{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T20:10:35Z","timestamp":1775160635018,"version":"3.50.1"},"reference-count":78,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T00:00:00Z","timestamp":1575244800000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61473196"],"award-info":[{"award-number":["61473196"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Auburn University MRI Research Center"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Inf."],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objective<\/jats:title>\n                <jats:p>It is important to identify brain-based biomarkers that progressively deteriorate from healthy to mild cognitive impairment (MCI) to Alzheimer\u2019s disease (AD). Cortical thickness, amyloid-\u00df deposition, and graph measures derived from functional connectivity (FC) networks obtained using functional MRI (fMRI) have been previously identified as potential biomarkers. Specifically, in the latter case, betweenness centrality (BC), a nodal graph measure quantifying information flow, is reduced in both AD and MCI. However, all such reports have utilized BC calculated from undirected networks that characterize synchronization rather than information flow, which is better characterized using directed networks.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Therefore, we estimated BC from directed networks using Granger causality (GC) on resting-state fMRI data (<jats:italic>N<\/jats:italic>\u2009=\u2009132) to compare the following populations (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05, FDR corrected for multiple comparisons): normal control (NC), early MCI (EMCI), late MCI (LMCI) and AD. We used an additional metric called middleman power (MP), which not only characterizes nodal information flow as in BC, but also measures nodal power critical for information flow in the entire network.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>MP detected more brain regions than BC that progressively deteriorated from NC to EMCI to LMCI to AD, as well as exhibited significant associations with behavioral measures. Additionally, graph measures obtained from conventional FC networks could not identify a single node, underscoring the relevance of GC.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Our findings demonstrate the superiority of MP over BC as well as GC over FC in our case. MP obtained from GC networks could serve as a potential biomarker for progressive deterioration of MCI and AD.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s40708-019-0101-x","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T10:09:09Z","timestamp":1575281349000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Deterioration from healthy to mild cognitive impairment and Alzheimer\u2019s disease mirrored in corresponding loss of centrality in directed brain networks"],"prefix":"10.1186","volume":"6","author":[{"given":"Sinan","family":"Zhao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9553-1354","authenticated-orcid":false,"given":"D.","family":"Rangaprakash","sequence":"additional","affiliation":[]},{"given":"Peipeng","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Gopikrishna","family":"Deshpande","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,2]]},"reference":[{"issue":"9533","key":"101_CR1","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1016\/S0140-6736(06)69113-7","volume":"368","author":"K Blennow","year":"2006","unstructured":"Blennow K, de Leon MJ, Zetterberg H (2006) Alzheimer\u2019s disease. Lancet 368(9533):387\u2013403","journal-title":"Lancet"},{"issue":"4","key":"101_CR2","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.nic.2005.09.008","volume":"15","author":"SG Mueller","year":"2005","unstructured":"Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack C, Jagust W, Trojanowski JQ, Toga AW, Beckett L (2005) The Alzheimer\u2019s disease neuroimaging initiative. Neuroimaging Clin N Am 15(4):869\u2013877","journal-title":"Neuroimaging Clin N Am"},{"issue":"3","key":"101_CR3","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.jalz.2010.03.007","volume":"6","author":"MW Weiner","year":"2010","unstructured":"Weiner MW, Aisen PS, Jack CR, Jagust WJ, Trojanowski JQ, Shaw L, Saykin AJ, Morris JC, Cairns N, Beckett LA, Toga A, Green R, Walter S, Soares H, Snyder P, Siemers E, Potter W, Cole PE, Schmidt M (2010) The Alzheimer\u2019s disease neuroimaging initiative: progress report and future plans. Alzheimers Dement 6(3):202\u2013211","journal-title":"Alzheimers Dement"},{"issue":"Pt 4","key":"101_CR4","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1093\/brain\/awr044","volume":"134","author":"D Tosun","year":"2011","unstructured":"Tosun D, Schuff N, Mathis CA, Jagust W, Weiner MW (2011) Spatial patterns of brain amyloid-beta burden and atrophy rate associations in mild cognitive impairment. Brain 134(Pt 4):1077\u20131088","journal-title":"Brain"},{"key":"101_CR5","doi-asserted-by":"publisher","first-page":"1985","DOI":"10.1001\/archneur.58.12.1985","volume":"58","author":"RC Petersen","year":"2001","unstructured":"Petersen RC, Doody R, Kurz A, Mohs RC, Morris JC, Rabins PV, Ritchie K, Rossor M, Thal L, Winblad B (2001) Current concepts in mild cognitive impairment. Arch Neurol 58:1985\u20131992","journal-title":"Arch Neurol"},{"issue":"1","key":"101_CR6","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1148\/radiol.12120319","volume":"266","author":"IK Amlien","year":"2013","unstructured":"Amlien IK, Fjell AM, Walhovd KB, Selnes P, Stenset V, Grambaite R, Bj\u00f8rnerud A, Due-T\u00f8nnessen P, Skinningsrud A, Gjerstad L, Reinvang I, Fladby T (2013) Mild cognitive impairment: cerebrospinal fluid tau biomarker pathologic levels and longitudinal changes in white matter integrity. Radiology 266(1):295\u2013303","journal-title":"Radiology"},{"issue":"5","key":"101_CR7","doi-asserted-by":"publisher","first-page":"2147","DOI":"10.1523\/JNEUROSCI.4437-12.2013","volume":"33","author":"G Douaud","year":"2013","unstructured":"Douaud G, Menke RAL, Gass A, Monsch AU, Rao A, Whitcher B, Zamboni G, Matthews PM, Sollberger M, Smith S (2013) Brain microstructure reveals early abnormalities more than two years prior to clinical progression from mild cognitive impairment to Alzheimer\u2019s disease. J Neurosci 33(5):2147\u20132155","journal-title":"J Neurosci"},{"issue":"4","key":"101_CR8","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1002\/mrm.1910340409","volume":"34","author":"B Biswal","year":"1995","unstructured":"Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537\u2013541","journal-title":"Magn Reson Med"},{"issue":"2","key":"101_CR9","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1016\/j.neuroimage.2010.09.024","volume":"54","author":"G Deshpande","year":"2011","unstructured":"Deshpande G, Santhanam P, Hu X (2011) Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data. Neuroimage 54(2):1043\u20131052","journal-title":"Neuroimage"},{"key":"101_CR10","first-page":"3","volume":"9","author":"P Liang","year":"2014","unstructured":"Liang P, Li Z, Deshpande G, Wang Z, Hu X, Li K (2014) Altered causal connectivity of resting state brain networks in amnesic MCI. PLoS ONE 9:3","journal-title":"PLoS ONE"},{"key":"101_CR11","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1371\/journal.pcbi.1000100","volume":"4","author":"K Supekar","year":"2008","unstructured":"Supekar K, Menon V, Rubin D, Musen M, Greicius MD (2008) Network analysis of intrinsic functional brain connectivity in Alzheimer\u2019s disease. PLoS Comput Biol 4:6","journal-title":"PLoS Comput Biol"},{"issue":"10","key":"101_CR12","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1002\/hbm.20324","volume":"28","author":"K Wang","year":"2007","unstructured":"Wang K, Liang M, Wang L, Tian L, Zhang X, Li K, Jiang T (2007) Altered functional connectivity in early Alzheimer\u2019s disease: a resting-state fMRI study. Hum Brain Mapp 28(10):967\u2013978","journal-title":"Hum Brain Mapp"},{"key":"101_CR13","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1093\/brain\/124.4.739","volume":"124","author":"CL Grady","year":"2001","unstructured":"Grady CL, Furey ML, Pietrini P, Horwitz B, Rapoport SI (2001) Altered brain functional connectivity and impaired short-term memory in Alzheimer\u2019s disease. Brain 124:739\u2013756","journal-title":"Brain"},{"issue":"2","key":"101_CR14","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1159\/000094558","volume":"22","author":"R Heun","year":"2006","unstructured":"Heun R, Freymann K, Erb M, Leube DT, Jessen F, Kircher TT, Grodd W (2006) Successful verbal retrieval in elderly subjects is related to concurrent hippocampal and posterior cingulate activation. Dement Geriatr Cogn Disord 22(2):165\u2013172","journal-title":"Dement Geriatr Cogn Disord"},{"issue":"1","key":"101_CR15","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1073\/pnas.0135058100","volume":"100","author":"MD Greicius","year":"2003","unstructured":"Greicius MD, Krasnow B, Reiss AL, Menon V (2003) Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci USA 100(1):253\u2013258","journal-title":"Proc Natl Acad Sci USA"},{"issue":"3","key":"101_CR16","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1016\/j.neuroimage.2009.12.120","volume":"50","author":"S Huang","year":"2010","unstructured":"Huang S, Li J, Sun L, Ye J, Fleisher A, Wu T, Chen K, Reiman E (2010) Learning brain connectivity of Alzheimer\u2019s disease by sparse inverse covariance estimation. Neuroimage 50(3):935\u2013949","journal-title":"Neuroimage"},{"issue":"47","key":"101_CR17","doi-asserted-by":"publisher","first-page":"18760","DOI":"10.1073\/pnas.0708803104","volume":"104","author":"C Sorg","year":"2007","unstructured":"Sorg C, Riedl V, M\u00fchlau M, Calhoun VD, Eichele T, L\u00e4er L, Drzezga A, F\u00f6rstl H, Kurz A, Zimmer C, Wohlschl\u00e4ger AM (2007) Selective changes of resting-state networks in individuals at risk for Alzheimer\u2019s disease. Proc Natl Acad Sci USA 104(47):18760\u201318765","journal-title":"Proc Natl Acad Sci USA"},{"key":"101_CR18","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.brainres.2009.09.028","volume":"1302","author":"F Bai","year":"2009","unstructured":"Bai F, Watson DR, Yu H, Shi Y, Yuan Y, Zhang Z (2009) Abnormal resting-state functional connectivity of posterior cingulate cortex in amnestic type mild cognitive impairment. Brain Res 1302:167\u2013174","journal-title":"Brain Res"},{"issue":"7","key":"101_CR19","doi-asserted-by":"publisher","first-page":"754","DOI":"10.2174\/15672050113109990146","volume":"10","author":"B Zhou","year":"2013","unstructured":"Zhou B, Liu Y, Zhang Z, An N, Yao H, Wang P, Wang L, Zhang X, Jiang T (2013) Impaired functional connectivity of the thalamus in Alzheimer\u2019 s disease and mild cognitive impairment: a resting-state fMRI study. Curr Alzheimer Res 10(7):754\u2013766","journal-title":"Curr Alzheimer Res"},{"issue":"12","key":"101_CR20","doi-asserted-by":"publisher","first-page":"1791","DOI":"10.1212\/WNL.58.12.1791","volume":"58","author":"DR Thal","year":"2002","unstructured":"Thal DR, R\u00fcb U, Orantes M, Braak H (2002) Phases of A beta-deposition in the human brain and its relevance for the development of AD. Neurology 58(12):1791\u20131800","journal-title":"Neurology"},{"issue":"6","key":"101_CR21","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1177\/1073858406293182","volume":"12","author":"DS Bassett","year":"2006","unstructured":"Bassett DS, Bullmore E (2006) Small-world brain networks. Neuroscientist 12(6):512\u2013523","journal-title":"Neuroscientist"},{"key":"101_CR22","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1111\/j.1749-6632.2008.03752.x","volume":"1158","author":"Y Gong","year":"2009","unstructured":"Gong Y, Zhang Z (2009) Global robustness and identifiability of random, scale-free, and small-world networks. Ann N Y Acad Sci 1158:82\u201392","journal-title":"Ann N Y Acad Sci"},{"key":"101_CR23","first-page":"10","volume":"5","author":"BCM van Wijk","year":"2010","unstructured":"van Wijk BCM, Stam CJ, Daffertshofer A (2010) Comparing brain networks of different size and connectivity density using graph theory. PLoS ONE 5:10","journal-title":"PLoS ONE"},{"issue":"5","key":"101_CR24","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1089\/brain.2014.0235","volume":"4","author":"EJ Friedman","year":"2014","unstructured":"Friedman EJ, Young K, Asif D, Jutla I, Liang M, Wilson S, Landsberg AS, Schuff N (2014) Directed progression brain networks in Alzheimer\u2019s disease: properties and classification. Brain Connect 4(5):384\u2013393","journal-title":"Brain Connect"},{"issue":"4","key":"101_CR25","first-page":"191","volume":"31","author":"D Ottavia","year":"2016","unstructured":"Ottavia D, Mara C (2016) Network functional connectivity and whole-brain functional connectomics to investigate cognitive decline in neurodegenerative conditions. Funct Neurol 31(4):191\u2013203","journal-title":"Funct Neurol"},{"key":"101_CR26","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1371\/journal.pone.0013788","volume":"5","author":"EJ Sanz-Arigita","year":"2010","unstructured":"Sanz-Arigita EJ, Schoonheim MM, Damoiseaux JS, Rombouts SARB, Maris E, Barkhof F, Scheltens P, Stam CJ (2010) Loss of \u2018Small-World\u2019 networks in Alzheimer\u2019s disease: graph analysis of fMRI resting-state functional connectivity. PLoS ONE 5:11","journal-title":"PLoS ONE"},{"issue":"1","key":"101_CR27","doi-asserted-by":"publisher","first-page":"e53922","DOI":"10.1371\/journal.pone.0053922","volume":"8","author":"EH Seo","year":"2013","unstructured":"Seo EH, Lee DSY, Lee J, Park J-S, Sohn BK, Choe YM, Woo JI (2013) Whole-brain functional networks in cognitively normal, mild cognitive impairment, and Alzheimer\u2019s disease. PLoS ONE 8(1):e53922","journal-title":"PLoS ONE"},{"key":"101_CR28","unstructured":"Owen S, Robert G (2014) Critical nodes in directed networks. arXiv Prepr. arXiv1401.0655"},{"key":"101_CR29","first-page":"1","volume":"12","author":"G Bellucci","year":"2016","unstructured":"Bellucci G, Chernyak S, Hoffman M, Deshpande G, DalMonte O, Knutson KM, Grafman J, Krueger F (2016) Effective connectivity of brain regions underlying third-party punishment: Functional MRI and Granger causality evidence. Soc Neurosci 12:1\u201311","journal-title":"Soc Neurosci"},{"issue":"4","key":"101_CR30","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1002\/cb.1575","volume":"15","author":"V Chattaraman","year":"2016","unstructured":"Chattaraman V, Deshpande G, Kim H, Sreenivasan KR (2016) Form \u2018defines\u2019 function: neural connectivity between aesthetic perception and product purchase decisions in an fMRI study. J Consum Behav 15(4):335\u2013347","journal-title":"J Consum Behav"},{"issue":"4","key":"101_CR31","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.1002\/hbm.20606","volume":"30","author":"G Deshpande","year":"2009","unstructured":"Deshpande G, LaConte S, James GA, Peltier S, Hu X (2009) Multivariate granger causality analysis of fMRI data. Hum Brain Mapp 30(4):1361\u20131373","journal-title":"Hum Brain Mapp"},{"issue":"2","key":"101_CR32","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1002\/hbm.23057","volume":"37","author":"C Feng","year":"2016","unstructured":"Feng C, Deshpande G, Liu C, Gu R, Luo YJ, Krueger F (2016) Diffusion of responsibility attenuates altruistic punishment: a functional magnetic resonance imaging effective connectivity study. Hum Brain Mapp 37(2):663\u2013677","journal-title":"Hum Brain Mapp"},{"issue":"7","key":"101_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/npp.2015.28","volume":"40","author":"MM Grant","year":"2015","unstructured":"Grant MM, Wood K, Sreenivasan K, Wheelock M, White D, Thomas J, Knight DC, Deshpande G (2015) Influence of early life stress on intra- and extra-amygdaloid causal connectivity. Neuropsychopharmacology 40(7):1\u201312","journal-title":"Neuropsychopharmacology"},{"key":"101_CR34","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1016\/j.neuroimage.2015.10.002","volume":"124","author":"BM Hampstead","year":"2016","unstructured":"Hampstead BM, Khoshnoodi M, Yan W, Deshpande G, Sathian K (2016) Patterns of effective connectivity during memory encoding and retrieval differ between patients with mild cognitive impairment and healthy older adults. Neuroimage 124:997\u20131008","journal-title":"Neuroimage"},{"issue":"4","key":"101_CR35","doi-asserted-by":"publisher","first-page":"1442","DOI":"10.1002\/hbm.22714","volume":"36","author":"NL Hutcheson","year":"2015","unstructured":"Hutcheson NL, Sreenivasan KR, Deshpande G, Reid MA, Hadley J, White DM, Ver Hoef L, Lahti AC (2015) Effective connectivity during episodic memory retrieval in schizophrenia participants before and after antipsychotic medication. Hum Brain Mapping 36(4):1442\u20131457","journal-title":"Hum Brain Mapping"},{"key":"101_CR36","first-page":"3","volume":"4320","author":"X Wang","year":"2016","unstructured":"Wang X, Katwal S, Rogers B, Gore J, Deshpande G (2016) Experimental validation of dynamic granger causality for inferring stimulus-evoked sub-100\u00a0ms timing differences from fMRI. IEEE Trans Neural Syst Rehabil Eng 4320:3","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"101_CR37","first-page":"13","volume":"4","author":"Y Chao-Gan","year":"2010","unstructured":"Chao-Gan Y, Yu-Feng Z (2010) DPARSF: a MATLAB Toolbox for \u2018Pipeline\u2019 data analysis of resting-state fMRI. Front Syst Neurosci 4:13","journal-title":"Front Syst Neurosci"},{"key":"101_CR38","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1002\/hbm.460020402","volume":"2","author":"KJ Friston","year":"1995","unstructured":"Friston KJ, Holmes AP, Worsley KJ, Poline P, Frith CD, Frackowiak RSJ (1995) Statistical parametric mapping in functional imaging: a general linear approach. Hum Brain Mapping 2:189\u2013210","journal-title":"Hum Brain Mapping"},{"issue":"8","key":"101_CR39","doi-asserted-by":"publisher","first-page":"1914","DOI":"10.1002\/hbm.21333","volume":"33","author":"RC Craddock","year":"2012","unstructured":"Craddock RC, James GA, Holtzheimer PE, Hu XP, Mayberg HS (2012) A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum Brain Mapping 33(8):1914\u20131928","journal-title":"Hum Brain Mapping"},{"key":"101_CR40","first-page":"670","volume":"9","author":"X Wang","year":"2015","unstructured":"Wang X, Xia M, Liao X, Evans A, He Y (2015) Corrigendum: GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci 9:670","journal-title":"Front Hum Neurosci"},{"key":"101_CR41","doi-asserted-by":"publisher","first-page":"670","DOI":"10.3389\/fnhum.2013.00670","volume":"7","author":"G Deshpande","year":"2013","unstructured":"Deshpande G, Libero LE, Sreenivasan KR, Deshpande HD, Kana RK (2013) Identification of neural connectivity signatures of autism using machine learning. Front Hum Neurosci 7:670","journal-title":"Front Hum Neurosci"},{"issue":"9","key":"101_CR42","doi-asserted-by":"publisher","first-page":"4815","DOI":"10.1002\/hbm.22514","volume":"35","author":"MM Grant","year":"2014","unstructured":"Grant MM, White D, Hadley J, Hutcheson N, Shelton R, Sreenivasan K, Deshpande G (2014) Early life trauma and directional brain connectivity within major depression. Hum Brain Mapp 35(9):4815\u20134826","journal-title":"Hum Brain Mapp"},{"issue":"1","key":"101_CR43","first-page":"70","volume":"4","author":"D Kapogiannis","year":"2014","unstructured":"Kapogiannis D, Deshpande G, Krueger F, Thornburg MP, Grafman JH (2014) Brain networks shaping religious belief. Brain Connect. 4(1):70\u201379","journal-title":"Brain Connect."},{"issue":"1","key":"101_CR44","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.neuropsychologia.2014.05.008","volume":"60","author":"S Lacey","year":"2014","unstructured":"Lacey S, Stilla R, Sreenivasan K, Deshpande G, Sathian K (2014) Spatial imagery in haptic shape perception. Neuropsychologia 60(1):144\u2013158","journal-title":"Neuropsychologia"},{"issue":"30","key":"101_CR45","doi-asserted-by":"publisher","first-page":"e4222","DOI":"10.1097\/MD.0000000000004222","volume":"95","author":"P Liang","year":"2016","unstructured":"Liang P, Deshpande G, Zhao S, Liu J, Hu X, Li K (2016) Altered directional connectivity between emotion network and motor network in Parkinson\u02bcs disease with depression. Medicine (Baltimore) 95(30):e4222","journal-title":"Medicine (Baltimore)"},{"issue":"P2","key":"101_CR46","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1016\/j.neuroimage.2014.08.005","volume":"102","author":"MD Wheelock","year":"2014","unstructured":"Wheelock MD, Sreenivasan KR, Wood KH, VerHoef LW, Deshpande G, Knight DC (2014) Threat-related learning relies on distinct dorsal prefrontal cortex network connectivity. Neuroimage 102(P2):904\u2013912","journal-title":"Neuroimage"},{"issue":"5","key":"101_CR47","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1089\/brain.2012.0091","volume":"2","author":"G Deshpande","year":"2012","unstructured":"Deshpande G, Hu X (2012) Investigating effective brain connectivity from fMRI data: past findings and current issues with reference to Granger causality analysis. Brain Connect 2(5):235\u2013245","journal-title":"Brain Connect"},{"issue":"9","key":"101_CR48","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1002\/hbm.21119","volume":"32","author":"F Krueger","year":"2011","unstructured":"Krueger F, Landgraf S, Van Der Meer E, Deshpande G, Hu X (2011) Effective connectivity of the multiplication network: a functional MRI and multivariate granger causality mapping study. Hum Brain Mapp 32(9):1419\u20131431","journal-title":"Hum Brain Mapp"},{"key":"101_CR49","doi-asserted-by":"publisher","first-page":"22","DOI":"10.3389\/fnhum.2011.00022","volume":"5","author":"F Preusse","year":"2011","unstructured":"Preusse F, van Elke M, Deshpande G, Krueger F, Wartenburger I (2011) Fluid intelligence allows flexible recruitment of the parieto-frontal network in analogical reasoning. Front Hum Neurosci 5:22","journal-title":"Front Hum Neurosci"},{"issue":"2","key":"101_CR50","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.neuroimage.2011.05.001","volume":"57","author":"K Sathian","year":"2011","unstructured":"Sathian K, Lacey S, Stilla R, Gibson GO, Deshpande G, Hu X, LaConte S, Glielmi C (2011) Dual pathways for haptic and visual perception of spatial and texture information. Neuroimage 57(2):462\u2013475","journal-title":"Neuroimage"},{"issue":"3","key":"101_CR51","doi-asserted-by":"publisher","first-page":"424","DOI":"10.2307\/1912791","volume":"37","author":"CWJ Granger","year":"1969","unstructured":"Granger CWJ (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3):424\u2013438","journal-title":"Econometrica"},{"issue":"4","key":"101_CR52","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1002\/mrm.27146","volume":"80","author":"D Rangaprakash","year":"2018","unstructured":"Rangaprakash D, Wu G-R, Marinazzo D, Hu X, Deshpande G (2018) Hemodynamic response function (HRF) variability confounds resting-state fMRI functional connectivity. Magn Reson Med 80(4):1697\u20131713","journal-title":"Magn Reson Med"},{"issue":"3","key":"101_CR53","doi-asserted-by":"publisher","first-page":"1991","DOI":"10.1016\/j.neuroimage.2009.08.052","volume":"49","author":"G Deshpande","year":"2010","unstructured":"Deshpande G, Hu X, Lacey S, Stilla R, Sathian K (2010) Object familiarity modulates effective connectivity during haptic shape perception. Neuroimage 49(3):1991\u20132000","journal-title":"Neuroimage"},{"key":"101_CR54","doi-asserted-by":"crossref","unstructured":"Rangaprakash D, Wu G-R, Marinazzo D, Hu X, Deshpande G (2017) Parametrized hemodynamic response function data of healthy individuals obtained from resting-state functional MRI in a 7T MRI scanner. Data in Brief (in press)","DOI":"10.1016\/j.dib.2018.01.003"},{"issue":"3","key":"101_CR55","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.media.2013.01.003","volume":"17","author":"G Wu","year":"2013","unstructured":"Wu G, Liao W, Stramaglia S, Ding J-R, Chen H, Marinazzo D (2013) A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Med Image Anal 17(3):365\u2013374","journal-title":"Med Image Anal"},{"key":"101_CR56","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/j.nicl.2017.07.016","volume":"16","author":"D Rangaprakash","year":"2017","unstructured":"Rangaprakash D, Dretsch MN, Yan W, Katz JS, Denney TS, Deshpande G (2017) Hemodynamic variability in soldiers with trauma: implications for functional MRI connectivity studies. NeuroImage 16:409\u2013417","journal-title":"NeuroImage"},{"key":"101_CR57","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1016\/j.dib.2017.07.072","volume":"14","author":"D Rangaprakash","year":"2017","unstructured":"Rangaprakash D, Dretsch MN, Yan W, Katz JS, Denney TS, Deshpande G (2017) Hemodynamic response function parameters obtained from resting-state functional MRI data in soldiers with trauma. Data Brief 14:558\u2013562","journal-title":"Data Brief"},{"key":"101_CR58","first-page":"32","volume":"13","author":"W Yan","year":"2018","unstructured":"Yan W, Rangaprakash D, Deshpande G (2018) Aberrant hemodynamic responses in autism: implications for resting state fMRI functional connectivity studies. Neuroimage 13:32","journal-title":"Neuroimage"},{"key":"101_CR59","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1016\/j.dib.2018.04.126","volume":"19","author":"W Yan","year":"2018","unstructured":"Yan W, Rangaprakash D, Deshpande G (2018) Hemodynamic response function parameters obtained from resting state BOLD fMRI data in subjects with autism spectrum disorder and matched healthy controls. Data Brief 19:1305\u20131309","journal-title":"Data Brief"},{"key":"101_CR60","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1371\/journal.pone.0014277","volume":"5","author":"G Deshpande","year":"2010","unstructured":"Deshpande G, Li Z, Santhanam P, Coles CD, Lynch ME, Hamann S, Hu X (2010) Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity. PLoS ONE 5:12","journal-title":"PLoS ONE"},{"issue":"3","key":"101_CR61","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1177\/1545968310382424","volume":"25","author":"BM Hampstead","year":"2011","unstructured":"Hampstead BM, Stringer AY, Stilla RF, Deshpande G, Hu X, Moore AB, Sathian K (2011) Activation and effective connectivity changes following explicit-memory training for face-name pairs in patients with mild cognitive impairment: a pilot study. Neurorehabil Neural Repair 25(3):210\u2013222","journal-title":"Neurorehabil Neural Repair"},{"key":"101_CR62","unstructured":"Kintali S (2008) Betweenness centrality\u202f: algorithms and lower bounds. arXiv"},{"issue":"3","key":"101_CR63","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","volume":"52","author":"M Rubinov","year":"2010","unstructured":"Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3):1059\u20131069","journal-title":"Neuroimage"},{"key":"101_CR64","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1371\/annotation\/dc754ef5-000d-4362-be14-e8b04d5e77e1","volume":"8","author":"M Xia","year":"2013","unstructured":"Xia M, Wang J, He Y (2013) BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE 8:7","journal-title":"PLoS ONE"},{"issue":"1","key":"101_CR65","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1002\/hbm.23841","volume":"39","author":"D Rangaprakash","year":"2018","unstructured":"Rangaprakash D, Dretsch M, Venkatraman A, Katz J, Denney T, Deshpande G (2018) Identifying disease foci from static and dynamic effective connectivity networks: illustration in soldiers with trauma. Hum Brain Mapp 39(1):264\u2013287","journal-title":"Hum Brain Mapp"},{"key":"101_CR66","first-page":"803","volume":"13","author":"D Rangaprakash","year":"2018","unstructured":"Rangaprakash D, Dretsch M, Katz J, Denney T, Deshpande G (2018) Dynamics of segregation and integration in directional brain networks: illustration in soldiers with PTSD and neurotrauma. Neuroimage 13:803","journal-title":"Neuroimage"},{"key":"101_CR67","doi-asserted-by":"publisher","DOI":"10.1007\/s11682-019-00191-8","author":"P Lanka","year":"2019","unstructured":"Lanka P, Rangaprakash D, Dretsch M, Katz J, Denney T, Deshpande G (2019) Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets. Brain Imaging Behav. https:\/\/doi.org\/10.1007\/s11682-019-00191-8","journal-title":"Brain Imaging Behav"},{"issue":"3","key":"101_CR68","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1093\/cercor\/10.3.243","volume":"10","author":"GW Van Hoesen","year":"2000","unstructured":"Van Hoesen GW, Parvizi J, Chu CC (2000) Orbitofrontal cortex pathology in Alzheimer\u2019s disease. Cereb Cortex 10(3):243\u2013251","journal-title":"Cereb Cortex"},{"issue":"7","key":"101_CR69","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1016\/j.neurobiolaging.2008.08.013","volume":"31","author":"KB Walhovd","year":"2010","unstructured":"Walhovd KB, Fjell AM, Dale AM, McEvoy LK, Brewer J, Karow DS, Salmon DP, Fennema-Notestine C (2010) Multi-modal imaging predicts memory performance in normal aging and cognitive decline. Neurobiol Aging 31(7):1107\u20131121","journal-title":"Neurobiol Aging"},{"issue":"8","key":"101_CR70","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1016\/j.neurobiolaging.2004.09.017","volume":"26","author":"DH Salat","year":"2005","unstructured":"Salat DH, Tuch DS, Greve DN, Van Kouwe AJW, Hevelone ND, Zaleta AK, Rosen BR, Fischl B, Corkin S, DianaRosas H, Dale AM (2005) Age-related alterations in white matter microstructure measured by diffusion tensor imaging. Neurobiol Aging 26(8):1215\u20131227","journal-title":"Neurobiol Aging"},{"issue":"6","key":"101_CR71","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1212\/WNL.0b013e3181b16431","volume":"73","author":"CR McDonald","year":"2009","unstructured":"McDonald CR, McEvoy LK, Gharapetian L, Fennema-Notestine C, Hagler DJ, Holland D, Koyama A, Brewer JB, Dale AM (2009) Regional rates of neocortical atrophy from normal aging to early Alzheimer disease. Neurology 73(6):457\u2013465","journal-title":"Neurology"},{"issue":"5","key":"101_CR72","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1089\/brain.2014.0223","volume":"4","author":"H Yao","year":"2014","unstructured":"Yao H, Zhou B, Zhang Z, Wang P, Guo Y, Shang Y, Wang L, Zhang X, An N, Liu Y (2014) Longitudinal alteration of amygdalar functional connectivity in mild cognitive impairment subjects revealed by resting-state FMRI. Brain Connect 4(5):361\u2013370","journal-title":"Brain Connect"},{"issue":"34","key":"101_CR73","doi-asserted-by":"publisher","first-page":"7709","DOI":"10.1523\/JNEUROSCI.2177-05.2005","volume":"25","author":"RL Buckner","year":"2005","unstructured":"Buckner RL (2005) Molecular, structural, and functional characterization of Alzheimer\u2019s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci 25(34):7709\u20137717","journal-title":"J Neurosci"},{"issue":"40","key":"101_CR74","doi-asserted-by":"publisher","first-page":"12686","DOI":"10.1523\/JNEUROSCI.3189-09.2009","volume":"29","author":"T Hedden","year":"2009","unstructured":"Hedden T, Van Dijk KR, Becker JA, Mehta A, Sperling RA, Johnson KA, Buckner RL (2009) Disruption of functional connectivity in clinically normal older adults harboring amyloid burden. J Neurosci 29(40):12686\u201312694","journal-title":"J Neurosci"},{"issue":"5594","key":"101_CR75","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1126\/science.1074069","volume":"298","author":"DJ Selkoe","year":"2002","unstructured":"Selkoe DJ (2002) Alzheimer\u2019s disease is a synaptic failure. Science 298(5594):789\u2013791","journal-title":"Science"},{"issue":"4","key":"101_CR76","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1016\/j.neurobiolaging.2013.10.081","volume":"35","author":"MR Brier","year":"2014","unstructured":"Brier MR, Thomas JB, Fagan AM, Hassenstab J, Holtzman DM, Benzinger TL, Morris JC, Ances BM (2014) Functional connectivity and graph theory in preclinical Alzheimer\u2019s disease. Neurobiol Aging 35(4):757\u2013768","journal-title":"Neurobiol Aging"},{"issue":"December","key":"101_CR77","first-page":"1","volume":"10","author":"J Mutlu","year":"2016","unstructured":"Mutlu J, Landeau B, Tomadesso C, de Flores R, M\u00e9zenge F, de La Sayette V, Eustache F, Ch\u00e9telat G (2016) Connectivity disruption, atrophy, and hypometabolism within posterior cingulate networks in Alzheimer\u2019s disease. Front Neurosci 10(December):1\u201310","journal-title":"Front Neurosci"},{"issue":"10","key":"101_CR78","doi-asserted-by":"publisher","first-page":"3723","DOI":"10.1093\/cercor\/bhu246","volume":"25","author":"Z Dai","year":"2015","unstructured":"Dai Z, Yan C, Li K, Wang Z, Wang J, Cao M, Lin Q, Shu N, Xia M, Bi Y, He Y (2015) Identifying and mapping connectivity patterns of brain network hubs in Alzheimer\u2019s disease. Cereb Cortex 25(10):3723\u20133742","journal-title":"Cereb Cortex"}],"container-title":["Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-019-0101-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s40708-019-0101-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-019-0101-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T19:23:48Z","timestamp":1606764228000},"score":1,"resource":{"primary":{"URL":"https:\/\/braininformatics.springeropen.com\/articles\/10.1186\/s40708-019-0101-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":78,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["101"],"URL":"https:\/\/doi.org\/10.1186\/s40708-019-0101-x","relation":{},"ISSN":["2198-4018","2198-4026"],"issn-type":[{"value":"2198-4018","type":"print"},{"value":"2198-4026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12]]},"assertion":[{"value":"17 July 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"8"}}