Abstract
Chemotherapy-related cognitive impairment and associated brain changes may reflect accelerated brain aging; however, empirical evidence for this theory is limited. The purpose of this study was to measure brain aging in newly diagnosed patients with breast cancer treated with chemotherapy (n = 43) and compare its longitudinal change to that of controls (n = 50). Brain age indices, derived from cortical measures, were compared between women with breast cancer and matched healthy controls across 3 timepoints (time 1: pre-surgery, time 2: 1 month following chemotherapy completion, and time 3: 1-year post-chemotherapy). The breast cancer group showed a significant decrease in cortical thickness across the 3 timepoints (p < .001) and a trend towards significant increase in predicted brain age especially from pre-treatment (time 1) to post-chemotherapy (time 2) compared to controls (p = 0.08). Greater increase in predicted brain age was related to several clinical factors (HER-2 status, surgery type, and history of neoadjuvant chemotherapy) and greater decrease in cortical thickness was associated with greater decrease in performance on a verbal learning task from time 1 to time 3 (r = − 0.48, p < .01). This study demonstrated evidence of increased cortical brain aging in middle-aged patients with breast cancer following chemotherapy treatment that was associated with decreased verbal memory performance.
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References
Aghakhani, A., & Chan, E. K (2007). Test Reviews: Bracken, B. A., & Howell, K. (2004). Clinical assessment of depression. Odessa, FL: psychological assessment resources. J Psychoeduc Assess, 25(4), 416–422. doi:https://doi.org/10.1177/0734282907300383
Ahles TA, Root JC, Ryan EL (2012) Cancer- and cancer treatment-associated cognitive change: an update on the state of the science. J Clin Oncol 30(30):3675–3686. https://doi.org/10.1200/JCO.2012.43.0116
Ahles TA, Saykin AJ (2007) Candidate mechanisms for chemotherapy-induced cognitive changes. Nat Rev Cancer 7(3):192–201. https://doi.org/10.1038/nrc2073
Beheshti I, Maikusa N, Matsuda H (2018) The association between “Brain-Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease. Brain Behav 8(8):e01020. https://doi.org/10.1002/brb3.1020
Bianchini G, Gianni L (2014) The immune system and response to HER2-targeted treatment in breast cancer. Lancet Oncol 15(2):e58–e68. https://doi.org/10.1016/S1470-2045(13)70477-7
Blalock EM, Chen KC, Sharrow K, Herman JP, Porter NM, Foster TC, Landfield PW (2003) Gene microarrays in hippocampal aging: statistical profiling identifies novel processes correlated with cognitive impairment. J Neurosci 23(9):3807–3819 https://www.ncbi.nlm.nih.gov/pubmed/12736351
Chen BT, Sethi SK, Jin T, Patel SK, Ye N, Sun CL, Rockne RC, Haacke EM, Root JC, Saykin AJ, Ahles TA, Holodny AI, Prakash N, Mortimer J, Waisman J, Yuan Y, Somlo G, Li D, Yang R, Tan H, Katheria V, Morrison R, Hurria A (2018) Assessing brain volume changes in older women with breast cancer receiving adjuvant chemotherapy: a brain magnetic resonance imaging pilot study. Breast Cancer Res 20(1):38. https://doi.org/10.1186/s13058-018-0965-3
Cole JH, Franke K (2017) Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci 40(12):681–690. https://doi.org/10.1016/j.tins.2017.10.001
Cole JH, Leech R, Sharp DJ, Alzheimer’s Disease Neuroimaging, I (2015) Prediction of brain age suggests accelerated atrophy after traumatic brain injury. Ann Neurol 77(4):571–581. https://doi.org/10.1002/ana.24367
Cole JH, Ritchie SJ, Bastin ME, Valdes Hernandez MC, Munoz Maniega S, Royle N et al (2018) Brain age predicts mortality. Mol Psychiatry 23(5):1385–1392. https://doi.org/10.1038/mp.2017.62
Cole JH, Underwood J, Caan MW, De Francesco D, van Zoest RA, Leech R et al (2017) Increased brain-predicted aging in treated HIV disease. Neurology 88(14):1349–1357. https://doi.org/10.1212/WNL.0000000000003790
Conroy SK, McDonald BC, Smith DJ, Moser LR, West JD, Kamendulis LM et al (2013) Alterations in brain structure and function in breast cancer survivors: effect of post-chemotherapy interval and relation to oxidative DNA damage. Breast Cancer Res Treat 137(2):493–502. https://doi.org/10.1007/s10549-012-2385-x
Crawford JR, Henry JD, Ward AL, Blake J (2006) The Prospective and Retrospective Memory Questionnaire (PRMQ): latent structure, normative data and discrepancy analysis for proxy-ratings. Br J Clin Psychol 45(Pt 1):83–104. https://doi.org/10.1348/014466505X28748
Crocco E, Curiel RE, Acevedo A, Czaja SJ, Loewenstein DA (2014) An evaluation of deficits in semantic cueing and proactive and retroactive interference as early features of Alzheimer’s disease. Am J Geriatr Psychiatry 22(9):889–897. https://doi.org/10.1016/j.jagp.2013.01.066
Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9(2):179–194. https://doi.org/10.1006/nimg.1998.0395
Dale W, Mohile SG, Eldadah BA, Trimble EL, Schilsky RL, Cohen HJ, Muss HB, Schmader KE, Ferrell B, Extermann M, Nayfield SG, Hurria A, Cancer and Aging Research Group (2012) Biological, clinical, and psychosocial correlates at the interface of cancer and aging research. J Natl Cancer Inst 104(8):581–589. https://doi.org/10.1093/jnci/djs145
de Ruiter MB, Schagen SB (2013) Functional MRI studies in non-CNS cancers. Brain Imaging Behav 7(4):388–408. https://doi.org/10.1007/s11682-013-9249-9
Deprez S, Vandenbulcke M, Peeters R, Emsell L, Smeets A, Christiaens MR et al (2014) Longitudinal assessment of chemotherapy-induced alterations in brain activation during multitasking and its relation with cognitive complaints. J Clin Oncol. https://doi.org/10.1200/JCO.2013.53.6219
Dieci MV, Griguolo G, Miglietta F, Guarneri V (2016) The immune system and hormone-receptor positive breast cancer: is it really a dead end. Cancer Treat Rev 46:9–19. https://doi.org/10.1016/j.ctrv.2016.03.011
Ferreira D, Bartres-Faz D, Nygren L, Rundkvist LJ, Molina Y, Machado A et al (2016) Different reserve proxies confer overlapping and unique endurance to cortical thinning in healthy middle-aged adults. Behav Brain Res 311:375–383. https://doi.org/10.1016/j.bbr.2016.05.061
Fischl B (2012) FreeSurfer. Neuroimage 62(2):774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021
Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images (research support, U.S. Gov’t, P.H.S.). Proc Natl Acad Sci U S A 97(20):11050–11055. https://doi.org/10.1073/pnas.200033797
Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3):341–355 http://www.ncbi.nlm.nih.gov/pubmed/11832223
Franke K, Gaser C (2012) Longitudinal changes in individual BrainAGE in healthy aging, mild cognitive impairment, and Alzheimer’s disease. GeroPsych 25(4)
Gaser C, Franke K, Kloppel S, Koutsouleris N, Sauer H, Alzheimer's Disease Neuroimaging I (2013) BrainAGE in mild cognitive impaired patients: predicting the conversion to Alzheimer’s disease. PLoS One 8(6):e67346. https://doi.org/10.1371/journal.pone.0067346
Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S, Busa E, Pacheco J, Albert M, Killiany R, Maguire P, Rosas D, Makris N, Dale A, Dickerson B, Fischl B (2006) Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. Neuroimage 32(1):180–194. https://doi.org/10.1016/j.neuroimage.2006.02.051
Hatton SN, Franz CE, Elman JA, Panizzon MS, Hagler DJ Jr, Fennema-Notestine C et al (2018) Negative fateful life events in midlife and advanced predicted brain aging. Neurobiol Aging 67:1–9. https://doi.org/10.1016/j.neurobiolaging.2018.03.004
Henneghan A, Palesh O, Harrison M, Kesler S (2018a) Identifying cytokine predictors of cognitive functioning in breast cancer survivors up to 10 years post chemotherapy using machine learning. J Neuroimmunol 320:38–47. https://doi.org/10.1016/j.jneuroim.2018.04.012
Henneghan AM, Carter P, Stuifbergan A, Parmelee B, Kesler S (2018b) Relationships between self-reported sleep quality components and cognitive functioning in breast cancer survivors up to 10 years following chemotherapy. Psychooncology. https://doi.org/10.1002/pon.4745
Inagaki M, Yoshikawa E, Matsuoka Y, Sugawara Y, Nakano T, Akechi T, Wada N, Imoto S, Murakami K, Uchitomi Y (2007) Smaller regional volumes of brain gray and white matter demonstrated in breast cancer survivors exposed to adjuvant chemotherapy. Cancer 109(1):146–156 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17131349
Janelsins MC, Kesler SR, Ahles TA, Morrow GR (2014) Prevalence, mechanisms, and management of cancer-related cognitive impairment (article). Int Rev Psychiatry 26(1):102–113. https://doi.org/10.3109/09540261.2013.864260
Jaskelioff M, Muller FL, Paik JH, Thomas E, Jiang S, Adams AC, Sahin E, Kost-Alimova M, Protopopov A, Cadiñanos J, Horner JW, Maratos-Flier E, Depinho RA (2011) Telomerase reactivation reverses tissue degeneration in aged telomerase-deficient mice. Nature 469(7328):102–106. https://doi.org/10.1038/nature09603
Jernigan TL, Archibald SL, Fennema-Notestine C, Gamst AC, Stout JC, Bonner J, Hesselink JR (2001) Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiol Aging 22(4):581–594 https://www.ncbi.nlm.nih.gov/pubmed/11445259
Kesler S, Janelsins M, Koovakkattu D, Palesh O, Mustian K, Morrow G et al (2013a) Reduced hippocampal volume and verbal memory performance associated with interleukin-6 and tumor necrosis factor-alpha levels in chemotherapy-treated breast cancer survivors (research support, N.I.H., extramural research support, non-U.S. Gov’t). Brain Behav Immun (30 Suppl):S109–S116. https://doi.org/10.1016/j.bbi.2012.05.017
Kesler S, Rao A, Blayney DW, Oakley Girvan I, Karuturi M, Palesh O (2017a) Predicting long-term cognitive outcome following breast cancer with pre-treatment resting state fMRI and random forest machine learning. Front Hum Neurosci 11:555. https://doi.org/10.3389/fnhum.2017.00555
Kesler SR (2014) Default mode network as a potential biomarker of chemotherapy-related brain injury. Neurobiol Aging 35(Suppl 2):S11–S19. https://doi.org/10.1016/j.neurobiolaging.2014.03.036
Kesler SR, Adams M, Packer M, Rao V, Henneghan AM, Blayney DW, Palesh O (2017b) Disrupted brain network functional dynamics and hyper-correlation of structural and functional connectome topology in patients with breast cancer prior to treatment. Brain Behav 7(3):e00643. https://doi.org/10.1002/brb3.643
Kesler SR, Blayney DW (2016) Neurotoxic effects of anthracycline- vs nonanthracycline-based chemotherapy on cognition in breast cancer survivors. JAMA Oncol 2(2):185–192. https://doi.org/10.1001/jamaoncol.2015.4333
Kesler SR, Kent JS, O'Hara R (2011) Prefrontal cortex and executive function impairments in primary breast cancer. JAMA Neurol 68(11):1447–1453. https://doi.org/10.1001/archneurol.2011.245
Kesler SR, Rao V, Ray WJ, Rao A (2017c) Probability of Alzheimer’s disease in breast cancer survivors based on gray-matter structural network efficiency. Alzheimers Dement 9:67–75. https://doi.org/10.1016/j.dadm.2017.10.002
Kesler SR, Watson CL, Blayney DW (2015) Brain network alterations and vulnerability to simulated neurodegeneration in breast cancer. Neurobiol Aging 36(8):2429–2442. https://doi.org/10.1016/j.neurobiolaging.2015.04.015
Kesler SR, Wefel JS, Hosseini SM, Cheung M, Watson CL, Hoeft F (2013b) Default mode network connectivity distinguishes chemotherapy-treated breast cancer survivors from controls. Proc Natl Acad Sci U S A 110(28):11600–11605. https://doi.org/10.1073/pnas.1214551110
Koppelmans V, Breteler MM, Boogerd W, Seynaeve C, Gundy C, Schagen SB (2012a) Neuropsychological performance in survivors of breast cancer more than 20 years after adjuvant chemotherapy. J Clin Oncol 30(10):1080–1086. https://doi.org/10.1200/JCO.2011.37.0189
Koppelmans V, de Groot M, de Ruiter MB, Boogerd W, Seynaeve C, Vernooij MW, Niessen WJ, Schagen SB, Breteler MM (2014) Global and focal white matter integrity in breast cancer survivors 20 years after adjuvant chemotherapy. Hum Brain Mapp 35(3):889–899. https://doi.org/10.1002/hbm.22221
Koppelmans V, de Ruiter MB, van der Lijn F, Boogerd W, Seynaeve C, van der Lugt A, Vrooman H, Niessen WJ, Breteler MM, Schagen SB (2012b) Global and focal brain volume in long-term breast cancer survivors exposed to adjuvant chemotherapy. Breast Cancer Res Treat 132(3):1099–1106. https://doi.org/10.1007/s10549-011-1888-1
Koshiyama D, Fukunaga M, Okada N, Yamashita F, Yamamori H, Yasuda Y, Fujimoto M, Ohi K, Fujino H, Watanabe Y, Kasai K, Hashimoto R (2018) Role of subcortical structures on cognitive and social function in schizophrenia. Sci Rep 8(1):1183. https://doi.org/10.1038/s41598-017-18950-2
Kuhn T, Kaufmann T, Doan NT, Westlye LT, Jones J, Nunez RA et al (2018) An augmented aging process in brain white matter in HIV. Hum Brain Mapp. https://doi.org/10.1002/hbm.24019
Kuperberg GR, Broome MR, McGuire PK, David AS, Eddy M, Ozawa F et al (2003) Regionally localized thinning of the cerebral cortex in schizophrenia. Arch Gen Psychiatry 60(9):878–888. https://doi.org/10.1001/archpsyc.60.9.878
Li W, Gan C, Lv Y, Wang SH, Cheng HD (2017) Chemotherapy-induced prospective memory impairment in breast cancer patients with different hormone receptor expression. Medicine 96(13). https://doi.org/10.1097/MD.0000000000006514
Liem, F., Gorgolewski, C. (2017). BIDS-apps/baracus: v1.1.2. https://zenodo.org/record/1018841#.WvtveC-ZPNI. Accessed
Liem F, Varoquaux G, Kynast J, Beyer F, Kharabian Masouleh S, Huntenburg JM, Lampe L, Rahim M, Abraham A, Craddock RC, Riedel-Heller S, Luck T, Loeffler M, Schroeter ML, Witte AV, Villringer A, Margulies DS (2017) Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage 148:179–188. https://doi.org/10.1016/j.neuroimage.2016.11.005
Little R, Rubin D (2002) Statistical analysis with missing data. Wiley, New York, NY
Loewenstein DA, Curiel RE, Greig MT, Bauer RM, Rosado M, Bowers D, Wicklund M, Crocco E, Pontecorvo M, Joshi AD, Rodriguez R, Barker WW, Hidalgo J, Duara R (2016) A novel cognitive stress test for the detection of preclinical Alzheimer disease: discriminative properties and relation to amyloid load. Am J Geriatr Psychiatry 24(10):804–813. https://doi.org/10.1016/j.jagp.2016.02.056
Loewenstein DA, Curiel RE, Wright C, Sun X, Alperin N, Crocco E, Czaja SJ, Raffo A, Penate A, Melo J, Capp K, Gamez M, Duara R (2017) Recovery from proactive semantic interference in mild cognitive impairment and normal aging: relationship to atrophy in brain regions vulnerable to Alzheimer’s disease. J Alzheimers Dis 56(3):1119–1126. https://doi.org/10.3233/JAD-160881
Lowe LC, Gaser C, Franke K, Alzheimer's Disease Neuroimaging I (2016) The effect of the APOE genotype on individual BrainAGE in normal aging, mild cognitive impairment, and Alzheimer’s disease. PLoS One 11(7):e0157514. https://doi.org/10.1371/journal.pone.0157514
Lustig C, Jantz T (2015) Questions of age differences in interference control: when and how, not if. Brain Res 1612:59–69. https://doi.org/10.1016/j.brainres.2014.10.024
Lyon DE, Cohen R, Chen H, Kelly DL, Starkweather A, Ahn HC, Jackson-Cook CK (2016) The relationship of cognitive performance to concurrent symptoms, cancer- and cancer-treatment-related variables in women with early-stage breast cancer: a 2-year longitudinal study. J Cancer Res Clin Oncol 142(7):1461–1474. https://doi.org/10.1007/s00432-016-2163-y
Maherali N, Sridharan R, Xie W, Utikal J, Eminli S, Arnold K et al (2007) Directly reprogrammed fibroblasts show global epigenetic remodeling and widespread tissue contribution. Cell Stem Cell 1(1):55–70. https://doi.org/10.1016/j.stem.2007.05.014
Mandelblatt JS, Hurria A, McDonald BC, Saykin AJ, Stern RA, VanMeter JW et al (2013) Cognitive effects of cancer and its treatments at the intersection of aging: what do we know; what do we need to know. Semin Oncol 40(6):709–725. https://doi.org/10.1053/j.seminoncol.2013.09.006
Mandelblatt JS, Stern RA, Luta G, McGuckin M, Clapp JD, Hurria A et al (2014) Cognitive impairment in older patients with breast cancer before systemic therapy: is there an interaction between cancer and comorbidity. J Clin Oncol. https://doi.org/10.1200/JCO.2013.54.2050
Mather KA, Jorm AF, Parslow RA, Christensen H (2011) Is telomere length a biomarker of aging? A review. J Gerontol A Biol Sci Med Sci 66(2):202–213. https://doi.org/10.1093/gerona/glq180
McDonald BC, Conroy SK, Ahles TA, West JD, Saykin AJ (2010) Gray matter reduction associated with systemic chemotherapy for breast cancer: a prospective MRI study. Breast Cancer Res Treat 123(3):819–828. https://doi.org/10.1007/s10549-010-1088-4
McDonald BC, Conroy SK, Smith DJ, West JD, Saykin AJ (2013) Frontal gray matter reduction after breast cancer chemotherapy and association with executive symptoms: a replication and extension study. Brain Behav Immun 30(Suppl):S117–S125. https://doi.org/10.1016/j.bbi.2012.05.007
Mor V, Laliberte L, Morris JN, Wiemann M (1984) The Karnofsky Performance Status Scale. An examination of its reliability and validity in a research setting. Cancer 53(9):2002–2007 http://www.ncbi.nlm.nih.gov/pubmed/6704925
Moses J (2004) Comprehensive Trail Making Test (CTMT)By Cecil R. Reynolds. Austin, Texas: PRO-ED, Inc. Arch Clin Neuropsychol 19(5):703–708. https://doi.org/10.1016/j.acn.2004.02.004
Newman LA (2017) Decision making in the surgical management of invasive breast cancer-part 1: lumpectomy, mastectomy, and contralateral prophylactic mastectomy. Oncology (Williston Park) 31(5):359–368 https://www.ncbi.nlm.nih.gov/pubmed/28512732
Ocampo, A., Reddy, P., Martinez-Redondo, P., Platero-Luengo, A., Hatanaka, F., Hishida, T., Li, M., Lam, D., Kurita, M., Beyret, E., Araoka, T., Vazquez-Ferrer, E., Donoso, D., Roman, J.L., Xu, J., Rodriguez Esteban, C., Nuñez, G., Nuñez-Delicado, E., Campistol, J.M., Guillen, I., Guillen, P. and Izpisua Belmonte, J.C. (2016). In vivo amelioration of aging hallmarks by partial reprogramming
Okita K, Ichisaka T, Yamanaka S (2007) Generation of germline-competent induced pluripotent stem cells. Nature 448(7151):313–317. https://doi.org/10.1038/nature05934
Ornish D, Lin J, Daubenmier J, Weidner G, Epel E, Kemp C et al (2008) Increased telomerase activity and comprehensive lifestyle changes: a pilot study. Lancet Oncol 9(11):1048–1057. https://doi.org/10.1016/S1470-2045(08)70234-1
Pare R, Shin JS, Lee CS (2016) Increased expression of senescence markers p14(ARF) and p16(INK4a) in breast cancer is associated with an increased risk of disease recurrence and poor survival outcome. Histopathology 69(3):479–491. https://doi.org/10.1111/his.12948
Park HS, Kim CJ, Kwak HB, No MH, Heo JW, Kim TW (2018) Physical exercise prevents cognitive impairment by enhancing hippocampal neuroplasticity and mitochondrial function in doxorubicin-induced chemobrain. Neuropharmacology 133:451–461. https://doi.org/10.1016/j.neuropharm.2018.02.013
Podolskiy DI, Lobanov AV, Kryukov GV, Gladyshev VN (2016) Analysis of cancer genomes reveals basic features of human aging and its role in cancer development. Nat Commun 7:12157–12112. https://doi.org/10.1038/ncomms12157
Reuter M, Schmansky NJ, Rosas HD, Fischl B (2012) Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61(4):1402–1418. https://doi.org/10.1016/j.neuroimage.2012.02.084
Rosas HD, Liu AK, Hersch S, Glessner M, Ferrante RJ, Salat DH, van der Kouwe A, Jenkins BG, Dale AM, Fischl B (2002) Regional and progressive thinning of the cortical ribbon in Huntington’s disease. Neurology 58(5):695–701 http://www.ncbi.nlm.nih.gov/pubmed/11889230
Ruff RM, Light RH, Parker SB, Levin HS (1996) Benton controlled oral word association test: reliability and updated norms. Arch Clin Neuropsychol 11(4):329–338 https://www.ncbi.nlm.nih.gov/pubmed/14588937
Salat DH, Buckner RL, Snyder AZ, Greve DN, Desikan RS, Busa E, Morris JC, Dale AM, Fischl B (2004) Thinning of the cerebral cortex in aging. Cereb Cortex 14(7):721–730. https://doi.org/10.1093/cercor/bhh032
Sanoff HK, Deal AM, Krishnamurthy J, Torrice C, Dillon P, Sorrentino J et al (2014) Effect of cytotoxic chemotherapy on markers of molecular age in patients with breast cancer. J Natl Cancer Inst 106(4):dju057. https://doi.org/10.1093/jnci/dju057
Saykin AJ, Ahles TA, Schoenfield JD (2003) Gray matter reduction on voxel-based morphometry in chemotherapy-treated cancer survivors. J Int Neuropsychol 9:246
Saykin AJ, de Ruiter MB, McDonald BC, Deprez S, Silverman DH (2013) Neuroimaging biomarkers and cognitive function in non-CNS cancer and its treatment: current status and recommendations for future research. Brain Imaging Behav 7(4):363–373. https://doi.org/10.1007/s11682-013-9283-7
Schmidt M (2012) Rey Auditory Verbal Learning Test (RAVLT): a handbook. Psychological Assessment Resources, Lutz, FL
Scuric Z, Carroll JE, Bower JE, Ramos-Perlberg S, Petersen L, Esquivel S, Hogan M, Chapman AM, Irwin MR, Breen EC, Ganz PA, Schiestl R (2017) Biomarkers of aging associated with past treatments in breast cancer survivors. NPJ Breast Cancer 3:50. https://doi.org/10.1038/s41523-017-0050-6
Simo M, Rifa-Ros X, Rodriguez-Fornells A, Bruna J (2013) Chemobrain: a systematic review of structural and functional neuroimaging studies. Neurosci Biobehav Rev 37(8):1311–1321. https://doi.org/10.1016/j.neubiorev.2013.04.015
Sosa V, Moline T, Somoza R, Paciucci R, Kondoh H, Me LL (2013) Oxidative stress and cancer: an overview. Ageing Res Rev 12(1):376–390. https://doi.org/10.1016/j.arr.2012.10.004
Untch M, Konecny GE, Paepke S, von Minckwitz G (2014) Current and future role of neoadjuvant therapy for breast cancer. Breast 23(5):526–537. https://doi.org/10.1016/j.breast.2014.06.004
Walhovd KB, Fjell AM, Reinvang I, Lundervold A, Dale AM, Eilertsen DE, Quinn BT, Salat D, Makris N, Fischl B (2005) Effects of age on volumes of cortex, white matter and subcortical structures. Neurobiol Aging 26(9):1261–1270; discussion 1275-8. https://doi.org/10.1016/j.neurobiolaging.2005.05.020
Wefel JS, Kesler SR, Noll KR, Schagen SB (2015) Clinical characteristics, pathophysiology, and management of noncentral nervous system cancer-related cognitive impairment in adults. CA Cancer J Clin 65(2):123–138. https://doi.org/10.3322/caac.21258
Wood WA, Krishnamurthy J, Mitin N, Torrice C, Parker JS, Snavely AC, Shea TC, Serody JS, Sharpless NE (2016) Chemotherapy and stem cell transplantation increase p16(INK4a) expression, a biomarker of T-cell aging. EBioMedicine 11:227–238. https://doi.org/10.1016/j.ebiom.2016.08.029
Yates JW, Chalmer B, McKegney FP (1980) Evaluation of patients with advanced cancer using the Karnofsky performance status (research support, U.S. Gov’t, P.H.S.). Cancer 45(8):2220–2224 http://www.ncbi.nlm.nih.gov/pubmed/7370963
Zheng F, Liu Y, Yuan Z, Gao X, He Y, Liu X et al (2018) Age-related changes in cortical and subcortical structures of healthy adult brains: a surface-based morphometry study. J Magn Reson Imaging. https://doi.org/10.1002/jmri.26037
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The authors wish to thank the faculty and staff at the Stanford University Richard M. Lucas Center for their assistance with neuroimaging acquisitions.
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This research was funded by a grant from the National Cancer Institute (National Institutes of Health, 1R01CA172145, MPIs: SK, OP).
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AH assisted with data analyses and interpretation and wrote the manuscript and edited the manuscript.
VR assisted with neuroimaging preprocessing, statistical analyses, and writing the manuscript.
RH provided consultation and interpretation regarding the relationships between medical/treatment variables and neurologic status and writing the manuscript.
MK provided consultation and interpretation regarding the clinical implications and writing the manuscript.
DB provided consultation regarding patient recruitment and approval for publication of the content.
OP co-supervised data acquisition and oversaw study coordination and provided editing of the manuscript.
SK is the PI for this study. She was responsible for to the conception or design of the work, the acquisition, statistical analysis and interpretation of data for the work, edited the manuscript and is the corresponding author.
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Henneghan, A., Rao, V., Harrison, R.A. et al. Cortical Brain Age from Pre-treatment to Post-chemotherapy in Patients with Breast Cancer. Neurotox Res 37, 788–799 (2020). https://doi.org/10.1007/s12640-019-00158-z
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DOI: https://doi.org/10.1007/s12640-019-00158-z