A within-subject voxel-wise constant-block partial least squares correlation method to explore MRI-based brain structure–function relationship

Altan A, Karasu S (2020) Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique. Chaos Solitons Fractals 140:1–10 Aydin S (2022) Cross-validated adaboost classification of emotion regulation strategies identified by spectral coherence in resting-state. Neuroinformatics 20(3):627–639 Aydın S, Çetin FH, Uytun MÇ, Babadaği Z, Güven AS, Işık Y (2022) Comparison of domain specific connectivity metrics for estimation brain network indices in boys with ADHD-C. Biomed Signal Process Control 76:1–10 Baecker L, Garcia-Dias R, Vieira S, Scarpazza C, Mechelli A (2021) Machine learning for brain age prediction: Introduction to methods and clinical applications. EBioMedicine 72:1–9 Bajic D, Craig MM, Mongerson CRL, Borsook D, Becerra L (2017) Identifying rodent resting-state brain networks with independent component analysis. Front Neurosci 11:1–24 Breakspear M (2017) Dynamic models of large-scale brain activity. Nat Neurosci 20(3):340–352 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):230–244 Campbell KL, Grigg O, Saverino C, Churchill N, Grady CL (2013) Age differences in the intrinsic functional connectivity of default network subsystems. Front Aging Neurosci 5:1–12 Cao M, Wang JH, Dai ZJ, Cao XY (2014) Topological organization of the human brain functional connectome across the lifespan. Dev Cogn Neurosci 7:76–93 Chen K, Reiman EM, Huan Z, Caselli RJ, Bandy D, Ayutyanont N et al (2009) Linking functional and structural brain images with multivariate network analyses: a novel application of the partial least square method. Neuroimage 47(2):602–610 Crofts JJ, Higham DJ (2009) A weighted communicability measure applied to complex brain networks. J R Soc Interface 6(33):411–414 Damoiseaux JS (2017) Effects of aging on functional and structural brain connectivity. Neuroimage 160:32–40 Deco G, Jirsa V, McIntoshe AR (2009) Key role of coupling, delay, and noise in resting brain fluctuations. Proc Natl Acad Sci 106(29):12207–12208 Deligianni F, Carmichael DW, Zhang GH, Clark CA, Clayden JD (2016) NODDI and tensor-based microstructural indices as predictors of functional connectivity. PLoS ONE 11(4):1–17 DuPre E, Spreng RN (2017) Structural covariance networks across the lifespan, from 6–94 years of age. Neuroscience 1(3):302–323 Erhardt EB, Allen EA, Wei Y, Eichele T, Calhoun VD (2012) SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability. Neuroimage 59(4):4160–4167 Goni J, van den Heuvel MP, Avena-Koenigsberger A (2013) Resting-brain functional connectivity predicted by analytic measures of network communication. Proc Natl Acad Sci 111(2):833–838 Graham D, Rockmore D (2011) The Packet Switching Brain. J Cogn Neurosci 23(2):267–276 Grigg O, Grady CL (2010a) The default network and processing of personally relevant information: converging evidence from task-related modulations and functional connectivity. Neuropsychologia 48(13):3815–3823 Grigg O, Grady CL (2010b) Task-related effects on the temporal and spatial dynamics of resting-state functional connectivity in the default network. PLoS ONE 5(10):1–12 Gudbjartsson H, Patz S (1995) The Rician distribution of noisy MRI data. Magn Reson Med 34(6):1–15 Haimovici A, Tagliazucchi E, Balenzuela P, Chialvo DR (2013) Brain organization into resting state networks emerges at criticality on a model of the human connectome. Phys Rev Lett 110:1–4 Hansen ECA, Battaglia D, Spiegler A (2014) Functional connectivity dynamics: modeling the switching behavior of the resting state. Neuroimage 1:1–11 Hervé Abdi, Williams LJ (2012) Partial least squares methods: partial least squares correlation and partial least square regression. Methods Mol Biol 930(1):549–579 van den Heuvel MP, Sporns O (2011) Rich-club organization of the human connectome. J Neurosci 2:15775–15786 van den Heuvel MP, Sporns O (2013) Network hubs in the human brain. Trends Cogn Sci 17(12):683–696 Honey CJ, Kötter R, Breakspear M (2007) Network structure of cerebral cortex shapes functional connectivity on multiple time scales-PNAS. PNAS 104(24):10240–10245 Keshavamurthy R, Dixon S, Pazdernik KT, Charles LE (2022) Predicting infectious disease for biopreparedness and response: a systematic review of machine learning and deep learning approaches. One Health 15:1–13 Krishnan A, Williams LJ, McIntosh AR, Abdi H (2011) Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review. Neuroimage 56(2):455–475 Levakov G, Rosenthal G, Shelef I, Raviv TR, Avidan G (2020) From a deep learning model back to the brain-Identifying regional predictors and their relation to aging. Hum Brain Mapp 41(12):3235–3252 Liu K, Yao S, Chen K, Zhang J, Yao L, Li K et al (2017) Structural brain network changes across the adult lifespan. Front Aging Neurosci 9:1–10 Marstaller L, Williams M, Rich A, Savage G, Burianova H (2015) Aging and large-scale functional networks: white matter integrity, gray matter volume, and functional connectivity in the resting state. Neuroscience 290(2015):369–378 McIntosh AR, Chau WK, Protzner AB (2004) Spatiotemporal analysis of event-related fMRI data using partial least squares. Neuroimage 23(2):764–775 McIntosh AR, Lobaugh NJ (2004a) Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage 23(2004):250–263 McIntosh AR, Lobaugh NJ (2004b) Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage 23:250–263 Meskaldjia D-E, Pretia MG, Boltona TA (2016) Prediction of long-term memory scores in MCI based on resting-state fMRI. NeuroImage: Clin 12(2016):785–795. Messe A, Rudrauf D, Benali H (2014) Relating structure and function in the human brain: relative contributions of anatomy, stationary dynamics, and non-stationarities. PLoS Comput Biol 10(3):1–9 Misic B, Betzel RF, Nematzadeh A (2015) Cooperative and competitive spreading dynamics on the human connectome. Neuron 86(6):1518–1529 Misic B, Betzel RF, de Reus MA, van den Heuvel MP, Berman MG, McIntosh AR et al (2016) Network-level structure-function relationships in human neocortex. Cereb Cortex 26(7):3285–3296 Neudorf J, Kress S, Borowsky R (2022) Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity. Brain Struct Funct 227(1):331–343 Nooner KB, Colcombe SJ, Tobe RH, Mennes M, Benedict MM, Moreno AL et al (2012) The NKI-Rockland sample: a model for accelerating the pace of discovery science in psychiatry. Front Neurosci 6:1–11 Park HJ, Friston K (2013) Structural and functional brain networks: from connections to cognition. Science 342(6158):579–586 Ponce-Alvarez A, Deco G, Hagmann P (2015) Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity. PLoS Comput Biol 11(2):1–23 Ren C, Kim D-K, Jeong D (2020) A survey of deep learning in agriculture techniques and their applications. J Inform Process Syst 16:1015–1033 Van Roon P, Zakizadeh J, Chartier S (2014) Partial least squares tutorial for analyzing neuroimaging data. Quant Methods Psychol 10(2):200–215 Rosenthal G, Vasa F, Griffa A, Hagmann P, Amico E, Goni J et al (2018) Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes. Nat Commun 9(1):1–12 Sanz-Leon P, Knock SA, Spiegler A, Jirsa VK (2015) Mathematical framework for large-scale brain network modeling in The Virtual Brain. Neuroimage 111(2):385–430 Sarwar T, Tian Y, Yeo BTT, Ramamohanarao K, Zalesky A (2021) Structure-function coupling in the human connectome: a machine learning approach. Neuroimage 226:1–11 Segall JM, Allen EA, Jung RE, Erhardt EB, Arja SK, Kiehl K et al (2012) Correspondence between structure and function in the human brain at rest. Front Neuroinform 6(10):1–17 Shaw DJ, Marecek R, Grosbras MH, Leonard G, Pike GB, Paus T (2016) Co-ordinated structural and functional covariance in the adolescent brain underlies face processing performance. Soc Cogn Affect Neurosci 11(4):556–568 Spreng RN, Turner GR (2013) Structural covariance of the default network in healthy and pathological aging. J Neurosci 33(38):15226–15234 Stephen JM, Coffman BA, Jung RE, Bustillo JR, Aine CJ, Calhoun VD (2013) Using joint ICA to link function and structure using MEG and DTI in schizophrenia. Neuroimage 83:418–430 Straathof M, Sinke MR (2019) A systematic review on the quantitative relationship between structural and functional network connectivity strength in mammalian brains. J Cereb Blood Flow Metab 39(2):189–209 Suarez LE, Markello RD, Betzel RF, Misic B (2020) Linking structure and function in macroscale brain networks. Trends Cogn Sci 24(4):302–315 Sui J, Huster R, Yu Q, Segall JM, Calhoun VD (2014) Function-structure associations of the brain: evidence from multimodal connectivity and covariance studies. Neuroimage 102(2014):11–23 Wang Z, Dai Z, Gong G, Zhou C, He Y (2015) Understanding structural-functional relationships in the human brain: a large-scale network perspective. Neuroscientist 21(3):290–305 Wang X, Lin Q, Xia M, He Y (2018) Differentially categorized structural brain hubs are involved in different microstructural, functional, and cognitive characteristics and contribute to individual identification. Hum Brain Mapp 39(4):1647–1663 Yan CG, Cheung B, Kelly C, Colcombe S, Craddock RC, Di Martino A et al (2013) A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76(1):183–201 Yağ İ, Altan A (2022) Artificial intelligence-based robust hybrid algorithm design and implementation for real-time detection of plant diseases in agricultural environments. Biology 11(12):1–30 Zhang L, Wang M, Liu M, Zhang D (2020) A survey on deep learning for neuroimaging-based brain disorder analysis. Front Neurosci 14:779 Zhang C, Yao L, Song S, Wen X, Zhao X, Long Z (2018) Euler elastica regularized logistic regression for whole-brain decoding of fMRI data. IEEE Trans Biomed Eng 65(7):1639–1653 Zhao X, Kewei Chen L (2019) Changes in the functional and structural default mode network across the adult lifespan based on partial least squares. IEEE Access 7:82256–82265 Zhao X, Yao LI, Chen K, Li KE, Zhang J, Guo X (2019) Changes in the functional and structural default mode network across the adult lifespan based on partial least squares. IEEE Access 7:82256–82265 Zhuang X, Yang Z, Cordes D (2020) A technical review of canonical correlation analysis for neuroscience applications. Hum Brain Mapp 41(13):3807–3833 Zimmermann J, Ritter P, Shen K (2016) Structural architecture supports functional organization in the human aging brain at a regionwise and network level. Hum Brain Mapp 37:2645–2661