Resting-state fMRI in the human connectome project
Open accessAbstract
Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of 1h of whole-brain rfMRI data at 3 T, with a spatial resolution of 2×2×2 mm and a temporal resolution of 0.7s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses.
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A growing number of studies are using AI to evaluate eye tracking data during digital cognitive and oculomotor tasks.
“Beyond task performance, a growing number of studies are also using AI to evaluate eye tracking data during digital cognitive and oculomotor tasks.64”
From Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias (2026) · cites this paper as [64]
The Introduction’s acquisition-protocol section describes resting-state fMRI with eyes-open fixation, while its temporal-preprocessing section describes automated classification of fMRI components. Searches for eye tracking, oculomotor tasks, and AI analysis of gaze data found no passage addressing the attributed claim. Our copy provides no basis to assess whether a growing number of studies use AI for that purpose. (LLM, conf=0.76)
Experimental — accuracy not yet measuredChoosing the best multiband factor and other sequence settings involves complex trade-offs and required extensive pilot work.
“The optimal multiband factor and other pulse sequence parameters depend on a complex set of trade-offs that entailed extensive piloting and analysis (Ugurbil et al., 2013; Smith et al. 2013; Sotiropoulos et al., 2013c).”
From The WU-Minn Human Connectome Project: An overview (2013) · cites this paper as Smith et al. 2013
The resulting resting-state network timeseries show exceptionally clean power spectra.
“The resulting resting-state network timeseries show exceptionally clean power spectra (Smith et al., 2013).”
From The WU-Minn Human Connectome Project: An overview (2013) · cites this paper as Smith et al., 2013
HCP fMRI data include robust task activations and resting-state networks.
“Although not emphasized in the present article, the HCP data do include robust task activations and resting-state networks from the fMRI data (Barch et al., 2013; Smith et al., 2013).”
From The WU-Minn Human Connectome Project: An overview (2013) · cites this paper as Smith et al., 2013
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Brain–phenotype models fail for individuals who defy sample stereotypes
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- Authors
- Smith, S. M., Beckmann, C. F., Andersson, J., Auerbach, E. J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D. A., Griffanti, L., Harms, M. P., Kelly, M., Laumann, T., Miller, K. L., Moeller, S., Petersen, S., Power, J., Salimi-Khorshidi, G., Snyder, A. Z., Vu, A. T., Woolrich, M. W., Xu, J., Yacoub, E., Uğurbil, K., Van Essen, D. C., & Glasser, M. F.
- Affiliations
- University of Oxford (GB)
- Radboud University Nijmegen (NL)
- University of Minnesota (US)
- University of California, Berkeley (US)
- Washington University in St. Louis (US)
- Corresponding author
- Smith, Stephen M.
- ORCID
- Smith, Stephen M.
- Beckmann, Christian F.
- Andersson, Jesper
- Auerbach, Edward J.
- Bijsterbosch, Janine
- Douaud, Gwenaëlle
- Duff, Eugene
- Griffanti, Ludovica
- Harms, Michael P.
- Laumann, Timothy
- Miller, Karla L.
- Moeller, Steen
- Petersen, Steve
- Power, Jonathan
- Snyder, Abraham Z.
- Vu, An T.
- Woolrich, Mark W.
- Xu, Junqian
- Yacoub, Essa
- Glasser, Matthew F.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 80
- Pages
- 144-168
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3720828
- OpenAlex
- W2096672020
- Cited by
- 1,502
- References
- 65
- Citations by year
- 2026: 74 · 2025: 147 · 2024: 139 · 2023: 138 · 2022: 149 · 2021: 166 · 2020: 143 · 2019: 130 · 2018: 117 · 2017: 114 · 2016: 85 · 2015: 54 · 2014: 32 · 2013: 14
- FWCI
- 33.82
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- University of Twente Research Information (publishedVersion)
- PubMed Central (acceptedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion)
- UniTERAMO Research Catalog (University of Teramo) (submittedVersion)
- University of Twente Research Information (submittedVersion)
- Funders
- NIH (1U54MH091657-01, P30-NS057091, P41-RR08079/EB015894, F30-MH097312)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- resting-state fMRI, functional connectivity, Human Connectome Project, echo planar imaging, data quality, healthy adults, whole-brain mapping
- MeSH terms
- Brain, Humans, Magnetic Resonance Imaging, Models, Anatomic, Models, Neurological, Nerve Net, Rest, Connectome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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