Altered transient brain dynamics in multiple sclerosis: Treatment or pathology?

Abstract

Multiple sclerosis (MS) is a demyelinating, neuroinflammatory, and ‐degenerative disease that affects the brain's neurophysiological functioning through brain atrophy, a reduced conduction velocity and decreased connectivity. Currently, little is known on how MS affects the fast temporal dynamics of activation and deactivation of the different large‐scale, ongoing brain networks. In this study, we investigated whether these temporal dynamics are affected in MS patients and whether these changes are induced by the pathology or by the use of benzodiazepines (BZDs), an important symptomatic treatment that aims at reducing insomnia, spasticity and anxiety and reinforces the inhibitory effect of GABA. To this aim, we employed a novel method capable of detecting these fast dynamics in 90 MS patients and 46 healthy controls. We demonstrated a less dynamic frontal default mode network in male MS patients and a reduced activation of the same network in female MS patients, regardless of BZD usage. Additionally, BZDs strongly altered the brain's dynamics by increasing the time spent in the deactivating sensorimotor network and the activating occipital network. Furthermore, BZDs induced a decreased power in the theta band and an increased power in the beta band. The latter was strongly expressed in those states without activation of the sensorimotor network. In summary, we demonstrate gender‐dependent changes to the brain dynamics in the frontal DMN and strong effects from BZDs. This study is the first to characterise the effect of multiple sclerosis and BZDs in vivo in a spatially, temporally and spectrally defined way.

Brain dysconnectivity relates to disability and cognitive impairment in multiple sclerosis

“…MS may also alter these dynamics, which might provide further insight into its physiopathology, as was shown in a comparison between static and dynamic rsFC in neurodevelopment (Hunt et al, 2018), although interpreting time‐dependent rsFC remains challenging (Hutchison et al, 2013). Results from our group in a similar participants' population demonstrated that transient brain dynamics is slightly altered in MS with a less dynamic frontal DMN in males with MS and a reduced activation of the same network in females with MS (Van Schependom et al, 2019). On the other hand, our current results are statistically robust (see discussion below).…”

“…The difference in nodewise rsFC or mean network rsFC between patients and healthy subjects was assessed by a mass‐univariate statistical contrast of the two corresponding “multi‐layer” matrices. Specifically, we considered for each matrix entry (i.e., two nodes or networks in their respective frequency band) Welch's t statistic comparing the 99 rsFC values in patients and the 47 values in healthy subjects, from which the effect of several confounding factors was regressed out beforehand (7 regressors for patients and 6 for healthy subjects): power estimates of the two corresponding nodes or within‐network averages, age, sex, educational level, and MEG system type (Vectorview vs. Triux) for both groups of participants, and additionally benzodiazepine status for patients to mitigate the effect of this psychotropic drug on brain activity (see, for example, Van Schependom et al, 2019). Regressing out power avoids power‐induced rsFC changes (Muthukumaraswamy & Singh, 2011), and regressing out system type eliminates any possible effect related to the MEG system upgrade (notwithstanding the absence of important data quality changes, see Naeije et al, 2019; Coquelet et al, 2020).…”

Hidden state dynamics reveal the prolonged inactive state across the adult lifespan

“…Therefore, in contrast to the co-activation patterns approach (Liu and Dyun, 2013; Liu et al, 2013; Chen et al, 2015) which directly clustering the single frames of the observation sequence, HMM allows more robust characterization of the dynamic properties.The transition probability between the hidden states, the fractional occupancy (FO) and mean lifetime (the average time spent in each state during each visit) are often used to quantify the temporal dynamics of the inferred hidden states (Baker et al 2014). HMM has been applied to neuroimaging studies of multiple modalities such as EEG (Hunyadi et al 2019; Stevner et al 2019), MEG (Baker et al 2014; Vidaurre et al 2016; Quinn et al 2018; Van Schependom et al 2019) and fMRI (Chen et al 2016; Ryali et al 2016; Vidaurre et al 2017; Vidaurre et al 2018; Taghia et al 2018; Kottaram et al 2019). Because of the expanded feature space inherent in the probabilistic model, HMM is the most effective when sample size is large due to Chen et al (2017) and is sufficient to capture quasi-stationary states of activity that are consistently recurring over a population (Baker et al 2014).…”

“…Because of the expanded feature space inherent in the probabilistic model, HMM is the most effective when sample size is large due to Chen et al (2017) and is sufficient to capture quasi-stationary states of activity that are consistently recurring over a population (Baker et al 2014). The capacity of HMM in discovering the switching dynamics in developmental maturation (Ryali et al 2016), schizophrenia (Kottaram et al 2019), multiple sclerosis (Van Schependom et al 2019) and non-REM sleep (Stevner et al 2019) proves that HMM is capable of characterizing the dynamic pattern of spontaneous brain activity across the adult lifespan.…”

“…HMM has been applied to neuroimaging studies of multiple modalities such as EEG (Hunyadi et al 2019;Stevner et al 2019), MEG (Baker et al 2014;Vidaurre et al 2016;Quinn et al 2018;Van Schependom et al 2019) and fMRI (Chen et al 2016;Ryali et al 2016;Vidaurre et al 2017;Vidaurre et al 2018;Taghia et al 2018;Kottaram et al 2019). Because of the expanded feature space inherent in the probabilistic model, HMM is the most effective when sample size is large due to Chen et al (2017) and is sufficient to capture quasi-stationary states of activity that are consistently recurring over a population (Baker et al 2014).…”