Graph theory reveals amygdala modules consistent with its anatomical subdivisions
Tóm tắt
Similarities on the cellular and neurochemical composition of the amygdaloid subnuclei suggests their clustering into subunits that exhibit unique functional organization. The topological principle of community structure has been used to identify functional subnetworks in neuroimaging data that reflect the brain effective organization. Here we used modularity to investigate the organization of the amygdala using resting state functional magnetic resonance imaging (rsfMRI) data. Our goal was to determine whether such topological organization would reliably reflect the known neurobiology of individual amygdaloid nuclei, allowing for human imaging studies to accurately reflect the underlying neurobiology. Modularity analysis identified amygdaloid elements consistent with the main anatomical subdivisions of the amygdala that embody distinct functional and structural properties. Additionally, functional connectivity pathways of these subunits and their correlation with task-induced amygdala activation revealed distinct functional profiles consistent with the neurobiology of the amygdala nuclei. These modularity findings corroborate the structure–function relationship between amygdala anatomical substructures, supporting the use of network analysis techniques to generate biologically meaningful partitions of brain structures.
Từ khóa
Tài liệu tham khảo
Baxter, M. G. & Murray, E. A. The amygdala and reward. Nat Rev Neurosci 3, 563–573, https://doi.org/10.1038/nrn875 (2002).
Freese, J. L. & Amaral, D. G. In The human amygdala (eds P J Whalen & s E A Phelp) 3–42 (2009).
Amunts, K. et al. Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anatomy and embryology 210, 343–352, https://doi.org/10.1007/s00429-005-0025-5 (2005).
Roy, A. K. et al. Functional connectivity of the human amygdala using resting state fMRI. Neuroimage 45, 614–626 (2009).
Sah, P., Faber, E. S., Lopez De Armentia, M. & Power, J. The amygdaloid complex: anatomy and physiology. Physiol Rev 83, 803–834, https://doi.org/10.1152/physrev.00002.2003 (2003).
Puelles, L. Thoughts on the development, structure and evolution of the mammalian and avian telencephalic pallium. Philos Trans R Soc Lond B Biol Sci 356, 1583–1598, https://doi.org/10.1098/rstb.2001.0973 (2001).
Unal, C. T., Pare, D. & Zaborszky, L. Impact of basal forebrain cholinergic inputs on basolateral amygdala neurons. J Neurosci 35, 853–863, https://doi.org/10.1523/jneurosci.2706-14.2015 (2015).
Mishra, A., Rogers, B. P., Chen, L. M. & Gore, J. C. Functional connectivity-based parcellation of amygdala using self-organized mapping: a data driven approach. Hum. Brain Mapping 35, 1247–1260, https://doi.org/10.1002/hbm.22249 (2014).
Bach, D. R., Behrens, T. E., Garrido, L., Weiskopf, N. & Dolan, R. J. Deep and superficial amygdala nuclei projections revealed in vivo by probabilistic tractography. J Neurosci 31, 618–623, https://doi.org/10.1523/jneurosci.2744-10.2011 (2011).
Saygin, Z. M., Osher, D. E., Augustinack, J., Fischl, B. & Gabrieli, J. D. Connectivity-based segmentation of human amygdala nuclei using probabilistic tractography. Neuroimage 56, 1353–1361, https://doi.org/10.1016/j.neuroimage.2011.03.006 (2011).
Bickart, K. C., Hollenbeck, M. C., Barrett, L. F. & Dickerson, B. C. Intrinsic Amygdala-Cortical Functional Connectivity Predicts Social Network Size in Humans. J Neurosci 32, 14729–14741 (2012).
Bzdok, D., Laird, A. R., Zilles, K., Fox, P. T. & Eickhoff, S. B. An investigation of the structural, connectional, and functional subspecialization in the human amygdala. Hum Brain Mapp 34, 3247–3266, https://doi.org/10.1002/hbm.22138 (2013).
Zhou, C., Zemanova, L., Zamora, G., Hilgetag, C. C. & Kurths, J. Hierarchical organization unveiled by functional connectivity in complex brain networks. Physical review letters 97, 238103, https://doi.org/10.1103/PhysRevLett.97.238103 (2006).
Ferrarini, L. et al. Hierarchical functional modularity in the resting-state human brain. Hum. Brain Mapping 30, 2220–2231, https://doi.org/10.1002/hbm.20663 (2009).
Meunier, D., Achard, S., Morcom, A. & Bullmore, E. Age-related changes in modular organization of human brain functional networks. Neuroimage 44, 715–723, https://doi.org/10.1016/j.neuroimage.2008.09.062 (2009).
Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature reviews. Neuroscience 10, 186–198, https://doi.org/10.1038/nrn2575 (2009).
Uğurbil, K. et al. Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project. Neuroimage 80, 80–104, https://doi.org/10.1016/j.neuroimage.2013.05.012 (2013).
Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124, https://doi.org/10.1016/j.neuroimage.2013.04.127 (2013).
Brierley, B., Shaw, P. & David, A. S. The human amygdala: a systematic review and meta-analysis of volumetric magnetic resonance imaging. Brain Res Brain Res Rev 39, 84–105 (2002).
Newman, M. E. Modularity and community structure in networks. Proc Natl Acad Sci USA 103, 8577–8582, https://doi.org/10.1073/pnas.0601602103 (2006).
Blondel, V., Guillaume, J., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J Stat Mech, P10008, https://doi.org/10.1088/1742-5468/2008/10/P10008 (2008).
Rubinov, M. & Sporns, O. Weight-conserving characterization of complex functional brain networks. Neuroimage 56, 2068–2079, https://doi.org/10.1016/j.neuroimage.2011.03.069 (2011).
Eickhoff, S. B., Thirion, B., Varoquaux, G. & Bzdok, D. Connectivity-based parcellation: Critique and implications. Hum Brain Mapp 36, 4771–4792, https://doi.org/10.1002/hbm.22933 (2015).
Nicolini, C. & Bifone, A. Modular structure of brain functional networks: breaking the resolution limit by Surprise. Scientific Reports 6, 19250, https://doi.org/10.1038/srep19250 (2016).
Rosvall, M. & Bergstrom, C. T. An information-theoretic framework for resolving community structure in complex networks. Proc Natl Acad Sci USA 104, 7327–7331, https://doi.org/10.1073/pnas.0611034104 (2007).
Glerean, E. et al. Reorganization of functionally connected brain subnetworks in high-functioning autism. Hum Brain Mapp 37, 1066–1079, https://doi.org/10.1002/hbm.23084 (2016).
Phelps, E. A. & LeDoux, J. E. Contributions of the amygdala to emotion processing: from animal models to human behavior. Neuron 48, 175–187 (2005).
Hariri, A. R., Bookheimer, S. Y. & Mazziotta, J. C. Modulating emotional responses: effects of a neocortical network on the limbic system. Neuroreport 11, 43–48 (2000).
Grezes, J., Valabregue, R., Gholipour, B. & Chevallier, C. A direct amygdala-motor pathway for emotional displays to influence action: A diffusion tensor imaging study. Hum Brain Mapp 35, 5974–5983, https://doi.org/10.1002/hbm.22598 (2014).
Stefanacci, L. & Amaral, D. G. Some observations on cortical inputs to the macaque monkey amygdala: an anterograde tracing study. J Comp Neurol 451, 301–323, https://doi.org/10.1002/cne.10339 (2002).
Kemppainen, S., Jolkkonen, E. & Pitkänen, A. Projections from the posterior cortical nucleus of the amygdala to the hippocampal formation and parahippocampal region in rat. Hippocampus 12, 735–755 (2002).
Moreno, N. & Gonzalez, A. Evolution of the amygdaloid complex in vertebrates, with special reference to the anamnio-amniotic transition. J Anat 211, 151–163, https://doi.org/10.1111/j.1469-7580.2007.00780.x (2007).
Li, Y., Qin, W., Jiang, T., Zhang, Y. & Yu, C. Sex-dependent correlations between the personality dimension of harm avoidance and the resting-state functional connectivity of amygdala subregions. PLoS One 7, e35925, https://doi.org/10.1371/journal.pone.0035925 (2012).
Stephan, H. & Andy, O. J. Quantitative comparison of the amygdala in insectivores and primates. Acta Anat (Basel) 98, 130–153 (1977).
Vogt, B. A. & Pandya, D. N. Cingulate cortex of the rhesus monkey: II. Cortical afferents. J Comp Neurol 262, 271–289, https://doi.org/10.1002/cne.902620208 (1987).
Chase, H. W., Moses-Kolko, E. L., Zevallos, C., Wisner, K. L. & Phillips, M. L. Disrupted posterior cingulate-amygdala connectivity in postpartum depressed women as measured with resting BOLD fMRI. Soc Cogn Affect Neurosci 9, 1069–1075, https://doi.org/10.1093/scan/nst083 (2014).
Marchetti, I., Koster, E. H., Sonuga-Barke, E. J. & De Raedt, R. The default mode network and recurrent depression: a neurobiological model of cognitive risk factors. Neuropsychol Rev 22, 229–251, https://doi.org/10.1007/s11065-012-9199-9 (2012).
Cauda, F. et al. Functional connectivity of the posteromedial cortex. PLoS One 5, https://doi.org/10.1371/journal.pone.0013107 (2010).
Pfefferbaum, A. et al. Cerebral blood flow in posterior cortical nodes of the default mode network decreases with task engagement but remains higher than in most brain regions. Cerebral cortex (New York, N. Y.: 1991) 21, 233–244, https://doi.org/10.1093/cercor/bhq090 (2011).
Zhang, S. & Li, C. S. Functional connectivity mapping of the human precuneus by resting state fMRI. Neuroimage 59, 3548–3562, https://doi.org/10.1016/j.neuroimage.2011.11.023 (2012).
Parvizi, J., Van Hoesen, G. W., Buckwalter, J. & Damasio, A. Neural connections of the posteromedial cortex in the macaque. Proc Natl Acad Sci USA 103, 1563–1568, https://doi.org/10.1073/pnas.0507729103 (2006).
Leichnetz, G. R. Connections of the medial posterior parietal cortex (area 7m) in the monkey. Anat Rec 263, 215–236 (2001).
Van Schuerbeek, P., Baeken, C., Luypaert, R., De Raedt, R. & De Mey, J. Does the amygdala response correlate with the personality trait ‘harm avoidance’ while evaluating emotional stimuli explicitly? Behav Brain Funct 10, 18, https://doi.org/10.1186/1744-9081-10-18 (2014).
Seeley, W. W. et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 27, 2349–2356, https://doi.org/10.1523/jneurosci.5587-06.2007 (2007).
Kapp, B. S., Supple, W. F. J. & Whalen, P. J. Effects of electrical stimulation of the amygdaloid central nucleus on neocortical arousal in the rabbit. Behav Neurosci 108, 81–93 (1994).
Touroutoglou, A., Bickart, K. C., Barrett, L. F. & Dickerson, B. C. Amygdala task-evoked activity and task-free connectivity independently contribute to feelings of arousal. Hum Brain Mapp 35, 5316–5327, https://doi.org/10.1002/hbm.22552 (2014).
Phillips, M. L., Drevets, W. C., Rauch, S. L. & Lane, R. Neurobiology of emotion perception I: The neural basis of normal emotion perception. Biol Psychiatry 54, 504–514 (2003).
Vogt, B. A. Pain and emotion interactions in subregions of the cingulate gyrus. Nat Rev Neurosci 6, 533–544 (2005).
Aggleton, J. P. The contribution of the amygdala to normal and abnormal emotional states. Trends Neurosci 16, 328–333 (1993).
Whalen, P. J. et al. Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge. J Neurosci 18, 411–418 (1998).
Craig, A. D. How do you feel-now? The anterior insula and human awareness. Nat Rev Neurosci 10, 59–70, https://doi.org/10.1038/nrn2555 (2009).
Muhlberger, A. et al. Stop looking angry and smile, please: start and stop of the very same facial expression differentially activate threat- and reward-related brain networks. Soc Cogn Affect Neurosci 6, 321–329, https://doi.org/10.1093/scan/nsq039 (2011).
Vogel, S. et al. Blocking the mineralocorticoid receptor in humans prevents the stress-induced enhancement of centromedial amygdala connectivity with the dorsal striatum. Neuropsychopharmacology 40, 947–956, https://doi.org/10.1038/npp.2014.271 (2015).
Wilensky, A. E., Schafe, G. E., Kristensen, M. P. & LeDoux, J. E. Rethinking the fear circuit: the central nucleus of the amygdala is required for the acquisition, consolidation, and expression of Pavlovian fear conditioning. J Neurosci 26, 12387–12396, https://doi.org/10.1523/jneurosci.4316-06.2006 (2006).
Blanchard, D. C. & Blanchard, R. J. Innate and conditioned reactions to threat in rats with amygdaloid lesions. J Comp Physiol Psychol 81, 281–290 (1972).
Gregg, T. R. & Siegel, A. Brain structures and neurotransmitters regulating aggression in cats: implications for human aggression. Prog Neuropsychopharmacol Biol Psychiatry 25, 91–140 (2001).
Ball, T. et al. Response properties of human amygdala subregions: evidence based on functional MRI combined with probabilistic anatomical maps. PLoS One 2, e307, https://doi.org/10.1371/journal.pone.0000307 (2007).
Baker, K. B. & Kim, J. J. Amygdalar lateralization in fear conditioning: evidence for greater involvement of the right amygdala. Behav Neurosci 118, 15–23, https://doi.org/10.1037/0735-7044.118.1.15 (2004).
Ji, G. & Neugebauer, V. Hemispheric Lateralization of Pain Processing by Amygdala Neurons. J Neurophysiol 102, 2253–2264 (2009).
Quirk, G. J. & Beer, J. S. Prefrontal involvement in the regulation of emotion: convergence of rat and human studies. Curr Opin Neurobiol 16, 723–727, https://doi.org/10.1016/j.conb.2006.07.004 (2006).
Llamas, A., Avendano, C. & Reinoso-Suarez, F. Amygdaloid projections to prefrontal and motor cortex. Science 195, 794–796 (1977).
Amaral, D. G. & Price, J. L. Amygdalo-cortical projections in the monkey (Macaca fascicularis). J Comp Neurol 230, 465–496, https://doi.org/10.1002/cne.902300402 (1984).
Gotts, S. J. et al. Fractionation of social brain circuits in autism spectrum disorders. Brain 135, 2711–2725, https://doi.org/10.1093/brain/aws160 (2012).
Hoffman, K. L., Gothard, K. M., Schmid, M. C. & Logothetis, N. K. Facial-expression and gaze-selective responses in the monkey amygdala. Curr Biol 17, 766–772, https://doi.org/10.1016/j.cub.2007.03.040 (2007).
Ball, T. et al. Anatomical specificity of functional amygdala imaging of responses to stimuli with positive and negative emotional valence. J Neurosci Methods 180, 57–70, https://doi.org/10.1016/j.jneumeth.2009.02.022 (2009).
Reichenbach, J. R. et al. Theory and application of static field inhomogeneity effects in gradient-echo imaging. J Magn Reson Imaging 7, 266–279 (1997).
Cordes, D., Nandy, R. R., Schafer, S. & Wager, T. D. Characterization and reduction of cardiac- and respiratory-induced noise as a function of the sampling rate (TR) in fMRI. Neuroimage 89, 314–330, https://doi.org/10.1016/j.neuroimage.2013.12.013 (2014).
Jacob, Y. et al. Dependency Network Analysis (DEPNA) Reveals Context Related Influence of Brain Network Nodes. Sci Rep 6, 27444, https://doi.org/10.1038/srep27444 (2016).
Glasser, M. F. et al. The Human Connectome Project’s neuroimaging approach. Nat Neurosci 19, 1175–1187, https://doi.org/10.1038/nn.4361 (2016).
Moeller, S. et al. Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn Reson Med 63, 1144–1153, https://doi.org/10.1002/mrm.22361 (2010).
WU-Minn HCP 500 Subjects + MEG2 Data Release: Reference Manual http://www.humanconnectome.org/documentation/S500/
Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res. 29, 162–173 (1996).
Smith, S. M. In Functional MRI: an introduction to methods (eds P. P. M. Jezzard, Matthews, & S. Smith, M.) (Oxford University Press, 2003).
Foerster, B., Tomasi, D. & Caparelli, E. C. Magnetic field shift due to mechanical vibration in functional magnetic resonance imaging. Magn. Reson. Med. 54, 1261–1267 (2005).
Cordes, D. et al. Frequencies Contributing to Functional Connectivity in the Cerebral Cortex in “Resting-state” Data. AJNR 22, 1326–1333 (2001).
Lund, T. E., Madsen, K. H., Sidaros, K., Luo, W. L. & Nichols, T. E. Non-white noise in fMRI: does modelling have an impact? Neuroimage 29, 54–66, https://doi.org/10.1016/j.neuroimage.2005.07.005 (2006).
Chai, X. J., Castanon, A. N., Ongur, D. & Whitfield-Gabrieli, S. Anticorrelations in resting state networks without global signal regression. Neuroimage 59, 1420–1428, https://doi.org/10.1016/j.neuroimage.2011.08.048 (2012).
Ashburner, J. & Friston, K. J. Unified segmentation. Neuroimage 26, 839–851, https://doi.org/10.1016/j.neuroimage.2005.02.018 (2005).
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154, https://doi.org/10.1016/j.neuroimage.2011.10.018 (2012).
Nielsen, F. A. & Hansen, L. K. Automatic anatomical labelling of Talairach coordinates and generation of volumes of interest via the BrainMap database, http://neuro.imm.dtu.dk/services/jerne/ninf/voi (2002).
Fox, P. T., Mikiten, S., Davis, G. & Lancaster, J. L. In Functional Neuroimaging: Technical Foundations (eds R. W. Thatcher et al.) Ch. 9, 95–105 (Academic Press, 1994).
Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069, https://doi.org/10.1016/j.neuroimage.2009.10.003 (2010).
Chen, G., Saad, Z. S., Britton, J. C., Pine, D. S. & Cox, R. W. Linear mixed-effects modeling approach to FMRI group analysis. Neuroimage 73, 176–190, https://doi.org/10.1016/j.neuroimage.2013.01.047 (2013).
Ward, B. D. Simultaneous inference for FMRI data. http://afni.nimh.nih.gov/pub/dist/doc/manual/AlphaSim.pdf (2000).
Shrout, P. E. & Fleiss, J. L. Intraclass correlations: uses in assessing rater reliability. Psychol Bull 86, 420–428 (1979).