[en] Subclinical depressive symptoms are associated with increased risk of Alzheimer's disease (AD), but the brain mechanisms underlying this relationship are still unclear. We aimed to provide a comprehensive overview of the brain substrates of subclinical depressive symptoms in cognitively unimpaired older adults using complementary multimodal neuroimaging data. We included cognitively unimpaired older adults from the baseline data of the primary cohort Age-Well (n = 135), and from the replication cohort ADNI (n = 252). In both cohorts, subclinical depressive symptoms were assessed using the 15-item version of the Geriatric Depression Scale; based on this scale, participants were classified as having depressive symptoms (>0) or not (0). Voxel-wise between-group comparisons were performed to highlight differences in gray matter volume, glucose metabolism and amyloid deposition; as well as white matter integrity (only available in Age-Well). Age-Well participants with subclinical depressive symptoms had lower gray matter volume in the hippocampus and lower white matter integrity in the fornix and the posterior parts of the cingulum and corpus callosum, compared to participants without symptoms. Hippocampal atrophy was recovered in ADNI, where participants with subclinical depressive symptoms also showed glucose hypometabolism in the hippocampus, amygdala, precuneus/posterior cingulate cortex, medial and dorsolateral prefrontal cortex, insula, and temporoparietal cortex. Subclinical depressive symptoms were not associated with brain amyloid deposition in either cohort. Subclinical depressive symptoms in ageing are linked with neurodegeneration biomarkers in the frontolimbic network including brain areas particularly sensitive to AD. The relationship between depressive symptoms and AD may be partly underpinned by neurodegeneration in common brain regions.
Disciplines :
Neurosciences & behavior
Author, co-author :
Touron, Edelweiss ; Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
Moulinet, Inès; Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
Kuhn, Elizabeth ; Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
Sherif, Siya; Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
Ourry, Valentin; Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France ; Unité 1077 NIMH "Neuropsychologie et Imagerie de la Mémoire Humaine," Institut National de la Santé et de la Recherche Médicale, Normandie Université, Université de Caen, PSL Université, EPHE, CHU de Caen-Normandie, GIP Cyceron, Caen, France
Landeau, Brigitte ; Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
Mézenge, Florence; Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
Vivien, Denis ; Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France ; Département de Recherche Clinique, CHU de Caen-Normandie, Caen, France
Klimecki, Olga M; Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, 01187, Dresden, Germany
Poisnel, Géraldine ; Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
Marchant, Natalie L ; Division of Psychiatry, University College London, London, UK
Chételat, Gaël ; Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France. chetelat@cyceron.fr
Requier, Florence ; Université de Liège - ULiège > Département de Psychologie > Neuropsychologie ; Université de Liège - ULiège > Psychologie et Neuroscience Cognitives (PsyNCog) ; ULiège - Université de Liège [BE] > GIGA > GIGA CRC In vivo Imaging - Aging and Memory
Language :
English
Title :
Depressive symptoms in cognitively unimpaired older adults are associated with lower structural and functional integrity in a frontolimbic network.
H2020 - 667696 - MEDIT-AGEING - Investigating the impact of meditation training on mental health and wellbeing in the ageing population
Name of the research project :
MEDIT-AGEING - Investigating the impact of meditation training on mental health and wellbeing in the ageing population
Funders :
EU - European Union
Funding text :
The Age-Well randomized clinical trial is part of the Medit-Ageing project and is funded through the European Union’s Horizon 2020 Research and Innovation Program (grant 667696), Institut National de la Santé et de la Recherche Médicale, Région Normandie, and Fondation MMA des Entrepreneurs du Futur. 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: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; 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.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. 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 Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The funders and sponsor had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.One hundred thirty-five CU older adults were included from the baseline visit of the Age-Well randomized controlled trial of the Medit-Ageing European project [], sponsored by the French National Institute of Health and Medical Research (INSERM). Participants were recruited from the general population with the main following eligibility criteria: native French speaker, aged at least 65 years, retired for at least 1 year, educated for at least 7 years and showing performance within the normal range for age and educational level on standardized cognitive tests (see Tables 1 and 2 in [] for details). Participants had no evidence of a major neurological or psychiatric disorder, chronic disease or acute unstable illness, no history of cerebrovascular disease, and no current or recent medication that may interfere with cognitive functioning (including antidepressants and anxiolytics). Notably, the absence of major depression was assessed using a clinician-administered questionnaire, the Montgomery-Åsberg Depression Rating Scale (MADRS) [], with a cut-off value of 6 (participants with MADRS > 6 were excluded). All participants gave their written informed consent prior to the examinations, and the Age-Well randomized clinical trial was approved by the ethics committee (Comité de Protection des Personnes Nord-Ouest III, Caen, France; trial registration number: EudraCT: 2016-002441-36; IDRCB: 2016-A01767-44; ClinicalTrials.gov Identifier: NCT02977819).OK, GP, NM and GC reported grants from European Union’s Horizon 2020 Research and Innovation Program under grant agreement No. 667696 during the conduct of the study. NM reported grants from a Senior Fellowship from the Alzheimer’s Society (AS-SF-15b-002). GC reported grants, personal fees and nonfinancial support from Institut National de la Santé et de la Recherche Médicale (INSERM); personal fees from Fondation Entrepreneurs MMA, grants and personal fees from Fondation Alzheimer, grants from Région Normandie, grants from Fondation Recherche Alzheimer, grants from Association France Alzheimer, outside the submitted work. No other disclosures were reported.
Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396:413–46. DOI: 10.1016/S0140-6736(20)30367-6
Barnes DE, Yaffe K. The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurol. 2011;10:819–28. DOI: 10.1016/S1474-4422(11)70072-2
Lyness JM, Kim J, Tang W, Tu X, Conwell Y, King DA, et al. The clinical significance of subsyndromal depression in older primary care patients. Am J Geriatr Psychiatry. 2007;15:214–23. DOI: 10.1097/01.JGP.0000235763.50230.83
Meeks TW, Vahia IV, Lavretsky H, Kulkarni G, Jeste DV. A tune in “a minor” can “b major”: a review of epidemiology, illness course, and public health implications of subthreshold depression in older adults. J Affect Disord. 2011;129:126–42. DOI: 10.1016/j.jad.2010.09.015
Wilson RS, Barnes LL, Leon CFM, de, Aggarwal NT, Schneider JS, Bach J, et al. Depressive symptoms, cognitive decline, and risk of AD in older persons. Neurology. 2002;59:364–70. DOI: 10.1212/WNL.59.3.364
Barnes DE, Alexopoulos GS, Lopez OL, Williamson JD, Yaffe K. Depressive symptoms, vascular disease, and mild cognitive impairment: findings from the Cardiovascular Health Study. Arch Gen Psychiatry. 2006;63:273–279. DOI: 10.1001/archpsyc.63.3.273
Rosenberg PB, Mielke MM, Appleby BS, Oh ES, Geda YE, Lyketsos CG. The association of neuropsychiatric symptoms in MCI with incident dementia and Alzheimer disease. Am J Geriatr Psychiatry. 2013;21:685–95. DOI: 10.1016/j.jagp.2013.01.006
Lanctôt KL, Amatniek J, Ancoli-Israel S, Arnold SE, Ballard C, Cohen-Mansfield J, et al. Neuropsychiatric signs and symptoms of Alzheimer’s disease: new treatment paradigms. Alzheimers Dement. 2017;3:440–9. DOI: 10.1016/j.trci.2017.07.001
Dotson VM, Davatzikos C, Kraut MA, Resnick SM. Depressive symptoms and brain volumes in older adults: a longitudinal magnetic resonance imaging study. J Psychiatry Neurosci. 2009;34:367–75.
Brown ES, Hughes CW, McColl R, Peshock R, King KS, Rush AJ. Association of depressive symptoms with hippocampal volume in 1936 adults. Neuropsychopharmacology. 2014;39:770–9. DOI: 10.1038/npp.2013.271
Tudorascu DL, Rosano C, Venkatraman VK, MacCloud RL, Harris T, Yaffe K, et al. Multimodal MRI markers support a model of small vessel ischemia for depressive symptoms in very old adults. Psychiatry Res. 2014;224:73–80. DOI: 10.1016/j.pscychresns.2014.08.009
Donovan NJ, Hsu DC, Dagley AS, Schultz AP, Amariglio RE, Mormino EC, et al. Depressive symptoms and biomarkers of Alzheimer’s disease in cognitively normal older adults. J Alzheimers Dis. 2015;46:63–73. DOI: 10.3233/JAD-142940
Elbejjani M, Fuhrer R, Abrahamowicz M, Mazoyer B, Crivello F, Tzourio C, et al. Depression, depressive symptoms, and rate of hippocampal atrophy in a longitudinal cohort of older men and women. Psychol Med. 2015;45:1931–44. DOI: 10.1017/S0033291714003055
Zhou H, Li R, Ma Z, Rossi S, Zhu X, Li J. Smaller gray matter volume of hippocampus/parahippocampus in elderly people with subthreshold depression: a cross-sectional study. BMC Psychiatry. 2016;16:219. DOI: 10.1186/s12888-016-0928-0
Pink A, Przybelski SA, Krell-Roesch J, Stokin GB, Roberts RO, Mielke MM, et al. Cortical thickness and depressive symptoms in cognitively normal individuals: the Mayo Clinic Study of Aging. J Alzheimers Dis. 2017;58:1273–81. DOI: 10.3233/JAD-170041
O’Shea DM, Dotson VM, Woods AJ, Porges EC, Williamson JB, O’Shea A, et al. Depressive symptom dimensions and their association with hippocampal and entorhinal cortex volumes in community dwelling older adults. Front Aging Neurosci. 2018;10:40.
Szymkowicz SM, Woods AJ, Dotson VM, Porges EC, Nissim NR, O’Shea A, et al. Associations between subclinical depressive symptoms and reduced brain volume in middle-aged to older adults. Aging Ment Health. 2019;23:819–30. DOI: 10.1080/13607863.2018.1432030
Dotson VM, Beason-Held L, Kraut MA, Resnick SM. Longitudinal study of chronic depressive symptoms and regional cerebral blood flow in older men and women. Int J Geriatr Psychiatry. 2009;24:809–19. DOI: 10.1002/gps.2298
Brendel M, Reinisch V, Kalinowski E, Levin J, Delker A, Därr S, et al. Hypometabolism in brain of cognitively normal patients with depressive symptoms is accompanied by atrophy-related partial volume effects. Curr Alzheimer Res. 2016;13:475–86. DOI: 10.2174/1567205013666160314143922
Krell-Roesch J, Ruider H, Lowe VJ, Stokin GB, Pink A, Roberts RO, et al. FDG-PET and neuropsychiatric symptoms among cognitively normal elderly persons: the Mayo Clinic Study of Aging. J Alzheimer’s Dis. 2016;53:1609–16. DOI: 10.3233/JAD-160326
Yasuno F, Kazui H, Morita N, Kajimoto K, Ihara M, Taguchi A, et al. High amyloid-β deposition related to depressive symptoms in older individuals with normal cognition: a pilot study. Int J Geriatr Psychiatry. 2016;31:920–8. DOI: 10.1002/gps.4409
Donovan NJ, Locascio JJ, Marshall GA, Gatchel J, Hanseeuw BJ, Rentz DM, et al. Longitudinal association of amyloid beta and anxious-depressive symptoms in cognitively normal older adults. Am J Psychiatry. 2018;175:530–7. DOI: 10.1176/appi.ajp.2017.17040442
Harrington KD, Gould E, Lim YY, Ames D, Pietrzak RH, Rembach A, et al. Amyloid burden and incident depressive symptoms in cognitively normal older adults. Int J Geriatr Psychiatry. 2017;32:455–63. DOI: 10.1002/gps.4489
Babulal GM, Ghoshal N, Head D, Vernon EK, Holtzman DM, Benzinger TLS, et al. Mood changes in cognitively normal older adults are linked to Alzheimer disease biomarker levels. Am J Geriatr Psychiatry. 2016;24:1095–104. DOI: 10.1016/j.jagp.2016.04.004
Krell-Roesch J, Lowe VJ, Neureiter J, Pink A, Roberts RO, Mielke MM, et al. Depressive and anxiety symptoms and cortical amyloid deposition among cognitively normal elderly persons: the Mayo Clinic Study of Aging. Int Psychogeriatr. 2018;30:245–51. DOI: 10.1017/S1041610217002368
Perin S, Harrington KD, Lim YY, Ellis K, Ames D, Pietrzak RH, et al. Amyloid burden and incident depressive symptoms in preclinical Alzheimer’s disease. J Affect Disord. 2018;229:269–74. DOI: 10.1016/j.jad.2017.12.101
Poisnel G, Arenaza-Urquijo E, Collette F, Klimecki OM, Marchant NL, Wirth M, et al. The Age-Well randomized controlled trial of the Medit-Ageing European project: effect of meditation or foreign language training on brain and mental health in older adults. Alzheimers Dement. 2018;4:714–23. DOI: 10.1016/j.trci.2018.10.011
Montgomery SA, Asberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry. 1979;134:382–9. DOI: 10.1192/bjp.134.4.382
Sheikh JI, Yesavage JA. Geriatric Depression Scale (GDS): recent evidence and development of a shorter version. Clin Gerontologist J Aging Ment Health. 1986;5:165–73.
Wancata J, Alexandrowicz R, Marquart B, Weiss M, Friedrich F. The criterion validity of the Geriatric Depression Scale: a systematic review. Acta Psychiatr Scand. 2006;114:398–410. DOI: 10.1111/j.1600-0447.2006.00888.x
Rodríguez MR, Nuevo R, Chatterji S, Ayuso-Mateos JL. Definitions and factors associated with subthreshold depressive conditions: a systematic review. BMC Psychiatry. 2012;12:181. DOI: 10.1186/1471-244X-12-181
Ezzati A, Katz MJ, Derby CA, Zimmerman ME, Lipton RB. Depressive symptoms predict incident dementia in a community sample of older adults: results from the Einstein Aging Study. J Geriatr Psychiatry Neurol. 2019;32:97–103. DOI: 10.1177/0891988718824036
Folstein MF, Folstein SE, McHugh PR. Mini-mental state’. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–98. DOI: 10.1016/0022-3956(75)90026-6
Mattis S. Mental status examination for organic mental syndrome in the elderly patient. In: Bellack L, Karusu TB, editors. Geriatric Psychiatry. New York: Grune & Stratton; 1976. p. 77–121.
Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatrics Soc. 2005;53:695–699. DOI: 10.1111/j.1532-5415.2005.53221.x
Delis D. California Verbal Learning Test®—Second Edition. 2018. https://www.pearsonclinical.com/psychology/products/100000166/california-verballearning-test-second-edition-cvlt-ii.html.
Moradi E, Hallikainen I, Hänninen T, Tohka J. Alzheimer’s Disease Neuroimaging Initiative. Rey’s Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer’s disease. Neuroimage Clin. 2017;13:415–27. DOI: 10.1016/j.nicl.2016.12.011
Falangola MF, Jensen JH, Babb JS, Hu C, Castellanos FX, Martino AD, et al. Age-related non-Gaussian diffusion patterns in the prefrontal brain. J Magn Reson Imaging. 2008;28:1345–50. DOI: 10.1002/jmri.21604
Falangola MF, Jensen JH, Tabesh A, Hu C, Deardorff RL, Babb JS, et al. Non-Gaussian diffusion MRI assessment of brain microstructure in mild cognitive impairment and Alzheimer’s disease. Magn Reson Imaging. 2013;31:840–6. DOI: 10.1016/j.mri.2013.02.008
La Joie R, Perrotin A, de La Sayette V, Egret S, Doeuvre L, Belliard S, et al. Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer’s disease and semantic dementia. Neuroimage Clin. 2013;3:155–62. DOI: 10.1016/j.nicl.2013.08.007
Watson D, Clark LA, Tellegen A. Development and validation of brief measures of positive and negative affect: the PANAS scales. J Pers Soc Psychol. 1988;54:1063–70. DOI: 10.1037/0022-3514.54.6.1063
Treynor W, Gonzalez R, Nolen-Hoeksema S. Rumination reconsidered: a psychometric analysis. Cogn Ther Res. 2003;27:247–59. DOI: 10.1023/A:1023910315561
Forman EM, Herbert JD, Juarascio AS, Yeomans PD, Zebell JA, Goetter EM, et al. The Drexel defusion scale: a new measure of experiential distancing. J Contextual Behav Sci. 2012;1:55–65. DOI: 10.1016/j.jcbs.2012.09.001
Gross JJ, John OP. Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. J Pers Soc Psychol. 2003;85:348–62. DOI: 10.1037/0022-3514.85.2.348
den Heijer T, Tiemeier H, Luijendijk HJ, van der Lijn F, Koudstaal PJ, Hofman A, et al. A study of the bidirectional association between hippocampal volume on magnetic resonance imaging and depression in the elderly. Biol Psychiatry. 2011;70:191–7. DOI: 10.1016/j.biopsych.2011.04.014
Elbejjani M, Fuhrer R, Abrahamowicz M, Mazoyer B, Crivello F, Tzourio C, et al. Hippocampal atrophy and subsequent depressive symptoms in older men and women: results from a 10-year prospective cohort. Am J Epidemiol. 2014;180:385–93. DOI: 10.1093/aje/kwu132
Buddeke J, Kooistra M, Zuithoff NPA, Gerritsen L, Biessels GJ, Graaf Y, et al. Hippocampal volume and the course of depressive symptoms over eight years of follow-up. Acta Psychiatr Scand. 2017;135:78–86. DOI: 10.1111/acps.12662
Zhang Z, Wei F, Shen X-N, Ma Y-H, Chen K-L, Dong Q, et al. Associations of subsyndromal symptomatic depression with cognitive decline and brain atrophy in elderly individuals without dementia: a longitudinal study. J Affect Disord. 2020;274:262–8. DOI: 10.1016/j.jad.2020.05.097
Sexton CE, Mackay CE, Ebmeier KP. A systematic review and meta-analysis of magnetic resonance imaging studies in late-life depression. Am J Geriatr Psychiatry. 2013;21:184–95. DOI: 10.1016/j.jagp.2012.10.019
Teipel S, Drzezga A, Grothe MJ, Barthel H, Chételat G, Schuff N, et al. Multimodal imaging in Alzheimer’s disease: validity and usefulness for early detection. Lancet Neurol. 2015;14:1037–53. DOI: 10.1016/S1474-4422(15)00093-9
Geerlings MI, Gerritsen L. Late-life depression, hippocampal volumes, and hypothalamic-pituitary-adrenal axis regulation: a systematic review and meta-analysis. Biol Psychiatry. 2017;82:339–50. DOI: 10.1016/j.biopsych.2016.12.032
Wang Q, Van Heerikhuize J, Aronica E, Kawata M, Seress L, Joels M, et al. Glucocorticoid receptor protein expression in human hippocampus; stability with age. Neurobiol Aging. 2013;34:1662–73. DOI: 10.1016/j.neurobiolaging.2012.11.019
Alexopoulos GS, Morimoto SS. The inflammation hypothesis in geriatric depression. Int J Geriatr Psychiatry. 2011;26:1109–18.
Gatchel JR, Donovan NJ, Locascio JJ, Schultz AP, Becker JA, Chhatwal J, et al. Depressive symptoms and tau accumulation in the inferior temporal lobe and entorhinal cortex in cognitively normal older adults: a pilot study. J Alzheimers Dis. 2017;59:975–85. DOI: 10.3233/JAD-170001
Babulal GM, Roe CM, Stout SH, Rajasekar G, Wisch JK, Benzinger TLS, et al. Depression is associated with tau and not amyloid positron emission tomography in cognitively normal adults. J Alzheimers Dis. 2020;74:1045–55. DOI: 10.3233/JAD-191078
Hayakawa YK, Sasaki H, Takao H, Mori H, Hayashi N, Kunimatsu A, et al. Structural brain abnormalities in women with subclinical depression, as revealed by voxel-based morphometry and diffusion tensor imaging. J Affect Disord. 2013;144:263–8. DOI: 10.1016/j.jad.2012.10.023
Hayakawa YK, Sasaki H, Takao H, Hayashi N, Kunimatsu A, Ohtomo K, et al. Depressive symptoms and neuroanatomical structures in community-dwelling women: a combined voxel-based morphometry and diffusion tensor imaging study with tract-based spatial statistics. Neuroimage Clin. 2014;4:481–7. DOI: 10.1016/j.nicl.2014.03.002
Allan CL, Sexton CE, Filippini N, Topiwala A, Mahmood A, Zsoldos E, et al. Sub-threshold depressive symptoms and brain structure: a magnetic resonance imaging study within the Whitehall II cohort. J Affect Disord. 2016;204:219–25. DOI: 10.1016/j.jad.2016.06.049
Lamar M, Charlton RA, Morris RG, Markus HS. The impact of subcortical white matter disease on mood in euthymic older adults: a diffusion tensor imaging study. Am J Geriatr Psychiatry. 2010;18:634–42. DOI: 10.1097/JGP.0b013e3181cabad1
Wen M-C, Steffens DC, Chen M-K, Zainal NH. Diffusion tensor imaging studies in late-life depression: systematic review and meta-analysis. Int J Geriatr Psychiatry. 2014;29:1173–84. DOI: 10.1002/gps.4129
Benedetti F, Poletti S, Hoogenboezem TA, Mazza E, Ambrée O, de Wit H, et al. Inflammatory cytokines influence measures of white matter integrity in Bipolar Disorder. J Affect Disord. 2016;202:1–9. DOI: 10.1016/j.jad.2016.05.047
Sexton CE, Kalu UG, Filippini N, Mackay CE, Ebmeier KP. A meta-analysis of diffusion tensor imaging in mild cognitive impairment and Alzheimer’s disease. Neurobiol Aging. 2011;32:2322.e5–18. DOI: 10.1016/j.neurobiolaging.2010.05.019
Oishi K, Lyketsos CG. Alzheimer’s disease and the fornix. Front Aging Neurosci. 2014;6:241. DOI: 10.3389/fnagi.2014.00241
Knyazeva MG. Splenium of Corpus Callosum: patterns of interhemispheric interaction in children and adults. Neural Plasticity. 2013;2013:e639430. DOI: 10.1155/2013/639430
Bubb EJ, Metzler-Baddeley C, Aggleton JP. The cingulum bundle: anatomy, function, and dysfunction. Neurosci Biobehav Rev. 2018;92:104–127. DOI: 10.1016/j.neubiorev.2018.05.008
Villain N, Fouquet M, Baron J-C, Mézenge F, Landeau B, de La Sayette V, et al. Sequential relationships between grey matter and white matter atrophy and brain metabolic abnormalities in early Alzheimer’s disease. Brain. 2010;133:3301–14. DOI: 10.1093/brain/awq203
Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106:1125–65. DOI: 10.1152/jn.00338.2011
Lindquist KA, Wager TD, Kober H, Bliss-Moreau E, Barrett LF. The brain basis of emotion: a meta-analytic review. Behav Brain Sci. 2012;35:121–43. DOI: 10.1017/S0140525X11000446
Tadayonnejad R, Ajilore O. Brain network dysfunction in late-life depression: a literature review. J Geriatr Psychiatry Neurol. 2014;27:5–12. DOI: 10.1177/0891988713516539
Alexopoulos GS. Mechanisms and treatment of late-life depression. Transl Psychiatry. 2019;9:1–16. DOI: 10.1038/s41398-019-0514-6
Alexopoulos GS, Hoptman MJ, Kanellopoulos D, Murphy CF, Lim KO, Gunning FM. Functional connectivity in the cognitive control network and the default mode network in late-life depression. J Affect Disord. 2012;139:56–65. DOI: 10.1016/j.jad.2011.12.002
Andrews-Hanna JR, Smallwood J, Spreng RN. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Ann N Y Acad Sci. 2014;1316:29–52. DOI: 10.1111/nyas.12360
Makovac E, Fagioli S, Rae CL, Critchley HD, Ottaviani C. Can’t get it off my brain: meta-analysis of neuroimaging studies on perseverative cognition. Psychiatry Res Neuroimaging. 2020;295:0925–4927.
Lugo SB, Deza-Araujo Y, Collette F, Vuilleumier P, Klimecki O. The Medit-Ageing Research. Exposure to negative socio-emotional events induces sustained alteration of resting-state brain networks in the elderly. 2020 (Preprint at Research Square).
Buckner RL. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci. 2005;25:7709–17. DOI: 10.1523/JNEUROSCI.2177-05.2005
Sheline YI, Raichle ME. Resting state functional connectivity in preclinical Alzheimer’s disease. Biol Psychiatry. 2013;74:340–7. DOI: 10.1016/j.biopsych.2012.11.028
Badhwar A, Tam A, Dansereau C, Orban P, Hoffstaedter F, Bellec P. Resting-state network dysfunction in Alzheimer’s disease: a systematic review and meta-analysis. Alzheimers Dement. 2017;8:73–85.
Menon V. Salience network. In: Toga AW, editor. Brain mapping. Waltham: Academic Press; 2015. p. 597–611.
Breukelaar IA, Antees C, Grieve SM, Foster SL, Gomes L, Williams LM, et al. Cognitive control network anatomy correlates with neurocognitive behavior: a longitudinal study. Hum Brain Mapp. 2017;38:631–43. DOI: 10.1002/hbm.23401
Manoliu A, Meng C, Brandl F, Doll A, Tahmasian M, Scherr M, et al. Insular dysfunction within the salience network is associated with severity of symptoms and aberrant inter-network connectivity in major depressive disorder. Front Hum Neurosci. 2014;7:930.
Demnitz-King H, Göehre I, Marchant NL. The neuroanatomical correlates of repetitive negative thinking: a systematic review. Psychiatry Res Neuroimaging. 2021;316:111353. DOI: 10.1016/j.pscychresns.2021.111353
Marchant NL, Lovland LR, Jones R, Pichet Binette A, Gonneaud J, Arenaza-Urquijo EM, et al. Repetitive negative thinking is associated with amyloid, tau, and cognitive decline. Alzheimers Dement. 2020;16:1054–64. DOI: 10.1002/alz.12116
