Investigating White Matter Lesion Load, Intrinsic Functional Connectivity, and Cognitive Abilities in Older Adults

Changes to the while matter of the brain disrupt neural communication between spatially distributed brain regions and are associated with cognitive changes in later life. While approximately 95% of older adults experience these brain changes, not everyone who has significant white matter damage displays cognitive impairment. Few studies have investigated the association between white matter changes and cognition in the context of functional brain network integrity. This study used a data-driven, multivariate analytical model to investigate intrinsic functional connectivity patterns associated with individual variability in white matter lesion load as related to fluid and crystallized intelligence in a sample of healthy older adults (n = 84). Several primary findings were noted. First, a reliable pattern emerged associating whole-brain resting-state functional connectivity with individual variability in measures of white matter lesion load, as indexed by total white matter lesion volume and number of lesions. Secondly, white matter lesion load was associated with increased network disintegration and dedifferentiation. Specifically, lower white matter lesion load was associated with greater within- versus between-network connectivity. Higher white matter lesion load was associated with greater between-network connectivity compared to within. These associations between intrinsic functional connectivity and white matter lesion load were not reliably associated with crystallized and fluid intelligence performance. These results suggest that changes to the white matter of the brain in typically aging older adults are characterized by increased functional brain network dedifferentiation. The findings highlight the role of white matter lesion load in altering the functional network architecture of the brain

Concepção e realização de um framework para sistemas embarcados baseados em FPGA aplicado a um classificador Floresta de Caminhos Ótimos

Orientadores: Eurípedes Guilherme de Oliveira Nóbrega, Isabelle Fantoni-Coichot, Vincent FrémontTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica, Université de Technologie de CompiègneResumo: Muitas aplicações modernas dependem de métodos de Inteligência Artificial, tais como classificação automática. Entretanto, o alto custo computacional associado a essas técnicas limita seu uso em plataformas embarcadas com recursos restritos. Grandes quantidades de dados podem superar o poder computacional disponível em tais ambientes, o que torna o processo de projetá-los uma tarefa desafiadora. As condutas de processamento mais comuns usam muitas funções de custo computacional elevadas, o que traz a necessidade de combinar alta capacidade computacional com eficiência energética. Uma possível estratégia para superar essas limitações e prover poder computacional suficiente aliado ao baixo consumo de energia é o uso de hardware especializado como, por exemplo, FPGA. Esta classe de dispositivos é amplamente conhecida por sua boa relação desempenho/consumo, sendo uma alternativa interessante para a construção de sistemas embarcados eficazes e eficientes. Esta tese propõe um framework baseado em FPGA para a aceleração de desempenho de um algoritmo de classificação a ser implementado em um sistema embarcado. A aceleração do desempenho foi atingida usando o esquema de paralelização SIMD, aproveitando as características de paralelismo de grão fino dos FPGA. O sistema proposto foi implementado e testado em hardware FPGA real. Para a validação da arquitetura, um classificador baseado em Teoria dos Grafos, o OPF, foi avaliado em uma proposta de aplicação e posteriormente implementado na arquitetura proposta. O estudo do OPF levou à proposição de um novo algoritmo de aprendizagem para o mesmo, usando conceitos de Computação Evolutiva, visando a redução do tempo de processamento de classificação, que, combinada à implementação em hardware, oferece uma aceleração de desempenho suficiente para ser aplicada em uma variedade de sistemas embarcadosAbstract: Many modern applications rely on Artificial Intelligence methods such as automatic classification. However, the computational cost associated with these techniques limit their use in resource constrained embedded platforms. A high amount of data may overcome the computational power available in such embedded environments while turning the process of designing them a challenging task. Common processing pipelines use many high computational cost functions, which brings the necessity of combining high computational capacity with energy efficiency. One of the strategies to overcome this limitation and provide sufficient computational power allied with low energy consumption is the use of specialized hardware such as FPGA. This class of devices is widely known for their performance to consumption ratio, being an interesting alternative to building capable embedded systems. This thesis proposes an FPGA-based framework for performance acceleration of a classification algorithm to be implemented in an embedded system. Acceleration is achieved using SIMD-based parallelization scheme, taking advantage of FPGA characteristics of fine-grain parallelism. The proposed system is implemented and tested in actual FPGA hardware. For the architecture validation, a graph-based classifier, the OPF, is evaluated in an application proposition and afterward applied to the proposed architecture. The OPF study led to a proposition of a new learning algorithm using evolutionary computation concepts, aiming at classification processing time reduction, which combined to the hardware implementation offers sufficient performance acceleration to be applied in a variety of embedded systemsDoutoradoMecanica dos Sólidos e Projeto MecanicoDoutor em Engenharia Mecânica3077/2013-09CAPE

The Development of Bio-Inspired Cortical Feature Maps for Robot Sensorimotor Controllers

Full version unavailable due to 3rd party copyright restrictions.This project applies principles from the field of Computational Neuroscience to Robotics research, in particular to develop systems inspired by how nature manages to solve sensorimotor coordination tasks. The overall aim has been to build a self-organising sensorimotor system using biologically inspired techniques based upon human cortical development which can in the future be implemented in neuromorphic hardware. This can then deliver the benefits of low power consumption and real time operation but with flexible learning onboard autonomous robots. A core principle is the Self-Organising Feature Map which is based upon the theory of how 2D maps develop in real cortex to represent complex information from the environment. A framework for developing feature maps for both motor and visual directional selectivity representing eight different directions of motion is described as well as how they can be coupled together to make a basic visuomotor system. In contrast to many previous works which use artificially generated visual inputs (for example, image sequences of oriented moving bars or mathematically generated Gaussian bars) a novel feature of the current work is that the visual input is generated by a DVS 128 silicon retina camera which is a neuromorphic device and produces spike events in a frame-free way. One of the main contributions of this work has been to develop a method of autonomous regulation of the map development process which adapts the learning dependent upon input activity. The main results show that distinct directionally selective maps for both the motor and visual modalities are produced under a range of experimental scenarios. The adaptive learning process successfully controls the rate of learning in both motor and visual map development and is used to indicate when sufficient patterns have been presented, thus avoiding the need to define in advance the quantity and range of training data. The coupling training experiments show that the visual input learns to modulate the original motor map response, creating a new visual-motor topological map.EPSRC, University of Plymouth Graduate Schoo

Improving nonlinear search with Self-Organizing Maps - Application to Magnetic Resonance Relaxometry

Quantification of myelin in vivo is crucial for the understanding of neurological diseases, like multiple sclerosis (MS). Multi-Component Driven Equilibrium Single Pulse Observation T1 and T2 (mcDESPOT) is a rapid and precise method for determination of the longitudinal and transverse relaxation times in a voxel wise fashion. Briefly, mcDESPOT couples sets of SPGR (spoiled gradient-recalled echo) and bSSFP (fully balance steady-state free precession) data acquired over a range of flip angles (α) with constant interpulse spacing (TR) to derive 6 parameters (free-water T1 and T2, myelin-associated water T1 and T2, relative myelin-associated water volume fraction, and the myelin-associated water proton residence time) based on water exchange models. However, this procedure is computationally expensive and extremely difficult due to the need to find the best fit to the 24 MRI signals volumes in a search of nonlinear 6 dimensional space of model parameters. In this context, the aim of this work is to improve mcDESPOT efficiency and accuracy using tissue information contained in the sets of signals (SPGR and bSSFP) acquired. The basic hypothesis is that similar acquired signals are referred to tissue portions with close features, which translate in similar parameters. This similarity could be used to drive the nonlinear mcDESPOT fitting, leading the optimization algorithm (that is based on a stochastic region contraction approach) to look for a solution (i.e. the 6 parameters vector) also in regions defined by previously computed solutions of others voxels with similar signals. For this reason, we clustered the sets of SPGR and bSSFP using the neural network called Self Organizing Map (SOM), which uses a competitive learning technique to train itself in an unsupervised manner. The similarity information obtained from the SOM was then used to accordingly suggest solutions to the optimization algorithm. A first validation phase with in silico data was performed to evaluate the performances of the SOM and of the modified method, SOM+mcDESPOT. The latter was further validated using real magnetic resonance images. The last step consisted of applying the SOM+mcDESPOT to a group of healthy subjects ( ) and a group of MS patients ( ) to look for differences in myelin-associated water fractions values between the two groups. The validation phases with in silico data verified the initial hypothesis: in more the 74% of the times, the correct solution of a certain voxel is in the space dictated by the cluster which that voxel is mapped to. Adding the information of similar solutions extracted from that cluster helps to improve the signals fitting and the accuracy in the determination of the 7 parameters. This result is still present even if the data are corrupted by a high level of noise (SNR=50). Using real images allowed to confirm the power of SOM+mcDESPOT underlined through the in silico data. The application of SOM+mcDESPOT to the controls and to the MS patients allowed firstly obtaining more feasible results than the traditional mcDESPOT. Moreover, a statistically significant difference of the myelin-associated water fraction values in the normal appearing white matter was found between the two groups: the MS patients, in fact, show lower fraction values compared to the normal subjects, indicating an abnormal presence of myelin in the normal appearing white matter of MS patients. In conclusion, we proposed the novel method SOM+mcDESPOT that is able to extract and exploit the information contained in the MRI signals to drive appropriately the optimization algorithm implemented in mcDESPOT. In so doing, the overall accuracy of the method in both the signals fitting and in the determination of the 7 parameters improves. Thus, the outstanding potentiality of SOM+mcDESPOT could assume a crucial role in improving the indirect quantification of myelin in both healthy subjects and patient

Swarm-Organized Topographic Mapping

Topographieerhaltende Abbildungen versuchen, hochdimensionale oder komplexe Datenbestände auf einen niederdimensionalen Ausgaberaum abzubilden, wobei die Topographie der Daten hinreichend gut wiedergegeben werden soll. Die Qualität solcher Abbildung hängt gewöhnlich vom eingesetzten Nachbarschaftskonzept des konstruierenden Algorithmus ab. Die Schwarm-Organisierte Projektion ermöglicht eine Lösung dieses Parametrisierungsproblems durch die Verwendung von Techniken der Schwarmintelligenz. Die praktische Verwendbarkeit dieser Methodik wurde durch zwei Anwendungen auf dem Feld der Molekularbiologie sowie der Finanzanalytik demonstriert

Event characteristics of intra-seasonal climate circulations

An analysis of rainfall characteristics over the summer rainfall areas of South Africa is done in order to find links between rainfall variability and the general circulation. Seasonal rainfall totals are clearly linked to significant rain days, indicating the importance of evaluating synoptic-scale event characteristics. Rank ordered rainfall characteristics reveal that normal rainfall may be considered as the middle three quintiles, with the outer quintiles representing wet and dry conditions. The general circulation in tenns of atmospheric state (humidity and temperature), moisture and heat transport, and energy exchange - with attention to tropical-extratropical cloud-bands, are central to wet seasons in South Africa. These are generally associated with deep convection that is driven by vertical instability. Transport of moisture into South Africa generally takes place from the east with the Indian Ocean being a major source of water vapour. Although important, fluctuations in moisture transport are not clearly associated with rainfall. This is because moisture is a necessary but not a sufficient condition for rainfall. Atmospheric dynamics producing rainfall are a combination of disturbances of mid-latitude and tropical origin. These disturbances often initiate the tropical-temperate cloud-band where eddy available potential energy, generated through surface heating over land, is converted to eddy kinetic energy. It is generally mid-latitude disturbances with stronger vertical shear that are associated with rainfall events. Increased baroclinic activity in the Southern Ocean south of South Africa, as such, is generally associated with dry seasons. During these seasons there is usually a greater amount of available potential energy which strengthens the southern branch of the Hadley Cell. The effect of this is to increase the strength of the subtropical jet through enhanced poleward flux of angular momentum. The resulting increase in baroclinicity in the South Atlantic Ocean then disrupts rainfall over South Africa through the advection of dry air from the west by the vertical-mean wind component. General circulation models are shown to capture inter-annual variability such that forecasts of regional rainfall, of useful skill at a seasonal scale, may be made. However, the skill level with regard to daily circulation, restricts their use to the large-scale circulation. Nonetheless, the analysis of rainfall and the links to large-scale circulation, discussed in the thesis, provide information to produce more skilful seasonal forecasts without having to model small-scale features directly. Such forecasts may also include additional information on rainfall characteristics, such as number of rain-days and length of dry spells