Results 21 to 30 of about 1,068,311 (337)
A mixed-modeling framework for analyzing multitask whole-brain network data [PDF]
Network Neuroscience, 2019 The emerging area of brain network analysis considers the brain as a system, providing profound insight into links between system-level properties and health outcomes.
Sean L. Simpson +2 more
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Alpha power during task performance predicts individual language comprehension
NeuroImage, 2022 Alpha power attenuation during cognitive task performing has been suggested to reflect a process of release of inhibition, increase of excitability, and thereby benefit the improvement of performance.
P. Wang +7 more
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Topological learning for brain networks [PDF]
The Annals of Applied Statistics, 2020 AbstractThis paper proposes a novel topological learning framework that can integrate networks of different sizes and topology through persistent homology. This is possible through the introduction of a new topological loss function that enables such challenging task. The use of the proposed loss function bypasses the intrinsic computational bottleneck
Songdechakraiwut, Tananun, Chung, Moo K.
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Aging and neural vulnerabilities in overeating: A conceptual overview and model to guide treatment
PCN Reports, 2022 Given the vulnerability of older adults to chronic disease and physical disability, coupled with the threat that obesity poses to healthy aging, there is an urgent need to understand the causes of positive energy balance and the struggle that many older ...
W. Jack Rejeski +5 more
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Embedding Functional Brain Networks in Low Dimensional Spaces Using Manifold Learning Techniques
Frontiers in Neuroinformatics, 2021 Background: fMRI data is inherently high-dimensional and difficult to visualize. A recent trend has been to find spaces of lower dimensionality where functional brain networks can be projected onto manifolds as individual data points, leading to new ways
Ramon Casanova +10 more
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Hodge Laplacian of Brain Networks
IEEE Transactions on Medical Imaging, 2023 The closed loops or cycles in a brain network embeds higher order signal transmission paths, which provide fundamental insights into the functioning of the brain. In this work, we propose an efficient algorithm for systematic identification and modeling of cycles using persistent homology and the Hodge Laplacian.
D. Vijay Anand, Moo K. Chung
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The envirome and the connectome: exploring the structural noise in the human brain associated with socioeconomic deprivation [PDF]
, 2013 Complex cognitive functions are widely recognized to be the result of a number of brain regions working together as large-scale networks. Recently, complex network analysis has been used to characterize various structural properties of the large scale ...
Batty, D. +7 more
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Dopamine role in learning and action inference
eLife, 2020 This paper describes a framework for modelling dopamine function in the mammalian brain. It proposes that both learning and action planning involve processes minimizing prediction errors encoded by dopaminergic neurons.
Rafal Bogacz
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Brain signatures of chronic gut inflammation
Frontiers in Psychiatry, 2023 Gut inflammation is thought to modify brain activity and behaviour via modulation of the gut-brain axis. However, how relapsing and remitting exposure to peripheral inflammation over the natural history of inflammatory bowel disease (IBD) contributes to ...
Caitlin V. Hall +10 more
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Pygpc: A sensitivity and uncertainty analysis toolbox for Python
SoftwareX, 2020 We present a novel Python package for the uncertainty and sensitivity analysis of computational models. The mathematical background is based on the non-intrusive generalized polynomial chaos method allowing one to treat the investigated models as black ...
Konstantin Weise +4 more
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