Cognitive processing of global and local visual stimuli in autism spectrum disorder

An ongoing debate is whether people with autism spectrum disorder (ASD) have a local processing bias and to what extent impaired contextual processing is associated with this bias. The set of experiments employed in this project examined global and local processing, shifts between global and local processing, and low- and high-level visual processing in an attempt to address this issue. This thesis tested the hypotheses that (1) a local processing bias is associated with impaired global processing in ASD individuals, and (2) atypical processing style is linked with ASD severity. Twenty ASD individuals and 20 IQ and age (15-30 years) matched normal controls were administered a novel embedded figures task (local processing advantageous), a novel form matching task and novel shape integration task (global processing advantageous), a local-global switching task (which assessed attention broadening and attention narrowing ability), and a local and global motion detection task. The Social Responsiveness Scale was used to assess ASD severity. The ASD group correctly detected significantly more embedded shapes than controls. Compared to controls, ASD participants were disproportionately slower on the shape integration task relative to the form perception task. No overall group differences were found in attention broadening or attention narrowing ability. In addition, no group differences were found in local or global motion perception. Results also revealed a significant correlation between ASD severity and (1) faster response time on the embedded figures test, (2) slower response time on the shape integration task, (3) reduced attention broadening ability, and (4) reduced global motion perception. These findings confirm previous reports of enhanced local visual processing in ASD, and suggest that while global form perception is intact in ASD, global integration is more problematic. There was no evidence of generalized attentional impairments or motion perception abnormalities in ASD participants, suggesting that lower-level perceptual functions may be spared in people with ASD. Perhaps most intriguing was the observed association between ASD severity and enhanced local perception and impaired global processing. This association suggests that both a local processing bias and impaired global processing may play a role in the behavioral aspects of ASD symptomatology

Multistage classification of multispectral Earth observational data: The design approach

An algorithm is proposed which predicts the optimal features at every node in a binary tree procedure. The algorithm estimates the probability of error by approximating the area under the likelihood ratio function for two classes and taking into account the number of training samples used in estimating each of these two classes. Some results on feature selection techniques, particularly in the presence of a very limited set of training samples, are presented. Results comparing probabilities of error predicted by the proposed algorithm as a function of dimensionality as compared to experimental observations are shown for aircraft and LANDSAT data. Results are obtained for both real and simulated data. Finally, two binary tree examples which use the algorithm are presented to illustrate the usefulness of the procedure

Contributions to the study of Austism Spectrum Brain conectivity

164 p.Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this Thesis we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines

Bilingualism and Attention in Typically Developing Children and Children With Developmental Language Disorder

Purpose: The aim of the current study was to investigate whether dual language experience modulates the efficiency of the 3 attentional networks (alerting, orienting, and executive control) in typically developing (TD) children and in children with developmental language disorder (DLD).Method: We examined the attentional networks in monolingual and bilingual school-aged children (ages 8–12 years) with and without DLD. TD children (35 monolinguals, 23 bilinguals) and children with DLD (17 monolinguals, 9 bilinguals) completed the Attention Network Test (Fan et al., 2002; Fan, McCandliss, Fossella, Flombaum, & Posner, 2005).Results: Children with DLD exhibited poorer executive control than TD children, but executive control was not modified by bilingual experience. The bilingual group with DLD and both TD groups exhibited an orienting effect, but the monolingual group with DLD did not. No group differences were found for alerting.Conclusions: Children with DLD have weak executive control skills. These skills are minimally influenced by dual language experience, at least in this age range. A potential bilingual advantage in orienting may be present in the DLD group.</p

On Practical machine Learning and Data Analysis

This thesis discusses and addresses some of the difficulties associated with practical machine learning and data analysis. Introducing data driven methods in e.g industrial and business applications can lead to large gains in productivity and efficiency, but the cost and complexity are often overwhelming. Creating machine learning applications in practise often involves a large amount of manual labour, which often needs to be performed by an experienced analyst without significant experience with the application area. We will here discuss some of the hurdles faced in a typical analysis project and suggest measures and methods to simplify the process. One of the most important issues when applying machine learning methods to complex data, such as e.g. industrial applications, is that the processes generating the data are modelled in an appropriate way. Relevant aspects have to be formalised and represented in a way that allow us to perform our calculations in an efficient manner. We present a statistical modelling framework, Hierarchical Graph Mixtures, based on a combination of graphical models and mixture models. It allows us to create consistent, expressive statistical models that simplify the modelling of complex systems. Using a Bayesian approach, we allow for encoding of prior knowledge and make the models applicable in situations when relatively little data are available. Detecting structures in data, such as clusters and dependency structure, is very important both for understanding an application area and for specifying the structure of e.g. a hierarchical graph mixture. We will discuss how this structure can be extracted for sequential data. By using the inherent dependency structure of sequential data we construct an information theoretical measure of correlation that does not suffer from the problems most common correlation measures have with this type of data. In many diagnosis situations it is desirable to perform a classification in an iterative and interactive manner. The matter is often complicated by very limited amounts of knowledge and examples when a new system to be diagnosed is initially brought into use. We describe how to create an incremental classification system based on a statistical model that is trained from empirical data, and show how the limited available background information can still be used initially for a functioning diagnosis system. To minimise the effort with which results are achieved within data analysis projects, we need to address not only the models used, but also the methodology and applications that can help simplify the process. We present a methodology for data preparation and a software library intended for rapid analysis, prototyping, and deployment. Finally, we will study a few example applications, presenting tasks within classification, prediction and anomaly detection. The examples include demand prediction for supply chain management, approximating complex simulators for increased speed in parameter optimisation, and fraud detection and classification within a media-on-demand system

Bases cérébrales de la perception auditive simple et complexe dans l’autisme

La perception est décrite comme l’ensemble des processus permettant au cerveau de recueillir et de traiter l’information sensorielle. Un traitement perceptif atypique se retrouve souvent associé au phénotype autistique habituellement décrit en termes de déficits des habilités sociales et de communication ainsi que par des comportements stéréotypés et intérêts restreints. Les particularités perceptives des autistes se manifestent à différents niveaux de traitement de l’information; les autistes obtiennent des performances supérieures à celles des non autistes pour discriminer des stimuli simples, comme des sons purs, ou encore pour des tâches de plus haut niveau comme la détection de formes enchevêtrées dans une figure complexe. Spécifiquement pour le traitement perceptif de bas niveau, on rapporte une dissociation de performance en vision. En effet, les autistes obtiennent des performances supérieures pour discriminer les stimuli définis par la luminance et inférieures pour les stimuli définis par la texture en comparaison à des non autistes. Ce pattern dichotomique a mené à l’élaboration d’une hypothèse suggérant que l’étendue (ou complexité) du réseau de régions corticales impliquées dans le traitement des stimuli pourrait sous-tendre ces différences comportementales. En effet, les autistes obtiennent des performances supérieures pour traiter les stimuli visuels entièrement décodés au niveau d’une seule région corticale (simples) et inférieures pour les stimuli dont l’analyse requiert l’implication de plusieurs régions corticales (complexes). Un traitement perceptif atypique représente une caractéristique générale associée au phénotype autistique, avec de particularités rapportées tant dans la modalité visuelle qu’auditive. Étant donné les parallèles entre ces deux modalités sensorielles, cette thèse vise à vérifier si l’hypothèse proposée pour expliquer certaines particularités du traitement de l’information visuelle peut possiblement aussi caractériser le traitement de l’information auditive dans l’autisme. Le premier article (Chapitre 2) expose le niveau de performance des autistes, parfois supérieur, parfois inférieur à celui des non autistes lors du traitement de l’information auditive et suggère que la complexité du matériel auditif à traiter pourrait être en lien avec certaines des différences observées. Le deuxième article (Chapitre 3) présente une méta-analyse quantitative investiguant la représentation au niveau cortical de la complexité acoustique chez les non autistes. Ce travail confirme l’organisation fonctionnelle hiérarchique du cortex auditif et permet d’identifier, comme en vision, des stimuli auditifs pouvant être définis comme simples et complexes selon l’étendue du réseau de régions corticales requises pour les traiter. Le troisième article (Chapitre 4) vérifie l’extension des prédictions de l’hypothèse proposée en vision au traitement de l’information auditive. Spécifiquement, ce projet compare les activations cérébrales sous-tendant le traitement des sons simples et complexes chez des autistes et des non autistes. Tel qu’attendu, les autistes montrent un patron d’activité atypique en réponse aux stimuli complexes, c’est-à-dire ceux dont le traitement nécessitent l’implication de plusieurs régions corticales. En bref, l’ensemble des résultats suggèrent que les prédictions de l’hypothèse formulée en vision peuvent aussi s’appliquer en audition et possiblement expliquer certaines particularités du traitement de l’information auditive dans l’autisme. Ce travail met en lumière des différences fondamentales du traitement perceptif contribuant à une meilleure compréhension des mécanismes d’acquisition de l’information dans cette population.Perception involves the processes allowing the brain to extract and understand sensory information. Atypical perceptual processing has been associated with the autistic phenotype usually described in terms of impairments in social and communication abilities, as well as restricted interests and repetitive behaviours. Perceptual atypicalities are reported across a range of tasks. For instance, superior performance in autistics compared to non autistics is observed for pure tone discrimination as well as for complex figure disembodying tasks. One particular study reported atypical low-level visual processing in autism. In this experiment, autistics displayed enhanced performance for identifying the orientation of luminance-defined gratings and inferior performance for texture-defined gratings in comparison to non autistics. This dichotomous pattern led to the formulation of a hypothesis suggesting an inverse relation between the level of performance and the extent (or complexity) of the cortical network required for processing the stimuli. Specifically, autistics would perform better than non autistics during processing visual stimuli involving one cortical region (luminance-defined or simple stimuli), while they would show decreased performance for processing stimuli involving a network of cortical region (texture-defined or complex stimuli). Atypical perceptual processing is described as a general feature associated with the autistic phenotype and is reported for both the visual and the auditory modalities. Considering the existing parallels between the two sensory modalities, the principal purpose of the presented doctoral dissertation it to verify whether the hypothesis proposed to explain atypical visual processing in autism could also apply to audition. The first article (Chapter 2) is an exhaustive literature review of studies on autistics’ auditory processing abilities. Taken together, the results suggest that the level of performance of autistics on auditory tasks could be related to the acoustic complexity of the stimuli. The second article (Chapter 3) uses quantitative meta-analysis to investigate how auditory complexity is represented at the cortical level in non autistics. This study confirms the hierarchical functional organization of the auditory cortex and allows defining simple and complex auditory stimuli based on the extent of the cortical network involved in their processing, as it was done in vision. The third article (Chapter 4) verifies if the predictions of the hypothesis proposed in vision could also apply in audition. Specifically, this study examines the cortical auditory response to simple and complex sounds in autistics and non autistics. As expected, autistics display atypical cortical activity in response to complex auditory material that is stimuli involving a network of multiple cortical regions to be processed. In sum, the studies in this dissertation indicate that the predictions of the hypothesis proposed in vision could extend to audition and possibly explain some of the atypical behaviours related to auditory processing in autism. This thesis demonstrates fundamentally different auditory cortical processing in autistics that could help define a general model of perceptual differences in autism which could represent a key factor in the understanding of information acquisition

A Neural Signal Processor for Low-Latency Spike Inference

This thesis describes the development of a system that can assign identities to a population of single-units, in multi-electrode recordings, at single-spike resolution with low-latency. The system has two parts. The first is a Field-Programmable Gate Array (FPGA)-based Neural Signal Processor (NSP) that receives raw input and generates labelled spikes as output, a process referred to as real-time spike inference. The second is a piece of software (Spiketag) that runs on a PC, communicates with the NSP, and generates a spike-sorted model to guide the real-time spike inference. The NSP provides clocks and control signals to five 32-channel INTAN RHD2132 chips to manage the acquisition of 160 channels of raw neural data. In parallel, the NSP further filters, detects and extracts extracellular spike waveforms from the raw neural data recorded by tetrodes or silicon probes and assigns single-unit identity to each detected spike. A set of Python application programming interfaces (APIs) was developed in Spiketag to enable the communication between the NSP and the PC. These APIs allow the NSP to obtain a model from the PC, which holds parameters such as reference channels, spike detection thresholds, spike feature transformation matrix and vector quantized clusters generated by spike sorting a short recording session. Using the spike-sorted model, the NSP performs data acquisition and real-time spike inference simultaneously. Algorithmic modules were implemented in the FPGA and pipelined to compute during 40 ms acquisition intervals. At the output end of the FPGA NSP, the real-time assigned single-unit identity (spike-id) is packaged with the timestamp, the electrode group, and the spike features as a spike-id packet. Spike-id packets are asynchronously transmitted through a low-latency Peripheral Component Interconnect Express (PCIe) interface to the PC, producing the real-time spike trains. The real-time spike trains can be used for further processing, such as real-time decoding. Several types of ground-truth data, including intracellular/extracellular paired recordings, synthesized tetrode extracellular waveforms with ground-truth spike timing and high-channel-count silicon probe recordings with ground-truth animal positions during navigation were used to validate the low-latency (1 ms) and high-accuracy (as high as state-of-the-art offline sorting and decoding algorithms) of the NSP’s real-time spike inference and the NSP-based real-time population decoding performance

Goddard Visiting Scientist Program for the Space and Earth Sciences Directorate

Progress reports of the Visiting Scientist Program covering the period from 1 Jul. - 30 Sep. 1992 are included. Topics covered include space science and earth science. Other topics covered include cosmic rays, magnetic clouds, solar wind, satellite data, high resolution radiometer, and microwave scattering