Multivariate Bayesian decoding of single-trial event-related fMRI responses for memory retrieval of voluntary actions.

This study proposes a method for classifying event-related fMRI responses in a specialized setting of many known but few unknown stimuli presented in a rapid event-related design. Compared to block design fMRI signals, classification of the response to a single or a few stimulus trial(s) is not a trivial problem due to contamination by preceding events as well as the low signal-to-noise ratio. To overcome such problems, we proposed a single trial-based classification method of rapid event-related fMRI signals utilizing sparse multivariate Bayesian decoding of spatio-temporal fMRI responses. We applied the proposed method to classification of memory retrieval processes for two different classes of episodic memories: a voluntarily conducted experience and a passive experience induced by watching a video of others' actions. A cross-validation showed higher classification performance of the proposed method compared to that of a support vector machine or of a classifier based on the general linear model. Evaluation of classification performances for one, two, and three stimuli from the same class and a correlation analysis between classification accuracy and target stimulus positions among trials suggest that presenting two target stimuli at longer inter-stimulus intervals is optimal in the design of classification experiments to identify the target stimuli. The proposed method for decoding subject-specific memory retrieval of voluntary behavior using fMRI would be useful in forensic applications in a natural environment, where many known trials can be extracted from a simulation of everyday tasks and few target stimuli from a crime scene.ope

What information is represented in the human hippocampus?

The hippocampus plays a critical role in supporting memories of our personal past experiences (episodic memories). However, it is not known how individual episodic memories are represented by neuronal populations within the hippocampus. The aim of my thesis was to explore the nature of the information represented in the human hippocampus, with a particular focus on episodic memories. I conducted five experiments using high-resolution and standard functional MRI (fMRI). In four of these projects I used and further developed a method known as multi-voxel pattern analysis (MVPA). This enabled me to interrogate the fMRI data to look for functionally-relevant patterns of information encoded across multiple voxels. My findings revealed that episodic memories were represented in the hippocampus more so than in neighbouring brain regions, that this was true even of memories that were highly overlapping in terms of content and context, and for recently-formed and very remote memories. Furthermore, I found that the episodic information within individual hippocampal subfields was consistent with computational models of episodic memory. One important contribution to the representation of an episodic memory is scene construction - the mental construction of a complex spatially coherent scene into which event details are bound. In order to explore the role of the hippocampus in scene construction, I used fMRI to study boundary extension – a scene-related phenomenon whereby people extrapolate beyond the edges of a given view. This revealed that hippocampal activity tracked the emergence of boundary extension, suggesting that scene construction can be rapid, automatic, and implicit. Overall, my findings shed new light on the nature of episodic representations within the human hippocampus, and offer an empirical link between episodic memory and computational theory. Moreover, they provide further evidence regarding scene construction, which is a key component of episodic representations within the hippocampus

Predicting Text Quality: Metrics for Content, Organization and Reader Interest

When people read articles---news, fiction or technical---most of the time if not always, they form perceptions about its quality. Some articles are well-written and others are poorly written. This thesis explores if such judgements can be automated so that they can be incorporated into applications such as information retrieval and automatic summarization. Text quality does not involve a single aspect but is a combination of numerous and diverse criteria including spelling, grammar, organization, informative nature, creative and beautiful language use, and page layout. In the education domain, comprehensive lists of such properties are outlined in the rubrics used for assessing writing. But computational methods for text quality have addressed only a handful of these aspects, mainly related to spelling, grammar and organization. In addition, some text quality aspects could be more relevant for one genre versus another. But previous work have placed little focus on specialized metrics based on the genre of texts. This thesis proposes new insights and techniques to address the above issues. We introduce metrics that score varied dimensions of quality such as content, organization and reader interest. For content, we present two measures: specificity and verbosity level. Specificity measures the amount of detail present in a text while verbosity captures which details are essential to include. We measure organization quality by quantifying the regularity of the intentional structure in the article and also using the specificity levels of adjacent sentences in the text. Our reader interest metrics aim to identify engaging and interesting articles. The development of these measures is backed by the use of articles from three different genres: academic writing, science journalism and automatically generated summaries. Proper presentation of content is critical during summarization because summaries have a word limit. Our specificity and verbosity metrics are developed with this genre as the focus. The argumentation structure of academic writing lends support to the idea of using intentional structure to model organization quality. Science journalism articles convey research findings in an engaging manner and are ideally suited for the development and evaluation of measures related to reader interest

Cost-Quality Trade-Offs in One-Class Active Learning

Active learning is a paradigm to involve users in a machine learning process. The core idea of active learning is to ask a user to annotate a specific observation to improve the classification performance. One important application of active learning is detecting outliers, i.e., unusual observations that deviate from the regular ones in a data set. Applying active learning for outlier detection in practice requires to design a system that consists of several components: the data, the classifier that discerns between inliers and outliers, the query strategy that selects the observations for feedback collection, and an oracle, e.g., the human expert that annotates the queries. Each of these components and their interplay influences the classification quality. Naturally, there are cost budgets limiting certain parts of the system, e.g., the number of queries one can ask a human. Thus, to configure efficient active learning systems, one must decide on several trade-offs between costs and quality. The existing literature on active learning systems does not provide an overview nor a formal description of the cost-quality trade-offs of active learning. All this makes the configuration of efficient active learning systems in practice difficult. In this thesis, we study different cost-quality trade-offs that are pivotal for configuring an active learning system for outlier detection. We first provide an overview of the costs of an active learning system. Then, we analyze three important trade-offs and propose ways to model and quantify them. In our first contribution, we study how one can reduce classification training costs by training only on a sample of the data set. We formalize the sampling trade-off between classifier training costs and resulting quality as an optimization problem and propose an efficient algorithm to solve it. Compared to the existing sampling methods in literature, our approach guarantees that a classifier trained on our sample makes the same predictions as if trained on the complete data set. We can therefore reduce the classification training costs without a loss of classification quality. In our second contribution, we investigate how selecting multiple queries allows trading off costs against quality. So-called batch queries reduce classifier training costs because the system only updates the classifier once for each batch. But the annotation of a batch may give redundant information, which reduces the achievable quality with a fixed query budget. We are the first to consider batch queries for outlier detection, a generalization of the more common case to query sequentially. We formalize batch active learning and propose several strategies to construct batches by modeling the expected utility of a batch. In our third contribution, we propose query synthesis for outlier detection. Query synthesis allows to artificially generate queries at any point in the data space without being restricted by a pool of query candidates. We propose a framework to efficiently synthesize queries and develop a novel query strategy to improve the generalization of a classifier beyond a biased data set with active learning. For all contributions, we derive recommendations for the cost-quality trade-offs from formal investigations and empirical studies to facilitate the configuration of robust and efficient active learning systems for outlier detection

What is the function of the human retrosplenial cortex?

The retrosplenial cortex (RSC) comprises Brodmann areas 29/30 and is an integral part of a brain system that is engaged by spatial navigation, scene processing, recollection of the past and imagining the future. Damage involving the RSC in humans can result in significant memory and navigation deficits, while the earliest metabolic decline in Alzheimer's disease is centred upon this region. The precise function of the RSC, however, remains elusive. In this thesis I sought to determine the key contribution of the RSC in a series of six studies that each comprised behavioural and functional magnetic resonance imaging (fMRI) experiments. Specifically, I discovered that the RSC is acutely responsive to landmarks in the environment that maintain a fixed, permanent location in space, and moreover is sensitive to the exact number of permanent landmarks in view. Using a virtual reality environment populated with entirely novel ‘alien’ landmarks I then tracked the de novo acquisition of landmark knowledge and observed the selective engagement of the RSC as information about landmark permanence accrued. In three further studies I established the parameters within which the RSC operates by contrasting permanent landmarks in large- and small-scale space, by comparing landmark permanence with orientation value, and by investigating permanence in non-spatial domains. In parallel lines of inquiry, I uncovered evidence that a fully functional RSC may be a prerequisite for successful navigation, while also characterising RSC interactions with other brain regions, such as the hippocampus, that could have importance for constructing reliable representations of the world. Together my findings provide new insights into the role of the RSC in a range of cognitive functions. The RSC’s processing of permanent predictable features may represent a key building block for spatial and scene representations that are central to navigation, recalling past experiences and imagining the future

Utilisation du contexte pour l’indexation sémantique des images et vidéos

The automated indexing of image and video is a difficult problem because of the``distance'' between the arrays of numbers encoding these documents and the concepts (e.g. people, places, events or objects) with which we wish to annotate them. Methods exist for this but their results are far from satisfactory in terms of generality and accuracy. Existing methods typically use a single set of such examples and consider it as uniform. This is not optimal because the same concept may appear in various contexts and its appearance may be very different depending upon these contexts. In this thesis, we considered the use of context for indexing multimedia documents. The context has been widely used in the state of the art to treat various problems. In our work, we use relationships between concepts as a source of semantic context. For the case of videos, we exploit the temporal context that models relationships between the shots of the same video. We propose several approaches using both types of context and their combination, in different levels of an indexing system. We also present the problem of multiple concept detection. We assume that it is related to the context use problematic. We consider that detecting simultaneously a set of concepts is equivalent to detecting one or more concepts forming the group in a context where the others are present. To do that, we studied and compared two types of approaches. All our proposals are generic and can be applied to any system for the detection of any concept. We evaluated our contributions on TRECVID and VOC collections, which are of international standards and recognized by the community. We achieved good results comparable to those of the best indexing systems evaluated in recent years in the evaluation campaigns cited previously.L'indexation automatisée des documents image fixe et vidéo est un problème difficile en raison de la ``distance'' existant entre les tableaux de nombres codant ces documents et les concepts avec lesquels on souhaite les annoter (personnes, lieux, événements ou objets, par exemple). Des méthodes existent pour cela mais leurs résultats sont loin d'être satisfaisants en termes de généralité et de précision. Elles utilisent en général un ensemble unique de tels exemples et le considère d'une manière uniforme. Ceci n'est pas optimal car un même concept peut apparaître dans des contextes très divers et son apparence peut être très différente en fonction de ces contextes. Dans le cadre de cette thèse, nous avons considéré l'utilisation du contexte pour l'indexation des documents multimédia. Le contexte a largement été utilisé dans l'état de l'art pour traiter diverses problématiques. Dans notre travail, nous retenons les relations entre les concepts comme source de contexte sémantique. Pour le cas des vidéos, nous exploitons le contexte temporel qui modélise les relations entre les plans d'une même vidéo. Nous proposons plusieurs approches utilisant les deux types de contexte ainsi que leur combinaison, dans différents niveaux d'un système d'indexation. Nous présentons également le problème de détection simultanée de groupes de concepts que nous jugeons lié à la problématique de l'utilisation du contexte. Nous considérons que la détection d'un groupe de concepts revient à détecter un ou plusieurs concepts formant le groupe dans un contexte ou les autres sont présents. Nous avons étudié et comparé pour cela deux catégories d'approches. Toutes nos propositions sont génériques et peuvent être appliquées à n'importe quel système pour la détection de n'importe quel concept. Nous avons évalué nos contributions sur les collections de données TRECVid et VOC, qui sont des standards internationaux et reconnues par la communauté. Nous avons obtenu de bons résultats, comparables à ceux des meilleurs systèmes d'indexation évalués ces dernières années dans les compagnes d'évaluation précédemment citées

Apicomplexan Parasite Interactions with Host Organelles: Recruitment, Lipid Scavenging, and Consequences

Apicomplexa parasites are harmful pathogens of humans and animals. They invade mammalian cells wherein they reside within a parasitophorous vacuole (PV) that protects them from cytosolic destructive pathways but forms a physical barrier from host-derived nutrients. Among Apicomplexa, Toxoplasma gondii has proficiently evolved to recruit host organelles to its PV to facilitate nutrient uptake. Almost nothing is known about the intracellular development of Neospora caninum, a closely relative of Toxoplasma. We show that N. caninum is also able to attract host organelles (ER, Golgi, endosomes) to its PV from which it retrieves cholesterol and ceramides, revealing conserved strategies among these parasites to exploit nutrient-filled host organelles. We next explore the role of host lipid droplets (LD) as sources of neutral lipids for Toxoplasma. We demonstrate that host LD cluster around the PV, and infection leads to an increase, then decrease in host LD numbers, suggesting a manipulation of these structures by Toxoplasma. Indeed, Toxoplasma is able to scavenge lipids from host LD, in part through the interception of Rab vesicles associated with LD and the translocation of host LD into its PV. Ectopic addition of oleic acid (OA) up to 1 mM (non toxic concentration for mammalian cells) stimulates LD biogenesis. When exposed to 0.2 mM OA, Toxoplasma scavenges this fatty acid in excess, channels it to LD that accumulate in the cytoplasm, as a result of increased transcription of its enzymes generating neutral lipids. However, this condition slows down both parasite replication and egress. By comparison, 0.2 mM palmitic acid does not affect the parasite’s intracellular development. Interestingly, ultrastructural analyses of OA-loaded Toxoplasma reveal for the first time, the presence of coated pits at the plasma membrane and structures potentially involved in endocytosis. More dramatically, addition of 0.4 mM OA to the medium results in massive accumulation of lipid deposits in the PV and the parasite, leading to replication defects and death. This highlights the high sensitivity of Toxoplasma towards deleterious effects of accumulating fatty acids. Deciphering the lipotoxic response of the parasite would reveal new vulnerabilities amenable to controlling Toxoplasma infections

Developmental trajectories of social signal processing

Most of the social cognitive and affective neuroscience in the past 3 decades has focussed on the face. By contrast, the understanding of processing social cues from the body and voice have been somewhat neglected in the literature. One could argue that, from an evolutionary point of view, body recognition (and particularly emotional body perception) is more important than that of the face. It may be beneficial for survival to be able to predict another’s behaviour or emotional state from a distance, without having to rely on facial expressions. If there are relatively few cognitive and affective neuroscience studies of body and voice perception, there are even fewer on the development of these processes. In this thesis, we set out to explore the behavioural and functional developmental trajectories of body and voice processing in children, adolescents and adults using fMRI, behavioural measures, and a wide range of univariate and multivariate analytical techniques. We found, using simultaneously recorded point-light and full-light displays of affective body movements, an increase in emotion recognition ability until 8.5 years old, followed by a slower rate of accuracy improvement through adolescence into adulthood (Chapter 2). Using fMRI we show, for the first time, that the body-selective areas of the visual cortex are not yet ‘adult-like’ in children (Chapter 3). We go on to show in Chapter 4, that although the body- selective regions are still maturing in the second decade of life, there is no difference between children, adolescents and adults in the amount of emotion modulation that these regions exhibit when presented with happy or angry bodies. We also show a positive correlation between amygdala activation and amount of emotion modulation of the body-selective areas in all subjects except the adolescents. Finally, we turn our attention to the development of the voice- selective areas in the temporal cortex, finding that, contrary to face and body processing, these areas are already ‘adult-like’ in children in terms of strength and extent of activation (Chapter 5). These results are discussed in relation to current developmental literature, limitations are considered, direction for future research is given and the wider clinical application of this work is explored