{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:47:18Z","timestamp":1790750838795,"version":"4.1.0"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Commun Biol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Network architecture is a brain-organizational motif present across spatial scales from cell assemblies to distributed systems. Structural pathology in some neurodegenerative disorders selectively afflicts a subset of functional networks, motivating the network degeneration hypothesis (NDH). Recent evidence suggests that structural pathology recapitulating physiology may be a general property of neuropsychiatric disorders. To test this possibility, we compared functional and structural network meta-analyses drawing upon the BrainMap database. The functional meta-analysis included results from &gt;7,000 experiments of subjects performing &gt;100 task paradigms; the structural meta-analysis included &gt;2,000 experiments of patients with &gt;40 brain disorders. Structure-function network concordance was high: 68% of networks matched (pFWE\u2009&lt;\u20090.01), confirming the broader scope of NDH. This correspondence persisted across higher model orders. A positive linear association between disease and behavioral entropy (p\u2009=\u20090.0006;R\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20090.53) suggests nodal stress as a common mechanism. Corroborating this interpretation with independent data, we show that metabolic \u2018cost\u2019 significantly differs along this transdiagnostic\/multimodal gradient.\n                  <\/jats:p>","DOI":"10.1038\/s42003-021-01832-9","type":"journal-article","created":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T06:03:05Z","timestamp":1615183385000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Brain pathology recapitulates physiology: A network meta-analysis"],"prefix":"10.1038","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4672-0049","authenticated-orcid":false,"given":"Thomas J.","family":"Vanasse","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0465-2028","authenticated-orcid":false,"given":"Peter T.","family":"Fox","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4997-0003","authenticated-orcid":false,"given":"P. Mickle","family":"Fox","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franco","family":"Cauda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tommaso","family":"Costa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8166-069X","authenticated-orcid":false,"given":"Stephen M.","family":"Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon B.","family":"Eickhoff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jack L.","family":"Lancaster","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"1832_CR1","doi-asserted-by":"crossref","unstructured":"Sporns, O. in Micro-, Meso- and Macro-Connectomics of the Brain (eds Kennedy, H., Van Essen, D. C. & Christen, Y.) 107\u2013127 (Springer International Publishing, 2016).","DOI":"10.1007\/978-3-319-27777-6_8"},{"key":"1832_CR2","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1002\/(SICI)1097-0193(1998)6:3<160::AID-HBM5>3.0.CO;2-1","volume":"6","author":"MJ Mckeown","year":"1998","unstructured":"Mckeown, M. J. et al. Analysis of fMRI data by blind separation into independent spatial components. Hum. Brain Mapp. 6, 160\u2013188 (1998).","journal-title":"Hum. Brain Mapp."},{"key":"1832_CR3","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1098\/rstb.2005.1634","volume":"360","author":"CF Beckmann","year":"2005","unstructured":"Beckmann, C. F., DeLuca, M., Devlin, J. T. & Smith, S. M. Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. B 360, 1001\u20131013 (2005).","journal-title":"Philos. Trans. R. Soc. B"},{"key":"1832_CR4","doi-asserted-by":"publisher","first-page":"3031","DOI":"10.7554\/eLife.44443","volume":"8","author":"A Llera","year":"2019","unstructured":"Llera, A., Wolfers, T., Mulders, P. & Beckmann, C. F. Inter-individual differences in human brain structure and morphology link to variation in demographics and behavior. Elife 8, 3031 (2019).","journal-title":"Elife"},{"key":"1832_CR5","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1002\/mrm.1910340409","volume":"34","author":"B Biswal","year":"1995","unstructured":"Biswal, B., Zerrin Yetkin, F., Haughton, V. M. & Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537\u2013541 (1995).","journal-title":"Magn. Reson. Med."},{"key":"1832_CR6","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.neuron.2018.03.035","volume":"98","author":"C Gratton","year":"2018","unstructured":"Gratton, C. et al. Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron 98, 439\u2013452.e5 (2018).","journal-title":"Neuron"},{"key":"1832_CR7","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1038\/nn.4125","volume":"18","author":"SM Smith","year":"2015","unstructured":"Smith, S. M. et al. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat. Neurosci. 18, 1565\u20131567 (2015).","journal-title":"Nat. Neurosci."},{"key":"1832_CR8","doi-asserted-by":"publisher","first-page":"4734","DOI":"10.1073\/pnas.0911855107","volume":"107","author":"BB Biswal","year":"2010","unstructured":"Biswal, B. B. et al. Toward discovery science of human brain function. Proc. Natl Acad. Sci. USA 107, 4734\u20134739 (2010).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"1832_CR9","doi-asserted-by":"publisher","first-page":"237","DOI":"10.3389\/fnins.2013.00237","volume":"7","author":"KL Ray","year":"2013","unstructured":"Ray, K. L. et al. ICA model order selection of task co-activation networks. Front. Neurosci. 7, 237 (2013).","journal-title":"Front. Neurosci."},{"key":"1832_CR10","doi-asserted-by":"publisher","first-page":"4022","DOI":"10.1162\/jocn_a_00077","volume":"23","author":"AR Laird","year":"2011","unstructured":"Laird, A. R. et al. Behavioral interpretations of intrinsic connectivity networks. J. Cogn. Neurosci. 23, 4022\u20134037 (2011).","journal-title":"J. Cogn. Neurosci."},{"key":"1832_CR11","doi-asserted-by":"publisher","first-page":"990","DOI":"10.1016\/j.neuron.2012.06.002","volume":"74","author":"JW Buckholtz","year":"2012","unstructured":"Buckholtz, J. W. & Meyer-Lindenberg, A. Psychopathology and the human connectome: toward a transdiagnostic model of risk for mental illness. Neuron 74, 990\u20131004 (2012).","journal-title":"Neuron"},{"key":"1832_CR12","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1176\/appi.ajp.2010.09091379","volume":"167","author":"T Insel","year":"2010","unstructured":"Insel, T. et al. Research Domain Criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167, 748\u2013751 (2010).","journal-title":"Am. J. Psychiatry"},{"key":"1832_CR13","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1016\/j.neuron.2012.03.004","volume":"73","author":"J Zhou","year":"2012","unstructured":"Zhou, J., Gennatas, E. D., Kramer, J. H., Miller, B. L. & Seeley, W. W. Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron 73, 1216\u20131227 (2012).","journal-title":"Neuron"},{"key":"1832_CR14","doi-asserted-by":"publisher","first-page":"3211","DOI":"10.1093\/brain\/awy252","volume":"141","author":"F Cauda","year":"2018","unstructured":"Cauda, F. et al. Brain structural alterations are distributed following functional, anatomic and genetic connectivity. Brain 141, 3211\u20133232 (2018).","journal-title":"Brain"},{"key":"1832_CR15","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.nicl.2018.01.002","volume":"18","author":"E Kotkowski","year":"2018","unstructured":"Kotkowski, E., Price, L. R., Mickle Fox, P., Vanasse, T. J. & Fox, P. T. The hippocampal network model: a transdiagnostic metaconnectomic approach. Neuroimage Clin. 18, 115\u2013129 (2018).","journal-title":"Neuroimage Clin."},{"key":"1832_CR16","doi-asserted-by":"publisher","first-page":"816.e19","DOI":"10.1016\/j.crad.2019.07.005","volume":"74","author":"FL Chiang","year":"2019","unstructured":"Chiang, F. L. et al. Localised grey matter atrophy in multiple sclerosis is network-based: a coordinate-based meta-analysis. Clin. Radiol. 74, 816.e19\u2013816.e28\u00a0(2019).","journal-title":"Clin. Radiol."},{"key":"1832_CR17","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.neuron.2009.03.024","volume":"62","author":"WW Seeley","year":"2009","unstructured":"Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L. & Greicius, M. D. Neurodegenerative diseases target large-scale human brain networks. Neuron 62, 42\u201352 (2009).","journal-title":"Neuron"},{"key":"1832_CR18","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1001\/jamapsychiatry.2014.2206","volume":"72","author":"M Goodkind","year":"2015","unstructured":"Goodkind, M. et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry 72, 305\u2013315 (2015).","journal-title":"JAMA Psychiatry"},{"key":"1832_CR19","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.neuroimage.2018.09.036","volume":"184","author":"F Cauda","year":"2018","unstructured":"Cauda, F. et al. The alteration landscape of the cerebral cortex. Neuroimage 184, 359\u2013371 (2018).","journal-title":"Neuroimage"},{"key":"1832_CR20","doi-asserted-by":"publisher","first-page":"17757","DOI":"10.1073\/pnas.1010459107","volume":"107","author":"SN Vaishnavi","year":"2010","unstructured":"Vaishnavi, S. N. et al. Regional aerobic glycolysis in the human brain. Proc. Natl Acad. Sci. USA 107, 17757\u201317762 (2010).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"1832_CR21","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1146\/annurev-neuro-062012-170320","volume":"37","author":"PT Fox","year":"2014","unstructured":"Fox, P. T., Lancaster, J. L., Laird, A. R. & Eickhoff, S. B. Meta-analysis in human neuroimaging: computational modeling of large-scale databases. Annu. Rev. Neurosci. 37, 409\u2013434 (2014).","journal-title":"Annu. Rev. Neurosci."},{"key":"1832_CR22","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.nic.2017.06.012","volume":"27","author":"VD Calhoun","year":"2017","unstructured":"Calhoun, V. D. & de Lacy, N. Ten key observations on the analysis of resting-state functional MR imaging data using independent component analysis. Neuroimaging Clin. North Am. 27, 561\u2013579 (2017).","journal-title":"Neuroimaging Clin. North Am."},{"key":"1832_CR23","doi-asserted-by":"publisher","first-page":"13040","DOI":"10.1073\/pnas.0905267106","volume":"106","author":"SM Smith","year":"2009","unstructured":"Smith, S. M. et al. Correspondence of the brain\u2019s functional architecture during activation and rest. Proc. Natl Acad. Sci. USA 106, 13040\u201313045 (2009).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"1832_CR24","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1162\/neco.1995.7.6.1129","volume":"7","author":"AJ Bell","year":"1995","unstructured":"Bell, A. J. & Sejnowski, T. J. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7, 1129\u20131159 (1995).","journal-title":"Neural Comput."},{"key":"1832_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-08546-x","volume":"10","author":"E Shokri-Kojori","year":"2019","unstructured":"Shokri-Kojori, E. et al. Correspondence between cerebral glucose metabolism and BOLD reveals relative power and cost in human brain. Nat. Commun. 10, 1\u201312 (2019).","journal-title":"Nat. Commun."},{"key":"1832_CR26","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1002\/hbm.24866","volume":"41","author":"A Mess\u00e9","year":"2020","unstructured":"Mess\u00e9, A. Parcellation influence on the connectivity\u2010based structure\u2013function relationship in the human brain. Hum. Brain Mapp. 41, 1167\u20131180 (2020).","journal-title":"Hum. Brain Mapp."},{"key":"1832_CR27","first-page":"664250","volume":"56","author":"EJ Cornblath","year":"2019","unstructured":"Cornblath, E. J., Robinson, J. L., Lee, V. M. Y., Trojanowski, J. Q. & Bassett, D. S. Defining and predicting transdiagnostic categories of neurodegenerative disease. bioRxiv 56, 664250 (2019).","journal-title":"bioRxiv"},{"key":"1832_CR28","doi-asserted-by":"publisher","first-page":"2382","DOI":"10.1093\/brain\/awu132","volume":"137","author":"NA Crossley","year":"2014","unstructured":"Crossley, N. A. et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 137, 2382\u20132395 (2014).","journal-title":"Brain"},{"key":"1832_CR29","doi-asserted-by":"publisher","first-page":"798","DOI":"10.1016\/j.neuron.2013.07.035","volume":"79","author":"JD Power","year":"2013","unstructured":"Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N. & Petersen, S. E. Evidence for hubs in human functional brain networks. Neuron 79, 798\u2013813 (2013).","journal-title":"Neuron"},{"key":"1832_CR30","doi-asserted-by":"publisher","first-page":"14496","DOI":"10.1523\/JNEUROSCI.4004-09.2009","volume":"29","author":"AR Laird","year":"2009","unstructured":"Laird, A. R. et al. Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling. J. Neurosci. 29, 14496\u201314505 (2009).","journal-title":"J. Neurosci."},{"key":"1832_CR31","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1016\/j.tics.2013.09.012","volume":"17","author":"MP van den Heuvel","year":"2013","unstructured":"van den Heuvel, M. P. & Sporns, O. Network hubs in the human brain. Trends Cogn. Sci. 17, 683\u2013696 (2013).","journal-title":"Trends Cogn. Sci."},{"key":"1832_CR32","doi-asserted-by":"publisher","first-page":"1860","DOI":"10.1523\/JNEUROSCI.5062-08.2009","volume":"29","author":"RL Buckner","year":"2009","unstructured":"Buckner, R. L. et al. cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer\u2019s disease. J. Neurosci. 29, 1860\u20131873 (2009).","journal-title":"J. Neurosci."},{"key":"1832_CR33","doi-asserted-by":"publisher","first-page":"2235","DOI":"10.1083\/jcb.201803152","volume":"217","author":"G Yellen","year":"2018","unstructured":"Yellen, G. Fueling thought: management of glycolysis and oxidative phosphorylation in neuronal metabolism. J. Cell Biol. 217, 2235\u20132246 (2018).","journal-title":"J. Cell Biol."},{"key":"1832_CR34","first-page":"238","volume":"5","author":"JMN Duarte","year":"2013","unstructured":"Duarte, J. M. N., Schuck, P. F., Wenk, G. L. & Ferreira, G. C. Metabolic disturbances in diseases with neurological involvement. Aging Dis. 5, 238\u2013255 (2013).","journal-title":"Aging Dis."},{"key":"1832_CR35","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1016\/S0006-3223(00)00235-3","volume":"47","author":"H-P Volz","year":"2000","unstructured":"Volz, H.-P. et al. Reduced phosphodiesters and high-energy phosphates in the frontal lobe of schizophrenic patients: a 31P chemical shift spectroscopic-imaging study. Biol. Psychiatry 47, 954\u2013961 (2000).","journal-title":"Biol. Psychiatry"},{"key":"1832_CR36","first-page":"167","volume":"17","author":"MA Mohamed","year":"2015","unstructured":"Mohamed, M. A. & Sheikh, A. Magnetic resonance spectroscopy in major depressive disorder. Int. J. Emerg. Mental Health Hum. Resil. 17, 167\u2013187 (2015).","journal-title":"Int. J. Emerg. Mental Health Hum. Resil."},{"key":"1832_CR37","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.3389\/fnins.2017.00493","volume":"11","author":"GS Zuccoli","year":"2017","unstructured":"Zuccoli, G. S., Saia-Cereda, V. M., Nascimento, J. M. & Martins-de-Souza, D. The energy metabolism dysfunction in psychiatric disorders postmortem brains: focus on proteomic evidence. Front. Neurosci. 11, 1320 (2017).","journal-title":"Front. Neurosci."},{"key":"1832_CR38","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1038\/npp.2017.167","volume":"43","author":"H Hagihara","year":"2018","unstructured":"Hagihara, H. et al. Decreased brain pH as a shared endophenotype of psychiatric disorders. Neuropsychopharmacology 43, 459\u2013468 (2018).","journal-title":"Neuropsychopharmacology"},{"key":"1832_CR39","doi-asserted-by":"publisher","first-page":"3288","DOI":"10.2741\/2926","volume":"13","author":"AA Fatokun","year":"2008","unstructured":"Fatokun, A. A. Oxidative stress in neurodegeneration and available means of protection. Front. Biosci. 13, 3288 (2008).","journal-title":"Front. Biosci."},{"key":"1832_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-15701-2","volume":"11","author":"JW Vogel","year":"2020","unstructured":"Vogel, J. W. et al. Spread of pathological tau proteins through communicating neurons in human Alzheimer\u2019s disease. Nat. Commun. 11, 1\u201315 (2020).","journal-title":"Nat. Commun."},{"key":"1832_CR41","doi-asserted-by":"publisher","first-page":"e115913","DOI":"10.1371\/journal.pone.0115913","volume":"9","author":"C Cioli","year":"2014","unstructured":"Cioli, C., Abdi, H., Beaton, D., Burnod, Y. & Mesmoudi, S. Differences in human cortical gene expression match the temporal properties of large-scale functional networks. PLoS ONE 9, e115913 (2014).","journal-title":"PLoS ONE"},{"key":"1832_CR42","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1111\/ene.13413","volume":"25","author":"P McColgan","year":"2018","unstructured":"McColgan, P. & Tabrizi, S. J. Huntington\u2019s disease: a clinical review. Eur. J. Neurol. 25, 24\u201334 (2018).","journal-title":"Eur. J. Neurol."},{"key":"1832_CR43","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1001\/archneur.55.1.84","volume":"55","author":"RI Mesholam","year":"1998","unstructured":"Mesholam, R. I., Moberg, P. J., Mahr, R. N. & Doty, R. L. Olfaction in neurodegenerative disease: a meta-analysis of olfactory functioning in Alzheimer\u2019s and Parkinson\u2019s diseases. Arch. Neurol. 55, 84\u201390 (1998).","journal-title":"Arch. Neurol."},{"key":"1832_CR44","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1176\/appi.ajp.2019.19050560","volume":"177","author":"JP Gray","year":"2020","unstructured":"Gray, J. P., M\u00fcller, V. I., Eickhoff, S. B. & Fox, P. T. Multi-modal abnormalities of brain structure and function in major depressive disorder: a meta-analysis of neuroimaging studies. Am. J. Psychiatry\u00a0177, 422\u2013434\u00a0(2020).","journal-title":"Am. J. Psychiatry"},{"key":"1832_CR45","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1001\/jamapsychiatry.2016.2783","volume":"74","author":"VI M\u00fcller","year":"2017","unstructured":"M\u00fcller, V. I. et al. Altered brain activity in unipolar depression revisited: meta-analyses of neuroimaging studies. JAMA Psychiatry 74, 47\u201355 (2017).","journal-title":"JAMA Psychiatry"},{"key":"1832_CR46","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1002\/hbm.20166","volume":"27","author":"VD Calhoun","year":"2006","unstructured":"Calhoun, V. D. et al. Method for multimodal analysis of independent source differences in schizophrenia: combining gray matter structural and auditory oddball functional data. Hum. Brain Mapp. 27, 47\u201362 (2006).","journal-title":"Hum. Brain Mapp."},{"key":"1832_CR47","doi-asserted-by":"publisher","first-page":"2198","DOI":"10.1016\/j.neuroimage.2010.09.073","volume":"54","author":"AR Groves","year":"2011","unstructured":"Groves, A. R., Beckmann, C. F., Smith, S. M. & Woolrich, M. W. Linked independent component analysis for multimodal data fusion. Neuroimage 54, 2198\u20132217 (2011).","journal-title":"Neuroimage"},{"key":"1832_CR48","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.neuroimage.2012.06.038","volume":"63","author":"AR Groves","year":"2012","unstructured":"Groves, A. R. et al. Benefits of multi-modal fusion analysis on a large-scale dataset: life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure. Neuroimage 63, 365\u2013380 (2012).","journal-title":"Neuroimage"},{"key":"1832_CR49","first-page":"230","volume":"1","author":"VD Calhoun","year":"2016","unstructured":"Calhoun, V. D. & Sui, J. Multimodal fusion of brain imaging data: a key to finding the missing link(s) in complex mental illness. Biol. Psychiatry 1, 230\u2013244 (2016).","journal-title":"Biol. Psychiatry"},{"key":"1832_CR50","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1016\/j.neuroimage.2015.07.060","volume":"124","author":"AT Reid","year":"2016","unstructured":"Reid, A. T. et al. ANIMA: a data-sharing initiative for neuroimaging meta-analyses. Neuroimage 124, 1245\u20131253 (2016).","journal-title":"Neuroimage"},{"key":"1832_CR51","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1038\/nrn789","volume":"3","author":"PT Fox","year":"2002","unstructured":"Fox, P. T. & Lancaster, J. L. Mapping context and content: the BrainMap model. Nat. Rev. Neurosci. 3, 319\u2013321 (2002).","journal-title":"Nat. Rev. Neurosci."},{"key":"1832_CR52","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1002\/hbm.20854","volume":"31","author":"JL Robinson","year":"2010","unstructured":"Robinson, J. L., Laird, A. R., Glahn, D. C., Lovallo, W. R. & Fox, P. T. Metaanalytic connectivity modeling: delineating the functional connectivity of the human amygdala. Hum. Brain Mapp. 31, 173\u2013184 (2010).","journal-title":"Hum. Brain Mapp."},{"key":"1832_CR53","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.neuroimage.2016.12.037","volume":"149","author":"AR Laird","year":"2017","unstructured":"Laird, A. R. et al. Heterogeneous fractionation profiles of meta-analytic coactivation networks. Neuroimage 149, 424\u2013435 (2017).","journal-title":"Neuroimage"},{"key":"1832_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/hbm.21186","volume":"33","author":"PE Turkeltaub","year":"2011","unstructured":"Turkeltaub, P. E. et al. Minimizing within-experiment and within-group effects in activation likelihood estimation meta-analyses. Hum. Brain Mapp. 33, 1\u201313 (2011).","journal-title":"Hum. Brain Mapp."},{"key":"1832_CR55","doi-asserted-by":"publisher","first-page":"3308","DOI":"10.1002\/hbm.24078","volume":"39","author":"TJ Vanasse","year":"2018","unstructured":"Vanasse, T. J. et al. BrainMap VBM: an environment for structural meta-analysis. Hum. Brain Mapp. 39, 3308\u20133325 (2018).","journal-title":"Hum. Brain Mapp."},{"key":"1832_CR56","doi-asserted-by":"publisher","first-page":"S208","DOI":"10.1016\/j.neuroimage.2004.07.051","volume":"23","author":"SM Smith","year":"2004","unstructured":"Smith, S. M. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, S208\u2013S219 (2004).","journal-title":"Neuroimage"},{"key":"1832_CR57","doi-asserted-by":"publisher","unstructured":"Alexander-Bloch, A. et al. On testing for spatial correspondence between maps of human brain structure and function. Neuroimage https:\/\/doi.org\/10.1016\/j.neuroimage.2018.05.070 (2018).","DOI":"10.1016\/j.neuroimage.2018.05.070"},{"key":"1832_CR58","doi-asserted-by":"publisher","first-page":"6136","DOI":"10.1523\/JNEUROSCI.2912-18.2019","volume":"39","author":"S Smith","year":"2019","unstructured":"Smith, S. et al. Structural variability in the human brain reflects fine-grained functional architecture at the population level. J. Neurosci. 39, 6136\u20136149 (2019).","journal-title":"J. Neurosci."},{"key":"1832_CR59","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1002\/hbm.20141","volume":"25","author":"PT Fox","year":"2005","unstructured":"Fox, P. T. et al. Brainmap taxonomy of experimental design: description and evaluation. Hum. Brain Mapp. 25, 185\u2013198 (2005).","journal-title":"Hum. Brain Mapp."},{"key":"1832_CR60","unstructured":"World Health Organization. ICD-10: International Statistical Classification of Diseases And Related Health Problems: 10th revision (World Health Organization, 2004)."},{"key":"1832_CR61","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.neuroimage.2013.01.071","volume":"73","author":"ML Anderson","year":"2013","unstructured":"Anderson, M. L., Kinnison, J. & Pessoa, L. Describing functional diversity of brain regions and brain networks. Neuroimage 73, 50\u201358 (2013).","journal-title":"Neuroimage"},{"key":"1832_CR62","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379\u2013423 (1948).","journal-title":"Bell Syst. Tech. J."},{"key":"1832_CR63","unstructured":"Bishop, C. M. Pattern Recognition and Machine Learning (Springer, 2006)."},{"key":"1832_CR64","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3389\/fninf.2014.00014","volume":"8","author":"A Abraham","year":"2014","unstructured":"Abraham, A. et al. Machine learning for neuroimaging with scikit-learn. Front. Neuroinform. 8, 14 (2014).","journal-title":"Front. Neuroinform."},{"key":"1832_CR65","doi-asserted-by":"publisher","unstructured":"Brett, M. et al. nipy\/nibabel: 2.5.1 https:\/\/doi.org\/10.5281\/zenodo.3458246 (2019).","DOI":"10.5281\/zenodo.3458246"},{"key":"1832_CR66","doi-asserted-by":"publisher","unstructured":"Esteban, O. et al. nipy\/nipype: 1.5.1 https:\/\/doi.org\/10.5281\/zenodo.4035081 (2020).","DOI":"10.5281\/zenodo.4035081"}],"container-title":["Communications Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42003-021-01832-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42003-021-01832-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42003-021-01832-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,22]],"date-time":"2023-10-22T13:55:27Z","timestamp":1697982927000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42003-021-01832-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,8]]},"references-count":66,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["1832"],"URL":"https:\/\/doi.org\/10.1038\/s42003-021-01832-9","relation":{},"ISSN":["2399-3642"],"issn-type":[{"value":"2399-3642","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,8]]},"assertion":[{"value":"7 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"301"}}