Abraham, A., Milham, M. P., Di Martino, A., et al. (2017). Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example. NeuroImage, 147, 736–745.
Aertsen, A., Gerstein, G., Habib, M., et al. (1989). Dynamics of neuronal firing correlation: modulation of “effective connectivity’’. Journal of neurophysiology, 61(5), 900–917.
Article CAS PubMed Google Scholar
Almuqhim, F., & Saeed, F. (2021). ASD-SAENET: a sparse autoencoder, and deep-neural network model for detecting autism spectrum disorder (ASD) using fMRI data. Frontiers in Computational Neuroscience, 15, 27.
An, H. S., Moon, W. J., Ryu, J. K., et al. (2017). Inter-vender and test-retest reliabilities of resting-state functional magnetic resonance imaging: Implications for multi-center imaging studies. Magnetic resonance imaging, 44, 125–130.
Arslan, S., Ktena, S. I., Makropoulos, A., et al. (2018). Human brain mapping: a systematic comparison of parcellation methods for the human cerebral cortex. NeuroImage, 170, 5–30. https://doi.org/10.1016/j.neuroimage.2017.04.014. https://www.sciencedirect.com/science/article/pii/S1053811917303026, segmenting the Brain.
Badhwar, A., Collin-Verreault, Y., Orban, P., et al. (2020). Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors. NeuroImage, 205, 116210.
Bassett, D. S., & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20(3), 353–364. https://doi.org/10.1038/nn.4502. https://doi.org/10.1038/nn.4502
Benkarim, O., Paquola, C., Park, B., et al. (2022). Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging. PLoS Biology, 20(4), e3001627.
Birn, R. M., Molloy, E. K., Patriat, R., et al. (2013). The effect of scan length on the reliability of resting-state fMRI connectivity estimates. Neuroimage, 83, 550–558.
Biswal, B. B., Mennes, M., Zuo, X. N., et al. (2010). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences, 107(10), 4734–4739.
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., et al. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic resonance in medicine, 34(4), 537–541.
Article CAS PubMed Google Scholar
Brown, G. G., Mathalon, D. H., Stern, H., et al. (2011). Multisite reliability of cognitive bold data. Neuroimage, 54(3), 2163–2175.
Bullmore, E. T., & Bassett, D. S. (2011). Brain graphs: graphical models of the human brain connectome. Annual Review of Clinical Psychology, 7(1), 113–140. https://doi.org/10.1146/annurev-clinpsy-040510-143934. pMID: 21128784. https://arxiv.org/abs/https://doi.org/10.1146/annurev-clinpsy-040510-143934
Chavez, S., Viviano, J., Zamyadi, M., et al. (2018). A novel DTI-QA tool: automated metric extraction exploiting the sphericity of an agar filled phantom. Magnetic resonance imaging, 46, 28–39.
Chen, C. P., Keown, C. L., Jahedi, A., et al. (2015). Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in Autism. NeuroImage: Clinical, 8, 238–245.
Chen, C. P., Keown, C. L., & Müller, R. A. (2013). Towards understanding autism risk factors: a classification of brain images with support vector machines. International Journal of Semantic Computing, 7(2), 205.
Chen, J., Liu, J., Calhoun, V. D., et al. (2014). Exploration of scanning effects in multi-site structural MRI studies. Journal of neuroscience methods, 230, 37–50.
Article PubMed PubMed Central Google Scholar
Chen, A. A., Srinivasan, D., Pomponio, R., et al. (2022). Harmonizing functional connectivity reduces scanner effects in community detection. NeuroImage, 256, 119198.
Combrisson, E., & Jerbi, K. (2015). Exceeding chance level by chance: the caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. Journal of neuroscience methods, 250, 126–136.
Corder, G. W., & Foreman, D. I. (2014). Nonparametric statistics: a step-by-step approach. Hoboken, New Jersey: John Wiley and Sons.
Cox, R. W., & Jesmanowicz, A. (1999). Real-time 3D image registration for functional MRI. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 42(6), 1014–1018.
Craddock, C., Benhajali, Y., Chu, C., et al. (2013). The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives. Frontiers in Neuroinformatics, 7, 3.
Craddock, R. C., James, G. A., Holtzheimer, P. E., III., et al. (2012). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human brain mapping, 33(8), 1914–1928.
Dansereau, C., Benhajali, Y., Risterucci, C., et al. (2017). Statistical power and prediction accuracy in multisite resting-state fMRI connectivity. Neuroimage, 149, 220–232.
Di Martino, A., O’connor, D., Chen, B., et al. (2017). Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Scientific data, 4(1), 1–15.
Di Martino, A., Yan, C. G., Li, Q., et al. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659–667.
Dukart, J., Schroeter, M. L., Mueller, K., et al. (2011). Age correction in dementia-matching to a healthy brain. PloS one, 6(7), e22193.
Article CAS PubMed PubMed Central Google Scholar
Eslami, T., Mirjalili, V., Fong, A., et al. (2019). ASD-diagnet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data. Frontiers in Neuroinformatics, 13, 70. https://doi.org/10.3389/fninf.2019.00070. https://www.frontiersin.org/article/10.3389/fninf.2019.00070
Faskowitz, J., Betzel, R. F., & Sporns, O. (2022). Edges in brain networks: Contributions to models of structure and function. Network Neuroscience, 6(1), 1–28.
PubMed PubMed Central Google Scholar
Faskowitz, J., Esfahlani, F. Z., Jo, Y., et al. (2020). Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Nature Neuroscience, 23(12), 1644–1654. https://doi.org/10.1038/s41593-020-00719-y
Article CAS PubMed Google Scholar
Feis, R. A., Smith, S. M., Filippini, N., et al. (2015). ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI. Frontiers in neuroscience, 9, 395.
Article PubMed PubMed Central Google Scholar
Fisher, R. A. (1915). Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population. Biometrika, 10(4), 507–521.
Forsyth, J. K., McEwen, S. C., Gee, D. G., et al. (2014). Reliability of functional magnetic resonance imaging activation during working memory in a multi-site study: analysis from the North American Prodrome longitudinal study. Neuroimage, 97, 41–52.
Fortin, J. P., Cullen, N., Sheline, Y. I., et al. (2018). Harmonization of cortical thickness measurements across scanners and sites. Neuroimage, 167, 104–120.
Fortin, J. P., Parker, D., Tunç, B., et al. (2017). Harmonization of multi-site diffusion tensor imaging data. Neuroimage, 161, 149–170.
Friedman, L., Glover, G. H., Consortium, F., et al. (2006). Reducing interscanner variability of activation in a multicenter fMRI study: controlling for signal-to-fluctuation-noise-ratio (SFNR) differences. Neuroimage, 33(2), 471–481.
Friedman, L., Stern, H., Brown, G. G., et al. (2008). Test-retest and between-site reliability in a multicenter fMRI study. Human brain mapping, 29(8), 958–972.
Glover, G. H., Mueller, B. A., Turner, J. A., et al. (2012). Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies. Journal of Magnetic Resonance Imaging, 36(1), 39–54.
Article PubMed PubMed Central Google Scholar
Gountouna, V. E., Job, D. E., McIntosh, A. M., et al. (2010). Functional magnetic resonance imaging (fMRI) reproducibility and variance components across visits and scanning sites with a finger tapping task. Neuroimage, 49(1), 552–560.
Gradin, V., Gountouna, V. E., Waiter, G., et al. (2010). Between-and within-scanner variability in the calibrain study n-back cognitive task. Psychiatry Research: Neuroimaging, 184(2), 86–95.
Guo, X., Dominick, K. C., Minai, A. A., et al. (2017). Diagnosing autism spectrum disorder from brain resting-state functional connectivity patterns using a deep neural network with a novel feature selection method. Frontiers in neuroscience, 11, 460.
Article PubMed PubMed Central Google Scholar
Heinsfeld, A. S., Franco, A. R., Craddock, R. C., et al. (2018). Identification of autism spectrum disorder using deep learning and the abide dataset. NeuroImage: Clinical, 17, 16–23.
Iidaka, T. (2015). Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex, 63, 55–67.
Jenkinson, M., & Chappell, M. (2018). Introduction to neuroimaging analysis. Oxford University Press.
Johnson, W. E., Li, C., & Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics, 8(1), 118–127.
Kam, T. E., Suk, H. I., & Lee, S. W. (2017). Multiple functional networks modeling for autism spectrum disorder diagnosis. Human brain mapping, 38(11), 5804–5821.
Article PubMed PubMed Central Google Scholar
Kassraian-Fard, P., Matthis, C., Balsters, J. H., et al. (2016). Promises, pitfalls, and basic guidelines for applying machine learning classifiers to psychiatric imaging data, with autism as an example. Frontiers in psychiatry, 7, 177.
Article PubMed PubMed Central Google Scholar
Khosla, M., Jamison, K., Kuceyeski, A., et al. (2019). Ensemble learning with 3D convolutional neural netw
Comments (0)