{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T13:04:04Z","timestamp":1771074244331,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T00:00:00Z","timestamp":1693958400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T00:00:00Z","timestamp":1693958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11517-023-02915-x","type":"journal-article","created":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T04:01:36Z","timestamp":1693972896000},"page":"3335-3344","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["3DCNN predicting brain age using diffusion tensor imaging"],"prefix":"10.1007","volume":"61","author":[{"given":"Yuqi","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jingxi","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Jiang","family":"Xin","sequence":"additional","affiliation":[]},{"given":"Yunhao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hua","family":"Xie","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5642-545X","authenticated-orcid":false,"given":"Yan","family":"Tang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,6]]},"reference":[{"key":"2915_CR1","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1212\/WNL.57.4.632","volume":"57","author":"M O\u2019Sullivan","year":"2001","unstructured":"O\u2019Sullivan M et al (2001) Evidence for cortical \u201cdisconnection\u201d as a mechanism of age-related cognitive decline. Neurology 57:4. https:\/\/doi.org\/10.1212\/WNL.57.4.632","journal-title":"Neurology"},{"issue":"1","key":"2915_CR2","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1073\/pnas.0135058100","volume":"100","author":"MD Greicius","year":"2018","unstructured":"Greicius MD et al (2018) Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceed Nat Acad Sci United States of America 100(1):253\u2013258. https:\/\/doi.org\/10.1073\/pnas.0135058100","journal-title":"Proceed Nat Acad Sci United States of America"},{"issue":"2","key":"2915_CR3","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.neuroimage.2009.01.068","volume":"46","author":"SW Davis","year":"2009","unstructured":"Davis SW et al (2009) Assessing the effects of age on long white matter tracts using diffusion tensor tractography. Neuroimage 46(2):530\u2013541. https:\/\/doi.org\/10.1016\/j.neuroimage.2009.01.068","journal-title":"Neuroimage"},{"key":"2915_CR4","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1038\/s41380-018-0098-1","volume":"24","author":"JH Cole","year":"2019","unstructured":"Cole JH et al (2019) Brain age and other bodily \u201cages\u201d: implications for neuropsychiatry. Molecular Psychiatry 24:266\u2013281. https:\/\/doi.org\/10.1038\/s41380-018-0098-1","journal-title":"Molecular Psychiatry"},{"key":"2915_CR5","doi-asserted-by":"publisher","unstructured":"Huang TW et al (2017) Age estimation from brain MRI images using deep learning. in IEEE International Symposium on Biomedical Imaging. https:\/\/doi.org\/10.1109\/ISBI.2017.7950650","DOI":"10.1109\/ISBI.2017.7950650"},{"issue":"5","key":"2915_CR6","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.1109\/jbhi.2016.2559938","volume":"20","author":"A Cherubini","year":"2016","unstructured":"Cherubini A et al (2016) Importance of multimodal MRI in characterizing brain tissue and its potential application for individual age prediction. Biomed Health Informatics, IEEE J 20(5):1232\u20131239. https:\/\/doi.org\/10.1109\/jbhi.2016.2559938","journal-title":"Biomed Health Informatics, IEEE J"},{"issue":"1","key":"2915_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-20655-6","volume":"12","author":"A Abrol","year":"2021","unstructured":"Abrol A et al (2021) Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning. Nature Comm 12(1):1\u201317. https:\/\/doi.org\/10.1038\/s41467-020-20655-6","journal-title":"Nature Comm"},{"key":"2915_CR8","doi-asserted-by":"publisher","unstructured":"Sarker IH (2021) Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN computer science 2(6):420. https:\/\/doi.org\/10.20944\/preprints202108.0060.v1","DOI":"10.20944\/preprints202108.0060.v1"},{"key":"2915_CR9","doi-asserted-by":"publisher","first-page":"185","DOI":"10.3389\/fnins.2019.00185","volume":"13","author":"J Xin","year":"2019","unstructured":"Xin J et al (2019) Brain differences between men and women: evidence from deep learning. Front neurosci 13:185. https:\/\/doi.org\/10.3389\/fnins.2019.00185","journal-title":"Front neurosci"},{"key":"2915_CR10","doi-asserted-by":"publisher","unstructured":"Feng X et al (2019) Estimating brain age based on a healthy population with deep learning and structural MRI. arXiv preprint arXiv:1907.00943. https:\/\/doi.org\/10.48550\/arXiv.1907.00943.","DOI":"10.48550\/arXiv.1907.00943"},{"key":"2915_CR11","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.neuroimage.2017.07.059","volume":"163","author":"JH Cole","year":"2017","unstructured":"Cole JH et al (2017) Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage 163:115\u2013124. https:\/\/doi.org\/10.1016\/j.neuroimage.2017.07.059","journal-title":"NeuroImage"},{"key":"2915_CR12","doi-asserted-by":"crossref","unstructured":"Feng X et al (2020) Estimating brain age based on a healthy population with deep learning and structural MRI. arXiv preprint arXiv:1907.00943. https:\/\/doi.org\/https:\/\/doi.org\/10.1016\/j.neurobiolaging.2020.02.009","DOI":"10.1016\/j.neurobiolaging.2020.02.009"},{"key":"2915_CR13","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.3389\/fneur.2019.01346","volume":"10","author":"H Jiang","year":"2020","unstructured":"Jiang H et al (2020) Predicting brain age of healthy adults based on structural MRI parcellation using convolutional neural networks. Front Neurol 10:1346. https:\/\/doi.org\/10.3389\/fneur.2019.01346","journal-title":"Frontiers in neurology"},{"key":"2915_CR14","doi-asserted-by":"publisher","first-page":"118871","DOI":"10.1016\/j.neuroimage.2022.118871","volume":"249","author":"DA Wood","year":"2022","unstructured":"Wood DA et al (2022) Accurate brain-age models for routine clinical MRI examinations. Neuroimage 249:118871. https:\/\/doi.org\/10.1016\/j.neuroimage.2022.118871","journal-title":"Neuroimage"},{"key":"2915_CR15","doi-asserted-by":"publisher","unstructured":"Peng H et al (2021) Accurate brain age prediction with lightweight deep neural networks. Medical image analysis 68:101871. https:\/\/doi.org\/10.1016\/j.media.2020.101871.","DOI":"10.1016\/j.media.2020.101871"},{"issue":"12","key":"2915_CR16","doi-asserted-by":"publisher","first-page":"3235","DOI":"10.1002\/hbm.25011","volume":"41","author":"G Levakov","year":"2020","unstructured":"Levakov G et al (2020) From a deep learning model back to the brain\u2014Identifying regional predictors and their relation to aging. Human brain mapping 41(12):3235\u20133252. https:\/\/doi.org\/10.1002\/hbm.25011","journal-title":"Human brain mapping"},{"issue":"46","key":"2915_CR17","doi-asserted-by":"publisher","first-page":"15425","DOI":"10.1523\/jneurosci.0203-14.2014","volume":"34","author":"CE Sexton","year":"2014","unstructured":"Sexton CE et al (2014) Accelerated changes in white matter microstructure during aging: a longitudinal diffusion tensor imaging study. J Neurosci 34(46):15425\u201315436. https:\/\/doi.org\/10.1523\/jneurosci.0203-14.2014","journal-title":"J Neurosci"},{"issue":"1","key":"2915_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/ncomms13629","volume":"7","author":"SR Cox","year":"2016","unstructured":"Cox SR et al (2016) Ageing and brain white matter structure in 3,513 UK Biobank participants. Nature Comm 7(1):1\u201313. https:\/\/doi.org\/10.1038\/ncomms13629","journal-title":"Nature Comm"},{"key":"2915_CR19","doi-asserted-by":"publisher","first-page":"117441","DOI":"10.1016\/j.neuroimage.2020.117441","volume":"224","author":"D Beck","year":"2021","unstructured":"Beck D et al (2021) White matter microstructure across the adult lifespan: a mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction. NeuroImage 224:117441. https:\/\/doi.org\/10.1016\/j.neuroimage.2020.117441","journal-title":"NeuroImage"},{"key":"2915_CR20","doi-asserted-by":"publisher","unstructured":"Chattopadhyay A et al (2018) Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. IEEE: 839\u2013847. https:\/\/doi.org\/10.1109\/WACV.2018.00097","DOI":"10.1109\/WACV.2018.00097"},{"issue":"9","key":"2915_CR21","doi-asserted-by":"publisher","first-page":"2302","DOI":"10.1002\/hbm.22065","volume":"34","author":"AR Mayer","year":"2013","unstructured":"Mayer AR et al (2013) Functional imaging of the hemodynamic sensory gating response in schizophrenia. Human Brain Mapping 34(9):2302\u20132312. https:\/\/doi.org\/10.1002\/hbm.22065","journal-title":"Human Brain Mapping"},{"issue":"2","key":"2915_CR22","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1093\/cercor\/bhq111","volume":"21","author":"C Yan","year":"2011","unstructured":"Yan C et al (2011) Sex-and brain size\u2013related small-world structural cortical networks in young adults: a DTI tractography study. Cerebral cortex 21(2):449\u2013458. https:\/\/doi.org\/10.1093\/cercor\/bhq111","journal-title":"Cerebral cortex"},{"issue":"1","key":"2915_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12883-014-0204-1","volume":"14","author":"MA Shafto","year":"2014","unstructured":"Shafto MA et al (2014) The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC neurology 14(1):1\u201325. https:\/\/doi.org\/10.1186\/s12883-014-0204-1","journal-title":"BMC neurology"},{"key":"2915_CR24","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","volume":"80","author":"DC Van Essen","year":"2013","unstructured":"Van Essen DC et al (2013) The WU-Minn human connectome project: an overview. Neuroimage 80:62\u201379. https:\/\/doi.org\/10.1016\/j.neuroimage.2013.05.041","journal-title":"Neuroimage"},{"issue":"1","key":"2915_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.17","volume":"4","author":"W Liu","year":"2017","unstructured":"Liu W et al (2017) Longitudinal test-retest neuroimaging data from healthy young adults in southwest China. Scientific data 4(1):1\u20139. https:\/\/doi.org\/10.1038\/sdata.2017.17","journal-title":"Scientific data"},{"issue":"4","key":"2915_CR26","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1212\/wnl.78.1_meetingabstracts.p06.083","volume":"95","author":"K Marek","year":"2011","unstructured":"Marek K et al (2011) The Parkinson progression marker initiative (PPMI). Progress in neurobiology 95(4):629\u2013635. https:\/\/doi.org\/10.1212\/wnl.78.1_meetingabstracts.p06.083","journal-title":"Progress in neurobiology"},{"key":"2915_CR27","doi-asserted-by":"publisher","unstructured":"Jenkinson M et al (2012) Fsl. Neuroimage 62(2):782\u2013790. https:\/\/doi.org\/10.1016\/j.neuroimage.2011.09.015","DOI":"10.1016\/j.neuroimage.2011.09.015"},{"issue":"4","key":"2915_CR28","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1016\/j.neuroimage.2006.02.024","volume":"31","author":"SM Smith","year":"2006","unstructured":"Smith SM et al (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31(4):1487\u20131505. https:\/\/doi.org\/10.1016\/j.neuroimage.2006.02.024","journal-title":"Neuroimage"},{"issue":"6","key":"2915_CR29","first-page":"1384","volume":"7","author":"S Mori","year":"2006","unstructured":"Mori S et al (2006) MRI atlas of human white matter. Am JNeuroradiol 7(6):1384","journal-title":"Am JNeuroradiol"},{"issue":"4","key":"2915_CR30","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/tpami.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen L-C et al (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transact Pattern Anal Machine Intelligence 40(4):834\u2013848. https:\/\/doi.org\/10.1109\/tpami.2017.2699184","journal-title":"IEEE Transact Pattern Anal Machine Intelligence"},{"key":"2915_CR31","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.neuroimage.2016.10.038","volume":"145","author":"G Varoquaux","year":"2017","unstructured":"Varoquaux G et al (2017) Assessing and tuning brain decoders: cross-validation, caveats, and guidelines. NeuroImage 145:166\u2013179. https:\/\/doi.org\/10.1016\/j.neuroimage.2016.10.038","journal-title":"NeuroImage"},{"key":"2915_CR32","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization.  arXiv preprint arXiv:1412.6980. https:\/\/doi.org\/https:\/\/doi.org\/10.48550\/arXiv.1412.6980"},{"key":"2915_CR33","unstructured":"Abadi M et al (2016) Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467. https:\/\/doi.org\/https:\/\/doi.org\/10.48550\/arXiv.1603.04467"},{"issue":"1","key":"2915_CR34","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neuroimage.2008.03.061","volume":"44","author":"SM Smith","year":"2009","unstructured":"Smith SM, Nichols TE (2009) Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 44(1):83\u201398. https:\/\/doi.org\/10.1016\/j.neuroimage.2008.03.061","journal-title":"Neuroimage"},{"issue":"1","key":"2915_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/03610927408827101","volume":"3","author":"T Cali\u0144ski","year":"1974","unstructured":"Cali\u0144ski T, Harabasz J (1974) A dendrite method for cluster analysis. Comm Statistics-Theory Methods 3(1):1\u201327. https:\/\/doi.org\/10.1080\/03610927408827101","journal-title":"Comm Statistics-Theory Methods"},{"key":"2915_CR36","doi-asserted-by":"crossref","unstructured":"He K et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2915_CR37","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.neuroscience.2013.11.026","volume":"276","author":"IJ Bennett","year":"2014","unstructured":"Bennett IJ, Madden DJ (2014) Disconnected aging: cerebral white matter integrity and age-related differences in cognition. Neuroscience 276:187\u2013205. https:\/\/doi.org\/10.1016\/j.neuroscience.2013.11.026","journal-title":"Neuroscience"},{"issue":"3","key":"2915_CR38","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1016\/j.neuroimage.2010.03.004","volume":"51","author":"A Giorgio","year":"2010","unstructured":"Giorgio A et al (2010) Age-related changes in grey and white matter structure throughout adulthood. Neuroimage 51(3):943\u2013951. https:\/\/doi.org\/10.1016\/j.neuroimage.2010.03.004","journal-title":"Neuroimage"},{"key":"2915_CR39","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.neuroscience.2015.01.049","volume":"290","author":"L Marstaller","year":"2015","unstructured":"Marstaller L et al (2015) Aging and large-scale functional networks: white matter integrity, gray matter volume, and functional connectivity in the resting state. Neuroscience 290:369\u2013378. https:\/\/doi.org\/10.1016\/j.neuroscience.2015.01.049","journal-title":"Neuroscience"},{"key":"2915_CR40","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.neuroimage.2013.02.055","volume":"75","author":"B Mwangi","year":"2013","unstructured":"Mwangi B, Hasan KM, Soares JC (2013) Prediction of individual subject\u2019s age across the human lifespan using diffusion tensor imaging: a machine learning approach. Neuroimage 75:58\u201367. https:\/\/doi.org\/10.1016\/j.neuroimage.2013.02.055","journal-title":"Neuroimage"},{"key":"2915_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2013\/639430","volume":"2013","author":"MG Knyazeva","year":"2013","unstructured":"Knyazeva MG (2013) Splenium of corpus callosum: patterns of interhemispheric interaction in children and adults. Neural plasticity 2013:1\u201312. https:\/\/doi.org\/10.1155\/2013\/639430","journal-title":"Neural plasticity"},{"key":"2915_CR42","doi-asserted-by":"publisher","first-page":"71","DOI":"10.3389\/fnagi.2016.00071","volume":"8","author":"RA Kanaan","year":"2016","unstructured":"Kanaan RA et al (2016) White matter microstructural organization is higher with age in adult superior cerebellar peduncles. Frontiers Aging Neurosci 8:71. https:\/\/doi.org\/10.3389\/fnagi.2016.00071","journal-title":"Frontiers Aging Neurosci"},{"key":"2915_CR43","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.neurobiolaging.2020.09.005","volume":"96","author":"S Raghavan","year":"2020","unstructured":"Raghavan S et al (2020) Reduced fractional anisotropy of the genu of the corpus callosum as a cerebrovascular disease marker and predictor of longitudinal cognition in MCI. Neurobiology Aging 96:176\u2013183. https:\/\/doi.org\/10.1016\/j.neurobiolaging.2020.09.005","journal-title":"Neurobiology Aging"},{"issue":"6","key":"2915_CR44","doi-asserted-by":"publisher","first-page":"1698","DOI":"10.1016\/j.neuropsychologia.2007.12.010","volume":"46","author":"A Fellgiebel","year":"2008","unstructured":"Fellgiebel A et al (2008) Functional relevant loss of long association fibre tracts integrity in early Alzheimer\u2019s disease. Neuropsychologia 46(6):1698\u20131706. https:\/\/doi.org\/10.1016\/j.neuropsychologia.2007.12.010","journal-title":"Neuropsychologia"},{"key":"2915_CR45","doi-asserted-by":"publisher","unstructured":"Pareek V, Rallabandi VS, Roy PK (2018) A Correlational study between microstructural white matter properties and macrostructural gray matter volume across normal ageing: conjoint DTI and VBM analysis. Magnetic Resonance Insights. https:\/\/doi.org\/10.1177\/1178623x18799926","DOI":"10.1177\/1178623x18799926"},{"issue":"2","key":"2915_CR46","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1016\/j.neuroimage.2010.12.053","volume":"55","author":"M-C Chiang","year":"2011","unstructured":"Chiang M-C et al (2011) BDNF gene effects on brain circuitry replicated in 455 twins. Neuroimage 55(2):448\u2013454. https:\/\/doi.org\/10.1016\/j.neuroimage.2010.12.053","journal-title":"Neuroimage"},{"issue":"30","key":"2915_CR47","doi-asserted-by":"publisher","first-page":"10937","DOI":"10.1523\/jneurosci.5302-10.2011","volume":"31","author":"C Lebel","year":"2011","unstructured":"Lebel C, Beaulieu C (2011) Longitudinal development of human brain wiring continues from childhood into adulthood. J Neurosci 31(30):10937\u201310947. https:\/\/doi.org\/10.1523\/jneurosci.5302-10.2011","journal-title":"J Neurosci"},{"issue":"1","key":"2915_CR48","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.neuroimage.2008.11.010","volume":"45","author":"P Kochunov","year":"2009","unstructured":"Kochunov P et al (2009) Loss of cerebral white matter structural integrity tracks the gray matter metabolic decline in normal aging. Neuroimage 45(1):17\u201328. https:\/\/doi.org\/10.1016\/j.neuroimage.2008.11.010","journal-title":"Neuroimage"},{"issue":"47","key":"2915_CR49","doi-asserted-by":"publisher","first-page":"19006","DOI":"10.1073\/pnas.1313735110","volume":"110","author":"DC Glahn","year":"2013","unstructured":"Glahn DC et al (2013) Genetic basis of neurocognitive decline and reduced white-matter integrity in normal human brain aging. Proceed Nat Acad Sci 110(47):19006\u201319011. https:\/\/doi.org\/10.1073\/pnas.1313735110","journal-title":"Proceed Nat Acad Sci"},{"issue":"12","key":"2915_CR50","doi-asserted-by":"publisher","first-page":"3685","DOI":"10.1161\/strokeaha.109.565390","volume":"40","author":"P Kochunov","year":"2009","unstructured":"Kochunov P et al (2009) Analysis of genetic variability and whole genome linkage of whole-brain, subcortical, and ependymal hyperintense white matter volume. Stroke 40(12):3685\u20133690. https:\/\/doi.org\/10.1161\/strokeaha.109.565390","journal-title":"Stroke"},{"issue":"3","key":"2915_CR51","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1080\/87565641003696775","volume":"35","author":"BB Bendlin","year":"2010","unstructured":"Bendlin BB et al (2010) White matter in aging and cognition: a cross-sectional study of microstructure in adults aged eighteen to eighty-three. Developmental Neuropsychol 35(3):257\u2013277. https:\/\/doi.org\/10.1080\/87565641003696775","journal-title":"Developmental Neuropsychol"},{"key":"2915_CR52","doi-asserted-by":"publisher","first-page":"116831","DOI":"10.1016\/j.neuroimage.2020.116831","volume":"217","author":"C-L Chen","year":"2020","unstructured":"Chen C-L et al (2020) Generalization of diffusion magnetic resonance imaging\u2013based brain age prediction model through transfer learning. NeuroImage 217:116831. https:\/\/doi.org\/10.1016\/j.neuroimage.2020.116831","journal-title":"NeuroImage"},{"key":"2915_CR53","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.brainres.2013.03.028","volume":"1512","author":"R Kumar","year":"2013","unstructured":"Kumar R et al (2013) Brain axial and radial diffusivity changes with age and gender in healthy adults. Brain research 1512:22\u201336. https:\/\/doi.org\/10.1016\/j.brainres.2013.03.028","journal-title":"Brain research"},{"issue":"6","key":"2915_CR54","doi-asserted-by":"publisher","first-page":"1626","DOI":"10.1002\/hbm.24899","volume":"41","author":"X Niu","year":"2020","unstructured":"Niu X et al (2020) Improved prediction of brain age using multimodal neuroimaging data. Human brain mapping 41(6):1626\u20131643. https:\/\/doi.org\/10.1002\/hbm.24899","journal-title":"Human brain mapping"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02915-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02915-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02915-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T03:13:43Z","timestamp":1703301223000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02915-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,6]]},"references-count":54,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["2915"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02915-x","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,6]]},"assertion":[{"value":"12 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}