Results 61 to 70 of about 60,482 (258)
Sampling Rate Effects on Resting State fMRI Metrics [PDF]
Frontiers in Neuroscience, 2019 Low image sampling rates used in resting state functional magnetic resonance imaging (rs-fMRI) may cause aliasing of the cardiorespiratory pulsations over the very low frequency (VLF) BOLD signal fluctuations which reflects to functional connectivity (FC). In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-
Niko Huotari +21 more
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Assessing Strengths and Limitations of Magnetoencephalography Source Imaging With Intracerebral EEG
Advanced Science, EarlyView.Simultaneous MEG and stereotactic EEG (SEEG) recordings provide a direct validation framework for MEG source imaging in focal epilepsy. Virtual SEEG signals derived from MEG reconstructions reveal significant agreement with intracranial measures of spike localization, resting‐state oscillations, and functional connectivity, while also identifying ...
Jawata Afnan +10 more
wiley +1 more source
Optimization of scan time for resting ⁃state fMRI in clinical application
Chinese Journal of Contemporary Neurology and Neurosurgery, 2011 Objective To study the optimization of scan time for resting⁃state functional magnetic resonance imaging (fMRI) in clinical application. Methods Fractional amplitude of low ⁃ frequency fluctuation (fALFF) was performed to analyse the resting ⁃ state fMRI
Cheng ZHAO +5 more
doaj
Frontiers in Human Neuroscience, 2013 In resting-state functional magnetic resonance imaging (fMRI), the temporal correlation between spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal from different brain regions is used to assess functional connectivity ...
Omer eTal +13 more
doaj +1 more source
Evaluation of Statistical Inference on Empirical Resting State fMRI [PDF]
IEEE Transactions on Biomedical Engineering, 2014 Modern statistical inference techniques may be able to improve the sensitivity and specificity of resting state functional magnetic resonance imaging (rs-fMRI) connectivity analysis through more realistic assumptions. In simulation, the advantages of such methods are readily demonstrable.
Xue Yang +3 more
openaire +2 more sources
Advanced Science, EarlyView.Using grilled lamb skewers as a model system, this work builds a multiscale coupling framework from oral processing to retronasal aroma perception, reveals dual‐kinetic release patterns and Electroencephalogram‐characterized central encoding features, and proposes an interpretable physics‐guided deep learning model validated by multiphysics simulation,
Che Shen +12 more
wiley +1 more source
Data in Brief, 2019 It has been reported that it is possible to observe transient changes in resting-state functional connectivity (FC) in the attention networks of healthy adults during treatment with prism adaptation. by using functional magnetic resonance imaging (fMRI) (
Kengo Tsujimoto +8 more
doaj +1 more source
Machine learning in resting-state fMRI analysis
Magnetic Resonance Imaging, 2019 51 pages, 6 ...
Meenakshi Khosla +4 more
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On the generalizability of resting-state fMRI machine learning classifiers [PDF]
Frontiers in Human Neuroscience, 2014 Machine learning classifiers have become increasingly popular tools to generate single-subject inferences from fMRI data. With this transition from the traditional group level difference investigations to single-subject inference, the application of machine learning methods can be seen as a considerable step forward.
Wolfgang eHuf +15 more
openaire +3 more sources
Advanced Science, EarlyView.Frontal lobe traumatic brain injury is associated with hyperactivity of an insular–orbitofrontal circuit in both patients and mice. By combination of integrating functional imaging, cell‐type–specific circuit manipulation, single‐cell transcriptomics, and whole‐cell recordings, this work identifies the downregulation of the potassium channel KCNC3 in ...
Meng‐Ge Li +10 more
wiley +1 more source