Longitudinal Network Changes and Conversion to Cognitive Impairment in Multiple Sclerosis

Huiskamp, Marijn; Eijlers, A. J. C.; Broeders, Tommy A.A.; Pasteuning, Jasmin; Dekker, Iris; Uitdehaag, Bernard M. J.; Barkhof, Frederik; Wink, Alle Meije; Geurts, Jeroen J. G.; Hulst, Hanneke E.; Schoonheim, Menno M.

doi:10.1212/wnl.0000000000012341

Cited by 28 publications

(40 citation statements)

References 38 publications

(67 reference statements)

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Section: Methodsmentioning

confidence: 99%

“…This study involves a retrospective analysis of prospectively attained longitudinal data from the Amsterdam multiple sclerosis cohort, 23 , 24 including a total of 332 multiple sclerosis patients and 96 healthy controls (HCs) with available functional MRI data who were recruited between 2008 and 2012. 12–14 , 24–29 Functional network dynamics in these participants was described previously. 14 In total, 234 multiple sclerosis patients and 60 HCs returned for a 5-year follow-up between 2014 and 2017, of whom rs-fMRI and neuropsychological assessment was available at both time points for 230 patients (48 ± 11 years; 83 male) and 59 HCs (46 ± 10 years; 28 male).…”

Section: Methodsmentioning

confidence: 99%

“…The same approach was applied again to classify patients based on the follow-up data, these classifications were exclusively used for a validation analysis. 24 Classification of longitudinal cognitive change has been described previously 23 and was based on the practice-corrected reliable change index 34 adjusted for the time-interval between baseline and follow-up. Patients with yearly change rates of more than 0.25 on two or more cognitive domains were considered cognitively declining and all others as cognitively stable.…”

Section: Methodsmentioning

confidence: 99%

“…The scanner underwent a major upgrade between baseline and follow-up, which was corrected for using time-point specific z -scores based on the distribution of HCs for all longitudinal analyses, as reported previously. 24 The scanning protocol included a 3D T 1 -weighted (3DT1) fast-spoiled gradient-echo sequence [repetition time (TR)/echo time (TE) = 7.8/3 ms; inversion time = 450 ms; flip angle = 12°; sagittal slice thickness = 1.0 mm; in-plane resolution = 0.9 × 0.9 mm], a 3D T 2 -weighted fluid-attenuated inversion recovery (FLAIR) sequence (TR/TE = 8000/125 ms; inversion time = 2350 ms; sagittal slice thickness = 1.2 mm; in-plane resolution =1.0 × 1.0 mm), a rs-fMRI echo planar imaging sequence (202 volumes; TR/TE = 2200/35 ms; flip angle = 80°; axial slice thickness = 3 mm, contiguous; in-plane resolution = 3.3 × 3.3 mm) and a diffusion tensor imaging sequence using five volumes without directional weighting ( b = 0 s/mm 2 ) and 30 with non-collinear diffusion gradients ( b = 1000 s/mm 2 , TR/TE = 13000/91 ms, flip angle = 90°, axial slice thickness = 2.4 mm, contiguous; in-plane resolution = 2 × 2 mm).…”

Section: Methodsmentioning

confidence: 99%

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A more unstable resting-state functional network in cognitively declining multiple sclerosis

Broeders

Douw

Eijlers

et al. 2022

Brain Communications

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Cognitive impairment is common in people with multiple sclerosis and strongly affects their daily functioning. Reports have linked disturbed cognitive functioning in multiple sclerosis to changes in the organization of the functional network. In the healthy brain, communication between brain regions and which network a region belongs to is continuously and dynamically adapted to enable adequate cognitive function. However, this dynamic network adaptation has not been investigated in multiple sclerosis, and longitudinal network data remains particularly rare. Therefore, the aim of this study was to longitudinally identify patterns of dynamic network reconfigurations that are related to the worsening of cognitive decline in multiple sclerosis. Resting-state functional MRI and cognitive scores (expanded Brief Repeatable Battery of Neuropsychological tests) were acquired in 230 patients with multiple sclerosis and 59 matched healthy controls, at baseline (mean disease duration 15 years) and at 5 year follow-up. A sliding-window approach was used for functional MRI analyses where brain regions were dynamically assigned to one of seven literature-based subnetworks. Dynamic reconfigurations of subnetworks was characterised using measures of promiscuity (number of subnetworks switched to), flexibility (number of switches), cohesion (mutual switches) and disjointedness (independent switches). Cross-sectional differences between cognitive groups and longitudinal changes were assessed, as well as relations with structural damage and performance on specific cognitive domains. At baseline, 23% of patients were cognitively impaired (≥ 2/7 domains Z< -2) and 18% were mildly impaired (≥ 2/7 domains Z< -1.5). Longitudinally, 28% of patients declined over time (0.25 yearly change on ≥ 2/7 domains based on reliable change index). Cognitively impaired patients displayed more dynamic network reconfigurations across the whole brain compared to cognitively preserved patients and controls, i.e., showing higher promiscuity (p=0.047), flexibility (p=0.008) and cohesion (p=0.008). Over time, cognitively declining patients showed a further increase in cohesion (p=0.004), which was not seen in stable patients (p=0.544). More cohesion was related to more severe structural damage (average r=0.166, p=0.015) and worse verbal memory (r=-0.156, p=0.022), information processing speed (r=-0.202, p=0.003) and working memory (r=-0.163, p=0.017). Cognitively impaired multiple sclerosis patients exhibited a more unstable network reconfiguration compared to preserved patients, i.e. brain regions switched between subnetworks more often, which was related to structural damage. This shift to more unstable network reconfigurations was also demonstrated longitudinally in patients that demonstrated cognitive decline only. These results indicate the potential relevance of a progressive destabilization of network topology for understanding cognitive decline in multiple sclerosis.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A more unstable resting-state functional network in cognitively declining multiple sclerosis

Broeders

Douw

Eijlers

et al. 2022

Brain Communications

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“…Decreased anticorrelation between DMN and attention networks was related to worse behavioral performance in HC 55 , moreover increased functional connectivity between VAN and DMN was observed in pwMS who had cognitive impairment compared to those without cognitive impairment and HC. 56 Even though we did not have cognitive scores for our cohort, previous studies have identified a strong association between disability and cognitive impairment in MS. 57 Our results suggest that dysfunction of the interaction between the VAN and DMN states may result in greater energetic demand between these states in pwMS with evidence of disability compared to the other groups. Finally, our results also showed greater transition energy between SOM+ and VIS-was associated with pwMS who had disability.…”

Section: Discussionmentioning

confidence: 96%

Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity

Tozlu

Card²,

Jamison

et al. 2022

Preprint

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Background: Neuronal loss, demyelination, and inflammation in the brains of people with multiple sclerosis (pwMS) can cause impairment or disability across a range of domains. However, the mapping between severity or location of this brain tissue damage and an individual's impairment remains challenging. Advanced neuroimaging techniques may enable us to better understand the neuropathological or compensatory mechanisms in MS and quantify the link between the brain's functional activity and its structural backbone. One recently developed approach uses network control theory to characterize the brain's energetic landscape using an individual's structural connectome and brain activity dynamics. While brain activity dynamics have previously been analyzed in MS cohorts, investigating how structural network damage, one of the hallmarks of MS, relates to shifts in activity dynamics has not yet been performed. Aims: We apply brain activity dynamics analysis and network control theory (NCT) to functional time series and structural connectomes in healthy controls (HC) and pwMS with and without disability and compare the three groups' resulting metrics. We also assessed entropy of the three groups' brain activity time series. Our hypotheses were that pwMS with disability would have increased brain state transition energies compared to pwMS without disability and controls, and that greater energies would be related to greater lesion burden and decreased entropy of brain activity. Materials and Methods: Ninety-nine pwMS and 19 HC were included in our study; individuals underwent MRI, including anatomical, diffusion and resting-state functional scans. Expanded Disability Status Scale (EDSS) was used to assess disability; 66 pwMS with EDSS<2 were considered as not having disability. Commonly recurring brain states were identified using k-means clustering of the regional brain activity time series. NCT was used to compute the energy required to transition between each pair of states or to remain in the same state, taking into account an individual's structural connectome. The entropy of brain activity time series was quantified and correlated with overall transition energy and lesion volume. Finally, logistic regression with ridge regularization was used to classify the groups using transition energies and demographics/clinical information and the model's feature importance assessed. Results: Six brain states were identified, including pairs of states with high and low visual (VIS+/-), ventral attention (VAN)+/- and somatomotor (SOM+/-) network activity. Global entropy was negatively correlated with lesion volume (r=-0.20, p=0.03) as was global transition energy (r=-0.18, p=0.04) in pwMS. Smaller transition energies were associated with pwMS without disability, while larger transition energies were associated with pwMS with disability. Discussion: We conjecture that functional compensation of brain activity in pwMS without disability results in decreased transition energies between states compared to controls, but, as this mechanism diminishes, transition energies increase and disability occurs. We also conjecture that larger volumes of MS-related lesions result in greater energetic demands to transition between brain states which then leads to lower overall entropy of brain activity.

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Functional gradients reveal cortical hierarchy changes in multiple sclerosis

De Rosa,

d'Ambrosio,

Bisecco

et al. 2024

Human Brain Mapping

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Functional gradient (FG) analysis represents an increasingly popular methodological perspective for investigating brain hierarchical organization but whether and how network hierarchy changes concomitant with functional connectivity alterations in multiple sclerosis (MS) has remained elusive. Here, we analyzed FG components to uncover possible alterations in cortical hierarchy using resting‐state functional MRI (rs‐fMRI) data acquired in 122 MS patients and 97 healthy control (HC) subjects. Cortical hierarchy was assessed by deriving regional FG scores from rs‐fMRI connectivity matrices using a functional parcellation of the cerebral cortex. The FG analysis identified a primary (visual‐to‐sensorimotor) and a secondary (sensory‐to‐transmodal) component. Results showed a significant alteration in cortical hierarchy as indexed by regional changes in FG scores in MS patients within the sensorimotor network and a compression (i.e., a reduced standard deviation across all cortical parcels) of the sensory‐transmodal gradient axis, suggesting disrupted segregation between sensory and cognitive processing. Moreover, FG scores within limbic and default mode networks were significantly correlated (, p < .005 after Bonferroni correction for both) with the symbol digit modality test (SDMT) score, a measure of information processing speed commonly used in MS neuropsychological assessments. Finally, leveraging supervised machine learning, we tested the predictive value of network‐level FG features, highlighting the prominent role of the FG scores within the default mode network in the accurate prediction of SDMT scores in MS patients (average mean absolute error of 1.22 ± 0.07 points on a hold‐out set of 24 patients). Our work provides a comprehensive evaluation of FG alterations in MS, shedding light on the hierarchical organization of the MS brain and suggesting that FG connectivity analysis can be regarded as a valuable approach in rs‐fMRI studies across different MS populations.

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Longitudinal Network Changes and Conversion to Cognitive Impairment in Multiple Sclerosis

Cited by 28 publications

References 38 publications

A more unstable resting-state functional network in cognitively declining multiple sclerosis

A more unstable resting-state functional network in cognitively declining multiple sclerosis

Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity

Functional gradients reveal cortical hierarchy changes in multiple sclerosis

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