Mapping hybrid functional-structural connectivity traits in the human connectome

One of the crucial questions in neuroscience is how a rich functional repertoire of brain states relates to its underlying structural organization. How to study the associations between these structural and functional layers is an open problem that involves novel conceptual ways of tackling this question. We here propose an extension of the Connectivity Independent Component Analysis (connICA) framework, to identify joint structural-functional connectivity traits. Here, we extend connICA to integrate structural and functional connectomes by merging them into common hybrid connectivity patterns that represent the connectivity fingerprint of a subject. We test this extended approach on the 100 unrelated subjects from the Human Connectome Project. The method is able to extract main independent structural-functional connectivity patterns from the entire cohort that are sensitive to the realization of different tasks. The hybrid connICA extracted two main task-sensitive hybrid traits. The first, encompassing the within and between connections of dorsal attentional and visual areas, as well as fronto-parietal circuits. The second, mainly encompassing the connectivity between visual, attentional, DMN and subcortical networks. Overall, these findings confirms the potential ofthe hybrid connICA for the compression of structural/functional connectomes into integrated patterns from a set of individual brain networks.Comment: article: 34 pages, 4 figures; supplementary material: 5 pages, 5 figure

Aplicación de los anticuerpos monoclonales para la detección de enterotoxinas estafilocócicas

Tesis de la Universidad Complutense de Madrid, Facultad de Veterinaria, Departamento de Sanidad Animal, leída el 20-03-1991Se produjeron en este estudio un total de 22 anticuerpos monoclonales frente a enterotoxinas estafilococicas (ees). De ellos, 14 fueron obtenidos frente a eeb, mientras que los 8 restantes lo fueron frente a eea. Estos anticuerpos fueron caracterizados, determinándose su titulo y curvas de dilución, capacidad de detección de ee homologa en elisa indirecto, estudios de inhibición con determinación de la constante de disociación y posibles reacciones con otros serotipos de ees y subclase de inmunoglobulina a la que pertenecían. Se comprobó la validez de estos anticuerpos monoclonales para su aplicación en un sistema de detección elisa tipo das, comprobándose que no todos realizaban adecuadamente las funciones de tapizado o conjugado, seleccionándose aquellas parejas que lo hacían de forma correcta. Una de estas parejas (la formada por los anticuerpos monoclonales a5 en función de tapizado y a7 como conjugado) permite la determinación en una sola prueba de la presencia o ausencia de ees, tanto en substrato laboratoriales, como en alimentos, detectando las enterotoxinas en cantidades bajas (0,6 ng/ml) entre otras sustancias presentes en los extractos alimenticios analizados.Fac. de VeterinariaTRUEpu

Semiparametric Estimation of Task-Based Dynamic Functional Connectivity on the Population Level

Dynamic functional connectivity (dFC) estimates time-dependent associations between pairs of brain region time series as typically acquired during functional MRI. dFC changes are most commonly quantified by pairwise correlation coefficients between the time series within a sliding window. Here, we applied a recently developed bootstrap-based technique (Kudela et al., 2017) to robustly estimate subject-level dFC and its confidence intervals in a task-based fMRI study (24 subjects who tasted their most frequently consumed beer and Gatorade as an appetitive control). We then combined information across subjects and scans utilizing semiparametric mixed models to obtain a group-level dFC estimate for each pair of brain regions, flavor, and the difference between flavors. The proposed approach relies on the estimated group-level dFC accounting for complex correlation structures of the fMRI data, multiple repeated observations per subject, experimental design, and subject-specific variability. It also provides condition-specific dFC and confidence intervals for the whole brain at the group level. As a summary dFC metric, we used the proportion of time when the estimated associations were either significantly positive or negative. For both flavors, our fully-data driven approach yielded regional associations that reflected known, biologically meaningful brain organization as shown in prior work, as well as closely resembled resting state networks (RSNs). Specifically, beer flavor-potentiated associations were detected between several reward-related regions, including the right ventral striatum (VST), lateral orbitofrontal cortex, and ventral anterior insular cortex (vAIC). The enhancement of right VST-vAIC association by a taste of beer independently validated the main activation-based finding (Oberlin et al., 2016). Most notably, our novel dFC methodology uncovered numerous associations undetected by the traditional static FC analysis. The data-driven, novel dFC methodology presented here can be used for a wide range of task-based fMRI designs to estimate the dFC at multiple levels-group-, individual-, and task-specific, utilizing a combination of well-established statistical methods

A morphospace of functional configuration to assess configural breadth based on brain functional networks

The best approach to quantify human brain functional reconfigurations in response to varying cognitive demands remains an unresolved topic in network neuroscience. We propose that such functional reconfigurations may be categorized into three different types: i) Network Configural Breadth, ii) Task-to-Task transitional reconfiguration, and iii) Within-Task reconfiguration. In order to quantify these reconfigurations, we propose a mesoscopic framework focused on functional networks (FNs) or communities. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology and integration of information within and between a reference set of FNs. In this study, we use this framework to quantify the Network Configural Breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.Comment: main article: 24 pages, 8 figures, 2 tables. supporting information: 11 pages, 5 figure

Measuring the hierarchy of feedforward networks

12 páginas, 6 figuras.In this paper we explore the concept of hierarchy as a quantifiable descriptor of ordered structures, departing from the definition of three conditions to be satisfied for a hierarchical structure: order, predictability, and pyramidal structure. According to these principles, we define a hierarchical index taking concepts from graph and information theory. This estimator allows to quantify the hierarchical character of any system susceptible to be abstracted in a feedforward causal graph, i.e., a directed acyclic graph defined in a single connected structure. Our hierarchical index is a balance between this predictability and pyramidal condition by the definition of two entropies: one attending the onward flow and the other for the backward reversion.We show how this index allows to identify hierarchical, antihierarchical, and nonhierarchical structures. Our formalism reveals that departing from the defined conditions for a hierarchical structure, feedforward trees and the inverted tree graphs emerge as the only causal structures of maximal hierarchical and antihierarchical systems respectively. Conversely, null values of the hierarchical index are attributed to a number of different configuration networks; from linear chains, due to their lack of pyramid structure, to full-connected feedforward graphs where the diversity of onward pathways is canceled by the uncertainty (lack of predictability) when going backward. Some illustrative examples are provided for the distinction among these three types of hierarchical causal graphs.This work was supported by the EU 6th framework project ComplexDis (NEST-043241, CRC and JG), the UTE project CIMA (JG), Fundación Marcelino Botín (CRC), the James McDonnell Foundation (BCM and RVS) and the Santa Fe Institute (RVS)

Dinámica de Sistemas Biológicos: modelando complejidad

El estudio de la dinámica de los sistemas biológicos es, desde hace décadas, uno de los grandes objetivos en los que han aunado sus fuerzas las Ciencias de la Vida y las Ciencias de la Complejidad. La elaboración de modelos que reflejen dinámicas esperadas, lejos de ser un mero ejercicio de autocomplacencia, resulta un paso esencial para la comprensión de cualquier sistema. Un sistema del cual tenemos muchos datos experimentales, no es un sistema del cual tenemos gran conocimiento, sino gran cantidad de información. La síntesis de dicha información para lograr confirmar o desechar las hipótesis previas y la búsqueda de representaciones adecuadas que capturen la esencia del comportamiento son las bases para un buen modelo, y, por tanto, para una adquisición de conocimiento.En este artículo se presentan dos metodologías muy útiles para la modelización de sistemas biológicos: la Dinámica de Sistemas y los Sistemas Basados en Agentes. Ambas son de muy distinta naturaleza, y vienen a representar una gran disyuntiva a la hora de realizar un modelo: modelar las poblaciones o modelar los individuos. Es importante comprender la naturaleza y el contexto de estos paradigmas, para ser conscientes de cuándo es adecuado aplicar cada uno de ellos, explotando así sus virtudes y tratando de evitar sus carencias