The WU-Minn Human Connectome Project: An overview
Open accessAbstract
The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 66 cited sources checked · 0 supported · 0 flagged
- Findingown data
Answers to a four-item satisfaction questionnaire given after testing indicate that participants were highly satisfied with the experience.
“A four-question satisfaction survey administered at the end of testing shows that participants report a very high level of satisfaction with their experiences.”
Quote in the paperNot a substantial claim - Findingown data
Through April 2013, participants generally handled the whole protocol well, even the long sessions in the customized narrow-bore scanner.
“In general, the participants studied to date (through April, 2013) have tolerated the entire experience very well, including the extended time in a customized scanner with a reduced bore diameter (see below).”
Quote in the paperNot a substantial claim - Findingreasoning
On the WU-Minn 3T, custom hardware raises the top gradient strength from 40 to 100 mT/m.
“Relative to a standard commercial Skyra, the customized hardware includes a gradient coil and gradient power amplifiers that together increase the maximum gradient strength from 40 mT/m to 100 mT/m on the WU-Minn 3T.”
Quote in the paperSubstantial claim - Findingnone given
Many practical problems were solved so that enrollment and visits could proceed fast enough to enroll 1200 people across three years at one imaging site.
“Many practical issues have been resolved to allow recruitment and visits to occur at a pace sufficient to study 1200 subjects over 3 years at a single imaging site, as discussed below.”
Quote in the paperNot a substantial claim - Findingown data
Preliminary analyses using the finalized acquisition and preprocessing protocols show that data from each modality are of exceptionally high quality.
“Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality.”
Quote in the paperSubstantial claim
- Findingnone given
None of the people who cleared the first phone screen were later ruled out by the SSAGA.
“To date, no participants who have passed the initial telephone screening have been subsequently excluded during the SSAGA.”
Quote in the paperNot a substantial claim - Findingcitation
Work over two years to improve the 3T scanning protocols led to major progress in all four MR imaging modalities.
“A two-year effort to develop and refine the scanning protocols for the 3T Connectome Scanner has yielded important advances in each of the four MR-based imaging modalities (Ugurbil et al., 2013).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Ugurbil et al., 2013 source
Not checked in the free run.
- Findingown data
Most participants gave their overall experience a score of 9 or 10 on a 10-point scale.
“The majority of participants rate their experience as a 9 or 10 (out of 10) overall (Supplemental Table S4).”
Quote in the paperNot a substantial claim - Findingown data
For 68 of those 76 people, scans were complete or nearly complete across every modality.
“Complete or near-complete scans for all modalities were obtained from 68 of these subjects (see Supplemental Table S3).”
Quote in the paperNot a substantial claim - Findingreasoning
Therefore every scan has stronger gradient distortions than a conventional scanner would produce.
“As a consequence, all scans have gradient distortions larger than in a conventional scanner.”
Quote in the paperNot a substantial claim - Findingown data
Early analyses suggest that most HCP participants move their heads relatively little.
“Fortunately, our preliminary analyses indicate that head motion is relatively low in the majority of HCP subjects.”
Quote in the paperNot a substantial claim - Findingcitation
Together with 1.25 mm isotropic resolution, this gives unusually good 3T whole-brain diffusion data.
“Combined with the spatial resolution of 1.25 mm isotropic, this yields exceptional data quality for in vivo whole brain diffusion imaging at 3T (Sotiropoulos et al., 2013c; Ugurbil et al., 2013).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Sotiropoulos et al., 2013c
Not checked in the free run.
- cites Ugurbil et al., 2013 source
Not checked in the free run.
- Findingcitation
The main pulse-sequence advance for HCP was developing and tuning slice-accelerated multiband acquisition for fMRI and dMRI.
“The most significant pulse sequence development for the HCP was the implementation and optimization of slice-accelerated multiband (MB) acquisitions for fMRI and dMRI (Larkman et al., 2001; Moeller et al., 2008; Feinberg et al., 2010; Moeller et al., 2010; Setsompop et al., 2012; Ugurbil et al., 2013).”
Quote in the paperSubstantial claim0 of 3 sources checked - Findingown data
Early pilot work on fMRI yielded good images at about 1 mm resolution, finer than the 2 mm voxels used for 3T fMRI.
“Initial pilot studies have focused on fMRI and have produced high quality images at higher spatial resolutions (~ 1mm) than the 2mm isotropic voxels used for fMRI data acquired in HCP subjects at 3T.”
Quote in the paperNot a substantial claim - Findingcitation
Initial findings suggest whole-brain isotropic resolution near 1 mm is achievable with a TR near 2 s.
“Despite this, early results (see Ugurbil et al 2013) indicate that ~ 1mm isotropic resolutions over the whole brain are feasible with a TR of around 2 s.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ugurbil et al 2013 source
Not checked in the free run.
- Findingown data
HCP structural images and FreeSurfer cortical segmentation are high quality, including in thin cortical regions such as the calcarine and precentral sulci.
“This illustrates the high quality of the structural images themselves and of the cortical segmentation, including regions where cortex is notably thin, such as the calcarine sulcus (red arrow) and precentral sulcus (black arrow).”
Quote in the paperNot a substantial claim - Findingreasoning
These pipelines yield major gains for every MRI modality: structural, fMRI, and dMRI.
“The resulting preprocessing pipelines provide substantial improvements used for each of the MRI modalities, including structural MRI, fMRI (both rfMRI and tfMRI), and dMRI.”
Quote in the paperSubstantial claim- Rests on an assumption: Raw MRI images always contain spatial distortions, lack a common anatomical space, and do not align across modalities.
- Rests on an assumption: Raw MRI images also contain noise, artifacts, and biases specific to each modality.
- Findingcitation
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).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Smith et al., 2013 source
Not checked in the free run.
- Findingown data
The HCP structural images show fine cerebellar detail, with most lamellae and many individual folia discernible.
“The fine detail in the cerebellum is also notable, as most lamellae and even many individual folia are discernible.”
Quote in the paperNot a substantial claim - Findingown data
Differences in cortical shape maps between identical twins are comparable to differences between unrelated individuals.
“The differences in shape maps for identical twins (A1 vs A2; B1 vs B2) are comparable to those between unrelated individuals (either ‘A’ subject vs either ‘B’ subject).”
Quote in the paperNot a substantial claim - Findingown data
In the illustrated HCP subject, myelin content is symmetric between hemispheres for selected vertices centered on myelin hotspots.
“The symmetry in the pattern of myelin content between the two hemispheres can be appreciated by comparing the location of corresponding vertices in the two hemispheres, which were selected to be centered on myelin hotspots in the left hemisphere (black circles) and are approximately centered on corresponding hotspots in the right hemisphere (blue dots).”
Quote in the paperNot a substantial claim - Findingcitation
Myelin maps for HCP subjects are generally higher quality than those originally reported, owing to higher spatial resolution and algorithmic improvements.
“In general, the myelin maps for this and the other HCP subjects are higher in quality than those originally reported (Glasser et al., 2011), thanks to the higher spatial resolution (0.7 vs 1 mm isotropic voxels) coupled with several algorithmic improvements (Glasser et al., 2013a,b).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Glasser et al., 2011 source
Not checked in the free run.
- cites Glasser et al., 2013a,b
Not checked in the free run.
- Findingown data
In a 22-component group-level ICA on 20 HCP subjects, component 1 covers higher-level visual areas.
“ICA component 1 covers higher-level visual areas.”
Quote in the paperNot a substantial claim - Findingreasoning
Correlations estimated without regression of the mean gray timecourse yield smaller anti-correlated regions because the mean is not forced to zero.
“In contrast to Fig. 3, these correlations are estimated without regression of the mean gray timecourse. Hence, the anti-correlated regions (blue, purple) are smaller in extent, because the mean is not forced to be zero.”
Quote in the paperNot a substantial claim - Findingown data
In an individual HCP subject, many regions strongly correlated with a retrosplenial seed are poorly correlated or anti-correlated with a nearby posterior cingulate seed.
“Many of the regions that are strongly correlated (yellow, red) with the retrosplenial seed are poorly correlated or anti-correlated with the nearby seed in cingulate cortex (blue, purple; but note this is after regression of the mean gray-matter timecourse – see below).”
Quote in the paperNot a substantial claim - Findingown data
In a group dense connectome from 20 HCP subjects, signal-to-noise improves substantially with the large group size.
“The signal-to-noise improves substantially by virtue of the large group size.”
Quote in the paperNot a substantial claim - Findingreasoning
Functional connectivity hotspots from group data are spatially more blurred than single-subject maps because shape-based inter-subject registration can be inaccurate in aligning functionally defined areas, especially in high folding variability.
“The functional connectivity hotspots associated with this location are spatially more blurred than equivalent maps derived from single subject datasets, owing to the fact that shape-based inter-subject registration can be inaccurate in aligning functionally defined areas, especially in regions of high folding variability.”
Quote in the paperNot a substantial claim - Findingown data
In the language processing task contrast story versus math, robust group-level activation occurs in anterior and inferior temporal regions and ventral prefrontal regions typically associated with language processing.
“Figure 9 displays results from the language processing task developed by Binder et al. (2011), with the specific contrast being story processing versus math. These data are acquired in approximately 8 minutes, and show robust group level activation in anterior and inferior temporal regions, as well as ventral prefrontal regions typically associated with various components of language processing.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Binder et al. (2011) source
Not checked in the free run.
- Findingown data
In the language task, activation in the same regions is seen in the majority of individual subjects.
“As with the working memory task, we also see activation in these same regions in the majority of individual subjects.”
Quote in the paperNot a substantial claim - Findingown data
In the working memory task contrast 2-back versus 0-back, robust mixed-effects group-level activation occurs in dorsal frontal-parietal and cingulate systems typically associated with working memory and cognitive control, in both volume and grayordinate analyses.
“Figure 8 displays the results from the working memory task, a variant of the N-back task, with the specific contrast a high working memory load (“2-back”) versus a low working memory load (“0-back”). The data for this task are acquired in ~10 minutes and show robust mixed-effects group level activation in dorsal frontal-parietal and cingulate systems typically associated with working memory and”
Quote in the paperNot a substantial claim - Findingown data
HCP diffusion MRI data show clearly improved anatomical detail compared with conventional 2 mm data.
“Figure 6 shows representative fractional anisotropy and color-encoded principal diffusion direction images from the HCP dMRI data, compared with a more conventional 2mm data set (from a different subject). The improvement in anatomical detail is clearly visible.”
Quote in the paperNot a substantial claim - Findingown data
In the working memory task, significant activation in the same regions is seen in the majority of individual subjects.
“Further, we see significant activation in these same regions in the majority of individual subjects, a result important for the individual difference and genetic analysis goals of the HCP.”
Quote in the paperSubstantial claim - Findingown data
A spatially corresponding ICA component from a 100-component group-level ICA on 66 HCP subjects shows striking spatial correspondence with the task-fMRI activation.
“The bottom row shows a spatially corresponding ICA component from a 100-component group-level ICA-based network decomposition (with 82 ‘signal’ components), carried out on 66 HCP subjects from the Q1 data release. The correspondence in spatial patterns between the rfMRI ICA component and the task-fMRI activation is striking.”
Quote in the paperNot a substantial claim - Findingcitation
A novel algorithm developed by HCP greatly improves correction of susceptibility and eddy-current induced distortions and subject motion effects.
“We also developed a novel algorithm that greatly improves the correction of susceptibility and eddy-current induced distortions and the effects of subject motion (Andersson et al., 2012; Sotiropoulos et al., 2013c).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Andersson et al., 2012
Not checked in the free run.
- cites Sotiropoulos et al., 2013c
Not checked in the free run.
- Findingnone given
One hour of resting-state fMRI per subject provides roughly 5,000 time frames for each of about 90,000 grayordinates.
“The hour’s worth of rfMRI scanning accumulated per subject yields ~5,000 frames (TR’s) of data for each of the ~90,000 grayordinates that represent the anatomical substrate on which a dense functional connectome is generated.”
Quote in the paperNot a substantial claim - Findingreasoning
Urging consortium members to question conventional assumptions and to let pilot data and proposed analyses guide choices produced numerous improvements.
“By encouraging all consortium members to challenge standard assumptions, then let the pilot data and results from proposed analyses drive the decision, many improvements have been realized.”
Quote in the paperNot a substantial claim - Findingcitation
Signal-to-noise for subcortical regions is generally weaker than for cerebral and cerebellar cortical regions, largely because of their buried location relative to the 32-channel head coil.
“However, the SNR for subcortical regions is generally weaker than for cerebral and cerebellar cortical regions, in large part because of their buried location relative to the 32-channel head coil (Ugurbil et al, 2013).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Ugurbil et al, 2013 source
Not checked in the free run.
- Interpretationreasoning
A large number of subjects is needed to identify relationships between brain circuit phenotype and genotype.
“Consequently, a large number of subjects will be needed in order to identify relationships between brain circuit phenotype and genotype.”
Quote in the paperSubstantial claim- Rests on an assumption: Many features of brain circuitry and behavior probably arise from small effects of many genes rather than from a few dominant genes.
- Premisereasoning
Studying sibships that include twins allows systematic assessment of heritability of neural circuits.
“Studying sibships that include twins offers multiple advantages. Most obviously, it enables systematic assessment of the heritability of neural circuits.”
Quote in the paperSubstantial claim - Premisereasoning
Combined analyses of MZ and DZ twin pairs allow estimation of how much genotype, shared environment, and non-shared influences contribute to trait variation.
“Combined analyses of MZ and DZ pairs will allow estimation of the extent to which genotype, shared environment, and non-shared influences each contribute to variation in traits.”
Quote in the paperSubstantial claim - Study design and conductcitation
The refinements were packaged into defined pipelines that perform distortion correction and spatial alignment reliably and consistently for all four modalities.
“These refinements have been consolidated into a set of well-defined preprocessing pipelines that consistently and reliably carry out distortion correction and spatial alignment for each of the four imaging modalities (Glasser et al., 2013b).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Glasser et al., 2013b
Not checked in the free run.
- Premisereasoning
Multiband sequences raise data yield per unit time by exciting and reading several slices at once, then separating them in reconstruction using receive-coil sensitivity profiles.
“In general, multiband pulse sequences greatly increase the amount of data acquired per unit time, using a strategy of simultaneously exciting and acquiring multiple brain slices, which are then separated from one another during image reconstruction, based on the spatial sensitivity profiles of the multiple receive coils (32 channels for the HCP standard Siemens 3T head coil).”
Quote in the paperSubstantial claim - Interpretationreasoning
The two scanners offer complementary ways to test possible tractography improvements.
“Thus, the two hardware systems provide complementary platforms for exploring the possible improvements that are available for tractography.”
Quote in the paperNot a substantial claim - Interpretationcitation
With the WU-Minn diffusion method and b-values, 100 mT/m captures much of the benefit that 300 mT/m could offer.
“For the specific method and diffusion weighting (b values) chosen in the WU-Minn consortium, 100 mT/m maximal gradient strength provides much of the gain that would be available at 300 mT/m (Ugurbil et al. 2013); the relative merit of each depends on the method and b- values employed.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ugurbil et al. 2013 source
Not checked in the free run.
- Study design and conductreasoning
Diffusion scans are run twice with opposite phase encoding so susceptibility distortions can be corrected.
“Each scan is repeated along two phase encoding directions (L/R and R/L) to allow correction of susceptibility induced distortions.”
Quote in the paperSubstantial claim - Interpretationreasoning
That allows much finer-resolution images to be acquired.
“This in turn permits the acquisition of much higher resolution images.”
Quote in the paperSubstantial claim - Study design and conductnone given
Only structural scans with one or more good or excellent T1w and T2w scans from the same session, accompanied by corresponding bias field maps, are used for preprocessing and released.
“The only scans used for structural preprocessing pipelines and released to the community are those in which one or more good/excellent T1w and T2w scans were acquired in the same session (and accompanied by corresponding receive and transmit bias field maps that are used in preprocessing).”
Quote in the paperSubstantial claim - Premisereasoning
Acquiring structural MRI at 0.7 mm isotropic resolution rather than conventional 1 mm is important because many HCP analyses depend on accurate cortical surfaces.
“Obtaining higher resolution than conventional 1mm isotropic voxels is important because many HCP analyses rely on cortical surfaces that are as accurate as possible.”
Quote in the paperSubstantial claim - Interpretationcitation
Even with voxels about eight times smaller, functional contrast-to-noise holds up because image SNR and BOLD contrast rise.
“The functional contrast to noise at such high resolutions is not compromised, despite the ~ 8 times smaller voxel size, because of the aforementioned increases in image SNR and BOLD based contrast (Ugurbil et al., 2013).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ugurbil et al., 2013 source
Not checked in the free run.
- Interpretationreasoning
Such fine images will have poorer temporal resolution than 2 mm 3T data, since covering the brain needs more slices and thus a longer TR.
“The acquisition of such high resolution images will result in lower temporal resolution than the 2 mm isotropic resolution 3T data, because the many more slices needed to cover the entire brain results in a substantial increase in the TR.”
Quote in the paperNot a substantial claim - Interpretationown data
The observed twin shape-map differences are consistent with prior research suggesting cortical folding patterns are only modestly heritable.
“This is consistent with previous research suggesting that cortical folding patterns are only modestly heritable (Botteron et al., 2008)”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Botteron et al., 2008
Not checked in the free run.
- Interpretationreasoning
Accurate interhemispheric registration facilitates cross-hemisphere comparisons, such as correspondence of myelin maps between left and right hemispheres in individual subjects.
“Accurate interhemispheric registration facilitates a variety of cross-hemisphere comparisons, such as the correspondence of myelin maps in the left and right hemispheres in individual subjects.”
Quote in the paperSubstantial claim - Premisecitation
Neurobiologically relevant fluctuations represent only about 4% of total temporal variance in the minimally preprocessed datasets.
“Neurobiologically relevant fluctuations, which ideally should be the only signals used to drive functional connectivity analyses, represent only a small fraction (~4%) of the total temporal variance in the minimally preprocessed datasets (Glasser et al., 2013b; Marcus et al., 2013).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Glasser et al., 2013b
Not checked in the free run.
- cites Marcus et al., 2013 source
Not checked in the free run.
- Premisecitation
Registration to the Conte69 atlas brings the left and right hemispheres into precise geographic alignment using interhemispheric landmark-constrained registration.
“This is followed by registration to the Conte69 atlas, which brings the left and right hemispheres into precise geographic alignment using interhemispheric landmark-constrained registration (Van Essen et al., 2012).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Van Essen et al., 2012
Not checked in the free run.
- Premisecitation
Cortical myelin maps can be extracted from structural images by computing the T1w/T2w ratio at each voxel and mapping it to the cortical surface.
“Cortical myelin maps are another useful type of data that can be extracted from structural images by computing the ratio of the T1w and T2w image values at each voxel and mapping this ratio to the cortical surface (Glasser and Van Essen, 2011).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Glasser and Van Essen, 2011 source
Not checked in the free run.
- Premisecitation
Combining data across multiple receive coils using SENSE-1 increases the dynamic range of the signal relative to the conventional root-sum-of-squares approach.
“For example, combining data across multiple receive coils using a sensitivity-encoding method (SENSE-1) increases the dynamic range of the signal relative to the conventional root-sum-of-squares approach (Lenglet et al., 2012; Sotiropoulos et al., 2012; Sotiropoulos et al., 2013c).”
Quote in the paperNot a substantial claim0 of 3 sources checked - Interpretationnone given
ICA provides an alternative approach to subdividing the brain into functionally independent regions that are not constrained to be topologically contiguous or non-overlapping.
“ICA provides a powerful alternative approach to subdividing the brain into regions that functionally have a high degree of independence, but are not constrained to be topologically contiguous or non-overlapping.”
Quote in the paperNot a substantial claim - Premisecitation
Current tractography approaches are much more likely to trace to gyral crowns than to sulcal depths.
“For example, a notable bias, present for clear geometric reasons, is that current tractography approaches are much more likely to trace to gyral crowns than to sulcal depths (Van Essen et al., 2013).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Van Essen et al., 2013
Not checked in the free run.
- Interpretationown data
Taken together, the tfMRI data illustrate the ability to acquire high quality task fMRI data from a range of paradigms.
“Taken together, these data illustrate our ability to acquire high quality tfMRI data from a range of paradigms.”
Quote in the paperSubstantial claim - Interpretationnone given
Diffusion MRI connectomes inevitably contain biases and errors resulting from limitations of the technique.
“While containing a wealth of information, dMRI connectomes will inevitably contain biases and errors resulting from limitations of the technique.”
Quote in the paperNot a substantial claim - Premisecitation
HCP developed novel fiber reconstruction algorithms optimized for multi-shell diffusion data.
“HCP has developed novel fiber reconstruction algorithms that are optimized for multi-shell data (Jbabdi et al. 2012).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Jbabdi et al. 2012 source
Not checked in the free run.
- Interpretationreasoning
Because of temporal and spatial correlations, the true information content is likely much lower, perhaps by about two orders of magnitude.
“However, given the strong correlations in time (owing to the slow hemodynamic response function) and in space (neighboring grayordinates tend to be highly correlated), the actual amount of information is presumably much smaller, perhaps by around two orders of magnitude.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The HCP's released imaging and behavioral data are not treated as de-identified, since some combinations of restricted data could permit individual identification.
“The released HCP data are not considered de-identified, insofar as certain combinations of HCP Restricted Data (available through a separate process) might allow identification of individuals as discussed below.”
Quote in the paperSubstantial claim - Interpretationreasoning
Since HCP participants belong to families containing twins and non-twin siblings, combining certain personal details (such as exact age, height and weight, or handedness) could inadvertently reveal identity, especially to relatives, if those details were made public.
“Because HCP participants come from families with twins and non-twin siblings, there is a risk that combinations of information about an individual (e.g., age by year; body weight and height; handedness) might lead to inadvertent identification, particularly by other family members, if these combinations were publicly released.”
Quote in the paperSubstantial claim- Rests on an assumption: The restricted personal information (age, height and weight, handedness) is required for many scientific studies of the heritability of brain circuits and of links between brain circuits and behavioral and demographic traits.
- Interpretationreasoning
For both fMRI and tractography, the effective spatial resolution is not always as fine as the acquired voxel size would suggest.
“Hence, for both modalities, the effective spatial resolution does not always achieve that implied by the size of the acquired voxels.”
Quote in the paperSubstantial claim- Rests on an assumption: fMRI scans can be affected by signal bleeding across opposing sulcal banks.
- Rests on an assumption: Tractography tends to show stronger connections with gyral blades than with sulcal banks and fundi.
- Interpretationreasoning
If spatial correlations span roughly 50 to 100 grayordinates, this also implies about 10^7 bits per subject.
“If spatial correlations typically extend over ~50 – 100 grayordinates (e.g., patches ~15 – 20 mm in diameter), this would also suggest about 107 information bits per subject.”
Quote in the paperSubstantial claim - Premisecitation
At the macroscopic level, individual cortical areas differ in surface area by at least twofold across people.
“At a macroscopic level, we know that individual cortical areas vary in surface area by two-fold or more across individuals (cf. Van Essen et al., 2012b)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Van Essen et al., 2012b
Not checked in the free run.
- Interpretationreasoning
Therefore, high sensitivity to sequence variants is essential for characterizing individual genomic differences and linking them to phenotypes.
“Hence, high sensitivity to sequence variants is critical for being able to characterize individual genomic differences and to relate these differences to phenotypes of interest.”
Quote in the paperNot a substantial claim - Premisecitation
Nucleotide diversity among individuals is very low, about one part in a thousand.
“However, the level of nucleotide diversity across individuals is remarkably low (only about 1 part in 1000; Jorde and Wooding, 2004; Tishkoff and Kidd, 2004).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Jorde and Wooding, 2004
Not checked in the free run.
- cites Tishkoff and Kidd, 2004
Not checked in the free run.
- May combine two claims. 42
- Classified as probably not a substantial claim. 29
May combine two claims.
Findingown dataClaustrophobia and body or head size were among the causes of missing or incomplete imaging data.
“Reasons for partial or complete loss of imaging data include claustrophobia and physical size (body or head).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataAbout six or seven families are screened on average for each family that has a twin pair plus another sibling meeting every criterion and agreeing to take part.
“On average, approximately 6–7 families are screened in order to identify one family with a twin pair and at least one other sibling who meet all inclusion criteria and are willing to participate.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataData from 76 people who came in by November 2012 are part of the Q1 release.
“The Q1 data release includes data from 76 subjects who visited through November, 2012.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenIn Q1, visits were completed by an average of 2.6 people per family.
“An average of 2.6 subjects per family completed visits in Q1.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThe March 2013 Q1 release included imaging and behavioral data from 68 subjects scanned during the first quarter of Phase II.
“This sharing includes the release in March 2013 of data from 68 subjects scanned during the first quarter (Q1) of Phase II data collection.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingreasoningBecause the bore was smaller, the table had to sit higher, leaving the head off the gradient isocenter.
“in the absence of a custom designed patient table, this smaller bore necessitated the placement of the patient table higher in the bore, resulting in the subject’s head not being centered along the gradient isocenter.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe custom 100 mT/m gradient set gave the Siemens 3T Skyra a clear bore of 56 cm, less than the standard 70 cm or the Trio's 60 cm.
“Placing the customized 100 mT/m gradient set into the Siemens 3T Skyra system resulted in a clear inner bore diameter of 56 cm, smaller than the standard Siemens 3T Skyra bore size (70 cm diameter) or a Siemens Trio 3T Trio (60 cm diameter);”
Quote in the paperSubstantial claimMay combine two claims.
FindingcitationThe standard reconstruction was substantially improved to raise data quality, particularly for dMRI, and to shorten reconstruction time for HCP's large datasets.
“Major improvements to the standard reconstruction process have been made in order to improve the data quality (especially for dMRI, Sotiropoulos et al. 2013c) and to reduce the reconstruction time for the very large HCP datasets (Ugurbil et al., 2013).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Sotiropoulos et al. 2013c
Not checked in the free run.
- cites Ugurbil et al., 2013 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenSeveral improvements are now part of current FSL, FreeSurfer, and Connectome Workbench releases.
“Some of the refinements have already been incorporated into the latest versions of FSL, FreeSurfer, and Connectome Workbench, three major software packages used by the HCP pipelines.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationSpatially more global artifacts, including motion artifacts not fully removed, can remain and may artifactually influence correlation-based functional connectivity estimates.
“Despite the success of the above cleanup process for structured artifacts, spatially more global artifacts can remain in the data. This may include motion artifacts (Power et al., 2012) that are not fully removed by the above processing steps, and which may artifactually influence correlation-based estimates of functional connectivity.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Power et al., 2012
Not checked in the free run.
May combine two claims.
Findingown dataIn that ICA analysis, component 7 includes the central visual field representation of V1 and V2, while component 3 mainly involves the peripheral visual field representation of these areas.
“ICA Component 7 includes the central visual field representation of V1 and V2, whereas component 3 mainly involves the peripheral visual field representation of these two areas.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenDense connectomes are full correlation matrices of every voxel or grayordinate with every other, and the voxel and grayordinate matrices are 190 GB and 32 GB respectively.
“A dense connectome is the full (voxels X voxels) or (grayordinates X grayordinates) correlation matrix obtained by correlating the timeseries of every brain voxel or every grayordinate with every other. These matrices are massive (190GB and 32GB respectively)”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationFunctional connectivity-based parcellation efforts face two challenges: transitions in connectivity vary in strength and can be influenced by noise and biases, and inter-subject alignment is imperfect in high folding variability, causing misalignment and blurring of connectivity gradients.
“These efforts are still in early stages of development and must cope with two fundamental challenges: (i) the strength, or sharpness of transitions in functional connectivity vary widely and can be influenced by noise and biases in individual subjects; and (ii) the fidelity of inter-subject alignment using shape-based surface registration methods is imperfect in regions of high folding variability”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Van Essen et al., 2012b
Not checked in the free run.
- cites Robinson et al., 2013
Not checked in the free run.
May combine two claims.
Findingown dataBoth volume-based and grayordinate-based tfMRI processing indicated excellent quality data from the HCP paradigms, with clear group-level activation and robust activation within individual subjects in many paradigms and contrasts.
“Both approaches indicated excellent quality data from these paradigms, with clear group level activation as well as robust activation within individual subjects in many of the paradigms and contrasts.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
FindingcitationHCP 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).”
Quote in the paperNot a substantial claim0 of 2 sources checkedMay combine two claims.
FindingcitationIn group-average task activation from the right-hand movement task in 20 unrelated subjects, activation occurs in expected left motor cortex and in two distinct dorsal and ventral cerebellar locations matching published reports.
“The top row shows the group-average task activation from the right-hand hand movement task, analyzed for the same group of 20 unrelated subjects shown in preceding figures. It includes activation in the expected location in left motor cortex (left panel), and also at two distinct locations in dorsal and ventral cerebellum matching published reports (Buckner et al., 2011).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Buckner et al., 2011 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenWhen the article was submitted, the WU-Minn HCP had reached the midpoint of its five-year grant.
“At the time this article was submitted, the WU-Minn HCP is at the midway point of the 5-year grant.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingreasoningThe project's accomplishments so far are substantial, and it is progressing according to its original ambitious timeline.
“Nonetheless, the achievements to date are considerable, and the project remains on track relative to its original ambitious schedule.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Findingnone givenCertain advances, for instance the choice to use multiband imaging, had a major effect by themselves.
“Some of the advances, such as the decision to use multiband imaging, have had a large impact on their own.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
FindingreasoningThese refinements concerned pulse sequences, image reconstruction methods, and preprocessing and analysis pipelines.
“This applies to the extensive efforts to refine pulse sequences, image reconstruction algorithms, and also to the preprocessing and analysis pipelines.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenSeveral of these refinements were adopted into other analysis packages, such as FSL, FreeSurfer, and Connectome Workbench, extending their benefits beyond the HCP.
“A number of these refinements have already been incorporated into other analysis platforms, including FSL, FreeSurfer, and Connectome Workbench, so that the benefits extend well outside the HCP proper.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe consortium currently involves over 100 investigators and technical staff drawn from ten institutions.
“This reflects dedicated efforts and hard work by a large team that currently includes more than 100 investigators and technical staff from ten institutions in the consortium (Supplemental Table S5).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThe first-quarter data release represented only about 6% of the planned 1200 subjects.
“The Q1 data release constitutes only ~6% of the target number of 1200 subjects.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
FindingreasoningThe companion papers in the special issue presented many promising preliminary findings and methodological developments, but had not yet reported major neuroscientific discoveries.
“The companion articles in this special issue report many encouraging preliminary results as well as methodological advances, but not surprisingly they do not yet report major neuroscientific discoveries.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThe later stages of data analysis needed to characterize structural and functional connectivity were still undergoing refinement and optimization.
“Moreover, the more advanced stages of data analysis that are essential for characterizing structural and functional connectivity are still being refined and optimized.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
FindingreasoningAt 2 mm resolution, the covariance matrix has about 8 × 10^9 elements, corresponding to about 4 × 10^10 bits if each element carries 2 bits.
“At 2 mm resolution the covariance matrix contains ~8 × 109 (90,0002) elements, or ~4 × 1010 information bits if there are 2 bits per element”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationIncluding non-twin siblings increases statistical power for heritability analyses, for separating genetic and environmental influences, and for relating genotype to phenotype.
“Including additional (non-twin) siblings provides a further increase in statistical power for analyzing heritability, distinguishing between genetic and environmental influences (Posthuma and Boomsma, 2000; Van Essen et al., 2012a) and relating genotype to phenotype.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Posthuma and Boomsma, 2000
Not checked in the free run.
- cites Van Essen et al., 2012a
Not checked in the free run.
May combine two claims.
InterpretationreasoningAlthough 1200 subjects is small compared with many GWAS studies, the statistical power from studying twins and siblings should enable valuable exploratory genome-wide analyses of how genes and regulatory sequences influence brain connectivity.
“While 1200 subjects is small relative to many GWAS studies, the statistical power gained by studying twins and their siblings should nonetheless enable valuable exploratory genome-wide analyses of how specific genes, interacting genes, and genetic regulatory sequences may influence brain connectivity.”
Quote in the paperSubstantial claim- Rests on an assumption: Many features of brain circuitry and behavior probably arise from small effects of many genes rather than from a few dominant genes.
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisereasoning16-bit DICOMs give a wider range of signal intensities, useful for multi-channel arrays with large intensity differences.
“The 16 bit DICOMs allow for an extended dynamic range of signal intensity values, which is advantageous with such multi-channel receiver arrays where signal intensity variations can be quite large.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThat gain can improve functional SNR, allow more diffusion samples, or raise fMRI/dMRI spatial resolution.
“This efficiency increase can lead to substantially improved functional SNR (Feinberg et al., 2010; (Smith et al., 2011), the ability to acquire more diffusion data points (Sotiropoulos et al., 2013c), and/or increases in spatial resolution for fMRI or dMRI (Ugurbil et al. 2013).”
Quote in the paperSubstantial claim0 of 3 sources checkedMay combine two claims.
Study design and conductcitationChoosing 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).”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
Study design and conductreasoningThe 3T HCP protocol does not include prospective motion correction, since the method appeared late and was not tested enough before the protocol was fixed.
“However, prospective motion correction is not part of our 3T HCP acquisition protocol because the technology became available only late in the HCP methods development phase and was not sufficiently tested and developed before the data collection protocol was finalized.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationTheory suggests that 100 mT/m helps diffusion imaging substantially more than 40 mT/m, but less than the 300 mT/m system used by MGH/UCLA.
“This specifically benefits diffusion imaging, and on theoretical grounds (Ugurbil et al., 2013) it should provide significant gains over the standard 40 mT/m though not as much as the 300 mT/m customized gradients used by the MGH/UCLA HCP consortium.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ugurbil et al., 2013 source
Not checked in the free run.
May combine two claims.
InterpretationcitationCompared with standard 1 mm scans, the higher resolution makes cortical surface reconstruction more faithful and myelin maps better.
“The higher resolution compared to standard 1 mm structural scans improves the fidelity of cortical surface reconstruction and provides higher quality myelin maps (Glasser et al., 2013a; see below).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Glasser et al., 2013a
Not checked in the free run.
May combine two claims.
Study design and conductreasoningHCP preprocessing fixes these distortions, but users who begin with raw HCP scans must correct them on their own.
“These distortions have been corrected in HCP preprocessed data, but must be carried out separately by anyone starting with the unprocessed (raw) HCP scan data (see below).”
Quote in the paperNot a substantial claim- Rests on an assumption: Raw MRI images always contain spatial distortions, lack a common anatomical space, and do not align across modalities.
Classified as probably not a substantial claim.
Study design and conductnone givenEach HCP structural scan is rated by a trained rater for overall quality based on visual inspection of tissue contrast, blurring, ringing, and other artifacts.
“Each structural scan is evaluated by a trained rater to assess overall quality (poor, fair, good, and excellent), based on visual inspection of tissue contrast, spatial blurring, ringing, and other possible artifacts.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningBecause 7T images need in-plane acceleration and have shorter T2*, the multiband factor is capped, as multiband also depends on coil sensitivity.
“Further, the requirement of in-plane acceleration, due to the higher resolution images combined with the much shorter T2* at 7T, limits the achievable multiband factor, because it also relies on the coil’s sensitivity profile to accelerate the acquisition.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductreasoningOptimization compared old and new methods critically, then bundled the best into pipelines meant for uniform, systematic use across HCP data.
“The optimization process entailed critical evaluation and comparisons of how various existing and new methods performed, then packaging the best methods into a set of preprocessing pipelines appropriate for consistent and systematic application to all HCP datasets.”
Quote in the paperSubstantial claim- Rests on an assumption: Raw MRI images always contain spatial distortions, lack a common anatomical space, and do not align across modalities.
- Rests on an assumption: Raw MRI images also contain noise, artifacts, and biases specific to each modality.
May combine two claims.
PremisecitationCompared with lower field strengths, 7T raises image SNR and functional contrast-to-noise.
“7T provides increases in both the image SNR (Vaughan et al., 2001) and functional contrast–to-noise (Yacoub et al., 2001), compared to lower fields.”
Quote in the paperSubstantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductcitationAny scan that could show identifiable facial features is defaced, and visual QC checks that defacing worked.
“all scan types containing potentially identifiable facial features are defaced (Milchenko and Marcus, 2013), with visual QC inspection to confirm successful defacing.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Milchenko and Marcus, 2013
Not checked in the free run.
May combine two claims.
PremisecitationAt higher fields, BOLD images are relatively more sensitive to microvasculature, which narrows the point spread function.
“Additionally, higher fields increase the relative sensitivity to the microvasculature in BOLD-based functional images (Uludag et al., 2009; Ugurbil et al., 2003b; Ogawa et al., 1993), resulting in a smaller point spread function (Shmuel et al., 2007).”
Quote in the paperSubstantial claim0 of 3 sources checkedMay combine two claims.
Premisenone givenSome ICA components represent artifacts such as head motion or cardiac pulsation, while others represent valid neuronally related spontaneous fluctuations.
“Some components represent artifacts such as head motion or cardiac pulsation, while others represent valid neuronally-related spontaneous fluctuations.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductown dataFIX was hand-trained and tested on 100 15-minute HCP datasets and achieved better than 99% accuracy in classifying ICA components.
“FIX has been hand-trained and tested on one hundred 15-minute HCP datasets, and has achieved better than 99% accuracy rate in correctly classifying components.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationCortical surface alignment uses surface-based registration to a population-average surface with FreeSurfer, based on matching cortical folding patterns.
“Cortical surface alignment benefits from a subsequent stage of surface-based registration to a population-average surface, using FreeSurfer to register each hemisphere to a separate left and right atlas surfaces based on matching of cortical folding patterns (Fischl et al., 1999).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Fischl et al., 1999 source
Not checked in the free run.
May combine two claims.
InterpretationcitationFull and partial correlation matrices are mathematically well-defined, but neither should be regarded as an explicit validated indicator of direct anatomical connectivity, although significant partial correlations may have a high probability of reflecting genuine connections.
“Both the full correlation matrix and the partial correlation matrix represent mathematically well-defined entities; however, as alluded to above, neither should be regarded as an explicit, validated indicator of direct anatomical connectivity, although significant values in the partial correlation matrix will hopefully have a high probability of reflecting genuine connections (Smith, 2012).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Smith, 2012 source
Not checked in the free run.
May combine two claims.
Interpretationown dataThe V1/V2 connectivity pattern fits evidence for a major mid-eccentricity transition and implies that functional connectivity network boundaries do not always respect classical areal boundaries.
“This fits with evidence for a major transition in functional connectivity that cuts across both V1 and V2 in their mid-eccentricity range (Yeo et al., 2011); it implies that network (parcel) boundaries defined by functional connectivity do not always respect classical areal boundaries”
Quote in the paperSubstantial claim0 of 3 sources checkedMay combine two claims.
Interpretationnone givenThe neurobiological interpretation of full correlation, mean-regressed correlation, and partial correlation is not well understood, and none should be considered a perfect measure of direct anatomical connectivity.
“The neurobiological interpretation of different types of representation (full correlation; correlation after mean gray-matter timecourse regression; and the partial correlation approach illustrated below) is not well understood, and none should be considered a perfect measure of direct anatomical connectivity.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenClassical parcellations of cortical areas and subcortical nuclei commonly assume that each parcel is topologically contiguous and non-overlapping with neighboring parcels.
“Classical parcellations of cortical areas and subcortical nuclei commonly assume that each parcel is topologically contiguous and is non-overlapping with neighboring parcels (aside from the experimental uncertainties in areal boundaries).”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationICA components 12 and 15 include several parts of the default mode network and support the hypothesis that this network includes functionally distinct subregions.
“ICA components 12 and 15 include several parts of the default mode network, and support the hypothesis that this network includes functionally distinct subregions (Andrews-Hanna et al., 2010).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Andrews-Hanna et al., 2010 source
Not checked in the free run.
May combine two claims.
InterpretationreasoningThe striking functional connectivity differences for nearby seed locations reflect high data quality and amount, topology-respecting preprocessing, and advanced noise and artifact reduction.
“These striking differences in functional connectivity for nearby locations reflect several factors, including the high quality (and large amount) of data acquired from each subject; the use of preprocessing and analysis steps that respect the topology of the cortical sheet; and the advanced methods used to reduce noise and artifacts.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationSubcortical gray matter excluding cerebellar cortex constitutes about 8% of brain volume, and its vital roles are achieved with fewer than 1% of total brain neurons.
“Subcortical gray matter (excluding cerebellar cortex) constitutes about 8% of brain volume; remarkably, the many vital roles of subcortical nuclei in brain function are achieved with fewer than 1% of the total number of brain neurons (Azevedo et al., 2009).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Azevedo et al., 2009 source
Not checked in the free run.
May combine two claims.
Interpretationnone givenData from any individual diffusion shell can be used with standard fiber reconstruction, but methods using all three shells get the largest benefit.
“Data from any individual shell (b = 1000, 2000, and 3000 s/mm2) can be used with standard fiber reconstruction techniques, but methods that make use of all three shells will get the largest benefit.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningIf each time point could carry 2 bits of independent information, the theoretical maximum would be about one gigabit per subject.
“If, hypothetically, each time point could encode just 2 bits of information that was statistically independent of other time points and other grayordinates, then the theoretical upper bound would be about one gigabit (109 bits) of information per subject.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenAccurate source reconstruction is a critical prerequisite for comparing electrophysiological results with those from other imaging modalities.
“Accurate source reconstruction is a critical prerequisite for comparing electrophysiological results to those obtained from other imaging modalities.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationLinear constrained minimum variance beamformers reconstruct source-space data in the time domain and are useful for inferring connectivity in oscillatory brain activity.
“Linear constrained minimum variance beamformers (LCMV) reconstruct source space data in the time domain and are useful for inferring connectivity in oscillatory brain activity (Schoffelen and Gross, 2009; Brookes et al., 2011).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Schoffelen and Gross, 2009
Not checked in the free run.
- cites Brookes et al., 2011 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationDynamic imaging of coherent source reconstructs source-space data in the frequency domain.
“Dynamic imaging of coherent source (DICS) reconstructs source-space data in the frequency domain (Van Veen et al., 1997; Gross et al., 2001).”
Quote in the paperNot a substantial claim0 of 2 sources checkedMay combine two claims.
InterpretationreasoningOther refinements were individually modest, but combining many small gains produced large aggregate benefits.
“Many other refinements represent incremental improvements individually, but the concatenation of many small increments has led to large gains in the aggregate.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningA second approach using the covariance matrix of the fMRI time series gives a similar estimate.
“An alternative assessment that yields a similar estimate comes from considering the covariance matrix of the fMRI timeseries, which presumably should be more reproducible across different scan sessions than the timeseries itself.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningMRI-based connectivity analyses could distinguish connectivity 'brainprints' among many individuals, though not uniquely for everyone on Earth.
“It suggests that MRI-based connectivity analyses have the potential to discriminate connectivity ‘brainprints’ among large numbers of individuals, albeit not unique for every individual on the planet.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningUnder that assumption, resting-state fMRI would supply roughly 10^7 bits of information about brain circuits per HCP subject.
“If so, the amount of information about brain circuits provided by rfMRI would be in the range of 107 bits per HCP subject.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationMacaque evidence indicates that connection strength between any two cortical areas can vary by one or two orders of magnitude.
“and evidence from the macaque monkey suggests that the strength of pathways between any pair of cortical areas can vary by one or two orders of magnitude (Markov et al., 2011).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Markov et al., 2011 source
Not checked in the free run.
May combine two claims.
InterpretationreasoningFor 7T scans, the smaller voxels (about 1 mm^3) increase spatial elements about eightfold, while lower temporal resolution by two- to threefold, suggesting total information may be about twice as large.
“For the 7T HCP scans, the smaller voxel size attainable (~1 mm3) will increase the number of spatial elements about 8-fold, but the anticipated temporal resolution will be lower by 2- or 3-fold, suggesting that the total amount of information may be about 2-fold greater.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningHuman brain connectivity can be measured much less accurately than genome sequencing, but its individual variability is much greater.
“In contrast, the accuracy with which human brain connectivity can be quantitatively assessed is much lower than for genome sequencing, but the degree of individual variability is far greater.”
Quote in the paperSubstantial claim
The paper relies on this without support.
Many features of brain circuitry and behavior probably arise from small effects of many genes rather than from a few dominant genes.
“Many aspects of brain circuitry and its relation to behavior are likely to involve small contributions from many genes, rather than dominant contributions from one or a few genes.”
The paper relies on this without support.
Even small head movements can harm MR data quality across modalities.
“Head movements, even small in magnitude, can have deleterious effects on MR data quality for all modalities.”
The paper relies on this without support.
Raw MRI images always contain spatial distortions, lack a common anatomical space, and do not align across modalities.
“Unprocessed images from MRI scanners invariably contain several types of spatial distortion, are not in a standard anatomical space, and are misaligned across modalities.”
The paper relies on this without support.
Raw MRI images also contain noise, artifacts, and biases specific to each modality.
“They also contain various types of modality-specific noise, artifacts, and biases.”
The paper relies on this without support.
DZ twins are genetically as related as ordinary full siblings but share more childhood environment, including in utero.
“Dizygotic (DZ) twins are no more related genetically than ordinary full siblings, but they share childhood environment, including in utero environment, to a greater degree.”
The paper relies on this without support.
MZ twins are genetically nearly identical.
“Monozygotic (MZ) twins should have the greatest similarity because they are genetically nearly identical.”
The paper relies on this without support.
MEG has much higher temporal resolution than MRI but coarser spatial resolution.
“The obvious advantage of MEG over MRI is the much higher temporal resolution (milliseconds vs seconds), but it occurs at the expense of coarser spatial resolution (centimeters instead of millimeters).”
The paper relies on this without support.
Subcortical regions are important enough that they must be well integrated into HCP analyses of brain connectivity and function.
“It is obviously very important that subcortical regions be well integrated into HCP analyses of brain connectivity and function.”
The paper relies on this without support.
The restricted personal information (age, height and weight, handedness) is required for many scientific studies of the heritability of brain circuits and of links between brain circuits and behavioral and demographic traits.
“On the other hand, this information will be needed for many types of scientific inquiry aimed at characterizing the heritability of brain circuits and relating brain circuits to behavioral and demographic phenotypes.”
The paper relies on this without support.
fMRI scans can be affected by signal bleeding across opposing sulcal banks.
“For example, fMRI scans can be impacted by signals “bleeding across” opposing banks of sulci.”
The paper relies on this without support.
Tractography tends to show stronger connections with gyral blades than with sulcal banks and fundi.
“Tractography has a bias for showing stronger connections with gyral blades compared to sulcal banks and fundi.”
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?
Participant recruitment procedures and informed consent forms, including consent to share de-identified data, were previously approved by the Washington University institutional review board as part of the Human Connectome Project.
“Participant recruitment procedures and informed consent forms, including consent to share de-identified data, were previously approved by the Washington University institutional review board as part of the Human Connectome Project [39].”
From Situating the default-mode network along a principal gradient of macroscale cortical organization (2016) · cites this paper as [39]
The HCP Progress section on subject recruitment describes participants reviewing and signing an informed consent document, and the Data sharing section describes sharing HCP data. Neither passage says that the Washington University IRB previously approved the recruitment procedures or consent forms, or that participants consented specifically to data sharing. The Informatics and data sharing section says the released HCP data are not considered de-identified. (LLM, conf=0.85)
Experimental — accuracy not yet measuredDropout can be viewed as Bernoulli approximate inference in Bayesian neural networks.
“using dropout [57], which can be viewed as (Bernoulli) approximate inference in Bayesian neural networks [58, 59].”
From Quicksilver: Fast predictive image registration – A deep learning approach (2017) · cites this paper as [59]
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 connectivity.
“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 [29]
The 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.
“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.”
From Functional connectomics from resting-state fMRI (2013) · cites this paper as [67]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Covariance-matrix bit estimate is too high
MinorAt two bits per element, approximately 8 billion elements would contain 16 billion bits, not the reported 40 billion bits.
Reported 40000000000 · recomputed 16000000000
“At 2 mm resolution the covariance matrix contains ~8 × 109 (90,0002) elements, or ~4 × 1010 information bits if there are 2 bits per element”
Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282 · see citing works
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589 · see citing works
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/156869 · Cited by 10,323 · see citing works
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 · see citing works
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553 · see citing works
Self-citationThe 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 · see citing works
The minimal preprocessing pipelines for the Human Connectome Project
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.127 · Cited by 5,253 · see citing works
Self-citationFunctional Network Organization of the Human Brain
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.006 · Cited by 4,124 · see citing works
Ogawa, S., Tank, D. W., Menon, R., Ellermann, J. M., Kim, S. G., Merkle, H., & Ugurbil, K. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.13.5951 · Cited by 3,681 · see citing works
Self-citationProbabilistic diffusion tractography with multiple fibre orientations: What can we gain?
Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.09.018 · Cited by 3,471 · see citing works
Self-citationTemporal Autocorrelation in Univariate Linear Modeling of FMRI Data
Woolrich, M. W., Ripley, B. D., Brady, M., & Smith, S. M. (2001) · NeuroImage · doi:10.1006/nimg.2001.0931 · Cited by 2,859 · see citing works
Self-citationFunctional-anatomic fractionation of the brain’s default network
Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., & Buckner, R. L. (2010) · Neuron · doi:10.1016/j.neuron.2010.02.005 · Cited by 2,767 · see citing works
Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging
Beckmann, C. F., & Smith, S. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.822821 · Cited by 2,707 · see citing works
Self-citationThe Human Connectome Project: A data acquisition perspective
Essen, D. C. V., Uǧurbil, K., Auerbach, E. J., Barch, D. M., Behrens, T. E. J., Bucholz, R. D., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.018 · Cited by 2,605 · see citing works
Self-citationVan Veen, B. D., Van Drongelen, W., Yuchtman, M., & Suzuki, A. (1997) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.623056 · Cited by 2,561 · see citing works
Azevedo, F. A. C., Carvalho, L. R. B., Grinberg, L. T., Farfel, J. M., Ferretti, R. E. L., Leite, R. E. P., Filho, W. J., Lent, R., & Herculano‐Houzel, S. (2009) · Journal of Comparative Neurology · doi:10.1002/cne.21974 · Cited by 2,337 · see citing works
Network modelling methods for FMRI
Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., Ramsey, J. D., & Woolrich, M. W. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.063 · Cited by 1,878 · see citing works
Self-citationDynamic imaging of coherent sources: studying neural interactions in the human brain
Gross, J., Kujala, J., Hämäläinen, M., Timmermann, L., Schnitzler, A., & Salmelin, R. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.694 · Cited by 1,810 · see citing works
Ogawa, S., Menon, R. S., Tank, D. W., Kim, S. G., Merkle, H., Ellermann, J. M., & Ugurbil, K. (1993) · Biophysical Journal · doi:10.1016/s0006-3495(93)81441-3 · Cited by 1,772 · see citing works
Self-citationBucholz, K. K., Cadoret, R. J., Cloninger, C. R., Dinwiddie, S. H., HESSELBROCK, V. M., Nürnberger, J. I., Reich, T., Schmidt, I., & Schuckit, M. A. (1994) · Journal of Studies on Alcohol · doi:10.15288/jsa.1994.55.149 · Cited by 1,758 · see citing works
Moeller, S., Yacoub, E., Olman, C. A., Auerbach, E., Strupp, J., Harel, N., & Uğurbil, K. (2010) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22361 · Cited by 1,586 · see citing works
Self-citationFunction in the human connectome: task-fMRI and individual differences in behavior
Barch, D. M., Burgess, G. C., Harms, M. P., Petersen, S. E., Schlaggar, B. L., Corbetta, M., Glasser, M. F., Curtiss, S., Dixit, S., Feldt, C., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.033 · Cited by 1,529 · see citing works
Self-citationResting-state fMRI in the human connectome project
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., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.039 · Cited by 1,502 · see citing works
Self-citationGlasser, M. F., & Van Essen, D. C. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.2180-11.2011 · Cited by 1,447 · see citing works
Self-citationMultiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging
Feinberg, D. A., Moeller, S., Smith, S. M., Auerbach, E., Ramanna, S., Glasser, M. F., Miller, K. L., Ugurbil, K., & Yacoub, E. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015710 · Cited by 1,296 · see citing works
Self-citationSetsompop, K., Gagoski, B. A., Polimeni, J. R., Witzel, T., Wedeen, V. J., & Wald, L. L. (2012) · Magnetic Resonance in Medicine · doi:10.1002/mrm.23097 · Cited by 1,285 · see citing works
7T vs. 4T: RF power, homogeneity, and signal-to-noise comparison in head images
Vaughan, J. T., Garwood, M., Collins, C. M., Liu, W., DelaBarre, L., Adriany, G., Andersen, P., Merkle, H., Goebel, R., Smith, M. B., et al. (2001) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1156 · Cited by 890 · see citing works
Self-citationAdvances in diffusion MRI acquisition and processing in the Human Connectome Project
Sotiropoulos, S. N., Jbabdi, S., Xu, J., Andersson, J., Moeller, S., Auerbach, E. J., Glasser, M. F., Hernandez, M., Sapiro, G., Jenkinson, M., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.057 · Cited by 872 · see citing works
Self-citationSource connectivity analysis with MEG and EEG
Schoffelen, J., & Groß, J. (2009) · Human Brain Mapping · doi:10.1002/hbm.20745 · Cited by 854 · see citing works
Uğurbil, K., Xu, J., Auerbach, E. J., Moeller, S., Vu, A. T., Duarte-Carvajalino, J. M., Lenglet, C., Wu, X., Schmitter, S., Van de Moortele, P. F., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.012 · Cited by 812 · see citing works
Self-citationTemporal dynamics of spontaneous MEG activity in brain networks
de Pasquale, F., Della Penna, S., Snyder, A. Z., Lewis, C., Mantini, D., Marzetti, L., Belardinelli, P., Ciancetta, L., Pizzella, V., Romani, G. L., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0913863107 · Cited by 698 · see citing works
Genetic variation, classification and ‘race’
Jorde, L. B., & Wooding, S. P. (2004) · Nature Genetics · doi:10.1038/ng1435 · Cited by 637 · see citing works
Parcellations and Hemispheric Asymmetries of Human Cerebral Cortex Analyzed on Surface-Based Atlases
Van Essen, D. C., Glasser, M. F., Dierker, D. L., Harwell, J., & Coalson, T. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhr291 · Cited by 616 · see citing works
Self-citationTractography: Where Do We Go from Here?
Jbabdi, S., & Johansen-Berg, H. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0033 · Cited by 614 · see citing works
Defining functional areas in individual human brains using resting functional connectivity MRI
Cohen, A. L., Fair, D. A., Dosenbach, N. U. F., Miezin, F. M., Dierker, D., Van Essen, D. C., Schlaggar, B. L., & Petersen, S. E. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.01.066 · Cited by 582 · see citing works
Self-citationMeasuring functional connectivity using MEG: methodology and comparison with fcMRI
Brookes, M. J., Hale, J. R., Zumer, J. M., Stevenson, C. M., Francis, S. T., Barnes, G. R., Owen, J. P., Morris, P. G., & Nagarajan, S. S. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.054 · Cited by 519 · see citing works
Implications of biogeography of human populations for 'race' and medicine
Tishkoff, S., & Kidd, K. (2004) · Nature Genetics · doi:10.1038/ng1438 · Cited by 468 · see citing works
Human Connectome Project informatics: Quality control, database services, and data visualization
Marcus, D. S., Harms, M. P., Snyder, A. Z., Jenkinson, M., Wilson, J. A., Glasser, M. F., Barch, D. M., Archie, K. A., Burgess, G. C., Ramaratnam, M., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.077 · Cited by 465 · see citing works
Self-citationTrends and properties of human cerebral cortex: Correlations with cortical myelin content
Glasser, M. F., Goyal, M. S., Preuss, T. M., Raichle, M. E., & Essen, D. C. V. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.060 · Cited by 456 · see citing works
Uludağ, K., Müller-Bierl, B., & Uğurbil, K. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.051 · Cited by 450 · see citing works
Imaging brain function in humans at 7 Tesla
Yacoub, E., Shmuel, A., Pfeuffer, J., Van De Moortele, P., Adriany, G., Andersen, P., Vaughan, J. T., Merkle, H., Ugurbil, K., & Hu, X. (2001) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1080 · Cited by 448 · see citing works
Self-citationImaging human connectomes at the macroscale
Craddock, R. C., Jbabdi, S., Yan, C.-G., Vogelstein, J. T., Castellanos, F. X., Di Martino, A., Kelly, C., Heberlein, K., Colcombe, S., & Milham, M. P. (2013) · Nature Methods · doi:10.1038/nmeth.2482 · Cited by 419 · see citing works
A Cortical Core for Dynamic Integration of Functional Networks in the Resting Human Brain
de Pasquale, F., Penna, S. D., Snyder, A. Z., Marzetti, L., Pizzella, V., Romani, G. L., & Corbetta, M. (2012) · Neuron · doi:10.1016/j.neuron.2012.03.031 · Cited by 413 · see citing works
Jbabdi, S., Sotiropoulos, S. N., Savio, A. M., Graña, M., & Behrens, T. E. J. (2012) · Magnetic Resonance in Medicine · doi:10.1002/mrm.24204 · Cited by 399 · see citing works
Self-citationZaitsev, M., Dold, C., Sakas, G., Hennig, J., & Speck, O. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.039 · Cited by 392 · see citing works
Weight consistency specifies regularities of macaque cortical networks
Markov, N. T., Misery, P., Falchier, A., Lamy, C., Vezoli, J., Quilodran, R., Gariel, M. A., Giroud, P., Ercsey-Ravasz, M., Pilaz, L. J., et al. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq201 · Cited by 386 · see citing works
The future of fMRI connectivity
Smith, S. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.022 · Cited by 355 · see citing works
Self-citationSteps toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.017 · Cited by 336 · see citing works
Mapping anterior temporal lobe language areas with fMRI: a multicenter normative study
Binder, J. R., Gross, W. L., Allendorfer, J. B., Bonilha, L., Chapin, J., Edwards, J. C., Grabowski, T. J., Langfitt, J. T., Loring, D. W., Lowe, M. J., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.048 · Cited by 300 · see citing works
Spatio-temporal point-spread function of fMRI signal in human gray matter at 7 Tesla
Shmuel, A., Yacoub, E., Chaimow, D., Logothetis, N. K., & Ugurbil, K. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.12.030 · Cited by 294 · see citing works
Self-citationA Note on the Statistical Power in Extended Twin Designs
Posthuma, D., & Boomsma, D. I. (2000) · Behavior Genetics · doi:10.1023/a:1001959306025 · Cited by 272 · see citing works
Ultrahigh field magnetic resonance imaging and spectroscopy
Uǧurbil, K., Adriany, G., Andersen, P. M., Chen, W., Garwood, M., Rolf Gruetter, A., Henry, P.-G., Kim, S., Lieu, H., Tkáč, I., et al. (2003) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2003.08.027 · Cited by 252 · see citing works
Self-citationSpatially constrained hierarchical parcellation of the brain with resting-state fMRI
Blumensath, T., Jbabdi, S., Glasser, M. F., Essen, D. C. V., Uǧurbil, K., Behrens, T. E. J., & Smith, S. M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.024 · Cited by 233 · see citing works
Self-citationAdding dynamics to the Human Connectome Project with MEG
Larson‐Prior, L. J., Oostenveld, R., Penna, S. D., Michalareas, G., Prior, F., Abbas Babajani-feremi, A., Schoffelen, J., Marzetti, L., de Pasquale, F., Pompeo, F., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.056 · Cited by 226 · see citing works
Jbabdi, S., Lehman, J. F., Haber, S. N., & Behrens, T. E. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.2457-12.2013 · Cited by 213 · see citing works
Self-citationHow accurate is magnetic resonance imaging of brain function?
Uǧurbil, K., Toth, L. J., & Kim, D. (2003) · Trends in Neurosciences · doi:10.1016/s0166-2236(02)00039-5 · Cited by 198 · see citing works
Sotiropoulos, S. N., Moeller, S., Jbabdi, S., Xu, J., Andersson, J., Auerbach, E. J., Yacoub, E. S., Feinberg, D. A., Setsompop, K., Wald, L. L., et al. (2013) · Magnetic Resonance in Medicine · doi:10.1002/mrm.24623 · Cited by 185 · see citing works
Self-citationLenglet, C., Abosch, A., Yacoub, E., De Martino, F., Sapiro, G., & Harel, N. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029153 · Cited by 164 · see citing works
Self-citationBall and rackets: inferring fiber fanning from diffusion-weighted MRI
Sotiropoulos, S. N., Behrens, T. E. J., & Jbabdi, S. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.056 · Cited by 156 · see citing works
Self-citationObscuring Surface Anatomy in Volumetric Imaging Data
Milchenko, M., & Marcus, D. (2013) · Neuroinformatics · doi:10.1007/s12021-012-9160-3 · Cited by 145 · see citing works
A Signal-Processing Pipeline for Magnetoencephalography Resting-State Networks
Mantini, D., Penna, S. D., Marzetti, L., de Pasquale, F., Pizzella, V., Corbetta, M., & Romani, G. L. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0001 · Cited by 128 · see citing works
Artifact Removal in Magnetoencephalogram Background Activity With Independent Component Analysis
Escudero, J., Hornero, R., Abásolo, D., Fernández, A., & López-Coronado, M. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.894968 · Cited by 71 · see citing works
Robinson, E. C., Jbabdi, S., Andersson, J., Smith, S. M., Glasser, M. F., Essen, D. C. V., Burgess, G., Harms, M. P., Barch, D. M., & Jenkinson, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38868-2_40 · Cited by 38 · see citing works
Self-citationLost in localization — But found with foci?!
Van Essen, D. C. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.050 · Cited by 22 · see citing works
Self-citationBotteron, K. N. (2008) · Biological Psychiatry · doi:10.1016/j.biopsych.2008.01.015 · Cited by 6 · see citing works
High-resolution intersubject averaging and a coordinate system for the cortical surface
Fischl, B., Sereno, M. I., Tootell, R. B. H., & Dale, A. M. (1999) · Human Brain Mapping · doi:10.1002/(sici)1097-0193(1999)8:4
Larkman (2001) · doi:10.1002/1522-2586(200102)13:2
Flagged first, then by citations
Research works printed but not linked: 3
- Moeller, 2008, fMRI with 16 fold reduction using multibanded multislice sampling, Proc. Int. Soc. Magn. Reson. Med., 16
- Van Essen, 2013, In vivo architectonics: a cortico-centric perspective, NeuroImage
- Wig, 2013, Parcellating an individual subject's cortical and subcortical brain structures using snowball sampling of resting-state correlations, Cereb. Cortex
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 · see citing works
Self-citationCluster failure: why fMRI inferences for spatial extent have inflated false-positive rates
Eklund, A., Nichols, T. E., & Knutsson, H. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602413113 · Cited by 3,487 · see citing works
Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., & Constable, R. T. (2015) · Nature Neuroscience · doi:10.1038/nn.4135 · Cited by 2,747 · see citing works
Situating the default-mode network along a principal gradient of macroscale cortical organization
Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., Bezgin, G., Eickhoff, S. B., Castellanos, F. X., Petrides, M., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1608282113 · Cited by 2,321 · see citing works
A mesoscale connectome of the mouse brain
Oh, S. W., Harris, J. A., Ng, L., Winslow, B., Cain, N., Mihalas, S., Wang, Q., Lau, C., Kuan, L., Henry, A. M., et al. (2014) · Nature · doi:10.1038/nature13186 · Cited by 2,612 · see citing works
Multimodal 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 · see citing works
Salimi-Khorshidi, G., Douaud, G., Beckmann, C. F., Glasser, M. F., Griffanti, L., & Smith, S. M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.046 · Cited by 1,770 · see citing works
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 · see citing works
Function in the human connectome: task-fMRI and individual differences in behavior
Barch, D. M., Burgess, G. C., Harms, M. P., Petersen, S. E., Schlaggar, B. L., Corbetta, M., Glasser, M. F., Curtiss, S., Dixit, S., Feldt, C., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.033 · Cited by 1,529 · see citing works
Self-citationResting-state fMRI in the human connectome project
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., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.039 · Cited by 1,502 · see citing works
Self-citationPrecision Functional Mapping of Individual Human Brains
Gordon, E. M., Laumann, T. O., Gilmore, A. W., Newbold, D. J., Greene, D. J., Berg, J. J., Ortega, M., Hoyt-Drazen, C., Gratton, C., Sun, H., et al. (2017) · Neuron · doi:10.1016/j.neuron.2017.07.011 · Cited by 1,335 · see citing works
Scanning the horizon: towards transparent and reproducible neuroimaging research
Poldrack, R. A., Baker, C. I., Durnez, J., Gorgolewski, K. J., Matthews, P. M., Munafò, M. R., Nichols, T. E., Poline, J.-B., Vul, E., & Yarkoni, T. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.167 · Cited by 1,410 · see citing works
Dipy, a library for the analysis of diffusion MRI data
Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., van der Walt, S., Descoteaux, M., Nimmo-Smith, I., & Contributors, D. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00008 · Cited by 1,371 · see citing works
Tractography-based connectomes are dominated by false-positive connections
Maier-Hein, K. H., Neher, P., Houde, J.-C., Côté, M.-A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.-C., Lin, Y.-C., Ji, Q., et al. (2016) · doi:10.1101/084137 · Cited by 1,361 · see citing works
The Human Connectome Project's neuroimaging approach
Glasser, M. F., Smith, S. M., Marcus, D. S., Andersson, J. L. R., Auerbach, E. J., Behrens, T. E. J., Coalson, T. S., Harms, M. P., Jenkinson, M., Moeller, S., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4361 · Cited by 1,090 · see citing works
Self-citationPreparing Medical Imaging Data for Machine Learning
Willemink, M. J., Koszek, W. A., Hardell, C., Wu, J., Fleischmann, D., Harvey, H., Folio, L. R., Summers, R. M., Rubin, D. L., & Lungren, M. P. (2020) · Radiology · doi:10.1148/radiol.2020192224 · Cited by 1,020 · see citing works
The default mode network in cognition: a topographical perspective
Smallwood, J., Bernhardt, B. C., Leech, R., Bzdok, D., Jefferies, E., & Margulies, D. S. (2021) · Nature Reviews Neuroscience · doi:10.1038/s41583-021-00474-4 · Cited by 935 · see citing works
Smith, S. M., Nichols, T. E., Vidaurre, D., Winkler, A. M., Behrens, T. E. J., Glasser, M. F., Ugurbil, K., Barch, D. M., Van Essen, D. C., & Miller, K. L. (2015) · Nature Neuroscience · doi:10.1038/nn.4125 · Cited by 859 · see citing works
Self-citationDomain adaptation for medical image analysis: A survey
Guan, H., & Liu, M. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3117407 · Cited by 1,057 · see citing works
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.079 · Cited by 912 · see citing works
Functional connectomics from resting-state fMRI
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., et al. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.016 · Cited by 952 · see citing works
Self-citationUğurbil, K., Xu, J., Auerbach, E. J., Moeller, S., Vu, A. T., Duarte-Carvajalino, J. M., Lenglet, C., Wu, X., Schmitter, S., Van de Moortele, P. F., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.012 · Cited by 812 · see citing works
Self-citationCross-validation failure: Small sample sizes lead to large error bars
Varoquaux, G. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.061 · Cited by 897 · see citing works
Time-resolved resting-state brain networks
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., & Breakspear, M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400181111 · Cited by 753 · see citing works
Hansen, J. Y., Shafiei, G., Markello, R. D., Smart, K., Cox, S. M. L., Nørgaard, M., Beliveau, V., Wu, Y., Gallezot, J.-D., Aumont, É., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01186-3 · Cited by 797 · see citing works
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 · see citing works
Self-citationUnderstanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies
Marquand, A. F., Rezek, I., Buitelaar, J., & Beckmann, C. F. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.12.023 · Cited by 683 · see citing works
Elliott, M. L., Knodt, A. R., Ireland, D., Morris, M. L., Poulton, R., Ramrakha, S., Sison, M. L., Moffitt, T. E., Caspi, A., & Hariri, A. R. (2020) · Psychological Science · doi:10.1177/0956797620916786 · Cited by 714 · see citing works
Towards a universal taxonomy of macro-scale functional human brain networks
Uddin, L. Q., Yeo, B. T. T., & Spreng, R. N. (2019) · Brain Topography · doi:10.1007/s10548-019-00744-6 · Cited by 683 · see citing works
Precision psychiatry: a neural circuit taxonomy for depression and anxiety
Williams, L. M. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00579-9 · Cited by 641 · see citing works
Burt, J. B., Demirtaş, M., Eckner, W. J., Navejar, N. M., Ji, J. L., Martin, W. J., Bernacchia, A., Anticevic, A., & Murray, J. D. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0195-0 · Cited by 657 · see citing works
Canonical 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 · see citing works
Quicksilver: Fast predictive image registration – A deep learning approach
Yang, X., Kwitt, R., Styner, M., & Niethammer, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.008 · Cited by 630 · see citing works
Nonsocial and social cognition in schizophrenia: current evidence and future directions
Green, M. F., Horan, W. P., & Lee, J. (2019) · World Psychiatry · doi:10.1002/wps.20624 · Cited by 612 · see citing works
Psilocybin for treatment-resistant depression: fMRI-measured brain mechanisms
Carhart-Harris, R. L., Roseman, L., Bolstridge, M., Demetriou, L., Pannekoek, J. N., Wall, M. B., Tanner, M., Kaelen, M., McGonigle, J., Murphy, K., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-13282-7 · Cited by 587 · see citing works
Diffusion MRI fiber tractography of the brain
Jeurissen, B., Descoteaux, M., Mori, S., & Leemans, A. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3785 · Cited by 574 · see citing works
“Fiber tracking has also claimed a central role in the field of “connectomics,” a technique that builds and studies comprehensive maps of the complex network of connections within the brain,(7) and to which significant resources are allocated worldwide.(8,9) Despite its unique abilities and exciting applications, fiber…”
BackgroundGenerative modeling of brain maps with spatial autocorrelation
Burt, J. B., Helmer, M., Shinn, M., Anticevic, A., & Murray, J. D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117038 · Cited by 528 · see citing works
Parcellating cortical functional networks in individuals
Wang, D., Buckner, R. L., Fox, M. D., Holt, D. J., Holmes, A. J., Stoecklein, S., Langs, G., Pan, R., Qian, T., Li, K., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4164 · Cited by 527 · see citing works
Human Connectome Project informatics: Quality control, database services, and data visualization
Marcus, D. S., Harms, M. P., Snyder, A. Z., Jenkinson, M., Wilson, J. A., Glasser, M. F., Barch, D. M., Archie, K. A., Burgess, G. C., Ramaratnam, M., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.077 · Cited by 465 · see citing works
Self-citationFunctional gradients of the cerebellum
Guell, X., Schmahmann, J. D., Gabrieli, J. D., & Ghosh, S. S. (2018) · eLife · doi:10.7554/elife.36652 · Cited by 472 · see citing works
neuromaps: structural and functional interpretation of brain maps
Markello, R. D., Hansen, J. Y., Liu, Z.-Q., Bazinet, V., Shafiei, G., Suárez, L. E., Blostein, N., Seidlitz, J., Baillet, S., Satterthwaite, T. D., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01625-w · Cited by 479 · see citing works
Distinct Global Brain Dynamics and Spatiotemporal Organization of the Salience Network
Chen, T., Cai, W., Ryali, S., Supekar, K., & Menon, V. (2016) · PLOS Biology · doi:10.1371/journal.pbio.1002469 · Cited by 480 · see citing works
Conceptualizing mental disorders as deviations from normative functioning
Marquand, A. F., Kia, S. M., Zabihi, M., Wolfers, T., Buitelaar, J. K., & Beckmann, C. F. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0441-1 · Cited by 433 · see citing works
Human 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 · see citing works
Cooperative and competitive spreading dynamics on the human connectome
Mišić, B., Betzel, R. F., Nematzadeh, A., Goñi, J., Griffa, A., Hagmann, P., Flammini, A., Ahn, Y.-Y., & Sporns, O. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.035 · Cited by 342 · see citing works
Li, J., Kong, R., Liégeois, R., Orban, C., Tan, Y., Sun, N., Holmes, A. J., Sabuncu, M. R., Ge, T., & Yeo, B. T. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.016 · Cited by 380 · see citing works
Langlotz, C. P., Allen, B., Erickson, B. J., Kalpathy-Cramer, J., Bigelow, K., Cook, T. S., Flanders, A. E., Lungren, M. P., Mendelson, D. S., Rudie, J. D., et al. (2019) · Radiology · doi:10.1148/radiol.2019190613 · Cited by 368 · see citing works
A Synergistic Core for Human Brain Evolution and Cognition
Luppi, A. I., Mediano, P. A. M., Rosas, F. E., Holland, N., Fryer, T. D., O’Brien, J. T., Rowe, J. B., Menon, D. K., Bor, D., & Stamatakis, E. A. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01070-0 · Cited by 314 · see citing works
Measuring 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 · see citing works
Self-citationBurgess, G. C., Kandala, S., Nolan, D., Laumann, T. O., Power, J. D., Adeyemo, B., Harms, M. P., Petersen, S. E., & Barch, D. M. (2016) · Brain Connectivity · doi:10.1089/brain.2016.0435 · Cited by 216 · see citing works
Self-citationHe, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116276 · Cited by 271 · see citing works
Evaluation and statistical inference for human connectomes
Pestilli, F., Yeatman, J. D., Rokem, A., Kay, K. N., & Wandell, B. A. (2014) · Nature Methods · doi:10.1038/nmeth.3098 · Cited by 259 · see citing works
Neurite imaging reveals microstructural variations in human cerebral cortical gray matter
Fukutomi, H., Glasser, M. F., Zhang, H., Autio, J. A., Coalson, T. S., Okada, T., Togashi, K., Van Essen, D. C., & Hayashi, T. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.02.017 · Cited by 234 · see citing works
Wiring cost and topological participation of the mouse brain connectome
Rubinov, M., Ypma, R. J. F., Watson, C., & Bullmore, E. T. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1420315112 · Cited by 219 · see citing works
White, T., Blok, E., & Calhoun, V. D. (2022) · Human Brain Mapping · doi:10.1002/hbm.25120 · Cited by 176 · see citing works
“Assuring that the shared data has the optimum data quality, or including metrics that allow users to understand the underlying quality of pre-processed images (Esteban et al., 2017; White et al., 2018) is important to reduce noise-related variability and to increase power (Zuo, Xu, & Milham, 2019).”
BackgroundMapping Structural Connectivity Using Diffusion <scp>MRI</scp> : Challenges and Opportunities
Yeh, C., Jones, D. K., Liang, X., Descoteaux, M., & Connelly, A. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27188 · Cited by 187 · see citing works
Brain–phenotype models fail for individuals who defy sample stereotypes
Greene, A. S., Shen, X., Noble, S., Horien, C., Hahn, C. A., Arora, J., Tokoglu, F., Spann, M. N., Carrión, C. I., Barron, D. S., et al. (2022) · Nature · doi:10.1038/s41586-022-05118-w · Cited by 167 · see citing works
Networks of task co-activations
Laird, A. R., Eickhoff, S. B., Rottschy, C., Bzdok, D., Ray, K. L., & Fox, P. T. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.073 · Cited by 168 · see citing works
Revisiting the global workspace orchestrating the hierarchical organization of the human brain
Deco, G., Vidaurre, D., & Kringelbach, M. L. (2021) · Nature Human Behaviour · doi:10.1038/s41562-020-01003-6 · Cited by 162 · see citing works
Genç, E., Fraenz, C., Schlüter, C., Friedrich, P., Hossiep, R., Voelkle, M. C., Ling, J. M., Güntürkün, O., & Jung, R. E. (2018) · Nature Communications · doi:10.1038/s41467-018-04268-8 · Cited by 167 · see citing works
TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry
Frässle, S., Aponte, E. A., Bollmann, S., Brodersen, K. H., Do, C. T., Harrison, O. K., Harrison, S. J., Heinzle, J., Iglesias, S., Kasper, L., et al. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.680811 · Cited by 169 · see citing works
Assessing inter individual differences with task related functional neuroimaging
Lebreton, M., Bavard, S., Daunizeau, J., & Palminteri, S. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0681-8 · Cited by 160 · see citing works
Two distinct scene processing networks connecting vision and memory
Baldassano, C., Esteva, A., Fei-Fei, L., & Beck, D. M. (2016) · eneuro · doi:10.1523/eneuro.0178-16.2016 · Cited by 144 · see citing works
Reconstructing the Neanderthal brain using computational anatomy
Kochiyama, T., Ogihara, N., Tanabe, H. C., Kondo, O., Amano, H., Hasegawa, K., Suzuki, H., Ponce de León, M. S., Zollikofer, C. P. E., Bastir, M., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-24331-0 · Cited by 156 · see citing works
Role of brain imaging in disorders of brain–gut interaction: a Rome Working Team Report
Mayer, E. A., Labus, J., Aziz, Q., Tracey, I., Kilpatrick, L., Elsenbruch, S., Schweinhardt, P., Van Oudenhove, L., & Borsook, D. (2019) · Gut · doi:10.1136/gutjnl-2019-318308 · Cited by 138 · see citing works
Transfer learning in magnetic resonance brain imaging: a systematic review
Valverde, J. M., Imani, V., Abdollahzadeh, A., De Feo, R., Prakash, M., Ciszek, R., & Tohka, J. (2021) · Journal of Imaging · doi:10.3390/jimaging7040066 · Cited by 137 · see citing works
Personality and intelligence: A meta-analysis.
Anglim, J., Dunlop, P. D., Wee, S., Horwood, S., Wood, J. K., & Marty, A. (2022) · Psychological Bulletin · doi:10.1037/bul0000373 · Cited by 125 · see citing works
Detect and correct bias in multi-site neuroimaging datasets
Wachinger, C., Rieckmann, A., & Pölsterl, S. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101879 · Cited by 115 · see citing works
Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review
Borchert, R. J., Azevedo, T., Badhwar, A., Bernal, J., Betts, M., Bruffaerts, R., Burkhart, M. C., Dewachter, I., Gellersen, H. M., Low, A., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13412 · Cited by 125 · see citing works
Turbulent-like Dynamics in the Human Brain
Deco, G., & Kringelbach, M. L. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108471 · Cited by 113 · see citing works
An afferent white matter pathway from the pulvinar to the amygdala facilitates fear recognition
McFadyen, J., Mattingley, J. B., & Garrido, M. I. (2019) · eLife · doi:10.7554/elife.40766 · Cited by 115 · see citing works
Modeling transcranial electrical stimulation in the aging brain
Indahlastari, A., Albizu, A., O’Shea, A., Forbes, M. A., Nissim, N. R., Kraft, J. N., Evangelista, N. D., Hausman, H. K., & Woods, A. J. (2020) · Brain Stimulation · doi:10.1016/j.brs.2020.02.007 · Cited by 113 · see citing works
Current methods and new directions in resting state fMRI
Yang, J., Gohel, S., & Vachha, B. (2020) · Clinical Imaging · doi:10.1016/j.clinimag.2020.04.004 · Cited by 110 · see citing works
Sotiropoulos, S. N., Hernández-Fernández, M., Vu, A. T., Andersson, J. L., Moeller, S., Yacoub, E., Lenglet, C., Ugurbil, K., Behrens, T. E. J., & Jbabdi, S. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.014 · Cited by 110 · see citing works
Self-citationProgress toward openness, transparency, and reproducibility in cognitive neuroscience
Gilmore, R. O., Diaz, M. T., Wyble, B. A., & Yarkoni, T. (2017) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13325 · Cited by 95 · see citing works
The Brain Analysis Library of Spatial maps and Atlases (BALSA) database
Van Essen, D. C., Smith, J., Glasser, M. F., Elam, J., Donahue, C. J., Dierker, D. L., Reid, E. K., Coalson, T., & Harwell, J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.002 · Cited by 89 · see citing works
Self-citationEklund, A., Knutsson, H., & Nichols, T. E. (2019) · Human Brain Mapping · doi:10.1002/hbm.24350 · Cited by 82 · see citing works
The neurocognitive correlates of brain entropy estimated by resting state fMRI
Wang, Z. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117893 · Cited by 77 · see citing works
Aydogan, D. B., Jacobs, R., Dulawa, S., Thompson, S. L., Francois, M. C., Toga, A. W., Dong, H., Knowles, J. A., & Shi, Y. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1663-8 · Cited by 72 · see citing works
St-Jean, S., Coupé, P., & Descoteaux, M. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.02.010 · Cited by 74 · see citing works
Functional connectivity change as shared signal dynamics
Cole, M. W., Yang, G. J., Murray, J. D., Repovš, G., & Anticevic, A. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.11.011 · Cited by 60 · see citing works
Bernal, J., & Mazo, C. (2022) · Applied Sciences · doi:10.3390/app122010228 · Cited by 70 · see citing works
Fiber tractography using machine learning
Neher, P. F., Côté, M.-A., Houde, J.-C., Descoteaux, M., & Maier-Hein, K. H. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.028 · Cited by 68 · see citing works
Morphological and functional variability in central and subcentral motor cortex of the human brain
Eichert, N., Watkins, K. E., Mars, R. B., & Petrides, M. (2021) · Brain Structure and Function · doi:10.1007/s00429-020-02180-w · Cited by 58 · see citing works
Lee, E., Choi, J.-S., Kim, M., & Suk, H.-I. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116113 · Cited by 61 · see citing works
The emergence of a functionally flexible brain during early infancy
Yin, W., Li, T., Hung, S.-C., Zhang, H., Wang, L., Shen, D., Zhu, H., Mucha, P. J., Cohen, J. R., & Lin, W. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2002645117 · Cited by 56 · see citing works
Pure Apraxia of Speech After Resection Based in the Posterior Middle Frontal Gyrus
Chang, E. F., Kurteff, G., Andrews, J. P., Briggs, R. G., Conner, A. K., Battiste, J. D., & Sughrue, M. E. (2020) · Neurosurgery · doi:10.1093/neuros/nyaa002 · Cited by 58 · see citing works
Yazdan-Panah, A., Schmidt-Mengin, M., Ricigliano, V. A. G., Soulier, T., Stankoff, B., & Colliot, O. (2023) · NeuroImage: Clinical · doi:10.1016/j.nicl.2023.103368 · Cited by 58 · see citing works
Fiber length profiling: a novel approach to structural brain organization
Bajada, C. J., Schreiber, J., & Caspers, S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.070 · Cited by 55 · see citing works
Refacing: Reconstructing Anonymized Facial Features Using GANS
Abramian, D., & Eklund, A. (2019) · 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) · doi:10.1109/isbi.2019.8759515 · Cited by 63 · see citing works
Analysis of cortical shape in children with simplex autism
Dierker, D. L., Feczko, E., Pruett, J. R., Petersen, S. E., Schlaggar, B. L., Constantino, J. N., Harwell, J. W., Coalson, T. S., & Van Essen, D. C. (2015) · Cerebral Cortex · doi:10.1093/cercor/bht294 · Cited by 50 · see citing works
Self-citationOnset pattern of nigrostriatal denervation in early Parkinson’s disease
Pineda-Pardo, J. A., Sánchez-Ferro, Á., Monje, M. H. G., Pavese, N., & Obeso, J. A. (2022) · Brain · doi:10.1093/brain/awab378 · Cited by 51 · see citing works
Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample
Boeke, E. A., Holmes, A. J., & Phelps, E. A. (2020) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.05.018 · Cited by 50 · see citing works
RNA-Seq Reveals Novel Transcriptional Reorganization in Human Alcoholic Brain
Farris, S. P., & Dayne Mayfield, R. (2014) · International Review of Neurobiology · doi:10.1016/b978-0-12-801105-8.00011-4 · Cited by 49 · see citing works
Dementia ConnEEGtome: towards multicentric harmonization of EEG connectivity in neurodegeneration
Prado, P., Birba, A., Cruzat, J., Santamaría-García, H., Parra, M., Moguilner, S., Tagliazucchi, E., & Ibáñez, A. (2022) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2021.12.008 · Cited by 43 · see citing works
Pushing the Boundaries of Psychiatric Neuroimaging to Ground Diagnosis in Biology
Saggar, M., & Uddin, L. Q. (2019) · eneuro · doi:10.1523/eneuro.0384-19.2019 · Cited by 38 · see citing works
Warnaby, C. E., Seretny, M., Ní Mhuircheartaigh, R., Rogers, R., Jbabdi, S., Sleigh, J., & Tracey, I. (2016) · Anesthesiology · doi:10.1097/aln.0000000000001027 · Cited by 38 · see citing works
Molecular neuroanatomy: a generation of progress
Pollock, J. D., Wu, D.-Y., & Satterlee, J. S. (2014) · Trends in Neurosciences · doi:10.1016/j.tins.2013.11.001 · Cited by 36 · see citing works
Whitman, E. T., Elliott, M. L., Knodt, A. R., Abraham, W. C., Anderson, T. J., Cutfield, N. J., Hogan, S., Ireland, D., Melzer, T. R., Ramrakha, S., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00897-z · Cited by 37 · see citing works
Park, B.-Y., Kim, M., Seo, J., Lee, J.-M., & Park, H. (2016) · Brain Topography · doi:10.1007/s10548-015-0463-1 · Cited by 31 · see citing works
- Authors
- Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K.
- Affiliations
- University of Oxford (GB)
- Washington University in St. Louis (US)
- University of Minnesota (US)
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 80
- Pages
- 62-79
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3724347
- OpenAlex
- W2024729467
- Cited by
- 5,651
- References
- 67
- Citations by year
- 2026: 513 · 2025: 688 · 2024: 684 · 2023: 567 · 2022: 599 · 2021: 577 · 2020: 526 · 2019: 448 · 2018: 339 · 2017: 271 · 2016: 204 · 2015: 140 · 2014: 72 · 2013: 22
- FWCI
- 12.41
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- UniTERAMO Research Catalog (University of Teramo) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Funders
- NIH grant (1U54MH091657)
- 16 NIH Institutes and Centers
- McDonnell Center for Systems Neuroscience at Washington University
- Biotechnology Research Center (BTRC) (P41 EB015894)
- NIBIB
- NINDS Institutional Center Core Grant (P30 NS076408)
- 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
- Human Connectome Project, human brain connectivity, neuroimaging preprocessing, open science, healthy adults, brain-behavior relationship
- MeSH terms
- Brain, Humans, Models, Anatomic, Models, Neurological, Nerve Net, 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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-05.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-05.
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 ›
Are you an author, or do you see a problem with this page? Request a review