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Resting-state blood-oxygen-level-dependent (BOLD) signal acquired through functional magnetic resonance imaging is a proxy of neural activity and a key mechanism for assessing neurological conditions. Therefore, practical tools to filter out artefacts that can compromise the assessment are required. On the one hand, a variety of tailored methods to preprocess the data to deal with identified sources of noise (e.g., head motion, heart beating, and breathing, just to mention a few) are in place. But, on the other hand, there might be unknown sources of unstructured noise present in the data. Therefore, to mitigate the effects of such unstructured noises, we propose a model-based filter that explores the statistical properties of the underlying signal (i.e., long-term memory). Specifically, we consider autoregressive fractional integrative process filters. Remarkably, we provide evidence that such processes can model the signals at different regions of interest to attain stationarity. Furthermore, we use a principled analysis where a ground-truth signal with statistical properties similar to the BOLD signal under the injection of noise is retrieved using the proposed filters. Next, we considered preprocessed (i.e., the identified sources of noise removed) resting-state BOLD data of 98 subjects from the Human Connectome Project. Our results demonstrate that the proposed filters decrease the power in the higher frequencies. However, unlike the low-pass filters, the proposed filters do not remove all high-frequency information, instead they preserve process-related higher frequency information. Additionally, we considered four different metrics (power spectrum, functional connectivity using the Pearson’s correlation, coherence, and eigenbrains) to infer the impact of such filter. We provided evidence that whereas the first three keep most of the features of interest from a neuroscience perspective unchanged, the latter exhibits some variations that could be due to the sporadic activity filtered out.

A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. This dynamical systems model of the brain offers information on the structure and dimensionality of the BOLD signal’s external drivers and shines a light on the likely external sources contributing to the BOLD signal’s non-stationarity. Our findings show the role of exogenous inputs in the BOLD dynamics and highlight the importance of accounting for external inputs to unravel the brain’s time-varying functional dynamics.

Large-scale climatic indices are extensively used as predictors of ecological processes, but the mechanisms and the spatio-temporal scales at which climatic indices influence these processes are often speculative. Here, we use long-term data to evaluate how a measure of individual breeding investment (the egg volume) of three long-lived and long-distance-migrating seabirds is influenced by i) a large-scale climatic index (the North Atlantic Oscillation) and ii) local-scale variables (food abundance, foraging conditions, and competition). Winter values of the North Atlantic Oscillation did not correlate with local-scale variables measured in spring, but surprisingly, both had a high predictive power of the temporal variability of the egg volume in the three study species, even though they have different life-history strategies. The importance of the winter North Atlantic Oscillation suggests carry-over effects of winter conditions on subsequent breeding investment. Interestingly, the most important local-scale variables measured in spring were associated with food detectability (foraging conditions) and the factors influencing its accessibility (foraging conditions and competition by density-dependence). Large-scale climatic indices may work better as predictors of foraging conditions when organisms perform long distance migrations, while local-scale variables are more appropriate when foraging areas are more restricted (e.g. during the breeding season). Contrary to what is commonly assumed, food abundance does not directly translate into food intake and its detectability and accessibility should be considered in the study of food-related ecological processes.

The ability to control infections is a key trait for migrants that must be balanced against other costly features of the migratory life. In this study we explored the links between migration and disease ecology by examining natural variation in parasite exposure and immunity in several populations of Lesser Black-backed Gulls (Larus fuscus) with different migratory strategies. We found higher activity of natural antibodies in long distance migrants from the nominate subspecies L.f.fuscus. Circulating levels of IgY showed large variation at the population level, while immune parameters associated with antimicrobial activity showed extensive variation at the individual level irrespective of population or migratory strategy. Pathogen prevalence showed large geographical variation. However, the seroprevalence of one of the gull-specific subtypes of avian influenza (H16) was associated to the migratory strategy, with lower prevalence among the long-distance migrants, suggesting that migration may play a role in disease dynamics of certain pathogens at the population level.

Small and peripheral populations are typically vulnerable to local extinction processes but important for the metapopulation dynamics of species. The Slender-billed gull (Chroicocephalus genei) is a long-lived species breeding in unstable ephemeral coastal habitats. Their Western Mediterranean populations are relatively small and represent the edge of their global geographical distribution. At a local scale, using long-term data (14 years) on annual breeding success and capture-resights of marked individuals, we estimated and compared the vital rates and evaluated the connectivity of two Spanish populations (Ebro Delta and Doñana) varying in their local environmental conditions. At a metapopulation scale, we analyzed 22 years of data on breeding numbers to predict their future prospects by means of population demographic models. Local survival and breeding success of gulls from the Ebro Delta was lower than those from Doñana, which is likely the result of higher permanent emigration and/or winter mortality in the former. Gulls from the Ebro Delta wintered mostly in Mediterranean areas whereas those from Doñana did so in Atlantic coasts, where food availability is higher. Whereas adult local survival was constant, juvenile local survival showed temporal parallel variations between colonies, probably related to natal dispersal to other breeding colonies. Our results suggested that dispersal was higher at the Ebro Delta and gulls emigrating from their natal colonies settled preferentially in close patches. We found large fluctuations in breeding numbers among local populations probably related to the fact that the Slender-billed gull is a species adapted to unstable and unpredictable habitats with high abilities to disperse between suitable patches depending on environmental stochastic conditions during breeding.