Spatiotemporal distribution pattern of white matter lesion volumes and their association with regional grey matter volume reductions in relapsing-remitting multiple sclerosis

Decoding the Best Automated Segmentation Tools for Vascular White Matter Hyperintensities in the Aging Brain: A Clinician's Guide to Precision and Purpose

Cerebrovascular damage from small vessel disease (SVD) occurs in healthy and pathological aging. SVD markers, such as white matter hyperintensities (WMH), are commonly found in individuals over 60 and increase in prevalence with age. WMHs are detectable on standard MRI by adhering to the STRIVE criteria. Currently, visual assessment scales are used in clinical and research scenarios but is time-consuming and has rater variability, limiting its practicality. Addressing this issue, our study aimed to determine the most precise WMH segmentation software, offering insights into methodology and usability to balance clinical precision with practical application. This study employed a dataset comprising T1, FLAIR, and DWI images from 300 cognitively healthy older adults. WMHs in this cohort were evaluated using four automated neuroimaging tools: Lesion Prediction Algorithm (LPA) and Lesion Growth Algorithm (LGA) from Lesion Segmentation Tool (LST), Sequence Adaptive Multimodal Segmentation (SAMSEG), and Brain Intensity Abnormalities Classification Algorithm (BIANCA). Additionally, clinicians manually segmented WMHs in a subsample of 45 participants to establish a gold standard. The study assessed correlations with the Fazekas scale, algorithm performance, and the influence of WMH volume on reliability. Results indicated that supervised algorithms were superior, particularly in detecting small WMHs, and can improve their consistency when used in parallel with unsupervised tools. The research also proposed a biomarker for moderate vascular damage, derived from the top 95th percentile of WMH volume in healthy individuals aged 50 to 60. This biomarker effectively differentiated subgroups within the cohort, correlating with variations in brain structure and behavior.

Low-frequency MR elastography reveals altered deep gray matter viscoelasticity in multiple sclerosis

INTRODUCTION

Brain viscoelasticity as assessed by magnetic resonance elastography (MRE) has been discussed as a promising surrogate of microstructural alterations due to neurodegenerative processes. Existing studies indicate that multiple sclerosis (MS) is associated with a global reduction in brain stiffness. However, no study to date systematically investigated the MS-related characteristics of brain viscoelasticity separately in normal-appearing white matter (NAWM), deep gray matter (DGM) and T2-hyperintense white matter (WM) lesions.

METHODS

70 MS patients and 42 healthy volunteers underwent whole-cerebral MRE using a stimulated echo sequence (DENSE) with a low-frequency mechanical excitation at 20 Hertz. The magnitude |G∗| (Pa) and phase angle φ (rad) of the complex shear modulus G∗ were reconstructed by multifrequency dual elasto-visco (MDEV) inversion and related to structural imaging and clinical parameters.

RESULTS

We observed φ in the thalamus to be higher by 4.3 % in patients relative to healthy controls (1.11 ± 0.07 vs. 1.06 ± 0.07, p < 0.0001). Higher Expanded Disability Status Scale (EDSS) scores were negatively associated with φ in the basal ganglia (p = 0.01). We measured φ to be lower in MS lesions compared to surrounding NAWM (p = 0.001), which was most prominent for lesions in the temporal lobe (1.01 ± 0.22 vs. 1.06 ± 0.19, p = 0.003). Age was associated with lower values of |G∗| (p = 0.04) and φ (p = 0.004) in the thalamus of patients. No alteration in NAWM stiffness relative to WM in healthy controls was observed.

CONCLUSION

Low-frequency elastography in MS patients reveals age-independent alterations in the viscoelasticity of deep gray matter at early stages of disease.

Women in the field of multiple sclerosis: How they contributed to paradigm shifts

History is full of women who made enormous contributions to science. While there is little to no imbalance at the early career stage, a decreasing proportion of women is found as seniority increases. In the multiple sclerosis (MS) field, 44% of first authors and only 35% of senior authors were female. So, in this review, we highlight ground-breaking research done by women in the field of MS, focusing mostly on their work as principal investigators. MS is an autoimmune disorder of the central nervous system (CNS), with evident paradigm shifts in the understating of its pathophysiology. It is known that the immune system becomes overactivated and attacks myelin sheath surrounding axons. The resulting demyelination disrupts the communication signals to and from the CNS, which causes unpredictable symptoms, depending on the neurons that are affected. Classically, MS was reported to cause mostly physical and motor disabilities. However, it is now recognized that cognitive impairment affects more than 50% of the MS patients. Another shifting paradigm was the involvement of gray matter in MS pathology, formerly considered to be a white matter disease. Additionally, the identification of different T cell immune subsets and the mechanisms underlying the involvement of B cells and peripheral macrophages provided a better understanding of the immunopathophysiological processes present in MS. Relevantly, the gut-brain axis, recognized as a bi-directional communication system between the CNS and the gut, was found to be crucial in MS. Indeed, gut microbiota influences not only different susceptibilities to MS pathology, but it can also be modulated in order to positively act in MS course. Also, after the identification of the first microRNA in 1993, the role of microRNAs has been investigated in MS, either as potential biomarkers or therapeutic agents. Finally, concerning MS therapeutical approaches, remyelination-based studies have arisen on the spotlight aiming to repair myelin loss/neuronal connectivity. Altogether, here we emphasize the new insights of remarkable women that have voiced the impact of cognitive impairment, white and gray matter pathology, immune response, and that of the CNS-peripheral interplay on MS diagnosis, progression, and/or therapy efficacy, leading to huge breakthroughs in the MS field.

3D finite-element brain modeling of lateral ventricular wall loading to rationalize periventricular white matter hyperintensity locations

Aging-related periventricular white matter hyperintensities (pvWMHs) are a common observation in medical images of the aging brain. The underlying tissue damage is part of the complex pathophysiology associated with age-related microstructural changes and cognitive decline. PvWMH formation is linked to blood-brain barrier dysfunction from cerebral small vessel disease as well as the accumulation of cerebrospinal fluid in periventricular tissue due to progressive denudation of the ventricular wall. In need of a unifying theory for pvWMH etiology, image-based finite-element modeling is used to demonstrate that ventricular expansion from age-related cerebral atrophy and hemodynamic loading leads to maximum mechanical loading of the ventricular wall in the same locations that show pvWMHs. Ventricular inflation, induced via pressurization of the ventricular wall, creates significant ventricular wall stretch and stress on the ependymal cells lining the wall, that are linked to cerebrospinal fluid leaking from the lateral ventricles into periventricular white matter tissue. Eight anatomically accurate 3D brain models of cognitively healthy subjects with a wide range of ventricular shapes are created. For all models, our simulations show that mechanomarkers of mechanical wall loading are consistently highest in pvWMHs locations (p < 0.05). Maximum principal strain, the ependymal cell thinning ratio, and wall curvature are on average 14%, 8%, and 24% higher in pvWMH regions compared to the remaining ventricular wall, respectively. Computational modeling provides a powerful framework to systematically study pvWMH formation and growth with the goal to develop pharmacological interventions in the future.

Peak ependymal cell stretch overlaps with the onset locations of periventricular white matter lesions

Deep and periventricular white matter hyperintensities (dWMH/pvWMH) are bright appearing white matter tissue lesions in T2-weighted fluid attenuated inversion recovery magnetic resonance images and are frequent observations in the aging human brain. While early stages of these white matter lesions are only weakly associated with cognitive impairment, their progressive growth is a strong indicator for long-term functional decline. DWMHs are typically associated with vascular degeneration in diffuse white matter locations; for pvWMHs, however, no unifying theory exists to explain their consistent onset around the horns of the lateral ventricles. We use patient imaging data to create anatomically accurate finite element models of the lateral ventricles, white and gray matter, and cerebrospinal fluid, as well as to reconstruct their WMH volumes. We simulated the mechanical loading of the ependymal cells forming the primary brain-fluid interface, the ventricular wall, and its surrounding tissues at peak ventricular pressure during the hemodynamic cycle. We observe that both the maximum principal tissue strain and the largest ependymal cell stretch consistently localize in the anterior and posterior horns. Our simulations show that ependymal cells experience a loading state that causes the ventricular wall to be stretched thin. Moreover, we show that maximum wall loading coincides with the pvWMH locations observed in our patient scans. These results warrant further analysis of white matter pathology in the periventricular zone that includes a mechanics-driven deterioration model for the ventricular wall.

Dynamic topology analysis for spatial patterns of multifocal lesions on MRI

Quantitatively analysing the spatial patterns of multifocal lesions on clinical MRI is an important step towards a better understanding of the disease and for precision medicine, which is yet to be properly explored by feature engineering and deep learning methods. Network science addresses this issue by explicitly modeling the inter-lesion topology. However, the construction of the informative graph with optimal edge sparsity and quantification of community graph structures are the current challenges in network science. In this paper, we address these challenges with a novel Dynamic Topology Analysis framework on the basis of persistent homology, aiming to investigate the predictive values of global geometry and local clusters of multifocal lesions. Firstly, Dynamic Hierarchical Network is proposed to construct informative global and community-level topology over multi-scale networks from sparse to dense. Multi-scale global topology is constructed with a nested sequence of Rips complexes, from which a new K-simplex Filtration is designed to generate a higher-level topological abstraction for community identification based on the connectivity of k-simplices in the Rips Complex. Secondly, to quantify multi-scale community structures, we design a new Decomposed Community Persistence algorithm to track the dynamic evolution of communities, and then summarise the evolutionary communities incorporated with a customisable descriptor. The quantified community features are encapsulated with global geometric invariants for topological pattern analysis. The proposed framework was evaluated on both diagnostic differentiation and prognostic prediction for multiple sclerosis that is a typical multifocal disease, and achieved ROC_AUC 0.875 and 0.767, respectively, outperforming seven state-of-the-art persistent homology methods and the reported performance of six feature engineering and deep learning methods.

A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

BACKGROUND AND PURPOSE

Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.

MATERIALS AND METHODS

Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer's Disease Neuroimaging Initiative-3 (n = 20).

RESULTS

StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.

CONCLUSIONS

A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

Gray Matter Matters: A Longitudinal Magnetic Resonance Voxel-Based Morphometry Study of Primary Progressive Multiple Sclerosis

Background: Multiple Sclerosis (MS) lesions in white matter (WM) are easily detected with conventional MRI which induce inflammation thereby generating contrast. WM lesions do not consistently explain the extent of clinical disability, cognitive impairment, or the source of an exacerbation. Gray matter (GM) structures including the cerebral cortex and various deep nuclei are known to be affected early in Primary Progressive Multiple Sclerosis (PPMS) and drive disease progression, disability, fatigue, and cognitive dysfunction. However, little is known about how rapidly GM lesions develop and accumulate over time.

Objective: The purpose of this study is to analyze the degree and rate of progression in 25 patients with PPMS using voxel-based automated volumetric quantitation.

Methods: This is a retrospective single-center study which includes a cohort of 25 patients with PPMS scanned utilizing NeuroQuant® 3 dimensional voxel-based morphometry (3D VBM) automated analysis and database and restudied after a period of ~1 year (11–14 months). Comparisons with normative data were acquired for whole brain, forebrain parenchyma, cortical GM, hippocampus, thalamus, superior and inferior lateral ventricles. GM volume changes were correlated with their clinical motor and cognitive scores using Extended Disability Status Scales (EDSS) and Montreal Cognitive Assessments (MoCA).

Results: Steep reductions occurred in cerebral cortical GM and deep GM nuclei volumes which correlated with each patient's clinical and cognitive impairment. The median observed percentile volume losses were statistically significant compared with the 50th percentile for each GM component. Longitudinal assessments of an unselected sample of one dozen patients involved in the PPMS study showed prominent losses occurring mainly in cortical GM and hippocampus which were reflected in their EDSS and MoCA. The longitudinal results were compared with a similar sample of patients having Relapsing MS (RMS) whose GM values were largely in normal range, annualized volume GM changes were much less, while WM hyperintensities were in abnormal range in half the unselected cases.

Conclusions: Knowledge of the degree and rapidity with which cortical atrophy and deep GM volume loss develops clarifies the source of progressive cognitive and clinical decline in PPMS.

Impact of Lesion Location on Longitudinal Myelin Water Fraction Change in Chronic Multiple Sclerosis Lesions

BACKGROUND AND PURPOSE

To examine the impact of lesion location on longitudinal myelin water fraction (MWF) changes in chronic multiple sclerosis (MS) lesions. Relative hypoxia, due to vascular watershed regions of the cerebrum, has been implicated in lesion development but impact on ongoing demyelination is unknown.

METHODS

Forty-eight patients with relapsing-remitting and secondary progressive MS had two MWF scans with fast acquisition, spiral trajectory, and T2prep (FAST-T2) sequence, at an interval of 2.0 (±.3) years. Lesion location was identified based upon cerebral lobe and relation to the ventricles. Change in MWF was assessed using a mixed effects model, controlling for lesion location and patient covariates.

RESULTS

Average age was 42.3 (±12) years, mean disease duration was 9.7 (±9.1) years, and median Expanded Disability Status Score (EDSS) was 2.5 (±2.3). The majority of 512 chronic lesions was located in the frontal and parietal lobes (75.6%) and more often periventricular (44.7%). All occipital lesions were periventricular. The average lesion MWF decreased from baseline (.07 ± .03) to 2 years (.06 ±.03) P < .01. Lesions within the occipital lobe showed a significant reduction in MWF as compared to other lobes.

CONCLUSIONS

Chronic lesions in the occipital lobe showed the greatest reduction in MWF. Neuroanatomical localization of lesions to the occipital horns of the lateral ventricles, a watershed region, may contribute to ongoing demyelination in this lesion type.

Urgent challenges in quantification and interpretation of brain grey matter atrophy in individual MS patients using MRI

Atrophy of the brain grey matter (GM) is an accepted and important feature of multiple sclerosis (MS). However, its accurate measurement is hampered by various technical, pathological and physiological factors. As a consequence, it is challenging to investigate the role of GM atrophy in the disease process as well as the effect of treatments that aim to reduce neurodegeneration. In this paper we discuss the most important challenges currently hampering the measurement and interpretation of GM atrophy in MS. The focus is on measurements that are obtained in individual patients rather than on group analysis methods, because of their importance in clinical trials and ultimately in clinical care. We discuss the sources and possible solutions of the current challenges, and provide recommendations to achieve reliable measurement and interpretation of brain GM atrophy in MS.

Structural MRI correlates of cognitive function in multiple sclerosis

BACKGROUND

Cognitive impairment (CI) has been associated with numerous magnetic resonance imaging (MRI) indices in multiple sclerosis (MS) patients. In this study we investigated the association of a large set of 2D and 3D MRI markers with cognitive function in MS.

METHODS

A sample of 61 RRMS patients (mean age 41.8 ± 10.6 years old, 44 women, mean disease duration 137.9 ± 83.9 months) along with 51 age and gender matched healthy controls was used in this cross-sectional study. Neuropsychological and other tests, along with a large set of 2D/3D MRI evaluations were made.

RESULTS

44.3% of patients had CI. CI patients had more disability, physical fatigue than non-CI patients and more psychological distress than non-CI patients and HCs. Also, CI patients had significantly larger third ventricle width and volume, smaller coprus callosum index and larger lesion volume than non-CI patients. These MRI markers also significantly predicted cognitive scores after adjusting for age and education, explaining about 30.6% of the variance of the total cognitive score.

CONCLUSIONS

Selected linear and volumetric MRI indices predict cognitive function in MS. Future studies should expand these results by exploring longitudinal changes and producing normative data.

White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks

White matter hyperintensities (WMH) are a feature of sporadic small vessel disease also frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The accurate assessment of WMH burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clinical data; their causes, and the effects of new treatments in randomized trials. The manual delineation of WMHs is a very tedious, costly and time consuming process, that needs to be carried out by an expert annotator (e.g. a trained image analyst or radiologist). The problem of WMH delineation is further complicated by the fact that other pathological features (i.e. stroke lesions) often also appear as hyperintense regions. Recently, several automated methods aiming to tackle the challenges of WMH segmentation have been proposed. Most of these methods have been specifically developed to segment WMH in MRI but cannot differentiate between WMHs and strokes. Other methods, capable of distinguishing between different pathologies in brain MRI, are not designed with simultaneous WMH and stroke segmentation in mind. Therefore, a task specific, reliable, fully automated method that can segment and differentiate between these two pathological manifestations on MRI has not yet been fully identified. In this work we propose to use a convolutional neural network (CNN) that is able to segment hyperintensities and differentiate between WMHs and stroke lesions. Specifically, we aim to distinguish between WMH pathologies from those caused by stroke lesions due to either cortical, large or small subcortical infarcts. The proposed fully convolutional CNN architecture, called uResNet, that comprised an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up-samples the low and high level features into a class likelihood semantic segmentation. Quantitatively, the proposed CNN architecture is shown to outperform other well established and state-of-the-art algorithms in terms of overlap with manual expert annotations. Clinically, the extracted WMH volumes were found to correlate better with the Fazekas visual rating score than competing methods or the expert-annotated volumes. Additionally, a comparison of the associations found between clinical risk-factors and the WMH volumes generated by the proposed method, was found to be in line with the associations found with the expert-annotated volumes.

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Robust, fully automatic white matter hyperintensity and stroke lesion segmentation and differentiation

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A novel patch sampling strategy used during CNN training that avoids the introduction of erroneous locality assumptions

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Improved segmentation accuracy in terms of Dice scores when compared to well established state-of-the-art methods

Disability-Specific Atlases of Gray Matter Loss in Relapsing-Remitting Multiple Sclerosis

IMPORTANCE

Multiple sclerosis (MS) is characterized by progressive gray matter (GM) atrophy that strongly correlates with clinical disability. However, whether localized GM atrophy correlates with specific disabilities in patients with MS remains unknown.

OBJECTIVE

To understand the association between localized GM atrophy and clinical disability in a biology-driven analysis of MS.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study, magnetic resonance images were acquired from 133 women with relapsing-remitting MS and analyzed using voxel-based morphometry and volumetry. A regression analysis was used to determine whether voxelwise GM atrophy was associated with specific clinical deficits. Data were collected from June 28, 2007, to January 9, 2014.

MAIN OUTCOMES AND MEASURES

Voxelwise correlation of GM change with clinical outcome measures (Expanded Disability Status Scale and Multiple Sclerosis Functional Composite scores).

RESULTS

Among the 133 female patients (mean [SD] age, 37.4 [7.5] years), worse performance on the Multiple Sclerosis Functional Composite correlated with voxelwise GM volume loss in the middle cingulate cortex (P < .001) and a cluster in the precentral gyrus bilaterally (P = .004). In addition, worse performance on the Paced Auditory Serial Addition Test correlated with volume loss in the auditory and premotor cortices (P < .001), whereas worse performance on the 9-Hole Peg Test correlated with GM volume loss in Brodmann area 44 (Broca area; P = .02). Finally, voxelwise GM loss in the right paracentral lobulus correlated with bowel and bladder disability (P = .03). Thus, deficits in specific clinical test results were directly associated with localized GM loss in clinically eloquent locations.

CONCLUSIONS AND RELEVANCE

These biology-driven data indicate that specific disabilities in MS are associated with voxelwise GM loss in distinct locations. This approach may be used to develop disability-specific biomarkers for use in future clinical trials of neuroprotective treatments in MS.

Exploring the origins of grey matter damage in multiple sclerosis

Multiple sclerosis is characterized at the gross pathological level by the presence of widespread focal demyelinating lesions of the myelin-rich white matter. However, it is becoming clear that grey matter is not spared, even during the earliest phases of the disease. Furthermore, grey matter damage may have an important role both in physical and cognitive disability. Grey matter pathology involves both inflammatory and neurodegenerative mechanisms, but the relationship between the two is unclear. Histological, immunological and neuroimaging studies have provided new insight in this rapidly expanding field, and form the basis of the most recent hypotheses on the pathogenesis of grey matter damage.

Lesion load may predict long-term cognitive dysfunction in multiple sclerosis patients

Background

Magnetic Resonance Imaging (MRI) techniques provided evidences into the understanding of cognitive impairment (CIm) in Multiple Sclerosis (MS).

Objectives

To investigate the role of white matter (WM) and gray matter (GM) in predicting long-term CIm in a cohort of MS patients.

Methods

303 out of 597 patients participating in a previous multicenter clinical-MRI study were enrolled (49.4% were lost at follow-up). The following MRI parameters, expressed as fraction (f) of intracranial volume, were evaluated: cerebrospinal fluid (CSF-f), WM-f, GM-f and abnormal WM (AWM-f), a measure of lesion load. Nine years later, cognitive status was assessed in 241 patients using the Symbol Digit Modalities Test (SDMT), the Semantically Related Word List Test (SRWL), the Modified Card Sorting Test (MCST), and the Paced Auditory Serial Addition Test (PASAT). In particular, being SRWL a memory test, both immediate recall and delayed recall were evaluated. MCST scoring was calculated based on the number of categories, number of perseverative and non-perseverative errors.

Results

AWM-f was predictive of an impaired performance 9 years ahead in SDMT (OR 1.49, CI 1.12–1.97 p = 0.006), PASAT (OR 1.43, CI 1.14–1.80 p = 0.002), SRWL-immediate recall (OR 1.72 CI 1.35–2.20 p<0.001), SRWL-delayed recall (OR 1.61 CI 1.28–2.03 p<0.001), MCST-category (OR 1.52, CI 1.2–1.9 p<0.001), MCST-perseverative error(OR 1.51 CI 1.2–1.9 p = 0.001), MCST-non perseverative error (OR 1.26 CI 1.02–1.55 p = 0.032).

Conclusion

In our large MS cohort, focal WM damage appeared to be the most relevant predictor of the long-term cognitive outcome.

Corticospinal tract integrity is related to primary motor cortex thinning in relapsing–remitting multiple sclerosis

Background:

The relationship between white matter injury and cortical atrophy development in relapsing–remitting multiple sclerosis (RRMS) remains unclear.

Objectives:

To investigate the associations between corticospinal tract integrity and cortical morphology measures of the primary motor cortex in RRMS patients and healthy controls.

Methods:

51 RRMS patients and 30 healthy controls underwent MRI examination for cortical reconstruction and assessment of corticospinal tract integrity. Partial correlation and multiple linear regression analyses were used to investigate the associations of focal and normal appearing white matter (NAWM) injury of the corticospinal tract with thickness and surface area measures of the primary motor cortex. Relationships between MRI measures and clinical disability as assessed by the Expanded Disability Status Scale and disease duration were also investigated.

Results:

In patients only, decreased cortical thickness was related to increased corticospinal tract NAWM mean, axial and radial diffusivities in addition to corticospinal tract lesion volume. The final multiple linear regression model for PMC thickness retained only NAWM axial diffusivity as a significant predictor (adjusted R2= 0.270, p= 0.001). Clinical measures were associated with NAWM corticospinal tract integrity measures.

Conclusions:

Primary motor cortex thinning in RRMS is related to alterations in connected white matter and is best explained by decreased NAWM integrity.

White matter lesion load is associated with resting state functional MRI activity and amyloid PET but not FDG in mild cognitive impairment and early Alzheimer's disease patients

PURPOSE

To quantify and investigate the interactions between multimodal MRI/positron emission tomography (PET) imaging metrics in elderly patients with early Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy controls.

MATERIALS AND METHODS

Thirteen early AD, 17 MCI patients, and 14 age-matched healthy aging controls from the Alzheimer's Disease Neuroimaging Initiative database were selected based on availability of data. Default mode network (DMN) functional connectivity and fractional amplitude of low frequency fluctuation (fALFF) were obtained for resting state functional MRI (RS-fMRI). White matter lesion load (WMLL) was quantified from MRI T2-weighted FLAIR images. Amyloid deposition with PET [(18)F]-Florbetapir tracer and metabolism of glucose by means of [(18)F]-fluoro-2-deoxyglucose (FDG) images were quantified using ratio of standard uptake values (rSUV).

RESULTS

Whole-brain WMLL and amyloid deposition were significantly higher (P < 0.005) in MCI and AD patients compared with controls. RS-fMRI results showed significantly reduced (corrected P < 0.05) DMN connectivity and altered fALFF activity in both MCI and AD groups. FDG uptake results showed hypometabolism in AD and MCI patients compared with controls. Correlations (P < 0.05) were found between WMLL and amyloid load, FDG uptake and amyloid load, as well as between amyloid load (rSUV) and fALFF.

CONCLUSION

Our quantitative results of four MRI and PET imaging metrics (fALFF/DMN, WMLL, amyloid, and FDG rSUV values) agree with published values. Significant correlations between MRI metrics, including WMLL/functional activity and PET amyloid load suggest the potential of MRI and PET-based biomarkers for early detection of AD.

Health effects of lesion localization in multiple sclerosis: spatial registration and confounding adjustment

Brain lesion localization in multiple sclerosis (MS) is thought to be associated with the type and severity of adverse health effects. However, several factors hinder statistical analyses of such associations using large MRI datasets: 1) spatial registration algorithms developed for healthy individuals may be less effective on diseased brains and lead to different spatial distributions of lesions; 2) interpretation of results requires the careful selection of confounders; and 3) most approaches have focused on voxel-wise regression approaches. In this paper, we evaluated the performance of five registration algorithms and observed that conclusions regarding lesion localization can vary substantially with the choice of registration algorithm. Methods for dealing with confounding factors due to differences in disease duration and local lesion volume are introduced. Voxel-wise regression is then extended by the introduction of a metric that measures the distance between a patient-specific lesion mask and the population prevalence map.

Corpus callosum atrophy correlates with gray matter atrophy in patients with multiple sclerosis

OBJECTIVE

Atrophy of the corpus callosum is a recognized characteristic of multiple sclerosis (MS). We describe a new reliable method for measuring corpus callosum atrophy and correlate this with global cerebral atrophy measures.

METHODS

Whole brain 3T MRI was performed in 38 relapsing-remitting MS subjects and 21 healthy controls (HC). Brain global gray and white matter volumes were segmented with SPM8. The contour of the corpus callosum was outlined on the midline of 3-D T1-weighted images by a semiautomated edge-detection technique to determine the corpus callosum area (CCA). Normalized CCA was correlated with other brain atrophy measures in MS subjects.

RESULTS

CCA was disproportionately lower in MS subjects vs. HC (20.1% mean decrease; P < .001), with a large effect size (d = .62) when compared with global atrophy measures. In MS subjects, CCA correlated with brain parenchymal fraction (r = .55; P < .001) and gray matter fraction (r = .45; P = .005) but not white matter fraction (r = .18; P = .29). An inverse correlation with FLAIR hyperintense lesion volume (r = -.40; P = .01) was detected for CCA.

CONCLUSION

Measurement of atrophy of the corpus callosum can have sensitivity as a useful imaging biomarker in patients with MS, even in patients with low disability levels. Both gray and white matter involvement in MS contribute to corpus callosum atrophy.

Subcortical deep gray matter pathology in patients with multiple sclerosis is associated with white matter lesion burden and atrophy but not with cortical atrophy: a diffusion tensor MRI study

BACKGROUND AND PURPOSE

The association between subcortical deep gray matter, white matter, and cortical pathology is not well understood in MS. The aim of this study was to use DTI to investigate the subcortical deep gray matter alterations and their relationship with lesion burden, white matter, and cortical atrophy in patients with MS and healthy control patients.

MATERIALS AND METHODS

A total of 210 patients with relapsing-remitting MS, 75 patients with progressive MS, and 110 healthy control patients were included in the study. DTI metrics in whole brain, normal-appearing white matter, normal-appearing gray matter, and subcortical deep gray matter structures were compared. The association between DTI metrics of the subcortical deep gray matter structures with lesion burden, normalized white matter volume, and normalized cortical volume was investigated.

RESULTS

DTI measures were significantly different in whole brain, normal-appearing white matter, and normal-appearing gray matter among the groups (P < .01). Significant differences in DTI diffusivity of total subcortical deep gray matter, caudate, thalamus, and hippocampus (P < .001) were found. DTI diffusivity of total subcortical deep gray matter was significantly associated with normalized white matter volume (P < .001) and normalized cortical volume (P = .033) in healthy control patients. In both relapsing and progressive MS groups, the DTI subcortical deep gray matter measures were associated with the lesion burden and with normalized white matter volume (P < .001), but not with normalized cortical volume.

CONCLUSIONS

These findings suggest that subcortical deep gray matter abnormalities are associated with white matter lesion burden and atrophy, whereas cortical atrophy is not associated with microstructural alterations of subcortical deep gray matter structures in patients with MS.

An exploratory study on the spatial relationship between regional cortical volume changes and white matter integrity in multiple sclerosis

Multiple sclerosis (MS) is a chronic inflammatory central nervous system disorder with a neurodegenerative component. While in the past, MS has been predominantly viewed as a white matter (WM) disease, gray matter (GM) pathology receives increasing attention in MS research. In this study, we tested hypothesis-free for a possible spatial relationship between cortical volume changes and disturbed integrity of projecting WM tracts. We used voxel-based morphometry (VBM), lesion probability maps (LPM), and probabilistic tractography to compare brain magnetic resonance imaging (MRI) scans obtained at 3 Tesla of 15 low disabled MS patients with 15 matched healthy controls (HCs). Areas of decreased cortical volume in the patients identified by VBM were used as seeds for tractography. Volume in two cortical areas in the left inferior frontal gyrus (IFG) and the left lateral occipital cortex (LOC) was reduced in patients compared to HCs. Starting from the IFG-region, tractography suggested impaired connections between left and right portions of the frontal lobe in the patients. Using the LOC as a seed, in patients, the left inferior longitudinal and fronto-occipital pathways appeared disintegrated compared to HCs. Swapping the seeds to homologous contralateral areas showed similar results for frontal, but different results for occipital brain areas. This at least partly could be explained by differential interference with WM lesions. These findings suggest a regional dependence between cortical GM and WM tract alterations in MS patients. While confirmation in larger and more heterogenic samples is needed, this study indicates that combining several MRI methods (VBM, LPM, and Probabilistic Tractography) may provide important insights into interacting processes related to the fiber tract and GM changes in MS.

White-matter lesions drive deep gray-matter atrophy in early multiple sclerosis: support from structural MRI

BACKGROUND

In MS, the relationship between lesions within cerebral white matter (WM) and atrophy within deep gray matter (GM) is unclear.

OBJECTIVE

To investigate the spatial relationship between WM lesions and deep GM atrophy.

METHODS

We performed a cross-sectional structural magnetic resonance imaging (MRI) study (3 Tesla) in 249 patients with clinically-isolated syndrome or relapsing-remitting MS (Expanded Disability Status Scale score: median, 1.0; range, 0-4) and in 49 healthy controls. Preprocessing of T1-weighted and fluid-attenuated T2-weighted images resulted in normalized GM images and WM lesion probability maps. We performed two voxel-wise analyses: 1. We localized GM atrophy and confirmed that it is most pronounced within deep GM; 2. We searched for a spatial relationship between WM lesions and deep GM atrophy; to this end we analyzed WM lesion probability maps by voxel-wise multiple regression, including four variables derived from maxima of regional deep GM atrophy (caudate and pulvinar, each left and right).

RESULTS

Atrophy of each deep GM region was explained by ipsilateral WM lesion probability, in the area most densely connected to the respective deep GM region.

CONCLUSION

We demonstrated that WM lesions and deep GM atrophy are spatially related. Our results are best compatible with the hypothesis that WM lesions contribute to deep GM atrophy through axonal pathology.

When the single matters more than the group: very high false positive rates in single case Voxel Based Morphometry

Voxel Based Morphometry (VBM) studies typically involve a comparison between groups of individuals; this approach however does not allow inferences to be made at the level of the individual. In recent years, an increasing number of research groups have attempted to overcome this issue by performing single case studies, which involve the comparison between a single subject and a control group. However, the interpretation of the results is problematic; for instance, any significant difference might be driven by individual variability in neuroanatomy rather than the neuropathology of the disease under investigation, or might represent a false positive due to the data being sampled from non-normally distributed populations. The aim of the present investigation was to empirically estimate the likelihood of detecting significant differences in gray matter volume in individuals free from neurological or psychiatric diagnosis. We compared a total of 200 single subjects against a group of 16 controls matched for age and gender, using two independent datasets from the Neuroimaging Informatics Tools and Resources Clearinghouse. We report that the chance of detecting a significant difference in a disease-free individual is much higher than previously expected; for instance, using a standard voxel-wise threshold of p<0.05 (corrected) and an extent threshold of 10 voxels, the likelihood of a single subject showing at least one significant difference is as high as 93.5% for increases and 71% for decreases. We also report that the chance of detecting significant differences was greatest in frontal and temporal cortices and lowest in subcortical regions. The chance of detecting significant differences was inversely related to the degree of smoothing applied to the data, and was higher for unmodulated than modulated data. These results were replicated in the two independent datasets. By providing an empirical estimation of the number of significant increases and decreases to be expected in each cortical and subcortical region in disease-free individuals, the present investigation could inform the interpretation of future single case VBM studies.

Recommendations to improve imaging and analysis of brain lesion load and atrophy in longitudinal studies of multiple sclerosis

Focal lesions and brain atrophy are the most extensively studied aspects of multiple sclerosis (MS), but the image acquisition and analysis techniques used can be further improved, especially those for studying within-patient changes of lesion load and atrophy longitudinally. Improved accuracy and sensitivity will reduce the numbers of patients required to detect a given treatment effect in a trial, and ultimately, will allow reliable characterization of individual patients for personalized treatment. Based on open issues in the field of MS research, and the current state of the art in magnetic resonance image analysis methods for assessing brain lesion load and atrophy, this paper makes recommendations to improve these measures for longitudinal studies of MS. Briefly, they are (1) images should be acquired using 3D pulse sequences, with near-isotropic spatial resolution and multiple image contrasts to allow more comprehensive analyses of lesion load and atrophy, across timepoints. Image artifacts need special attention given their effects on image analysis results. (2) Automated image segmentation methods integrating the assessment of lesion load and atrophy are desirable. (3) A standard dataset with benchmark results should be set up to facilitate development, calibration, and objective evaluation of image analysis methods for MS.

Spatiotemporal distribution of white matter lesions in relapsing-remitting and secondary progressive multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. MS lesions show a typical distribution pattern and primarily affect the white matter (WM) in the periventricular zone and in the centrum semiovale.

OBJECTIVE

To track lesion development during disease progression, we compared the spatiotemporal distribution patterns of lesions in relapsing-remitting MS (RRMS) and secondary progressive MS (SPMS).

METHODS

We used T1 and T2 weighted MR images of 209 RRMS and 62 SPMS patients acquired on two different 1.5 Tesla MR scanners in two clinical centers followed up for 25 (± 1.7) months. Both cross-sectional and longitudinal differences in lesion distribution between RRMS and SPMS patients were analyzed with lesion probability maps (LPMs) and permutation-based inference.

RESULTS

MS lesions clustered around the lateral ventricles and in the centrum semiovale. Cross-sectionally, compared to RRMS patients, the SPMS patients showed a significantly higher regional probability of T1 hypointense lesions (p ≤ 0.03) in the callosal body, the corticospinal tract, and other tracts adjacent to the lateral ventricles, but not of T2 lesions (peak probabilities were RRMS: T1 9%, T2 18%; SPMS: T1 21%, T2 27%). No longitudinal changes of regional T1 and T2 lesion volumes between baseline and follow-up scan were found.

CONCLUSION

The results suggest a particular vulnerability to neurodegeneration during disease progression in a number of WM tracts.

Multivariate pattern classification of gray matter pathology in multiple sclerosis

Univariate analyses have identified gray matter (GM) alterations in different groups of MS patients. While these methods detect differences on the basis of the single voxel or cluster, multivariate methods like support vector machines (SVM) identify the complex neuroanatomical patterns of GM differences. Using multivariate linear SVM analysis and leave-one-out cross-validation, we aimed at identifying neuroanatomical GM patterns relevant for individual classification of MS patients. We used SVM to separate GM segmentations of T1-weighted three-dimensional magnetic resonance (MR) imaging scans within different age- and sex-matched groups of MS patients with either early (n=17) or late MS (n=17) (contrast I), low (n=20) or high (n=20) white matter lesion load (contrast II), and benign MS (BMS, n=13) or non-benign MS (NBMS, n=13) (contrast III) scanned on a single 1.5 T MR scanner. GM patterns most relevant for individual separation of MS patients comprised cortical areas of all the cerebral lobes as well as deep GM structures, including the thalamus and caudate. The patterns detected were sufficiently informative to separate individuals of the respective groups with high sensitivity and specificity in 85% (contrast I), 83% (contrast II) and 77% (contrast III) of cases. The study demonstrates that neuroanatomical spatial patterns of GM segmentations contain information sufficient for correct classification of MS patients at the single case level, thus making multivariate SVM analysis a promising clinical application.

The effect of hypointense white matter lesions on automated gray matter segmentation in multiple sclerosis

Previous imaging studies assessing the relationship between white matter (WM) damage and matter (GM) atrophy have raised the concern that Multiple Sclerosis (MS) WM lesions may affect measures of GM volume by inducing voxel misclassification during intensity-based tissue segmentation. Here, we quantified this misclassification error in simulated and real MS brains using a lesion-filling method. Using this method, we also corrected GM measures in patients before comparing them with controls in order to assess the impact of this lesion-induced misclassification error in clinical studies. We found that higher WM lesion volumes artificially reduced total GM volumes. In patients, this effect was about 72% of that predicted by simulation. Misclassified voxels were located at the GM/WM border and could be distant from lesions. Volume of individual deep gray matter (DGM) structures generally decreased with higher lesion volumes, consistent with results from total GM. While preserving differences in GM volumes between patients and controls, lesion-filling correction revealed more lateralised DGM shape changes in patients, which were not evident with the original images. Our results confirm that WM lesions can influence MRI measures of GM volume and shape in MS patients through their effect on intensity-based GM segmentation. The greater effect of lesions at increasing levels of damage supports the use of lesion-filling to correct for this problem and improve the interpretability of the results. Volumetric or morphometric imaging studies, where lesion amount and characteristics may vary between groups of patients or change over time, may especially benefit from this correction.

MR imaging of multiple sclerosis

Owing to its ability to depict the pathologic features of multiple sclerosis (MS) in exquisite detail, conventional magnetic resonance (MR) imaging has become an established tool in the diagnosis of this disease and in monitoring its evolution. MR imaging has been formally included in the diagnostic work-up of patients who present with a clinically isolated syndrome suggestive of MS, and ad hoc diagnostic criteria have been proposed and are updated on a regular basis. In patients with established MS and in those participating in treatment trials, examinations performed with conventional MR pulse sequences provide objective measures to monitor disease activity and progression; however, they have a limited prognostic role. This has driven the application of newer MR imaging technologies, including higher-field-strength MR units, to estimate overall MS burden and mechanisms of recovery in patients at different stages of the disease. These techniques have allowed in vivo assessment of the heterogeneity of MS pathologic features in focal lesions and in normal-appearing tissues. More recently, some of the finer details of MS, including macrophage infiltration and abnormal iron deposition, have become quantifiable with MR imaging. The utility of these modern MR techniques in clinical trial monitoring and in the assessment of the individual patient's response to treatment still need to be evaluated.

Longitudinal gray matter changes in multiple sclerosis--differential scanner and overall disease-related effects

Voxel-based morphometry (VBM) has been used repeatedly in single-center studies to investigate regional gray matter (GM) atrophy in multiple sclerosis (MS). In multi-center trials, across-scanner variations might interfere with the detection of disease-specific structural abnormalities, thereby potentially limiting the use of VBM. Here we evaluated longitudinally inter-site differences and inter-site comparability of regional GM in MS using VBM. Baseline and follow up 3D T1-weighted magnetic resonance imaging (MRI) data of 248 relapsing-remitting (RR) MS patients, recruited in two clinical centers, (center1/2: n = 129/119; mean age 42.6 ± 10.7/43.3 ± 9.3; male:female 33:96/44:75; median disease duration 150 [72-222]/116 [60-156]) were acquired on two different 1.5T MR scanners. GM volume changes between baseline and year 2 while controlling for age, gender, disease duration, and global GM volume were analyzed. The main effect of time on regional GM volume was larger in data of center two as compared to center one in most of the brain regions. Differential effects of GM volume reductions occurred in a number of GM regions of both hemispheres, in particular in the fronto-temporal and limbic cortex (cluster P corrected <0.05). Overall disease-related effects were found bilaterally in the cerebellum, uncus, inferior orbital gyrus, paracentral lobule, precuneus, inferior parietal lobule, and medial frontal gyrus (cluster P corrected <0.05). The differential effects were smaller as compared to the overall effects in these regions. These results suggest that the effects of different scanners on longitudinal GM volume differences were rather small and thus allow pooling of MR data and subsequent combined image analysis.

Neuroimaging in multiple sclerosis: neurotherapeutic implications

Imaging techniques, in particular magnetic resonance imaging (MRI), play an important role in the diagnosis and management of multiple sclerosis (MS) and related demyelinating diseases. Findings on MRI studies of the brain and spinal cord are critical for MS diagnosis, are used to monitor treatment response and may aid in predicting disease progression in individual patients. In addition, results of imaging studies serve as essential biomarkers in clinical trials of putative MS therapies and have led to important insights into disease pathophysiology. Although they are useful tools and provide in vivo measures of disease-related activity, there are some important limitations of MRI findings in MS, including the non-specific nature of detectable white matter changes, the poor correlation with clinical disability, the limited sensitivity and ability of standard measures of gadolinium enhancing lesions and T2 lesions to predict future clinical course, and the lack of validated biomarkers of long term outcomes. Advancements that hold promise for the future include new techniques that are sensitive to diffuse changes, the increased use of higher field scanners, measures that capture disease related changes in gray matter, and the use of combined structural and functional imaging approaches to assess the complex and evolving disease process that occurs during the course of MS.

Cognitive impairment in multiple sclerosis is associated to different patterns of gray matter atrophy according to clinical phenotype

OBJECTIVE

To investigate whether cognitive impairment in multiple sclerosis (MS) patients is associated to different patterns of gray matter (GM) atrophy and T2-visible lesion distribution according to the clinical phenotype.

EXPERIMENTAL DESIGN

Twenty-two relapsing remitting (RR), 29 secondary progressive (SP), and 22 primary progressive (PP) MS patients, and 39 healthy controls underwent high-field structural magnetic resonance imaging and an extensive neuropsychological battery. Voxel-wise distribution of GM damage and T2-lesions was compared between cognitively impaired (CI) and cognitively preserved (CP) patients according to their clinical phenotype.

PRINCIPAL OBSERVATIONS

Thirty-nine MS patients were CI. In all MS groups, regional GM loss was correlated with cognitive impairment. Different patterns of regional distribution of GM atrophy and T2-visible lesions were found between CI vs. CP MS patients, according to their clinical phenotype. No areas were significantly more atrophied in CI SPMS vs. CI RRMS patients. Conversely, compared with CI PPMS, CI SPMS patients had a significant GM loss in several regions of the fronto-temporal lobes, the left hypothalamus and thalami. While in RRMS and SPMS patients there was a correspondence between presence of T2 visible lesions and GM atrophy in several areas, this was not the case in PPMS patients.

CONCLUSION

Distinct patterns of regional distribution of GM damage and T2-visible lesions are associated with cognitive impairment in MS patients with different clinical phenotypes. The correspondence between lesion formation and GM atrophy distribution varies in the different forms of MS.

Effect of immunomodulatory medication on regional gray matter loss in relapsing-remitting multiple sclerosis--a longitudinal MRI study

Prevention of global gray matter (GM) volume changes in multiple sclerosis (MS) are an objective in clinical trials, but the effect of immunomodulatory medication on regional GM atrophy progression is unclear. MRIs from 86 patients with relapsing-remitting MS (RRMS) followed up for 24 months were analyzed using voxel-based morphometry. An analysis of covariance model (cluster threshold, corrected p<0.05) was used to compare GM volumes between baseline and follow-up while stratified by immunomodulatory medication (IM): Interferone INF-beta-1a (n=34), INF-beta-1b (n=16), glatiramer acetate (GA) (n=15), and no-immunomodulatory treatment (n=21). In the INF-beta-1a/1b group (n=50), significant GM volume reductions were observed during follow-up in fronto-temporal, cingulate and cerebellar cortical brain regions, without significant differences between the INF-beta-1a and INF-beta-1b patients. In the GA group and in unmedicated patients, no significant regional GM volume reductions were observed. In contrast to GA, INF-beta-1a/1b treatment was associated with GM volume reductions in hippocampal/parahippocampal and anterior cingulate cortex. This is the first longitudinal study investigating the effects of IMs on GM in RRMS. Results suggest differences in the dynamics of regional GM volume atrophy in differentially treated or untreated RRMS patients.