A mathematical perspective on edge-centric functional connectivity

Edge-centric functional connectivity (eFC) has recently been proposed to characterise the finest time resolution on the FC dynamics without the concomitant assumptions of sliding-window approaches. Here, we lay the mathematical foundations for the edge-centric analysis and examine its main findings from a quantitative perspective. The proposed framework provides a theoretical explanation for the observed occurrence of high-amplitude edge cofluctuations across datasets and clarifies why a few large events drive the node-centric FC (nFC). Our exposition also constitutes a critique of the edge-centric approach as currently applied to functional MRI (fMRI) time series. The central argument is that the existing findings based on edge time series can be derived from the static nFC under a null hypothesis that only accounts for the observed static spatial correlations and not the temporal ones. Challenging our analytic predictions against fMRI data from the Human Connectome Project confirms that the nFC is sufficient to replicate the eFC matrix, the edge communities, the large cofluctuations, and the corresponding brain activity mode. We conclude that the temporal structure of the edge time series has not so far been exploited sufficiently and encourage further work to explore features that cannot be explained by the presented static null model

Inferring network properties from time series using transfer entropy and mutual information: validation of multivariate versus bivariate approaches

Functional and effective networks inferred from time series are at the core of network neuroscience. Interpreting their properties requires inferred network models to reflect key underlying structural features; however, even a few spurious links can distort network measures, challenging functional connectomes. We study the extent to which micro- and macroscopic properties of underlying networks can be inferred by algorithms based on mutual information and bivariate/multivariate transfer entropy. The validation is performed on two macaque connectomes and on synthetic networks with various topologies (regular lattice, small-world, random, scale-free, modular). Simulations are based on a neural mass model and on autoregressive dynamics (employing Gaussian estimators for direct comparison to functional connectivity and Granger causality). We find that multivariate transfer entropy captures key properties of all networks for longer time series. Bivariate methods can achieve higher recall (sensitivity) for shorter time series but are unable to control false positives (lower specificity) as available data increases. This leads to overestimated clustering, small-world, and rich-club coefficients, underestimated shortest path lengths and hub centrality, and fattened degree distribution tails. Caution should therefore be used when interpreting network properties of functional connectomes obtained via correlation or pairwise statistical dependence measures, rather than more holistic (yet data-hungry) multivariate models

Spectral Dynamic Causal Modelling: A Didactic Introduction and its Relationship with Functional Connectivity

We present a didactic introduction to spectral Dynamic Causal Modelling (DCM), a Bayesian state-space modelling approach used to infer effective connectivity from non-invasive neuroimaging data. Spectral DCM is currently the most widely applied DCM variant for resting-state functional MRI analysis. Our aim is to explain its technical foundations to an audience with limited expertise in state-space modelling and spectral data analysis. Particular attention will be paid to cross-spectral density, which is the most distinctive feature of spectral DCM and is closely related to functional connectivity, as measured by (zero-lag) Pearson correlations. In fact, the model parameters estimated by spectral DCM are those that best reproduce the cross-correlations between all variables--at all time lags--including the zero-lag correlations that are usually interpreted as functional connectivity. We derive the functional connectivity matrix from the model equations and show how changing a single effective connectivity parameter can affect all pairwise correlations. To complicate matters, the pairs of brain regions showing the largest changes in functional connectivity do not necessarily coincide with those presenting the largest changes in effective connectivity. We discuss the implications and conclude with a comprehensive summary of the assumptions and limitations of spectral DCM

Intensificación de las secuencias de cultivos en un molisol y un vertisol : cambios en la estabilidad estructural y en el almacenaje de C en agregados

Los actuales sistemas agrícolas de la Región Pampeana argentina presentan una elevada frecuencia de barbechos en las secuencias de cultivos lo que resulta en el desperdicio de recursos ambientales y en un mayor impacto de los procesos erosivos sobre la agregación y el almacenaje de carbono orgánico del suelo (COS). La intensificación de las secuencias de cultivos, por medio de la reducción de los períodos de barbecho es una alternativa para mejorar la eficiencia y productividad de dichos sistemas, aunque existe escasa información sobre su impacto en la agregación y en el almacenaje de COS en suelos con agentes de agregación contrastantes. El objetivo de esta tesis fue evaluar en un molisol y un vertisol los mecanismos involucrados en la consolidación de la estructura y en el almacenaje de COS ante cambios en el nivel de intensificación y en la composición de la secuencia de cultivos. A través de diferentes escalas de evaluación (lotes de producción, ensayos de campo, incubaciones en laboratorio)se analizó el impacto del tiempo de ocupación con cobertura vegetal viva y el efecto de la calidad y frecuencia de adición de residuos sobre la dinámica de la agregación y el almacenaje de COS. En comparación con el vertisol, el molisol demostró una mayor dependencia de agentes orgánicos de la agregación para la formación de agregados. Además, la agregación y el COS almacenado en los macroagregados se asociaron negativamente con la frecuencia de soja en las secuencias. En ambos tipos de suelo, la adición más frecuente de residuos vegetales permitió mantener elevadas las tasas respiratorias y de agregación en comparación con una única adición de residuos en ensayos de laboratorio. A pesar del menor impacto de las secuencias de cultivos en el vertisol y a la elevada variación temporal en la agregación detectada en los suelos, mantener el suelo ocupado por prolongados períodos de tiempo favoreció el mayor aporte de biomasa, la agregación y las ganancias de COS en macroagregados en ambos tipos de suelo

Dynamic reorganization of the cortico-basal ganglia-thalamo-cortical network during task learning

Adaptive behavior is coordinated by neuronal networks that are distributed across multiple brain regions such as in the cortico-basal ganglia-thalamo-cortical (CBGTC) network. Here, we ask how cross-regional interactions within such mesoscale circuits reorganize when an animal learns a new task. We apply multi-fiber photometry to chronically record simultaneous activity in 12 or 48 brain regions of mice trained in a tactile discrimination task. With improving task performance, most regions shift their peak activity from the time of reward-related action to the reward-predicting stimulus. By estimating cross-regional interactions using transfer entropy, we reveal that functional networks encompassing basal ganglia, thalamus, neocortex, and hippocampus grow and stabilize upon learning, especially at stimulus presentation time. The internal globus pallidus, ventromedial thalamus, and several regions in the frontal cortex emerge as salient hub regions. Our results highlight the learning-related dynamic reorganization that brain networks undergo when task-appropriate mesoscale network dynamics are established for goal-oriented behavior

Impact of soybean cropping frequency on soil carbon storage in Mollisols and Vertisols

The high cropping frequency of soybean (Glycine max [L.] Merr.), mainly as a single annual crop, in the extensive agricultural systems of South America may adversely affect the soil organic carbon (SOC) storage, which may be different between soils depending on aggregation agents. The aim of this work was to evaluate the impact of the soybean cropping frequency on the SOC storage in different soil aggregate size classes in a Mollisol and in a Vertisol in the Northeastern Pampas of Argentina under no-tillage management. In each soil, the samples were collected at 0-5, 5-15 and 15-30 cm depths in eleven cropped and one uncropped fields. The number of months occupied with soybean in relation to the total number of months occupied with crops within crop sequences, over a 6-year period, was used to calculate the soybean cropping frequency. The SOC stocks in equivalent soil mass, the SOC concentration both in the whole sample and in different aggregate size classes, and the stratification ratio of the SOC stock and of the SOC concentration were determined. The increase in soybean cropping frequency reduced the SOC stock in both soils at 0-5 cm, and in the Vertisol at 5-15 and 0-30 cm but the change was evident only between the cropped and the uncropped situation. A decrease in soybean cropping frequency resulted in a higher amount of macroaggregates (> 250 um), a higher SOC concentration and a higher stratification ratio in the Mollisol at 0-5 cm, whereas in the Vertisol the soybean cropping frequency did not affect the stratification ratio or the aggregate distribution in any size class. The increase in soybean cropping frequency reduced SOC storage only in macroaggregates (> 250 µm) in both soils at 0-5 cm, particularly in the largest macroaggregates (> 2000 µm), and more in the Mollisol than in the Vertisol. Our results show that a high soybean cropping frequency may severely affect the SOC storage in the Mollisol, and suggest that in the Vertisol this effect may lead to a reduction in the SOC storage in the long term.Fil: Novelli, Leonardo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Paraná; ArgentinaFil: Caviglia, Octavio Pedro. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Paraná; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos; ArgentinaFil: Melchiori, R. J. M.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Paraná; Argentin

Land use intensity and cropping sequence effects on aggregate stability and C storage in a Vertisol and a Mollisol

The relevant change in land use due mainly to the rapid expansion of soybean cropping towards areas traditionally occupied for livestock purposes or with native grasslands of South America may have negative consequences on soil organic carbon (SOC) storage and aggregate stability, although the effect may be different between soils with contrasting aggregation agents. The aim of our work was to assess the impact of the land use, measured as the intensification and/or frequency of a given crop, on SOC storage and aggregate stability in two soils differing in their main agents of aggregation. The study was conducted in a Mollisol and a Vertisol of Argentina. Eleven cropped fields (agricultural and crop-pasture rotation) under no-tillage and one uncropped situation (pristine native grassland) were selected in each soil type. The fraction of annual time with plant cover (as a measure of the intensification in the land use) and the frequency of a given crop in the cropping sequence over a 6-year period were calculated. Undisturbed soil samples were collected from each soil at 0-5, 5-15 and 15-30 cm depths. The SOC stocks in equivalent soil mass were calculated using the native grassland as the baseline system. Aggregate stability was evaluated using a method that involved three pretreatments: fast wetting, stirring after prewetting and slow wetting. The intensification improved the aggregate stability in the Mollisol, whereas a low impact of land use on aggregate stability was recorded in the Vertisol. Overall, both the intensification sequence index and the soybean cropping frequency were the best indexes to evaluate the impact of land use on aggregate stability and SOC storage, mainly in the Mollisol. The stirring after prewetting pretreatment was mainly associated with SOC concentration in the Mollisol, appearing as a method with high potential capacity to discriminate land use in the Mollisol, in which the SOC is the main aggregation agent. In contrast, the slow wetting pretreatment was more appropriate to evaluate the impact of land use in the Vertisol. The approach used to evaluate the land use, which included agricultural lands, crop-pasture rotation and native grasslands, evaluated through indexes of occupation with plant cover, was more suitable for the Mollisol than for the Vertisol. This reveals that the evaluation of land use through several indexes should be based on the soil typeFil: Novelli, Leonardo Esteban. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Paraná; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Caviglia, Octavio Pedro. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Paraná; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Wilson, M. G.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Paraná; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; ArgentinaFil: Casal, M. C.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Paraná; Argentin

Effective Connectivity of Functionally Anticorrelated Networks Under Lysergic Acid Diethylamide

Background: Classic psychedelic-induced ego dissolution involves a shift in the sense of self and a blurring of the boundary between the self and the world. A similar phenomenon is identified in psychopathology and is associated with the balance of anticorrelated activity between the default mode network, which directs attention inward, and the salience network, which recruits the dorsal attention network to direct attention outward. Methods: To test whether changes in anticorrelated networks underlie the peak effects of lysergic acid diethylamide (LSD), we applied dynamic causal modeling to infer effective connectivity of resting-state functional magnetic resonance imaging scans from a study of 25 healthy adults who were administered 100 μg of LSD or placebo. Results: We found that inhibitory effective connectivity from the salience network to the default mode network became excitatory, and inhibitory effective connectivity from the default mode network to the dorsal attention network decreased under the peak effect of LSD. Conclusions: The effective connectivity changes we identified may reflect diminution of the functional anticorrelation between resting-state networks that may be a key neural mechanism of LSD and underlie ego dissolution. Our findings suggest that changes to the sense of self and subject-object boundaries across different states of consciousness may depend upon the organized balance of effective connectivity of resting-state networks

IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks

Producción CientíficaWe present IDTxl (the Information Dynamics Toolkit xl), a new open source Python toolbox for effective network inference from multivariate time series using information theory, available from GitHub (https://github.com/pwollstadt/IDTxl). Information theory (Cover & Thomas, 2006; MacKay, 2003; Shannon, 1948) is the math- ematical theory of information and its transmission over communication channels. In- formation theory provides quantitative measures of the information content of a single random variable (entropy) and of the information shared between two variables (mutual information). The defined measures build on probability theory and solely depend on the probability distributions of the variables involved. As a consequence, the dependence between two variables can be quantified as the information shared between them, without the need to explicitly model a specific type of dependence. Hence, mutual information is a model-free measure of dependence, which makes it a popular choice for the analysis of systems other than communication channels