Advertisement

Rostral-Caudal Hippocampal Functional Convergence Is Reduced Across the Alzheimer’s Disease Spectrum

  • Joseph TherriaultEmail author
  • S. Wang
  • S. Mathotaarachchi
  • Tharick A. Pascoal
  • M. Parent
  • T. Beaudry
  • M. Shin
  • Benedet AL
  • M. S. Kang
  • K. P. Ng
  • C. Dansereau
  • M. T. M. Park
  • V. Fonov
  • F. Carbonell
  • E. Zimmer
  • M. Mallar Chakravarty
  • P. Bellec
  • S. Gauthier
  • P. Rosa-Neto
  • for the Alzheimer’s Disease Neuroimaging Initiative
Article

Abstract

Beginning in the early stages of Alzheimer’s disease (AD), the hippocampus reduces its functional connections to other cortical regions due to synaptic depletion. However, little is known regarding connectivity abnormalities within the hippocampus. Here, we describe rostral-caudal hippocampal convergence (rcHC), a metric of the overlap between the rostral and caudal hippocampal functional networks, across the clinical spectrum of AD. We predicted a decline in rostral-caudal hippocampal convergence in the early stages of the disease. Using fMRI, we generated resting-state hippocampal functional networks across 56 controls, 48 early MCI (EMCI), 35 late MCI (LMCI), and 31 AD patients from the Alzheimer’s Disease Neuroimaging Initiative cohort. For each diagnostic group, we performed a conjunction analysis and compared the rostral and caudal hippocampal network changes using a mixed effects linear model to estimate the convergence and differences between these networks, respectively. The conjunction analysis showed a reduction of rostral-caudal hippocampal convergence strength from early MCI to AD, independent of hippocampal atrophy. Our results demonstrate a parallel between the functional convergence within the hippocampus and disease stage, which is independent of brain atrophy. These findings support the concept that network convergence might contribute as a biomarker for connectivity dysfunction in early stages of AD.

Keywords

Alzheimer’s disease Brain network Functional connectivity Hippocampus Mild cognitive impairment 

Notes

Funding

This work was supported by the Canadian Institutes of Health Research (CIHR) (MOP-11-51-31); the Alan Tiffin Foundation; the Alzheimer’s Association (NIRG-08-92090); and the Fonds de recherche du Québec—Santé (chercheur boursier); the CAPES Foundation (0327/13-1), and a fellowship from the Stop-AD Centre, provided by McGill University and the Douglas Hospital Research Centre Institutional Funding. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd., and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Compliance with Ethical Standards

In this section, we outline our manuscript’s compliance with all relevant ethical standards.

Conflict of Interest

Therriault J, Wang S, Mathotaarachchi S, Pascoal TA, Parent M, Beaudry T, Shin M, Benedet AL, Kang MS, Ng KP, Dansereau C, Park MTM, Fonov V, Carbonell F, Zimmer E, Chakravarty M, Bellec P, and Rosa-Neto P have no conflicts of interest to disclose. S. Gauthier has received honoraria for serving on the scientific advisory boards of Alzheon, Axovant, Lilly, Lundbeck, Novartis, Schwabe, and TauRx and on the Data Safety Monitoring Board of a study sponsored by Eisai and studies run by the Alzheimer’s Disease Cooperative Study and by the Alzheimer’s Therapeutic Research Institute.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

The ADNI study was approved by the Institutional Review Boards of all of the participating institutions. Informed written consent was obtained from all participants in the study.

Supplementary material

12035_2019_1671_MOESM1_ESM.pdf (187 kb)
Supplementary Figure 1: To ensure that connectivity measures were not confounded by hippocampal atrophy or signal dropout, we produced probability maps of the left hippocampus for each diagnostic group by averaging MAGeT Brain segmentations (passing quality control) warped into the MNI space using linear and non-linear transformations from the CIVET pipeline. Seeds were selected to avoid areas with significant atrophy or signal dropout. Crosses indicate the posterior (upper left) and anterior (lower right) seed centers. (PDF 187 kb)
12035_2019_1671_MOESM2_ESM.png (324 kb)
Supplementary Figure 2: To ensure that hippocampal seeds were representative across subjects, we produced a Coefficient of Variance map. The value corresponds to the average BOLD signal divided by the standard deviation for all subjects. Coordinates of the seeds are in the MNI space. Seeds were selected to avoid areas with large coefficient of variance. With a spherical seed of 3.5mm, the left anterior and left posterior hippocampus seeds had an average coefficient of variance of 0.388 and 0.347, respectively. (PNG 323 kb)
12035_2019_1671_MOESM3_ESM.png (684 kb)
Supplementary Figure 3: Network architectures depicting rostral and caudal hippocampal seeds per disease state. The maps have been overlaid on a MNI template surface and represent t-statistics within areas that have survived multiple testing correction. While the anterior hippocampus was uniquely connected to the precuneus, PCC, ACC, inferior parietal lobe and the caudate nucleus (red), the posterior hippocampus was functionally related with the occipital and the orbitofrontal regions (blue). An overlap between the hippocampal seeds’ networks (yellow) was present in the medial temporal lobe. This overlap diminished with increased disease severity (see Figure 4). *RFT-corrected with a corrected threshold of p≤0.05, with minimum cluster size = 321mm3 and supra-threshold = 3.2485. (PNG 684 kb)
12035_2019_1671_MOESM4_ESM.pdf (247 kb)
Supplementary Figure 4: Differences between the left and right hippocampal seeds. Red areas are more connected to the left than the right seed, and blue for the reverse. The left hemisphere present ipsilateral changes, and the right hemisphere present contralateral ones. No laterality difference was detected in CN. In EMCI and LMCI, left > right anterior seed to the contralateral temporal lobe, and the left > right posterior seed to the contralateral occipital lobe. In AD, the left > right anterior seed to the precuneus, whereas the right > left posterior seed to the anterior lateral temporal lobe. *RFT-corrected (EMCI, LMCI and AD, corrected threshold p≤0.05). (PDF 247 kb)

References

  1. 1.
    Braak H, Braak E (1991) Neuropathological stageing of Alzheimer-related changes. Acta Neuropathologica 82:239–259Google Scholar
  2. 2.
    Chiotis K, Leuzy A, Almkvist O, et al (2017) Longitudinal changes of tau PET imaging in relation to hypometabolism in prodromal and Alzheimer’s disease dementia. Mol Psychiatry 1–8. doi:  https://doi.org/10.1038/mp.2017.108
  3. 3.
    Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD (2009) Neurodegenerative diseases target large-scale human brain networks. Neuron 62:42–52.  https://doi.org/10.1016/j.neuron.2009.03.024 CrossRefGoogle Scholar
  4. 4.
    Allen G, Barnard H, Mccoll R et al (2007) Reduced hippocampal functional connectivity in Alzheimer disease. Arch Neurol 64:1482–1487CrossRefGoogle Scholar
  5. 5.
    Badhwar A, Tam A, Dansereau C et al (2017) Resting-state network dysfunction in Alzheimer’s disease: a systematic review and meta-analysis. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring 8:73–85Google Scholar
  6. 6.
    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 101:4637–4642CrossRefGoogle Scholar
  7. 7.
    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 1302:167–174.  https://doi.org/10.1016/j.brainres.2009.09.028 CrossRefGoogle Scholar
  8. 8.
    Sorg C, Riedl V (2007) Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. Proceedings of the National Academy of Sciences 104:18760–18765Google Scholar
  9. 9.
    Wu L, Soder RB, Schoemaker D, Carbonnell F, Sziklas V, Rowley J, Mohades S, Fonov V et al (2014) Resting state executive control network adaptations in amnestic mild cognitive impairment. J Alzheimers Dis 40:1–12.  https://doi.org/10.3233/JAD-131574 CrossRefGoogle Scholar
  10. 10.
    Zhou Y, Dougherty JH, Hubner KF et al (2008) Abnormal connectivity in the posterior cingulate and hippocampus in early Alzheimer’s disease and mild cognitive impairment. Alzheimers Dement 4:265–270.  https://doi.org/10.1016/j.jalz.2008.04.006 CrossRefGoogle Scholar
  11. 11.
    Roy AK, Shehzad Z, Margulies DS, Kelly AMC, Uddin LQ, Gotimer K, Biswal BB, Castellanos FX et al (2009) Functional connectivity of the human amygdala using resting state fMRI. NeuroImage 45:614–626.  https://doi.org/10.1016/j.neuroimage.2008.11.030 CrossRefGoogle Scholar
  12. 12.
    Taylor KS, Seminowicz DA, Davis KD (2009) Two systems of resting state connectivity between the insula and cingulate cortex. Hum Brain Mapp 30:2731–2745.  https://doi.org/10.1002/hbm.20705 CrossRefGoogle Scholar
  13. 13.
    Pasquini L, Scherr M, Tahmasian M et al (2014) Link between hippocampus’ raised local and eased global intrinsic connectivity in AD. Alzheimers Dement 11:475–484CrossRefGoogle Scholar
  14. 14.
    Zarei M, Beckmann CF, Binnewijzend MAA et al (2013) Functional segmentation of the hippocampus in the healthy human brain and in Alzheimer’s disease. NeuroImage 66:28–35.  https://doi.org/10.1016/j.neuroimage.2012.10.071 CrossRefGoogle Scholar
  15. 15.
    Alzheimer’s Disease Neuroimaging Initiative (2005) ADNI2 procedures manualGoogle Scholar
  16. 16.
    Biomedical Research Forum LLC (2015) When there’s no amyloid, it’s not Alzheimer’s. AlzForumGoogle Scholar
  17. 17.
    Hedden T, Van Dijk KRA, Becker JA et al (2009) Disruption of functional connectivity in clinically normal older adults harboring amyloid burden. J Neurosci 29:12686–12694.  https://doi.org/10.1523/JNEUROSCI.3189-09.2009 CrossRefGoogle Scholar
  18. 18.
    Bellec P, Carbonell FM, Perlbarg V, et al (2011) A neuroimaging analysis kit for Matlab and Octave. In: Proceedings of the 17th international conference on functional mapping of the human brain pp. 2735–46Google Scholar
  19. 19.
    Collins DL, Evans AC (1997) Animal: validation and applications of nonlinear registration-based segmentation. Int J Pattern Recognit Artif Intell 11:1271–1294.  https://doi.org/10.1142/S0218001497000597 CrossRefGoogle Scholar
  20. 20.
    Fonov V, Evans AC, Botteron K, Almli CR, McKinstry R, Collins DL, Brain Development Cooperative Group (2011) Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54:313–327.  https://doi.org/10.1016/j.neuroimage.2010.07.033 CrossRefGoogle Scholar
  21. 21.
    Zijdenbos AP, Forghani R, Evans AC (2002) Automatic “pipeline” analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imaging 21:1280–1291.  https://doi.org/10.1109/TMI.2002.806283 CrossRefGoogle Scholar
  22. 22.
    Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59:2142–2154.  https://doi.org/10.1016/j.neuroimage.2011.10.018 CrossRefGoogle Scholar
  23. 23.
    Giove F, Gili T, Iacovella V, Macaluso E, Maraviglia B (2009) Images-based suppression of unwanted global signals in resting-state functional connectivity studies. Magn Reson Imaging 27:1058–1064.  https://doi.org/10.1016/j.mri.2009.06.004 CrossRefGoogle Scholar
  24. 24.
    Lund TE, Madsen KH, Sidaros K, Luo WL, Nichols TE (2006) Non-white noise in fMRI: does modelling have an impact? NeuroImage 29:54–66.  https://doi.org/10.1016/j.neuroimage.2005.07.005 CrossRefGoogle Scholar
  25. 25.
    Chakravarty MM, Steadman P, Eede MC et al (2013) Performing label-fusion-based segmentation using multiple automatically generated templates. Hum Brain Mapp 34:2635–2654CrossRefGoogle Scholar
  26. 26.
    Pipitone J, Park MTM, Winterburn J, Lett TA, Lerch JP, Pruessner JC, Lepage M, Voineskos AN et al (2014) Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage 101:494–512CrossRefGoogle Scholar
  27. 27.
    Winterburn JL, Pruessner JC, Chavez S, Schira MM, Lobaugh NJ, Voineskos AN, Chakravarty MM (2013) A novel in vivo atlas of human hippocampal subfields using high-resolution 3T magnetic resonance imaging. Neuroimage 74:254–265CrossRefGoogle Scholar
  28. 28.
    Treadway MT, Waskom ML, Dillon DG, Holmes AJ, Park MTM, Chakravarty MM, Dutra SJ, Polli FE et al (2015) Illness progression, recent stress, and morphometry of hippocampal subfields and medial prefrontal cortex in major depression. Biol Psychiatry 77:285–294CrossRefGoogle Scholar
  29. 29.
    Worsley KJ, Liao CH, Aston J, Petre V, Duncan GH, Morales F, Evans AC (2002) A general statistical analysis for fMRI data. NeuroImage 15:1–15.  https://doi.org/10.1006/nimg.2001.0933 CrossRefGoogle Scholar
  30. 30.
    Worsley K, Friston K (2000) A test for a conjunction. Statistics and Probability Letters 47:135–140.  https://doi.org/10.1016/S0167-7152(99)00149-2
  31. 31.
    Friston KJ, Holmes AP, Price CJ, et al (1999) Multisubject fMRI studies and conjunction analyses. NeuroImage 10:385–396Google Scholar
  32. 32.
    Persson J, Nyberg L (2000) Conjunction analysis of cortical activations common to encoding and retrieval. Microsc Res Tech 51:39–44CrossRefGoogle Scholar
  33. 33.
    Friston KJ, Penny WD, Glaser DE (2005) Conjunction revisited. doi:  https://doi.org/10.1016/j.neuroimage.2005.01.013
  34. 34.
    Friston KJ, Worsley KJ, Frackowiak RSJ et al (1993) Assessing the significance of focal activations using their spatial extent. Hum Brain Mapp 1:210–220CrossRefGoogle Scholar
  35. 35.
    Worsley KJ, Cao J, Paus T, Petrides M, Evans AC (1998) Applications of random field theory to functional connectivity. Hum Brain Mapp 6:364–367CrossRefGoogle Scholar
  36. 36.
    Gorgolewski KJ, Yarkoni T, Ghosh SS, et al (2013) NeuroVault. org: a web database for sharing statistical parametric maps. In: 19th Annual Meeting of the Organization for Human Brain MappingGoogle Scholar
  37. 37.
    Jones DT, Knopman DS, Gunter JL, Graff-Radford J, Vemuri P, Boeve BF, Petersen RC, Weiner MW et al (2016) Cascading network failure across the Alzheimer’s disease spectrum. Brain 139:547–562.  https://doi.org/10.1093/brain/awv338 CrossRefGoogle Scholar
  38. 38.
    Lee E-S, Yoo K, Lee Y-B, Chung J, Lim JE, Yoon B, Jeong Y, Alzheimer’s Disease Neuroimaging Initiative (2016) Default mode network functional connectivity in early and late mild cognitive impairment. Alzheimer Dis Assoc Disord 30:289–296.  https://doi.org/10.1097/wad.0000000000000143 CrossRefGoogle Scholar
  39. 39.
    Bondi MW, Houston WS, Eyler LT, Brown GG (2005) fMRI evidence of compensatory mechanisms in older adults at genetic risk for Alzheimer disease. Neurology 64:501–508.  https://doi.org/10.1212/01.WNL.0000150885.00929.7E CrossRefGoogle Scholar
  40. 40.
    Huijbers W, Mormino EC, Schultz AP, Wigman S, Ward AM, Larvie M, Amariglio RE, Marshall GA et al (2015) Amyloid-β deposition in mild cognitive impairment is associated with increased hippocampal activity, atrophy and clinical progression. Brain 138:1023–1035.  https://doi.org/10.1093/brain/awv007 CrossRefGoogle Scholar
  41. 41.
    Fanselow MS, Dong H-W (2010) Are the dorsal and ventral hippocampus functionally distinct structures? Neuron 65:7–19.  https://doi.org/10.1016/j.neuron.2009.11.031 CrossRefGoogle Scholar
  42. 42.
    Strange BA, Witter MP, Lein ES, Moser EI (2014) Functional organization of the hippocampal longitudinal axis. Nat Rev Neurosci 15:655–669.  https://doi.org/10.1038/nrn3785 CrossRefGoogle Scholar
  43. 43.
    Hamann S, Monarch ES, Goldstein FC (2002) Impaired fear conditioning in Alzheimer’s disease. Neuropsychologia 40:1187–1195.  https://doi.org/10.1016/S0028-3932(01)00223-8 CrossRefGoogle Scholar
  44. 44.
    Landes AM, Sperry SD, Strauss ME, Geldmacher DS (2001) Apathy in Alzheimer’s disease. J Am Geriatr Soc 49:1700–1707CrossRefGoogle Scholar
  45. 45.
    Therriault J, Ng KP, Pascoal TA, Mathotaarachchi S, Kang MS, Struyfs H, Shin M, Benedet AL et al (2018) Anosognosia predicts default mode network hypometabolism and clinical progression to dementia. Neurology 90:e932–e939.  https://doi.org/10.1212/WNL.0000000000005120 CrossRefGoogle Scholar
  46. 46.
    Viswanathan A, Freeman RD (2007) Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity. Nature Neuroscience 10:1308–1312Google Scholar
  47. 47.
    Counts SE, Alldred MJ, Che S, Ginsberg SD, Mufson EJ (2014) Synaptic gene dysregulation within hippocampal CA1 pyramidal neurons in mild cognitive impairment. Neuropharmacology 79:172–179CrossRefGoogle Scholar
  48. 48.
    Padurariu M, Ciobica A, Mavroudis I et al (2012) Hippocampal neuronal loss in the Ca1 and Ca3 areas of Alzheimer’s disease patients. Psychiatr Danub 24:152–158Google Scholar
  49. 49.
    Scheff SW, Price DA, Schmitt FA, Mufson EJ (2006) Hippocampal synaptic loss in early Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging 27:1372–1384CrossRefGoogle Scholar
  50. 50.
    West MJ, Coleman PD, Flood DG, Troncoso JC (1994) Differences in the pattern of hippocampal neuronal loss in normal aging and Alzheimer’s-disease. Lance 344:769–772.  https://doi.org/10.1016/S0140-6736(94)92338-8 CrossRefGoogle Scholar
  51. 51.
    West MJ, Kawas CH, Martin LJ, Troncoso JC (2000) The CA1 region of the human hippocampus is a hot spot in Alzheimer’s disease. Annals of the New York Academy of Sciences 908:255–259CrossRefGoogle Scholar
  52. 52.
    Scheff SW, Price DA (2006) Alzheimer’s disease-related alterations in synaptic density: neocortex and hippocampus. J Alzheimers Dis 9:101–115CrossRefGoogle Scholar
  53. 53.
    Mai JK, Paxinos G (2011) The human nervous system. Academic pressGoogle Scholar
  54. 54.
    Voineskos AN, Winterburn JL, Felsky D, Pipitone J, Rajji TK, Mulsant BH, Chakravarty MM (2015) Hippocampal (subfield) volume and shape in relation to cognitive performance across the adult lifespan. Hum Brain Mapp 36:3020–3037.  https://doi.org/10.1002/hbm.22825 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Joseph Therriault
    • 1
    • 2
    • 3
    Email author
  • S. Wang
    • 1
    • 2
  • S. Mathotaarachchi
    • 1
    • 2
  • Tharick A. Pascoal
    • 1
    • 2
  • M. Parent
    • 1
    • 2
  • T. Beaudry
    • 1
    • 2
  • M. Shin
    • 1
    • 2
  • Benedet AL
    • 1
    • 2
  • M. S. Kang
    • 1
    • 2
  • K. P. Ng
    • 1
    • 2
  • C. Dansereau
    • 4
  • M. T. M. Park
    • 2
  • V. Fonov
    • 1
    • 5
  • F. Carbonell
    • 5
  • E. Zimmer
    • 6
  • M. Mallar Chakravarty
    • 2
    • 5
  • P. Bellec
    • 4
    • 5
  • S. Gauthier
    • 1
    • 4
    • 7
  • P. Rosa-Neto
    • 1
    • 4
    • 7
  • for the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.McGill University Research Centre for Studies in AgingDouglas Mental Health University InstituteMontrealCanada
  2. 2.Douglas Mental Health University InstituteMontrealCanada
  3. 3.Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas HospitalMcGill UniversityMontrealCanada
  4. 4.Institut universitaire de gériatrie de MontréalUniversité de MontréalMontrealCanada
  5. 5.McConnell Brain Imaging CentreMontreal Neurological InstituteMontrealCanada
  6. 6.Brain Institute of Rio Grande do SulPontifical Catholic University of Rio Grande do Sul (PUCRS)Porto AlegreBrazil
  7. 7.Department of Neurology and NeurosurgeryMcGill UniversityMontrealCanada

Personalised recommendations