Five years functional connectivity reorganization without clinical or cognitive decline in MS

ABSTRACTObjective1) To assess fMRI-based functional connectivity (FC) anomalies in early multiple sclerosis (MS), 2) To determine the relation between FC changes and structural brain damage due to disease progression 3) To study the association between FC changes and cognitive and physical disability.MethodsStructural MRI and resting-state fMRI were acquired from 76 early relapsing-remitting MS patients at baseline (average disease duration 71.7 months ± 63) and after five years. Ninety-four healthy controls (HCs) matched for age and sex were included at baseline. Independent component analysis (ICA) and network modelling were used to measure FC. FC variation was related to expanded disability status scale and neuropsychological outcomes. Brain and lesion volumes were quantified using standard methods. We used the 25 independent components obtained from ICA to estimate the longitudinal stability of the brain connectome as a proxy for functional reorganization over time.ResultsThe MS subjects were clinically and cognitively stable. Compared to HCs, FC abnormalities were detected within networks and in single connections in patients with early MS at baseline. Over time, FC was relatively invariable, but changes in FC were associated with progression of brain atrophy (ρ = 0.39, p = .06). No significant relationship with clinical and cognitive measures or lesion load was detected.ConclusionPatients with MS showed evidence of altered FC in the early stages of the disease. Over time, changes in FC seem to be related to a progression of brain atrophy, which are known to precede changes in clinical and cognitive functioning.

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Functional connectivity in multiple sclerosis modelled as connectome stability: A 5-year follow-up study

Multiple Sclerosis Journal ◽

10.1177/13524585211030212 ◽

2021 ◽

pp. 135245852110302

Author(s):

Einar August Høgestøl ◽

Samuele Ghezzo ◽

Gro Owren Nygaard ◽

Thomas Espeseth ◽

Piotr Sowa ◽

...

Keyword(s):

Multiple Sclerosis ◽

Functional Connectivity ◽

Cognitive Performance ◽

Large Scale ◽

Resting State Fmri ◽

Network Modelling ◽

Cross Sectional ◽

Relapsing Remitting ◽

Structural Brain Damage ◽

Brain Functional Connectivity

Background: Brain functional connectivity (FC) in multiple sclerosis (MS) is abnormal compared to healthy controls (HCs). More longitudinal studies in MS are needed to evaluate whether FC stability is clinically relevant. Objective: To compare functional magnetic resonance imaging (fMRI)-based FC between MS and HC, and to determine the relationship between longitudinal FC changes and structural brain damage, cognitive performance and physical disability. Methods: T1-weighted MPRAGE and resting-state fMRI (1.5T) were acquired from 70 relapsing-remitting MS patients and 94 matched HC at baseline (mean months since diagnosis 14.0 ± 11) and from 60 MS patients after 5 years. Independent component analysis and network modelling were used to measure longitudinal FC stability and cross-sectional comparisons with HC. Linear mixed models, adjusted for age and sex, were used to calculate correlations. Results: At baseline, patients with MS showed FC abnormalities both within networks and in single connections compared to HC. Longitudinal analyses revealed functional stability and no significant relationships with clinical disability, cognitive performance, lesion or brain volume. Conclusion: FC abnormalities occur already at the first decade of MS, yet we found no relevant clinical correlations for these network deviations. Future large-scale longitudinal fMRI studies across a range of MS subtypes and outcomes are required.

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Deletion of the mu opioid receptor gene in mice reshapes the reward–aversion connectome

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1601640113 ◽

2016 ◽

Vol 113 (41) ◽

pp. 11603-11608 ◽

Cited By ~ 35

Author(s):

Anna E. Mechling ◽

Tanzil Arefin ◽

Hsu-Lei Lee ◽

Thomas Bienert ◽

Marco Reisert ◽

...

Keyword(s):

Functional Connectivity ◽

Opioid Receptor ◽

Resting State ◽

Receptor Gene ◽

Resting State Fmri ◽

Mu Opioid Receptor ◽

Major Influence ◽

Mu Opioid ◽

Brain Connectome ◽

Intergroup Comparison

Connectome genetics seeks to uncover how genetic factors shape brain functional connectivity; however, the causal impact of a single gene’s activity on whole-brain networks remains unknown. We tested whether the sole targeted deletion of the mu opioid receptor gene (Oprm1) alters the brain connectome in living mice. Hypothesis-free analysis of combined resting-state fMRI diffusion tractography showed pronounced modifications of functional connectivity with only minor changes in structural pathways. Fine-grained resting-state fMRI mapping, graph theory, and intergroup comparison revealed Oprm1-specific hubs and captured a unique Oprm1 gene-to-network signature. Strongest perturbations occurred in connectional patterns of pain/aversion-related nodes, including the mu receptor-enriched habenula node. Our data demonstrate that the main receptor for morphine predominantly shapes the so-called reward/aversion circuitry, with major influence on negative affect centers.

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A cortical hierarchy of localized and distributed processes revealed via dissociation of task activations, connectivity changes, and intrinsic timescales

10.1101/262626 ◽

2018 ◽

Author(s):

Takuya Ito ◽

Luke J. Hearne ◽

Michael W. Cole

Keyword(s):

Functional Connectivity ◽

Negative Relationship ◽

Structural Mri ◽

Resting State Fmri ◽

List Type ◽

Related Activity ◽

Distributed Processes ◽

Cortical Hierarchy ◽

Evoked Activity ◽

Cortical Regions

AbstractMany studies have identified the role of localized and distributed cognitive functionality by mapping either local task-related activity or distributed functional connectivity (FC). However, few studies have directly explored the relationship between a brain region’s localized task activity and its distributed task FC. Here we systematically evaluated the differential contributions of task-related activity and FC changes to identify a relationship between localized and distributed processes across the cortical hierarchy. We found that across multiple tasks, the magnitude of regional task-evoked activity was high in unimodal areas, but low in transmodal areas. In contrast, we found that task-state FC was significantly reduced in unimodal areas relative to transmodal areas. This revealed a strong negative relationship between localized task activity and distributed FC across cortical regions that was associated with the previously reported principal gradient of macroscale organization. Moreover, this dissociation corresponded to hierarchical cortical differences in the intrinsic timescale estimated from resting-state fMRI and region myelin content estimated from structural MRI. Together, our results contribute to a growing literature illustrating the differential contributions of a hierarchical cortical gradient representing localized and distributed cognitive processes.HighlightsTask activations and functional connectivity changes are negatively correlated across cortexTask activation and connectivity dissociations reflect differences in localized and distributed processes in cortexDifferences in localized and distributed processes are associated with differences in intrinsic timescale organizationDifferences in localized and distributed processes are associated with differences in cortical myelin contentCortical heterogeneity in localized and distributed processes revealed by activity flow mapping prediction error

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Optimising network modelling methods for fMRI

10.1101/741595 ◽

2019 ◽

Cited By ~ 2

Author(s):

Usama Pervaiz ◽

Diego Vidaurre ◽

Mark W. Woolrich ◽

Stephen M. Smith

Keyword(s):

Functional Connectivity ◽

Predictive Models ◽

Tangent Space ◽

Resting State Fmri ◽

Shrinkage Estimators ◽

Network Modelling ◽

Common Procedure ◽

Human Connectome Project ◽

Performance Validation ◽

Modelling Methods

AbstractA major goal of neuroimaging studies is to develop predictive models to analyse the relationship between whole brain functional connectivity patterns and behavioural traits. However, there is no single widely-accepted standard pipeline for analyzing functional connectivity. The common procedure for designing functional connectivity based predictive models entails three main steps: parcellating the brain, estimating the interaction between defined parcels, and lastly, using these integrated associations between brain parcels as features fed to a classifier for predicting non-imaging variables e.g., behavioural traits, demographics, emotional measures, etc. There are also additional considerations when using correlation-based measures of functional connectivity, resulting in three supplementary steps: utilising Riemannian geometry tangent space parameterization to preserve the geometry of functional connectivity; penalizing the connectivity estimates with shrinkage approaches to handle challenges related to short time-series (and noisy) data; and removing confounding variables from brain-behaviour data. These six steps are contingent on each-other, and to optimise a general framework one should ideally examine these various methods simultaneously. In this paper, we investigated strengths and short-comings, both independently and jointly, of the following measures: parcellation techniques of four kinds (categorized further depending upon number of parcels), five measures of functional connectivity, the decision of staying in the ambient space of connectivity matrices or in tangent space, the choice of applying shrinkage estimators, six alternative techniques for handling confounds and finally four novel classifiers/predictors. For performance evaluation, we have selected two of the largest datasets, UK Biobank and the Human Connectome Project resting state fMRI data, and have run more than 9000 different pipeline variants on a total of ∼14000 individuals to determine the optimum pipeline. For independent performance validation, we have run some best-performing pipeline variants on ABIDE and ACPI datasets (∼1000 subjects) to evaluate the generalisability of proposed network modelling methods.

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Patterns of brain neurodegeneration in early-stage relapsing-remitting multiple sclerosis

10.1101/2021.11.18.21266506 ◽

2021 ◽

Author(s):

Rozanna Meijboom ◽

Elizabeth N. York ◽

Agniete Kampaite ◽

Mathew A. Harris ◽

Nicole White ◽

...

Keyword(s):

Early Stage ◽

Occipital Lobe ◽

Structural Mri ◽

Longitudinal Change ◽

Subcortical Structures ◽

Normal Appearing White Matter ◽

Relapsing Remitting ◽

Cortical Regions ◽

The Brain ◽

Over Time

Recurrent neuroinflammation in relapsing-remitting MS (RRMS) is thought to lead to neurodegeneration, resulting in progressive disability. Repeated magnetic resonance imaging (MRI) of the brain provides non-invasive measures of atrophy over time, a key marker of neurodegeneration. This study investigates regional neurodegeneration of the brain in early-stage RRMS using volumetry and voxel-based morphometry (VBM). RRMS patients (N=354) underwent 3T structural MRI at diagnosis and 1-year follow-up, as part of the Scottish multicentre 'FutureMS' study. MRI data were processed using FreeSurfer to derive volumetrics, and FSL for VBM (grey matter [GM] only), to establish patterns of change in GM and normal-appearing white matter (NAWM) over time throughout the cerebrum, cerebellum and brainstem. Volumetric analyses showed a decrease over time (q<0.05) in bilateral cortical GM and NAWM, multiple subcortical structures, cerebellar GM and the brainstem. Additionally, NAWM and GM volume decreased respectively in the following cortical regions, frontal: 14 out of 28 regions and 17/28; temporal: 18/18 and 15/18; parietal: 14/14 and 11/14; occipital: 7/8 and 8/8. Left GM and NAWM asymmetry was observed in the frontal lobe. GM VBM analysis showed three major clusters of decrease over time: 1) temporal lobe and subcortical areas, 2) cerebellum, 3) anterior cingulum and supplementary motor cortex; and four smaller clusters within the occipital lobe. Widespread neurodegeneration was observed in early-stage RRMS; particularly in the brainstem, cerebellar GM, and subcortical and occipital-temporal regions. Volumetric and VBM results emphasise different as well as overlapping patterns of longitudinal change, and provide potential response markers for existing therapies and trials of neuroprotective agents.

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