Background:
The development of tailored recovery-oriented strategies in multiple sclerosis requires early identification of an individual’s potential for functional recovery.
Objective:
To identify ...predictors of visuomotor performance improvements, a proxy of functional recovery, using a predictive statistical model that combines demographic, clinical and magnetic resonance imaging (MRI) data.
Methods:
Right-handed multiple sclerosis patients underwent baseline disability assessment and MRI of the brain structure, function and vascular health. They subsequently undertook 4 weeks of right upper limb visuomotor practice. Changes in performance with practice were our outcome measure. We identified predictors of improvement in a training set of patients using lasso regression; we calculated the best performing model in a validation set and applied this model to a test set.
Results:
Patients improved their visuomotor performance with practice. Younger age, better visuomotor abilities, less severe disease burden and concurrent use of preventive treatments predicted improvements. Neuroimaging localised outcome-relevant sensory motor regions, the microstructure and activity of which correlated with performance improvements.
Conclusion:
Initial characteristics, including age, disease duration, visuo-spatial abilities, hand dexterity, self-evaluated disease impact and the presence of disease-modifying treatments, can predict functional recovery in individual patients, potentially improving their clinical management and stratification in clinical trials. MRI is a correlate of outcome, potentially supporting individual prognosis.
Cerebrovascular reactivity (CVR) reflects the capacity of the brain's vasculature to increase blood flow following a vasodilatory stimulus. Reactivity is an essential property of the brain's blood ...vessels that maintains nutrient supplies in the face of changing demand. In Multiple Sclerosis (MS), CVR may be diminished with brain inflammation and this may contribute to neurodegeneration. We test the hypothesis that CVR is altered with MS neuroinflammation and that it is restored when inflammation is reduced. Using a breath-hold task during functional Magnetic Resonance Imaging (MRI), we mapped grey matter and white matter CVRs (CVR
and CVR
, respectively) in 23 young MS patients, eligible for disease modifying therapy, before and during Interferon beta treatment. Inflammatory activity was inferred from the presence of Gadolinium enhancing lesions at MRI. Eighteen age and gender-matched healthy controls (HC) were also assessed. Enhancing lesions were observed in 12 patients at the start of the study and in 3 patients during treatment. Patients had lower pre-treatment CVR
(p = 0.04) and CVR
(p = 0.02) compared to HC. In patients, a lower pre-treatment CVR
was associated with a lower GM volume (r = 0.60, p = 0.003). On-treatment, there was an increase in CVR
(p = 0.02) and CVR
(p = 0.03) that negatively correlated with pre-treatment CVR (GM: r = - 0.58, p = 0.005; WM: r = - 0.60, p = 0.003). CVR increased when enhancing lesions reduced in number (GM: r = - 0.48, p = 0.02, WM: r = - 0.62, p = 0.003). Resolution of inflammation may restore altered cerebrovascular function limiting neurodegeneration in MS. Imaging of cerebrovascular function may thereby inform tissue physiology and improve treatment monitoring.
Metabolomics-based technologies map in vivo biochemical changes that may be used as early indicators of pathological abnormalities prior to the development of clinical symptoms in neurological ...conditions. Metabolomics may also reveal biochemical pathways implicated in tissue dysfunction and damage and thus assist in the development of novel targeted therapeutics for neuroinflammation and neurodegeneration. Metabolomics holds promise as a non-invasive, high-throughput and cost-effective tool for early diagnosis, follow-up and monitoring of treatment response in multiple sclerosis (MS), in combination with clinical and imaging measures. In this review, we offer evidence in support of the potential of metabolomics as a biomarker and drug discovery tool in MS. We also use pathway analysis of metabolites that are described as potential biomarkers in the literature of MS biofluids to identify the most promising molecules and upstream regulators, and show novel, still unexplored metabolic pathways, whose investigation may open novel avenues of research.
This cross-sectional study investigated the effects of aerobic fitness on cerebrovascular function in the healthy brain. Gray matter cerebral blood flow (CBF) and cerebrovascular reactivity (CVR) ...were quantified in a sample of young adults within a normal fitness range. Based on existing Transcranial Doppler ultrasound and fMRI evidence, we predicted a positive relationship between fitness and resting gray matter CBF and CVR. Exploratory hypotheses that higher
O
peak would be associated with higher GM volume and cognitive performance were also investigated. 20 adults underwent a
O
peak test and a battery of cognitive tests. All subjects also underwent an MRI scan where multiple inversion time (MTI) pulsed arterial spin labeling (PASL) was used to quantify resting CBF and CVR to 5% CO
. Region of interest analysis showed a non-significant inverse correlation between whole-brain gray matter CBF and
O
peak;
= -0.4,
= 0.08, corrected
(
') = 0.16 and a significant positive correlation between
O
peak and whole-brain averaged gray matter CVR;
= 0.62,
= 0.003,
' = 0.006. Voxel-wise analysis revealed a significant inverse association between
O
peak and resting CBF in the left and right thalamus, brainstem, right lateral occipital cortex, left intra-calcarine cortex and cerebellum. The results of this study suggest that aerobic fitness is associated with lower baseline CBF and greater CVR in young adults.
Myelin oligodendrocyte glycoprotein (MOG) antibody disease (MOG-AD) is now recognised as a nosological entity with specific clinical and paraclinical features to aid early diagnosis. Although no age ...group is exempt, median age of onset is within the fourth decade of life, with optic neuritis being the most frequent presenting phenotype. Disease course can be either monophasic or relapsing, with subsequent relapses most commonly involving the optic nerve. Residual disability develops in 50–80% of patients, with transverse myelitis at onset being the most significant predictor of long-term outcome. Recent advances in MOG antibody testing offer improved sensitivity and specificity. To avoid misdiagnosis, MOG antibody testing should be undertaken in selected cases presenting clinical and paraclinical features that are felt to be in keeping with MOG-AD, using a validated cell-based assay. MRI characteristics can help in differentiating MOG-AD from other neuroinflammatory disorders, including multiple sclerosis and neuromyelitis optica. Cerebrospinal fluid oligoclonal bands are uncommon. Randomised control trials are limited, but observational open-label experience suggests a role for high-dose steroids and plasma exchange in the treatment of acute attacks, and for immunosuppressive therapies, such as steroids, oral immunosuppressants and rituximab as maintenance treatment.
•Elucidating the neural correlates of cognitive impairment in MS is a research priority for improving symptom management.•Tractometry was used to identify patterns of white matter pathology in ...MS.•Results showed a single pattern of co-variance across white matter tracts.•Variance in normal appearing white matter significantly contributes to cognitive domains in MS.•Findings question shared susceptibility among tracts to pathology and highlight need for further research.
Understanding the brain changes underlying cognitive dysfunction is a key priority in multiple sclerosis (MS) to improve monitoring and treatment of this debilitating symptom. Functional connectivity network changes are associated with cognitive dysfunction, but it is less well understood how changes in normal appearing white matter relate to cognitive symptoms. If white matter tracts have network structure it would be expected that tracts within a network share susceptibility to MS pathology. In the present study, we used a tractometry approach to explore patterns of variance in white matter metrics across white matter (WM) tracts, and assessed how such patterns relate to neuropsychological test performance across cognitive domains. A sample of 102 relapsing-remitting MS patients and 27 healthy controls underwent MRI and neuropsychological testing. Tractography was performed on diffusion MRI data to extract 40 WM tracts and microstructural measures were extracted from each tract. Principal component analysis (PCA) was used to decompose metrics from all tracts to assess the presence of any co-variance structure among the tracts. Similarly, PCA was applied to cognitive test scores to identify the main cognitive domains. Finally, we assessed the ability of tract co-variance patterns to predict test performance across cognitive domains. We found that a single co-variance pattern which captured microstructure across all tracts explained the most variance (65% variance explained) and that there was little evidence for separate, smaller network patterns of pathology. Variance in this pattern was explained by effects related to lesions, but one main co-variance pattern persisted after this effect was regressed out. This main WM tract co-variance pattern contributed to explaining a modest degree of variance in one of our four cognitive domains in MS. These findings highlight the need to investigate the relationship between the normal appearing white matter and cognitive impairment further and on a more granular level, to improve the understanding of the network structure of the brain in MS.
The brain retains a lifelong ability to adapt through learning and in response to injury or disease-related damage, a process known as functional neuroplasticity. The neural energetics underlying ...functional brain plasticity have not been thoroughly investigated experimentally in the healthy human brain. A better understanding of the blood flow and metabolic changes that accompany motor skill acquisition, and which facilitate plasticity, is needed before subsequent translation to treatment interventions for recovery of function in disease. The aim of the current study was to characterize cerebral blood flow (CBF) and oxygen consumption (relative CMRO
) responses, using calibrated fMRI conducted in 20 healthy participants, during performance of a serial reaction time task which induces rapid motor adaptation. Regions of interest (ROIs) were defined from areas showing task-induced BOLD and CBF responses that decreased over time. BOLD, CBF and relative CMRO
responses were calculated for each block of the task. Motor and somatosensory cortices and the cerebellum showed statistically significant positive responses to the task compared to baseline, but with decreasing amplitudes of BOLD, CBF, and CMRO
response as the task progressed. In the cerebellum, there was a sustained positive BOLD response in the absence of a significant CMRO
increase from baseline, for all but the first task blocks. This suggests that the brain may continue to elevate the supply energy even after CMRO
has returned to near baseline levels. Relying on BOLD fMRI data alone in studies of plasticity may not reveal the nature of underlying metabolic responses and their changes over time. Calibrated fMRI approaches may offer a more complete picture of the energetic changes supporting plasticity and learning.
Neuroimaging experiments have identified several brain regions that appear to play roles in motor learning. Here we apply a novel multivariate analytical approach to explore the dynamic interactions ...of brain activation regions as spatio-temporally coherent functional networks. We acquired BOLD fMRI signal during explicit motor sequence learning task to characterize the adaptive functional changes in the early phase of motor learning. Subjects practiced a 10-digit, visually cued, fixed motor sequence during 15 consecutive 30 s practice blocks interleaved with similarly cued random sequence blocks. Tensor Independent Component Analysis (TICA) decomposed the data into statistically independent spatio-temporal processes.
Two components were identified that represented task-related activations. The first component showed decreasing activity of a fronto-parieto-cerebellar network during task conditions. The other exclusively related to sequence learning blocks showed activation in a network including the posterior parietal and premotor cortices. Variation in expression of this component across individual subjects correlated with differences in behavior.
Relative deactivations also were found in patterns similar to those described previously as “resting state” networks. Some of these deactivation components also showed task- and time-related modulations and were related to the behavioral improvement.
The spatio-temporal coherence within these networks suggests that their elements are functionally integrated. Their anatomical plausibility and correlation with behavioral measures also suggest that this approach allows characterization of the interactions of functional networks relevant to the task. Particular value for multi-variant, model-free methods such as TICA lies in the potential for generating hypotheses regarding functional anatomical networks underlying specific behaviors.
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