Abstract
Sleep disturbance is a recognized risk factor for Alzheimer’s disease (AD), but the underlying micro-pathological evidence remains limited. To bridge this gap, we established an amyloid-β oligomers (AβO)-induced rat model of AD and subjected it to intermittent sleep deprivation (SD). Diffusion tensor imaging (DTI) and transmission electron microscopy were employed to assess white matter (WM) integrity and ultrastructural changes in myelin sheaths. Our findings demonstrated that SD exacerbated AβO-induced cognitive decline. Furthermore, we found SD aggravated AβO-induced asymmetrical impairments in WM, presenting with reductions in tract integrity observed in commissural fibers and association fasciculi, particularly the right anterior commissure, right corpus callosum, and left cingulum. Ultrastructural changes in myelin sheaths within the hippocampus and corpus callosum further confirmed a lateralized effect. Moreover, SD worsened AβO-induced lateralized disruption of the brain structural network, with impairments in critical nodes of the left hemisphere strongly correlated with cognitive dysfunction. This work represents the first identification of a lateralized impact of SD on the mesoscopic network and cognitive deficits in an AD rat model. These findings could deepen our understanding of the complex interplay between sleep disturbance and AD pathology, providing valuable insights into the early progression of the disease, as well as the development of neuroimaging biomarkers for screening early AD patients with self-reported sleep disturbances. Enhanced understanding of these mechanisms may pave the way for targeted interventions to alleviate cognitive decline and improve the quality of life for individuals at risk of or affected by AD.
Graphical Abstract
a AβO injection and sleep deprivation were conducted on adult rats. b Sleep deprivation and AβO-induced neurotoxicity aggravated cognitive disability with a synergistic effect. c Sleep deprivation and AβO-induced neurotoxicity reduced the integrity of specific association fasciculi and commissural fibers with ultrastructural demyelination. d On the basis of white matter integrity destruction, the structural connection was disrupted. The exacerbated topological properties with lateralized effects were correlated with cognitive decline.
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Data availability
All data associated with this study are present in the paper or the Supplementary Materials. Any additional information that supports the findings reported in this study is available by contacting the corresponding author upon reasonable request.
Abbreviations
- Aβ :
-
Amyloid-β oligomer injection group
- AβO :
-
Amyloid-β oligomer
- AD :
-
Alzheimer’s disease
- ADC :
-
Apparent diffusion coefficient
- AxD :
-
Axial diffusivity
- CC :
-
Corpus callosum
- CG :
-
Cingulate gyrus
- Con :
-
Control
- Cp :
-
Clustering coefficient
- DR :
-
Discrimination ratio
- DTI :
-
Diffusion tensor imaging
- Eg :
-
Global efficiency
- Eloc :
-
Local efficiency
- Ent :
-
Entorhinal cortex
- FA :
-
Fractional anisotropy
- HIP :
-
Hippocampus
- L :
-
Left
- Lp :
-
Length of shortest path
- mPFC :
-
Medial prefrontal cortex
- MCI :
-
Mild cognitive impairment
- MR :
-
Magnetic resonance
- R :
-
Right
- RD :
-
Radial diffusivity
- ROI :
-
Regions of interest
- SD :
-
Sleep deprivation or sleep deprivation group
- SA :
-
Sleep deprivation combined with amyloid-β oligomer injection group
- SWN :
-
Small world network
- SCD :
-
Subjective cognitive decline
- T-Lobe :
-
Temporal lobe
- VBM :
-
Voxel-based morphometry
References
Panza F, Lozupone M, Logroscino G, Imbimbo BP. A critical appraisal of amyloid-β-targeting therapies for Alzheimer disease. Nat Rev Neurol. 2019;15(2):73–88. https://doi.org/10.1038/s41582-018-0116-6.
Dean DC 3rd, Hurley SA, Kecskemeti SR, O’Grady JP, Canda C, Davenport-Sis NJ, Carlsson CM, Zetterberg H, Blennow K, Asthana S, et al. Association of amyloid pathology with myelin alteration in preclinical Alzheimer disease. JAMA Neurol. 2017;74(1):41–9. https://doi.org/10.1001/jamaneurol.2016.3232.
Wang Q, Wang Y, Liu J, Sutphen CL, Cruchaga C, Blazey T, Gordon BA, Su Y, Chen C, Shimony JS, et al. Quantification of white matter cellularity and damage in preclinical and early symptomatic Alzheimer’s disease. Neuroimage Clin. 2019;22:101767. https://doi.org/10.1016/j.nicl.2019.101767.
Xu MY, Xu ZQ, Wang YJ. White matter “matters” in Alzheimer’s disease. Neurosci Bull. 2022;38(3):323–6. https://doi.org/10.1007/s12264-021-00803-8.
Mayo CD, Mazerolle EL, Ritchie L, Fisk JD, Gawryluk JR. Longitudinal changes in microstructural white matter metrics in Alzheimer’s disease. Neuroimage Clin. 2017;13:330–8. https://doi.org/10.1016/j.nicl.2016.12.012.
Depp C, Sun T, Sasmita AO, Spieth L, Berghoff SA, Nazarenko T, Overhoff K, Steixner-Kumar AA, Subramanian S, Arinrad S, et al. Myelin dysfunction drives amyloid-β deposition in models of Alzheimer’s disease. Nature. 2023;618(7964):349–57. https://doi.org/10.1038/s41586-023-06120-6.
Emery B. Regulation of oligodendrocyte differentiation and myelination. Science. 2010;330(6005):779–82. https://doi.org/10.1126/science.1190927.
Wang Y, Olson IR. The original social network: white matter and social cognition. Trends Cogn Sci. 2018;22(6):504–16. https://doi.org/10.1016/j.tics.2018.03.005.
Saab AS, Tzvetavona ID, Trevisiol A, Baltan S, Dibaj P, Kusch K, Möbius W, Goetze B, Jahn HM, Huang W, et al. Oligodendroglial NMDA receptors regulate glucose import and axonal energy metabolism. Neuron. 2016;91(1):119–32. https://doi.org/10.1016/j.neuron.2016.05.016.
Jia L, Du Y, Chu L, Zhang Z, Li F, Lyu D, Li Y, Li Y, Zhu M, Jiao H, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health. 2020;5(12):e661–71. https://doi.org/10.1016/s2468-2667(20)30185-7.
Shi L, Chen SJ, Ma MY, Bao YP, Han Y, Wang YM, Shi J, Vitiello MV, Lu L. Sleep disturbances increase the risk of dementia: a systematic review and meta-analysis. Sleep Med Rev. 2018;40:4–16. https://doi.org/10.1016/j.smrv.2017.06.010.
de Vivo L, Bellesi M. The role of sleep and wakefulness in myelin plasticity. Glia. 2019;67(11):2142–52. https://doi.org/10.1002/glia.23667.
Kocevska D, Tiemeier H, Lysen TS, de Groot M, Muetzel RL, Van Someren EJW, Ikram MA, Vernooij MW, Luik AI. The prospective association of objectively measured sleep and cerebral white matter microstructure in middle-aged and older persons. Sleep. 2019;42(10):zsz140. https://doi.org/10.1093/sleep/zsz140.
Wu Y, Liu M, Zeng S, Ma X, Yan J, Lin C, Xu G, Li G, Yin Y, Fu S, et al. Abnormal topology of the structural connectome in the limbic cortico-basal-ganglia circuit and default-mode network among primary insomnia patients. Front Neurosci. 2018;12:860. https://doi.org/10.3389/fnins.2018.00860.
Li S, Tian J, Bauer A, Huang R, Wen H, Li M, Wang T, Xia L, Jiang G. Reduced integrity of right lateralized white matter in patients with primary insomnia: a diffusion-tensor imaging study. Radiology. 2016;280(2):520–8. https://doi.org/10.1148/radiol.2016152038.
Hoyer C, Gass N, Weber-Fahr W, Sartorius A. Advantages and challenges of small animal magnetic resonance imaging as a translational tool. Neuropsychobiology. 2014;69(4):187–201. https://doi.org/10.1159/000360859.
Zhang W, Chen X, Du Z, Mao X, Gao R, Chen Z, Wang H, Zhang G, Zhang N, Li H, et al. Knockdown of astrocytic Grin2a exacerbated sleep deprivation-induced cognitive impairments and elevation of amyloid-beta. Sleep Med. 2022;100:280–90. https://doi.org/10.1016/j.sleep.2022.08.021.
Chang L, Zhang Y, Liu J, Song Y, Lv A, Li Y, Zhou W, Yan Z, Almeida OF, Wu Y. Differential regulation of N-methyl-D-aspartate receptor subunits is an early event in the actions of soluble amyloid-β(1–40) oligomers on hippocampal neurons. J Alzheimers Dis. 2016;51(1):197–212. https://doi.org/10.3233/jad-150942.
Du Z, Song Y, Chen X, Zhang W, Zhang G, Li H, Chang L, Wu Y. Knockdown of astrocytic Grin2a aggravates β-amyloid-induced memory and cognitive deficits through regulating nerve growth factor. Aging Cell. 2021;20(8):e13437. https://doi.org/10.1111/acel.13437.
Sawangjit A, Oyanedel CN, Niethard N, Salazar C, Born J, Inostroza M. The hippocampus is crucial for forming non-hippocampal long-term memory during sleep. Nature. 2018;564(7734):109–13. https://doi.org/10.1038/s41586-018-0716-8.
Winters BD, Saksida LM, Bussey TJ. Object recognition memory: neurobiological mechanisms of encoding, consolidation and retrieval. Neurosci Biobehav Rev. 2008;32(5):1055–70. https://doi.org/10.1016/j.neubiorev.2008.04.004.
Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23(Suppl 1):S208-219. https://doi.org/10.1016/j.neuroimage.2004.07.051.
Nie B, Wang L, Hu Y, Liang S, Tan Z, Chai P, Tang Y, Shang J, Pan Z, Zhao X, et al. A population stereotaxic positron emission tomography brain template for the macaque and its application to ischemic model. Neuroimage. 2019;203:116163. https://doi.org/10.1016/j.neuroimage.2019.116163.
Nie B, Chen K, Zhao S, Liu J, Gu X, Yao Q, Hui J, Zhang Z, Teng G, Zhao C, et al. A rat brain MRI template with digital stereotaxic atlas of fine anatomical delineations in paxinos space and its automated application in voxel-wise analysis. Hum Brain Mapp. 2013;34(6):1306–18. https://doi.org/10.1002/hbm.21511.
Liang S, Wu S, Huang Q, Duan S, Liu H, Li Y, Zhao S, Nie B, Shan B. Rat brain digital stereotaxic white matter atlas with fine tract delineation in Paxinos space and its automated applications in DTI data analysis. Magn Reson Imaging. 2017;43:122–8. https://doi.org/10.1016/j.mri.2017.07.011.
Kreilkamp BAK, Lisanti L, Glenn GR, Wieshmann UC, Das K, Marson AG, Keller SS. Comparison of manual and automated fiber quantification tractography in patients with temporal lobe epilepsy. Neuroimage Clin. 2019;24:102024. https://doi.org/10.1016/j.nicl.2019.102024.
Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS One. 2013;8(7):e68910. https://doi.org/10.1371/journal.pone.0068910.
Wang R, Han H, Shi K, Alberts IL, Rominger A, Yang C, Yan J, Cui D, Peng Y, He Q, et al. The alteration of brain interstitial fluid drainage with myelination development. Aging Dis. 2021;12(7):1729–40. https://doi.org/10.14336/ad.2021.0305.
Chen JF, Liu K, Hu B, Li RR, Xin W, Chen H, Wang F, Chen L, Li RX, Ren SY, et al. Enhancing myelin renewal reverses cognitive dysfunction in a murine model of Alzheimer’s disease. Neuron. 2021;109(14):2292-2307.e2295. https://doi.org/10.1016/j.neuron.2021.05.012.
Aggleton JP, O’Mara SM. The anterior thalamic nuclei: core components of a tripartite episodic memory system. Nat Rev Neurosci. 2022;23(8):505–16. https://doi.org/10.1038/s41583-022-00591-8.
Thomason ME, Thompson PM. Diffusion imaging, white matter, and psychopathology. Annu Rev Clin Psychol. 2011;7:63–85. https://doi.org/10.1146/annurev-clinpsy-032210-104507.
Bubb EJ, Metzler-Baddeley C, Aggleton JP. The cingulum bundle: anatomy, function, and dysfunction. Neurosci Biobehav Rev. 2018;92:104–27. https://doi.org/10.1016/j.neubiorev.2018.05.008.
Paus T. Tracking development of connectivity in the human brain: axons and dendrites. Biol Psychiatry. 2023;93(5):455–63. https://doi.org/10.1016/j.biopsych.2022.08.019.
Uddin MS, Tewari D, Mamun AA, Kabir MT, Niaz K, Wahed MII, Barreto GE, Ashraf GM. Circadian and sleep dysfunction in Alzheimer’s disease. Ageing Res Rev. 2020;60:101046. https://doi.org/10.1016/j.arr.2020.101046.
Oliveira AM, Hawk JD, Abel T, Havekes R. Post-training reversible inactivation of the hippocampus enhances novel object recognition memory. Learn Mem. 2010;17(3):155–60. https://doi.org/10.1101/lm.1625310.
Bonetto G, Belin D, Káradóttir RT. Myelin: A gatekeeper of activity-dependent circuit plasticity? Science. 2021;374(6569):eaba6905. https://doi.org/10.1126/science.aba6905.
Kiely M, Triebswetter C, Cortina LE, Gong Z, Alsameen MH, Spencer RG, Bouhrara M. Insights into human cerebral white matter maturation and degeneration across the adult lifespan. Neuroimage. 2022;247:118727. https://doi.org/10.1016/j.neuroimage.2021.118727.
Bartzokis G. Alzheimer’s disease as homeostatic responses to age-related myelin breakdown. Neurobiol Aging. 2011;32(8):1341–71. https://doi.org/10.1016/j.neurobiolaging.2009.08.007.
Lloret A, Esteve D, Lloret MA, Monllor P, López B, León JL, Cervera-Ferri A. Is oxidative stress the link between cerebral small vessel disease, sleep disruption, and oligodendrocyte dysfunction in the onset of Alzheimer’s disease? Front Physiol. 2021;12:708061. https://doi.org/10.3389/fphys.2021.708061.
Nasrabady SE, Rizvi B, Goldman JE, Brickman AM. White matter changes in Alzheimer’s disease: a focus on myelin and oligodendrocytes. Acta Neuropathol Commun. 2018;6(1):22. https://doi.org/10.1186/s40478-018-0515-3.
Kenigsbuch M, Bost P, Halevi S, Chang Y, Chen S, Ma Q, Hajbi R, Schwikowski B, Bodenmiller B, Fu H, et al. A shared disease-associated oligodendrocyte signature among multiple CNS pathologies. Nat Neurosci. 2022;25(7):876–86. https://doi.org/10.1038/s41593-022-01104-7.
Josselyn SA, Tonegawa S. Memory engrams: recalling the past and imagining the future. Science. 2020;367(6473):eaaw4325. https://doi.org/10.1126/science.aaw4325.
Gudberg C, Stevelink R, Douaud G, Wulff K, Lazari A, Fleming MK, Johansen-Berg H. Individual differences in slow wave sleep architecture relate to variation in white matter microstructure across adulthood. Front Aging Neurosci. 2022;14:745014. https://doi.org/10.3389/fnagi.2022.745014.
Steadman PE, Xia F, Ahmed M, Mocle AJ, Penning ARA, Geraghty AC, Steenland HW, Monje M, Josselyn SA, Frankland PW. Disruption of oligodendrogenesis impairs memory consolidation in adult mice. Neuron. 2020;105(1):150-164.e156. https://doi.org/10.1016/j.neuron.2019.10.013.
He F, Li Y, Li C, Zhao J, Liu T, Fan L, Zhang X, Wang J. Changes in the connection network of whole-brain fiber tracts in patients with Alzheimer’s disease have a tendency of lateralization. Neuroreport. 2021;32(14):1175–82. https://doi.org/10.1097/wnr.0000000000001708.
Yan J, Raja VV, Huang Z, Amico E, Nho K, Fang S, Sporns O, Wu YC, Saykin A, Goni J, et al. Brain-wide structural connectivity alterations under the control of Alzheimer risk genes. Int J Comput Biol Drug Des. 2020;13(1):58–70. https://doi.org/10.1504/ijcbdd.2020.10026789.
Lavrador JP, Ferreira V, Lourenço M, Alexandre I, Rocha M, Oliveira E, Kailaya-Vasan A, Neto L. White-matter commissures: a clinically focused anatomical review. Surg Radiol Anat. 2019;41(6):613–24. https://doi.org/10.1007/s00276-019-02218-7.
Gazzaniga MS. Cerebral specialization and interhemispheric communication: does the corpus callosum enable the human condition? Brain. 2000;123(Pt 7):1293–326. https://doi.org/10.1093/brain/123.7.1293.
Wang Z, Wang J, Zhang H, McHugh R, Sun X, Li K, Yang QX. Interhemispheric functional and structural disconnection in Alzheimer’s disease: a combined resting-state fMRI and DTI study. PLoS One. 2015;10(5):e0126310. https://doi.org/10.1371/journal.pone.0126310.
Cabeza R. Hemispheric asymmetry reduction in older adults: the HAROLD model. Psychol Aging. 2002;17(1):85–100. https://doi.org/10.1037//0882-7974.17.1.85.
Li K, Luo X, Zeng Q, Jiaerken Y, Wang S, Xu X, Xu X, Xu J, Wang C, Zhou J, et al. Interactions between sleep disturbances and Alzheimer’s disease on brain function: a preliminary study combining the static and dynamic functional MRI. Sci Rep. 2019;9(1):19064. https://doi.org/10.1038/s41598-019-55452-9.
Güntürkün O, Ströckens F, Ocklenburg S. Brain lateralization: a comparative perspective. Physiol Rev. 2020;100(3):1019–63. https://doi.org/10.1152/physrev.00006.2019.
Roe JM, Vidal-Piñeiro D, Sørensen Ø, Brandmaier AM, Düzel S, Gonzalez HA, Kievit RA, Knights E, Kühn S, Lindenberger U, et al. Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer’s disease. Nat Commun. 2021;12(1):721. https://doi.org/10.1038/s41467-021-21057-y.
Kong XZ, Mathias SR, Guadalupe T, Glahn DC, Franke B, Crivello F, Tzourio-Mazoyer N, Fisher SE, Thompson PM, Francks C. Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium. Proc Natl Acad Sci U S A. 2018;115(22):E5154-e5163. https://doi.org/10.1073/pnas.1718418115.
Jordan JT. The rodent hippocampus as a bilateral structure: a review of hemispheric lateralization. Hippocampus. 2020;30(3):278–92. https://doi.org/10.1002/hipo.23188.
Mengotti P, Käsbauer AS, Fink GR, Vossel S. Lateralization, functional specialization, and dysfunction of attentional networks. Cortex. 2020;132:206–22. https://doi.org/10.1016/j.cortex.2020.08.022.
Duboc V, Dufourcq P, Blader P, Roussigné M. Asymmetry of the brain: development and implications. Annu Rev Genet. 2015;49:647–72. https://doi.org/10.1146/annurev-genet-112414-055322.
Esteves M, Lopes SS, Almeida A, Sousa N, Leite-Almeida H. Unmasking the relevance of hemispheric asymmetries-Break on through (to the other side). Prog Neurobiol. 2020;192:101823. https://doi.org/10.1016/j.pneurobio.2020.101823.
Vallortigara G, Rogers LJ. Survival with an asymmetrical brain: advantages and disadvantages of cerebral lateralization. Behav Brain Sci. 2005;28(4):575–89. https://doi.org/10.1017/s0140525x05000105. (discussion 589-633).
Klur S, Muller C, Pereira de Vasconcelos A, Ballard T, Lopez J, Galani R, Certa U, Cassel JC. Hippocampal-dependent spatial memory functions might be lateralized in rats: an approach combining gene expression profiling and reversible inactivation. Hippocampus. 2009;19(9):800–16. https://doi.org/10.1002/hipo.20562.
Rattenborg NC, Ungurean G. The evolution and diversification of sleep. Trends Ecol Evol. 2023;38(2):156–70. https://doi.org/10.1016/j.tree.2022.10.004.
Anafi RC, Kayser MS, Raizen DM. Exploring phylogeny to find the function of sleep. Nat Rev Neurosci. 2019;20(2):109–16. https://doi.org/10.1038/s41583-018-0098-9.
Krueger JM, Frank MG, Wisor JP, Roy S. Sleep function: toward elucidating an enigma. Sleep Med Rev. 2016;28:46–54. https://doi.org/10.1016/j.smrv.2015.08.005.
Fenk LA, Riquelme JL, Laurent G. Interhemispheric competition during sleep. Nature. 2023;616(7956):312–8. https://doi.org/10.1038/s41586-023-05827-w.
Hartwigsen G, Bengio Y, Bzdok D. How does hemispheric specialization contribute to human-defining cognition? Neuron. 2021;109(13):2075–90. https://doi.org/10.1016/j.neuron.2021.04.024.
Mohtasib R, Alghamdi J, Jobeir A, Masawi A, Pedrosa de Barros N, Billiet T, Struyfs H, Phan TV, Van Hecke W, Ribbens A. MRI biomarkers for Alzheimer’s disease: the impact of functional connectivity in the default mode network and structural connectivity between lobes on diagnostic accuracy. Heliyon. 2022;8(2):e08901. https://doi.org/10.1016/j.heliyon.2022.e08901.
Frisoni GB, Boccardi M, Barkhof F, Blennow K, Cappa S, Chiotis K, Démonet JF, Garibotto V, Giannakopoulos P, Gietl A, et al. Strategic roadmap for an early diagnosis of Alzheimer’s disease based on biomarkers. Lancet Neurol. 2017;16(8):661–76. https://doi.org/10.1016/s1474-4422(17)30159-x.
Lei Y, Han H, Yuan F, Javeed A, Zhao Y. The brain interstitial system: anatomy, modeling, in vivo measurement, and applications. Prog Neurobiol. 2017;157:230–46. https://doi.org/10.1016/j.pneurobio.2015.12.007.
Stokin GB, Lillo C, Falzone TL, Brusch RG, Rockenstein E, Mount SL, Raman R, Davies P, Masliah E, Williams DS, et al. Axonopathy and transport deficits early in the pathogenesis of Alzheimer’s disease. Science. 2005;307(5713):1282–8. https://doi.org/10.1126/science.1105681.
Zamponi E, Pigino GF. Protein misfolding, signaling abnormalities and altered fast axonal transport: implications for Alzheimer and prion diseases. Front Cell Neurosci. 2019;13:350. https://doi.org/10.3389/fncel.2019.00350.
Rasmussen MK, Mestre H, Nedergaard M. The glymphatic pathway in neurological disorders. Lancet Neurol. 2018;17(11):1016–24. https://doi.org/10.1016/s1474-4422(18)30318-1.
Yuan T, Zhan W, Dini D. Linking fluid-axons interactions to the macroscopic fluid transport properties of the brain. Acta Biomater. 2023;160:152–63. https://doi.org/10.1016/j.actbio.2023.02.010.
Wang A, Wang R, Cui D, Huang X, Yuan L, Liu H, Fu Y, Liang L, Wang W, He Q, et al. The drainage of interstitial fluid in the deep brain is controlled by the integrity of myelination. Aging Dis. 2019;10(5):937–48. https://doi.org/10.14336/ad.2018.1206.
Vaquer-Alicea J, Diamond MI. propagation of protein aggregation in neurodegenerative diseases. Annu Rev Biochem. 2019;88:785–810. https://doi.org/10.1146/annurev-biochem-061516-045049.
Mueller SG, Weiner MW. Amyloid associated intermittent network disruptions in cognitively intact older subjects: structural connectivity matters. Front Aging Neurosci. 2017;9:418. https://doi.org/10.3389/fnagi.2017.00418.
Funding
This study was supported by grants from the Scientific Research Key Program of the Beijing Municipal Commission of Education (KZ202110025032), the National Natural Science Foundation of China (81771370, 82071514), the National Key Research and Development Program of China (2020YFC2005300, 2022YFC2503900) and the Scientific Research Common Program of Beijing Municipal Commission of Education (KM201810025004, KM202110025029).
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Y. W. and L. C. conceived and designed the study. X. M. and D. H. performed animal experiments. B. N. and H. L. performed MRI data processing, L. J. performed the experiment of transmission electron microscopy, and W. G., W. Z., and H. W. performed data analyses. X. M., D. H., and L. C. interpreted the data and wrote the original manuscript. G. Z., N. Z., Y. S., and Y. W. provided constructive comments and discussion of the manuscript. All authors read and approved the final version of the manuscript.
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11357_2023_1000_MOESM1_ESM.tif
Supplementary file1 (TIF 2752 KB) Sleep deprivation combined with AβO aggravated the cognitive decline. (a) Timeline of the experimental design, indicating OPR task was conducted after sleep deprivation. (b) Diagram of the object place recognition test. (c) Quantification of average locomotor speed. (d) Quantification of discrimination ratio for novel place of object. (e) Quantification of times of interaction with novel place of object. Error bars represent standard deviations; One-way ANOVA with Turkey's multiple comparison test between four groups. * vs Con, ***p < 0.001; △vs SD, △△p < 0.01, △△△p < 0.001; n = 5-8 rats /group.
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Supplementary file2 (TIF 13138 KB) Maps of volume for white matter in voxel-wise analysis. Regional difference between Con and SD (a), Aβ (b), SA (c) groups were compared, and specific significant clusters were showed in Supplementary Table S1-S3.
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Supplementary file3 (TIF 474 KB) Bilateral difference between left and right hippocampus and mPFC of rats in tractography. (a, c) Quantification of the number (a) and mean FA value (c) of tracts between left and right hippocampus. (b, d) Estimation plots reevaluate the bilateral difference of hippocampal tracts. (e, g) Quantification of bilateral difference of the number (e) and mean FA value (g) of tracts in left and right mPFC. (f, h) Estimation plots reevaluate the difference between left and right mPFCs. Dots with wathet, pink, green and purple in estimation plots represent Con, SD, Aβ and SA groups respectively. Error bars represent standard deviations; Paired samples t test between bilateral number and mean FA value of tracts in four groups, #p < 0.05, ##p < 0.01; n = 6-11 rats /group.
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Supplementary file4 (TIF 2112 KB) The AxD and RD values changed asymmetrically in specific tracts linking cognition related regions. (a) A heat map for AxD value change in related association fasciculi and commissural fibers. (b-h) Quantification of AxD value difference in bilateral forceps minor of corpus callosum (b), anterior commissure (c), cingulum (d), ventral hippocampal commissure (e), fornix (f), dorsal hippocampal commissure (g) and forceps major of corpus callosum (h) within four groups. (i) A bar chart for bilateral difference of AxD value in above tracts. (j) A heat map for RD value change in related association fasciculus and commissural fibers. (k-q) Quantification of RD value difference in bilateral forceps minor of corpus callosum (k), anterior commissure (l), cingulum (m), ventral hippocampal commissure (n), fornix (o), dorsal hippocampal commissure (p) and forceps major of corpus callosum (q) within four groups. (r) A bar chart for bilateral difference of RD value in above tracts. Error bars represent standard deviations; One-way ANOVA with Turkey's multiple comparison test between ipsilateral tracts in four groups. * vs Con, *p < 0.05, **p < 0.01, ***p < 0.001; △ vs SD, △p < 0.05, △△p < 0.01, △△△p < 0.001; + vs Aβ, +p < 0.05, ++p < 0.01; Paired samples T test between bilateral ROIs in four groups, #p < 0.05, ##p < 0.01; n = 6-11 rats /group.
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Supplementary file5 (TIF 812 KB) The FA, ADC and volume changed in corpus callosum and hippocampus commissure. (a-d) Quantification of FA value difference in forceps minor of corpus callosum (a), forceps major of corpus callosum (b), ventral hippocampal commissure (c), and dorsal hippocampal commissure (d) within four groups. (e-g) Quantification of ADC value difference in forceps minor of corpus callosum (e), forceps major of corpus callosum (f), ventral hippocampal commissure (g), and dorsal hippocampal commissure (h) within four groups. (i-l) Quantification of difference of white matter volume in forceps minor of corpus callosum (i), forceps major of corpus callosum (j), ventral hippocampal commissure (k), and dorsal hippocampal commissure (l) within four groups. Error bars represent standard deviations; One-way ANOVA with Turkey's multiple comparison test between ipsilateral tracts in four groups. * vs Con, *p < 0.05, **p < 0.01, ***p < 0.001; △ vs SD, △p < 0.05, △△p < 0.01, △△△p < 0.001; + vs Aβ, +p < 0.05, +++p < 0.001; Paired samples T test between bilateral ROIs in four groups, #p < 0.05, ##p < 0.01, ###p < 0.001; n = 6-11 rats /group.
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Supplementary file6 (TIF 1030 KB) Bilateral difference of FA value in association fasciculi and commissural fibers. (a) Abar chart for bilateral difference of FA value in some related association fasciculi and commissural fibers. (b, d, f, h, j, l and n) Quantification of the FA value change within bilateral forceps minor of corpus callosum (b), anterior commissure (d), cingulum (f), ventral hippocampal commissure (h), fornix (j), dorsal hippocampal commissure (l) and forceps major of corpus callosum (n) within four groups. (c, e, g, i, k, m and o) Estimation plots reevaluate the bilateral susceptibility in above tracts when weighting different interventions. Dots with wathet, pink, green and purple represent Con, SD, Aβ and SA groups respectively. Error bars represent standard deviations; Paired samples t test between bilateral mean FA values of tracts in four groups, #p < 0.05, ##p < 0.01, ###p < 0.001; n = 6-11 rats /group.
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Supplementary file7 (TIF 1033 KB) Bilateral difference of ADC value in association fasciculi and commissural fibers. (a) A bar chart for bilateral difference of ADC value in some related association fasciculi and commissural fibers. (b, d, f, h, j, l and n) Quantification of the ADC value change within bilateral forceps minor of corpus callosum (b), anterior commissure (d), cingulum (f), ventral hippocampal commissure (h), fornix (j), dorsal hippocampal commissure (l) and forceps major of corpus callosum (n) within four groups. (c, e, g, i, k, m and o) Estimation plots reevaluate the bilateral susceptibility in above tracts when weighting different interventions. Dots with wathet, pink, green and purple represent Con, SD, Aβ and SA groups respectively. Error bars represent standard deviations; Paired samples t test between bilateral mean ADC value of tracts in four groups, #p < 0.05, ##p < 0.01, ###p < 0.001; n = 6-11 rats /group.
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Supplementary file8 (TIF 978 KB) Bilateral difference of volumes in association fasciculi and commissural fibers. (a) A bar chart for bilateral difference of volumes in some related association fasciculi and commissural fibers. (b, d, f, h, j, l and n) Quantification of the volumes change within bilateral forceps minor of corpus callosum (b), anterior commissure (d), cingulum (f), ventral hippocampal commissure (h), fornix (j), dorsal hippocampal commissure (l) and forceps major of corpus callosum (n) within four groups. (c, e, g, i, k, m and o) Estimation plots reevaluate the bilateral susceptibility in above tracts when weighting different interventions. Dots with wathet, pink, green and purple represent Con, SD, Aβ and SA groups respectively. Error bars represent standard deviations; Paired samples t test between bilateral mean volume of tracts in four groups, #p < 0.05, ##p < 0.01; n = 6-11 rats /group.
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Mao, X., Han, D., Guo, W. et al. Lateralized brunt of sleep deprivation on white matter injury in a rat model of Alzheimer’s disease. GeroScience 46, 2295–2315 (2024). https://doi.org/10.1007/s11357-023-01000-3
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DOI: https://doi.org/10.1007/s11357-023-01000-3