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Original Research

Altered Intrinsic Brain Activities in Patients with Diabetic Retinopathy Using Amplitude of Low-frequency Fluctuation: A Resting-state fMRI Study

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Pages 2833-2842 | Published online: 13 Aug 2020

References

  • Mark B, David A. Moving past Anti-VEGF: novel therapies for treating diabetic retinopathy. Int J Mol Sci. 2016;17(9):1498–1521. doi:10.3390/ijms17091498
  • Ranchod TM, Fine SL. Primary treatment of diabetic macular edema. Clin Interv Aging. 2009;4:101–107. doi:10.2147/CIA.S4357
  • Ting DSW, Cheung GCM, Wong TY. Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review. Clin Exp Ophthalmol. 2016;44(4):260–277. doi:10.1111/ceo.12696
  • Resnikoff S, Pascolini D, Etya’ale D, et al. Global data on visual impairment in the year 2002. Bull World Health Organ. 2004;82(11):844–851.
  • Yau JWY, Rogers S, Kawasaki R, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35(3):556–564. doi:10.2337/dc11-1909
  • Patton N, Aslam T, Macgillivray T, Pattie A, Deary IJ, Dhillon B. Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures. J Anat. 2005;206(4):319–348. doi:10.1111/j.1469-7580.2005.00395.x
  • Wessels AM, Simsek S, Remijnse PL, et al. Voxel-based morphometry demonstrates reduced grey matter density on brain MRI in patients with diabetic retinopathy. Diabetologia. 2006;49(10):2474–2480. doi:10.1007/s00125-006-0283-7
  • Lin Y, Zhou J, Sha L, et al. Metabolite differences in the lenticular nucleus in type 2 diabetes mellitus shown by proton MR spectroscopy. AJNR Am J Neuroradiol. 2013;34(9):1692–1696. doi:10.3174/ajnr.A3492
  • Li YM, Zhou HM, Xu XY, Shi HS. Research progress in MRI of the visual pathway in diabetic retinopathy. Curr Med Sci. 2018;38(6):968–975. doi:10.1007/s11596-018-1971-5
  • Dogan M, Ozsoy E, Doganay S, et al. Brain diffusion-weighted imaging in diabetic patients with retinopathy. Eur Rev Med Pharmacol Sci. 2012;16(1):126–131.
  • Ding J, Strachan MW, Reynolds RM, et al. Diabetic retinopathy and cognitive decline in older people with type 2 diabetes: the Edinburgh type 2 diabetes study. Diabetes. 2010;59(11):2883–2889. doi:10.2337/db10-0752
  • Liao JL, Xiong ZY, Yang ZK, et al. An association of cognitive impairment with diabetes and retinopathy in end stage renal disease patients under peritoneal dialysis. PLoS One. 2017;12(8):e0183965. doi:10.1371/journal.pone.0183965
  • Wei FF, Raaijmakers A, Zhang ZY, et al. Association between cognition and the retinal microvasculature in 11-year old children born preterm or at term. Early Hum Dev. 2018;118:1–7. doi:10.1016/j.earlhumdev.2018.01.018
  • Gupta P, Gan ATL, Man REK, et al. Association between diabetic retinopathy and incident cognitive impairment. Br J Ophthalmol. 2019;103(11):1605–1609. doi:10.1136/bjophthalmol-2018-312807
  • Dai H, Zhang Y, Lai L, et al. Brain functional networks: correlation analysis with clinical indexes in patients with diabetic retinopathy. Neuroradiology. 2017;59(11):1121–1131. doi:10.1007/s00234-017-1900-5
  • Cordes D, Haughton VM, Arfanakis K, et al. Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. AJNR Am J Neuroradiol. 2001;22(7):1326–1333.
  • Wang YF, Dai GS, Liu F, Long ZL, Yan JH, Chan HF. Steady-state BOLD response to higher-order cognition modulates low-frequency neural oscillations. J Cogn Neurosci. 2015;27(12):2406–2415. doi:10.1162/jocn_a_00864
  • Gao Q, Peng B, Huang X, et al. Assessment of cerebral low-frequency oscillations in patients with retinal vein occlusion: a preliminary functional MRI study. Acta Radiol. 2019.
  • Zhang YF, He Y, Zhu CZ, et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev. 2007;29(2):83–91. doi:10.1016/j.braindev.2006.07.002
  • Qi R, Zhang L, Wu S, et al. Altered resting-state brain activity at functional MR imaging during the progression of hepatic encephalopathy. Radiology. 2012;264(1):187–195. doi:10.1148/radiol.12111429
  • Li T, Liu Z, Li J, et al. Altered amplitude of low-frequency fluctuation in primary open-angle glaucoma: a resting-state FMRI study. Invest Ophthalmol Vis Sci. 2014;56(1):322–329. doi:10.1167/iovs.14-14974
  • Huang X, Zhou FQ, Dan HD, Shen Y. Abnormal intrinsic brain activity in individuals with peripheral vision loss because of retinitis pigmentosa using amplitude of low-frequency fluctuations. Neuroreport. 2018;29(15):1323–1332. doi:10.1097/WNR.0000000000001116
  • Wilkinson CP, Ferris FL, Klein RE, et al. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology. 2003;110(9):1677–1682. doi:10.1016/S0161-6420(03)00475-5
  • Tu S, Qiu J, Martens U, Zhang Q. Category-selective attention modulates unconscious processes in the middle occipital gyrus. Conscious Cogn. 2013;22(2):479–485. doi:10.1016/j.concog.2013.02.007
  • Caprara C, Grimm C. From oxygen to erythropoietin: relevance of hypoxia for retinal development, health and disease. Prog Retin Eye Res. 2012;31(1):89–119. doi:10.1016/j.preteyeres.2011.11.003
  • Persson J, Pudas S, Nilsson LG, Nybery L. Longitudinal assessment of default-mode brain function in aging. Neurobiol Aging. 2014;35(9):2107–2117. doi:10.1016/j.neurobiolaging.2014.03.012
  • Buzsaki G, Moser EI. Memory, navigation and theta rhythm in the hippocampal-entorhinal system. Nat Neurosci. 2013;16(2):130–138. doi:10.1038/nn.3304
  • Teng C, Zhou J, Ma H, et al. Abnormal resting state activity of left middle occipital gyrus and its functional connectivity in female patients with major depressive disorder. BMC Psychiatry. 2018;18(1):370–379. doi:10.1186/s12888-018-1955-9
  • Flier JS. Obesity wars: molecular progress confronts an expanding epidemic. Cell. 2004;116(2):337–350. doi:10.1016/S0092-8674(03)01081-X
  • Zhou X, Zhang J, Chen Y, et al. Aggravated cognitive and brain functional impairment in mild cognitive impairment patients with type 2 diabetes: a resting-state functional MRI study. J Alzheimers Dis. 2014;41(3):925–935. doi:10.3233/JAD-132354
  • Herzfeld DJ, Kojima Y, Soetedjo R, Shadmehr R. Encoding of action by the Purkinje cells of the cerebellum. Nature. 2015;526(7573):439–442. doi:10.1038/nature15693
  • Stoodley CJ, Schmahmann JD. Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex. 2010;46(7):831–844. doi:10.1016/j.cortex.2009.11.008
  • Izana J, Criscimagna-Hemminger SE, Shadmehr R. Cerebellar contributions to reach adaptation and learning sensory consequences of action. J Neurosci. 2012;32(12):4230–4239. doi:10.1523/JNEUROSCI.6353-11.2012
  • Kawamura T, Umemura T, Hotta N. Curious relationship between cognitive impairment and diabetic retinopathy. J Diabetes Investig. 2015;6(1):21–23. doi:10.1111/jdi.12234
  • Wang CX, Fu KL, Liu HJ, Xing F, Zhang SY, Valdes-Sosa PA. Spontaneous brain activity in type 2 diabetics revealed by amplitude of low-frequency fluctuations and its association with diabetic vascular disease: a resting-state FMRI study. PLoS One. 2014;9(10):e108883. doi:10.1371/journal.pone.0108883
  • Zlokovic BV. The blood-brain barrier in health and chronic neurodegenerative disorders. Neuron. 2008;57(2):178–201. doi:10.1016/j.neuron.2008.01.003
  • Mckee JL, Riesenhhuber M, Miller EK, Freedman DJ. Task dependence of visual and category representations in prefrontal and inferior temporal cortices. J Neurosci. 2014;34(48):16065–16075. doi:10.1523/JNEUROSCI.1660-14.2014
  • Verhoef BE, Vogels R, Janssen P. Inferotemporal cortex subserves three-dimensional structure categorization. Neuron. 2012;73(1):171–182. doi:10.1016/j.neuron.2011.10.031
  • van Duinkerken E, Schoonheim MM, Sanz-Arigita EJ, et al. Resting-state brain networks in type 1 diabetic patients with and without microangiopathy and their relation to cognitive functions and disease variables. Diabetes. 2012;61(7):1814–1821. doi:10.2337/db11-1358
  • Yu Y, Yan LF, Sun Q, et al. Neurovascular decoupling in type 2 diabetes mellitus without mild cognitive impairment: potential biomarker for early cognitive impairment. Neuroimage. 2019;200:644–658. doi:10.1016/j.neuroimage.2019.06.058