The virtual multiple sclerosis patient: on the clinical-radiological paradox
Poster No:
1656
Submission Type:
Abstract Submission
Authors:
Pierpaolo Sorrentino1, Anagh Pathak2, Abolfazl Ziaeemehr1, Emahnuel Troisi Lopez3, Lorenzo Cipriano4, Antonella Romano5, Simona Bonavita6, Mario Quarantelli7, Arpan Banerjee8, Giuseppe Sorrentino5, Meysam Hashemi1, Viktor Jirsa9
Institutions:
1Aix-Marseille University, Marseille, Please Select, 2National Brain Research Centre, Gurgaon, Haryana, 3National Research Council of Italy, Naples, NA, 4Parhtenope university of Naples, Naples, Italy, 5Parthenope university of Naples, Naples, Italy, 6University of Naples L Vanvitelli, Naples, Italy, 7National Research Council, Naples, Naples, 8National Brain Research Centre, Manesar, India, 9nstitut de Neurosciences des Systèmes, Marseille, N/A
First Author:
Co-Author(s):
Introduction:
Multiple sclerosis (MS) is typically diagnosed based on the clinical presentation, the presence of structural MRI lesions, and a "no better explanation" criterion. The structural lesions, disseminated in time and space, are a consequence of autoimmune processes leading to the damage of the myelin sheath in the central nervous system. As such, one would expect that more lesions would relate to higher clinical disability. However, a conflicting scenario is often present, with a high lesion load related to mild clinical impairment, and vice versa, a phenomenon referred to as the "clinico-radiological paradox". The myelin damage in MS is widespread, which is likely mirrored in a widespread slowing of conduction velocities. However, conduction velocities are typically measured on selected white-matter tracts (e.g., visual evoked potentials), which do not directly relate to clinical impairment. In this paper, we hypothesize that the overall slowing of conduction velocities (i.e., across all brain tracts) is a better predictor of clinical disability. However, estimating the whole-brain average velocities is challenging.
Methods:
To overcome this obstacle, we estimated patient-specific conduction velocities in MS patients by merging multimodal data (i.e., DTI and source-reconstructed magnetoencephalography) to inform large-scale brain models, fitted on each individual patient. We started from the known reduction of the power of the alpha frequency band, as well as the shift in its peak, observed in MS patients. We then reproduced these individual spectral features in silico using large-scale models based on the individual connectomes. We then used state-of-the-art deep neural networks for Bayesian model inversion to estimate the most likely average conduction velocity in each patient, given the observed spectral features (and the connectomes). Finally, we used the inferred conduction velocities to predict the individual clinical disability.
·Pipeline
Results:
We find that the conduction velocities inferred for patients are significantly lower than those inferred for controls and that they are predictive of individual clinical disability, well above the predictive power of demographic and clinical variables and lesion load.
·Results
Conclusions:
Our results suggest a biologically and physically plausible solution to the "clinico-radiological" paradox, where the inferred, individual changes in conduction velocities across the whole networks are proposed as causative to the clinical disability.
Modeling and Analysis Methods:
Bayesian Modeling
Connectivity (eg. functional, effective, structural)
EEG/MEG Modeling and Analysis 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Cyto- and Myeloarchitecture 2
Keywords:
Computational Neuroscience
Demyelinating
MEG
Modeling
Myelin
1|2Indicates the priority used for review
Provide references using author date format
2. P. Sorrentino, C. Seguin, R. Rucco, M. Liparoti, E. Troisi Lopez, S. Bonavita, M. Quarantelli, G. Sorrentino, V. Jirsa, A. Zalesky, The structural connectome constrains fast brain dynamics. eLife. 10, e67400 (2021).
3. J. Cabral, F. Castaldo, J. Vohryzek, V. Litvak, C. Bick, R. Lambiotte, K. Friston, M. L. Kringelbach, G. Deco, Metastable oscillatory modes emerge from synchronization in the brain spacetime connectome. Commun. Phys. 5, 1–13 (2022).
4. A. Pathak, V. Sharma, D. Roy, A. Banerjee, Biophysical mechanism underlying compensatory preservation of neural synchrony over the adult lifespan. Commun. Biol. 5, 1–12 (2022).
5. M. Hashemi, A. N. Vattikonda, V. Sip, M. Guye, F. Bartolomei, M. M. Woodman, V. K. Jirsa, The Bayesian Virtual Epileptic Patient: A probabilistic framework designed to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread. NeuroImage. 217, 116839 (2020).
6. P. Sorrentino, R. Rucco, F. Baselice, R. De Micco, A. Tessitore, A. Hillebrand, L. Mandolesi, M. Breakspear, L. L. Gollo, G. Sorrentino, Flexible brain dynamics underpins complex behaviours as observed in Parkinson’s disease. Sci. Rep. 11, 4051 (2021).