87 research outputs found
Functional MRI correlates of cognitive performance in patients with a clinically isolated syndrome suggestive of MS at presentation: an activation and connectivity study
Background/Objective: To assess whether abnormalities on functional magnetic resonance imaging (fMRI) are related to cognitive function in patients at presentation with clinically isolated syndrome (CIS) suggestive of multiple sclerosis.
Methods: Eighteen patients with CIS and 15 healthy controls (HCs) performed an adapted fMRI version of the Paced Auditory Serial Addition Test (PASAT). According to their PASAT performance, CIS patients were divided into two groups: 10 with a low PASAT performance (<1 SD from the mean value of HCs) were considered ‘cognitive impairment’ (CI); eight patients were defined as ‘cognitively preserved’ (CP). Between-group differences in the patterns of brain activations and effective connectivity were assessed.
Results: During PASAT, compared to HCs, CIS patients showed increased activations of the bilateral inferior parietal lobe (IPL), bilateral precuneus, bilateral middle frontal gyrus (MFG), left anterior cingulate cortex (ACC), left claustrum, right thalamus and right caudate nucleus. When CIS patients were analyzed, the CI group had a more significant activation of the bilateral IPL than HCs and CP patients. Compared to CP patients, they also had more significant recruitment of the right superior parietal lobe, right cerebellum, left MFG and left ACC. The analysis of effective connectivity showed stronger connections between several regions of the right hemisphere involved in working memory function in CI patients versus CP and HC.
Conclusions: During performance of the PASAT, CIS patients show abnormalities in the patterns of cortical recruitment and connectivity related to the level of their cognitive impairment.This research has been supported by the following grants: Alicia Koplowitz Foundation; CSD2007-00012 (Consolider-Ingenio 2010 Programme), SEJ2007-65929/PSIC and PSI2010-20168 from the Spanish Ministry of Science and Education; the Dávalos Foundation Grant and P1·1A2010-07 from Bancaixa
Quantification of Cervical Cord Cross-Sectional Area: Which Acquisition, Vertebra Level, and Analysis Software? A Multicenter Repeatability Study on a Traveling Healthy Volunteer
Atrofia; Área transversal; Médula espinalAtròfia; Àrea transversal; Medul·la espinalAtrophy; Cross-sectional area; Spinal cordBackground: Considerable spinal cord (SC) atrophy occurs in multiple sclerosis (MS). While MRI-based techniques for SC cross-sectional area (CSA) quantification have improved over time, there is no common agreement on whether to measure at single vertebral levels or across larger regions and whether upper SC CSA can be reliably measured from brain images.
Aim: To compare in a multicenter setting three CSA measurement methods in terms of repeatability at different anatomical levels. To analyze the agreement between measurements performed on the cervical cord and on brain MRI.
Method: One healthy volunteer was scanned three times on the same day in six sites (three scanner vendors) using a 3T MRI protocol including sagittal 3D T1-weighted imaging of the brain (covering the upper cervical cord) and of the SC. Images were analyzed using two semiautomated methods [NeuroQLab (NQL) and the Active Surface Model (ASM)] and the fully automated Spinal Cord Toolbox (SCT) on different vertebral levels (C1–C2; C2/3) on SC and brain images and the entire cervical cord (C1–C7) on SC images only.
Results: CSA estimates were significantly smaller using SCT compared to NQL and ASM (p < 0.001), regardless of the cord level. Inter-scanner repeatability was best in C1–C7: coefficients of variation for NQL, ASM, and SCT: 0.4, 0.6, and 1.0%, respectively. CSAs estimated in brain MRI were slightly lower than in SC MRI (all p ≤ 0.006 at the C1–C2 level). Despite protocol harmonization between the centers with regard to image resolution and use of high-contrast 3D T1-weighted sequences, the variability of CSA was partly scanner dependent probably due to differences in scanner geometry, coil design, and details of the MRI parameter settings.
Conclusion: For CSA quantification, dedicated isotropic SC MRI should be acquired, which yielded best repeatability in the entire cervical cord. In the upper part of the cervical cord, use of brain MRI scans entailed only a minor loss of CSA repeatability compared to SC MRI. Due to systematic differences between scanners and the CSA quantification software, both should be kept constant within a study. The MRI dataset of this study is available publicly to test new analysis approaches.Parts of this work were funded by the German Federal Ministry for Education and Research, BMBF, German Competence Network Multiple Sclerosis KKNMS (Grant Nos. 01GI1601I and 01GI0914) and by grants from the UK MS Society. FP, CG, and MY were supported by the National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Center. The funding institutions did not interfere with the study design, the collection, analysis and interpretation of data, the writing of the report, or the decision to submit the article for publication
Characterizing 1-year development of cervical cord atrophy across different MS phenotypes: A voxel-wise, multicentre analysis
Background: Spatio-temporal evolution of cord atrophy in multiple sclerosis (MS) has not been investigated yet. Objective: To evaluate voxel-wise distribution and 1-year changes of cervical cord atrophy in a multicentre MS cohort. Methods: Baseline and 1-year 3D T1-weighted cervical cord scans and clinical evaluations of 54 healthy controls (HC) and 113 MS patients (14 clinically isolated syndromes (CIS), 77 relapsing-remitting (RR), 22 progressive (P)) were used to investigate voxel-wise cord volume loss in patients versus HC, 1-year volume changes and clinical correlations (SPM12). Results: MS patients exhibited baseline cord atrophy versus HC at anterior and posterior/lateral C1/C2 and C4–C6 (p < 0.05, corrected). While CIS patients showed baseline volume increase at C4 versus HC (p < 0.001, uncorrected), RRMS exhibited posterior/lateral C1/C2 atrophy versus CIS, and PMS showed widespread cord atrophy versus RRMS (p < 0.05, corrected). At 1 year, 13 patients had clinically worsened. Cord atrophy progressed in MS, driven by RRMS, at posterior/lateral C2 and C3–C6 (p < 0.05, corrected). CIS patients showed no volume changes, while PMS showed circumscribed atrophy progression. Baseline cord atrophy at posterior/lateral C1/C2 and C3–C6 correlated with concomitant and 1-year disability (r = −0.40/–0.62, p < 0.05, corrected). Conclusions: Voxel-wise analysis characterized spinal cord neurodegeneration over 1 year across MS phenotypes and helped to explain baseline and 1-year disability
MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies
There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resource
Relation of sensorimotor and cognitive cerebellum functional connectivity with brain structural damage in patients with multiple sclerosis and no disability
Background and purpose To investigate the relationship between the functional connectivity (FC) of the sensorimotor and cognitive cerebellum and measures of structural damage in patients with multiple sclerosis (MS) and no physical disability. Methods We selected 144 relapsing-remitting MS patients with an Expanded Disability Status Scale score of <= 1.5 and 98 healthy controls from the Italian Neuroimaging Network Initiative database. From multimodal 3T magnetic resonance imaging (MRI), including functional MRI at rest, we calculated lesion load, cortical thickness, and white matter, cortical gray matter, and caudate, putamen, thalamic, and cerebellar volumes. Voxel-wise FC of the sensorimotor and cognitive cerebellum was assessed with seed-based analysis, and multiple regression analysis was used to evaluate the relationship between FC and structural damage. Results Whole brain, white matter, caudate, putamen, and thalamic volumes were reduced in patients compared to controls, whereas cortical gray matter was not significantly different in patients versus controls. Both the sensorimotor and cognitive cerebellum showed a widespread pattern of increased and decreased FC that were negatively associated with structural measures, indicating that the lower the FC, the greater the tissue loss. Lastly, among multiple structural measures, cortical gray matter and white matter volumes were the best predictors of cerebellar FC alterations. Conclusions Increased and decreased cerebellar FC with several brain areas coexist in MS patients with no disability. Our data suggest that white matter loss hampers FC, whereas, in the absence of atrophy, cortical volume represents the framework for FC to increase
Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis : A multicenter study
Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining. Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor. In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ ρ =(-0.42)-(-0.76); p- values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings. Automated and manual thalamus segmentation consistently demonstrated an association between thalamus atrophy and cognitive impairment in MS. However, a proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings
Extra-Visual Functional and Structural Connection Abnormalities in Leber's Hereditary Optic Neuropathy
We assessed abnormalities within the principal brain resting state networks (RSNs) in patients with Leber's hereditary optic neuropathy (LHON) to define whether functional abnormalities in this disease are limited to the visual system or, conversely, tend to be more diffuse. We also defined the structural substrates of fMRI changes using a connectivity-based analysis of diffusion tensor (DT) MRI data. Neuro-ophthalmologic assessment, DT MRI and RS fMRI data were acquired from 13 LHON patients and 13 healthy controls. RS fMRI data were analyzed using independent component analysis and SPM5. A DT MRI connectivity-based parcellation analysis was performed using the primary visual and auditory cortices, bilaterally, as seed regions. Compared to controls, LHON patients had a significant increase of RS fluctuations in the primary visual and auditory cortices, bilaterally. They also showed decreased RS fluctuations in the right lateral occipital cortex and right temporal occipital fusiform cortex. Abnormalities of RS fluctuations were correlated significantly with retinal damage and disease duration. The DT MRI connectivity-based parcellation identified a higher number of clusters in the right auditory cortex in LHON vs. controls. Differences of cluster-centroid profiles were found between the two groups for all the four seeds analyzed. For three of these areas, a correspondence was found between abnormalities of functional and structural connectivities. These results suggest that functional and structural abnormalities extend beyond the visual network in LHON patients. Such abnormalities also involve the auditory network, thus corroborating the notion of a cross-modal plasticity between these sensory modalities in patients with severe visual deficits
Monoaminergic network abnormalities: a marker for multiple sclerosis-related fatigue and depression
Objective: To investigate monoaminergic network abnormalities in patients with multiple sclerosis (MS) according to their fatigue and depressive status through a positron emission tomography (PET)-based constrained independent component analysis (ICA) on resting state (RS) functional MRI (fMRI). Methods: In this prospective study, 213 patients with MS (mean age=40.6±12.5 years; 94/119 men/women; 153 relapsing-remitting; 60 progressive) and 62 healthy controls (HCs, mean age=39.0±10.4 years; 30/32 men/women) underwent neurological, fatigue, depression and RS fMRI assessment. Patterns of dopamine, norepinephrine-related and serotonin-related RS functional connectivity (FC) were derived by ICA, constrained to PET atlases for dopamine, norepinephrine and serotonin transporters, obtained in HCs' brain. Results: Compared with HCs, patients with MS showed abnormalities in all three explored monoaminergic networks, mostly with decreased RS FC within PET-guided monoaminergic networks in frontal regions and subcortical areas including the cerebellum and thalamus, and increased RS FC in temporo-parieto-occipital cortical areas, including bilateral precunei.MS-related fatigue was associated with decreased RS FC within the PET-guided dopamine network in the left thalamus and left cerebellum, and with increased RS FC within the PET-guided serotonin network in the left middle occipital gyrus. MS-related depression was associated with more distributed abnormalities involving the three explored monoaminergic networks, resulting in overall reduced RS FC in the frontal lobe, limbic areas and the precuneus. Conclusions: Patients with MS present diffuse dysregulation in the monoaminergic networks. Specific alterations in these networks were associated with fatigue and depression, providing a pathological marker for these bothersome symptoms and putative targets for their treatment
Task- and resting-state fMRI studies in multiple sclerosis: From regions to systems and time-varying analysis. Current status and future perspective
Multiple sclerosis (MS) is a neurological disorder affecting the central nervous system and features extensive functional brain changes that are poorly understood but relate strongly to clinical impairments. Functional magnetic resonance imaging (fMRI) is a non-invasive, powerful technique able to map activity of brain regions and to assess how such regions interact for an efficient brain network. FMRI has been widely applied to study functional brain changes in MS, allowing to investigate functional plasticity consequent to disease-related structural injury. The first studies in MS using active fMRI tasks mainly aimed to study such plastic changes by identifying abnormal activity in salient brain regions (or systems) involved by the task. In later studies the focus shifted towards resting state (RS) functional connectivity (FC) studies, which aimed to map large-scale functional networks of the brain and to establish how MS pathology impairs functional integration, eventually leading to the hypothesized network collapse as patients clinically progress. This review provides a summary of the main findings from studies using task-based and RS fMRI and illustrates how functional brain alterations relate to clinical disability and cognitive deficits in this condition. We also give an overview of longitudinal studies that used task-based and RS fMRI to monitor disease evolution and effects of motor and cognitive rehabilitation. In addition, we discuss the results of studies using newer technologies involving time-varying FC to investigate abnormal dynamism and flexibility of network configurations in MS. Finally, we show some preliminary results from two recent topics (i.e., multimodal MRI analysis and artificial intelligence) that are receiving increasing attention. Together, these functional studies could provide new (conceptual) insights into disease stage-specific mechanisms underlying progression in MS, with recommendations for future research
Dynamic Functional Connectivity For The Classification Of Multiple Sclerosis Phenotype: A Hidden Markov Model Approach
We present a pipeline for the classification of subjects according to multiple sclerosis phenotype. The approach is based on a hidden Markov model built on dynamic functional connectivity. More in detail, a sequence of correlation matrices is built from the fMRI time slices, then projected on the tangent Euclidean space. The number of considered dimensions is reduced through PCA, then dominant set clustering is applied to group similar correlation matrices in a limited number of ”mental” states, which are then used as the hidden states of the Markov model. Subjects in the test set are then classified according to the likelihood of their sequence of observations with the obtained class-wise Markov models. We demonstrate that our approach is capable of discriminating multiple sclerosis phenotypes and that the persistence of some of the identified states is a possible neurohpyisiological marker of disease severity
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