Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Capturing functional connectomics using Riemannian partial least squares (2306.17371v1)

Published 30 Jun 2023 in stat.ME and stat.ML

Abstract: For neurological disorders and diseases, functional and anatomical connectomes of the human brain can be used to better inform targeted interventions and treatment strategies. Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique that captures spatio-temporal brain function through blood flow over time. FMRI can be used to study the functional connectome through the functional connectivity matrix; that is, Pearson's correlation matrix between time series from the regions of interest of an fMRI image. One approach to analysing functional connectivity is using partial least squares (PLS), a multivariate regression technique designed for high-dimensional predictor data. However, analysing functional connectivity with PLS ignores a key property of the functional connectivity matrix; namely, these matrices are positive definite. To account for this, we introduce a generalisation of PLS to Riemannian manifolds, called R-PLS, and apply it to symmetric positive definite matrices with the affine invariant geometry. We apply R-PLS to two functional imaging datasets: COBRE, which investigates functional differences between schizophrenic patients and healthy controls, and; ABIDE, which compares people with autism spectrum disorder and neurotypical controls. Using the variable importance in the projection statistic on the results of R-PLS, we identify key functional connections in each dataset that are well represented in the literature. Given the generality of R-PLS, this method has potential to open up new avenues for multi-model imaging analysis linking structural and functional connectomics.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (67)
  1. The structural and functional connectome and prediction of risk for cognitive impairment in older adults. \JournalTitleCurrent behavioral neuroscience reports 2, 234–245 (2015).
  2. Functional connectome mediates the association between sleep disturbance and mental health in preadolescence: a longitudinal mediation study. \JournalTitleHuman Brain Mapping 43, 2041–2050 (2022).
  3. Resting-State Functional Connectivity in Psychiatric Disorders. \JournalTitleThe Journal of the American Medical Association Psychiatry 72, 743–744, DOI: 10.1001/JAMAPSYCHIATRY.2015.0484 (2015).
  4. Connectome imaging for mapping human brain pathways. \JournalTitleMolecular psychiatry 22, 1230–1240 (2017).
  5. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. \JournalTitleProceedings of the National Academy of Sciences of the United States of America 87, 9868–9872 (1990).
  6. Wold, H. Soft Modelling by Latent Variables: The Non-Linear Iterative Partial Least Squares (NIPALS) Approach. \JournalTitleJournal of Applied Probability 12, 117–142, DOI: 10.1017/S0021900200047604 (1975).
  7. Spatial Pattern Analysis of Functional Brain Images Using Partial Least Squares. \JournalTitleNeuroImage 3, 143–157, DOI: 10.1006/NIMG.1996.0016 (1996).
  8. Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review. \JournalTitleNeuroImage 56, 455–475, DOI: 10.1016/j.neuroimage.2010.07.034 (2011).
  9. A Riemannian Framework for Tensor Computing. \JournalTitleInternational Journal of Computer Vision 66, 41–66, DOI: 10.1007/s11263-005-3222-z (2006).
  10. Riemannian Geometric Statistics in Medical Image Analysis (Elsevier, 2019).
  11. Riemannian Regression and Classification Models of Brain Networks Applied to autism. \JournalTitleConnectomics in neuroImaging : second international workshop, CNI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018 : proceedings. CNI (Workshop) (2nd : 2018 : Granada, Spain) 11083, 78, DOI: 10.1007/978-3-030-00755-3_9 (2018).
  12. Chu, Y. et al. Decoding multiclass motor imagery EEG from the same upper limb by combining Riemannian geometry features and partial least squares regression. \JournalTitleJournal of Neural Engineering 17, 046029, DOI: 10.1088/1741-2552/ABA7CD (2020).
  13. Region Constraint Person Re-Identification via Partial Least Square on Riemannian Manifold. \JournalTitleIEEE Access 6, 17060–17066, DOI: 10.1109/ACCESS.2018.2808602 (2018).
  14. Partial Least Squares Regression on Symmetric Positive-Definite Matrices. \JournalTitleRevista Colombiana de Estadistica 36, 177–192 (2013).
  15. PLS: partial least squares projections to latent structures. \JournalTitle3D QSAR Drug Des 523–550 (1993).
  16. Aine, C. J. et al. Multimodal Neuroimaging in Schizophrenia: Description and Dissemination. \JournalTitleNeuroinformatics 15, 343–364, DOI: 10.1007/s12021-017-9338-9 (2017).
  17. LNCS 6361 - Detection of Brain Functional-Connectivity Difference in Post-stroke Patients Using Group-Level Covariance Modeling (2010).
  18. Craddock, C. et al. The Neuro Bureau Preprocessing Initiative: open sharing of preprocessed neuroimaging data and derivatives. \JournalTitleFrontiers in Neuroinformatics 7, DOI: 10.3389/CONF.FNINF.2013.09.00041/EVENT_ABSTRACT (2013).
  19. Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. \JournalTitleNeuroImage 15, 273–289, DOI: 10.1006/nimg.2001.0978 (2002).
  20. Multi-subject dictionary learning to segment an atlas of brain spontaneous activity. vol. 6801 LNCS, 562–573, DOI: 10.1007/978-3-642-22092-0_46 (Springer, 2011).
  21. Functional connections between and within brain subnetworks under resting-state. \JournalTitleScientific Reports 2020 10:1 10, 1–13, DOI: 10.1038/s41598-020-60406-7 (2020).
  22. The Effect of Aging on Resting State Connectivity of Predefined Networks in the Brain. \JournalTitleFrontiers in Aging Neuroscience 11, 234, DOI: 10.3389/FNAGI.2019.00234/BIBTEX (2019).
  23. Functional brain connectivity changes across the human life span: From fetal development to old age. \JournalTitleJournal of Neuroscience Research 99, 236–262, DOI: 10.1002/JNR.24669 (2021).
  24. Ferreira, L. K. et al. Aging Effects on Whole-Brain Functional Connectivity in Adults Free of Cognitive and Psychiatric Disorders. \JournalTitleCerebral Cortex 26, 3851–3865, DOI: 10.1093/CERCOR/BHV190 (2016).
  25. Aging and functional brain networks. \JournalTitleMolecular Psychiatry 2012 17:5 17, 549–558, DOI: 10.1038/mp.2011.81 (2011).
  26. Vidal-Piñiro, D. et al. Decreased Default Mode Network connectivity correlates with age-associated structural and cognitive changes. \JournalTitleFrontiers in Aging Neuroscience 6, 256, DOI: 10.3389/FNAGI.2014.00256/BIBTEX (2014).
  27. Abnormal cortico-cerebellar functional connectivity in autism spectrum disorder. \JournalTitleFrontiers in systems neuroscience 12, 74 (2019).
  28. Evaluating functional connectivity alterations in autism spectrum disorder using network-based statistics. \JournalTitleDiagnostics 8, 51 (2018).
  29. Assaf, M. et al. Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients. \JournalTitleNeuroImage 53, 247–256, DOI: 10.1016/J.NEUROIMAGE.2010.05.067 (2010).
  30. Smith, R. E. et al. Sex differences in resting-state functional connectivity of the cerebellum in autism spectrum disorder. \JournalTitleFrontiers in Human Neuroscience 13, 104, DOI: 10.3389/FNHUM.2019.00104/BIBTEX (2019).
  31. Zhang, B. et al. Altered Functional Connectivity of Striatum Based on the Integrated Connectivity Model in First-Episode Schizophrenia. \JournalTitleFrontiers in Psychiatry 10, 756, DOI: 10.3389/FPSYT.2019.00756/BIBTEX (2019).
  32. Orliac, F. et al. Links among resting-state default-mode network, salience network, and symptomatology in schizophrenia. \JournalTitleSchizophrenia research 148, 74–80, DOI: 10.1016/J.SCHRES.2013.05.007 (2013).
  33. Duan, M. et al. Altered basal ganglia network integration in schizophrenia. \JournalTitleFrontiers in Human Neuroscience 9, 561, DOI: 10.3389/FNHUM.2015.00561/BIBTEX (2015).
  34. Resting-state networks in schizophrenia. \JournalTitleCurrent topics in medicinal chemistry 12, 2404–2414, DOI: 10.2174/156802612805289863 (2012).
  35. Functional resting-state networks are differentially affected in schizophrenia. \JournalTitleSchizophrenia Research 130, 86–93, DOI: 10.1016/J.SCHRES.2011.03.010 (2011).
  36. Dysfunction of Large-Scale Brain Networks in Schizophrenia: A Meta-analysis of Resting-State Functional Connectivity. \JournalTitleSchizophrenia Bulletin 44, 168–181, DOI: 10.1093/SCHBUL/SBX034 (2018).
  37. Yu, Q. et al. Brain connectivity networks in schizophrenia underlying resting state functional magnetic resonance imaging. \JournalTitleCurrent topics in medicinal chemistry 12, 2415, DOI: 10.2174/156802612805289890 (2012).
  38. Wang, H. et al. Evidence of a dissociation pattern in default mode subnetwork functional connectivity in schizophrenia. \JournalTitleScientific reports 5, 14655 (2015).
  39. Yan, C. et al. Spontaneous Brain Activity in the Default Mode Network Is Sensitive to Different Resting-State Conditions with Limited Cognitive Load. \JournalTitlePLOS ONE 4, e5743, DOI: 10.1371/JOURNAL.PONE.0005743 (2009).
  40. Han, J. et al. Eyes-Open and Eyes-Closed Resting State Network Connectivity Differences. \JournalTitleBrain Sciences 13, 122 (2023).
  41. Resting state connectivity differences in eyes open versus eyes closed conditions. \JournalTitleHuman Brain Mapping 40, 2488, DOI: 10.1002/HBM.24539 (2019).
  42. Ryan, M. Riemannian statistical techniques with applications in fMRI. Ph.D. thesis, The University of Adelaide (2023).
  43. Bellec, P. et al. A neuroimaging analyses kit for Matlab and Octave. 1–5 (Organization on Human Brain Mapping, 2011).
  44. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. \JournalTitleNeuroImage 37, 90–101, DOI: 10.1016/j.neuroimage.2007.04.042 (2007).
  45. Tumor classification by partial least squares using microarray gene expression data. \JournalTitleBioinformatics 18, 39–50, DOI: 10.1093/BIOINFORMATICS/18.1.39 (2002).
  46. Hulland, J. Use of partial least squares (PLS) in strategic management research: A review of four recent studies. \JournalTitleStrategic Management Journal 20, 195–204, DOI: 10.1002/(SICI)1097-0266(199902)20:2 (1999).
  47. Partial least squares analysis of neuroimaging data: applications and advances. \JournalTitleNeuroImage 23, S250–S263, DOI: 10.1016/J.NEUROIMAGE.2004.07.020 (2004).
  48. Lin, F. H. et al. Multivariate analysis of neuronal interactions in the generalized partial least squares framework: simulations and empirical studies. \JournalTitleNeuroImage 20, 625–642, DOI: 10.1016/S1053-8119(03)00333-1 (2003).
  49. Overview and Recent Advances in Partial Least Squares. \JournalTitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3940 LNCS, 34–51, DOI: 10.1007/11752790_2 (2006).
  50. Garthwaite, P. H. An interpretation of partial least squares. \JournalTitleJournal of the American Statistical Association 89, 122–127, DOI: 10.1080/01621459.1994.10476452 (1994).
  51. Partial least-squares regression: a tutorial. \JournalTitleAnalytica Chimica Acta 185, 1–17, DOI: 10.1016/0003-2670(86)80028-9 (1986).
  52. Höskuldsson, A. PLS regression methods. \JournalTitleJournal of Chemometrics 2, 211–228, DOI: 10.1002/CEM.1180020306 (1988).
  53. Tenenhaus, M. La régression PLS: Théorie et pratique (Technip, 1998).
  54. Interpretation of variable importance in Partial Least Squares with Significance Multivariate Correlation (sMC). \JournalTitleChemometrics and Intelligent Laboratory Systems 138, 153–160, DOI: 10.1016/J.CHEMOLAB.2014.08.005 (2014).
  55. Variable influence on projection (VIP) for orthogonal projections to latent structures (OPLS). \JournalTitleJournal of Chemometrics 28, 623–632, DOI: 10.1002/cem.2627 (2014).
  56. Use of the bootstrap and permutation methods for a more robust variable importance in the projection metric for partial least squares regression. \JournalTitleAnalytica Chimica Acta 768, 49–56, DOI: 10.1016/J.ACA.2013.01.004 (2013).
  57. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. \JournalTitleJournal of the Royal Statistical Society: Series B (Methodological) 57, 289–300, DOI: 10.1111/j.2517-6161.1995.tb02031.x (1995).
  58. Lee, J. M. Introduction to Topological Manifolds, vol. 202 (Springer New York, 2011).
  59. Lee, J. M. Introduction to Smooth Manifolds, vol. 218 (Springer New York, 2012).
  60. Lee, J. M. Introduction to Riemannian Manifolds, vol. 176 (Springer International Publishing, 2018).
  61. do Carmo, M. P. Riemannian Geometry (Birkhauser Boston Inc, 1992).
  62. Fréchet, M. Les éléments aléatoires de nature quelconque dans un espace distancié. \JournalTitleAnnales de l’institut Henri Poincaré 10, 215–310 (1948).
  63. Kim, H. J. et al. Canonical Correlation Analysis on Riemannian Manifolds and Its Applications. 251–267, DOI: 10.1007/978-3-319-10605-2_17 (Springer, 2014).
  64. Fletcher, P. T. Geodesic regression and the theory of least squares on Riemannian manifolds. \JournalTitleInternational Journal of Computer Vision 105, 171–185, DOI: 10.1007/s11263-012-0591-y (2013).
  65. Comparing functional connectivity matrices: A geometry-aware approach applied to participant identification. \JournalTitleNeuroImage 207, 116398, DOI: 10.1016/j.neuroimage.2019.116398 (2020).
  66. The Elements of Statistical Learning (Springer New York, 2009).
  67. R Core Team. R: A Language and Environment for Statistical Computing (2022).

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now