The Rhesus Monkey Connectome Predicts Disrupted Functional Networks Resulting from Pharmacogenetic Inactivation of the Amygdala

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.

[1]  C. J. Honeya,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009 .

[2]  Jonathan D. Power,et al.  Network measures predict neuropsychological outcome after brain injury , 2014, Proceedings of the National Academy of Sciences.

[3]  O. Sporns,et al.  Functional connectivity between anatomically unconnected areas is shaped by collective network-level effects in the macaque cortex. , 2012, Cerebral cortex.

[4]  Ernesto Estrada,et al.  Communicability in complex networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Anthony Randal McIntosh,et al.  Hundreds of brain maps in one atlas: Registering coordinate-independent primate neuro-anatomical data to a standard brain , 2012, NeuroImage.

[6]  S. Everling,et al.  Monkey in the middle: why non-human primates are needed to bridge the gap in resting-state investigations , 2012, Front. Neuroanat..

[7]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[8]  M. Breakspear,et al.  The connectomics of brain disorders , 2015, Nature Reviews Neuroscience.

[9]  Bill Seeley,et al.  Neurodegenerative diseases target large-scale human brain networks , 2010, Alzheimer's & Dementia.

[10]  N. Volkow,et al.  Whole-brain circuit dissection in free-moving animals reveals cell-specific mesocorticolimbic networks. , 2013, The Journal of clinical investigation.

[11]  Ravi S. Menon,et al.  Information Processing Architecture of Functionally Defined Clusters in the Macaque Cortex , 2012, The Journal of Neuroscience.

[12]  Biyu J. He,et al.  Breakdown of Functional Connectivity in Frontoparietal Networks Underlies Behavioral Deficits in Spatial Neglect , 2007, Neuron.

[13]  Danielle S. Bassett,et al.  Resolving Anatomical and Functional Structure in Human Brain Organization: Identifying Mesoscale Organization in Weighted Network Representations , 2013, PLoS Comput. Biol..

[14]  Olaf Sporns,et al.  Modeling the Impact of Lesions in the Human Brain , 2009, PLoS Comput. Biol..

[15]  Desmond J. Higham,et al.  Network analysis detects changes in the contralesional hemisphere following stroke , 2011, NeuroImage.

[16]  Kevin E. Bassler,et al.  Improved community structure detection using a modified fine-tuning strategy , 2009, ArXiv.

[17]  Mark D'Esposito,et al.  Focal Brain Lesions to Critical Locations Cause Widespread Disruption of the Modular Organization of the Brain , 2012, Journal of Cognitive Neuroscience.

[18]  G. Tononi,et al.  Diaschisis: past, present, future. , 2014, Brain : a journal of neurology.

[19]  Gustavo Deco,et al.  Functional connectivity dynamics: Modeling the switching behavior of the resting state , 2015, NeuroImage.

[20]  H. Gundersen,et al.  Unbiased stereological estimation of the total number of neurons in the subdivisions of the rat hippocampus using the optical fractionator , 1991, The Anatomical record.

[21]  R. Saunders,et al.  Medial Temporal Lobe Projections to the Retrosplenial Cortex of the Macaque Monkey , 2012, Hippocampus.

[22]  B. Roth,et al.  Evolving the lock to fit the key to create a family of G protein-coupled receptors potently activated by an inert ligand , 2007, Proceedings of the National Academy of Sciences.

[23]  M. Sigman,et al.  Signature of consciousness in the dynamics of resting-state brain activity , 2015, Proceedings of the National Academy of Sciences.

[24]  D. Amaral,et al.  Some observations on cortical inputs to the macaque monkey amygdala: An anterograde tracing study , 2002, The Journal of comparative neurology.

[25]  Richard C Saunders,et al.  Chemogenetic disconnection of monkey orbitofrontal and rhinal cortex reversibly disrupts reward value , 2015, Nature Neuroscience.

[26]  Paul J. Laurienti,et al.  Erratum to “An exponential random graph modeling approach to creating group-based representative whole-brain connectivity networks” [NeuroImage 60/2 (2012) 1117–1126] , 2012, NeuroImage.

[27]  Paul J. Laurienti,et al.  The Human Functional Brain Network Demonstrates Structural and Dynamical Resilience to Targeted Attack , 2013, PLoS Comput. Biol..

[28]  W. Mehler.  Subcortical afferent connections of the amygdala in the monkey , 1980, The Journal of comparative neurology.

[29]  D. Amaral,et al.  Organization of the intrinsic connections of the monkey amygdaloid complex: Projections originating in the lateral nucleus , 1998, The Journal of comparative neurology.

[30]  M. Young,et al.  Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac). , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[31]  Edward M. Reingold,et al.  Graph drawing by force‐directed placement , 1991, Softw. Pract. Exp..

[32]  D. Higham,et al.  A weighted communicability measure applied to complex brain networks , 2009, Journal of The Royal Society Interface.

[33]  Peter Stiers,et al.  Comparative Analysis of the Macroscale Structural Connectivity in the Macaque and Human Brain , 2014, PLoS Comput. Biol..

[34]  Habib Benali,et al.  Relating Structure and Function in the Human Brain: Relative Contributions of Anatomy, Stationary Dynamics, and Non-stationarities , 2014, PLoS Comput. Biol..

[35]  Olaf Sporns,et al.  Weight-conserving characterization of complex functional brain networks , 2011, NeuroImage.

[36]  D. Amaral,et al.  The amygdalostriatal projections in the monkey. An anterograde tracing study , 1985, Brain Research.

[37]  Richard F. Betzel,et al.  Resting-brain functional connectivity predicted by analytic measures of network communication , 2013, Proceedings of the National Academy of Sciences.

[38]  D. Amaral,et al.  Amygdalo‐cortical projections in the monkey (Macaca fascicularis) , 1984, The Journal of comparative neurology.

[39]  R. E. Passingham,et al.  Cortical and subcortical afferents to the amygdala of the rhesus monkey (Macaca mulatta) , 1980, Brain Research.

[40]  Mason A. Porter,et al.  Robust Detection of Dynamic Community Structure in Networks , 2012, Chaos.

[41]  Jean M. Vettel,et al.  Controllability of structural brain networks , 2014, Nature Communications.

[42]  Brian D. Mills,et al.  Bridging the Gap between the Human and Macaque Connectome: A Quantitative Comparison of Global Interspecies Structure-Function Relationships and Network Topology , 2014, The Journal of Neuroscience.

[43]  D. Fair,et al.  Dietary Omega-3 Fatty Acids Modulate Large-Scale Systems Organization in the Rhesus Macaque Brain , 2014, The Journal of Neuroscience.

[44]  O. Sporns,et al.  Dynamical consequences of lesions in cortical networks , 2008, Human brain mapping.

[45]  Ravi S. Menon,et al.  Identification of Optimal Structural Connectivity Using Functional Connectivity and Neural Modeling , 2014, The Journal of Neuroscience.