Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease

The joint analysis of biomedical data in Alzheimer's Disease (AD) is important for better clinical diagnosis and to understand the relationship between biomarkers. However, jointly accounting for heterogeneous measures poses important challenges related to the modeling of the variability and the interpretability of the results. These issues are here addressed by proposing a novel multi-channel stochastic generative model. We assume that a latent variable generates the data observed through different channels (e.g., clinical scores, imaging, ...) and describe an efficient way to estimate jointly the distribution of both latent variable and data generative process. Experiments on synthetic data show that the multi-channel formulation allows superior data reconstruction as opposed to the single channel one. Moreover, the derived lower bound of the model evidence represents a promising model selection criterion. Experiments on AD data show that the model parameters can be used for unsupervised patient stratification and for the joint interpretation of the heterogeneous observations. Because of its general and flexible formulation, we believe that the proposed method can find important applications as a general data fusion technique.Comment: accepted for presentation at MLCN 2018 workshop, in Conjunction with MICCAI 2018, September 20, Granada, Spai

Inunditos como análogos de reservatórios: um exemplo nos depósitos gonduânicos triássicos do Gráben Arroio Moirão, RS

O estudo de reservatórios de óleo e gás de sistemas continentais tem enfatizado, na última década, arenitos associados a inunditos, provenientes de fluxos hiperpicnais. Contudo, há poucos estudos para entender sua arquitetura e heterogeneidade, resultando em dificuldades de reconhecimento e de exploração. O presente trabalho visa à análise de pacotes triássicos da Bacia do Paraná pertencentes à Formação Santa Maria, que ocorrem como fragmentos isolados sobre o Escudo Sul-Rio-Grandense na região do Gráben Arroio Moirão (RS). Para tanto, fez-se uso de mapeamento sistemático, análise de heterogeneidade de fácies e estratigrafia de sequências, que incluem levantamento de perfis colunares, identificação de associações e sucessões de fácies e superfícies-chave. Além disso, classificaram-se os arenitos e qualificou-se a porosidade com base na análise petrográfica. Foi possível delimitar os principais depósitos de arenitos, no quais predomina geometria tabular, grãos mal selecionados e matriz argilosa. Distinguiram-se cinco ciclos deposicionais granodecrescentes ascendentes, limitados na base e no topo por superfícies erosivas, que marcam heterogeneidades recorrentes. As fácies-reservatório foram classificadas como subarcósios, de matriz argilosa oxidada, com agregados de caulinita, e porosidade intergranular do tipo shrinkage. A integração dos dados resultou na elaboração de um modelo de variação lateral e vertical de fácies de depósitos de inunditos. Nele, identificaram-se fácies constituídas por arenitos grossos a conglomeráticos, com estratificações cruzada tangencial e plano-paralela, como potenciais modelos análogos de reservatórios. Esses resultados possibilitam prospectar outros depósitos arenosos dessa unidade estratigráfica da Bacia do Paraná, para fins de dimensionar regionalmente o análogo de reservatório.The study of oil and gas reservoirs in continental systems has emphasized, in the last decade, sandstones associated with inundites, coming from hyperpicnal flows. However, there are few studies to understand its architecture and heterogeneity, resulting in difficulties for exploration and exploitation. The current work aims at the analysis of Triassic strata from the Paraná Basin belonging to the Santa Maria Formation, which occur as isolated fragments on the Sul-rio-grandense Shield in Arroio Moirão Graben (RS). For this, systematic mapping, facies heterogeneity analysis and sequence stratigraphy were used, including columnar profiles, identification of associations and sequences of facies and key surfaces. In addition, the sandstones were classified and the porosity was qualified based on the petrographic analysis. It was possible to define the main deposits of sandstones, in which predominate tabular geometry, poorly selected grains and clayey matrix. Five ascending granodecrescent depositional cycles were distinguished, limited at the base and at the top by erosive surfaces, which marked recurrent heterogeneities. The reservoir facies were classified as subarcósios, of oxidized clay matrix, with aggregates of kaolinite, and intergranular porosity of the shrinkage type. The integration of the data resulted in the elaboration of a model of lateral and vertical variation inundites deposits facies. In it, facies composed of conglomeratic thick sandstones were identified, with tangential cross stratification and planar stratification, as potential analog models of reservoirs. These results allow the prospection of other sandy deposits from this stratigraphic unit of the Paraná Basin, in order to size the reservoir analogue regionally

Determining a Role for Ventromedial Prefrontal Cortex in Encoding Action-Based Value Signals During Reward-Related Decision Making

Considerable evidence has emerged to implicate ventromedial prefrontal cortex in encoding expectations of future reward during value-based decision making. However, the nature of the learned associations upon which such representations depend is much less clear. Here, we aimed to determine whether expected reward representations in this region could be driven by action–outcome associations, rather than being dependent on the associative value assigned to particular discriminative stimuli. Subjects were scanned with functional magnetic resonance imaging while performing 2 variants of a simple reward-related decision task. In one version, subjects made choices between 2 different physical motor responses in the absence of discriminative stimuli, whereas in the other version, subjects chose between 2 different stimuli that were randomly assigned to different responses on a trial-by-trial basis. Using an extension of a reinforcement learning algorithm, we found activity in ventromedial prefrontal cortex tracked expected future reward during the action-based task as well as during the stimulus-based task, indicating that value representations in this region can be driven by action–outcome associations. These findings suggest that ventromedial prefrontal cortex may play a role in encoding the value of chosen actions irrespective of whether those actions denote physical motor responses or more abstract decision options

The increase of the functional entropy of the human brain with age

We use entropy to characterize intrinsic ageing properties of the human brain. Analysis of fMRI data from a large dataset of individuals, using resting state BOLD signals, demonstrated that a functional entropy associated with brain activity increases with age. During an average lifespan, the entropy, which was calculated from a population of individuals, increased by approximately 0.1 bits, due to correlations in BOLD activity becoming more widely distributed. We attribute this to the number of excitatory neurons and the excitatory conductance decreasing with age. Incorporating these properties into a computational model leads to quantitatively similar results to the fMRI data. Our dataset involved males and females and we found significant differences between them. The entropy of males at birth was lower than that of females. However, the entropies of the two sexes increase at different rates, and intersect at approximately 50 years; after this age, males have a larger entropy

Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

This paper introduces a novel methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1mm space and map this parcellation to each individual subject data. We use deep neural network architectures together with hard negative mining to achieve the final voxel level classification. For survival prediction, we present a new method for combining features from connectomics data, brain parcellation information, and the brain tumor mask. We leverage the average connectome information from the Human Connectome Project and map each subject brain volume onto this common connectome space. From this, we compute tractographic features that describe potential neural disruptions due to the brain tumor. These features are then used to predict the overall survival of the subjects. The main novelty in the proposed methods is the use of normalized brain parcellation data and tractography data from the human connectome project for analyzing MR images for segmentation and survival prediction. Experimental results are reported on the BraTS2018 dataset.Comment: 14 pages, 5 figures, 4 tables, accepted by BrainLes 2018 MICCAI worksho

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Effective connectivity reveals strategy differences in an expert calculator

Mathematical reasoning is a core component of cognition and the study of experts defines the upper limits of human cognitive abilities, which is why we are fascinated by peak performers, such as chess masters and mental calculators. Here, we investigated the neural bases of calendrical skills, i.e. the ability to rapidly identify the weekday of a particular date, in a gifted mental calculator who does not fall in the autistic spectrum, using functional MRI. Graph-based mapping of effective connectivity, but not univariate analysis, revealed distinct anatomical location of “cortical hubs” supporting the processing of well-practiced close dates and less-practiced remote dates: the former engaged predominantly occipital and medial temporal areas, whereas the latter were associated mainly with prefrontal, orbitofrontal and anterior cingulate connectivity. These results point to the effect of extensive practice on the development of expertise and long term working memory, and demonstrate the role of frontal networks in supporting performance on less practiced calculations, which incur additional processing demands. Through the example of calendrical skills, our results demonstrate that the ability to perform complex calculations is initially supported by extensive attentional and strategic resources, which, as expertise develops, are gradually replaced by access to long term working memory for familiar material

A Study of Brain Networks Associated with Swallowing Using Graph-Theoretical Approaches

Functional connectivity between brain regions during swallowing tasks is still not well understood. Understanding these complex interactions is of great interest from both a scientific and a clinical perspective. In this study, functional magnetic resonance imaging (fMRI) was utilized to study brain functional networks during voluntary saliva swallowing in twenty-two adult healthy subjects (all females, 23.1±1.52 years of age). To construct these functional connections, we computed mean partial correlation matrices over ninety brain regions for each participant. Two regions were determined to be functionally connected if their correlation was above a certain threshold. These correlation matrices were then analyzed using graph-theoretical approaches. In particular, we considered several network measures for the whole brain and for swallowing-related brain regions. The results have shown that significant pairwise functional connections were, mostly, either local and intra-hemispheric or symmetrically inter-hemispheric. Furthermore, we showed that all human brain functional network, although varying in some degree, had typical small-world properties as compared to regular networks and random networks. These properties allow information transfer within the network at a relatively high efficiency. Swallowing-related brain regions also had higher values for some of the network measures in comparison to when these measures were calculated for the whole brain. The current results warrant further investigation of graph-theoretical approaches as a potential tool for understanding the neural basis of dysphagia. © 2013 Luan et al

Effect of parasympathetic stimulation on brain activity during appraisal of fearful expressions

Autonomic nervous system activity is an important component of human emotion. Mental processes influence bodily physiology, which in turn feeds back to influence thoughts and feelings. Afferent cardiovascular signals from arterial baroreceptors in the carotid sinuses are processed within the brain and contribute to this two-way communication with the body. These carotid baroreceptors can be stimulated non-invasively by externally applying focal negative pressure bilaterally to the neck. In an experiment combining functional neuroimaging (fMRI) with carotid stimulation in healthy participants, we tested the hypothesis that manipulating afferent cardiovascular signals alters the central processing of emotional information (fearful and neutral facial expressions). Carotid stimulation, compared with sham stimulation, broadly attenuated activity across cortical and brainstem regions. Modulation of emotional processing was apparent as a significant expression-by-stimulation interaction within left amygdala, where responses during appraisal of fearful faces were selectively reduced by carotid stimulation. Moreover, activity reductions within insula, amygdala, and hippocampus correlated with the degree of stimulation-evoked change in the explicit emotional ratings of fearful faces. Across participants, individual differences in autonomic state (heart rate variability, a proxy measure of autonomic balance toward parasympathetic activity) predicted the extent to which carotid stimulation influenced neural (amygdala) responses during appraisal and subjective rating of fearful faces. Together our results provide mechanistic insight into the visceral component of emotion by identifying the neural substrates mediating cardiovascular influences on the processing of fear signals, potentially implicating central baroreflex mechanisms for anxiolytic treatment targets

Functional near infrared spectroscopy (fNIRS) to assess cognitive function in infants in rural Africa

Cortical mapping of cognitive function during infancy is poorly understood in low-income countries due to the lack of transportable neuroimaging methods. We have successfully piloted functional near infrared spectroscopy (fNIRS) as a neuroimaging tool in rural Gambia. Four-to-eight month old infants watched videos of Gambian adults perform social movements, while haemodynamic responses were recorded using fNIRS. We found distinct regions of the posterior superior temporal and inferior frontal cortex that evidenced either visual-social activation or vocally selective activation (vocal > non-vocal). The patterns of selective cortical activation in Gambian infants replicated those observed within similar aged infants in the UK. These are the first reported data on the measurement of localized functional brain activity in young infants in Africa and demonstrate the potential that fNIRS offers for field-based neuroimaging research of cognitive function in resource-poor rural communities