Luca Prosperini

Davide Cattaneo

Yuyang Zhai

Navina Nasseri

Jana Pöttgen

Eghbal Gezhelbash

Christoph Heesen

Jan-Patrick Stellmann

Smartphone Accelerometry: A Smart and Reliable Measurement of Real-Life Physical Activity in Multiple Sclerosis and Healthy Individuals

Keywords: smartphone, multiple sclerosis, accelerometry, physical activity, ambulation

Background: Mobility impairment is common in persons with multiple sclerosis (pwMS) and can be assessed with clinical tests and surveys that have restricted ecological validity. Commercial research-based accelerometers are considered to be more valuable as they measure real-life mobility. Smartphone accelerometry might be an easily accessible alternative.

Objective: To explore smartphone accelerometry in comparison to clinical tests, surveys, and a wrist-worn ActiGraph in pwMS and controls.

Zhai et al.

Smartphone Accelerometry in Multiple Sclerosis

Conclusions: Smartphone accelerometry provides better estimates of mobility and disability than a wrist-worn standard accelerometer in a free-living context for both controls and pwMS. Given the fact that no additional device is needed, smartphone accelerometry might be a convenient outcome of real-life ambulation in healthy individuals and chronic diseases such as MS.

Methods: Sixty-seven pwMS and 70 matched controls underwent mobility tests and surveys. Real-life data were collected with a smartphone and an ActiGraph over 7 days. We explored different smartphone metrics in a technical validation course and computed afterward correlation between ActiGraph (steps per minute), smartphone accelerometry (variance of vector magnitude), clinical tests, and surveys. We also determined the ability to separate between patients and controls as well as between different disability groups.

Results: Based on the technical validation, we found the variance of the vector magnitude as a reliable estimate to discriminate wear time and no wear-time of the smartphone. Due to a further association with different activity levels, it was selected for real-life analyses. In the cross-sectional study, ActiGraph correlated moderately (r = 0.43, p < 0.05) with the smartphone but less with clinical tests (rho between |0.211| and |0.337|). Smartphone data showed stronger correlations with age (rho = -0.487) and clinical tests (rho between |0.565| and |0.605|). ActiGraph only differed between pwMS and controls (p < 0.001) but not between disability groups. At the same time, the smartphone showed differences between pwMS and controls, between RRMS and PP-/SPMS, and between participants with/without ambulatory impairment (all p < 0.001).

INTRODUCTION

Multiple sclerosis (MS) is the most common autoimmune disease of the central nervous system (CNS) and leads to an accumulation of disability by chronic inflammation and neurodegeneration [START_REF] Friese | Mechanisms of neurodegeneration and axonal dysfunction in multiple sclerosis[END_REF]. The patterns of disability are heterogeneous, but impaired mobility occurs in up to 75% of persons with multiple sclerosis (pwMS) (2) and represents one of the most disrupting physical features of MS [START_REF] Larocca | Impact of walking impairment in multiple sclerosis: perspectives of patients and care partners[END_REF]. Regarding the perceptions of bodily functions, ambulation is rated as one of the three most valuable abilities [START_REF] Heesen | Perceptions on the value of bodily functions in multiple sclerosis[END_REF]. Besides, walking is the most frequent type of self-selected physical activity [START_REF] Sosnoff | Real-life walking impairment in multiple sclerosis: preliminary comparison of four methods for processing accelerometry data[END_REF] and represents with over 50% of dynamic activity over a 24h period, the primary mode of physical activity in pwMS [START_REF] Weikert | Most common types of physical activity self-Selected by people with multiple sclerosis[END_REF]. Walking impairment could cause physical inactivity, which results in physical deconditioning, and in this negative feedback mechanism, walking impairment could be driven further down [START_REF] Sandroff | Relationships among physical inactivity, deconditioning, and walking impairment in persons with multiple sclerosis[END_REF]. In the clinical setting, walking impairment can be used to monitor disability progression, and ambulatory improvement can be used as an indicator of efficacy in therapeutic trials [START_REF] Schwid | Quantitative functional measures in MS: what is a reliable change?[END_REF]. However, while the importance of the walking ability in MS is widely accepted, the ideal measurement approach is still under discussion [START_REF] Kieseier | Assessing walking disability in multiple sclerosis[END_REF].

The Extended Disability Status Scale (EDSS) is an accepted standard of disability measurement in MS and relies in its middle range mainly on walking abilities in the range between 20 and 500 m [START_REF] Kurtzke | Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS)[END_REF]. However, the scale suffers for its increased variability for longer walking distances and other factors like fatigue, patient's mood, and the time the test was performed [START_REF] Dalla-Costa | Smart watch, smarter EDSS: improving disability assessment in multiple sclerosis clinical practice[END_REF]. EDSS also has limitations to measure small but clinically meaningful changes in ambulation, and it fails to capture the performance fluctuation over time in the natural environment [START_REF] Block | Association of continuous assessment of step count by remote monitoring with disability progression among adults with multiple sclerosis[END_REF]. Standard clinical performance-based measures, such as the Timed 25-Foot Walk (T25FW), 2-Minute Walking Test (2MWT), and 6-Minute Walking Test (6MWT) [START_REF] Stellmann | A 3 meter timed tandem walk is an early marker of motor and cerebellar impairment in fully ambulatory MS patients[END_REF], provide objective snapshots of the day-to-day variable ambulatory capacity [START_REF] Albrecht | Day-to-day variability of maximum walking distance in MS patients can mislead to relevant changes in the expanded disability status scale (EDSS): average walking speed is a more constant parameter[END_REF]. They may not reflect the continuous walking activity in the real-world environment due to the lack of ecological validity [START_REF] Stellmann | Ecological validity of walking capacity tests in multiple sclerosis[END_REF]. Patientreported outcome measures (PROMS) (e.g., 12-Items Multiple Sclerosis Walking Scale (MSWS-12) [START_REF] Hobart | Walking scale (MSWS-12) measuring the impact of MS on[END_REF] or Godin Leisure-Time Exercise Questionnaire (GLTEQ) [START_REF] Godin | A simple method to assess exercise behavior in the community[END_REF] are limited by recall bias and variability in self-perception of physical activity.

To that end, the total ambulatory activity undertaken in the habitual environment in performing a usual range of daily activities is recognized as the gold standard for measuring ambulatory mobility in neurological disorders [START_REF] Pearson | Quantification of walking mobility in neurological disorders[END_REF], and there is an emerging body of research supporting the application of accelerometry for measuring physical data in MS [START_REF] Yousef | Biosensing in multiple sclerosis[END_REF][START_REF] Block | Remote physical activity monitoring in neurological disease: a systematic review[END_REF]. ActiGraph (Pensacola, FL, USA) is one of the most common accelerometers used in research [START_REF] Block | Remote physical activity monitoring in neurological disease: a systematic review[END_REF][START_REF] John | ActiGraph and actical physical activity monitors: a peek under the hood[END_REF]. Associations between the output of ActiGraph (i.e., activity counts, step counts, MVPA, and sedentary time) and clinical outcomes in the free-living setting have been intensively investigated [START_REF] Motl | Walking and cognition, but not symptoms, correlate with dual task cost of walking in multiple sclerosis[END_REF][START_REF] Block | Continuous daily assessment of multiple sclerosis disability using remote step count monitoring[END_REF][START_REF] Klaren | Objectively quantified physical activity in persons with multiple sclerosis[END_REF]. Nevertheless, there are neither standard protocols of application of commercially available accelerometers nor standard accelerometer output-for example, estimates of energy consumption, step number, or walking speed [START_REF] Yousef | Biosensing in multiple sclerosis[END_REF][START_REF] Motl | Accuracy of the actibelt R accelerometer for measuring walking speed in a controlled environment among persons with multiple sclerosis[END_REF]. The need and burden of wearing an additional device restricts its use to short-term usage and may, due to the perceived invasiveness, affect the ecological validity. Smartphone with built-in accelerometry might overcome this shortcoming and has been considered as a possible measurement for motion data. The cost and the burden of measurement are low due to a high usage rate in the general population and among pwMS and the lack of need for a further device [START_REF] Griffin | A questionnaire study to explore the views of people with multiple sclerosis of using smartphone technology for health care purposes[END_REF]. Studies in recent years supported the application of smartphones for assessing mobility and physical activity in clinical as well as in a free-living setting [START_REF] Donaire-Gonzalez | Comparison of physical activity measures using mobile phone-based calfit and actigraph[END_REF][START_REF] Balto | Accuracy and precision of smartphone applications and commercially available motion sensors in multiple sclerosis[END_REF][START_REF] Hekler | Validation of physical activity tracking via android smartphones compared to actiGraph accelerometer: laboratory-Based and free-Living validation studies[END_REF][START_REF] Case | Accuracy of smartphone applications and wearable devices for tracking physical activity data[END_REF]. However, there is a lack of studies investigating smartphone accelerometry as a putative outcome for neurological diseases such as MS.

Here, we aimed to investigate the value of built-in smartphone accelerometers as a valid outcome for disability and mobility compared to a wrist-worn ActiGraph in a representative group of pwMS compared to healthy controls in a free-living setting.

METHODS

The validation and exploration of the smartphone accelerometry were done in two steps: first, we performed a technical validation course for wear time validation and selection of outcomes. Second, we performed a cross-sectional analysis in pwMS and healthy controls with clinical outcomes, PROMS, and ActiGraph measurements. The value of an outcome metric was estimated by its discriminant ability between different disability groups (e.g., mild vs. moderate impairment) and its correlation with self-reported physical activity and clinical performancebased measures.

Technical Validation Course

To define periods of wear time and non-wear time and to explore summary measures from the raw accelerometry data, we performed a technical validation course using 28 smartphones, Samsung Galaxy (model S4 mini) with a built-in tri-axis accelerometer. First, we collected no-wear data over 10 min while all smartphones were lying in different positions on a table. Then, the smartphones were carried by three members from the staff for investigating wear time assessments, which included sitting, standing, walking, running, and stair climbing 10 min each. Passive movements were recorded in an elevator and during a bus trip.

Participants

Participants were recruited at the MS outpatient clinic at the University Medical Centre Hamburg-Eppendorf. The inclusion criteria for pwMS were (1) age 18-65 years, (2) a confirmed diagnosis of MS according to McDonald criteria 2010 [START_REF] Polman | Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria[END_REF], [START_REF] Larocca | Impact of walking impairment in multiple sclerosis: perspectives of patients and care partners[END_REF] an Extended Disability Status Scale (EDSS) (10) score below 6.5, and (4) no relapse in the last 30 days. The inclusion criteria for the controls were ( 1) not reporting disease with potential impact on mobility and (2) matching the age distribution of the sample with MS. The exclusion criteria for both samples were severe medical conditions other than MS, severe cognitive impairment, or any other condition that might relevantly compromise the use of a smartphone (e.g., very low visual acuity or severe ataxia). All participants gave written informed consent prior to any testing under this protocol, and the local ethical review board (Ärztekammer Hamburg, PVN 5001) approved the investigation.

Procedures

After inclusion, we collected demographic data and participants filled in the following questionnaires: Godin Leisure-Time Exercise Questionnaire (GLTEQ) [START_REF] Godin | A simple method to assess exercise behavior in the community[END_REF], the Frenchay Activity Index (FAI) [START_REF] Monteiro | Validation of the frenchay activity index on stroke victims[END_REF], and the International Physical Activity Questionnaire (IPAQ) [START_REF] Craig | International physical activity questionnaire: 12-country reliability and validity[END_REF]. pwMS completed also the 12-Item Multiple Sclerosis Walking Scale (MSWS-12) [START_REF] Hobart | Walking scale (MSWS-12) measuring the impact of MS on[END_REF][START_REF] Motl | Accelerometry and its association with objective markers of walking limitations in ambulatory adults with multiple sclerosis[END_REF]. Clinicbased measures of ambulation included Five-Times Sit-To-Stand test (FTSTS), Timed 25-Foot Walk (T25FW), 2-Minute Walking Test (2MWT), 6-Minute Walking Test (6MWT), and a 3-Meter Timed Tandem Walk (TTW) [START_REF] Stellmann | A 3 meter timed tandem walk is an early marker of motor and cerebellar impairment in fully ambulatory MS patients[END_REF]. EDSS scoring was performed within the clinical examination by a neurologist [START_REF] Kurtzke | Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS)[END_REF].

All participants were supplied with an ActiGraph (model GT3X+) and a smartphone (Samsung Galaxy S4 mini). We asked the participants to wear the ActiGraph on the non-dominant wrist [START_REF] Lehnkering | Actigraphic investigations on the activity-rest behavior of right-and left-handed students[END_REF] and the smartphone in the habitual position like their phones for the following 7 days. They were asked to wear both devices during the entire day, except for showering, swimming, or while sleeping.

Data Processing

All the written data, including demography, clinical performance-based measures, and PROMS were collected in an electronic case report file. The raw ActiGraph data were processed, and standard outcomes [mean vector magnitude (meanVM), daily MVPA, steps/minute] were downloaded with ActiLife 6 software version 6.13.3 (ActiGraph, Pensacola, FL USA) in 60-s epoch intervals. Non-wear time was filtered out with the Choi algorithm [START_REF] Choi | Validation of accelerometer wear and nonwear time classification algorithm[END_REF]. The smartphone accelerometer data were collected via a small Android-based application, which had been developed by the Institute of Neuroimmunology and Multiple Sclerosis (INIMS). The raw accelerometer axis (X, Y, and Z) values were filed at a sampling rate of 2 Hz.

Selection of Smartphone Outcomes

For the selection of putative smartphone outcomes in the technical validation course, we computed and explored the following summary metrics for epochs of 60 s (same bout length as for the ActiGraph): Sum of absolute axis values (sumX, sumY, and sumZ), variance of axis values (varX, varY, and varZ), Pearson's correlations between each pair of axes (corXY, corXZ, and corYZ), sum of all absolute axis values (sumXYZ), mean absolute correlation (corXYZ), sum of absolute vector magnitude (sumVM), mean vector magnitude (VM), and mean variance of the vector magnitude (varVM). Most of the metrics reflect standard accelerometry metrics-such as the vector magnitude and sum of acceleration of selected axes [START_REF] Howe | Accelerometer prediction of energy expenditure: vector magnitude versus vertical axis[END_REF]. However, several commonly used accelerometry outcomes rely on the proper orientation in space, for example the vertical axis counts. For smartphones, such orientation-dependent metrics are not reasonable under the concept of using the patient's device in the future. Thus, we decided to explore orientation-independent metrics. We hypothesized that increasing physical activity might translate into the reduced correlation of the axes counts and increased variance of acceleration measurements.

To compare the potential metrics, the available dataset was split in a ratio of 1:1 randomly in an explorative and a validation subset. First, we used the explorative data to visually inspect boxplots of all measurements for the selection of candidates with the high discriminant ability of no-wear vs. wear time and over different activities. The potential metrics were then formally tested for discriminant abilities of wear and no-wear time by receiver operating characteristic (ROC) analyses. Finally, we validated the metrics from the explorative dataset in the validation sample and defined cutoff values for separation of wear and no-wear time. For further analysis, all accelerometry outcomes were wear-time corrected average values.

Statistical Analysis

For the statistical analysis, we divided the total sample into healthy controls and the pwMS. The pwMS were further divided into the following subgroups: (1) disease course (relapsing vs. progressive) representing conceptually early and late MS and (2) by EDSS <3.5 vs. ≥3.5 representing a cutoff for ambulatory impairment in MS (10) (minimal ambulatory impaired vs. ambulatory impaired). We performed descriptive statistics of the demography with mean/SD, median/range, or number/rates according to the nature of the data. Student's t-test was used to detect the differences of demography, clinical performance-based metrics, PROMS, wear time, and metrics of accelerometry within the above-mentioned groups. Associations between smartphone accelerometry and ActiGraph were first estimated by Spearman's rank-order coefficient within the groups. The most correlating metric of each accelerometer was then chosen to be tested with the clinical performance-based metrics and PROMS by Spearman's rank order. We used Mann-Whitney U-test to determine the ability of the accelerometers to separate between groups. In addition, we computed ROC analysis to examine the predictability of the accelerometers for disease course and severity of the disability. P < 0.05 was used for judging the significance level. Due to multiple comparisons, we corrected the p-values with the false discovery rate (FDR). All analyses were performed with statistics in R.

RESULTS

Technical Validation and Selection of Outcomes

Exploration of smartphone metrics (see Figures 1, 2 andFigures e1, e2) revealed a high sensitivity and specificity for wear time detection for sumXYZ [area under the curve (AUC) = 0.928, p < 0.001, accuracy = 0.901, sensitivity = 0.874, and specificity = 0.959] and several variance metrics including varVM (AUC = 0.984, p < 0.001, Figures 1A-C, accuracy = 0.975, sensitivity = 0.987, specificity = 0.941]. Discriminant abilities could be confirmed in the validation subset, and the AUC from the validation set did not differ from the explorative estimation for varVM (p = 0.507). However, varVM showed significantly higher accuracy than sumXYZ (p < 0.001) and was chosen for wear time detection. Moreover, both metrics tended to increase with estimated physical activity level, and we used these two outcomes for further analyses (Figures 2A,B).

Participants and Clinical Characteristics of the Subgroups

We included 137 subjects: 70 HC and 67 pwMS (see Figure 3). Demographic data are presented in Table 1. Patients with primary or secondary progressive MS (PP-/SPMS) were elder (49.6 vs. 35.9 years) than patients with relapsing-remitting MS (RRMS). pwMS with impaired ambulation had longer disease duration (12.9 vs. 6.4) than its comparison group. Otherwise, we observed no group differences in age, body mass index (BMI), and waist. Moreover, the median EDSS in patients with primary or secondary progressive MS (PP-/SPMS) was 1.8 higher (p < 0.001) than in relapsing-remitting MS (RRMS).

Table 2 shows the descriptive statistics of clinical tests, PROMs, and accelerometry measures of ActiGraph and smartphone. The average measurement times within 7 days were 55 h for the smartphone and 76 h for the ActiGraph, which represent an average active wear time of 7.5 and 10.9 h/day, respectively.

Correlations Between Smartphone Metrics and ActiGraph

First, we were interested in analyzing the correlation between standard ActiGraph outcomes, and smartphone-derived metrics (see Table 3 and Figure e3). Among all metrics, varVM correlated best with ActiGraph steps/minute within all participants (rho = 0.44, p < 0.001). However, this association was mainly driven by the correlation in healthy controls (rho = 0.478, p < 0.001), while it was clearly weaker but still significant in pwMS (rho = 0.29, p = 0.022).

Correlations Between Accelerometer

Outcomes, Clinical Performance-Based Measures, and PROMS

Next, we investigated the association of both accelerometers with demography, clinical measures, and PROMS (Figure 4). Among the variables derived from ActiGraph, steps/minute showed within all participants the strongest but still only weak to moderate correlations with clinical measures (2MWT, 6MWT, FTSTS,T25FW rho = |0.21| to |0.34|, p < 0.05) and PROMS (FAI and IPAQ, rho = |0.27|, p < 0.05). In healthy controls and pwMS with ambulatory impairment, MVPA had a stronger association with some of the clinical measures than steps/minute (rho = |0.28| to |0.59|, p < 0.05). Among all variables derived from the smartphone, varVM showed the strongest correlations among all participants. varVM correlated mildly to moderately with the demography (age and waist, rho = |0.25| to |0.49|, p < 0.01), the clinical measures (TTW, 2MWT, 6MWT, FTSTS, T25FW, rho = |0.56| to |0.61|, p < 0.0001), and with PROMS (GLTEQ and FAI, rho = |0.39| and |0.25|, p < 0.01).

Thus, we will describe the smartphone outcome varVM and the ActiGraph outcome steps/minute as the most correlating outcomes in the subgroups more in detail. The association of both outcomes with demography, clinical measures, and PROMS are summarized in Figure 4 (for correlations of other accelerometer outcomes, see Supplemental Material). In healthy controls, varVM correlated moderately with most clinical measures (2MWT, 6MWT, F25WT, and FTSTS, rho = |0.30| to |0.39|, p < 0.05) but not with PROMs. Steps/minute did not correlate with any clinical measures nor with PROMs. Within all pwMS, varVM again moderately to strongly correlated with age (rho = -0.63, p < 0.0001), all clinical measures (TTW, 2MWT, 6MWT, FTSTS and T25FW, rho = |0.56| to |0.67|, p < 0.0001), GLTEQ (rho = 0.37, p < 0.01), EDSS (rho = -0.62, p < 0.0001), and MSWS-12 (rho = -0.73, p < 0.0001). Steps/minute did not, neither within all pwMS nor in the subgroups, correlate with any variable. Within minimal impaired pwMS, varVM showed moderate to strong correlation with age (rho = -0.74, p < 0.0001), clinical measures (TTW, 2MWT, 6MWT, FTSTS, and T25FW, rho = |0.45| to |0.64|, p < 0.01), EDSS (rho = -0.602, p < 0.0001), and MSWS-12 (rho = -0.74, p < 0.0001). Within ambulatory impaired subgroup, none of the accelerometer metrics correlated with any variable. Overall, varVM showed in comparison to steps/minute not only in the healthy subgroup but also in pwMS a stronger association with age, clinical measures, MSWS-12, and EDSS, representing walking ability and ambulatory impairment.

The Ability of Accelerometry to Differentiate Between Subgroups

In addition, we wanted to compare the discriminant abilities of accelerometry data for MS subgroups. Again, we used steps/minute and varVM as outcomes of interest. ROC analysis (Table 4) revealed that varVM was the better classifier for differentiating pwMS from control (AUC = 0.75 vs. 0.68, Figure 5). Moreover, only varVM was able to differentiate between relapsing-remitting and progressive MS (AUC = 0.946, p < 0.0001, Figure 6) and to differentiate between severe ambulatory impairment and mild ambulatory impairment patients (AUC = 0.728, p < 0.01, Figure 7).

DISCUSSION

This study examined smartphone accelerometry as an outcome of real-life ambulation and physical activity in healthy individuals and pwMS. To follow this aim, we analyzed the relationship of putative smartphone metrics with a research-grade accelerometer (ActiGraph) during free-living conditions, with objectively measured walking ability and with self-reported physical activity. Overall, results showed that the smartphone accelerometer correlated only moderately with ActiGraph in HC and pwMS. However, the smartphone accelerometer seems to be more closely associated with walking ability, represented by the clinical performance-based measures, such as TTW, T25FW, 2-/6-MWT, FTSTS, and with ambulatory impairment, represented by MSWS and EDSS. Moreover, the smartphone accelerometer differentiated the levels of ambulation among all participants and ambulatory impairment among the pwMS better than the ActiGraph. In our study, smartphone metrics seem more reliable than a wrist-worn research-grade accelerometer. For this study, we used a new metric to capture ambulation and body motion based on accelerometry data-the variance of the vector magnitude. From a conceptual point of view, the metric represents the movement of the smartphone in all dimensions in a given time. The metric was chosen based on a technical validation course and provided two important features: high specificity and sensitivity to identify wear time periods and a positive association with increased ambulation. An advantage of this metric is its independence from the orientation of the smartphone. The value of this metric was evaluated in comparison to a battery of different outcomes and was chosen by applying a strict selection methodology based on an explorative and a validation data set. Moreover, the promising results of being a good discriminator between ambulation levels and its association with ambulatory impairment metrics indicate a successful proof-of-concept.

The rather weak to moderate association between smartphone varVM and ActiGraph outcomes in both HC and pwMS contrasts one study that android smartphones provided similar raw counts as ActiGraph in a free-living setting [START_REF] Hekler | Validation of physical activity tracking via android smartphones compared to actiGraph accelerometer: laboratory-Based and free-Living validation studies[END_REF]. Although ActiGraph is a validated tool, most of those validating studies chose the hip-worn position [START_REF] Block | Remote physical activity monitoring in neurological disease: a systematic review[END_REF][START_REF] John | ActiGraph and actical physical activity monitors: a peek under the hood[END_REF], and the literature provides controversial data for the wrist-worn position of accelerometers [START_REF] Block | Association of continuous assessment of step count by remote monitoring with disability progression among adults with multiple sclerosis[END_REF][START_REF] Block | Continuous daily assessment of multiple sclerosis disability using remote step count monitoring[END_REF][START_REF] Barreira | Comparison of step outputs for waist and wrist accelerometer attachment sites[END_REF][START_REF] Scott | Comparability and feasibility of wrist-and hip-worn accelerometers in free-living adolescents[END_REF][START_REF] Höchsmann | Validity of activity trackers, smartphones, and phone applications to measure steps in various walking conditions[END_REF]. However, the acceptance for the wrist-worn position may be higher [START_REF] Scott | Comparability and feasibility of wrist-and hip-worn accelerometers in free-living adolescents[END_REF]. In this study, we also used wristworn ActiGraph data, which might explain the unexpected low to moderate correlations with clinical measures and PROMS. Another reason for the lower correlation could be explained by the arm movements during the non-walking time included in the high active wear time, while the participants might move the smartphone mostly when they were walking.

Regarding the ecological validity, ActiGraph could influence the exercising behavior and, for example, increase the physical activity, since the notable visibility and discomfort on the wrist could be perceived invasive as a "reminder." On the other hand, the possibly perceived invasiveness of a wrist-worn accelerometer could be intentionally used to motivate users for more exercising. Eventually, using smartphones as an ubiquitous, available measuring tool might overcome these shortcomings as usual smartphone positions such as handbags, rucksacks, or pants pocket, which are less perceivable and provide a high accuracy [START_REF] Höchsmann | Validity of activity trackers, smartphones, and phone applications to measure steps in various walking conditions[END_REF][START_REF] Leong | Accuracy of three android-based pedometer applications in laboratory and free-living settings[END_REF]. These positions are closer to the body's center of mass that has been recommended as the best sensor position [START_REF] Schimpl | Development and validation of a new method to measure walking speed in free-living environments using the actibelt R platform[END_REF]. Thus, smartphones could refer more to real life and offer higher ecological validity. However, the perceived invasiveness was not studied here and needs to be addressed in future studies. An important result supporting the value of our smartphonebased approach is the clear association with age. Walking abilities, and especially walking speed, are known to be an important indicator of health status and associated with age [START_REF] Schimpl | Association between walking speed and age in healthy, free-living individuals using mobile accelerometry-a cross-sectional study[END_REF]. Interestingly, this important coherence with age was wellreproducible with our smartphone metric but not with the ActiGraph. Proving a well-known and fundamental relationship emphasizes the reliability of smartphone accelerometry as an outcome of real-life ambulation. Furthermore, the discriminating ability of smartphone varVM confirmed the assumption that smartphones could differentiate levels of walking ability and ambulatory dysfunction. However, these findings are based on a cross-sectional study, and its sensitivity to disability progression or improvement must be analyzed in a longitudinal setting.

However, it remains uncertain which dimension of physical activity or ambulation is captured explicitly by smartphones in general: There are controversial results addressing this issue in the literature of former research [START_REF] Donaire-Gonzalez | Comparison of physical activity measures using mobile phone-based calfit and actigraph[END_REF][START_REF] Hekler | Validation of physical activity tracking via android smartphones compared to actiGraph accelerometer: laboratory-Based and free-Living validation studies[END_REF][START_REF] Weikert | Accelerometry is associated with walking mobility, not physical activity, in persons with multiple sclerosis[END_REF]. Here, our approach using a research-grade accelerometer as a reference failed. However, varVM correlated much stronger with the clinical measures, representing walking ability than with the PROMs and representing self-reported physical activity. Thus, we assume that smartphone measures rather the walking ability than the physical activity. It might be due to a measurement gap during exercising or other vigorous activities performed without the smartphone. Conceptually, this assumption is supported by the fact that smartphones are usually worn during habitual activities like traveling, shopping, and walking outside. At the same time, it is preferably placed aside during exercising and other vigorous activities. However, this assumption needs further investigation.

The association between smartphone metrics and clinical outcomes was, in general, higher than for the ActiGraph. However, both smartphone and ActiGraph correlated with clinical measures or PROMs more among the ambulatory mildly to moderately impaired pwMS than those with severely impaired ambulation. This links to the still open question of whether accelerometry can generally measure walking ability or rather physical activity in patients with very low activity levels and variable gait patterns, such as in progressive MS. However, the ability of the clinical test to mirror real-life ambulation and motion is also limited, and they have a rather low ecological validity [START_REF] Stellmann | Ecological validity of walking capacity tests in multiple sclerosis[END_REF][START_REF] Stellmann | Fampridine and real-life walking in multiple sclerosis: low predictive value of clinical test for habitual short-term changes[END_REF]. Thus, a poor association might be due to the low performance of the real-life device or shortcomings of the clinical tests. Further research is needed to provide better objective estimates of low activity levels in more severely disabled patients. Moreover, our technical validation indicated a meaningful increase in the chosen smartphone metric with increasing physical activity. However, these findings could not be validated in the real-life setting in this study.

One of the limitations in our study was the wear time of the devices that might have been too short for reliable estimates of real-life walking or activity. The original wristlet of ActiGraph was often reported as unfeasible during specific exercising like weightlifting; on the other hand, the smartphone has a relatively short battery life and needed to be charged at least once a day. Wear time alone cannot be considered as evidence for the smartphone as an outcome of real-life activity. However, smartphone covered ∼72% of the ActiGraph measurement time. Future studies need to validate against other outcomes or devices. Moreover, we asked the participants to wear the smartphone in their habitual wearing position, aiming to simulate the reallife condition and to avoid the possibly perceived invasiveness. Although the usual position like handbag, backpack, and pants pocket probably does not have differences in the accuracy of measurement [START_REF] Höchsmann | Validity of activity trackers, smartphones, and phone applications to measure steps in various walking conditions[END_REF], the smartphone secured on the upper arm showed a lower accuracy [START_REF] Leong | Accuracy of three android-based pedometer applications in laboratory and free-living settings[END_REF]. Another limitation is that it is impossible to determine if the phone estimates would remain comparable with other phone models that have not been tested. However, one of the most prominent android brands was used in this study. Finally, we only investigated a rather simple summary metric of 60-s epochs, which reduces the complexity of the raw data. Advanced algorithms, for example, estimating walking speed, might improve the validity of smartphone accelerometry, as it has been shown for research-grade devices [START_REF] Motl | Accuracy of the actibelt R accelerometer for measuring walking speed in a controlled environment among persons with multiple sclerosis[END_REF].

Even with these limitations, there seems to be a strong opportunity for smartphone accelerometry in the context of several diseases and healthy living [START_REF] Donaire-Gonzalez | Comparison of physical activity measures using mobile phone-based calfit and actigraph[END_REF][START_REF] Case | Accuracy of smartphone applications and wearable devices for tracking physical activity data[END_REF]. It might help clinicians to monitor ambulatory dysfunction, disease progress, or rehabilitation in diverse clinical conditions with high ecological validity. It could also help patients to monitor their individual changes of walking ability from a personal baseline over time and to achieve ability goals. Combined with motivational, educational tools, it may as well help to improve physical activity independent from diseases.

CONCLUSION

Smartphone accelerometry provides better estimates of mobility and disability than a wrist-worn standard accelerometer in a freeliving context for both controls and pwMS. Given the fact that no additional device is needed and despite further validation, smartphone accelerometry might be a convenient outcome of real-life ambulation in healthy individuals and chronic diseases such as MS. Moreover, activity estimates from smartphones might be more ecological valid as the perceived invasiveness of assessment is lower than for additional and clearly visible devices.

FIGURE 1 |

 1 FIGURE 1 | Technical validation of smartphone metrics: wear time detection. Exploration of different smartphone metrics revealed good discriminant abilities for (A,B) sumXYZ and varVM that could be confirmed in receiver operating characteristic (ROC) analyses of the (C) explorative and the (D) validation subset.

FIGURE 2 |

 2 FIGURE 2 | Smartphone metrics and physical activities. Boxplots of the smartphone metrics (A) sumXYZ and (B) varVM during different activities. Both metrics also tended to increase with the activity intensity.

FIGURE 3 |

 3 FIGURE 3 | Study flow chart. HC, healthy controls; pwMS, patients with MS; RR, relapsing-remitting MS; PP-/SPMS, primary and secondary progressive MS; EDSS, expanded disability status scale (EDSS > 3 indicates walking impairment).

FIGURE 4 |

 4 FIGURE 4 | Association of clinical outcomes and questionnaires with smartphone varVM and ActiGraph steps/minute. Correlogram of Spearman rho; corrected p-values for multiple comparisons through FDR; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

FIGURE 5 |

 5 FIGURE 5 | Group differences between healthy control and patients with multiple sclerosis (pwMS). Left: Boxplots showing smartphone and ActiGraph metrics for controls and pwMS, ***p < 0.001, ****p < 0.0001. Right: receiver operating characteristic (ROC) curves of smartphone (red) and ActiGraph (blue) showing the ability of differentiation between the groups.

FIGURE 6 |

 6 FIGURE 6 | Group differences between relapsing-remitting and progressive multiple sclerosis (MS). Left: Boxplots showing smartphone and ActiGraph metrics for controls, progressive (or late) MS and relapsing (or early) MS. *p < 0.05, ***p < 0.001, ****p < 0.0001. Right: receiver operating characteristic (ROC) curves of smartphone (red) and ActiGraph (blue) showing the ability of differentiation between the MS groups.

FIGURE 7 |

 7 FIGURE 7 | Group differences between ambulatory minimally impaired and ambulatory impaired patients with multiple sclerosis (pwMS). Left: Boxplots showing differences between controls minimally ambulatory impaired pwMS [Extended Disability Status Scale (EDSS) < 3.5] and severely impaired pwMS (EDSS > 3.5), **p < 0.01, ***p < 0.001, ****p < 0.0001. Right: receiver operating characteristic (ROC) curves of smartphone (red) and ActiGraph (blue) showing the ability of differentiation of levels of ambulatory impairment.

TABLE 1 |

 1 Demographic and clinical data. Values represent mean ± SD, if not otherwise indicated; p-values for group comparison (patients vs. controls, RRMS vs. PP-/SPMS, and EDSS groups) based on chi-square test for rates or Student's t-test. BMI, body mass index; EDSS, expanded disability status score.

		Control	pwMS	RRMS	PP-/SPMS	EDSS ≤ 3.5	EDSS > 3.5
	N (Sex)	70 (47F/23M)	67 (42F/25M)	34 (18F/16M)	33 (24F/9M)	49 (32F/17M)	18 (10F/8M)
			(p = 0.713)		(p = 0.155)		(p = 0.655)
	Age (years)	41.5 ± 12.8	42.9 ± 10.9	35.9 ± 9.1	49.6 ± 7.7	41.6 ± 11.4	46.4 ± 8.4
			(p = 0.496)		(p < 0.001)		(p = 0.067)
	Weight (kg)	71.9 ± 15.2	73.3 ± 16.8	72.2 ± 18.5	74.4 ± 15.2	74.4 ± 17.0	70.4 ± 16.6
			(p = 0.613)		(p = 0.595)		(p = 0.396)
	BMI	24.0 ± 3.9	24.4 ± 4.7	24.5 ± 5.3	24.4 ± 4.2	24.8 ± 4.7	23.3 ± 4.7
			(p = 0.585)		(p = 0.905)		(p = 0.242)
	Waist (cm)	89.1 ± 12.8	92.3 ± 14.8	89.9 ± 16.4	94.6 ± 12.9	93.1 ± 14.7	90.2 ± 15.5
			(p = 0.177)		(p = 0.197)		(p = 0.499)
	Disease duration (years)		8.5 ± 8.1	6.5 ± 5.6	9.7 ± 8.6	6.4 ± 6.2	12.9 ± 9.1
					(p = 0.097)		(p = 0.012)
	Median EDSS (range)		3 (1.0-6.0)	2.0 (1.0-5.5)	3.5 (2.0-6.0)	2.5 (1.0-3.5)	5.5 (4.0-6.0)

TABLE 2 |

 2 Clinical outcomes and accelerometry data.

		Control	pwMS	RRMS	PP-/SPMS	EDSS ≤ 3.5	EDSS > 3.5
	Clinical test
	T25FW (s)	4.0 ± 0.6	5.6 ± 2.8	4.2 ± 0.8	6.8 ± 3.5	4.7 ± 0.8	7.8 ± 3.5
			(p < 0.001)		(p < 0.001)		(p = 0.020)
	FTSTS (s)	7.4 ± 1.7	11.7 ± 4.8	9.6 ± 1.7	13.8 ± 5.9	10.4 ± 2.7	15.8 ± 7.4
			(p < 0.001)		(p < 0.001)		(p = 0.039)
	TTW (s)	6.6 ± 1.8	11.8 ± 5.6	8.9 ± 3.3	14.4 ± 6.2	10.4 ± 4.1	15.8 ± 7.5
			(p < 0.001)		(p < 0.001)		(p = 0.030)
	2MWT (m)	201 ± 35	164 ± 38	179 ± 29	148 ± 40	176 ± 29	131 ± 41
			(p < 0.001)		(p = 0.002)		(p = 0.003)
	6MWT (m)	609 ± 75	477 ± 123	530 ± 86	424 ± 132	517 ± 90	362 ± 136
			(p < 0.001)		(p < 0.001)		(p = 0.002)
	PROMS
	GLTEQ	43.1 ± 27.1	25.1 ± 20.9	30.1 ± 21.6	20.6 ± 19.2	29.5 ± 20.9	13.9 ± 16.4
			(p < 0.001)		(p = 0.105)		(p = 0.014)
	FAI	34.6 ± 4.6	32.0 ± 6.1	32.4 ± 5.1	32.1 ± 6.8	33.6 ± 5.1	28.6 ± 6.7
			(p = 0.023)		(p < 0.868)		(p = 0.028)
	IPAQ	7,553 ± 7,454	6,314 ± 5,919	6,449 ± 5,451	6,188 ± 6,415	6,459 ± 5,059	5,900 ± 8,099
			(p = 0.347)		(p = 0.868)		(p = 0.805)
	MSWS-12		26.4 ± 13.9	18.8 ± 9.7	34.5 ± 12.5	21.0 ± 10.3	42.0 ± 9.4
					(p < 0.001)		(p < 0. 001)
	Wear time of actiGraph and smartphone
	Wear time ActiGraph minutes	4,498 ± 1,305	4,556 ± 1,692	4,056 ± 1,715	5,038 ± 1,547	4,638 ± 1,437	4,278 ± 2,407
			(p = 0.869)		(p = 0.036)		(p = 0.740)
	Wear time smartphone minutes	3,684 ± 1,390	2,769 ± 1,980	2,783 ± 1,582	2,557 ± 2,315	2,884 ± 2,071	2,081 ± 1,742
			(p < 0.005)		(p = 0.733)		(p = 0.171)
	Smartphone outcomes
	meanVM	9.86 ± 0.08	9.83 ± 0.07	9.83 ± 0.07	9.88 ± 0.07	9.85 ± 0.07	9.87 ± 0.09
			(p = 0.103)		(p = 0.007)		(p = 0.467)
	varVM	0.485 ± 0.26	0.264 ± 0.22	0.430 ± 0.19	0.103 ± 0.10	0.311 ± 0.23	0.138 ± 0.16
			(p < 0.001)		(p < 0.001)		(p = 0.007)
	ActiGraph outcomes
	meanVM	2,405 ± 714	2,286 ± 583	2,375 ± 580	2,205 ± 583	2,275 ± 592	2,319 ± 575
			(p = 0.347)		(p = 0.309)		(p = 0.805)
	Daily MVPA	218 ± 80	216 ± 83	189 ± 77	240 ± 81	213 ± 82	223 ± 88
			(p = 0.869)		(p = 0.023)		(p = 0.791)
	Steps/minute	13.5 ± 3.43	11.6 ± 2.80	12.1 ± 2.24	11.0 ± 3.17	11.8 ± 2.94	10.9 ± 2.24
			(p < 0.001)		(p = 0.165)		(p = 0.421)

Values represent mean ± SD if not otherwise indicated. p-values for group comparison (patients vs. controls, RRMS vs. PP-/SPMS and EDSS groups) based on chi-square test for rates or Student's t-test. p-values are corrected p-values through Benjamini-Hochberg. T25FW, Timed 25-Foot Walk; FTSTS, Five-Times Sit-to-Stand Test; TTW, 3-Meter Timed Tandem Walk; 2MWT, 2-Minute Walk Time; 6MWT, 6-Minute Walk Time; GLTEQ, Godin Leisure-Time Exercise Questionnaire; FAI, Frenchay Activity Index; IPAQ, International Physical Activity Questionnaire; MSWS-12, 12-Item MS Walking Scale; meanVM, mean vector magnitude; varVM, variance of vector magnitude; daily MVPA, daily moderate to vigorous physical activity.

TABLE 3 |

 3 Spearman rho rank correlations between ActiGraph and smartphone data.

			Control	pwMS
	Smartphone vs. actiGraph
	Smartphone (varVM)	ActiGraph (steps/minute)	0.478**** 0.288*
		ActiGraph (meanVM)	0.378**	0.201
		ActiGraph (daily MVPA)	0.169	-0.128
	Smartphone (meanVM)	ActiGraph (steps/minute)	0.003	0.058
		ActiGraph (meanVM)	-0.077	0.173
		ActiGraph (daily MVPA)	-0.162	0.327**
	Within accelerometer itself
	Smartphone (varVM)	Smartphone (meanVM)	0.102	-0.243
	ActiGraph (steps/minute)	ActiGraph (meanVM)	0.828**** 0.796****
	ActiGraph (steps/minute)	ActiGraph (daily MVPA)	0.533**** 0.325*
	ActiGraph (meanVM)	ActiGraph (daily MVPA)	0.727**** 0.443**
	FDR corrected p-values: *p < 0.05, **p < 0.01, ****p < 0.0001.

TABLE 4 |

 4 Sensitivity and specificity of accelerometry metrics to differentiate subgroups. ROC analyses. The area under the curve (AUC) results were compared with Delong's test. AUC, area under the curve; NPV, negative predictive value; PPV, positive predictive value.

	Groups	Accelerometry	AUC	Sensitivity	Specificity	NPV	PPV	Delong's test
								p-value
	Control vs. patient	ActiGraph steps/minute	0.683	0.746	0.586	0.618	0.719	p = 0.286
		Smartphone varVM	0.750	0.567	0.843	0.776	0.670
	RRMS vs. PP-SP/MS	ActiGraph steps/minute	0.613	0,767	0.484	0.696	0.575	p < 0.001
		Smartphone varVM	0.946	0.939	0.853	0.935	0.861
	Impaired vs. minimal impaired	ActiGraph steps/minute	0.567	0.426	0.750	0.308	0.833	p = 0.153
		Smartphone varVM	0.728	0.694	0.833	0.500	0.919

Frontiers in Neurology | www.frontiersin.org

August 2020 | Volume 11 | Article 688

ACKNOWLEDGMENTS

We thank Lilija Gutmann for her support.

DATA AVAILABILITY STATEMENT

The datasets generated for this study are available on request to the corresponding author.

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by Ärztekammer Hamburg. The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

YZ: design and conceptualized study, major role in the acquisition of data, analyzed the data, and drafted the manuscript for intellectual content. NN: major role in the acquisition of data and revised the manuscript for intellectual content. JP: interpreted the data and revised the manuscript for intellectual content. EG: major role in the acquisition of data, interpreted the data, and revised the manuscript for intellectual content. CH: interpreted the data and revised the manuscript for intellectual content. J-PS: design and conceptualized study, analyzed the data, and drafted the manuscript for intellectual content. All authors contributed to the article and approved the submitted version.

FUNDING

Parts of this study were funded by a grant from Biogen.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fneur. 2020.00688/full#supplementary-material Conflict of Interest: JP reports grants from Deutsche Rentenversicherung Bund and from MerckSerono outside the submitted work. CH reports grants and personal fees from Biogen, Genzyme, MerckSerono, Novartis, Roche outside the submitted work. J-PS receives research funding from Deutsche Forschungsgemeinschaft and reports a grant from Biogen that partially funded the submitted work. A further grant is reported from Genzyme outside the submitted work. J-PS received travel support and personal fees from Alexion, Biogen, and Genzyme outside the submitted work.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Jan-Patrick Stellmann
email: patrickstellmannjan-patrick.stellmann@univ-amu.fr

Adil Maarouf

Karl-Heinz Schulz

Lisa Baquet

Jana Pöttgen

Stefan Patra

Iris-Katharina Penner

Susanne Gellissen

Gesche Ketels

Pierre Besson

Xiaoluan Xia

Susanne Gellißen

Jean-Philippe Ranjeva

Maxime Guye

Guido Nolte

Andreas K Engel

Bertrand Audoin

Christoph Heesen

Stefan M Gold

A Randomized Controlled Trial

Keywords: CNS networks, neuroplasticity, exercise, multiple sclerosis, randomized controlled trial

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers,

Objectives: Evidence from animal studies suggests that aerobic exercise may promote neuroplasticity and could, therefore, provide therapeutic benefits for neurological diseases such as multiple sclerosis (MS). However, the effects of exercise in human CNS disorders on the topology of brain networks, which might serve as an outcome at the interface between biology and clinical performance, remain poorly understood.

Methods: We investigated functional and structural networks in patients with relapsingremitting MS in a clinical trial of standardized aerobic exercise. Fifty-seven patients were randomly assigned to moderate-intensity exercise for 3 months or a non-exercise control group. We reconstructed functional networks based on resting-state functional magnetic resonance imaging (MRI) and used probabilistic tractography on diffusionweighted imaging data for structural networks.

Results: At baseline, compared to 30 healthy controls, patients exhibited decreased structural connectivity that was most pronounced in hub regions of the brain. Vice versa, functional connectivity was increased in hubs. After 3 months, we observed hub independent increased functional connectivity in the exercise group while the control group presented a loss of functional hub connectivity. On a structural level, the control group remained unchanged, while the exercise group had also increased connectivity. Increased clustering of hubs indicates a better structural integration and internal connectivity at the top of the network hierarchy.

INTRODUCTION

Promoting neuroplasticity is an unmet need to counter neurodegeneration and disability in acute and chronic neurological diseases. Preclinical studies have provided accumulating evidence for the ''neuroregenerative'' potential of exercise e.g., by modulating molecular signaling pathways, neurogenesis, and cognitive performance in mice [START_REF] Aguiar | Short bouts of mild-intensity physical exercise improve spatial learning and memory in aging rats: involvement of hippocampal plasticity via AKT, CREB and BDNF signaling[END_REF][START_REF] Mattson | Intermittent metabolic switching, neuroplasticity and brain health[END_REF]. Moreover, exerciseinduced synaptogenesis and neurogenesis correlate with the intensity of exercise and are detectable in different brain areas including the motor cortex, cerebellum, and the hippocampus [START_REF] Biedermann | The hippocampus and exercise: histological correlates of MR-detected volume changes[END_REF][START_REF] Gutierrez | The effects of acrobatic exercise on brain plasticity: a systematic review of animal studies[END_REF]. First clinical studies have suggested that exercises may enhance cognitive performance-a putative clinical readout of neuroplasticity-in healthy individuals and patients with neurodegenerative diseases [START_REF] Hillman | Be smart, exercise your heart: exercise effects on brain and cognition[END_REF][START_REF] Hötting | Beneficial effects of physical exercise on neuroplasticity and cognition[END_REF][START_REF] Mak | Long-term effects of exercise and physical therapy in people with Parkinson disease[END_REF]. These beneficial effects of exercise can be framed as an evolutionary scenario that enhances brain function and resilience of neurons [START_REF] Mattson | Intermittent metabolic switching, neuroplasticity and brain health[END_REF].

Based on these observations, exercise has received increasing attention as a putatively disease-modifying treatment for complex human CNS disorders. Multiple sclerosis (MS) is an inflammatory, demyelinating CNS disorder with a pronounced neurodegenerative component and can serve as a paradigmatic disease model of CNS network disruption [START_REF] Reich | Multiple sclerosis[END_REF]. Indeed, some experimental evidence suggests that exercise can protect mice from MS-like disease [START_REF] Klaren | Effects of exercise in experimental autoimmune encephalomyelitis (an animal model of multiple sclerosis)[END_REF]. In MS, a couple of small clinical trials have indicated that short-term exercise intervention can improve cognition, mobility, and other symptoms of MS [START_REF] Motl | Effect of exercise training on quality of life in multiple sclerosis: a meta-analysis[END_REF][START_REF] Dalgas | Exercise and disease progression in multiple sclerosis: can exercise slow down the progression of multiple sclerosis?[END_REF][START_REF] Briken | Effects of exercise on fitness and cognition in progressive MS: a randomized, controlled trial[END_REF][START_REF] Heine | Exercise therapy for fatigue in multiple sclerosis[END_REF][START_REF] Motl | Exercise in patients with multiple sclerosis[END_REF][START_REF] Kjølhede | Can resistance training impact MRI outcomes in relapsing-remitting multiple sclerosis?[END_REF]. However, the mechanisms of action of exercise on brain structure and function remain elusive.

Combining structural and functional magnetic resonance imaging (MRI) is a feasible method to investigate morphology, microstructure, and large scale organization of the human brain [START_REF] Hötting | Beneficial effects of physical exercise on neuroplasticity and cognition[END_REF][START_REF] Hamaide | Neuroplasticity and MRI: a perfect match[END_REF][START_REF] Oberlin | White matter microstructure mediates the relationship between cardiorespiratory fitness and spatial working memory in older adults[END_REF][START_REF] Suo | Therapeutically relevant structural and functional mechanisms triggered by physical and cognitive exercise[END_REF][START_REF] Kjølhede | Can resistance training impact MRI outcomes in relapsing-remitting multiple sclerosis?[END_REF]. Over the last decade, MRI based functional and structural network analyses have deepened our understanding of the complex organization of the human brain and they have been recommended as an outcome measure for brain diseases including MS [START_REF] Kaiser | The potential of the human connectome as a biomarker of brain disease[END_REF][START_REF] Griffa | Structural connectomics in brain diseases[END_REF][START_REF] Betzel | Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivity[END_REF][START_REF] Fornito | The connectomics of brain disorders[END_REF][START_REF] Bede | Deciphering neurodegeneration[END_REF][START_REF] Yuan | Brain hubs in lesion models: predicting functional network topology with lesion patterns in patients[END_REF]. A fundamental feature is the hierarchical organization of neuronal networks in so-called hubs or rich-club nodes on one hand and peripheral nodes on the other (van den [START_REF] Van Den Heuvel | Rich-club organization of the human connectome[END_REF].

Hubs have an essential managing and integrating role in the brain, show altered connectivity in several neurological diseases, and explain disability better than other MRI metrics [START_REF] Achard | Hubs of brain functional networks are radically reorganized in comatose patients[END_REF][START_REF] Collin | Connectome organization is related to longitudinal changes in general functioning, symptoms and IQ in chronic schizophrenia[END_REF][START_REF] Daianu | Disrupted rich club network in behavioral variant frontotemporal dementia and early-onset alzheimer's disease[END_REF][START_REF] Stellmann | Reduced rich-club connectivity is related to disability in primary progressive MS[END_REF]. Based on the combined evidence from preclinical research and the potential of network analysis to interrogate brain organization, we aimed to decipher how exercise impacts functional and structural CNS reorganization in MS and how such changes are related to the network topology [START_REF] Tewarie | Disruption of structural and functional networks in long-standing multiple sclerosis[END_REF][START_REF] Enzinger | Longitudinal fMRI studies: exploring brain plasticity and repair in MS[END_REF][START_REF] Rocca | Impaired functional integration in multiple sclerosis: a graph theory study[END_REF][START_REF] Schoonheim | Functional reorganization is a maladaptive response to injury-commentary[END_REF].

MATERIALS AND METHODS

Patients and Study Design

The AERCONN trial [Exercise in Multiple Sclerosis: Effects on Cognitive Function and Brain Connectivity (NCT02005237)] is a rater-blinded, 1:1 randomized, controlled trial of aerobic exercise training over 3 months in MS compared to a waitlist control group. Details on trial design, intervention, and clinical outcomes have been published before [START_REF] Baquet | Short-term interval aerobic exercise training does not improve memory functioning in relapsing-remitting multiple sclerosis-a randomized controlled trial[END_REF]. Briefly, we recruited patients with relapsing-remitting MS (RRMS) according to the McDonald criteria 2010 [START_REF] Polman | Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria[END_REF] without relapse or disability progression during the last 3 months (January 2013-November 2015). A bicycle ergometer training was tailored to the individual level of physical fitness and consisted of 2-3 supervised sessions per week for 12 weeks. Out of 68 recruited patients, 58 had a complete baseline and follow-up assessment including cognition, fitness, and disability: EDSS (expanded disability status scale), SDMT (symbol digit modality test), VLMT (verbal learning and memory test), PASAT (Paced Auditory Serial Addition Test), PA (Phasic altertness), TA (Tonic altertness), Digit_bw (Digit backwards), Digit_fw (Digits forward), T25FW (timed 25 foot walk), 6MWT (6 min Walking Test), VO2_peak and VO2 max/kg. The study did not reach the primary endpoint to improve performance in the VLMT [START_REF] Helmstädter | VLMT Verbaler Lern-und Merkfähigkeitstest: VLMT Manual[END_REF], failed to improve other clinical endpoints but showed an improvement in physical fitness. A predefined explorative analysis of the trial was to investigate effects of aerobic exercise on functional and structural connectivity. To follow this aim, we pursued the previous hypothesis and methods developed in our group [START_REF] Faivre | Depletion of brain functional connectivity enhancement leads to disability progression in multiple sclerosis: a longitudinal restingstate fMRI study[END_REF][START_REF] Stellmann | Reduced rich-club connectivity is related to disability in primary progressive MS[END_REF]. We predefined a blinded graph theoretical analysis of hub dependency as an outcome of interest. Data from 57 patients could be included as MRI assessments were missing in one patient. Thirty healthy controls were included as a reference from an observational cohort study with the same MRI assessments (flow diagram as

MRI Data Acquisition and Processing

A detailed description of sequences and processing steps are available in the Supplementary Material. Briefly, MRI data were acquired with a 3T scanner and the protocol included T1/T2 weighted sequences, diffusion tensor imaging (DTI, 32 directions), and 10 min of resting-state activity as represented by the BOLD signal. We used T2 images for lesion mapping and applied the longitudinal stream [START_REF] Reuter | Within-subject template estimation for unbiased longitudinal image analysis[END_REF] in FreeSurfer software (Version 5.2.0) to extract 80 cortical regions per hemisphere for our structural and functional connectivity analyses. We used an established pipeline [START_REF] Besson | NeuroImage Structural connectivity differences in left and right temporal lobe epilepsy[END_REF] to build individual structural networks based on wholebrain probabilistic fiber tracking with average FA along the tracts as edge weight. To reconstruct individual functional connectomes, we adapted an existing processing pipeline [START_REF] Wirsich | Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy[END_REF] by computing Pearson-correlation between each region's wavelet coefficient time series, thresholding, and binarizing networks assuring better comparability of networks [START_REF] Achard | Hubs of brain functional networks are radically reorganized in comatose patients[END_REF].

Descriptive Statistics, Graph Metrics, and Hub Disruption Index

We performed descriptive statistics with a mean (SD) or median (range) corresponding to the nature of the data. We computed individual global and nodal graph metrics (igraph and tnet packages in R; [START_REF] Csardi | The igraph software package for complex network research Int[END_REF][START_REF] Opsahl | Structure and Evolution of Weighted Networks[END_REF]. Global graph metrics of structural networks included global strength (i.e., the sum of edge weights in each network) as a measure of total connectivity in the network, the global clustering coefficient, and the average shortest path length (APL) between all nodes. For functional connectomes, we computed the clustering coefficient and APL. On a node level, we extracted strength, weighted betweenness and weighted clustering using the arithmetic mean method [START_REF] Opsahl | Clustering in weighted networks[END_REF] for structural networks. Functional node metrics included degree, global efficiency (Eglob), and local efficiency (Eloc; [START_REF] Achard | Hubs of brain functional networks are radically reorganized in comatose patients[END_REF].

The hub disruption index κ allows to investigate topology dependent network alterations and has originally been defined as the linear regression estimate between the mean degree of the nodes in controls (x-axis) and the difference between the mean degree of nodes in comatose patients and controls (y-axis; [START_REF] Achard | Hubs of brain functional networks are radically reorganized in comatose patients[END_REF]. We followed this approach by defining the order of the nodes, which is the representation of the topology, according to the mean degree in functional networks of controls and similar by the strength of nodes in structural networks. κ slope is the slope estimate and determines the topological dependency of abnormalities. κ inter is the intercept of the regression estimate and represents a global increase or decrease in connectivity if κ slope is not significant. For changes in the hub disruption index, we computed for each node the differences between baseline values and month 3 values (labeled as ∆ 0-3 ). To preserve a standardized topological representation overall analyses, controls' degree and strength served always as reference. In addition to group-level analysis, we computed individual hub disruption indices by regressing individual nodal values against the reference values from controls.

Statistics

For group comparison, we used either Student's t-test or Chi-Square test. Differences between groups in the averaged hub disruption indices as well as changes over time were investigated with ANOVA. For individual data, we applied a linear mixed effect model with random intercepts to investigate group differences. Finally, we computed Pearson correlation coefficients to investigate the association between graph metrics and clinical data, respectively their changes. After false discovery rate correction, p-values below 0.05 were considered statistically significant.

RESULTS

Markers of Global Network Organization

At baseline, MS patients showed lower global structural connectivity compared to healthy controls as measured by the total strength (p < 0.001, Supplementary Figure S1A). Structural clustering and APL, however, were similar between patients and controls (p = 0.924 and p = 0.500, respectively). In functional networks, patients had a higher clustering coefficient (p = 0.006) and a shorter APL (p = 0.038), both indicating a more densely connected network topology. However, these metrics did not change or differ between the exercise and waiting group at follow-up (Supplementary Figure S1B). We also observed no group differences for changes in lesion volume or total brain volume (Supplementary Figure S2).

Topology Dependent Network Organization

Applying the hub disruption index, we observed baseline differences between MS patients and controls indicating specific topological abnormalities in functional and structural connectomes in MS. Functional connectivity rose with ascending hubness of the nodes (κ slope positive, p < 0.001, Figure 1A). Investigating structural connectomes, we observed an opposing effect with a pronounced loss of connectivity in hubs in MS (p < 0.001, Figure 2A).

Next, we examined the effects of exercise on these network parameters. Topology dependent changes in functional connectivity were observed between the waiting and the exercise group (time × group interaction p < 0.001, Figures 1B,C). A negative κ slope (p = 0.018) indicated a decrease in hub connectivity in the waiting group. In contrast, the exercise group showed no differences between hubs and non-hubs (κ slope = -0.01, p = 0.819) but a topology independent increase in connectivity (κ inter = 2.30, p = 0.002). We observed similar results for structural connectivity with a group difference (time × group interaction p < 0.001): A topology independent increase of connectivity (κ inter = 1.26, p = 0.003) was detected in the exercise group with somewhat less pronounced effects in hub regions (κ slope = -0.02, p = 0.063). In contrast, we did not detect any significant changes from baseline for the waiting group (Figures 2B,C). Further graph metrics in functional networks provided less marked results for global and local efficiency of nodes (details are summarized in the Supplementary Material).

The local clustering coefficient in structural networks is usually inversely correlated with the node hubness as found also in our cohorts (MS: r = -0.69, p < 0.001; Controls: r = -0.72, p < 0.001). At baseline, both patient groups showed an increased local clustering pronounced in non-hubs. After 3 months of exercise, we observed an increase in local clustering (κ inter = 0.06, p < 0.001) that was accentuated in hubs (κ slope = 0.07, p < 0.001) indicating a better structural integration and internal connectivity at the top of the network hierarchy. The control group did not show any changes from baseline (time × group interaction p < 0.001, Figure 3).

On an individual level, a group difference (p = 0.026) with a pronounced loss of node degree in hubs of the waiting control group compared to the exercise group confirmed group-level results for functional connectomes. No group differences were seen for the strength of nodes in structural connectomes (p = 0.345) while increased hub clustering in the exercise group compared to the control group was also confirmed (p = 0.007).

The Association Between Functional and Structural Connectivity

To elucidate the association between functional and structural reorganization of networks, we investigated correlations between nodal graph metrics. Here, no associations were observed between changes in structural and functional metrics (Supplementary Results and Supplementary Figure S5). It should be noted that node metrics are also summary statistics and might not sufficiently account for the underlying reorganization within the network. To interrogate such effects, we extended our analysis to the edge level of the structural networks (see Supplementary Results and Supplementary Figure S6). We found the increase in connectivity to be accentuated in interhemispherical connections while deep gray matter connections seem to be unchanged.

To link structural and functional reorganization, we aimed to compare how efficient new functional connections are structurally wired. The length of the shortest structural path indicates the wiring costs between two nodes. Thus, we computed the differences in path lengths between baseline and month 3 for each pair of nodes. Overall, both groups had a gain in path length (p < 0.001), i.e., lower wiring costs. However, the gain was more pronounced in the exercise group (1.5% vs. 1.3%, p < 0.001).

Association Between Network Metrics and Clinical Parameters

The associations between clinical outcomes of disability, cognition, mobility, and fitness and network metrics at baseline are summarized in Figure 4. On a global level, we observed mainly associations of network strength with EDSS, SDMT, VLMT, T25FW, and physical fitness (Figure 4A). In contrast, individual κ slope linked increased functional hub connectivity with higher fitness and walking tests at baseline (Figure 4B). This short-term study failed to reach the primary endpoint and only physical fitness increased in the exercise group over 3 months. Thus, we did not expect to find a strong association between clinical outcomes and network metrics. However, we observed weak associations between EDSS change and increased structural strength (rho = -0.28, p = 0.039) and shorter APL (rho = 0.29, p = 0.038). Out of the individual hub disruption indices, only higher betweenness of hubs was correlated with better VLMT performance (rho = 0.33, p = 0.013).

DISCUSSION

Besides clinical endpoints, the AERCONN study was designed to explore the impact of an exercise intervention on functional and structural connectivity in mildly disabled patients with RRMS. Aerobic exercise was chosen as the best available framework to investigate neuroplasticity [START_REF] Hötting | Beneficial effects of physical exercise on neuroplasticity and cognition[END_REF]. We observed an increase of structural and functional connectivity induced by a moderate aerobic exercise training over 3 months. We were able to show that these network changes follow distinctive patterns that are associated with the fundamental hierarchical organization of the brain networks (van den Heuvel and Sporns, 2011). Moreover, exercise seems to increase the efficiency of new functional connections utilizing their structural wiring costs.

Topology Dependent Alteration of Brain Networks in MS

Within recent years, investigating the topology of brain networks and their alterations in development, aging, neuroinflammatory and neurodegenerative diseases including MS has received increasing attention [START_REF] Yuan | Brain hubs in lesion models: predicting functional network topology with lesion patterns in patients[END_REF]. The most prominent nodes in these networks, which act as managing nodes with an important integrative role for the overall network performance are of special interest [START_REF] Griffa | Structural connectomics in brain diseases[END_REF][START_REF] Collin | Impaired rich club connectivity in unaffected siblings of schizophrenia patients[END_REF][START_REF] Tewarie | Disruption of structural and functional networks in long-standing multiple sclerosis[END_REF][START_REF] Baker | Developmental changes in brain network hub connectivity in late adolescence[END_REF][START_REF] Daianu | Disrupted rich club network in behavioral variant frontotemporal dementia and early-onset alzheimer's disease[END_REF][START_REF] Rocca | Impaired functional integration in multiple sclerosis: a graph theory study[END_REF][START_REF] Li | Abnormal rich club organization and impaired correlation between structural and functional connectivity in migraine sufferers[END_REF][START_REF] Meijer | Increased connectivity of hub networks and cognitive impairment in multiple sclerosis[END_REF][START_REF] Stellmann | Reduced rich-club connectivity is related to disability in primary progressive MS[END_REF][START_REF] Wierenga | A multisample study of longitudinal changes in brain network architecture in 4-13-year-old Children[END_REF]. Our findings underline the feasibility of such an approach in clinical settings. Compared to healthy individuals, we observed a distinctive pattern of network alteration in MS patients at baseline, as the loss of structural connectivity was pronounced in hub regions. This observation is in line with a previous study in primary progressive MS from our group [START_REF] Stellmann | Reduced rich-club connectivity is related to disability in primary progressive MS[END_REF] and points towards a specific network pathology of MS. Interestingly, this alteration of the network topology is not detectable with global network metrics such as APL or global clustering-even though total structural connectivity is lower in MS. This discrepancy has been observed before and can be explained by the robustness of natural scale-free networks against local and diffuse attacks [START_REF] Aerts | Brain networks under attack: robustness properties and the impact of lesions[END_REF][START_REF] Stellmann | Reduced rich-club connectivity is related to disability in primary progressive MS[END_REF]. Metrics like APL are only altered if the large-scale organization is severely affected. For example on a functional level, even comatose patients do not show an alteration of global graph metrics in comparison to healthy controls [START_REF] Achard | Hubs of brain functional networks are radically reorganized in comatose patients[END_REF].

A recent study also linked spatial lesion distributions in neurological diseases to a pronounced alteration of connectivity in the managing regions of the brain [START_REF] Yuan | Brain hubs in lesion models: predicting functional network topology with lesion patterns in patients[END_REF]. As MS lesions show a typical spatial distribution, a disease-specific pattern is not surprising. Trans-synaptic anterograde and retrograde neurodegeneration is a known pathophysiological process in MS [START_REF] Gabilondo | Trans-synaptic axonal degeneration in the visual pathway in Multiple Sclerosis[END_REF] and explains the promotion of connectivity loss in the top of the hierarchy independent from the actual distance between lesions and hubs.

The structural alteration was accompanied by increased functional connectivity in hub regions. The inverse relationship between structure and function might indicate a compensatory mechanism. However, increased connectivity has been described before as typical observation in MS (especially in early disease stages) and it remains controversial if such changes should be interpreted as adaptive or maladaptive [START_REF] Audoin | Compensatory cortical activation observed by fMRI during a cognitive task at the earliest stage of MS[END_REF][START_REF] Meijer | Increased connectivity of hub networks and cognitive impairment in multiple sclerosis[END_REF][START_REF] Schoonheim | Functional reorganization is a maladaptive response to injury-commentary[END_REF]. While positive associations of clinical performance with higher activation levels might indicate a beneficial effect, others argue that high energy costs might be unfavorable given the importance of mitochondrial dysfunction and energy consumption in neurodegeneration [START_REF] Lin | Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases[END_REF][START_REF] Campbell | The central role of mitochondria in axonal degeneration in multiple sclerosis[END_REF][START_REF] Schoonheim | Functional reorganization is a maladaptive response to injury-commentary[END_REF]. Here, we observed at baseline an association between increased functional hub connectivity and better physical fitness pointing towards a rather beneficial effect. However, the vast majority of data are cross-sectional and the need to investigate functional reorganization longitudinally has been emphasized [START_REF] Schoonheim | Network collapse and cognitive impairment in multiple sclerosis[END_REF][START_REF] Enzinger | Longitudinal fMRI studies: exploring brain plasticity and repair in MS[END_REF][START_REF] Schoonheim | Functional reorganization is a maladaptive response to injury-commentary[END_REF].

Topology Dependent Effects of Exercising on Brain Networks in MS

Our study provides insight into topology-dependent changes in structural and functional reorganization over 3 months. We observed a global increase in functional connectivity associated with a similar increase in the structural connectome under the exercise condition. An increased clustering of hub nodes indicated that such changes are reflecting the topology of network alterations observed at baseline. Thus, considering exercise as a beneficial and neuroprotective framework, increasing functional connectivity under-exercising seems rather reflect an adaptive mechanism than a maladaptive one.

In contrast to our findings, a recent cross-sectional study described a peripheral increase or non-hub increase in cognitively impaired patients, while cognitively preserved patients showed a similar pattern as controls [START_REF] Meijer | Increased connectivity of hub networks and cognitive impairment in multiple sclerosis[END_REF]. The patients enrolled in our trial were still in the early disease stage, younger and less disabled, both physically and cognitively. The peripheral increase might, therefore, be explained as a feature of the later disease course where the adaptive increase in hub connectivity is exhausted. This explanation is supported by the observation, that our control group already showed a decrease in functional hub connectivity within 3 months. Moreover, a first longitudinal study observed an association between the loss of increased baseline connectivity and disability progression in MS [START_REF] Faivre | Depletion of brain functional connectivity enhancement leads to disability progression in multiple sclerosis: a longitudinal restingstate fMRI study[END_REF]. Thus, one might hypothesize that increased functional connectivity represents a basic adaptive principle that only translates to sustained neuroplasticity on a molecular or neuronal level in a biophysiological context like exercising [START_REF] Thomas | NeuroImage Multi-modal characterization of rapid anterior hippocampal volume increase associated with aerobic exercise[END_REF].

Changes in structural connectivity in our exercise cohort represent primarily increased fractional anisotropy within the white matter, which is known to correlate with axonal density and myelinization and thus support our interpretation of exercise-induced neuroplasticity on a biological level [START_REF] Mallik | Imaging outcomes for trials of remyelination in multiple sclerosis[END_REF]. The suspected mechanism of action is in line with previous findings that describe white matter microstructure, as assessed with diffusion tensor imaging, as an important mediator between physical fitness and cognition [START_REF] Oberlin | White matter microstructure mediates the relationship between cardiorespiratory fitness and spatial working memory in older adults[END_REF][START_REF] Sexton | A systematic review of MRI studies examining the relationship between physical fitness and activity and the white matter of the ageing brain[END_REF]. Finally, there is recent evidence that increased neuronal activity, as represented by increased functional connectivity, induces oligodendrogenesis, and adaptive remyelination that is furthermore associated with behavior [START_REF] Gibson | Oligodendrogenesis and adaptive myelination in the mammalian brain[END_REF]. The latter mechanism might also explain the gain in the path length of new functional connections that we observed in the waiting group.

LIMITATIONS

Our study has several limitations. With only two visits, 3 months apart, our study cannot address whether structural changes follow functional reorganization or vice versa. As increased co-activation of brain regions can already be observed after a single session of exercising in healthy individuals [START_REF] Perini | Acute effects of aerobic exercise promote learning[END_REF], it seems reasonable to expect functional changes to occur before structural changes. Moreover, the short follow-up does not allow for conclusions about sustained neuroplasticity and long-term effects in a chronic disease such as MS. As our trial failed to show significant effects on clinical outcomes, our analyses were further restricted, as we were only able to detect weak associations between network changes and disability. Whether network adaptation can predict subsequent clinical improvement thus remains to be addressed in future trials. Moreover, our sample size was rather small, and as a result, we might have missed weak effects. Finally, without a healthy control group undergoing the same intervention, we were not able to distinguish between general and MS-specific effects of exercising on brain networks.

Taken together, we here provide evidence that increased functional connectivity of hubs seems to represent a fundamental adaptive mechanism of the brain to compensate for a loss of structural connectivity in neurological diseases. Moreover, regular exercise may provide a biophysiological framework that induces repair of disturbed structural connections in complex human brain disorders such as MS.

FIGURE 1 |

 1 FIGURE 1 | Reorganization of functional connectivity. Reorganization of functional connectivity based on the adapted hub disruption index. (A) Baseline: the mean degree of nodes from controls is plotted against the difference between mean baseline values from patients and controls. (B) Mean differences from baseline to month 3 (∆0-3) in both patient groups are plotted against mean values from healthy controls. (C) Changes of a degree from baseline, node size indicates absolute change while the color indicates the direction.

FIGURE 2 |

 2 FIGURE 2 | Reorganization of structural connectivity. Reorganization of structural connectivity based on the adapted hub disruption index. (A) Baseline: the mean strength of nodes from controls is plotted against the difference between mean baseline values from patients and controls. (B) Mean differences from baseline to month 3 (∆0-3) in both patient groups are plotted against mean values from healthy controls. (C) Changes of strength from baseline, node size indicates absolute change while the color indicates the direction.

FIGURE 3 |

 3 FIGURE 3 | Clustering of nodes in structural networks: Baseline and changes. Nodal clustering in structural networks analyzed with the adapted hub disruption index. (A) Baseline: mean differences of the clustering coefficient between patients and controls plotted vs. strength of the nodes as an indicator of the hubness. (B) Mean differences from baseline to month 3 (∆0-3) are plotted against mean strength from healthy controls for both patient groups. (C) Changes of clustering from baseline, node size indicates absolute change while the color indicates the direction.

FIGURE 4 |

 4 FIGURE 4 | Association between graph metrics and clinical scores. Baseline correlations between clinical outcomes and global network metrics (top, A), respectively individual hub disruption (bottom, B). Bright colors indicate p-values < 0.05, asterisks indicate p-values below 0.05 after FDR correction. EDSS, expanded disability status scale; SDMT, symbol digit modality test; VLMT, Verbal learning and memory test; Digit_bw, digit span backward; Digit_fw, digit span forward; TA, tonic alertness; PA, phasic alertness; PASAT, Paced Auditory Serial Addition Test; T25FW = timed 25 foot walk; 6MWT, 6-Minute-Walking-Test; VO2max/kg, VO2 max per kg body weight.

TABLE 1 |

 1 Descriptive statistics.

		Waiting group N = 27	Exercise group N = 30	p-value	Healthy controls N = 30	p-value
	Females n (%)	21 (77.8)	18 (60.0)	0.248 §	20 (66.7)	0.353
	Age years	40.0 (9.8)	38.3 (10.1)	0.530 #	40.6 (8.7)	0.477
	Disease duration years	5.3 (6.3)	6.1 (5.5)	0.646 #
	Right handed n (%)	25 (93.0)	28 (93.3)	0.259 §	28 (93.3)	1.000
	Education >12 years	21 (77.8)	24 (80.0)	0.897 §	19 (63.3)	0.055
	Non smokers n (%)	17 (63.0)	24 (80.0)	0.305 §	24 (80.0)	0.293
	Immunotherapy n (%)	11 (40.7)	19 (63.3)	0.150 §
	Disability
	EDSS median (range)	1.5 (0-3.5)	1.5 (0-3.5)	0.574 §
	SDMT correct answers	58.0 (9.9)	60.0 (11.8)	0.491 #
	T25FW seconds	4.8 (0.8)	4.6 (0.8)	0.434 #
	6MWT meters	437 (78)	441 (107)	0.851 #
	FSMC score	49.4 (22.0)	49.3 (21.1)	0.990 #
	HADS Depression score	3.6 (3.6)	3.5 (3.3)	0.949 #
	Fitness
	Weight kg	68.1 (10.6)	80.5 (19.9)	0.005 #
	VO 2 -peak ml kg -1 min -1	25.4 (5.2)	28.5 (7.5)	0.078 #
	Training sessions median (range)	0	22 (10-36)

EDSS, Expanded Disability Status Scale (Global disability, range 0-10); SDMT, Symbol Digit Modality Test (Cognition); T25FW, Timed 25 Foot Walk (Mobility); 6MWT, 6-Minute Walking Test (Mobility); FSMC, Fatigue Scale for Motor and Cognitive Functions; HADS, Hospital Anxiety and Depression Scale; Data as mean (sd) if not otherwise indicated, § = Chi-Square-Test, # = Student's t-Test. Supplementary Material). Demographic data are summarized in Table 1. The ethics committee of the Hamburg Chamber of Physicians approved the trial (Registration Number PV4356) and all participants gave written consent before any testing under the protocol.

Frontiers in Human Neuroscience | www.frontiersin.org

June 2020 | Volume 14 | Article 255

FUNDING

This study was supported through the NEU 2 consortium by grant 161A130 (NEUCONN to AE, SMG, K-HS, and CH) within the Biopharma Initiative (Bundesministerium für Bildung und Forschung, BMBF). Additional funding was provided by the Deutsche Forschungsgemeinschaft (Research Fellowship STE 2593/1-1 to J-PS and Heisenberg Professorship GO1357/5-2 to SMG) and the National MS Society [Mentor Based Postdoctoral Fellowship Program National Multiple Sclerosis Society (NMSS) Grant No.: 1359477155683 to CH]. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.

DATA AVAILABILITY STATEMENT

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

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by Ethics committee of the Hamburg Chamber of Physicians (Registration Number PV4356). The patients/participants provided their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS

J-PS, JP, K-HS, GN, AE, CH, and SMG have analyzed and interpreted data, drafted/revised the manuscript for content, and have contributed to the study concept and design. AM, PB, J-PR, MG, and BA have analyzed and interpreted data and have revised the manuscript for content. LB, SP, I-KP, SG, and GK have contributed to the study concept and design, the conduction of the trial and have revised the manuscript for content.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnhum. 2020.00255/full#supplementary-material.

Conflict of Interest: J-PS receives research funding from Deutsche Forschungsgemeinschaft and reports grants from Alexion, Biogen and Genzyme outside the submitted work. JP reports grants from Deutsche Rentenversicherung Bund outside the submitted work. I-KP has received honoraria for speaking at scientific meetings, serving at scientific advisory boards and consulting activities from Adamas Pharma, Almirall, Bayer Pharma, Biogen, Genzyme, Merck Serono, Novartis, and Teva. She has received research support from Merck Serono, Novartis, the German MS Society, and Teva. I-KP is the founder of the company COGITO GmbH, Zentrum für Angewandte Neurokognition und Neuropsychologische Forschung, Merowingerplatz 1, 40225 Düsseldorf, Germany. AE receives research funding from the Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, and the EU. CH reports grants and personal fees from Biogen, personal fees from Genzyme, grants and personal fees from Novartis, grants from Merck Serono, outside the submitted work. SG reports honoraria from Mylan GmbH and Almirall S.A., research grants from Biogen, outside the submitted work. He receives research funding from the Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, and the National MS Society.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.