Abstract
Brain-predicted age difference scores are calculated by subtracting chronological age from ‘brain’ age, which is estimated using neuroimaging data. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted age difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age in each dataset: Dokuz Eylül University (n = 175), the Cognitive Reserve/Reference Ability Neural Network study (n = 380), and The Irish Longitudinal Study on Ageing (n = 487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brain-predicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ardila, A., Ostrosky-Solís, F., and Bernal, B. (2006). Cognitive testing toward the future: The example of semantic verbal fluency (ANIMALS). International Journal of Psychology, 41(5), 324–332. https://doi.org/10.1080/00207590500345542.
Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Stat. Surv., 4(0), 40–79. https://doi.org/10.1214/09-SS054.
Ashendorf, L., Jefferson, A. L., O’connor, M. K., CHAISSON, C., Green, R. C., Stern, R. A., et al. (2008). Trail making test errors in normal aging, mild cognitive impairment, and dementia. Archives of Clinical Neuropsychology, 23(2), 129–137. https://doi.org/10.1016/j.acn.2007.11.005.
Azor, A. M., Cole, J. H., Holland, A. J., Dumba, M., Patel, M. C., Sadlon, A., Goldstone, A. P., & Manning, K. E. (2019). Increased brain age in adults with Prader-Willi syndrome. Neuroimage Clin., 21, 101664. https://doi.org/10.1016/j.nicl.2019.101664.
Bartels, C., Wegrzyn, M., Wiedl, A., Ackermann, V., & Ehrenreich, H. (2010). Practice effects in healthy adults: A longitudinal study on frequent repetitive cognitive testing. BMC Neuroscience, 11, 118. https://doi.org/10.1186/1471-2202-11-118.
Beheshti, I., Maikusa, N., & Matsuda, H. (2018). The association between “brain-age score” (BAS) and traditional neuropsychological screening tools in Alzheimer’s disease. Brain and Behavior: A Cognitive Neuroscience Perspective, 8(8), e01020. https://doi.org/10.1002/brb3.1020.
Benton, A. L., Varney, N. R., & Hamsher, K. D. (1978). Visuospatial judgment. A clinical test. Arch. Neurol., 35(6), 364–367 Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/655909.
Bland, J. M., & Altman, D. G. (2011). Correlation in restricted ranges of data. BMJ, 342, d556. https://doi.org/10.1136/bmj.d556.
Ble, A., Volpato, S., Zuliani, G., Guralnik, J. M., Bandinelli, S., Lauretani, F., Bartali, B., Maraldi, C., Fellin, R., & Ferrucci, L. (2005). Executive function correlates with walking speed in older persons: The InCHIANTI study. Journal of the American Geriatrics Society, 53(3), 410–415. https://doi.org/10.1111/j.1532-5415.2005.53157.x.
Bunea, F., She, Y., Ombao, H., Gongvatana, A., Devlin, K., & Cohen, R. (2011). Penalized least squares regression methods and applications to neuroimaging. NeuroImage, 55(4), 1519–1527. https://doi.org/10.1016/j.neuroimage.2010.12.028.
Buschke, H., & Fuld, P. A. (1974). Evaluating storage, retention, and retrieval in disordered memory and learning. Neurology, 24(11), 1019–1025. https://doi.org/10.1212/WNL.24.11.1019.
Butler, R. N., Sprott, R., Warner, H., Bland, J., Feuers, R., Forster, M., et al. (2004). Aging: The reality: Biomarkers of aging: From primitive organisms to humans. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 59(6), B560–B567. https://doi.org/10.1093/gerona/59.6.B560.
Chanraud, S., Martelli, C., Delain, F., Kostogianni, N., Douaud, G., Aubin, H. J., Reynaud, M., & Martinot, J. L. (2007). Brain Morphometry and cognitive performance in detoxified alcohol-dependents with preserved psychosocial functioning. Neuropsychopharmacology, 32(2), 429–438. https://doi.org/10.1038/sj.npp.1301219.
Ciulli, S., Citi, L., Salvadori, E., Valenti, R., Poggesi, A., Inzitari, D., et al. (2016). Prediction of impaired performance in trail making test in MCI patients with small vessel disease using DTI data. IEEE J. Biomed. Health, 20(4), 1026–1033. https://doi.org/10.1109/JBHI.2016.2537808.
Clark, L. J., Gatz, M., Zheng, L., Chen, Y.-L., McCleary, C., & Mack, W. J. (2009). Longitudinal verbal fluency in Normal aging, preclinical, and prevalent Alzheimer’s disease. American Journal of Alzheimer's Disease and Other Dementias, 24(6), 461–468. https://doi.org/10.1177/1533317509345154.
Cole, J. H., Franke, K., & Cherbuin, N. (2019). Quantification of the biological age of the brain using neuroimaging. In A. Moskalev (Ed.), Biomarkers of human aging (pp. 293–328). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-24970-0_19.
Cole, J. H., Leech, R., & Sharp, D. J. (2015). Prediction of brain age suggests accelerated atrophy after traumatic brain injury. Annals of Neurology, 77(4), 571–581. https://doi.org/10.1002/ana.24367.
Cole, J. H., Poudel, R. P. K., Tsagkrasoulis, D., Caan, M. W. A., Steves, C., Spector, T. D., & Montana, G. (2017a). Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage, 163, 115–124. https://doi.org/10.1016/j.neuroimage.2017.07.059.
Cole, J. H., Ritchie, S. J., Bastin, M. E., Valdés Hernández, M. C., Muñoz Maniega, S., Royle, N., Corley, J., Pattie, A., Harris, S. E., Zhang, Q., Wray, N. R., Redmond, P., Marioni, R. E., Starr, J. M., Cox, S. R., Wardlaw, J. M., Sharp, D. J., & Deary, I. J. (2018). Brain age predicts mortality. Molecular Psychiatry, 23(5), 1385–1392. https://doi.org/10.1038/mp.2017.62.
Cole, J. H., Underwood, J., Caan, M. W. A. A., De Francesco, D., van Zoest, R. A., Leech, R., et al. (2017b). Increased brain-predicted aging in treated HIV disease. Neurology, 88(14), 1349–1357. https://doi.org/10.1212/WNL.0000000000003790.
Cooper, D. B., Epker, M., Lacritz, L., Weiner, M., Rosenberg, R. N., Honig, L., & Cullum, C. M. (2001). Effects of practice on category fluency in Alzheimers disease*. The Clinical Neuropsychologist, 15(1), 125–128. https://doi.org/10.1076/clin.15.1.125.1914.
Cruz-Almeida, Y., Fillingim, R. B., Riley, J. L. I. I. I., Woods, A. J., Porges, E., Cohen, R., & Cole, J. (2019). Chronic pain is associated with a brain aging biomarker in community-dwelling older adults. PAIN, 160(5), 1119–1130. https://doi.org/10.1097/j.pain.0000000000001491.
D’Elia, L. F., Satz, P., Uchiyama, C. L., & White, T. (1996). Color trials test. Professional manual. Odessa: Psychological Assessment Resources.
Dean, W., & Morgan, R. F. (1988). In defense of the concept of biological aging measurement--current status. Archives of Gerontology and Geriatrics, 7(3), 191–210. https://doi.org/10.1016/0167-4943(88)90002-7.
Dickerson, B. C., Fenstermacher, E., Salat, D. H., Wolk, D. A., Maguire, R. P., Desikan, R., Pacheco, J., Quinn, B. T., van der Kouwe, A., Greve, D. N., Blacker, D., Albert, M. S., Killiany, R. J., & Fischl, B. (2008). Detection of cortical thickness correlates of cognitive performance: Reliability across MRI scan sessions, scanners, and field strengths. NeuroImage, 39(1), 10–18. https://doi.org/10.1016/j.neuroimage.2007.08.042.
Dohmatob, E., Eickenberg, M., Thirion, B., & Varoquaux, G. (2015). Speeding-up model-selection in Graphnet via early-stopping and Univariate feature-screening. In 2015 International Workshop on Pattern Recognition in NeuroImaging (pp. 17–20). IEEE. https://doi.org/10.1109/PRNI.2015.19.
Dorrian, J., Rogers, N., & Dinges, D. (2005). Psychomotor vigilance performance: Neurocognitive assay sensitive to sleep loss. In C. A. Kushida (Ed.), Sleep deprivation: Clinical issues, pharmacology, and sleep loss effects (Vol. 193, pp. 39–70). Boca Raton: CRC Press.
Doshi-Velez, F., & Kim, B. (2017). Towards A Rigorous Science of Interpretable Machine Learning. Retrieved from http://arxiv.org/abs/1702.08608
Dubois, M., Hadj-Selem, F., Lofstedt, T., Perrot, M., Fischer, C., Frouin, V., & Duchesnay, E. (2014). Predictive support recovery with TV-elastic net penalty and logistic regression: An application to structural MRI. In Proceedings - 2014 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2014 (pp. 1–4). Tubingen. https://doi.org/10.1109/PRNI.2014.6858517.
Dugbartey, A. T., Townes, B. D., & Mahurin, R. K. (2000). Equivalence of the color trails test and trail making test in nonnative English-speakers. Archives of Clinical Neuropsychology, 15(5), 425–431. https://doi.org/10.1016/S0887-6177(99)00034-7.
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology, 14, 91–118. https://doi.org/10.1146/annurev-clinpsy-032816-045037.
Eastman, J. A., Hwang, K. S., Lazaris, A., Chow, N., Ramirez, L., Babakchanian, S., et al. (2013). Cortical thickness and semantic fluency in Alzheimer’s disease and mild cognitive impairment. Am. J. Alzheimers Dis. (Columbia), 1(2), 81–92. https://doi.org/10.7726/ajad.2013.1006.
Elkin-Frankston, S., Lebowitz, B. K., Kapust, L. R., Hollis, H. H., & O’Connor, M. G. (2007). The use of the color trails test in the assessment of driver competence: Preliminary report of a culture-fair instrument. Archives of Clinical Neuropsychology, 22(5), 631–635. https://doi.org/10.1016/j.acn.2007.04.004.
Elman, J. A., Jak, A. J., Panizzon, M. S., Tu, X. M., Chen, T., Reynolds, C. A., Gustavson, D. E., Franz, C. E., Hatton, S. N., Jacobson, K. C., Toomey, R., McKenzie, R., Xian, H., Lyons, M. J., & Kremen, W. S. (2018). Underdiagnosis of mild cognitive impairment: A consequence of ignoring practice effects. Alzheimers Dement. (Amst), 10, 372–381. https://doi.org/10.1016/J.DADM.2018.04.003.
Emek-Savaş, D., Yerlikaya, D., Yener, G., & Öktem, Ö. (2019). Validity, reliability and norm scores of the Stroop test Çapa version. Türk Psikiyatri Dergisi, in press. https://doi.org/10.5080/u23549.
Emery, V. O. B., Gillie, E. X., & Smith, J. A. (1996). Reclassification of the vascular dementias: Comparisons of infarct and noninfarct vascular dementias. International Psychogeriatrics, 8(1), 33–61. https://doi.org/10.1017/S1041610296002475.
Farokhian, F., Yang, C., Beheshti, I., Matsuda, H., & Wu, S. (2017). Age-related gray and White matter changes in Normal adult brains. Aging and Disease, 8(6), 899–909. https://doi.org/10.14336/AD.2017.0502.
Feeney, J., Savva, G. M., O’Regan, C., King-Kallimanis, B., Cronin, H., & Kenny, R. A. (2016). Measurement error, reliability, and minimum detectable change in the mini-mental state examination, Montreal cognitive assessment, and color trails test among community living middle-aged and older adults. Journal of Alzheimer's Disease, 53(3), 1107–1114. https://doi.org/10.3233/JAD-160248.
Fiorito, G., McCrory, C., Robinson, O., Carmeli, C., Rosales, C. O., Zhang, Y., et al. (2019). Socioeconomic position, lifestyle habits and biomarkers of epigenetic aging: A multi-cohort analysis. Aging, 11(7), 2045–2070. https://doi.org/10.18632/aging.101900.
Fjell, A. M., Westlye, L. T., Grydeland, H., Amlien, I., Espeseth, T., Reinvang, I., et al. (2013). Critical ages in the life course of the adult brain: Nonlinear subcortical aging. Neurobiology of Aging, 34(10), 2239–2247. https://doi.org/10.1016/j.neurobiolaging.2013.04.006.
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198. https://doi.org/10.1016/0022-3956(75)90026-6.
Franke, K., & Gaser, C. (2012). Longitudinal changes in individual BrainAGE in healthy aging, mild cognitive impairment, and Alzheimer’s disease. GeroPsych, 25(4), 235–245. https://doi.org/10.1024/1662-9647/a000074.
Franke, K., Gaser, C., Manor, B., & Novak, V. (2013). Advanced BrainAGE in older adults with type 2 diabetes mellitus. Frontiers in Aging Neuroscience, 5, 90. https://doi.org/10.3389/fnagi.2013.00090.
Franke, K., Ristow, M., Gaser, C., & Alzheimer’s Disease Neuroimaging Initiative. (2014). Gender-specific impact of personal health parameters on individual brain aging in cognitively unimpaired elderly subjects. Frontiers in Aging Neuroscience, 6, 94. https://doi.org/10.3389/fnagi.2014.00094.
Franke, K., Ziegler, G., Klöppel, S., & Gaser, C. (2010). Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters. Neuroimage, 50(3), 883–892. https://doi.org/10.1016/j.neuroimage.2010.01.005.
Galasko, D., Abramson, I., Corey-Bloom, J., & Thal, L. J. (1993). Repeated exposure to the mini-mental state examination and the information-memory-concentration test results in a practice effect in Alzheimer’s disease. Neurology, 43(8), 1559–1563. https://doi.org/10.1212/wnl.43.8.1559.
Gaser, C., Franke, K., Klöppel, S., Koutsouleris, N., Sauer, H., & Alzheimer’s Disease Neuroimaging Initiative. (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.
Ge, Y., Grossman, R. I., Babb, J. S., Rabin, M. L., Mannon, L. J., & Kolson, D. L. (2002). Age-related total gray matter and white matter changes in normal adult brain. Part I: Volumetric MR imaging analysis. AJNR Am. J. Neuroradiol., 23(8), 1327–1333. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12223373.
Golden, C. J. (1978). Stroop color and word test: A manual for clinical and experimental uses. Chicago: Stoelting. https://doi.org/10.1002
Goodwin, L. D., & Leech, N. L. (2006). Understanding correlation: Factors that affect the size of r. The Journal of Experimental Education, 74(3), 249–266. https://doi.org/10.3200/JEXE.74.3.249-266.
Green, R. C., Woodard, J. L., & Green, J. (1995). Validity of the Mattis dementia rating scale for detection of cognitive impairment in the elderly. The Journal of Neuropsychiatry and Clinical Neurosciences, 7(3), 357–360. https://doi.org/10.1176/jnp.7.3.357.
Grober, E., & Sliwinski, M. (1991). Development and validation of a model for estimating premorbid verbal intelligence in the elderly. Journal of Clinical and Experimental Neuropsychology. https://doi.org/10.1080/01688639108405109.
Guggenmos, M., Schmack, K., Sekutowicz, M., Garbusow, M., Sebold, M., Sommer, C., et al. (2017). Quantitative neurobiological evidence for accelerated brain aging in alcohol dependence. Transl. Psychiatry, 7(12), 1279. https://doi.org/10.1038/s41398-017-0037-y.
Guo, Q. H., Cao, X. Y., Zhou, Y., Zhao, Q. H., Ding, D., & Hong, Z. (2010). Application study of quick cognitive screening test in identifying mild cognitive impairment. Neuroscience Bulletin, 26(1), 47–54. https://doi.org/10.1007/s12264-010-0816-4.
Gutierrez Becker, B., Klein, T., & Wachinger, C. (2018). Gaussian process uncertainty in age estimation as a measure of brain abnormality. Neuroimage, 175, 246–258. https://doi.org/10.1016/J.NEUROIMAGE.2018.03.075.
Han, L. K. M., Dinga, R., Hahn, T., Ching, C., Eyler, L., Aftanas, L., et al. (2019). Brain aging in major depressive disorder: Results from the ENIGMA major depressive disorder working group. BioRxiv, 560623. https://doi.org/10.1101/560623.
Harrison, J. E., Buxton, P., Husain, M., & Wise, R. (2000). Short test of semantic and phonological fluency: Normal performance, validity and test-retest reliability. Br. J. Clin. Psychol., (2), 181–191. https://doi.org/10.1348/014466500163202.
Heaton, R. K. K., Chelune, G. J., Talley, J. L., Kay, G. G., & Curtiss, G. (1993). Wisconsin card sorting test manual: Revised and expanded. Psychological reports. Odessa, FL: Psychological Assessment Resources. https://doi.org/10.2466/pr0.1995.76.2.623.
Honeine, P., & Richard, C. (2009). Solving the pre-image problem in kernel machines: A direct method. In 2009 IEEE International Workshop on Machine Learning for Signal Processing (pp. 1–6). https://doi.org/10.1109/MLSP.2009.5306204.
Irimia, A., Torgerson, C. M., Goh, S.-Y. M., & Van Horn, J. D. (2015). Statistical estimation of physiological brain age as a descriptor of senescence rate during adulthood. Brain Imaging and Behavior, 9(4), 678–689. https://doi.org/10.1007/s11682-014-9321-0.
Jollans, L., Boyle, R., Artiges, E., Banaschewski, T., Desrivières, S., Grigis, A., et al. (2019). Quantifying performance of machine learning methods for neuroimaging data. Neuroimage. https://doi.org/10.1016/J.NEUROIMAGE.2019.05.082.
Jollans, L., & Whelan, R. (2016). The clinical added value of imaging: A perspective from outcome prediction. Biol. Psychiatry Cogn. Neurosci. Neuroimaging, 1(5), 423–432. https://doi.org/10.1016/j.bpsc.2016.04.005.
Jollans, L., & Whelan, R. (2018). Neuromarkers for mental disorders: Harnessing population neuroscience. Frontiers in Psychiatry, 9, 242. https://doi.org/10.3389/fpsyt.2018.00242.
Jurica, P. J., Leitten, C. L., & Mattis, S. (2001). DRS-2: Dementia rating Scale-2: Professional manual. Psychological assessment resources. Retrieved from https://books.google.ie/books?id=tovFPwAACAAJ
Kaufmann, T., van der Meer, D., Doan, N. T., Schwarz, E., Lund, M. J., Agartz, I., et al. (2019). Common brain disorders are associated with heritable patterns of apparent aging of the brain. Nature Neuroscience, 22(10), 1617–1623. https://doi.org/10.1038/s41593-019-0471-7.
Kearney, P. M., Cronin, H., O’Regan, C., Kamiya, Y., Savva, G. M., Whelan, B., & Kenny, R. (2011). Cohort profile: The Irish longitudinal study on ageing. International Journal of Epidemiology, 40(4), 877–884. https://doi.org/10.1093/ije/dyr116.
Koutsouleris, N., Davatzikos, C., Borgwardt, S., Gaser, C., Bottlender, R., Frodl, T., Falkai, P., Riecher-Rössler, A., Möller, H. J., Reiser, M., Pantelis, C., & Meisenzahl, E. (2014). Accelerated brain aging in schizophrenia and beyond: A neuroanatomical marker of psychiatric disorders. Schizophrenia Bulletin, 40(5), 1140–1153. https://doi.org/10.1093/schbul/sbt142.
Kwok, J. T., & Tsang, I. W. (2004). The pre-image problem in kernel methods. IEEE Transactions on Neural Networks, 15(6), 1517–1525. https://doi.org/10.1109/TNN.2004.837781.
Lancaster, J., Lorenz, R., Leech, R., & Cole, J. H. (2018). Bayesian optimization for neuroimaging pre-processing in brain age classification and prediction. Front. Aging Neurosci., 10(FEB), 1–10. https://doi.org/10.3389/fnagi.2018.00028.
Le, T. T., Kuplicki, R. T., McKinney, B. A., Yeh, H.-W., Thompson, W. K., Paulus, M. P., & Tulsa 1000 Investigators, T. 1000. (2018). A nonlinear simulation framework supports adjusting for age when analyzing BrainAGE. Frontiers in Aging Neuroscience, 10, 317. https://doi.org/10.3389/fnagi.2018.00317.
Lee, T. M. C., Cheung, C. C. Y., Chan, J. K. P., & Chan, C. C. H. (2000). Trail making across languages. Journal of Clinical and Experimental Neuropsychology, 22(6), 772–778. https://doi.org/10.1076/jcen.22.6.772.954.
Liem, F., Varoquaux, G., Kynast, J., Beyer, F., Kharabian Masouleh, S., Huntenburg, J. M., Lampe, L., Rahim, M., Abraham, A., Craddock, R. C., Riedel-Heller, S., Luck, T., Loeffler, M., Schroeter, M. L., Witte, A. V., Villringer, A., & Margulies, D. S. (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.
Lin, Y. C., Shih, Y. C., Tseng, W. Y. I., Chu, Y. H., Wu, M. T., Chen, T. F., et al. (2014). Cingulum correlates of cognitive functions in patients with mild cognitive impairment and early Alzheimer’s disease: A diffusion Spectrum imaging study. Brain Topography, 27(3), 393–402. https://doi.org/10.1007/s10548-013-0346-2.
Lipton, Z. C. (2018). The mythos of model interpretability. ACM Queue, 16(3), 30:31–30:57. https://doi.org/10.1145/3236386.3241340.
Liu, W., & Li, Q. (2017). An efficient elastic net with regression coefficients method for variable selection of Spectrum data. PLoS One, 12(2), e0171122. https://doi.org/10.1371/journal.pone.0171122.
Lou, Y., Caruana, R., & Gehrke, J. (2012). Intelligible models for classification and regression. In Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 150–158). New York: ACM. https://doi.org/10.1145/2339530.2339556.
Löwe, L. C., Gaser, C., & Franke, K. (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.
Luders, E., Cherbuin, N., & Gaser, C. (2016). Estimating brain age using high-resolution pattern recognition: Younger brains in long-term meditation practitioners. Neuroimage, 134, 508–513. https://doi.org/10.1016/j.neuroimage.2016.04.007.
Luo, Y., Tseng, H.-H., Cui, S., Wei, L., Ten Haken, R. K., & El Naqa, I. (2019). Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling. BJR|Open. https://doi.org/10.1259/bjro.20190021.
Madan, C. R., & Kensinger, E. A. (2018). Predicting age from cortical structure across the lifespan. The European Journal of Neuroscience, 47(5), 399–416. https://doi.org/10.1111/ejn.13835.
Mateos-Pérez, J. M., Dadar, M., Lacalle-Aurioles, M., Iturria-Medina, Y., Zeighami, Y., & Evans, A. C. (2018). Structural neuroimaging as clinical predictor: A review of machine learning applications. Neuroimage Clin., 20, 506–522. https://doi.org/10.1016/j.nicl.2018.08.019.
McCaffrey, R. J., & Westervelt, H. J. (1995). Issues associated with repeated neuropsychological assessments. Neuropsychology Review, 5(3), 203–221. https://doi.org/10.1007/BF02214762.
McCrory, C., & Kenny, R. A. (2018). Rebuking the concept of ageing as a disease. Lancet Diabetes Endocrinol., 6(10), 768. https://doi.org/10.1016/S2213-8587(18)30266-3.
McIntyre, R. S., Cha, D. S., Soczynska, J. K., Woldeyohannes, H. O., Gallaugher, L. A., Kudlow, P., et al. (2013). Cognitive deficits and functional outcomes in major depressive disorder: Determinants, substrates, and treatment interventions. Depression and Anxiety, 30(6), 515–527. https://doi.org/10.1002/da.22063.
Mendoza, J. L., & Mumford, M. (1987). Corrections for attenuation and range restriction on the predictor. Journal of Educational Statistics, 12(3), 282. https://doi.org/10.2307/1164688.
Messinis, L., Malegiannaki, A.-C., Christodoulou, T., Panagiotopoulos, V., & Papathanasopoulos, P. (2011). Color trails test: Normative data and criterion validity for the Greek adult population. Archives of Clinical Neuropsychology, 26(4), 322–330. https://doi.org/10.1093/arclin/acr027.
Miciak, J., Taylor, W. P., Stuebing, K. K., Fletcher, J. M., & Vaughn, S. (2016). Designing intervention studies: Selected populations, range restrictions, and statistical power. J. Res. Educ. Eff., 9(4), 556–569. https://doi.org/10.1080/19345747.2015.1086916.
Mitrushina, M. N., Boone, K. B., Razani, J. L., & D’Elia, L. F. (2005). Handbook of normative data for neuropsychological assessment (second). New York: Oxford University Press. Retrieved from https://global.oup.com/academic/product/handbook-of-normative-data-for-neuropsychological-assessment-9780195169300?cc=ie&lang=en&
Mwangi, B., Tian, T. S., & Soares, J. C. (2014). A review of feature reduction techniques in neuroimaging. Neuroinformatics, 12(2), 229–244. https://doi.org/10.1007/s12021-013-9204-3.
Nelson, H. E., & Willinson, J. (1982). The National Adult Reading Test (NART): Test manual. Windsor, UK: NFER: Nelson.
Nenadić, I., Dietzek, M., Langbein, K., Sauer, H., & Gaser, C. (2017). BrainAGE score indicates accelerated brain aging in schizophrenia, but not bipolar disorder. Psychiatry Research: Neuroimaging, 266, 86–89. https://doi.org/10.1016/j.pscychresns.2017.05.006.
Öktem, O. (1992). A verbal test of memory processes: A preliminary study. Noro Psikiyatri Arsivi, 29(4), 196–206.
Pfeffer, R. I., Kurosaki, T. T., Chance, J. M., Filos, S., & Bates, D. (1984). Use of the mental function index in older adults: Reliability, validity, and measurement of change over time. American Journal of Epidemiology, 120(6), 922–935. https://doi.org/10.1093/oxfordjournals.aje.a113963.
Ranganathan, P., Pramesh, C. S., & Buyse, M. (2016). Common pitfalls in statistical analysis: The perils of multiple testing. Perspectives in Clinical Research, 7(2), 106–107. https://doi.org/10.4103/2229-3485.179436.
Reitan, R. M. (1955). The relation of the trail making test to organic brain damage. Journal of Consulting Psychology. https://doi.org/10.1037/h0044509.
Richard, G., Kolskår, K., Sanders, A.-M., Kaufmann, T., Petersen, A., Doan, N. T., Monereo Sánchez, J., Alnæs, D., Ulrichsen, K. M., Dørum, E. S., Andreassen, O. A., Nordvik, J. E., & Westlye, L. T. (2018). Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry. PeerJ, 6, e5908. https://doi.org/10.7717/peerj.5908.
Robertson, I. H., Manly, T., Andrade, J., Baddeley, B. T., & Yiend, J. (1997). ‘oops!’: Performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia, 35(6), 747–758. https://doi.org/10.1016/S0028-3932(97)00015-8.
Rodríguez-Aranda, C., Waterloo, K., Johnsen, S. H., Eldevik, P., Sparr, S., Wikran, G. C., Herder, M., & Vangberg, T. R. (2016). Neuroanatomical correlates of verbal fluency in early Alzheimer’s disease and normal aging. Brain and Language, 155–156, 24–35. https://doi.org/10.1016/J.BANDL.2016.03.001.
Sackett, P. R., & Yang, H. (2000). Correction for range restriction: An expanded typology. The Journal of Applied Psychology, 85(1), 112–118. https://doi.org/10.1037/0021-9010.85.1.112.
Saeb, S., Lonini, L., Jayaraman, A., Mohr, D. C., & Kording, K. P. (2016). Voodoo machine learning for clinical predictions. BioRxiv, 059774. https://doi.org/10.1101/059774.
Santos Nogueira, D., Azevedo Reis, E., & Vieira, A. (2016). Verbal fluency tasks: Effects of age, gender, and education. Folia Phoniatr. Logo., 68(3), 124–133. https://doi.org/10.1159/000450640.
Savva, G. M., Maty, S. C., Setti, A., & Feeney, J. (2013). Cognitive and physical health of the older populations of England, the United States, and Ireland: International comparability of the Irish longitudinal study on ageing. Journal of the American Geriatrics Society, 61, S291–S298. https://doi.org/10.1111/jgs.12196.
Scheinost, D., Noble, S., Horien, C., Greene, A. S., Lake, E. M., Salehi, M., Gao, S., Shen, X., O'Connor, D., Barron, D. S., Yip, S. W., Rosenberg, M. D., & Constable, R. T. (2019). Ten simple rules for predictive modeling of individual differences in neuroimaging. Neuroimage, 193, 35–45. https://doi.org/10.1016/J.NEUROIMAGE.2019.02.057.
Scheller, E., Schumacher, L. V, Peter, J., Lahr, J., Wehrle, J., Kaller, C. P., … Klöppel, S. (2018). Brain aging and APOE ε4 interact to reveal potential neuronal compensation in healthy older adults. Frontiers in Aging Neuroscience, 10, 74. https://doi.org/10.3389/fnagi.2018.00074.
Schnack, H. G., van Haren, N. E. M., Nieuwenhuis, M., Hulshoff Pol, H. E., Cahn, W., & Kahn, R. S. (2016). Accelerated brain aging in schizophrenia: A longitudinal pattern recognition study. The American Journal of Psychiatry, 173(6), 607–616. https://doi.org/10.1176/appi.ajp.2015.15070922.
Skocik, M., Collins, J., Callahan-Flintoft, C., Bowman, H., & Wyble, B. (2016). I tried a bunch of things: The dangers of unexpected overfitting in classification. BioRxiv, 078816. https://doi.org/10.1101/078816.
Smith, S. M., Vidaurre, D., Alfaro-Almagro, F., Nichols, T. E., & Miller, K. L. (2019). Estimation of brain Age Delta from brain imaging. Neuroimage. https://doi.org/10.1016/j.neuroimage.2019.06.017.
Snyder, J. C., Mika, S., Burke, K., & Müller, K.-R. (2013). Kernels, Pre-images and Optimization. In B. Schölkopf, Z. Luo, & V. Vovk (Eds.), Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik (pp. 245–259). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-41136-6_21.
Sprott, R. L. (2010). Biomarkers of aging and disease: Introduction and definitions. Experimental Gerontology, 45(1), 2–4. https://doi.org/10.1016/J.EXGER.2009.07.008.
Steffener, J., Habeck, C., O’Shea, D., Razlighi, Q., Bherer, L., & Stern, Y. (2016). Differences between chronological and brain age are related to education and self-reported physical activity. Neurobiology of Aging, 40, 138–144. https://doi.org/10.1038/nn.3945.Dopaminergic.
Stern, Y., Gazes, Y., Razlighi, Q., Steffener, J., & Habeck, C. (2018). A task-invariant cognitive reserve network. Neuroimage, 178, 36–45. https://doi.org/10.1016/J.NEUROIMAGE.2018.05.033.
Stern, Y., Habeck, C., Steffener, J., Barulli, D., Gazes, Y., Razlighi, Q., et al. (2014). The reference ability neural network study: Motivation, design, and initial feasibility analyses. Neuroimage, 103, 139–151. https://doi.org/10.1016/J.NEUROIMAGE.2014.09.029.
Strauss, E. H., Sherman, E. M. S., & Spreen, O. (2006). A compendium of neuropsychological tests; administration norms and commentary (3rd ed.). New York: Oxford University Press. https://doi.org/10.1016/j.jvolgeores.2008.06.015.
Tombaugh, T. N., & McIntyre, N. J. (1992). The mini-mental state examination: A comprehensive review. Journal of the American Geriatrics Society, 40(9), 922–935. https://doi.org/10.1111/j.1532-5415.1992.tb01992.x.
Varikuti, D. P., Genon, S., Sotiras, A., Schwender, H., Hoffstaedter, F., Patil, K. R., Jockwitz, C., Caspers, S., Moebus, S., Amunts, K., Davatzikos, C., & Eickhoff, S. B. (2018). Evaluation of non-negative matrix factorization of grey matter in age prediction. Neuroimage, 173(March), 394–410. https://doi.org/10.1016/j.neuroimage.2018.03.007.
Varoquaux, G., Raamana, P. R., Engemann, D. A., Hoyos-Idrobo, A., Schwartz, Y., & Thirion, B. (2017). Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. Neuroimage, 145, 166–179. https://doi.org/10.1016/J.NEUROIMAGE.2016.10.038.
Vaucher, P., Herzig, D., Cardoso, I., Herzog, M. H., Mangin, P., & Favrat, B. (2014). The trail making test as a screening instrument for driving performance in older drivers; a translational research. BMC Geriatrics, 14(1), 123. https://doi.org/10.1186/1471-2318-14-123.
Vazzana, R., Bandinelli, S., Lauretani, F., Volpato, S., Lauretani, F., Di Iorio, A., et al. (2010). Trail making test predicts physical impairment and mortality in older persons. Journal of the American Geriatrics Society, 58(4), 719–723. https://doi.org/10.1111/j.1532-5415.2010.02780.x.
Wechsler, D. (1987). Wechsler memory scale - revised manual. San Antonio, Texas: Psychological Corporation.
Wechsler, D. (1997). WAIS-III administration and scoring manual. The Psychological Corporation, San Antonio, Texas. https://doi.org/10.1177/1073191102009001003.
Whelan, B. J., & Savva, G. M. (2013). Design and methodology of the Irish longitudinal study on ageing. Journal of the American Geriatrics Society, 61, S265–S268. https://doi.org/10.1111/jgs.12199.
Whelan, R., & Garavan, H. (2014). When optimism hurts: Inflated predictions in psychiatric neuroimaging. Biological Psychiatry, 75(9), 746–748. https://doi.org/10.1016/j.biopsych.2013.05.014.
Willer, L., Pedersen, P. M., Forchhammer, H. B., & Christensen, H. (2016). Cognitive assessment at bedside for iPad: A preliminary validation of a novel cognitive test for stroke patients. European Stroke Journal, 1(4), 294–301. https://doi.org/10.1177/2396987316665233.
Wilson, B. A., Watson, P. C., Baddeley, A. D., Emslie, H., & Evans, J. J. (2000). Improvement or simply practice? The effects of twenty repeated assessments on people with and without brain injury. J. Int. Neuropsychol. Soc., 6(4), 469–479. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10902416.
Woo, C. W., Chang, L. J., Lindquist, M. A., & Wager, T. D. (2017). Building better biomarkers: Brain models in translational neuroimaging. Nature Neuroscience, 20(3), 365–377. https://doi.org/10.1038/nn.4478.
Woods, D. L., Wyma, J. M., Herron, T. J., & Yund, E. W. (2016). Computerized analysis of verbal fluency: Normative data and the effects of repeated testing, simulated malingering, and traumatic brain injury. PLoS One, 11(12), e0166439. https://doi.org/10.1371/journal.pone.0166439.
Zanetti, M., Ballabio, C., Abbate, C., Cutaia, C., Vergani, C., & Bergamaschini, L. (2006). Mild cognitive impairment subtypes and vascular dementia in community-dwelling elderly people: A 3-year follow-up study. Journal of the American Geriatrics Society, 54(4), 580–586. https://doi.org/10.1111/j.1532-5415.2006.00658.x.
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. J. R. Stat. Soc. Series B Stat. Methodol., 67(2), 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x.
Acknowledgements
The authors would like to thank all participants who participated in the various studies which are used here.
Funding
RB is supported by the Irish Research Council under grant number EPSPG/2017/277. LMRD and RW are supported by the Science Foundation Ireland under grant number 16/ERCD/3797. RR is supported by a PhD scholarship funded by the Region Calabria. Data collection in Dokuz Eylul University, managed and supervised by GGY and DDS, was partially supported by the Turkish National Science and Research Council (TUBITAK, Grant number: 112S459) and the Dokuz Eylul University Scientific Research Projects (Grant number: 2018.KB.SAG.084). The Irish Longitudinal Study on Ageing is funded by core grants from the Health Research Board, Atlantic Philanthropies and Irish Life. MRI data collection in TILDA was supported by the Centre for Advanced Medical Imaging (CAMI) at St. James’ Hospital, Dublin. IHR thanks The Atlantic Philanthropies for their grant to the Global Brain Health Institute. YS is supported by NIA RF1 AG038465 and R01 AG026158. The funding agencies had no involvement in the conduct of the research or preparation of the article.
Author information
Authors and Affiliations
Contributions
Author contributions included conception and study design (RW and RB), data collection or acquisition (GGY, DDES, YS, JPM, SPK, DC, and RAK), preprocessing and quality control of MRI data (RR and RB), statistical analysis (LMRD, LJ, RW, and RB), interpretation of results (RW and RB), drafting the manuscript work (RW and RB), revising the manuscript critically for important intellectual content (DC, DDES, IHR, LMRD, LJ, YS, RAK, RW and RB) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (All authors).
Corresponding author
Ethics declarations
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.
Conflict of interest
The authors report no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(DOCX 3196 kb)
Rights and permissions
About this article
Cite this article
Boyle, R., Jollans, L., Rueda-Delgado, L.M. et al. Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis. Brain Imaging and Behavior 15, 327–345 (2021). https://doi.org/10.1007/s11682-020-00260-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11682-020-00260-3