A structural connectivity disruption one decade before the typical age for dementia: a study in healthy subjects with family history of Alzheimer’s disease

  1. Ramírez-Toraño, F 12
  2. Abbas, Kausar 45
  3. Bruña, Ricardo 127
  4. Marcos de Pedro, Silvia 110
  5. Gómez-Ruiz, Natividad 3
  6. Barabash, Ana 89
  7. Pereda, Ernesto 112
  8. Marcos, Alberto 11
  9. López-Higes, Ramón 2
  10. Maestu, Fernando 127
  11. Goñi, Joaquín 456
  1. 1 Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid 28223, Comunidad de Madrid, Spain
  2. 2 Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid 28223, Comunidad de Madrid, Spain
  3. 3 Sección Neurorradiología, Servicio de Diagnóstico por Imagen, Hospital Clínico San Carlos, Madrid 28040, Comunidad de Madrid, Spain
  4. 4 Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN 46202, USA
  5. 5 School of Industrial Engineering, Purdue University, West Lafayette, IN 46202, USA
  6. 6 Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 46202, USA
  7. 7 Networking Research Center on Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid 28029, Comunidad de Madrid, Spain
  8. 8 Endocrinology and Nutrition Department, Hospital Clinico San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid 28040, Comunidad de Madrid, Spain
  9. 9 Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Madrid 28029, Comunidad de Madrid, Spain
  10. 10 Facultad de Educación y Salud, Universidad Camilo José Cela, Madrid 28010, Comunidad de Madrid, Spain
  11. 11 Neurology Department, Hospital Clinico San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid 28040, Comunidad de Madrid, Spain
  12. 12 Electrical Engineering and Bioengineering Group, Department of Industrial Engineering & IUNE & ITB, Universidad de La Laguna, Santa Cruz de Tenerife 38205, Spain
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Revista:
Cerebral Cortex Communications

ISSN: 2632-7376

Año de publicación: 2021

Volumen: 2

Número: 4

Tipo: Artículo

DOI: 10.1093/TEXCOM/TGAB051 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Cerebral Cortex Communications

Objetivos de desarrollo sostenible

Resumen

The concept of the brain has shifted to a complex system where different subnetworks support the human cognitive functions. Neurodegenerative diseases would affect the interactions among these subnetworks and, the evolution of impairment and the subnetworks involved would be unique for each neurodegenerative disease. In this study, we seek for structural connectivity traits associated with the family history of Alzheimer’s disease, that is, early signs of subnetworks impairment due to Alzheimer’s disease.The sample in this study consisted of 123 first-degree Alzheimer’s disease relatives and 61 nonrelatives. For each subject, structural connectomes were obtained using classical diffusion tensor imaging measures and different resolutions of cortical parcellation. For the whole sample, independent structural-connectome-traits were obtained under the framework of connICA. Finally, we tested the association of the structural-connectome-traits with different factors of relevance for Alzheimer’s disease by means of a multiple linear regression.The analysis revealed a structural-connectome-trait obtained from fractional anisotropy associated with the family history of Alzheimer’s disease. The structural-connectome-trait presents a reduced fractional anisotropy pattern in first-degree relatives in the tracts connecting posterior areas and temporal areas.The family history of Alzheimer’s disease structural-connectome-trait presents a posterior–posterior and posterior–temporal pattern, supplying new evidences to the cascading network failure model.

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Referencias bibliográficas

  • Albert, (2011), Alzheimer’s Dementia, 7, pp. 270, 10.1016/j.jalz.2011.03.008
  • Amico, (2020), Neuroimage, 209, pp. 116515, 10.1016/j.neuroimage.2020.116515
  • Amico, (2018), Netw Neurosci, 2, pp. 306, 10.1162/netn_a_00049
  • Amico, (2017), Neuroimage, 148, pp. 201, 10.1016/j.neuroimage.2017.01.020
  • Andersson, (2016), Neuroimage, 125, pp. 1063, 10.1016/j.neuroimage.2015.10.019
  • Behrens, (2003), Magn Resonance Med, 50, pp. 1077, 10.1002/mrm.10609
  • Bendlin, (2010), Maturitas, 65, pp. 131, 10.1016/j.maturitas.2009.12.014
  • Bendlin, (2010), Alzheimer’s Dement, 6, pp. 394, 10.1016/j.jalz.2009.11.003
  • Le Bihan, (1985), Comptes-Rendus l’Académie des Sci
  • Bozzali, (2002), J Neurol Neurosurg Psychiatry, 72, pp. 742, 10.1136/jnnp.72.6.742
  • Caballero, (2018), Brain, 141, pp. 3065, 10.1093/brain/awy229
  • Chalmers, (2005), Neuropathol Appl Neurobiol, 31, pp. 623, 10.1111/j.1365-2990.2005.00678.x
  • Chua, (2009), Am J Geriatr Psychiatry, 17, pp. 602, 10.1097/JGP.0b013e3181a76e0b
  • Chua, (2008), Curr Opin Neurol, 21, pp. 83, 10.1097/WCO.0b013e3282f4594b
  • Contreras, (2017), Alzheimer’s Dement Diagnosis Assess Dis Monit, 6, pp. 40
  • Cuesta, (2015), J Alzheimer’s Dis, 44, pp. 493, 10.3233/JAD-141872
  • Dale, (1999), Neuroimage, 9, pp. 179, 10.1006/nimg.1998.0395
  • Dhollander, (2016), 24th International Society of magnetic resonance in medicine
  • Dhollander, (2016), ISMRM Work Break Barriers Diffus MRI
  • Douaud, (2011), Neuroimage, 55, pp. 880, 10.1016/j.neuroimage.2010.12.008
  • Fischl, (2001), IEEE Trans Med Imaging, 20, pp. 70, 10.1109/42.906426
  • Fischl, (1999), Neuroimage, 9, pp. 195, 10.1006/nimg.1998.0396
  • Fischl, (1999), Hum Brain Mapp, 8, pp. 272, 10.1002/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4
  • Gold, (2010), Neuroimage, 52, pp. 1487, 10.1016/j.neuroimage.2010.05.036
  • Goñi, (2013), PLoS One, 8, pp. e58070, 10.1371/journal.pone.0058070
  • Gosche, (2002), Neurology, 58, pp. 1476, 10.1212/WNL.58.10.1476
  • Hof, (1990), J Comp Neurol., 301, pp. 44, 10.1002/cne.903010105
  • Huang, (2020), J Alzheimer’s Dis, 77, pp. 1025, 10.3233/JAD-200173
  • Huff, (1987), J Am Geriatr Soc, 35, pp. 27, 10.1111/j.1532-5415.1987.tb01315.x
  • Hyvärinen, (1999), IEEE Trans Neural Networks, 10.1109/72.761722
  • Jack, (2017), Alzheimer’s Dementia, 13, pp. 205, 10.1016/j.jalz.2016.08.005
  • Jenkinson, (2002), Neuroimage, 17, pp. 825, 10.1006/nimg.2002.1132
  • Jenkinson, (2001), Med Image Anal, 5, pp. 143, 10.1016/S1361-8415(01)00036-6
  • Jones, (2003), Magn Resonance Med, 49, pp. 7, 10.1002/mrm.10331
  • Jones, (2016), Brain, 139, pp. 547, 10.1093/brain/awv338
  • Kantarci, (2017), Neurobiol Aging, 56, pp. 172, 10.1016/j.neurobiolaging.2017.04.024
  • Kellner, (2015), Magn Reson Med, 76, pp. 1574, 10.1002/mrm.26054
  • Liu, (2011), Neurobiol Aging, 32, pp. 1558, 10.1016/j.neurobiolaging.2009.10.006
  • Martinez, (1998), Arch Neurol, 55, pp. 810, 10.1001/archneur.55.6.810
  • Mayo, (2019), Front Aging Neurosci, 10, pp. 436, 10.3389/fnagi.2018.00436
  • McKhann, (1984), Neurology, 34, pp. 939, 10.1212/WNL.34.7.939
  • (2011), Alzheimers Dementia, 7, pp. 263, 10.1016/j.jalz.2011.03.005
  • Mishkin, (1983), Trends in neurosciences, pp. 414
  • Nir, (2013), Neuro Image Clin, 3, pp. 180, 10.1016/j.nicl.2013.07.006
  • Patenaude, (2007), Dep Clin Neurol, 10.1016/j.neuroimage.2011.02.046
  • Polders, (2011), J Magn Resonance Imaging, 33, pp. 1456, 10.1002/jmri.22554
  • Raffelt, (2017), 25th International Society of magnetic resonance in medicine
  • Ramírez-Toraño, (2020), Cereb Cortex, 31, pp. 1201, 10.1093/cercor/bhaa286
  • Schaefer, (2018), Cereb Cortex, 28, pp. 3095, 10.1093/cercor/bhx179
  • Seeley, (2009), Neuron, 62, pp. 42, 10.1016/j.neuron.2009.03.024
  • Ségonne, (2004), Neuroimage, 22, pp. 1060, 10.1016/j.neuroimage.2004.03.032
  • Ségonne, (2007), IEEE Trans Med Imaging, 26, pp. 518, 10.1109/TMI.2006.887364
  • Sintini, (2019), Hum Brain Mapp, 40, pp. 1618, 10.1002/hbm.24473
  • Smith, (2010), Neurobiol Aging, 31, pp. 1122, 10.1016/j.neurobiolaging.2008.08.006
  • Smith, (2012), Neuroimage, 62, pp. 1924, 10.1016/j.neuroimage.2012.06.005
  • Smith, (2015), Neuroimage, 119, pp. 338, 10.1016/j.neuroimage.2015.06.092
  • Sperling, (2011), Alzheimer’s Dementia, 7, pp. 280, 10.1016/j.jalz.2011.03.003
  • Tournier, (2010), Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions
  • Tournier, (2019), Neuroimage, 202, pp. 116137, 10.1016/j.neuroimage.2019.116137
  • Veraart, (2016), Neuroimage, 142, pp. 394, 10.1016/j.neuroimage.2016.08.016
  • Wang, (2009), Am J Neuroradiol, 30, pp. 893, 10.3174/ajnr.A1484
  • Yamin, (2017), J Neurochem, 140, pp. 210, 10.1111/jnc.13887
  • Zhuang, (2012), Neurology, 79, pp. 748, 10.1212/WNL.0b013e3182661f4d