Shape-related characteristics of age-related differences in subcortical structures

References

• Adduru, V. R., Michael, A. M., Helguera, M., Baum, S. A., & Moore, G. J. (in press). Leveraging clinical imaging archives for radiomics: Reliability of automated methods for brain volume measurement. Radiology. doi:10.1148/radiol.2017161928
   Web of Science ®Google Scholar
• Allen, J. S., Bruss, J., Brown, C. K., & Damasio, H. (2005). Normal neuroanatomical variation due to age: The major lobes and a parcellation of the temporal region. Neurobiology of Aging, 26, 1245–1260. doi:10.1016/j.neurobiolaging.2005.05.023
   PubMed Web of Science ®Google Scholar
• Bae, C.-Y., Kang, Y. G., Suh, Y.-S., Han, J. H., Kim, S.-S., & Shim, K. W. (2013). A model for estimating body shape biological age based on clinical parameters associated with body composition. Clinical Interventions in Aging, 8, 11–18. doi:10.2147/CIA.S38220
   PubMed Web of Science ®Google Scholar
• Blinn, J. F. (1978). Simulation of wrinkled surfaces. SIGGRAPH Computer Graphics, 12, 286–292. doi:10.1145/965139.507101
   Google Scholar
• Blum, H. (1973). Biological shape and visual science (Part I). Journal of Theoretical Biology, 38, 205–287. doi:10.1016/0022-5193(73)90175-6
   PubMed Web of Science ®Google Scholar
• Brechbühler, C., Gerig, G., & Kübler, O. (1995). Parametrization of closed surfaces for 3-D shape description. Computer Vision and Image Understanding, 61, 154–170. doi:10.1006/cviu.1995.1013
   Web of Science ®Google Scholar
• Buckner, R. L., Head, D., Parker, J., Fotenos, A. F., Marcus, D., Morris, J. C., & Snyder, A. Z. (2004). A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: Reliability and validation against manual measurement of total intracranial volume. NeuroImage, 23, 724–738. doi:10.1016/j.neuroimage.2004.06.018
   PubMed Web of Science ®Google Scholar
• Burnham, K. P., Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods & Research, 33, 261–304. doi:10.1177/0049124104268644.
   Web of Science ®Google Scholar
• Chan, M. Y., Park, D. C., Savalia, N. K., Petersen, S. E., & Wig, G. S. (2014). Decreased segregation of brain systems across the healthy adult lifespan. Proceedings of the National Academy of Sciences, 111, E4997–E5006. doi:10.1073/pnas.1415122111
   PubMed Web of Science ®Google Scholar
• Chen, W., Qian, W., Wu, G., Chen, W., Xian, B., Chen, X., … Han, J.-D. J. (2015). Three-dimensional human facial morphologies as robust aging markers. Cell Research, 25, 574–587. doi:10.1038/cr.2015.36
   PubMed Web of Science ®Google Scholar
• Chung, M. K. (2013). Computational neuroanatomy: The methods. Toh Tuck Link, Singapore: World Scientific.
   Google Scholar
• Chung, M. K. (2014). Statistical and computational methods in brain image analysis. Boca Raton, FL: CRC Press.
   Google Scholar
• Chung, M. K., Nacewicz, B. M., Wang, S., Dalton, K. M., Pollak, S., & Davidson, R. J. (2008). Amygdala surface modeling with weighted spherical harmonics. In Dohi T., Sakuma I., Liao H. (Eds.), Lecture notes in computer science, 5128 (pp. 177–184). Berlin Heidelberg: Springer. doi:10.1007/978-3-540-79982-5_20
   Google Scholar
• Chung, M. K., Worsley, K. J., Nacewicz, B. M., Dalton, K. M., & Davidson, R. J. (2010). General multivariate linear modeling of surface shapes using SurfStat. NeuroImage, 53, 491–505. doi:10.1016/j.neuroimage.2010.06.032
   PubMed Web of Science ®Google Scholar
• Cong, S., Rizkalla, M., Du, E. Y., West, J., Risacher, S., Saykin, A., & Shen, L. (2014). Building a surface atlas of hippocampal subfields from MRI scans using FreeSurfer, FIRST and SPHARM. In IEEE 57th International Midwest Symposium on Circuits and Systems (MWSCAS). College Station, TX: IEEE. doi:10.1109/mwscas.2014.6908539
   Google Scholar
• de Miras, J. R., Costumero, V., Belloch, V., Escudero, J., Ávila, C., & Sepulcre, J. (in press). Complexity analysis of cortical surface detects changes in future Alzheimers disease converters. Human Brain Mapping. doi:10.1002/hbm.23773
   Google Scholar
• Di Ieva, A., Esteban, F. J., Grizzi, F., Klonowski, W., & Martín-Landrove, M. (2015). Fractals in the neurosciences, Part II: Clinical applications and future perspectives. The Neuroscientist, 21, 30–43. doi:10.1177/1073858413513928
   PubMed Web of Science ®Google Scholar
• Di Ieva, A., Grizzi, F., Jelinek, H., Pellionisz, A. J., & Losa, G. A. (2014). Fractals in the neurosciences, Part I: General principles and basic neurosciences. The Neuroscientist, 20, 403–417. doi:10.1177/1073858413513927
   PubMed Web of Science ®Google Scholar
• Fischl, B. (2012). FreeSurfer. NeuroImage, 62, 774–781. doi:10.1016/j.neuroimage.2012.01.021
   PubMed Web of Science ®Google Scholar
• Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences, 97, 11050–11055. doi:10.1073/pnas.200033797
   PubMed Web of Science ®Google Scholar
• Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., … Dale, A. M. (2002). Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron, 33, 341–355. doi:10.1016/s0896-6273(02)00569-x
   PubMed Web of Science ®Google Scholar
• Fischl, B., Salat, D. H., van der Kouwe, A. J. W., Makris, N., Ségonne, F., Quinn, B. T., & Dale, A. M. (2004). Sequence-independent segmentation of magnetic resonance images. NeuroImage, 23, S69–S84. doi:10.1016/j.neuroimage.2004.07.016
   PubMed Web of Science ®Google Scholar
• Fjell, A. M., Walhovd, K. B., Westlye, L. T., Østby, Y., Tamnes, C. K., Jernigan, T. L., … Dale, A. M. (2010). When does brain aging accelerate? Dangers of quadratic fits in cross-sectional studies. NeuroImage, 50, 1376–1383. doi:10.1016/j.neuroimage.2010.01.061
   PubMed Web of Science ®Google Scholar
• Fjell, A. M., Westlye, L. T., Grydeland, H., Amlien, I., Espeseth, T., Reinvang, I., … Walhovd, K. B. (2013). Critical ages in the life course of the adult brain: Nonlinear subcortical aging. Neurobiology of Aging, 34, 2239–2247. doi:10.1016/j.neurobiolaging.2013.04.006
   PubMed Web of Science ®Google Scholar
• Gerig, G., Styner, M., Jones, D., Weinberger, D., & Lieberman, J. (2001b). Shape analysis of brain ventricles using SPHARM. In Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001). College Station, TX: IEEE Comput. Soc. doi:10.1109/mmbia.2001.991731
   Google Scholar
• Gerig, G., Styner, M., Shenton, M. E., & Lieberman, J. A. (2001a). Shape versus size: Improved understanding of the morphology of brain structures. In W. J. Niessen & M. A. Viergever (Eds.), Medical image computing and computer-assisted intervention – MICCAI 2001 (pp. 24–32). Berlin: Springer. doi:10.1007/3-540-45468-3_4
   Google Scholar
• Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016). Radiomics: Images are more than pictures, they are data. Radiology, 278, 563–577. doi:10.1148/radiol.2015151169
   PubMed Web of Science ®Google Scholar
• Goodro, M., Sameti, M., Patenaude, B., & Fein, G. (2012). Age effect on subcortical structures in healthy adults. Psychiatry Research: Neuroimaging, 203, 38–45. doi:10.1016/j.pscychresns.2011.09.014
   PubMed Web of Science ®Google Scholar
• Gårding, J. (1988). Properties of fractal intensity surfaces. Pattern Recognition Letters, 8, 319–324. doi:10.1016/0167-8655(88)90081-5
   Web of Science ®Google Scholar
• Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., … Zhang, K. (2013). Genome-wide methylation profiles reveal quantitative views of human aging rates. Molecular Cell, 49, 359–367. doi:10.1016/j.molcel.2012.10.016
   PubMed Web of Science ®Google Scholar
• Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biology, 14, R115. doi:10.1186/gb-2013-14-10-r115
   PubMed Web of Science ®Google Scholar
• Huang, H., Shen, L., Zhang, R., Makedon, F., Saykin, A., & Pearlman, J. (2007). A novel surface registration algorithm with biomedical modeling applications. IEEE Transactions on Information Technology in Biomedicine, 11, 474–482. doi:10.1109/titb.2007.897577
   PubMedGoogle Scholar
• Inano, S., Takao, H., Hayashi, N., Yoshioka, N., Mori, H., Kunimatsu, A., … Ohtomo, K. (20122013). Effects of age and gender on neuroanatomical volumes. Journal of Magnetic Resonance Imaging, 37, 1072–1076. doi:10.1002/jmri.23910
   PubMed Web of Science ®Google Scholar
• Keller, S. S., Gerdes, J. S., Mohammadi, S., Kellinghaus, C., Kugel, H., Deppe, K., … Deppe, M. (2012). Volume estimation of the thalamus using freesurfer and stereology: Consistency between methods. Neuroinformatics, 10, 341–350. doi:10.1007/s12021-012-9147-0
   PubMed Web of Science ®Google Scholar
• Kennedy, D. N., Haselgrove, C., Riehl, J., Preuss, N., & Buccigrossi, R. (2016). The NITRC image repository. NeuroImage, 124, 1069–1073. doi:10.1016/j.neuroimage.2015.05.074
   PubMed Web of Science ®Google Scholar
• Kennedy, K. M., Rodrigue, K. M., Bischof, G. N., Hebrank, A. C., Reuter-Lorenz, P. A., & Park, D. C. (2015). Age trajectories of functional activation under conditions of low and high processing demands: An adult lifespan fMRI study of the aging brain. NeuroImage, 104, 21–34. doi:10.1016/j.neuroimage.2014.09.056
   PubMed Web of Science ®Google Scholar
• Kennedy, S. K., & Lin, W.-H. (1986). Fract–A Fortran subroutine to calculate the variables necessary to determine the fractal dimension of closed forms. Computers & Geosciences, 12, 705–712. doi:10.1016/0098-3004(86)90046-4
   Web of Science ®Google Scholar
• Keshavan, A., Datta, E., McDonough, I. M., Madan, C. R., Jordan, K., & Henry, R. G. (in press). Mindcontrol: A web application for brain segmentation quality control. Neuro Image. doi:10.1016/j.neuroimage.2017.03.055
   Web of Science ®Google Scholar
• King, R. D., Brown, B., Hwang, M., Jeon, T., & George, A. T. (2010). Fractal dimension analysis of the cortical ribbon in mild Alzheimer's disease. NeuroImage, 53, 471–479. doi:10.1016/j.neuroimage.2010.06.050
   PubMed Web of Science ®Google Scholar
• Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., … Walsh, S. (in press). Radiomics: The bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology. doi:10.1038/nrclinonc.2017.141
   Web of Science ®Google Scholar
• Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., … Aerts, H. J. W. L. (2012). Radiomics: Extracting more information from medical images using advanced feature analysis. European Journal of Cancer, 48, 441–446. doi:10.1016/j.ejca.2011.11.036
   PubMed Web of Science ®Google Scholar
• Lee, A., Moreton, H., & Hoppe, H. (2000). Displaced subdivision surfaces. SIGGRAPH Computer Graphics, 27, 85–94. doi:10.1145/344779.344829
   Google Scholar
• Lehmann, M., Douiri, A., Kim, L. G., Modat, M., Chan, D., Ourselin, S., … Fox, N. C. (2010). Atrophy patterns in Alzheimer's disease and semantic dementia: A comparison of FreeSurfer and manual volumetric measurements. NeuroImage, 49, 2264–2274. doi:10.1016/j.neuroimage.2009.10.056
   PubMed Web of Science ®Google Scholar
• Lespessailles, E., Chappard, C., Bonnet, N., & Benhamou, C. L. (2006). Imaging techniques for evaluating bone microarchitecture. Joint Bone Spine, 73, 254–261. doi:10.1016/j.jbspin.2005.12.002
   PubMed Web of Science ®Google Scholar
• Lewis, W. M. (1976). Surface/volume ratio: Implications for phytoplankton morphology. Science, 192, 885–887. doi:10.1126/science.192.4242.885
   PubMed Web of Science ®Google Scholar
• Long, X., Liao, W., Jiang, C., Liang, D., Qiu, B., & Zhang, L. (2012). Healthy aging: An automatic analysis of global and regional morphological alterations of human brain. Academic Radiology, 19, 785–793. doi:10.1016/j.acra.2012.03.006
   PubMed Web of Science ®Google Scholar
• Lopes, R., & Betrouni, N. (2009). Fractal and multifractal analysis: A review. Medical Image Analysis, 13, 634–649. doi:10.1016/j.media.2009.05.003
   PubMed Web of Science ®Google Scholar
• Lopes, R., Dubois, P., Bhouri, I., Bedoui, M. H., Maouche, S., & Betrouni, N. (2011). Local fractal and multifractal features for volumic texture characterization. Pattern Recognition, 44, 1690–1697. doi:10.1016/j.patcog.2011.02.017
   Web of Science ®Google Scholar
• Lorensen, W. E., & Cline, H. E. (1987). Marching cubes: A high resolution 3D surface construction algorithm. SIGGRAPH Computer Graphics, 21, 163–169. doi:10.1145/37402.37422
   Google Scholar
• Madan, C. R. (2015). Creating 3D visualizations of MRI data: A brief guide. F1000Research, 4, 466. doi:10.12688/f1000research.6838.1
   PubMedGoogle Scholar
• Madan, C. R. (2016). Prism: Multiple spline regression with regularization, dimensionality reduction, and feature selection. Journal of Open Source Software, 1, 31. doi:10.21105/joss.00031
   Google Scholar
• Madan, C. R. (2017). Advances in studying brain morphology: The benefits of open-access data. Frontiers in Human Neuroscience, 11, 405. doi:10.3389/fnhum.2017.00405
   PubMed Web of Science ®Google Scholar
• Madan, C. R., & Kensinger, E. A. (2016). Cortical complexity as a measure of age-related brain atrophy. NeuroImage, 134, 617–629. doi:10.1016/j.neuroimage.2016.04.029
   PubMed Web of Science ®Google Scholar
• Madan, C. R., & Kensinger, E. A. (2017a). Age-related differences in the structural complexity of subcortical and ventricular structures. Neurobiology of Aging, 50, 87–95. doi:10.1016/j.neurobiolaging.2016.10.023
   PubMed Web of Science ®Google Scholar
• Madan, C. R., & Kensinger, E. A. (2017b). Test–retest reliability of brain morphology estimates. Brain Informatics, 4, 107–121. doi:10.1007/s40708-016-0060-4
   PubMedGoogle Scholar
• Mandelbrot, B. B. (1967). How long is the coast of Britain? Statistical self-similarity and fractional dimension. Science, 156, 636–638. doi:10.1126/science.156.3775.636
   PubMed Web of Science ®Google Scholar
• Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007). Open access series of imaging studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience, 19, 1498–1507. doi:10.1162/jocn.2007.19.9.1498
   PubMed Web of Science ®Google Scholar
• Mennes, M., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2013). Making data sharing work: The FCP/INDI experience. NeuroImage, 82, 683–691. doi:10.1016/j.neuroimage.2012.10.064
   PubMed Web of Science ®Google Scholar
• Nenadic, I., Yotter, R. A., Sauer, H., & Gaser, C. (2014). Cortical surface complexity in frontal and temporal areas varies across subgroups of schizophrenia. Human Brain Mapping, 35, 1691–1699. doi:10.1002/hbm.22283
   PubMed Web of Science ®Google Scholar
• Parekh, V., & Jacobs, M. A. (2016). Radiomics: A new application from established techniques. Expert Review of Precision Medicine and Drug Development, 1, 207–226. doi:10.1080/23808993.2016.1164013
   PubMed Web of Science ®Google Scholar
• Park, D. C., Lautenschlager, G., Hedden, T., Davidson, N. S., Smith, A. D., & Smith, P. K. (2002). Models of visuospatial and verbal memory across the adult life span. Psychology and Aging, 17, 299–320. doi:10.1037/0882-7974.17.2.299
   PubMed Web of Science ®Google Scholar
• Pentland, A. P. (1985). On describing complex surface shapes. Image and Vision Computing, 3, 153–162. doi:10.1016/0262-8856(85)90002-2
   Web of Science ®Google Scholar
• Potvin, O., Mouiha, A., Dieumegarde, L., & Duchesne, S. (2016). Normative data for subcortical regional volumes over the lifetime of the adult human brain. NeuroImage, 137, 9–20. doi:10.1016/j.neuroimage.2016.05.016
   PubMed Web of Science ®Google Scholar
• Raz, N., Lindenberger, U., Rodrigue, K. M., Kennedy, K. M., Head, D., Williamson, A., … Acker, J. D. (2005). Regional brain changes in aging healthy adults: General trends, individual differences and modifiers. Cerebral Cortex, 15, 1676–1689. doi:10.1093/cercor/bhi044
   PubMed Web of Science ®Google Scholar
• Reagh, Z., & Yassa, M. (2017). Selective vulnerabilities and biomarkers in neurocognitive aging. F1000Research, 6, 491. doi:10.12688/f1000research.10652.1
   PubMedGoogle Scholar
• Salthouse, T. A. (2011). Neuroanatomical substrates of age-related cognitive decline. Psychological Bulletin, 137, 753–784. doi:10.1037/a0023262
   PubMed Web of Science ®Google Scholar
• Sandu, A.-L., Rasmussen, I.-A., Lundervold, A., Kreuder, F., Neckelmann, G., Hugdahl, K., & Specht, K. (2008). Fractal dimension analysis of MR images reveals grey matter structure irregularities in schizophrenia. Computerized Medical Imaging and Graphics, 32, 150–158. doi:10.1016/j.compmedimag.2007.10.005
   PubMed Web of Science ®Google Scholar
• Sargolzaei, S., Sargolzaei, A., Cabrerizo, M., Chen, G., Goryawala, M., Pinzon-Ardila, A., … Adjouadi, M. (2015). Estimating intracranial volume in brain research: An evaluation of methods. Neuroinformatics, 13, 427–441. doi:10.1007/s12021-015-9266-5
   PubMed Web of Science ®Google Scholar
• Sarkar, N., & Chaudhuri, B. B. (1992). An efficient approach to estimate fractal dimension of textural images. Pattern Recognition, 25, 1035–1041. doi:10.1016/0031-3203(92)90066-r
   Web of Science ®Google Scholar
• Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464. doi:10.1214/aos/1176344136
   Web of Science ®Google Scholar
• Shen, L., & Makedon, F. (2006). Spherical mapping for processing of 3D closed surfaces. Image and Vision Computing, 24, 743–761. doi:10.1016/j.imavis.2006.01.011
   Web of Science ®Google Scholar
• Shen, L., Firpi, H. A., Saykin, A. J., & West, J. D. (2009). Parametric surface modeling and registration for comparison of manual and automated segmentation of the hippocampus. Hippocampus, 19, 588–595. doi:10.1002/hipo.20613
   PubMed Web of Science ®Google Scholar
• Shen, L., Huang, H., Makedon, F., & Saykin, A. J. (2007). Efficient Registration of 3D SPHARM surfaces. In Fourth Canadian Conference on Computer and Robot Vision (CRV 07). College Station, TX: IEEE. doi:10.1109/crv.2007.26
   Google Scholar
• Small, B. J., Dixon, R. A., & McArdle, J. J. (20102011). Tracking cognition-health changes from 55 to 95 years of age. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 66B, i153–i161. doi:10.1093/geronb/gbq093
   Google Scholar
• Staib, L. H., & Duncan, J. S. (1996). Model-based deformable surface finding for medical images. IEEE Transactions on Medical Imaging, 15, 720–731. doi:10.1109/42.538949
   PubMed Web of Science ®Google Scholar
• Tamnes, C. K., Walhovd, K. B., Dale, A. M., Østby, Y., Grydeland, H., Richardson, G., … Fjell, A. M. (2013). Brain development and aging: Overlapping and unique patterns of change. NeuroImage, 68, 63–74. doi:10.1016/j.neuroimage.2012.11.039
   PubMed Web of Science ®Google Scholar
• Thomas, T., Rosén, B.-G., & Amini, N. (1999). Fractal characterisation of the anisotropy of rough surfaces. Wear, 232, 41–50. doi:10.1016/s0043-1648(99)00128-3
   Web of Science ®Google Scholar
• Thompson, P. M., Lee, A. D., Dutton, R. A., Geaga, J. A., Hayashi, K. M., Eckert, M. A., … Reiss, A. L. (2005). Abnormal cortical complexity and thickness profiles mapped in Williams syndrome. Journal of Neuroscience, 25, 4146–4158. doi:10.1523/JNEUROSCI.0165-05.2005
   PubMed Web of Science ®Google Scholar
• Tipping, M. E. (2000). The Relevance Vector Machine. Advances in Neural Information Processing Systems, 12 (), 652–658.
   Google Scholar
• Wadell, H. (1933). Sphericity and roundness of rock particles. Journal of Geology, 41, 310–331. doi:10.1086/624040
   Google Scholar
• Wadell, H. (1935). Volume, shape, and roundness of quartz particles. Journal of Geology, 43, 250–280. doi:10.1086/624298
   Google Scholar
• Walhovd, K. B., Fjell, A. M., Reinvang, I., Lundervold, A., Dale, A. M., Eilertsen, D. E., … Fischl, B. (2005). Effects of age on volumes of cortex, white matter and subcortical structures. Neurobiology of Aging, 26, 1261–1270. doi:10.1016/j.neurobiolaging.2005.05.020
   PubMed Web of Science ®Google Scholar
• Walhovd, K. B., Westlye, L. T., Amlien, I., Espeseth, T., Reinvang, I., Raz, N., … Fjell, A. M. (2011). Consistent neuroanatomical age-related volume differences across multiple samples. Neurobiology of Aging, 32, 916–932. doi:10.1016/j.neurobiolaging.2009.05.013
   PubMed Web of Science ®Google Scholar
• Weibel, E. R., Kistler, G. S., & Scherle, W. F. (1966). Practical stereological methods for morphometric cytology. Journal of Cell Biology, 30, 23–38. doi:10.1083/jcb.30.1.23
   PubMed Web of Science ®Google Scholar
• Wentworth, C. K. (1933). The shapes of rock particles: A discussion. Journal of Geology, 41, 306–309. doi:10.1086/624039
   Google Scholar
• Yang, Z., Wen, W., Jiang, J., Crawford, J. D., Reppermund, S., Levitan, C., … Sachdev, P. S. (2016). Age-associated differences on structural brain MRI in nondemented individuals from 71 to 103 years. Neurobiology of Aging, 40, 86–97. doi:10.1016/j.neurobiolaging.2016.01.006
   PubMed Web of Science ®Google Scholar
• Yip, S. S. F., & Aerts, H. J. W. L. (2016). Applications and limitations of radiomics. Physics in Medicine and Biology, 61, R150–R166. doi:10.1088/0031-9155/61/13/r150
   PubMed Web of Science ®Google Scholar
• Yotter, R. A., Nenadic, I., Ziegler, G., Thompson, P. M., & Gaser, C. (2011). Local cortical surface complexity maps from spherical harmonic reconstructions. NeuroImage, 56, 961–973. doi:10.1016/j.neuroimage.2011.02.007
   PubMed Web of Science ®Google Scholar
• Yushkevich, P. A., Detre, J. A., Mechanic-Hamilton, D., Fernández-Seara, M. A., Tang, K. Z., Hoang, A., … Gee, J. C. (2007). Hippocampus-specific fMRI group activation analysis using the continuous medial representation. NeuroImage, 35, 1516–1530. doi:10.1016/j.neuroimage.2007.01.029
   PubMed Web of Science ®Google Scholar
• Yushkevich, P. A., Zhang, H., & Gee, J. C. (2006). Continuous medial representation for anatomical structures. IEEE Transactions on Medical Imaging, 25, 1547–1564. doi:10.1109/tmi.2006.884634
   PubMed Web of Science ®Google Scholar