nach oben

Neuropsychology Review

Erschienen in:

01.03.2014 | Review

Functional Brain Connectivity Using fMRI in Aging and Alzheimer’s Disease

verfasst von: Emily L. Dennis, Paul M. Thompson

Erschienen in: Neuropsychology Review | Ausgabe 1/2014

Einloggen, um Zugang zu erhalten

Abstract

Normal aging and Alzheimer’s disease (AD) cause profound changes in the brain’s structure and function. AD in particular is accompanied by widespread cortical neuronal loss, and loss of connections between brain systems. This degeneration of neural pathways disrupts the functional coherence of brain activation. Recent innovations in brain imaging have detected characteristic disruptions in functional networks. Here we review studies examining changes in functional connectivity, measured through fMRI (functional magnetic resonance imaging), starting with healthy aging and then Alzheimer’s disease. We cover studies that employ the three primary methods to analyze functional connectivity—seed-based, ICA (independent components analysis), and graph theory. At the end we include a brief discussion of other methodologies, such as EEG (electroencephalography), MEG (magnetoencephalography), and PET (positron emission tomography). We also describe multi-modal studies that combine rsfMRI (resting state fMRI) with PET imaging, as well as studies examining the effects of medications. Overall, connectivity and network integrity appear to decrease in healthy aging, but this decrease is accelerated in AD, with specific systems hit hardest, such as the default mode network (DMN). Functional connectivity is a relatively new topic of research, but it holds great promise in revealing how brain network dynamics change across the lifespan and in disease.

Achard, S., & Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Computational Biology, 3, e17.PubMedCentralPubMedCrossRef

Agosta, F., Pievani, M., Geroldi, C., Copetti, M., Frisoni, G. B., & Filippi, M. (2012). Resting state fMRI in Alzheimer’s disease: beyond the default mode network. Neurobiology of Aging, 33, 1564–1578.PubMedCrossRef

Allen, G., Barnard, H., McColl, R., Hester, A. L., Fields, J. A., Weiner, M. F., Ringe, W. K., Lipton, A. M., Brooker, M., McDonald, E., Rubin, C. D., & Cullum, C. M. (2007). Reduced Hippocampal functional connectivity in Alzheimer disease. Archives of Neurology, 64, 1482–1487.PubMedCrossRef

Andrews-Hanna, J. R., Snyder, A. Z., Vincent, J. L., Lustig, C., Head, D., Raichle, M. E., & Buckner, R. L. (2007). Disruption of large-scale brain systems in advanced aging. Neuron, 56, 924–935.PubMedCentralPubMedCrossRef

Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., & Buckner, R. L. (2010). Functional-anatomic fractionation of the brain’s default network. Neuron, 65, 550–562.PubMedCentralPubMedCrossRef

Bartrés-Faz, D., Serra-Grabulosa, J. M., Sun, F. T., Solé-Padullés, C., Rami, L., Molinuevo, J. L., et al. (2008). Functional connectivity of the hippocampus in elderly with mild memory dysfunction carrying the APOE ε4 allele. Neurobiology of Aging, 29, 1644–1653.PubMedCrossRef

Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society, 360, 1001–1013.CrossRef

Binnewijzend, M. A. A., Schoonheim, M. M., Sanz-Arigita, E., Wink, A. M., van der Flier, W. M., Tolboom, N., et al. (2012). Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiology of Aging, 33, 2018–2028.PubMedCrossRef

Biswal, B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34, 537–541.PubMedCrossRef

Braskie, M. N., Klunder, A. D., Hayashi, K. M., Protas, H., Kepe, V., Miller, K. J., Huang, S. C., Barrio, J. R., Ercoli, L., Toga, A. W., Bookheimer, S. Y., Small, G. W., & Thompson, P. M. (2010). Plaque and tangle imaging and cognition in normal aging and Alzheimer’s disease. Neurobiology of Aging, 31, 1669–1678.PubMedCentralPubMedCrossRef

Brier, M. R., Thomas, J. B., Snyder, A. Z., Benzinger, T. L., Zhang, D., Raichle, M. E., Holtzman, D. M., Morris, J. C., & Ances, B. M. (2012). Loss of intranetwork and internetwork resting state functional connections with Alzheimer’s disease progression. Journal of Neuroscience, 32, 8890–8899.PubMedCentralPubMedCrossRef

Brier, M. R., Thomas, J. B., Fagan, A. M., Hassenstab, J., Holtzman, D. M., Benzinger, T. L., Morris, J. C., & Ances, B. M. (2013). Functional connectivity and graph theory in preclinical Alzheimer’s disease. Neurobiology of Aging, 35, 757–768.PubMedCrossRef

Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain’s default network: anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences, 1124, 1–38.PubMedCrossRef

Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., Andrews-Hanna, J. R., Sperling, R. A., & Johnson, K. A. (2009). Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. Journal of Neuroscience, 29, 1860–1873.PubMedCentralPubMedCrossRef

Corder, E. H., Saunders, A. M., Strittmatter, W. J., Schmechel, D. E., Gaskell, P. C., Small, G. W., Roses, A. D., Haines, J. L., & Pericak-Vance, M. A. (1993). Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science, 261, 921–923.PubMedCrossRef

Daianu, M., Dennis, E. L., Nir, T. M., Jahanshad, N., Toga, A. W., Jack, Jr. C. R., et al. (2013). Alzheimer’s disease disrupts rich club organization in brain connectivity networks. In Proc. 10th IEEE ISBI (pp 266–269).

Daianu, M., Jahanshad, N., Nir, T. M., Toga, A. W., Jack, C. R., Jr., Weiner, M. W., et al. (2013b). Breakdown of brain connectivity between normal aging and Alzheimer’s disease: a structural k-core network analysis. Brain Connectivity, 3, 407–422.PubMedCrossRef

Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006). Consistent resting-state networks across healthy subjects. PNAS, 103, 13848–13853.PubMedCentralPubMedCrossRef

Damoiseaux, J. S., Beckmann, C. F., Arigita, E. J. S., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Rombouts, S. A. R. B. (2008). Reduced resting-state brain activity in the “default network” in normal aging. Cerebral Cortex, 18, 1856–1864.PubMedCrossRef

Damoiseaux, J. S., Prater, K. E., Miller, B. L., & Greicius, M. D. (2012). Functional connectivity tracks clinical deterioration in Alzheimer’s disease. Neurobiology of Aging, 33, 828.e19–30.CrossRef

Dennis, N. A., Browndyke, J. N., Stokes, J., Need, A., Burke, J. R., Welsh-Bohmer, K. A., et al. (2010). Temporal lobe functional activity and connectivity in young adult APOE ε4 carriers. Alzheimer’s & Dementia, 6, 303–311.CrossRef

Drzezga, A., Becker, J. A., Van Dijk, K. R. A., Sreenivasan, A., Talukdar, T., Sullivan, C., et al. (2011). Neuronal dysfunction and disconnection of cortical hubs in non-demented subjects with elevated amyloid burden. Brain, 134, 1635–1646.PubMedCentralPubMedCrossRef

Farrer, L. A., Cupples, L. A., Haines, J. L., Hyman, B., Kukull, W. A., Mayeux, R., et al. (1997). Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. JAMA, 278, 1349–1356.PubMedCrossRef

Filippini, N., MacIntosh, B. J., Hough, M. G., Goodwin, G. M., Frisoni, G. B., Smith, S. M., et al. (2009). Distinct patterns of brain activity in young carriers of the APOE-ε4 allele. PNAS, 106, 7209–7214.PubMedCentralPubMedCrossRef

Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience, 8, 700–711.PubMedCrossRef

Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Essen, D. C. V., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. PNAS, 102, 9673–9678.PubMedCentralPubMedCrossRef

Fox, M. D., Zhang, D., Snyder, A. Z., & Raichle, M. E. (2009). The global signal and observed anticorrelated resting state brain networks. Journal of Neurophysiology, 101, 3270–3283.PubMedCentralPubMedCrossRef

Goveas, J. S., Xie, C., Ward, B. D., Wu, Z., Li, W., Franczak, M., et al. (2011). Recovery of hippocampal network connectivity correlates with cognitive improvement in mild Alzheimer’s disease patients treated with Donepezil assessed by resting-state fMRI. Journal of Magnetic Resonance Imaging, 34, 764–773.PubMedCentralPubMedCrossRef

Grady, C. L., Furey, M. L., Pietrini, P., Horwitz, B., & Rapoport, S. I. (2001). Altered brain functional connectivity and impaired short-term memory in Alzheimer’s disease. Brain, 124, 739–756.PubMedCrossRef

Grady, C. L., McIntosh, A. R., & Craik, F. I. M. (2003). Age-related differences in the functional connectivity of the hippocampus during memory encoding. Hippocampus, 13, 572–586.PubMedCrossRef

Greicius, M. (2008). Resting-state functional connectivity in neuropsychiatric disorders. Current Opinion in Neurology, 21, 424–430.PubMedCrossRef

Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004). Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. PNAS, 101, 4637–4642.PubMedCentralPubMedCrossRef

Hedden, T., Van Dijk, K. R. A., Becker, J. A., Mehta, A., Sperling, R. A., Johnson, K. A., & Buckner, R. L. (2009). Disruption of functional connectivity in clinically normal older adults harboring amyloid burden. Journal of Neuroscience, 29, 12686–12694.PubMedCentralPubMedCrossRef

Ho, A. J., Stein, J. L., Hua, X., Lee, S., Hibar, D. P., Leow, A. D., Dinov, I. D., Toga, A. W., Saykin, A. J., Shen, L., Foroud, T., Pankratz, N., Huentelman, M. J., Craig, D. W., Gerber, J. D., Allen, A., Corneveaux, J., Stephan, D. A., Webster, J., DeChairo, B. M., Potkin, S. G., Jack, C. R., Weiner, M. W., Raji, C. A., Lopez, O. L., Becker, J. T., & Thompson, P. M. (2010). A commonly carried allele of the obesity-related FTO gene is associated with reduced brain volume in the healthy elderly. Proceedings of the National Academy of Sciences, 107, 8404–8409.CrossRef

Jack, C. R., Lowe, V. J., Weigand, S. D., Wiste, H. J., Senjem, M. L., Knopman, D. S., Shiung, M. M., Gunter, J. L., Boeve, B. F., Kemp, B. J., Weiner, M., Petersen, E. C., & the Alzheimer’s Disease Neuroimaging Initiative. (2009). Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer’s disease: implications for sequence of pathological events in Alzheimer’s disease. Brain, 132, 1355–1365.PubMedCentralPubMedCrossRef

Jahanshad, N., Rajagopalan, P., Thompson, P. (2013). Neuroimaging, nutrition, and iron-related genes. Invited Review for Cellular Molecular and Life Science Reviews (CMLS Reviews): 1–13.

Jelic, V., Julin, P., Shigeta, M., Nordberg, A., Lannfelt, L., Winblad, B., & Wahlund, L.-O. (1997). Apolipoprotein E e4 allele decreases functional connectivity in Alzheimer’s disease as measured by EEG coherence. Journal of Neurology, Neurosurgery, and Psychiatry, 63, 59–65.PubMedCentralPubMedCrossRef

Jie, B., Zhang, D., Suk, H.-I., Wee, C.-Y., Shen, D. (2013). Integrating multiple network properties for MCI identification. Workshop on Machine Learning in Medical Imaging, Medical Image Computing and Computer Assisted Intervention (MICCAI):9–16.

Johnson, K. A., Gregas, M., Becker, J. A., Kinnecom, C., Salat, D. H., Moran, E. K., Smith, E. E., Rosand, J., Rentz, D. M., Klunk, W. E., Mathis, C. A., Price, J. C., DeKosky, S. T., Fischman, A. J., & Greenberg, S. M. (2007). Imaging of amyloid burden and distribution in cerebral amyloid angiopathy. Annals of Neurology, 62, 229–234.PubMedCrossRef

Koch, W., Teipel, S., Mueller, S., Buerger, K., Bokde, A. L. W., Hampel, H., Coates, U., Reiser, M., & Meindl, T. (2010). Effects of aging on default mode network activity in resting state fMRI: does the method of analysis matter? NeuroImage, 51, 280–287.PubMedCrossRef

Larson-Prior, L., Zempel, J., Nolan, T., Prior, F., Snyder, A., & Raichle, M. E. (2009). Cortical network functional connectivity in the descent to sleep. PNAS, 106, 4489–4494.PubMedCentralPubMedCrossRef

Levy, R. (1994). Aging-associated cognitive decline. International Psychogeriatrics, 6, 63–68.PubMedCrossRef

Li, W., Antuono, P. G., Xie, C., Chen, G., Jones, J. L., Ward, D. B., et al. (2012). Changes in regional cerebral blood flow and functional connectivity in the cholinergic pathway associated with cognitive performance in subjects with mild Alzheimer’s disease after 12-week donepezil treatment. NeuroImage, 60, 1083–1091.PubMedCentralPubMedCrossRef

Lorenzi, M., Beltramello, A., Mercuri, N. B., Canu, E., Zoccatelli, G., Pizzini, F. B., et al. (2012). Effect of memantine on resting state default mode network activity in Alzheimer’s disease. Drugs & Aging, 28, 205–217.CrossRef

Machulda, M. M., Jones, D. T., Vemuri, P., McDade, E., Avula, R., Przybelski, S., et al. (2011). Effect of APOE ε4 status on intrinsic network connectivity in cognitively normal elderly subjects. Archives of Neurology, 68, 1131–1136.PubMedCentralPubMedCrossRef

Madsen, S., Rajagopalan, P., Joshi, S. H., Toga, A. W., Thompson, P. M., the Alzheimer’s Disease Neuroimaging Initiative (ADNI). (2013). Elevated homocysteine is associated with thinner cortical gray matter in 803 ADNI subjects. Neurobiology of Aging Accepted.

Meunier, D., Achard, S., Morcom, A., & Bullmore, E. (2009). Age-related changes in modular organization of human brain functional networks. NeuroImage, 44, 715–723.PubMedCrossRef

Milham, M. P., Erickson, K. I., Banich, M. T., Kramer, A. F., Webb, A., Wszalek, T., & Cohen, N. J. (2002). Attentional control in the aging brain: insights from an fMRI study of the stroop task. Brain and Cognition, 49, 277–296.PubMedCrossRef

Mormino, E. C., Kluth, J. T., Madison, C. M., Rabinovici, G. D., Baker, S. L., Miller B. L., et al. (2009). Episodic memory loss is related to hippocampal-mediated β-amyloid deposition in elderly subjects. Brain, 132, 1310–1323.

Mormino, E. C., Smiljic, A., Hayenga, A. O., Onami, S. H., Greicius,M. D., Rabinovici, G. D., et al. (2011). Relationships between beta-amyloid and functional connectivity in different components of the default mode network in aging. Cerebral Cortex, 21, 2399–2407.

Mowinckel, A. M., Espeseth, T., & Westlye, L. T. (2012). Network-specific effects of age and in-scanner subject motion: a resting-state fMRI study of 238 healthy adults. NeuroImage, 63, 1364–1373.PubMedCrossRef

Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., & Bandettini, P. A. (2009). The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? NeuroImage, 44, 893–905.PubMedCentralPubMedCrossRef

Nir, T. M., Jahanshad, N., Villalon-Reina, J. E., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2013). Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging. NeuroImage: Clinical, 3, 180–195.CrossRef

O’Sullivan, M., Jones, D. K., Summers, P. E., Morris, R. G., Williams, S. C., & Markus, H. S. (2001). Evidence for cortical “disconnection” as a mechanism of age-related cognitive decline. Neurologia i Neurochirurgia Polska, 57, 632–638.

Petrella, J. R., Sheldon, F. C., Prince, S. E., Calhoun, V. D., & Doraiswamy, P. M. (2011). Default mode network connectivity in stable vs progressive mild cognitive impairment. Neurology, 76, 511–517.PubMedCentralPubMedCrossRef

Plassman, B. L., Langa, K. M., Fisher, G. G., Heeringa, S. G., Weir, D. R., Ofstedal, M. B., Burke, J. R., Hurd, M. D., Potter, G. G., Rodger, W. L., Steffens, D. C., Willis, R. J., & Wallace, R. B. (2007). Prevalence of dementia in the United States: the aging, demographics, and memory study. Neuroepidemiology, 29, 125–132.PubMedCentralPubMedCrossRef

Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59, 2142–2154.PubMedCentralPubMedCrossRef

Protas, H. D., Kepe, V., Hayashi, K. M., Klunder, A. D., Braskie, M. N., Ercoli, L., Siddarth, P., Bookheimer, S. Y., Thompson, P. M., Small, G. W., Barrio, J. R., & Huang, S. C. (2012). Prediction of cognitive decline based on hemispheric cortical surface maps of FDDNP PET. NeuroImage, 61, 749–760.PubMedCrossRef

Rabinovici, G. D., Furst, A. J., O’Neil, J. P., Racine, C. A., Mormino, E. C., Baker, S. L., Chetty, S., Patel, P., Pagliaro, T. A., Klunk, W. E., Mathis, C. A., Rosen, H. J., Miller, B. L., & Jagust, W. J. (2007). 11C-PIB PET imaging in Alzheimer disease and frontotemporal lobar degeneration. Neurologia i Neurochirurgia Polska, 68, 1205–1212.

Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. PNAS, 98, 676–682.PubMedCentralPubMedCrossRef

Rajagopalan, P., Hua, X., Jack, C. R., Weiner, M. W., Toga, A. W., Thompson, P. M., & the ADNI. (2011). Homocysteine levels are associated with regional brain volumes in 732 elderly subjects. NeuroReport, 22, 391–395.PubMedCentralPubMedCrossRef

Rajagopalan, P., Jahanshad, N., Stein, J. L., Kohannim, O., Hibar, D. P., Hua, X., Toga, A. W., Jack, C. R., Jr., Saykin, A. J., Green, R. C., Weiner, M. W., Thompson, P. M., & the Alzheimer’s Disease Neuroimaging Initiative. (2012). Commonly carried C677T risk variant in the folate pathway candidate gene, MTHFR, promotes brain deficits in the cognitively impaired elderly. NeuroImage: Clinical, 1, 179–187.CrossRef

Rajagopalan, P., Toga, A. W., Jack, C. R., Jr., Weiner, M. W., Thompson, P. M., & for the Alzheimer’s Disease Neuroimaging Initiative. (2013). Fat-mass related hormone, plasma leptin, predicts brain volumes in the elderly. NeuroReport, 24, 58–62.PubMedCentralPubMedCrossRef

Raz, N., Gunning, F. M., Head, D., Dupuis, J. H., McQuain, J., Briggs, S. D., Loken, W. J., Thornton, A. E., & Acker, J. D. (1997). Selective aging of the human cerebral cortex observed in vivo: differential vulnerability of the prefrontal gray matter. Cerebral Cortex, 7, 268–282.PubMedCrossRef

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52, 1059–1069.PubMedCrossRef

Sanz-Arigita, E. J., Schoonheim, M. M., Damoiseaux, J. S., Rombouts, S. A. R. B., Maris, E., Barkhof, F., Scheltens, P., & Stam, C. J. (2010). Loss of “small-world” networks in Alzheimer’s disease: graph analysis of fMRI resting-state functional connectivity. (J. C. S Breitner, Ed.). PLoS ONE, 5, e13788.PubMedCentralPubMedCrossRef

Satterthwaite, T. D., Wolf, D. H., Loughead, J., Ruparel, K., Elliot, M. A., Hakonarson, H., et al. (2012). Impact of in-scanner head motion in multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. NeuroImage, 60, 623–632.PubMedCentralPubMedCrossRef

Scarmeas, N., Luchsinger, J. A., Schupf, N., Brickman, A. M., Cosentino, S., Tang, M. X., & Stern, Y. (2009). Physical activity, diet, and risk of Alzheimer disease. JAMA, 302, 627–637.PubMedCentralPubMedCrossRef

Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L., & Greicius, M. D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. Journal of Neuroscience, 27, 2349–2356.PubMedCentralPubMedCrossRef

Sheline, Y. I. & Raichle, M. E. (2013). Resting state functional connectivity in preclinical Alzheimer’s disease. Biological Psychiatry, 74, 340-347.

Sheline, Y. I., Morris, J. C., Snyder, A. Z., Price, J. L., Yan, Z., D’Angelo, G., et al. (2010a). APOE4 allele disrupts resting state fMRI connectivity in the absence of amyloid plaques of decreased CSF Aβ42. Journal of Neuroscience, 30, 17035–17040.PubMedCentralPubMedCrossRef

Sheline, Y. I., Raichle, M. E., Snyder, A. Z., Morris, J. C., Head, D., Wang, S., & Mintun, M. A. (2010b). Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. BPS, 67, 584–587.

Sorg, C., Riedl, V., Mühlau, M., Calhoun, V. D., Eichele, T., Läer, L., Drzezga, A., Förstl, H., Kurz, A., Zimmer, C., & Wohlschläger. (2007). Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. PNAS, 104, 18760–18765.PubMedCentralPubMedCrossRef

St Jacques, P., Dolcos, F., & Cabeza, R. (2010). Effects of aging on functional connectivity of the amygdala during negative evaluation: a network analysis of fMRI data. Neurobiology of Aging, 31, 315–327.PubMedCentralPubMedCrossRef

Stam, C., Jones, B., Nolte, G., Breakspear, M., & Scheltens, P. (2007). Small-world networks and functional connectivity in Alzheimer’s disease. Cerebral Cortex, 17, 92–99.PubMedCrossRef

Stam, C. J., de Haan, W., Daffertshofer, A., Jones, B. F., Manshanden, I., Van Cappellen van Walsum, A. M., Montez, T., Verbunt, J. P. A., de Munck, J. C., van Dijk, B. W., Berendse, H. W., & Scheltens, P. (2009). Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease. Brain, 132, 213–224.PubMedCrossRef

Supekar, K., Menon, V., Rubin, D., Musen, M., & Greicius, M. D. (2008). Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. (O. Sporns, Ed.). PLoS Computational Biology, 4, e1000100.PubMedCentralPubMedCrossRef

Thomason, M. E., Dassanayake, M. T., Shen, S., Katkuri, Y., Alexis, M., Anderson, A. L., Yeo, L., Mody, S., Hernandez-Andrade, E., Hassan, S. S., Studholme, C., Jeong, J.-W., & Romero, R. (2013). Cross-hemispheric functional connectivity in the human fetal brain. Science Translational Medicine, 5, 173ra24.PubMedCentralPubMedCrossRef

Thompson, P. M., Stein, J. L., Medland, S. E., Hibar, D. P., Vasquez, A. A., Renteria, M., et al. (2014). The ENIGMA Consortium: Large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging & Behavior, Special Issue on Imaging Genetics (ed. Van Horn, J. D.). In Press.

Toga, A. W., & Thompson, P. M. (2013). Connectomics sheds new light on Alzheimer’s disease. Biological Psychiatry, 73, 390–392.PubMedCrossRef

Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002). Automatic anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15, 273–289.PubMedCrossRef

Van Dijk, K. R. A., Sabuncu, M. R., & Buckner, R. L. (2012). The influence of head motion on intrinsic functional connectivity MRI. NeuroImage, 59, 431–438.PubMedCentralPubMedCrossRef

Wang, L., Zang, Y., He, Y., Liang, M., Zhang, X., Tian, L., Wu, T., Jiang, T., & Li, K. (2006). Changes in Hippocampal connectivity in the early stages of Alzheimer’s disease: evidence from resting state fMRI. NeuroImage, 31, 496–504.PubMedCrossRef

Wang, K., Liang, M., Wang, L., Tian, L., Zhang, X., Li, K., & Jiang, T. (2007). Altered functional connectivity in early Alzheimer’s disease: a resting-state fMRI study. Human Brain Mapping, 28, 967–978.PubMedCrossRef

Wu, J.-T., Wu, H.-Z., Yan, C.-G., Chen, W.-X., Zhang, H.-Y., He, Y., & Yang, H.-S. (2011). Neuroscience letters. Neuroscience Letters, 504, 62–67.PubMedCrossRef

Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yücel, M., Pantelis, C., & Bullmore, E. T. (2010). Whole-brain anatomical networks: does the choice of nodes matter? NeuroImage, 50, 970–983.PubMedCrossRef

Zhang, H.-Y., Wang, S.-J., Xing, J., Liu, B., Ma, Z.-L., Yang, M., Zhang, Z.-J., & Teng, G.-J. (2009). Detection of PCC functional connectivity characteristics in resting-state fMRI in mild Alzheimer’s disease. Behavioural Brain Research, 197, 103–108.PubMedCrossRef

Titel: Functional Brain Connectivity Using fMRI in Aging and Alzheimer’s Disease
verfasst von: Emily L. Dennis
Paul M. Thompson
Publikationsdatum: 01.03.2014
Verlag: Springer US
Erschienen in: Neuropsychology Review / Ausgabe 1/2014
Print ISSN: 1040-7308
Elektronische ISSN: 1573-6660
DOI: https://doi.org/10.1007/s11065-014-9249-6

Leitlinien kompakt für die Neurologie

Mit medbee Pocketcards sicher entscheiden.

^{Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag}

Kostenlos registrieren

Neu im Fachgebiet Neurologie

Niedriger diastolischer Blutdruck erhöht Risiko für schwere kardiovaskuläre Komplikationen

25.04.2024 Hypotonie Nachrichten

Wenn unter einer medikamentösen Hochdrucktherapie der diastolische Blutdruck in den Keller geht, steigt das Risiko für schwere kardiovaskuläre Ereignisse: Darauf deutet eine Sekundäranalyse der SPRINT-Studie hin.

Frühe Alzheimertherapie lohnt sich

25.04.2024 AAN-Jahrestagung 2024 Nachrichten

Ist die Tau-Last noch gering, scheint der Vorteil von Lecanemab besonders groß zu sein. Und beginnen Erkrankte verzögert mit der Behandlung, erreichen sie nicht mehr die kognitive Leistung wie bei einem früheren Start. Darauf deuten neue Analysen der Phase-3-Studie Clarity AD.

Viel Bewegung in der Parkinsonforschung

25.04.2024 Parkinson-Krankheit Nachrichten

Neue arznei- und zellbasierte Ansätze, Frühdiagnose mit Bewegungssensoren, Rückenmarkstimulation gegen Gehblockaden – in der Parkinsonforschung tut sich einiges. Auf dem Deutschen Parkinsonkongress ging es auch viel um technische Innovationen.

Demenzkranke durch Antipsychotika vielfach gefährdet

23.04.2024 Demenz Nachrichten

Wenn Demenzkranke aufgrund von Symptomen wie Agitation oder Aggressivität mit Antipsychotika behandelt werden, sind damit offenbar noch mehr Risiken verbunden als bislang angenommen.

Update Neurologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 1/2014

Brain Networks in Schizophrenia

Strengthening Connections: Functional Connectivity and Brain Plasticity

The Implications of Brain Connectivity in the Neuropsychology of Autism

Brain Connectivity and Applications to Neuropsychology: Introduction to the Special Issue of Neuropsychology Review