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.
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“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
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Dissociable intrinsic connectivity networks for salience processing and executive control
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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
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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
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Resting state neural networks and energy metabolism
Noack, R., Manjesh, C., Ruszinko, M., Siegelmann, H., & Kozma, R. (2017) · 2017 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7965859 · Cited by 12
van Kleef, R. S., Kaushik, P., Besten, M., Marsman, J.-B. C., Bockting, C. L. H., van Vugt, M., Aleman, A., & van Tol, M.-J. (2023) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2023.07.034 · Cited by 7
Association of brain network dynamics with plasma biomarkers in subjective memory complainers
Chiesa, P. A., Houot, M., Vergallo, A., Cavedo, E., Lista, S., Potier, M. C., Zetterberg, H., Blennow, K., Vanmechelen, E., De Vos, A., et al. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.12.017 · Cited by 7
Shan, H.-D., Wang, Y.-M., Hu, H.-X., Jiang, S.-Y., Chu, M.-Y., Wang, Y., Lui, S. S. Y., Cheung, E. F. C., Wang, Z., & Chan, R. C. K. (2022) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-020-01222-y · Cited by 6
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
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