The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Decoding Subject-Driven Cognitive States with Whole-Brain Connectivity Patterns
Open accessAbstract
Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continuous and purely subject-driven cognitive states have not been available. Here, we demonstrate that free-streaming subject-driven cognitive states can be decoded using a novel whole-brain functional connectivity analysis. Ninety functional regions of interest (ROIs) were defined across 14 large-scale resting-state brain networks to generate a 3960 cell matrix reflecting whole-brain connectivity. We trained a classifier to identify specific patterns of whole-brain connectivity as subjects rested quietly, remembered the events of their day, subtracted numbers, or (silently) sang lyrics. In a leave-one-out cross-validation, the classifier identified these 4 cognitive states with 84% accuracy. More critically, the classifier achieved 85% accuracy when identifying these states in a second, independent cohort of subjects. Classification accuracy remained high with imaging runs as short as 30-60 s. At all temporal intervals assessed, the 90 functionally defined ROIs outperformed a set of 112 commonly used structural ROIs in classifying cognitive states. This approach should enable decoding a myriad of subject-driven cognitive states from brief imaging data samples.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The cited source says what the claim attributes to it.
In the citing paper
“In between-group analysis using functionally defined ROIs (Shirer et al. 2012) based upon our a priori hypotheses, the left DLPFC displayed significantly enhanced connectivity to ipsilateral insular cortex and contralateral supramarginal gyrus in highs compared to lows during hypnosis compared to rest (P < 0.05, corrected for multiple comparisons with FEAT's GRF correction; Fig. 3A; Table 3).”
From Brain Activity and Functional Connectivity Associated with Hypnosis (2016) · cites this paper as Shirer et al. 2012
In the citing paper
“Functionally defined regions of interest have recently been suggested in whole-brain connectivity analysis (Shirer et al., 2012).”
From Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis (2012) · cites this paper as Shirer et al., 2012
In the citing paper
“Using the 90 functional regions of interest defined by Shirer et al. (2012), the support vector machine classifier performed with a classification accuracy of 92.5%.”
From Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis (2012) · cites this paper as Shirer et al., 2012
In the citing paper
“This kind of analysis has previously been used (albeit from full correlation network matrices) to predict subjects’ psychiatric condition [77] and cognitive state [78];”
From Functional connectomics from resting-state fMRI (2013) · cites this paper as [78]
In the citing paper
“To provide a framework for such a network classification, we used the Stanford functional ROI atlas (http://findlab.stanford.edu/func- tional_ROIs.html),consisting of 14 individual ICNs, including Anterior-and Posterior Salience, Auditory, Basal Ganglia, Dorsal-and Ventral Default Mode, Higher Visual, Language, Left-and Right Executive Control, Sensorimotor, Precuneus, Primary Visual and Visuosp”
From Specific default mode subnetworks support mentalizing as revealed through opposing network recruitment by social and semantic FMRI tasks: Mentalizing and Default-Mode Subnetworks (2015) · cites this paper as Shirer et al., 2012
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
James, W. (1891) · Personality Culture Society · doi:10.1037/11059-000 · Cited by 25,848
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
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
Dissociable intrinsic connectivity networks for salience processing and executive control
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L., & Greicius, M. D. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.5587-06.2007 · Cited by 7,426
Self-citationFunctional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
Self-citationCorrespondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150
Consistent resting-state networks across healthy subjects
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173
Distributed and overlapping representations of faces and objects in ventral temporal cortex
Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001) · Science · doi:10.1126/science.1063736 · Cited by 3,940
Spatial registration and normalization of images
Friston, K. J., Ashburner, J., Frith, C., Poline, J.-B., Heather, J. D., & Frackowiak, R. S. J. (1995) · Human Brain Mapping · doi:10.1002/hbm.460030303 · Cited by 3,744
Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308627101 · Cited by 3,684
Self-citationInvestigations into resting-state connectivity using independent component analysis
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1634 · Cited by 3,398
Predicting human resting-state functional connectivity from structural connectivity
Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., & Hagmann, P. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0811168106 · Cited by 3,307
Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network
Greicius, M. D., Supekar, K., Menon, V., & Dougherty, R. F. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhn059 · Cited by 2,175
Self-citationDetecting awareness in the vegetative state
Owen, A. M., Coleman, M. R., Boly, M., Davis, M. H., Laureys, S., & Pickard, J. D. (2006) · Science · doi:10.1126/science.1130197 · Cited by 1,978
Decoding mental states from brain activity in humans
Haynes, J., & Rees, G. E. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1931 · Cited by 1,868
Decoding the visual and subjective contents of the human brain
Kamitani, Y., & Tong, F. (2005) · Nature Neuroscience · doi:10.1038/nn1444 · Cited by 1,829
Time–frequency dynamics of resting-state brain connectivity measured with fMRI
Chang, C., & Glover, G. H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.011 · Cited by 1,808
Sources of Mathematical Thinking: Behavioral and Brain-Imaging Evidence
Dehaene, S., Spelke, E., Pinel, P., Stanescu, R., & Tsivkin, S. (1999) · Science · doi:10.1126/science.284.5416.970 · Cited by 1,739
Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data.
Cordes, D., Haughton, V. M., Arfanakis, K., Carew, J. D., Turski, P. A., Moritz, C. H., Quigley, M., & Meyerand, M. E. (2001) · PubMed Central · Cited by 1,402
Identifying natural images from human brain activity
Kay, K., Naselaris, T., Prenger, R., & Gallant, J. L. (2008) · Nature · doi:10.1038/nature06713 · Cited by 1,272
Whole-brain anatomical networks: does the choice of nodes matter?
Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yücel, M., Pantelis, C., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.027 · Cited by 1,169
Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease
Supekar, K., Menon, V., Rubin, D., Musen, M., & Greicius, M. D. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000100 · Cited by 1,148
Self-citationMental Imagery of Faces and Places Activates Corresponding Stimulus-Specific Brain Regions
O’Craven, K. M., & Kanwisher, N. (2000) · Journal of Cognitive Neuroscience · doi:10.1162/08989290051137549 · Cited by 1,057
Predicting the orientation of invisible stimuli from activity in human primary visual cortex
Haynes, J., & Rees, G. E. (2005) · Nature Neuroscience · doi:10.1038/nn1445 · Cited by 866
Brain connectivity related to working memory performance
Hampson, M., Driesen, N. R., Skudlarski, P., Gore, J. C., & Constable, R. T. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3408-06.2006 · Cited by 853
Mourão-Miranda, J., Bokde, A. L. W., Born, C., Hampel, H. J., & Stetter, M. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.06.070 · Cited by 700
Detection of functional connectivity using temporal correlations in MR images
Hampson, M., Peterson, B. S., Skudlarski, P., Gatenby, J. C., & Gore, J. C. (2002) · Human Brain Mapping · doi:10.1002/hbm.10022 · Cited by 699
Category-Specific Cortical Activity Precedes Retrieval During Memory Search
Polyn, S. M., Natu, V. S., Cohen, J. D., & Norman, K. A. (2005) · Science · doi:10.1126/science.1117645 · Cited by 690
Combining spatial extent and peak intensity to test for activations in functional imaging
Poline, J.-B., Worsley, K. J., Evans, A. C., & Friston, K. J. (1997) · NeuroImage · doi:10.1006/nimg.1996.0248 · Cited by 670
How default is the default mode of brain function?
Fransson, P. (2006) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2006.06.017 · Cited by 611
Spiral‐in/out BOLD fMRI for increased SNR and reduced susceptibility artifacts
Glover, G. H., & Law, C. S. W. (2001) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1222 · Cited by 600
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
Chang, C., & Glover, G. H. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.012 · Cited by 503
Towards a network theory of cognition
McIntosh, A. R. (2000) · Neural Networks · doi:10.1016/s0893-6080(00)00059-9 · Cited by 500
Self‐navigated spiral fMRI: Interleaved versus single‐shot
Glover, G. H., & Lai, S. (1998) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910390305 · Cited by 476
Davatzikos, C. A., Ruparel, K., Yong Fan, A., Shen, D. G., Acharyya, M., Loughead, J., Gur, R. C., & Langleben, D. D. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.08.009 · Cited by 470
Dissociating Prefrontal and Parietal Cortex Activation during Arithmetic Processing
Menon, V., Rivera, S. M., White, C. D., Glover, G. H., & Reiss, A. L. (2000) · NeuroImage · doi:10.1006/nimg.2000.0613 · Cited by 430
Self-citationNeural Correlates of Simple and Complex Mental Calculation
Zago, L., Pesenti, M., Mellet, E., Crivello, F., Mazoyer, B. M., & Tzourio‐Mazoyer, N. (2001) · NeuroImage · doi:10.1006/nimg.2000.0697 · Cited by 423
Connection Patterns Distinguish 3 Regions of Human Parietal Cortex
Rushworth, M. F. S., Behrens, T. E. J., & Johansen‐Berg, H. (2005) · Cerebral Cortex · doi:10.1093/cercor/bhj079 · Cited by 390
Independent component analysis of nondeterministic fMRI signal sources
Kiviniemi, V. J., Kantola, J.-H., Jauhiainen, J., Hyvärinen, A., & Tervonen, O. A. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00097-1 · Cited by 381
Functional segmentation of the brain cortex using high model order group PICA
Kiviniemi, V. J., Starck, T., Remes, J. J., Long, X., Nikkinen, J., Haapea, M., Veijola, J. M., Moilanen, I. K., Isohanni, M. K., Zang, Y., et al. (2009) · Human Brain Mapping · doi:10.1002/hbm.20813 · Cited by 376
Natural Scene Categories Revealed in Distributed Patterns of Activity in the Human Brain
Walther, D. B., Caddigan, E., Fei-Fei, L., & Beck, D. M. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.0559-09.2009 · Cited by 336
Predicting the Stream of Consciousness from Activity in Human Visual Cortex
Haynes, J., & Rees, G. E. (2005) · Current Biology · doi:10.1016/j.cub.2005.06.026 · Cited by 327
Decoding brain states from fMRI connectivity graphs
Richiardi, J., Eryilmaz, H., Schwartz, S., Vuilleumier, P. O., & Ville, D. V. D. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.081 · Cited by 321
Dissociating Neural Correlates of Cognitive Components in Mental Calculation
Gruber, O. (2001) · Cerebral Cortex · doi:10.1093/cercor/11.4.350 · Cited by 305
Decoding Individual Episodic Memory Traces in the Human Hippocampus
Chadwick, M. J., Hassabis, D., Weiskopf, N., & Maguire, E. A. (2010) · Current Biology · doi:10.1016/j.cub.2010.01.053 · Cited by 235
&NA;, &. (1877) · The Journal of Nervous and Mental Disease · doi:10.1097/00005053-187704000-00009 · Cited by 220
Modulation of Brain Resting-State Networks by Sad Mood Induction
Harrison, B. J., Pujol, J., Ortiz, H., Fornito, A., Pantelis, C., & Yücel, M. (2008) · PLoS ONE · doi:10.1371/journal.pone.0001794 · Cited by 206
Regularized higher‐order in vivo shimming
Kim, D., Elfar Adalsteinsson, A., Glover, G. H., & Spielman, D. M. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10267 · Cited by 200
Decoding cognitive control in human parietal cortex
Esterman, M., Chiu, Y.-C., Tamber-Rosenau, B. J., & Yantis, S. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0903593106 · Cited by 158
Identification of large-scale networks in the brain using fMRI
Bellec, P., Perlbarg, V., Jbabdi, S., Pélégrini‐Issac, M., Anton, J., Doyon, J., & Benali, H. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.08.044 · Cited by 154
Imaging neuroscience: Principles or maps?
Friston, K. J. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.3.796 · Cited by 130
Classifying instantaneous cognitive states from fMRI data
Mitchell, T. M., Hutchinson, R., Just, M. A., Niculescu, R. S., Pereira, F., & Wang, X. (2018) · PubMed · doi:10.1184/r1/6613946 · Cited by 114
Inferring behavior from functional brain images
Dehaene, S., Clec’H, G. L., Cohen, L., et al. (1998) · Nature Neuroscience · doi:10.1038/2785 · Cited by 38
Decoding Task-based Attentional Modulation during Face Categorization
Chiu, Y., Esterman, M. S., Han, Y., Rosen, H., & Yantis, S. (2010) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2010.21503 · Cited by 36
Flagged first, then by citations
Cited by
Sorted by citations
Large-scale brain networks and psychopathology: a unifying triple network model
Menon, V. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.08.003 · Cited by 4,168
Self-citationDynamic functional connectivity: Promise, issues, and interpretations
Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.079 · Cited by 2,962
Tracking whole-brain connectivity dynamics in the resting state
Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs352 · Cited by 2,992
Kaiser, R. H., Andrews-Hanna, J. R., Wager, T. D., & Pizzagalli, D. A. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.0071 · Cited by 2,281
Resting-state connectivity biomarkers define neurophysiological subtypes of depression
Drysdale, A. T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., Fetcho, R. N., Zebley, B., Oathes, D. J., Etkin, A., et al. (2017) · Nature Medicine · doi:10.1038/nm.4246 · Cited by 2,170
Metadata
- Authors
- Shirer, W. R., Ryali, S., Rykhlevskaia, E., Menon, V., & Greicius, M. D.
- Affiliations
- Stanford Medicine (US)
- Stanford University (US)
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-05-26
- Volume
- 22
- Issue
- 1
- Pages
- 158-165
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- PMC
- PMC3236795
- OpenAlex
- W2101219946
- Cited by
- 1,843
- References
- 59
- Citations by year
- 2026: 59 · 2025: 98 · 2024: 111 · 2023: 126 · 2022: 173 · 2021: 207 · 2020: 161 · 2019: 174 · 2018: 158 · 2017: 161 · 2016: 131 · 2015: 113 · 2014: 76 · 2013: 55 · 2012: 32
- FWCI
- 27.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cerebral Cortex (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS073498, NS058899, R21NS058899, NS073498)
- National Center for Complementary and Integrative Health (AT005733, RC1AT005733)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD057610, HD057610, HD059205, R01HD059205)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- cognitive state decoding, whole-brain functional connectivity, resting-state brain networks, brain state classification, memory retrieval
- MeSH terms
- Adolescent, Adult, Brain, Brain Mapping, Cognition, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Neural Pathways, Neuropsychological Tests, Oxygen, Mental Recall, Cohort Studies, Young Adult
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.
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
