Modeling the relationship among gray matter atrophy, abnormalities in connecting white matter, and cognitive performance in early multiple sclerosis

The sequence of regional structural disconnectivity due to multiple sclerosis lesions

Prediction of disease progression is challenging in multiple sclerosis as the sequence of lesion development and retention of inflammation within a subset of chronic lesions is heterogeneous among patients. We investigated the sequence of lesion-related regional structural disconnectivity across the spectrum of disability and cognitive impairment in multiple sclerosis. In a full cohort of 482 multiple sclerosis patients (age: 41.83 ± 11.63 years, 71.57% females), the Expanded Disability Status Scale was used to classify patients into (i) no or mild (Expanded Disability Status Scale <3) versus (ii) moderate or severe disability groups (Expanded Disability Status Scale ≥3). In 363 out of 482 patients, quantitative susceptibility mapping was used to identify paramagnetic rim lesions, which are maintained by a rim of iron-laden innate immune cells. In 171 out of 482 patients, Brief International Cognitive Assessment was used to identify subjects as being cognitively preserved or impaired. Network Modification Tool was used to estimate the regional structural disconnectivity due to multiple sclerosis lesions. Discriminative event-based modelling was applied to investigate the sequence of regional structural disconnectivity due to (i) all representative T2 fluid-attenuated inversion recovery lesions, (ii) paramagnetic rim lesions versus non-paramagnetic rim lesions separately across disability groups ('no to mild disability' to 'moderate to severe disability'), (iii) all representative T2 fluid-attenuated inversion recovery lesions and (iv) paramagnetic rim lesions versus non-paramagnetic rim lesions separately across cognitive status ('cognitively preserved' to 'cognitively impaired'). In the full cohort, structural disconnection in the ventral attention and subcortical networks, particularly in the supramarginal and putamen regions, was an early biomarker of moderate or severe disability. The earliest biomarkers of disability progression were structural disconnections due to paramagnetic rim lesions in the motor-related regions. Subcortical structural disconnection, particularly in the ventral diencephalon and thalamus regions, was an early biomarker of cognitive impairment. Our data-driven model revealed that the structural disconnection in the subcortical regions, particularly in the thalamus, is an early biomarker for both disability and cognitive impairment in multiple sclerosis. Paramagnetic rim lesions-related structural disconnection in the motor cortex may identify the patients at risk for moderate or severe disability in multiple sclerosis. Such information might be used to identify people with multiple sclerosis who have an increased risk of disability progression or cognitive decline in order to provide personalized treatment plans.

The novel small molecule TPN10518 alleviates EAE pathogenesis by inhibiting AP1 to depress Th1/Th17 cell differentiation

Multiple sclerosis (MS) is one of the most common autoimmune diseases of central nervous system (CNS) demyelination. Experimental autoimmune encephalomyelitis (EAE) is the most classic animal model for simulating the onset of clinical symptoms in MS. Previous research has reported the anti-inflammatory effects of artemisinin on autoimmune diseases. In our study, we identified a novel small molecule, TPN10518, an artemisinin derivative, which plays a protective role on the EAE model. We found that TPN10518 reduced CNS inflammatory cell infiltration and alleviated clinical symptoms of EAE. In addition, TPN10518 downregulated the production of Th1 and Th17 cells in vivo and in vitro, and decrease the levels of related chemokines. RNA-seq assay combined with the experimental results demonstrated that TPN10518 lowered the mRNA and protein levels of the AP1 subunits c-Fos and c-Jun in EAE mice. It was further confirmed that TPN10518 was dependent on AP1 to inhibit the differentiation of Th1 and Th17 cells. The results suggest that TPN10518 reduces the production of Th1 and Th17 cells through inhibition of AP1 to alleviate the severity of EAE disease. It is expected to be a potential drug for the treatment of MS.

Regional white matter and gray matter damage and cognitive performances in multiple sclerosis according to sex

In this study, we investigated whether regional distribution of white matter (WM) lesions, normal-appearing [NA] WM microstructural abnormalities and gray matter (GM) atrophy may differently contribute to cognitive performance in multiple sclerosis (MS) patients according to sex. Using the same scanner, brain 3.0T MRI was acquired for 287 MS patients (females = 173; mean age = 42.1 [standard deviation, SD = 12.7] years; relapsing-remitting = 196, progressive = 91; median Expanded Disability Status Scale = 2.5 [interquartile range, IQR = 1.5-5.0]; median disease duration = 12.1 [IQR = 6.3-19.0] years; treatment: none = 70, first-line = 130, second-line = 87) and 172 healthy controls (HC) (females = 92; mean age = 39.3 [SD = 14.8] years). MS patients underwent also Rao's neuropsychological battery. Using voxel-wise analyses, we investigated in patients sex-related differences in the association of cognitive performances with WM lesions, NAWM fractional anisotropy (FA) and GM volumes (p < 0.01, family-wise error [FWE]). Sixty-six female (38%) and 48 male (42%) MS patients were cognitively impaired, with no significant between-group difference (p = 0.704). However, verbal memory performance was worse in males (p = 0.001), whereas verbal fluency performance was worse in females (p = 0.004). In both sexes, a higher T2-hyperintense lesion prevalence in cognitively-relevant WM tracts was significantly associated with worse cognitive performance (p ≤ 0.006), with stronger associations in females than males in global cognition (p ≤ 0.004). Compared to sex-matched HC, male and female MS patients had widespread lower NAWM FA and GM volume (p < 0.01). In both sexes, worse cognitive performance was associated with widespread reduced NAWM FA (p < 0.01), with stronger associations in females than males in global cognition and verbal memory (p ≤ 0.009). Worse cognitive performance was significantly associated with clusters of cortical GM atrophy in males (p ≤ 0.007) and mainly with deep GM atrophy in females (p ≤ 0.006). In this study, only limited differences in cognitive performances were found between male and female MS patients. A disconnection syndrome due to focal WM lesions and diffuse NAWM microstructural abnormalities seems to be more relevant in female MS patients to explain cognitive impairment.

The sequence of regional structural disconnectivity due to multiple sclerosis lesions

Objective

Prediction of disease progression is challenging in multiple sclerosis (MS) as the sequence of lesion development and retention of inflammation within a subset of chronic lesions is heterogeneous among patients. We investigated the sequence of lesion-related regional structural disconnectivity across the spectrum of disability and cognitive impairment in MS.

Methods

In a full cohort of 482 patients, the Expanded Disability Status Scale was used to classify patients into (i) no or mild vs (ii) moderate or severe disability groups. In 363 out of 482 patients, Quantitative Susceptibility Mapping was used to identify paramagnetic rim lesions (PRL), which are maintained by a rim of iron-laden innate immune cells. In 171 out of 482 patients, Brief International Cognitive Assessment was used to identify subjects with cognitive impairment. Network Modification Tool was used to estimate the regional structural disconnectivity due to MS lesions. Discriminative event-based modeling was applied to investigate the sequence of regional structural disconnectivity due to all representative lesions across the spectrum of disability and cognitive impairment.

Results

Structural disconnection in the ventral attention and subcortical networks was an early biomarker of moderate or severe disability. The earliest biomarkers of disability progression were structural disconnections due to PRL in the motor-related regions. Subcortical structural disconnection was an early biomarker of cognitive impairment.

Interpretation

MS lesion-related structural disconnections in the subcortex is an early biomarker for both disability and cognitive impairment in MS. PRL-related structural disconnection in the motor cortex may identify the patients at risk for moderate or severe disability in MS.

Time course of lesion-induced atrophy in multiple sclerosis

BACKGROUND AND PURPOSE

White matter (WM) tract disruption impacts volume loss in connected deep gray matter (DGM) over 5 years in people with multiple sclerosis (PwMS). However, the timeline of this phenomenon remains poorly characterized.

MATERIALS AND METHODS

Annual serial MRI for 181 PwMS was retrospectively analyzed from a 10-year clinical trial database. Annualized thalamic atrophy, DGM atrophy, and disruption of connected WM tracts were measured. For time series analysis, ~700 epochs were collated using a sliding 5-year window, and regression models predicting 1-year atrophy were applied to characterize the influence of new tract disruption from preceding years, while controlling for whole brain atrophy and other relevant factors.

RESULTS

Disruptions of WM tracts connected to the thalamus were significantly associated with thalamic atrophy 1 year later (β: 0.048-0.103). This effect was not observed for thalamic tract disruption concurrent with the time of atrophy nor for thalamic tract disruption preceding the atrophy by 2-4 years. Similarly, disruptions of white matter tracts connected to the DGM were significantly associated with DGM atrophy 1 year later (β: 0.078-0.111), but not for tract disruption concurrent with, nor preceding the atrophy by 2-4 years.

CONCLUSION

Increased rates of thalamic and DGM atrophy were restricted to 1 year following newly developed disruption in connected WM tracts. In research and clinical settings, additional gray matter atrophy may be expected 1 year following new lesion growth in connected white matter.

Validating atlas-based lesion disconnectomics in multiple sclerosis: A retrospective multi-centric study

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Structural disconnectomes can be modelled without diffusion using tractography atlases.

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Atlas-based and DTI-derived disconnectome topological metrics correlate strongly.

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MS patient disconnectomes relate to clinical scores.

The translational potential of MR-based connectivity modelling is limited by the need for advanced diffusion imaging, which is not part of clinical protocols for many diseases. In addition, where diffusion data is available, brain connectivity analyses rely on tractography algorithms which imply two major limitations. First, tracking algorithms are known to be sensitive to the presence of white matter lesions and therefore leading to interpretation pitfalls and poor inter-subject comparability in clinical applications such as multiple sclerosis. Second, tractography quality is highly dependent on the acquisition parameters of diffusion sequences, leading to a trade-off between acquisition time and tractography precision.

Here, we propose an atlas-based approach to study the interplay between structural disconnectivity and lesions without requiring individual diffusion imaging. In a multi-centric setting involving three distinct multiple sclerosis datasets (containing both 1.5 T and 3 T data), we compare our atlas-based structural disconnectome computation pipeline to disconnectomes extracted from individual tractography and explore its clinical utility for reducing the gap between radiological findings and clinical symptoms in multiple sclerosis. Results using topological graph properties showed that overall, our atlas-based disconnectomes were suitable approximations of individual disconnectomes from diffusion imaging. Small-worldness was found to decrease for larger total lesion volumes thereby suggesting a loss of efficiency in brain connectivity of MS patients. Finally, the global efficiency of the created brain graph, combined with total lesion volume, allowed to stratify patients into subgroups with different clinical scores in all three cohorts.

Structural disconnectivity from paramagnetic rim lesions is related to disability in multiple sclerosis

BACKGROUND

In people with multiple sclerosis (pwMS), lesions with a hyperintense rim (rim+) on Quantitative Susceptibility Mapping (QSM) have been shown to have greater myelin damage compared to rim- lesions, but their association with disability has not yet been investigated. Furthermore, how QSM rim+ and rim- lesions differentially impact disability through their disruptions to structural connectivity has not been explored. We test the hypothesis that structural disconnectivity due to rim+ lesions is more predictive of disability compared to structural disconnectivity due to rim- lesions.

METHODS

Ninety-six pwMS were included in our study. Individuals with Expanded Disability Status Scale (EDSS) <2 were considered to have lower disability (n = 59). For each gray matter region, a Change in Connectivity (ChaCo) score, that is, the percent of connecting streamlines also passing through a rim- or rim+ lesion, was computed. Adaptive Boosting was used to classify the pwMS into lower versus greater disability groups based on ChaCo scores from rim+ and rim- lesions. Classification performance was assessed using the area under ROC curve (AUC).

RESULTS

The model based on ChaCo from rim+ lesions outperformed the model based on ChaCo from rim- lesions (AUC = 0.67 vs 0.63, p-value < .05). The left thalamus and left cerebellum were the most important regions in classifying pwMS into disability categories.

CONCLUSION

rim+ lesions may be more influential on disability through their disruptions to the structural connectome than rim- lesions. This study provides a deeper understanding of how rim+ lesion location/size and resulting disruption to the structural connectome can contribute to MS-related disability.

Gray matter atrophy in relapsing-remitting multiple sclerosis is associated with white matter lesions in connecting fibers

Background:

Lesions of brain white matter (WM) and atrophy of brain gray matter (GM) are well-established surrogate parameters in multiple sclerosis (MS), but it is unclear how closely these parameters relate to each other.

Objective:

To assess across the whole cerebrum whether GM atrophy can be explained by lesions in connecting WM tracts.

Methods:

GM images of 600 patients with relapsing-remitting MS (women = 68%; median age = 33.0 years, median expanded disability status scale score = 1.5) were converted to atrophy maps by data from a healthy control cohort. An atlas of WM tracts from the Human Connectome Project and individual lesion maps were merged to identify potentially disconnected GM regions, leading to individual disconnectome maps. Across the whole cerebrum, GM atrophy and potentially disconnected GM were tested for association both cross-sectionally and longitudinally.

Results:

We found highly significant correlations between disconnection and atrophy across most of the cerebrum. Longitudinal analysis demonstrated a close temporal relation of WM lesion formation and GM atrophy in connecting fibers.

Conclusion:

GM atrophy is associated with WM lesions in connecting fibers. Caution is warranted when interpreting group differences in GM atrophy exclusively as differences in early neurodegeneration independent of WM lesion formation.

Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups

BACKGROUND

Multiple Sclerosis (MS), a neurodegenerative and neuroinflammatory disease, causing lesions that disrupt the brain's anatomical and physiological connectivity networks, resulting in cognitive, visual and/or motor disabilities. Advanced imaging techniques like diffusion and functional MRI allow measurement of the brain's structural connectivity (SC) and functional connectivity (FC) networks, and can enable a better understanding of how their disruptions cause disability in people with MS (pwMS). However, advanced MRI techniques are used mainly for research purposes as they are expensive, time-consuming and require high-level expertise to acquire and process. As an alternative, the Network Modification (NeMo) Tool can be used to estimate SC and FC using lesion masks derived from pwMS and a reference set of controls' connectivity networks.

OBJECTIVE

Here, we test the hypothesis that estimated SC and FC (eSC and eFC) from the NeMo Tool, based only on an individual's lesion masks, can be used to classify pwMS into disability categories just as well as SC and FC extracted from advanced MRI directly in pwMS. We also aim to find the connections most important for differentiating between no disability vs evidence of disability groups.

MATERIALS AND METHODS

One hundred pwMS (age:45.5 ± 11.4 years, 66% female, disease duration: 12.97 ± 8.07 years) were included in this study. Expanded Disability Status Scale (EDSS) was used to assess disability, 67 pwMS had no disability (EDSS < 2). Observed SC and FC were extracted from diffusion and functional MRI directly in pwMS, respectively. The NeMo Tool was used to estimate the remaining structural connectome (eSC), by removing streamlines in a reference set of tractograms that intersected the lesion mask. The NeMo Tool's eSC was used then as input to a deep neural network to estimate the corresponding FC (eFC). Logistic regression with ridge regularization was used to classify pwMS into disability categories (no disability vs evidence of disability), based on demographics/clinical information (sex, age, race, disease duration, clinical phenotype, and spinal lesion burden) and either pairwise entries or regional summaries from one of the following matrices: SC, FC, eSC, and eFC. The area under the ROC curve (AUC) was used to assess the classification performance. Both univariate statistics and parameter coefficients from the classification models were used to identify features important to differentiating between the groups.

RESULTS

The regional eSC and eFC models outperformed their observed FC and SC counterparts (p-value < 0.05), while the pairwise eSC and SC performed similarly (p = 0.10). Regional eSC and eFC models had higher AUC (0.66-0.68) than the pairwise models (0.60-0.65), with regional eFC having highest classification accuracy across all models. Ridge regression coefficients for the regional eFC and regional observed FC models were significantly correlated (Pearson's r = 0.52, p-value < 10e-7). Decreased estimated SC node strength in default mode and ventral attention networks and increased eFC node strength in visual networks was associated with evidence of disability.

DISCUSSION

Here, for the first time, we use clinically acquired lesion masks to estimate both structural and functional connectomes in patient populations to better understand brain lesion-dysfunction mapping in pwMS. Models based on the NeMo Tool's estimates of SC and FC better classified pwMS by disability level than SC and FC observed directly in the individual using advanced MRI. This work provides a viable alternative to performing high-cost, advanced MRI in patient populations, bringing the connectome one step closer to the clinic.

Microstructural MRI Correlates of Cognitive Impairment in Multiple Sclerosis: The Role of Deep Gray Matter

Although cognitive impairment (CI) is frequently observed in people with multiple sclerosis (pwMS), its pathogenesis is still controversial. Conflicting results emerged concerning the role of microstructural gray matter (GM) damage especially when involving the deep GM structures. In this study, we aimed at evaluating whether differences in cortical and deep GM structures between apparently cognitively normal (ACN) and CI pwMS (36 subjects in total) are present, using an extensive set of diffusion MRI (dMRI) indices and conventional morphometry measures. The results revealed increased anisotropy and restriction over several deep GM structures in CI compared with ACN pwMS, while no changes in volume were present in the same areas. Conversely, reduced anisotropy/restriction values were detected in cortical regions, mostly the pericalcarine cortex and precuneus, combined with reduced thickness of the superior frontal gyrus and insula. Most of the dMRI metrics but none of the morphometric indices correlated with the Symbol Digit Modality Test. These results suggest that deep GM microstructural damage can be a strong anatomical substrate of CI in pwMS and might allow identifying pwMS at higher risk of developing CI.

Axonal damage in spinal cord is associated with gray matter atrophy in sensorimotor cortex in experimental autoimmune encephalomyelitis

BACKGROUND

Gray matter (GM) atrophy in brain is one of the best predictors of long-term disability in multiple sclerosis (MS), and recent findings have revealed that localized GM atrophy is associated with clinical disabilities. GM atrophy associated with each disability mapped to a distinct brain region, revealing a disability-specific atlas (DSA) of GM loss.

OBJECTIVE

To uncover the mechanisms underlying the development of localized GM atrophy.

METHODS

We used voxel-based morphometry (VBM) to evaluate localized GM atrophy and Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging-compatible Tissue-hYdrogel (CLARITY) to evaluate specific pathologies in mice with experimental autoimmune encephalomyelitis (EAE).

RESULTS

We observed extensive GM atrophy throughout the cerebral cortex, with additional foci in the thalamus and caudoputamen, in mice with EAE compared to normal controls. Next, we generated pathology-specific atlases (PSAs), voxelwise mappings of the correlation between specific pathologies and localized GM atrophy. Interestingly, axonal damage (end-bulbs and ovoids) in the spinal cord strongly correlated with GM atrophy in the sensorimotor cortex of the brain.

CONCLUSION

The combination of VBM with CLARITY in EAE can localize GM atrophy in brain that is associated with a specific pathology in spinal cord, revealing a PSA of GM loss.

Impact of Focal White Matter Damage on Localized Subcortical Gray Matter Atrophy in Multiple Sclerosis: A 5-Year Study

BACKGROUND AND PURPOSE

It is unclear to what extent subcortical gray matter atrophy is a primary process as opposed to a result of focal white matter damage. Correlations between WM damage and atrophy of subcortical gray matter have been observed but may be partly attributable to indirect relationships between co-occurring processes arising from a common cause. Our aim was to cross-sectionally and longitudinally characterize the unique impact of focal WM damage on the atrophy of connected subcortical gray matter regions, beyond what is explainable by global disease progression.

MATERIALS AND METHODS

One hundred seventy-six individuals with MS and 47 healthy controls underwent MR imaging at baseline and 5 years later. Atrophy and lesion-based disruption of connected WM tracts were evaluated for 14 subcortical gray matter regions. Hierarchic regressions were applied, predicting regional atrophy from focal WM disruption, controlling for age, sex, disease duration, whole-brain volume, and T2-lesion volume.

RESULTS

When we controlled for whole-brain volume and T2-lesion volume, WM tract disruption explained little additional variance of subcortical gray matter atrophy and was a significant predictor for only 3 of 14 regions cross-sectionally (ΔR² = 0.004) and 5 regions longitudinally (ΔR² = 0.016). WM tract disruption was a significant predictor for even fewer regions when correcting for multiple comparisons.

CONCLUSIONS

WM tract disruption accounts for a small percentage of atrophy in connected subcortical gray matter when controlling for overall disease burden and is not the primary driver in most cases.

White matter tract network disruption explains reduced conscientiousness in multiple sclerosis

Quantifying white matter (WM) tract disruption in people with multiple sclerosis (PwMS) provides a novel means for investigating the relationship between defective network connectivity and clinical markers. PwMS exhibit perturbations in personality, where decreased Conscientiousness is particularly prominent. This trait deficit influences disease trajectory and functional outcomes such as work capacity. We aimed to identify patterns of WM tract disruption related to decreased Conscientiousness in PwMS. Personality assessment and brain MRI were obtained in 133 PwMS and 49 age- and sex-matched healthy controls (HC). Lesion maps were applied to determine the severity of WM tract disruption between pairs of gray matter regions. Next, the Network-Based-Statistics tool was applied to identify structural networks whose disruption negatively correlates with Conscientiousness. Finally, to determine whether these networks explain unique variance above conventional MRI measures and cognition, regression models were applied controlling for age, sex, brain volume, T2-lesion volume, and cognition. Relative to HCs, PwMS exhibited lower Conscientiousness and slowed cognitive processing speed (p = .025, p = .006). Lower Conscientiousness in PwMS was significantly associated with WM tract disruption between frontal, frontal-parietal, and frontal-cingulate pathways in the left (p = .02) and right (p = .01) hemisphere. The mean disruption of these pathways explained unique additive variance in Conscientiousness, after accounting for conventional MRI markers of pathology and cognition (ΔR²  = .049, p = .029). Damage to WM tracts between frontal, frontal-parietal, and frontal-cingulate cortical regions is significantly correlated with reduced Conscientiousness in PwMS. Tract disruption within these networks explains decreased Conscientiousness observed in PwMS as compared with HCs.

Baseline biomarkers of connectome disruption and atrophy predict future processing speed in early multiple sclerosis

The development of accurate prognoses in multiple sclerosis is difficult, as the disease is characterized by heterogeneous patterns of brain abnormalities that relate in an unclear way to future impairments. Here, we use a statistical modeling approach to determine if the baseline pattern of connectome disruption due to T2-FLAIR lesions could predict a patient's future processing speed, as measured using the Symbol Digits Modality Test scores. Imaging data, demographics and Symbol Digits Modality Test scores were collected from 61 early relapsing remitting multiple sclerosis patients. The Network Modification Tool was used to estimate damage to the connectome by quantifying white matter abnormalities' effects on 1) global network properties, 2) regional connectivity and 3) connectivity between pairs of regions. MS subjects showed significant improvement of processing speed between baseline and follow-up (t = −2.6, p = 0.0096); however, both baseline (t = −4.01, p = 0.00012) and follow-up (t = −2.10, p = 0.038) processing speed were significantly lower than age-matched healthy controls. Partial Least Squares Regression was used to create models that predict future processing speed from between baseline imaging metrics and demographics. The model based on region-pair disconnection and gray matter atrophy had the lowest AIC and highest prediction accuracy (R² = 0.79) compared to models based on global (R² = 0.41) or regional (R² = 0.66) disconnection and gray matter atrophy, overlap with an ROI-based atlas and gray matter atrophy (R² = 0.73) or gray matter atrophy alone (R² = 0.71). We found that baseline measures of connectivity disruption in various parietal, temporal, occipital and subcortical regions and atrophy in the putamen were important predictors of future processing speed. We conclude that information about disruptions to pairwise brain connections is more informative of future processing speed than regional or global metrics or gray matter atrophy alone. The combination of quantitative disconnectome metrics, gray matter atrophy and statistical modeling approaches could enable clinicians in developing more accurate, individualized prognoses of future cognitive status in multiple sclerosis patients.

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Atrophy and structural disconnection estimates via NeMo Tool were collected in MS.

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Future cognitive functioning in MS patients was predicted by baseline MRI measures.

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Measures of atrophy and disconnection between region-pairs had best goodness-of-fit.

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More caudate atrophy was significantly predictive of worse future cognition.

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Disconnections in parietal/temporal/occipital areas predicted worse future cognition.

CCR5Δ32 - A piece of protection in the inflammatory puzzle of multiple sclerosis susceptibility

BACKGROUND

Leucocyte infiltration and activation in the central nervous system (CNS) is an important step in the pathogenesis of multiple sclerosis (MS). The Chemokine receptor 5 (CCR5) is implicated in immune cell migration and cytokine release in the CNS, and it was demonstrated to strongly contribute to CNS inflammation and damage in several models of sterile and pathogen-mediated CNS diseases. Although the inhibition of CCR5 results in a beneficial effect in experimental models of MS, conflicting results have been found about the loss-of-function variant CCR5Δ32 (rs333) in MS patients. The aim of this study was to evaluate the association of CCR5Δ32 and MS in a Brazilian case/control cohort.

PATIENTS AND METHODS

261 MS patients and 435 healthy controls were genotyped for CCR5Δ32. Allelic and genotypic frequencies were compared between patients and controls (case/control analysis), and among patients classified according to the MS clinical form (relapsing remitting versus progressive) and severity (EDSS, MSSS and progression index).

RESULTS AND DISCUSSION

The CCR5Δ32 variant frequency was statistically higher in controls as compared to patients presenting European-derived ethnic background. The variant was more frequent in progressive MS as compared to RR-MS patients, and, although not statistically significant, a higher frequency of the truncated allele was observed among patients with less severe forms of MS. These findings emphasize the potential involvement of CCR5 signaling in CNS inflammation and damage in MS.

CONCLUSION

The CCR5Δ32 deletion is a protective factor against the development and progression of MS in European-derived Brazilian patients.

Predicting language outcomes after stroke: Is structural disconnection a useful predictor?

For many years, researchers have sought to understand whether and when stroke survivors with acquired language impairment (aphasia) will recover. There is broad agreement that lesion location information should play some role in these predictions, but still no consensus on the best or right way to encode that information. Here, we address the emerging emphasis on the structural connectome in this work - specifically the claim that disrupted white matter connectivity conveys important, unique prognostic information for stroke survivors with aphasia. Our sample included 818 stroke patients extracted from the PLORAS database, which associates structural MRI from stroke patients with language assessment scores from the Comprehensive Aphasia Test (CAT) and basic demographic. Patients were excluded when their lesions were too diffuse or small (<1 cm3) to be detected by the Automatic Lesion Identification toolbox, which we used to encode patients' lesions as binary lesion images in standard space. Lesions were encoded using the 116 regions defined by the Automatic Anatomical Labelling atlas. We examined prognostic models driven by both "lesion load" in these regions (i.e. the proportion of each region destroyed by each patient's lesion), and by the disconnection of the white matter connections between them which was calculated via the Network Modification toolbox. Using these data, we build a series of prognostic models to predict first one ("naming"), and then all of the language scores defined by the CAT. We found no consistent evidence that connectivity disruption data in these models improved our ability to predict any language score. This may be because the connectivity disruption variables are strongly correlated with the lesion load variables: correlations which we measure both between pairs of variables in their original form, and between principal components of both datasets. Our conclusion is that, while both types of structural brain data do convey useful, prognostic information in this domain, they also appear to convey largely the same variance. We conclude that connectivity disruption variables do not help us to predict patients' language skills more accurately than lesion location (load) data alone.

The impact of epilepsy surgery on the structural connectome and its relation to outcome

Background

Temporal lobe surgical resection brings seizure remission in up to 80% of patients, with long-term complete seizure freedom in 41%. However, it is unclear how surgery impacts on the structural white matter network, and how the network changes relate to seizure outcome.

Methods

We used white matter fibre tractography on preoperative diffusion MRI to generate a structural white matter network, and postoperative T1-weighted MRI to retrospectively infer the impact of surgical resection on this network. We then applied graph theory and machine learning to investigate the properties of change between the preoperative and predicted postoperative networks.

Results

Temporal lobe surgery had a modest impact on global network efficiency, despite the disruption caused. This was due to alternative shortest paths in the network leading to widespread increases in betweenness centrality post-surgery. Measurements of network change could retrospectively predict seizure outcomes with 79% accuracy and 65% specificity, which is twice as high as the empirical distribution. Fifteen connections which changed due to surgery were identified as useful for prediction of outcome, eight of which connected to the ipsilateral temporal pole.

Conclusion

Our results suggest that the use of network change metrics may have clinical value for predicting seizure outcome. This approach could be used to prospectively predict outcomes given a suggested resection mask using preoperative data only.

MRI Analysis of White Matter Myelin Water Content in Multiple Sclerosis: A Novel Approach Applied to Finding Correlates of Cortical Thinning

A novel lesion-mask free method based on a gamma mixture model was applied to myelin water fraction (MWF) maps to estimate the association between cortical thickness and myelin content, and how it differs between relapsing-remitting (RRMS) and secondary-progressive multiple sclerosis (SPMS) groups (135 and 23 patients, respectively). It was compared to an approach based on lesion masks. The gamma mixture distribution of whole brain, white matter (WM) MWF was characterized with three variables: the mode (most frequent value) m₁ of the gamma component shown to relate to lesion, the mode m₂ of the component shown to be associated with normal appearing (NA) WM, and the mixing ratio (λ) between the two distributions. The lesion-mask approach relied on the mean MWF within lesion and within NAWM. A multivariate regression analysis was carried out to find the best predictors of cortical thickness for each group and for each approach. The gamma-mixture method was shown to outperform the lesion-mask approach in terms of adjusted R², both for the RRMS and SPMS groups. The predictors of the final gamma-mixture models were found to be m₁ (β = 1.56, p < 0.005), λ (β = −0.30, p < 0.0005) and age (β = −0.0031, p < 0.005) for the RRMS group (adjusted R² = 0.16), and m₂ (β = 4.72, p < 0.0005) for the SPMS group (adjusted R² = 0.45). Further, a DICE coefficient analysis demonstrated that the lesion mask had more overlap to an ROI associated with m₁, than to an ROI associated with m₂ (p < 0.00001), and vice versa for the NAWM mask (p < 0.00001). These results suggest that during the relapsing phase, focal WM damage is associated with cortical thinning, yet in SPMS patients, global WM deterioration has a much stronger influence on secondary degeneration. Through these findings, we demonstrate the potential contribution of myelin loss on neuronal degeneration at different disease stages and the usefulness of our statistical reduction technique which is not affected by the typical bias associated with approaches based on lesion masks.

The Contribution of Cortical Lesions to a Composite MRI Scale of Disease Severity in Multiple Sclerosis

Objective

To test a new version of the Magnetic Resonance Disease Severity Scale (v.3 = MRDSS3) for multiple sclerosis (MS), incorporating cortical gray matter lesions (CLs) from 3T magnetic resonance imaging (MRI).

Background

MRDSS1 was a cerebral MRI-defined composite scale of MS disease severity combining T2 lesion volume (T2LV), the ratio of T1 to T2LV (T1/T2), and whole brain atrophy [brain parenchymal fraction (BPF)]. MRDSS2 expanded the scale to include cerebral gray matter fraction (GMF) and upper cervical spinal cord area (UCCA). We tested the contribution of CLs to the scale (MRDSS3) in modeling the MRI relationship to clinical status.

Methods

We studied 51 patients [3 clinically isolated syndrome, 43 relapsing-remitting, 5 progressive forms, age (mean ± SD) 40.7 ± 9.1 years, Expanded Disability Status Scale (EDSS) score 1.6 ± 1.7] and 20 normal controls by high-resolution cerebrospinal MRI. CLs required visibility on both fluid-attenuated inversion-recovery (FLAIR) and modified driven equilibrium Fourier transform sequences. The MACFIMS battery defined cognitively impaired (n = 18) vs. preserved (n = 33) MS subgroups.

Results

EDSS significantly correlated with only BPF, UCCA, MRDSS2, and MRDSS3 (all p < 0.05). After adjusting for depressive symptoms, the cognitively impaired group had higher severity of MRI metrics than the cognitively preserved group in regard to only BPF, GMF, T1/T2, MRDSS1, and MRDSS2 (all p < 0.05). CL number was not significantly related to EDSS score or cognition status.

Conclusion

CLs from 3T MRI did not appear to improve the validity of the MRDSS. Further studies employing advanced sequences or higher field strengths may show more utility for the incorporation of CLs into composite scales.

Structural connectome disruption at baseline predicts 6-months post-stroke outcome

In this study, models based on quantitative imaging biomarkers of post-stroke structural connectome disruption were used to predict six-month outcomes in various domains. Demographic information and clinical MRIs were collected from 40 ischemic stroke subjects (age: 68.1 ± 13.2 years, 17 female, NIHSS: 6.8 ± 5.6). Diffusion-weighted images were used to create lesion masks, which were uploaded to the Network Modification (NeMo) Tool. The NeMo Tool, using only clinical MRIs, allows estimation of connectome disruption at three levels: whole brain, individual gray matter regions and between pairs of gray matter regions. Partial Least Squares Regression models were constructed for each level of connectome disruption and for each of the three six-month outcomes: applied cognitive, basic mobility and daily activity. Models based on lesion volume were created for comparison. Cross-validation, bootstrapping and multiple comparisons corrections were implemented to minimize over-fitting and Type I errors. The regional disconnection model best predicted applied cognitive (R(2)  = 0.56) and basic mobility outcomes (R(2)  = 0.70), while the pairwise disconnection model best predicted the daily activity measure (R(2)  = 0.72). These results demonstrate that models based on connectome disruption metrics were more accurate than ones based on lesion volume and that increasing anatomical specificity of disconnection metrics does not always increase model accuracy, likely due to statistical adjustments for concomitant increases in data dimensionality. This work establishes that the NeMo Tool's measures of baseline connectome disruption, acquired using only routinely collected MRI scans, can predict 6-month post-stroke outcomes in various functional domains including cognition, motor function and daily activities. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

Classification, diagnosis, and differential diagnosis of multiple sclerosis

PURPOSE OF REVIEW

The increasing availability of effective therapies for multiple sclerosis as well as research demonstrating the benefits of early treatment highlights the importance of expedient and accurate multiple sclerosis diagnosis. This review will discuss the classification, diagnosis, and differential diagnosis of multiple sclerosis.

RECENT FINDINGS

An international panel of multiple sclerosis experts, the MS Phenotype Group, recently revised the multiple sclerosis phenotypic classifications and published their recommendations in 2014. Recent research developments have helped improve the accuracy of multiple sclerosis diagnosis, especially with regard to differentiating multiple sclerosis from neuromyelitis optica spectrum disorders.

SUMMARY

Current multiple sclerosis phenotypic classifications include relapsing-remitting multiple sclerosis, clinically isolated syndrome, radiologically isolated syndrome, primary-progressive multiple sclerosis, and secondary-progressive multiple sclerosis. The McDonald 2010 diagnostic criteria provide formal guidelines for the diagnosis of relapsing-remitting multiple sclerosis and primary-progressive multiple sclerosis. These require demonstration of dissemination in space and time, with consideration given to both clinical findings and imaging data. The criteria also require that there exist no better explanation for the patient's presentation. The clinical history, examination, and MRI should be most consistent with multiple sclerosis, including the presence of features typical for the disease as well as the absence of features that suggest an alternative cause, for a diagnosis of multiple sclerosis to be proposed.

Profilometry: A new statistical framework for the characterization of white matter pathways, with application to multiple sclerosis

AIMS

describe a new "profilometry" framework for the multimetric analysis of white matter tracts, and demonstrate its application to multiple sclerosis (MS) with radial diffusivity (RD) and myelin water fraction (MWF).

METHODS

A cohort of 15 normal controls (NC) and 141 MS patients were imaged with T1, T2 FLAIR, T2 relaxometry and diffusion MRI (dMRI) sequences. T1 and T2 FLAIR allowed for the identification of patients having lesion(s) on the tracts studied, with a special focus on the forceps minor. T2 relaxometry provided MWF maps, while dMRI data yielded RD maps and the tractography required to compute MWF and RD tract profiles. The statistical framework combined a multivariate analysis of covariance (MANCOVA) and a linear discriminant analysis (LDA) both accounting for age and gender, with multiple comparison corrections.

RESULTS

In the single-case case study the profilometry visualization showed a clear departure of MWF and RD from the NC normative data at the lesion location(s). Group comparison from MANCOVA demonstrated significant differences at lesion locations, and a significant age effect in several tracts. The follow-up LDA analysis suggested MWF better discriminates groups than RD.

DISCUSSION AND CONCLUSION

While progress has been made in both tract-profiling and metrics for white matter characterization, no single framework for a joint analysis of multimodality tract profiles accounting for age and gender is known to exist. The profilometry analysis and visualization appears to be a promising method to compare groups using a single score from MANCOVA while assessing the contribution of each metric with LDA.

Exploring the brain's structural connectome: A quantitative stroke lesion-dysfunction mapping study

The aim of this work was to quantitatively model cross-sectional relationships between structural connectome disruptions caused by cerebral infarction and measures of clinical performance. Imaging biomarkers of 41 ischemic stroke patients (72.0 ± 12.0 years, 20 female) were related to their baseline performance in 18 cognitive, physical and daily life activity assessments. Individual estimates of structural connectivity disruption in gray matter regions were computed using the Change in Connectivity (ChaCo) score. ChaCo scores were utilized because they can be calculated using routinely collected clinical magnetic resonance imagings. Partial Least Squares Regression (PLSR) was used to predict various acute impairment and activity measures from ChaCo scores and patient demographics. Statistical methods of cross-validation, bootstrapping and multiple comparisons correction were implemented to minimize over-fitting and Type I errors. Multiple linear regression models based on lesion volume and lateralization information were constructed for comparison. All models based on connectivity disruption had lower Akaike Information Criterion and almost all had better goodness-of-fit values (R(2) : 0.26-0.92) than models based on lesion characteristics (R(2) : 0.06-0.50). Confidence intervals of PLSR coefficients identified brain regions important in predicting each clinical assessment. Appropriate mapping of eloquent functions, that is, language and motor, and replication of results across pathologies provided validation of this method. Models of complex functions provided new insights into brain-behavior relationships. In addition to the potential applications in prognostication and rehabilitation development, this quantitative approach provides insight into the structural networks underlying complex functions like activities of daily living and cognition. Quantitative analysis of big data will be invaluable in understanding complex brain-behavior relationships.