Skip to main content
Top

18-02-2023 | Research Article

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

Authors: Xiaoyu Zhao, Kewei Chen, Hailing Wang, Yufei Gao, Xiangmin Ji, Yanping Li

Published in: Cognitive Neurodynamics

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The brain structure–function relationship is crucial to how the human brain works under normal or diseased conditions. Exploring such a relationship is challenging when using the 3-dimensional magnetic resonance imaging (MRI) functional dataset which is temporal dynamic and the structural MRI which is static. Partial Least Squares Correlation (PLSC) is one of the classical methods for exploring the joint spatial and temporal relationship. The goal of PLSC is to identify covarying patterns via linear voxel-wise combinations in each of the structural and functional data sets to maximize the covariance. However, existing PLSC cannot adequately deal with the unmatched temporal dimensions between structural and functional data sets. We proposed a new alternative variant of the PLSC, termed within-subject, voxel-wise, and constant-block PLSC, to address this problem. To validate our method, we used two data sets with weak and strong relationships in simulated data. Additionally, the analysis of real brain data was carried out based on gray matter volume hubs derived from sMRI and whole-brain voxel-wise measures from resting-state fMRI for aging effect based on healthy subjects aged 16–85 years. Our results showed that our constant-block PLSC can detect weak structure–function relationships and has better robustness to noise. In fact, it adequately unearthed the true simulated number of significant and more accurate latent variables for the simulated data and more meaningful LVs for the real data, with covariance improvement from 16.19 to 41.48% (simulated) and 13.29–53.68% (real data), respectively. More interestingly in the real data analysis, our method identified simultaneously the well-known brain networks such as the default mode, sensorimotor, auditory, and dorsal attention networks both functionally and structurally, implying the hubs we derived from gray matter volumes are the basis of brain function, supporting diverse functions. Constant-block PLSC is a feasible tool for analyzing the brain structure–function relationship.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
go back to reference 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 140:1–10CrossRef 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 140:1–10CrossRef
go back to reference Aydin S (2022) Cross-validated adaboost classification of emotion regulation strategies identified by spectral coherence in resting-state. Neuroinformatics 20(3):627–639PubMedCrossRef Aydin S (2022) Cross-validated adaboost classification of emotion regulation strategies identified by spectral coherence in resting-state. Neuroinformatics 20(3):627–639PubMedCrossRef
go back to reference 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 76:1–10CrossRef 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 76:1–10CrossRef
go back to reference 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 72:1–9CrossRef 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 72:1–9CrossRef
go back to reference Bajic D, Craig MM, Mongerson CRL, Borsook D, Becerra L (2017) Identifying rodent resting-state brain networks with independent component analysis. Front Neurosci 11:1–24CrossRef Bajic D, Craig MM, Mongerson CRL, Borsook D, Becerra L (2017) Identifying rodent resting-state brain networks with independent component analysis. Front Neurosci 11:1–24CrossRef
go back to reference 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 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
go back to reference 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 5:1–12CrossRef 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 5:1–12CrossRef
go back to reference Cao M, Wang JH, Dai ZJ, Cao XY (2014) Topological organization of the human brain functional connectome across the lifespan. Dev Cogn Neurosci 7:76–93PubMedCrossRef Cao M, Wang JH, Dai ZJ, Cao XY (2014) Topological organization of the human brain functional connectome across the lifespan. Dev Cogn Neurosci 7:76–93PubMedCrossRef
go back to reference 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):602–610PubMedCrossRef 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):602–610PubMedCrossRef
go back to reference Damoiseaux JS (2017) Effects of aging on functional and structural brain connectivity. Neuroimage 160:32–40PubMedCrossRef Damoiseaux JS (2017) Effects of aging on functional and structural brain connectivity. Neuroimage 160:32–40PubMedCrossRef
go back to reference Deco G, Jirsa V, McIntoshe AR (2009) Key role of coupling, delay, and noise in resting brain fluctuations. Proc Natl Acad Sci 106(29):12207–12208CrossRef Deco G, Jirsa V, McIntoshe AR (2009) Key role of coupling, delay, and noise in resting brain fluctuations. Proc Natl Acad Sci 106(29):12207–12208CrossRef
go back to reference 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):1–17CrossRef 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):1–17CrossRef
go back to reference DuPre E, Spreng RN (2017) Structural covariance networks across the lifespan, from 6–94 years of age. Neuroscience 1(3):302–323 DuPre E, Spreng RN (2017) Structural covariance networks across the lifespan, from 6–94 years of age. Neuroscience 1(3):302–323
go back to reference 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):4160–4167PubMedCrossRef 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):4160–4167PubMedCrossRef
go back to reference 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):833–838PubMedPubMedCentralCrossRef 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):833–838PubMedPubMedCentralCrossRef
go back to reference 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):3815–3823PubMedPubMedCentralCrossRef 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):3815–3823PubMedPubMedCentralCrossRef
go back to reference 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):1–12CrossRef 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):1–12CrossRef
go back to reference Gudbjartsson H, Patz S (1995) The Rician distribution of noisy MRI data. Magn Reson Med 34(6):1–15CrossRef Gudbjartsson H, Patz S (1995) The Rician distribution of noisy MRI data. Magn Reson Med 34(6):1–15CrossRef
go back to reference 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 110:1–4CrossRef 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 110:1–4CrossRef
go back to reference Hansen ECA, Battaglia D, Spiegler A (2014) Functional connectivity dynamics: modeling the switching behavior of the resting state. Neuroimage 1:1–11 Hansen ECA, Battaglia D, Spiegler A (2014) Functional connectivity dynamics: modeling the switching behavior of the resting state. Neuroimage 1:1–11
go back to reference 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 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
go back to reference van den Heuvel MP, Sporns O (2011) Rich-club organization of the human connectome. J Neurosci 2:15775–15786CrossRef van den Heuvel MP, Sporns O (2011) Rich-club organization of the human connectome. J Neurosci 2:15775–15786CrossRef
go back to reference van den Heuvel MP, Sporns O (2013) Network hubs in the human brain. Trends Cogn Sci 17(12):683–696PubMedCrossRef van den Heuvel MP, Sporns O (2013) Network hubs in the human brain. Trends Cogn Sci 17(12):683–696PubMedCrossRef
go back to reference Honey CJ, Kötter R, Breakspear M (2007) Network structure of cerebral cortex shapes functional connectivity on multiple time scales-PNAS. PNAS 104(24):10240–10245PubMedPubMedCentralCrossRef Honey CJ, Kötter R, Breakspear M (2007) Network structure of cerebral cortex shapes functional connectivity on multiple time scales-PNAS. PNAS 104(24):10240–10245PubMedPubMedCentralCrossRef
go back to reference 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 15:1–13CrossRef 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 15:1–13CrossRef
go back to reference Krishnan A, Williams LJ, McIntosh AR, Abdi H (2011) Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review. Neuroimage 56(2):455–475PubMedCrossRef Krishnan A, Williams LJ, McIntosh AR, Abdi H (2011) Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review. Neuroimage 56(2):455–475PubMedCrossRef
go back to reference 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):3235–3252PubMedPubMedCentralCrossRef 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):3235–3252PubMedPubMedCentralCrossRef
go back to reference 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 9:1–10CrossRef 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 9:1–10CrossRef
go back to reference 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):369–378PubMedCrossRef 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):369–378PubMedCrossRef
go back to reference McIntosh AR, Chau WK, Protzner AB (2004) Spatiotemporal analysis of event-related fMRI data using partial least squares. Neuroimage 23(2):764–775PubMedCrossRef McIntosh AR, Chau WK, Protzner AB (2004) Spatiotemporal analysis of event-related fMRI data using partial least squares. Neuroimage 23(2):764–775PubMedCrossRef
go back to reference McIntosh AR, Lobaugh NJ (2004a) Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage 23(2004):250–263CrossRef McIntosh AR, Lobaugh NJ (2004a) Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage 23(2004):250–263CrossRef
go back to reference McIntosh AR, Lobaugh NJ (2004b) Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage 23:250–263CrossRef McIntosh AR, Lobaugh NJ (2004b) Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage 23:250–263CrossRef
go back to reference 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. 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.
go back to reference 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):1–9CrossRef 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):1–9CrossRef
go back to reference Misic B, Betzel RF, Nematzadeh A (2015) Cooperative and competitive spreading dynamics on the human connectome. Neuron 86(6):1518–1529PubMedCrossRef Misic B, Betzel RF, Nematzadeh A (2015) Cooperative and competitive spreading dynamics on the human connectome. Neuron 86(6):1518–1529PubMedCrossRef
go back to reference 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):3285–3296PubMedPubMedCentralCrossRef 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):3285–3296PubMedPubMedCentralCrossRef
go back to reference 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):331–343PubMedCrossRef 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):331–343PubMedCrossRef
go back to reference 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 6:1–11CrossRef 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 6:1–11CrossRef
go back to reference Park HJ, Friston K (2013) Structural and functional brain networks: from connections to cognition. Science 342(6158):579–586CrossRef Park HJ, Friston K (2013) Structural and functional brain networks: from connections to cognition. Science 342(6158):579–586CrossRef
go back to reference Ponce-Alvarez A, Deco G, Hagmann P (2015) Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity. PLoS Comput Biol 11(2):1–23CrossRef Ponce-Alvarez A, Deco G, Hagmann P (2015) Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity. PLoS Comput Biol 11(2):1–23CrossRef
go back to reference 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 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
go back to reference Van Roon P, Zakizadeh J, Chartier S (2014) Partial least squares tutorial for analyzing neuroimaging data. Quant Methods Psychol 10(2):200–215CrossRef Van Roon P, Zakizadeh J, Chartier S (2014) Partial least squares tutorial for analyzing neuroimaging data. Quant Methods Psychol 10(2):200–215CrossRef
go back to reference 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):1–12CrossRef 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):1–12CrossRef
go back to reference 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):385–430PubMedCrossRef 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):385–430PubMedCrossRef
go back to reference Sarwar T, Tian Y, Yeo BTT, Ramamohanarao K, Zalesky A (2021) Structure-function coupling in the human connectome: a machine learning approach. Neuroimage 226:1–11CrossRef Sarwar T, Tian Y, Yeo BTT, Ramamohanarao K, Zalesky A (2021) Structure-function coupling in the human connectome: a machine learning approach. Neuroimage 226:1–11CrossRef
go back to reference 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 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
go back to reference 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):556–568PubMedCrossRef 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):556–568PubMedCrossRef
go back to reference 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 83:418–430PubMedCrossRef 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 83:418–430PubMedCrossRef
go back to reference 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):189–209PubMedCrossRef 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):189–209PubMedCrossRef
go back to reference Suarez LE, Markello RD, Betzel RF, Misic B (2020) Linking structure and function in macroscale brain networks. Trends Cogn Sci 24(4):302–315PubMedCrossRef Suarez LE, Markello RD, Betzel RF, Misic B (2020) Linking structure and function in macroscale brain networks. Trends Cogn Sci 24(4):302–315PubMedCrossRef
go back to reference 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):11–23PubMedCrossRef 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):11–23PubMedCrossRef
go back to reference 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):290–305PubMedCrossRef 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):290–305PubMedCrossRef
go back to reference 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):1647–1663PubMedPubMedCentralCrossRef 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):1647–1663PubMedPubMedCentralCrossRef
go back to reference 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):183–201PubMedCrossRef 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):183–201PubMedCrossRef
go back to reference 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):1–30CrossRef 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):1–30CrossRef
go back to reference 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):1639–1653PubMedCrossRef 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):1639–1653PubMedCrossRef
go back to reference 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 7:82256–82265PubMedPubMedCentralCrossRef 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 7:82256–82265PubMedPubMedCentralCrossRef
go back to reference 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 7:82256–82265PubMedPubMedCentralCrossRef 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 7:82256–82265PubMedPubMedCentralCrossRef
go back to reference Zhuang X, Yang Z, Cordes D (2020) A technical review of canonical correlation analysis for neuroscience applications. Hum Brain Mapp 41(13):3807–3833PubMedPubMedCentralCrossRef Zhuang X, Yang Z, Cordes D (2020) A technical review of canonical correlation analysis for neuroscience applications. Hum Brain Mapp 41(13):3807–3833PubMedPubMedCentralCrossRef
go back to reference 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 37:2645–2661PubMedPubMedCentralCrossRef 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 37:2645–2661PubMedPubMedCentralCrossRef
Metadata
Title
A within-subject voxel-wise constant-block partial least squares correlation method to explore MRI-based brain structure–function relationship
Authors
Xiaoyu Zhao
Kewei Chen
Hailing Wang
Yufei Gao
Xiangmin Ji
Yanping Li
Publication date
18-02-2023
Publisher
Springer Netherlands
Published in
Cognitive Neurodynamics
Print ISSN: 1871-4080
Electronic ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-023-09941-3