No CrossRef data available.
Article contents
Neuroimaging genetics and developmental psychopathology: A systematic review
Published online by Cambridge University Press: 01 September 2025
Abstract
Imaging genetics is an interdisciplinary field that integrates neuroimaging and genetic data to improve behavioral prediction and investigate the genetic bases of brain structure and function. It aims to identify associations between genetic markers and brain imaging phenotypes, with a behavioral or clinical trait as the outcome of interest. Since its emergence nearly 30 years ago, the field has advanced substantially, fueled by rapid developments in molecular-genetic and neuroimaging techniques. These advances have opened new avenues for exploring individual differences in cognitive and socio-emotional development and their links to neurodevelopmental disorders. This systematic review examined studies published between 2020 and 2024, focusing on developmental psychopathology. We screened 769 articles from PubMed/MEDLINE and PsycINFO and selected 42 publications that met specific inclusion criteria for review. The studies were categorized into three groups based on the developmental ages in which conditions typically develop: birth/early childhood, late childhood or early adolescence, and late adolescence. Although the field has seen considerable progress, multiple challenges in data acquisition, analysis, and interpretation remain. Larger sample sizes and novel analytical techniques are crucial for the continued advancement of imaging genetics, with animal studies offering potential complementary insights.
Information
- Type
- Regular Article
- Information
- Copyright
- © The Author(s), 2025. Published by Cambridge University Press
References
Agarwala, P., Narang, B., Geetha, T. S., Kurwale, N., Samson, P. L., Golani, T., Mahadevia, U., Vedam, R., Murugan, S., Chatterjee, S., Goyal, P., & Jain, V. (2023). Early-infantile developmental and epileptic encephalopathy: The aetiologies, phenotypic differences and outcomes-a prospective observational study. Brain Communications, 5(5), fcad243. https://doi.org/10.1093/braincomms/fcad243
CrossRefGoogle ScholarPubMed
Alemany, S., Blok, E., Jansen, P. R., Muetzel, R. L., & White, T. (2021). Brain morphology, autistic traits, and polygenic risk for autism: A population-based neuroimaging study. Autism Research, 14(10), 2085–2099. https://doi.org/10.1002/aur.2576 psyhCrossRefGoogle ScholarPubMed
Alemi, R., Wolfe, J., Neumann, S., Manning, J., Hanna, L., Towler, W., Wilson, C., Bien, A., Miller, S., Schafer, E., Gemignani, J., Koirala, N., Gracco, V. L., & Deroche, M. (2024). Motor processing in children with cochlear implants as assessed by functional near-infrared spectroscopy. Perceptual and Motor Skills, 131(1), 74–105. https://doi.org/10.1177/00315125231213167
CrossRefGoogle ScholarPubMed
Alemi, R., Wolfe, J., Neumann, S., Manning, J., Towler, W., Koirala, N., Gracco, V. L., & Deroche, M. (2023). Audiovisual integration in children with cochlear implants revealed through EEG and fNIRS. Brain Research Bulletin, 205 https://doi.org/10.1016/j.brainresbull.2023.110817
CrossRefGoogle Scholar
Alex, A. M., Buss, C., Davis, E. P., Campos,. G. de Los, Donald, K. A., Fair, D. A., Gaab, N., Gao, W., Gilmore, J. H., Girault, J. B., Grewen, K., Groenewold, N. A., Hankin, B. L., Ipser, J., Kapoor, S., Kim, P., Lin, W., Luo, S., Norton, E. S., …Knickmeyer, R. (2023). Genetic Influences on the Developing Young Brain and Risk for Neuropsychiatric Disorders. Biological Psychiatry, 93(10), 905–920. https://doi.org/10.1016/j.biopsych.2023.01.013
Google Scholar
Alexander, L. M., Escalera, J., Ai, L., Andreotti, C., Febre, K., Mangone, A., Vega-Potler, N., Langer, N., Alexander, A., Kovacs, M., Litke, S., O’Hagan, B., Andersen, J., Bronstein, B., Bui, A., Bushey, M., Butler, H., Castagna, V., Camacho, N.,…Milham, M. P. (2017). An open resource for transdiagnostic research in pediatric mental health and learning disorders. Scientific Data, 4, Article 170181. https://doi.org/10.1038/sdata.2017.181
CrossRefGoogle ScholarPubMed
Alexander-Bloch, A., Huguet, G., Schultz, L. M., Huffnagle, N., Jacquemont, S., Seidlitz, J., Saci, Z., Moore, T. M., Bethlehem, R. A. I., Mollon, J., Knowles, E. K., Raznahan, A., Merikangas, A., Chaiyachati, B. H., Raman, H., Schmitt, J. E., Barzilay, R., Calkins, M. E., Shinohara, R. T., …Glessner, J. (2022). Copy number variant risk scores associated with cognition, psychopathology, and brain structure in youths in the philadelphia neurodevelopmental cohort. JAMA Psychiatry, 79(7), 699–709. https://doi.org/10.1001/jamapsychiatry.2022.1017
CrossRefGoogle ScholarPubMed
Almajidy, R. K., Mankodiya, K., Abtahi, M., & Hofmann, U. G. (2020). A newcomer’s guide to functional near infrared spectroscopy experiments. IEEE Reviews in Biomedical Engineering, 13, 292–308. https://doi.org/10.1109/Rbme.2019.2944351
CrossRefGoogle ScholarPubMed
Ardesch, D. J., Libedinsky, I., Scholtens, L. H., Wei, Y., & van den Heuvel, M. P. (2023). Convergence of brain transcriptomic and neuroimaging patterns in schizophrenia, bipolar disorder, autism spectrum disorder, and major depressive disorder. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 8(6), 630–639. https://doi.org/10.1016/j.bpsc.2022.12.013 psyh.Google ScholarPubMed
Arnatkeviciute, A., Fulcher, B. D., Bellgrove, M. A., & Fornito, A. (2022). Imaging transcriptomics of brain disorders. Biological Psychiatry Global Open Science, 2(4), 319–331. https://doi.org/10.1016/j.bpsgos.2021.10.002
CrossRefGoogle ScholarPubMed
Arnatkeviciute, A., Markello, R. D., Fulcher, B. D., Misic, B., & Fornito, A. (2023). Toward best practices for imaging transcriptomics of the human brain. Biological Psychiatry, 93(5), 391–404. https://doi.org/10.1016/j.biopsych.2022.10.016
CrossRefGoogle ScholarPubMed
Arnett, A. B., & Flaherty, B. P. (2022). A framework for characterizing heterogeneity in neurodevelopmental data using latent profile analysis in a sample of children with ADHD. Journal of Neurodevelopmental Disorders, 14(1), 45. https://doi.org/10.1186/s11689-022-09454-w
CrossRefGoogle Scholar
Avinun, R., Nevo, A., Knodt, A. R., Elliott, M. L., & Hariri, A. R. (2018). Reproducibility in imaging genetics: The case of threat-related amygdala reactivity. Biological Psychiatry, 84(2), 148–159. https://doi.org/10.1016/j.biopsych.2017.11.010
CrossRefGoogle Scholar
Backhausen, L. L., Herting, M. M., Tamnes, C. K., & Vetter, N. C. (2022). Best practices in structural neuroimaging of neurodevelopmental disorders. Neuropsychology Review, 32(2), 400–418. https://doi.org/10.1007/s11065-021-09496-2
CrossRefGoogle ScholarPubMed
Baehne, C. G., Ehlis, A. C., Plichta, M. M., Conzelmann, A., Pauli, P., Jacob, C., Gutknecht, L., Lesch, K. P., & Fallgatter, A. J. (2009). Gene variants modulate response control processes in adult ADHD patients and healthy individuals. Molecular Psychiatry, 14(11), 1032–1039. https://doi.org/10.1038/mp.2008.39
CrossRefGoogle ScholarPubMed
Bandettini, P. A. (2012). Twenty years of functional MRI: The science and the stories. NeuroImage, 62(2), 575–588. https://doi.org/10.1016/j.neuroimage.2012.04.026
CrossRefGoogle ScholarPubMed
Barker, E. D., Ing, A., Biondo, F., Jia, T., Pingault, J.-B., Du Rietz, E., Zhang, Y., Ruggeri, B., Banaschewski, T., Hohmann, S., Bokde, A. L. W., Bromberg, U., Büchel, C., Quinlan, E. B., Sounga-Barke, E., Bowling, A. B., Desrivières, S., Flor, H., Frouin, V.…Schumann, G. (2021). Do ADHD-impulsivity and BMI have shared polygenic and neural correlates? Molecular Psychiatry, 26(3), 1019–1028. https://doi.org/10.1038/s41380-019-0444-y psyh.CrossRefGoogle ScholarPubMed
Bassett, D. S., & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20(3), 353–364. https://doi.org/10.1038/nn.4502
CrossRefGoogle ScholarPubMed
Battel, L., Cunegatto, F., Viduani, A., Fisher, H. L., Kohrt, B. A., Mondelli, V., Swartz, J. R., & Kieling, C. (2021). Mind the brain gap: The worldwide distribution of neuroimaging research on adolescent depression. NeuroImage, 231, Article 117865. https://doi.org/10.1016/j.neuroimage.2021.117865
CrossRefGoogle Scholar
Benkarim, O., Paquola, C., Park, B.-Y., Hong, S.-J., Royer, J., Vos de Wael, R., Lariviere, S., Valk, S., Bzdok, D., Mottron, L., & C. Bernhardt, B. (2021). Connectivity alterations in autism reflect functional idiosyncrasy. Communications Biology, 4(1), 1078. https://doi.org/10.1038/s42003-021-02572-6
CrossRefGoogle ScholarPubMed
Berg, L. M., Gurr, C., Leyhausen, J., Seelemeyer, H., Bletsch, A., Schaefer, T., Pretzsch, C. M., Oakley, B., Loth, E., Floris, D. L., Buitelaar, J. K., Beckmann, C. F., Banaschewski, T., Charman, T., Jones, E. J. H., Tillmann, J., Chatham, C. H., Bourgeron, T., Ambrosino, S., …Ecker, C. (2023). The neuroanatomical substrates of autism and ADHD and their link to putative genomic underpinnings. Molecular Autism, 14(1), 36. https://doi.org/10.1186/s13229-023-00568-z
CrossRefGoogle ScholarPubMed
Biessmann, F., Plis, S., Meinecke, F. C., Eichele, T., & Müller, K. R. (2011). Analysis of multimodal neuroimaging data. IEEE Reviews in Biomedical Engineering, 4, 26–58. https://doi.org/10.1109/rbme.2011.2170675
CrossRefGoogle ScholarPubMed
Bigos, K. L., & Weinberger, D. R. (2010). Imaging genetics-days of future past. NeuroImage, 53(3), 804–809. https://doi.org/10.1016/j.neuroimage.2010.01.035
CrossRefGoogle ScholarPubMed
Bolhuis, K., Mulder, R. H., de Mol, C. L., Defina, S., Warrier, V., White, T., Tiemeier, H., Muetzel, R. L., & Cecil, C. A. M. (2022). Mapping gene by early life stress interactions on child subcortical brain structures: A genome-wide prospective study. JCPP Advances, 2(4), jcv2.12113. https://doi.org/10.1002/jcv2.12113
CrossRefGoogle ScholarPubMed
Brown, A. B., Biederman, J., Valera, E. M., Doyle, A. E., Bush, G., Spencer, T., Monuteaux, M. C., Mick, E., Whitfield-Gabrieli, S., Makris, N., LaViolette, P. S., Oscar-Berman, M., Faraone, S. V., & Seidman, L. J. (2010). Effect of dopamine transporter gene (SLC6A3) variation on dorsal anterior cingulate function in attention-deficit/hyperactivity disorder. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 153B(2), 365–375. https://doi.org/10.1002/ajmg.b.31022
CrossRefGoogle ScholarPubMed
Buch, A. M., Vértes, P. E., Seidlitz, J., Kim, S. H., Grosenick, L., & Liston, C. (2023). Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder. Nature Neuroscience, 26(4), 650–663. https://doi.org/10.1038/s41593-023-01259-x psyh.CrossRefGoogle ScholarPubMed
Cao, Z., Cupertino, R. B., Ottino-Gonzalez, J., Murphy, A., Pancholi, D., Juliano, A., Chaarani, B., Albaugh, M., Yuan, D., Schwab, N., Stafford, J., Goudriaan, A. E., Hutchison, K., Li, C.-S. R., Luijten, M., Groefsema, M., Momenan, R., Schmaal, L., Sinha, R.,…IMAGEN Consortium
ENIGMA Addiction Working Group (2023). Cortical profiles of numerous psychiatric disorders and normal development share a common pattern. Molecular Psychiatry, 28(2), 698–709. https://doi.org/10.1038/s41380-022-01855-6 psyh.CrossRefGoogle Scholar
Carter, C. S., Bearden, C. E., Bullmore, E. T., Geschwind, D. H., Glahn, D. C., Gur, R. E., Meyer-Lindenberg, A., & Weinberger, D. R. (2017). Enhancing the informativeness and replicability of imaging genomics studies. Biological Psychiatry, 82(3), 157–164. https://doi.org/10.1016/j.biopsych.2016.08.019
CrossRefGoogle ScholarPubMed
Cerasa, A., Gioia, M. C., Labate, A., Liguori, M., Lanza, P., & Quattrone, A. (2008). Impact of catechol-O-methyltransferase Val108/158 Met genotype on hippocampal and prefrontal gray matter volume. NeuroReport, 19(4), 405–408. https://doi.org/10.1097/wnr.0b013e3282f5f784
CrossRefGoogle Scholar
Chand, G. B., Singhal, P., Dwyer, D. B., Wen, J., Erus, G., Doshi, J., Srinivasan, D., Mamourian, E., Varol, E., Sotiras, A., Hwang, G., Dazzan, P., Kahn, R. S., Schnack, H. G., Zanetti, M. V., Meisenzahl, E., Busatto, G. F., Crespo-Facorro, B., Pantelis, C., …Davatzikos, C. (2022). Schizophrenia imaging signatures and their associations with cognition, psychopathology, and genetics in the general population. American Journal of Psychiatry, 179(9), 650–660. https://doi.org/10.1176/appi.ajp.21070686
CrossRefGoogle ScholarPubMed
Cheek, C. L., Lindner, P., & Grigorenko, E. L. (2024). Statistical and machine learning analysis in brain-imaging genetics: A review of methods. Behavior Genetics, 54, 233–251. https://doi.org/10.1007/s10519-024-10177-y
CrossRefGoogle ScholarPubMed
Chen, J., Zhang, C., Wang, R., Jiang, P., Cai, H., Zhao, W., Zhu, J., & Yu, Y. (2022). Molecular basis underlying functional connectivity of fusiform gyrus subregions: A transcriptome-neuroimaging spatial correlation study. Cortex; A Journal Devoted to the Study of the Nervous System and Behavior, 152, 59–73. https://doi.org/10.1016/j.cortex.2022.03.016
CrossRefGoogle ScholarPubMed
Chiang, M. C., Barysheva, M., Toga, A. W., Medland, S. E., Hansel, N. K., James, M. R., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Wright, M. J., & Thompson, P. M. (2011). BDNF gene effects on brain circuitry replicated in 455 twins. Neuroimage, 55(2), 448–454. https://doi.org/10.1016/j.neuroimage.2010.12.053
CrossRefGoogle ScholarPubMed
Chilosi, A. M., Podda, I., Ricca, I., Comparini, A., Franchi, B., Fiori, S., Pasquariello, R., Casalini, C., Cipriani, P., & Santorelli, F. M. (2022). Differences and commonalities in children with childhood apraxia of speech and comorbid neurodevelopmental disorders: A multidimensional perspective. Journal of Personalized Medicine, 12(2), Article 313. https://doi.org/10.3390/jpm12020313
CrossRefGoogle ScholarPubMed
Choi, S. W., Mak, T. S.-H., & O’Reilly, P. F. (2020). Tutorial: A guide to performing polygenic risk score analyses. Nature Protocols, 15(9), 2759–2772. https://doi.org/10.1038/s41596-020-0353-1
CrossRefGoogle ScholarPubMed
Chubb, J. E., Bradshaw, N. J., Soares, D. C., Porteous, D. J., & Millar, J. K. (2008). The DISC locus in psychiatric illness. Molecular Psychiatry, 13(1), 36–64. https://doi.org/10.1038/sj.mp.4002106
CrossRefGoogle ScholarPubMed
Chyzhyk, D., Varoquaux, G., Milham, M., & Thirion, B. (2022). How to remove or control confounds in predictive models, with applications to brain biomarkers. GigaScience, 11, Article giac014. https://doi.org/10.1093/gigascience/giac014
CrossRefGoogle ScholarPubMed
Collier, J. J., Guissart, C., Oláhová, M., Sasorith, S., Piron-Prunier, F., Suomi, F., Zhang, D., Martinez-Lopez, N., Leboucq, N., Bahr, A., Azzarello-Burri, S., Reich, S., Schöls, L., Polvikoski, T. M., Meyer, P., Larrieu, L., Schaefer, A. M., Alsaif, H. S., Alyamani, S.…Taylor, R.W. (2021). Developmental consequences of defective ATG7-mediated autophagy in humans. The New England Journal of Medicine, 384(25), 2406–2417. https://doi.org/10.1056/NEJMoa1915722
CrossRefGoogle ScholarPubMed
Cross-Disorder Group of the Psychiatric Genomics Consortium. (2013). Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet, 381(9875), 1371–1379. https://doi.org/10.1016/S0140-6736(12)62129-1
CrossRefGoogle Scholar
Demontis, D., Walters, R. K., Martin, J., Mattheisen, M., Als, T. D., Agerbo, E., Baldursson, G., Belliveau, R., Bybjerg-Grauholm, J., Bækvad-Hansen, M., Cerrato, F., Chambert, K., Churchhouse, C., Dumont, A., Eriksson, N., Gandal, M., Goldstein, J. I., Grasby, K. L., Grove, J.…Neale, B. M. (2019). Discovery of the first genome-wide significant risk loci for attention-deficit/hyperactivity disorder. Nature Genetics, 51(1), 63–75. https://doi.org/10.1038/s41588-018-0269-7
CrossRefGoogle ScholarPubMed
Dempster, E., Toulopoulou, T., McDonald, C., Bramon, E., Walshe, M., Filbey, F., Wickham, H., Sham, P. C., Murray, R. M., & Collier, D. A. (2005). Association between BDNF Val Met genotype and episodic memory. American Journal of Medical Genetics Part B-Neuropsychiatric Genetics, 134B(1), 73–75. https://doi.org/10.1002/ajmg.b.30150
CrossRefGoogle Scholar
Dennis, E. L., Jahanshad, N., Rudie, J. D., Brown, J. A., Johnson, K., McMahon, K. L., de Zubicaray, G. I., Montgomery, G., Martin, N. G., Wright, M. J., Bookheimer, S. Y., Dapretto, M., Toga, A. W., & Thompson, P. M. (2011). Altered structural brain connectivity in healthy carriers of the autism risk gene, CNTNAP2. Brain Connectivity, 1(6), 447–459. https://doi.org/10.1089/brain.2011.0064 Medline.CrossRefGoogle ScholarPubMed
Deroche, M. L. D., Wolfe, J., Neumann, S., Manning, J., Towler, W., Alemi, R., Bien, A. G., Koirala, N., Hanna, L., Henry, L., & Gracco, V. L. (2023). Auditory evoked response to an oddball paradigm in children wearing cochlear implants. Clinical Neurophysiology, 149, 133–145. https://doi.org/10.1016/j.clinph.2023.02.179
CrossRefGoogle Scholar
Di Giorgio, A., Blasi, G., Sambataro, F., Rampino, A., Papazacharias, A., Gambi, F., Romano, R., Caforio, G., Rizzo, M., Latorre, V., Popolizio, T., Kolachana, B., Callicott, J. H., Nardini, M., Weinberger, D. R., & Bertolino, A. (2008). Association of the SerCys DISC1 polymorphism with human hippocampal formation gray matter and function during memory encoding. The European Journal of Neuroscience, 28(10), 2129–2136. https://doi.org/10.1111/j.1460-9568.2008.06482.x
CrossRefGoogle ScholarPubMed
Diamond, A. (2007). Consequences of variations in genes that affect dopamine in prefrontal cortex. Cerebral Cortex, 17(suppl 1), i161–i170. https://doi.org/10.1093/cercor/bhm082
CrossRefGoogle ScholarPubMed
Du, L., Zhang, J., Liu, F., Wang, H., Guo, L., Han, J., & Disease Neuroimaging Initiative, the A’s (2021). Identifying associations among genomic, proteomic and imaging biomarkers via adaptive sparse multi-view canonical correlation analysis. Medical Image Analysis, 70, 102003. https://doi.org/10.1016/j.media.2021.102003
CrossRefGoogle ScholarPubMed
Dudbridge, F. (2013). Power and predictive accuracy of polygenic risk scores. PLOS Genetics, 9(3), e1003348. https://doi.org/10.1371/journal.pgen.1003348
CrossRefGoogle ScholarPubMed
Duff, B. J., Macritchie, K. A. N., Moorhead, T. W. J., Lawrie, S. M., & Blackwood, D. H. R. (2013). Human brain imaging studies of DISC1 in schizophrenia, bipolar disorder and depression: A systematic review. Schizophrenia Research, 147(1), 1–13. https://doi.org/10.1016/j.schres.2013.03.015
CrossRefGoogle ScholarPubMed
Duman, R. S., Sanacora, G., & Krystal, J. H. (2019). Altered connectivity in depression: GABA and glutamate neurotransmitter deficits and reversal by novel treatments. Neuron, 102(1), 75–90. https://doi.org/10.1016/j.neuron.2019.03.013
CrossRefGoogle ScholarPubMed
Ecker, C., Pretzsch, C. M., Bletsch, A., Mann, C., Schaefer, T., Ambrosino, S., Tillmann, J., Yousaf, A., Chiocchetti, A., Lombardo, M. V., Warrier, V., Bast, N., Moessnang, C., Baumeister, S., Dell’Acqua, F., Floris, D. L., Zabihi, M., Marquand, A., Cliquet, F.,…Murphy, D. G. M. (2022). Interindividual differences in cortical thickness and their genomic underpinnings in autism spectrum disorder. The American Journal of Psychiatry, 179(3), 242–254. https://doi.org/10.1176/appi.ajp.2021.20050630
CrossRefGoogle ScholarPubMed
Egan, M. F., Goldberg, T. E., Kolachana, B. S., Callicott, J. H., Mazzanti, C. M., Straub, R. E., Goldman, D., & Weinberger, D. R. (2001). Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proceedings of the National Academy of Sciences, 98(12), 6917–6922. https://doi.org/10.1073/pnas.111134598
CrossRefGoogle ScholarPubMed
Egan, M. F., Kojima, M., Callicott, J. H., Goldberg, T. E., Kolachana, B. S., Bertolino, A., Zaitsev, E., Gold, B., Goldman, D., Dean, M., Lu, B., & Weinberger, D. R. (2003). The BDNF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell, 112(2), 257–269. https://doi.org/10.1016/S0092-8674(03)00035-7
CrossRefGoogle ScholarPubMed
Elliott, L. T., Sharp, K., Alfaro-Almagro, F., Shi, S., Miller, K. L., Douaud, G., Marchini, J., & Smith, S. M. (2018). Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature, 562(7726), 210–216. https://doi.org/10.1038/s41586-018-0571-7
CrossRefGoogle ScholarPubMed
Fan, C. C., Smeland, O. B., Schork, A. J., Chen, C.-H., Holland, D., Lo, M.-T., Sundar, V. S., Frei, O., Jernigan, T. L., Andreassen, O. A., & Dale, A. M. (2018). Beyond heritability: Improving discoverability in imaging genetics. Human Molecular Genetics, 27(R1), R22–R28. https://doi.org/10.1093/hmg/ddy082
CrossRefGoogle ScholarPubMed
Fan, J., Han, F., & Liu, H. (2014). Challenges of big data analysis. National Science Review, 1(2), 293–314. https://doi.org/10.1093/nsr/nwt032
CrossRefGoogle ScholarPubMed
Fernandez-Cabello, S., Alnæs, D., van der Meer, D., Dahl, A., Holm, M., Kjelkenes, R., Maximov, I. I., Norbom, L. B., Pedersen, M. L., Voldsbekk, I., Andreassen, O. A., & Westlye, L. T. (2022). Associations between brain imaging and polygenic scores of mental health and educational attainment in children aged 9 – 11. NeuroImage, 263, 119611. https://doi.org/10.1016/j.neuroimage.2022.119611 psyh.CrossRefGoogle ScholarPubMed
Fernandez-Jaen, A., Lopez-Martin, S., Albert, J., Fernandez-Mayoralas, D. M., Fernandez-Perrone, A. L., de La Pena, M. J., Calleja-Perez, B., Rodriguez, M. R., Lopez-Arribas, S., & Munoz-Jareno, N. (2015). Cortical thickness differences in the prefrontal cortex in children and adolescents with ADHD in relation to dopamine transporter (DAT1) genotype. Psychiatry Research: Neuroimaging, 233(3), 409–417. https://doi.org/10.1016/j.pscychresns.2015.07.005 Medline.CrossRefGoogle ScholarPubMed
Ferrari, M., Giannini, I., Sideri, G., & Zanette, E. (1985). Continuous non invasive monitoring of human-brain by near-infrared spectroscopy. Advances in Experimental Medicine and Biology, 191, 873–882.10.1007/978-1-4684-3291-6_88CrossRefGoogle ScholarPubMed
Flores-Dorantes, M. T., Díaz-López, Y. E., & Gutiérrez-Aguilar, R. (2020). Environment and gene association with obesity and their impact on neurodegenerative and neurodevelopmental diseases. Frontiers in Neuroscience, 14, Article 863. https://doi.org/10.3389/fnins.2020.00863
CrossRefGoogle ScholarPubMed
Fornito, A., & Bullmore, E. T. (2012). Connectomic intermediate phenotypes for psychiatric disorders. Frontiers in Psychiatry, 3, 32. https://doi.org/10.3389/fpsyt.2012.00032
CrossRefGoogle ScholarPubMed
Fu, J., Liu, F., Qin, W., Xu, Q., Yu, C., & Alzheimer’s Disease Neuroimaging Initiative (ADNI) (2020). Individual-level identification of gene expression associated with volume differences among neocortical areas. Cerebral Cortex, 30(6), 3655–3666. https://doi.org/10.1093/cercor/bhz333 psyh.CrossRefGoogle ScholarPubMed
Gaiteri, C., Ding, Y., French, B., Tseng, G. C., & Sibille, E. (2014). Beyond modules and hubs: The potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes, Brain and Behavior, 13(1), 13–24. https://doi.org/10.1111/gbb.12106
CrossRefGoogle ScholarPubMed
Gao, J., Xu, Y., Li, Y., Lu, F., & Wang, Z. (2024). Comprehensive exploration of multi-modal and multi-branch imaging markers for autism diagnosis and interpretation: Insights from an advanced deep learning model. Cerebral Cortex, 34(2), bhad521. https://doi.org/10.1093/cercor/bhad521 (New York, N.Y.: 1991).CrossRefGoogle ScholarPubMed
Gao, W., Grewen, K., Knickmeyer, R. C., Qiu, A., Salzwedel, A., Lin, W., & Gilmore, J. H. (2019). A review on neuroimaging studies of genetic and environmental influences on early brain development. NeuroImage, 185, 802–812. https://doi.org/10.1016/j.neuroimage.2018.04.032
CrossRefGoogle ScholarPubMed
Ge, Y.-J., Wu, B.-S., Zhang, Y., Chen, S.-D., Zhang, Y.-R., Kang, J.-J., Deng, Y.-T., Ou, Y.-N., He, X.-Y., Zhao, Y.-L., Kuo, K., Ma, Q., Banaschewski, T., Barker, G. J., Bokde, A. L. W., Desrivières, S., Flor, H., Grigis, A., Garavan, H., …Yu, J.-T. (2024). Genetic architectures of cerebral ventricles and their overlap with neuropsychiatric traits. Nature Human Behaviour, 8(1), 164–180. https://doi.org/10.1038/s41562-023-01722-6
CrossRefGoogle ScholarPubMed
Glaab, E., Trezzi, J.-P., Greuel, A., Jäger, C., Hodak, Z., Drzezga, A., Timmermann, L., Tittgemeyer, M., Diederich, N. J., & Eggers, C. (2019). Integrative analysis of blood metabolomics and PET brain neuroimaging data for Parkinson’s disease. Neurobiology of Disease, 124, 555–562. https://doi.org/10.1016/j.nbd.2019.01.003
CrossRefGoogle ScholarPubMed
Glahn, D. C., Winkler, A. M., Kochunov, P., Almasy, L., Duggirala, R., Carless, M. A., Curran, J. C., Olvera, R. L., Laird, A. R., Smith, S. M., Beckmann, C. F., Fox, P. T., & Blangero, J. (2010). Genetic control over the resting brain. Proceedings of the National Academy of Sciences of the United States of America, 107(3), 1223–1228.10.1073/pnas.0909969107CrossRefGoogle ScholarPubMed
González-Peñas, J., Alloza, C., Brouwer, R., Díaz-Caneja, C. M., Costas, J., González-Lois, N., Gallego, A. G., de Hoyos, L., Gurriarán, X., Andreu-Bernabeu, Á., Romero-García, R., Fañanas, L., Bobes, J., Pinto, A. G., Crespo-Facorro, B., Martorell, L., Arrojo, M., Vilella, E., Guitiérrez-Zotes, A.,…Schnac, H. (2024). Accelerated cortical thinning in schizophrenia is associated with rare and common predisposing variation to schizophrenia and neurodevelopmental disorders. Biological Psychiatry, S0006-3223(24
01170–3. https://doi.org/10.1016/j.biopsych.2024.03.011
Google ScholarPubMed
González-Peñas, J., Costas, J. C., García-Alcón, A., Penzol, M. J., Rodríguez, J., Rodríguez-Fontenla, C., Alonso-González, A., Fernández-Prieto, M., Carracedo, Á., Arango, C., & Parellada, M. (2020). Psychiatric comorbidities in Asperger syndrome are related with polygenic overlap and differ from other autism subtypes. Translational Psychiatry, 10(1), 1–9. https://doi.org/10.1038/s41398-020-00939-7
CrossRefGoogle ScholarPubMed
Gouveris, H., Koirala, N., Anwar, A. R., Ding, H., Ludwig, K., Huppertz, T., Matthias, C., Groppa, S., & Muthuraman, M. (2022). Reduced cross-frequency coupling and daytime sleepiness in obstructive sleep apnea patients. Biology, 11(5), 700.10.3390/biology11050700CrossRefGoogle ScholarPubMed
Grant, S. F. A., & Hakonarson, H. (2008). Microarray technology and applications in the arena of genome-wide association. Clinical Chemistry, 54(7), 1116–1124. https://doi.org/10.1373/clinchem.2008.105395
CrossRefGoogle Scholar
Grossmann, T., Johnson, M. H., Vaish, A., Hughes, D. A., Quinque, D., Stoneking, M., & Friederici, A. D. (2011). Genetic and neural dissociation of individual responses to emotional expressions in human infants. Developmental Cognitive Neuroscience, 1(1), 57–66. https://doi.org/10.1016/j.dcn.2010.07.001
CrossRefGoogle ScholarPubMed
Grove, J., Ripke, S., Als, T. D., Mattheisen, M., Walters, R. K., Won, H., Pallesen, J., Agerbo, E., Andreassen, O. A., Anney, R., Awashti, S., Belliveau, R., Bettella, F., Buxbaum, J. D., Bybjerg-Grauholm, J., Bækvad-Hansen, M., Cerrato, F., Chambert, K., Christensen, J. H.…Børglum, A. D. (2019). Identification of common genetic risk variants for autism spectrum disorder. Nature Genetics, 51(3), 431–444. https://doi.org/10.1038/s41588-019-0344-8
CrossRefGoogle ScholarPubMed
Guhn, A., Domschke, K., Müller, L. D., Dresler, T., Eff, F., Kopf, J., Deckert, J., Reif, A., & Herrmann, M. J. (2015). Neuropeptide S receptor gene variation and neural correlates of cognitive emotion regulation. Social Cognitive and Affective Neuroscience, 10(12), 1730–1737. https://doi.org/10.1093/scan/nsv061
CrossRefGoogle ScholarPubMed
Gulsuner, S., Walsh, T., Watts, A C., Lee, M K., Thornton, A M., Casadei, S., Rippey, C., Shahin, H., Nimgaonkar, V. L., Go, R. C. P., Savage, R. M., Swerdlow, N. R., Gur, R. E., Braff, D. L., King, M.-C., McClellan, J. M., Braff, D., Cadenhead, K S., Calkins, M E., …Wilson, W. (2013). Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network. Cell, 154(3), 518–529. https://doi.org/10.1016/j.cell.2013.06.049
CrossRefGoogle Scholar
Günther, T., Nettelblad, C., & Di Rienzo, A. (2019). The presence and impact of reference bias on population genomic studies of prehistoric human populations. PLoS Genetics, 15(7), e1008302.10.1371/journal.pgen.1008302CrossRefGoogle ScholarPubMed
Guo, X., Liu, L., Li, T., Zhao, Q., Li, H., Huang, F., Wang, Y., Qian, Q., Cao, Q., Wang, Y., Calhoun, V. D., Sui, J., & Sun, L. (2021). Inhibition-directed multimodal imaging fusion patterns in adults with ADHD and its potential underlying “gene-brain-cognition” relationship. CNS Neuroscience & Therapeutics, 27(6), 664–673. https://doi.org/10.1111/cns.13625 psyh.CrossRefGoogle ScholarPubMed
Hale, A. T., Bastarache, L., Morales, D. M., Wellons, J. C. III, Limbrick, D. D. Jr., & Gamazon, E. R. (2021). Multi-omic analysis elucidates the genetic basis of hydrocephalus. Cell Reports, 35(5), 109085. https://doi.org/10.1016/j.celrep.2021.109085
CrossRefGoogle ScholarPubMed
Han, K.-M., Han, M.-R., Kim, A., Kang, W., Kang, Y., Kang, J., Tae, W.-S., Cho, Y., & Ham, B.-J. (2020). A study combining whole-exome sequencing and structural neuroimaging analysis for major depressive disorder. Journal of Affective Disorders, 262, 31–39.10.1016/j.jad.2019.10.039CrossRefGoogle ScholarPubMed
Harrisberger, F., Spalek, K., Smieskova, R., Schmidt, A., Coynel, D., Milnik, A., Fastenrath, M., Freytag, V., Gschwind, L., Walter, A., Vogel, T., Bendfeldt, K., de Quervain, D. J.-F., Papassotiropoulos, A., & Borgwardt, S. (2014). The association of the BDNF Val66Met polymorphism and the hippocampal volumes in healthy humans: A joint meta-analysis of published and new data. Neuroscience & Biobehavioral Reviews, 42, 267–278. https://doi.org/10.1016/j.neubiorev.2014.03.011
CrossRefGoogle ScholarPubMed
Hashimoto, R., Ohi, K., Yamamori, H., Yasuda, Y., Fujimoto, M., Umeda-Yano, S., Watanabe, Y., Fukunaga, M., & Takeda, M. (2015). Imaging genetics and psychiatric disorders. Current Molecular Medicine, 15(2), 168–175. https://doi.org/10.2174/1566524015666150303104159
CrossRefGoogle ScholarPubMed
Herrmann, M. J., Huter, T., Müller, F., Mühlberger, A., Pauli, P., Reif, A., Renner, T., Canli, T., Fallgatter, A. J., & Lesch, K. P. (2007). Additive effects of serotonin transporter and tryptophan hydroxylase-2 gene variation on emotional processing. Cerebral Cortex, 17(5), 1160–1163. https://doi.org/10.1093/cercor/bhl026
CrossRefGoogle ScholarPubMed
Hess, J. L., Radonjić, N. V., Patak, J., Glatt, S. J., & Faraone, S. V. (2021). Autophagy, apoptosis, and neurodevelopmental genes might underlie selective brain region vulnerability in attention-deficit/hyperactivity disorder. Molecular Psychiatry, 26(11), 6643–6654. https://doi.org/10.1038/s41380-020-00974-2
CrossRefGoogle ScholarPubMed
Hiraiwa, A., Matsui, K., Nakayama, Y., Komatsubara, T., Magara, S., Kobayashi, Y., Hojo, M., Kato, M., Yamamoto, T., & Tohyama, J. (2021). Polymicrogyria with calcification in Pallister-Killian syndrome detected by microarray analysis. Brain & Development, 43(3), 448–453. https://doi.org/10.1016/j.braindev.2020.11.003 psyh.CrossRefGoogle ScholarPubMed
Holland, D., Wang, Y., Thompson, W. K., Schork, A., Chen, C.-H., Lo, M.-T., Witoelar, A., Schizophrenia Working Group of the Psychiatric Genomics Consortium, Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, Werge, T., O’Donovan, M., Andreassen, O. A., & Dale, A. M. (2016). Estimating effect sizes and expected replication probabilities from GWAS summary statistics. Frontiers in Genetics, 7, Article 14. https://doi.org/10.3389/fgene.2016.00015
CrossRefGoogle ScholarPubMed
Hong, S. B., Zalesky, A., Park, S., Yang, Y. H., Park, M. H., Kim, B., Song, I. C., Sohn, C. H., Shin, M. S., Kim, B. N., Cho, S. C., & Kim, J. W. (2015). COMT genotype affects brain white matter pathways in attention-deficit/Hyperactivity disorder. Human Brain Mapping, 36(1), 367–377. https://doi.org/10.1002/hbm.22634
CrossRefGoogle ScholarPubMed
Hughes, D. E., Kunitoki, K., Elyounssi, S., Luo, M., Bazer, O. M., Hopkinson, C. E., Dowling, K. F., Doyle, A. E., Dunn, E. C., Eryilmaz, H., Gilman, J. M., Holt, D. J., Valera, E. M., Smoller, J. W., Cecil, C. A. M., Tiemeier, H., Lee, P. H., & Roffman, J. L. (2023). Genetic patterning for child psychopathology is distinct from that for adults and implicates fetal cerebellar development. Nature Neuroscience, 26(6), 959–969. https://doi.org/10.1038/s41593-023-01321-8 psyh.CrossRefGoogle ScholarPubMed
Hüls, A., Wedderburn, C. J., Groenewold, N. A., Gladish, N., Jones, M., Koen, N., MacIsaac, J. L., Lin, D. T., Ramadori, K. E., Epstein, M. P., Donald, K. A., Kobor, M. S., Zar, H. J., & Stein, D. J. (2022). Newborn differential DNA methylation and subcortical brain volumes as early signs of severe neurodevelopmental delay in a South African birth cohort study. The World Journal of Biological Psychiatry : The Official Journal of the World Federation of Societies of Biological Psychiatry, 23(8), 601–612. https://doi.org/10.1080/15622975.2021.2016955
CrossRefGoogle Scholar
Jabbi, M., Arasappan, D., Eickhoff, S. B., Strakowski, S. M., Nemeroff, C. B., & Hofmann, H. A. (2020). Neuro-transcriptomic signatures for mood disorder morbidity and suicide mortality. Journal of Psychiatric Research, 127, 62–74. https://doi.org/10.1016/j.jpsychires.2020.05.013 psyh.CrossRefGoogle ScholarPubMed
Jacob, S., Wolff, J. J., Steinbach, M. S., Doyle, C. B., Kumar, V., & Elison, J. T. (2019). Neurodevelopmental heterogeneity and computational approaches for understanding autism. Translational Psychiatry, 9(1), 63. https://doi.org/10.1038/s41398-019-0390-0
CrossRefGoogle ScholarPubMed
Jernigan, T. L., Brown, T. T., Hagler, D. J. Jr., Akshoomoff, N., Bartsch, H., Newman, E., Thompson, W. K., Bloss, C. S., Murray, S. S., Schork, N., Kennedy, D. N., Kuperman, J. M., McCabe, C., Chung, Y., Libiger, O., Maddox, M., Casey, B. J., Chang, L., Ernst, T. M., …Dale, A. M. (2016). The pediatric imaging, neurocognition, and genetics (PING) data repository. NeuroImage, 124(Pt B), 1149–1154. https://doi.org/10.1016/j.neuroimage.2015.04.057
CrossRefGoogle ScholarPubMed
Jia, T., Chu, C., Liu, Y., van Dongen, J., Papastergios, E., Armstrong, N. J., Bastin, M. E., Carrillo-Roa, T., den Braber, A., Harris, M., Jansen, R., Liu, J., Luciano, M., Ori, A. P. S., Roiz Santiañez, R., Ruggeri, B., Sarkisyan, D., Shin, J., Sungeun, , … Desrivières, S. (2021). Epigenome-wide meta-analysis of blood DNA methylation and its association with subcortical volumes: Findings from the ENIGMA epigenetics working group. Molecular Psychiatry, 26(8), 3884–3895. https://doi.org/10.1038/s41380-019-0605-z psyh.CrossRefGoogle ScholarPubMed
Jiang, X., Zai, C. C., Kennedy, K. G., Zou, Y., Nikolova, Y. S., Felsky, D., Young, L. T., MacIntosh, B. J., & Goldstein, B. I. (2023). Association of polygenic risk for bipolar disorder with grey matter structure and white matter integrity in youth. Translational Psychiatry, 13(1), 322. https://doi.org/10.1038/s41398-023-02607-y
CrossRefGoogle ScholarPubMed
Jindal, M., Chhetri, A., Ludhiadch, A., Singh, P., Peer, S., Singh, J., Brar, R. S., & Munshi, A. (2024). Neuroimaging genomics a predictor of major depressive disorder (MDD). Molecular Neurobiology, 61(6), 3427–3440. https://doi.org/10.1007/s12035-023-03775-0
CrossRefGoogle Scholar
Johnson, P. C., & Haydon, D. T. (2007). Maximum-likelihood estimation of allelic dropout and false allele error rates from microsatellite genotypes in the absence of reference data. Genetics, 175(2), 827–842.10.1534/genetics.106.064618CrossRefGoogle ScholarPubMed
Kabukcu Basay, B., Buber, A., Basay, O., Alacam, H., Ozturk, O., Suren, S., Izci Ay, O., Acikel, C., Agladıoglu, K., Erdal, M. E., Ercan, E. S., & Herken, H. (2016). White matter alterations related to attention-deficit hyperactivity disorder and COMT val(158)met polymorphism: Children with valine homozygote attention-deficit hyperactivity disorder have altered white matter connectivity in the right cingulum (cingulate gyrus). Neuropsychiatric Disease and Treatment, 12, 969–981. https://doi.org/10.2147/ndt.S104450
Google Scholar
Kang, J. H., & Chung, J.-K. (2008). Molecular-genetic imaging based on reporter gene expression. Journal of Nuclear Medicine: Official Publication, Society of Nuclear Medicine, 49(Suppl 2), 164S–179S. https://doi.org/10.2967/jnumed.107.045955
CrossRefGoogle ScholarPubMed
Kebschull, J. M., & Zador, A. M. (2015). Sources of PCR-induced distortions in high-throughput sequencing data sets. Nucleic Acids Research, 43(21), e143. https://doi.org/10.1093/nar/gkv717
Google ScholarPubMed
Kelly, R. E., & Hoptman, M. J. (2022). Replicability in brain imaging. Brain Sciences, 12(3), Article 397. https://doi.org/10.3390/brainsci12030397
CrossRefGoogle ScholarPubMed
Khanna, N., Altmeyer, W., Zhuo, J., & Steven, A. (2015). Functional neuroimaging: Fundamental principles and clinical applications. The Neuroradiology Journal, 28(2), 87–96. https://doi.org/10.1177/1971400915576311
CrossRefGoogle ScholarPubMed
Khundrakpam, B., Bhutani, N., Vainik, U., Gong, J., Al-Sharif, N., Dagher, A., White, T., & Evans, A. C. (2023). A critical role of brain network architecture in a continuum model of autism spectrum disorders spanning from healthy individuals with genetic liability to individuals with ASD. Molecular Psychiatry, 28(3), 1210–1218. https://doi.org/10.1038/s41380-022-01916-w psyh.CrossRefGoogle Scholar
Khundrakpam, B., Vainik, U., Gong, J., Al-Sharif, N., Bhutani, N., Kiar, G., Zeighami, Y., Kirschner, M., Luo, C., Dagher, A., & Evans, A. (2020). Neural correlates of polygenic risk score for autism spectrum disorders in general population. Brain Communications, 2(2), Article fcaa092. https://doi.org/10.1093/braincomms/fcaa092
CrossRefGoogle ScholarPubMed
Kim, J. I., Yoo, J. H., Kim, D., Jeong, B., & Kim, B. N. (2018). The effects of GRIN2B and DRD4 gene variants on local functional connectivity in attention-deficit/hyperactivity disorder. Brain Imaging and Behavior, 12(1), 247–257. https://doi.org/10.1007/s11682-017-9690-2
CrossRefGoogle ScholarPubMed
Kim, J.-W., Park, K., Kang, R. J., Gonzales, E. L., Oh, H. A., Seung, H., Ko, M. J., Cheong, J. H., Chung, C., & Shin, C. Y. (2019). Gene-environment interaction counterbalances social impairment in mouse models of autism. Scientific Reports, 9(1), 11490. https://doi.org/10.1038/s41598-019-47680-w
CrossRefGoogle ScholarPubMed
Klein, R. A., Ratliff, K. A., Vianello, M., Adams, R. B. Jr., Bahník, Š., Bernstein, M. J., Bocian, K., Brandt, M. J., Brooks, B., Brumbaugh, C. C., Cemalcilar, Z., Chandler, J., Cheong, W., Davis, W. E., Devos, T., Eisner, M., Frankowska, N., Furrow, D., Galliani, E. M., …Nosek, B. A. (2014). Investigating variation in replicability: A “many labs” replication project. Social Psychology, 45(3), 142–152. https://doi.org/10.1027/1864-9335/a000178
CrossRefGoogle Scholar
Kockum, I., Huang, J., & Stridh, P. (2023). Overview of genotyping technologies and methods. Current Protocols, 3(4), e727. https://doi.org/10.1002/cpz1.727
CrossRefGoogle ScholarPubMed
Kohannim, O., Hibar, D. P., Stein, J. L., Jahansha, N., Hua, X., Rajagopalan, P., Toga, A. W., Jack, C. R., Weiner, M. W., de Zubicaray, G. I., McMahon, K. L., Hansell, N. K., Martin, N. G., Wright, M. J., & Thompson, P. M. (2012). Discovery and replication of gene influences on brain structure using LASSO regression. Frontiers in Neuroscience, 6, Article 115. https://doi.org/10.3389/fnins.2012.00115
CrossRefGoogle ScholarPubMed
Kohshour, M. O., Papiol, S., Ching, C. R., & Schulze, T. G. (2022). Genomic and neuroimaging approaches to bipolar disorder. British Journal of Psychiatry Open, 8(2), Article e36.Google Scholar
Koirala, N., Deroche, M. L. D., Wolfe, J., Neumann, S., Bien, A. G., Doan, D., Goldbeck, M., Muthuraman, M., & Gracco, V. L. (2023). Dynamic networks differentiate the language ability of children with cochlear implants. Frontiers in Neuroscience, 17, Article 1141886. https://doi.org/10.3389/fnins.2023.1141886
CrossRefGoogle ScholarPubMed
Koirala, N., Fleischer, V., Glaser, M., Zeuner, K. E., Deuschl, G., Volkmann, J., Muthuraman, M., & Groppa, S. (2018). Frontal lobe connectivity and network community characteristics are associated with the outcome of subthalamic nucleus deep brain stimulation in patients with Parkinson’s disease. Brain Topography, 31(2), 311–321. https://doi.org/10.1007/s10548-017-0597-4
CrossRefGoogle ScholarPubMed
Koirala, N., Kleinman, D., Perdue, M. V., Su, X., Villa, M., Grigorenko, E. L., & Landi, N. (2021). Widespread effects of dMRI data quality on diffusion measures in children. Human Brain Mapping, 43(4), 1326–1341. https://doi.org/10.1002/hbm.25724
CrossRefGoogle ScholarPubMed
Koirala, N., Perdue, M. V., Su, X., Grigorenko, E. L., & Landi, N. (2021). Neurite density and arborization is associated with reading skill and phonological processing in children. NeuroImage, 241, 118426. https://doi.org/10.1016/j.neuroimage.2021.118426
CrossRefGoogle ScholarPubMed
Kosmicki, J. A., Churchhouse, C. L., Rivas, M. A., & Neale, B. M. (2016). Discovery of rare variants for complex phenotypes. Human Genetics, 135(6), 625–634. https://doi.org/10.1007/s00439-016-1679-1
CrossRefGoogle ScholarPubMed
Lan, J. H., Yin, Y., Reed, E. F., Moua, K., Thomas, K., & Zhang, Q. (2015). Impact of three Illumina library construction methods on GC bias and HLA genotype calling. Human Immunology, 76(2-3), 166–175. https://doi.org/10.1016/j.humimm.2014.12.016
CrossRefGoogle ScholarPubMed
Lancaster, K., Morris, J. P., & Connelly, J. J. (2018). Neuroimaging epigenetics: Challenges and recommendations for best practices. Neuroscience, 370, 88–100. https://doi.org/10.1016/j.neuroscience.2017.08.004
CrossRefGoogle ScholarPubMed
Landi, N., Frost, S. J., Mencl, W. E., Preston, J. L., Jacobsen, L. K., Lee, M., Yrigollen, C., Pugh, K. R., & Grigorenko, E. L. (2013). The COMT Val/Met polymorphism is associated with reading related skills and consistent patterns of functional neural activation. Developmental Science, 16(1), 13–23. https://doi.org/10.1111/j.1467-7687.2012.01180.x
CrossRefGoogle ScholarPubMed
Lemvigh, C., Brouwer, R., Johansen, L. B., Hilker, R., Pantelis, C., Glenthoj, B., & Fagerlund, B. (2020). Genetic influences on memory functions and related brain structures and associations with schizophrenia spectrum disorders: A nation-wide twin study. Schizophrenia Bulletin, 46, S254–S254.10.1093/schbul/sbaa029.620CrossRefGoogle Scholar
Lenroot, R. K., & Giedd, J. N. (2008). The changing impact of genes and environment on brain development during childhood and adolescence: Initial findings from a neuroimaging study of pediatric twins. Development and Psychopathology, 20(4), 1161–1175. https://doi.org/10.1017/s0954579408000552
CrossRefGoogle ScholarPubMed
Leyhausen, J., Schäfer, T., Gurr, C., Berg, L. M., Seelemeyer, H., Pretzsch, C. M., Loth, E., Oakley, B., Buitelaar, J. K., Beckmann, C. F., Floris, D. L., Charman, T., Bourgeron, T., Banaschewski, T., Jones, E. J. H., Tillmann, J., Chatham, C., Murphy, D. G., Ecker, C., …Zwiers, M. P. (2024). Differences in intrinsic gray matter connectivity and their genomic underpinnings in autism spectrum disorder. Biological Psychiatry, 95(2), 175–186. https://doi.org/10.1016/j.biopsych.2023.06.010 psyh.CrossRefGoogle ScholarPubMed
Li, Q., Xu, X., Qian, Y., Cai, H., Zhao, W., Zhu, J., & Yu, Y. (2023). Resting-state brain functional alterations and their genetic mechanisms in drug-naive first-episode psychosis. Schizophrenia, 9(1), 13. https://doi.org/10.1038/s41537-023-00338-z
CrossRefGoogle ScholarPubMed
Li, X., Jiang, M., Zhao, L., Yang, K., Lu, T., Zhang, D., Li, J., & Wang, L. (2024). Relationship between autism and brain cortex surface area: Genetic correlation and a two-sample Mendelian randomization study. BMC Psychiatry, 24(1), Article 69. https://doi.org/10.1186/s12888-024-05514-8
Google Scholar
Li, Y., Zhou, F., Li, R., Gu, J., & He, J. (2023). Exploring the correlation between genetic transcription and multi-temporal developmental autism spectrum disorder using resting-state functional magnetic resonance imaging. Frontiers in Neuroscience, 17, Article 1219753. https://doi.org/10.3389/fnins.2023.1219753
CrossRefGoogle ScholarPubMed
Liu, J., & Calhoun, V. (2014). A review of multivariate analyses in imaging genetics. Frontiers in Neuroinformatics, 8, Article 29. https://doi.org/10.3389/fninf.2014.00029
CrossRefGoogle ScholarPubMed
Lorenzi, M., Deprez, M., Balelli, I., Aguila, A. L., & Altmann, A. (2023). Integration of multimodal data. In Colliot, O. (Ed.), Machine learning for brain disorders (pp. 573–597). Springer US, https://doi.org/10.1007/978-1-0716-3195-9_19
CrossRefGoogle Scholar
Ma, L., Yuan, T., Li, W., Guo, L., Zhu, D., Wang, Z., Liu, Z., Xue, K., Wang, Y., Liu, J., Man, W., Ye, Z., Liu, F., & Wang, J. (2021). Dynamic functional connectivity alterations and their associated gene expression pattern in autism spectrum disorders. Frontiers in Neuroscience, 15, 794151. https://doi.org/10.3389/fnins.2021.794151
CrossRefGoogle ScholarPubMed
Maes, H. H. M., Lapato, D. M., Schmitt, J. E., Luciana, M., Banich, M. T., Bjork, J. M., Hewitt, J. K., Madden, P. A., Heath, A. C., Barch, D. M., Thompson, W. K., Iacono, W. G., & Neale, M. C. (2023). Genetic and environmental variation in continuous phenotypes in the ABCD study®. Behavior Genetics, 53(1), 1–24. https://doi.org/10.1007/s10519-022-10123-w psyh.CrossRefGoogle ScholarPubMed
Malhotra, A. K., Kestler, L. J., Mazzanti, C., Bates, J. A., Goldberg, T., & Goldman, D. (2002). A functional polymorphism in the COMT gene and performance on a test of prefrontal cognition. American Journal of Psychiatry, 159(4), 652–654. https://doi.org/10.1176/appi.ajp.159.4.652
CrossRefGoogle ScholarPubMed
Mandelman, S. D., & Grigorenko, E. L. (2012). BDNF Val66Met and cognition: All, none, or some? A meta-analysis of the genetic association. Genes, Brain, and Behavior, 11(2), 127–136. https://doi.org/10.1111/j.1601-183X.2011.00738.x
CrossRefGoogle ScholarPubMed
Mascarell Maričić, L., Walter, H., Rosenthal, A., Ripke, S., Quinlan, E. B., Banaschewski, T., Barker, G. J., Bokde, A. L. W., Bromberg, U., Büchel, C., Desrivières, S., Flor, H., Frouin, V., Garavan, H., Itterman, B., Martinot, J.-L., Martinot, M.-L. P., Nees, F., Orfanos, D. P., …Tahmasebi, A. (2020). The IMAGEN study: A decade of imaging genetics in adolescents. Molecular Psychiatry, 25(11), 2648–2671. https://doi.org/10.1038/s41380-020-0822-5
CrossRefGoogle ScholarPubMed
Matsumoto, J., Fukunaga, M., Miura, K., Nemoto, K., Okada, N., Hashimoto, N., Morita, K., Koshiyama, D., Ohi, K., Takahashi, T., Koeda, M., Yamamori, H., Fujimoto, M., Yasuda, Y., Ito, S., Yamazaki, R., Hasegawa, N., Narita, H., Yokoyama, S., …Hashimoto, R. (2023). Cerebral cortical structural alteration patterns across four major psychiatric disorders in 5549 individuals. Molecular Psychiatry, 28(11), 4915–4923. https://doi.org/10.1038/s41380-023-02224-7 psyh.CrossRefGoogle ScholarPubMed
McFarquhar, M., McKie, S., Emsley, R., Suckling, J., Elliott, R., & Williams, S. (2016). Multivariate and repeated measures (MRM): A new toolbox for dependent and multimodal group-level neuroimaging data. NeuroImage, 132, 373–389. https://doi.org/10.1016/j.neuroimage.2016.02.053
CrossRefGoogle ScholarPubMed
Medland, S. E., Grasby, K. L., Jahanshad, N., Painter, J. N., Colodro‐Conde, L. D.;a, Bralten, J., Hibar, D. P., Lind, P. A., Pizzagalli, F., Thomopoulos, S. I., Stein, J. L., Franke, B., Martin, N. G., Thompson, P. M., & ENIGMA Genetics Working Group (2022). Ten years of enhancing neuro-imaging genetics through meta-analysis: An overview from the ENIGMA genetics working group. Human Brain Mapping, 43(1), 292–299. https://doi.org/10.1002/hbm.25311
CrossRefGoogle ScholarPubMed
Meyer-Lindenberg, A., Nicodemus, K. K., Egan, M. F., Callicott, J. H., Mattay, V., & Weinberger, D. R. (2008). False positives in imaging genetics. NeuroImage, 40(2), 655–661. https://doi.org/10.1016/j.neuroimage.2007.11.058
CrossRefGoogle ScholarPubMed
Mezinska, S., Gallagher, L., Verbrugge, M., & Bunnik, E. M. (2021). Ethical issues in genomics research on neurodevelopmental disorders: A critical interpretive review. Human Genomics, 15(1), Article 16. https://doi.org/10.1186/s40246-021-00317-4
CrossRefGoogle ScholarPubMed
Michels, L., Koirala, N., Groppa, S., Luechinger, R., Gantenbein, A. R., Sandor, P. S., Kollias, S., Riederer, F., & Muthuraman, M. (2021). Structural brain network characteristics in patients with episodic and chronic migraine. Journal of Headache and Pain, 22(1), 8. https://doi.org/10.1186/s10194-021-01216-8
CrossRefGoogle ScholarPubMed
Milone, R., Cesario, C., Goldoni, M., Pasquariello, R., Fusilli, C., Giovannetti, A., Giglio, S., Novelli, A., Caputo, V., Cioni, G., Mazza, T., Battaglia, A., Bernardini, L., & Battini, R. (2021). Correlating neuroimaging and CNVs data: 7 years of cytogenomic microarray analysis on patients affected by neurodevelopmental disorders. Journal of Pediatric Genetics, 10(4), 292–299. https://doi.org/10.1055/s-0040-1716398
CrossRefGoogle ScholarPubMed
Miranda, M., Morici, J. F., Zanoni, M. B., & Bekinschtein, P. (2019). Brain-derived neurotrophic factor: A key molecule for memory in the healthy and the pathological brain. Frontiers in Cellular Neuroscience, 13, 363. https://doi.org/10.3389/fncel.2019.00363
CrossRefGoogle ScholarPubMed
Mizuno, Y., Jung, M., Fujisawa, T. X., Takiguchi, S., Shimada, K., Saito, D. N., Kosaka, H., & Tomoda, A. (2017). Catechol-O-methyltransferase polymorphism is associated with the cortico-cerebellar functional connectivity of executive function in children with attention-deficit/hyperactivity disorder. Scientific Reports, 7, Article 4850. https://doi.org/10.1038/s41598-017-04579-8
CrossRefGoogle ScholarPubMed
Mohammadi, D. (2015). ENIGMA: Crowdsourcing meets neuroscience. The Lancet Neurology, 14(5), 462–463.10.1016/S1474-4422(15)00005-8CrossRefGoogle ScholarPubMed
Monuteaux, M. C., Seidman, L. J., Faraone, S. V., Makris, N., Spencer, T., Valera, E., Brown, A., Bush, G., Doyle, A. E., Hughes, S., Helliesen, M., Mick, E., & Biederman, J. (2008). A preliminary study of dopamine D4 receptor genotype and structural brain alterations in adults with ADHD. American Journal of Medical Genetics Part B-Neuropsychiatric Genetics, 147B(8), 1436–1441. https://doi.org/10.1002/ajmg.b.30870
CrossRefGoogle ScholarPubMed
Moriguchi, Y., & Shinohara, I. (2018). Effect of the Val158Met genotype on lateral prefrontal activations in young children. Developmental Science, 21(5), Article e12649. https://doi.org/10.1111/desc.12649
CrossRefGoogle ScholarPubMed
Morris, D. W. (2023). Cell-specific gene expression in the prenatal brain and schizophrenia risk. Biological Psychiatry, 93(2), 105–106. https://doi.org/10.1016/j.biopsych.2022.10.008 psyh.CrossRefGoogle ScholarPubMed
Mueller, S., Keeser, D., Samson, A. C., Kirsch, V., Blautzik, J., Grothe, M., Erat, O., Hegenloh, M., Coates, U., Reiser, M. F., Hennig-Fast, K., Meindl, T., & Draganski, B. (2013). Convergent findings of altered functional and structural brain connectivity in individuals with high functioning autism: A multimodal MRI study. PloS One, 8(6), e67329.10.1371/journal.pone.0067329CrossRefGoogle ScholarPubMed
Nho, K., Corneveaux, J. J., Kim, S., Lin, H., Risacher, S. L., Shen, L., Swaminathan, S., Ramanan, V. K., Liu, Y., Foroud, T., Inlow, M. H., Siniard, A. L., Reiman, R. A., Aisen, P. S., Petersen, R. C., Green, R. C., Jack, C. R., Weiner, M. W., Baldwin, C. T., …for the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (2013). Whole-exome sequencing and imaging genetics identify functional variants for rate of change in hippocampal volume in mild cognitive impairment. Molecular Psychiatry, 18(7), 781–787.10.1038/mp.2013.24CrossRefGoogle ScholarPubMed
Nho, K., Horgusluoglu, E., Kim, S., Risacher, S. L., Kim, D., Foroud, T., Aisen, P. S., Petersen, R. C., Jack, C. R., Shaw, L. M., Trojanowski, J. Q., Weiner, M. W., Green, R. C., Toga, A. W., & Saykin, A. J. (2016). Integration of bioinformatics and imaging informatics for identifying rare PSEN1 variants in Alzheimer’s disease. BMC Medical Genomics, 9(Suppl 1), 30. https://doi.org/10.1186/s12920-016-0190-9
CrossRefGoogle ScholarPubMed
Nickl-Jockschat, T., Janouschek, H., Eickhoff, S. B., & Eickhoff, C. R. (2015). Lack of meta-analytic evidence for an impact of COMT Val158Met genotype on brain activation during working memory tasks. Biological Psychiatry, 78(11), e43–46. https://doi.org/10.1016/j.biopsych.2015.02.030
CrossRefGoogle ScholarPubMed
Nickl-Jockschat, T., & Wassink, T. (2021). Genetic imaging: Promises and pitfalls. In Brain network dysfunction in neuropsychiatric illness: Methods, applications, and implications (pp. 413–431). Springer Nature Switzerland AG, https://doi.org/10.1007/978-3-030-59797-9_20
CrossRefGoogle Scholar
Ning, K., Chen, B., Sun, F., Hobel, Z., Zhao, L., Matloff, W., & Toga, A. W. (2018). Classifying Alzheimer’s disease with brain imaging and genetic data using a neural network framework. Neurobiology of Aging, 68, 151–158. https://doi.org/10.1016/j.neurobiolaging.2018.04.009
CrossRefGoogle ScholarPubMed
Nisar, S., & Haris, M. (2023). Neuroimaging genetics approaches to identify new biomarkers for the early diagnosis of autism spectrum disorder. Molecular Psychiatry, 28, 4995–5008. https://doi.org/10.1038/s41380-023-02060-9
Google ScholarPubMed
Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990). Brain magnetic-resonance-imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences of the United States of America, 87(24), 9868–9872. https://doi.org/10.1073/pnas.87.24.9868
CrossRefGoogle ScholarPubMed
Oh, E.-Y., Han, K.-M., Kim, A., Kang, Y., Tae, W.-S., Han, M.-R., & Ham, B.-J. (2024). Integration of whole-exome sequencing and structural neuroimaging analysis in major depressive disorder: A joint study. Translational Psychiatry, 14(1), 1–11. https://doi.org/10.1038/s41398-024-02849-4
CrossRefGoogle ScholarPubMed
Öner, Ö., Akin, A., Herken, H., Erdal, M. E., Çiftçi, K., Ay, M. E., Bicer, D., Öncü, B., Bozkurt, O. H., Münir, K., & Yazgan, Y. (2011). Association among SNAP-25 gene I and I polymorphisms and hemodynamic changes during methylphenidate use: A functional near-infrared spectroscopy study. Journal of Attention Disorders, 15(8), 628–637. https://doi.org/10.1177/1087054710374597
CrossRefGoogle ScholarPubMed
Onnink, A. M. H., Franke, B., van Hulzen, K., Zwiers, M. P., Mostert, J. C., Schene, A. H., Heslenfeld, D. J., Oosterlaan, J., Hoekstra, P. J., Hartman, C. A., Vasquez, A. A., Kan, C. C., Buitelaar, J., & Hoogman, M. (2016). Enlarged striatal volume in adults with ADHD carrying the 9-6 haplotype of the dopamine transporter gene. Journal of Neural Transmission, 123(8), 905–915. https://doi.org/10.1007/s00702-016-1521-x
CrossRefGoogle ScholarPubMed
Open Science Collaboration (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. https://doi.org/10.1126/science.aac4716
CrossRefGoogle Scholar
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, , … Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. British Medical Journal, 372, Article n71. https://doi.org/10.1136/bmj.n71
Google ScholarPubMed
Pala, D., Lee, B., Ning, X., Kim, D., & Shen, L. (2022). Mediation analysis and mixed-effects models for the identification of stage-specific imaging genetics patterns in Alzheimer’s disease. Proceedings (IEEE Int Conf Bioinformatics Biomed), 2667–2673. https://doi.org/10.1109/BIBM55620.2022.9995405
Google ScholarPubMed
Palk, A., Illes, J., Thompson, P. M., & Stein, D. J. (2020). Ethical issues in global neuroimaging genetics collaborations. NeuroImage, 221, 117208. https://doi.org/10.1016/j.neuroimage.2020.117208
CrossRefGoogle ScholarPubMed
Palla, L., & Dudbridge, F. (2015). A fast method that uses polygenic scores to estimate the variance explained by genome-wide marker panels and the proportion of variants affecting a trait. American Journal of Human Genetics, 97(2), 250–259. https://doi.org/10.1016/j.ajhg.2015.06.005
CrossRefGoogle ScholarPubMed
Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015). Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nature Reviews Genetics, 16(8), 441–458. https://doi.org/10.1038/nrg3934
CrossRefGoogle ScholarPubMed
Patel, Y., Parker, N., Shin, J., Howard, D., French, L., Thomopoulos, S. I., Pozzi, E., Rooij, D. van, Buitelaar, J. K., Hoogman, M., Franke, B., Andreassen, O. A., Hibar, D. P., Ching, C. R. K., Schmaal, L., Veltman, D. J., Heuvel, O. A. van den, Stein, D. J., Turner, J. A., … Paus, T. (2020). Virtual histology of cortical thickness reveals shared neurobiology across six psychiatric disorders. Biological Psychiatry, 87(9), S239–S240. https://doi.org/10.1016/j.biopsych.2020.02.1188
CrossRefGoogle Scholar
Peper, J. S., Brouwer, R. M., Boomsma, D. I., Kahn, R. S., & Hulshoff Pol, H. E. (2007). Genetic influences on human brain structure: A review of brain imaging studies in twins. Human Brain Mapping, 28(6), 464–473. https://doi.org/10.1002/hbm.20398
CrossRefGoogle ScholarPubMed
Petrican, R., & Fornito, A. (2023). Adolescent neurodevelopment and psychopathology: The interplay between adversity exposure and genetic risk for accelerated brain ageing. Developmental Cognitive Neuroscience, 60, 1–18. https://doi.org/10.1016/j.dcn.2023.101229 psyh.CrossRefGoogle ScholarPubMed
Petryshen, T. L., Sabeti, P. C., Aldinger, K. A., Fry, B., Fan, J. B., Schaffner, S. F., Waggoner, S. G., Tahl, A. R., & Sklar, P. (2010). Population genetic study of the brain-derived neurotrophic factor (BDNF) gene. Molecular Psychiatry, 15(8), 810–815. https://doi.org/10.1038/mp.2009.24
CrossRefGoogle ScholarPubMed
Poldrack, R. A., Baker, C. I., Durnez, J., Gorgolewski, K. J., Matthews, P. M., Munafò, M. R., Nichols, T. E., Poline, J.-B., Vul, E., & Yarkoni, T. (2017). Scanning the horizon: Towards transparent and reproducible neuroimaging research. Nature Reviews Neuroscience, 18(2), 115–126. https://doi.org/10.1038/nrn.2016.167
CrossRefGoogle ScholarPubMed
Potkin, S. G., Turner, J. A., Fallon, J. A., Lakatos, A., Keator, D. B., Guffanti, G., & Macciardi, F. (2009). Gene discovery through imaging genetics: Identification of two novel genes associated with schizophrenia. Molecular Psychiatry, 14(4), 416–428. https://doi.org/10.1038/mp.2008.127
CrossRefGoogle ScholarPubMed
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., & Petersen, S. E. (2011). Functional network organization of the human brain. Neuron, 72(4), 665–678. https://doi.org/10.1016/j.neuron.2011.09.006
CrossRefGoogle ScholarPubMed
Pretzsch, C. M., Schäfer, T., Lombardo, M. V., Warrier, V., Mann, C., Bletsch, A., Chatham, C. H., Floris, D. L., Tillmann, J., Yousaf, A., Jones, E., Charman, T., Ambrosino, S., Bourgeron, T., Dumas, G., Loth, E., Oakley, B., Buitelaar, J. K., Cliquet, F., …Ecker, C. (2022). Neurobiological correlates of change in adaptive behavior in autism. American Journal of Psychiatry, 179(5), 336–349. https://doi.org/10.1176/appi.ajp.21070711
CrossRefGoogle ScholarPubMed
Qian, A. D., Wang, X., Liu, H. R., Tao, J. J., Zhou, J. J., Ye, Q., Li, J. C., Yang, C., Cheng, J. L., Zhao, K., & Wang, M. H. (2018). Dopamine D4 receptor gene associated with the frontal-striatal-cerebellar loop in children with ADHD: A resting-state fMRI study. Neuroscience Bulletin, 34(3), 497–506. https://doi.org/10.1007/s12264-018-0217-7
CrossRefGoogle ScholarPubMed
Quinlan, E. B., Barker, E. D., Luo, Q., Banaschewski, T., Bokde, A. L. W., Bromberg, U., Büchel, C., Desrivières, S., Flor, H., Frouin, V., Garavan, H., Chaarani, B., Gowland, P., Heinz, A., Brühl, R. C., Martinot, J.-L., Martinot, M.-L. P., Nees, F., Orfanos, D. P., …Schumann, G. (2020). Peer victimization and its impact on adolescent brain development and psychopathology. Molecular Psychiatry, 25(11), 3066–3076. https://doi.org/10.1038/s41380-018-0297-9
CrossRefGoogle ScholarPubMed
Rahman, M. M., & Fatema, K. (2021). Genetic diagnosis in children with epilepsy and developmental disorders by targeted gene panel analysis in a developing country. Journal of Epilepsy Research, 11(1), 22–31. https://doi.org/10.14581/jer.21004
CrossRefGoogle ScholarPubMed
Rastogi, S., Lee, C., & Salamon, N. (2008). Neuroimaging in pediatric epilepsy: A multimodality approach. Radiographics, 28(4), 1079–1095.10.1148/rg.284075114CrossRefGoogle ScholarPubMed
Ravagnani Salto, A. B., Santoro, M. L., Hoexter, M. Q., Jackowski, A. P., Pan, P. M., Rosário, M. C., Belangero, S. I., Alvarenga, P. G., Doretto, V. F., Fumo, A. M. T., Batistuzzo, M. C., Macul Ferreira de Barros, P., Timpano, K. R., Ota, V. K., Rohde, L. A., Miguel, E. C., Leckman, J. F., & Zugman, A. (2021). Obsessive-compulsive symptoms, polygenic risk score, and thalamic development in children from the Brazilian high-risk cohort for mental conditions (BHRCS). Frontiers in Psychiatry, 12 https://doi.org/10.3389/fpsyt.2021.673595
CrossRefGoogle ScholarPubMed
Richards, A. L., Pardiñas, A. F., Frizzati, A., Tansey, K. E., Lynham, A. J., Holmans, P., Legge, S. E., Savage, J. E., Agartz, I., Andreassen, O. A., Blokland, G. A. M., Corvin, A., Cosgrove, D., Degenhardt, F., Djurovic, S., Espeseth, T., Ferraro, L., Gayer-Anderson, C., Giegling, I., …Walters, J. T. R. (2020). The relationship between polygenic risk scores and cognition in schizophrenia. Schizophrenia Bulletin, 46(2), 336–344. https://doi.org/10.1093/schbul/sbz061 psyh.Google ScholarPubMed
Rieber, N., Zapatka, M., Lasitschka, Bärbel, Jones, D., Northcott, P., Hutter, B., Jäger, N., Kool, M., Taylor, M., Lichter, P., Pfister, S., Wolf, S., Brors, B., Eils, R., & Hofmann, O. (2013). Coverage bias and sensitivity of variant calling for four whole-genome sequencing technologies. PLOS ONE, 8(6), e66621. https://doi.org/10.1371/journal.pone.0066621
CrossRefGoogle ScholarPubMed
Roberts, G., Wen, W., Ridgway, K., Ho, C., Gooch, P., Leung, V., Williams, T., Breakspear, M., & Mitchell, P. B. (2022). Hippocampal cingulum white matter increases over time in young people at high genetic risk for bipolar disorder. Journal of Affective Disorders, 314, 325–332. https://doi.org/10.1016/j.jad.2022.07.025
CrossRefGoogle Scholar
Rosenberg, M. D., & Finn, E. S. (2022). How to establish robust brain–behavior relationships without thousands of individuals. Nature Neuroscience, 25(7), 835–837. https://doi.org/10.1038/s41593-022-01110-9
CrossRefGoogle ScholarPubMed
Roshchupkin, G. V., Gutman, B. A., Vernooij, M. W., Jahanshad, N., Martin, N. G., Hofman, A., McMahon, K. L., van der Lee, S. J., van Duijn, C. M., de Zubicaray, G. I., Uitterlinden, A. G., Wright, M. J., Niessen, W. J., Thompson, P. M., Ikram, M. A., & Adams, H. H. H. (2016). Heritability of the shape of subcortical brain structures in the general population. Nature Communications, 7, Article 13738. https://doi.org/10.1038/ncomms13738
CrossRefGoogle ScholarPubMed
Ross, J. P., Dion, P. A., & Rouleau, G. A. (2020). Exome sequencing in genetic disease: Recent advances and considerations. F1000Research, 9, Article F1000 Faculty Rev-336.10.12688/f1000research.19444.1CrossRefGoogle ScholarPubMed
Sarnowski, C., Satizabal, C. L., DeCarli, C., Pitsillides, A. N., Cupples, L. A., Vasan, R. S., Wilson, J. G., Bis, J. C., Fornage, M., Beiser, A. S., DeStefano, A. L., Dupuis, J., Seshadri, S., & NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, & TOPMed Neurocognitive Working Group. (2018). Whole genome sequence analyses of brain imaging measures in the Framingham Study. Neurology, 90(3), e188–e196. https://doi.org/10.1212/WNL.0000000000004820
CrossRefGoogle ScholarPubMed
Sato, J. R., Biazoli, C. E., Bueno, A. P. A., Caye, A., Pan, P. M., Santoro, M., Honorato-Mauer, J., Salum, G. A., Hoexter, M. Q., Bressan, R. A., Jackowski, A. P., Miguel, E. C., Belangero, S., & Rohde, L. A. (2023). Polygenic risk score for attention-deficit/hyperactivity disorder and brain functional networks segregation in a community-based sample. Genes, Brain & Behavior, 22(2), 1–9. https://doi.org/10.1111/gbb.12838 psyh.CrossRefGoogle Scholar
Schmaal, L., Pozzi, E., C. Ho, T., van Velzen, L. S., Veer, I. M., Opel, N., Van Someren, E. J. W., Han, L. K. M., Aftanas, L., Aleman, Aé, Baune, B. T., Berger, K., Blanken, T. F., Capitão, L., Couvy-Duchesne, B., R. Cullen, K., Dannlowski, U., Davey, C., Erwin-Grabner, T., …Veltman, D. J. (2020). ENIGMA MDD: Seven years of global neuroimaging studies of major depression through worldwide data sharing. Translational Psychiatry, 10(1), 1–19. https://doi.org/10.1038/s41398-020-0842-6
CrossRefGoogle ScholarPubMed
Schomer, D. L., & Lopes da Silva, F. H. (2017). Niedermeyer’s electroencephalography: Basic principles, clinical applications, and related fields. Oxford University Press, https://doi.org/10.1093/med/9780190228484.001.0001
CrossRefGoogle Scholar
Schweren, L. J. S., Hartman, C. A., Heslenfeld, D. J., Groenman, A. P., Franke, B., Oosterlaan, J., Buitelaar, J. K., & Hoekstra, P. J. (2016). Age and DRD4 genotype moderate associations between stimulant treatment history and cortex structure in attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 55(10), 877–885. https://doi.org/10.1016/j.jaac.2016.06.013
CrossRefGoogle ScholarPubMed
Seidlitz, J., Nadig, A., Liu, S., Bethlehem, R. A. I., Vértes, P. E., Morgan, S. E., Váša, F., Romero-Garcia, R., Lalonde, F. M., Clasen, L. S., Blumenthal, J. D., Paquola, C., Bernhardt, B., Wagstyl, K., Polioudakis, D., de la Torre-Ubieta, L., Geschwind, D. H., Han, J. C., Lee, N. R., …Raznahan, A. (2020). Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders. Nature Communications, 11(1), 3358. https://doi.org/10.1038/s41467-020-17051-5
CrossRefGoogle ScholarPubMed
Shang, C. Y., Lin, H. Y., Tseng, W. Y., & Gau, S. S. (2018). A haplotype of the dopamine transporter gene modulates regional homogeneity, gray matter volume, and visual memory in children with attention-deficit/hyperactivity disorder. Psychological Medicine, 48(15), 2530–2540. https://doi.org/10.1017/S0033291718000144
CrossRefGoogle ScholarPubMed
Sharma, E., Vaidya, N., Iyengar, U., Zhang, Y., Holla, B., Purushottam, M., Chakrabarti, A., Fernandes, G. S., Heron, J., Hickman, M., Desrivieres, S., Kartik, K., Jacob, P., Rangaswamy, M., Bharath, R. D., Barker, G., Orfanos, D. P., Ahuja, C., Murthy, P.…Benegal, V. (2020). Consortium on vulnerability to externalizing disorders and addictions (cVEDA): A developmental cohort study protocol. BMC Psychiatry, 20(1), 2. https://doi.org/10.1186/s12888-019-2373-3
CrossRefGoogle Scholar
Shimada, K., Fujisawa, T. X., Takiguchi, S., Naruse, H., Kosaka, H., Okazawa, H., & Tomoda, A. (2017). Ethnic differences in COMT genetic effects on striatal grey matter alterations associated with childhood ADHD: A voxel-based morphometry study in a Japanese sample. World Journal of Biological Psychiatry, 18(4), 322–328. https://doi.org/10.3109/15622975.2015.1102325
CrossRefGoogle Scholar
Simões, B., Vassos, E., Shergill, S., McDonald, C., Toulopoulou, T., Kalidindi, S., Kane, F., Murray, R., Bramon, E., Ferreira, H., & Prata, D. (2020). Schizophrenia polygenic risk score influence on white matter microstructure. Journal of Psychiatric Research, 121, 62–67. https://doi.org/10.1016/j.jpsychires.2019.11.011 psyh.CrossRefGoogle ScholarPubMed
Simpson, E. H., Morud, J., Winiger, V., Biezonski, D., Zhu, J. P., Bach, M. E., Malleret, G., Polan, H. J., Ng-Evans, S., Phillips, P. E., Kellendonk, C., & Kandel, E. R. (2014). Genetic variation in COMT activity impacts learning and dopamine release capacity in the striatum. Learning & Memory, 21(4), 205–214. https://doi.org/10.1101/lm.032094.113
CrossRefGoogle ScholarPubMed
Smeland, O. B., Frei, O., Dale, A. M., & Andreassen, O. A. (2020). The polygenic architecture of schizophrenia—Rethinking pathogenesis and nosology. Nature Reviews Neurology, 16(7), 366–379. https://doi.org/10.1038/s41582-020-0364-0
CrossRefGoogle ScholarPubMed
Stein, J. L., Hua, X., Lee, S., Ho, A. J., Leow, A. D., Toga, A. W., Saykin, A. J., Shen, L., Foroud, T., Pankratz, N., Huentelman, M. J., Craig, D. W., Gerber, J. D., Allen, A. N., Corneveaux, J. J., DeChairo, B. M., Potkin, S. G., Weiner, M. W., & Thompson, P. M. (2010). Voxelwise genome-wide association study (vGWAS). NeuroImage, 53(3), 1160–1174. https://doi.org/10.1016/j.neuroimage.2010.02.032
CrossRefGoogle ScholarPubMed
Sun, X., Huang, W., Wang, J., Xu, R., Zhang, X., Zhou, J., Zhu, J., & Qian, Y. (2023). Cerebral blood flow changes and their genetic mechanisms in major depressive disorder: A combined neuroimaging and transcriptome study. Psychological Medicine, 53(14), 1–13. https://doi.org/10.1017/S0033291722003750
CrossRefGoogle ScholarPubMed
Sun, Y., Jia, T., Barker, E. D., Chen, D., Zhang, Z., Xu, J., Chang, S., Zhou, G., Liu, Y., Tay, N., Luo, Q., Chang, X., Banaschewski, T., Bokde, A. L. W., Flor, H., Grigis, A., Garavan, H., Heinz, A., Martinot, J.-L.,…Desrivières, S. (2023). Associations of DNA methylation with behavioral problems, gray matter volumes, and negative life events across adolescence: Evidence from the longitudinal IMAGEN study. Biological Psychiatry, 93(4), 342–351. https://doi.org/10.1016/j.biopsych.2022.06.012 psyh.CrossRefGoogle ScholarPubMed
Suresh, P., Ray, B., Duan, K., Chen, J., Schoenmacker, G., Franke, B., Buitelaar, J. K., Sprooten, E., Arias-Vasquez, A., Turner, J. A., & Liu, J. (2021). Evaluating the Neuroimaging-Genetic Prediction of Symptom Changes in Individuals with ADHD. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021, pp. 1950–1956. https://doi.org/10.1109/EMBC46164.2021.9630229
CrossRefGoogle Scholar
Takahashi, Y., Seki, N., Ishiura, H., Mitsui, J., Matsukawa, T., Kishino, A., Onodera, O., Aoki, M., Shimozawa, N., Murayama, S., Itoyama, Y., Suzuki, Y., Sobue, G., Nishizawa, M., Goto, J., & Tsuji, S. (2008). Development of a high-throughput microarray-based resequencing system for neurological disorders and its application to molecular genetics of amyotrophic lateral sclerosis. Archives of Neurology, 65(10), 1326–1332. https://doi.org/10.1001/archneur.65.10.1326
CrossRefGoogle ScholarPubMed
Takeuchi, H., Kimura, R., Tomita, H., Taki, Y., Kikuchi, Y., Ono, C., Yu, Z., Matsudaira, I., Nouchi, R., Yokoyama, R., Kotozaki, Y., Nakagawa, S., Hanawa, S., Iizuka, K., Sekiguchi, A., Araki, T., Miyauchi, C. M., Ikeda, S., Sakaki, K., …Kawashima, R. (2021). Polygenic risk score for bipolar disorder associates with divergent thinking and brain structures in the prefrontal cortex. Human Brain Mapping, 42(18), 6028–6037. https://doi.org/10.1002/hbm.25667 psyh.CrossRefGoogle ScholarPubMed
Takeuchi, H., Tomita, H., Taki, Y., Kikuchi, Y., Ono, C., Yu, Z., Sekiguchi, A., Nouchi, R., Kotozaki, Y., Nakagawa, S., Miyauchi, C. M., Iizuka, K., Yokoyama, R., Shinada, T., Yamamoto, Y., Hanawa, S., Araki, T., Hashizume, H., Kunitoki, K., …Kawashima, R. (2015). Cognitive and neural correlates of the 5-repeat allele of the dopamine D4 receptor gene in a population lacking the 7-repeat allele. Neuroimage, 110, 124–135. https://doi.org/10.1016/j.neuroimage.2015.01.053
CrossRefGoogle Scholar
The 1000 Genomes Project Consortium (2012). An integrated map of genetic variation from 1,092 human genomes. Nature, 491(7422), 56–65. https://doi.org/10.1038/nature11632
CrossRefGoogle Scholar
Thompson, P. M., Cannon, T. D., Narr, K. L., van Erp, T., Poutanen, V. P., Huttunen, M., Lönnqvist, J., Standertskjöld-Nordenstam, C. G., Kaprio, J., Khaledy, M., Dail, R., Zoumalan, C. I., & Toga, A. W. (2001). Genetic influences on brain structure. Nature Neuroscience, 4(12), 1253–1258. https://doi.org/10.1038/nn758
CrossRefGoogle ScholarPubMed
Thompson, P. M., Jahanshad, N., Ching, C. R. K., Salminen, L. E., Thomopoulos, S. I., Bright, J., Baune, B. T., Bertolín, S., Bralten, J., Bruin, W. B., Bülow, R., Chen, J., Chye, Y., Dannlowski, U., de Kovel, C. G. F., Donohoe, G., Eyler, L. T., Faraone, S. V., Favre, , … for the ENIGMA Consortium (2020). ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries. Translational Psychiatry, 10(1), 100. https://doi.org/10.1038/s41398-020-0705-1
CrossRefGoogle ScholarPubMed
Thompson, P. M., Stein, J. L., Medland, S. E., Hibar, D. P., Vasquez, A. A., Renteria, M. E., Toro, R., Jahanshad, N., Schumann, G., & Franke, B. (2014). The ENIGMA consortium: Large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging and Behavior, 8, 153–182.10.1007/s11682-013-9269-5CrossRefGoogle ScholarPubMed
Tooley, U. A., Bassett, D. S., & Mackey, A. P. (2021). Environmental influences on the pace of brain development. Nature Reviews Neuroscience, 22(6), 372–384. https://doi.org/10.1038/s41583-021-00457-5
CrossRefGoogle ScholarPubMed
Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021). Genome-wide association studies. Nature Reviews Methods Primers, 1(1), 59. https://doi.org/10.1038/s43586-021-00056-9
CrossRefGoogle Scholar
van der Meer, D., & Kaufmann, T. (2022). Mapping the genetic architecture of cortical morphology through neuroimaging: Progress and perspectives. Translational Psychiatry, 12(1), 447. https://doi.org/10.1038/s41398-022-02193-5
CrossRefGoogle Scholar
van der Meer, D., Kaufmann, T., Shadrin, A. A., Makowski, C., Frei, O., Roelfs, D., Monereo-Sánchez, J., Linden, D. E. J., Rokicki, J., Alnæs, D., de Leeuw, C., Thompson, W. K., Loughnan, R., Fan, C. C., Westlye, L. T., Andreassen, O. A., & Dale, A. M. (2021). The genetic architecture of human cortical folding. Science Advances, 7(51), eabj9446. https://doi.org/10.1126/sciadv.abj9446
CrossRefGoogle ScholarPubMed
Vértes, P. E., Rittman, T., Whitaker, K. J., Romero-Garcia, R., Váša, F., Kitzbichler, M. G., Wagstyl, K., Fonagy, P., Dolan, R. J., Jones, P. B., Goodyer, I. M., & the NSPN Consortium
Bullmore, E. T. (2016). Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1705), 20150362. https://doi.org/10.1098/rstb.2015.0362
CrossRefGoogle Scholar
Viding, E., Williamson, D. E., & Hariri, A. R. (2006). Developmental imaging genetics: Challenges and promises for translational research. Development and Psychopathology, 18(3), 877–892. https://doi.org/10.1017/S0954579406060433
CrossRefGoogle ScholarPubMed
Villemonteix, T., De Brito, S. A., Slama, H., Kavec, M., Balériaux, D., Metens, T., Baijot, S., Mary, A., Ramoz, N., Septier, M., Gorwood, P., Peigneux, P., & Massat, I. (2015). Structural correlates of COMT Val158Met polymorphism in childhood ADHD: A voxel-based morphometry study. World Journal of Biological Psychiatry, 16(3), 190–199. https://doi.org/10.3109/15622975.2014.984629
CrossRefGoogle ScholarPubMed
Vounou, M., Janousova, E., Wolz, R., Stein, J. L., Thompson, P. M., Rueckert, D., & Montana, G. (2012). Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer’s disease. NeuroImage, 60(1), 700–716. https://doi.org/10.1016/j.neuroimage.2011.12.029
CrossRefGoogle ScholarPubMed
Wainschtein, P., Jain, D., Zheng, Z., Becker, D., Bi, W., Brody, J., Carlson, J. C., Correa, A., Du, M. M., Fernandez-Rhodes, L., Ferrier, K. R., Graff, M., Guo, X., He, J., Heard-Costa, N. L., Highland, H. M., Hirschhorn, J. N., Howard-Claudio, C. M., Isasi, C. R., …Visscher, P. M. (2022). Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data. Nature Genetics, 54(3), 263–273. https://doi.org/10.1038/s41588-021-00997-7
CrossRefGoogle ScholarPubMed
Wang, H., Nie, F., Huang, H., Kim, S., Nho, K., Risacher, S. L., Saykin, A. J., & Shen, L. (2012). Identifying quantitative trait loci via group-sparse multitask regression and feature selection: An imaging genetics study of the ADNI cohort. Bioinformatics, 28(2), 229–237. https://doi.org/10.1093/bioinformatics/btr649
CrossRefGoogle ScholarPubMed
Wang, L.-J., Li, S.-C., Kuo, H.-C., Chou, W.-J., Lee, M.-J., Chou, M.-C., Tseng, H.-H., Hsu, C.-F., Lee, S.-Y., & Lin, W.-C. (2020). Gray matter volume and microRNA levels in patients with attention-deficit/hyperactivity disorder. European Archives of Psychiatry and Clinical Neuroscience, 270(8), 1037–1045. https://doi.org/10.1007/s00406-019-01032-x psyh.CrossRefGoogle ScholarPubMed
Wassink, T. H., Nelson, J. J., Crowe, R. R., & Andreasen, N. C. (1999). Heritability of BDNF alleles and their effect on brain morphology in schizophrenia. American Journal of Medical Genetics, 88(6), 724–728. https://doi.org/10.1002/(SICI)1096-8628(19991215)88:63.0.CO;2-7.3.0.CO;2-7>CrossRefGoogle ScholarPubMed
Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D., Jack, C. R. Jr., Jagust, W., Morris, J. C., Petersen, R. C., Salazar, J., Saykin, A. J., Shaw, L. M., Toga, A. W., Trojanowski, J. Q., & Alzheimer’s Disease Neuroimaging Initiative (2017). The Alzheimer’s disease neuroimaging initiative 3: Continued innovation for clinical trial improvement. Alzheimer’s & Dementia : The Journal of the Alzheimer’s Association, 13(5), 561–571. https://doi.org/10.1016/j.jalz.2016.10.006
CrossRefGoogle Scholar
Williams, J. A., Burgess, S., Suckling, J., Lalousis, P. A., Batool, F., Griffiths, S. L., Palmer, E., Karwath, A., Barsky, A., Gkoutos, G. V., Wood, S., Barnes, N. M., David, A. S., Donohoe, G., Neill, J. C., Deakin, B., Khandaker, G. M., Upthegrove, R., Mondelli, , … Harrison, N. (2022). Inflammation and brain structure in schizophrenia and other neuropsychiatric disorders: A Mendelian randomization study. JAMA Psychiatry, 79(5), 498–507. https://doi.org/10.1001/jamapsychiatry.2022.0407
CrossRefGoogle ScholarPubMed
Yadav, D., Tanveer, A., Malviya, N., & Yadav, S. (2018). Overview and principles of bioengineering. In Omics technologies and bio-engineering (pp. 3–23). Elsevier, https://doi.org/10.1016/B978-0-12-804659-3.00001-4
CrossRefGoogle Scholar
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. https://doi.org/10.1152/jn.00338.2011
Google ScholarPubMed
Yoo, J. H., Kim, J. I., Kim, B.-N., & Jeong, B. (2020). Exploring characteristic features of attention-deficit/hyperactivity disorder: Findings from multi-modal MRI and candidate genetic data. Brain Imaging and Behavior, 14(6), 2132–2147. https://doi.org/10.1007/s11682-019-00164-x psyh.CrossRefGoogle ScholarPubMed
Yu, G., Liu, Z., Wu, X., Becker, B., Zhang, K., Fan, H., Peng, S., Kuang, N., Kang, J., Dong, G., Zhao, X.-M., Schumann, G., Feng, J., Sahakian, B. J., Robbins, T. W., Palaniyappan, L., & Zhang, J. (2023). Common and disorder-specific cortical thickness alterations in internalizing, externalizing and thought disorders during early adolescence: An adolescent brain and cognitive development study. Journal of Psychiatry and Neuroscience, 48(5), E345–E356. https://doi.org/10.1503/jpn.220202
CrossRefGoogle ScholarPubMed
Yuan, B., Wang, M., Wu, X., Cheng, P., Zhang, R., Zhang, R., Yu, S., Zhang, J., Du, Y., Wang, X., & Qiu, Z. (2023). Identification of de novo mutations in the Chinese autism spectrum disorder cohort via whole-exome sequencing unveils brain regions implicated in autism. Neuroscience Bulletin, 39(10), 1469–1480. https://doi.org/10.1007/s12264-023-01037-6
CrossRefGoogle Scholar
Zhang, S., Zhao, J., Guo, Z., Jones, J. A., Liu, P., & Liu, H. (2018). The association between genetic variation in FOXP2 and sensorimotor control of speech production. Frontiers in Neuroscience, 12, 666. https://doi.org/10.3389/fnins.2018.00666
CrossRefGoogle ScholarPubMed
Zhang, X., Gao, W., Cao, W., Niu, J., Guo, Y., Cui, D., Yu, G., Dou, R., Jiao, Q., Qiu, J., Su, L., & Lu, G. (2023). Cortical thickness alterations are associated with astrocytes and excitatory neuron-specific transcriptome signatures in pediatric bipolar disorder. 33(12), 7540–7552. https://doi.org/10.1093/cercor/bhad059
CrossRefGoogle Scholar
Zhang, Y., Li, M., Wang, Q., Hsu, J. S., Deng, W., Ma, X., Ni, P., Zhao, L., Tian, Y., Sham, P. C., & Li, T. (2020). A joint study of whole exome sequencing and structural MRI analysis in major depressive disorder. Psychological Medicine, 50(3), 384–395. https://doi.org/10.1017/S0033291719000072
CrossRefGoogle ScholarPubMed
Zhao, Y., Li, L., & Caffo, B. S. (2021). Multimodal neuroimaging data integration and pathway analysis. Biometrics, 77(3), 879–889. https://doi.org/10.1111/biom.13351
CrossRefGoogle ScholarPubMed
Zheng, J., Womer, F. Y., Tang, L., Guo, H., Zhang, X., Tang, Y., & Wang, F. (2024). Integrative omics analysis reveals epigenomic and transcriptomic signatures underlying brain structural deficits in major depressive disorder. Translational Psychiatry, 14(1), 17. https://doi.org/10.1038/s41398-023-02724-8
CrossRefGoogle ScholarPubMed
Zhou, T., Thung, K.-H., Liu, M., & Shen, D. (2019). Brain-wide genome-wide association study for Alzheimer’s disease via joint projection learning and sparse regression model. IEEE Transactions on Biomedical Engineering, 66(1), 165–175. https://doi.org/10.1109/TBME.2018.2824725
CrossRefGoogle ScholarPubMed
Cheek et al. supplementary material
Cheek et al. supplementary material
File
26.4 KB