Author Of 3 Presentations
P0570 - Dynamic functional connectivity as a neural correlate of fatigue in multiple sclerosis (ID 1455)
Abstract
Background
More than 80% of multiple sclerosis (MS) patients experience symptoms of fatigue. MS-related fatigue can only partly be explained by structural (lesions and atrophy) and functional (brain activation and conventional static functional connectivity) brain changes.
Objectives
To investigate the relationship of dynamic functional connectivity (dFC) with present and future fatigue in MS patients and compare this with commonly used clinical and MRI parameters.
Methods
In 35 relapsing-remitting MS patients (age: 42.8, female/male: 20/15, disease duration: 11 years) and 19 healthy controls (HC) (age: 41.4, female/male: 11/8), fatigue was measured using the CIS-20r questionnaire at baseline and at a 6-month follow-up. Furthermore, disability (EDSS) was assessed for patients. All subjects underwent structural MRI and resting-state functional MRI at baseline. We calculated global static functional connectivity (sFC) and assessed dynamic connectivity using a tapered sliding-window approach by calculating the summed difference (diff) and coefficient of variation (cov). Moreover, we calculated connectivity between basal ganglia and cortical regions previously associated with fatigue in MS (medial prefrontal cortex, posterior cingulate cortex, and precuneus). We performed hierarchical regression analyses with forward selection to identify the most important predictors of fatigue at baseline and follow-up.
Results
Patients were more fatigued than HCs at baseline (MS: 74.36 ± 29.33; HC: 46.72 ± 17.06; p=0.001) and follow-up (MS: 69.91 ± 27.01; HC: 45.11 ± 19.84; p=0.002). No difference in sFC was found between patients and controls. Patients had higher baseline global dFC than controls (p<0.05) but no difference in basal ganglia-cortical dFC. Basal ganglia-cortical dFC-cov added 12.5% extra explained variance (standardized β=-0.353, p=0.032) on top of EDSS (standardized β=0.380, p=0.022) to a regression model for baseline fatigue in patients (adjusted R2=0.211, p=0.011). Post-hoc analysis revealed lower basal ganglia-cortical dFC-cov in patients with severe fatigue at baseline (0.89 ± 0.06) compared to non-fatigued patients (0.93 ± 0.05; p=0.036).
Conclusions
Less dynamic connectivity between the basal ganglia and the cortex is associated with greater fatigue in MS patients, independent of disability status. These findings may reflect less efficient network reconfigurations of those connections as a potential additional neural correlate of fatigue in MS.
P0605 - More dynamic functional network switching in cognitively declining multiple sclerosis patients (ID 777)
Abstract
Background
Cognitive impairment in multiple sclerosis (MS) is strongly related to functional network dysfunction. In the absence of MS, optimal cognitive functioning of an individual is ensured by dynamically adapting the configuration of the functional network as needed. How these dynamic patterns are altered in MS remains unclear.
Objectives
Our aim was to investigate the dynamic reconfiguration of cognitively relevant brain networks in MS, to identify specific brain network patterns related to progression of cognitive impairment.
Methods
Resting-state functional MRI (rs-fMRI) and cognitive scores were acquired from 230 patients with MS and 59 matched healthy controls, at baseline and at 5 year follow-up. Seven cognitive domains were examined with the expanded Brief Repeatable Battery of Neuropsychological tests. A sliding-window approach was used on the rs-fMRI data, for which brain regions were assigned to one of seven classic literature-based resting-state networks based on connectivity patterns at that point in time. How regions switched between networks was described using measures of promiscuity (number of networks switched to), flexibility (number of switches), cohesion (switches with another region), and disjointedness (independent switches). Linear mixed models were used for baseline and longitudinal analyses, controlling for age, sex, and education.
Results
At baseline, 42% of patients showed cognitive impairment (CI) (18% Mild CI, ≥2 tests Z<-1.5; 23% severe CI, ≥2 tests Z<-2) and 28% of patients declined over time (≥2 tests yearly reliable decline>0.25). At baseline, CI patients showed increased promiscuity, flexibility and cohesion (i.e. more switching between networks) compared to preserved patients. Patients displaying cognitive deterioration showed increases in cohesion over time. Higher baseline cohesion was related to less gray matter volume, and more white matter integrity loss and lesion volume. Within cognitive domains, cohesion was inversely related to verbal memory, information processing speed, and working memory.
Conclusions
In patients with MS, increased switching between brain networks was related to cognitive impairment and structural damage. Cohesion particularly increased over time in patients showing cognitive decline, indicating that switching together with other regions might be particularly more common. These results provide support for the hypothesis of a progressive destabilization of the functional brain network in MS.
P0640 - Sensorimotor network dynamics predicts loss of upper and lower limb function in people with multiple sclerosis (ID 1048)
Abstract
Background
Both upper and lower limb disability is common in multiple sclerosis (MS), but do not always occur together, suggesting partially independent underlying mechanisms. Physical disability strongly relates to brain network disturbances in MS, yet network mechanisms underlying upper and lower disability progression remain unclear.
Objectives
To investigate the relationship between upper and lower limb progression and functional sensorimotor network changes in MS.
Methods
Longitudinal data was included from a prospectively acquired cohort, with baseline data collected between 2008 and 2012 and follow-up assessments between 2014 and 2017. Participants underwent MRI and dexterity (9-Hole Peg Test) and mobility (Timed 25-Foot Walk) tests at baseline and after 5 years. Patients were stratified into progressors (>20% decline) or non-progressors for both tests. Measures of network efficiency were calculated from resting-state functional MRI data using both static (i.e. calculated on the entire scan) and dynamic (i.e. fluctuations during the scan) approaches and compared between patient groups. Multiple logistic regression was used to identify independent predictors of upper and lower limb progression and baseline connectivity patterns.
Results
This study included 214 people with MS (age 47±11; 149 women) and 58 healthy controls (age 46±10; 31 women). Compared to respective non-progressors, upper limb progression (n=24) was related to higher dynamic efficiency of the right premotor cortex, somatosensory cortex and thalamus, while lower limb progression (n=37) was related to higher dynamic efficiency of the right supplementary motor area at baseline (p<0.05). Logistic regression showed that dynamic efficiency of the thalamus and supplementary motor area best predicted upper and lower limb progression respectively, independent of the severity of structural damage (p<0.01). Both areas displayed widespread higher dynamic connectivity in progressing compared to non-progressing patients at baseline (p<0.05).
Conclusions
Disability progression can be predicted by the severity of fluctuations (i.e. higher dynamics) in the efficiency of the sensorimotor network. The dynamic behavior of the thalamus and supplementary motor area were respectively related to upper and lower limb progression, possibly indicating different mechanisms underlying these types of progression in MS.