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Functional connectomics from resting-state fMRI
Open accessAbstract
Spontaneous fluctuations in activity in different parts of the brain can be used to study functional brain networks. We review the use of resting-state functional MRI for the purpose of mapping the macroscopic functional connectome. After describing MRI acquisition and image processing methods commonly used to generate data in a form amenable to connectomics network analysis, we discuss different approaches for estimating network structure from that data. Finally, we describe new possibilities resulting from the high-quality rfMRI data being generated by the Human Connectome Project, and highlight some upcoming challenges in functional connectomics.
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TL;DR
This review describes how resting-state fMRI (rfMRI) can map the macroscopic functional connectome, covering acquisition, preprocessing, network modelling, and Human Connectome Project (HCP) data. It highlights high-quality HCP rfMRI enabling detailed node-and-edge connectomes and links to behaviour and sex, while noting major challenges in causality, alignment, and dynamic network modelling.
Key findings
- The HCP is generating rfMRI from over 1000 healthy adults aged 22–35, including twins and non-twin siblings: 1 h of whole-brain 3T rfMRI in two pairs of 15-min runs on separate days, at 2×2×2 mm spatial resolution, 0.7 s temporal resolution, and EPI acceleration factor ×8; over 200 subjects’ datasets were already publicly released, and 3T rfMRI is represented using about 90,000 grayordinates at ~2 mm spacing.
- In the first 131 HCP subjects, aggregating over 600,000 timepoints, group-ICA at dimensionality 100 yielded 98 network nodes after discarding 2 artefactual components; partial correlation matrices were as strong and consistent across subjects as full correlation matrices, which the authors call unusual and indicative of high data quality and many timepoints.
- Multimodal surface matching improved cross-subject network similarity: average between-subject network correlations were FreeSurfer=0.613, MSMfolding=0.618, and MSMmultimodal=0.649; sex prediction with leave-one-out linear discriminant analysis reached 87% for MSMmultimodal versus 78% for FreeSurfer and 82% for MSMfolding, and using full correlation instead of partial correlation reduced sex classification accuracy from 87% to 70%.
- A significant association was found between individual subjects’ network matrices and fluid intelligence, with the 24 strongest edges shown; the node-pair contributing most strongly had only a weak average population connection but varied across subjects in a way that correlated with fluid intelligence. A separate analysis identified 9 edges significantly different in strength between the two sexes.
Methods
- Design: a review of rfMRI-based functional connectomics, including illustrative analyses using publicly released HCP rfMRI data.
- Data/sample: HCP cohort of over 1000 healthy adult twins and non-twin siblings aged 22–35; the illustrative analyses used the first 131 subjects’ rfMRI data, aggregating over 600,000 timepoints.
- Main analysis: preprocessing with FSL, FreeSurfer, and Connectome Workbench; ICA-based artefact removal with FIX (reported >99% classification accuracy on HCP data); group-ICA at dimensionality 100 yielding 98 nodes; full and partial correlation, regularised inverse covariance, multimodal surface matching, permutation testing, linear discriminant analysis, and temporal-ICA.
Limitations
- Stated in paper: correlation cannot reveal causality or whether connectivity is direct versus indirect; partial correlation estimates direct connections but not directionality; directionality estimation is harder than edge detection, temporal lag is confounded by slow and variable haemodynamics, and observational rfMRI is not a robust tool for inferring causality compared with interventional task-fMRI.
- Stated in paper: dynamic causal modelling is practical only for networks with fewer than 10 nodes; for 50–500 nodes, partial correlation is a pragmatic compromise but inverse covariance estimation is often unstable and benefits from regularisation; graph-theory measures risk inappropriate node definitions, use of thresholded full correlation matrices, and confounds such as head motion or heart rate.
- Stated in paper: temporal-ICA forces modes to be fully independent over time, assumes within-mode connection strengths are static, and cannot well identify mutually exclusive modes with negative correlation; sliding-window correlation can be confounded by window length and overlapping networks; no single parcellation technique has emerged as best; and larger heterogeneous datasets such as the Thousand Functional Connectomes have greater scanning-parameter heterogeneity and lower data quality than HCP. Evident from text: the paper is a review with illustrative HCP analyses rather than a single primary empirical study, so its quantitative results are presented as examples of methodological potential and issues.
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50 Own interpretation · 42 Not checked yet · 12 Own result · 11 No basis given · 1 Partly supported · accuracy not yet measured
In the paper
“Significant improvements in the alignment of task activation regions were found, both quantitatively and qualitatively [Robinson, in submission];”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
What the sources say
- Not checked yetcites [Robinson, in submission]
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“When MSM is only given folding information to drive the alignment (“MSMfolding”), the overall degree of alignment is similar to that achieved by FreeSurfer.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“for example, Figure 5B shows stronger and more sharply defined activation patterns from a group-level analysis of two different tasks (“maths vs story” and a social interaction task) when MSMmultimodal is utilised (lower panels) compared to MSMfolding (upper panels), especially in parts of prefrontal cortex (blue arrows).”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“When we used the full correlation network matrices instead of partial correlation, the sex classification accuracy was reduced from 87% to 70%.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“However, if MSM is also fed cortical myelin-weighted maps [76], as well as low-dimensional resting-state network maps, it can utilise richer structural and connectional information, and further improve the intersubject alignment (“MSMmultimodal”) [43].”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
What the sources say
- Not matchedcites [76] Glasser MF 2011 Comparison of Surface Gradients Derived from Myelin Maps and Functional Connectivity Analysis Int. Conf. on Functional Mapping of the Human Brain
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [43] Glasser MF 2013 The Minimal Preprocessing Pipelines for the Human Connectome Project NeuroImage 80 105 124 23668970 PMC3720813 source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using leave-one-out training and testing, sex was successfully predicted in 87% of subjects”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“This identified 9 edges that were significantly different in strength between the two sexes.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In Figure 7, we utilise the above approach to show a significant association between individual subjects’ network matrices and fluid intelligence (a component of IQ).”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Notably, the overall range of values is similar for both matrices.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plainn = 131- Asserted: passes — The authors put it forward as true.
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In the paper
“and are consistent across subjects [4]”
§ Functional connectomics from resting-state fMRI > Resting-state fMRI and the connectome – what’s the connection?
What the sources say
- Not checked yetcites [4] Yeo BT 2011 The organization of the human cerebral cortex estimated by intrinsic functional connectivity J Neurophysiol 106 1125 1165 21653723 PMC3174820 source
plain- Asserted: passes — The authors put it forward as true.
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- Probably not a substantial claim. 23
- It may state two separate claims as one. 45
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In the paper
““multiband accelerated EPI” acquires multiple slices simultaneously [39, 40]. Such approaches enable major improvements in spatial and/or temporal resolution, for example acquiring data with 2mm spatial resolution in less than a second.”
§ Functional connectomics from resting-state fMRI > rfMRI acquisition and image processing overview
What the sources say
- Partly supportedcites [39] Moeller S 2010 Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI Magn Reson Med 63 1144 1153 20432285 PMC2906244 sourceExperimental — accuracy not yet measured
The Abstract and Results (Functional Imaging) show that multiband EPI acquired multiple slices simultaneously and achieved 2 × 2 × 2 mm³ whole-brain fMRI. The reported repetition time for that acquisition was 1.25 seconds, not less than a second, so the speed in the example is not supported. (LLM, conf=0.90)
- Supportedcites [40] Setsompop K 2012 Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planer imaging with reduced g-factor penalty Magnetic Resonance in Medicine 67 1210 1224 21858868 PMC3323676 source
The Introduction says simultaneous acquisition of multiple slices directly reduces the time needed to acquire a fixed number of slices. The Methods > Acquisitions passage reports 2-mm isotropic resolution with a TR of 0.77 s, and the Discussion and Conclusion explicitly describes 2-mm isotropic whole-brain coverage in 0.77 s. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Indeed, the histogram of edge-strength mean group differences is centred at zero, with no gross asymmetry.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found the following average correlation values for the 3 alignment methods: FreeSurfer=0.613, MSMfolding=0.618 and MSMmultimodal=0.649 (Figure 5A).”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Indeed, the node-pair which contributes most strongly to the regression against fluid intelligence (shown in more detail in B) has only a weak connection on average in the population - but it varies across subjects in a way that correlates with fluid intelligence.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The 3 alignment methods described above gave classification accuracies of: FreeSurfer=78%, MSMfolding=82% and MSMmultimodal=87%.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The 24 edges (node-pairs) having the strongest weights in the regression are shown;”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“In both cases we show strong spatial correspondence to specific task activation maps from the HCP task-fMRI datasets, thus helping to provide interpretation for the functional modes identified from the resting-state data.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Figure 9 shows 2 example results from feeding 98 nodes’ timeseries into temporal-ICA, where we analysed the high quality HCP rfMRI data containing more time points (>600,000, concatenating data from 131 subjects) than spatial points (~90,000 grayordinates).”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plainn = 131- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“These patterns can be distinguished from each other because, while each has a relatively consistent timecourse across its set of involved regions, the different networks have distinct timecourses [2].”
§ Functional connectomics from resting-state fMRI > Resting-state fMRI and the connectome – what’s the connection?
What the sources say
- Not checked yetcites [2] Beckmann CF 2005 Investigations into Resting-State Connectivity using Independent Component Analysis Philosophical Transactions of the Royal Society 360 1001 1013 PMC1854918 16087444 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“These “resting-state networks” (RSNs) persist even during sleep and under anaesthesia [3]”
§ Functional connectomics from resting-state fMRI > Resting-state fMRI and the connectome – what’s the connection?
What the sources say
- Not checked yetcites [3] Picchioni D 2013 Sleep and the functional connectome Neuroimage 80 387 396 23707592 PMC3733088 source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Such an approach contains fine spatial detail, but only informs about average correlation with the selected seed(s).”
§ Functional connectomics from resting-state fMRI > rfMRI acquisition and image processing overview
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Effective artefact removal is particularly important for resting-state analyses, which rely fundamentally on correlations between different voxels’ timeseries, as these will be corrupted by artefacts that span multiple voxels.”
§ Functional connectomics from resting-state fMRI > rfMRI acquisition and image processing overview
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“a high-dimensional parcellation into many nodes (potentially hundreds) allows a richer analysis of the network connections – by shifting the emphasis from large-scale maps with fine spatial detail into a network description of “nodes” and “edges”, new information can be obtained.”
§ Functional connectomics from resting-state fMRI > rfMRI acquisition and image processing overview
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
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In the paper
“although the sluggish response of the brain’s haemodynamics (to neural activity) will ultimately place a limit on the usefulness of further improvements in temporal resolution.”
§ Functional connectomics from resting-state fMRI > rfMRI acquisition and image processing overview
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“(In contrast, task-fMRI has the advantage of fitting a pre-specified temporal model, which provides greater robustness against artefactual influences.)”
§ Functional connectomics from resting-state fMRI > rfMRI acquisition and image processing overview
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“accurate alignment of functionally corresponding cortical parcels is an exceedingly challenging problem, largely owing to individual variability of cortical folding patterns along with variability of functional parcel locations relative to these folds”
§ Functional connectomics from resting-state fMRI > rfMRI acquisition and image processing overview
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In order to combine or compare connectomes across subjects, it is important to strive for “the same” parcellation in each subject – comparisons between two network models are inherently flawed if they are derived from non-corresponding sets of nodes.”
§ Functional connectomics from resting-state fMRI > rfMRI acquisition and image processing overview
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“and also increase the information content of the data (e.g., in terms of reflecting the richness of the neural dynamics) [41, 42]”
§ Functional connectomics from resting-state fMRI > rfMRI acquisition and image processing overview
What the sources say
- Not checked yetcites [41] Feinberg DA 2010 Multiplexed Echo Planar Imaging for Sub-second Whole Brain FMRI and Fast Diffusion Imaging PLoS ONE 5 e15710 21187930 PMC3004955 source
- Not checked yetcites [42] Smith SM 2012 Temporally-independent functional modes of spontaneous brain activity Proc Natl Acad Sci USA (PNAS) 109 3131 3136 PMC3286957 22323591 source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“a detailed nodes+edges network model may reveal which nodes (sub-parts of the large-scale networks) are responsible for the correlations seen between the larger-scale networks.”
§ Functional connectomics from resting-state fMRI > rfMRI acquisition and image processing overview
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“Furthermore, correlation cannot reveal anything about causality, or even whether connectivity is direct vs indirect [49].”
§ Functional connectomics from resting-state fMRI > Functional network modelling
What the sources say
- Not checked yetcites [49] Marrelec G 2006 Partial correlation for functional brain interactivity investigation in functional MRI NeuroImage 32 228 237 16777436 source
plain- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This therefore generates a richer description of multiple networks in the brain, but at the expense of no longer dictating in advance the regions with which the connectivity map(s) are related.”
§ Functional connectomics from resting-state fMRI > rfMRI acquisition and image processing overview
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, in a 3-node network A→B→C, all three nodes’ timeseries will be correlated, so correlation will incorrectly estimate a fully-connected network (including an A-C connection).”
§ Functional connectomics from resting-state fMRI > Functional network modelling
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“while DCM, originally developed for task data, has recently been extended [54] to allow modelling of resting-state data, and to search across a wide range of possible network matrix models”
§ Functional connectomics from resting-state fMRI > Functional network modelling
What the sources say
- Not checked yetcites [54] Friston KJ 2011 Network discovery with DCM NeuroImage 56 1202 1221 21182971 PMC3094760 source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“L1-norm regularisation of the inverse covariance matrix [55] shrinks the estimated values in the partial correlation matrix, so that very small, noisy values are forced to zero, and all other values are better estimated.”
§ Functional connectomics from resting-state fMRI > Functional network modelling
What the sources say
- Not checked yetcites [55] Friedman J 2008 Sparse inverse covariance estimation with the Graphical Lasso Biostat 9 432 441 PMC3019769 18079126 source
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Naturally, these techniques are dependent on accurate network modelling at the lower level (carefully constructed network matrices); the abstraction of these metrics from the underlying data makes it challenging to evaluate results”
§ Functional connectomics from resting-state fMRI > Functional network modelling
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The HCP is generating a detailed in vivo mapping of functional connectivity in a large cohort (over 1000 subjects), and is making these datasets freely available for use by the neuroimaging community (over 200 subjects’ datasets are already acquired and publicly released – available via humanconnectome.org).”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plainn = 200- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Bayes nets model the full set of covariances (or conditional dependencies) between all nodes’ timeseries [59] and show some promise.”
§ Functional connectomics from resting-state fMRI > Functional network modelling
What the sources say
- Not checked yetcites [59] Ramsey JD 2010 Six problems for causal inference from fMRI NeuroImage 49 1545 1558 19747552 source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Another concern is that it is frequently the (thresholded) full correlation matrix that is input into graph theoretical analyses, rather than an estimate of direct network connections; interpretations of graph-theory-based measures (such as communication path length) are often, as a result, somewhat questionable.”
§ Functional connectomics from resting-state fMRI > Functional network modelling
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Many methods, such as correlation, give no directionality information at all.”
§ Functional connectomics from resting-state fMRI > Functional network modelling
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This growing dataset comes with the caveat of having greater heterogeneity of scanning parameters (and lower data quality) than the HCP”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“another simple estimation method, partial correlation, aims to more accurately estimate the “direct” connections (though not their directionalities).”
§ Functional connectomics from resting-state fMRI > Functional network modelling
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The MGH-UCLA HCP is using extremely powerful MRI gradients to generate leading-edge 3T diffusion MRI data.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
What the sources say
- Not checked yetcites [16] Setsompop K 2013 Pushing the limits of in vivo diffusion MRI for the Human Connectome Project Neuroimage 80 220 233 23707579 PMC3725309 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“it cannot be assumed that the brain’s connections are static.”
§ Functional connectomics from resting-state fMRI > Functional network modelling
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“the WashU-UMinn-Oxford (“WU-Minn”) HCP is generating high quality and high resolution 3T rfMRI and diffusion MRI data from over 1000 healthy adults, as well as task-fMRI, genotyping and behavioural data.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
What the sources say
- Not checked yetcites [67] Van Essen DC 2013 The WU-Minn Human Connectome Project: An Overview NeuroImage 80 62 79 23684880 PMC3724347 source
plainn = 1,000- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this case, regression of B out of A and C removes the correlation between A and C, and hence the spurious third edge (A–C).”
§ Functional connectomics from resting-state fMRI > Functional network modelling
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The simpler methods are in general more robust (with respect to fitting the model to the data), and faster to compute.”
§ Functional connectomics from resting-state fMRI > Functional network modelling
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“they do not require the scope of possible network models to be pre-constrained, making them computationally feasible for attempting network discovery.”
§ Functional connectomics from resting-state fMRI > Functional network modelling
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Subjects are drawn from a population of healthy adult twins and their non-twin siblings (in the age range of 22–35 years), thus aiding the study of the influence of heritability and environmental factors on the connectome.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Temporal lag between pairs of timeseries [58] is often used, though it is arguably too confounded by the slow and variable haemodynamics to be useful with fMRI data [56], and is better-suited to electrophysiological modalities.”
§ Functional connectomics from resting-state fMRI > Functional network modelling
What the sources say
- Not checked yetcites [56] Smith SM 2011 Network Modelling Methods for FMRI NeuroImage 54 875 891 20817103 source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimation of partial correlation effectively involves inverting the full correlation matrix, a process which is often quite unstable, depending on the quality of the data and the number of original timepoints.”
§ Functional connectomics from resting-state fMRI > Functional network modelling
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“causality estimation when the brain is “at rest” may involve many different functional processes mixed together, and would be estimated as the average “relative causality” over all possible spontaneous fluctuations.”
§ Functional connectomics from resting-state fMRI > Functional network modelling
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The Thousand Functional Connectomes (and related) projects [74, 75], led by Mike Milham, are seeking to generate even larger numbers of subjects than the HCP, bringing together data from a wide range of imaging studies and scanners, and covering a wider range of subject groups, including a range of pathologies.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
What the sources say
- Not checked yetcites [74] Biswal BB 2010 Towards discovery science of human brain function Proc Natl Acad Sci USA (PNAS) 107 4734 4739 PMC2842060 20176931 source
- Not checked yetcites [75] Yan CG 2013 Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes Neuroimage 80 246 262 23631983 PMC4074397 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Once each subject’s cortical surface has been estimated from the structural data using FreeSurfer, FreeSurfer also provides one way to improve intersubject alignment of features on the surface.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For the partial correlation matrices to be as strong and consistent across subjects as the full correlation is unusual and is indicative of the high quality of the data and large number of timepoints in each subject’s rfMRI dataset.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This suggests that multimodal MSM improves the alignment of function across subjects (as it improves the accuracy with which the derived network matrices can predict sex), and also supports the hypothesis that this sex prediction is indeed primarily being driven by network connection strength and not (for example) by systematic functional misalignments across (groups of) subjects.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“This kind of analysis has previously been used (albeit from full correlation network matrices) to predict subjects’ psychiatric condition [77] and cognitive state [78];”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
What the sources say
- Not checked yetcites [77] Craddock RC 2009 Disease state prediction from resting state functional connectivity Magnetic Resonance in Medicine 62 1619 1628 19859933 PMC3749911 source
- Not checked yetcites [78] Shirer WR 2012 Decoding subject-driven cognitive states with whole-brain connectivity patterns Cereb Cortex 22 158 165 21616982 PMC3236795 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“One important issue is to distinguish between changes in correlation due to some nodes being part of multiple overlapping functional networks, vs. the internal connections within any given network being non-constant.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“We have recently developed an alternative surface warping approach, Multimodal Surface Matching (MSM) [70].”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
What the sources say
- Not checked yetcites [70] Robinson E 2013 Multimodal Surface Matching: Fast and Generalisable Cortical Registration using Discrete Optimisation Springer Berlin Heidelberg 24683992
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, a problem can arise when applying a window length that does not encompass (at least) several cycles of the resting fluctuations, as the rfMRI signal is dominated by low frequencies (~0.015Hz), and a sliding window containing a fraction of a cycle is expected to show correlations appearing to fluctuate wildly over time, even if the underlying network structure is stationary.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The rfMRI signal is dominated by low frequencies (~0.015Hz).
In the paper
“FIX achieves classification accuracy on HCP data of over 99%.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
What the sources say
- Not checked yetcites Salimi-Khorshidi, unpublished manuscript
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“At this point, no single parcellation technique has emerged as the best approach, and investigations are ongoing.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, better alignments should yield more accurate network matrices, because the functional parcels defined at the group level should be more accurately aligned to the true subject-specific versions of those same parcels.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The functional parcels defined at the group level correspond to true subject-specific versions of those same parcels.
In the paper
“For example, nonstationarities in correlation have been studied utilising either wavelet decompositions or sliding-window correlation [88].”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
What the sources say
- Not checked yetcites [88] Hutchison RM 2013 Dynamic functional connectivity: Promise, issues, and interpretations Neuroimage 80 360 378 23707587 PMC3807588
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, although this is encouraging, an increase in cross-subject network similarity is not conclusive proof of a better alignment method; for example, it might indicate over-strong application of prior information (e.g., in the extreme, setting all subjects to a constant network), which would reduce sensitivity when attempting to relate the network matrices to other measures.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A limitation, however, is that multiple distinct functional networks might overlap each other in space and also vary over time in how they interact with each other.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It does not provide the more direct view of neuronal activity and information flow that can be obtained from electrophysiological techniques, or inform us regarding the biochemistry of brain connectivity.”
§ Functional connectomics from resting-state fMRI > Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“[42] showed several such modes, with plausible functional interpretability and large amounts of spatial overlap; for example, the patterns of average correlation and anti-correlation associated with the default-mode network can be decomposed into several modes of distinct but overlapping spatial layout (and hence presumably distinct function).”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
What the sources say
- Not checked yetcites [42] Smith SM 2012 Temporally-independent functional modes of spontaneous brain activity Proc Natl Acad Sci USA (PNAS) 109 3131 3136 PMC3286957 22323591 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“A related challenge is raised by evidence that rfMRI signal includes complex spatiotemporal patterns of spontaneous activity that propagate across the brain [92], and are therefore not at all well modelled by spatially fixed network modelling approaches.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
What the sources say
- Not checked yetcites [92] Majeed W 2010 Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans Neuroimage 54 1140 1150 20728554 PMC2997178
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, rfMRI does provide us with something valuable and unique – the ability to create a richly detailed mapping of the functional connectome, at the millimetre scale, covering the whole brain, with in vivo, non-invasive imaging across thousands of subjects.”
§ Functional connectomics from resting-state fMRI > Conclusions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It does not tell us directly about the microscopic, neuronal-level, structural connectome, or even about the macroscopic-level structural connectome such as is inferred from diffusion MRI.”
§ Functional connectomics from resting-state fMRI > Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Another limitation with temporal-ICA is the assumption that the connections within a given mode are not changing in strength over time; this is surely an oversimplistic view of brain networks.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, two distinct functional modes might in reality be mutually exclusive, if they share one node that strongly participates in only one function at any given time; this would imply a negative correlation between the modes’ timeseries, and this situation will not be well identified through the application of temporal-ICA.”
§ Functional connectomics from resting-state fMRI > The Human Connectome Project and future directions for macroscopic functional connectomics
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
The biological significance of the rich and continuously-present set of spontaneous, correlated activities in the resting brain remains poorly understood.
“The biological significance of such a rich and continuously-present set of spontaneous, correlated activities in the “resting” brain remains poorly understood.”
The authors state this as an assumption.
The brain is continuously engaged in undirected cognitive activities, including conscious thought processes and sub-conscious activity such as learning/unlearning, and also responds to uncontrolled external stimuli.
“Presumably, the brain is continuously engaged in undirected cognitive activities (both conscious thought processes and sub-conscious activity such as learning/unlearning), and also responds to uncontrolled external stimuli.”
The paper relies on this without support.
The functional parcels defined at the group level correspond to true subject-specific versions of those same parcels.
“because the functional parcels defined at the group level should be more accurately aligned to the true subject-specific versions of those same parcels.”
The paper relies on this without support.
The subject-wise variations of all edges are not fully independent of each other.
“because we do not expect the subject-wise variations of all edges to be fully independent of each other.”
The paper relies on this without support.
The rfMRI signal is dominated by low frequencies (~0.015Hz).
“as the rfMRI signal is dominated by low frequencies (~0.015Hz)”
The paper relies on this without support.
There is a true macroscopic functional connectome that partial correlation matrices estimate better than full correlation.
“partial correlation matrices provide a better estimate of the “true” macroscopic functional connectome than full correlation.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Functional connectivity as measured using fMRI is a powerful but indirect measure of network organization that can provide high spatial resolution but should not be considered a surrogate for direct anatomical connectivity29, 30.”
From Canonical genetic signatures of the adult human brain (2015) · cites this paper as [30]
In the citing paper
“Each of four 15-min resting state runs for each subject in the group was cleaned (denoised) using 24-parameter motion regression followed by ICA+FIX denoising30, 48.”
From Canonical genetic signatures of the adult human brain (2015) · cites this paper as [30]
Citations
Cites
Investigating causal relations by econometric models and cross-spectral methods
Granger, C. W. J. (1969) · Econometrica · doi:10.2307/1912791 · Cited by 22,989
Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.015 · Cited by 11,314
Self-citationFunctional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.021 · Cited by 9,526
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304
Sparse inverse covariance estimation with the graphical lasso
Friedman, J., Hastie, T., & Tibshirani, R. (2008) · Biostatistics · doi:10.1093/biostatistics/kxm045 · Cited by 6,355
The WU-Minn Human Connectome Project: An overview
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651
Self-citation
Cited by
Sorted by citations
A multi-modal parcellation of human cerebral cortex
Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., et al. (2016) · Nature · doi:10.1038/nature18933 · Cited by 5,111
Self-citationMultimodal population brain imaging in the UK Biobank prospective epidemiological study
Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., Bartsch, A. J., Jbabdi, S., Sotiropoulos, S. N., Andersson, J. L. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4393 · Cited by 2,020
Self-citationIntrinsic and task-evoked network architectures of the human brain
Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014) · Neuron · doi:10.1016/j.neuron.2014.05.014 · Cited by 1,564
Using connectome-based predictive modeling to predict individual behavior from brain connectivity
Shen, X.-L., Finn, E., Scheinost, D., Rosenberg, M., Chun, M. M., Papademetris, X., & Constable, R. T. (2017) · Nature Protocols · doi:10.1038/nprot.2016.178 · Cited by 1,088
Ciric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., Shinohara, R. T., Elliott, M. A., Eickhoff, S. B., Davatzikos, C., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.020 · Cited by 1,042
A neuromarker of sustained attention from whole-brain functional connectivity
Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2015) · Nature Neuroscience · doi:10.1038/nn.4179 · Cited by 1,051
Brain network dynamics are hierarchically organized in time
Vidaurre, D., Smith, S. M., & Woolrich, M. W. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1705120114 · Cited by 883
Self-citationCanonical genetic signatures of the adult human brain
Hawrylycz, M., Miller, J. A., Menon, V., Feng, D., Dolbeare, T., Guillozet-Bongaarts, A. L., Jegga, A. G., Aronow, B. J., Lee, C.-K., Bernard, A., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4171 · Cited by 663
Self-citationZuo, X., & Xing, X.-X. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.05.009 · Cited by 678
Network dysfunction after traumatic brain injury
Sharp, D. J., Scott, G., & Leech, R. (2014) · Nature Reviews Neurology · doi:10.1038/nrneurol.2014.15 · Cited by 657
MSM: a new flexible framework for Multimodal Surface Matching
Robinson, E. C., Jbabdi, S., Glasser, M. F., Andersson, J., Burgess, G. C., Harms, M. P., Smith, S. M., Van Essen, D. C., & Jenkinson, M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.069 · Cited by 640
Self-citationHarms, M. P., Somerville, L. H., Ances, B. M., Andersson, J., Barch, D. M., Bastiani, M., Bookheimer, S. Y., Brown, T., Buckner, R. L., Burgess, G. C., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.060 · Cited by 596
Self-citationReorganization of brain networks in aging: a review of functional connectivity studies
Sala‐Llonch, R., Bartrés‐Faz, D., & Junqué, C. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.00663 · Cited by 550
Activity flow over resting-state networks shapes cognitive task activations
Cole, M. W., Ito, T., Bassett, D. S., & Schultz, D. H. (2016) · Nature Neuroscience · doi:10.1038/nn.4406 · Cited by 511
An open science resource for establishing reliability and reproducibility in functional connectomics
Zuo, X., Anderson, J. S., Bellec, P., Birn, R. M., Biswal, B. B., Blautzik, J., Breitner, J. C. S., Buckner, R. L., Calhoun, V. D., Castellanos, F. X., et al. (2014) · Scientific Data · doi:10.1038/sdata.2014.49 · Cited by 449
Stimulation mapping of white matter tracts to study brain functional connectivity.
Duffau, H. (2015) · Nature Reviews Neurology · doi:10.1038/nrneurol.2015.51 · Cited by 437
Reveley, C., Seth, A. K., Pierpaoli, C., Silva, A. C., Yu, D., Saunders, R. C., Leopold, D. A., & Ye, F. Q. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418198112 · Cited by 417
How reliable are MEG resting-state connectivity metrics?
Colclough, G. L., Woolrich, M. W., Tewarie, P. K. B., Brookes, M. J., Quinn, A. J., & Smith, S. M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.070 · Cited by 409
Self-citationCabral, J., Vidaurre, D., Marques, P., Magalhães, R., Silva Moreira, P., Miguel Soares, J., Deco, G., Sousa, N., & Kringelbach, M. L. (2017) · Scientific Reports · doi:10.1038/s41598-017-05425-7 · Cited by 386
Self-citationHuman brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex
Arslan, S., Ktena, S. I., Makropoulos, A., Robinson, E. C., Rueckert, D., & Parisot, S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.014 · Cited by 374
Self-citationKrienen, F. M., Yeo, B. T. T., & Buckner, R. L. (2014) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0526 · Cited by 372
Common Dysfunction of Large-Scale Neurocognitive Networks Across Psychiatric Disorders
Sha, Z., Wager, T. D., Mechelli, A., & He, Y. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.11.011 · Cited by 367
Somerville, L. H., Bookheimer, S. Y., Buckner, R. L., Burgess, G. C., Curtiss, S. W., Dapretto, M., Elam, J. S., Gaffrey, M. S., Harms, M. P., Hodge, C., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.050 · Cited by 373
Self-citationMeasuring macroscopic brain connections in vivo
Jbabdi, S., Sotiropoulos, S. N., Haber, S. N., Van Essen, D. C., & Behrens, T. E. (2015) · Nature Neuroscience · doi:10.1038/nn.4134 · Cited by 334
A Domain-General Cognitive Core Defined in Multimodally Parcellated Human Cortex
Assem, M., Glasser, M. F., Essen, D. C. V., & Duncan, J. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa023 · Cited by 340
Self-citationDu, Y., Fu, Z., & Calhoun, V. D. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00525 · Cited by 334
Frontostriatal salience network expansion in individuals in depression
Lynch, C. J., Elbau, I., Ng, T. H. B., Ayaz, A., Zhu, S., Wolk, D., Manfredi, N., Johnson, M. A., Chang, M., Chou, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07805-2 · Cited by 353
Movie-watching outperforms rest for functional connectivity-based prediction of behavior
Finn, E. S., & Bandettini, P. A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117963 · Cited by 290
Network-Level Structure-Function Relationships in Human Neocortex
Mišić, B., Betzel, R. F., de Reus, M. A., van den Heuvel, M. P., Berman, M. G., McIntosh, A. R., & Sporns, O. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw089 · Cited by 293
A Hitchhiker's Guide to Functional Magnetic Resonance Imaging
Soares, J. M., Magalhães, R. J. D. S., Moreira, P. S., Sousa, A., Ganz, E., Sampaio, A., Alves, V., Marques, P., & Sousa, N. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00515 · Cited by 304
Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration
Schultz, D. H., & Cole, M. W. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.0358-16.2016 · Cited by 272
Connectivity‐based parcellation: Critique and implications
Eickhoff, S. B., Thirion, B., Varoquaux, G. P., & Bzdok, D. (2015) · Human Brain Mapping · doi:10.1002/hbm.22933 · Cited by 278
The human connectome in Alzheimer disease — relationship to biomarkers and genetics
Yu, M., Sporns, O., & Saykin, A. J. (2021) · Nature Reviews Neurology · doi:10.1038/s41582-021-00529-1 · Cited by 265
Connectopic mapping with resting-state fMRI
Haak, K. V., Marquand, A. F., & Beckmann, C. F. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.075 · Cited by 256
Self-citationInterpreting temporal fluctuations in resting-state functional connectivity MRI
Liégeois, R., Laumann, T. O., Snyder, A. Z., Zhou, J. H., & Yeo, B. T. T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.012 · Cited by 264
Glasser, M. F., Coalson, T. S., Bijsterbosch, J. D., Harrison, S. J., Harms, M. P., Anticevic, A., Essen, D. C. V., & Smith, S. M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.076 · Cited by 260
Self-citationTask-induced brain activity in aphasic stroke patients: what is driving recovery?
Geranmayeh, F., Brownsett, S. L. E., & Wise, R. J. S. (2014) · Brain · doi:10.1093/brain/awu163 · Cited by 252
Childhood trauma history is linked to abnormal brain connectivity in major depression
Yu, M., Linn, K. A., Shinohara, R. T., Oathes, D. J., Cook, P. A., Duprat, R., Moore, T. M., Oquendo, M. A., Phillips, M. L., McInnis, M. G., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900801116 · Cited by 230
Network Dysfunction in Alzheimer’s Disease and Frontotemporal Dementia: Implications for Psychiatry
Zhou, J. H., & Seeley, W. W. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.01.020 · Cited by 238
Modern views of machine learning for precision psychiatry
Chen, Z. S., Kulkarni, P., Galatzer‐Levy, I. R., Bigio, B., Nasca, C., & Zhang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100602 · Cited by 247
The relationship between spatial configuration and functional connectivity of brain regions
Bijsterbosch, J. D., Woolrich, M. W., Glasser, M. F., Robinson, E. C., Beckmann, C. F., Essen, D. C. V., Harrison, S. J., & Smith, S. M. (2018) · eLife · doi:10.7554/elife.32992 · Cited by 237
Self-citationUnderstanding Structural-Functional Relationships in the Human Brain
Wang, Z., Dai, Z., Gong, G., Zhou, C., & Yong He, A. (2014) · The Neuroscientist · doi:10.1177/1073858414537560 · Cited by 232
Zhang, C., Dougherty, C. C., Baum, S. A., White, T. J. H., & Michael, A. M. (2018) · Human Brain Mapping · doi:10.1002/hbm.23950 · Cited by 221
Sezer, I., Pizzagalli, D. A., & Sacchet, M. D. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104583 · Cited by 214
“Moreover, future mindfulness and brain network research will benefit from recent developments in approaches in this field (e.g., connectomics; Smith et al., 2013).”
BackgroundTask activations produce spurious but systematic inflation of task functional connectivity estimates
Cole, M. W., Ito, T., Schultz, D. H., Mill, R. D., Chen, R. J., & Cocuzza, C. V. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.12.054 · Cited by 218
Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis
Gadgil, S. U., Zhao, Q., Adolf Pfefferbaum, A., Sullivan, E. V., Adeli, E., & Pohl, K. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59728-3_52 · Cited by 215
Functional brain networks related to individual differences in human intelligence at rest
Hearne, L. J., Mattingley, J. B., & Cocchi, L. (2016) · Scientific Reports · doi:10.1038/srep32328 · Cited by 217
The Dorsal Frontoparietal Network: A Core System for Emulated Action
Ptak, R., Schnider, A., & Fellrath, J. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.05.002 · Cited by 218
Salehi, M., Greene, A. S., Karbasi, A., Shen, X., Scheinost, D., & Constable, R. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116366 · Cited by 203
Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI
Lynch, C. J., Power, J. D., Scult, M. A., Dubin, M. J., Gunning, F. M., & Liston, C. M. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108540 · Cited by 192
de Vos, F., Koini, M., Schouten, T. M., Seiler, S., van der Grond, J., Lechner, A., Schmidt, R., de Rooij, M., & Rombouts, S. A. R. B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.025 · Cited by 192
Advances in resting state fMRI acquisitions for functional connectomics
Raimondo, L., de Oliveira, I. A. F., Heij, J., Priovoulos, N., Kundu, P., Leoni, R. F., & van der Zwaag, W. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118503 · Cited by 188
Matsui, T., Murakami, T., & Ohki, K. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1521299113 · Cited by 194
Large-scale DCMs for resting-state fMRI
Razi, A., Seghier, M. L., Zhou, Y., McColgan, P., Zeidman, P., Park, H., Sporns, O., Rees, G. E., & Friston, K. J. (2017) · Network Neuroscience · doi:10.1162/netn_a_00015 · Cited by 181
Magnetic Resonance Imaging at Ultrahigh Fields
Uǧurbil, K. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2313619 · Cited by 182
Self-citationWeak correlations between hemodynamic signals and ongoing neural activity during the resting state
Winder, A. T., Echagarruga, C., Zhang, Q., & Drew, P. J. (2017) · Nature Neuroscience · doi:10.1038/s41593-017-0007-y · Cited by 178
Sex Classification by Resting State Brain Connectivity
Weis, S., Patil, K. R., Hoffstaedter, F., Nostro, A. D., Yeo, B. T. T., & Eickhoff, S. B. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz129 · Cited by 173
Dubois, J., Galdi, P., Han, Y., Paul, L. K., & Adolphs, R. (2018) · Personality Neuroscience · doi:10.1017/pen.2018.8 · Cited by 170
A Connectome Computation System for discovery science of brain
Xu, T., Yang, Z., Jiang, L., Xing, X.-X., & Zuo, X. (2015) · 中国科学通报：英文版 · doi:10.1007/s11434-014-0698-3 · Cited by 173
Prediction complements explanation in understanding the developing brain
Rosenberg, M. D., Casey, B. J., & Holmes, A. J. (2018) · Nature Communications · doi:10.1038/s41467-018-02887-9 · Cited by 169
Dynamic graph metrics: Tutorial, toolbox, and tale
Sizemore, A. E., & Bassett, D. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.081 · Cited by 171
Resting‐state functional connectivity and nicotine addiction: prospects for biomarker development
Fedota, J. R., & Stein, E. A. (2015) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12882 · Cited by 170
Challenges and future directions for representations of functional brain organization
Bijsterbosch, J. D., Harrison, S. J., Jbabdi, S., Woolrich, M. W., Beckmann, C. F., Smith, S. M., & Duff, E. P. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00726-z · Cited by 167
Self-citationHyper-connectivity of functional networks for brain disease diagnosis
Jie, B., Chong-yaw Wee, A., Shen, D. G., & Zhang, D. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.03.003 · Cited by 166
An update on semantic dementia: genetics, imaging, and pathology
Landín-Romero, R., Tan, R. H., Hodges, J. R., & Kumfor, F. (2016) · Alzheimer s Research & Therapy · doi:10.1186/s13195-016-0219-5 · Cited by 170
Nazeri, A., Chakravarty, M. M., Rotenberg, D. J., Rajji, T. K., Rathi, Y., Michailovich, O. V., & Voineskos, A. N. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.3979-14.2015 · Cited by 162
Braga, R. M., Van Dijk, K. R. A., Polimeni, J. R., Eldaief, M. C., & Buckner, R. L. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00808.2018 · Cited by 162
Multisite reliability of MR-based functional connectivity
Noble, S., Scheinost, D., Finn, E. S., Shen, X., Papademetris, X., McEwen, S. C., Bearden, C. E., Addington, J., Goodyear, B. G., Cadenhead, K. S., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.020 · Cited by 168
Data-driven graph construction and graph learning: A review
Qiao, L., Zhang, L., Chen, S., & Shen, D. G. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.084 · Cited by 161
Patterns of functional connectivity in an aging population: The Rotterdam Study
Zonneveld, H. I., Pruim, R. H. R., Bos, D., Vrooman, H. A., Muetzel, R. L., Albert Hofman, D., Rombouts, S. A. R. B., van der Lugt, A., Niessen, W. J., Ikram, M. A., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.041 · Cited by 171
Sulpizio, S., Maschio, N. D., Mauro, G. D., Fedeli, D., & Abutalebi, J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116306 · Cited by 172
Handedness, language areas and neuropsychiatric diseases: insights from brain imaging and genetics
Wiberg, A., Ng, M., Omran, Y. A., Alfaro‐Almagro, F., McCarthy, P., Marchini, J., Bennett, D., Smith, S. M., Douaud, G., & Furniss, D. (2019) · Brain · doi:10.1093/brain/awz257 · Cited by 161
Self-citationZhang, Y., Zhang, H., Chen, X., Lee, S., & Shen, D. G. (2017) · Scientific Reports · doi:10.1038/s41598-017-06509-0 · Cited by 154
Valenza, G., Sclocco, R., Duggento, A., Passamonti, L., Napadow, V., Barbieri, R., & Toschi, N. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.075 · Cited by 160
Moeller, S., Pisharady, P. K., Ramanna, S., Lenglet, C., Wu, X., Dowdle, L. T., Yacoub, E. S., Uǧurbil, K., & Akçakaya, M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117539 · Cited by 148
Self-citationEffects of Early and Late Bilingualism on Resting-State Functional Connectivity
Berken, J. A., Chai, X. J., Chen, J.-K., Gracco, V. L., & Klein, D. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1960-15.2016 · Cited by 156
Langner, R., Leiberg, S., Hoffstaedter, F., & Eickhoff, S. B. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.04.022 · Cited by 156
Shah, L. M., Cramer, J. A., Ferguson, M. A., Birn, R. M., & Anderson, J. S. (2016) · Brain and Behavior · doi:10.1002/brb3.456 · Cited by 150
Characterizing Attention with Predictive Network Models
Rosenberg, M. D., Finn, E. S., Scheinost, D., Constable, R. T., & Chun, M. M. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.01.011 · Cited by 151
Brain connectivity dynamics during social interaction reflect social network structure
Schmälzle, R., O’Donnell, M. B., Garcia, J. O., Cascio, C. N., Bayer, J., Bassett, D. S., Vettel, J. M., & Falk, E. B. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1616130114 · Cited by 141
Zhang, H., Chen, X., Shi, F., Li, G., Kim, M., Giannakopoulos, P., Haller, S., & Shen, D. G. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160092 · Cited by 142
Frequency-specific electrophysiologic correlates of resting state fMRI networks
Hacker, C. D., Snyder, A. Z., Pahwa, M., Corbetta, M., & Leuthardt, E. C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.054 · Cited by 146
Prediction of neurocognition in youth from resting state fMRI
Sripada, C., Rutherford, S. E., Angstadt, M., Thompson, W. K., Luciana, M. M., Weigard, A. S., Hyde, L. W., & Heitzeg, M. M. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0481-6 · Cited by 146
Parcellating Cerebral Cortex: How Invasive Animal Studies Inform Noninvasive Mapmaking in Humans
Essen, D. C. V., & Glasser, M. F. (2018) · Neuron · doi:10.1016/j.neuron.2018.07.002 · Cited by 143
Self-citationRelating resting-state fMRI and EEG whole-brain connectomes across frequency bands
Deligianni, F., Centeno, M., Carmichael, D. W., & Clayden, J. D. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00258 · Cited by 136
Chen, B., Xu, T., Zhou, C., Wang, L., Yang, N., Wang, Z., Dong, H.-M., Yang, Z., Zang, Y., Zuo, X., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0144963 · Cited by 136
Estimating functional brain networks by incorporating a modularity prior
Qiao, L., Zhang, H., Kim, M., Teng, S., Zhang, L., & Shen, D. G. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.058 · Cited by 133
Wang, D. J. J., Jann, K., Fan, C., Qiao, Y., Zang, Y., Lu, H., & Yang, Y. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00352 · Cited by 140
Intrinsic functional network architecture of human semantic processing: Modules and hubs
Xu, Y., Lin, Q., Han, Z., Yong He, A., & Bi, Y. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.004 · Cited by 139
Bhargava, A., Arnold, A. P., Bangasser, D. A., Denton, K. M., Gupta, A., Hilliard Krause, L. M., Mayer, E. A., McCarthy, M., Miller, W. L., Raznahan, A., et al. (2021) · Endocrine Reviews · doi:10.1210/endrev/bnaa034 · Cited by 130
A number-form area in the blind
Abboud, S., Maidenbaum, S., Dehaene, S., & Amedi, A. (2015) · Nature Communications · doi:10.1038/ncomms7026 · Cited by 144
Human lifespan changes in the brain’s functional connectome
Sun, L., Zhao, T., Liang, X., Xia, M., Li, Q., Liao, X., Gong, G., Wang, Q., Pang, C., Yu, Q., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01907-4 · Cited by 132
Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study
de Lange, A. G., Anatürk, M., Suri, S., Kaufmann, T., Cole, J. H., Griffanti, L., Zsoldos, E., Jensen, D. E. A., Filippini, N., Singh‐Manoux, A., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117292 · Cited by 129
The heritability of multi-modal connectivity in human brain activity
Colclough, G. L., Smith, S. M., Nichols, T. E., Winkler, A. M., Sotiropoulos, S. N., Glasser, M. F., Essen, D. C. V., & Woolrich, M. W. (2017) · eLife · doi:10.7554/elife.20178 · Cited by 127
Self-citationPan, J., Zhan, L., Hu, C., Yang, J., Wang, C., Gu, L., Zhong, S., Huang, Y., Wu, Q., Xie, X., et al. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00070 · Cited by 130
Schouten, T. M., Koini, M., de Vos, F., Seiler, S., van der Grond, J., Lechner, A., Hafkemeijer, A., Möller, C., Schmidt, R., de Rooij, M., et al. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.01.002 · Cited by 125
Geerligs, L., Cam‐CAN, & Henson, R. N. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.047 · Cited by 127
Du, J., DiNicola, L. M., Angeli, P. A., Saadon‐Grosman, N., Sun, W., Kaiser, S., Ladopoulou, J., Xue, A., Yeo, B. T. T., Eldaief, M. C., et al. (2024) · Journal of Neurophysiology · doi:10.1152/jn.00308.2023 · Cited by 122
Machine-learning to characterise neonatal functional connectivity in the preterm brain
Ball, G., Aljabar, P., Arichi, T., Tusor, N., Cox, D., Merchant, N., Nongena, P., Hajnal, J. V., Edwards, A. D., & Counsell, S. J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.055 · Cited by 114
Methylphenidate Modulates Functional Network Connectivity to Enhance Attention
Rosenberg, M. D., Zhang, S., Hsu, T. W., Scheinost, D., Finn, E. S., Shen, X., Constable, R. T., Li, C. R., & Chun, M. M. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1746-16.2016 · Cited by 113
Metadata
- Authors
- Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., Nichols, T. E., Robinson, E. C., Salimi-Khorshidi, G., Woolrich, M. W., Barch, D. M., Uğurbil, K., & Van Essen, D. C.
- Affiliations
- University of Oxford (GB)
- Radboud University Nijmegen (NL)
- Washington University in St. Louis (US)
- University of Warwick (GB)
- Corresponding author
- Smith, Stephen M.
- ORCID
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2013-12
- Volume
- 17
- Issue
- 12
- Pages
- 666-682
- ISSN
- 1364-6613 (ISSN-L)
- PMC
- PMC4004765
- OpenAlex
- W2124698428
- Semantic Scholar
- c8812e9136cba9ff05045d5d65844af68e7ceddb
- Cited by
- 952
- References
- 93
- Citations by year
- 2026: 55 · 2025: 77 · 2024: 84 · 2023: 76 · 2022: 83 · 2021: 82 · 2020: 88 · 2019: 77 · 2018: 81 · 2017: 72 · 2016: 71 · 2015: 63 · 2014: 38 · 2013: 3
- FWCI
- 4.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Trends in Cognitive Sciences (publishedVersion)
- PubMed (publishedVersion)
- University of Twente Research Information (publishedVersion)
- PubMed Central (acceptedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- University of Twente Research Information (submittedVersion)
- Funders
- Wellcome Trust (1U54MH091657, 098369/Z/12/Z, 098369, 100309/A/12/Z, 100309)
- National Institutes of Health (5u54mh091657-05, 1u54mh091657-01, 1U54‐MH‐091657, RR08079)
- National Institute of Mental Health (1U54-MH091657-01, U54-MH091657, F30MH097312)
- National Institute of Neurological Disorders and Stroke (P30NS076408, P30NS057091)
- National Institute of Biomedical Imaging and Bioengineering (P41EB015894, P41-RR08079/EB015894)
- National Center for Research Resources (P41RR008079)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- functional connectomics, resting-state fMRI, spontaneous brain activity, functional brain networks, network structure estimation, Human Connectome Project, MRI image processing, connectome mapping
- MeSH terms
- Animals, Brain, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Nerve Net, Neural Pathways, Oxygen, Rest
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.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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