112 research outputs found
A mechanistic model of connector hubs, modularity, and cognition
The human brain network is modular--comprised of communities of tightly
interconnected nodes. This network contains local hubs, which have many
connections within their own communities, and connector hubs, which have
connections diversely distributed across communities. A mechanistic
understanding of these hubs and how they support cognition has not been
demonstrated. Here, we leveraged individual differences in hub connectivity and
cognition. We show that a model of hub connectivity accurately predicts the
cognitive performance of 476 individuals in four distinct tasks. Moreover,
there is a general optimal network structure for cognitive
performance--individuals with diversely connected hubs and consequent modular
brain networks exhibit increased cognitive performance, regardless of the task.
Critically, we find evidence consistent with a mechanistic model in which
connector hubs tune the connectivity of their neighbors to be more modular
while allowing for task appropriate information integration across communities,
which increases global modularity and cognitive performance
Non-equilibrium dynamics and entropy production in the human brain
Living systems operate out of thermodynamic equilibrium at small scales,
consuming energy and producing entropy in the environment in order to perform
molecular and cellular functions. However, it remains unclear whether
non-equilibrium dynamics manifest at macroscopic scales, and if so, how such
dynamics support higher-order biological functions. Here we present a framework
to probe for non-equilibrium dynamics by quantifying 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 fundamentally
operates out of equilibrium at large scales. Moreover, we find that the brain
produces more entropy -- operating further from equilibrium -- when performing
physically and cognitively demanding tasks. By simulating an Ising model, we
show that macroscopic non-equilibrium dynamics can arise from asymmetries in
the interactions at the microscale. Together, these results suggest that
non-equilibrium dynamics are vital for cognition, and provide a general tool
for quantifying the non-equilibrium nature of macroscopic systems.Comment: 18 pages, 14 figure
Multiscale and multimodal network dynamics underpinning working memory
Working memory (WM) allows information to be stored and manipulated over
short time scales. Performance on WM tasks is thought to be supported by the
frontoparietal system (FPS), the default mode system (DMS), and interactions
between them. Yet little is known about how these systems and their
interactions relate to individual differences in WM performance. We address
this gap in knowledge using functional MRI data acquired during the performance
of a 2-back WM task, as well as diffusion tensor imaging data collected in the
same individuals. We show that the strength of functional interactions between
the FPS and DMS during task engagement is inversely correlated with WM
performance, and that this strength is modulated by the activation of FPS
regions but not DMS regions. Next, we use a clustering algorithm to identify
two distinct subnetworks of the FPS, and find that these subnetworks display
distinguishable patterns of gene expression. Activity in one subnetwork is
positively associated with the strength of FPS-DMS functional interactions,
while activity in the second subnetwork is negatively associated. Further, the
pattern of structural linkages of these subnetworks explains their differential
capacity to influence the strength of FPS-DMS functional interactions. To
determine whether these observations could provide a mechanistic account of
large-scale neural underpinnings of WM, we build a computational model of the
system composed of coupled oscillators. Modulating the amplitude of the
subnetworks in the model causes the expected change in the strength of FPS-DMS
functional interactions, thereby offering support for a mechanism in which
subnetwork activity tunes functional interactions. Broadly, our study presents
a holistic account of how regional activity, functional interactions, and
structural linkages together support individual differences in WM in humans
Predator arrival elicits differential dispersal, change in age structure and reproductive performance in a prey population
Acknowledgements We thank everyone monitoring colonies over the years, in particular Carles Domingo from the Ebro Delta NP Staff, Ràpita, Castelló and the Tarragona Port Authorities and José Manuel Igual from the Group of Ecology and Animal Demography. We also thank the editorial board and the reviewers for their constructive comments. 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).Peer reviewedPublisher PD
The community structure of functional brain networks exhibits scale-specific patterns of inter- and intra-subject variability
The network organization of the human brain varies across individuals, changes with development and aging, and differs in disease. Discovering the major dimensions along which this variability is displayed remains a central goal of both neuroscience and clinical medicine. Such efforts can be usefully framed within the context of the brain\u27s modular network organization, which can be assessed quantitatively using computational techniques and extended for the purposes of multi-scale analysis, dimensionality reduction, and biomarker generation. Although the concept of modularity and its utility in describing brain network organization is clear, principled methods for comparing multi-scale communities across individuals and time are surprisingly lacking. Here, we present a method that uses multi-layer networks to simultaneously discover the modular structure of many subjects at once. This method builds upon the well-known multi-layer modularity maximization technique, and provides a viable and principled tool for studying differences in network communities across individuals and within individuals across time. We test this method on two datasets and identify consistent patterns of inter-subject community variability, demonstrating that this variability - which would be undetectable using past approaches - is associated with measures of cognitive performance. In general, the multi-layer, multi-subject framework proposed here represents an advance over current approaches by straighforwardly mapping community assignments across subjects and holds promise for future investigations of inter-subject community variation in clinical populations or as a result of task constraints
Agricultural policies against invasive species generate contrasting outcomes for climate change mitigation and biodiversity conservation
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.info:eu-repo/semantics/acceptedVersio
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