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1,359 claims from 845 papers are on the record. 46 have been checked so far; the other 1,313 have no check with a result yet.

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Status: Unchecked ×Keyword: resting-state networks ×Clear all

3 claims from 2 papers

1. Neuroscience › Functional Brain Connectivity Studies

   Consistent resting-state networks across healthy subjects

   Damoiseaux, Rombouts, Barkhof et al. · Proceedings of the National Academy of Sciences · 2006

   Using a statistical method on resting fMRI scans, the study found brain activity patterns that were consistent across subjects and sessions, with signal changes comparable to those seen in task experiments.

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   1. UncheckedResting-state fMRI analysis found 10 brain patterns, linked to regions for movement, vision, memory and others, with signal changes up to 3%.“The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3%.”
   2. UncheckedIn this resting-state fMRI study, brain areas with a higher mean percentage BOLD signal were generally more consistent and varied least around the mean.“In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean.”

2. Neuroscience › Functional Brain Connectivity Studies

   ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging

   Griffanti, Salimi‐Khorshidi, Beckmann et al. · NeuroImage · 2014

   The study compared three data-driven ways of cleaning resting-state fMRI of artefacts, and compared standard with faster, higher-resolution accelerated acquisitions, to improve brain network imaging.

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   1. UncheckedWith accelerated fMRI data, the authors could run ICA decompositions with more components, which they say helps detailed network analyses.“Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.”