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Here, we test three often proposed hypotheses about socioeconomic status (SES), affect, and the brain, for which evidence is mixed or lacking. The first hypothesis, that negative affect is more common at lower levels of SES, has ample evidence from studies of psychiatric symptoms but is tested for the first time here across multiple measures of negative emotions in healthy young adults. The second hypothesis is actually a set of hypotheses, that SES is associated with three structural and functional properties of the amygdala. Third, and most important for the affective neuroscience of SES, is the hypothesis that SES differences in the amygdala are responsible for the affective differences. Despite the intuitive appeal of this hypothesis, it has rarely been tested and has never been confirmed. Here, we review the literature for evidence on each of these hypotheses and find in a number of cases that the evidence is weak or nonexistant. We then subject each hypothesis to a new empirical test with a large sample of healthy young adults. We confirm that negative affect is more common at lower levels of SES and we find a positive relation between SES and amygdala volume. However, evidence is weak on the relation of SES to functional properties of amygdala. Finally, the tendency toward negative affect in lower SES individuals cannot be accounted for by the structural or functional characteristics of the amygdala measured here.

The white matter architecture of the human brain undergoes substantial development throughout childhood and adolescence, allowing for more efficient signaling between brain regions that support executive function. Increasingly, the field understands grey matter development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. While white matter development also appears asynchronous, previous studies have largely relied on anatomical atlases to characterize white matter tracts, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Here, we leveraged advances in diffusion modeling and unsupervised machine learning to delineate white matter fiber covariance networks comprised of structurally similar areas of white matter in a cross-sectional sample of 939 youth aged 8-22 years. We then evaluated associations between fiber covariance network structural properties with both age and executive function using generalized additive models. The identified fiber covariance networks aligned with the known architecture of white matter while simultaneously capturing novel spatial patterns of coordinated maturation. Fiber covariance networks showed heterochronous increases in fiber density and cross section that generally followed hierarchically organized temporal patterns of cortical development, with the greatest increases in unimodal sensorimotor networks and the most prolonged increases in superior and anterior transmodal networks. Notably, we found that executive function was associated with structural features of limbic and association networks. Taken together, this study delineates data-driven patterns of white matter network development that support cognition and align with major axes of brain maturation.

Fil: Pérez Méndez, Néstor. Institut de Recerca i Tecnologia Agroalimentàries. Sustainable Field Crops Program. España. Fil: Alcaraz, Carles. Institut de Recerca i Tecnologia Agroalimentàries. Marine and Continental Waters Program. España. Fil: Bertolero, Albert. Associació Ornitológica Picampall de les Terres de l'Ebre. España. Fil: Català Forner, Mar. Sustainable Field Crops Program. España. Fil: Garibaldi, Lucas Alejandro. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina. Fil: Garibaldi, Lucas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina. Fil: González Varo, Juan P. Universidad de Cádiz. IVAGRO. Departamento de Biología. España. Fil: Rivaes, Sofía. SEO/Birdlife. España. Fil: Martínez Eixarch, Maite. Institut de Recerca i Tecnologia Agroalimentàries. Marine and Continental Waters Program. España. Direct consequences of biological invasions on biodiversity and the environment have been largely documented. Yet collateral indirect effects mediated by changes in agri-environmental policies aimed at combating invasions remain little explored. Here we assessed the effects of recent changes in water management in rice farming, which are aimed at buffering the impact of the invasive apple snail (Pomacea maculata, Lamarck) on greenhouse gas emissions and diversity of waterbird communities. We used observational data from a 2-year field monitoring (2015–2016) performed at the Ebro Delta regional scale. We found that drying rice fields reduced methane emission rates by 82% (2015) and 51% (2016), thereby reducing the contribution of rice farming to climate change. However, there was a marked reduction (75% in 2015 and 57% in 2016) in waterbird diversity in dry fields compared with flooded fields, thus suggesting that post-invasion policies might hinder biodiversity conservation. Our results highlight the need for accounting for potential collateral effects during the policy decision-making process to design efficient agricultural management plans that lessen undesirable agri-environmental outcomes.

Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain’s functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive DevelopmentSMStudy. Across matched discovery (n=3,525) and replication (n=3,447) samples, the total cortical representation of fronto-parietal PFNs positively correlated with general cognition. Cross-validated ridge regressions trained on PFN topography predicted cognition across domains, with prediction accuracy increasing along the cortex’s sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.

Este artículo contiene 9 páginas, 2 tablas, 2 figuras., Age drives diferences in ftness components typically due to lower performances of younger and senescent individuals, and changes in breeding age structure infuence population dynamics and persistence. However, determining age and age structure is challenging in most species, where distinctive age features are lacking and available methods require substantial eforts or invasive procedures. Here we explore the potential to assess the age of breeders, or at least to identify young and senescent individuals, by measuring some breeding parameters partially driven by age (e.g. egg volume in birds). Taking advantage of a long-term population monitored seabird, we frst assessed whether age infuenced egg volume, and identifed other factors driving this trait by using general linear models. Secondly, we developed and evaluated a machine learning algorithm to assess the age of breeders using measurable variables. We confrmed that both younger and older individuals performed worse (less and smaller eggs) than middle-aged individuals. Our ensemble training algorithm was only able to distinguish young individuals, but not senescent breeders. We propose to test the combined use of feld monitoring, classic regression analysis and machine learning methods in other wild populations were measurable breeding parameters are partially driven by age, as a possible tool for assessing age structure in the wild., Funding came partially from the Spanish Ministry of Science (CGL2017-85210) (MICINN/FEDER, UE), MG was partially supported by the European Union (MINOUW Project, H2020-634495) and KK was partially supported by the Erasmus+ programme of the European Union.

Cortical arealization arises during neurodevelopment from the confluence of molecular gradients representing patterned expression of morphogens and transcription factors. However, how these gradients relate to adult brain function, and whether they are maintained in the adult brain, remains unknown. Here we uncover three axes of topographic variation in gene expression in the adult human brain that specifically capture previously identified rostral-caudal, dorsal-ventral and medial-lateral axes of early developmental patterning. The interaction of these spatiomolecular gradients i) accurately predicts the location of unseen brain tissue samples, ii) delineates known functional territories, and iii) explains the topographical variation of diverse cortical features. The spatiomolecular gradients are distinct from canonical cortical functional hierarchies differentiating primary sensory cortex from association cortex, but radiate in parallel with the axes traversed by local field potentials along the cortex. We replicate all three molecular gradients in three independent human datasets as well as two non-human primate datasets, and find that each gradient shows a distinct developmental trajectory across the lifespan. The gradients are composed of several well known morphogens (e.g., PAX6 and SIX3), and a small set of genes shared across gradients are strongly enriched for multiple diseases. Together, these results provide insight into the developmental sculpting of functionally distinct brain regions, governed by three robust transcriptomic axes embedded within brain parenchyma.

Animal studies of neurodevelopmental plasticity have shown that intrinsic brain activity evolves from high amplitude and globally synchronized to suppressed and sparse as plasticity declines and the cortex matures. Leveraging resting-state functional MRI data from 1033 individuals (8-23 years), we reveal that this stereotyped refinement of intrinsic activity occurs during human development and provides evidence for a cortical gradient of neurodevelopmental plasticity during childhood and adolescence. Specifically, we demonstrate that declines in the amplitude of intrinsic activity are initiated heterochronously across regions, coupled to the maturation of a plasticity-restricting structural feature, and temporally staggered along a hierarchical sensorimotor-association axis from ages 8 to 18. Youth from disadvantaged environments exhibit reduced intrinsic activity in regions further up the sensorimotor-association axis, suggestive of a reduced level of plasticity in late-maturing cortices. Our results uncover a hierarchical axis of neurodevelopment and offer insight into the temporal sequence of protracted neurodevelopmental plasticity in humans.

SummaryMany scientific questions can be formulated as hypotheses about conditional correlations. For instance, in tests of cognitive and physical performance, the trade-off between speed and accuracy motivates study of the two variables together. A natural question is whether speed-accuracy coupling depends on other variables, such as sustained attention. Classical regression techniques, which posit models in terms of covariates and outcomes, are insufficient to investigate the effect of a third variable on the symmetric relationship between speed and accuracy. In response, we propose CoCoA (Conditional Correlation Model with Association Size), a likelihood-based statistical framework to estimate the conditional correlation between speed and accuracy as a function of additional variables. We propose novel measures of the association size, which are analogous to effect sizes on the correlation scale, while adjusting for confound variables. In simulation studies, we compare likelihood-based estimators of conditional correlation to semi-parametric estimators adapted from genome association studies, and find that the former achieves lower bias and variance under both ideal settings and model assumption misspecification. Using neurocognitive data from the Philadelphia Neurodevelopmental Cohort, we demonstrate that greater sustained attention is associated with stronger speed-accuracy coupling in a complex reasoning task while controlling for age. By highlighting conditional correlations as the outcome of interest, our model provides complementary insights to traditional regression modelling and partitioned correlation analyses.

Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy ( P < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography.

Treball de fi de grau d'Humanitats. Curs 2021-2022 Tutora: Marició Janué i Miret

Hierarchical processing requires activity propagating between higher and lower-order cortical areas. However, studies of brain development have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leveraged advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n=388). We found that propagations robustly ascend and descend the cortical hierarchy, and that top-down propagations become both more prevalent with cognitive control demands and with development in youth.

SUMMARYThe brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derived personalized functional networks across 29 scales in a large sample of youths (n=693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling systematically adhered to and strengthened a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks dissociably mediated the emergence of executive function. These results delineate maturation of multi-scale brain networks, which varies according to a functional hierarchy and impacts cognitive development.

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled “Curation of BIDS” (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad––a version control software package for data––to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images’ metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.HIGHLIGHTSCuBIDS is a workflow and software package for curating BIDS data.CuBIDS summarizes the heterogeneity in a BIDS dataset.CuBIDS prepares BIDS data for successful preprocessing pipeline runs.CuBIDS helps users perform metadata-based quality control.

In multiple academic disciplines, having a perceived gender of `woman' is associated with a lower than expected rate of citations. In some fields, that disparity is driven primarily by the citations of men and is increasing over time despite increasing diversification of the profession. It is likely that complex social interactions and individual ideologies shape these disparities. Computational models of select factors that reproduce empirical observations can help us understand some of the minimal driving forces behind these complex phenomena and therefore aid in their mitigation. Here, we present a simple agent-based model of citation practices within academia, in which academics generate citations based on three factors: their estimate of the collaborative network of the field, how they sample that estimate, and how open they are to learning about their field from other academics. We show that increasing homophily -- or the tendency of people to interact with others more like themselves -- in these three domains is sufficient to reproduce observed biases in citation practices. We find that homophily in sampling an estimate of the field influences total citation rates, and openness to learning from new and unfamiliar authors influences the change in those citations over time. We next model a real-world intervention -- the citation diversity statement -- which has the potential to influence both of these parameters. We determine a parameterization of our model that matches the citation practices of academics who use the citation diversity statement. This parameterization paired with an openness to learning from many new authors can result in citation practices that are equitable and stable over time. Ultimately, our work underscores the importance of homophily in shaping citation practices and provides evidence that specific actions may mitigate biased citation practices in academia.

The processes underlying macroecological gradients in body size are widely debated, in part because their intraspecific variability remains poorly described even in well-studied taxa such as vertebrates. In this study, we investigated how climate, habitat, genetic lineage and sex explain body size variations in French populations of the European pond turtle (Emys orbicularis). We measured 7016 adult individuals captured in 41 populations, covering most of the species’ distribution in metropolitan France, including Corsica. Body size variation in our sample was wide and comparable to that found across the species’ worldwide range. Variation was similar in magnitude at regional and local levels, suggesting that body size is influenced by local factors as much as by regional factors such as climate or genetic lineage. Smaller sizes were associated with Mediterranean or altered oceanic climates, and with two lineages (E. o. galloitalica and E. o. galloitalica/E. o. orbicularis), while larger sizes were associated with northern environments and the orbicularis lineage. Body size variations recorded at local level reflect an adaptive response to environmental constraints, suggesting that habitat is also an important factor in understanding size variation.

Complex human cognition arises from the integrated processing of multiple brain systems. However, little is known about how brain systems and their interactions might relate to, or perhaps even explain, human cognitive capacities. Here, we address this gap in knowledge by proposing a mechanistic framework linking frontoparietal system activity, default mode system activity, and the interactions between them, with individual differences in working memory capacity. We show that working memory performance depends on the strength of functional interactions between the frontoparietal and default mode systems. We find that this strength is modulated by the activation of two newly described brain regions, and demonstrate that the functional role of these systems is underpinned by structural white matter. Broadly, our study presents a holistic account of how regional activity, functional connections, and structural linkages together support integrative processing across brain systems in order for the brain to execute a complex cognitive process., Working memory is a critical component of executive function that allows people to complete complex tasks in the moment. Here, the authors show that this ability is underpinned by two newly defined brain networks.

Turtles and tortoises (chelonians) have been integral components of global ecosystems for about 220 million years and have played important roles in human culture for at least 400,000 years. The chelonian shell is a remarkable evolutionary adaptation, facilitating success in terrestrial, freshwater and marine ecosystems. Today, more than half of the 360 living species and 482 total taxa (species and subspecies combined) are threatened with extinction. This places chelonians among the groups with the highest extinction risk of any sizeable vertebrate group. Turtle populations are declining rapidly due to habitat loss, consumption by humans for food and traditional medicines and collection for the international pet trade. Many taxa could become extinct in this century. Here, we examine survival threats to turtles and tortoises and discuss the interventions that will be needed to prevent widespread extinction in this group in coming decades.

Adolescence is hypothesized to be a critical period for the development of association cortex. A reduction of the excitation:inhibition (E:I) ratio is a hallmark of critical period development; however it has been unclear how to assess the development of the E:I ratio using non-invasive neuroimaging techniques. Here, we used pharmacological fMRI with a GABAergic benzodiazepine challenge to empirically generate a model of E:I ratio based on multivariate patterns of functional connectivity. In an independent sample of 879 youth (ages 8-22 years), this model predicted reductions in the E:I ratio during adolescence, which were specific to association cortex and related to psychopathology. These findings support hypothesized shifts in E:I balance of association cortices during a neurodevelopmental critical period in adolescence.TeaserInhibitory maturation of the association cortex reflects an adolescent critical period.

Signal propagation along the structural connectome of the brain induces changes in the patterns of activity. These activity patterns define global brain states and contain information in accordance with their expected probability of occurrence. The structural connectome, in conjunction with the dynamics, determines the set of possible brain states and constrains the transition between accessible states. Yet, precisely how these structural constraints on state-transitions relate to their information content remains unexplored. To address this gap in knowledge, we defined the information content as a function of the activation distribution, where statistically rare values of activation correspond to high information content. With this numerical definition in hand, we studied the spatiotemporal distribution of information content in fMRI data from the Human Connectome Project during different tasks, and report four key findings. First, information content strongly depends on the cognitive task. Second, while information content shows similarities to other measures of brain activity, it is distinct from both Neurosynth maps and task contrast maps generated by a general linear model applied to the fMRI data. Third, the brain's structural wiring constrains the cost to control its state, where the cost to transition into high information content states is larger than that to transition into low information content states. Finally, all state transitions - especially those to high information content states - are less costly than expected from random network null models, thereby indicating the brain's marked efficiency. Taken together, our findings establish an explanatory link between the information contained in a brain state and the energetic cost of attaining that state, thereby laying important groundwork for our understanding of large-scale cognitive computations., 16 pages, 4 figures + supplement (5 pages, 5 figures)

The document contains all additional material (tables and figures) cited in the main text.

Este artículo contiene 15 páginas, 3 tablas, 3 figuras., 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-distancemigrating 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 localscale 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., Funds were supplied by grants CGL2013-42203-R and CGL2017-85210-P (MCIU/AEI/FEDER, UE) respectively. The study also received funding from the European Commission’s Horizon 2020 Research and Innovation Program under Grant Agreement no. 634495 for the project Sci-ence, Technology, and Society Initiative to Minimize Unwanted Catches in European Fisheries (MINOUW). MG was partially funded by Govern Balear. ASA was supported by a Ramón y Cajal contract (RYC-2017- 22796).

13 pages, 4 figures, 1 table, supplementary information https://doi.org/10.1007/s10750-022-04996-5.-- Data availability: The isotopic values generated from this study are available on request from the corresponding author, The only wetland in Europe colonized successfully by the apple snail Pomacea maculata Perry, 1810, is the Ebro Delta (Spain). After 10 years, it has become widespread there, despite significant eradication attempts. In spite of its great negative ecological and economic impacts, its incorporation into food webs can result in an abundant potential resource for native and exotic predators. We identified the potential predators, including fishes, amphibians, crustaceans, birds, turtles and mammals in natural conditions using stable isotopic analyses (δ13C and δ15N). Six predator species, three native (two birds and one frog) and three invasive (one crustacean, one turtle and one mammal), were confirmed to consume apple snails. None of the 10 fish species analyzed consumed apple snails, although some have been previously observed consuming this snail under laboratory conditions. This study emphasizes the need to assess the consumption of apple snail, as well as other invasive organisms, by potential predators in natural conditions to confirm the establishment of new trophic relationships and to understand whether these potential predators actually act as biological control agents in nature, This work was funded by SEO/BirdLife and Forestal Catalana. JN was supported by Spanish National Program Ramón y Cajal (RYC-2015-17809). MV was supported by a predoctoral fellowship of the FPU program of the Spanish Government (FPU18/01775), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

The Ebro Delta is a wetland of international importance for waterbird conservation but severally affected by intensive agriculture, toxic waste discharges from a past chloro-alkali industry and affluence of tourism. The discharge of contaminants associated to these activities pose waterbirds breeding in the Ebro Delta at risk. The aim of this study is to evaluate the exposure of 91 emerging and legacy micropollutants in flamingo chicks (Phoenicopterus roseus), an emblematic species of the area. Fifty chicks of 45-60 days were captured, biometric parameters measured and whole blood collected. Compounds analyzed included perfluoroalkyl substances (PFASs), pharmaceuticals, organophosphate esters (OPEs), in-use pesticides, polychlorinated biphenyls (PCBs), organochlorine pesticides (OCs), and polycyclic aromatic hydrocarbons (PAHs). The results indicate a multi-exposure of flamingo's chicks from a very young age. PFASs were the most ubiquitous compounds with ∑PFASs ranging from 9.34 to 576 ng/mL, being PFOA, PFOS and PFHxS detected in all samples. ∑PAHs ranged from 0.19 to 423 ng/mL, ∑PCBs from 0.5 to 15.6 ng/mL and ∑OCs from 1.35 to 37.8 ng/mL. Pharmaceuticals, OPEs and in-use pesticides were not detected. The flamingo's filtering behavior on mud and maternal ovo-transference are the more likely routes of exposure of organic micropollutants to flamingos' chicks. The reported levels of micropollutants were not associated with any alteration in the body condition of chicks. This is the first study to describe flamingos chicks' exposure to multiple contaminants, highlighting the importance of biomonitoring for wildlife conservation and biodiversity preservation., The Spanish Ministry of Science and Innovation is acknowledged for financial support [PID2019-105732GB-C21] from MCIN/AEI/10.13039/501100011033. This study was also supported by the Spanish Ministry of Science and Innovation and the IDAEA-CSIC, a Centre of Excellence Severo Ochoa (CEX2018-000794-S). We thank the Ebro Delta Natural Park, specially Francesc Vidal and Toni Curcó, for the opportunity to obtain the samples and the team of veterinaries for the extraction of blood.

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.

Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of eight different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence, and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability, fingerprinting accuracy, and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability and fingerprinting accuracy. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

Socioeconomic status (SES) can impact cognitive performance, including working memory (WM). As executive systems that support WM undergo functional neurodevelopment during adolescence, environmental stressors at both individual and community levels may influence cognitive outcomes. Here, we sought to examine how SES at the neighborhood and family level impacts task-related activation of the executive system during adolescence and determine whether this effect mediates the relationship between SES and WM performance. To address these questions, we studied 1,150 youths (age 8–23) that completed a fractal n-back WM task during functional magnetic resonance imaging at 3T as part of the Philadelphia Neurodevelopmental Cohort. We found that both higher neighborhood SES and parental education were associated with greater activation of the executive system to WM load, including the bilateral dorsolateral prefrontal cortex, posterior parietal cortex, and precuneus. The association of neighborhood SES remained significant when controlling for task performance, or related factors like exposure to traumatic events. Furthermore, high-dimensional multivariate mediation analysis identified distinct patterns of brain activity within the executive system that significantly mediated the relationship between measures of SES and task performance. These findings underscore the importance of multilevel environmental factors in shaping executive system function and WM in youth.

The spatial layout of large-scale functional brain networks differs between individuals and is particularly variable in association cortex that has been implicated in a broad range of psychiatric disorders. However, it remains unknown whether this variation in functional topography is related to major dimensions of psychopathology in youth. Capitalizing on a large sample with 27-minutes of high-quality functional MRI data (n=790, ages 8-23 years) and advances in machine learning, we examined associations between functional topography and four correlated dimensions of psychopathology (fear, psychosis, externalizing, anxious-misery) as well as an overall psychopathology factor. We found that functional topography significantly predicted individual differences in dimensions of psychopathology, driven mainly by robust associations between topography and overall psychopathology. Reduced cortical representations of association networks were among the most important features of the model. Our results emphasize the value of considering systematic differences in functional neuroanatomy for personalized diagnostics and therapeutics in psychiatry.

The Adolescent Brain Cognitive Development Study (ABCD), a 10 year longitudinal neuroimaging study of the largest population based and demographically distributed cohort of 9-10 year olds (N=11,877), was designed to overcome reproducibility limitations of prior child mental health studies. Besides the fantastic wealth of research opportunities, the extremely large size of the ABCD data set also creates enormous data storage, processing, and analysis challenges for researchers. To ensure data privacy and safety, researchers are not currently able to share neuroimaging data derivatives through the central repository at the National Data Archive (NDA). However, sharing derived data amongst researchers laterally can powerfully accelerate scientific progress, to ensure the maximum public benefit is derived from the ABCD study. To simultaneously promote collaboration and data safety, we developed the ABCD-BIDS Community Collection (ABCC), which includes both curated processed data and software utilities for further analyses. The ABCC also enables researchers to upload their own custom-processed versions of ABCD data and derivatives for sharing with the research community. This NeuroResource is meant to serve as the companion guide for the ABCC. In section we describe the ABCC. Section II highlights ABCC utilities that help researchers access, share, and analyze ABCD data, while section III provides two exemplar reproducibility analyses using ABCC utilities. We hope that adoption of the ABCC’s data-safe, open-science framework will boost access and reproducibility, thus facilitating progress in child and adolescent mental health research.

Arterial spin labeled (ASL) magnetic resonance imaging (MRI) is the primary method for noninvasively measuring regional brain perfusion in humans. We introduce ASLPrep, a suite of software pipelines that ensure the reproducible and generalizable processing of ASL MRI data.

Gull eggs are excellent bioindicators of environmental pollution as reflect the contamination levels of coastal areas, especially of persistent and bioacumulative compounds such as perfluoroalkyl substances (PFAS). This study aims to evaluate the geographical distribution and 10-year temporal trends (2009-2018) of 17 PFAS in eggs of two gull species (Larus michahellis and Larus audouinii) from 5 main Spanish colonies. ∑PFAS ranged from 13.7 ± 5.9 to 164 ± 17 ng g-1 wet weight and higher concentrations were observed in L. audouinii than in L. michahellis. Perfluorooctane sulfonate (PFOS) was the predominant compound in all samples, followed by perfluoroundecanoic acid (PFUnA) and perfluorotridecanoic acid (PFTriDA). Perfluorododecanoic acid (PFDoA), perfluorodecanoic acid (PFDA) and perfluorooctanoic acid (PFNA) were also found in all studied areas but at lower concentrations, while perfluorooctanoic acid (PFOA) was only detected in the Medes Islands. Principal Component Analysis revealed the co-occurrence of the 6 detected PFAS, and differentiated samples from Ebro Delta and Medes Islands, both located in the North-Eastern Mediterranean Sea, with high contribution of all PFAS, from Chafarinas and Atlantic Islands with lower concentration levels and variability. Also, different patterns were observed among colonies, suggesting the fish-based diet plays an important role in PFAS bioaccumulation. In all colonies, except for the Medes Islands, ∑PFAS decreased through the 10-year study period, with PFOS, PFUnA, and PFTriDA showing a significant concentration reduction in a colony-specific manner. This study demonstrates the usefulness and importance of continuous systematic long-term monitoring to determine the geographical distribution and temporal variations of PFAS in marine protected areas using gull eggs as bioindicators of environmental pollution., We acknowledge the Islas Atlánticas National Park, Refugio de Caza de las Islas Chafarinas, Delta de l'Ebre Natural Park, and Montgrí, les Illes Medes i el Baix Ter Natural Park to authorize sampling for our work. The Servei de Protecció i Gestió de la Fauna (Catalan Government) is acknowledged for sampling permission. We are especially grateful to José Antonio Fernández-Bouza and Vicente Piorno (Atlantic Islands), Javier Zapata (Chafarinas) for organizing the sampling, logistic support and transport of samples and for their valuable contributions to the study, and Mònica Cárdenas and Marc Bosch (Medes Islands) and Francesc Vidal and Antoni Curcó (PN Delta de l’Ebre) for facilitating sampling. We also thank Carles Domingo and Julia Piccardo (Forestal Catalana) for valuable field assistance. This research has been funded by the Spanish Autonomous Organism of National Parks (projects 038/2009 and 2012/768) and by the Spanish Ministry of Science and Innovation [PID 2019-105732 GB-C21, MCIN/AEI/10.13039/501100011033.

The recent and growing focus on reproducibility in neuroimaging studies has led many major academic centers to use cloud-based imaging databases for storing, analyzing, and sharing complex imaging data. Flywheel is one such database platform that offers easily accessible, large-scale data management, along with a framework for reproducible analyses through containerized pipelines. The Brain Imaging Data Structure (BIDS) is a data storage specification for neuroimaging data, but curating neuroimaging data into BIDS can be a challenging and time-consuming task. In particular, standard solutions for BIDS curation are not designed for use on cloud-based systems such as Flywheel. To address these challenges, we developed “FlywheelTools”, a software toolbox for reproducible data curation and manipulation on Flywheel. FlywheelTools includes two elements: fw-heudiconv, for heuristic-driven curation of data into BIDS, and flaudit, which audits and inventories projects on Flywheel. Together, these tools accelerate reproducible neuroscience research on the widely used Flywheel platform.

Graciá et al., Chelonians are among the animal groups with the poorest conservation status. Since tortoises are long-lived species that need very long time to reach sexual maturity, they are extremely vulnerable to human pressure. Despite their endangered status, there are no common strategies for the development of conservation actions. At the “Mediterranean workshop to develop tortoise conservation strategies”, scientists, conservation associations, environmental managers, IUCN advisers and CITES inspectors met in October 2019 in Alicante (Spain). The aims were to update the diagnosis of the conservation status of the Hermann’s tortoise (Testudo hermanni) and the spur-thighed tortoise (Testudo graeca) and to suggest strategies that guarantee their preservation at the Mediterranean Region. The main conclusions are summarized by: i) Mediterranean tortoises are wild endangered species that must be protected in, and together with, their natural habitat. Their main threats are habitat loss and fragmentation and pet trade, but we should also pay attention to wildfires, spread of diseases, the introduction of exotic species or lineages and climate change. ii) Long-term efforts and stablished protocols are needed to accurately diagnose and monitor the conservation status of wild populations, as well as flows among captive and wild animals. iii) Tortoise trade should be banned because it threatens the conservation of wild populations of tortoises. Illegal practices related to tortoises should be decidedly persecuted. People should consider tortoises as wild animals that are endangered and deserving of protection. It is possible to enjoy them by responsibly observing them in their habitat. iv) Tortoises may carry pathogens or parasites and their sanitary status cannot be fully assessed. They should always be considered potential vectors of tortoise diseases and of zoonosis. Hygiene protocols should be implemented when managing them. v) The researchers attending this workshop aimed to develop a scientific network for the long-term monitoring of graeca and T. hermanni populations in the Mediterranean Region. They will need the support from public administrations., The work of the researchers leading this work (EG, RCRC and AG) was funded by the Span‐ ish Ministry of Science through projects PID2019‐105682RA‐I00/AEI/10.13039/5011 00011033 and CGL2015‐64144 (the latter with the support of the European Regional Development Fund, MINECO/FEDER). MVJF and RCRC are supported by post‐doctoral grants funded by the Regional Valencian Government and the European Social Fund (APOSTD/2018/043 and APOSTD/2020/090, respectively).

Phalangeal reduction is a common and widespread phenomenon among tortoises that has been associated with the adaptation to terrestrial life. While reduced manual digit 1 appears characteristic in almost all Testudo species, it is uncertain why the metacarpal I and distal carpal of the same digit are completely missing in some individuals of Hermann's tortoise (Testudo hermanni hermanni). To clarify this issue, we investigated the number of manual claws in six populations of Hermann's tortoise (one from the Ebro Delta in the Iberian Peninsula and five from Minorca Island), their age, sex, genetic lineage, and the substrate type that they inhabit. The number of claws was ascertained based on direct counts (n > 1500 individuals) and by X-rays (n = 32 individuals), obtaining three different phalangeal formulae: (1-2-2-2-1, D-2-2-2-1, 0-2-2-2-1). Thus, claw counts through both methodologies (direct count and X-ray) further confirm that the observed claws serve as a good proxy to assess the actual number of digits. Our results show no loss of phalanges, metacarpal and carpal bones in digit 1 associated with age, sex, or substrate, contrary to some previous authors who hypothesized a relationship between this loss and sexual dimorphism. Therefore, variations in the number of manual digits and the loss of metacarpal I and distal carpal in digit 1 in Hermann's tortoise are related to population and genetic lineage. More detailed comparisons with other Testudo hermanni populations from elsewhere in Europe would be required to understand the evolutionary significance concerning the intrapopulation variability in the number of digits remaining.

Resumen del póster presentado al SETAC Europe 31st Annual Meeting, celebrado de forma virtual del 3 al 6 de mayo de 2021., Ebro delta is a wetland of international importance for water bird conservation. In a relatively small surface, it comprises a great diversity of habitats and species: a total of 343 species of birds have been observed in the delta, from which 100 species breed regularly and 15 occasionally. Over the last 150 years the extensive wetland reclamation for rice cultivation has resulted in the loss of 65% of the natural habitats. The agricultural activities and the runoffs inputs from industries and wastewater treatment plants are an important source of organic contaminants to Ebro delta. In the recent years the presence of pesticides, pharmaceuticals, perfluorinated compounds and organophosphorus compounds has been reported in soils, sediments and water from Ebro delta. These organic contaminants can be bioaccumulated and transferred along the food chain affecting avifauna. The accumulation of pollutants in Yellow-legged gulls and Audouin’s gulls from the delta Ebro have been reported in the recent years. The Greater Flamingo (Phoenicopterus roseus) is an emblematic species of the Ebro Delta Natural Park, since it is the only place where it breeds in Catalonia and one of the few stable places in the western Mediterranean. Conserving the species in one of the main objectives of the Natural Park, implying an accurate monitoring program. However, polluted sediments and water can affect flamingos and induce potential adverse effects on reproduction and survival, although this has never been assessed before. In this study, we have analysed for the first-time a near one hundred of contaminants, including pharmaceuticals, perfluorinated compounds, organophosphorus flame retardants and pesticides in blood from chicks of greater flamingos with aim to value their potential impact in the conservation status of the species. Our results suggest ovo- transfer of pollutants and sediment ingestion as a source of contamination of flamingo’s flagging’s.

Discrimination against racial and ethnic minority groups exists in the academy, and the associated biases impact hiring and promotion, publication rates, grant funding, and awards. Precisely how racial and ethnic bias impacts the manner in which the scientific community engages with the ideas of academics in minority groups has yet to be fully elucidated. Citations are a marker of such community engagement, as well as a currency used to attain career milestones. Here we assess the extent and drivers of racial and ethnic imbalance in the reference lists of papers published in five top neuroscience journals over the last 25 years. We find that reference lists tend to include more papers with a White person as first and last author than would be expected if race and ethnicity were unrelated to referencing. We show that this imbalance is driven largely by the citation practices of White authors, and is increasing over time even as the field diversifies. To further explain our findings, we examine co-authorship networks and find that while the network has become markedly more integrated in general, the current degree of segregation by race/ethnicity is greater now than it has been in the past. Citing further from oneself on the network is associated with greater balance, but White authors’ preferential citation of White authors remains even at high levels of network exploration. We also quantify the effects of intersecting identities, determining the relative costs of gender and race/ethnicity, and their combination in women of color. Our findings represent a call to scientists and journal editors of all disciplines to consider the ethics of citation practices, and actions to be taken in support of an equitable future.

How an individual’s unique brain connectivity determines that individual’s cognition, behavior, and risk for pathology is a fundamental question in basic and clinical neuroscience. In seeking answers, many have turned to machine learning, with some noting the particular promise of deep neural networks in modelling complex non-linear functions. However, it is not clear that complex functions actually exist between brain connectivity and behavior, and thus if deep neural networks necessarily outperform simpler linear models, or if their results would be interpretable. Here we show that, across 52 subject measures of cognition and behavior, deep neural networks fit to each brain region’s connectivity outperform linear regression, particularly for the brain’s connector hubs—regions with diverse brain connectivity—whereas the two approaches perform similarly when fit to brain systems. Critically, averaging deep neural network predictions across brain regions results in the most accurate predictions, demonstrating the ability of deep neural networks to easily model the various functions that exists between regional brain connectivity and behavior, carving the brain at its joints. Finally, we shine light into the black box of deep neural networks using multislice network models. We determined that the relationship between connector hubs and behavior is best captured by modular deep neural networks. Our results demonstrate that both simple and complex relationships exist between brain connectivity and behavior, and that deep neural networks can fit both. Moreover, deep neural networks are particularly powerful when they are first fit to the various functions of a system independently and then combined. Finally, deep neural networks are interpretable when their architectures are structurally characterized using multislice network models.

Living systems break detailed balance at small scales, consuming energy and producing entropy in the environment in order to perform molecular and cellular functions. However, it remains unclear how broken detailed balance manifests at macroscopic scales, and how such dynamics support higher-order biological functions. Here we present a framework to quantify broken detailed balance by measuring entropy production in macroscopic systems. We apply our method to the human brain, an organ whose immense metabolic consumption drives a diverse range of cognitive functions. Using whole-brain imaging data, we demonstrate that the brain nearly obeys detailed balance when at rest, but strongly breaks detailed balance when performing physically and cognitively demanding tasks. Using a dynamic Ising model, we show that these large-scale violations of detailed balance can emerge from fine-scale asymmetries in the interactions between elements, a known feature of neural systems. Together, these results suggest that violations of detailed balance are vital for cognition, and provide a general tool for quantifying entropy production in macroscopic systems., Comment: 19 pages, 15 figures

A central challenge in the computational modeling of neural dynamics is the trade-off between accuracy and simplicity. At the level of individual neurons, nonlinear dynamics are both experimentally established and essential for neuronal functioning. One may therefore expect the collective dynamics of massive networks of such neurons to only increase in their complexity, thereby supporting an expanded repertoire of nonlinear behaviors. An implicit assumption has thus formed that an “accurate” computational model of whole-brain dynamics must inevitably be nonlinear whereas linear models may provide a first-order approximation. To what extent this assumption holds, however, has remained an open question. Here, we provide a rigorous and data-driven answer at the level of whole-brain blood-oxygen-level-dependent (BOLD) and macroscopic field potential dynamics by leveraging the theory of system identification. Using functional magnetic resonance imaging (fMRI) and intracranial electroencephalography (iEEG), we model the spontaneous, resting state activity of 700 subjects in the Human Connectome Project (HCP) and 122 subjects from the Restoring Active Memory (RAM) project using state-of-the-art linear and nonlinear model families. We assess relative model fit using predictive power, computational complexity, and the extent of residual dynamics unexplained by the model. Contrary to our expectations, linear auto-regressive models achieve the best measures across all three metrics, eliminating the trade-off between accuracy and simplicity. To understand and explain this linearity, we highlight four properties of macroscopic neurodynamics which can counteract or mask microscopic nonlinear dynamics: averaging over space, averaging over time, observation noise, and limited data samples. Whereas the latter two are technological limitations and can improve in the future, the former two are inherent to aggregated macroscopic brain activity. Our results demonstrate the discounted potential of linear models in accurately capturing macroscopic brain dynamics. This, together with the unparalleled interpretability of linear models, can greatly facilitate our understanding of macroscopic neural dynamics, which in turn may facilitate the principled design of model-based interventions for the treatment of neuropsychiatric disorders.

Este artículo contiene 9 páginas, 3 figuras, 1 tabla., Social interactions, through infuence on behavioural processes, can play an important role in populations’ resilience (i.e. ability to cope with perturbations). However little is known about the efects of perturbations on the strength of social cohesion in wild populations. Long-term associations between individuals may refect the existence of social cohesion for seizing the evolutionary advantages of social living. We explore the existence of social cohesion and its dynamics under perturbations by analysing long-term social associations, in a colonial seabird, the Audouin’s gull Larus audouinii, living in a site experiencing a shift to a perturbed regime. Our goals were namely (1) to uncover the occurrence of long-term social ties (i.e. associations) between individuals and (2) to examine whether the perturbation regime afected this form of social cohesion. We analysed a dataset of more than 3500 individuals from 25 years of monitoring by means of contingency tables and within the Social Network Analysis framework. We showed that associations between individuals are not only due to philopatry or random gregariousness but that there are social ties between individuals over the years. Furthermore, social cohesion decreased under the perturbation regime. We sustain that perturbations may lead not only to changes in individuals’ behaviour and ftness but also to a change in populations’ social cohesion. The consequences of decreasing social cohesion are still not well understood, but they can be critical for the population dynamics of social species., Funding came from the Spanish Ministry of Science (CGL2017-85210) and Grant PICS INTERACT n°07699 (2016, CSIC-CNRS). MG was partially supported by the European Union (MINOUW Project, H2020-634495).

The aim of the present work was to comparatively assess the occurrence and impact of persistent organic pollutants (POPs) in nine natural and national parks from Spain and Portugal using gull eggs ( Larus michahellis and L. audouinii ) as bioindicators of environmental contamination. Sampling was performed during the breeding season of 2016. Compounds studied include polychlorinated biphenyls (PCBs), organochlorinated pesticides (OC pesticides), perfluorooctane sulfonic acid (PFOS) and polybrominated diphenyl ethers (PBDEs), and were analyzed using mass spectrometric based techniques. The results showed a high contamination by PCBs in all colonies, with total levels ranging from 59 to 1278 ng/g wet weight (ww), despite their use is not currently authorized. OC pesticides were also present in all colonies, with a high incidence of 4,4′-DDE in gull eggs at levels up to 218 ± 50 ng/g ww in L. michahellis and 760 ± 412 ng/g ww in L. audouinii from the Ebro Delta natural park. PBDEs and PFOS were also detected at levels up to 91.7 ± 21.3 ng/g ww, which can be attributed to a more recent use. Except for PBDEs, the POP levels in eggs from L. audouinii were higher than in L. michahellis , presumably associated to the fish-based diet of the former. Finally, the effect of POP levels on eggshell parameters (volume, eggshell thickness and desiccation index) were investigated for each colony and gull species in order to evaluate the egg viability and, therefore, the reproduction success.

Este artículo contiene 7 páginas, 3 figuras., Predators are an important ecological and evolutionary force shaping prey population dynamics. Ecologists have extensively assessed the lethal effects of invasive predators on prey populations. However, the role of non-lethal effects, such as physiological stress or behavioural responses like dispersal, has been comparatively overlooked and their potential population effects remain obscure. Over the last 23 years, we developed a mark-recapture program for the Audouin’s gull and an intensive carnivore monitoring program to assess how the appearance and invasion of the study site by carnivores affects population dynamics. We evaluate changes in turnover of discrete breeding patches within the colony, age structure and breeding performance. Once carnivores entered the colony, the number of occupied patches increased, indicating a higher patch turnover. Breeders responded by moving to areas less accessible to carnivores. More importantly, the presence of carnivores caused differential (and density-independent) breeding dispersal: experienced, better-performing breeders were more likely to leave the colony than younger breeders. This differential dispersal modified the age structure and reduced the reproductive performance of the population. Our results confirm the importance experience in the study of populations. The role of differential dispersal for animal population dynamics might be more important than previously thought, especially under scenarios of global change., RESET (ref.CGL2017-85210-P), FPU (ref. FPU2012-000869), IBISES (ref. CGL2013-42203-R) and MINOW (ref. H2020- 634495). ASA and MG are supported by a postdoctoral contract co-funded by the Regional Government of the Balearic Islands and the European Social Fund (ref. PD/003/2016 and PD/023/2015).

A complex system can be represented and analyzed as a network, where nodes represent the units of the network and edges represent connections between those units. For example, a brain network represents neurons as nodes and axons between neurons as edges. In many networks, some nodes have a disproportionately high number of edges as well as many edges between each other and are referred to as the “rich club”. In many different networks, the nodes of this club are assumed to support global network integration. Here we show that another set of nodes, which have edges diversely distributed across the network, form a “diverse club”. The diverse club exhibits, to a greater extent than the rich club, properties consistent with an integrative network function—these nodes are more highly interconnected and their edges are more critical for efficient global integration. Finally, these two clubs potentially evolved via distinct selection pressures., Complex networks represent systems such as neural networks and air traffic as interconnected nodes that organize themselves into subsets. Here Bertolero et al. propose a subset which they call the diverse club, which offers an alternative to the commonly used rich club.