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A generalized form of context-dependent psychophysiological interactions (gPPI): A comparison to standard approaches
Open accessAbstract
Functional MRI (fMRI) allows one to study task-related regional responses and task-dependent connectivity analysis using psychophysiological interaction (PPI) methods. The latter affords the additional opportunity to understand how brain regions interact in a task-dependent manner. The current implementation of PPI in Statistical Parametric Mapping (SPM8) is configured primarily to assess connectivity differences between two task conditions, when in practice fMRI tasks frequently employ more than two conditions. Here we evaluate how a generalized form of context-dependent PPI (gPPI; http://www.nitrc.org/projects/gppi), which is configured to automatically accommodate more than two task conditions in the same PPI model by spanning the entire experimental space, compares to the standard implementation in SPM8. These comparisons are made using both simulations and an empirical dataset. In the simulated dataset, we compare the interaction beta estimates to their expected values and model fit using the Akaike information criterion (AIC). We found that interaction beta estimates in gPPI were robust to different simulated data models, were not different from the expected beta value, and had better model fits than when using standard PPI (sPPI) methods. In the empirical dataset, we compare the model fit of the gPPI approach to sPPI. We found that the gPPI approach improved model fit compared to sPPI. There were several regions that became non-significant with gPPI. These regions all showed significantly better model fits with gPPI. Also, there were several regions where task-dependent connectivity was only detected using gPPI methods, also with improved model fit. Regions that were detected with all methods had more similar model fits. These results suggest that gPPI may have greater sensitivity and specificity than standard implementation in SPM. This notion is tempered slightly as there is no gold standard; however, data simulations with a known outcome support our conclusions about gPPI. In sum, the generalized form of context-dependent PPI approach has increased flexibility of statistical modeling, and potentially improves model fit, specificity to true negative findings, and sensitivity to true positive findings.
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Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Model selection and multimodel inference: a practical information-theoretic approach.
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495
ANew Look at the Statistical Model Identification
AKAlKE, H. (2011) · Cited by 4,177
Psychophysiological and Modulatory Interactions in Neuroimaging
Friston, K. J., Buechel, C., Fink, G. R., Morris, J., Rolls, E., & Dolan, R. J. (1997) · NeuroImage · doi:10.1006/nimg.1997.0291 · Cited by 3,097
Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1
Boynton, G., Engel, S., Glover, G., & Heeger, D. (1996) · Journal of Neuroscience · doi:10.1523/jneurosci.16-13-04207.1996 · Cited by 2,522
Analysis of fMRI Time-Series Revisited
Friston, K. J., Holmes, A. P., Poline, J., Grasby, P. M., Williams, S., Frackowiak, R. S. J., & Turner, R. (1995) · NeuroImage · doi:10.1006/nimg.1995.1007 · Cited by 2,504
A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex
Van Essen, D. C. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.06.058 · Cited by 1,151
Erroneous analyses of interactions in neuroscience: A problem of significance
Nieuwenhuis, S., Forstmann, B. U., & Wagenmakers, E.-J. (2011) · Nature Neuroscience · doi:10.1038/nn.2886 · Cited by 864
Gitelman, D. R., Penny, W. D., Ashburner, J., & Friston, K. J. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00058-2 · Cited by 799
Characterizing evoked hemodynamics with fMRI.
Friston, K. J., Frith, C., Turner, R., & Frackowiak, R. (1995) · NeuroImage · doi:10.1006/nimg.1995.1018 · Cited by 711
Cited by
Sorted by citations
Items 401–500 of 1,000
Bartoň, M., Mareček, R., Krajčovičová, L., Slavicek, T., Kašpárek, T., Zemánková, P., Říha, P., & Mikl, M. (2018) · Human Brain Mapping · doi:10.1002/hbm.24433 · Cited by 29
Ventromedial prefrontal cortex connectivity during and after psychological stress in women
Ginty, A. T., Kraynak, T. E., Kuan, D. C.-H., & Gianaros, P. J. (2019) · Psychophysiology · doi:10.1111/psyp.13445 · Cited by 35
Neural Systems for Own-body Processing Align with Gender Identity Rather Than Birth-assigned Sex
Majid, D. S. A., Burke, S., Manzouri, A., Moody, T. D., Dhejne, C., Feusner, J. D., & Savic, I. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz282 · Cited by 36
Frontostriatal functional connectivity supports reward-enhanced memory in older adults
Bowen, H. J., Ford, J. H., Grady, C. L., & Spaniol, J. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.02.013 · Cited by 32
Hawco, C. S., Yoganathan, L., Voineskos, A. N., Lyon, R. E., Tan, T., Daskalakis, Z. J., Blumberger, D. M., Croarkin, P. E., Lai, M., Szatmári, P., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102260 · Cited by 32
Olfactory perceptual decision-making is biased by motivational state
Shanahan, L. K., Bhutani, S., & Kahnt, T. (2021) · PLoS Biology · doi:10.1371/journal.pbio.3001374 · Cited by 33
Soleimani, G., Kuplicki, R., Bodurka, J., Paulus, M. P., & Ekhtiari, H. (2022) · Brain stimulation · doi:10.1016/j.brs.2022.01.008 · Cited by 31
Functional connectivity during masked and unmasked face emotion processing in bipolar disorder
Tseng, W., Thomas, L. A., Harkins, E., Stoddard, J., Zarate, C. A., Pine, D. S., Leibenluft, E., & Brotman, M. A. (2016) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2016.10.006 · Cited by 30
Reward enhances connectivity between the ventral striatum and the default mode network
Dobryakova, E., & Smith, D. V. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119398 · Cited by 31
Davis, M. M., Modi, H. H., Skymba, H. V., Finnegan, M. K., Haigler, K., Telzer, E. H., & Rudolph, K. D. (2022) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsac055 · Cited by 40
Baltruschat, S., Ventura‐Campos, N., Cruz-Gómez, Á. J., Belenguer, A., & Forn, C. (2015) · Journal of Neuroradiology · doi:10.1016/j.neurad.2015.02.006 · Cited by 30
Fournier, J. C. J., Chase, H. W., Greenberg, T., Etkin, A., Almeida, J., Stiffler, R. S., Deckersbach, T., Weyandt, S., Cooper, C. M., Toups, M. S. P., et al. (2016) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.11.008 · Cited by 31
Shigemune, Y., Tsukiura, T., Nouchi, R., Kambara, T., & Kawashima, R. (2017) · Human Brain Mapping · doi:10.1002/hbm.23599 · Cited by 30
Common and distinct neural correlates of self‐serving and prosocial dishonesty
Pornpattananangkul, N., Zhen, S., & Yu, R. (2018) · Human Brain Mapping · doi:10.1002/hbm.24062 · Cited by 29
Reliability of neural activation and connectivity during implicit face emotion processing in youth
Haller, S. P., Kircanski, K., Stoddard, J., White, L. K., Chen, G., Sharif‐Askary, B., Zhang, S., Towbin, K. E., Pine, D. S., Leibenluft, E., et al. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.03.010 · Cited by 31
Neural substrates of social facilitation effects on incentive-based performance
Chib, V. S., Adachi, R., & O’Doherty, J. P. (2018) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsy024 · Cited by 31
Cognitive control network dysconnectivity and response to antipsychotic treatment in schizophrenia
Cadena, E. J., White, D. M., Kraguljac, N. V., Reid, M. A., Jindal, R. D., Pixley, R. M., & Lahti, A. C. (2018) · Schizophrenia Research · doi:10.1016/j.schres.2018.07.045 · Cited by 30
Affective iconic words benefit from additional sound–meaning integration in the left amygdala
Aryani, A., Hsu, C., & Jacobs, A. M. (2019) · Human Brain Mapping · doi:10.1002/hbm.24772 · Cited by 32
Marečková, K., Holsen, L. M., Admon, R., Makris, N., Seidman, L. J., Buka, S. L., Whitfield, S., & Goldstein, J. M. (2016) · Human Brain Mapping · doi:10.1002/hbm.23271 · Cited by 30
Linked networks for learning and expressing location-specific threat
Suarez‐Jimenez, B., Bisby, J. A., Horner, A. J., King, J. A., Pine, D. S., & Burgess, N. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1714691115 · Cited by 31
Abend, R., Swetlitz, C., White, L. K., Shechner, T., Bar‐Haim, Y., Filippi, C. A., Kircanski, K., Haller, S. P., Benson, B. E., Chen, G., et al. (2019) · Psychological Medicine · doi:10.1017/s0033291718003999 · Cited by 35
Quidé, Y., Girshkin, L., Watkeys, O. J., Carr, V. J., & Green, M. J. (2021) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-020-01190-3 · Cited by 32
Ly, M., Adluru, N., Destiche, D. J., Lu, S. Y.-F., Oh, J. M., Hoscheidt, S. M., Alexander, A. L., Okonkwo, O. C., Rowley, H. A., Sager, M. A., et al. (2016) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617715001216 · Cited by 30
Self-citationLohmann, G., Stelzer, J., Zuber, V., Buschmann, T., Margulies, D. S., Bartels, A., & Scheffler, K. (2016) · PLoS ONE · doi:10.1371/journal.pone.0158185 · Cited by 29
Reagh, Z. M., Murray, E. A., & Yassa, M. A. (2016) · Hippocampus · doi:10.1002/hipo.22681 · Cited by 29
Hernaus, D., Santa, M. M. C., Offermann, J. S., & van Amelsvoort, T. (2017) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2017.02.004 · Cited by 33
Reckless, G. E., Andreassen, O. A., Server, A., Østefjells, T., & Jensen, J. (2015) · NeuroImage Clinical · doi:10.1016/j.nicl.2015.04.025 · Cited by 28
Impaired cortico-limbic functional connectivity in schizophrenia patients during emotion processing
Comte, M., Zendjidjian, X. Y., Coull, J. T., Cancel, A., Boutet, C., Schneider, F., Sage, T., Lazerges, P., Jaafari, N., Ibrahim, E. C., et al. (2017) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsx083 · Cited by 32
Hampshire, A., Daws, R. E., das Neves, I., Soreq, E., Sandrone, S., & Violante, I. R. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.002 · Cited by 29
Kowalski, J., Wypych, M., Marchewka, A., & Dragan, M. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00648 · Cited by 30
McCurry, K. L., Frueh, B. C., Chiu, P. H., & King‐Casas, B. (2019) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.09.006 · Cited by 35
Neural mechanisms of self-affirmation’s stress buffering effects
Dutcher, J. M., Eisenberger, N. I., Woo, H., Klein, W. M. P., Harris, P. R., Levine, J. M., & Creswell, J. D. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa042 · Cited by 34
Functional connectivity with the anterior and posterior hippocampus during spatial memory
Fritch, H. A., Spets, D. S., & Slotnick, S. D. (2020) · Hippocampus · doi:10.1002/hipo.23283 · Cited by 29
Colich, N. L., Hanford, L. C., Weissman, D. G., Allen, N. B., Shirtcliff, E. A., Lengua, L. J., Sheridan, M. A., & McLaughlin, K. A. (2022) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2022.101187 · Cited by 31
Olivé, I., Tempelmann, C., Berthoz, A., & Heinze, H.-J. (2014) · Human Brain Mapping · doi:10.1002/hbm.22659 · Cited by 29
Letting the good times roll: adolescence as a period of reduced inhibition to appetitive social cues
Perino, M. T., Miernicki, M. E., & Telzer, E. H. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsw096 · Cited by 33
Hu, X., Kleinschmidt, H., Martin, J. A., Han, Y., Thelen, M., Meiberth, D. U., Jessen, F., & Weber, B. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00663 · Cited by 29
Orbitofrontal Cortex Activity and Connectivity Predict Future Depression Symptoms in Adolescence
Jin, J., Narayanan, A., Perlman, G., Luking, K. R., DeLorenzo, C., Hajcak, G., Klein, D. N., Kotov, R., & Mohanty, A. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.02.002 · Cited by 31
Breukelaar, I. A., Williams, L. M., Antees, C., Grieve, S. M., Foster, S., Gomes, L. J., & Korgaonkar, M. S. (2018) · Human Brain Mapping · doi:10.1002/hbm.24342 · Cited by 29
Butera, C. D., Kaplan, J., Kilroy, E., Harrison, L., Jayashankar, A., Loureiro, F., & Aziz‐Zadeh, L. (2023) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2023.108469 · Cited by 32
Chen, S., Chen, C., Yang, J., & Yuan, J. (2017) · Scientific Reports · doi:10.1038/s41598-017-00073-3 · Cited by 33
Neural Integration in Body Perception
Ramsey, R. (2018) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01299 · Cited by 29
Coster, L. D., Lin, L. D., Mathalon, D. H., & Woolley, J. D. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-019-0417-5 · Cited by 31
Bryant, R. A., Erlinger, M., Felmingham, K. L., Klimova, A., Williams, L. M., Malhi, G. S., Forbes, D., & Korgaonkar, M. S. (2020) · Psychological Medicine · doi:10.1017/s0033291720001129 · Cited by 32
Lengersdorff, L., Wagner, I. C., Lockwood, P. L., & Lamm, C. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.0842-20.2020 · Cited by 26
Frequency-specific task modulation of human brain functional networks: A fast fMRI study
Sasai, S., Koike, T., Sugawara, S. K., Hamano, Y. H., Sumiya, M., Okazaki, S., Takahashi, H. K., Taga, G., & Sadato, N. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117375 · Cited by 31
Hodgdon, E. A., Yu, Q., Kryza‐Lacombe, M., Liuzzi, M. T., Aspe, G., Menacho, V. C., Bozzetto, L. E., Dougherty, L. R., & Wiggins, J. L. (2021) · Developmental Psychobiology · doi:10.1002/dev.22167 · Cited by 28
Ben‐Zion, Z., Shany, O., Admon, R., Keynan, J. N., Avisdris, N., Balter, S. R., Shalev, A. Y., Liberzon, I., & Hendler, T. (2021) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.09.001 · Cited by 28
Disrupted network interactions serve as a neural marker of dyslexia
Turker, S., Kuhnke, P., Jiang, Z., & Hartwigsen, G. (2023) · Communications Biology · doi:10.1038/s42003-023-05499-2 · Cited by 29
Krebs, R. M., Fias, W., Achten, E., & Boehler, C. N. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.02.027 · Cited by 31
The relation between structural and functional connectivity depends on age and on task goals
Ford, J. H., & Kensinger, E. A. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00307 · Cited by 31
Differential Neural Correlates Underlie Judgment of Learning and Subsequent Memory Performance
Yang, H., Cai, Y., Liu, Q., Zhao, X. Q., Wang, Q., Chen, C., & Xue, G. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01699 · Cited by 33
Shiota, S., Okamoto, Y., Okada, G., Takagaki, K., Takamura, M., Mori, A., Yokoyama, S., Nishiyama, Y., Jinnin, R., Hashimoto, R., et al. (2016) · Psychological Medicine · doi:10.1017/s0033291716002956 · Cited by 29
Opposing roles for amygdala and vmPFC in the return of appetitive conditioned responses in humans
Ebrahimi, C., Koch, S., Pietrock, C., Fydrich, T., Heinz, A., & Schlagenhauf, F. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0482-x · Cited by 28
Valadez, E. A., Tottenham, N., Korom, M., Tabachnick, A. R., Pine, D. S., & Dozier, M. (2023) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2023.06.015 · Cited by 27
Sex differences in somatomotor representations of others’ pain: a permutation-based analysis
Christov‐Moore, L., & Iacoboni, M. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1814-y · Cited by 27
A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping
Caballero‐Gaudes, C., Moia, S., Panwar, P., Bandettini, P. A., & González-Castillo, J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116081 · Cited by 27
Abnormal reward valuation and event-related connectivity in unmedicated major depressive disorder
Rupprechter, S., Stankevicius, A., Huys, Q. J. M., Series, P., & Steele, J. D. (2021) · Psychological Medicine · doi:10.1017/s0033291719003799 · Cited by 27
Multimodal imaging of brain reorganization in hearing late learners of sign language
Banaszkiewicz, A., Matuszewski, J., Bola, Ł., Szczepanik, M., Kossowski, B., Rutkowski, P., Marcin Szwed, A., Emmorey, K. D., Jednoróg, K., & Marchewka, A. (2020) · Human Brain Mapping · doi:10.1002/hbm.25229 · Cited by 27
Gard, A. M., Hein, T. C., Mitchell, C. M., Jeanne Brooks-gunn, B., McLanahan, S. S., Monk, C. S., & Hyde, L. W. (2021) · Development and Psychopathology · doi:10.1017/s0954579420001583 · Cited by 26
Wang, J., Pines, J. R., Joanisse, M. F., & Booth, J. R. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118083 · Cited by 27
An fMRI investigation of the intention-outcome interactions in second- and third-party punishment
Feng, C., Yang, Q., Azem, L., Atanasova, K., Gu, R., Luo, W., Hoffman, M. B., Lis, S., & Krüeger, F. (2021) · Brain Imaging and Behavior · doi:10.1007/s11682-021-00555-z · Cited by 27
Functional mapping and cooperation between the cerebellum and cerebrum during word reading
Li, H., Wu, J., Marks, R. A., Huang, H., Li, L., Dong, L., Luo, Y., Tao, W., & Ding, G. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac006 · Cited by 27
Xu, L., Zheng, X., Yao, S., Li, J., Fu, M., Li, K., Zhao, W., Li, H., Becker, B., & Kendrick, K. M. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119010 · Cited by 27
Hanuka, S., Olson, E. A., Admon, R., Webb, C. A., Killgore, W. D. S., Rauch, S. L., Rosso, I. M., & Pizzagalli, D. A. (2022) · Psychological Medicine · doi:10.1017/s0033291722001106 · Cited by 31
Decreased reward circuit connectivity during reward anticipation in major depression
Geugies, H., Groenewold, N. A., Meurs, M., Doornbos, B., de Klerk‐Sluis, J. M., van Eijndhoven, P., Roest, A. M., & Ruhé, H. G. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103226 · Cited by 31
Tsuchiyagaito, A., Misaki, M., Kirlić, N., Yu, X., Sánchez, S. M., Cochran, G., Stewart, J. L., Smith, R. S., Fitzgerald, K. D., Rohan, M. L., et al. (2023) · Psychotherapy and Psychosomatics · doi:10.1159/000528377 · Cited by 27
Entorhinal grid-like codes and time-locked network dynamics track others navigating through space
Wagner, I. C., Graichen, L. P., Todorova, B., Lüttig, A., Omer, D. B., Stangl, M., & Lamm, C. (2023) · Nature Communications · doi:10.1038/s41467-023-35819-3 · Cited by 33
Cope, T., Sohoglu, E., Peterson, K. A., Jones, P. S., Rua, C., Passamonti, L., Sedley, W., Post, B., Coebergh, J. W. W., Butler, C., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112422 · Cited by 27
Deficit of state-dependent risk attitude modulation in gambling disorder
Fujimoto, A., Tsurumi, K., Kawada, R., Murao, T., Takeuchi, H., Murai, T., & Takahashi, H. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.55 · Cited by 27
Yamawaki, R., Nakamura, K., Aso, T., Shigemune, Y., Fukuyama, H., & Tsukiura, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23662 · Cited by 28
Kireev, M. V., Korotkov, A. D., Medvedeva, N., Masharipov, R. S., & Medvedev, S. V. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00482 · Cited by 27
Guo, D., & Yang, J. (2019) · Hippocampus · doi:10.1002/hipo.23154 · Cited by 32
Hippocampal-cortical functional connectivity during memory encoding and retrieval
Raud, L., Sneve, M. H., Dídac Vidal-piñeiro, A., Sørensen, Ø., Folvik, L., Ness, H. T., Mowinckel, A. M., Grydeland, H., Walhovd, K. B., & Fjell, A. M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120309 · Cited by 25
Martin, S., Frieling, R., Saur, D., & Hartwigsen, G. (2023) · Brain stimulation · doi:10.1016/j.brs.2023.09.009 · Cited by 29
Targeted memory reactivation to augment treatment in post-traumatic stress disorder
van der Heijden, A. C., van der Werf, Y. D., van den Heuvel, O. A., Talamini, L. M., & van Marle, H. J. F. (2024) · Current Biology · doi:10.1016/j.cub.2024.07.019 · Cited by 30
Flexible modulation of network connectivity related to cognition in Alzheimer's disease
McLaren, D. G., Sperling, R. A., & Atri, A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.032 · Cited by 25
Self-citationCultural influences on neural basis of inhibitory control
Pornpattananangkul, N., Hariri, A. R., Harada, T., Mano, Y., Komeda, H., Parrish, T. B., Sadato, N., Iidaka, T., & Chiao, J. Y. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.061 · Cited by 28
A human subcortical network underlying social avoidance revealed by risky economic choices
Schultz, J., Willems, T., Gädeke, M., Chakkour, G., Franke, A., Weber, B., & Hurlemann, R. (2019) · eLife · doi:10.7554/elife.45249 · Cited by 27
Comparison of Food Cue–Evoked and Resting-State Functional Connectivity in Obesity
Donofry, S. D., Jakicic, J. M., Rogers, R. J., Watt, J. C., Roecklein, K. A., & Erickson, K. I. (2019) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000769 · Cited by 26
Masson, H. L., Pillet, I., Boets, B., & de Beeck, H. O. (2020) · Cortex · doi:10.1016/j.cortex.2019.12.011 · Cited by 27
Neural processes for live pro-social dialogue between dyads with socioeconomic disparity
Descorbeth, O., Zhang, X., Noah, J. A., & Joy Hirsch, C. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa120 · Cited by 27
Sehatpour, P., Dondé, C., Hoptman, M. J., Kreither, J., Adair, D., Dias, E. C., Vail, B., Rohrig, S. N., Silipo, G. S., López‐Calderón, J., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117311 · Cited by 25
Ho, S.-H. S., Muzik, M., Rosenblum, K. L., Morelen, D. M., Nakamura, Y., & Swain, J. E. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.568824 · Cited by 29
Zhang, W., Li, G., Manza, P., Hu, Y., Wang, J., Lv, G., He, Y., von Deneen, K. M., Yu, J., Han, Y., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab333 · Cited by 25
Naim, R., Haller, S. P., Linke, J. O., Jaffe, A., Stoddard, J., Jones, M., Harrewijn, A., Kircanski, K., Bar‐Haim, Y., & Brotman, M. A. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-022-01307-3 · Cited by 23
Martin, U., & Kiefer, M. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv070 · Cited by 27
Altered neural function to happy faces in adolescents with and at risk for depression
Kerestes, R., Segreti, A. M., Pan, L. A., Phillips, M. L., Birmaher, B. B., Brent, D. A., & Ladouceur, C. D. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.12.013 · Cited by 25
Filbey, F. M., & Yezhuvath, U. S. (2016) · Obesity · doi:10.1002/oby.21713 · Cited by 26
Miller, J. A., Scult, M. A., Drabant, E. M., Chen, Q., Weinberger, D. R., & Hariri, A. R. (2017) · Schizophrenia Bulletin · doi:10.1093/schbul/sbx140 · Cited by 26
Luo, L., Becker, B., Zheng, X., Zhao, Z., Xu, X., Zhou, F., Wang, J., Kou, J., Dai, J., & Kendrick, K. M. (2017) · Human Brain Mapping · doi:10.1002/hbm.23880 · Cited by 24
Hart, H. C., Lim, L. L., Mehta, M. A., Curtis, C. J. C., Xu, X., Breen, G. D., Simmons, A., Mirza, K., & Rubia, K. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00007 · Cited by 27
Sleep Spindle-dependent Functional Connectivity Correlates with Cognitive Abilities
Fang, Z., Ray, L. B., Houldin, E., Smith, D., Owen, A. M., & Fogel, S. M. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01488 · Cited by 27
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Ousdal, O. T., Specht, K., Server, A., Andreassen, O. A., Dolan, R. J., & Jensen, J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.07.055 · Cited by 26
Anxiety-potentiated amygdala–medial frontal coupling and attentional control
Robinson, O. J., Krimsky, M., Lieberman, L., Vytal, K. E., Ernst, M., & Grillon, C. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.105 · Cited by 27
Shao, R., & Lee, T. M. C. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.147 · Cited by 27
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Garbarini, F., Cecchetti, L., Bruno, V., Mastropasqua, A., Fossataro, C., Massazza, G., Sacco, K., Valentini, M. C., Ricciardi, E., & Berti, A. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy134 · Cited by 25
Farber, M. J., Kim, M. J., Knodt, A. R., & Hariri, A. R. (2019) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2019.100711 · Cited by 28
Aberg, K. C., Kramer, E. E., & Schwartz, S. (2020) · Nature Communications · doi:10.1038/s41467-020-15542-z · Cited by 25
Metadata
- Authors
- McLaren, D. G., Ries, M. L., Xu, G., & Johnson, S. C.
- Affiliations
- University of Wisconsin–Madison (US)
- Corresponding author
- Johnson, Sterling C.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-07
- Volume
- 61
- Issue
- 4
- Pages
- 1277-1286
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3376181
- OpenAlex
- W2132137022
- Cited by
- 1,445
- References
- 24
- Citations by year
- 2026: 99 · 2025: 121 · 2024: 113 · 2023: 100 · 2022: 117 · 2021: 125 · 2020: 139 · 2019: 115 · 2018: 129 · 2017: 120 · 2016: 122 · 2015: 71 · 2014: 44 · 2013: 19 · 2012: 7
- FWCI
- 23.11
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Department of Veterans Affairs Merit Review (I01CX000165)
- National Institutes of Health (AG021155, AG000213)
- 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
- generalized psychophysiological interaction, functional MRI, statistical parametric mapping, model fit, Akaike information criterion, brain connectivity
- MeSH terms
- Brain, Brain Mapping, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Models, Neurological, Psychophysiology, Sensitivity and Specificity
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