Skip to main content
Springer Nature Link
Log in

Machine Learning for Brain Imaging Genomics Methods: A Review

  • Review
  • Published:
Machine Intelligence Research Aims and scope Submit manuscript

Abstract

In the past decade, multimodal neuroimaging and genomic techniques have been increasingly developed. As an interdisciplinary topic, brain imaging genomics is devoted to evaluating and characterizing genetic variants in individuals that influence phenotypic measures derived from structural and functional brain imaging. This technique is capable of revealing the complex mechanisms by macroscopic intermediates from the genetic level to cognition and psychiatric disorders in humans. It is well known that machine learning is a powerful tool in the data-driven association studies, which can fully utilize priori knowledge (intercorrelated structure information among imaging and genetic data) for association modelling. In addition, the association study is able to find the association between risk genes and brain structure or function so that a better mechanistic understanding of behaviors or disordered brain functions is explored. In this paper, the related background and fundamental work in imaging genomics are first reviewed. Then, we show the univariate learning approaches for association analysis, summarize the main idea and modelling in genetic-imaging association studies based on multivariate machine learning, and present methods for joint association analysis and outcome prediction. Finally, this paper discusses some prospects for future work.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. A. R. Hariri, D. R. Weinberger. Imaging genomics. British Medical Bulletin, vol. 65, no. 1, pp. 259–270, 2003. DOI: https://doi.org/10.1093/bmb/65.1.259.

    Article  Google Scholar 

  2. P. M. Thompson, N. G. Martin, M. J. Wright. Imaging genomics. Current Opinion in Neurology, vol. 23, no. 4, pp. 368–373, 2010. DOI: https://doi.org/10.1097/WCO.0b013e32833b764c.

    Article  Google Scholar 

  3. D. C. Glahn, P. M. Thompson, J. Blangero. Neuroimaging endophenotypes: Strategies for finding genes influencing brain structure and function. Human Brain Mapping, vol. 28, no. 6, pp. 488–501, 2007. DOI: https://doi.org/10.1002/hbm.20401.

    Article  Google Scholar 

  4. M. W. Weiner, D. P. Veitch, P. S. Aisen, L. A. Beckett, N. J. Cairns, R. C. Green, D. Harvey, C. R. Jack Jr, W. Jagust, J. C. Morris, R. C. Petersen, A. J. Saykin, L. M. Shaw, A. W. Toga, J. Q. Trojanowski, Alzheimer’s Disease Neuroimaging Initiative. Recent publications from the Alzheimer’s disease neuroimaging initiative: Reviewing progress toward improved AD clinical trials. Alzheimer’s & Dementia, vol. 13, no. 4, pp. e1–e85, 2017. DOI: https://doi.org/10.1016/j.jalz.2016.11.007.

    Article  Google Scholar 

  5. P. M. Thompson, N. Jahanshad, C. R. K. Ching, L. E. Salminen, S. I. Thomopoulos, J. Bright, B. T. Baune, S. Bertolín, J. Bralten, W. B. Bruin, R. Bülow, J. Chen, Y. Chye, U. Dannlowski, C. G. F. de Kovel, G. Donohoe, L. T. Eyler, S. V. Faraone, P. Favre, C. A. Filippi, T. Frodl, D. Garijo, Y. Gil, H. J. Grabe, K. L. Grasby, T. Hajek, L. K. M. Han, S. N. Hatton, K. Hilbert, T. C. Ho, L. Holleran, G. Homuth, N. Hosten, J. Houenou, I. Ivanov, T. Y. Jia, S. Kelly, M. Klein, J. S. Kwon, M. A. Laansma, J. Leerssen, U. Lueken, A. Nunes, J. O’Neill, N. Opel, F. Piras, F. Piras, M. C. Postema, E. Pozzi, N. Shatokhina, C. Soriano-Mas, G. Spalletta, D. Q. Sun, A. Teumer, A. K. Tilot, L. Tozzi, C. van der Merwe, E. J. W. van Someren, G. A. van Wingen, H. Völzke, E. Walton, L. Wang, A. M. Winkler, K. Wittfeld, M. J. Wright, J. Y. Yun, G. H. Zhang, Y. Zhang-James, B. M. Adhikari, I. Agartz, M. Aghajani, A. Aleman, R. R. Althoff, A. Altmann, O. A. Andreassen, D. A. Baron, B. L. Bartnik-Olson, J. M. Bas-Hoogendam, A. R. Baskin-Sommers, C. E. Bearden, L. A. Berner, P. S. W. Boedhoe, R. M. Brouwer, J. K. Buitelaar, K. Caeyenberghs, C. A. M. Cecil, R. A. Cohen, J. H. Cole, P. J. Conrod, S. A. de Brito, S. M. C. de Zwarte, E. L. Dennis, S. Desrivieres, D. Dima, S. Ehrlich, C. Esopenko, G. Fairchild, S. E. Fisher, J. P. Fouche, C. Francks, S. Frangou, B. Franke, H. P. Garavan, D. C. Glahn, N. A. Groenewold, T. P. Gurholt, B. A. Gutman, T. Hahn, I. H. Harding, D. Hernaus, D. P. Hibar, F. G. Hillary, M. Hoogman, H. E. H. Pol, M. Jalbrzikowski, G. A. Karkashadze, E. T. Klapwijk, R. C. Knickmeyer, P. Kochunov, I. K. Koerte, X. Z. Kong, S. L. Liew, A. P. Lin, M. W. Logue, E. Luders, F. Macciardi, S. Mackey, A. R. Mayer, C. R. McDonald, A. B. McMahon, S. E. Medland, G. Modinos, R. A. Morey, S. C. Mueller, P. Mukherjee, L. Namazova-Baranova, T. M. Nir, A. Olsen, P. Paschou, D. S. Pine, F. Pizzagalli, M. E. Rentería, J. D. Rohrer, P. G. Sämann, L. Schmaal, G. Schumann, M. S. Shiroishi, S. M. Sisodiya, D. J. A. Smit, I. E. Sønderby, D. J. Stein, J. L. Stein, M. Tahmasian, D. F. Tate, J. A. Turner, O. A. van den Heuvel, N. J. A. van der Wee, Y. D. van der Werf, T. G. M. van Erp, N. E. M. van Haren, D. van Rooij, L. S. van Velzen, I. M. Veer, D. J. Veltman, J. E. Villalon-Reina, H. Walter, C. D. Whelan, E. A. Wilde, M. Zarei, Vladimir Zelman for the ENIGMA Consortium. ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries. Translational Psychiatry, vol. 10, no. 1, Article number 100, 2020. DOI: https://doi.org/10.1038/s41398-020-0705-1.

  6. T. D. Satterthwaite, M. A. Elliott, K. Ruparel, J. Loughead, K. Prabhakaran, M. E. Calkins, R. Hopson, C. Jackson, J. Keefe, M. Riley, F. D. Mentch, P. Sleiman, R. Verma, C. Davatzikos, H. Hakonarson, R. C. Gur, R. E. Gur. Neuroimaging of the Philadelphia neurodevelopmental cohort. NeuroImage, vol. 86, pp. 544–553, 2014. DOI: https://doi.org/10.1016/j.neuroimage.2013.07.064.

    Article  Google Scholar 

  7. K. Marek, S. Chowdhury, A. Siderowf, S. Lasch, C. S. Coffey, C. Caspell-Garcia, T. Simuni, D. Jennings, C. M. Tanner, J. Q. Trojanowski, L. M. Shaw, J. Seibyl, N. Schuff, A. Singleton, K. Kieburtz, A. W. Toga, B. Mollenhauer, D. Galasko, L. M. Chahine, D. Weintraub, T. Foroud, D. Tosun-Turgut, K. Poston, V. Arnedo, M. Frasier, T. Sherer, Parkinson’s Progression Markers Initiative. The Parkinson’s progression markers initiative (PPMI)-Establishing a PD biomarker cohort. Annals of Clinical and Translational Neurology, vol. 5, no. 12, pp. 1460–1477, 2018. DOI: https://doi.org/10.1002/acn3.644.

    Article  Google Scholar 

  8. I. I. Gottesman, T. D. Gould. The endophenotype concept in psychiatry: Etymology and strategic intentions. The American Journal of Psychiatry, vol. 160, no. 4, pp. 636–645, 2003. DOI: https://doi.org/10.1176/appi.ajp.160.4.636.

    Article  Google Scholar 

  9. A. Meyer-Lindenberg, D. R. Weinberger. Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nature Reviews Neuroscience, vol. 7, no. 10, pp. 818–827, 2006. DOI: https://doi.org/10.1038/nrn1993.

    Article  Google Scholar 

  10. T. Ge, G. Schumann, J. F. Feng. Imaging genetics-to-wards discovery neuroscience. Quantitative Biology, vol. 1, no. 4, pp. 227–245, 2013. DOI: https://doi.org/10.1007/s40484-013-0023-1.

    Article  Google Scholar 

  11. A. M. Winkler, P. Kochunov, J. Blangero, L. Almasy, K. Zilles, P. T. Fox, R. Duggirala, D. C. Glahn. Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. NeuroImage, vol. 53, no. 3, pp. 1135–1146, 2010. DOI: https://doi.org/10.1016/j.neuroimage.2009.12.028.

    Article  Google Scholar 

  12. S. M. Smith, P. T. Fox, K. L. Miller, D. C. Glahn, P. M. Fox, C. E. Mackay, N. Filippini, K. E. Watkins, R. Toro, A. R. Laird, C. F. Beckmann. Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 31, pp. 13040–13045, 2009. DOI: https://doi.org/10.1073/pnas.090526710.

    Article  Google Scholar 

  13. H. Tost, E. Bilek, A. Meyer-Lindenberg. Brain connectivity in psychiatric imaging genetics. NeuroImage, vol. 62, no. 4, pp. 2250–2260, 2012. DOI: https://doi.org/10.1016/j.neuroimage.2011.11.007.

    Article  Google Scholar 

  14. M. Rubinov, O. Sporns. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, vol. 52, no. 3, pp. 1059–1069, 2010. DOI: https://doi.org/10.1016/j.neuroimage.2009.10.003.

    Article  Google Scholar 

  15. J. Hardy, A. Singleton. Genomewide association studies and human disease. The New England Journal of Medicine, vol. 360, no. 17, pp. 1759–1768, 2009. DOI: https://doi.org/10.1056/NEJMra0808700.

    Article  Google Scholar 

  16. R. J. Klein, C. Zeiss, E. Y. Chew, J. Y. Tsai, R. S. Sackler, C. Haynes, A. K. Henning, J. P. SanGiovanni, S. M. Mane, S. T. Mayne, M. B. Bracken, F. L. Ferris, J. Ott, C. Barnstable, J. Hoh. Complement factor H polymorphism in age-related macular degeneration. Science, vol. 308, no. 5720, pp. 385–389, 2005. DOI: https://doi.org/10.1126/science.1109557.

    Article  Google Scholar 

  17. C. Esslinger, H. Walter, P. Kirsch, S. Erk, K. Schnell, C. Arnold, L. Haddad, D. Mier, C. O. von Boberfeld, K. Raab, S. H. Witt, M. Rietschel, S. Cichon, A. Meyer-Lindenberg. Neural mechanisms of a genome-wide supported psychosis variant. Science, vol. 324, no. 5927, Article number 605, 2009. DOI: https://doi.org/10.1126/science.1167768.

  18. S. E. Medland, N. Jahanshad, B. M. Neale, P. M. Thompson. Whole-genome analyses of whole-brain data: Working within an expanded search space. Nature Neuroscience, vol. 17, no. 6, pp. 791–800, 2014. DOI: https://doi.org/10.1038/nn.3718.

    Article  Google Scholar 

  19. J. Y. Liu, V. D. Calhoun. A review of multivariate analyses in imaging genetics. Frontiers in Neuroinformatics, vol. 8, Article number 29, 2014. DOI: https://doi.org/10.3389/fninf.2014.00029.

  20. P. M. Thompson, T. Ge, D. C. Glahn, N. Jahanshad, T. E. Nichols. Genetics of the connectome. NeuroImage, vol. 80, pp. 475–488, 2013. DOI: https://doi.org/10.1016/j.neuroimage.2013.05.013.

    Article  Google Scholar 

  21. D. M. Witten, R. Tibshirani, T. Hastie. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics, vol. 10, no. 3, pp. 515–534, 2009. DOI: https://doi.org/10.1093/biostatistics/kxp008.

    Article  MATH  Google Scholar 

  22. K. A. Lê Cao, P. G. P. Martin, C. Robert-Granié, P. Besse. Sparse canonical methods for biological data integration: Application to a cross-platform study. BMC Bioinformatics, vol. 10, Article number 34, 2009. DOI: https://doi.org/10.1186/1471-2105-10-34.

  23. E. C. Chi, G. I. Allen, H. Zhou, O. Kohannim, K. Lange, P. M. Thompson. Imaging genetics via sparse canonical correlation analysis. In Proceedings of the 10th IEEE International Symposium on Biomedical Imaging, San Francisco, USA, pp. 740–743, 2013. DOI: https://doi.org/10.1109/ISBI.2013.6556581.

  24. J. Y. Liu, O. Demirci, V. D. Calhoun. A parallel independent component analysis approach to investigate genomic influence on brain function. IEEE Signal Processing Letters, vol. 15, pp. 413–416, 2008. DOI: https://doi.org/10.1109/LSP.2008.922513.

    Article  Google Scholar 

  25. V. D. Calhoun, J. Y. Liu, T. Adalimath. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. NeuroImage, vol. 45, no. 1 Suppl 1, pp. S163–S172, 2009. DOI: https://doi.org/10.1016/j.neuroimage.2008.10.057.

    Article  Google Scholar 

  26. W. W. Daniel, C. L. Cross. Biostatistics: A Foundation for Analysis in the Health Sciences, 10th ed., Hoboken, USA: Wiley, 2013.

    MATH  Google Scholar 

  27. S. G. Potkin, G. Guffanti, A. Lakatos, J. A. Turner, F. Kruggel, J. H. Fallon, A. J. Saykin, A. Orro, S. Lupoli, E. Salvi, M. Weiner, F. Macciardi, Alzheimer’s Disease Neuroimaging Initiative. Hippocampal atrophy as a quantitative trait in a genome-wide association study identifying novel susceptibility genes for Alzheimer’s disease. PLoS One, vol. 4, no. 8, Article number e6501, 2009. DOI: https://doi.org/10.1371/journal.pone.0006501.

  28. L. Shen, P. M. Thompson, S. G. Potkin, L. Bertram, L. A. Farrer, T. M. Foroud, R. C. Green, X. L. Hu, M. J. Huentelman, S. Kim, J. S. K. Kauwe, Q. Q. Li, E. C. Liu, F. Macciardi, J. H. Moore, L. Munsie, K. Nho, V. K. Ramanan, S. L. Risacher, D. J. Stone, S. Swaminathan, A. W. Toga, M. W. Weiner, A. J. Saykin, Alzheimer’s Disease Neuroimaging Initiative. Genetic analysis of quantitative phenotypes in AD and MCI: Imaging, cognition and biomarkers. Brain Imaging and Behavior, vol. 8, no. 2, pp. 183–207, 2014. DOI: https://doi.org/10.1007/s11682-013-9262-z.

    Article  Google Scholar 

  29. S. L. Risacher, L. Shen, J. D. West, S. Kim, B. C. McDonald, L. A. Beckett, D. J. Harvey, C. R. Jack Jr, M. W. Weiner, A. J. Saykin, Alzheimer’s Disease Neuroimaging Initiative. Longitudinal MRI atrophy biomarkers: Relationship to conversion in the ADNI cohort. Neurobiology of Aging, vol. 31, no. 8, pp. 1401–1418, 2010. DOI: https://doi.org/10.1016/j.neurobiolaging.2010.04.029.

    Article  Google Scholar 

  30. S. L. Risacher, S. Kim, L. Shen, K. Nho, T. Foroud, R. C. Green, R. C. Petersen, C. R. Jack Jr, P. S. Aisen, R. A. Koeppe, W. J. Jagust, L. M. Shaw, J. Q. Trojanowski, M. W. Weiner, A. J. Saykin, Alzheimer’s Disease Neuroimaging Initiative. The role of apolipoprotein E (APOE) genotype in early mild cognitive impairment (E-MCI). Frontiers in Aging Neuroscience, vol. 5, Article number 11, 2013. DOI: https://doi.org/10.3389/fnagi.2013.00011.

  31. A. J. Ho, J. L. Stein, X. Hua, S. Lee, D. P. Hibar, A. D. Leow, I. D. Dinov, A. W. Toga, A. J. Saykin, L. Shen, T. Foroud, N. Pankratz, M. J. Huentelman, D. W. Craig, J. D. Gerber, A. N. Allen, J. J. Corneveaux, D. A. Stephan, C. S. DeCarli, B. M. DeChairo, S. G. Potkin, C. R. Jack Jr, M. W. Weiner, C. A. Raji, O. L. Lopez, J. T. Becker, O. T. Carmichael, P. M. Thompson, the Alzheimer’s Disease Neuroimaging Initiative, M. Weiner, L. Thal, R. Petersen, C. R. Jack Jr, W. Jagust, J. Trojanowki, A. W. Toga, L. Beckett, R. C. Green, A. Gamst, W. Z. Potter, T. Montine, D. Anders, M. Bernstein, J. Felmlee, N. Fox, P. Thompson, N. Schuff, G. Alexander, D. Bandy, R. A. Koeppe, N. Foster, E. M. Reiman, K. W. Chen, J. Trojanowki, L. Shaw, V. M. Y. Lee, M. Korecka, A. W. Toga, K. Crawford, S. Neu, D. Harvey, A. Gamst, J. Kornak, Z. Kachaturian, R. Frank, P. J. Snyder, S. Molchan, J. Kaye, R. Vorobik, J. Quinn, L. Schneider, S. Pawluczyk, B. Spann, A. S. Fleisher, H. Vanderswag, J. L. Heidebrink, J. L. Lord, K. Johnson, R. S. Doody, J. Villanueva-Meyer, M. Chowdhury, Y. Stern, L. S. Honig, K. L. Bell, J. C. Morris, M. A. Mintun, S. Schneider, D. Marson, R. Griffith, B. Badger, H. Grossman, C. Tang, J. Stern, L. deToledo-Morrell, R. C. Shah, J. Bach, R. Duara, R. Isaacson, S. Strauman, M. S. Albert, J. Pedroso, J. Toroney, H. Rusinek, M. J. de Leon, S. M. de Santi, P. M. Doraiswamy, J. R. Petrella, M. Aiello, C. M. Clark, C. Pham, J. Nunez, C. D. Smith, C. A. Given II, P. Hardy, S. T. DeKosky, M. Oakley, D. M. Simpson, M. S. Ismail, A. Porsteinsson, C. McCallum, S. C. Cramer, R. A. Mulnard, C. McAdams-Ortiz, R. Diaz-Arrastia, K. Martin-Cook, M. DeVous, A. I. Levey, J. J. Lah, J. S. Cellar, J. M. Burns, H. S. Anderson, M. M. Laubinger, G. Bartzokis, D. H. S. Silverman, P. H. Lu, R. Fletcher, F. Parfitt, H. Johnson, M. Farlow, S. Herring, A. M. Hake, C. H. van Dyck, M. G. MacAvoy, L. A. Bifano, H. Chertkow, H. Bergman, C. Hosein, S. Black, S. Graham, C. Caldwell, H. Feldman, M. Assaly, G. Y. R. Hsiung, A. Kertesz, J. Rogers, D. Trost, C. Bernick, D. Gitelman, N. Johnson, M. Mesulam, C. Sadowsky, T. Villena, S. Mesner, P. S. Aisen, K. B. Johnson, K. E. Behan, R. A. Sperling, D. M. Rentz, K. A. Johnson, A. Rosen, J. Tinklenberg, W. Ashford, M. Sabbagh, D. Connor, S. Obradov, R. Killiany, A. Norbash, T. O. Obisesan, A. Jayam-Trouth, P. Wang, A. P. Auchus, J. B. Huang, R. P. Friedland, C. DeCarli, E. Fletcher, O. Carmichael, S. Kittur, S. Mirje, S. C. Johnson, M. Borrie, T. Y. Lee, S. Asthana, C. M. Carlsson, S. G. Potkin, D. Highum, A. Preda, D. Nguyen, P. N. Tariot, B. A. Hendin, D. W. Scharre, M. Kataki, D. Q. Beversdorf, E. A. Zimmerman, D. Celmins, A. D. Brown, S. Gandy, M. E. Marenberg, B. W. Rovner, G. Pearlson, K. Blank, K. Anderson, A. J. Saykin, R. B. Santulli, N. Pare, J. D. Williamson, K. M. Sink, H. Potter, B. A. Raj, A. Giordano, B. R. Ott, C. K. Wu, R. Cohen, K. L. Wilks. A commonly carried allele of the obesity-related FTO gene is associated with reduced brain volume in the healthy elderly. Proceedings of the National Academy of Sciences of the United States of America, vol. 107, no. 18, pp. 8404–8409, 2010. DOI: https://doi.org/10.1073/pnas.0910878107.

    Article  Google Scholar 

  32. E. M. Reiman, K. W. Chen, X. F. Liu, D. Bandy, M. X. Yu, D. Lee, N. Ayutyanont, J. Keppler, S. A. Reeder, J. B. S. Langbaum, G. E. Alexander, W. E. Klunk, C. A. Mathis, J. C. Price, H. J. Aizenstein, S. T. DeKosky, R. J. Caselli. Fibrillar amyloid-β burden in cognitively normal people at 3 levels of genetic risk for Alzheimer’s disease. Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 16, pp. 6820–6825, 2009. DOI: https://doi.org/10.1073/pnas.090034510.

    Article  Google Scholar 

  33. C. D. Sloan, L. Shen, J. D. West, H. A. Wishart, L. A. Flashman, L. A. Rabin, R. B. Santulli, S. J. Guerin, C. H. Rhodes, G. J. Tsongalis, T. W. McAllister, T. A. Ahles, S. L. Lee, J. H. Moore, A. J. Saykin. Genetic pathway-based hierarchical clustering analysis of older adults with cognitive complaints and amnestic mild cognitive impairment using clinical and neuroimaging phenotypes. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, vol. 153B, no. 5, pp. 1060–1069, 2010. DOI: https://doi.org/10.1002/ajmg.b.31078.

    Article  Google Scholar 

  34. S. Swaminathan, L. Shen, S. L. Risacher, K. K. Yoder, J. D. West, S. Kim, K. Nho, T. Foroud, M. Inlow, S. G. Potkin, M. J. Huentelman, D. W. Craig, W. J. Jagust, R. A. Koeppe, C. A. Mathis, C. R. Jack Jr, M. W. Weiner, A. J. Saykin, Alzheimer’s Disease Neuroimaging Initiative. Amyloid pathway-based candidate gene analysis of [11C]PiB-PET in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Brain Imaging and Behavior, vol. 6, no. 1, pp. 1–15, 2012. DOI: https://doi.org/10.1007/s11682-011-9136-1.

    Article  Google Scholar 

  35. M. C. Chiang, M. Barysheva, K. L. McMahon, G. I. de Zubicaray, K. Johnson, G. W. Montgomery, N. G. Martin, A. W. Toga, M. J. Wright, P. Shapshak, P. M. Thompson. Gene network effects on brain microstructure and intellectual performance identified in 472 twins. Journal of Neuroscience, vol. 32, no. 25, pp. 8732–8745, 2012. DOI: https://doi.org/10.1523/JNEUROSCI.5993-11.2012.

    Article  Google Scholar 

  36. A. J. Saykin, L. Shen, T. M. Foroud, S. G. Potkin, S. Swaminathan, S. Kim, S. L. Risacher, K. Nho, M. J. Huentelman, D. W. Craig, P. M. Thompson, J. L. Stein, J. H. Moore, L. A. Farrer, R. C. Green, L. Bertram, C. R. Jack Jr, M. W. Weiner, Alzheimer’s Disease Neuroimaging Initiative. Alzheimer’s Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: Genetics core aims, progress, and plans. Alzheimer’s & Dementia, vol. 6, no. 3, pp. 265–273, 2010. DOI: https://doi.org/10.1016/j.jalz.2010.03.013.

    Article  Google Scholar 

  37. S. G. Potkin, J. A. Turner, J. A. Fallon, A. Lakatos, D. B. Keator, G. Guffanti, F. Macciardi. Gene discovery through imaging genetics: Identification of two novel genes associated with schizophrenia. Molecular Psychiatry, vol. 14, no. 4, pp. 416–428, 2009. DOI: https://doi.org/10.1038/mp.2008.127.

    Article  Google Scholar 

  38. L. Shen, S. Kim, S. L. Risacher, K. Nho, S. Swaminathan, J. D. West, T. Foroud, N. Pankratz, J. H. Moore, C. D. Sloan, M. J. Huentelman, D. W. Craig, B. M. DeChairo, S. G. Potkin, C. R. Jack Jr, M. W. Weiner, A. J. Saykin, Alzheimer’s Disease Neuroimaging Initiative. Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. NeuroImage, vol. 53, no. 3, pp. 1051–1063, 2010. DOI: https://doi.org/10.1016/j.neuroimage.2010.01.042.

    Article  Google Scholar 

  39. J. L. Stein, X. Hua, S. Lee, A. J. Ho, A. D. Leow, A. W. Toga, A. J. Saykin, L. Shen, T. Foroud, N. Pankratz, M. J. Huentelman, D. W. Craig, J. D. Gerber, A. N. Allen, J. J. Corneveaux, B. M. DeChairo, S. G. Potkin, M. W. Weiner, P. M. Thompson, Alzheimer’s Disease Neuroimaging Initiative. Voxelwise genome-wide association study (vGWAS). NeuroImage, vol. 53, no. 3, pp. 1160–1174, 2010. DOI: https://doi.org/10.1016/j.neuroimage.2010.02.032.

    Article  Google Scholar 

  40. A. Biffi, C. D. Anderson, R. S. Desikan, M. Sabuncu, L. Cortellini, N. Schmansky, D. Salat, J. Rosand, Alzheimer’s Disease Neuroimaging Initiative. Genetic variation and neuroimaging measures in Alzheimer disease. Archives of Neurology, vol. 67, no. 6, pp. 677–685, 2010. DOI: https://doi.org/10.1001/archneurol.2010.108.

    Article  Google Scholar 

  41. J. S. K. Kauwe, S. Bertelsen, K. Mayo, C. Cruchaga, R. Abraham, P. Hollingworth, D. Harold, M. J. Owen, J. Williams, S. Lovestone, J. C. Morris, A. M. Goate, Alzheimer’s Disease Neuroimaging Initiative. Suggestive synergy between genetic variants in TF and HFE as risk factors for Alzheimer’s disease. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, vol. 153B, no. 4, pp. 955–959, 2010. DOI: https://doi.org/10.1002/ajmg.b.31053.

    Article  Google Scholar 

  42. B. C. Dickerson, D. A. Wolk. Dysexecutive versus amnesic phenotypes of very mild Alzheimer’s disease are associated with distinct clinical, genetic and cortical thinning characteristics. Journal of Neurology, Neurosurgery & Psychiatry, vol. 82, no. 1, pp. 45–51, 2011. DOI: https://doi.org/10.1136/jnnp.2009.199505.

    Article  Google Scholar 

  43. S. Purcell, B. Neale, K. Todd-Brown, L. Thomas, M. A. R. Ferreira, D. Bender, J. Maller, P. Sklar, P. I. W. de Bakker, M. J. Daly, P. C. Sham. PLINK: A tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, vol. 81, no. 3, pp. 559–575, 2007. DOI: https://doi.org/10.1086/519795.

    Article  Google Scholar 

  44. S. Gombar, H. J. Jung, F. Dong, B. Calder, G. Atzmon, N. Barzilai, X. L. Tian, J. Pothof, J. H. J. Hoeijmakers, J. Campisi, J. Vijg, Y. Suh. Comprehensive microRNA profiling in B-cells of human centenarians by massively parallel sequencing. BMC Genomics, vol. 13, Article number 353, 2012. DOI: https://doi.org/10.1186/1471-2164-13-353.

  45. Y. Benjamini, D. Yekutieli. The control of the false discovery rate in multiple testing under dependency. The Annals of Statistics, vol. 29, no. 4, pp. 1165–1188, 2001. DOI: https://doi.org/10.1214/aos/1013699998.

    Article  MathSciNet  MATH  Google Scholar 

  46. D. P. Hibar, J. L. Stein, O. Kohannim, N. Jahanshad, A. J. Saykin, L. Shen, S. Kim, N. Pankratz, T. Foroud, M. J. Huentelman, S. G. Potkin, C. R. Jack Jr, M. W. Weiner, A. W. Toga, P. M. Thompson, Alzheimer’s Disease Neuroimaging Initiative. Voxelwise gene-wide association study (vGeneWAS): Multivariate gene-based association testing in 731 elderly subjects. NeuroImage, vol. 56, no. 4, pp. 1875–1891, 2011. DOI: https://doi.org/10.1016/j.neuroimage.2011.03.077.

    Article  Google Scholar 

  47. D. P. Hibar, J. L. Stein, O. Kohannim, N. Jahanshad, C. R. Jack, M. W. Weiner, A. W. Toga, P. M. Thompson. Principal components regression: Multivariate, gene-based tests in imaging genomics. In Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, USA, pp. 289–293, 2011. DOI: https://doi.org/10.1109/ISBI.2011.5872408.

  48. D. P. Hibar, O. Kohannim, J. L. Stein, M. C. Chiang, P. M. Thompson. Multilocus genetic analysis of brain images. Frontiers in Genetics, vol. 2, Article number 73, 2011. DOI: https://doi.org/10.3389/fgene.2011.00073.

  49. O. Kohannim, D. P. Hibar, J. L. Stein, N. Jahanshad, X. Hua, P. Rajagopalan, A. W. Toga, C. R. Jack Jr, M. W. Weiner, G. I. de Zubicaray, K. L. McMahon, N. K. Hansell, N. G. Martin, M. J. Wright, P. M. Thompson, Alzheimer’s Disease Neuroimaging Initiative. Discovery and replication of gene influences on brain structure using LASSO regression. Frontiers in Neuroscience, vol. 6, Article number 115, 2012. DOI: https://doi.org/10.3389/fnins.2012.00115.

  50. T. Yang, J. Wang, Q. Sun, D. P. Hibar, N. Jahanshad, L. Liu, Y. L. Wang, L. Zhan, P. M. Thompson, J. P. Ye. Detecting genetic risk factors for Alzheimer’s disease in whole genome sequence data via Lasso screening. In Proceedings of the 12th IEEE International Symposium on Biomedical Imaging, Brooklyn, USA, pp. 985–989, 2015. DOI: https://doi.org/10.1109/ISBI.2015.7164036.

  51. M. Silver, G. Montana, Alzheimer’s Disease Neuroimaging Initiative. Fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps. Statistical Applications in Genetics and Molecular Biology, vol. 11, no. 1, Article number 7, 2012. DOI: https://doi.org/10.2202/1544-6115.1755.

  52. M. Yuan, Y. Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 68, no. 1, pp. 49–67, 2006. DOI: https://doi.org/10.1111/j.1467-9868.2005.00532.x.

    Article  MathSciNet  MATH  Google Scholar 

  53. M. Silver, P. Chen, R. Y. Li, C. Y. Cheng, T. Y. Wong, E. S. Tai, Y. Y. Teo, G. Montana. Pathways-driven sparse regression identifies pathways and genes associated with high-density lipoprotein cholesterol in two Asian cohorts. PLoS Genetics, vol. 9, no. 11, Article number e1003939, 2013. DOI: https://doi.org/10.1371/journal.pgen.l003939.

  54. J. C. Barrett, B. Fry, J. Maller, M. J. Daly. Haploview: Analysis and visualization of LD and haplotype maps. Bioinformatics, vol. 21, no. 2, pp. 263–265, 2005. DOI: https://doi.org/10.1093/bioinformatics/bth457.

    Article  Google Scholar 

  55. X. K. Hao, J. T. Yu, D. Q. Zhang. Identifying genetic associations with MRI-derived measures via tree-guided sparse learning. In Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Boston, USA, pp. 757–764, 2014. DOI: https://doi.org/10.1007/978-3-319-10470-6_94.

    Google Scholar 

  56. X. K. Hao, X. H. Yao, S. L. Risacher, A. J. Saykin, J. T. Yu, H. F. Wang, L. Tan, L. Shen, D. Q. Zhang. Identifying candidate genetic associations with MRI-Derived AD-related ROI via tree-guided sparse learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 16, no. 6, pp. 1986–1996, 2019. DOI: https://doi.org/10.1109/TCBB.2018.2833487.

    Article  Google Scholar 

  57. J. Wang, J. P. Ye. Multi-layer feature reduction for tree structured group lasso via hierarchical projection. In Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, Canada, pp. 1279–1287, 2015. DOI: https://doi.org/10.5555/2969239.2969382.

  58. X. K. Hao, J. W. Yan, X. H. Yao, S. L. Risacher, A. J. Saykin, D. Q. Zhang, L. Shen. Diagnosis-guided method for identifying multi-modality neuroimaging biomarkers associated with genetic risk factors in Alzheimer’s disease. In Proeedings of Pacific Symposium, Kohala Coast, USA, pp. 108–119, 2016.

  59. X. K. Hao, X. H. Yao, J. W. Yan, S. L. Risacher, A. J. Saykin, D. Q. Zhang, L. Shen, Alzheimer’s Disease Neuroimaging Initiative. Identifying multimodal intermediate phenotypes between genetic risk factors and disease status in Alzheimer’s disease. Neuroinformatics, vol. 14, no. 4, pp. 439–452, 2016. DOI: https://doi.org/10.1007/s12021-016-9307-8.

    Article  Google Scholar 

  60. M. L. Wang, X. K. Hao, J. Huang, W. Shao, D. Q. Zhang. Discovering network phenotype between genetic risk factors and disease status via diagnosis-aligned multi-modality regression method in Alzheimer’s disease. Bioinformatics, vol. 35, no. 11, pp. 1948–1957, 2019. DOI: https://doi.org/10.1093/bioinformatics/bty911.

    Article  Google Scholar 

  61. R. Tibshirani. Regression shrinkage and selection via the lasso: A retrospective. Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 73, no. 3, pp. 273–282, 2011. DOI: https://doi.org/10.1111/j.1467-9868.2011.00771.x.

    Article  MathSciNet  MATH  Google Scholar 

  62. M. Vounou, T. E. Nichols, G. Montana, Alzheimer’s Disease Neuroimaging Initiative. Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach. NeuroImage, vol. 53, no. 3, pp. 1147–1159, 2010. DOI: https://doi.org/10.1016/j.neuroimage.2010.07.002.

    Article  Google Scholar 

  63. M. Vounou, E. Janousova, R. Wolz, J. L. Stein, P. M. Thompson, D. Rueckert, G. Montana, Alzheimer’s Disease Neuroimaging Initiative. Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer’s disease. NeuroImage, vol. 60, no. 1, pp. 700–716, 2012. DOI: https://doi.org/10.1016/j.neuroimage.2011.12.029.

    Article  Google Scholar 

  64. M. Silver, E. Janousova, X. Hua, P. M. Thompson, G. Montana, Alzheimer’s Disease Neuroimaging Initiative. Identification of gene pathways implicated in Alzheimer’s disease using longitudinal imaging phenotypes with sparse regression. NeuroImage, vol. 63, no. 3, pp. 1681–1694, 2012. DOI: https://doi.org/10.1016/j.neuroimage.2012.08.002.

    Article  Google Scholar 

  65. H. Wang, F. P. Nie, H. Huang, S. Kim, K. Nho, S. L. Risacher, A. J. Saykin, L. Shen, Alzheimer’s Disease Neuroimaging Initiative. Identifying quantitative trait loci via group-sparse multitask regression and feature selection: An imaging genetics study of the ADNI cohort. Bioinformatics, vol. 28, no. 2, pp. 229–237, 2012. DOI: https://doi.org/10.1093/bioinformatics/btr649.

    Article  Google Scholar 

  66. T. Park, G. Casella. The Bayesian lasso. Journal of the American Statistical Association, vol. 103, no. 482, pp. 681–686, 2008. DOI: https://doi.org/10.1198/016214508000000337.

    Article  MathSciNet  MATH  Google Scholar 

  67. G. Casella, M. Ghosh, J. Gill, M. Kyung. Penalized regression, standard errors, and Bayesian lassos. Bayesian Analysis, vol. 5, no. 2, pp. 369–411, 2010. DOI: https://doi.org/10.1214/10-BA607.

    Article  MathSciNet  MATH  Google Scholar 

  68. H. T. Zhu, Z. Khondker, Z. H. Lu, J. G. Ibrahim, Alzheimer’s Disease Neuroimaging Initiative. Bayesian generalized low rank regression models for neuroimaging phenotypes and genetic markers. Journal of the American Statistical Association, vol. 109, no. 507, pp. 977–990, 2014. DOI: https://doi.org/10.1080/01621459.2014.923775.

    Article  MathSciNet  MATH  Google Scholar 

  69. Z. H. Lu, Z. Khondker, J. G. Ibrahim, Y. Wang, H. T. Zhu, Alzheimer’s Disease Neuroimaging Initiative. Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies. NeuroImage, vol. 149, pp. 305–322, 2017. DOI: https://doi.org/10.1016/j.neuroimage.2017.01.052.

    Article  Google Scholar 

  70. H. Wang, F. P. Nie, H. Huang, J. W. Yan, S. Kim, K. Nho, S. L. Risacher, A. J. Saykin, L. Shen, Alzheimer’s Disease Neuroimaging Initiative. From phenotype to genotype: An association study of longitudinal phenotypic markers to Alzheimer’s disease relevant SNPs. Bioinformatics, vol. 28, no. 18, pp. i619–i625, 2012. DOI: https://doi.org/10.1093/bioinformatics/bts411.

    Article  Google Scholar 

  71. X. Q. Wang, J. W. Yan, X. H. Yao, S. Kim, K. Nho, S. L. Risacher, A. J. Saykin, L. Shen, H. Huang. Longitudinal genotype-phenotype association study through temporal structure auto-learning predictive model. Journal of Computational Biology, vol. 25, no. 7, pp. 809–824, 2018. DOI: https://doi.org/10.1089/cmb.2018.0008.

    Article  MathSciNet  MATH  Google Scholar 

  72. T. Zhou, K. H. Thung, M. X. Liu, D. G. Shen. Brain-wide genome-wide association study for Alzheimer’s disease via joint projection learning and sparse regression model. IEEE Transactions on Biomedical Engineering, vol. 66, no. 1, pp. 165–175, 2019. DOI: https://doi.org/10.1109/TBME.2018.2824725.

    Article  Google Scholar 

  73. X. F. Zhu, H. I. Suk, H. Huang, D. G. Shen. Structured sparse low-rank regression model for brain-wide and genome-wide associations. In Proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Athens, Greece, pp. 344–352, 2016. DOI: https://doi.org/10.1007/978-3-319-46720-7_40.

    Google Scholar 

  74. X. F. Zhu, H. I. Suk, H. Huang, D. G. Shen. Low-rank graph-regularized structured sparse regression for identifying genetic biomarkers. IEEE Transactions on Big Data, vol. 3, no. 4, pp. 405–414, 2017. DOI: https://doi.org/10.1109/TB-DATA.2017.2735991.

    Article  Google Scholar 

  75. X. F. Zhu, W. H. Zhang, Y. Fan, Alzheimer’s Disease Neuroimaging Initiative. A robust reduced rank graph regression method for neuroimaging genetic analysis. Neuroinformatics, vol. 16, no. 3, pp. 351–361, 2018. DOI: https://doi.org/10.1007/s12021-018-9382-0.

    Article  Google Scholar 

  76. H. Wang, F. P. Nie, H. Huang, S. L. Risacher, A. J. Saykin, L. Shen, Alzheimer’s Disease Neuroimaging Initiative. Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning. Bioinformatics, vol. 28, no. 12, pp. i127–i136, 2012. DOI: https://doi.org/10.1093/bioinformatics/bts228.

    Article  Google Scholar 

  77. J. Y. Liu, G. Pearlson, A. Windemuth, G. Ruano G, N. L. Perrone-Bizzozero, V. Calhoun. Combining fMRI and SNP data to investigate connections between brain function and genetics using parallel ICA. Human Brain Mapping, vol. 30, no. 1, pp. 241–255, 2009. DOI: https://doi.org/10.1002/hbm.20508.

    Article  Google Scholar 

  78. S. A. Meda, B. Narayanan, J. Y. Liu, N. I. Perrone-Bizzozero, M. C. Stevens, V. D. Calhoun, D. C. Glahn, L. Shen, S. L. Risacher, A. J. Saykin, G. D. Pearlson. A large scale multivariate parallel ICA method reveals novel imaging-genetic relationships for Alzheimer’s disease in the ADNI cohort. NeuroImage, vol. 60, no. 3, pp. 1608–1621, 2012. DOI: https://doi.org/10.1016/j.neuroimage.2011.12.076.

    Article  Google Scholar 

  79. H. Hotelling. The most predictable criterion. Journal of Educational Psychology, vol. 26, no. 2, pp. 139–142, 1935. DOI: https://doi.org/10.1037/h0058165.

    Article  Google Scholar 

  80. N. M. Correa, Y. O. Li, T. Adali, V. D. Calhoun. Canonical correlation analysis for feature-based fusion of biomedical imaging modalities and its application to detection of associative networks in schizophrenia. IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 6, pp. 998–1007, 2008. DOI: https://doi.org/10.1109/JSTSP.2008.2008265.

    Article  Google Scholar 

  81. S. Wold, H. Martens, H. Wold. The multivariate calibration problem in chemistry solved by the PLS method: Section C generalized singular values and data analysis. In Proceedings of a Conference Held at Pite Havsbad, Springer, Pite Havsbad, Sweden, pp. 286–293, 1983. DOI: https://doi.org/10.1007/BFb0062108.

    Google Scholar 

  82. A. Krishnan, L. J. Williams, A. R. McIntosh, H. Abdi. Partial least squares (PLS) methods for neuroimaging: A tutorial and review. NeuroImage, vol. 56, no. 2, pp. 455–475, 2011. DOI: https://doi.org/10.1016/j.neuroimage.2010.07.034.

    Article  Google Scholar 

  83. E. Le Floch, V. Guillemot, V. Frouin, P. Pinel, C. Lalanne, L. Trinchera, A. Tenenhaus, A. Moreno, M. Zilbovicius, T. Bourgeron, S. Dehaene, B. Thirion, J. B. Poline, É. Duchesnay. Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse partial least squares. NeuroImage, vol. 63, no. 1, pp. 11–24, 2012. DOI: https://doi.org/10.1016/j.neuroimage.2012.06.061.

    Article  Google Scholar 

  84. K. A. Lê Cao, D. Rossouw, C. Robert-Granié, P. Besse. A sparse PLS for variable selection when integrating omics data. Statistical Applications in Genetics and Molecular Biology, vol. 7, no. 1, Article number 35, 2008. DOI: https://doi.org/10.2202/1544-6115.1390.

  85. J. W. Yan, L. Du, S. Kim, S. L. Risacher, H. Huang, J. H. Moore, A. J. Saykin, L. Shen, Alzheimer’s Disease Neuroimaging Initiative. Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm. Bioinformatics, vol. 30, no. 17, pp. i564–i571, 2014. DOI: https://doi.org/10.1093/bioinformatics/btu465.

    Article  Google Scholar 

  86. L. Du, H. Huang, J. W. Yan, S. Kim, S. L. Risacher, M. Inlow, J. H. Moore, A. J. Saykin, L. Shen, Alzheimer’s Disease Neuroimaging Initiative. Structured sparse canonical correlation analysis for brain imaging genetics: An improved Graphnet method. Bioinformatics, vol. 32, no. 10, pp. 1544–1551, 2016. DOI: https://doi.org/10.1093/bioinformatics/btw033.

    Article  Google Scholar 

  87. D. D. Lin, V. D. Calhoun, Y. P. Wang. Correspondence between fMRI and SNP data by group sparse canonical correlation analysis. Medical Image Analysis, vol. 18, no. 6, pp. 891–902, 2014. DOI: https://doi.org/10.1016/j.media.2013.10.010.

    Article  Google Scholar 

  88. J. Fang, D. D. Lin, S. C. Schulz, Z. B. Xu, V. D. Calhoun, Y. P. Wang. Joint sparse canonical correlation analysis for detecting differential imaging genetics modules. Bioinformatics, vol. 32, no. 22, pp. 3480–3488, 2016. DOI: https://doi.org/10.1093/bioinformatics/btw485.

    Google Scholar 

  89. L. Du, J. W. Yan, S. Kim, S. L. Risacher, H. Huang, M. Inlow, J. H. Moore, A. J. Saykin, L. Shen. A novel structure-aware sparse learning algorithm for brain imaging genetics. In Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention Springer, Boston, USA, pp. 329–336, 2014. DOI: https://doi.org/10.1007/978-3-319-10443-0_42.

    Google Scholar 

  90. L. Du, K. F. Liu, X. H. Yao, S. L. Risacher, J. W. Han, L. Guo, A. J. Saykin, L. Shen. Fast multi-task SCCA learning with feature selection for multi-modal brain imaging genetics. In Proceedings of IEEE International Conference on Bioinformatics and Biomedicine, Madrid, Spain, pp. 356–361, 2018. DOI: https://doi.org/10.1109/BIBM.2018.8621298.

  91. L. Du, K. F. Liu, X. H. Yao, S. L. Risacher, J. W. Han, A. J. Saykin, L. Guo, L. Shen. Multi-task sparse canonical correlation analysis with application to multi-modal brain imaging genetics. IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 1, pp. 227–239, 2021. DOI: https://doi.org/10.1109/TCBB.2019.2947428.

    Article  Google Scholar 

  92. L. Du, K. F. Liu, X. H. Yao, J. W. Yan, S. L. Risacher, J. W. Han, L. Guo, A. J. Saykin, L. Shen, Alzheimer’s Disease Neuroimaging Initiative. Pattern discovery in brain imaging genetics via SCCA modeling with a generic non-convex penalty. Scientific Reports, vol. 7, no. 1, Article number 14052, 2017. DOI: https://doi.org/10.1038/s41598-017-13930-y.

  93. L. Du, K. F. Liu, T. Zhang, X. H. Yao, J. W. Yan, S. L. Risacher J. W. Han, L. Guo, A. J. Saykin, L. Shen, Alzheimer’s Disease Neuroimaging Initiative. A novel SCCA approach via truncated ℓ1-norm and truncated group lasso for brain imaging genetics. Bioinformatics, vol. 34, no. 2, pp. 278–285, 2018. DOI: https://doi.org/10.1093/bioinformatics/btx594.

    Article  Google Scholar 

  94. L. Grosenick, B. Klingenberg, K. Katovich, B. Knutson, J. E. Taylor. Interpretable whole-brain prediction analysis with GraphNet. NeuroImage, vol. 72, pp. 304–321, 2013. DOI: https://doi.org/10.1016/j.neuroimage.2012.12.062.

    Article  Google Scholar 

  95. X. K. Hao, C. X. Li, J. W. Yan, X. H. Yao, S. L. Risacher, A. J. Saykin, L. Shen, D. Q. Zhang. Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis. Bioinformatics, vol. 33, no. 14, pp. i341–i349, 2017. DOI: https://doi.org/10.1093/bioinformatics/btx245.

    Article  Google Scholar 

  96. J. Fang, C. Xu, P. Zille, D. D. Lin, H. W. Deng, V. D. Calhoun, Y. P. Wang. Fast and accurate detection of complex imaging genetics associations based on greedy projected distance correlation. IEEE Transactions on Medical Imaging, vol. 37, no. 4, pp. 860–870, 2018. DOI: https://doi.org/10.1109/TMI.2017.2783244.

    Article  Google Scholar 

  97. J. Q. Fan, Y. Feng, L. Xia. A projection-based conditional dependence measure with applications to high-dimensional undirected graphical models. Journal of Econometrics, vol. 218, no. 1, pp. 119–139, 2020. DOI: https://doi.org/10.1016/j.je-conom.2019.12.016.

    Article  MathSciNet  MATH  Google Scholar 

  98. X. K. Hao, C. X. Li, L. Du, X. H. Yao, J. W. Yan, S. L. Risacher, A. J. Saykin, L. Shen, D. Q. Zhang, Alzheimer’s Disease Neuroimaging Initiative. Mining outcome-relevant brain imaging genetic associations via three-way sparse canonical correlation analysis in Alzheimer’s disease. Scientific Reports, vol. 7, Article number 44272, 2017. DOI: https://doi.org/10.1038/srep44272.

  99. W. X. Hu, A. Y. Zhang, B. Cai, V. Calhoun, Y. P. Wang. Distance canonical correlation analysis with application to an imaging-genetic study. Journal of Medical Imaging, vol. 6, no. 2, Article number 026501, 2019. DOI: https://doi.org/10.1117/1.JMI.6.2.026501.

  100. M. L. Wang, W. Shao, X. K. Hao, L. Shen, D. Q. Zhang. Identify consistent cross-modality imaging genetic patterns via discriminant sparse canonical correlation analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 4, pp. 1549–1561, 2021. DOI: https://doi.org/10.1109/TCBB.2019.2944825.

    Article  Google Scholar 

  101. M. L. Wang, W. Shao, X. K. Hao, D. Q. Zhang. Identify complex imaging genetic patterns via fusion self-expressive network analysis. IEEE Transactions on Medical Imaging, vol. 40, no. 6, pp. 1673–1686, 2021. DOI: https://doi.org/10.1109/TMI.2021.3063785.

    Article  Google Scholar 

  102. M. L. Wang, W. Shao, S. Huang, D. Q. Zhang. Deep self-reconstruction sparse canonical correlation analysis for brain imaging genetics. In Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, Nice, France, pp. 1790–1793, 2021. DOI: https://doi.org/10.1109/ISBI48211.2021.9434077. no. 12, pp. 2561–2571, 2018. DOI: https://doi.org/10.1109/TMI.2017.2721301.

  103. M. L. Wang, W. Shao, X. K. Hao, S. Huang, D. Q. Zhang. Identify connectome between genotypes and brain network phenotypes via deep self-reconstruction sparse canonical correlation analysis. Bioinformatics, vol. 38, no. 8, pp. 2323–2332, 2022. DOI: https://doi.org/10.1093/bioinformatics/btac074.

    Article  Google Scholar 

  104. A. Gossmann, P. Zille, V. Calhoun, Y. P. Wang. FDR-corrected sparse canonical correlation analysis with applications to imaging genomics. IEEE Transactions on Medical Imaging, vol. 37, no. 8, pp. 1761–1774, 2018. DOI: https://doi.org/10.1109/TMI.2018.2815583.

    Article  Google Scholar 

  105. J. Dukart, F. Sambataro, A. Bertolino, Alzheimer’s Disease Neuroimaging Initiative. Accurate prediction of conversion to Alzheimer’s disease using imaging, genetic, and neuropsychological biomarkers. Journal of Alzheimer’s Disease, vol. 49, no. 4, pp. 1143–1159, 2016. DOI: https://doi.org/10.3233/JAD-150570.

    Article  Google Scholar 

  106. R. Filipovych, B. Gaonkar, C. Davatzikos. A composite multivariate polygenic and neuroimaging score for prediction of conversion to Alzheimer’s disease. In Proceedings of the 2nd International Workshop on Pattern Recognition in Neuroimaging, IEEE, London, UK, pp. 105–108, 2012. DOI: https://doi.org/10.1109/PRNI.2012.9.

    Google Scholar 

  107. Y. Fan, D. G. Shen, R. C. Gur, R. E. Gur, C. Davatzikos. COMPARE: Classification of morphological patterns using adaptive regional elements. IEEE Transactions on Medical Imaging, vol. 26, no. 1, pp. 93–105, 2007. DOI: https://doi.org/10.1109/TMI.2006.886812.

    Article  Google Scholar 

  108. J. L. Peng, L. An, X. F. Zhu, Y. Jin, D. G. Shen. Structured sparse kernel learning for imaging genetics based Alzheimer’s disease diagnosis. In Proceedings of the 19th International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, Athens, Greece, pp. 70–78, 2016. DOI: https://doi.org/10.1007/978-3-319-46723-8_9.

    Google Scholar 

  109. A. Singanamalli, H. B. Wang, A. Madabhushi, Alzheimer’s Disease Neuroimaging Initiative. Cascaded multi-view canonical correlation (CaMCCo) for early diagnosis of Alzheimer’s disease via fusion of clinical, imaging and omic features. Scientific Reports, vol. 7, no. 1, Article number 8137, 2017. DOI: https://doi.org/10.1038/s41598-017-03925-0.

  110. J. W. Yan, S. L. Risacher, K. Nho, A. J. Saykin, L. Shen, Alzheimer’s Disease Neuroimaging Initiative. Identification of discriminative imaging proteomics associations in Alzheimer’s disease via a novel sparse correlation model. In Proceedings of Pacific Symposium, Big Island, USA, pp. 94–104, 2017. DOI: https://doi.org/10.1142/9789813207813_0010.

  111. L. Du, K. F. Liu, X. H. Yao, S. L. Risacher, L. Guo, A. J. Saykin, L. Shen. Diagnosis status guided brain imaging genetics via integrated regression and sparse canonical correlation analysis. In Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, Venice, Italy, pp. 356–359, 2019. DOI: https://doi.org/10.1109/ISBI.2019.8759489.

  112. P. Zille, V. D. Calhoun, Y. P. Wang. Enforcing co-expression within a brain-imaging genomics regression framework. IEEE Transactions on Medical Imaging, vol. 37

  113. X. Bi, L. Q. Yang, T. F. Li, B. S. Wang, H. T. Zhu, H. P. Zhang. Genome-wide mediation analysis of psychiatric and cognitive traits through imaging phenotypes. Human Brain Mapping, vol. 38, no. 8, pp. 4088–4097, 2017. DOI: https://doi.org/10.1002/hbm.23650.

    Article  Google Scholar 

  114. N. K. Batmanghelich, A. Dalca, G. Quon, M. Sabuncu, P. Golland. Probabilistic modeling of imaging, genetics and diagnosis. IEEE Transactions on Medical Imaging, vol. 35, no. 7, pp. 1765–1779, 2016. DOI: https://doi.org/10.1109/TMI.2016.2527784.

    Article  Google Scholar 

  115. D. Q. Zhang, Y. P. Wang, L. P. Zhou, H. Yuan, D. G. Shen, Alzheimer’s Disease Neuroimaging Initiative. Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage, vol. 55, no. 3, pp. 856–867, 2011. DOI: https://doi.org/10.1016/j.neuroimage.2011.01.008.

    Article  Google Scholar 

  116. Y. Wang, W. Goh, L. Wong, G. Montana, Alzheimer’s Disease Neuroimaging Initiative. Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes. BMC Bioinformatics, vol. 14, no. Suppl 16, Article number S6, 2013. DOI: https://doi.org/10.1186/1471-2105-14-S16-S6.

  117. X. H. Yao, J. W. Yan, S. Kim, K. Nho, S. L. Risacher, M. Inlow, J. H. Moore, A. J. Saykin, L. Shen, Alzheimer’s Disease Neuroimaging Initiative. Two-dimensional enrichment analysis for mining high-level imaging genetic associations. In Proceedings of the 8th International Conference on Brain Informatics and Health, Springer, London, UK, pp. 115–124, 2015. DOI: https://doi.org/10.1007/978-3-319-23344-4_12.

    Chapter  Google Scholar 

  118. Q. J. M. Huys, T. V. Maia, M. J. Frank. Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, vol. 19, no. 3, pp. 404–413, 2016. DOI: https://doi.org/10.1038/nn.4238.

    Article  Google Scholar 

  119. R. Birnbaum, D. R. Weinberger. Functional neuroimaging and schizophrenia: A view towards effective connectivity modeling and polygenic risk. Dialogues in Clinical Neuroscience, vol. 15, no. 3, pp. 279–289, 2013. DOI: https://doi.org/10.31887/DCNS.2013.15.3/rbirnbaum.

    Article  Google Scholar 

  120. D. P. Hibar, J. L. Stein, N. Jahanshad, O. Kohannim, A. W. Toga, K. L. McMahon, G. I. de Zubicaray, G. W. Montgomery, N. G. Martin, M. J. Wright, M. W. Weiner, P. M. Thompson. Exhaustive search of the SNP-SNP interactome identifies epistatic effects on brain volume in two cohorts. In Proceedings of the 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Nagoya, Japan, pp. 600–607, 2013. DOI: https://doi.org/10.1007/978-3-642-40760-4_75.

    Google Scholar 

  121. S. M. Gross, R. Tibshirani. Collaborative regression. Biostatistics, vol. 16, no. 2, pp. 326–338, 2015. DOI: https://doi.org/10.1093/biostatistics/kxu047.

    Article  MathSciNet  Google Scholar 

  122. T. Zhou, K. H. Thung, X. Zhu, D. G. Shen. Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. Human Brain Mapping, vol. 40, no. 3, pp. 1001–1016, 2019. DOI: https://doi.org/10.1002/hbm.24428.

    Article  Google Scholar 

  123. G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, C. I. Sánchez. A survey on deep learning in medical image analysis. Medical Image Analysis, vol. 42, pp. 60–88, 2017. DOI: https://doi.org/10.1016/j.media.2017.07.005.

    Article  Google Scholar 

  124. D. Grapov, J. Fahrmann, K. Wanichthanarak, S. Khoomrung. Rise of deep learning for genomic, proteomic, and metabolomic data integration in precision medicine. OMICS: A Journal of Integrative Biology, vol. 22, no. 10, pp. 630–636, 2018. DOI: https://doi.org/10.1089/omi.2018.0097.

    Article  Google Scholar 

  125. J. H. Wen, E. Thibeau-Sutre, M. Diaz-Melo, J. Samper-Gonzalez, A. Routier, S. Bottani, D. Dormont, S. Durrleman, N. Burgos, O. Colliot, Alzheimer’s Disease Neuroimaging Initiative, Australian Imaging Biomarkers and Lifestyle flagship study of ageing. Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation. Medical Image Analysis, vol. 63, Article number 101694, 2020. DOI: https://doi.org/10.1016/j.media.2020.101694.

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 62106104, 62136004, 6190 2183, 61876082, 61861130366 and 61732006), the Project funded by China Postdoctoral Science Foundation (No. 2022T150320), and the National Key Research and Development Program of China (Nos. 2018YFC2001600 and 2018YFC2001602).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Mei-Ling Wang, Wei Shao & Dao-Qiang Zhang

  2. Key Laboratory of Pattern Analysis and Machine Intelligence, Ministry of Industry and Information Technology, Nanjing, 211106, China

    Mei-Ling Wang, Wei Shao & Dao-Qiang Zhang

  3. School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China

    Xiao-Ke Hao

Authors
  1. Mei-Ling Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Wei Shao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Xiao-Ke Hao
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Dao-Qiang Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Dao-Qiang Zhang.

Additional information

Mei-Ling Wang received the M. Sc. degree in information and communication engineering from Nanjing University of Information Science and Technology, China in 2016, and the Ph. D. degree in computer science and technology from Nanjing University of Aeronautics and Astronautics, China in 2020. She is currently a postdoctor with College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China.

Her research interests include machine learning and brain imaging genetics.

Wei Shao received the B.Sc. and M.Sc. degrees in information and computing science from Nanjing University of Technology, China in 2009 and 2012, respectively, and the Ph. D. degree in software engineering from Nanjing University of Aeronautics and Astronautics, China in 2018. He is currently an associate professor with College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China.

His research interests include machine learning and bioinformatics.

Xiao-Ke Hao received the B.Sc. and M.Sc. degrees in computer science and technology from Nanjing University of Information Science and Technology, China in 2009 and 2012, respectively, and the Ph.D. degree in computer science and technology from Nanjing University of Aeronautics and Astronautics, China in 2017. He is currently an associate professor with School of Artificial Intelligence, Hebei University of Technology, China. He has published over 20 scientific articles in refereed journals such as IEEE Transactions on Image Processing, Medical Image Analysis, Bioinformatics. He is a member of the Artificial Intelligence and Pattern Recognition Society of the China Computer Federation (CCF).

His research interests include machine learning, pattern recognition and medical image analysis.

Dao-Qiang Zhang received the B. Sc. and Ph.D. degrees in computer science from Nanjing University of Aeronautics and Astronautics (NUAA), China in 1999 and 2004, respectively. He joined Department of Computer Science and Engineering of NUAA, as a lecturer in 2004, and is a professor at present. He has published over 200 scientific articles in refereed international journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Medical Imaging, IEEE Transactions on Image Processing, Neuroimage, Human Brain Mapping, and Medical Image Analysis, and conference proceedings such as IJCAI, AAAI, NIPS, CVPR, MICCAI, and KDD, with 12 000+ citations by Google Scholar. He was nominated for the National Excellent Doctoral Dissertation Award of China in 2006, and won the Best Paper Award and the Best Student Award of several international conferences such as PRICAI’06, STMI’12 and BICS’16, etc. He has served as a program committee member for some international conferences like IJCAI, AAAI, NIPS, MICCAI, SDM, PRICAI, ACML, etc. He is a member of the Machine Learning Society of the Chinese Association of Artificial Intelligence (CAAI), and the Artificial Intelligence and Pattern Recognition Society of the China Computer Federation (CCF).

His research interests include machine learning, pattern recognition, data mining and medical image analysis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, ML., Shao, W., Hao, XK. et al. Machine Learning for Brain Imaging Genomics Methods: A Review. Mach. Intell. Res. 20, 57–78 (2023). https://doi.org/10.1007/s11633-022-1361-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-022-1361-0

Keywords