Disparate connectivity for structural and functional networks is revealed when physical location of the connected nodes is considered

Macroscopic brain networks have been widely described with the manifold of metrics available using graph theory. However, most analyses do not incorporate information about the physical position of network nodes. Here, we provide a multimodal macroscopic network characterization while considering the physical positions of nodes. To do so, we examined anatomical and functional macroscopic brain networks in a sample of twenty healthy subjects. Anatomical networks are obtained with a graph based tractography algorithm from diffusion-weighted magnetic resonance images (DW-MRI). Anatomical con- nections identified via DW-MRI provided probabilistic constraints for determining the connectedness of 90 dif- ferent brain areas. Functional networks are derived from temporal linear correlations between blood-oxygenation level-dependent signals derived from the same brain areas. Rentian Scaling analysis, a technique adapted from very- large-scale integration circuits analyses, shows that func- tional networks are more random and less optimized than the anatomical networks. We also provide a new metric that allows quantifying the global connectivity arrange- ments for both structural and functional networks. While the functional networks show a higher contribution of inter-hemispheric connections, the anatomical networks highest connections are identified in a dorsal?ventral arrangement. These results indicate that anatomical and functional networks present different connectivity organi- zations that can only be identified when the physical locations of the nodes are included in the analysis.

The default mode network is functionally and structurally disrupted in amnestic mild cognitive impairment — a bimodal MEG–DTI study

Over the past years, several studies on Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) have reported Default Mode Network (DMN) deficits. This network is attracting increasing interest in the AD community, as it seems to play an important role in cognitive functioning and in beta amyloid deposition. Attention has been particularly drawn to how different DMN regions are connected using functional or structural connectivity. To this end, most studies have used functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET) or Diffusion Tensor Imaging (DTI). In this study we evaluated (1) functional connectivity from resting state magnetoencephalography (MEG) and (2) structural connectivity from DTI in 26 MCI patients and 31 age-matched controls. Compared to controls, the DMN in the MCI group was functionally disrupted in the alpha band, while no differences were found for delta, theta, beta and gamma frequency bands. In addition, structural disconnection could be assessed through a decreased fractional anisotropy along tracts connecting different DMN regions. This suggests that the DMN functional and anatomical disconnection could represent a core feature of MCI.

Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of mild cognitive impaired conditions

Whole brain resting state connectivity is a promising biomarker that might help to obtain an early diagnosis in many neurological diseases, such as dementia. Inferring resting-state connectivity is often based on correlations, which are sensitive to indirect connections, leading to an inaccurate representation of the real backbone of the network. The precision matrix is a better representation for whole brain connectivity, as it considers only direct connections. The network structure can be estimated using the graphical lasso (GL), which achieves sparsity through l1-regularization on the precision matrix. In this paper, we propose a structural connectivity adaptive version of the GL, where weaker anatomical connections are represented as stronger penalties on the corre- sponding functional connections. We applied beamformer source reconstruction to the resting state MEG record- ings of 81 subjects, where 29 were healthy controls, 22 were single-domain amnestic Mild Cognitive Impaired (MCI), and 30 were multiple-domain amnestic MCI. An atlas-based anatomical parcellation of 66 regions was ob- tained for each subject, and time series were assigned to each of the regions. The fiber densities between the re- gions, obtained with deterministic tractography from diffusion-weighted MRI, were used to define the anatomical connectivity. Precision matrices were obtained with the region specific time series in five different frequency bands. We compared our method with the traditional GL and a functional adaptive version of the GL, in terms of log-likelihood and classification accuracies between the three groups. We conclude that introduc- ing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups.

Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI

The structural connectivity of the brain is considered to encode species-wise and subject-wise patterns that will unlock large areas of understanding of the human brain. Currently, diffusion MRI of the living brain enables to map the microstructure of tissue, allowing to track the pathways of fiber bundles connecting the cortical regions across the brain. These bundles are summarized in a network representation called connectome that is analyzed using graph theory. The extraction of the connectome from diffusion MRI requires a large processing flow including image enhancement, reconstruction, segmentation, registration, diffusion tracking, etc. Although a concerted effort has been devoted to the definition of standard pipelines for the connectome extraction, it is still crucial to define quality assessment protocols of these workflows. The definition of quality control protocols is hindered by the complexity of the pipelines under test and the absolute lack of gold-standards for diffusion MRI data. Here we characterize the impact on structural connectivity workflows of the geometrical deformation typically shown by diffusion MRI data due to the inhomogeneity of magnetic susceptibility across the imaged object. We propose an evaluation framework to compare the existing methodologies to correct for these artifacts including whole-brain realistic phantoms. Additionally, we design and implement an image segmentation and registration method to avoid performing the correction task and to enable processing in the native space of diffusion data. We release PySDCev, an evaluation framework for the quality control of connectivity pipelines, specialized in the study of susceptibility-derived distortions. In this context, we propose Diffantom, a whole-brain phantom that provides a solution to the lack of gold-standard data. The three correction methodologies under comparison performed reasonably, and it is difficult to determine which method is more advisable. We demonstrate that susceptibility-derived correction is necessary to increase the sensitivity of connectivity pipelines, at the cost of specificity. Finally, with the registration and segmentation tool called regseg we demonstrate how the problem of susceptibility-derived distortion can be overcome allowing data to be used in their original coordinates. This is crucial to increase the sensitivity of the whole pipeline without any loss in specificity.

Study of OLSR protocol with Network Simulator 2 over Linux

Until a few years ago, most of the network communications were based in the wire as the physical media, but due to the advances and the maturity of the wireless communications, this is changing. Nowadays wireless communications offers fast, secure, efficient and reliable connections. Mobile communications are in expansion, clearly driven by the use of smart phones and other mobile devices, the use of laptops, etc… Besides that point, the inversion in the installation and maintenance of the physical medium is much lower than in wired communications, not only because the air has no cost, but because the installation and maintenance of the wire require a high economic cost. Besides the economic cost we find that wire is a more vulnerable medium to external threats such as noise, sabotages, etc… There are two different types of wireless networks: those which the structure is part of the network itself and those which have a lack of structure or any centralization, in a way that the devices that form part of the network can connect themselves in a dynamic and random way, handling also the routing of every control and information messages, this kind of networks is known as Ad-hoc. In the present work we will proceed to study one of the multiple wireless protocols that allows mobile communications, it is Optimized Link State Routing, from now on, OLSR, it is an pro-active routing, standard mechanism that works in a distributed in order to stablish the connections among the different nodes that belong to a wireless network. Thanks to this protocol it is possible to get all the routing tables in all the devices correctly updated every moment through the periodical transmission of control messages and on this way allow a complete connectivity among the devices that are part of the network and also, allow access to other external networks such as virtual private networks o Internet. This protocol could be perfectly used in environments such as airports, malls, etc… The update of the routing tables in all the devices is got thanks to the periodical transmission of control messages and finally it will offer connectivity among all the devices and the corresponding external networks. For the study of OLSR protocol we will have the help of the network simulator “Network Simulator 2”, a freeware network simulator programmed in C++ based in discrete events. This simulator is used mainly in educational and research environments and allows a very extensive range of protocols, both, wired networks protocols and wireless network protocols, what is going to be really useful to proceed to the simulation of different configurations of networks and protocols. In the present work we will also study different simulations with Network Simulator 2, in different scenarios with different configurations, wired networks, and Ad-hoc networks, where we will study OLSR Protocol. RESUMEN. Hasta hace pocos años, la mayoría de las comunicaciones de red estaban basadas en el cable como medio físico pero debido al avance y madurez alcanzados en el campo de las comunicaciones inalámbricas esto está cambiando. Hoy día las comunicaciones inalámbricas nos ofrecen conexiones veloces, seguras, eficientes y fiables. Las comunicaciones móviles se encuentran en su momento de máxima expansión, claramente impulsadas por el uso de teléfonos y demás dispositivos móviles, el uso de portátiles, etc… Además la inversión a realizar en la instalación y el mantenimiento del medio físico en las comunicaciones móviles es muchísimo menor que en comunicaciones por cable, ya no sólo porque el aire no tenga coste alguno, sino porque la instalación y mantenimiento del cable precisan de un elevado coste económico por norma. Además del coste económico nos encontramos con que es un medio más vulnerable a amenazas externas tales como el ruido, escuchas no autorizadas, sabotajes, etc… Existen dos tipos de redes inalámbricas: las constituidas por una infraestructura que forma parte más o menos de la misma y las que carecen de estructura o centralización alguna, de modo que los dispositivos que forman parte de ella pueden conectarse de manera dinámica y arbitraria entre ellos, encargándose además del encaminamiento de todos los mensajes de control e información, a este tipo de redes se las conoce como redes Ad-hoc. En el presente Proyecto de Fin de Carrera se procederá al estudio de uno de los múltiples protocolos inalámbricos que permiten comunicaciones móviles, se trata del protocolo inalámbrico Optimized Link State Routing, de ahora en adelante OLSR, un mecanismo estándar de enrutamiento pro-activo, que trabaja de manera distribuida para establecer las conexiones entre los nodos que formen parte de las redes inalámbricas Ad-hoc, las cuales carecen de un nodo central y de una infraestructura pre-existente. Gracias a este protocolo es posible conseguir que todos los equipos mantengan en todo momento las tablas de ruta actualizadas correctamente mediante la transmisión periódica de mensajes de control y así permitir una completa conectividad entre todos los equipos que formen parte de la red y, a su vez, también permitir el acceso a otras redes externas tales como redes privadas virtuales o Internet. Este protocolo sería usado en entornos tales como aeropuertos La actualización de las tablas de enrutamiento de todos los equipos se conseguirá mediante la transmisión periódica de mensajes de control y así finalmente se podrá permitir conectividad entre todos los equipos y con las correspondientes redes externas. Para el estudio del protocolo OLSR contaremos con el simulador de redes Network Simulator 2, un simulador de redes freeware programado en C++ basado en eventos discretos. Este simulador es usado principalmente en ambientes educativos y de investigación y permite la simulación tanto de protocolos unicast como multicast. El campo donde más se utiliza es precisamente en el de la investigación de redes móviles Ad-hoc. El simulador Network Simulator 2 no sólo implementa el protocolo OLSR, sino que éste implementa una amplia gama de protocolos, tanto de redes cableadas como de redes inalámbricas, lo cual va a sernos de gran utilidad para proceder a la simulación de distintas configuraciones de redes y protocolos. En el presente Proyecto de Fin de Carrera se estudiarán también diversas simulaciones con el simulador NS2 en diferentes escenarios con diversas configuraciones; redes cableadas, redes inalámbricas Ad-hoc, donde se estudiará el protocolo antes mencionado: OLSR. Este Proyecto de Fin de Carrera consta de cuatro apartados distintos: Primeramente se realizará el estudio completo del protocolo OLSR, se verán los beneficios y contrapartidas que ofrece este protocolo inalámbrico. También se verán los distintos tipos de mensajes existentes en este protocolo y unos pequeños ejemplos del funcionamiento del protocolo OLSR. Seguidamente se hará una pequeña introducción al simulador de redes Network Simulator 2, veremos la historia de este simulador, y también se hará referencia a la herramienta extra NAM, la cual nos permitirá visualizar el intercambio de paquetes que se produce entre los diferentes dispositivos de nuestras simulaciones de forma intuitiva y amigable. Se hará mención a la plataforma MASIMUM, encargada de facilitar en un entorno académico software y documentación a sus alumnos con el fin de facilitarles la investigación y la simulación de redes y sensores Ad-hoc. Finalmente se verán dos ejemplos, uno en el que se realizará una simulación entre dos PCs en un entorno Ethernet y otro ejemplo en el que se realizará una simulación inalámbrica entre cinco dispositivos móviles mediante el protocolo a estudiar, OLSR.