Results 51 to 60 of about 362,106 (369)
Model averaging, optimal inference and habit formation
Frontiers in Human Neuroscience, 2014 Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function – the subject of much current interest in neuroscience and related disciplines.
Thomas H B FitzGerald +2 more
doaj +1 more source
Journal of NeuroEngineering and Rehabilitation, 2020 Background After stroke, kinematic measures obtained with non-robotic and robotic devices are highly recommended to precisely quantify the sensorimotor impairments of the upper-extremity and select the most relevant therapeutic strategies.
Nabila Brihmat +4 more
doaj +1 more source
A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis
npj Digital Medicine, 2023 Modern management of MS targets No Evidence of Disease Activity (NEDA): no clinical relapses, no magnetic resonance imaging (MRI) disease activity and no disability worsening.
Michael Barnett +24 more
doaj +1 more source
Predicting Responses from Weighted Networks with Node Covariates in an Application to Neuroimaging [PDF]
arXiv, 2022 We consider the setting where many networks are observed on a common node set, and each observation comprises edge weights of a network, covariates observed at each node, and an overall response. The goal is to use the edge weights and node covariates to predict the response while identifying an interpretable set of predictive features.
arxiv
The coordinate-based meta-analysis of neuroimaging data [PDF]
Statist. Sci. Volume 32, Number 4 (2017), 580-599, 2016 Neuroimaging meta-analysis is an area of growing interest in statistics. The special characteristics of neuroimaging data render classical meta-analysis methods inapplicable and therefore new methods have been developed. We review existing methodologies, explaining the benefits and drawbacks of each. A demonstration on a real dataset of emotion studies
arxiv +1 more source
ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data [PDF]
, 2022 Brain extraction and registration are important preprocessing steps in neuroimaging data analysis, where the goal is to extract the brain regions from MRI scans (i.e., extraction step) and align them with a target brain image (i.e., registration step). Conventional research mainly focuses on developing methods for the extraction and registration tasks ...
arxiv +1 more source
Journal of Integrative Neuroscience, 2022 Background: While amyotrophic lateral sclerosis (ALS) is widely recognised as a multi-network disorder with extensive frontotemporaland cerebellar involvement, sensory dysfunction is relatively under evaluated.
Rangariroyashe H. Chipika +5 more
doaj +1 more source
CNS & Neurological Disorders - Drug Targets, 2009 Neuroimaging techniques have refined the characterization of neural structures involved in the regulation of normal sleep-wake cycle in healthy humans. Yet brain imaging studies in patients with sleep disorders still remain scarce. In narcoleptic patients, structural and functional brain imaging studies have suggested the involvement of the ...
Dang-Vu, T. T. +3 more
openaire +5 more sources
Nature Neuroscience, 2018 Hierarchy provides a unifying principle for the macroscale organization of anatomical and functional properties across primate cortex, yet microscale bases of specialization across human cortex are poorly understood.
J. Burt +8 more
semanticscholar +1 more source
Biological Psychiatry, 2020 The neuroimaging community has witnessed a paradigm shift in biomarker discovery from using traditional univariate brain mapping approaches to multivariate predictive models, allowing the field to move towards a translational neuroscience era. Regression-
J. Sui +3 more
semanticscholar +1 more source