{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T13:03:52Z","timestamp":1771074232140,"version":"3.50.1"},"reference-count":96,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T00:00:00Z","timestamp":1711843200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["213038\/Z\/18\/Z"],"award-info":[{"award-number":["213038\/Z\/18\/Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012621","name":"NIHR University College London Hospitals Biomedical Research Centre","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012317","name":"UCLH Biomedical Research Centre","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012317","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000627","name":"Guarantors Of Brain","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000627","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"UKRI Medical Research Council","doi-asserted-by":"publisher","award":["MR\/X00046X\/1"],"award-info":[{"award-number":["MR\/X00046X\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["NeuroImage"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1016\/j.neuroimage.2024.120600","type":"journal-article","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T01:57:05Z","timestamp":1712023025000},"page":"120600","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"C","title":["Computational limits to the legibility of the imaged human brain"],"prefix":"10.1016","volume":"291","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6248-7203","authenticated-orcid":false,"given":"James K.","family":"Ruffle","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert J","family":"Gray","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samia","family":"Mohinta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guilherme","family":"Pombo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaitanya","family":"Kaul","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harpreet","family":"Hyare","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geraint","family":"Rees","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2718-4423","authenticated-orcid":false,"given":"Parashkev","family":"Nachev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.neuroimage.2024.120600_bib0030","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3389\/fninf.2014.00014","article-title":"Machine learning for neuroimaging with scikit-learn","volume":"8","author":"Abraham","year":"2014","journal-title":"Front. Neuroinform."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0060","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.3390\/app10061959","article-title":"Convolutional Neural Networks Using Skip Connections with Layer Groups for Super-Resolution Image Reconstruction Based on Deep Learning","volume":"10","author":"Ahn","year":"2020","journal-title":"Applied Sciences"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0072","first-page":"7","article-title":"A practical tool for maximal information coefficient analysis","author":"Albanese","year":"2018","journal-title":"Gigascience"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0021","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.neuroimage.2017.10.034","article-title":"Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank","volume":"166","author":"Alfaro-Almagro","year":"2018","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0045","article-title":"Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge","author":"Bakas","year":"2018","journal-title":"ArXiv."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0005","doi-asserted-by":"crossref","first-page":"2850","DOI":"10.1038\/s41467-023-38585-4","article-title":"Assortative mixing in micro-architecturally annotated brain connectomes","volume":"14","author":"Bazinet","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0059","unstructured":"Benchmarks, A.I. MNIST, https:\/\/benchmarks.ai\/mnist(2021)."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0039","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1198\/016214504000001907","article-title":"False Discovery Rate\u2013Adjusted Multiple Confidence Intervals for Selected Parameters","volume":"100","author":"Benjamini","year":"2005","journal-title":"J. Am. Stat. Assoc."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0002","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1038\/s41586-022-04554-y","article-title":"Brain charts for the human lifespan","volume":"604","author":"Bethlehem","year":"2022","journal-title":"Nature"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0073","article-title":"nipy\/nibabel: 3.2.1 (Version 3.2.1)","author":"Brett","year":"2020","journal-title":"Zenodo"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0032","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1038\/nrn2575","article-title":"Complex brain networks: graph theoretical analysis of structural and functional systems","volume":"10","author":"Bullmore","year":"2009","journal-title":"Nat. Rev. Neurosci."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0088","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41586-018-0579-z","article-title":"The UK Biobank resource with deep phenotyping and genomic data","volume":"562","author":"Bycroft","year":"2018","journal-title":"Nature"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.patter.2020.100119","article-title":"Inference and Prediction Diverge in Biomedicine","volume":"1","author":"Bzdok","year":"2020","journal-title":"Patterns"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0086","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2022.119534","article-title":"Machine learning of large-scale multimodal brain imaging data reveals neural correlates of hand preference","volume":"262","author":"Chormai","year":"2022","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0092","first-page":"20","article-title":"Deep Neural Network for Visual Stimulus-Based Reaction Time Estimation Using the Periodogram of Single-Trial EEG","author":"Chowdhury","year":"2020","journal-title":"Sensors. (Basel)"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0041","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1093\/brain\/awac304","article-title":"Graph lesion-deficit mapping of fluid intelligence","volume":"146","author":"Cipolotti","year":"2022","journal-title":"Brain"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0080","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.neurobiolaging.2020.03.014","article-title":"Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors","volume":"92","author":"Cole","year":"2020","journal-title":"Neurobiol. Aging"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0049","article-title":"Project MONAI","author":"Consortium","year":"2020","journal-title":"Zenodo"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0062","unstructured":"Cortes, C., Mohri, M. & Rostamizadeh, A. L2 Regularization for Learning Kernels. (2012). https:\/\/ui.adsabs.harvard.edu\/abs\/2012arXiv1205.2653C."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0079","unstructured":"Developers, N. CUDA Toolkit 11.0, https:\/\/developer.nvidia.com\/cuda-11.0-download-archive(2021)."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0003","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1038\/s41586-018-0571-7","article-title":"Genome-wide association studies of brain imaging phenotypes in UK Biobank","volume":"562","author":"Elliott","year":"2018","journal-title":"Nature"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0093","doi-asserted-by":"crossref","unstructured":"Farazi, H. & Nogga, J. Semantic Prediction: Which One Should Come First, Recognition or Prediction?, (2021), https:\/\/ui.adsabs.harvard.edu\/abs\/2021arXiv211002829F.","DOI":"10.14428\/esann\/2021.ES2021-23"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0094","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0231627","article-title":"Reliability and validity of the UK Biobank cognitive tests","volume":"15","author":"Fawns-Ritchie","year":"2020","journal-title":"PLoS. One"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0013","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1038\/nn.4135","article-title":"Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity","volume":"18","author":"Finn","year":"2015","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0009","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1093\/cercor\/bhm225","article-title":"Cortical Folding Patterns and Predicting Cytoarchitecture","volume":"18","author":"Fischl","year":"2008","journal-title":"Cerebral Cortex"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0089","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.jocn.2009.06.027","article-title":"Effect of image analysis software on neurofunctional activation during processing of emotional human faces","volume":"17","author":"Fusar-Poli","year":"2010","journal-title":"J. Clin. Neurosci."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0031","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/nature18933","article-title":"A multi-modal parcellation of human cerebral cortex","volume":"536","author":"Glasser","year":"2016","journal-title":"Nature"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0038","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1038\/nn.4361","article-title":"The Human Connectome Project's neuroimaging approach","volume":"19","author":"Glasser","year":"2016","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0082","doi-asserted-by":"crossref","DOI":"10.3389\/fpsyt.2021.627996","article-title":"Optimising a Simple Fully Convolutional Network for Accurate Brain Age Prediction in the PAC 2019 Challenge","volume":"12","author":"Gong","year":"2021","journal-title":"Front. Psychiatry"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0087","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1006\/nimg.2001.0857","article-title":"Cerebral asymmetry and the effects of sex and handedness on brain structure: a voxel-based morphometric analysis of 465 normal adult human brains","volume":"14","author":"Good","year":"2001","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0017","series-title":"Deep Learning","author":"Goodfellow","year":"2017"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0028","unstructured":"Grabner, G. et al. in Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2006. (eds Rasmus Larsen, Mads Nielsen, & Jon Sporring) 58\u201366 (Springer Berlin Heidelberg)."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0069","author":"Haas","year":"2021","journal-title":"gravis"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0090","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1002\/hbm.26147","article-title":"Multisite test-retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3","volume":"44","author":"Haddad","year":"2023","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0011","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1038\/s41562-021-01082-z","article-title":"Mapping gene transcription and neurocognition across human neocortex","volume":"5","author":"Hansen","year":"2021","journal-title":"Nat. Hum. Behav."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0006","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1038\/s41593-022-01186-3","article-title":"Mapping neurotransmitter systems to the structural and functional organization of the human neocortex","volume":"25","author":"Hansen","year":"2022","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0074","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0056","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"770","article-title":"Deep Residual Learning for Image Recognition","author":"He","year":"2016"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0096","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2019.116276","article-title":"Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics","volume":"206","author":"He","year":"2020","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0058","unstructured":"Heinz, S. A performance benchmark of Google AutoML Vision using Fashion-MNIST, https:\/\/towardsdatascience.com\/a-performance-benchmark-of-google-automl-vision-using-fashion-mnist-a9bf8fc1c74f(2018)."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0053","article-title":"Gaussian Error Linear Units (GELUs)","author":"Hendrycks","year":"2016","journal-title":"ArXiv."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0008","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1073\/pnas.0811168106","article-title":"Predicting human resting-state functional connectivity from structural connectivity","volume":"106","author":"Honey","year":"2009","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0071","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D Graphics Environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0052","unstructured":"Ioffe, S. & Szegedy, C. in Proceedings of the 32nd International Conference on Machine Learning Vol. 37 (eds Bach Francis & Blei David) 448\u2013456 (PMLR, Proceedings of Machine Learning Research, 2015)."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0046","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0027","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1006\/nimg.2002.1132","article-title":"Improved optimization for the robust and accurate linear registration and motion correction of brain images","volume":"17","author":"Jenkinson","year":"2002","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0047","doi-asserted-by":"crossref","first-page":"5409","DOI":"10.1038\/s41467-019-13163-9","article-title":"Brain age prediction using deep learning uncovers associated sequence variants","volume":"10","author":"Jonsson","year":"2019","journal-title":"Nat. Commun."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0061","first-page":"6980","article-title":"A Method for Stochastic Optimization","volume":"1412","author":"Kingma","year":"2017","journal-title":"ArXiv."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0055","first-page":"25","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0016","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0084","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2021.118409","article-title":"Deep learning for sex classification in resting-state and task functional brain networks from the UK Biobank","volume":"241","author":"Leming","year":"2021","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0001","doi-asserted-by":"crossref","first-page":"2624","DOI":"10.1038\/s41467-020-15948-9","article-title":"The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions","volume":"11","author":"Littlejohns","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0035","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1038\/35084005","article-title":"Neurophysiological investigation of the basis of the fMRI signal","volume":"412","author":"Logothetis","year":"2001","journal-title":"Nature"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0012","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41586-022-04492-9","article-title":"Reproducible brain-wide association studies require thousands of individuals","volume":"603","author":"Marek","year":"2022","journal-title":"Nature"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0051","article-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","author":"Paszke","year":"2019","journal-title":"NeurIPS"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0023","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0043","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevE.85.056122","article-title":"Entropy of stochastic blockmodel ensembles","volume":"85","author":"Peixoto","year":"2012","journal-title":"Physical Review E"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0042","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevE.89.012804","article-title":"Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models","volume":"89","author":"Peixoto","year":"2014","journal-title":"Physical Review E"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0068","article-title":"The graph-tool python library","author":"Peixoto","year":"2014","journal-title":"figshare"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0040","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevE.92.042807","article-title":"Inferring the mesoscale structure of layered, edge-valued, and time-varying networks","volume":"92","author":"Peixoto","year":"2015","journal-title":"Physical Review E"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0083","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevE.97.012306","article-title":"Nonparametric weighted stochastic block models","volume":"97","author":"Peixoto","year":"2018","journal-title":"Physical Review E"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0081","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101871","article-title":"Accurate brain age prediction with lightweight deep neural networks","volume":"68","author":"Peng","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0050","article-title":"Generative AI for Medical Imaging: extending the MONAI Framework","author":"Pinaya","year":"2023","journal-title":"arXiv e-prints"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0066","first-page":"1","article-title":"Package \u2018nlme\u2019","author":"Pinheiro","year":"2022","journal-title":"cran"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0065","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1001\/jamapsychiatry.2019.3671","article-title":"Establishment of Best Practices for Evidence for Prediction: A Review","volume":"77","author":"Poldrack","year":"2020","journal-title":"JAMa Psychiatry"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0036","doi-asserted-by":"crossref","first-page":"S91","DOI":"10.1016\/j.neurobiolaging.2014.05.040","article-title":"Thickness network features for prognostic applications in dementia","volume":"36","author":"Raamana","year":"2015","journal-title":"Neurobiol. Aging"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0075","article-title":"jbrockmendel. pandas-dev\/pandas: Pandas 1.0.3 (Version v1.0.3)","author":"Reback","year":"2020","journal-title":"Zenodo"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0024","first-page":"1518","article-title":"Detecting novel associations in large data sets","volume":"334","author":"Reshef","year":"2011","journal-title":"Science (1979)"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0018","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1038\/s41593-019-0520-2","article-title":"A deep learning framework for neuroscience","volume":"22","author":"Richards","year":"2019","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0034","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.cortex.2021.06.012","article-title":"The autonomic brain: Multi-dimensional generative hierarchical modelling of the autonomic connectome","volume":"143","author":"Ruffle","year":"2021","journal-title":"Cortex"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0044","doi-asserted-by":"crossref","DOI":"10.1093\/brain\/awad199","article-title":"Brain tumour genetic network signatures of survival","author":"Ruffle","year":"2023","journal-title":"Brain"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0048","doi-asserted-by":"crossref","DOI":"10.1093\/braincomms\/fcad118","article-title":"Brain tumour segmentation with incomplete imaging data","author":"Ruffle","year":"2023","journal-title":"Brain Commun."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0020","doi-asserted-by":"crossref","first-page":"4238","DOI":"10.1038\/s41467-020-18037-z","article-title":"Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets","volume":"11","author":"Schulz","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0078","series-title":"Proc of the 9th Python in science conference","doi-asserted-by":"crossref","DOI":"10.25080\/Majora-92bf1922-011","article-title":"Econometric and Statistical Modeling with Python","author":"Seabold","year":"2010"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0085","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2113095118","article-title":"Handedness and its genetic influences are associated with structural asymmetries of the cerebral cortex in 31,864 individuals","volume":"118","author":"Sha","year":"2021","journal-title":"Proc. Natl. Acad. Sci. u S. a"},{"issue":"Suppl 1","key":"10.1016\/j.neuroimage.2024.120600_bib0025","doi-asserted-by":"crossref","first-page":"S208","DOI":"10.1016\/j.neuroimage.2004.07.051","article-title":"Advances in functional and structural MR image analysis and implementation as FSL","volume":"23","author":"Smith","year":"2004","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0029","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1016\/j.neuroimage.2006.02.024","article-title":"Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data","volume":"31","author":"Smith","year":"2006","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0026","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1002\/hbm.10062","article-title":"Fast robust automated brain extraction","volume":"17","author":"Smith","year":"2002","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0054","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0007","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.tics.2020.01.008","article-title":"Linking Structure and Function in Macroscale Brain Networks","volume":"24","author":"Su\u00e1rez","year":"2020","journal-title":"Trends. Cogn. Sci."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0022","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1001779","article-title":"UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age","volume":"12","author":"Sudlow","year":"2015","journal-title":"PLoS. Med."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2020.117164","article-title":"Sample size evolution in neuroimaging research: An evaluation of highly-cited studies (1990-2012) and of latest practices (2017-2018) in high-impact journals","volume":"221","author":"Szucs","year":"2020","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0070","first-page":"42","article-title":"GNU Parallel - The Command-Line Power Tool","author":"Tange","year":"2011","journal-title":"The USENIX Magazine"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0037","doi-asserted-by":"crossref","first-page":"5094","DOI":"10.1038\/s41467-020-18920-9","article-title":"Brain disconnections link structural connectivity with function and behaviour","volume":"11","author":"Thiebaut de Schotten","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0010","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1152\/jn.00338.2011","article-title":"The organization of the human cerebral cortex estimated by intrinsic functional connectivity","volume":"106","author":"Thomas Yeo","year":"2011","journal-title":"J. Neurophysiol."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0063","unstructured":"Trimarchi, D. Confusion Matrix, https:\/\/github.com\/DTrimarchi10\/confusion_matrix(2019)."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0064","doi-asserted-by":"crossref","unstructured":"Varoquaux, G. & Colliot, O. in Machine Learning for Brain Disorders (ed Olivier Colliot) 601\u2013630 (Springer US, 2023).","DOI":"10.1007\/978-1-0716-3195-9_20"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0076","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0004","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1038\/s41593-022-01074-w","article-title":"Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging","volume":"25","author":"Wang","year":"2022","journal-title":"Nat. Neurosci."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0077","article-title":"Seaborn_Development_Team. seaborn","author":"Waskom","year":"2020","journal-title":"Zenodo"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0067","doi-asserted-by":"crossref","DOI":"10.21105\/joss.01686","article-title":"Welcome to the Tidyverse","volume":"4","author":"Wickham","year":"2019","journal-title":"J. Open. Source Softw."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2022.119569","article-title":"Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns","volume":"262","author":"Wu","year":"2022","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0015","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1038\/s41562-023-01670-1","article-title":"The challenges and prospects of brain-based prediction of behaviour","volume":"7","author":"Wu","year":"2023","journal-title":"Nat. Hum. Behav."},{"key":"10.1016\/j.neuroimage.2024.120600_bib0057","first-page":"1077","article-title":"A Neural Network for Speaker-Independent Isolated Word Recognition","volume":"90","author":"Yamaguchi","year":"1990","journal-title":"ICSLP"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0033","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1016\/j.neuroimage.2010.06.041","article-title":"Network-based statistic: identifying differences in brain networks","volume":"53","author":"Zalesky","year":"2010","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2024.120600_bib0091","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1038\/s42003-022-03880-1","article-title":"Choice of Voxel-based Morphometry processing pipeline drives variability in the location of neuroanatomical brain markers","volume":"5","author":"Zhou","year":"2022","journal-title":"Commun. Biol."}],"container-title":["NeuroImage"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811924000958?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811924000958?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T18:39:31Z","timestamp":1714156771000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1053811924000958"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":96,"alternative-id":["S1053811924000958"],"URL":"https:\/\/doi.org\/10.1016\/j.neuroimage.2024.120600","relation":{},"ISSN":["1053-8119"],"issn-type":[{"value":"1053-8119","type":"print"}],"subject":[],"published":{"date-parts":[[2024,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Computational limits to the legibility of the imaged human brain","name":"articletitle","label":"Article Title"},{"value":"NeuroImage","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neuroimage.2024.120600","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"120600"}}