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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
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“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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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
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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
Multi-task connectivity reveals flexible hubs for adaptive task control
Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013) · Nature Neuroscience · doi:10.1038/nn.3470 · Cited by 1,704
GABA-modulating bacteria of the human gut microbiota
Strandwitz, P., Kim, K. H., Terekhova, D., Liu, J. K., Sharma, A., Levering, J., McDonald, D., Dietrich, D., Ramadhar, T. R., Lekbua, A., et al. (2018) · Nature Microbiology · doi:10.1038/s41564-018-0307-3 · Cited by 1,170
Discovering Event Structure in Continuous Narrative Perception and Memory
Baldassano, C., Chen, J., Zadbood, A., Pillow, J. W., Hasson, U., & Norman, K. A. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.041 · Cited by 952
Gratton, C., Laumann, T. O., Nielsen, A. N., Greene, D. J., Gordon, E. M., Gilmore, A. W., Nelson, S. M., Coalson, R. S., Snyder, A. Z., Schlaggar, B. L., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.035 · Cited by 846
Opportunities and limitations of intrinsic functional connectivity MRI
Buckner, R. L., Krienen, F. M., & Yeo, B. T. T. (2013) · Nature Neuroscience · doi:10.1038/nn.3423 · Cited by 912
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
20 years of the default mode network: A review and synthesis.
Menon, V. (2023) · Neuron · doi:10.1016/j.neuron.2023.04.023 · Cited by 934
“77,104,105 114 These findings challenge earlier notions of an undifferentiated PCC as a global hub and the DMN as a network with a unitary function.”
BackgroundSelf-citationGoulden, N., Khusnulina, A., Davis, N. J., Bracewell, R. M., Bokde, A. L., McNulty, J. P., & Mullins, P. G. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.052 · Cited by 810
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Leonardi, N., & Van De Ville, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.007 · Cited by 828
Default mode network mechanisms of transcranial magnetic stimulation in depression
Liston, C., Chen, A. C., Zebley, B. D., Drysdale, A. T., Gordon, R., Leuchter, B., Voss, H. U., Casey, B. J., Etkin, A., & Dubin, M. J. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.01.023 · Cited by 763
Zeng, L.-L., Shen, H., Liu, L., Wang, L., Li, B., Fang, P., Zhou, Z., Li, Y., & Hu, D. (2012) · Brain · doi:10.1093/brain/aws059 · Cited by 748
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
Abraham, A., Milham, M. P., Di Martino, A., Craddock, R. C., Samaras, D., Thirion, B., & Varoquaux, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.045 · Cited by 701
Shared memories reveal shared structure in neural activity across individuals
Chen, J., Leong, Y. C., Honey, C. J., Yong, C. H., Norman, K. A., & Hasson, U. (2017) · Nature Neuroscience · doi:10.1038/nn.4450 · Cited by 645
Dynamic reconfiguration of the default mode network during narrative comprehension
Simony, E., Honey, C. J., Chen, J., Lositsky, O., Yeshurun, Y., Wiesel, A., & Hasson, U. (2016) · Nature Communications · doi:10.1038/ncomms12141 · Cited by 588
Correlated gene expression supports synchronous activity in brain networks
Richiardi, J., Altmann, A., Milazzo, A.-C., Chang, C., Chakravarty, M. M., Banaschewski, T., Barker, G. J., Bokde, A. L. W., Bromberg, U., Büchel, C., et al. (2015) · Science · doi:10.1126/science.1255905 · Cited by 625
Self-citationThe role of PFC networks in cognitive control and executive function
Menon, V., & D’Esposito, M. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-021-01152-w · Cited by 654
Self-citationMcTeague, L. M., Huemer, J., Carreon, D. M., Jiang, Y., Eickhoff, S. B., & Etkin, A. (2017) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2017.16040400 · Cited by 635
“Given that the swath of cortex extending from the anterior to the midcingulate/presupplementary motor cortex has been characterized as part of a coherent salience network (10, 15), the discordant hypo- and hyperactivation observed here between the more anterior and posterior cingulate, respectively, might seem unexpec…”
BackgroundQuestions and controversies in the study of time-varying functional connectivity in resting fMRI
Lurie, D. J., Kessler, D., Bassett, D. S., Betzel, R. F., Breakspear, M., Kheilholz, S., Kucyi, A., Liégeois, R., Lindquist, M. A., McIntosh, A. R., et al. (2020) · Network Neuroscience · doi:10.1162/netn_a_00116 · Cited by 648
Kucyi, A., & Davis, K. D. (2015) · Trends in Neurosciences · doi:10.1016/j.tins.2014.11.006 · Cited by 587
The hierarchical basis of neurovisceral integration
Smith, R., Thayer, J. F., Khalsa, S. S., & Lane, R. D. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.02.003 · Cited by 523
Johnson, M. W., Hendricks, P. S., Barrett, F. S., & Griffiths, R. R. (2019) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2018.11.010 · Cited by 506
Menon, V. (2015) · Brain Mapping · doi:10.1016/b978-0-12-397025-1.00052-x · Cited by 625
Self-citationCognitive Network Neuroscience
Medaglia, J. D., Lynall, M.-E., & Bassett, D. S. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00810 · Cited by 419
McTeague, L. M., Rosenberg, B. M., Lopez, J. W., Carreon, D. M., Huemer, J., Jiang, Y., Chick, C. F., Eickhoff, S. B., & Etkin, A. (2020) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2019.18111271 · Cited by 388
Krienen, F. M., Yeo, B. T. T., & Buckner, R. L. (2014) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0526 · Cited by 372
Shared understanding of narratives is correlated with shared neural responses
Nguyen, M., Vanderwal, T., & Hasson, U. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.010 · Cited by 304
Lehmann, M., Ghosh, P. M., Madison, C., Laforce, R., Corbetta-Rastelli, C., Weiner, M. W., Greicius, M. D., Seeley, W. W., Gorno-Tempini, M. L., Rosen, H. J., et al. (2013) · Brain · doi:10.1093/brain/aws327 · Cited by 325
Self-citationBrain correlates of hypnosis: A systematic review and meta-analytic exploration
Landry, M., Lifshitz, M., & Raz, A. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.02.020 · Cited by 295
Zadbood, A., Chen, J., Leong, Y. C., Norman, K. A., & Hasson, U. (2016) · doi:10.1101/081208 · Cited by 281
Phillips, M. L., Chase, H. W., Sheline, Y. I., Etkin, A., Almeida, J. R. C., Deckersbach, T., & Trivedi, M. H. (2015) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2014.14010076 · Cited by 267
Zadbood, A., Chen, J., Leong, Y. C., Norman, K. A., & Hasson, U. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx202 · Cited by 281
The will to persevere Induced by electrical stimulation of the human cingulate gyrus
Parvizi, J., Rangarajan, V., Shirer, W. R., Desai, N., & Greicius, M. D. (2013) · Neuron · doi:10.1016/j.neuron.2013.10.057 · Cited by 254
Self-citationWhen the Brain Takes a Break: A Model-Based Analysis of Mind Wandering
Mittner, M., Boekel, W., Tucker, A. M., Turner, B. M., Heathcote, A., & Forstmann, B. U. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.2062-14.2014 · Cited by 228
Lanius, R. A., Frewen, P. A., Tursich, M., Jetly, R., & McKinnon, M. C. (2015) · European Journal of Psychotraumatology · doi:10.3402/ejpt.v6.27313 · Cited by 246
One-year test-retest reliability of intrinsic connectivity network fMRI in older adults
Guo, C. C., Kurth, F., Zhou, J., Mayer, E. A., Eickhoff, S. B., Kramer, J. H., & Seeley, W. W. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.027 · Cited by 252
Brain Activity and Functional Connectivity Associated with Hypnosis
Jiang, H., White, M. P., Greicius, M. D., Waelde, L. C., & Spiegel, D. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw220 · Cited by 246
Self-citationLearning and comparing functional connectomes across subjects
Varoquaux, G., & Craddock, R. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.007 · Cited by 233
Passamonti, L., Tsvetanov, K. A., Jones, P. S., Bevan-Jones, W. R., Arnold, R., Borchert, R. J., Mak, E., Su, L., O'Brien, J. T., & Rowe, J. B. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.2574-18.2019 · Cited by 220
Reineberg, A. E., Andrews-Hanna, J. R., Depue, B. E., Friedman, N. P., & Banich, M. T. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.045 · Cited by 206
Default Network Modulation and Large-Scale Network Interactivity in Healthy Young and Old Adults
Spreng, R. N., & Schacter, D. L. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhr339 · Cited by 214
Hemington, K. S., Wu, Q., Kucyi, A., Inman, R. D., & Davis, K. D. (2016) · Brain Structure and Function · doi:10.1007/s00429-015-1161-1 · Cited by 220
Armbruster, D. J. N., Ueltzhöffer, K., Basten, U., & Fiebach, C. J. (2012) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00286 · Cited by 202
Tyszka, J. M., Kennedy, D. P., Paul, L. K., & Adolphs, R. (2014) · Cerebral Cortex · doi:10.1093/cercor/bht040 · Cited by 187
Ekstrom, A. D., Huffman, D. J., & Starrett, M. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00531.2017 · Cited by 186
Evans, J. W., Szczepanik, J., Brutsché, N., Park, L. T., Nugent, A. C., & Zarate, C. A. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.01.027 · Cited by 173
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Zhou, F., Zhao, W., Qi, Z., Geng, Y., Yao, S., Kendrick, K. M., Wager, T. D., & Becker, B. (2021) · Nature Communications · doi:10.1038/s41467-021-26977-3 · Cited by 155
Hasson, U., Egidi, G., Marelli, M., & Willems, R. M. (2018) · Cognition · doi:10.1016/j.cognition.2018.06.018 · Cited by 157
Chen, J., Honey, C. J., Simony, E., Arcaro, M. J., Norman, K. A., & Hasson, U. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhv155 · Cited by 135
Çetin, M. S., Christensen, F., Abbott, C. C., Stephen, J. M., Mayer, A. R., Cañive, J. M., Bustillo, J. R., Pearlson, G. D., & Calhoun, V. D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.009 · Cited by 154
Distinct intrinsic network connectivity patterns of post‐traumatic stress disorder symptom clusters
Tursich, M., Ros, T., Frewen, P. A., Kluetsch, R. C., Calhoun, V. D., & Lanius, R. A. (2015) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.12387 · Cited by 148
Grothe, M. J., Teipel, S. J., & Initiative, F. T. A. D. N. (2016) · Human Brain Mapping · doi:10.1002/hbm.23018 · Cited by 150
Identification of depression subtypes and relevant brain regions using a data-driven approach
Tokuda, T., Yoshimoto, J., Shimizu, Y., Okada, G., Takamura, M., Okamoto, Y., Yamawaki, S., & Doya, K. (2018) · Scientific Reports · doi:10.1038/s41598-018-32521-z · Cited by 136
Anderson, A., Douglas, P. K., Kerr, W. T., Haynes, V. S., Yuille, A. L., Xie, J., Wu, Y. N., Brown, J. A., & Cohen, M. S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.12.015 · Cited by 136
Castellazzi, G., Cuzzoni, M. G., Cotta Ramusino, M., Martinelli, D., Denaro, F., Ricciardi, A., Vitali, P., Anzalone, N., Bernini, S., Palesi, F., et al. (2020) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2020.00025 · Cited by 135
Lehmann, M., Madison, C. M., Ghosh, P. M., Seeley, W. W., Mormino, E., Greicius, M. D., Gorno-Tempini, M. L., Kramer, J. H., Miller, B. L., Jagust, W. J., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1221536110 · Cited by 122
Self-citationPredicting individual brain maturity using dynamic functional connectivity
Qin, J., Chen, S.-G., Hu, D., Zeng, L.-L., Fan, Y.-M., Chen, X.-P., & Shen, H. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00418 · Cited by 117
The spatial structure of resting state connectivity stability on the scale of minutes
Gonzalez-Castillo, J., Handwerker, D. A., Robinson, M. E., Hoy, C. W., Buchanan, L. C., Saad, Z. S., & Bandettini, P. A. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00138 · Cited by 116
Yokoi, T., Watanabe, H., Yamaguchi, H., Bagarinao, E., Masuda, M., Imai, K., Ogura, A., Ohdake, R., Kawabata, K., Hara, K., et al. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00304 · Cited by 116
Bagarinao, E., Watanabe, H., Maesawa, S., Mori, D., Hara, K., Kawabata, K., Yoneyama, N., Ohdake, R., Imai, K., Masuda, M., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-47922-x · Cited by 108
Interoception primes emotional processing: multimodal evidence from neurodegeneration
Salamone, P. C., Legaz, A., Sedeño, L., Moguilner, S., Fraile-Vazquez, M., Campo, C. G., Fittipaldi, S., Yoris, A., Miranda, M., Birba, A., et al. (2021) · The Journal of Neuroscience · doi:10.1523/jneurosci.2578-20.2021 · Cited by 103
Cowan, E., Liu, A., Henin, S., Kothare, S., Devinsky, O., & Davachi, L. (2020) · The Journal of Neuroscience · doi:10.1523/jneurosci.1946-19.2020 · Cited by 97
Preferential degradation of cognitive networks differentiates Alzheimer’s disease from ageing
Chhatwal, J. P., Schultz, A. P., Johnson, K. A., Hedden, T., Jaimes, S., Benzinger, T. L. S., Jack, C., Ances, B. M., Ringman, J. M., Marcus, D. S., et al. (2018) · Brain · doi:10.1093/brain/awy053 · Cited by 101
Dynamic causal brain circuits during working memory and their functional controllability
Cai, W., Ryali, S., Pasumarthy, R., Talasila, V., & Menon, V. (2021) · Nature Communications · doi:10.1038/s41467-021-23509-x · Cited by 105
Self-citationUsing fMRI connectivity to define a treatment-resistant form of post-traumatic stress disorder
Etkin, A., Maron-Katz, A., Wu, W., Fonzo, G. A., Huemer, J., Vértes, P. E., Patenaude, B., Richiardi, J., Goodkind, M. S., Keller, C. J., et al. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aal3236 · Cited by 103
Karim, H. T., Andreescu, C., Tudorascu, D., Smagula, S. F., Butters, M. A., Karp, J. F., Reynolds, C., & Aizenstein, H. J. (2017) · Molecular Psychiatry · doi:10.1038/mp.2016.55 · Cited by 95
Ma, Q., Zeng, L.-L., Shen, H., Liu, L., & Hu, D. (2013) · Brain Research · doi:10.1016/j.brainres.2012.12.002 · Cited by 95
Brain functional connectivity correlates of coping styles
Santarnecchi, E., Sprugnoli, G., Tatti, E., Mencarelli, L., Neri, F., Momi, D., Di Lorenzo, G., Pascual-Leone, A., Rossi, S., & Rossi, A. (2018) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-018-0583-7 · Cited by 85
Reineberg, A. E., & Banich, M. T. (2016) · Human Brain Mapping · doi:10.1002/hbm.23219 · Cited by 88
Intrinsic Brain Network Biomarkers of Antidepressant Response: a Review
Dunlop, K., Talishinsky, A., & Liston, C. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1072-6 · Cited by 80
Brody, G. H., Yu, T., Nusslock, R., Barton, A. W., Miller, G. E., Chen, E., Holmes, C., McCormick, M., & Sweet, L. H. (2019) · Psychological Science · doi:10.1177/0956797619847989 · Cited by 82
Neuroimaging for psychotherapy research: current trends.
Weingarten, C. P., & Strauman, T. J. (2015) · Psychotherapy Research · doi:10.1080/10503307.2014.883088 · Cited by 81
“Journal of the American Psychoanalytic Association, 57, 361–398. doi:10.1177/0003065109333502 Siniatchkin, M., Glatthaar, N., von Müller, G. G., PrehnKristensen, A., Wolff, S., Knöchel, S., … Gerber, W.-D. (2012).”
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Dong, G., Lin, X., Hu, Y., Xie, C., & Du, X. (2015) · Scientific Reports · doi:10.1038/srep09197 · Cited by 76
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Hyatt, C. J., Calhoun, V. D., Pearlson, G. D., & Assaf, M. (2015) · Human Brain Mapping · doi:10.1002/hbm.22827 · Cited by 65
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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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