Relationship between brain white matter damage and grey matter atrophy in hereditary spastic paraplegia types 4 and 5

BACKGROUND AND PURPOSE

White matter (WM) damage is the main target of hereditary spastic paraplegia (HSP), but mounting evidence indicates that genotype-specific grey matter (GM) damage is not uncommon. Our aim was to identify and compare brain GM and WM damage patterns in HSP subtypes and investigate how gene expression contributes to these patterns, and explore the relationship between GM and WM damage.

METHODS

In this prospective single-centre cohort study from 2019 to 2022, HSP patients and controls underwent magnetic resonance imaging evaluations. The alterations of GM and WM patterns were compared between groups by applying a source-based morphometry approach. Spearman rank correlation was used to explore the associations between gene expression and GM atrophy patterns in HSP subtypes. Mediation analysis was conducted to investigate the interplay between GM and WM damage.

RESULTS

Twenty-one spastic paraplegia type 4 (SPG4) patients (mean age 50.7 years ± 12.0 SD, 15 men), 21 spastic paraplegia type 5 (SPG5) patients (mean age 29.1 years ± 12.8 SD, 14 men) and 42 controls (sex- and age-matched) were evaluated. Compared to controls, SPG4 and SPG5 showed similar WM damage but different GM atrophy patterns. GM atrophy patterns in SPG4 and SPG5 were correlated with corresponding gene expression (ρ = 0.30, p = 0.008, ρ = 0.40, p < 0.001, respectively). Mediation analysis indicated that GM atrophy patterns were mediated by WM damage in HSP.

CONCLUSIONS

Grey matter atrophy patterns were distinct between SPG4 and SPG5 and were not only secondary to WM damage but also associated with disease-related gene expression.

CLINICAL TRIAL REGISTRATION NO

NCT04006418.

Brain and cervical spinal cord MRI correlates of sensorimotor impairment in patients with multiple sclerosis

BACKGROUND

Cervical spinal cord (cSC) lesions and atrophy contribute to disability in multiple sclerosis (MS), but associations with specific sensorimotor dysfunction require further exploration.

OBJECTIVE

To investigate the associations of brain and cSC magnetic resonance imaging (MRI) measures with sensorimotor impairment in MS.

METHODS

One hundred fifty-one MS patients and 69 healthy controls underwent 3T MRI and clinical assessments including Expanded Disability Status Scale (EDSS), 9-hole peg test (9-HPT), finger tapping test (FTT), timed 25-foot walk test (T25FWT), and vibration detection threshold (VDT). Random forest ranked brain (T2-hyperintense lesion volume (T2-LV) and normalized deep gray matter (GM), cortical and white matter (WM) volumes) and cSC (T2-LV and total, GM, and WM cross-sectional areas (CSAs) at C2/C3 level) MRI measures relevance in explaining EDSS milestones (EDSS ⩾3.0, ⩾4.0, and ⩾6.0), VDT, pyramidal and sensory functional systems (P-FS and S-FS ⩾2), and motor tests impairment.

RESULTS

Various combinations of brain and cSC MRI measures explained EDSS milestones (area under the curve (AUC) =0.879-0.900), VDT (R2 = 0.194), and impaired P-FS (AUC = 0.820), S-FS (AUC = 0.795), 9-HPT (AUC = 0.793), FTT (AUC = 0.740), and T25FWT (AUC = 0.825). cSC GM CSA was the most informative feature for all outcomes, except 9-HPT.

CONCLUSION

cSC MRI measures, especially GM CSA, explain EDSS and sensorimotor dysfunction better than brain measures in MS.

Brain neuroplasticity in multiple sclerosis patients in functional magnetic resonance imaging. Part 1: Comparison with healthy volunteers

Purpose

The aim of this study was to assess the activity of motor cortical areas and the resting brain activity in a group of multiple sclerosis (MS) patients compared to a group of healthy individuals according to task-based functional magnetic resonance imaging (t-fMRI), resting state functional MRI (rs-fMRI), and volumetric MRI studies.

Material and methods

The study enrolled 28 MS patients and 20 healthy volunteers who underwent MRI examinations. Primary motor cortex (M1), premotor area (PMA), supplementary motor area, as well as resting state networks (RSN's) and volumes of selected brain structures were subjected to a detailed analysis.

Results

In MS patients, a motor task more often resulted in the activation of ipsilateral M1 cortex (observed in 39% of the studied group) as well as the PMA cortex (observed in 32% of MS patients). No differences in resting brain activity were found between the studied groups. Significant differences were observed in volumetric parameters of the total brain volume (healthy volunteers vs. MS patients, respectively): (1197 cm³ vs. 1150 cm³) and volumes of the grey matter (517 cm³ vs. 481 cm³), cerebellum (150 cm³ vs. 136 cm³), thalamus (16.3 cm³ vs. 12.6 cm³), putamen (8.9 cm³ vs. 7.7 cm³), and globus pallidus (4.57 cm³ vs. 3.57 cm³).

Conclusions

In the MS patients, the motor task required significantly more frequent activation of the primary and secondary ipsilateral motor cortex compared to the group of healthy volunteers. The rs-fMRI study showed no differences in activity patterns within the RSN's. Differences in the total cerebral volume and the volume of the grey matter, cerebellum, thalamus, putamen, and globus pallidus were observed.

Diffusion tensor imaging metrics associated with future disability in multiple sclerosis

The relationship between brain diffusion microstructural changes and disability in multiple sclerosis (MS) remains poorly understood. We aimed to explore the predictive value of microstructural properties in white (WM) and grey matter (GM), and identify areas associated with mid-term disability in MS patients. We studied 185 patients (71% female; 86% RRMS) with the Expanded Disability Status Scale (EDSS), timed 25-foot walk (T25FW), nine-hole peg test (9HPT), and Symbol Digit Modalities Test (SDMT) at two time-points. We used Lasso regression to analyse the predictive value of baseline WM fractional anisotropy and GM mean diffusivity, and to identify areas related to each outcome at 4.1 years follow-up. Motor performance was associated with WM (T25FW: RMSE = 0.524, R² = 0.304; 9HPT dominant hand: RMSE = 0.662, R² = 0.062; 9HPT non-dominant hand: RMSE = 0.649, R² = 0.139), and SDMT with GM diffusion metrics (RMSE = 0.772, R² = 0.186). Cingulum, longitudinal fasciculus, optic radiation, forceps minor and frontal aslant were the WM tracts most closely linked to motor dysfunction, and temporal and frontal cortex were relevant for cognition. Regional specificity related to clinical outcomes provide valuable information that can be used to develop more accurate predictive models that could improve therapeutic strategies.

Subtypes of relapsing-remitting multiple sclerosis identified by network analysis

We used network analysis to identify subtypes of relapsing-remitting multiple sclerosis subjects based on their cumulative signs and symptoms. The electronic medical records of 113 subjects with relapsing-remitting multiple sclerosis were reviewed, signs and symptoms were mapped to classes in a neuro-ontology, and classes were collapsed into sixteen superclasses by subsumption. After normalization and vectorization of the data, bipartite (subject-feature) and unipartite (subject-subject) network graphs were created using NetworkX and visualized in Gephi. Degree and weighted degree were calculated for each node. Graphs were partitioned into communities using the modularity score. Feature maps visualized differences in features by community. Network analysis of the unipartite graph yielded a higher modularity score (0.49) than the bipartite graph (0.25). The bipartite network was partitioned into five communities which were named fatigue, behavioral, hypertonia/weakness, abnormal gait/sphincter, and sensory, based on feature characteristics. The unipartite network was partitioned into five communities which were named fatigue, pain, cognitive, sensory, and gait/weakness/hypertonia based on features. Although we did not identify pure subtypes (e.g., pure motor, pure sensory, etc.) in this cohort of multiple sclerosis subjects, we demonstrated that network analysis could partition these subjects into different subtype communities. Larger datasets and additional partitioning algorithms are needed to confirm these findings and elucidate their significance. This study contributes to the literature investigating subtypes of multiple sclerosis by combining feature reduction by subsumption with network analysis.

Neural correlates of digital measures shown by structural MRI: a post-hoc analysis of a smartphone-based remote assessment feasibility study in multiple sclerosis

BACKGROUND

A study was undertaken to evaluate remote monitoring via smartphone sensor-based tests in people with multiple sclerosis (PwMS). This analysis aimed to explore regional neural correlates of digital measures derived from these tests.

METHODS

In a 24-week, non-randomized, interventional, feasibility study (NCT02952911), sensor-based tests on the Floodlight Proof-of-Concept app were used to assess cognition (smartphone-based electronic Symbol Digit Modalities Test), upper extremity function (Draw a Shape Test, Pinching Test), and gait and balance (Static Balance Test, Two-Minute Walk Test, U-Turn Test). In this post-hoc analysis, digital measures and standard clinical measures (e.g., Nine-Hole Peg Test [9HPT]) were correlated against regional structural magnetic resonance imaging outcomes. Seventy-six PwMS aged 18-55 years with an Expanded Disability Status Scale score of 0.0-5.5 were enrolled from two different sites (USA and Spain). Sixty-two PwMS were included in this analysis.

RESULTS

Worse performance on digital and clinical measures was associated with smaller regional brain volumes and larger ventricular volumes. Whereas digital and clinical measures had many neural correlates in common (e.g., putamen, globus pallidus, caudate nucleus, lateral occipital cortex), some were observed only for digital measures. For example, Draw a Shape Test and Pinching Test measures, but not 9HPT score, correlated with volume of the hippocampus (r = 0.37 [drawing accuracy over time on the Draw a Shape Test]/ - 0.45 [touching asynchrony on the Pinching Test]), thalamus (r = 0.38/ - 0.41), and pons (r = 0.35/ - 0.35).

CONCLUSIONS

Multiple neural correlates were identified for the digital measures in a cohort of people with early MS. Digital measures showed associations with brain regions that clinical measures were unable to demonstrate, thus providing potential novel information on functional ability compared with standard clinical assessments.

Machine learning-optimized Combinatorial MRI scale (COMRISv2) correlates highly with cognitive and physical disability scales in Multiple Sclerosis patients

Composite MRI scales of central nervous system tissue destruction correlate stronger with clinical outcomes than their individual components in multiple sclerosis (MS) patients. Using machine learning (ML), we previously developed Combinatorial MRI scale (COMRISv1) solely from semi-quantitative (semi-qMRI) biomarkers. Here, we asked how much better COMRISv2 might become with the inclusion of quantitative (qMRI) volumetric features and employment of more powerful ML algorithm. The prospectively acquired MS patients, divided into training (n = 172) and validation (n = 83) cohorts underwent brain MRI imaging and clinical evaluation. Neurological examination was transcribed to NeurEx™ App that automatically computes disability scales. qMRI features were computed by lesion-TOADS algorithm. Modified random forest pipeline selected biomarkers for optimal model(s) in the training cohort. COMRISv2 models validated moderate correlation with cognitive disability [Spearman Rho = 0.674; Lin's concordance coefficient (CCC) = 0.458; p < 0.001] and strong correlations with physical disability (Spearman Rho = 0.830-0.852; CCC = 0.789-0.823; p < 0.001). The NeurEx led to the strongest COMRISv2 model. Addition of qMRI features enhanced performance only of cognitive disability model, likely because semi-qMRI biomarkers measure infratentorial injury with greater accuracy. COMRISv2 models predict most granular clinical scales in MS with remarkable criterion validity, expanding scientific utilization of cohorts with missing clinical data.

Confounder-adjusted MRI-based predictors of multiple sclerosis disability

Introduction

Both aging and multiple sclerosis (MS) cause central nervous system (CNS) atrophy. Excess brain atrophy in MS has been interpreted as "accelerated aging." Current paper tests an alternative hypothesis: MS causes CNS atrophy by mechanism(s) different from physiological aging. Thus, subtracting effects of physiological confounders on CNS structures would isolate MS-specific effects.

Methods

Standardized brain MRI and neurological examination were acquired prospectively in 646 participants enrolled in ClinicalTrials.gov Identifier: NCT00794352 protocol. CNS volumes were measured retrospectively, by automated Lesion-TOADS algorithm and by Spinal Cord Toolbox, in a blinded fashion. Physiological confounders identified in 80 healthy volunteers were regressed out by stepwise multiple linear regression. MS specificity of confounder-adjusted MRI features was assessed in non-MS cohort (n = 158). MS patients were randomly split into training (n = 277) and validation (n = 131) cohorts. Gradient boosting machine (GBM) models were generated in MS training cohort from unadjusted and confounder-adjusted CNS volumes against four disability scales.

Results

Confounder adjustment highlighted MS-specific progressive loss of CNS white matter. GBM model performance decreased substantially from training to cross-validation, to independent validation cohorts, but all models predicted cognitive and physical disability with low p-values and effect sizes that outperform published literature based on recent meta-analysis. Models built from confounder-adjusted MRI predictors outperformed models from unadjusted predictors in the validation cohort.

Conclusion

GBM models from confounder-adjusted volumetric MRI features reflect MS-specific CNS injury, and due to stronger correlation with clinical outcomes compared to brain atrophy these models should be explored in future MS clinical trials.

Task- and resting-state fMRI studies in multiple sclerosis: From regions to systems and time-varying analysis. Current status and future perspective

•

Functional MRI is able to detect adaptive and maladaptive abnormalities at different MS stages.

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Increased fMRI activity is a feature of early MS, while progressive exhaustion of adaptive mechanisms is detected later on in the disease.

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Collapse of long-range connections and impaired hub integration characterize MS network reorganization.

•

Time-varying connectivity analysis provides useful and complementary pieces of information to static functional connectivity.

•

New perspectives might be the use of multimodal MRI and artificial intelligence.

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.

Linking lesions in sensorimotor cortex to contralateral hand function in multiple sclerosis: a 7 T MRI study

Cortical lesions constitute a key manifestation of multiple sclerosis and contribute to clinical disability and cognitive impairment. Yet it is unknown whether local cortical lesions and cortical lesion subtypes contribute to domain-specific impairments attributable to the function of the lesioned cortex.

In this cross-sectional study, we assessed how cortical lesions in the primary sensorimotor hand area (SM1-HAND) relate to corticomotor physiology and sensorimotor function of the contralateral hand. 50 relapse-free patients with relapsing-remitting or secondary-progressive multiple sclerosis and 28 healthy age- and sex-matched participants underwent whole-brain 7 T MRI to map cortical lesions. Brain scans were also used to estimate normalized brain volume, pericentral cortical thickness, white matter lesion fraction of the corticospinal tract, infratentorial lesion volume and the cross-sectional area of the upper cervical spinal cord. We tested sensorimotor hand function and calculated a motor and sensory composite score for each hand. In 37 patients and 20 healthy controls, we measured maximal motor evoked potential (MEP) amplitude, resting motor threshold and corticomotor conduction time with transcranial magnetic stimulation (TMS) and the N20 latency from somatosensory evoked potentials (SSEPs).

Patients showed at least one cortical lesion in the SM1-HAND in 47 of 100 hemispheres. The presence of a lesion was associated with worse contralateral sensory (P = 0.014) and motor (P = 0.009) composite scores. TMS of a lesion-positive SM1-HAND revealed a decreased maximal MEP amplitude (P &lt; 0.001) and delayed corticomotor conduction (P = 0.002) relative to a lesion-negative SM1-HAND. Stepwise mixed linear regressions showed that the presence of an SM1-HAND lesion, higher white-matter lesion fraction of the corticospinal tract, reduced spinal cord cross-sectional area and higher infratentorial lesion volume were associated with reduced contralateral motor hand function. Cortical lesions in SM1-HAND, spinal cord cross-sectional area and normalized brain volume were also associated with smaller maximal MEP amplitude and longer corticomotor conduction times. The effect of cortical lesions on sensory function was no longer significant when controlling for MRI-based covariates. Lastly, we found that intracortical and subpial lesions had the largest effect on reduced motor hand function, intracortical lesions on reduced MEP amplitude and leukocortical lesions on delayed corticomotor conduction.

Together, this comprehensive multi-level assessment of sensorimotor brain damage shows that the presence of a cortical lesion in SM1-HAND is associated with impaired corticomotor function of the hand, after accounting for damage at the subcortical level. The results also provide preliminary evidence that cortical lesion types may affect the various facets of corticomotor function differentially.

MRI of Transcallosal White Matter Helps to Predict Motor Impairment in Multiple Sclerosis

Background Altered callosal integrity has been associated with motor deficits in patients with multiple sclerosis (MS), but its contribution to disability has, to the knowledge of the authors, not been investigated by using multiparametric MRI approaches. Purpose To investigate structural and functional interhemispheric MRI substrates of global disability at different milestones and upper limb motor impairment in MS. Materials and Methods In this cross-sectional study, healthy control patients and patients with MS (between January 1, 2008, and December 31, 2016) were retrospectively selected from our hospital database. Clinical assessment included Expanded Disability Status Scale (EDSS), nine-hole peg test, and digital finger tapping test. By using structural and resting-state functional MRI sequences, probabilistic tractography of hand corticospinal tract fibers, and transcallosal fibers between hand-motor cortices (hereafter, referred to as hand-M1), supplementary motor areas (SMAs), premotor cortices (PMCs), and voxel-mirror homotopic connectivity (VMHC) were analyzed. Random forest analyses identified the MRI predictors of clinical disability at different milestones (EDSS scores of 3.0, 4.0, 6.0) and upper limb motor impairment (nine-hole peg test and finger tapping test z scores < healthy control patients 5th percentile). Results One-hundred thirty healthy control patients (median age, 39 years; interquartile range, 31-50 years; 70 women) and 340 patients with MS (median age, 43 years; interquartile range, 33-51 years; 213 women) were studied. EDSS 3.0 predictors (n = 159) were global measures of atrophy and lesions together with damage measures of corticospinal tracts and transcallosal fibers between PMCs and SMAs (accuracy, 86%; P = .001-.01). For EDSS 4.0 (n = 131), similar predictors were found in addition to damage in transcallosal fibers between hand-M1 (accuracy, 89%; P = .001-.049). No MRI predictors were found for EDSS 6.0 (n = 70). Nine-hole peg test (right, n = 161; left, n = 166) and finger tapping test (right, n = 117; left, n = 111) impairments were predicted by damage in transcallosal fibers between SMAs and PMCs (accuracy range, 69%-77%; P = .001-.049). VMHC abnormalities did not explain clinical outcomes. Conclusion Structural, not functional, abnormalities at MRI in transcallosal premotor and motor white matter fibers predicted severity of global disability and upper limb motor impairment in patients with multiple sclerosis. The informative role of such predictors appeared less evident at higher disability levels. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Barkhof and Pontillo in this issue.

Differential association of cortical, subcortical and spinal cord damage with multiple sclerosis disability milestones: A multiparametric MRI study

BACKGROUND

In multiple sclerosis (MS), cortical, subcortical and infratentorial structural damage may have a differential contribution to clinical disability according to disease phases.

PURPOSE

To determine the relative contributions of cortical, deep (D) grey matter (GM), cerebellar and cervical cord damage to MS disability milestones.

METHODS

Multi-centre 3T brain and cervical cord T₂- and three-dimensional (3D) T₁-weighted images were acquired from 198 MS patients (139 relapsing-remitting (RR) MS, 59 progressive (P) MS) and 67 healthy controls. Brain/cord lesion burden, cortical thickness (CTh), DGM and cerebellar volumetry and cord cross-sectional area (CSA) were quantified. Random forest analyses identified predictors of expanded disability status scale (EDSS) disability milestones (EDSS = 3.0, 4.0 and 6.0).

RESULTS

MS patients had widespread atrophy in all investigated compartments versus controls (p-range: ⩽0.001-0.05). Informative determinants of EDSS = 3.0 were cord CSA, brain lesion volume, frontal CTh and thalamic and cerebellar atrophy (out-of-bag (OOB) accuracy = 0.84, p-range: ⩽0.001-0.05). EDSS = 4.0 was mainly predicted by cerebellar and cord atrophy, frontal and sensorimotor CTh and cord lesion number (OOB accuracy = 0.84, p-range: ⩽0.001-0.04). Cervical cord CSA (p = 0.001) and cord lesion number (p = 0.003) predicted EDSS = 6.0 (OOB accuracy = 0.77).

CONCLUSION

Brain lesion burden, cortical and thalamic atrophy were the main determinants of EDSS = 3.0 and 4.0, while cord damage played a major contribution to EDSS = 6.0.