeling, comparing models with modulation of extrinsic and intrinsic connectivity at different levels of the auditory hierarchy. While MMN was explained by recursive interplay of sensory predictions and prediction errors, attention was linked to the gain of inhibitory interneurons, consistent with its modulation of sensory precision

A multiscale analysis of gene flow for the New England cottontail, an imperiled habitat specialist in a fragmented landscape

Landscape features of anthropogenic or natural origin can influence organisms\u27 dispersal patterns and the connectivity of populations. Understanding these relationships is of broad interest in ecology and evolutionary biology and provides key insights for habitat conservation planning at the landscape scale. This knowledge is germane to restoration efforts for the New England cottontail (Sylvilagus transitionalis), an early successional habitat specialist of conservation concern. We evaluated local population structure and measures of genetic diversity of a geographically isolated population of cottontails in the northeastern United States. We also conducted a multiscale landscape genetic analysis, in which we assessed genetic discontinuities relative to the landscape and developed several resistance models to test hypotheses about landscape features that promote or inhibit cottontail dispersal within and across the local populations. Bayesian clustering identified four genetically distinct populations, with very little migration among them, and additional substructure within one of those populations. These populations had private alleles, low genetic diversity, critically low effective population sizes (3.2-36.7), and evidence of recent genetic bottlenecks. Major highways and a river were found to limit cottontail dispersal and to separate populations. The habitat along roadsides, railroad beds, and utility corridors, on the other hand, was found to facilitate cottontail movement among patches. The relative importance of dispersal barriers and facilitators on gene flow varied among populations in relation to landscape composition, demonstrating the complexity and context dependency of factors influencing gene flow and highlighting the importance of replication and scale in landscape genetic studies. Our findings provide information for the design of restoration landscapes for the New England cottontail and also highlight the dual influence of roads, as both barriers and facilitators of dispersal for an early successional habitat specialist in a fragmented landscape

A tutorial on group effective connectivity analysis, part 2: second level analysis with PEB

This tutorial provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (effective connectivity). This involves specifying a hierarchical model with two or more levels. At the first level, state space models (DCMs) are used to infer the effective connectivity that best explains a subject's neuroimaging timeseries (e.g. fMRI, MEG, EEG). Subject-specific connectivity parameters are then taken to the group level, where they are modelled using a General Linear Model (GLM) that partitions between-subject variability into designed effects and additive random effects. The ensuing (Bayesian) hierarchical model conveys both the estimated connection strengths and their uncertainty (i.e., posterior covariance) from the subject to the group level; enabling hypotheses to be tested about the commonalities and differences across subjects. This approach can also finesse parameter estimation at the subject level, by using the group-level parameters as empirical priors. We walk through this approach in detail, using data from a published fMRI experiment that characterised individual differences in hemispheric lateralization in a semantic processing task. The preliminary subject specific DCM analysis is covered in detail in a companion paper. This tutorial is accompanied by the example dataset and step-by-step instructions to reproduce the analyses

Landscape genetics reveal broad and fine‐scale population structure due to landscape features and climate history in the northern leopard frog (Rana pipiens) in North Dakota

Prehistoric climate and landscape features play large roles structuring wildlife populations. The amphibians of the northern Great Plains of North America present an opportunity to investigate how these factors affect colonization, migration, and current population genetic structure. This study used 11 microsatellite loci to genotype 1,230 northern leopard frogs (Rana pipiens) from 41 wetlands (30 samples/wetland) across North Dakota. Genetic structure of the sampled frogs was evaluated using Bayesian and multivariate clustering methods. All analyses produced concordant results, identifying a major east–west split between two R. pipiens population clusters separated by the Missouri River. Substructuring within the two major identified population clusters was also found. Spatial principal component analysis (sPCA) and variance partitioning analysis identified distance, river basins, and the Missouri River as the most important landscape factors differentiating R. pipiens populations across the state. Bayesian reconstruction of coalescence times suggested the major east– west split occurred ~13–18 kya during a period of glacial retreat in the northern Great Plains and substructuring largely occurred ~5–11 kya during a period of extreme drought cycles. A range‐wide species distribution model (SDM) for R. pipiens was developed and applied to prehistoric climate conditions during the Last Glacial Maximum (21 kya) and the mid‐Holocene (6 kya) from the CCSM4 climate model to identify potential refugia. The SDM indicated potential refugia existed in South Dakota or further south in Nebraska. The ancestral populations of R. pipiens in North Dakota may have inhabited these refugia, but more sampling outside the state is needed to reconstruct the route of colonization. Using microsatellite genotype data, this study determined that colonization from glacial refugia, drought dynamics in the northern Great Plains, and major rivers acting as barriers to gene flow were the defining forces shaping the regional population structure of R. pipiens in North Dakota