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Abstract

The emergence of functional magnetic resonance imaging (fMRI) marked a significant technological breakthrough in the real-time measurement of the functioning human brain in vivo. In part because of their 4D nature (three spatial dimensions and time), fMRI data have inspired a great deal of statistical development in the past couple of decades to address their unique spatiotemporal properties. This article provides an overview of the current landscape in functional brain measurement, with a particular focus on fMRI, highlighting key developments in the past decade. Furthermore, it looks ahead to the future, discussing unresolved research questions in the community and outlining potential research topics for the future.

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2025-03-07
2025-06-22
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Literature Cited

  1. Airan RD, Vogelstein JT, Pillai JJ, Caffo B, Pekar JJ, Sair HI. 2016.. Factors affecting characterization and localization of interindividual differences in functional connectivity using MRI. . Hum. Brain Mapp. 37:(5):198697
    [Crossref] [Google Scholar]
  2. Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD. 2014.. Tracking whole-brain connectivity dynamics in the resting state. . Cereb. Cortex 24:(3):66376
    [Crossref] [Google Scholar]
  3. Andrew G, Arora R, Bilmes J, Livescu K. 2013.. Deep canonical correlation analysis. . Proc. Mach. Learn. Res. 28:(3):124755
    [Google Scholar]
  4. Antelmi L, Ayache N, Robert P, Lorenzi M. 2019.. Sparse multi-channel variational autoencoder for the joint analysis of heterogeneous data. . Proc. Mach. Learn. Res. 97::30211
    [Google Scholar]
  5. Arbabshirani MR, Plis S, Sui J, Calhoun VD. 2017.. Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. . Neuroimage 145::13765
    [Crossref] [Google Scholar]
  6. Asadi N, Olson IR, Obradovic Z. 2023.. A transformer model for learning spatiotemporal contextual representation in fMRI data. . Netw. Neurosci. 7:(1):2247
    [Crossref] [Google Scholar]
  7. Babiloni F, Mattia D, Babiloni C, Astolfi L, Salinari S, et al. 2004.. Multimodal integration of EEG, MEG and fMRI data for the solution of the neuroimage puzzle. . Magn. Reson. Imag. 22:(10):147176
    [Crossref] [Google Scholar]
  8. Bassett DS, Bullmore ET. 2009.. Human brain networks in health and disease. . Curr. Opin. Neurol. 22:(4):34047
    [Crossref] [Google Scholar]
  9. Bedel HA, Sivgin I, Dalmaz O, Dar SUH, Çukur T. 2023.. Bolt: fused window transformers for fMRI time series analysis. . Med. Image Anal. 88::102841
    [Crossref] [Google Scholar]
  10. Bengio Y, Simard P, Fransconi P. 1994.. Learning long-term dependencies with gradient descent is difficult. . IEEE Trans. Neural Netw. 5:(2):15766
    [Crossref] [Google Scholar]
  11. Berardi G, Frey-Law L, Sluka KA, Bayman EO, Coffey CS, et al. 2022.. Multi-site observational study to assess biomarkers for susceptibility or resilience to chronic pain: the Acute to Chronic Pain Signatures (A2CPS) study protocol. . Front. Med. 9::849214
    [Crossref] [Google Scholar]
  12. Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS. 1995.. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. . Magn. Reson. Med. 34:(4):53741
    [Crossref] [Google Scholar]
  13. Bookheimer SY, Salat DH, Terpstra M, Ances BM, Barch DM, et al. 2019.. The Lifespan Human Connectome Project in Aging: an overview. . NeuroImage 185::33548
    [Crossref] [Google Scholar]
  14. Botvinik-Nezer R, Holzmeister F, Camerer C, Dreber A, Huber J, et al. 2020.. Variability in the analysis of a single neuroimaging dataset by many teams. . Nature 582::8488
    [Crossref] [Google Scholar]
  15. Bridgeford EW, Powell M, Kiar G, Lawrence R, Caffo B, et al. 2021a.. Batch effects are causal effects: applications in human connectomics. . bioRxiv 2021.09.03.458920
    [Google Scholar]
  16. Bridgeford EW, Wang S, Wang Z, Xu T, Craddock C, et al. 2021b.. Eliminating accidental deviations to minimize generalization error and maximize replicability: applications in connectomics and genomics. . PLOS Comput. Biol. 17:(9):e1009279
    [Crossref] [Google Scholar]
  17. Brier MR, Thomas JB, Fagan AM, Hassenstab J, Holtzman DM, et al. 2014.. Functional connectivity and graph theory in preclinical Alzheimer's disease. . Neurobiol. Aging 35:(4):75768
    [Crossref] [Google Scholar]
  18. Bullmore E, Sporns O. 2012.. The economy of brain network organization. . Nat. Rev. Neurosci. 13:(5):33649
    [Crossref] [Google Scholar]
  19. Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J, et al. 2013.. Power failure: why small sample size undermines the reliability of neuroscience. . Nat. Rev. Neurosci. 14::36576
    [Crossref] [Google Scholar]
  20. Buxton RB, Frank LR, Wong EC, Siewert B, Warach S, Edelman RR. 1998a.. A general kinetic model for quantitative perfusion imaging with arterial spin labeling. . Magn. Reson. Med. 40:(3):38396
    [Crossref] [Google Scholar]
  21. Buxton RB, Wong EC, Frank LR. 1998b.. Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. . Magn. Reson. Med. 39:(6):85564
    [Crossref] [Google Scholar]
  22. Caballero-Gaudes C, Reynolds RC. 2017.. Methods for cleaning the BOLD fMRI signal. . Neuroimage 154::12849
    [Crossref] [Google Scholar]
  23. Caffo B, Chen S, Stewart W, Bolla K, Yousem D, et al. 2008.. Are brain volumes based on magnetic resonance imaging mediators of the associations of cumulative lead dose with cognitive function?. Am. J. Epidemiol. 167:(4):42937
    [Crossref] [Google Scholar]
  24. Calhoun VD, Adali T. 2008.. Feature-based fusion of medical imaging data. . IEEE Trans. Inform. Technol. Biomed. 13:(5):71120
    [Crossref] [Google Scholar]
  25. Calhoun VD, Liu J, Adal T. 2009.. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. . Neuroimage 45:(1):S16372
    [Crossref] [Google Scholar]
  26. Calhoun VD, Sui J. 2016.. Multimodal fusion of brain imaging data: a key to finding the missing link(s) in complex mental illness. . Biol. Psychiatry Cogn. Neurosci. Neuroimag. 1:(3):23044
    [Google Scholar]
  27. Carp J. 2012.. On the plurality of (methodological) worlds: Estimating the analytic flexibility of fMRI experiments. . Front. Neurosci. 6::149
    [Crossref] [Google Scholar]
  28. Casey B, Cannonier T, Conley MI, Cohen AO, Barch DM, et al. 2018.. The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. . Dev. Cogn. Neurosci. 32::4354
    [Crossref] [Google Scholar]
  29. Casey B, Tottenham N, Liston C, Durston S. 2005.. Imaging the developing brain: what have we learned about cognitive development?. Trends Cogn. Sci. 9:(3):10410
    [Crossref] [Google Scholar]
  30. Chang C, Glover GH. 2010.. Time–frequency dynamics of resting-state brain connectivity measured with fMRI. . NeuroImage 50:(1):8198
    [Crossref] [Google Scholar]
  31. Chén OY, Crainiceanu C, Ogburn EL, Caffo BS, Wager TD, Lindquist MA. 2018.. High-dimensional multivariate mediation with application to neuroimaging data. . Biostatistics 19:(2):12136
    [Crossref] [Google Scholar]
  32. Chen PHC, Chen J, Yeshurun Y, Hasson U, Haxby J, Ramadge PJ. 2015.. A reduced-dimension fMRI shared response model. . In Advances in Neural Information Processing Systems 28 (NIPS 2015), ed. C Cortes, N Lawrence, D Lee, M Sugiyama, R Garnett , pp. 46068. Red Hook, NY:: Curran
    [Google Scholar]
  33. Choe AS, Jones CK, Joel SE, Muschelli J, Belegu V, et al. 2015.. Reproducibility and temporal structure in weekly resting-state fMRI over a period of 3.5 years. . PLOS ONE 10:(10):e0140134
    [Crossref] [Google Scholar]
  34. Choe AS, Tang B, Smith KR, Honari H, Lindquist MA, et al. 2021.. Phase-locking of resting-state brain networks with the gastric basal electrical rhythm. . PLOS ONE 16:(1):e0244756
    [Crossref] [Google Scholar]
  35. Ciric R, Wolf DH, Power JD, Roalf DR, Baum GL, et al. 2017.. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. . Neuroimage 154::17487
    [Crossref] [Google Scholar]
  36. Correa N, Adal T, Calhoun VD. 2007.. Performance of blind source separation algorithms for fMRI analysis using a group ICA method. . Magn. Reson. Imag. 25:(5):68494
    [Crossref] [Google Scholar]
  37. Cox RW. 1996.. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. . Comput. Biomed. Res. 29:(3):16273
    [Crossref] [Google Scholar]
  38. Cribben I, Haraldsdottir R, Atlas LY, Wager TD, Lindquist MA. 2012.. Dynamic connectivity regression: determining state-related changes in brain connectivity. . Neuroimage 61:(4):90720
    [Crossref] [Google Scholar]
  39. Di CZ, Crainiceanu CM, Caffo BS, Punjabi NM. 2009.. Multilevel functional principal component analysis. . Ann. Appl. Stat. 3:(1):458
    [Crossref] [Google Scholar]
  40. Eklund A, Nichols TE, Knutsson H. 2016.. Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. . PNAS 113:(28):79005
    [Crossref] [Google Scholar]
  41. Eloyan A, Crainiceanu CM, Caffo BS. 2013.. Likelihood-based population independent component analysis. . Biostatistics 14:(3):51427
    [Crossref] [Google Scholar]
  42. Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, et al. 2015.. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. . Nat. Neurosci. 18:(11):166471
    [Crossref] [Google Scholar]
  43. Fischl B. 2012.. FreeSurfer. . NeuroImage 62:(2):77481
    [Crossref] [Google Scholar]
  44. Flury BK. 1987.. Two generalizations of the common principal component model. . Biometrika 74:(1):5969
    [Crossref] [Google Scholar]
  45. Friston K, Ashburner J, Kiebel S, Nichols T, Penny W, eds. 2007.. Statistical Parametric Mapping. London:: Academic
    [Google Scholar]
  46. Friston KJ. 2009.. Modalities, modes, and models in functional neuroimaging. . Science 326:(5951):399403
    [Crossref] [Google Scholar]
  47. Friston KJ. 2011.. Functional and effective connectivity: a review. . Brain Connectivity 1:(1):1336
    [Crossref] [Google Scholar]
  48. George JS, Aine C, Mosher J, Schmidt D, Ranken D, et al. 1995.. Mapping function in the human brain with magnetoencephalography, anatomical magnetic resonance imaging, and functional magnetic resonance imaging. . J. Clin. Neurophysiol. 12:(5):40631
    [Crossref] [Google Scholar]
  49. Geuter S, Reynolds Losin EA, Roy M, Atlas LY, Schmidt L, et al. 2020.. Multiple brain networks mediating stimulus–pain relationships in humans. . Cereb. Cortex 30:(7):420419
    [Crossref] [Google Scholar]
  50. Gordon EM, Laumann TO, Gilmore AW, Newbold DJ, Greene DJ, et al. 2017.. Precision functional mapping of individual human brains. . Neuron 95:(4):791807
    [Crossref] [Google Scholar]
  51. Gorgolewski K, Burns C, Madison C, Clark D, Halchenko Y, et al. 2011.. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. . Front. Neuroinformat. 5:. https://doi.org/10.3389/fninf.2011.00013
    [Crossref] [Google Scholar]
  52. Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, et al. 2016.. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. . Sci. Data 3:(1):160044
    [Crossref] [Google Scholar]
  53. Handwerker DA, Roopchansingh V, Gonzalez-Castillo J, Bandettini PA. 2012.. Periodic changes in fMRI connectivity. . NeuroImage 63:(3):171219
    [Crossref] [Google Scholar]
  54. Hari R, Puce A. 2023.. MEG-EEG Primer. Oxford, UK:: Oxford Univ. Press
    [Google Scholar]
  55. Haxby JV, Guntupalli JS, Connolly AC, Halchenko YO, Conroy BR, et al. 2011.. A common, high-dimensional model of the representational space in human ventral temporal cortex. . Neuron 72:(2):40416
    [Crossref] [Google Scholar]
  56. Haxby JV, Guntupalli JS, Nastase SA, Feilong M. 2020.. Hyperalignment: modeling shared information encoded in idiosyncratic cortical topographies. . eLife 9::e56601
    [Crossref] [Google Scholar]
  57. Haxby JV, Hoffman EA, Gobbini MI. 2000.. The distributed human neural system for face perception. . Trends Cogn. Sci. 4:(6):22333
    [Crossref] [Google Scholar]
  58. He T, Kong R, Holmes AJ, Nguyen M, Sabuncu MR, et al. 2020.. Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics. . NeuroImage 206::116276
    [Crossref] [Google Scholar]
  59. Henson RN, Flandin G, Friston KJ, Mattout J. 2010.. A parametric empirical Bayesian framework for fMRI-constrained MEG/EEG source reconstruction. . Hum. Brain Mapp. 31:(10):151231
    [Crossref] [Google Scholar]
  60. Hochreiter S, Schmidhuber J. 1997.. Long short-term memory. . Neural Comput. 9:(8):173580
    [Crossref] [Google Scholar]
  61. Huang X, Xiao J, Wu C. 2021.. Design of deep learning model for task-evoked fMRI data classification. . Comput. Intel. Neurosci. 2021::6660866
    [Crossref] [Google Scholar]
  62. Hutchison RM, Womelsdorf T, Allen EA, Bandettini PA, Calhoun VD, et al. 2013.. Dynamic functional connectivity: promise, issues, and interpretations. . NeuroImage 80::36078
    [Crossref] [Google Scholar]
  63. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. 2012.. FSL. . NeuroImage 62:(2):782790
    [Crossref] [Google Scholar]
  64. Joel SE, Caffo BS, Van Zijl PC, Pekar JJ. 2011.. On the relationship between seed-based and ICA-based measures of functional connectivity. . Magn. Reson. Med. 66:(3):64457
    [Crossref] [Google Scholar]
  65. Keilholz SD, Magnuson ME, Pan WJ, Willis M, Thompson GJ. 2013.. Dynamic properties of functional connectivity in the rodent. . Brain Connectivity 3:(1):3140
    [Crossref] [Google Scholar]
  66. Kingma DP, Welling M. 2013.. Auto-encoding variational Bayes. . arXiv:1312.6114 [stat.ML]
  67. Lee S, Shen H, Truong Y, Lewis M, Huang X. 2011.. Independent component analysis involving autocorrelated sources with an application to functional magnetic resonance imaging. . J. Am. Stat. Assoc. 106:(495):100924
    [Crossref] [Google Scholar]
  68. Li J, Binbin S, Qian C. 2022.. Diagnosis of Alzheimer's disease by feature weighted-LSTM: a preliminary study of temporal features in brain resting-state fMRI. . J. Integr. Neurosci. 21:(2):56
    [Crossref] [Google Scholar]
  69. Li S, Eloyan A, Joel S, Mostofsky S, Pekar J, et al. 2012.. Analysis of group ICA-based connectivity measures from fMRI: application to Alzheimer's disease. . PLOS ONE 7:(11):e49340
    [Crossref] [Google Scholar]
  70. Lindquist M. 2020.. Neuroimaging results altered by varying analysis pipelines. . Nature 582::3637
    [Crossref] [Google Scholar]
  71. Lindquist MA. 2008.. The statistical analysis of fMRI data. . Stat. Sci. 23:(4):43964
    [Crossref] [Google Scholar]
  72. Lindquist MA. 2012.. Functional causal mediation analysis with an application to brain connectivity. . J. Am. Stat. Assoc. 107:(500):1297309
    [Crossref] [Google Scholar]
  73. Lindquist MA, Geuter S, Wager TD, Caffo BS. 2019.. Modular preprocessing pipelines can reintroduce artifacts into fMRI data. . Hum. Brain Mapp. 40:(8):235876
    [Crossref] [Google Scholar]
  74. Lindquist MA, Loh JM, Atlas LY, Wager TD. 2009.. Modeling the hemodynamic response function in fMRI: efficiency, bias and mis-modeling. . Neuroimage 45:(1):S18798
    [Crossref] [Google Scholar]
  75. Lindquist MA, Mejia A. 2015.. Zen and the art of multiple comparisons. . Psychosom. Med. 77:(2):11425
    [Crossref] [Google Scholar]
  76. Lindquist MA, Sobel ME. 2016.. Effective connectivity and causal inference in neuroimaging. . In Handbook of Neuroimaging Data Analysis, ed. H Ombao, M Lindquist, W Thompson, J Aston , pp. 41940. Boca Raton, FL:: Chapman and Hall/CRC
    [Google Scholar]
  77. Lindquist MA, Wager TD. 2007.. Validity and power in hemodynamic response modeling: a comparison study and a new approach. . Hum. Brain Mapp. 28:(8):76484
    [Crossref] [Google Scholar]
  78. Lindquist MA, Xu Y, Nebel MB, Caffo BS. 2014.. Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach. . NeuroImage 101::53146
    [Crossref] [Google Scholar]
  79. Lindquist MA, Zhang CH, Glover G, Shepp L. 2008.. Rapid three-dimensional functional magnetic resonance imaging of the initial negative bold response. . J. Magn. Reson. 191:(1):10011
    [Crossref] [Google Scholar]
  80. Liu S, Cai W, Liu S, Zhang F, Fulham M, et al. 2015.. Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders. . Brain Inform. 2::16780
    [Crossref] [Google Scholar]
  81. Liu X, Lai Y, Wang X, Hao C, Chen L, et al. 2014.. A combined DTI and structural MRI study in medicated-naive chronic schizophrenia. . Magn. Reson. Imag. 32:(1):18
    [Crossref] [Google Scholar]
  82. Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A. 2001.. Neurophysiological investigation of the basis of the fMRI signal. . Nature 412:(6843):15057
    [Crossref] [Google Scholar]
  83. Lurie DJ, Kessler D, Bassett DS, Betzel RF, Breakspear M, et al. 2020.. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. . Netw. Neurosci. 4:(1):3069
    [Crossref] [Google Scholar]
  84. Malkiel I, Rosenman G, Wolf L, Hendler T. 2022.. Self-supervised transformers for fMRI representation. . Proc. Mach. Learn. Res. 172::895913
    [Google Scholar]
  85. Marek S, Tervo-Clemments B, Calabro FJ, Montez DF, Kay BP, et al. 2022.. Reproducible brain-wide association studies require thousands of individuals. . Nature 603::65460
    [Crossref] [Google Scholar]
  86. Martínez-Montes E, Valdés-Sosa PA, Miwakeichi F, Goldman RI, Cohen MS. 2004.. Concurrent EEG/fMRI analysis by multiway partial least squares. . NeuroImage 22:(3):102334
    [Crossref] [Google Scholar]
  87. Mascalchi M, Ginestroni A, Toschi N, Poggesi A, Cecchi P, et al. 2014.. The burden of microstructural damage modulates cortical activation in elderly subjects with MCI and leuko-araiosis. A DTI and fMRI study. . Hum. Brain Mapp. 35:(3):81930
    [Crossref] [Google Scholar]
  88. Mejia AF, Bolin D, Yue YR, Wang J, Caffo BS, Nebel MB. 2023.. Template independent component analysis with spatial priors for accurate subject-level brain network estimation and inference. . J. Comput. Graph. Stat. 32:(2):41333
    [Crossref] [Google Scholar]
  89. Mejia AF, Nebel MB, Wang Y, Caffo BS, Guo Y. 2020.. Template independent component analysis: targeted and reliable estimation of subject-level brain networks using big data population priors. . J. Am. Stat. Assoc. 115:(531):115177
    [Crossref] [Google Scholar]
  90. Menon RS, Luknowsky DC, Gati JS. 1998.. Mental chronometry using latency-resolved functional MRI. . PNAS 95:(18):109027
    [Crossref] [Google Scholar]
  91. Michon KJ, Khammash D, Simmonite M, Hamlin AM, Polk TA. 2022.. Person-specific and precision neuroimaging: current methods and future directions. . NeuroImage 263::119589
    [Crossref] [Google Scholar]
  92. Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, et al. 2016.. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. . Nat. Neurosci. 19:(11):152336
    [Crossref] [Google Scholar]
  93. Moeller S, Yacoub E, Olman CA, Auerbach E, Strupp J, et al. 2010.. Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. . Magn. Reson. Med. 63:(5):114453
    [Crossref] [Google Scholar]
  94. Monti MM. 2011.. Statistical analysis of fMRI time-series: a critical review of the GLM approach. . Front. Hum. Neurosci. 5::28
    [Crossref] [Google Scholar]
  95. Muschelli J, Gherman A, Fortin JP, Avants B, Whitcher B, et al. 2019.. Neuroconductor: an R platform for medical imaging analysis. . Biostatistics 20:(2):21839
    [Crossref] [Google Scholar]
  96. Nastase SA, Gazzola V, Hasson U, Keysers C. 2019.. Measuring shared responses across subjects using intersubject correlation. . Soc. Affect. Cogn. Neurosci. 14:(6):66785
    [Google Scholar]
  97. Nath T, Caffo B, Wager T, Lindquist MA. 2023.. A machine learning based approach towards high-dimensional mediation analysis. . NeuroImage 268::119843
    [Crossref] [Google Scholar]
  98. Nebel MB, Joel SE, Muschelli J, Barber AD, Caffo BS, et al. 2014.. Disruption of functional organization within the primary motor cortex in children with autism. . Hum. Brain Mapp. 35:(2):56780
    [Crossref] [Google Scholar]
  99. Newman MEJ. 2004.. Fast algorithm for detecting community structure in networks. . Phys. Rev. E 69:(6):066133
    [Crossref] [Google Scholar]
  100. Niso G, Gorgolewski KJ, Bock E, Brooks TL, Flandin G, et al. 2018.. MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. . Sci. Data 5::180110
    [Crossref] [Google Scholar]
  101. Niyogi PG, Lindquist MA, Maiti T. 2024.. A tensor based varying-coefficient model for multi-modal neuroimaging data analysis. . IEEE Trans. Signal Proc. 72::160719
    [Crossref] [Google Scholar]
  102. Ogawa S, Lee TM, Kay AR, Tank DW. 1990.. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. . PNAS 87:(24):986872
    [Crossref] [Google Scholar]
  103. Ombao H, Lindquist M, Thompson W, Aston J. 2016.. Handbook of Neuroimaging Data Analysis. Boca Raton, FL:: Chapman and Hall/CRC
    [Google Scholar]
  104. Pearl J. 2001.. Direct and indirect effects. . In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, UAI'01, pp. 41120. San Francisco:: Morgan Kaufmann
    [Google Scholar]
  105. Pereira F, Mitchell T, Botvinick M. 2009.. Machine learning classifiers and fMRI: a tutorial overview. . Neuroimage 45:(1):S199209
    [Crossref] [Google Scholar]
  106. Pernet CR, Appelhoff S, Gorgolewski KJ, Flandin G, Phillips C, et al. 2019.. EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. . Sci. Data 6:(1):103
    [Crossref] [Google Scholar]
  107. Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, et al. 2010.. Alzheimer's disease neuroimaging initiative (ADNI). . Neurology 74:(3):2019
    [Crossref] [Google Scholar]
  108. Pham DD, Muschelli J, Mejia AF. 2022.. ciftiTools: A package for reading, writing, visualizing, and manipulating CIFTI files in R. . NeuroImage 250::118877
    [Crossref] [Google Scholar]
  109. Poldrack RA, Laumann TO, Koyejo O, Gregory B, Hover A, et al. 2015.. Long-term neural and physiological phenotyping of a single human. . Nat. Commun. 6:(1):8885
    [Crossref] [Google Scholar]
  110. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. 2012.. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. . Neuroimage 59:(3):214254
    [Crossref] [Google Scholar]
  111. Power JD, Lynch CJ, Adeyemo B, Petersen SE. 2020.. A critical, event-related appraisal of denoising in resting-state fMRI studies. . Cereb. Cortex 30:(10):554459
    [Crossref] [Google Scholar]
  112. Power JD, Schlaggar BL, Petersen SE. 2015.. Recent progress and outstanding issues in motion correction in resting state fMRI. . Neuroimage 105::53651
    [Crossref] [Google Scholar]
  113. Preti MG, Bolton TA, Van De Ville D. 2017.. The dynamic functional connectome: state-of-the-art and perspectives. . NeuroImage 160::4154
    [Crossref] [Google Scholar]
  114. Qiang N, Dong Q, Liang H, Ge B, Zhang S, et al. 2021.. Modeling and augmenting of fMRI data using deep recurrent variational auto-encoder. . J. Neural Eng. 18::0460b6
    [Crossref] [Google Scholar]
  115. Qiu H, Han F, Liu H, Caffo B. 2016.. Joint estimation of multiple graphical models from high dimensional time series. . J. R. Stat. Soc. Ser. B 78:(2):487504
    [Crossref] [Google Scholar]
  116. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. 2001.. A default mode of brain function. . PNAS 98:(2):67682
    [Crossref] [Google Scholar]
  117. Rezende DJ, Mohamed S, Wierstra D. 2014.. Stochastic backpropagation and approximate inference in deep generative models. . Proc. Mach. Learn. Res. 32:(2):127886
    [Google Scholar]
  118. Risk BB, Matteson DS, Ruppert D, Eloyan A, Caffo BS. 2014.. An evaluation of independent component analyses with an application to resting-state fMRI. . Biometrics 70:(1):22436
    [Crossref] [Google Scholar]
  119. Rubinov M, Sporns O. 2010.. Complex network measures of brain connectivity: uses and interpretations. . NeuroImage 52:(3):105969
    [Crossref] [Google Scholar]
  120. Sabuncu MR, Konukoglu E, Alzheimer's Disease Neuroimaging Initiative. 2015.. Clinical prediction from structural brain MRI scans: a large-scale empirical study. . Neuroinformatics 13::3146
    [Crossref] [Google Scholar]
  121. Segall JM, Allen EA, Jung RE, Erhardt EB, Arja SK, et al. 2012.. Correspondence between structure and function in the human brain at rest. . Front. Neuroinform. 6::10
    [Crossref] [Google Scholar]
  122. Shappell H, Caffo BS, Pekar JJ, Lindquist MA. 2019.. Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models. . NeuroImage 191::24357
    [Crossref] [Google Scholar]
  123. Shou H, Eloyan A, Lee S, Zipunnikov V, Crainiceanu A, et al. 2013.. Quantifying the reliability of image replication studies: the image intraclass correlation coefficient (I2C2). . Cogn. Affect. Behav. Neurosci. 13::71424
    [Crossref] [Google Scholar]
  124. Sluka KA, Wager TD, Sutherland SP, Labosky PA, Balach T, et al. 2023.. Predicting chronic postsurgical pain: current evidence and a novel program to develop predictive biomarker signatures. . Pain 164:(9):191226
    [Crossref] [Google Scholar]
  125. Smirnova L, Caffo BS, Gracias DH, Huang Q, Morales Pantoja IE, et al. 2023.. Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish. . Front. Sci. 1::1017235
    [Crossref] [Google Scholar]
  126. Soldner J, Meindl T, Koch W, Bokde A, Reiser M, et al. 2012.. Structural and functional neuronal connectivity in Alzheimer's disease: a combined DTI and fMRI study. . Der Nervenarzt 83::87887
    [Crossref] [Google Scholar]
  127. Somerville LH, Bookheimer SY, Buckner RL, Burgess GC, Curtiss SW, et al. 2018.. The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5–21 year olds. . NeuroImage 183::45668
    [Crossref] [Google Scholar]
  128. Sporns O, Zwi JD. 2004.. The small world of the cerebral cortex. . Neuroinformatics 2::14562
    [Crossref] [Google Scholar]
  129. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, et al. 2015.. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. . PLOS Med. 12:(3):e1001779
    [Crossref] [Google Scholar]
  130. Sui J, Adali T, Yu Q, Chen J, Calhoun VD. 2012.. A review of multivariate methods for multimodal fusion of brain imaging data. . J. Neurosci. Methods 204:(1):6881
    [Crossref] [Google Scholar]
  131. Tang C, Eaves E, Dams-O'Connor K, Ho L, Leung E, et al. 2012.. Diffuse disconnectivity in traumatic brain injury: a resting state fMRI and DTI study. . Transl. Neurosci. 3:(1):914
    [Crossref] [Google Scholar]
  132. Tao Y, Ficek B, Rapp B, Tsapkini K. 2020.. Different patterns of functional network reorganization across the variants of primary progressive aphasia: a graph-theoretic analysis. . Neurobiol. Aging 96::18496
    [Crossref] [Google Scholar]
  133. Ter-Pogossian MM, Raichle ME, Sobel BE. 1980.. Positron-emission tomography. . Sci. Am. 243:(4):17081
    [Crossref] [Google Scholar]
  134. Thual A, Tran QH, Zemskova T, Courty N, Flamary R, et al. 2022.. Aligning individual brains with fused unbalanced Gromov-Wasserstein. . In NIPS'22: Proceedings of the 36th International Conference on Neural Information Processing Systems, ed. S Koyejo, S Mohamed, A Agarwal, D Belgrave, K Cho, A Oh , pp. 21792804. Red Hook, NY:: Curran
    [Google Scholar]
  135. Tillisch K, Mayer EA, Gupta A, Gill Z, Brazeilles R, et al. 2017.. Brain structure and response to emotional stimuli as related to gut microbial profiles in healthy women. . Psychosomat. Med. 79:(8):90513
    [Crossref] [Google Scholar]
  136. Tipping ME, Bishop CM. 1999.. Probabilistic principal component analysis. . J. R. Stat. Soc. Ser. B 61:(3):61122
    [Crossref] [Google Scholar]
  137. Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, et al. 2013.. The WU-Minn human connectome project: an overview. . Neuroimage 80::6279
    [Crossref] [Google Scholar]
  138. VanderWeele TJ. 2016.. Mediation analysis: a practitioner's guide. . Annu. Rev. Public Health 37::1732
    [Crossref] [Google Scholar]
  139. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, et al. 2017.. Attention is all you need. . In NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems, ed. UV Luxburg, I Guyon, S Bengio, H Wallach, R Fergus , pp. 600010. Red Hook, NY:: Curran
    [Google Scholar]
  140. Vidaurre D, Smith SM, Woolrich MW. 2017.. Brain network dynamics are hierarchically organized in time. . PNAS 114:(48):1282732
    [Crossref] [Google Scholar]
  141. Villringer A, Chance B. 1997.. Non-invasive optical spectroscopy and imaging of human brain function. . Trends Neursci. 20:(10):43542
    [Crossref] [Google Scholar]
  142. Villringer A, Planck J, Hock C, Schleinkofer L, Dirnagl U. 1993.. Near infrared spectroscopy (NIRS): a new tool to study hemodynamic changes during activation of brain function in human adults. . Neurosci. Lett. 154:(1):1014
    [Crossref] [Google Scholar]
  143. Wager TD, Atlas LY, Lindquist MA, Roy M, Woo CW, Kross E. 2013.. An fMRI-based neurologic signature of physical pain. . N. Engl. J. Med. 368:(15):138897
    [Crossref] [Google Scholar]
  144. Wang B, Luo X, Zhao Y, Caffo B. 2021a.. Semiparametric partial common principal component analysis for covariance matrices. . Biometrics 77:(4):117586
    [Crossref] [Google Scholar]
  145. Wang Y, Guo Y. 2019.. A hierarchical independent component analysis model for longitudinal neuroimaging studies. . NeuroImage 189::380400
    [Crossref] [Google Scholar]
  146. Wang Z, Bridgeford E, Wang S, Vogelstein JT, Caffo B. 2020.. Statistical analysis of data repeatability measures. . arXiv:2005.11911 [stat.AP]
  147. Wang Z, Sair HI, Crainiceanu C, Lindquist M, Landman BA, et al. 2021b.. On statistical tests of functional connectome fingerprinting. . Can. J. Stat. 49:(1):6388
    [Crossref] [Google Scholar]
  148. Woo CW, Chang LJ, Lindquist MA, Wager TD. 2017.. Building better biomarkers: brain models in translational neuroimaging. . Nat. Neurosci. 20:(3):36577
    [Crossref] [Google Scholar]
  149. Wu Y, Besson P, Azcona EA, Bandt SK, Parrish TB, Katsaggelos AK. 2022.. Reconstruction of resting state fMRI using LSTM variational auto-encoder on subcortical surface to detect epilepsy. . In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 15. Piscataway, NJ:: IEEE
    [Google Scholar]
  150. Yu M, Linn KA, Cook PA, Phillips ML, McInnis M, et al. 2018.. Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. . Hum. Brain Mapp. 39:(11):421327
    [Crossref] [Google Scholar]
  151. Zhao Y, Caffo B, Luo X, Alzheimer's Disease Neuroimaging Initiative. 2021a.. Principal regression for high dimensional covariance matrices. . Electron. J. Stat. 15:(2):4192235
    [Crossref] [Google Scholar]
  152. Zhao Y, Caffo BS, Luo X. 2024.. Longitudinal regression of covariance matrix outcomes. . Biostatistics 25:(2):385401
    [Crossref] [Google Scholar]
  153. Zhao Y, Li L, Alzheimer's Disease Neuroimaging Initiative. 2022.. Multimodal data integration via mediation analysis with high-dimensional exposures and mediators. . Hum. Brain Mapp. 43:(8):251933
    [Crossref] [Google Scholar]
  154. Zhao Y, Lindquist MA, Caffo BS. 2020.. Sparse principal component based high-dimensional mediation analysis. . Comput. Stat. Data Anal. 142::106835
    [Crossref] [Google Scholar]
  155. Zhao Y, Luo X. 2023.. Multilevel mediation analysis with structured unmeasured mediator-outcome confounding. . Comput. Stat. Data Anal. 179::107623
    [Crossref] [Google Scholar]
  156. Zhao Y, Wang B, Mostofsky SH, Caffo BS, Luo X. 2021b.. Covariate assisted principal regression for covariance matrix outcomes. . Biostatistics 22:(3):62945
    [Crossref] [Google Scholar]
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