Opportunities and Challenges for Psychiatry in the Connectomic Era

Alex Fornito1,2, Edward T. Bullmore3,4, Andrew Zalesky2,5
1Brain and Mental Health Laboratory, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Melbourne
2Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne, Australia
3Department of Psychiatry, University of Cambridge, and ImmunoPsychiatry, Cambridge, United Kingdom
4Department of Psychiatry Alternative Discovery and Development, GlaxoSmithKline R&D, Cambridge, United Kingdom
5Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Australia

Tài liệu tham khảo

Fornito, 2015, The connectomics of brain disorders, Nat Rev Neurosci, 16, 159, 10.1038/nrn3901 Deco, 2014, Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders, Neuron, 84, 892, 10.1016/j.neuron.2014.08.034 Insel, 2010, Research domain criteria (RDoC): toward a new classification framework for research on mental disorders, Am J Psychiatry, 167, 748, 10.1176/appi.ajp.2010.09091379 Insel, 2014, The NIMH Research Domain Criteria (RDoC) Project: precision medicine for psychiatry, Am J Psychiatry, 171, 395, 10.1176/appi.ajp.2014.14020138 Wernicke, 1906 Hughlings Jackson, 1888, On post-epileptic states: a contribution to the comparative study of insanities, Br J Psychiatry, 34, 349, 10.1192/bjp.34.147.349 York, 2011, Hughlings Jackson’s neurological ideas, Brain, 134, 3106, 10.1093/brain/awr219 Catani, 2013, Connectomic approaches before the connectome, Neuroimage, 80, 2, 10.1016/j.neuroimage.2013.05.109 Catani, 2005, The rises and falls of disconnection syndromes, Brain, 128, 2224, 10.1093/brain/awh622 Collin, 2016, Connectomics in schizophrenia: From early pioneers to recent brain network findings, Biol Psychiatry Cogn Neurosci Neuroimaging, 1, 199, 10.1016/j.bpsc.2016.01.002 Sejnowski, 2014, Putting big data to good use in neuroscience, Nat Neurosci, 17, 1440, 10.1038/nn.3839 Alivisatos, 2012, The brain activity map project and the challenge of functional connectomics, Neuron, 74, 970, 10.1016/j.neuron.2012.06.006 Lichtman, 2011, The big and the small: challenges of imaging the brain’s circuits, Science, 334, 618, 10.1126/science.1209168 Van Essen, 2012, The Human Connectome Project: a data acquisition perspective, Neuroimage, 62, 2222, 10.1016/j.neuroimage.2012.02.018 Sporns, 2005, The human connectome: a structural description of the human brain, PLoS Comput Biol, 1, e42, 10.1371/journal.pcbi.0010042 White, 1986, The structure of the nervous system of the nematode Caenorhabditis elegans, Philos Trans R Soc Lond B Biol Sci, 314, 1, 10.1098/rstb.1986.0056 Varshney, 2011, Structural properties of the Caenorhabditis elegans neuronal network, PLoS Comput Biol, 7, 10.1371/journal.pcbi.1001066 Chiang, 2011, Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution, Curr Biol, 21, 1, 10.1016/j.cub.2010.11.056 Shih, 2015, Connectomics-based analysis of information flow in the Drosophila brain, Curr Biol, 25, 1249, 10.1016/j.cub.2015.03.021 Oh, 2014, A mesoscale connectome of the mouse brain, Nature, 508, 207, 10.1038/nature13186 Zingg, 2014, Neural networks of the mouse neocortex, Cell, 156, 1096, 10.1016/j.cell.2014.02.023 Bota, 2015, Architecture of the cerebral cortical association connectome underlying cognition, Proc Natl Acad Sci U S A, 112, E2093, 10.1073/pnas.1504394112 Scannell, 1995, Analysis of connectivity in the cat cerebral cortex, J Neurosci, 15, 1463, 10.1523/JNEUROSCI.15-02-01463.1995 Markov, 2014, A weighted and directed interareal connectivity matrix for macaque cerebral cortex, Cereb Cortex, 24, 17, 10.1093/cercor/bhs270 Hagmann, 2007, Mapping human whole-brain structural networks with diffusion MRI, PLoS One, 2, e597, 10.1371/journal.pone.0000597 Iturria-Medina, 2007, Characterizing brain anatomical connections using diffusion weighted MRI and graph theory, Neuroimage, 36, 645, 10.1016/j.neuroimage.2007.02.012 Zalesky, 2009, A DTI-derived measure of cortico-cortical connectivity, IEEE Trans Med Imaging, 28, 1023, 10.1109/TMI.2008.2012113 Bullmore, 2009, Complex brain networks: graph theoretical analysis of structural and functional systems, Nat Rev Neurosci, 10, 186, 10.1038/nrn2575 Bullmore, 2011, Brain graphs: graphical models of the human brain connectome, 7, 113 Fornito, 2013, Graph analysis of the human connectome: promise, progress, and pitfalls, Neuroimage, 80, 426, 10.1016/j.neuroimage.2013.04.087 Rubinov, 2010, Complex network measures of brain connectivity: uses and interpretations, Neuroimage, 52, 1059, 10.1016/j.neuroimage.2009.10.003 Sporns, 2010 Sporns, 2012 Fornito, 2016 Newman, 2010 Newman, 2003, The structure and function of complex networks, SIAM Rev, 45, 167, 10.1137/S003614450342480 Albert, 2002, Statistical mechanics of complex networks, Rev Modern Physics, 74, 47, 10.1103/RevModPhys.74.47 Boccaletti, 2006, Complex networks: structure and dynamics, Phys Rep, 424, 175, 10.1016/j.physrep.2005.10.009 Ellison-Wright, 2008, The anatomy of first-episode and chronic schizophrenia: an anatomical likelihood estimation meta-analysis, Am J Psychiatry, 165, 1015, 10.1176/appi.ajp.2008.07101562 Fornito, 2009, Mapping grey matter reductions in schizophrenia: an anatomical likelihood estimation analysis of voxel-based morphometry studies, Schizophr Res, 108, 104, 10.1016/j.schres.2008.12.011 Bora, 2010, Voxelwise meta-analysis of gray matter abnormalities in bipolar disorder, Biol Psychiatry, 67, 1097, 10.1016/j.biopsych.2010.01.020 Bora, 2011, Gray matter abnormalities in major depressive disorder: a meta-analysis of voxel based morphometry studies, J Affect Disord, 138, 9, 10.1016/j.jad.2011.03.049 Wang, 2013, Disrupted functional brain connectome in individuals at risk for Alzheimer’s disease, Biol Psychiatry, 73, 472, 10.1016/j.biopsych.2012.03.026 Zhang, 2011, Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder, Biol Psychiatry, 70, 334, 10.1016/j.biopsych.2011.05.018 Liu, 2008, Disrupted small-world networks in schizophrenia, Brain, 131, 945, 10.1093/brain/awn018 Fornito, 2011, General and specific functional connectivity disturbances in first-episode schizophrenia during cognitive control performance, Biol Psychiatry, 70, 64, 10.1016/j.biopsych.2011.02.019 Zalesky, 2011, Disrupted axonal fiber connectivity in schizophrenia, Biol Psychiatry, 69, 80, 10.1016/j.biopsych.2010.08.022 Hong, 2014, Connectomic disturbances in attention-deficit/hyperactivity disorder: a whole-brain tractography analysis, Biol Psychiatry, 76, 656, 10.1016/j.biopsych.2013.12.013 Korgaonkar, 2014, Abnormal structural networks characterize major depressive disorder: a connectome analysis, Biol Psychiatry, 76, 567, 10.1016/j.biopsych.2014.02.018 Albert, 2000, Error and attack tolerance of complex networks, Nature, 406, 378, 10.1038/35019019 van den Heuvel, 2011, Rich-club organization of the human connectome, J Neurosci, 31, 15775, 10.1523/JNEUROSCI.3539-11.2011 Buckner, 2009, Cortical hubs revealed by intrinsic functional connectivity: Mapping, assessment of stability, and relation to Alzheimer’s disease, J Neurosci, 29, 1860, 10.1523/JNEUROSCI.5062-08.2009 Buckner, 2005, Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory, J Neurosci, 25, 7709, 10.1523/JNEUROSCI.2177-05.2005 van den Heuvel, 2013, Abnormal rich club organization and functional brain dynamics in schizophrenia, JAMA Psychiatry, 70, 783, 10.1001/jamapsychiatry.2013.1328 Fornito, 2014, Reconciling abnormalities of brain network structure and function in schizophrenia, Curr Opin Neurobiol, 30, 44, 10.1016/j.conb.2014.08.006 Klauser, 2016, White matter disruptions in schizophrenia are spatially widespread and topologically converge on brain network hubs [published online ahead of print August 17], Schizophr Bull, 10.1093/schbul/sbw100 Crossley, 2014, The hubs of the human connectome are generally implicated in the anatomy of brain disorders, Brain, 137, 2382, 10.1093/brain/awu132 van den Heuvel, 2012, High-cost, high-capacity backbone for global brain communication, Proc Natl Acad Sci U S A, 109, 11372, 10.1073/pnas.1203593109 de Haan, 2012, Activity dependent degeneration explains hub vulnerability in Alzheimer’s disease, PLoS Comput Biol, 8, e1002582, 10.1371/journal.pcbi.1002582 Mišić, 2014, Communication efficiency and congestion of signal traffic in large-scale brain networks, PLoS Comput Biol, 10, e1003427, 10.1371/journal.pcbi.1003427 Harriger, 2012, Rich club organization of macaque cerebral cortex and its role in network communication, PLoS One, 7, e46497, 10.1371/journal.pone.0046497 de Reus, 2013, Rich club organization and intermodule communication in the cat connectome, J Neurosci, 33, 12929, 10.1523/JNEUROSCI.1448-13.2013 Fulcher, 2016, A transcriptional signature of hub connectivity in the mouse connectome, Proc Natl Acad Sci U S A, 113, 1435, 10.1073/pnas.1513302113 Collin, 2014, Structural and functional aspects relating to cost and benefit of rich club organization in the human cerebral cortex, Cereb Cortex, 24, 2258, 10.1093/cercor/bht064 Bullmore, 2012, The economy of brain network organization, Nat Rev Neurosci, 13, 336, 10.1038/nrn3214 Tomasi, 2013, Energetic cost of brain functional connectivity, Proc Natl Acad Sci U S A, 110, 13642, 10.1073/pnas.1303346110 Liang, 2013, Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain, Proc Natl Acad Sci U S A, 110, 1929, 10.1073/pnas.1214900110 Sperling, 2007, Functional MRI studies of associative encoding in normal aging, mild cognitive impairment, and Alzheimer׳s disease, Ann N Y Acad Sci, 109, 146, 10.1196/annals.1379.009 Papoutsi, 2014, The cognitive burden in Huntington’s disease: Pathology, phenotype, and mechanisms of compensation, Mov Disord, 29, 673, 10.1002/mds.25864 Betzel, 2016, Optimally controlling the human connectome: the role of network topology, Sci Rep, 6, 30770, 10.1038/srep30770 Crossley, 2016, Altered hub functioning and compensatory activations in the connectome: a meta-analysis of functional neuroimaging studies in schizophrenia, Schizophr Bull, 42, 434, 10.1093/schbul/sbv146 Anticevic, 2015, N-methyl-D-aspartate receptor antagonist effects on prefrontal cortical connectivity better model early than chronic schizophrenia, Biol Psychiatry, 77, 569, 10.1016/j.biopsych.2014.07.022 Li, 2001, Aging cognition: from neuromodulation to representation, Trends Cogn Sci, 5, 479, 10.1016/S1364-6613(00)01769-1 Rajah, 2005, Region-specific changes in prefrontal function with age: a review of PET and fMRI studies on working and episodic memory, Brain, 128, 1964, 10.1093/brain/awh608 Bero, 2011, Neuronal activity regulates the regional vulnerability to amyloid-ß deposition, Nat Neurosci, 14, 750, 10.1038/nn.2801 Cirrito, 2005, Synaptic activity regulates interstitial fluid amyloid-ß levels in vivo, Neuron, 48, 913, 10.1016/j.neuron.2005.10.028 Alstott, 2009, Modeling the impact of lesions in the human brain, PLoS Comput Biol, 5, e1000408, 10.1371/journal.pcbi.1000408 Honey, 2008, Dynamical consequences of lesions in cortical networks, Hum Brain Mapp, 29, 802, 10.1002/hbm.20579 Warren, 2014, Network measures predict neuropsychological outcome after brain injury, Proc Natl Acad Sci U S A, 111, 14247, 10.1073/pnas.1322173111 Erdös, 1959, On random graphs, Publ Math Debrecen, 6, 290, 10.5486/PMD.1959.6.3-4.12 Barabási, 1999, Emergence of scaling in random networks, Science, 286, 509, 10.1126/science.286.5439.509 Watts, 1998, Collective dynamics of “small-world” networks, Nature, 393, 440, 10.1038/30918 Ramón y Cajal, 1995 Cherniak, 1999, Large-scale optimization of neuron arbors, Phys Rev E, 59, 6001, 10.1103/PhysRevE.59.6001 Cherniak, 2004, Global optimization of cerebral cortex layout, Proc Natl Acad Sci U S A, 101, 1081, 10.1073/pnas.0305212101 Chklovskii, 2002, Wiring optimization in cortical circuits, Neuron, 34, 341, 10.1016/S0896-6273(02)00679-7 Chklovskii, 2004, Synaptic connectivity and neuronal morphology: two sides of the same coin, Neuron, 43, 609 Mitchison, 1991, Neuronal branching patterns and the economy of cortical wiring, Proc Biol Sci, 245, 151, 10.1098/rspb.1991.0102 Sterling, 2015 van den Heuvel, 2016, Comparative connectomics, Trends Cogn Sci, 20, 345, 10.1016/j.tics.2016.03.001 Kaiser, 2006, Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems, PLoS Comput Biol, 2, e95, 10.1371/journal.pcbi.0020095 Fornito, 2011, Genetic influences on cost-efficient organization of human cortical functional networks, J Neurosci, 31, 3261, 10.1523/JNEUROSCI.4858-10.2011 Bassett, 2009, Cognitive fitness of cost-efficient brain functional networks, Proc Natl Acad Sci U S A, 106, 11747, 10.1073/pnas.0903641106 Vértes, 2012, Simple models of human brain functional networks, Proc Natl Acad Sci U S A, 109, 5868, 10.1073/pnas.1111738109 Betzel, 2016, Generative models of the human connectome, Neuroimage, 124, 1054, 10.1016/j.neuroimage.2015.09.041 Chen, 2013, Trade-off between multiple constraints enables simultaneous formation of modules and hubs in neural systems, PLoS Comput Biol, 9, e1002937, 10.1371/journal.pcbi.1002937 Vértes, 2014, Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks, Philos Trans R Soc Lond B Biol Sci, 369, 10.1098/rstb.2013.0531 Song, 2014, Spatial embedding of structural similarity in the cerebral cortex, Proc Natl Acad Sci U S A, 111, 16580, 10.1073/pnas.1414153111 Alexander-Bloch, 2013, The anatomical distance of functional connections predicts brain network topology in health and schizophrenia, Cereb Cortex, 23, 127, 10.1093/cercor/bhr388 Goñi, 2014, Resting-brain functional connectivity predicted by analytic measures of network communication, Proc Natl Acad Sci U S A, 111, 833, 10.1073/pnas.1315529111 Mišić, 2015, Cooperative and competitive spreading dynamics on the human connectome, Neuron, 86, 1518, 10.1016/j.neuron.2015.05.035 Raj, 2012, A network diffusion model of disease progression in dementia, Neuron, 73, 1204, 10.1016/j.neuron.2011.12.040 Raj, 2015, Network diffusion model of progression predicts longitudinal patterns of atrophy and metabolism in Alzheimer’s disease, Cell Report, 10, 359, 10.1016/j.celrep.2014.12.034 Abdelnour, 2015, Relating cortical atrophy in temporal lobe epilepsy with graph diffusion-based network models, PLoS Comput Biol, 11, e1004564, 10.1371/journal.pcbi.1004564 Schmidt, 2016, Simulating disease propagation across white matter connectome reveals anatomical substrate for neuropathology staging in amyotrophic lateral sclerosis, Neuroimage, 124, 762, 10.1016/j.neuroimage.2015.04.005 Weinberger, 2002, Neurotoxicity, neuroplasticity, and magnetic resonance imaging morphometry: what is happening in the schizophrenic brain?, Arch Gen Psychiatry, 59, 533, 10.1001/archpsyc.59.6.553 Sun, 2009, Progressive brain structural changes mapped as psychosis develops in “at risk” individuals, Schizophr Res, 108, 85, 10.1016/j.schres.2008.11.026 Wood, 2008, Progressive changes in the development toward schizophrenia: studies in subjects at increased symptomatic risk, Schizophr Bull, 34, 322, 10.1093/schbul/sbm149 Zipursky, 2013, the myth of schizophrenia as a progressive brain disease, Schizophr Bull, 39, 1363, 10.1093/schbul/sbs135 Deco, 2008, The dynamic brain: from spiking neurons to neural masses and cortical Fields, PLoS Comput Biol, 4, e1000092, 10.1371/journal.pcbi.1000092 Deco, 2011, Emerging concepts for the dynamical organization of resting-state activity in the brain, Nat Rev Neurosci, 12, 43, 10.1038/nrn2961 Breakspear, 2010, Computational models of the brain: from structure to function, Neuroimage, 52, 727, 10.1016/j.neuroimage.2010.05.061 Deco, 2012, Ongoing cortical activity at rest: criticality, multistability, and ghost attractors, J Neurosci, 32, 3366, 10.1523/JNEUROSCI.2523-11.2012 Honey, 2009, Predicting human resting-state functional connectivity from structural connectivity, Proc Natl Acad Sci U S A, 106, 2035, 10.1073/pnas.0811168106 Cabral, 2012, Modeling the outcome of structural disconnection on resting-state functional connectivity, Neuroimage, 62, 1342, 10.1016/j.neuroimage.2012.06.007 Yang, 2014, Altered global brain signal in schizophrenia, Proc Natl Acad Sci U S A, 111, 7438, 10.1073/pnas.1405289111 Yang, 2016, Functional hierarchy underlies preferential connectivity disturbances in schizophrenia, Proc Natl Acad Sci U S A, 113, E219, 10.1073/pnas.1508436113 Lynall, 2010, Functional connectivity and brain networks in schizophrenia, J Neurosci, 30, 9477, 10.1523/JNEUROSCI.0333-10.2010 Fornito, 2012, Schizophrenia, neuroimaging and connectomics, Neuroimage, 62, 2296, 10.1016/j.neuroimage.2011.12.090 Alexander-Bloch, 2010, Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia, Front Syst Neurosci, 4, 147, 10.3389/fnsys.2010.00147 Pettersson-Yeo, 2011, Dysconnectivity in schizophrenia: where are we now?, Neurosci Biobehav Rev, 35, 1110, 10.1016/j.neubiorev.2010.11.004 Fornito, 2013, Functional dysconnectivity of corticostriatal circuitry as a risk phenotype for psychosis, JAMA Psychiatry, 70, 1143, 10.1001/jamapsychiatry.2013.1976 Dandash, 2014, Altered striatal functional connectivity in subjects with an at-risk mental state for psychosis, Schizophr Bull, 40, 904, 10.1093/schbul/sbt093 Howes, 2009, Elevated striatal dopamine function linked to prodromal signs of schizophrenia, Arch Gen Psychiatry, 66, 13, 10.1001/archgenpsychiatry.2008.514 Howes, 2011, Dopamine synthesis capacity before onset of psychosis: a prospective [18F]-DOPA PET imaging study, Am J Psychiatry, 168, 1311, 10.1176/appi.ajp.2011.11010160 Anticevic, 2015, Association of thalamic dysconnectivity and conversion to psychosis in youth and young adults at elevated clinical risk, JAMA Psychiatry, 72, 882, 10.1001/jamapsychiatry.2015.0566 Baker, 2015, Developmental changes in brain network hub connectivity in late adolescence, J Neurosci, 35, 9078, 10.1523/JNEUROSCI.5043-14.2015 Jones, 2013, White matter integrity, fiber count, and other fallacies: the do’s and don’t’s of diffusion MRI, Neuroimage, 73, 239, 10.1016/j.neuroimage.2012.06.081 Thomas, 2014, Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited, Proc Natl Acad Sci U S A, 111, 16574, 10.1073/pnas.1405672111 Logothetis, 2008, What we can do and what we cannot do with fMRI, Nature, 453, 869, 10.1038/nature06976 Power, 2012, Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion, Neuroimage, 59, 2142, 10.1016/j.neuroimage.2011.10.018 Fox, 2009, The global signal and observed anticorrelated resting state brain networks, J Neurophysiol, 101, 3270, 10.1152/jn.90777.2008 Saad, 2012, Trouble at rest: how correlation patterns and group differences become distorted after global signal regression, Brain Connect, 2, 25, 10.1089/brain.2012.0080 Sanz Leon, 2013, The Virtual Brain: a simulator of primate brain network dynamics, Front Neuroinform, 7, 10, 10.3389/fninf.2013.00010 Hawrylycz, 2012, An anatomically comprehensive atlas of the adult human brain transcriptome, Nature, 489, 391, 10.1038/nature11405 Lein, 2006, Genome-wide atlas of gene expression in the adult mouse brain, Nature, 445, 168, 10.1038/nature05453 Rubinov, 2015, Wiring cost and topological participation of the mouse brain connectome, Proc Natl Acad Sci U S A, 112, 10032, 10.1073/pnas.1420315112 Richiardi, 2015, Correlated gene expression supports synchronous activity in brain networks, Science, 348, 1241, 10.1126/science.1255905 Vértes, 2016, Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks, Philos Trans R Soc Biol Sci, 371, 10.1098/rstb.2015.0362 Krienen, 2016, Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain, Proc Natl Acad Sci U S A, 113, E469, 10.1073/pnas.1510903113 van den Heuvel, 2015, Associated microscale spine density and macroscale connectivity disruptions in schizophrenia, Biol Psychiatry, 80, 293, 10.1016/j.biopsych.2015.10.005 Schroeter, 2015, Emergence of rich-club topology and coordinated dynamics in development of hippocampal functional networks in?vitro, J Neurosci, 35, 5459, 10.1523/JNEUROSCI.4259-14.2015 DeLong, 2015, Basal ganglia circuits as targets for neuromodulation in Parkinson disease, JAMA Neurol, 72, 1354, 10.1001/jamaneurol.2015.2397 Figee, 2013, Deep brain stimulation restores frontostriatal network activity in obsessive-compulsive disorder, Nat Neurosci, 16, 386, 10.1038/nn.3344 Riva-Posse, 2014, Defining critical white matter pathways mediating successful subcallosal cingulate deep brain stimulation for treatment-resistant depression, Biol Psychiatry, 76, 963, 10.1016/j.biopsych.2014.03.029 Fox, 2014, Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases, Proc Natl Acad Sci U S A, 111, E4367, 10.1073/pnas.1405003111 Friston, 2013, Analysing connectivity with Granger causality and dynamic causal modelling, Curr Opin Neurobiol, 23, 172, 10.1016/j.conb.2012.11.010 Friston, 1994, Functional and effective connectivity in neuroimaging: a synthesis, Hum Brain Mapp, 2, 56, 10.1002/hbm.460020107 Yeh, 2013, Deterministic diffusion fiber tracking improved by quantitative anisotropy, PLoS One, 8, e80713, 10.1371/journal.pone.0080713 Zalesky, 2010, Network-based statistic: identifying differences in brain networks, Neuroimage, 53, 1197, 10.1016/j.neuroimage.2010.06.041