Changes in resting-state functional connectivity in neuropsychiatric lupus: A dynamic approach based on recurrence quantification analysis

Pentari, Anastasia; Tzagkarakis, George; Tsakalides, Panagiotis; Simos, Panagiotis; Bertsias, George; Kavroulakis, Eleftherios; Marias, Kostas; Simos, Nikolaos-Ioannis; Papadaki, Efrosini

doi:10.1016/j.bspc.2021.103285

Article (Scientific journals)

Changes in resting-state functional connectivity in neuropsychiatric lupus: A dynamic approach based on recurrence quantification analysis

Pentari, Anastasia; Tzagkarakis, George; Tsakalides, Panagiotis et al.

2022 • In Biomedical Signal Processing and Control, 72, p. 103285

Peer Reviewed verified by ORBi

Permalink
https://hdl.handle.net/2268/316641

DOI
10.1016/j.bspc.2021.103285

Files (1)Send to Details Statistics Bibliography Similar publications

Files

Full Text

BSPC_rqa.pdf

Author postprint (2.5 MB)

Download

All documents in ORBi are protected by a user license.

Send to

RIS BibTex APA Chicago Permalink X Linkedin

Details

Keywords :

Dynamic functional connectivity; Neuropsychiatric systemic lupus erythematosus; Recurrence quantification analysis; Brain connectivity; Cross recurrences; Dynamic approaches; Effect size; Functional connectivity; Systemic lupus erythematosus; Visuomotors; Signal Processing; Health Informatics

Abstract :

[en] There is growing interest in dynamic approaches to functional brain connectivity (FC), and their potential applications in understanding atypical brain function. In this study, we assess the relative sensitivity of cross recurrence quantification analysis CRQA) to identify aberrant FC in patients with neuropsychiatric systemic lupus erythematosus (NPSLE) in comparison with conventional static and dynamic bivariate FC measures, as well as univariate (nodal) RQA. This technique was applied to resting-state fMRI data obtained from 45 NPSLE patients and 35 healthy volunteers (HC). Cross recurrence plots were computed for all pairs of 16 frontoparietal brain regions known to be critically involved in visuomotor control and suspected to show hemodynamic disturbance in NPSLE. Multivariate group comparisons revealed that the combination of six CRQA measures differentiated the two groups with large effect sizes (.214>η2>.061) in 40 out of the 120 region pairs. The majority of brain regions forming these pairs also showed group differences on nodal RQA indices (.146>η2>.09) Overall, larger values were found in NPSLE patients vs. HC with the exception of FC formed by the paracentral lobule. Determinism within five pairs of right-hemisphere sensorimotor regions (paracentral lobule, primary somatosensory, primary motor, and supplementary motor areas), correlated positively with visuomotor performance among NPSLE patients (p<.001). By comparison, group differences on static FC displayed large effect sizes in only 4 of the 120 region pairs (.126>η2>.061), none of which correlated significantly with visuomotor performance. Indices derived from dynamic, temporal-based FC analyses displayed large effect sizes in 11/120 region pairs (.11>η2>.063). These findings further support the importance of feature-based dFC in advancing current knowledge on correlates of cognitive dysfunction in a clinically challenging disorder, such as NPSLE.

Disciplines :

Neurosciences & behavior

Author, co-author :

Pentari, Anastasia; Computer Science Department, University of Crete, Heraklion, Greece ; Institute of Computer Science, Foundation for Research and Technology-Hellas, Greece

Tzagkarakis, George; Institute of Computer Science, Foundation for Research and Technology-Hellas, Greece

Tsakalides, Panagiotis; Computer Science Department, University of Crete, Heraklion, Greece ; Institute of Computer Science, Foundation for Research and Technology-Hellas, Greece

Simos, Panagiotis; Institute of Computer Science, Foundation for Research and Technology-Hellas, Greece ; School of Medicine, University of Crete, Heraklion, Greece

Bertsias, George; School of Medicine, University of Crete, Heraklion, Greece ; Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Greece

Kavroulakis, Eleftherios; Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece

Marias, Kostas; Institute of Computer Science, Foundation for Research and Technology-Hellas, Greece ; Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece

Simos, Nikolaos-Ioannis ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology

Papadaki, Efrosini; Institute of Computer Science, Foundation for Research and Technology-Hellas, Greece ; School of Medicine, University of Crete, Heraklion, Greece

Language :

English

Title :

Changes in resting-state functional connectivity in neuropsychiatric lupus: A dynamic approach based on recurrence quantification analysis

Publication date :

February 2022

Journal title :

Biomedical Signal Processing and Control

ISSN :

1746-8094

eISSN :

1746-8108

Publisher :

Elsevier Ltd

Volume :

Pages :

103285

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://api.elsevier.com/content/article/PII:S174680942100882X?httpAccept=text/xml

Funders :

SNF - Stavros Niarchos Foundation
HFRI - Hellenic Foundation for Research and Innovation

Funding text :

This work is funded by the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT) under grant agreements No. 2285 (neuronXnet) and No. 1725 (V4-ICARUS), by the HFRI faculty Grant No. 1725, and the Stavros Niarchos Foundation within the framework of the ARCHERS project.

Available on ORBi :

since 23 April 2024

Statistics

Number of views

7 (0 by ULiège)

Number of downloads

15 (0 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

OpenAlex citations

Bibliography

Menon, S.S., Krishnamurthy, K., A comparison of static and dynamic functional connectivities for identifying subjects and biological sex using intrinsic individual brain connectivity. Sci. Rep., 9, 2019, 5729.
Allen, E.A., Damaraju, E., Plis, S.M., Erhardt, B., Eichele, T., Calhoun, V.D., Tracking Whole-Brain Connectivity Dynamics in the Resting State. Cerebral Cortex 24 (2014), 663–676.
Damaraju, E., Allen, E.A., Belger, A., Ford, J.M., McEwen, S., Mathalon, D.H., Mueller, B.A., Pearlson, G.D., Potkin, S.G., Preda, A., Turner, J.A., Vaidya, J.G., van Erp, T.G., Calhoun, V.D., Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. NeuroImage: Clinical 5 (2014), 298–308.
Menon, S.S., Krishnamurthy, K.A., Comparison of Static and Dynamic Functional Connectivities for Identifying Subjects and Biological Sex Using Intrinsic Individual Brain Connectivity. Sci. Rep., 9, 2019, 5729.
Fukushima, M., Betzel, R.F., He, Y., van den Heuvel, M.P., Zuo, X.N., Sporns, O., Structure-function relationships during segregated and integrated network states of human brain functional connectivity. Brain Structure and Function 223:3 (2018), 1091–1106.
Luppi, A.I., Carhart-Harris, R.L., Roseman, L., Pappas, I., Menon, D.K., Stamatakis, E.A., LSD alters dynamic integration and segregation in the human brain. NeuroImage, 227, 2021.
Shine, J.M., Bissett, P.G., Bell, P.T., Koyejo, O., Balsters, J.H., Gorgolewski, K.J., Moodie, C.A., Poldrack, R.A., The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance. Neuron 92:2 (2016), 544–554.
Bianciardi, M., Sirabella, P., Hagberg, G., Giuliani, A., Zbilut, J., Colosimo, A., Model-free analysis of brain fMRI data by recurrence quantification. Neuroimage 37:2 (2007), 489–503.
Marwan, N., Kurths, J., Cross Recurrence Plots and Their Applications. Mathematical Physics Research at the Cutting Edge, 2004, 101–139.
Jonak, K., Syta, A., Karakuła-Juchnowicz, H., Krukow, P., The Clinical Application of EEG-Signals Recurrence Analysis as a Measure of Functional Connectivity: Comparative Case Study of Patients with Various Neuropsychiatric Disorders. Brain Sci., 10(6), 2020, 380.
Simos, N.J., Dimitriadis, S.I., Kavroulakis, E., Manikis, G.C., Bertsias, G., Simos, P., Maris, T.G., Papadaki, E., Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach. Brain Sci., 10(11), 2020, 777.
Papadaki, E., Fanouriakis, A., Kavroulakis, E., Karageorgou, D., Sidiropoulos, P., Bertsias, G., Simos, P., Boumpas, D.T., Neuropsychiatric lupus or not? Cerebral hypoperfusion by perfusion-weighted MRI in normal-appearing white matter in primary neuropsychiatric lupus erythematosus. Ann. Rheum. Dis. 77:3 (2018), 441–448.
Budisavljevic, S., Dell'Acqua, F., Zanatto, D., Begliomini, C., Miotto, D., Motta, R., Castiello, U., Asymmetry and Structure of the Fronto-Parietal Networks Underlie Visuomotor Processing in Humans. Cereb Cortex 27:2 (2017), 1532–1544.
Dadar, M., Narayanan, S., Arnold, D.L., Collins, D.L., Maranzano, J., Conversion of diffusely abnormal white matter to focal lesions is linked to progression in secondary progressive multiple sclerosis. Mult Scler, 2020.
Howells, H., Thiebaut de Schotten, M., Dell'Acqua, F., Beyh, A., Zappalà, G., Leslie, A., Simmons, A., Murphy, D.G., Catani, M., Frontoparietal Tracts Linked to Lateralized Hand Preference and Manual Specialization. Cereb Cortex 28:7 (2018), 2482–2494.
Berlucchi, G., Vallar, G., The history of the neurophysiology and neurology of the parietal lobe. Handb Clin Neurol 151 (2018), 3–30.
Luppi, A.I., Craig, M.M., Pappas, I., Finoia, P., Williams, G.B., Allanson, J., Pickard, J.D., Owen, A.M., Naci, L., Menon, D.K., Stamatakis, E.A., Consciousness-specific dynamic interactions of brain integration and functional diversity. Nature Communications, 10(1), 2019.
Bakula, M., Čikeš, N., Anić, B., Validation of the new classification criteria for systemic lupus erythematosus on a patient cohort from a national referral center: a retrospective study. Croat Med J 60:4 (2019), 333–344.
Behzadi, Y., Restom, K., Liau, J., Liu, Thomas T., A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37:1 (2007), 90–101.
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M., Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single subject brain. NeuroImage 15 (2002), 273–289.
Eckmann, J.P., Kamphorst, S.O., Ruelle, D., Recurrence plots of dynamical systems. World Sci. Seri. Nonlinear Sci. Seri. 16 (1995), 441–446.
Wallot, S., Mønster, D., Calculation of Average Mutual Information (AMI) and False-Nearest Neighbors (FNN) for the Estimation of Embedding Parameters of Multidimensional Time Series in Matlab. Front Psychol., 9, 2018, 1679.
Marwan, N., Carmen Romano, M., Thiel, M., Kurths, J., Recurrence plots for the analysis of complex systems. Physics Reports 438:5–6 (2007), 237–329.
Rubinov, M., Sporns, O., Weight-conserving characterization of complex functional brain networks. NeuroImage 56:4 (2011), 2068–2079.
Letellier, C., Estimating the Shannon entropy: recurrence plots versus symbolic dynamics. Phys Rev Lett, 96(25), 2006, 254102.
Xia, M., Wang, J., He, Y., BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics. PLoS ONE, 8(7), 2013, e68910.
Nystedt, J., Mannfolk, P., Jönsen, A., Bengtsson, A., Nilsson, P., Sundgren, P.C., Strandberg, T.O., Functional Connectivity Changes in Systemic Lupus Erythematosus: A Resting-State Study. Brain Connect 8:4 (2018), 220–234.
Bianciardi, M., Sirabella, P., Hagberg, G.E., Giuliani, A., Zbilut, J.P., Colosimo, A., Model-free analysis of brain fMRI data by recurrence quantification. Neuroimage 37:2 (2007), 489–503.
Syta, A., Bernardini, D., Litak, G., Savi, A., Marcelo, K. Jonak, A comparison of different approaches to detect the transitions from regular to chaotic motions in SMA oscillator. Meccanica 55 (2020), 1295–1308.