87 research outputs found

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    Neurogeometry of stereo vision

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    This work aims to develop a neurogeometric model of stereo vision, based on cortical architectures involved in the problem of 3D perception and neural mechanisms generated by retinal disparities. First, we provide a sub-Riemannian geometry for stereo vision, inspired by the work on the stereo problem by Zucker (2006), and using sub-Riemannian tools introduced by Citti-Sarti (2006) for monocular vision. We present a mathematical interpretation of the neural mechanisms underlying the behavior of binocular cells, that integrate monocular inputs. The natural compatibility between stereo geometry and neurophysiological models shows that these binocular cells are sensitive to position and orientation. Therefore, we model their action in the space R3xS2 equipped with a sub-Riemannian metric. Integral curves of the sub-Riemannian structure model neural connectivity and can be related to the 3D analog of the psychophysical association fields for the 3D process of regular contour formation. Then, we identify 3D perceptual units in the visual scene: they emerge as a consequence of the random cortico-cortical connection of binocular cells. Considering an opportune stochastic version of the integral curves, we generate a family of kernels. These kernels represent the probability of interaction between binocular cells, and they are implemented as facilitation patterns to define the evolution in time of neural population activity at a point. This activity is usually modeled through a mean field equation: steady stable solutions lead to consider the associated eigenvalue problem. We show that three-dimensional perceptual units naturally arise from the discrete version of the eigenvalue problem associated to the integro-differential equation of the population activity

    Assessing brain connectivity through electroencephalographic signal processing and modeling analysis

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    Brain functioning relies on the interaction of several neural populations connected through complex connectivity networks, enabling the transmission and integration of information. Recent advances in neuroimaging techniques, such as electroencephalography (EEG), have deepened our understanding of the reciprocal roles played by brain regions during cognitive processes. The underlying idea of this PhD research is that EEG-related functional connectivity (FC) changes in the brain may incorporate important neuromarkers of behavior and cognition, as well as brain disorders, even at subclinical levels. However, a complete understanding of the reliability of the wide range of existing connectivity estimation techniques is still lacking. The first part of this work addresses this limitation by employing Neural Mass Models (NMMs), which simulate EEG activity and offer a unique tool to study interconnected networks of brain regions in controlled conditions. NMMs were employed to test FC estimators like Transfer Entropy and Granger Causality in linear and nonlinear conditions. Results revealed that connectivity estimates reflect information transmission between brain regions, a quantity that can be significantly different from the connectivity strength, and that Granger causality outperforms the other estimators. A second objective of this thesis was to assess brain connectivity and network changes on EEG data reconstructed at the cortical level. Functional brain connectivity has been estimated through Granger Causality, in both temporal and spectral domains, with the following goals: a) detect task-dependent functional connectivity network changes, focusing on internal-external attention competition and fear conditioning and reversal; b) identify resting-state network alterations in a subclinical population with high autistic traits. Connectivity-based neuromarkers, compared to the canonical EEG analysis, can provide deeper insights into brain mechanisms and may drive future diagnostic methods and therapeutic interventions. However, further methodological studies are required to fully understand the accuracy and information captured by FC estimates, especially concerning nonlinear phenomena

    Bayesian Dynamic DAG Learning: Application in Discovering Dynamic Effective Connectome of Brain

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    Understanding the complex mechanisms of the brain can be unraveled by extracting the Dynamic Effective Connectome (DEC). Recently, score-based Directed Acyclic Graph (DAG) discovery methods have shown significant improvements in extracting the causal structure and inferring effective connectivity. However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data. In this paper, we introduce Bayesian Dynamic DAG learning with M-matrices Acyclicity characterization \textbf{(BDyMA)} method to address the challenges in discovering DEC. The presented dynamic causal model enables us to discover bidirected edges as well. Leveraging an unconstrained framework in the BDyMA method leads to more accurate results in detecting high-dimensional networks, achieving sparser outcomes, making it particularly suitable for extracting DEC. Additionally, the score function of the BDyMA method allows the incorporation of prior knowledge into the process of dynamic causal discovery which further enhances the accuracy of results. Comprehensive simulations on synthetic data and experiments on Human Connectome Project (HCP) data demonstrate that our method can handle both of the two main challenges, yielding more accurate and reliable DEC compared to state-of-the-art and baseline methods. Additionally, we investigate the trustworthiness of DTI data as prior knowledge for DEC discovery and show the improvements in DEC discovery when the DTI data is incorporated into the process

    Multisensory self-motion processing in humans

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    Humans obtain and process sensory information from various modalities to ensure successful navigation through the environment. While visual, vestibular, and auditory self-motion perception have been extensively investigated, studies on tac-tile self-motion perception are comparably rare. In my thesis, I have investigated tactile self-motion perception and its interaction with the visual modality. In one of two behavioral studies, I analyzed the influence of a tactile heading stimulus intro-duced as a distractor on visual heading perception. In the second behavioral study, I analyzed visuo-tactile perception of self-motion direction (heading). In both studies, visual self-motion was simulated as forward motion over a 2D ground plane. Tactile self-motion was simulated by airflow towards the subjects’ forehead, mimicking the experience of travel wind, e.g., during a bike ride. In the analysis of the subjects’ perceptual reports, I focused on possible visuo-tactile interactions and applied dif-ferent models to describe the integration of visuo-tactile heading stimuli. Lastly, in a functional magnetic resonance imaging study (fMRI), I investigated neural correlates of visual and tactile perception of traveled distance (path integration) and its modu-lation by prediction and cognitive task demands. In my first behavioral study, subjects indicated perceived heading from uni-modal visual (optic flow), unimodal tactile (tactile flow) or from a combination of stimuli from both modalities, simulating either congruent or incongruent heading (bimodal condition). In the bimodal condition, the subjects’ task was to indicate visually perceived heading. Hence, here tactile stimuli were behaviorally irrelevant. In bimodal trials, I found a significant interaction of stimuli from both modalities. Visually perceived heading was biased towards tactile heading direction for an offset of up to 10° between both heading directions. The relative weighting of stimuli from both modalities in the visuo-tactile in-teraction were examined in my second behavioral study. Subjects indicated per-ceived heading from unimodal visual, unimodal tactile and bimodal trials. Here, in bimodal trials, stimuli form both modalities were presented as behaviorally rele-vant. By varying eye- relative to head position during stimulus presentation, possi-ble influences of different reference frames of the visual and tactile modality were investigated. In different sensory modalities, incoming information is encoded rela-tive to the reference system of the receiving sensory organ (e.g., relative to the reti-na in vision or relative to the skin in somatosensation). In unimodal tactile trials, heading perception was shifted towards eye-position. In bimodal trials, varying head- and eye-position had no significant effect on perceived heading: subjects indicated perceived heading based on both, the vis-ual and tactile stimulus, independently of the behavioral relevance of the tactile stimulus. In sum, results of both studies suggest that the tactile modality plays a greater role in self-motion perception than previously thought. Besides the perception of travel direction (heading), information about trav-eled speed and duration are integrated to achieve a measure of the distance trav-eled (path integration). One previous behavioral study has shown that tactile flow can be used for the reproduction of travel distance (Churan et al., 2017). However, studies on neural correlates of tactile distance encoding in humans are lacking en-tirely. In my third study, subjects solved two path integration tasks from unimodal visual and unimodal tactile self-motion stimuli. Brain activity was measured by means of functional magnetic resonance imaging (fMRI). Both tasks varied in the engagement of cognitive task demands. In the first task, subjects replicated (Active trial) a previously observed traveled distance (Passive trial) (= Reproduction task). In the second task, subjects traveled a self-chosen distance (Active trial) which was then recorded and played back to them (Passive trial) (= Self task). The predictive coding theory postulates an internal model which creates predictions about sensory outcomes-based mismatches between predictions and sensory input which enables the system to sharpen future predictions (Teufel et al., 2018). Recent studies sug-gested a synergistical interaction between prediction and cognitive demands, there-by reversing the attenuating effect of prediction. In my study, this hypothesis was tested by manipulating cognitive demands between both tasks. For both tasks, Ac-tive trials compared to Passive trials showed BOLD enhancement of early sensory cortices and suppression of higher order areas (e.g., the intraparietal lobule (IPL)). For both modalities, enhancement of early sensory areas might facilitate task solv-ing processes at hand, thereby reversing the hypothesized attenuating effect of pre-diction. Suppression of the IPL indicates this area as an amodal comparator of pre-dictions and incoming self-motion signals. In conclusion, I was able to show that tactile self-motion information, i.e., tactile flow, provides significant information for the processing of two key features of self-motion perception: Heading and path integration. Neural correlates of tactile path-integration were investigated by means of fMRI, showing similarities between visual and tactile path integration on early processing stages as well as shared neu-ral substrates in higher order areas located in the IPL. Future studies should further investigate the perception of different self-motion parameters in the tactile modali-ty to extend the understanding of this less researched – but important – modality

    NOVEL STATISTICAL METHODS FOR MODELING BRAIN AND OTHER DENSE, WEIGHTED NETWORKS

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    Advances in brain imaging have led to the generation of new kinds of datasets. The novel structures of these datasets necessitate development of new modeling paradigms for analysis better suited to this domain. This work is composed of different projects that propose novel statistical methods for analyzing brain data and other kinds of networks, which are described below. The first project studies the problem of testing for the equality of autocovariances of two independent high-dimensional time series. Tests based on the suprema or sums of suitable averages across the dimensions are adapted from the available literature. Another test based on principal component analysis (PCA) is introduced and studied in theory. An extension is also considered to the setting of testing for the equality of autocovariances of two populations, having multiple individual high-dimensional series from the two populations. The proposed methodologies are assessed on simulated data, with the performance of the introduced PCA testing being superior overall. An application using fMRI data from individuals experiencing two different emotional states is provided. These tests are further developed into a method for detecting change points within a single session. A different notion of detecting a change is explored in the second project. Alzheimer's disease (AD) is a multi-factor neurodegenerative disease that selectively affects certain regions of the brain while other areas remain unaffected. The underlying mechanisms of this selectivity, however, are still largely elusive. A novel longitudinal network analysis method is proposed to address this challenge by employing sparse logistic regression to identify frequency-specific oscillation patterns which contribute to the selective network vulnerability for patients at risk of advancing to the more severe stage of dementia. The statistical method is fit and applied to more than 100 longitudinal brain networks, and validated on synthetic data. A set of critical connectome pathways are identified that exhibit strong association to the progression of AD. The third project attempts to create a mechanism for generating networks similar to those observed in brain scans. Dense networks with weighted connections often exhibit a community like structure, where although most nodes are connected to each other, different patterns of edge weights may emerge depending on each node's community membership. A new framework for generating and estimating dense, weighted networks with different connectivity patterns across different groups is introduced. The proposed model relies on a particular class of functions which map individual node characteristics to the edges connecting those nodes. By leveraging the estimation techniques, a bootstrap methodology for generating new networks on the same set of vertices is also developed, which may be useful in circumstances where it is costly to collect multiple data sets. Performance of these methods are analyzed in theory, simulations, and real data. An extension of the previous project to partially observed bipartite networks is also discussed. The application of interest is recommender systems, where nodes of users and items share weighted edges. In this construct, the weight of each edge reflects the affinity between a user and item. In general, most users have not interacted with most items, so those edges are unobserved, but the underlying affinity may still be estimated. Tools developed for analyzing dense weighted networks are updated to match this setting, and estimation results are compared to those achieved by graph neural networks at different levels of missingness.Doctor of Philosoph

    Identification of proprioceptive thalamocortical tracts in children : comparison of fMRI, MEG, and manual seeding of probabilistic tractography

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    Studying white matter connections with tractography is a promising approach to understand the development of different brain processes, such as proprioception. An emerging method is to use functional brain imaging to select the cortical seed points for tractography, which is considered to improve the functional relevance and validity of the studied connections. However, it is unknown whether different functional seeding methods affect the spatial and microstructural properties of the given white matter connection. Here, we compared functional magnetic resonance imaging, magnetoencephalography, and manual seeding of thalamocortical proprioceptive tracts for finger and ankle joints separately. We showed that all three seeding approaches resulted in robust thalamocortical tracts, even though there were significant differences in localization of the respective proprioceptive seed areas in the sensorimotor cortex, and in the microstructural properties of the obtained tracts. Our study shows that the selected functional or manual seeding approach might cause systematic biases to the studied thalamocortical tracts. This result may indicate that the obtained tracts represent different portions and features of the somatosensory system. Our findings highlight the challenges of studying proprioception in the developing brain and illustrate the need for using multimodal imaging to obtain a comprehensive view of the studied brain process.Peer reviewe
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