Ménard, C., Hodes, G. E. & Russo, S. J. Pathogenesis of depression: insights from human and rodent studies. Neuroscience 321, 138–162 (2016).
Google Scholar
Mitina, M., Young, S. & Zhavoronkov, A. Psychological aging, depression, and well-being. Aging (Albany NY). 12, 18765 (2020).
Google Scholar
World Health Organization. Depression and Other Common Mental Disorders: Global Health Estimates (World Health Organization, 2017).
Fiske, A., Wetherell, J. L. & Gatz, M. Depression in older adults. Ann. Rev. Clin. Psychol. 5, 363–389 (2009).
Google Scholar
Al-Harbi, K. S. Treatment-resistant depression: Therapeutic trends, challenges, and future directions. Patient Prefer. Adher., 369–388 (2012).
Rush, A. J. et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR* D report. Am. J. Psychiatry. 163, 1905–1917 (2006).
Google Scholar
Nuñez, N. A. et al. Augmentation strategies for treatment resistant major depression: a systematic review and network meta-analysis. J. Affect. Disord. 302, 385–400 (2022).
Google Scholar
Ma, L. Depression, anxiety, and apathy in mild cognitive impairment: current perspectives. Front. Aging Neurosci. 12, 9 (2020).
Google Scholar
Gao, Y. et al. Retracted: depression as a risk factor for dementia and mild cognitive impairment: a meta-analysis of longitudinal studies. Int. J. Geriatr. Psychiatry. 28, 441–449 (2013).
Google Scholar
Formánek, T. et al. Trajectories of depressive symptoms and associated patterns of cognitive decline. Sci. Rep. 10, 20888 (2020).
Google Scholar
Molinuevo, J. L. et al. Implementation of subjective cognitive decline criteria in research studies. Alzheimer’s Dement. 13, 296–311 (2017).
Google Scholar
Röhr, S. et al. Estimating prevalence of subjective cognitive decline in and across international cohort studies of aging: a COSMIC study. Alzheimers Res. Ther. 12, 1–14 (2020).
Google Scholar
Slot, R. E. et al. Subjective cognitive decline and rates of incident Alzheimer’s disease and non–Alzheimer’s disease dementia. Alzheimer’s Dement. 15, 465–476 (2019).
Google Scholar
Davey, C. G., Pujol, J. & Harrison, B. J. Mapping the self in the brain’s default mode network. NeuroImage 132, 390–397 (2016).
Google Scholar
Seeley, W. W. The salience network: a neural system for perceiving and responding to homeostatic demands. J. Neurosci. 39, 9878–9882 (2019).
Google Scholar
Fox, M. et al. (ed, D.) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. 102 9673–9678 (2005).
Google Scholar
Shi, L. et al. Brain networks of happiness: dynamic functional connectivity among the default, cognitive and salience networks relates to subjective well-being. Soc. Cognit. Affect. Neurosci. 13, 851–862 (2018).
Google Scholar
Sha, Z., Wager, T. D., Mechelli, A. & He, Y. Common dysfunction of large-scale neurocognitive networks across psychiatric disorders. Biol. Psychiatry. 85, 379–388 (2019).
Google Scholar
Kaiser, R. H., Andrews-Hanna, J. R., Wager, T. D. & Pizzagalli, D. A. Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiatry. 72, 603–611 (2015).
Google Scholar
Mulders, P. C., van Eijndhoven, P. F., Schene, A. H., Beckmann, C. F. & Tendolkar, I. Resting-state functional connectivity in major depressive disorder: a review. Neurosci. Biobehavioral Reviews. 56, 330–344 (2015).
Google Scholar
Yun, J. Y. & Kim, Y. K. Graph theory approach for the structural-functional brain connectome of depression. Prog. Neuropsychopharmacol. Biol. Psychiatry. 111, 110401 (2021).
Google Scholar
Alexopoulos, G. S. et al. Functional connectivity in the cognitive control network and the default mode network in late-life depression. J. Affect. Disord. 139, 56–65 (2012).
Google Scholar
Posner, J. et al. Antidepressants normalize the default mode network in patients with dysthymia. JAMA Psychiatry. 70, 373–382 (2013).
Google Scholar
Dichter, G. S., Gibbs, D. & Smoski, M. J. A systematic review of relations between resting-state functional-MRI and treatment response in major depressive disorder. J. Affect. Disord. 172, 8–17 (2015).
Google Scholar
Dunlop, K., Victoria, L. W., Downar, J., Gunning, F. M. & Liston, C. Accelerated brain aging predicts impulsivity and symptom severity in depression. Neuropsychopharmacology 46, 911–919 (2021).
Google Scholar
Fulop, T. et al. Immunology of aging: The birth of inflammaging. Clin. Rev. Allergy Immunol., 1–14 (2021).
Shaw, A. C., Joshi, S., Greenwood, H., Panda, A. & Lord, J. M. Aging of the innate immune system. Curr. Opin. Immunol. 22, 507–513 (2010).
Google Scholar
Alpert, A. et al. A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring. Nat. Med. 25, 487–495 (2019).
Google Scholar
Dinarello, C. A. Interleukin 1 and Interleukin 18 as mediators of inflammation and the aging process. Am. J. Clin. Nutr. 83, 447S–455S (2006).
Google Scholar
Rea, I. M. et al. Age and age-related diseases: role of inflammation triggers and cytokines. Front. Immunol. 9, 334076 (2018).
Google Scholar
Schumacher, B., Pothof, J., Vijg, J. & Hoeijmakers, J. H. The central role of DNA damage in the ageing process. Nature 592, 695–703 (2021).
Google Scholar
Walker, K. A., Basisty, N., Wilson, D. M. & Ferrucci, L. Connecting aging biology and inflammation in the omics era. J. Clin. Investig. 132 (2022).
DiSabato, D. J., Quan, N. & Godbout, J. P. Neuroinflammation: the devil is in the details. J. Neurochem. 139, 136–153 (2016).
Google Scholar
Allison, D. J. & Ditor, D. S. The common inflammatory etiology of depression and cognitive impairment: a therapeutic target. J. Neuroinflamm. 11, 1–12 (2014).
Google Scholar
Lavretsky, H. & Newhouse, P. A. Stress, inflammation and aging. Am. J. Geriatric Psychiatry: Official J. Am. Association Geriatric Psychiatry. 20, 729 (2012).
Google Scholar
Troubat, R. et al. Neuroinflammation and depression: A review. Eur. J. Neurosci. 53, 151–171 (2021).
Google Scholar
Corrigan, M., O’Rourke, A. M., Moran, B., Fletcher, J. M. & Harkin, A. Inflammation in the pathogenesis of depression: A disorder of neuroimmune origin. Neuronal Signal. 7 (2023).
Fan, N., Luo, Y., Ou, Y. & He, H. Altered serum levels of TNF-α, IL‐6, and IL‐18 in depressive disorder patients. Hum. Psychopharmacology: Clin. Experimental. 32, e2588 (2017).
Google Scholar
Bai, S. et al. Efficacy and safety of anti-inflammatory agents for the treatment of major depressive disorder: a systematic review and meta-analysis of randomised controlled trials. J. Neurol. Neurosurg. Psychiatry. 91, 21–32 (2020).
Google Scholar
Spellman, T. & Liston, C. Toward circuit mechanisms of pathophysiology in depression. Am. J. Psychiatry. 177, 381–390 (2020).
Google Scholar
(2023).
Bauer, M. E. & Teixeira, A. L. Inflammation in psychiatric disorders: what comes first? Ann. N. Y. Acad. Sci. 1437, 57–67 (2019).
Google Scholar
Golia, M. T. et al. Interplay between inflammation and neural plasticity: both immune activation and suppression impair LTP and BDNF expression. Brain. Behav. Immun. 81, 484–494 (2019).
Google Scholar
Yirmiya, R. & Goshen, I. Immune modulation of learning, memory, neural plasticity and neurogenesis. Brain. Behav. Immun. 25, 181–213 (2011).
Google Scholar
Lynch, M. A. Age-related neuroinflammatory changes negatively impact on neuronal function. Front. Aging Neurosci. 1, 1206 (2010).
Google Scholar
Kumar, A. Vol. 10, 413 (Frontiers Media SA, 2018).
Jessen, F. et al. The characterisation of subjective cognitive decline. Lancet Neurol. 19, 271–278 (2020).
Google Scholar
Harvey, P. D. et al. Technology and mental health: state of the Art for assessment and treatment. Am. J. Psychiatry. 179, 897–914 (2022).
Google Scholar
Abbadessa, G. et al. Digital therapeutics in neurology. J. Neurol. 269, 1209–1224 (2022).
Google Scholar
Moshe, I. et al. Digital interventions for the treatment of depression: A meta-analytic review. Psychol. Bull. 147, 749 (2021).
Google Scholar
Amedi, A., Shelly, S., Saporta, N. & Catalogna, M. Perceptual learning and neural correlates of virtual navigation in subjective cognitive decline: A pilot study. Science (2024).
Aggius-Vella, E., Chebat, D. R., Maidenbaum, S. & Amedi, A. Activation of human visual area V6 during egocentric navigation with and without visual experience. Curr. Biol. 33, 1211–1219 (2023). e1215.
Google Scholar
Pascual-Leone, A., Amedi, A., Fregni, F. & Merabet, L. B. The plastic human brain cortex. J. R N. 28, 377–401 (2005).
Google Scholar
Bursky, M., Egglefield, D. A., Schiff, S. G., Premnath, P. & Sneed, J. R. Mindfulness-enhanced computerized cognitive training for depression: an integrative review and proposed model targeting the cognitive control and default-mode networks. Brain Sci. 12, 663 (2022).
Google Scholar
Dunn, T. J. & Dimolareva, M. The effect of mindfulness-based interventions on immunity-related biomarkers: a comprehensive meta-analysis of randomised controlled trials. Clin. Psychol. Rev. 92, 102124 (2022).
Google Scholar
Shields, G. S., Spahr, C. M. & Slavich, G. M. Psychosocial interventions and immune system function: a systematic review and meta-analysis of randomized clinical trials. JAMA Psychiatry. 77, 1031–1043 (2020).
Google Scholar
Cohen, C. I., Magai, C. & Yaffee, R. Walcott-Brown, L. Racial differences in syndromal and subsyndromal depression in an older urban population. Psychiatric Serv. 56, 1556–1563 (2005).
Google Scholar
Briggs, R. G. et al. Parcellation-based tractographic modeling of the salience network through meta‐analysis. Brain Behav. 12, e2646 (2022).
Google Scholar
Sandhu, Z. et al. Parcellation-based anatomic modeling of the default mode network. Brain Behav. 11, e01976 (2021).
Google Scholar
Ingersoll-Dayton, B., Torges, C. & Krause, N. Unforgiveness, rumination, and depressive symptoms among older adults. Aging Ment. Health. 14, 439–449 (2010).
Google Scholar
Spinhoven, P., Drost, J., van Hemert, B. & Penninx, B. W. Common rather than unique aspects of repetitive negative thinking are related to depressive and anxiety disorders and symptoms. J. Anxiety Disord. 33, 45–52 (2015).
Google Scholar
Wu, M. et al. Default-mode network connectivity and white matter burden in late-life depression. Psychiatry Research: Neuroimaging. 194, 39–46 (2011).
Google Scholar
Zhou, Y. et al. Increased neural resources recruitment in the intrinsic organization in major depression. J. Affect. Disord. 121, 220–230 (2010).
Google Scholar
Kim, J. & Kim, Y. K. Crosstalk between depression and dementia with resting-state fMRI studies and its relationship with cognitive functioning. Biomedicines 9, 82 (2021).
Google Scholar
Damoiseaux, J. S. et al. Reduced resting-state brain activity in the default network in normal aging. Cereb. Cortex. 18, 1856–1864 (2008).
Google Scholar
De Vogelaere, F., Santens, P., Achten, E., Boon, P. & Vingerhoets, G. Altered default-mode network activation in mild cognitive impairment compared with healthy aging. Neuroradiology 54, 1195–1206 (2012).
Google Scholar
Brier, M. R. et al. Loss of intranetwork and internetwork resting state functional connections with Alzheimer’s disease progression. J. Neurosci. 32, 8890–8899 (2012).
Google Scholar
Tumati, S., Martens, S., de Jong, B. & Aleman, A. Lateral parietal cortex in the generation of behavior: implications for apathy. Prog. Neurobiol. 175, 20–34 (2019).
Google Scholar
Braga, R. M., Sharp, D. J., Leeson, C., Wise, R. J. & Leech, R. Echoes of the brain within default mode, association, and heteromodal cortices. J. Neurosci. 33, 14031–14039 (2013).
Google Scholar
Shao, J. et al. Common and distinct changes of default mode and salience network in schizophrenia and major depression. Brain Imaging Behav. 12, 1708–1719 (2018).
Google Scholar
Van Tol, M. J. et al. Local cortical thinning links to resting-state disconnectivity in major depressive disorder. Psychol. Med. 44, 2053–2065 (2014).
Google Scholar
Connolly, C. G. et al. Resting-state functional connectivity of subgenual anterior cingulate cortex in depressed adolescents. Biol. Psychiatry. 74, 898–907 (2013).
Google Scholar
Manoliu, A. 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. 7, 930 (2014).
Google Scholar
Chan, M. Y., Park, D. C., Savalia, N. K., Petersen, S. E. & Wig, G. S. Decreased segregation of brain systems across the healthy adult lifespan. Proc.Natl. Acad. Sci. 111, E4997–E5006 (2014).
Malagurski, B., Liem, F., Oschwald, J., Mérillat, S. & Jäncke, L. Functional dedifferentiation of associative resting state networks in older adults–a longitudinal study. NeuroImage 214, 116680 (2020).
Google Scholar
Putcha, D., Ross, R. S., Cronin-Golomb, A., Janes, A. C. & Stern, C. E. Altered intrinsic functional coupling between core neurocognitive networks in Parkinson’s disease. NeuroImage: Clin. 7, 449–455 (2015).
Google Scholar
Bossu, P. et al. Interleukin-18 produced by peripheral blood cells is increased in Alzheimer’s disease and correlates with cognitive impairment. Brain. Behav. Immun. 22, 487–492 (2008).
Google Scholar
Yamanishi, K. et al. Acute stress induces severe neural inflammation and overactivation of glucocorticoid signaling in interleukin-18-deficient mice. Translational Psychiatry. 12, 404 (2022).
Google Scholar
Szabo, A. et al. Increased Circulating IL-18 levels in severe mental disorders indicate systemic inflammasome activation. Brain. Behav. Immun. 99, 299–306 (2022).
Google Scholar
Mehta, D. et al. Using polymorphisms in FKBP5 to define biologically distinct subtypes of posttraumatic stress disorder: evidence from endocrine and gene expression studies. Arch. Gen. Psychiatry. 68, 901–910 (2011).
Google Scholar
Du, X. et al. Peripheral Interleukin-18 is negatively correlated with abnormal brain activity in patients with depression: a resting-state fMRI study. BMC Psychiatry. 22, 531 (2022).
Google Scholar
Kaufman, E. & Lamster, I. B. The diagnostic applications of saliva—a review. Crit. Reviews Oral Biology Med. 13, 197–212 (2002).
Google Scholar
Williamson, S., Munro, C., Pickler, R., Grap, M. J. & Elswick, R. Jr Comparison of biomarkers in blood and saliva in healthy adults. Nurs. Res. Pract. 246178 (2012).
Li, Y. et al. Normative dataset for plasma cytokines in healthy human adults. Data Brief. 35, 106857 (2021).
Google Scholar
Conti, B. et al. Cultures of astrocytes and microglia express Interleukin 18. Mol. Brain Res. 67, 46–52 (1999).
Google Scholar
Kim, T. K. et al. Local interleukin-18 system in the basolateral amygdala regulates susceptibility to chronic stress. Mol. Neurobiol. 54, 5347–5358 (2017).
Google Scholar
Muscatell, K. A. et al. Greater amygdala activity and dorsomedial prefrontal–amygdala coupling are associated with enhanced inflammatory responses to stress. Brain. Behav. Immun. 43, 46–53 (2015).
Google Scholar
Peng, X., Lau, W. K., Wang, C., Ning, L. & Zhang, R. Impaired left amygdala resting state functional connectivity in subthreshold depression individuals. Sci. Rep. 10, 17207 (2020).
Google Scholar
Cullen, K. R. et al. Abnormal amygdala resting-state functional connectivity in adolescent depression. JAMA Psychiatry. 71, 1138–1147 (2014).
Google Scholar
Fresco, D. M. & Mennin, D. S. All together now: utilizing common functional change principles to unify cognitive behavioral and mindfulness-based therapies. Curr. Opin. Psychol. 28, 65–70 (2019).
Google Scholar
Hallis, L., Cameli, L., Bekkouche, N. S. & Knäuper, B. Combining cognitive therapy with acceptance and commitment therapy for depression: a group therapy feasibility study. J. Cogn. Psychother. 31, 171–190 (2017).
Google Scholar
Farras-Permanyer, L. et al. Age-related changes in resting-state functional connectivity in older adults. Neural Regeneration Res. 14, 1544–1555 (2019).
Google Scholar
Edde, M., Leroux, G., Altena, E. & Chanraud, S. Functional brain connectivity changes across the human life span: from fetal development to old age. J. Neurosci. Res. 99, 236–262 (2021).
Google Scholar
Lee, J. S. et al. Distinct brain regions in physiological and pathological brain aging. Front. Aging Neurosci. 11, 147 (2019).
Google Scholar
Jessen, F. et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimer’s Dement. 10, 844–852 (2014).
Google Scholar
Yang, C. et al. Montreal cognitive assessment: Seeking a single cutoff score may not be optimal. Evid.-Based Complement. Altern. Med. 9984419 (2021). (2021).
Thomann, A. E., Berres, M., Goettel, N., Steiner, L. A. & Monsch, A. U. Enhanced diagnostic accuracy for neurocognitive disorders: a revised cut-off approach for the Montreal cognitive assessment. Alzheimers Res. Ther. 12, 1–10 (2020).
Google Scholar
Amedi, A., Hofstetter, S., Maidenbaum, S. & Heimler, B. Task selectivity as a comprehensive principle for brain organization. Trends Cogn. Sci. 21, 307–310 (2017).
Google Scholar
Amedi, A., Raz, N., Pianka, P., Malach, R. & Zohary, E. Early ‘visual’cortex activation correlates with superior verbal memory performance in the blind. Nat. Neurosci. 6, 758–766 (2003).
Google Scholar
Feinberg, D. A. et al. Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PloS One. 5, e15710 (2010).
Google Scholar
Moeller, S. et al. Multiband multislice GE-EPI at 7 Tesla, with 16‐fold acceleration using partial parallel imaging with application to high Spatial and Temporal whole‐brain fMRI. Magn. Reson. Med. 63, 1144–1153 (2010).
Google Scholar
Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2, 125–141 (2012).
Google Scholar
Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37, 90–101 (2007).
Google Scholar
Glasser, M. F. et al. The human connectome project’s neuroimaging approach. 19, 1175–1187 (2016).
link
