Continuous reorganization of cortical information flow in multiple sclerosis: A longitudinal fMRI effective connectivity study

Vinzenz Fleischer,René-Maxime Gracien,Gabriel Gonzalez-Escamilla,Stefan Bittner,Sergiu Groppa,Felix Luessi,Muthuraman Muthuraman,Angela Radetz,Abdul Rauf Anwar,Frauke Zipp,Sven G Meuth

doi:10.1038/s41598-020-57895-x

Abstract

Effective connectivity (EC) is able to explore causal effects between brain areas and can depict mechanisms that underlie repair and adaptation in chronic brain diseases. Thus, the application of EC techniques in multiple sclerosis (MS) has the potential to determine directionality of neuronal interactions and may provide an imaging biomarker for disease progression. Here, serial longitudinal structural and resting-state fMRI was performed at 12-week intervals over one year in twelve MS patients. Twelve healthy subjects served as controls (HC). Two approaches for EC quantification were used: Causal Bayesian Network (CBN) and Time-resolved Partial Directed Coherence (TPDC). The EC strength was correlated with the Expanded Disability Status Scale (EDSS) and Fatigue Scale for Motor and Cognitive functions (FSMC). Our findings demonstrated a longitudinal increase in EC between specific brain regions, detected in both the CBN and TPDC analysis in MS patients. In particular, EC from the deep grey matter, frontal, prefrontal and temporal regions showed a continuous increase over the study period. No longitudinal changes in EC were attested in HC during the study. Furthermore, we observed an association between clinical performance and EC strength. In particular, the EC increase in fronto-cerebellar connections showed an inverse correlation with the EDSS and FSMC. Our data depict continuous functional reorganization between specific brain regions indicated by increasing EC over time in MS, which is not detectable in HC. In particular, fronto-cerebellar connections, which were closely related to clinical performance, may provide a marker of brain plasticity and functional reserve in MS.

Highlights

Effective connectivity (EC) estimations as derived from Functional MRI (fMRI) allow quantification of information flows in neural networks
We analysed EC based on Causal Bayesian Networks (CBN) proposed by Smith et al.[17] and, since EC can be simultaneously estimated in two domains, we assessed EC based on the so-called Time-resolved Partial Directed Coherence (TPDC) method[18,19,20,21,22,23] developed in the context of electrophysiological measurements of frequency-filtered blood oxygen-level dependent (BOLD) fMRI time courses
Our findings provide evidence that EC in patients with clinically stable multiple sclerosis (MS) changes markedly over a short period of time with a distinct topographical specificity

Summary

Introduction

Effective connectivity (EC) estimations as derived from fMRI allow quantification of information flows in neural networks. EC studies in MS patients demonstrated higher EC levels during Paced Auditory Serial Addition Test (PASAT) performance in the working memory network[9], during a sensorimotor and information processing speed task within frontal networks[10,11] as well as during Stroop task performance in the sensorimotor cortex[10] each in a cross-sectional approach These differences in EC measures between MS patients and healthy controls (HC) were primarily interpreted as an adaptive response to maintain cognitive and/or sensorimotor function for review see[12]. Altered connectivity patterns were found during high cognitive control demands in the executive control network among different MS phenotypes[13], in particular a loss of top-down connections was seen in patients with progressive MS14 These task-related EC changes point towards dynamic connectivity patterns that change during the course of the disease. We investigated the link between the strength of EC and clinical disability, patient fatigue and disease duration

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