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Intrinsic functional connectivity, CSF biomarker profiles and their relation to cognitive function in mild cognitive impairment

Published online by Cambridge University Press:  05 December 2019

Silke Matura*
Affiliation:
Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
Jan Köhler
Affiliation:
Department of Child and Adolescent Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
Andreas Reif
Affiliation:
Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
Fabian Fusser
Affiliation:
Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
Tarik Karakaya
Affiliation:
Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
Monika Scheibe
Affiliation:
Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
Felix Ehret
Affiliation:
Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
Daniel Hartmann
Affiliation:
Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
Jun-Suk Kang
Affiliation:
Department of Neurology, University Hospital, Goethe University, Frankfurt, Germany
Christoph Mayer
Affiliation:
Department of Neurology, University Hospital, Goethe University, Frankfurt, Germany
David Prvulovic
Affiliation:
Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
Johannes Pantel
Affiliation:
Institute of General Practice, Goethe University, Frankfurt, Germany
*
Author for correspondence: Silke Matura, Email: Silke.Matura@kgu.de

Abstract

Mild cognitive impairment (MCI) often precedes Alzheimer’s Dementia (AD), and in a high proportion of individuals affected by MCI, there are already neuropathological processes ongoing that become more evident when patients progress to AD. Accordingly, there is a need for reliable biomarkers to distinguish between normal aging and incipient AD. Recent research suggests that, in addition to established biomarkers such as CSF Aß42, total tau and hyperphosphorylated tau, resting state connectivity established by functional magnetic resonance imaging might also be a feasible biomarker for prodromal stages of AD. In order to explore this possibility, we investigated resting state functional connectivity as well as cerebrospinal fluid (CSF) biomarker profiles in patients with MCI (n = 30; age 66.43 ± 7.06 years) and cognitively healthy controls (n = 38; age 66.89 ± 7.12 years). CSF Aß42, total tau and hyperphosphorylated tau concentrations were correlated with measures of cognitive performance (immediate and delayed recall, global cognition, processing speed). Moreover, MCI-related alterations in intrinsic functional connectivity within the default mode network were investigated using functional resting state MRI. As expected, MCI patients showed decreased CSF Aß42 and increased total tau concentrations. These alterations were associated with cognitive performance. However, there were no differences between MCI patients and cognitively healthy controls regarding intrinsic functional connectivity. In conclusion, our results indicate that CSF protein profiles seem to be more closely related to cognitive decline than alterations in resting state activity. Thus, resting state connectivity might not be a reliable biomarker for early stages of AD.

Type
Original Article
Copyright
© Scandinavian College of Neuropsychopharmacology 2019

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References

Adriaanse, SM, Sanz-Arigita, EJ, Binnewijzend, MA, Ossenkoppele, R, Tolboom, N, Van Assema, DM, Wink, AM, Boellaard, R, Yaqub, M, Windhorst, AD, Van Der Flier, WM, Scheltens, P, Lammertsma, AA, Rombouts, SA, Barkhof, F and Van Berckel, BN (2014) Amyloid and its association with default network integrity in Alzheimer’s disease. Human Brain Mapping 35, 779791.10.1002/hbm.22213CrossRefGoogle ScholarPubMed
Andreasen, N and Blennow, K (2005) CSF biomarkers for mild cognitive impairment and early Alzheimer’s disease. Clinical Neurology and Neurosurgery 107, 165173.CrossRefGoogle ScholarPubMed
Beck, AT, Steer, RA and Brown, GK (1996) Beck Depression Inventory, 2nd Edn. Manual, San Antonio: The Psychological Corporation.Google Scholar
Bertens, D, Knol, DL, Scheltens, P, Visser, PJ and Alzheimer’s Disease Neuroimaging Initiative (2015) Temporal evolution of biomarkers and cognitive markers in the asymptomatic, MCI, and dementia stage of Alzheimer’s disease. Alzheimer’s & Dementia 11, 511522.10.1016/j.jalz.2014.05.1754CrossRefGoogle ScholarPubMed
Binnewijzend, MA, Schoonheim, MM, Sanz-Arigita, E, Wink, AM, Van Der Flier, WM, Tolboom, N, Adriaanse, SM, Damoiseaux, JS, Scheltens, P, Van Berckel, BN and Barkhof, F (2012) Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiology of Aging 33, 20182028.10.1016/j.neurobiolaging.2011.07.003CrossRefGoogle ScholarPubMed
Birn, RM, Diamond, JB, Smith, MA and Bandettini, PA (2006) Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. Neuroimage 31, 15361548.10.1016/j.neuroimage.2006.02.048CrossRefGoogle ScholarPubMed
Buckner, RL, Sepulcre, J, Talukdar, T, Krienen, FM, Liu, H, Hedden, T, Andrews-Hanna, JR, Sperling, RA and Johnson, KA (2009) Cortical hubs revealed by intrinsic functional connectivity: Mapping, assessment of stability, and relation to Alzheimer’s disease. The Journal of Neuroscience 29, 18601873.10.1523/JNEUROSCI.5062-08.2009CrossRefGoogle ScholarPubMed
Buckner, RL, Snyder, AZ, Shannon, BJ, Larossa, G, Sachs, R, Fotenos, AF, Sheline, YI, Klunk, WE, Mathis, CA, Morris, JC and Mintun, MA (2005) Molecular, structural, and functional characterization of Alzheimer’s disease: Evidence for a relationship between default activity, amyloid, and memory. The Journal of Neuroscience 25, 77097717.CrossRefGoogle ScholarPubMed
Cha, J, Jo, HJ, Kim, HJ, Seo, SW, Kim, HS, Yoon, U, Park, H, Na, DL and Lee, JM (2013) Functional alteration patterns of default mode networks: Comparisons of normal aging, amnestic mild cognitive impairment and Alzheimer’s disease. European Journal of Neuroscience 37, 19161924.CrossRefGoogle ScholarPubMed
Chen, K, Ayutyanont, N, Langbaum, JB, Fleisher, AS, Reschke, C, Lee, W, Liu, X, Alexander, GE, Bandy, D, Caselli, RJ and Reiman, EM (2012) Correlations between FDG PET glucose uptake-MRI gray matter volume scores and apolipoprotein E epsilon4 gene dose in cognitively normal adults: A cross-validation study using voxel-based multi-modal partial least squares. Neuroimage 60, 23162322.CrossRefGoogle ScholarPubMed
Deichmann, R, Schwarzbauer, C and Turner, R (2004) Optimisation of the 3D MDEFT sequence for anatomical brain imaging: technical implications at 1.5 and 3 T. NeuroImage 21, 757767.CrossRefGoogle ScholarPubMed
Delis, DC, Kramer, JH, Kaplan, E and Ober, BA (1987) The California Verbal Learning Test, San Antonio, TX: Psychological Corporation.Google Scholar
Doraiswamy, PM, Sperling, RA, Coleman, RE, Johnson, KA, Reiman, EM, Davis, MD, Grundman, M, Sabbagh, MN, Sadowsky, CH, Fleisher, AS, Carpenter, A, Clark, CM, Joshi, AD, Mintun, MA, Skovronsky, DM, Pontecorvo, MJ and Group, AAS (2012) Amyloid-beta assessed by florbetapir F 18 PET and 18-month cognitive decline: A multicenter study. Neurology 79, 16361644.CrossRefGoogle ScholarPubMed
Drzezga, A, Becker, JA, Van Dijk, KR, Sreenivasan, A, Talukdar, T, Sullivan, C, Schultz, AP, Sepulcre, J, Putcha, D, Greve, D, Johnson, KA and Sperling, RA (2011) Neuronal dysfunction and disconnection of cortical hubs in non-demented subjects with elevated amyloid burden. Brain 134, 16351646.CrossRefGoogle ScholarPubMed
Dubois, B and Albert, ML (2004) Amnestic MCI or prodromal Alzheimer’s disease? The Lancet Neurology 3, 246248.CrossRefGoogle ScholarPubMed
Esposito, R, Mosca, A, Pieramico, V, Cieri, F, Cera, N and Sensi, SL (2013) Characterization of resting state activity in MCI individuals. PeerJ 1, e135.CrossRefGoogle ScholarPubMed
Fox, MD, Snyder, AZ, Vincent, JL, Corbetta, M, Van Essen, DC and Raichle, ME (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America 102, 96739678.CrossRefGoogle ScholarPubMed
Genovese, CR, Lazar, NA and Nichols, T (2002) Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15, 870878.CrossRefGoogle ScholarPubMed
Goebel, R, Esposito, F and Formisano, E (2006) Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: from singlesubject to cortically aligned group general linear model analysis and selforganizing group independent component analysis. Human Brain Mapping 27, 392401.CrossRefGoogle Scholar
Greicius, MD, Srivastava, G, Reiss, AL and Menon, V (2004) Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: Evidence from functional MRI. Proceedings of the National Academy of Sciences of the United States of America 101, 46374642.CrossRefGoogle ScholarPubMed
Greicius, MD, Supekar, K, Menon, V and Dougherty, RF (2009) Resting-state functional connectivity reflects structural connectivity in the default mode network. Cerebral Cortex 19, 7278.CrossRefGoogle ScholarPubMed
Hagmann, P, Cammoun, L, Gigandet, X, Meuli, R, Honey, CJ, Wedeen, VJ and Sporns, O (2008) Mapping the structural core of human cerebral cortex. PLoS Biology 6, e159.CrossRefGoogle ScholarPubMed
Hautzinger, M, Keller, F and Kühner, C (2006) BDI II Beck Depressions Inventar Revision, Frankfurt am Main: Harcourt Test Services.Google Scholar
Hedden, T, Mormino, EC, Amariglio, RE, Younger, AP, Schultz, AP, Becker, JA, Buckner, RL, Johnson, KA, Sperling, RA and Rentz, DM (2012) Cognitive profile of amyloid burden and white matter hyperintensities in cognitively normal older adults. The Journal of Neuroscience 32, 1623316242.CrossRefGoogle ScholarPubMed
Hodges, JR (2006) Alzheimer’s centennial legacy: Origins, landmarks and the current status of knowledge concerning cognitive aspects. Brain 129, 28112822.CrossRefGoogle ScholarPubMed
Ishii, K, Mori, T, Hirono, N. and Mori, E (2003) Glucose metabolic dysfunction in subjects with a clinical dementia rating of 0.5. The Journal of Neuroscience 215, 7174.Google ScholarPubMed
Jack, CR Jr. and Holtzman, DM (2013) Biomarker modeling of Alzheimer’s disease. Neuron 80, 13471358.CrossRefGoogle ScholarPubMed
Jin, M, Pelak, VS and Cordes, D (2012) Aberrant default mode network in subjects with amnestic mild cognitive impairment using resting-state functional MRI. Magnetic Resonance Imaging 30, 4861.CrossRefGoogle ScholarPubMed
Lim, YY, Ellis, KA, Harrington, K, Kamer, A, Pietrzak, RH, Bush, AI, Darby, D, Martins, RN, Masters, CL, Rowe, CC, Savage, G, Szoeke, C, Villemagne, VL, Ames, D and Maruff, P (2013) Cognitive consequences of high Abeta amyloid in mild cognitive impairment and healthy older adults: Implications for early detection of Alzheimer’s disease. Neuropsychology 27, 322332.CrossRefGoogle ScholarPubMed
Lim, YY, Kalinowski, P, Pietrzak, RH, Laws, SM, Burnham, SC, Ames, D, Villemagne, VL, Fowler, CJ, Rainey-Smith, SR, Martins, RN, Rowe, CC, Masters, CL and Maruff, PT (2018) Association of beta-amyloid and apolipoprotein E epsilon4 with memory decline in preclinical alzheimer disease. JAMA Neurology 75, 488494.CrossRefGoogle ScholarPubMed
Malpas, CB, Saling, MM, Velakoulis, D, Desmond, P, O’brien, TJ and Alzheimer’s Disease Neuroimaging Initiative (2015) Tau and Amyloid-beta cerebrospinal fluid biomarkers have differential relationships with cognition in mild cognitive impairment. Journal of Alzheimer’s Disease 47, 965975.CrossRefGoogle ScholarPubMed
Mccormick, C, Quraan, M, Cohn, M, Valiante, TA and Mcandrews, MP (2013) Default mode network connectivity indicates episodic memory capacity in mesial temporal lobe epilepsy. Epilepsia 54, 809818.CrossRefGoogle ScholarPubMed
Morris, JC, Mohs, RC, Rogers, H, Fillenbaum, G and Heyman, A (1988) Consortium to establish a registry for Alzheimer’s disease (CERAD) clinical and neuropsychological assessment of Alzheimer’s disease. Psychopharmacology Bulletin 24, 641652.Google ScholarPubMed
Nathan, PJ, Lim, YY, Abbott, R, Galluzzi, S, Marizzoni, M, Babiloni, C, Albani, D, Bartres-Faz, D, Didic, M, Farotti, L, Parnetti, L, Salvadori, N, Muller, BW, Forloni, G, Girtler, N, Hensch, T, Jovicich, J, Leeuwis, A, Marra, C, Molinuevo, JL, Nobili, F, Pariente, J, Payoux, P, Ranjeva, JP, Rolandi, E, Rossini, PM, Schonknecht, P, Soricelli, A, Tsolaki, M, Visser, PJ, Wiltfang, J, Richardson, JC, Bordet, R, Blin, O, Frisoni, GB and Pharmacog, C (2017) Association between CSF biomarkers, hippocampal volume and cognitive function in patients with amnestic mild cognitive impairment (MCI). Neurobiology of Aging 53, 110.CrossRefGoogle Scholar
Niemann, H, Sturm, W, Thöne-Otto, AIT and Willmes, K (2008) California Verbal Learning Test. Deutsche Adaptation, Frankfurt am Main: Pearson Assessment & Information GmbH.Google Scholar
Oertel-Knochel, V, Knochel, C, Matura, S, Prvulovic, D, Linden, DE and Van De Ven, V (2013) Reduced functional connectivity and asymmetry of the planum temporale in patients with schizophrenia and first-degree relatives. Schizophrenia Research 147, 331338.CrossRefGoogle ScholarPubMed
Raichle, ME, Macleod, AM, Snyder, AZ, Powers, WJ, Gusnard, DA and Shulman, GL (2001) A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America 98, 676682.CrossRefGoogle ScholarPubMed
Reiman, EM, Caselli, RJ, Yun, LS, Chen, K, Bandy, D, Minoshima, S, Thibodeau, SN and Osborne, D (1996) Preclinical evidence of Alzheimer’s disease in persons homozygous for the epsilon 4 allele for apolipoprotein E. The New England Journal of Medicine 334, 752758.CrossRefGoogle ScholarPubMed
Reiman, EM, Chen, K, Alexander, GE, Caselli, RJ, Bandy, D, Osborne, D, Saunders, AM and Hardy, J (2004) Functional brain abnormalities in young adults at genetic risk for late-onset Alzheimer’s dementia. Proceedings of the National Academy of Sciences of the United States of America 101, 284289.CrossRefGoogle Scholar
Rickham, PP (1964) Human experimentation. Code of ethics of the world medical association. Declaration of Helsinki. British Medical Journal 2, 177.Google ScholarPubMed
Riemenschneider, M, Lautenschlager, N, Wagenpfeil, S, Diehl, J, Drzezga, A and Kurz, A (2002) Cerebrospinal fluid tau and beta-amyloid 42 proteins identify Alzheimer disease in subjects with mild cognitive impairment. Archieves of Neurology 59, 17291734.CrossRefGoogle ScholarPubMed
Rombouts, SA, Damoiseaux, JS, Goekoop, R, Barkhof, F, Scheltens, P, Smith, SM and Beckmann, CF (2009) Model-free group analysis shows altered BOLD FMRI networks in dementia. Human Brain Mapping 30, 256266.CrossRefGoogle ScholarPubMed
Rowe, CC, Bourgeat, P, Ellis, KA, Brown, B, Lim, YY, Mulligan, R, Jones, G, Maruff, P, Woodward, M, Price, R, Robins, P, Tochon-Danguy, H, O’keefe, G, Pike, KE, Yates, P, Szoeke, C, Salvado, O, Macaulay, SL, O’meara, T, Head, R, Cobiac, L, Savage, G, Martins, R, Masters, CL, Ames, D and Villemagne, VL (2013) Predicting Alzheimer disease with beta-amyloid imaging: Results from the Australian imaging, biomarkers, and lifestyle study of ageing. Annals of Neurology 74, 905913.CrossRefGoogle ScholarPubMed
Sestieri, C, Corbetta, M, Romani, GL and Shulman, GL (2011) Episodic memory retrieval, parietal cortex, and the default mode network: Functional and topographic analyses. The Journal of Neuroscience 31, 44074420.CrossRefGoogle ScholarPubMed
Sheline, YI, Raichle, ME, Snyder, AZ, Morris, JC, Head, D, Wang, S and Mintun, MA (2010) Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biological Psychiatry 67, 584587.CrossRefGoogle ScholarPubMed
Small, GW, Ercoli, LM, Silverman, DH, Huang, SC, Komo, S, Bookheimer, SY, Lavretsky, H, Miller, K, Siddarth, P, Rasgon, NL, Mazziotta, JC, Saxena, S, Wu, HM, Mega, MS, Cummings, JL, Saunders, AM, Pericak-Vance, MA, Roses, AD., Barrio, JR and Phelps, ME (2000) Cerebral metabolic and cognitive decline in persons at genetic risk for Alzheimer’s disease. Proceedings of the National Academy of Sciences of the United States of America 97, 60376042.CrossRefGoogle ScholarPubMed
Sorg, C, Riedl, V, Muhlau, M, Calhoun, VD, Eichele, T, Laer, L, Drzezga, A, Forstl, H, Kurz, A, Zimmer, C and Wohlschlager, AM (2007) Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. Proceedings of the National Academy of Sciences of the United States of America 104, 1876018765.CrossRefGoogle ScholarPubMed
Sperling, RA, Laviolette, PS, O’keefe, K, O’brien, J, Rentz, DM, Pihlajamaki, M, Marshall, G, Hyman, BT, Selkoe, DJ, Hedden, T, Buckner, RL, Becker, JA and Johnson, KA (2009) Amyloid deposition is associated with impaired default network function in older persons without dementia. Neuron 63, 178188.CrossRefGoogle ScholarPubMed
Spreen, O and Strauss, E (1998) A Compendium of Neuropsychological Tests: Administration, Norms and Commentary, New York: Oxford University Press.Google Scholar
Spreng, RN, Mar, RA and Kim, AS (2009) The common neural basis of autobiographical memory, prospection, navigation, theory of mind, and the default mode: A quantitative meta-analysis. Journal of Cognitive Neuroscience 21, 489510.CrossRefGoogle ScholarPubMed
Wang, Y, Risacher, SL, West, JD, Mcdonald, BC, Magee, TR, Farlow, MR, Gao, S, O’neill, DP and Saykin, AJ (2013) Altered default mode network connectivity in older adults with cognitive complaints and amnestic mild cognitive impairment. Journal of Alzheimer’s Disease 35, 751760.CrossRefGoogle ScholarPubMed
Yang, XF, Bossmann, J, Schiffhauer, B, Jordan, M and Immordino-Yang, MH (2012) Intrinsic default mode network connectivity predicts spontaneous verbal descriptions of autobiographical memories during social processing. Frontiers in Psychology 3, 592.Google ScholarPubMed
Yi, D, Choe, YM, Byun, MS, Sohn, BK, Seo, EH, Han, J, Park, J, Woo, JI and Lee, DY (2015) Differences in functional brain connectivity alterations associated with cerebral amyloid deposition in amnestic mild cognitive impairment. Frontiers in Aging Neuroscience 7, 15.CrossRefGoogle ScholarPubMed
Zamboni, G, Wilcock, GK, Douaud, G, Drazich, E, Mcculloch, E, Filippini, N, Tracey, I, Brooks, JC, Smith, SM, Jenkinson, M and Mackay, CE (2013) Resting functional connectivity reveals residual functional activity in Alzheimer’s disease. Biological Psychiatry 74, 375383.CrossRefGoogle ScholarPubMed
Zetterberg, H, Wahlund, LO and Blennow, K (2003) Cerebrospinal fluid markers for prediction of Alzheimer’s disease. Neuroscience Letters 352, 6769.CrossRefGoogle ScholarPubMed
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