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PLOS ONE  2013 

Glucose Metabolism during Resting State Reveals Abnormal Brain Networks Organization in the Alzheimer’s Disease and Mild Cognitive Impairment

DOI: 10.1371/journal.pone.0068860

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Abstract:

This paper aims to study the abnormal patterns of brain glucose metabolism co-variations in Alzheimer disease (AD) and Mild Cognitive Impairment (MCI) patients compared to Normal healthy controls (NC) using the Alzheimer Disease Neuroimaging Initiative (ADNI) database. The local cerebral metabolic rate for glucose (CMRgl) in a set of 90 structures belonging to the AAL atlas was obtained from Fluro-Deoxyglucose Positron Emission Tomography data in resting state. It is assumed that brain regions whose CMRgl values are significantly correlated are functionally associated; therefore, when metabolism is altered in a single region, the alteration will affect the metabolism of other brain areas with which it interrelates. The glucose metabolism network (represented by the matrix of the CMRgl co-variations among all pairs of structures) was studied using the graph theory framework. The highest concurrent fluctuations in CMRgl were basically identified between homologous cortical regions in all groups. Significant differences in CMRgl co-variations in AD and MCI groups as compared to NC were found. The AD and MCI patients showed aberrant patterns in comparison to NC subjects, as detected by global and local network properties (global and local efficiency, clustering index, and others). MCI network’s attributes showed an intermediate position between NC and AD, corroborating it as a transitional stage from normal aging to Alzheimer disease. Our study is an attempt at exploring the complex association between glucose metabolism, CMRgl covariations and the attributes of the brain network organization in AD and MCI.

References

[1]  American Psychiatric Association (1994) Diagnostic and statistical manual of mental disorders. Washington,DC.
[2]  Villain N, Desgranges B, Viader F, de lS V, et al. (2008) Relationships between hippocampal atrophy, white matter disruption, and gray matter hypometabolism in Alzheimer’s disease. J Neurosci 28: 6174–6181.
[3]  Englund E, Brun A, Alling C (1988) White matter changes in dementia of Alzheimer’s type. Biochemical and neuropathological correlates. Brain 111 (Pt 6): 1425–1439.
[4]  Kuczynski B, Targan E, Madison C, Weiner M, Zhang Y, et al. (2010) White matter integrity and cortical metabolic associations in aging and dementia. Alzheimers Dement 6: 54–62.
[5]  Raj A, Kuceyeski A, Weiner M (2012) A Network Diffusion Model of Disease Progression in Dementia. Neuron 73: 1204–1215.
[6]  Zhou J, Gennatas E, Kramer J, Miller B, Seeley W (2012) Predicting Regional Neurodegeneration from the Healthy Brain Functional Connectome. Neuron 73: 1216–1227.
[7]  Liu L, Drouet V, Wu JW, Witter MP, Small SA, et al. (2012) Transsynaptic spread of tau pathology in vivo. PLoS One 7: e31302.
[8]  Ronnback A, Sagelius H, Bergstedt KD, Naslund J, Westermark GT, et al. (2012) Amyloid neuropathology in the single Arctic APP transgenic model affects interconnected brain regions. Neurobiol Aging 33: 831–839.
[9]  Delbeuck X, Van der LM, Collette F (2003) Alzheimer’s disease as a disconnection syndrome? Neuropsychol Rev 13: 79–92.
[10]  de Haan W, Pijnenburg YA, Strijers RL, van der MY, van der Flier WM, et al. (2009) Functional neural network analysis in frontotemporal dementia and Alzheimer’s disease using EEG and graph theory. BMC Neurosci 10: 101.
[11]  He Y, Chen Z, Evans A (2008) Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer’s Disease. Journal of Neuroscience 28: 4756–4766.
[12]  Lo CY, Wang PN, Chou KH, Wang J, He Y, et al. (2010) Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer’s disease. J Neurosci 30: 16876–16885.
[13]  Sanz-Arigita EJ, Schoonheim MM, Damoiseaux JS, Rombouts SA, Maris E, et al. (2010) Loss of ‘small-world’ networks in Alzheimer’s disease: graph analysis of FMRI resting-state functional connectivity. PLoS One 5: e13788.
[14]  Stam CJ, de HW, Daffertshofer A, Jones BF, Manshanden I, et al. (2009) Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease. Brain 132: 213–224.
[15]  Supekar K, Menon V, Rubin D, Musen M, Greicius MD (2008) Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Comput Biol 4: e1000100.
[16]  Yao Z, Zhang Y, Lin L, Zhou Y, Xu C, et al. (2010) Abnormal cortical networks in mild cognitive impairment and Alzheimer’s disease. PLoS Comput Biol 6: e1001006.
[17]  Tijms BM, Wink AM, de HW, van der Flier WM, Stam CJ, et al.. (2013) Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks. Neurobiol Aging.
[18]  McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, et al. (2011) The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia 7: 263–269.
[19]  Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, et al. (2011) Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7: 280–292.
[20]  Alexander GE, Chen K, Pietrini P, Rapoport SI, Reiman EM (2002) Longitudinal PET Evaluation of Cerebral Metabolic Decline in Dementia: A Potential Outcome Measure in Alzheimer’s Disease Treatment Studies. Am J Psychiatry 159: 738–745.
[21]  Chen K, Langbaum JB, Fleisher AS, Ayutyanont N, Reschke C, et al. (2010) Twelve-month metabolic declines in probable Alzheimer’s disease and amnestic mild cognitive impairment assessed using an empirically pre-defined statistical region-of-interest: findings from the Alzheimer’s Disease Neuroimaging Initiative. Neuroimage 51: 654–664.
[22]  Choo IH, Lee DY, Youn JC, Jhoo JH, Kim KW, et al. (2007) Topographic patterns of brain functional impairment progression according to clinical severity staging in 116 Alzheimer disease patients: FDG-PET study. Alzheimer Dis Assoc Disord 21: 77–84.
[23]  Langbaum JB, Chen K, Lee W, Reschke C, Bandy D, et al. (2009) Categorical and correlational analyses of baseline fluorodeoxyglucose positron emission tomography images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Neuroimage 45: 1107–1116.
[24]  Mosconi L, Mistur R, Switalski R, Tsui WH, Glodzik L, et al. (2009) FDG-PET changes in brain glucose metabolism from normal cognition to pathologically verified Alzheimer’s disease. Eur J Nucl Med Mol Imaging 36: 811–822.
[25]  Silverman DH, Small GW, Chang CY, Lu CS, Kung De Aburto MA, et al. (2001) Positron emission tomography in evaluation of dementia: Regional brain metabolism and long-term outcome. JAMA 286: 2120–2127.
[26]  Herholz K, Salmon E, Perani D, Baron JC, Holthoff V, et al. (2002) Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. Neuroimage 17: 302–316.
[27]  Reiman EM, Langbaum JB, Tariot PN (2010) Alzheimer’s prevention initiative: a proposal to evaluate presymptomatic treatments as quickly as possible. Biomark Med 4: 3–14.
[28]  Reiman EM, Langbaum JB, Fleisher AS, Caselli RJ, Chen K, et al. (2011) Alzheimer’s Prevention Initiative: a plan to accelerate the evaluation of presymptomatic treatments. J Alzheimers Dis 26 Suppl 3321–329.
[29]  Metter EJ, Riege WH, Kameyama M, Kuhl DE, Phelps ME (1984) Cerebral metabolic relationships for selected brain regions in Alzheimer’s, Huntington’s, and Parkinson’s diseases. J Cereb Blood Flow Metab 4: 500–506.
[30]  Horwitz B, Duara R, Rapoport SI (1984) Intercorrelations of glucose metabolic rates between brain regions: application to healthy males in a state of reduced sensory input. J Cereb Blood Flow Metab 4: 484–499.
[31]  Horwitz B, Grady CL, Schlageter NL, Duara R, Rapoport SI (1987) Intercorrelations of regional cerebral glucose metabolic rates in Alzheimer’s disease. Brain Res 407: 294–306.
[32]  Mosconi L, Perani D, Sorbi S, Herholz K, Nacmias B, et al. (2004) MCI conversion to dementia and the APOE genotype: a prediction study with FDG-PET. Neurology 63: 2332–2340.
[33]  Lee DS, Kang H, Kim H, Park H, Oh JS, et al. (2008) Metabolic connectivity by interregional correlation analysis using statistical parametric mapping (SPM) and FDG brain PET; methodological development and patterns of metabolic connectivity in adults. Eur J Nucl Med Mol Imaging 35: 1681–1691.
[34]  Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ, et al. (2006) Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A 103: 13848–13853.
[35]  Kerrouche N, Herholz K, Mielke R, Holthoff V, Baron JC (2006) 18FDG PET in vascular dementia: differentiation from Alzheimer’s disease using voxel-based multivariate analysis. J Cereb Blood Flow Metab 26: 1213–1221.
[36]  Pagani M, Salmaso D, Rodriguez G, Nardo D, Nobili F (2009) Principal component analysis in mild and moderate Alzheimer’s disease–a novel approach to clinical diagnosis. Psychiatry Res 173: 8–14.
[37]  Markiewicz PJ, Matthews JC, Declerck J, Herholz K (2011) Verification of predicted robustness and accuracy of multivariate analysis. Neuroimage 56: 1382–1385.
[38]  Illán IA, Górriz JM, Ramírez J, Salas-Gonzales D, López MM, et al. (2011) 18F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis. Information Sciences 181: 903–916 10.1016/j.ins.2010.10.027.
[39]  Toussaint PJ, Perlbarg V, Bellec P, Desarnaud S, Lacomblez L, et al. (2012) Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer’s disease using conjoint univariate and independent component analyses. Neuroimage 63: 936–946.
[40]  Huang S, Li J, Sun L, Ye J, Fleisher A, et al. (2010) Learning brain connectivity of Alzheimer’s disease by sparse inverse covariance estimation. Neuroimage 50: 935–949.
[41]  Zhang F, Zhang J, Zuo C, Guo W, Wang C (2011) Small-world properties of glucose metabolism based brain functional network. Chinese journal of medical instrumentation 35: 164–168.
[42]  Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack CR, et al. (2005) Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimers Dement 1: 55–66.
[43]  Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, et al. (2012) The Alzheimer’s Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement 8: S1–68.
[44]  Jagust WJ, Bandy D, Chen K, Foster NL, Landau SM, et al. (2010) The Alzheimer’s Disease Neuroimaging Initiative positron emission tomography core. Alzheimers Dement 6: 221–229.
[45]  Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12: 189–198.
[46]  Morris JC (1993) The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43: 2412–2414.
[47]  Petersen RC, Doody R, Kurz A, Mohs RC, Morris JC, et al. (2001) Current concepts in mild cognitive impairment. Arch Neurol 58: 1985–1992.
[48]  McKhann G, Drachman D, Folstein M, Katzman R, Price D, et al. (1984) Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34: 939–944.
[49]  Gray KR, Wolz R, Heckemann RA, Aljabar P, Hammers A, et al. (2012) Multiregion analysis of longitudinal FDG-PET for the classification of Alzheimer’s disease. Neuroimage 60: 221–229.
[50]  Landau SM, Harvey D, Madison CM, Koeppe RA, Reiman EM, et al. (2011) Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol Aging 32: 1207–1218.
[51]  Di X, Biswal B (2012) Metabolic Brain Covariant Networks as Revealed by FDG-PET with reference to resting-state fMRI networks. Brain Connect.
[52]  Rasmussen JM, Lakatos A, van Erp TG, Kruggel F, Keator DB, et al. (2012) Empirical derivation of the reference region for computing diagnostic sensitive (1)(8)fluorodeoxyglucose ratios in Alzheimer’s disease based on the ADNI sample. Biochim Biophys Acta 1822: 457–466.
[53]  Jovicich J, Czanner S, Greve D, Haley E, van der KA, et al. (2006) Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage 30: 436–443.
[54]  Jack CR Jr, Bernstein MA, Fox NC, Thompson P, Alexander G, et al. (2008) The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 27: 685–691.
[55]  Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17: 87–97.
[56]  Alemán-Gómez Y, Melie-García L, Valdes-Hernández P (2006) IBASPM: Toolbox for automatic parcellation of brain structures.
[57]  Moeller C, Vrenken H, Jiskoot L, Versteeg A, Barkhof F, et al.. (2013) Different patterns of gray matter atrophy in early- and late-onset Alzheimer’s disease. Neurobiology of Aging. Available: http://dx.doi.org/10.1016/j.neurobiolagi?ng.2013.02.013.
[58]  Yakushev I, Landvogt C, Buchholz HG, Fellgiebel A, Hammers A, et al. (2008) Choice of reference area in studies of Alzheimer’s disease using positron emission tomography with fluorodeoxyglucose-F18. Psychiatry Res 164: 143–153.
[59]  Borghammer P, Aanerud J, Gjedde A (2009) Data-driven intensity normalization of PET group comparison studies is superior to global mean normalization. Neuroimage 46: 981–988.
[60]  Yakushev I, Hammers A, Fellgiebel A, Schmidtmann I, Scheurich A, et al. (2009) SPM-based count normalization provides excellent discrimination of mild Alzheimer’s disease and amnestic mild cognitive impairment from healthy aging. Neuroimage 44: 43–50.
[61]  Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, et al. (2005) Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci 25: 7709–7717.
[62]  Blesa R, Mohr E, Miletich RS, Hildebrand K, Sampson M, et al. (1996) Cerebral metabolic changes in Alzheimer’s disease: neurobehavioral patterns. Dementia 7: 239–245.
[63]  Sanabria-Diaz G, Melie-Garcia L, Iturria-Medina Y, eman-Gomez Y, Hernandez-Gonzalez G, et al. (2010) Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks. Neuroimage 50: 1497–1510.
[64]  Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, et al. (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15: 273–289.
[65]  Melie-Garcia L, Sanabria-Diaz G, Sanchez-Catasus C (2013) Studying the topological organization of the cerebral blood flow fluctuations in resting state. Neuroimage 64: 173–184.
[66]  Ginestet CE, Nichols TE, Bullmore ET, Simmons A (2011) Brain network analysis: separating cost from topology using cost-integration. PLoS One 6: e21570.
[67]  Bassett DS, Nelson BG, Mueller BA, Camchong J, Lim KO (2012) Altered resting state complexity in schizophrenia. Neuroimage 59: 2196–2207.
[68]  He Y, Chen ZJ, Evans AC (2007) Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cerebral Cortex 17: 2407–2419.
[69]  Achard S, Bullmore E (2007) Efficiency and cost of economical brain functional networks.
[70]  Sporns O (2011) The non-random brain: efficiency, economy, and complex dynamics. Front Comput Neurosci 5: 5.
[71]  Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D-U (2006) Complex networks: Structure and dynamics. 175–308.
[72]  Watts DJ, Strogatz SH (1998) Collective dynamics of small-world networks. 440–442.
[73]  Watts DJ (1999) Small Worlds: The Dynamics of Networks between Order and Randomness.
[74]  Maslov S, Sneppen K (2002) Specificity and stability in topology of protein networks.
[75]  Milo R, Shen-Orr S, Itzkovitz S, Kashan N, Chklovskii D, et al.. (2002) Network motifs: simple building blocks of complex networks. 824–827.
[76]  Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87: 198701.
[77]  Freeman L (1977) A set of measures of centrality based upon betweenness.
[78]  Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E (2006) A resilient, lowfrequency, small-world human brain functional network with highly connected association cortical hubs. Journal of Neuroscience 26: 63–72.
[79]  Wu K, Taki Y, Sato K, Kinomura S, Goto R, et al.. (2011) Age-related changes in topological organization of structural brain networks in healthy individuals. Hum Brain Mapp.
[80]  Zhu W, Wen W, He Y, Xia A, Anstey KJ, et al.. (2010) Changing topological patterns in normal aging using large-scale structural networks. Neurobiol Aging.
[81]  Cohen J, Cohen P (1983) Applied multiple regression/correlation analysis for the behavioral sciences. Hillsdale, NJ: Erlbaum.
[82]  Genovese CR, Lazar NA, Nichols T (2002) Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15: 870–878.
[83]  He Y, Dagher A, Chen Z, Charil A, Zijdenbos A, et al. (2009) Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load. Brain 132: 3366–3379.
[84]  Mesulam MM (2000) Principles of Behavioral and Cognitive Neurology. Oxfor: OXFOR University Press.
[85]  Morbelli S, Drzezga A, Perneczky R, Frisoni GB, Caroli A, et al. (2012) Resting metabolic connectivity in prodromal Alzheimer’s disease. A European Alzheimer Disease Consortium (EADC) project. Neurobiol Aging 33: 2533–2550.
[86]  Rocher AB, Chapon F, Blaizot X, Baron JC, Chavoix C (2003) Resting-state brain glucose utilization as measured by PET is directly related to regional synaptophysin levels: a study in baboons. Neuroimage 20: 1894–1898.
[87]  Nobili F, Morbelli S (2010) 18F-FDG-PET as Biomarker for Early Alzheimer’s Disease. The Open Nuclear Medicine Journal 2: 46–52.
[88]  Mosconi L, Tsui WH, Herholz K, Pupi A, Drzezga A, et al. (2008) Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias. J Nucl Med 49: 390–398.
[89]  Mosconi L (2005) Brain glucose metabolism in the early and specific diagnosis of Alzheimer’s disease. FDG-PET studies in MCI and AD. Eur J Nucl Med Mol Imaging 32: 486–510.
[90]  Jagust W, Reed B, Mungas D, Ellis W, DeCarli C (2007) What does fluorodeoxyglucose PET imaging add to a clinical diagnosis of dementia? Neurology 69: 871–877.
[91]  Minoshima S, Giordani B, Berent S, Frey KA, Foster NL, et al. (1997) Metabolic reduction in the posterior cingulate cortex in very early Alzheimer’s disease. Ann Neurol 42: 85–94.
[92]  Eidelberg D (2009) Metabolic brain networks in neurodegenerative disorders: a functional imaging approach. Trends in Neurosciences 32: 548–557.
[93]  Rapoport SI, Horwitz B, Grady CL, Haxby JV, DeCarli C, et al.. (1991) Abnormal brain glucose metabolism in Alzheimer’s disease, as measured by positron emission tomography. In: Fuel homeostasis and the nervous system. Springer. 231–248.
[94]  Pietrini P, Azari NP, Grady CL, Salerno JA, Gonzales-Aviles A, et al. (1993) Pattern of cerebral metabolic interactions in a subject with isolated amnesia at risk for Alzheimer’s disease: a longitudinal evaluation. Dementia and Geriatric Cognitive Disorders 4: 94–101.
[95]  Greicius MD, Srivastava G, Reiss AL, Menon V (2004) Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A 101: 4637–4642.
[96]  Rombouts SA, Barkhof F, Goekoop R, Stam CJ, Scheltens P (2005) Altered resting state networks in mild cognitive impairment and mild Alzheimer’s disease: an fMRI study. Hum Brain Mapp 26: 231–239.
[97]  Sorg C, Riedl V, Muhlau M, Calhoun VD, Eichele T, et al. (2007) Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. Proc Natl Acad Sci U S A 104: 18760–18765.
[98]  Duffy CJ (2009) Visual Motion Processing in Aging and Alzheimer’s Disease. Annals of the New York Academy of Sciences 1170: 736–744.
[99]  Rizzo M, Anderson SW, Dawson J, Nawrot M (2000) Vision and cognition in Alzheimer’s disease. Neuropsychologia 38: 1157–1169.
[100]  Tetewsky SJ, Duffy CJ (1999) Visual loss and getting lost in Alzheimer’s disease. Neurology 52: 958.
[101]  Zalesky A, Fornito A, Egan GF, Pantelis C, Bullmore ET (2012) The relationship between regional and inter-regional functional connectivity deficits in schizophrenia. Hum Brain Mapp 33: 2535–2549.
[102]  Yu Q, Sui J, Liu J, Plis SM, Kiehl KA, et al. (2013) Disrupted correlation between low frequency power and connectivity strength of resting state brain networks in schizophrenia. Schizophrenia Research 143: 165–171.
[103]  Salmon E, Kerrouche N, Perani D, Lekeu F, Holthoff V, et al. (2009) On the multivariate nature of brain metabolic impairment in Alzheimer’s disease. Neurobiol Aging 30: 186–197.
[104]  Cabeza R, Nyberg L (2000) Imaging cognition II: An empirical review of 275 PET and fMRI studies. J Cogn Neurosci 12: 1–47.
[105]  Stern Y (2006) Cognitive reserve and Alzheimer disease. Alzheimer Dis Assoc Disord 20: S69–S74.
[106]  Grady CL, McIntosh AR, Beig S, Keightley ML, Burian H, et al. (2003) Evidence from functional neuroimaging of a compensatory prefrontal network in Alzheimer’s disease. J Neurosci 23: 986–993.
[107]  Becker JT, Mintun MA, Aleva K, Wiseman MB, Nichols T, et al. (1996) Compensatory reallocation of brain resources supporting verbal episodic memory in Alzheimer’s disease. Neurology 46: 692–700.
[108]  Wang K, Liang M, Wang L, Tian L, Zhang X, et al. (2007) Altered functional connectivity in early Alzheimer’s disease: a resting-state fMRI study. Hum Brain Mapp 28: 967–978.
[109]  Agosta F, Pievani M, Geroldi C, Copetti M, Frisoni GB, et al. (2012) Resting state fMRI in Alzheimer’s disease: beyond the default mode network. Neurobiol Aging 33: 1564–1578.
[110]  Barbas H (1988) Anatomic organization of basoventral and mediodorsal visual recipient prefrontal regions in the rhesus monkey. J Comp Neurol 276: 313–342.
[111]  Dolan RJ, Fletcher P, Morris J, Kapur N, Deakin JF, et al. (1996) Neural activation during covert processing of positive emotional facial expressions. Neuroimage 4: 194–200.
[112]  Geday J, Gjedde A, Boldsen AS, Kupers R (2003) Emotional valence modulates activity in the posterior fusiform gyrus and inferior medial prefrontal cortex in social perception. Neuroimage 18: 675–684.
[113]  Qi Z, Wu X, Wang Z, Zhang N, Dong H, et al. (2010) Impairment and compensation coexist in amnestic MCI default mode network. Neuroimage 50: 48–55.
[114]  Gili T, Cercignani M, Serra L, Perri R, Giove F, et al. (2011) Regional brain atrophy and functional disconnection across Alzheimer’s disease evolution. J Neurol Neurosurg Psychiatry 82: 58–66.
[115]  Bassett DS, Bullmore E (2006) Small-world brain networks. Neuroscientist 12: 512–523.
[116]  Buckner RL, Sepulcre J, Talukdar T, Krienen FM, Liu H, et al. (2009) Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci 29: 1860–1873.
[117]  Buckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124: 1–38.
[118]  Acosta-Cabronero J, Williams GB, Pengas G, Nestor PJ (2010) Absolute diffusivities define the landscape of white matter degeneration in Alzheimer’s disease. Brain 133: 529–539.
[119]  Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, et al. (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6: e159.
[120]  van den Heuvel MP, Sporns O (2011) Rich-club organization of the human connectome. J Neurosci 31: 15775–15786.
[121]  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 U S A 100: 253–258.
[122]  Raichle ME, Snyder AZ (2007) A default mode of brain function: a brief history of an evolving idea. Neuroimage 37: 1083–1090.
[123]  Kogure D, Matsuda H, Ohnishi T, Asada T, Uno M, et al. (2000) Longitudinal evaluation of early Alzheimer’s disease using brain perfusion SPECT. J Nucl Med 41: 1155–1162.
[124]  Resnick SM, Pham DL, Kraut MA, Zonderman AB, Davatzikos C (2003) Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J Neurosci 23: 3295–3301.
[125]  Park DC, Reuter-Lorenz P (2009) The adaptive brain: aging and neurocognitive scaffolding. Annu Rev Psychol 60: 173–196.
[126]  Wang JH, Zuo XN, Gohel S, Milham MP, Biswal BB, et al. (2011) Graph theoretical analysis of functional brain networks: test-retest evaluation on short- and long-term resting-state functional MRI data. PLoS One 6: e21976.
[127]  Schwarz AJ, McGonigle J (2011) Negative edges and soft thresholding in complex network analysis of resting state functional connectivity data. Neuroimage 55: 1132–1146.

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