Formal Models of the Network Co-occurrence Underlying Mental Operations

International audienceSystems neuroscience has identified a set of canonical large-scale networks in humans. These have predominantly been characterized by resting-state analyses of the task-uncon-strained, mind-wandering brain. Their explicit relationship to defined task performance is largely unknown and remains challenging. The present work contributes a multivariate statistical learning approach that can extract the major brain networks and quantify their configuration during various psychological tasks. The method is validated in two extensive datasets (n = 500 and n = 81) by model-based generation of synthetic activity maps from recombination of shared network topographies. To study a use case, we formally revisited the poorly understood difference between neural activity underlying idling versus goal-directed behavior. We demonstrate that task-specific neural activity patterns can be explained by plausible combinations of resting-state networks. The possibility of decomposing a mental task into the relative contributions of major brain networks, the "network co-occurrence architecture" of a given task, opens an alternative access to the neural substrates of human cognition

Pattern recognition and machine learning for magnetic resonance images with kernel methods

The aim of this thesis is to apply a particular category of machine learning and pattern recognition algorithms, namely the kernel methods, to both functional and anatomical magnetic resonance images (MRI). This work specifically focused on supervised learning methods. Both methodological and practical aspects are described in this thesis. Kernel methods have the computational advantage for high dimensional data, therefore they are idea for imaging data. The procedures can be broadly divided into two components: the construction of the kernels and the actual kernel algorithms themselves. Pre-processed functional or anatomical images can be computed into a linear kernel or a non-linear kernel. We introduce both kernel regression and kernel classification algorithms in two main categories: probabilistic methods and non-probabilistic methods. For practical applications, kernel classification methods were applied to decode the cognitive or sensory states of the subject from the fMRI signal and were also applied to discriminate patients with neurological diseases from normal people using anatomical MRI. Kernel regression methods were used to predict the regressors in the design of fMRI experiments, and clinical ratings from the anatomical scans

Deep Interpretability Methods for Neuroimaging

Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Nevertheless, the difficulty of reliable training on high-dimensional but small-sample datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this dissertation, we address these challenges by proposing a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. The developed model is pre-trainable and alleviates the need to collect an enormous amount of neuroimaging samples to achieve optimal training. We also provide a quantitative validation module, Retain and Retrain (RAR), that can objectively verify the higher predictability of the dynamics learned by the model. Results successfully demonstrate that the proposed framework enables learning the fMRI dynamics directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction. We also comprehensively reviewed deep interpretability literature in the neuroimaging domain. Our analysis reveals the ongoing trend of interpretability practices in neuroimaging studies and identifies the gaps that should be addressed for effective human-machine collaboration in this domain. This dissertation also proposed a post hoc interpretability method, Geometrically Guided Integrated Gradients (GGIG), that leverages geometric properties of the functional space as learned by a deep learning model. With extensive experiments and quantitative validation on MNIST and ImageNet datasets, we demonstrate that GGIG outperforms integrated gradients (IG), which is considered to be a popular interpretability method in the literature. As GGIG is able to identify the contours of the discriminative regions in the input space, GGIG may be useful in various medical imaging tasks where fine-grained localization as an explanation is beneficial

Text Classification

There is an abundance of text data in this world but most of it is raw. We need to extract information from this data to make use of it. One way to extract this information from raw text is to apply informative labels drawn from a pre-defined fixed set i.e. Text Classification. In this thesis, we focus on the general problem of text classification, and work towards solving challenges associated to binary/multi-class/multi-label classification. More specifically, we deal with the problem of (i) Zero-shot labels during testing; (ii) Active learning for text screening; (iii) Multi-label classification under low supervision; (iv) Structured label space; (v) Classifying pairs of words in raw text i.e. Relation Extraction. For (i), we use a zero-shot classification model that utilizes independently learned semantic embeddings. Regarding (ii), we propose a novel active learning algorithm that reduces problem of bias in naive active learning algorithms. For (iii), we propose neural candidate-selector architecture that starts from a set of high-recall candidate labels to obtain high-precision predictions. In the case of (iv), we proposed an attention based neural tree decoder that recursively decodes an abstract into the ontology tree. For (v), we propose using second-order relations that are derived by explicitly connecting pairs of words via context token(s) for improved relation extraction. We use a wide variety of both traditional and deep machine learning tools. More specifically, we used traditional machine learning models like multi-valued linear regression and logistic regression for (i, ii), deep convolutional neural networks for (iii), recurrent neural networks for (iv) and transformer networks for (v)

Encoding of Motor Behaviors by Cortical Neuronal Networks

Performance of motor behavior requires complex coordination of neural activity across diverse regions of cortex at multiples scales. At the level of coordination across large areas of cortex, this activity is thought to be related to similarly broad concepts of movement from goal identification to motor planning to generation of motor commands. At smaller scales on the level of local populations of individual neurons in motor and premotor cortex, we observe complex non-stationary firing patterns that appear to be related to the movement itself. Our understanding of the details of this relationship are incomplete, however. Earlier work by the community largely focused on the analysis of individual units in isolation. Technological advances and changes in experimental paradigms have led to the simultaneous recording of hundreds of neurons simultaneously. From an analysis standpoint, we are observing a similar shift in focus from the individual neuron to the population as a whole. This dissertation investigates encoding and decoding techniques that handle time-varying neuronal activity from within the context of a reach-to-grasp task. The first part of this work investigates the dynamics of neural coding of reach and grasp through a series of temporally localized classifiers. The second part of this thesis proposes a semi-supervised learning approach to identifying task relevant neurons for classification purposes and for identifying communities of neurons that co-modulate their activity in correlation to a common external variable. The third part of this work proposes and demonstrates an approach to modeling the firing of individual neurons as a weighted combination of other neurons with weighting dependent on the task being performed

Classical Statistics and Statistical Learning in Imaging Neuroscience

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques

Apprentissage d'atlas fonctionnel du cerveau modélisant la variabilité inter-individuelle

Recent studies have shown that resting-state spontaneous brain activity unveils intrinsic cerebral functioning and complete information brought by prototype task study. From these signals, we will set up a functional atlas of the brain, along with an across-subject variability model. The novelty of our approach lies in the integration of neuroscientific priors and inter-individual variability in a probabilistic description of the rest activity. These models will be applied to large datasets. This variability, ignored until now, may lead to learning of fuzzy atlases, thus limited in term of resolution. This program yields both numerical and algorithmic challenges because of the data volume but also because of the complexity of modelisation.De récentes études ont montré que l'activité spontanée du cerveau observée au repos permet d'étudier l'organisation fonctionnelle cérébrale en complément de l'information fournie par les protocoles de tâches. A partir de ces signaux, nous allons extraire un atlas fonctionnel du cerveau modélisant la variabilité inter-sujet. La nouveauté de notre approche réside dans l'intégration d'a-prioris neuroscientifiques et de la variabilité inter-sujet directement dans un modèles probabiliste de l'activité de repos. Ces modèles seront appliqués sur de larges jeux de données. Cette variabilité, ignorée jusqu'à présent, cont nous permettre d'extraire des atlas flous, donc limités en terme de résolution. Des challenges à la fois numériques et algorithmiques sont à relever de par la taille des jeux de données étudiés et la complexité de la modélisation considérée

Large-scale inference in the focally damaged human brain

Clinical outcomes in focal brain injury reflect the interactions between two distinct anatomically distributed patterns: the functional organisation of the brain and the structural distribution of injury. The challenge of understanding the functional architecture of the brain is familiar; that of understanding the lesion architecture is barely acknowledged. Yet, models of the functional consequences of focal injury are critically dependent on our knowledge of both. The studies described in this thesis seek to show how machine learning-enabled high-dimensional multivariate analysis powered by large-scale data can enhance our ability to model the relation between focal brain injury and clinical outcomes across an array of modelling applications. All studies are conducted on internationally the largest available set of MR imaging data of focal brain injury in the context of acute stroke (N=1333) and employ kernel machines at the principal modelling architecture. First, I examine lesion-deficit prediction, quantifying the ceiling on achievable predictive fidelity for high-dimensional and low-dimensional models, demonstrating the former to be substantially higher than the latter. Second, I determine the marginal value of adding unlabelled imaging data to predictive models within a semi-supervised framework, quantifying the benefit of assembling unlabelled collections of clinical imaging. Third, I compare high- and low-dimensional approaches to modelling response to therapy in two contexts: quantifying the effect of treatment at the population level (therapeutic inference) and predicting the optimal treatment in an individual patient (prescriptive inference). I demonstrate the superiority of the high-dimensional approach in both settings

Inimaju arvutuslikke protsesside mõistmine masinõpe mudelite tõlgendamise kaudu. Andmepõhine lähenemine arvutuslikku neuroteadusesse

Modelleerimine on inimkonna põline viis keerulistest nähtustest arusaamiseks. Planeetide liikumise mudel, gravitatsiooni mudel ja osakestefüüsika standardmudel on näited selle lähenemise edukusest. Neuroteaduses on olemas kaks viisi mudelite loomiseks: traditsiooniline hüpoteesipõhine lähenemine, mille puhul kõigepealt mudel sõnastatakse ja alles siis valideeritakse andmete peal; ja uuem andmepõhine lähenemine, mis toetub masinõpele, et sõnastada mudeleid automaatselt. Hüpoteesipõhine viis annab täieliku mõistmise sellest, kuidas mudel töötab, aga nõuab aega, kuna iga hüpotees peab olema sõnastatud ja valideeritud käsitsi. Andmepõhine lähenemine toetub ainult andmetele ja arvutuslikele ressurssidele mudelite otsimisel, aga ei seleta kuidas täpselt mudel jõuab oma tulemusteni. Me väidame, et neuroandmestike suur hulk ja nende mahu kiire kasv nõuab andmepõhise lähenemise laiemat kasutuselevõttu neuroteaduses, nihkes uurija rolli mudelite tööprintsiipide tõlgendamisele. Doktoritöö koosneb kolmest näitest neuroteaduse teadmisi avastamisest masinõppe tõlgendamismeetodeid kasutades. Esimeses uuringus tõlgendatava mudeli abiga me kirjeldame millised ajas muutuvad sageduskomponendid iseloomustavad inimese ajusignaali visuaalsete objektide tuvastamise ülesande puhul. Teises uuringus võrdleme omavahel signaale inimese aju ventraalses piirkonnas ja konvolutsiooniliste tehisnärvivõrkude aktivatsioone erinevates kihtides. Säärane võrdlus võimaldas meil kinnitada hüpoteesi, et mõlemad süsteemid kasutavad hierarhilist struktuuri. Viimane näide kasutab topoloogiat säilitavat mõõtmelisuse vähendamise ja visualiseerimise meetodit, et näha, millised ajusignaalid ja mõtteseisundid on üksteisele sarnased. Viimased tulemused masinõppes ja tehisintellektis näitasid et mõned mehhanismid meie ajus on sarnased mehhanismidega, milleni jõuavad õppimise käigus masinõppe algoritmid. Oma tööga me rõhutame masinõppe mudelite tõlgendamise tähtsust selliste mehhanismide avastamiseks.Building a model of a complex phenomenon is an ancient way of gaining knowledge and understanding of the reality around us. Models of planetary motion, gravity, particle physics are examples of this approach. In neuroscience, there are two ways of coming up with explanations of reality: a traditional hypothesis-driven approach, where a model is first formulated and then tested using the data, and a more recent data-driven approach, that relies on machine learning to generate models automatically. Hypothesis-driven approach provides full understanding of the model, but is time-consuming as each model has to be conceived and tested manually. Data-driven approach requires only the data and computational resources to sift through potential models, saving time, but leaving the resulting model itself to be a black box. Given the growing amount of neural data, we argue in favor of a more widespread adoption of the data-driven approach, reallocating part of the human effort from manual modeling. The thesis is based on three examples of how interpretation of machine-learned models leads to neuroscientific insights on three different levels of neural organization. Our first interpretable model is used to characterize neural dynamics of localized neural activity during the task of visual perceptual categorization. Next, we compare the activity of human visual system with the activity of a convolutional neural network, revealing explanations about the functional organization of human visual cortex. Lastly, we use dimensionality reduction and visualization techniques to understand relative organization of mental concepts within a subject's mental state space and apply it in the context of brain-computer interfaces. Recent results in neuroscience and AI show similarities between the mechanisms of both systems. This fact endorses the relevance of our approach: interpreting the mechanisms employed by machine learning models can shed light on the mechanisms employed by our brainhttps://www.ester.ee/record=b536057