The Rhesus Monkey connectome predicts disrupted functional networks resulting from pharmacogenetic inactivation of the Amygdala
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Contemporary research suggests that the mammalian brain is a complex system, implying that damage to even a single functional area could have widespread consequences across the system. To test this hypothesis, we pharmacogenetically inactivated the rhesus monkey amygdala, a subcortical region with distributed and well-defined cortical connectivity. We then examined the impact of that perturbation on global network organization using resting-state functional connectivity MRI. Amygdala inactivation disrupted amygdalocortical communication and distributed corticocortical coupling across multiple functional brain systems. Altered coupling was explained using a graph-based analysis of experimentally established structural connectivity to simulate disconnection of the amygdala. Communication capacity via monosynaptic and polysynaptic pathways, in aggregate, largely accounted for the correlational structure of endogenous brain activity and many of the non-local changes that resulted from amygdala inactivation. These results highlight the structural basis of distributed neural activity and suggest a strategy for linking focal neuropathology to remote neurophysiological changes.
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Experimentally induced local perturbations produce broad neurophysiological changes that can be partly accounted for by global network interactions.
“recent studies have found that even purely local, experimentally induced perturbations (e.g. exogenous stimulation or inactivation) result in widespread, complex neurophysiological changes, which are at least partly explainable as local interactions with global network structure (Andoh et al., 2015; Grayson et al., 2016; Gu et al., 2015a; Misic et al., 2015).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Grayson et al., 2016
A node's structural hub status has been associated with global integration of brain activity.
“Similarly, structural hubness has been linked to integration of global brain activity (Grayson et al., 2016).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Grayson et al., 2016
Focal pharmacogenetic inactivation and cortical transcranial magnetic stimulation demonstrate a causal effect on distributed resting-state connectivity and other neurophysiological signatures, driven by cascading neuronal interactions.
“In our recent paper, we presented experimental validation of these ideas through focal pharmacogenetic inactivation (Grayson et al., 2016), and similar findings were presented by (Andoh et al., 2015) using cortical transcranial magnetic stimulation. Both works demonstrate a causal effect on distributed resting-state connectivity and other neurophysiological signatures, driven by cascading neuronal”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Grayson et al., 2016
Site-specific pathology can be deduced from distributed functional network disturbances.
“This work also provides evidence that site-specific pathology can be deduced from distributed functional network disturbances (Grayson et al., 2016).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Grayson et al., 2016
Even the best-performing SC-FC models generally account for less than 50% of the variance in FC across the brain in both humans and non-human primates.
“Even the best-performing SC-FC models generally account for less than 50% of the variance in FC across the brain in both humans and non-human primates (Grayson et al., 2016; Messe et al., 2014; Misic et al., 2015).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Grayson et al., 2016
Without those efforts, critical structure-function relationships were much degraded.
“Without these efforts (which are not feasible in the large majority of human studies), critical structure-function relationships were much degraded.”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Grayson et al., 2016
Tight control of functional imaging conditions was needed in Grayson et al. to reveal novel structure-function relationships and maximize model performance.
“One interesting aspect of the work in (Grayson et al., 2016) is how tightly functional imaging conditions had to be controlled in order to reveal novel structure-function relationships and in order to maximize model performance”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Grayson et al., 2016
Animal models are proving to be an important and necessary bridge for identifying ground-truth structure-to-function principles.
“animal models are proving to be an important and necessary “bridge” for identifying ground-truth structure-to-function principles (Deco et al., 2014; Grayson et al., 2016; Miranda-Dominguez et al., 2014b; Stafford et al., 2014).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Grayson et al., 2016
Recent data reinforces concerns about artifactual contributions to the global signal in conventional 3T rs-fcMRI acquisitions sensitive to BOLD contrast.
“Recent data reinforces concerns about artifactual contributions to the global signal in conventional 3T rs-fcMRI acquisitions sensitive to BOLD contrast (Grayson et al., 2016; Power et al., 2016)”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Grayson et al., 2016
Recent data partly substantiates concerns regarding global signal regression under less conventional, high signal-to-noise scenarios where motion can be physically restricted.
“but also partly substantiates concerns regarding GSR under less conventional, high signal-to-noise scenarios where motion can be physically restricted (Grayson et al., 2016).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Grayson et al., 2016
Network properties and structure-function relationships supporting sensory and cognitive function are attainable across species.
“network properties and structure-function relationships supporting sensory and cognitive function (see Figure 5) are attainable across species (Grayson et al., 2016; Miranda-Dominguez et al., 2014b; Stafford et al., 2014).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Grayson et al., 2016
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Cites
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256 · see citing works
Graph drawing by force‐directed placement
Fruchterman, T. M. J., & Reingold, E. M. (1991) · Software: Practice and Experience · doi:10.1002/spe.4380211102 · Cited by 6,352 · see citing works
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 · see citing works
West, M. J., Slomianka, L., & Gundersen, H. J. G. (1991) · The Anatomical Record · doi:10.1002/ar.1092310411 · Cited by 2,994 · see citing works
Neurodegenerative Diseases Target Large-Scale Human Brain Networks
Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L., & Greicius, M. D. (2009) · Neuron · doi:10.1016/j.neuron.2009.03.024 · Cited by 2,350 · see citing works
Armbruster, B. N., Li, X., Pausch, M. H., Herlitze, S., & Roth, B. L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0700293104 · Cited by 2,046 · see citing works
The connectomics of brain disorders
Fornito, A., Zalesky, A., & Breakspear, M. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3901 · Cited by 1,734 · see citing works
Amygdalo‐cortical projections in the monkey ( <i>Macaca fascicularis</i> )
Amaral, D. G., & Price, J. L. (1984) · Journal of Comparative Neurology · doi:10.1002/cne.902300402 · Cited by 1,162 · see citing works
Self-citationHe, B. J., Snyder, A. Z., Vincent, J. L., Epstein, A. A., Shulman, G. L., & Corbetta, M. (2007) · Neuron · doi:10.1016/j.neuron.2007.02.013 · Cited by 916 · see citing works
Controllability of structural brain networks
Gu, S., Pasqualetti, F., Cieslak, M., Telesford, Q. K., Yu, A. B., Kahn, A. E., Medaglia, J. D., Vettel, J. M., Miller, M. B., Grafton, S. T., et al. (2015) · Nature Communications · doi:10.1038/ncomms9414 · Cited by 912 · see citing works
Weight-conserving characterization of complex functional brain networks
Rubinov, M., & Sporns, O. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.03.069 · Cited by 871 · see citing works
Diaschisis: past, present, future
Carrera, E., & Tononi, G. (2014) · Brain · doi:10.1093/brain/awu101 · Cited by 824 · see citing works
Communicability in complex networks
Estrada, E., & Hatano, N. (2008) · Physical Review E · doi:10.1103/physreve.77.036111 · Cited by 719 · see citing works
Signature of consciousness in the dynamics of resting-state brain activity
Barttfeld, P., Uhrig, L., Sitt, J. D., Sigman, M., Jarraya, B., & Dehaene, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418031112 · Cited by 670 · see citing works
Cortical and subcortical afferents to the amygdala of the rhesus monkey (Macaca mulatta)
Aggleton, J. P., Burton, M. J., & Passingham, R. E. (1980) · Brain Research · doi:10.1016/0006-8993(80)90279-6 · Cited by 610 · see citing works
Resting-brain functional connectivity predicted by analytic measures of network communication
Goñi, J., van den Heuvel, M. P., Avena‐Koenigsberger, A., de Mendizábal, N. V., Betzel, R. F., Griffa, A., Hagmann, P., Corominas‐Murtra, B., Thiran, J., & Sporns, O. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1315529111 · Cited by 579 · see citing works
Modeling the impact of lesions in the human brain
Alstott, J., Breakspear, M., Hagmann, P., Cammoun, L., & Sporns, O. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000408 · Cited by 561 · see citing works
Functional connectivity dynamics: modeling the switching behavior of the resting state
Hansen, E. C. A., Battaglia, D., Spiegler, A., Deco, G., & Jirsa, V. K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.11.001 · Cited by 528 · see citing works
Robust detection of dynamic community structure in networks
Bassett, D. S., Porter, M. A., Wymbs, N. F., Grafton, S. T., Carlson, J. M., & Mucha, P. J. (2013) · Chaos: An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4790830 · Cited by 425 · see citing works
Robust detection of dynamic community structure in networks
Bassett, D. S., Porter, M. A., Wymbs, N. F., Grafton, S. T., Carlson, J. M., & Mucha, P. J. (2013) · Oxford University Research Archive (ORA) (University of Oxford) · Cited by 411 · see citing works
The amygdalostriatal projections in the monkey. An anterograde tracing study
Russchen, F. T., Bakst, I., Amaral, D. G., & Price, J. L. (1985) · Brain Research · doi:10.1016/0006-8993(85)90530-x · Cited by 395 · see citing works
Self-citationAdvanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac)
Stephan, K. E., Kamper, L., Bozkurt, A., Burns, G., Young, M. P., & Kötter, R. (2001) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2001.0908 · Cited by 369 · see citing works
Dynamical consequences of lesions in cortical networks
Honey, C. J., & Sporns, O. (2008) · Human Brain Mapping · doi:10.1002/hbm.20579 · Cited by 353 · see citing works
Some observations on cortical inputs to the macaque monkey amygdala: An anterograde tracing study
Stefanacci, L., & Amaral, D. G. (2002) · Journal of Comparative Neurology · doi:10.1002/cne.10339 · Cited by 345 · see citing works
Self-citationGratton, C., Nomura, E. M., Pérez, F., & D’Esposito, M. (2012) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00222 · Cited by 336 · see citing works
Network measures predict neuropsychological outcome after brain injury
Warren, D. E., Power, J. D., Bruss, J., Denburg, N. L., Waldron, E. J., Sun, H., Petersen, S. E., & Tranel, D. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1322173111 · Cited by 249 · see citing works
Messé, A., Rudrauf, D., Benali, H., & Marrelec, G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003530 · Cited by 215 · see citing works
Adachi, Y., Osada, T., Sporns, O., Watanabe, T., Matsui, T., Miyamoto, K., & Miyashita, Y. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhr234 · Cited by 211 · see citing works
Network analysis detects changes in the contralesional hemisphere following stroke
Crofts, J. J., Higham, D. J., Bosnell, R., Jbabdi, S., Matthews, P. M., Behrens, T. E. J., & Johansen‐Berg, H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.032 · Cited by 210 · see citing works
Subcortical afferent connections of the amygdala in the monkey
Mehler, W. R. (1980) · The Journal of Comparative Neurology · doi:10.1002/cne.901900407 · Cited by 205 · see citing works
A weighted communicability measure applied to complex brain networks
Crofts, J. J., & Higham, D. J. (2009) · Journal of The Royal Society Interface · doi:10.1098/rsif.2008.0484 · Cited by 170 · see citing works
Identification of optimal structural connectivity using functional connectivity and neural modeling
Deco, G., McIntosh, A. R., Shen, K., Hutchison, R. M., Menon, R. S., Everling, S., Hagmann, P., & Jirsa, V. K. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4423-13.2014 · Cited by 162 · see citing works
Self-citationHutchison, R. M., & Everling, S. (2012) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2012.00029 · Cited by 140 · see citing works
Miranda-Dominguez, O., Mills, B. D., Grayson, D., Woodall, A., Grant, K. A., Kroenke, C. D., & Fair, D. A. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.4229-13.2014 · Cited by 130 · see citing works
Self-citationEldridge, M. A. G., Lerchner, W., Saunders, R. C., Kaneko, H., Krausz, K. W., Gonzalez, F. J., Ji, B., Higuchi, M., Minamimoto, T., & Richmond, B. J. (2015) · Nature Neuroscience · doi:10.1038/nn.4192 · Cited by 130 · see citing works
Improved community structure detection using a modified fine-tuning strategy
Sun, Y., Danila, B., Josić́, K., & Bassler, K. E. (2009) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/86/28004 · Cited by 125 · see citing works
Information processing architecture of functionally defined clusters in the macaque cortex
Shen, K., Bezgin, G., Hutchison, R. M., Gati, J. S., Menon, R. S., Everling, S., & McIntosh, A. R. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.2709-12.2012 · Cited by 123 · see citing works
Self-citationSimpson, S. L., Moussa, M. N., & Laurienti, P. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.071 · Cited by 112 · see citing works
Joyce, K. E., Hayasaka, S., & Laurienti, P. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002885 · Cited by 79 · see citing works
Michaelides, M., Anderson, S. A. R., Ananth, M. R., Smirnov, D. S., Thanos, P. K., Neumaier, J. F., Wang, G., Volkow, N. D., & Hurd, Y. L. (2013) · Journal of Clinical Investigation · doi:10.1172/jci72117 · Cited by 76 · see citing works
Comparative Analysis of the Macroscale Structural Connectivity in the Macaque and Human Brain
Goulas, A., Bastiani, M., Bezgin, G., Uylings, H. B. M., Roebroeck, A. F., & Stiers, P. L. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003529 · Cited by 76 · see citing works
Medial temporal lobe projections to the retrosplenial cortex of the macaque monkey
Aggleton, J. P., Wright, N. F., Vann, S. D., & Saunders, R. C. (2012) · Hippocampus · doi:10.1002/hipo.22024 · Cited by 68 · see citing works
Dietary Omega-3 Fatty Acids Modulate Large-Scale Systems Organization in the Rhesus Macaque Brain
Grayson, D. S., Kroenke, C. D., Neuringer, M., & Fair, D. A. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.3038-13.2014 · Cited by 67 · see citing works
Self-citationBezgin, G., Vakorin, V. A., van Opstal, A. J., McIntosh, A. R., & Bakker, R. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.04.013 · Cited by 63 · see citing works
Lohse, C., Bassett, D. S., Lim, K. O., & Carlson, J. M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003712 · Cited by 62 · see citing works
Rempel-Clower, N. L., & Barbas, H. (1998) · The Journal of Comparative Neurology · doi:10.1002/(sici)1096-9861(19980831)398:3
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Communication dynamics in complex brain networks
Avena-Koenigsberger, A., Misic, B., & Sporns, O. (2018) · Nature Reviews Neuroscience · doi:10.1038/nrn.2017.149 · Cited by 856 · see citing works
Linking Structure and Function in Macroscale Brain Networks
Suárez, L. E., Markello, R. D., Betzel, R. F., & Mišić, B. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.01.008 · Cited by 745 · see citing works
A Network Model of the Emotional Brain
Pessoa, L. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.03.002 · Cited by 526 · see citing works
A cross-disorder connectome landscape of brain dysconnectivity
van den Heuvel, M. P., & Sporns, O. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0177-6 · Cited by 462 · see citing works
Nagai, Y., Miyakawa, N., Takuwa, H., Hori, Y., Oyama, K., Ji, B., Takahashi, M., Huang, X., Slocum, S. T., DiBerto, J. F., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0661-3 · Cited by 408 · see citing works
Grayson, D. S., & Fair, D. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.079 · Cited by 411 · see citing works
Self-citationRapid Reconfiguration of the Functional Connectome after Chemogenetic Locus Coeruleus Activation
Zerbi, V., Floriou‐Servou, A., Markicevic, M., Vermeiren, Y., Sturman, O., Privitera, M., von Ziegler, L., Ferrari, K. D., Weber, B., Deyn, P. P. D., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.05.034 · Cited by 322 · see citing works
Chemogenetic Interrogation of a Brain-wide Fear Memory Network in Mice
Vetere, G., Kenney, J. W., Tran, L. M., Xia, F., Steadman, P. E., Parkinson, J., Josselyn, S. A., & Frankland, P. W. (2017) · Neuron · doi:10.1016/j.neuron.2017.03.037 · Cited by 293 · see citing works
Brain network communication: concepts, models and applications
Seguin, C., Sporns, O., & Zalesky, A. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00718-5 · Cited by 305 · see citing works
An Open Resource for Non-human Primate Imaging
Milham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., Baxter, M. G., Blezer, E. L. A., Brochier, T. G., Chen, A., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.08.039 · Cited by 247 · see citing works
Self-citationLesion Studies in Contemporary Neuroscience
Vaidya, A. R., Pujara, M. S., Petrides, M., Murray, E. A., & Fellows, L. K. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.05.009 · Cited by 224 · see citing works
Structural disconnections explain brain network dysfunction after stroke
Griffis, J. C., Metcalf, N. V., Corbetta, M., & Shulman, G. L. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.07.100 · Cited by 213 · see citing works
The Basal Forebrain Regulates Global Resting-State fMRI Fluctuations
Turchi, J. N., Chang, C. E., Ye, F. Q., Russ, B. E., Yu, D. K., Cortes, C. R., Monosov, I. E., Duyn, J. H., & Leopold, D. A. (2018) · Neuron · doi:10.1016/j.neuron.2018.01.032 · Cited by 213 · see citing works
Gratton, C., Sun, H., & Petersen, S. E. (2017) · Psychophysiology · doi:10.1111/psyp.13032 · Cited by 194 · see citing works
Fox, A. S., & Shackman, A. J. (2017) · Neuroscience Letters · doi:10.1016/j.neulet.2017.11.056 · Cited by 194 · see citing works
High-potency ligands for DREADD imaging and activation in rodents and monkeys
Bonaventura, J., Eldridge, M. A. G., Hu, F., Gomez, J. L., Sánchez‐Soto, M., Abramyan, A. M., Lam, S., Boehm, M. A., Ruiz, C. M., Farrell, M. R., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12236-z · Cited by 193 · see citing works
Plasticity and Spontaneous Activity Pulses in Disused Human Brain Circuits
Newbold, D. J., Laumann, T. O., Hoyt, C. R., Hampton, J. M., Montez, D. F., Raut, R. V., Ortega, M., Mitra, A., Nielsen, A. N., Miller, D. B., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.05.007 · Cited by 174 · see citing works
Seguin, C., van den Heuvel, M. P., & Zalesky, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1801351115 · Cited by 173 · see citing works
Mapping how local perturbations influence systems-level brain dynamics
Gollo, L. L., Roberts, J. A., & Cocchi, L. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.057 · Cited by 143 · see citing works
Stiso, J., Khambhati, A. N., Menara, T., Kahn, A. E., Stein, J. M., Das, S. R., Gorniak, R., Tracy, J. I., Litt, B., Davis, K. A., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2019.08.008 · Cited by 133 · see citing works
Campbell, E. J., & Marchant, N. J. (2018) · British Journal of Pharmacology · doi:10.1111/bph.14146 · Cited by 140 · see citing works
Individual-specific functional connectivity of the amygdala: A substrate for precision psychiatry
Sylvester, C. M., Yu, Q., Srivastava, A. B., Marek, S., Zheng, A., Alexopoulos, D., Smyser, C. D., Shimony, J. S., Ortega, M., Dierker, D. L., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1910842117 · Cited by 139 · see citing works
Chemogenetic Tools for Causal Cellular and Neuronal Biology
Atasoy, D., & Sternson, S. M. (2017) · Physiological Reviews · doi:10.1152/physrev.00009.2017 · Cited by 130 · see citing works
Animal Functional Magnetic Resonance Imaging: Trends and Path Toward Standardization
Mandino, F., Cerri, D. H., Garin, C. M., Straathof, M., van Tilborg, G. A. F., Chakravarty, M. M., Dhénain, M., Dijkhuizen, R. M., Gozzi, A., Heß, A., et al. (2020) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00078 · Cited by 119 · see citing works
Increased fMRI connectivity upon chemogenetic inhibition of the mouse prefrontal cortex
Rocchi, F., Canella, C., Noei, S., Gutierrez‐Barragan, D., Coletta, L., Galbusera, A., Stuefer, A., Vassanelli, S., Pasqualetti, M., Iurilli, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28591-3 · Cited by 114 · see citing works
Metabolism and Distribution of Clozapine-N-oxide: Implications for Nonhuman Primate Chemogenetics
Raper, J., Morrison, R. D., Daniels, J. S., Howell, L. L., Bachevalier, J., Wichmann, T., & Galván, A. (2017) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.7b00079 · Cited by 117 · see citing works
Seguin, C., Tian, Y., & Zalesky, A. (2020) · Network Neuroscience · doi:10.1162/netn_a_00161 · Cited by 114 · see citing works
Pessoa, L. (2022) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01908 · Cited by 116 · see citing works
Straathof, M., Sinke, M. R. T., Dijkhuizen, R. M., & Otte, W. M. (2018) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x18809547 · Cited by 109 · see citing works
Mahler, S. V., Brodnik, Z. D., Cox, B. M., Buchta, W. C., Bentzley, B. S., Quintanilla, J., Cope, Z. A., Lin, E. C., Riedy, M. D., Scofield, M. D., et al. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0537-18.2018 · Cited by 101 · see citing works
Hur, J., Stockbridge, M. D., Fox, A. S., & Shackman, A. J. (2019) · Progress in brain research · doi:10.1016/bs.pbr.2019.03.012 · Cited by 91 · see citing works
Intrinsic functional connectivity of the central extended amygdala
Tillman, R. M., Stockbridge, M. D., Nacewicz, B. M., Torrisi, S., Fox, A. S., Smith, J. F., & Shackman, A. J. (2017) · Human Brain Mapping · doi:10.1002/hbm.23917 · Cited by 85 · see citing works
Combining brain perturbation and neuroimaging in non-human primates
Klink, P. C., Aubry, J., Ferrera, V. P., Fox, A. S., Froudist‐Walsh, S., Jarraya, B., Konofagou, E. E., Krauzlis, R. J., Messinger, A., Mitchell, A. S., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118017 · Cited by 86 · see citing works
What do we know about laminar connectivity?
Rockland, K. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.032 · Cited by 83 · see citing works
Seguin, C., Jedynak, M., Olivier David, A., Sina Mansour, L., Sporns, O., & Zalesky, A. (2023) · Neuron · doi:10.1016/j.neuron.2023.01.027 · Cited by 83 · see citing works
Multimodal mapping of the face connectome
Wang, Y., Metoki, A., Smith, D. V., Medaglia, J. D., Zang, Y., Benear, S. L., Popal, H. S., Lin, Y., & Olson, I. R. (2020) · Nature Human Behaviour · doi:10.1038/s41562-019-0811-3 · Cited by 88 · see citing works
Functional networks and network perturbations in rodents
Chuang, K., & Nasrallah, F. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.038 · Cited by 80 · see citing works
Advances in optogenetic and chemogenetic methods to study brain circuits in non-human primates
Galván, A., Caiola, M. J., & Albaugh, D. L. (2017) · Journal of Neural Transmission · doi:10.1007/s00702-017-1697-8 · Cited by 74 · see citing works
Markicevic, M., Fulcher, B., Lewis, C., Helmchen, F., Rudin, M., Zerbi, V., & Wenderoth, N. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa084 · Cited by 74 · see citing works
Network-Based Asymmetry of the Human Auditory System
Mišić, B., Betzel, R. F., Griffa, A., de Reus, M. A., He, Y., Zuo, X., van den Heuvel, M. P., Hagmann, P., Sporns, O., & Zatorre, R. J. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy101 · Cited by 69 · see citing works
Xu, T., Sturgeon, D., Ramirez, J. S. B., Froudist‐Walsh, S., Margulies, D. S., Schroeder, C. E., Fair, D. A., & Milham, M. P. (2019) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.02.005 · Cited by 67 · see citing works
Self-citationMapping and comparing fMRI connectivity networks across species
Pagani, M., Gutierrez‐Barragan, D., de Guzman, A. E., Xu, T., & Gozzi, A. (2023) · Communications Biology · doi:10.1038/s42003-023-05629-w · Cited by 64 · see citing works
Seguin, C., Sina Mansour, L., Sporns, O., Zalesky, A., & Calamante, F. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119323 · Cited by 64 · see citing works
Topological Properties of Neuromorphic Nanowire Networks
Loeffler, A., Zhu, R., Hochstetter, J., Li, M., Fu, K., Álvarez, A. D., Nakayama, T., Shine, J. M., & Kuncic, Z. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00184 · Cited by 63 · see citing works
An open database of resting-state fMRI in awake rats
Liu, Y., Pérez, P. D., Ma, Z., Ma, Z., Dopfel, D., Cramer, S. R., Tu, W., & Zhang, N. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117094 · Cited by 58 · see citing works
System-level matching of structural and functional connectomes in the human brain
Osmanlıoğlu, Y., Tunç, B., Parker, D., Elliott, M. A., Baum, G. L., Ćirić, R., Satterthwaite, T. D., Gur, R. E., Gur, R. C., & Verma, R. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.064 · Cited by 50 · see citing works
Luppi, A. I., Singleton, S. P., Hansen, J. Y., Jamison, K. W., Bzdok, D., Kuceyeski, A. F., Betzel, R. F., & Mišić, B. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01242-2 · Cited by 56 · see citing works
Understanding Vulnerability and Adaptation in Early Brain Development using Network Neuroscience
Graham, A. M., Marr, M. C., Buß, C., Sullivan, E. L., & Fair, D. A. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.01.008 · Cited by 49 · see citing works
Self-citationUpright, N. A., & Baxter, M. G. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0660-9 · Cited by 47 · see citing works
Modulating Dopamine Signaling and Behavior with Chemogenetics: Concepts, Progress, and Challenges
Runegaard, A. H., Fitzpatrick, C. M., Woldbye, D. P. D., Andreasen, J. T., Sørensen, A. T., & Gether, U. (2019) · Pharmacological Reviews · doi:10.1124/pr.117.013995 · Cited by 46 · see citing works
Delineating the Macroscale Areal Organization of the Macaque Cortex In Vivo
Xu, T., Falchier, A., Sullivan, E. L., Linn, G. S., Ramirez, J. S. B., Ross, D. A., Feczko, E., Opitz, A., Bagley, J. L., Sturgeon, D., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.049 · Cited by 46 · see citing works
Self-citationFishman, I., Linke, A. C., Hau, J., Carper, R. A., & Müller, R. (2018) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2018.06.015 · Cited by 42 · see citing works
Ultrastructural localization of DREADD s in monkeys
Galván, A., Raper, J., Hu, X., Paré, J., Bonaventura, J., Richie, C. T., Michaelides, M., Mueller, S. A. L., Roseboom, P. H., Oler, J. A., et al. (2019) · European Journal of Neuroscience · doi:10.1111/ejn.14429 · Cited by 44 · see citing works
Structural and resting state functional connectivity beyond the cortex
Harrison, O. K., Guell, X., Klein-Flügge, M. C., & Barry, R. L. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118379 · Cited by 44 · see citing works
Connecting Circuits with Networks in Addiction Neuroscience: A Salience Network Perspective
Cushnie, A. K., Tang, W., & Heilbronner, S. R. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24109083 · Cited by 43 · see citing works
Raper, J., Murphy, L. M., Richardson, R., Romm, Z. L., Kovacs-Balint, Z., Payne, C. D., & Galván, A. (2019) · eNeuro · doi:10.1523/eneuro.0360-19.2019 · Cited by 42 · see citing works
Peeters, L. M., Hinz, R., Detrez, J. R., Missault, S., Vos, W. H. D., Verhoye, M., der Linden, A. V., & Keliris, G. A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117088 · Cited by 39 · see citing works
Using rAAV2-retro in rhesus macaques: Promise and caveats for circuit manipulation
Cushnie, A. K., El-Nahal, H. G., Bohlen, M. O., May, P. J., Basso, M. A., Grimaldi, P., Wang, M. Z., de Velasco, E. M. F., Sommer, M. A., & Heilbronner, S. R. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108859 · Cited by 39 · see citing works
The diversity and specificity of functional connectivity across spatial and temporal scales
Engel, T. A., Schölvinck, M. L., & Lewis, C. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118692 · Cited by 39 · see citing works
Morin, E. L., Howell, B. R., Feczko, E., Earl, E., Pincus, M., Reding, K., Kovacs-Balint, Z. A., Meyer, J. S., Styner, M., Fair, D., et al. (2020) · Development and Psychopathology · doi:10.1017/s0954579420001133 · Cited by 37 · see citing works
Self-citationApplications of optogenetic and chemogenetic methods to seizure circuits: Where to go next?
Forcelli, P. A. (2017) · Journal of Neuroscience Research · doi:10.1002/jnr.24135 · Cited by 37 · see citing works
Ethical and Legal Implications of the Methodological Crisis in Neuroimaging
Kellmeyer, P. (2017) · Cambridge Quarterly of Healthcare Ethics · doi:10.1017/s096318011700007x · Cited by 37 · see citing works
Brain network reorganization after targeted attack at a hub region
Tu, W., Ma, Z., & Zhang, N. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118219 · Cited by 37 · see citing works
Applications of chemogenetics in non-human primates
Raper, J., & Galván, A. (2022) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2022.102204 · Cited by 37 · see citing works
Mapping effective connectivity of human amygdala subdivisions with intracranial stimulation
Sawada, M., Adolphs, R., Dlouhy, B. J., Jenison, R. L., Rhone, A. E., Kovach, C. K., Greenlee, J. D. W., Howard, M. A., & Oya, H. (2022) · Nature Communications · doi:10.1038/s41467-022-32644-y · Cited by 38 · see citing works
Mills, B. D., Grayson, D. S., Shunmugavel, A., Miranda-Domínguez, Ó., Feczko, E., Earl, E., Neve, K. A., & Fair, D. A. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2910-17.2018 · Cited by 36 · see citing works
Self-citationRoseboom, P. H., Mueller, S. A. L., Oler, J. A., Fox, A. S., Riedel, M. K., Elam, V. R., Olsen, M. E., Gomez, J. L., Boehm, M. A., DiFilippo, A. H., et al. (2021) · Molecular Therapy · doi:10.1016/j.ymthe.2021.04.021 · Cited by 36 · see citing works
Causal mapping of emotion networks in the human brain: Framework and initial findings
Dubois, J., Oya, H., Tyszka, J. M., Howard, M. A., Eberhardt, F., & Adolphs, R. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.11.015 · Cited by 33 · see citing works
Nonhuman Primate Models to Explore Mechanisms Underlying Early-Life Temperamental Anxiety
Kenwood, M. M., & Kalin, N. H. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.08.028 · Cited by 35 · see citing works
Mapping stress networks using functional magnetic resonance imaging in awake animals
Dopfel, D., & Zhang, N. (2018) · Neurobiology of Stress · doi:10.1016/j.ynstr.2018.06.002 · Cited by 32 · see citing works
Hirabayashi, T., Nagai, Y., Hori, Y., Inoue, K., Aoki, I., Takada, M., Suhara, T., Higuchi, M., & Minamimoto, T. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.032 · Cited by 33 · see citing works
Chemogenetics as a neuromodulatory approach to treating neuropsychiatric diseases and disorders
Song, J., Patel, R. V., Sharif, M. S., Ashokan, A., & Michaelides, M. (2021) · Molecular Therapy · doi:10.1016/j.ymthe.2021.11.019 · Cited by 33 · see citing works
Han, J.-K., Kwon, S., Kim, Y. G., Choi, J., Kim, J., Lee, Y., Ye, S.-K., & Kim, S. J. (2021) · eLife · doi:10.7554/elife.63291 · Cited by 31 · see citing works
Cisler, J. M., Privratsky, A. A., Smitherman, S., Herringa, R. J., & Kilts, C. D. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.12.001 · Cited by 30 · see citing works
Fair, D. A., Dosenbach, N. U. F., Moore, A. H., Satterthwaite, T. D., & Milham, M. P. (2021) · Annual Review of Developmental Psychology · doi:10.1146/annurev-devpsych-121318-085124 · Cited by 30 · see citing works
Self-citationFujimoto, A., Elorette, C., Fredericks, J. M., Fujimoto, S. H., Fleysher, L., Rudebeck, P. H., & Russ, B. E. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0325-22.2022 · Cited by 28 · see citing works
Maternal Interleukin-6 Is Associated With Macaque Offspring Amygdala Development and Behavior
Ramirez, J. S. B., Graham, A. M., Thompson, J. R., Zhu, J. Y., Sturgeon, D., Bagley, J. L., Thomas, E., Papadakis, S., Bah, M., Perrone, A. J., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz188 · Cited by 29 · see citing works
Self-citationAn Honest Reckoning With the Amygdala and Mental Illness
Fox, A. S., & Shackman, A. J. (2024) · American Journal of Psychiatry · doi:10.1176/appi.ajp.20240941 · Cited by 26 · see citing works
Nagai, Y., Miyakawa, N., Takuwa, H., Hori, Y., Oyama, K., Ji, B., Takahashi, M., Huang, X., Slocum, S. T., DiBerto, J. F., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/854513 · Cited by 27 · see citing works
Behavioral Manipulation by Optogenetics in the Nonhuman Primate
Deng, C., Yuan, H., & Dai, J. (2017) · The Neuroscientist · doi:10.1177/1073858417728459 · Cited by 26 · see citing works
Oguchi, M., Tanaka, S., Pan, X., Kikusui, T., Moriya‐Ito, K., Kato, S., Kobayashi, K., & Sakagami, M. (2021) · Communications Biology · doi:10.1038/s42003-021-02623-y · Cited by 26 · see citing works
Elorette, C., Fujimoto, A., Stoll, F. M., Fujimoto, S. H., Bienkowska, N., London, L., Fleysher, L., Russ, B. E., & Rudebeck, P. H. (2024) · Nature Communications · doi:10.1038/s41467-024-49140-0 · Cited by 20 · see citing works
Froudist‐Walsh, S., Browning, P. G. F., Young, J., Murphy, K. L., Mars, R. B., Fleysher, L., & Croxson, P. L. (2018) · eLife · doi:10.7554/elife.34354 · Cited by 23 · see citing works
Phytocannabinoids as Potential Multitargeting Neuroprotectants in Alzheimer’s Disease
Rathod, S., & Agrawal, Y. O. (2023) · Current Drug Research Reviews · doi:10.2174/2589977515666230502104021 · Cited by 18 · see citing works
Intelligent architectures for robotics: The merging of cognition and emotion
Pessoa, L. (2019) · Physics of Life Reviews · doi:10.1016/j.plrev.2019.04.009 · Cited by 20 · see citing works
Deffains, M., Nguyen, T. H., Orignac, H., Biendon, N., Doveró, S., Bézard, E., & Boraud, T. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.14746 · Cited by 20 · see citing works
Kovacs-Balint, Z., Feczko, E., Pincus, M., Earl, E., Miranda-Domínguez, Ó., Howell, B., Morin, E. L., Maltbie, E. A., Li, L. Y., Steele, J., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy222 · Cited by 19 · see citing works
Self-citationResting state functional atlas and cerebral networks in mouse lemur primates at 11.7 Tesla
Garin, C. M., Nadkarni, N. A., Landeau, B., Chételat, G., Picq, J., Bougacha, S., & Dhénain, M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117589 · Cited by 19 · see citing works
Mapping Structure-Function Relationships in the Brain
Snyder, A. Z., & Bauer, A. Q. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.10.005 · Cited by 19 · see citing works
Update on Nonhuman Primate Models of Brain Disease and Related Research Tools
Qiao, N., Ma, L., Zhang, Y., & Wang, L. (2023) · Biomedicines · doi:10.3390/biomedicines11092516 · Cited by 18 · see citing works
Commentary on Pang et al. (2023) NaturePreprint
Faskowitz, J., Moyer, D., Handwerker, D. A., González-Castillo, J., Bandettini, P. A., Jbabdi, S., & Betzel, R. F. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.20.549785 · Cited by 17 · see citing works
Allen, D. C., Carlson, T. L., Xiong, Y., Jin, J., Grant, K. A., & Cuzon Carlson, V. C. (2019) · The Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.118.252031 · Cited by 17 · see citing works
Self-citationGrayson, D. S., Bliss‐Moreau, E., Bennett, J., Lavenex, P., & Amaral, D. G. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx080 · Cited by 14 · see citing works
Self-citationDamage to the structural connectome reflected in resting-state fMRI functional connectivity
Wodeyar, A., Cassidy, J. M., Cramer, S. C., & Srinivasan, R. (2020) · Network Neuroscience · doi:10.1162/netn_a_00160 · Cited by 15 · see citing works
Carotenoids improve the development of cerebral cortical networks in formula-fed infant macaques
Miranda-Domínguez, Ó., Ramirez, J. S. B., Mitchell, A. J., Perrone, A. J., Earl, E., Carpenter, S., Feczko, E., Graham, A. M., Jeon, S., Cohen, N. J., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-19279-1 · Cited by 15 · see citing works
Self-citationRagone, E., Tanner, J., Jo, Y., Esfahlani, F. Z., Faskowitz, J., Pope, M., Coletta, L., Gozzi, A., & Betzel, R. F. (2024) · Communications Biology · doi:10.1038/s42003-024-05766-w · Cited by 16 · see citing works
Hirabayashi, T., Nagai, Y., Hori, Y., Hori, Y., Oyama, K., Mimura, K., Miyakawa, N., Iwaoki, H., Inoue, K., Suhara, T., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49570-w · Cited by 14 · see citing works
Mueller, S. A. L., Oler, J. A., Roseboom, P. H., Aggarwal, N., Kenwood, M. M., Riedel, M. K., Elam, V. R., Olsen, M. E., DiFilippo, A. H., Christian, B. T., et al. (2023) · Current Research in Neurobiology · doi:10.1016/j.crneur.2023.100111 · Cited by 13 · see citing works
The use of chemogenetic actuator ligands in nonhuman primate DREADDs-fMRI
Cushnie, A. K., Bullock, D. N., Manea, A. M. G., Tang, W., Zimmermann, J., & Heilbronner, S. R. (2022) · Current Research in Neurobiology · doi:10.1016/j.crneur.2022.100072 · Cited by 13 · see citing works
Kraft, A. W., Mitra, A., Bauer, A. Q., Snyder, A. Z., Raichle, M. E., Culver, J. P., & Lee, J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1711789114 · Cited by 13 · see citing works
- Authors
- Grayson, D. S., Bliss-Moreau, E., Machado, C. J., Bennett, J., Shen, K., Grant, K. A., Fair, D. A., & Amaral, D. G.
- Affiliations
- University of California, Davis (US)
- Rotman Research Institute (CA)
- Oregon National Primate Research Center (US)
- Oregon Health & Science University (US)
- Corresponding author
- Amaral, David G.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2016-07
- Volume
- 91
- Issue
- 2
- Pages
- 453-466
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC5233431
- OpenAlex
- W2501822306
- Cited by
- 172
- References
- 46
- Citations by year
- 2026: 1 · 2025: 15 · 2024: 13 · 2023: 27 · 2022: 12 · 2021: 15 · 2020: 21 · 2019: 24 · 2018: 19 · 2017: 24 · 2016: 1
- FWCI
- 14.42
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (UL1TR000128, AA109431, OD-011107, P51OD011107)
- National Institute on Alcohol Abuse and Alcoholism (U01AA013510, R24AA019431)
- National Institute of Mental Health (R21MH098585, R01MH096773)
- National Center for Advancing Translational Sciences
- California National Primate Research Center (OD 011107)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- amygdala, rhesus monkeys, brain connectome, resting-state functional connectivity, graph theory, structural connectivity
- MeSH terms
- Amygdala, Animals, Macaca mulatta, Magnetic Resonance Imaging, Male, Models, Neurological, Nerve Net, Neural Pathways, Pharmacogenetics, Connectome
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