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:

Resting state effective connectivity abnormalities of the Papez circuit and cognitive performance in multiple sclerosis

Molecular Psychiatry volume 27, pages 3913–3919 (2022)Cite this article

Subjects

Abstract

The Papez circuit is central to memory and emotional processes. However, little is known about its involvement in multiple sclerosis (MS). We aimed to investigate abnormalities of resting state (RS) effective connectivity (EC) between regions of the Papez circuit in MS and their relationship with cognitive performances. Sixty-two MS patients and 64 healthy controls (HC) underwent neuropsychological assessment, 3D T1-weighted, and RS functional MRI. RS EC analysis was performed using SPM12 and dynamic causal modeling. RS EC abnormalities were investigated using parametric empirical Bayes models and were correlated with cognitive scores. Compared to HC, MS patients showed (posterior probability > 0.95) higher EC between the right entorhinal cortex and right subiculum, and lower EC from the anterior cingulate cortex (ACC) to the posterior cingulate cortex (PCC), from left to right subiculum, from left anterior thalamus to ACC, and within ACC and PCC. Lower RS EC from the ACC to the PCC correlated with worse global cognitive scores (rho = 0.19; p = 0.03), worse visuospatial memory (rho = 0.19; p = 0.03) and worse semantic fluency (rho = 0.21; p = 0.02). Lower RS EC from the left to the right subiculum correlated with worse verbal memory (rho = 0.20; p = 0.02), lower RS EC within the ACC correlated with worse attention (rho = −0.19; p = 0.04) and more severe brain atrophy (rho = −0.26; p = 0.003). Higher EC from the right entorhinal cortex to right subiculum correlated with worse semantic fluency (rho = 0.21; p = 0.02). In conclusion, MS patients showed altered RS EC within the Papez circuit. Abnormal RS EC involving cingulate cortices and hippocampal formation contributed to explain cognitive deficits.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Results of resting state effective connectivity analysis.

Similar content being viewed by others

References

  1. Ruano L, Portaccio E, Goretti B, Niccolai C, Severo M, Patti F, et al. Age and disability drive cognitive impairment in multiple sclerosis across disease subtypes. Mult Scler J. 2017;23:1258–67.

    Google Scholar 

  2. Feuillet L, Reuter F, Audoin B, Malikova I, Barrau K, Cherif AA, et al. Early cognitive impairment in patients with clinically isolated syndrome suggestive of multiple sclerosis. Mult Scler. 2007;13:124–7.

    CAS  PubMed  Google Scholar 

  3. Benedict RHB, Amato MP, DeLuca J, Geurts JJG. Cognitive impairment in multiple sclerosis: clinical management, MRI, and therapeutic avenues. Lancet Neurol. 2020;19:860–71.

    PubMed  PubMed Central  Google Scholar 

  4. Catani M, Dell’acqua F, Thiebaut de Schotten M. A revised limbic system model for memory, emotion, and behaviour. Neurosci Biobehav Rev. 2013;37:1724–37.

    PubMed  Google Scholar 

  5. Eshaghi A, Marinescu RV, Young AL, Firth NC, Prados F, Jorge Cardoso M, et al. Progression of regional grey matter atrophy in multiple sclerosis. Brain 2018;141:1665–77.

    PubMed  PubMed Central  Google Scholar 

  6. Bisecco A, Capuano R, Caiazzo G, d’Ambrosio A, Docimo R, Cirillo M, et al. Regional changes in thalamic shape and volume are related to cognitive performance in multiple sclerosis. Mult Scler. 2021;27:134–8.

    CAS  PubMed  Google Scholar 

  7. Parisi L, Rocca MA, Valsasina P, Panicari L, Mattioli F, Filippi M. Cognitive rehabilitation correlates with the functional connectivity of the anterior cingulate cortex in patients with multiple sclerosis. Brain Imaging Behav. 2014;8:387–93.

    PubMed  Google Scholar 

  8. 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.

    PubMed  Google Scholar 

  9. Rocca MA, De Meo E, Filippi M. Functional MRI in investigating cognitive impairment in multiple sclerosis. Acta Neurol Scand. 2016;134:39–46.

    PubMed  Google Scholar 

  10. Stephan KE, Friston KJ. Analyzing effective connectivity with functional magnetic resonance imaging. Wiley Interdiscip Rev Cogn Sci. 2010;1:446–59.

    PubMed  PubMed Central  Google Scholar 

  11. Friston KJ, Harrison L, Penny W. Dynamic causal modelling. Neuroimage 2003;19:1273–302.

    CAS  PubMed  Google Scholar 

  12. Rocca MA, Absinta M, Valsasina P, Ciccarelli O, Marino S, Rovira A, et al. Abnormal connectivity of the sensorimotor network in patients with MS: a multicenter fMRI study. Hum Brain Mapp. 2009;30:2412–25.

    PubMed  Google Scholar 

  13. Rocca MA, Valsasina P, Ceccarelli A, Absinta M, Ghezzi A, Riccitelli G, et al. Structural and functional MRI correlates of Stroop control in benign MS. Hum Brain Mapp. 2009;30:276–90.

    PubMed  Google Scholar 

  14. Friston KJ, Kahan J, Biswal B, Razi AA. DCM for resting state fMRI. Neuroimage 2014;94:396–407.

    PubMed  Google Scholar 

  15. Razi A, Kahan J, Rees G, Friston KJ. Construct validation of a DCM for resting state fMRI. Neuroimage 2015;106:1–14.

    PubMed  Google Scholar 

  16. Benhamou E, Marshall CR, Russell LL, Hardy CJD, Bond RL, Sivasathiaseelan H, et al. The neurophysiological architecture of semantic dementia: spectral dynamic causal modelling of a neurodegenerative proteinopathy. Sci Rep. 2020;10:16321.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Fridgeirsson EA, Figee M, Luigjes J, van den Munckhof P, Schuurman PR, van Wingen G, et al. Deep brain stimulation modulates directional limbic connectivity in obsessive-compulsive disorder. Brain. 2020;143:1603–12.

    PubMed  PubMed Central  Google Scholar 

  18. Ray D, Bezmaternykh D, Mel’nikov M, Friston KJ, Das M. Altered effective connectivity in sensorimotor cortices is a signature of severity and clinical course in depression. Proc Natl Acad Sci USA. 2021;118:e2105730118.

  19. Uscatescu LC, Kronbichler L, Stelzig-Scholer R, Pearce BG, Said-Yurekli S, Reich LA, et al. Effective connectivity of the hippocampus can differentiate patients with schizophrenia from healthy controls: a spectral DCM approach. Brain Topogr. 2021;34:762–78.

    PubMed  PubMed Central  Google Scholar 

  20. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17:162–73.

    PubMed  Google Scholar 

  21. Montgomery SA, Asberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry. 1979;134:382–9.

    CAS  PubMed  Google Scholar 

  22. Rao S A manual for the Brief Repeatable Battery of Neuropsychological Tests in multiple sclerosis. In: Wisconsin MCo, editor. Milwaukee, WI 1990.

  23. 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.

    CAS  PubMed  Google Scholar 

  24. Sepulcre J, Vanotti S, Hernandez R, Sandoval G, Caceres F, Garcea O, et al. Cognitive impairment in patients with multiple sclerosis using the Brief Repeatable Battery-Neuropsychology test. Mult Scler. 2006;12:187–95.

    CAS  PubMed  Google Scholar 

  25. Amato MP, Morra VB, Falautano M, Ghezzi A, Goretti B, Patti F, et al. Cognitive assessment in multiple sclerosis-an Italian consensus. Neurol Sci. 2018;39:1317–24.

    PubMed  Google Scholar 

  26. Behzadi Y, Restom K, Liau J, Liu TT. A component-based noise correction method (CompCor) for BOLD and perfusion-based fMRI. Neuroimage 2007;37:90–101.

    PubMed  Google Scholar 

  27. Whitfield-Gabrieli S, Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2012;2:125–41.

    PubMed  Google Scholar 

  28. Valverde S, Cabezas M, Roura E, Gonzalez-Villa S, Pareto D, Vilanova JC, et al. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. Neuroimage 2017;155:159–68.

    PubMed  Google Scholar 

  29. Bubb EJ, Kinnavane L, Aggleton JP. Hippocampal–diencephalic–cingulate networks for memory and emotion: An anatomical guide. Brain Neurosci Adv. 2017;1:2398212817723443.

    PubMed  PubMed Central  Google Scholar 

  30. Konishi K, Joober R, Poirier J, MacDonald K, Chakravarty M, Patel R, et al. Healthy versus Entorhinal Cortical Atrophy identification in asymptomatic APOE4 carriers at risk for Alzheimer’s disease. J Alzheimers Dis. 2018;61:1493–507.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Robinson JL, Barron DS, Kirby LA, Bottenhorn KL, Hill AC, Murphy JE, et al. Neurofunctional topography of the human hippocampus. Hum Brain Mapp. 2015;36:5018–37.

    PubMed  PubMed Central  Google Scholar 

  32. Bergsland N, Zivadinov R, Dwyer MG, Weinstock-Guttman B, Benedict RH. Localized atrophy of the thalamus and slowed cognitive processing speed in MS patients. Mult Scler. 2016;22:1327–36.

    PubMed  Google Scholar 

  33. Zeidman P, Jafarian A, Corbin N, Seghier ML, Razi A, Price CJ, et al. A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI. Neuroimage 2019;200:174–90.

    PubMed  Google Scholar 

  34. Zeidman P, Jafarian A, Seghier ML, Litvak V, Cagnan H, Price CJ, et al. A guide to group effective connectivity analysis, part 2: Second level analysis with PEB. Neuroimage 2019;200:12–25.

    PubMed  Google Scholar 

  35. Friston K, Penny W. Post hoc Bayesian model selection. Neuroimage 2011;56:2089–99.

    PubMed  Google Scholar 

  36. Friston KJ, Litvak V, Oswal A, Razi A, Stephan KE, van Wijk BCM, et al. Bayesian model reduction and empirical Bayes for group (DCM) studies. Neuroimage 2016;128:413–31.

    PubMed  Google Scholar 

  37. Penny WD, Stephan KE, Daunizeau J, Rosa MJ, Friston KJ, Schofield TM, et al. Comparing families of dynamic causal models. PLoS Comput Biol. 2010;6:e1000709.

    PubMed  PubMed Central  Google Scholar 

  38. Rocca MA, Valsasina P, Leavitt VM, Rodegher M, Radaelli M, Riccitelli GC, et al. Functional network connectivity abnormalities in multiple sclerosis: Correlations with disability and cognitive impairment. Mult Scler J. 2018;24:459–71.

    Google Scholar 

  39. Rocca MA, Valsasina P, Absinta M, Riccitelli G, Rodegher ME, Misci P, et al. Default-mode network dysfunction and cognitive impairment in progressive MS. Neurology 2010;74:1252–9.

    CAS  PubMed  Google Scholar 

  40. Crossley NA, Mechelli A, Scott J, Carletti F, Fox PT, McGuire P, et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders (vol 137, 2382, 2014). Brain 2015;138:E374–E.

    Google Scholar 

  41. Storelli L, Pagani E, Preziosa P, Filippi M, Rocca MA. Measurement of white matter fiber-bundle cross-section in multiple sclerosis using diffusion-weighted imaging. Mult Scler J. 2021;27:818–26.

    CAS  Google Scholar 

  42. Meijer KA, Steenwijk MD, Douw L, Schoonheim MM, Geurts JJG. Long-range connections are more severe y damaged and relevant for cognition in multiple sclerosis. Brain 2020;143:150–60.

    PubMed  Google Scholar 

  43. 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 Psychiatr. 2021;26:2163–74.

  44. Roosendaal SD, Hulst HE, Vrenken H, Feenstra HEM, Castelijns JA, Pouwels PJW, et al. Structural and functional hippocampal changes in multiple sclerosis patients with intact memory function. Radiology 2010;255:595–604.

    PubMed  Google Scholar 

  45. Hulst HE, Schoonheim MM, Van Geest Q, Uitdehaag BMJ, Barkhof F, Geurts JJG. Memory impairment in multiple sclerosis: Relevance of hippocampal activation and hippocampal connectivity. Mult Scler J. 2015;21:1705–12.

    CAS  Google Scholar 

  46. Hulst HE, Schoonheim MM, Roosendaal SD, Popescu V, Schweren LJS, van der Werf YD, et al. Functional adaptive changes within the hippocampal memory system of patients with multiple sclerosis. Hum Brain Mapp. 2012;33:2268–80.

    PubMed  Google Scholar 

  47. Mansouri FA, Tanaka K, Buckley MJ. Conflict-induced behavioural adjustment: a clue to the executive functions of the prefrontal cortex. Nat Rev Neurosci. 2009;10:141–52.

  48. Loitfelder M, Filippi M, Rocca M, Valsasina P, Ropele S, Jehna M, et al. Abnormalities of Resting State Functional Connectivity Are Related to Sustained Attention Deficits in MS. Plos One. 2012;7.

  49. Wagner S, Sebastian A, Lieb K, Tuscher O, Tadic A. A coordinate-based ALE functional MRI meta-analysis of brain activation during verbal fluency tasks in healthy control subjects. BMC Neurosci. 2014;15:19.

  50. Wang L, Negreira A, La Violette P, Bakkour A, Sperling RA, Dickerson BC. Intrinsic interhemispheric hippocampal functional connectivity predicts individual differences in memory performance ability. Hippocampus 2010;20:345–51.

    PubMed  PubMed Central  Google Scholar 

  51. Glikmann-Johnston Y, Oren N, Hendler T, Shapira-Lichter I. Distinct functional connectivity of the hippocampus during semantic and phonemic fluency. Neuropsychologia 2015;69:39–49.

    PubMed  Google Scholar 

  52. Weiskopf N, Hutton C, Josephs O, Deichmann R. Optimal EPI parameters for reduction of susceptibility-induced BOLD sensitivity losses: A whole-brain analysis at 3 T and 1.5 T. NeuroImage 2006;33:493–504.

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Olga Marchesi, Raffaello Bonacchi, Paola Valsasina, Maria A. Rocca & Massimo Filippi

  2. Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Raffaello Bonacchi, Maria A. Rocca & Massimo Filippi

  3. Vita-Salute San Raffaele University, Milan, Italy

    Maria A. Rocca & Massimo Filippi

  4. Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Massimo Filippi

  5. Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Massimo Filippi

Authors
  1. Olga Marchesi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raffaello Bonacchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paola Valsasina
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maria A. Rocca
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Massimo Filippi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

OM and RB contributed to the conception of the study, acquisition, analysis, and interpretation of data, drafting the text, and preparing the figures. PV contributed to the acquisition, analysis, and interpretation of MRI data and revising the manuscript. MAR and MF contributed to the conception of the study, drafting and revising the text, acting as the study supervisors. All the authors gave their approval to the current version of the manuscript.

Corresponding author

Correspondence to Massimo Filippi.

Ethics declarations

Competing interests

OM and RB have nothing to disclose. PV received speaker honoraria from Biogen Idec. 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. 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 Alexion, Almirall, 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).

Additional information

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Marchesi, O., Bonacchi, R., Valsasina, P. et al. Resting state effective connectivity abnormalities of the Papez circuit and cognitive performance in multiple sclerosis. Mol Psychiatry 27, 3913–3919 (2022). https://doi.org/10.1038/s41380-022-01625-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-022-01625-4

Search

Advanced search

Quick links