# Flexibility of brain dynamics is increased and predicts clinical impairment in relapsing–remitting but not in secondary progressive multiple sclerosis

Lorenzo Cipriano, Roberta Minino, Marianna Liparoti, Arianna Polverino, Antonella Romano, Simona Bonavita, Maria Agnese Pirozzi, Mario Quarantelli, Viktor Jirsa, Giuseppe Sorrentino, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Lorenzo Cipriano, Roberta Minino, Marianna Liparoti, Arianna Polverino, Antonella Romano, et al. Flexibility of brain dynamics is increased and predicts clinical impairment in relapsing–remitting but not in secondary progressive multiple sclerosis. Brain Communications, 2025, 6, 10.1093/brain-comms/fcae112. doi:10.1093/brain-comms/fcae112

HAL Id: hal-04890285

https://hal.science/hal-04890285v1

Submitted on 16 Jan 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
# Flexibility of brain dynamics is increased and predicts clinical impairment in relapsing–remitting but not in secondary progressive multiple sclerosis

Lorenzo Cipriano,¹,† Roberta Minino,¹ Marianna Liparoti,² Arianna Polverino,³

Antonella Romano,¹ Simona Bonavita,⁴

Maria Agnese Pirozzi,⁴ Mario Quarantelli,⁵

Viktor Jirsa,⁶ Giuseppe Sorrentino,¹,³,⁷

Pierpaolo Sorrentino,⁶,⁸

and Emahnuel Troisi Lopez⁷,†

† These authors contributed equally to this work.

Large-scale brain activity has long been investigated under the erroneous assumption of stationarity. Nowadays, we know that resting-state functional connectivity is characterized by aperiodic, scale-free bursts of activity (i.e. neuronal avalanches) that intermittently recruit different brain regions. These different patterns of activity represent a measure of brain flexibility, whose reduction has been found to predict clinical impairment in multiple neurodegenerative diseases such as Parkinson's disease, amyotrophic lateral sclerosis and Alzheimer's disease. Brain flexibility has been recently found increased in multiple sclerosis, but its relationship with clinical disability remains elusive. Also, potential differences in brain dynamics according to the multiple sclerosis clinical phenotypes remain unexplored so far. We performed a brain dynamics study quantifying brain flexibility utilizing the 'functional repertoire' (i.e. the number of configurations of active brain areas) through source reconstruction of magnetoencephalography signals in a cohort of 25 multiple sclerosis patients (10 relapsing–remitting multiple sclerosis and 15 secondary progressive multiple sclerosis) and 25 healthy controls. Multiple sclerosis patients showed a greater number of unique reconfigurations at fast time scales as compared with healthy controls. This difference was mainly driven by the relapsing–remitting multiple sclerosis phenotype, whereas no significant differences in brain dynamics were found between secondary progressive multiple sclerosis and healthy controls. Brain flexibility also showed a different predictive power on clinical disability according to the multiple sclerosis type. For the first time, we investigated brain dynamics in multiple sclerosis patients through high temporal resolution techniques, unveiling differences in brain flexibility according to the multiple sclerosis phenotype and its relationship with clinical disability.

1. Department of Medical, Motor and Wellness Sciences, University of Naples 'Parthenope', 80133 Naples, Italy

2. Department of Philosophical, Pedagogical and Quantitative-Economic Sciences, University of Chieti-Pescara 'G. d'Annunzio', 66100 Chieti, Italy

3. Institute of Diagnosis and Therapy Hermitage Capodimonte, 80145 Naples, Italy

4. Department of Advanced Medical and Surgical Sciences, University of Campania 'L. Vanvitelli', 81100 Naples, Italy

5. Biostructure and Bioimaging Institute, CNR, 80145 Naples, Italy

6. Institut de Neurosciences des Systèmes, Inserm, INS, Aix-Marseille University, 13005 Marseille, France

7. Institute of Applied Sciences and Intelligent Systems, National Research Council, 80078 Pozzuoli, Italy

8. Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
Correspondence to: Prof. Pierpaolo Sorrentino, MD, PhD
Department of Biomedical Sciences, University of Sassari, Viale S. Pietro, 43c, 07100 Sassari, Italy
E-mail: pierpaolo.sorrentino@univ-amu.fr

**Keywords:** magnetoencephalography; neuronal avalanches; brain flexibility; inflammation; neurodegeneration

## Graphical Abstract

### Neuronal avalanches and functional repertoire in patients with Multiple Sclerosis (MS)

<figure><img src="image_3.png" /></figure>

### Functional repertoire according to the MS type

Functional repertoire (No. of patterns)

<figure><img src="image_4.png" /></figure>

### Prediction of the Expanded Disability Status Scale (EDSS)

<figure><img src="image_5.png" /></figure>

### Correlation between EDSS and functional repertoire

<figure><img src="image_6.png" /></figure>

## Introduction

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system characterized by a complex association of both demyelination and diffuse neurodegeneration of the grey and white matter.¹ MS occurs most often with a clinical phenotype characterized by a relapsing–remitting course (RRMS). However, around 50% of MS patients can evolve to a secondary progressive form (SPMS), and a minority may show worsening from the onset, the primary progressive form

(PPMS).¹,² Recent studies support the idea that RRMS and SPMS are part of a disease continuum, in which the phase transition is driven by the change in the balance between inflammatory and neurodegenerative mechanisms.³ The different clinical presentations, which are a consequence of distinct underlying pathophysiological mechanisms, may also explain the large variability of responses to the currently available immunosuppressive and immunomodulatory treatments.¹,³,⁴

MRI is currently established as a key diagnostic tool in MS⁵ due to its ability to detect the spatial and temporal
distribution of MS-associated lesions. Nevertheless, years of use of MRI have shown that only a small fraction of MS clinical features and outcomes can be explained by the lesion load. This mismatch is called the clinical–radiological paradox and highlights our lack of understanding of this complex disease.⁶

Many functional MRI (fMRI) studies deployed neural network theory in the attempt to shed light on the functional effects of the neuropathological processes that characterize the different clinical phenotypes. Taken together, these studies demonstrated relationships between changes in functional connectivity (FC) among specific brain regions and clinical features of the disease.⁷⁻⁹ However, these results were internally inconsistent, as they often failed to replicate. For instance, some studies found that increased resting-state FC (RS-FC) of selected brain regions/networks was related to better cognitive performances,⁷,¹⁰ while others showed an increased RS-FC of the same regions/networks in cognitively impaired individuals.⁸,⁹

Potential explanations for these discrepancies include the temporal neuropathological evolution of the disease (i.e. different studies were performed in different disease stages), its clinical heterogeneity, the methodological differences across fMRI studies and, finally, the intrinsic limitations of the fMRI, including its low temporal resolution. The low temporal resolution represents a weak point of the fMRI, because the temporal smoothing induced by the slow haemodynamic response reduces the ability to evaluate brain reconfigurations at fast time scales. Conversely, magnetofluorography (MEG, EEG) provides a more direct assessment of the brain's fast activities.¹¹

Large-scale brain scans have been processed typically under the assumption of stationarity. However, now we know that RS-FC evolves over time in a non-linear fashion.¹² In particular, brain activity is characterized by aperiodic, scale-free bursts of activity (i.e. neuronal avalanches) that intermittently interconnect brain regions¹³⁻¹⁵ and that account for most of the time-averaged FC.¹⁶ In particular, aperiodic bursts reconfigure over time, giving rise to rich, non-stereotyped dynamics. In fact, healthy brains constantly recruit different brain regions, generating a high number of patterns of activations. Thus, the number of such patterns represents a measure of brain flexibility dynamics, whose reduction has been found to predict clinical impairment in multiple neurodegenerative diseases.¹⁷,¹⁸

Based on our previous observations about brain dynamics in neurodegenerative diseases such as probable Alzheimer's disease,¹⁹ Parkinson's disease¹⁷ and amyotrophic lateral sclerosis,¹⁸ in the present work, we hypothesize that MS could also be characterized by variations in brain flexibility, which would be related to, and predictive of, the subject-specific clinical impairment. We also wondered whether the underlying disease mechanisms that characterize the two distinct forms of MS (relapsing–remitting and progressive) could reflect itself in different brain dynamics and if this difference could help in predicting clinical disability.

To test these hypotheses, we source-reconstructed MEG scans performed in 25 MS patients (10 RRMS and 15 SPMS) and 25 healthy controls (HC). To estimate flexibility, we calculated the number of unique patterns of neuronal avalanches expressed in each MEG recording. Operationally, a neuronal avalanche is defined as an event starting when at least one brain region deviates from its baseline activity and ending when all regions return to their normal level of activity. An avalanche pattern is defined as the set of all the brain areas that were recruited at any moment during an avalanche. The functional repertoire is defined as the set of the unique patterns that occurred over time, and its size can be seen as a surrogate marker of brain flexibility. Finally, we implemented a multilinear regression model with 'k-fold cross-validation' to verify the ability of the size of the functional repertoire to predict, at individual level, the clinical impairment assessed by the Expanded Disability Status Scale (EDSS).

# Materials and methods

## Participants

Twenty-five MS patients (7 males and 18 females) and 25 age-, sex- and education-matched HC were recruited from 1 April 2018 to 9 November 2018. MS was diagnosed in accordance with the 2017 revision of the McDonald criteria.⁵ MS individuals were further classified in RRMS and SPMS. The eligibility of the patients was defined according to the following exclusion criteria: (i) use of illicit drugs, stimulants, amphetamines, barbiturates and cannabis; (ii) a history of CNS disorder other than MS; (iii) severe mental illness; and (iv) other systemic disorders with possible secondary involvement of the CNS.

MS patients underwent a clinical examination performed by an experienced neurologist. The EDSS²⁰ was used to evaluate disease-related disability. Fatigue was assessed by the Fatigue Severity Scale (FSS),²¹ while neurocognitive function was evaluated by the Symbol Digit Modalities Test (SDMT).²² Key symptoms of depression were studied through the Beck Depression Inventory (BDI) self-rated scale.²³ The study protocol was approved by the Local Ethics Committee (ASL-NA1) with protocol number Prot.n.93C.E./Reg. n.14-17OSS. All procedures performed were in accordance with the ethical standards of the institutional research committee, and all participants provided written informed consent in accordance with the Declaration of Helsinki.

## MEG and MRI acquisition, preprocessing, and source reconstruction

MRI and MEG acquisition, preprocessing, source reconstruction and connectivity estimation (Fig. 1A) have been performed according to our previous studies.²⁴⁻²⁹ In particular, patients and HC underwent MRI recorded by a 1.5 T GE
A Data pre-processing

Ai

<figure><img src="image_7.png" /></figure>

Neuronal activity in
the sensor space
(MEG helmet)

<figure><img src="image_8.png" /></figure>

Aii

<figure><img src="image_9.png" /></figure>

Coregistration of MEG signals and MRI

<figure><img src="image_10.png" /></figure>

Aiii

<figure><img src="image_11.png" /></figure>

Source reconstruction
through beamforming

<figure><img src="image_12.png" /></figure>

B Brain signals and avalanches identification

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>**Figure 1** Pipeline overview and neuronal avalanche representation. (A: Ai) Registration of neuronal activity through MEG. (Aii) Cleaned sensor signals (without physiological artefacts) coregistered with structural MRI of each participant. (Aiii) Through a beamformer algorithm, the time series of the sources were estimated in ROIs within the brain according to a parcellation based on the AAL atlas. (B) The highlighted boxes represent the time frame in which a neuronal avalanche occurred. Specifically, the bold dots indicate the frame in which the time series was above threshold (Z-score > 3). In the bottom section of the panel, a schematic representation of a neuronal avalanche is depicted.</figcaption></figure>

a dependent variable and MS type and functional repertoire as independent variables. Multicollinearity was assessed through the variance inflation factor (VIF). To validate our approach, we performed *k*-fold cross-validation, with *k* = 5.⁴¹ Specifically, *k* iterations were performed to train our model and at each iteration, and the *k*th subgroup was used as a test set.

Statistical analysis

Statistical analysis was carried out in MATLAB 2021a and R Studio (http://www.rstudio.com/). A *t*-test and a *χ*² were used to compare patients and controls for age, educational level and sex. A Wilcoxon rank sum test was used to compare HC and MS groups. The Kruskal–Wallis test was performed to compare HC, RRMS and SPMS groups. The results were corrected by the false discovery rate, and the significance level was set at *P*-value < 0.05. The relationship between the size of the functional repertoire and the clinical scores was investigated in the MS group using the Spearman’s correlation coefficient. The predictive power of the flexibility parameter

on clinical features has been investigated through a multi-linear regression model.

Results

Cohort characteristics

Sociodemographic and clinical characteristics of our cohort (as also reported in Cipriano *et al.*²⁴) are reported in Table 1 (and Supplementary Table 1).

Analysis of brain dynamics: the
functional repertoire

The comparison between MS and HC showed a larger func-
tional repertoire in MS patients (P = 0.006) (Fig. 2A). We
also performed a Kruskal–Wallis test to evaluate differences
in the size of the functional repertoire according to the MS
clinical form (χ²(2) = 9.8, P = 0.007) (RRMS and SPMS).
As shown in Fig. 2B, the difference in brain flexibility
<table><caption>**Table 1** Sociodemographic and clinical characteristics of the cohort</caption><thead><tr><th>Parameters</th><th>MS patients (n = 25), mean (±SD)</th><th>HC (n = 25), mean (±SD)</th><th>P-values</th></tr></thead><tbody><tr><td><strong>Demographic data</strong></td><td></td><td></td><td></td></tr><tr><td>Age</td><td>45.68 ± 9.47</td><td>45.8 ± 11.83</td><td>ns</td></tr><tr><td>Male/female</td><td>7/18</td><td>7/18</td><td>ns</td></tr><tr><td>Education (years)</td><td>13.36 ± 4.28</td><td>13.93 ± 3.91</td><td>ns</td></tr><tr><td><strong>MS-specific clinical characteristics</strong></td><td></td><td></td><td></td></tr><tr><td>MS phenotype (n)</td><td>RRMS</td><td>SPMS</td><td></td></tr><tr><td>DD (months ± SD)</td><td>101.20 ± 75.61</td><td>15</td><td></td></tr><tr><td>EDSS (mean)</td><td>3.75</td><td>5.10</td><td>ns</td></tr><tr><td>EDSS (range)</td><td>1.5–6</td><td>2.5–7</td><td></td></tr><tr><td>FSS (mean ± SD)</td><td>29.80 ± 13.54</td><td>42.80 ± 12.45</td><td>0.025</td></tr><tr><td>FSS (range)</td><td>9–49</td><td>19–59</td><td></td></tr><tr><td>BDI (mean ± SD)</td><td>11.70 ± 6.70</td><td>10.87 ± 10.11</td><td>ns</td></tr><tr><td>BDI (range)</td><td>0–19</td><td>0–36</td><td></td></tr><tr><td>SDMT (mean ± SD)</td><td>41.80 ± 13.93</td><td>35.00 ± 13.59</td><td>ns</td></tr><tr><td>SDMT (range)</td><td>15–59</td><td>11–58</td><td></td></tr><tr><td>LL</td><td>10.85 ± 17.14</td><td>19.55 ± 18.14</td><td>0.03</td></tr><tr><td>GM</td><td>666.07 ± 96.14</td><td>596.54 ± 72.30</td><td>ns</td></tr><tr><td>WM</td><td>346.56 ± 54.04</td><td>330.66 ± 70.84</td><td>ns</td></tr></tbody></table>

P-value from Wilcoxon-Mann-Whitney or Student's t-test, according to the sample data distribution checked with the Kolmogorov-Smirnov test. No significant difference in age, gender and education was found between the two groups. BDI, Beck Depression Inventory; DD, disease duration; EDSS, Expanded Disability Status Scale; FSS, Fatigue Severity Scale; GM, grey matter; HC, healthy controls; LL, lesion load; MS, multiple sclerosis; RRMS, relapsing-remitting MS; SDMT, Symbol Digit Modalities Test; SPMS, secondary progressive MS; WM, white matter. ROI volumes in the SPMS group were available only for 13 patients. Volumes of each ROI are provided in the supplementary materials (Supplementary Table 1). Volumes are defined in mL.

between MS and HC was primarily driven by the RRMS patients (P = 0.02).

## Impairment prediction according to MS type

We performed a k-fold cross-validated multilinear regression analysis setting the clinical variables (FSS, EDSS, SDMT, BDI) as dependent variables and tried to predict them by the means of the size of the functional repertoire. We also included the MS phenotype as a further predictor and its interaction with the size of the functional repertoire to account for a possible different behaviour in the relationship between the functional repertoire and the EDSS score. We obtained a significant regression model (F(3,21) = 3.61, P = 0.03) able to predict 34% of the EDSS variance, with a normalized root mean squared error of the prediction equal to 25%. The significant contribution was determined by the MS type and its interaction with the size of the functional repertoire (beta coefficient = 0.461, P = 0.018, and beta coefficient = -0.456, P = 0.0419, respectively) (Fig. 3A). The interaction effect between MS type and functional repertoire suggests that the MS type is worthy in obtaining a significant prediction. Indeed, when removing the interaction effect, the model was not predictive anymore, while the MS type only, as a categorical variable, was not enough to predict the EDSS score. Given the MS type relevance, we observed the comparison between actual and predicted EDSS in each group separately (Fig. 3B). It can be noticed that while the predicted values of the RRMS group (yellow) showed a direct agreement with the actual values, this was not the case with the SPMS predictions (red). The distribution of the standardized

residuals was then observed (Fig. 3C), and its absolute value was compared between RRMS and SPMS (Fig. 3D). The statistical comparison confirmed the higher error in the prediction of the EDSS of the SPMS group (P = 0.017). We found no significant results when trying to predict the FSS, the SDMT and the BDI.

The relationship highlighted by the regression model was confirmed through a Spearman correlation test, performed in each MS group, separately (Fig. 4). For SPMS patients, no significant relationship was found between dynamics and clinical features (r = -0.13, P = 0.64). Conversely, in RRMS subjects, the functional repertoire was significantly and positively related to the EDSS (r = 0.7, P = 0.024). Similar results were evident also with the other clinical characteristics, but again, only in the RRMS group. In particular, SDMT and BDI showed respectively a significant negative (r = -0.64, P = 0.044) and positive (r = 0.65, P = 0.049) correlation with the size of the functional repertoire. A trend towards a positive statistically significant correlation was found for the fatigue scale. No significant (or nearly significant) relationships between clinical data and brain flexibility were found in the SPMS group.

We also searched for a potential relationship between disease duration and brain flexibility in SPMS, performing a Spearman's correlation that showed a trend towards a negative correlation between brain dynamics and disease duration (r = -0.48, P = 0.067).

A multilinear model with the size of the functional repertoire as the dependent variable; age, gender and education as covariates; and each ROI-specific volume as an independent variable has been built. The same model has been used by replacing volume data with LL. In none of the performed
<figure><img src="image_15.png" /><figcaption>**Figure 2** Brain flexibility comparison. (A) Violin plots (HC in blue and individuals with MS in orange) of the number of unique avalanche patterns (i.e. functional repertoire). (B) Violin plots including MS type separation (RRMS in yellow and SPMS in red). The dots in the violins represent the size of the functional repertoire of each individual. The horizontal lines in the violins represent the mean value of each group. Significant *P*-values after *t*-test (A) and Kruskal–Wallis test (B), respectively: \* *P* < 0.05, \*\* *P* < 0.01, \*\*\* *P* < 0.001.</figcaption></figure>

models, either LL or volumes had a significant impact on the number of unique brain reconfigurations.

Comparable analyses were performed using clinical markers of active disease (e.g. 2 years with no evidence of disease activity NEDA and the number of clinical relapses) without finding any statistically significant impact of disease activity on the size of the functional repertoire.

We also assessed the impact of the EDSS nonlinearity on our results. To study weather this nonlinearity affects the prediction model and if the relationship between EDSS and functional repertoire was also valid in both lower disability and higher disability, we split the sample into two smaller ones according to the EDSS cut-off of 6. In the cohort with EDSS ≤ 6, we performed a Spearman's test according to the MS type. A positive correlation was evident in RRMS (*P*-value = 0.02), whereas no relationship was found in the SPMS (*r* = 0.3, *P*-value = 0.4). In the sample with EDSS over 6, composed only of SPMS patients, the absence of a relationship between the functional repertoire size and the EDSS was confirmed. To assess again whether these differences were valid for both lower and higher disability (and not dependent on the particular cut-off value we chose), we also divided the cohort according to the median EDSS value. In both the samples (under and over EDSS of 4.5), we found the same behaviour previously found in the non-split cohort but this time without reaching statistical significance (Supplementary Fig. 2).

## Discussion

In the present study, we investigated the flexibility of the brain dynamics in MS and its relationship with the clinical phenotypes. In particular, following our previous studies carried

out on neurodegenerative diseases, including amyotrophic lateral sclerosis,¹⁸ Alzheimer's disease¹⁹ and Parkinson's disease,¹⁷ where a reduction of the functional repertoire has been consistently found, we hypothesized that MS could express different behaviours as a function of the prevalence of neuroinflammation or neurodegeneration. When we looked at the different MS clinical phenotypes, we found that the significant differences in the flexibility of the brain dynamics were mainly attributable to the RRMS phenotype. In particular, RRMS patients showed a larger number of unique patterns when compared with HC, whereas the dynamics in SPMS patients did not differ significantly as compared with HC.

In the second part of our work, we searched for relationships between flexibility and MS clinical features. Even in this case, RRMS and SPMS behaved in two opposite ways. The size of the functional repertoire did not show significant predictive power on EDSS. Nevertheless, the interaction between the size of the functional repertoire and MS phenotype showed significant predictive power. That is to say that brain dynamics show different predictive power according to the MS phenotype. In particular, brain dynamics positively correlated with disability in RRMS patients without showing a significant relationship in SPMS patients. This means that only for the RRMS, the increased number of patterns was associated with a worse clinical condition. Accordingly, in the RRMS group, a significant correlation between increased brain flexibility and other clinical signs such as depression and cognitive impairment (assessed by BDI and SDMT, respectively) was also found.

In our opinion, these results could hide two different explanations. On one hand, the observation that only RRMS patients showed larger functional repertoires as compared with HC (and not SPMS patients) could suggest that the
<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

**Figure 3** Clinical impairment prediction. Multilinear regression analysis with *k*-fold cross-validation was performed to verify the ability of the size of the functional repertoire to predict clinical impairment assessed by the EDSS. The clinical phenotype of MS was added as a predictor into the model (individually and as an interaction with the functional repertoire), to verify whether the prediction would depend upon the MS form (RRMS or SPMS). (A) Regression analysis data: F-test ($F(3,21) = 3.61, P = 0.03$) $R^2 = 0.34$, normalized root mean square error (NRMSE) = 0.25; significant predictors in bold (MS type, $β = 0.461, P = 0.018$; MS type * functional repertoire, $β = -0.456, P = 0.0419$). (B) Scatter plot to compare actual EDSS values with the EDSS values predicted through cross-validation. Since the MS type and the interaction between the MS type and functional repertoire were significant, we used different colours to represent RRMS (yellow) and SPMS (red) and observe the predictions independently. (C) Scatter plot of the residual's distribution. (D) Statistical comparison (permutation test with 10,000 iterations) between RRMS and SPMS absolute value of the residuals. Significant lower residuals indicate better prediction of the EDSS values of the RRMS group. \*P-value < 0.05. Abbreviations: s.d., standard deviations.

increased number of reconfigurations observed in RRMS brain might represent a compensatory mechanism adopted by the nervous system in the early phase of disease to maintain a proper functionality. On the other hand, the difference in brain dynamics between RRMS and HC could be due, at

least partially, to the different pathophysiologies that characterize RRMS and SPMS.

However, the results on the correlation between brain flexibility and clinical condition in RRMS subjects would seem to reject the compensatory hypothesis. In fact, when
<figure><img src="image_18.png" /><figcaption>Figure 4 Correlation between EDSS and brain flexibility.</figcaption></figure>

Spearman correlation test between EDSS and functional repertoire was performed in RRMS and SPMS, separately. Significant correlation was found in the RRMS group only. For SPMS individuals (red points and line), no significant relationship was found between brain flexibility and EDSS (r = -0.13, *P* = 0.64). In RRMS subjects (yellow points and line), the functional repertoire was significantly and positively related to the EDSS (r = 0.7, *P* = 0.024).

we performed statistical correlations between brain dynamics and clinical characteristics such as processing speed, depression, fatigue and disability, we found a positive relation between higher brain flexibility and worse clinical condition. All this seems to make a compensatory mechanism a less likely explanation unless the compensatory mechanism is not efficient. On the contrary, the neuropathological differences between the two MS phenotypes could partly explain the diversity in brain flexibility according to the clinical form of the disease. In fact, RRMS is a phenotype with predominant neuroinflammation and demyelination, whereas neurodegeneration, independent from the inflammatory responses, represents the main mechanism of SPMS disease progression.³,⁴² Hence, while the RRMS pathology is dominated by a peripheral immune response (brain parenchymal lymphocyte infiltration through a disrupted blood-brain barrier) that leads to the formation of new active lesions,⁴³ progressive MS is characterized by subpial demyelinated lesions, with slow expansion of pre-existing white matter lesions and, most importantly, diffuse grey matter neurodegeneration.¹ Speculatively, one can suppose that the coexistence of neuroinflammation and neurodegeneration could result in an unchanged level of overall flexibility in SPMS patients. In other words, the similar overall flexibility observed in SPMS with respect to HC may be the result of two concurrent mechanisms that affect the brain dynamics in opposite ways. One pattern would lead to more

stereotyped brain dynamics, as previously found in neurodegenerative diseases. A different effect, as seen in the predominantly focal inflammatory RRMS phenotype, would lead to increased heterogeneity of the repertoires. In other words, the disruption of the myelin sheath observed in RRMS would lead to a dysregulation of the overall dynamics, which would become less effectively controlled, resulting in more disordered dynamics and a higher number of states. Widespread degeneration, on the other hand, prevents the brain from accessing certain configurations, which would result in an impoverished repertoire. A comparable amount of both neurodegenerative and focal neuroinflammatory processes might contrast each other.⁴⁰ According to this line of thought, we found a trend towards an inverse correlation between the disease duration and the flexibility of the brain dynamics in SPMS patients. That is to say, the more SPMS patients are close to the early phase of the disease (and, thus, the more similar they are to RRMS), the greater the flexibility of the brain dynamics, while longer disease durations (with increased neurodegenerative load) correspond to impaired flexibility (Supplementary Fig. 1).

Nevertheless, the compensatory hypothesis should not be completely discarded because the possibility of a partial compensation remains viable. Hence, to better interpret and quantify the role of neuroinflammation and neurodegeneration, as well as their impact on the brain dynamics, a larger sample, assessing both neurodegenerative (e.g. neurofilament, Tau and p-Tau, and amyloid-β 40/42, in both plasma and CSF) and inflammatory (such as several immune mediators and cytokines) biomarkers, will be necessary in future studies.

Overall, our results are in line with the very recent studies that, through different neuroimaging techniques, investigated brain dynamics in MS. von Schwanenflug et al.⁴⁴ conducted an fMRI study in a cohort of almost entirely RRMS subjects, showing an increased flexibility of brain dynamics in MS patients. Similarly, by means of fMRI, Broeders et al.⁴⁵ evaluated the brain dynamics in a cohort consisting of around 80% RRMS subjects. They found a greater number of brain network reconfigurations in patients with higher cognitive impairment. These results are in agreement with our findings that show a positive relationship between worse clinical condition and higher brain flexibility in RRMS subjects.

In addition, it is noteworthy that the fatigue burden was significantly different between RRMS and SPMS individuals, and the fact that this difference might have had a role in determining different dynamics between the two groups cannot be ruled out. Although the performed statistical analyses (above all the study of the interaction effect between FSS and MS type in predicting the size of the functional repertoire) do not seem to support this option, wider samples are needed to explore this aspect extensively. The study of the relationship between fatigue and the number of dynamic brain reconfigurations may also help to expand our knowledge about the aetiology of fatigue. Based on the well-known double nature, degenerative and inflammatory,⁴⁶ of fatigue,
the positive relationship between FSS score and the number of unique brain reconfigurations in the RRMS and its absence in SPMS could hint at a shift towards a more ‘atrophiedriven’ fatigue along with the disease progression.

Some limitations of the current work should be pointed out. First is the small sample size of the studied population. A second limitation is the absence of CSF/serum inflammatory biomarkers (or neuroinflammatory PET imaging biomarkers) to support the primary role of inflammation in affecting brain dynamics. An adjunctive weakness is the absence of a follow-up of the RRMS patients to demonstrate a change of brain dynamics in case of disease conversion to the progressive form. Future longitudinal studies evaluating the brain flexibility changes across the disease progression (and conversion) will be mandatory to confirm our hypothesis.

To our knowledge, we are the first to investigate brain dynamics through high temporal resolution techniques (M/EEG) in both RRMS and SPMS patients. These findings support the key role of temporal dynamics in understanding the link between brain connectivity and clinical features, also unveiling different dynamical features according to the MS phenotype. Additionally, if the supposed link between brain dynamics, inflammation and clinical outcome will be confirmed, this could allow monitoring in a minimal invasive and objective way (M/EEG analyses) the efficacy of MS immunotherapy.<sup>4,47,48</sup>

## Supplementary material

Supplementary material is available at *Brain Communications* online.

## Funding

This work was supported by the Ministero Sviluppo Economico, Contratto di sviluppo industriale ‘Farmaceutica e Diagnostica’ (CDS 000606) and European Union ‘Next-GenerationEU’ (Investimento 3.1.M4. C2) of PNRR (Piano Nazionale di Ripresa e Resilienza).

## Competing interests

The authors report no competing interests.

## Data availability

The data that support the findings of this study are available from the corresponding author, P.S., upon reasonable request.

## References

1. Lassmann H. Pathogenic mechanisms associated with different clinical courses of multiple sclerosis. *Front Immunol.* 2019;10(JAN): 1-14.

2. Cree BAC, Arnold DL, Chataway J, et al. Secondary progressive multiple sclerosis: New insights. *Neurology.* 2021;97(8):378-388.

3. Correale J, Gaitán MI, Ysraelit MC, Fiol MP. Progressive multiple sclerosis: From pathogenic mechanisms to treatment. *Brain.* 2017; 140(3):527-546.

4. McGinley MP, Goldschmidt CH, Rae-Grant AD. Diagnosis and treatment of multiple sclerosis: A review. *JAMA—J Am Med Assoc.* 2021;325(8):765-779.

5. Thompson AJ, Banwell BL, Barkhof F, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. *Lancet Neurol.* 2018;17(2):162-173.

6. Barkhof F. The clinico-radiological paradox in multiple sclerosis revisited. *Curr Opin Neurol.* 2002;15(3):239-245.

7. Rocca MA, Riccitelli G, Rodegher M, et al. Functional MR imaging correlates of neuropsychological impairment in primary-progressive multiple sclerosis. *Am J Neuroradiol.* 2010;31(7): 1240-1246.

8. Tona F, Petsas N, Sbardella E, et al. Multiple sclerosis: Altered thalamic resting-state functional connectivity and its effect on cognitive function. *Radiology.* 2014;271(3):814-821.

9. Hawellek DJ, Hipp JF, Lewis CM, Corbetta M, Engel AK. Increased functional connectivity indicates the severity of cognitive impairment in multiple sclerosis. *Proc Natl Acad Sci U S A.* 2011; 108(47):19066-19071.

10. Roosendaal SD, Schoonheim MM, Hulst HE, et al. Resting state networks change in clinically isolated syndrome. *Brain.* 2010; 133(6):1612-1621.

11. Hall EL, Robson SE, Morris PG, Brookes MJ. The relationship between MEG and fMRI. *Neuroimage.* 2014;102(P1):80-91.

12. Zalesky A, Fornito A, Cocchi L, Gollo LL, Breakspear M. Time-resolved resting-state brain networks. *Proc Natl Acad Sci U S A.* 2014;111(28):10341-10346.

13. Haldeman C, Beggs JM. Critical branching captures activity in living neural networks and maximizes the number of metastable states. *Phys Rev Lett.* 2005;94(5):1-4.

14. Tagliazucchi E, Balenzuela P, Fraiman D, Chialvo DR. Criticality in large-scale brain fmri dynamics unveiled by a novel point process analysis. *Front Physiol.* 2012;3(February):1-12.

15. Shriki O, Alstott J, Carver F, et al. Neuronal avalanches in the resting MEG of the human brain. *J Neurosci.* 2013;33(16):7079-7090.

16. Rabuffo G, Fousek J, Bernard C, Jirsa V. Neuronal cascades shape whole-brain functional dynamics at rest. *eNeuro.* 2021;8(5). doi: 10.1523/ENEURO.0283-21.2021.

17. Sorrentino P, Rucco R, Baselice F, et al. Flexible brain dynamics underpins complex behaviours as observed in Parkinson's disease. *Sci Rep.* 2021;11(1):1-12.

18. Polverino A, Troisi Lopez E, Minino R, et al. Flexibility of fast brain dynamics and disease severity in amyotrophic lateral sclerosis. *Neurology.* 2022;99(21):E2395-E2405.

19. Romano A, Troisi Lopez E, Cipriano L, et al. Topological changes of fast large-scale brain dynamics in mild cognitive impairment predict early memory impairment: A resting-state, source reconstructed, magnetencephalography study. *Neurobiol Aging.* 2023;132:36-46.

20. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: An Expanded Disability Status Scale (EDSS). *Neurology.* 1983;33(11): 1444-1452.

21. Krupp LB, Larocca NG, Muir Nash J, Steinberg AD. The Fatigue Severity Scale: Application to patients with multiple sclerosis and systemic lupus erythematosus. *Arch Neurol.* 1989;46(10): 1121-1123.

22. Smith A. The Symbol-Digit Modalities Test: A neuropsychologic test for economic screening of learning and other cerebral disorders. *Learn Disord.* 1968;3:83-91.

23. Beck A, Brown G, Steer R. BDI-II manual. *J Health Psychol.* 1996; 17(6). doi: 10.1037/t00742-000.

24. Cipriano L, Troisi E, Liparoti M, et al. NeuroImage: Clinical reduced clinical connectome fingerprinting in multiple sclerosis predicts fatigue severity. *NeuroImage Clin.* 2023;39(June):103464.
25. Polverino A, Lopez ET, Minino R, et al. Brain network topological changes in inflammatory bowel disease: an exploratory study. Published online 2023:1-2. doi: 10.22541/au.168441123.33854485/v1.

26. Liparoti M, Cipriano L, Lopez ET, et al. Brain flexibility increases during the peri-ovulatory phase of the menstrual cycle. *Sci Rep* 2023;14(1):1976.

27. Cipriano L, Liparoti M, Troisi E, et al. Brain fingerprint changes across the menstrual cycle correlate with emotional state of PNRR. medRxiv 23290292. Published online 2023;1-24. doi: 10.1101/2023.05.21.23290292.

28. Pesoli M, Rucco R, Liparoti M, et al. A night of sleep deprivation alters brain connectivity and affects specific executive functions. *Neurol Sci.* 2022;43(2):1025-1034.

29. Rucco R, Lardone A, Liparoti M, et al. Brain networks and cognitive impairment in Parkinson's disease. *Brain Connect.* 2022; 12(5):465-475.

30. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. *Neuroimage.* 2002;15(1):273-289.

31. Ashburner J. A fast diffeomorphic image registration algorithm. *Neuroimage.* 2007;38(1):95-113.

32. Rombetto S, Granata C, Vettoliere A, Russo M. Multichannel system based on a high sensitivity superconductive sensor for magnetoencephalography. *Sensors (Switzerland).* 2014;14(7): 12114-12126.

33. Fraschini M, Demuru M, Crobe A, Marrosu F, Stam CJ, Hillebrand A. The effect of epoch length on estimated EEG functional connectivity and brain network organisation. *J Neural Eng.* 2016;13(3):036015.

34. Gross J, Baillet S, Barnes GR, et al. Good practice for conducting and reporting MEG research. *Neuroimage.* 2013;65:349-363.

35. Oostenveld R, Fries P, Maris E, Schoffelen JM. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. *Comput Intell Neurosci.* 2011;2011. doi: 10.1155/2011/156869.

36. de Cheveigné A, Simon JZ. Denoising based on time-shift PCA. *J Neurosci Methods.* 2007;165(2):297-305.

37. Barbati G, Porcaro C, Zappasodi F, Rossini PM, Tecchio F. Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals. *Clin Neurophysiol.* 2004;115(5):1220-1232.

38. Nolte G. The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors. *Phys Med Biol.* 2003; 48(22):3637-3652.

39. Van Veen BD, Van Drongelen W, Yuchtman M, Suzuki A. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. *IEEE Trans Biomed Eng.* 1997; 44(9):867-880.

40. Sorrentino P, Seguin C, Rucco R, et al. The structural connectome constrains fast brain dynamics. *eLife.* 2021;10. doi: 10.7554/eLife.67400.

41. Varoquaux G, Raamana PR, Engemann DA, Hoyos-Idrobo A, Schwartz Y, Thirion B. Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. *Neuroimage.* 2017;145: 166-179.

42. Mahad DH, Trapp BD, Lassmann H. Pathological mechanisms in progressive multiple sclerosis. *Lancet Neurol.* 2015;14(2):183-193.

43. Ontaneda D, Thompson AJ, Fox RJ, Cohen JA. Progressive multiple sclerosis: Prospects for disease therapy, repair, and restoration of function. *Lancet.* 2017;389(10076):1357-1366.

44. von Schwanenflug N, Koch SP, Krohn S, et al. Increased flexibility of brain dynamics in patients with multiple sclerosis. *Brain Commun.* 2023;5(3):1-14.

45. Broeders TAA, Douw L, Eijlers AJC, et al. A more unstable resting-state functional network in cognitively declining multiple sclerosis. *Brain Commun.* 2022;4(2):1-15.

46. Manjaly ZM, Harrison NA, Critchley HD, et al. Pathophysiological and cognitive mechanisms of fatigue in multiple sclerosis. *J Neurol Neurosurg Psychiatry.* 2019;90(6):642-651.

47. Abbadessa G, Miele G, Maida E, et al. Immunomodulatory effects of ocelizumab and candidate biomarkers for monitoring treatment response in multiple sclerosis. *Mult Scler J.* 2023;29:779-788.

48. Hauser SL, Cree BAC. Treatment of multiple sclerosis: A review. *Am J Med.* 2020;133(12):1380-1390.e2.