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Alzheimer's Research & Therapy
Published in: Alzheimer's Research & Therapy 1/2018

Open Access 01-12-2018 | Research

Early brain connectivity alterations and cognitive impairment in a rat model of Alzheimer’s disease

Authors: Emma Muñoz-Moreno, Raúl Tudela, Xavier López-Gil, Guadalupe Soria

Published in: Alzheimer's Research & Therapy | Issue 1/2018

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Abstract

Background

Animal models of Alzheimer’s disease (AD) are essential to understanding the disease progression and to development of early biomarkers. Because AD has been described as a disconnection syndrome, magnetic resonance imaging (MRI)-based connectomics provides a highly translational approach to characterizing the disruption in connectivity associated with the disease. In this study, a transgenic rat model of AD (TgF344-AD) was analyzed to describe both cognitive performance and brain connectivity at an early stage (5 months of age) before a significant concentration of β-amyloid plaques is present.

Methods

Cognitive abilities were assessed by a delayed nonmatch-to-sample (DNMS) task preceded by a training phase where the animals learned the task. The number of training sessions required to achieve a learning criterion was recorded and evaluated. After DNMS, MRI acquisition was performed, including diffusion-weighted MRI and resting-state functional MRI, which were processed to obtain the structural and functional connectomes, respectively. Global and regional graph metrics were computed to evaluate network organization in both transgenic and control rats.

Results

The results pointed to a delay in learning the working memory-related task in the AD rats, which also completed a lower number of trials in the DNMS task. Regarding connectivity properties, less efficient organization of the structural brain networks of the transgenic rats with respect to controls was observed. Specific regional differences in connectivity were identified in both structural and functional networks. In addition, a strong correlation was observed between cognitive performance and brain networks, including whole-brain structural connectivity as well as functional and structural network metrics of regions related to memory and reward processes.

Conclusions

In this study, connectivity and neurocognitive impairments were identified in TgF344-AD rats at a very early stage of the disease when most of the pathological hallmarks have not yet been detected. Structural and functional network metrics of regions related to reward, memory, and sensory performance were strongly correlated with the cognitive outcome. The use of animal models is essential for the early identification of these alterations and can contribute to the development of early biomarkers of the disease based on MRI connectomics.
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Literature
1.
Dubois B, Hampel H, Feldman HH, Scheltens P, Aisen P, Andrieu S, et al. Preclinical Alzheimer’s disease: definition, natural history, and diagnostic criteria. Alzheimers Dement. 2016;12:292–323.CrossRefPubMed
2.
Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. 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. 2011;7:280–92.CrossRefPubMedPubMedCentral
3.
Vos SJ, Xiong C, Visser PJ, Jasielec MS, Hassenstab J, Grant EA, et al. Preclinical Alzheimer’s disease and its outcome: a longitudinal cohort study. Lancet Neurol. 2013;12:957–65.CrossRefPubMedPubMedCentral
4.
5.
Galeano P, Martino Adami PV, Do Carmo S, Blanco E, Rotondaro C, Capani F, et al. Longitudinal analysis of the behavioral phenotype in a novel transgenic rat model of early stages of Alzheimer’s disease. Front Behav Neurosci. 2014;8:321.CrossRefPubMedPubMedCentral
6.
Leon WC, Canneva F, Partridge V, Allard S, Ferretti MT, DeWilde A, et al. A novel transgenic rat model with a full Alzheimer’s-like amyloid pathology displays pre-plaque intracellular amyloid-β-associated cognitive impairment. J Alzheimers Dis. 2010;20:113–26.CrossRefPubMed
7.
Sabbagh JJ, Kinney JW, Cummings JL. Alzheimer’s disease biomarkers in animal models: closing the translational gap. Am J Neurodegener Dis. 2013;2:108–20.PubMedPubMedCentral
8.
Cohen RM, Rezai-Zadeh K, Weitz TM, Rentsendorj A, Gate D, Spivak I, et al. A transgenic Alzheimer rat with plaques, tau pathology, behavioral impairment, oligomeric Aβ and frank neuronal loss. J Neurosci. 2013;33:6245–56.CrossRefPubMedPubMedCentral
9.
10.
Windisch M. We can treat Alzheimer’s disease successfully in mice but not in men: failure in translation? A perspective. Neurodegener Dis. 2014;13:147–50.CrossRefPubMed
11.
Jack CR Jr, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 2010;9:119–28.CrossRefPubMedPubMedCentral
12.
Jack CR Jr, Barnes J, Bernstein MA, Borowski BJ, Brewer J, Clegg S, et al. Magnetic resonance imaging in Alzheimer’s Disease Neuroimaging Initiative 2. Alzheimers Dement. 2015;11:740–56.CrossRefPubMedPubMedCentral
13.
Knight M, Wood B, Kauppinen R, Coulthard EJ. Magnetic resonance imaging to detect early molecular and cellular changes in Alzheimer’s disease. Front Aging Neurosci. 2016;8:139.CrossRefPubMedPubMedCentral
14.
15.
Ridha BH, Barnes J, Bartlett JW, Godbolt A, Pepple T, Rossor MN, et al. Tracking atrophy progression in familial Alzheimer’s disease: a serial MRI study. Lancet Neurol. 2006;5:828–34.CrossRefPubMed
16.
Racine AM, Adluru N, Alexander AL, Christian BT, Okonkwo OC, Oh J, et al. Associations between white matter microstructure and amyloid burden in preclinical Alzheimer’s disease: a multimodal imaging investigation. Neuroimage Clin. 2014;4:604–14.CrossRefPubMedPubMedCentral
17.
Weston PSJ, Simpson IJA, Ryan NS, Ourselin S, Fox NC. Diffusion imaging changes in grey matter in Alzheimer’s disease: a potential marker of early neurodegeneration. Alzheimers Res Ther. 2015;7:47.CrossRefPubMedPubMedCentral
18.
Daianu M, Jahanshad N, Nir TM, Toga AW, Jack CR Jr, Weiner MW, et al. Breakdown of brain connectivity between normal aging and Alzheimer’s disease: a structural k-core network analysis. Brain Connect. 2013;3:407–22.CrossRefPubMedPubMedCentral
19.
Fischer FU, Wolf D, Scheurich A, Fellgiebel A. Altered whole-brain white matter networks in preclinical Alzheimer’s disease. Neuroimage Clin. 2015;8:660–6.CrossRefPubMedPubMedCentral
20.
Gour N, Felician O, Didic M, Koric L, Gueriot C, Chanoine V, et al. Functional connectivity changes differ in early and late-onset Alzheimer’s disease. Hum Brain Mapp. 2014;35:2978–94.CrossRefPubMed
21.
Lo C, Wang P, Chou K, Wang J, He Y, Lin C. Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer’s disease. J Neurosci. 2010;30:16876–85.CrossRefPubMed
22.
Wee CY, Yap PT, Li W, Denny K, Browndyke JN, Potter GG, et al. Enriched white matter connectivity networks for accurate identification of MCI patients. NeuroImage. 2011;54:1812–22.CrossRefPubMed
23.
24.
25.
26.
Palesi F, Castellazzi G, Casiraghi L, Sinforiani E, Vitali P, Gandini Wheeler-Kingshott CAM, et al. Exploring patterns of alteration in Alzheimer’s disease brain networks: a combined structural and functional connectomics analysis. Front Neurosci. 2016;10:380.CrossRefPubMedPubMedCentral
27.
Fornito A, Zalesky A, Breakspear M. Graph analysis of the human connectome: promise, progress, and pitfalls. NeuroImage. 2013;80:426–44.CrossRefPubMed
28.
Fornito A, Zalesky A, Breakspear M. The connectomics of brain disorders. Nat Rev Neurosci. 2015;16:159–72.CrossRefPubMed
29.
Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage. 2010;52:1059–69.CrossRefPubMed
30.
Hagmann P, Cammoun L, Gigandet X, Gerhard S, Grant PE, Weeden VJ, et al. MR connectomics: principles and challenges. J Neurosci Methods. 2010;194:34–45.CrossRefPubMed
31.
Supekar K, Menon V, Rubin D, Musen M, Greicius MD. Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Comput Biol. 2008;4:e1000100.CrossRefPubMedPubMedCentral
32.
He Y, Chen Z, Evans AC. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer’s disease. J Neurosci. 2008;28:4756–66.CrossRefPubMed
33.
He Y, Chen ZJ, Evans AC. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb Cortex. 2007;17:2407–19.CrossRefPubMed
34.
Tijms BM, Yeung HM, Sikkes SAM, Möller C, Smits LL, Stam CJ, et al. Single-subject gray matter graph properties and their relationship with cognitive impairment in early- and late-onset Alzheimer’s disease. Brain Connect. 2014;4:337–46.CrossRefPubMed
35.
Bai F, Shu N, Yuan Y, Shi Y, Yu H, Wu D, et al. Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment. J Neurosci. 2012;32:4307–18.CrossRefPubMed
36.
Stam CJ, de Haan W, Daffertshofer A, Jones BF, Manshanden I, van Cappellen van Walsum AM, et al. Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease. Brain. 2009;132:213–24.CrossRefPubMed
37.
de Haan W, Pijnenburg Y. Functional neural network analysis in frontotemporal dementia and Alzheimer’s disease using EEG and graph theory. BMC Neurosci. 2009;10:101.CrossRefPubMedPubMedCentral
38.
Xiang J, Guo H, Cao R, Liang H, Chen J. An abnormal resting-state functional brain network indicates progression towards Alzheimer’s disease. Neural Regen Res. 2013;8:2789–99.PubMedPubMedCentral
39.
López-Gil X, Amat-Roldan I, Tudela R, Castañé A, Prats-Galino A, Planas AM, et al. DWI and complex brain network analysis predicts vascular cognitive impairment in spontaneous hypertensive rats undergoing executive function tests. Front Aging Neurosci. 2014;6:167.PubMedPubMedCentral
40.
Shah D, Jonckers E, Praet J, Vanhoutte G, Delgado Y, Palacios R, Bigot C, et al. Resting state fMRI reveals diminished functional connectivity in a mouse model of amyloidosis. PLoS One. 2013;8:e84241.CrossRefPubMedPubMedCentral
41.
Zerbi V, Wiesmann M, Emmerzaal TL, Jansen D, van Beek M, Mutsaers MPC, et al. Resting-state functional connectivity changes in aging apoE4 and apoE-KO mice. J Neurosci. 2014;34:13963–75.CrossRefPubMed
42.
Callaghan CK, Hok V, Della-Chiesa A, Virley DJ, Upton N, O’Mara SM. Age-related declines in delayed non-match-to-sample performance (DNMS) are reversed by the novel 5HT6 receptor antagonist SB742457. Neuropharmacology. 2012;63:890–7.CrossRefPubMed
43.
Schwarz AJ, Danckaert A, Reese T, Gozzi A, Paxinos G, Watson C, et al. A stereotaxic MRI template set for the rat brain with tissue class distribution maps and co-registered anatomical atlas: application to pharmacological MRI. NeuroImage. 2006;32:538–50.CrossRefPubMed
44.
Valdés-Hernández PA, Sumiyoshi A, Nonaka H, Haga R, Aubert-Vásquez E, Ogawa T, et al. An in vivo MRI template set for morphometry, tissue segmentation, and fMRI localization in rats. Front Neuroinform. 2011;5:26.CrossRefPubMedPubMedCentral
45.
Paxinos G, Watson C. The rat brain in stereotaxic coordinates. 6th ed. London: Academic Press; 2007.
46.
Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal. 2008;12:26–41.CrossRefPubMed
47.
Coupé P, Yger P, Prima S, Hellier P, Kervrann C, Barillot C. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans Med Imaging. 2008;27:425–41.CrossRefPubMedPubMedCentral
48.
49.
50.
51.
Garyfallidis E, Brett M, Amirbekian B, Rokem A, van der Walt S, Descoteaux M, et al. Dipy, a library for the analysis of diffusion MRI data. Front Neuroinform. 2014;8:8.CrossRefPubMedPubMedCentral
52.
Batalle D, Muñoz-Moreno E, Arbat-Plana A, Illa M, Figueras F, Eixarch E, et al. Long-term reorganization of structural brain networks in a rabbit model of intrauterine growth restriction. NeuroImage. 2014;100:24–38.CrossRefPubMed
53.
Power JD, Plitt M, Laumann TO, Martin A. Sources and implications of whole-brain fMRI signals in humans. NeuroImage. 2017;146:609–25.CrossRefPubMed
54.
Benjamin Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 1995;57:289–300.
55.
Tsai Y, Lu B, Ljubimov AV, Girman S, Ross-Cisneros FN, Sadun AA, et al. Ocular changes in TGF344-AD rat model of Alzheimer’s disease. Investig Ophthalmol Vis Sci. 2014;55:523–34.CrossRef
56.
Barnes CA. Memory deficits associated with senescence: a neurophysiological and behavioral study in the rat. J Comp Physiol Psychol. 1979;93:74–104.CrossRefPubMed
57.
Morris RGM, Garrud P, Rawlins JNP, O’Keefe J. Place navigation in rats with hippocampal lesions. Nature. 1982;297:681–3.CrossRefPubMed
58.
Iulita MF, Allard S, Richter L, Munter LM, Ducatenzeiler A, Weise C, et al. Intracellular Aβ pathology and early cognitive impairments in a transgenic rat overexpressing human amyloid precursor protein: a multidimensional study. Acta Neuropathol Commun. 2014;2:61.CrossRefPubMedPubMedCentral
59.
Berlot R, Metzler-Baddeley C, Ikram MA, Jones DK, O’Sullivan MJ. Global efficiency of structural networks mediates cognitive control in mild cognitive impairment. Front Aging Neurosci. 2016;8:292.CrossRefPubMedPubMedCentral
60.
Shu N, Liang Y, Li H, Zhang J, Li X, Wang L, et al. Disrupted topological organization in white matter structural networks in amnestic mild cognitive impairment. Radiology. 2012;265:518–27.CrossRefPubMed
61.
Zhao T, Sheng C, Bi Q, Niu W, Shu N, Han Y. Age-related differences in the topological efficiency of the brain structural connectome in amnestic mild cognitive impairment. Neurobiol Aging. 2017;59:144–55.CrossRefPubMed
62.
Reijmer YD, Leemans A, Caeyenberghs K, Heringa SM, Koek H, Biessels GJ. Disruption of cerebral networks and cognitive impairment in Alzheimer’s disease. Neurology. 2013;80:1370–7.CrossRefPubMed
63.
Chen Y, Chen K, Zhang J, Li X, Shu N, Wang J, et al. Disrupted functional and structural networks in cognitively normal elderly subjects with the APOE ɛ4 allele. Neuropsychopharmacology. 2015;40:1181–91.CrossRefPubMed
64.
Shu N, Li X, Ma C, Zhang J, Chen K, Liang Y, et al. Effects of APOE promoter polymorphism on the topological organization of brain structural connectome in nondemented elderly. Hum Brain Mapp. 2015;36:4847–58.CrossRefPubMedPubMedCentral
65.
Tuladhar AM, van Uden IWM, Rutten-Jacobs LCA, Lawrence A, van Der Holst H, van Norden A, et al. Structural network efficiency predicts conversion to dementia. Neurology. 2016;86:1112–9.CrossRefPubMedPubMedCentral
66.
67.
Cardinal RN, Parkinson JA, Hall J, Everitt BJ. Emotion and motivation: the role of the amygdala, ventral striatum, and prefrontal cortex. Neurosci Biobehav Rev. 2002;26:321–52.CrossRefPubMed
68.
Brown JA, Terashima KH, Burggren AC, Ercoli LM, Miller KJ, Small GW, et al. Brain network local interconnectivity loss in aging APOE-4 allele carriers. Proc Natl Acad Sci U S A. 2011;108:20760–5.CrossRefPubMedPubMedCentral
69.
Liu Z, Zhang Y, Yan H, Bai L, Dai R, Wei W, et al. Altered topological patterns of brain networks in mild cognitive impairment and Alzheimer’s disease: a resting-state fMRI study. Psychiatry Res. 2012;202:118–25.CrossRefPubMed
70.
Sanz-Arigita EJ, Schoonheim MM, Damoiseaux JS, Rombouts SARB, Maris E, Barkhof F, et al. Loss of “small-world” networks in Alzheimer’s disease: graph analysis of fMRI resting-state functional connectivity. PLoS One. 2010;5:e13788.CrossRefPubMedPubMedCentral
71.
Wang J, Zuo X, Dai Z, Xia M, Zhao Z, Zhao X, et al. Disrupted functional brain connectome in individuals at risk for Alzheimer’s disease. Biol Psychiatry. 2013;73:472–81.CrossRefPubMed
72.
Wee CY, Yang S, Yap PT, Shen D. Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification. Brain Imaging Behav. 2016;10:342–56.CrossRefPubMedPubMedCentral
73.
Li Y, Qin Y, Chen X, Li W. Exploring the functional brain network of Alzheimer’s disease: based on the computational experiment. PLoS One. 2013;8:e73186.CrossRefPubMedPubMedCentral
74.
Minati L, Chan D, Mastropasqua C, Serra L, Spano B, Marra C, et al. Widespread alterations in functional brain network architecture in amnestic mild cognitive impairment. J Alzheimers Dis. 2014;40:213–20.PubMed
75.
Zhao X, Liu Y, Wang X, Liu B, Xi Q, Guo Q, et al. Disrupted small-world brain networks in moderate Alzheimer’s disease: a resting-state fMRI study. PLoS One. 2012;7:e33540.CrossRefPubMedPubMedCentral
76.
Brier MR, Thomas JB, Fagan AM, Hassenstab J, Holtzman DM, Benzinger TL, et al. Functional connectivity and graph theory in preclinical Alzheimer’s disease. Neurobiol Aging. 2014;35:757–68.CrossRefPubMed
77.
Phillips DJ, McGlaughlin A, Ruth D, Jager LR, Soldan A. Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer’s disease: the importance of graph creation methods. Neuroimage Clin. 2015;7:377–90.CrossRefPubMedPubMedCentral
78.
Lewis CM, Baldassarre A, Committeri G, Romani GL, Corbetta M. Learning sculpts the spontaneous activity of the resting human brain. Proc Natl Acad Sci U S A. 2009;106:17558–63.CrossRefPubMedPubMedCentral
79.
Uddin LQ. Salience processing and insular cortical function and dysfunction. Nat Rev Neurosci. 2014;16:55–61.CrossRefPubMed
80.
Sun Y, Yin Q, Fang R, Yan X, Wang Y, Bezerianos A, et al. Disrupted functional brain connectivity and its association to structural connectivity in amnestic mild cognitive impairment and Alzheimer’s disease. PLoS One. 2014;9:e906505.
81.
Dai Z, Yan C, Wang Z, Wang J, Xia M, Li K, et al. Discriminative analysis of early Alzheimer’s disease using multi-modal imaging and multi-level characterization with multi-classifier (M3). NeuroImage. 2012;59:2187–95.CrossRefPubMed
82.
Jie B, Zhang D, Wee CY, Shen D. Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification. Hum Brain Mapp. 2014;35:2876–97.CrossRefPubMed
83.
Khazaee A, Ebrahimzadeh A, Babajani-Feremi A. Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory. Clin Neurophysiol. 2015;126:2132–41.CrossRefPubMed
84.
Gabbott PLA, Warner TA, Jays PRL, Salway P, Busby SJ. Prefrontal cortex in the rat: Projections to subcortical autonomic, motor, and limbic centers. J Comp Neurol. 2005;492:145–77.CrossRefPubMed
Metadata
Title
Early brain connectivity alterations and cognitive impairment in a rat model of Alzheimer’s disease
Authors
Emma Muñoz-Moreno
Raúl Tudela
Xavier López-Gil
Guadalupe Soria
Publication date
01-12-2018
Publisher
BioMed Central
Published in
Alzheimer's Research & Therapy / Issue 1/2018
Electronic ISSN: 1758-9193
DOI
https://doi.org/10.1186/s13195-018-0346-2

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