Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Divergent time-varying connectivity of thalamic sub-regions characterizes clinical phenotypes and cognitive status in multiple sclerosis

Abstract

We aimed to investigate abnormal time-varying functional connectivity (FC) for thalamic sub-regions in multiple sclerosis (MS) and their clinical, cognitive and MRI correlates. Eighty-nine MS patients (49 relapsing-remitting [RR] MS; 40 progressive [P] MS) and 53 matched healthy controls underwent neurological, neuropsychological and resting state fMRI assessment. Time-varying connectivity (TVC) was quantified using sliding-window seed-voxel correlation analysis. Standard deviation of FC across windows was taken as measure of TVC, while mean connectivity across windows expressed static FC. MS patients showed reduced TVC vs controls between most of thalamic sub-regions and fronto-temporo-occipital regions. At the same time, they showed increased static FC between all thalamic sub-regions and structurally connected cortico-subcortical regions. TVC reduction was mainly driven by RRMS; while PMS exhibited a variable pattern of TVC abnormalities, characterized by reduced TVC between frontal/motor thalamic seeds and default-mode network areas and increased TVC vs controls/RRMS between posterior thalamic sub-regions and occipito-temporo-insular cortices, associated with severity of clinical disability. Compared with controls, both cognitively preserved and impaired patients showed reduced TVC between anterior thalamic sub-regions and frontal cortex. Cognitively impaired patients also showed increased TVC of the right postcentral thalamic sub-region with the cingulate cortex and postcentral gyrus vs both controls and cognitively preserved patients. Divergent patterns of TVC thalamic abnormalities were found between RRMS and PMS patients. TVC reduction in RRMS may represent the attempt of thalamic network to keep with stable connections. Conversely, increased TVC of posterior thalamic sub-regions characterized PMS and cognitively impaired MS, possibly reflecting maladaptive mechanisms.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Time-varying functional connectivity analysis: progressive vs relapsing-remitting multiple sclerosis patients.

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Chard DT, Alahmadi AAS, Audoin B, Charalambous T, Enzinger C, Hulst HE et al. Mind the gap: from neurons to networks to outcomes in multiple sclerosis. Nat Rev Neurol. 2021;17:173–84.

  2. Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, et al. Toward discovery science of human brain function. Proc Natl Acad Sci USA. 2010;107:4734–9.

    Article  CAS  Google Scholar 

  3. Chang C, Glover GH. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage. 2010;50:81–98.

    Article  Google Scholar 

  4. Sakoglu U, Pearlson GD, Kiehl KA, Wang YM, Michael AM, Calhoun VD. A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia. MAGMA. 2010;23:351–66.

    Article  Google Scholar 

  5. Choe AS, Nebel MB, Barber AD, Cohen JR, Xu Y, Pekar JJ, et al. Comparing test-retest reliability of dynamic functional connectivity methods. Neuroimage. 2017;158:155–75.

    Article  Google Scholar 

  6. Hindriks R, Adhikari MH, Murayama Y, Ganzetti M, Mantini D, Logothetis NK, et al. Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? Neuroimage. 2016;127:242–56.

    Article  CAS  Google Scholar 

  7. Tomasi D, Shokri-Kojori E, Volkow ND. Temporal changes in local functional connectivity density reflect the temporal variability of the amplitude of low frequency fluctuations in gray matter. PLoS One. 2016;11:e0154407.

    Article  CAS  Google Scholar 

  8. Zhang C, Baum SA, Adduru VR, Biswal BB, Michael AM. Test-retest reliability of dynamic functional connectivity in resting state fMRI. Neuroimage. 2018;183:907–18.

    Article  Google Scholar 

  9. Chen J, Sun D, Shi Y, Jin W, Wang Y, Xi Q, et al. Alterations of static functional connectivity and dynamic functional connectivity in motor execution regions after stroke. Neurosci Lett. 2018;686:112–21.

    Article  CAS  Google Scholar 

  10. Engels G, Vlaar A, McCoy B, Scherder E, Douw L. Dynamic functional connectivity and symptoms of Parkinson’s disease: a resting-state fMRI study. Front Aging Neurosci. 2018;10:388.

    Article  Google Scholar 

  11. Falahpour M, Thompson WK, Abbott AE, Jahedi A, Mulvey ME, Datko M, et al. Underconnected, but not broken? Dynamic functional connectivity mri shows underconnectivity in autism is linked to increased intra-individual variability across time. Brain Connect. 2016;6:403–14.

    Article  Google Scholar 

  12. Liu A, Lin SJ, Mi T, Chen X, Chan P, Wang ZJ, et al. Decreased subregional specificity of the putamen in Parkinson’s Disease revealed by dynamic connectivity-derived parcellation. Neuroimage Clin. 2018;20:1163–75.

    Article  Google Scholar 

  13. Yue JL, Li P, Shi L, Lin X, Sun HQ, Lu L. Enhanced temporal variability of amygdala-frontal functional connectivity in patients with schizophrenia. Neuroimage Clin. 2018;18:527–32.

    Article  Google Scholar 

  14. Nakajima M, Halassa MM. Thalamic control of functional cortical connectivity. Curr Opin Neurobiol. 2017;44:127–31.

    Article  CAS  Google Scholar 

  15. Bisecco A, Rocca MA, Pagani E, Mancini L, Enzinger C, Gallo A, et al. Connectivity-based parcellation of the thalamus in multiple sclerosis and its implications for cognitive impairment: A multicenter study. Hum Brain Mapp. 2015;36:2809–25.

    Article  Google Scholar 

  16. Minagar A, Barnett MH, Benedict RH, Pelletier D, Pirko I, Sahraian MA, et al. The thalamus and multiple sclerosis: modern views on pathologic, imaging, and clinical aspects. Neurology. 2013;80:210–9.

    Article  CAS  Google Scholar 

  17. Schoonheim MM, Pinter D, Prouskas SE, Broeders TA, Pirpamer L, Khalil M et al. Disability in multiple sclerosis is related to thalamic connectivity and cortical network atrophy. Mult Scler 2021. https://doi.org/10.1177/13524585211008743.

  18. Rocca MA, Barkhof F, De Luca J, Frisen J, Geurts JJG, Hulst HE, et al. The hippocampus in multiple sclerosis. Lancet Neurol. 2018;17:918–26.

    Article  CAS  Google Scholar 

  19. Schoonheim MM, Geurts J, Wiebenga OT, De Munck JC, Polman CH, Stam CJ, et al. Changes in functional network centrality underlie cognitive dysfunction and physical disability in multiple sclerosis. Mult Scler. 2014;20:1058–65.

    Article  CAS  Google Scholar 

  20. Tona F, Petsas N, Sbardella E, Prosperini L, Carmellini M, Pozzilli C, et al. Multiple sclerosis: altered thalamic resting-state functional connectivity and its effect on cognitive function. Radiology. 2014;271:814–21.

    Article  Google Scholar 

  21. Hidalgo de la Cruz M, Valsasina P, Mesaros S, Meani A, Ivanovic J, Martinovic V et al. Clinical predictivity of thalamic sub-regional connectivity in clinically isolated syndrome: a 7-year study. Mol Psychiatry 2021;26:2163–74.

  22. d’Ambrosio A, Hidalgo de la Cruz M, Valsasina P, Pagani E, Colombo B, Rodegher M, et al. Structural connectivity-defined thalamic subregions have different functional connectivity abnormalities in multiple sclerosis patients: Implications for clinical correlations. Hum Brain Mapp. 2017;38:6005–18.

    Article  Google Scholar 

  23. Hidalgo de la Cruz M, d’Ambrosio A, Valsasina P, Pagani E, Colombo B, Rodegher M, et al. Abnormal functional connectivity of thalamic sub-regions contributes to fatigue in multiple sclerosis. Mult Scler. 2018;24:1183–95.

    Article  Google Scholar 

  24. d’Ambrosio A, Valsasina P, Gallo A, De Stefano N, Pareto D, Barkhof F, et al. Reduced dynamics of functional connectivity and cognitive impairment in multiple sclerosis. Mult Scler. 2020;26:476–88.

    Article  Google Scholar 

  25. Eijlers AJC, Wink AM, Meijer KA, Douw L, Geurts JJG, Schoonheim MM. Reduced network dynamics on functional MRI signals cognitive impairment in multiple sclerosis. Radiology. 2019;292:449–57.

    Article  Google Scholar 

  26. Bommarito G, Tarun A, Farouj Y, Preti MG, Petracca M, Droby A et al. Functional network dynamics in progressive multiple sclerosis. medRxiv. 2020: https://www.medrxiv.org/content/10.1101/2020.11.26.20238923v1.

  27. Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971;9:97–113.

    Article  CAS  Google Scholar 

  28. Rao SM, Leo GJ, Bernardin L, Unverzagt F. Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns, and prediction. Neurology. 1991;41:685–91.

    Article  CAS  Google Scholar 

  29. Amato MP, Portaccio E, Goretti B, Zipoli V, Ricchiuti L, De Caro MF, et al. The Rao’s Brief Repeatable Battery and Stroop Test: normative values with age, education and gender corrections in an Italian population. Mult Scler. 2006;12:787–93.

    Article  CAS  Google Scholar 

  30. Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex. 2014;24:663–76.

    Article  Google Scholar 

  31. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34:537–41.

    Article  CAS  Google Scholar 

  32. Lowe MJ, Mock BJ, Sorenson JA. Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. Neuroimage. 1998;7:119–32.

    Article  CAS  Google Scholar 

  33. Schoonheim MM, Hulst HE, Brandt RB, Strik M, Wink AM, Uitdehaag BM, et al. Thalamus structure and function determine severity of cognitive impairment in multiple sclerosis. Neurology. 2015;84:776–83.

    Article  Google Scholar 

  34. Kaiser RH, Whitfield-Gabrieli S, Dillon DG, Goer F, Beltzer M, Minkel J, et al. Dynamic resting-state functional connectivity in major depression. Neuropsychopharmacology. 2016;41:1822–30.

    Article  CAS  Google Scholar 

  35. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: anatomy, function, and relevance to disease. Ann NY Acad Sci. 2008;1124:1–38.

    Article  Google Scholar 

  36. van Geest Q, Douw L, van ‘t Klooster S, Leurs CE, Genova HM, Wylie GR, et al. Information processing speed in multiple sclerosis: relevance of default mode network dynamics. Neuroimage Clin. 2018;19:507–15.

    Article  Google Scholar 

  37. Valsasina P, Hidalgo de la Cruz M, Filippi M, Rocca MA. Characterizing rapid fluctuations of resting state functional connectivity in demyelinating, neurodegenerative, and psychiatric conditions: from static to time-varying analysis. Front Neurosci. 2019;13:618.

    Article  Google Scholar 

  38. Zhou K, Zhu L, Hou G, Chen X, Chen B, Yang C, et al. The contribution of thalamic nuclei in salience processing. Front Behav Neurosci. 2021;15:634618.

    Article  CAS  Google Scholar 

  39. Hwang K, Bertolero MA, Liu WB, D’Esposito M. The human thalamus is an integrative hub for functional brain networks. J Neurosci. 2017;37:5594–607.

    Article  CAS  Google Scholar 

  40. Cabral J, Vidaurre D, Marques P, Magalhaes R, Silva Moreira P, Miguel Soares J, et al. Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest. Sci Rep. 2017;7:5135.

    Article  Google Scholar 

  41. Kornblith S, Buschman TJ, Miller EK. Stimulus load and oscillatory activity in higher cortex. Cereb Cortex. 2016;26:3772–84.

    Article  Google Scholar 

  42. Zhou F, Zhuang Y, Gong H, Zhan J, Grossman M, Wang Z. Resting state brain entropy alterations in relapsing remitting multiple sclerosis. PLoS One. 2016;11:e0146080.

    Article  Google Scholar 

Download references

Acknowledgements

AC is supported by a MAGNIMS/ECTRIMS research fellowship programme.

Author information

Authors and Affiliations

Authors

Contributions

MAR contributed to the conception and design of the study. AC, PV, MHdlC, LC, PP, OM, MF, MAR contributed to the acquisition and analysis of data. AC, PV, MHdlC, LC, PP, OM, MF, MAR contributed to drafting the text and preparing the figures. All the authors gave their approval to the current version of the manuscript.

Corresponding author

Correspondence to Maria A. Rocca.

Ethics declarations

Competing interests

AC has received research grants from the ECTRIMS-MAGNIMS and from Almirall, and served on advisory boards for: Merk, Novartis, Roche and Almirall. PV received speaker honoraria from Biogen Idec. MHdlC reports no disclosures. LC received speaker and consultant honoraria from ACCMED, Roche, BMS Celgene, and Sanofi. PP received speaker honoraria from Biogen, Novartis, Merck Serono, Bristol Myers Squibb and Genzyme. He is supported by a senior research fellowship FISM – Fondazione Italiana Sclerosi Multipla – cod. 2019/BS/009 and financed or co-financed with the ‘5 per mille’ public funding. OM reports no disclosures. MF is Editor-in-Chief of the Journal of Neurology, Associate Editor of Human Brain Mapping, Associate editor of Radiology, and Associate Editor of Neurological Sciences; received compensation for consulting services and/or speaking activities from Almiral, Alexion, Bayer, Biogen, Celgene, Eli Lilly, Genzyme, Merck-Serono, Novartis, Roche, Sanofi, Takeda, and Teva Pharmaceutical Industries; and receives research support from Biogen Idec, Merck-Serono, Novartis, Roche, Teva Pharmaceutical Industries, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla, and ARiSLA (Fondazione Italiana di Ricerca per la SLA). MAR received speaker honoraria from Bayer, Biogen, Bristol Myers Squibb, Celgene, Genzyme, Merck Serono, Novartis, Roche, and Teva, and receives research support from the MS Society of Canada and Fondazione Italiana Sclerosi Multipla.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Carotenuto, A., Valsasina, P., Hidalgo de la Cruz, M. et al. Divergent time-varying connectivity of thalamic sub-regions characterizes clinical phenotypes and cognitive status in multiple sclerosis. Mol Psychiatry 27, 1765–1773 (2022). https://doi.org/10.1038/s41380-021-01401-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-021-01401-w

This article is cited by

Search

Quick links