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Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Computational neuroimaging"

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Stephan, Klaas E., Sandra Iglesias, Jakob Heinzle, and Andreea O. Diaconescu. "Translational Perspectives for Computational Neuroimaging." Neuron 87, no. 4 (August 2015): 716–32. http://dx.doi.org/10.1016/j.neuron.2015.07.008.

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2

Potter, Aneirin. "044 At what resolution does the brain perform computations?" Journal of Neurology, Neurosurgery & Psychiatry 93, no. 9 (August 12, 2022): e2.239. http://dx.doi.org/10.1136/jnnp-2022-abn2.88.

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Анотація:
Computation is the transformation of inputs into outputs through logical operations such as AND, OR, and NOT. This literature review compares models of computation at different physiological resolutions, whole brain networks, multi-cell circuits, individual synapses and individual molecular interactions and discusses if these models might be useful for bridging between functional neuroimaging with molecular models of disease. While resolution in functional neuroimaging such as EEG, MEG, PET, and fMRI is of groups of neurons pharmacotherapy alters the brain at a molecular level. Bridging this resolution gap presents many difficulties for modellers and wider connectome projects. Sufficiently detailed models can quickly outstrip computational capacity while not including sufficient detail leads to models lacking physiological validity. This is particularly problematic when connectome projects overpromise in their capacity to understand brain disorders without basis in valid physiological models. Examples of computation at network and molecular levels suggests a lack of consensus about what resolution the brain performs computations and how these computations interact. This interaction should be a goal for further research, especially given its role in linking functional neuroimaging diagnostics and pharmacological treatments in neurology.
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3

Wandell, Brian A. "COMPUTATIONAL NEUROIMAGING OF HUMAN VISUAL CORTEX." Annual Review of Neuroscience 22, no. 1 (March 1999): 145–73. http://dx.doi.org/10.1146/annurev.neuro.22.1.145.

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4

Wandell, Brian A., and Jonathan Winawer. "Computational neuroimaging and population receptive fields." Trends in Cognitive Sciences 19, no. 6 (June 2015): 349–57. http://dx.doi.org/10.1016/j.tics.2015.03.009.

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5

Friston, Karl J., and Raymond J. Dolan. "Computational and dynamic models in neuroimaging." NeuroImage 52, no. 3 (September 2010): 752–65. http://dx.doi.org/10.1016/j.neuroimage.2009.12.068.

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6

Stephan, K. E., F. Schlagenhauf, Q. J. M. Huys, S. Raman, E. A. Aponte, K. H. Brodersen, L. Rigoux, et al. "Computational neuroimaging strategies for single patient predictions." NeuroImage 145 (January 2017): 180–99. http://dx.doi.org/10.1016/j.neuroimage.2016.06.038.

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7

Di Ieva, Antonio, Mounir Boukadoum, Salim Lahmiri, and Michael D. Cusimano. "Computational Analyses of Arteriovenous Malformations in Neuroimaging." Journal of Neuroimaging 25, no. 3 (December 17, 2014): 354–60. http://dx.doi.org/10.1111/jon.12200.

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8

Poldrack, Russell A., Krzysztof J. Gorgolewski, and Gaël Varoquaux. "Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging." Annual Review of Biomedical Data Science 2, no. 1 (July 20, 2019): 119–38. http://dx.doi.org/10.1146/annurev-biodatasci-072018-021237.

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The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data-sharing resources that have been developed for neuroimaging data, as well as the role of data standards (particularly the brain imaging data structure) in enabling the automated sharing, processing, and reuse of large neuroimaging data sets. We outline how the open source Python language has provided the basis for a data science platform that enables reproducible data analysis and visualization. We also discuss how new advances in software engineering, such as containerization, provide the basis for greater reproducibility in data analysis. The emergence of this new ecosystem provides an example for many areas of science that are currently struggling with reproducibility.
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9

Ritter, Petra, Michael Schirner, Anthony R. McIntosh, and Viktor K. Jirsa. "The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging." Brain Connectivity 3, no. 2 (April 2013): 121–45. http://dx.doi.org/10.1089/brain.2012.0120.

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10

Goldstein-Piekarski, Andrea N., Bailey Holt-Gosselin, Kathleen O’Hora, and Leanne M. Williams. "Integrating sleep, neuroimaging, and computational approaches for precision psychiatry." Neuropsychopharmacology 45, no. 1 (August 19, 2019): 192–204. http://dx.doi.org/10.1038/s41386-019-0483-8.

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Дисертації з теми "Computational neuroimaging"

1

Macoveanu, Julian. "Neural mechanisms underlying working memory : computational and neuroimaging studies /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-901-7/.

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2

Whalley, Matthew G. "Autobiographical memory in depression : neuroimaging and computational linguistic investigation." Thesis, University of London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.542382.

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3

Cattinelli, I. "INVESTIGATIONS ON COGNITIVE COMPUTATION AND COMPUTATIONAL COGNITION." Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155482.

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This Thesis describes our work at the boundary between Computer Science and Cognitive (Neuro)Science. In particular, (1) we have worked on methodological improvements to clustering-based meta-analysis of neuroimaging data, which is a technique that allows to collectively assess, in a quantitative way, activation peaks from several functional imaging studies, in order to extract the most robust results in the cognitive domain of interest. Hierarchical clustering is often used in this context, yet it is prone to the problem of non-uniqueness of the solution: a different permutation of the same input data might result in a different clustering result. In this Thesis, we propose a new version of hierarchical clustering that solves this problem. We also show the results of a meta-analysis, carried out using this algorithm, aimed at identifying specific cerebral circuits involved in single word reading. Moreover, (2) we describe preliminary work on a new connectionist model of single word reading, named the two-component model because it postulates a cascaded information flow from a more cognitive component that computes a distributed internal representation for the input word, to an articulatory component that translates this code into the corresponding sequence of phonemes. Output production is started when the internal code, which evolves in time, reaches a sufficient degree of clarity; this mechanism has been advanced as a possible explanation for behavioral effects consistently reported in the literature on reading, with a specific focus on the so called serial effects. This model is here discussed in its strength and weaknesses. Finally, (3) we have turned to consider how features that are typical of human cognition can inform the design of improved artificial agents; here, we have focused on modelling concepts inspired by emotion theory. A model of emotional interaction between artificial agents, based on probabilistic finite state automata, is presented: in this model, agents have personalities and attitudes that can change through the course of interaction (e.g. by reinforcement learning) to achieve autonomous adaptation to the interaction partner. Markov chain properties are then applied to derive reliable predictions of the outcome of an interaction. Taken together, these works show how the interplay between Cognitive Science and Computer Science can be fruitful, both for advancing our knowledge of the human brain and for designing more and more intelligent artificial systems.
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4

Gradin, Iade Victoria B. "Major depression and schizophrenia : investigation of neural mechanisms using neuroimaging and computational modeling of brain function." Thesis, University of Aberdeen, 2011. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=184011.

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Depression and schizophrenia are common psychiatric disorders that can be disabling and chronic. This thesis aimed to further elucidate the underlying neural substrates using functional magnetic resonance imaging (fMRI) based studies. Hypothesized impairments in reinforcement learning in depression and schizophrenia were investigated, as were the neural correlates of abnormalities of social information processing in schizophrenia. Computational models of reinforcement learning are based on the concept of a 'prediction error' (PE, discrepancy between the expected and actual outcome) signal to update predictions of rewards and improve action selection. It has been argued that the firing of dopamine neurons encode a reward PE signal that mediates the learning of associations and the attribution of motivational salience to reward-related stimuli. Using model-based fMRI, the encoding of neural PE signals in patients with depression and schizophrenia were investigated. Consistent with hypotheses, patients exhibited different abnormalities in neural PE signals, with the degree of abnormality correlating with increased anhedonia/psychotic symptoms in depression/schizophrenia. These findings are consistent with the suggestion that a disruption in the encoding of PE signals contributes to anhedonia symptoms in depression by disrupting learning and the acquisition of salience of rewarding events. In schizophrenia, abnormal PE signals may contribute to psychosis by promoting aberrant perceptions and abnormal associations. In a different study, the neural responses to social exclusion in schizophrenia were investigated. Schizophrenia patients failed to modulate activity in the medial prefrontal cortex with the degree of exclusion, unlike controls. This highlights the neural substrates of putatively impaired social information processing in schizophrenia. Overall, these findings are consistent with proposals that psychiatric syndromes reflect different disorders of neural valuation. This perspective may help bridge the gap between the biological and phenomenological levels of understanding of depression and schizophrenia, hopefully contributing in the long term to the development of more effective treatments.
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Salimi-Khorshidi, Gholamreza. "Statistical models for neuroimaging meta-analytic inference." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:40a10327-7f36-42e7-8120-ae04bd8be1d4.

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A statistical meta-analysis combines the results of several studies that address a set of related research hypotheses, thus increasing the power and reliability of the inference. Meta-analytic methods are over 50 years old and play an important role in science; pooling evidence from many trials to provide answers that any one trial would have insufficient samples to address. On the other hand, the number of neuroimaging studies is growing dramatically, with many of these publications containing conflicting results, or being based on only a small number of subjects. Hence there has been increasing interest in using meta-analysis methods to find consistent results for a specific functional task, or for predicting the results of a study that has not been performed directly. Current state of neuroimaging meta-analysis is limited to coordinate-based meta-analysis (CBMA), i.e., using only the coordinates of activation peaks that are reported by a group of studies, in order to "localize" the brain regions that respond to a certain type of stimulus. This class of meta-analysis suffers from a series of problems and hence cannot result in as accurate results as desired. In this research, we describe the problems that existing CBMA methods are suffering from and introduce a hierarchical mixed-effects image-based metaanalysis (IBMA) solution that incorporates the sufficient statistics (i.e., voxel-wise effect size and its associated uncertainty) from each study. In order to improve the statistical-inference stage of our proposed IBMA method, we introduce a nonparametric technique that is capable of adjusting such an inference for spatial nonstationarity. Given that in common practice, neuroimaging studies rarely provide the full image data, in an attempt to improve the existing CBMA techniques we introduce a fully automatic model-based approach that employs Gaussian-process regression (GPR) for estimating the meta-analytic statistic image from its corresponding sparse and noisy observations (i.e., the collected foci). To conclude, we introduce a new way to approach neuroimaging meta-analysis that enables the analysis to result in information such as “functional connectivity” and networks of the brain regions’ interactions, rather than just localizing the functions.
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6

D'ANGELO, LAURA. "Bayesian modeling of calcium imaging data." Doctoral thesis, Università degli Studi di Padova, 2022. https://hdl.handle.net/10281/399067.

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Recent advancements in miniaturized fluorescence microscopy have made it possible to investigate neuronal responses to external stimuli in awake behaving animals through the analysis of intra-cellular calcium signals. An ongoing challenge is deconvolving the noisy calcium signals to extract the spike trains, and understanding how this activity is affected by external stimuli and conditions. In this thesis, we aim to provide novel approaches to tackle various aspects of the analysis of calcium imaging data within a Bayesian framework. Following the standard methodology to the analysis of calcium imaging data based on a two-stage approach, we investigate efficient computational methods to link the output of the deconvolved fluorescence traces with the experimental conditions. In particular, we focus on the use of Poisson regression models to relate the number of detected spikes with several covariates. Motivated by this framework, but with a general impact in terms of application to other fields, we develop an efficient Metropolis-Hastings and importance sampling algorithm to simulate from the posterior distribution of the parameters of Poisson log-linear models under conditional Gaussian priors, with superior performance with respect to the state-of-the-art alternatives. Motivated by the lack of clear uncertainty quantification resulting from the use of a two-stage approach, and the impossibility to borrow information between the two stages, we focus on the analysis of individual neurons, and develop a coherent mixture model that allows for estimation of spiking activity and, simultaneously, reconstructing the distributions of the calcium transient spikes' amplitudes under different experimental conditions. More specifically, our modeling framework leverages two nested layers of random discrete mixture priors to borrow information between experiments and discover similarities in the distributional patterns of the neuronal response to different stimuli. Finally, we move to the multivariate analysis of populations of neurons. Here the interest is not only to detect and analyze the spiking activity but also to investigate the existence of groups of co-activating neurons. Estimation of such groups is a challenging problem due to the need to deconvolve the calcium traces and then cluster the resulting latent binary time series of activity. We describe a nonparametric mixture model that allows for simultaneous deconvolution and clustering of time series based on common patterns of activity. The model makes use of a latent continuous process for the spike probabilities to identify groups of co-activating cells. Neurons' dependence is taken into account by informing the mixture weights with their spatial location, following the common neuroscience assumption that neighboring neurons often activate together.
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7

Petitet, Pierre. "Sensorimotor adaptation : mechanisms, modulation and rehabilitation potential." Thesis, University of Oxford, 2018. http://ora.ox.ac.uk/objects/uuid:5935d96d-625a-4778-b42d-bb56c96d96cc.

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Adaptation is a fundamental property of the nervous system that underlies the maintenance of successful actions through flexible reconfiguration of sensorimotor processing. The primary aims of this thesis are 1) to investigate the computational and neural underpinnings of sensorimotor memory formation during prism adaptation (PA) in humans, and 2) how they interact with anodal transcranial direct current stimulation (a-tDCS) of the primary motor cortex (M1), in order to 3) improve efficacy of prism therapy for post-stroke spatial neglect. In chapter 4, we modify an influential state-space model of adaptation in order to characterize the contribution of short and long memory timescales to motor behaviour as sensorimotor after-effects (AEs) develop during PA. This enables us, in the multimodal 7 Tesla MRI experiment reported in chapter 5, to demonstrate that the level of M1 excitation:inhibition causally sets the relative contribution of long versus short memory timescales during PA, thus determining behavioural persistence of the AE at retention in young healthy adults. This finding offers a bridge between different levels of investigation by providing a biologically plausible neuro-computational model of how sensorimotor memories are formed and enhanced by a-tDCS. In chapter 6, we use the ageing motor system as a model of reduced GABAergic inhibition and show that the age-related decrease in M1 GABA explains why older adults demonstrate more persistent prism AEs. Taken together, these data indicate that the reduction in M1 GABAergic inhibition via excitatory a-tDCS during PA has the potential to enhance persistence of adaptation memory in both young and older adults. Informed by these results, we subsequently ask whether standard (multi-session) PA therapy combined with left M1 a-tDCS translates to greater and/or longer-lasting clinical improvements in post-stroke spatial neglect patients. In chapter 7, we compare the multimodal neuroimaging data of six neglect patients to normative data of age-matched controls. We show that in all patients, the lesion interrupted long-range frontoparietal connections, and we provide direct evidence for a pathological left dominance of activity within the lateral occipital cortex during deployment of bilateral visuospatial attention. In chapter 8, we present the behavioural performance of these patients throughout the two phases of the clinical study (i.e. before and after either PA + real M1 a-tDCS or PA + sham M1 atDCS). There was no clear effect of a-tDCS on the therapeutic effect of PA in these patients. The results of the studies presented in this thesis provide a novel insight into the neurocomputational mechanisms of sensorimotor memory formation and its modulation by a-tDCS in the healthy brain. Further investigation of how these mechanisms relate to therapeutic improvements following PA in certain neglect patients is needed.
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Weiler, Florian [Verfasser], Horst [Akademischer Betreuer] Hahn, Horst [Gutachter] Hahn, Lars [Gutachter] Linsen, Bernhard [Gutachter] Preim, and Jan [Gutachter] Klein. "Computational tools for objective assessment in Neuroimaging / Florian Weiler ; Gutachter: Horst Hahn, Lars Linsen, Bernhard Preim, Jan Klein ; Betreuer: Horst Hahn." Bremen : IRC-Library, Information Resource Center der Jacobs University Bremen, 2020. http://d-nb.info/1203875983/34.

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9

Iyappan, Anandhi [Verfasser]. "Conceptualization of computational modeling approaches and interpretation of the role of neuroimaging indices in pathomechanisms for pre-clinical detection of Alzheimer Disease / Anandhi Iyappan." Bonn : Universitäts- und Landesbibliothek Bonn, 2018. http://d-nb.info/1173789685/34.

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Gloaguen, Arnaud. "A statistical and computational framework for multiblock and multiway data analysis." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG016.

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L'étude des relations entre plusieurs ensembles de variables mesurées sur un même groupe d'individus est un défi majeur en statistique. La littérature fait référence à ce paradigme sous plusieurs termes : "analyse de données multimodales", "intégration de données", "fusion de données" ou encore "analyse de données multibloc". Ce type de problématique se retrouve dans des domaines aussi variés que la biologie, la chimie, l'analyse multi-capteurs, le marketing, la recherche agro-alimentaire, où l'objectif commun est d'identifier les variables de chaque bloc intervenant dans les intéractions entre blocs. Par ailleurs, il est possible que chaque bloc soit composé d'un très grand nombre de variables (~1M), nécessitant le calcul de milliards d'associations. L'élaboration d'un cadre statistique épousant la complexité et l'hétérogénéité des données est donc primordial pour mener une analyse pertinente.Le développement de méthodes d'analyse de données hétérogènes, potentiellement de grande dimension, est au coeur de ce travail. Ces développements se basent sur l'Analyse Canonique Généralisée Régularisée (RGCCA), un cadre général pour l'analyse de données multiblocs. Le coeur algorithmique de RGCCA se résume à un unique "update", répété jusqu'à convergence. Si cet update possède certaines "bonnes" propriétés, la convergence globale de l'algorithme est garantie. Au cours de ces travaux, le cadre algorithmique de RGCCA a été étendu dans plusieurs directions :(i) Du séquentiel au global. Plutôt que d'extraire de chaque bloc les composantes de manière séquentielle, un problème d'optimisation globale permettant de construire ces composantes simultanément a été proposé.(ii) De la matrice au tenseur. L'Analyse Canonique Généralisée Multivoie (MGCCA) étend RGCCA à l'analyse conjointe d'un ensemble de tenseurs. Des versions séquentielle et globale de MGCCA ont été proposées. La convergence globale de ces algorithmes est montrée.(iii) De la parcimonie à la parcimonie structurée. Le coeur de l'algorithme d'Analyse Canonique Généralisée Parcimonieuse (SGCCA) a été amélioré en fournissant un algorithme à convergence globale beaucoup plus rapide. Des contraintes de parcimonie structurée ont également été ajoutées à SGCCA.Dans une seconde partie, l'analyse de plusieurs jeux de données est menée à l'aide de ces nouvelles méthodes. La polyvalence des ces outils est démontrée sur (i) deux études en imagerie-génétique, (ii) deux études en électroencéphalographie ainsi (iii) qu'une étude en microscopie Raman. L'accent est mis sur l'interprétation des résultats facilitée par la prise en compte des structures multiblocs, tensorielles et/ou parcimonieuses
A challenging problem in multivariate statistics is to study relationships between several sets of variables measured on the same set of individuals. In the literature, this paradigm can be stated under several names as “learning from multimodal data”, “data integration”, “data fusion” or “multiblock data analysis”. Typical examples are found in a large variety of fields such as biology, chemistry, sensory analysis, marketing, food research, where the common general objective is to identify variables of each block that are active in the relationships with other blocks. Moreover, each block can be composed of a high number of measurements (~1M), which involves the computation of billion(s) of associations. A successful investigation of such a dataset requires developing a computational and statistical framework that fits both the peculiar structure of the data as well as its heterogeneous nature.The development of multivariate statistical methods constitutes the core of this work. All these developments find their foundations on Regularized Generalized Canonical Correlation Analysis (RGCCA), a flexible framework for multiblock data analysis that grasps in a single optimization problem many well known multiblock methods. The RGCCA algorithm consists in a single yet very simple update repeated until convergence. If this update is gifted with certain conditions, the global convergence of the procedure is guaranteed. Throughout this work, the optimization framework of RGCCA has been extended in several directions:(i) From sequential to global. We extend RGCCA from a sequential procedure to a global one by extracting all the block components simultaneously with a single optimization problem.(ii) From matrix to higher order tensors. Multiway Generalized Canonical Correlation Analysis (MGCCA) has been proposed as an extension of RGCCA to higher order tensors. Sequential and global strategies have been designed for extracting several components per block. The different variants of the MGCCA algorithm are globally convergent under mild conditions.(iii) From sparsity to structured sparsity. The core of the Sparse Generalized Canonical Correlation Analysis (SGCCA) algorithm has been improved. It provides a much faster globally convergent algorithm. SGCCA has been extended to handle structured sparse penalties.In the second part, the versatility and usefulness of the proposed methods have been investigated on various studies: (i) two imaging-genetic studies, (ii) two Electroencephalography studies and (iii) one Raman Microscopy study. For these analyses, the focus is made on the interpretation of the results eased by considering explicitly the multiblock, tensor and sparse structures
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Книги з теми "Computational neuroimaging"

1

Li, Ping, and Hua Shu. Language and the brain: computational and neuroimaging evidence from Chinese. Oxford University Press, 2010. http://dx.doi.org/10.1093/oxfordhb/9780199541850.013.0007.

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2

Tang, Xiaoying, Thomas Fletcher, and Michael I. Miller, eds. Bayesian Estimation and Inference in Computational Anatomy and Neuroimaging: Methods & Applications. Frontiers Media SA, 2019. http://dx.doi.org/10.3389/978-2-88945-984-1.

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3

Sensory Nervous System - Computational Neuroimaging Investigations of Topographical Organization in Human Sensory Cortex [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.98172.

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4

Adams, Reginald B., Daniel N. Albohn, and Kestutis Kveraga. A Social Vision Account of Facial Expression Perception. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190613501.003.0017.

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In this chapter, we discuss prospects for a future computational neuropsychology. Computerized approaches to assessment, the ability to implement life-like scenarios in a controlled virtual environment, and teleneuropsychology offer promise for expanding available approaches to cognitive remediation and self-monitoring. Computational models are also available increasingly for integrating neuroimaging into the assessment process. Neuropsychologists can use neuroimaging to develop new frameworks for neuropsychological testing that are rooted in the current evidence base on large-scale brain system interactions. This will allow for traditional assessment of discrete areas of neurocognitive functioning to be brought in line with recent findings that highly nuanced relations exist among brain networks. Furthermore, the new findings from systems neuroscience may allow for the development of neuropsychological assessments with greater accuracy and increased targeted testing. Neuroinfomatic approaches offer computational neuropsychology an approach to knowledge sharing via well-defined neuropsychological ontologies and collaborative knowledgebases.
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5

Chung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.

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6

Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.

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7

Chung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.

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8

Chung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.

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9

Roggeman, Chantal, Wim Fias, and Tom Verguts. Basic Number Representation and Beyond. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.68.

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We discuss recent computational network models of elementary number processing. One key issue to emerge from this work is a crucial distinction between symbolic and non-symbolic number representation, and the related distinction between number-selective and number-sensitive coding. Empirical predictions from the models were tested, and are here summarized. Another issue is the relation with task-based decision making mechanisms. In both lab and real-life settings, representations are seldomly accessed in a task-neutral manner, rather subjects are usually presented with a task. A related theme is the functional association between number representations and working memory. In these issues also, both modeling and neuroimaging work is summarized. To conclude, we propose that the combined modeling-neuroimaging approach should be extended to tackle more complex questions about number processing (e.g. fractions, development, dyscalculia).
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10

Shaikh, Mohd Faraz. Machine Learning in Detecting Auditory Sequences in Magnetoencephalography Data : Research Project in Computational Modelling and Simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.25368/2022.411.

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Does your brain replay your recent life experiences while you are resting? An open question in neuroscience is which events does our brain replay and is there any correlation between the replay and duration of the event? In this study I tried to investigate this question by using Magnetoencephalography data from an active listening experiment. Magnetoencephalography (MEG) is a non-invasive neuroimaging technique used to study the brain activity and understand brain dynamics in perception and cognitive tasks particularly in the fields of speech and hearing. It records the magnetic field generated in our brains to detect the brain activity. I build a machine learning pipeline which uses part of the experiment data to learn the sound patterns and then predicts the presence of sound in the later part of the recordings in which the participants were made to sit idle and no sound was fed. The aim of the study of test replay of learned sound sequences in the post listening period. I have used classification scheme to identify patterns if MEG responses to different sound sequences in the post task period. The study concluded that the sound sequences can be identified and distinguished above theoretical chance level and hence proved the validity of our classifier. Further, the classifier could predict the sound sequences in the post-listening period with very high probability but in order to validate the model results on post listening period, more evidence is needed.
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Частини книг з теми "Computational neuroimaging"

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Hanson, Stephen José, Michiro Negishi, and Catherine Hanson. "Connectionist Neuroimaging." In Emergent Neural Computational Architectures Based on Neuroscience, 560–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44597-8_40.

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2

Lahmiri, Salim, Mounir Boukadoum, and Antonio Di Ieva. "Fractals in Neuroimaging." In Springer Series in Computational Neuroscience, 295–309. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3995-4_19.

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3

Ray, Kimberly, and Angela Marie Richmond Laird. "Meta-analysis in Neuroimaging." In Encyclopedia of Computational Neuroscience, 1687–89. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_542.

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Ray, Kimberly, and Angela Laird. "Meta-analysis in Neuroimaging." In Encyclopedia of Computational Neuroscience, 1–3. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_542-1.

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Kawato, Mitsuo. "Brain-Machine Interface and Neuroimaging." In Encyclopedia of Computational Neuroscience, 441–43. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_523.

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6

Poline, Jean Baptiste, and David Kennedy. "Software for Neuroimaging Data Analysis." In Encyclopedia of Computational Neuroscience, 2733–44. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_538.

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7

Ozaki, Tohru. "Statistical Analysis of Neuroimaging Data." In Encyclopedia of Computational Neuroscience, 2868–70. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_539.

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Bojak, Ingo, and Michael Breakspear. "Neuroimaging, Neural Population Models for." In Encyclopedia of Computational Neuroscience, 1919–44. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_70.

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Kawato, Mitsuo. "Brain Machine Interface and Neuroimaging." In Encyclopedia of Computational Neuroscience, 1–3. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_523-1.

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Poline, Jean Baptiste, and David Kennedy. "Software for Neuroimaging Data Analysis." In Encyclopedia of Computational Neuroscience, 1–14. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_538-1.

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Тези доповідей конференцій з теми "Computational neuroimaging"

1

Wandell, Brian A., and Robert F. Dougherty. "Computational neuroimaging: maps and tracks in the human brain." In Electronic Imaging 2006, edited by Bernice E. Rogowitz, Thrasyvoulos N. Pappas, and Scott J. Daly. SPIE, 2006. http://dx.doi.org/10.1117/12.674141.

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2

Mayrand, Robin Perry, Christian Yaphet Freytes, Luana Okino Sawada, Micheal Adeyosoye, Rosie E. Curiel Cid, David Lowenstein, Ranjan Duara, and Malek Adjouadi. "Computational Analysis of a Light-Weight SUVr Processing Technique for Neuroimaging Alzheimer's Disease." In 2022 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2022. http://dx.doi.org/10.1109/csci58124.2022.00317.

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3

Steinkamp, Simon, Iyadh Chaker, Félix Hubert, David Meder, and Oliver Hulme. "Computational Parametric Mapping: A Method For Mapping Cognitive Models Onto Neuroimaging Data." In 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1124-0.

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4

Baykova, Reny, and Warrick Roseboom. "Effects of Sensory Precision on Behavioral and Neuroimaging Perceptual Biases in Duration Estimation." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1280-0.

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Obafemi-Ajayi, Tayo, Khalid Al-Jabery, Lauren Salminen, David Laidlaw, Ryan Cabeen, Donald Wunsch, and Robert Paul. "Neuroimaging biomarkers of cognitive decline in healthy older adults via unified learning." In 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017. http://dx.doi.org/10.1109/ssci.2017.8280937.

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Kemtur, Anirudha, Francois Paugam, Basile Pinsard, Pravish sainath, Yann Harel, Maximilien Le clei, Julie Boyle, Karim Jerbi, and Pierre Bellec. "AI-based modeling of brain and behavior: Combining neuroimaging, imitation learning and video games." In 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1303-0.

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Thomas, Armin, Hauke R. Heekeren, Klaus-Robert Müller, and Wojciech Samek. "DeepLight: A Structured Framework For The Analysis of Neuroimaging Data Through Recurrent Deep Learning Models." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1226-0.

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Enyagina, I., A. Polyakov, and D. Kokovin. "SERVICES OF COMPUTATIONAL NEUROBIOLOGY TASKS, BASED ON THE DISTRIBUTED MODULAR PLATFORM «DIGITAL LABORATORY» NRC «KURCHATOV INSTITUTE»." In 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". Crossref, 2021. http://dx.doi.org/10.54546/mlit.2021.13.75.001.

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This paper describes program services for performing tasks of computational neurobiology, based onthe distributed modular platform "Digital Laboratory" NRC «Kurchatov Institute». These servicesexpanded the system "Neuroimaging" for storing, processing and analyzing experimental MRI / fMRIdata of the human brain. The main goal of creating these program services is the softwareimplementation of methods for calculating the functional connectivity of regions of the human brain atrest using fMRI data, and visualization of the results.
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Niu, Xin, Hualou Liang, and Fengqing Zhang. "Brain age prediction for post-traumatic stress disorder patients with convolutional neural networks: a multi-modal neuroimaging study." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1121-0.

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10

Garimella, Harsha T., and Reuben H. Kraft. "Validation of Embedded Element Method in the Prediction of White Matter Disruption in Concussions." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67785.

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A better understanding of the axonal injury would help us develop improved diagnostic tools, protective measures, and rehabilitation treatments. Computational modeling coupled with advanced neuroimaging techniques might be a promising tool for this purpose. However, before the models can be used for real life applications, they need to be validated and cross-verified with real life scenarios to establish the credibility of the model. In this work, progress has been made in validating a human head finite element model with embedded axonal fiber tractography (using embedded element method) using pre- and post-diffusion tensor imaging data (DTI) of a concussed athlete. Fractional anisotropy (FA) was used to determine the microstructural changes during injury. These damaged locations correlated well with the damaged locations observed from the finite element model. This work could be characterized as a first step towards the development of a more comprehensively validated human head finite element model.
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Звіти організацій з теми "Computational neuroimaging"

1

Cohen, Jonathan D. Second Generation Flexible Computing Environment for Computational Modeling of Brain Function and Neuroimaging Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada530764.

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